Out of prison, Shkreli plans “Web3 drug discovery” platform backed by crypto

Martin Shkreli being photographed for his role as CIO of MSMB Capital Management.

Martin Shkreli being photographed for his role as CIO of MSMB Capital Management. (credit: Getty Images)

Martin Shkreli—the notorious ex-pharmaceutical executive fresh from prison after his 2017 fraud conviction—announced his latest, eyebrow-raising venture Monday: creating a blockchain-based “Web3 drug discovery platform” that traffics in his own cryptocurrency, MSI, aka Martin Shkreli Inu.

The platform, still in the early development phase, is called Druglike, according to a press release that circulated Monday. The platform’s goals are ostensibly lofty, but the details are extremely sketchy, and Shkreli’s intentions have already drawn skepticism. It’s also unclear if the enterprise will run Shkreli afoul of his lifetime ban from the pharmaceutical industry, which stemmed from the abrupt and callous 4,000 percent price hike of a life-saving drug that made him infamous.

Shkreli, who is named as a co-founder of Druglike, says the platform aims to make early-stage drug discovery more affordable and accessible. “Druglike will remove barriers to early-stage drug discovery, increase innovation and allow a broader group of contributors to share the rewards,” Shkreli said in the press release. “Underserved and underfunded communities, such as those focused on rare diseases or in developing markets, will also benefit from access to these tools.”

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#bitcoin, #blockchain, #cryptocurrency, #drug-discovery, #martin-shkreli, #science

The next healthcare revolution will have AI at its center

The global pandemic has heightened our understanding and sense of importance of our own health and the fragility of healthcare systems around the world. We’ve all come to realize how archaic many of our health processes are, and that, if we really want to, we can move at lightning speed. This is already leading to a massive acceleration in both the investment and application of artificial intelligence in the health and medical ecosystems.

Modern medicine in the 20th century benefited from unprec­edented scientific breakthroughs, resulting in improvements in every as­pect of healthcare. As a result, human life expectancy increased from 31 years in 1900 to 72 years in 2017. Today, I believe we are on the cusp of another healthcare revolution — one driven by artificial intelligence (AI). Advances in AI will usher in the era of modern medicine in truth.

Over the coming decades, we can expect medical diagnosis to evolve from an AI tool that provides analysis of options to an AI assistant that recommends treatments.

Digitization enables powerful AI

The healthcare sector is seeing massive digitization of everything from patient records and radiology data to wearable computing and multiomics. This will redefine healthcare as a data-driven industry, and when that happens, it will leverage the power of AI — its ability to continuously improve with more data.

When there is enough data, AI can do a much more accurate job of diagnosis and treatment than human doctors by absorbing and checking billions of cases and outcomes. AI can take into account everyone’s data to personalize treatment accordingly, or keep up with a massive number of new drugs, treatments and studies. Doing all of this well is beyond human capabilities.

AI-powered diagnosis

I anticipate diagnostic AI will surpass all but the best doctors in the next 20 years. Studies have shown that AI trained on sizable data can outperform physicians in several areas of medical diagnosis regarding brain tumors, eye disease, breast cancer, skin cancer and lung cancer. Further trials are needed, but as these technologies are deployed and more data is gathered, the AI stands to outclass doctors.

We will eventually see diagnostic AI for general practitioners, one disease at a time, to gradually cover all diagnoses. Over time, AI may become capable of acting as your general practitioner or family doctor.

#artificial-intelligence, #biotechnology, #cancer, #column, #drug-discovery, #ec-column, #ec-enterprise-health, #ec-robotics, #health, #healthcare, #medical-imaging, #pharmaceuticals, #precision-medicine, #robotics, #startups

Journey Clinical raises $3M to allow psychotherapists to prescribe psychedelics

Psychedelics companies are all the rage right now. Compass Pathways is working with the magic mushroom compound psilocybin to treat depression. It’s has raised $290 million in total. Atai Life Sciences — backed by PayPal co-founder Peter Thiel — brought in $258 million from its IPO. In the tech space, this has not gone unnoticed and the same business models that have been used in other platforms for health and wellness startups are coming to psychedelics.

The latest is Journey Clinical, based out of NYC, which has raised a $3 million seed round led by San Francisco VC firm Fifty Years. Also participating were Neo Kuma Ventures, Palo Santo, PsyMed Ventures, Lionheart Ventures, Christina Sass co-founder of Andela, ​​Edvard Engesæth, MD co-founder of Nurx and, Hans Gangeskar co-founder of Nurx.

Journey joins other startups in the space looking at psychedelic-assisted psychotherapy, where ketamine is used to treat depression, anxiety, PTSD, and trauma, known as ketamine-assisted psychotherapy (KAP). Miami-based startup NUE Life Health raised a $3.3 million seed round for the same purpose back in June. There is also Field Trip and Mindbloom playing in this space.

These startups are pushing at an open door on depression and anxiety. Pre-COVID-19, the National Center for Health Statistics estimated some 50 million Americans were fighting the afflictions. The pandemic has of course exacerbated this issue, with those figures doubling, by some estimates.

It’s still an early market. Journey says the market landscape for legal psychedelic therapies is very disparate, with over a million licensed mental health professionals lacking the infrastructure to offer these treatments as they lack access to prescribing clinicians. On the flip side, patients struggle to find psychotherapists who can prescribe psychedelics as treatment.

Journey says it has a “decentralized clinic model” that allows psychotherapists to offer legal psychedelic therapy treatments in their practice, starting with ketamine. The way it works is that Journey takes care of the pharmacology side, while psychotherapists that sign up to the platform take care of the psychotherapy of the patient. The treatment plans are then customized to meet the patient’s needs.

Jonathan Sabbagh, co-founder and CEO, was previously diagnosed with PTSD, but after discovering psychedelics, he went back to school to study clinical psychology, and went on to co-found Journey. He said: “We are on the verge of a paradigm shift in the field of mental health. Psychedelic-assisted psychotherapies are one of the most promising new means of treatment available; they will allow clinicians to tackle the growing global mental health crisis we are facing.”

Speaking to TechCrunch he added: “When we asked what was the main bottleneck for therapists to offer KAP to their patients, the #1 response was access to a prescribing doctor. Our alpha test group confirmed that guaranteeing access to a trained medical team and building a robust care management system would solve an essential bottleneck of mainstream adoption for KAP.”

Journey has two revenue streams. Psychotherapists pay them a $200 monthly membership fee which gives them access to a number of services including and access to the prescriber, an EHR (achieved through a white label), a KAP training (training materials created by a specialized training company), a profile on Journey’s directory, and a community of peers. Patients pay journey for medical services. They pay $250 for the intake consultation and $150 for follow-up consultations.

Ela Madej, Founding Partner at Fifty Years, said: “I dream of a world where those of us affected by trauma, anxiety, or depression don’t have to fall into learned helplessness. We’re lucky that powerful psychedelic treatments for the mind exist, but they need to be delivered responsibly, with proper screening, protocols, and follow-up. We’ve been incredibly impressed by Journey Clinical’s ambitious plan to empower psychotherapists to better treat their existing patients.”

The team also comprises Kyle Lapidus MD, Ph.D., who has over 20 years as a board-certified psychiatrist and has extensive experience working with ketamine; and Brigitte Gordon DNP a professor at Columbia University and also works for the Multidisciplinary Association for Psychedelic Studies (MAPS.)

#alpha, #andela, #atai-life-sciences, #christina-sass, #co-founder, #columbia-university, #compass-pathways, #depression, #drug-discovery, #drugs, #ela-madej, #fifty-years, #medicine, #mental-health, #miami, #multidisciplinary-association-for-psychedelic-studies, #nue-life-health, #nurx, #partner, #paypal, #peter-thiel, #ptsd, #tc

African genomics startup 54gene raises $25M to expand precision medicine capabilities

Less than 3% of genetic material used in global pharmaceutical research is from Africa. The staggering gap is quite surprising because Africans and people of African descent are reported to be more genetically diverse than any other population.

Since launching in 2019, African genomics startup 54gene has been at the forefront of bridging this divide in the global genomics market. Today, the company has secured $25 million in Series B funding to bolster its efforts.

This round comes a year after the company, founded by Dr Abasi Ene-Obong, raised $15 million in Series A and two years after closing a $4.5 million seed round.

In total, 54gene has raised more than $45 million since its inception.

With the world’s analyzed genomes coming mostly from anywhere that isn’t Africa, the continent remains a valuable source of new genetic information for health and drug discovery research.

This is where 54gene’s work is relevant. The company conducts and leverages this research to ensure Africans are recipients of upcoming drug and medical discoveries.

Last year when we covered the company last year, CEO Ene-Obong disclosed that for 54gene to conduct this research, it recruits voluntary participants who donate genetic samples via swab or blood tests.

It still very much works this way. However, instead of depending on third-party health centres like hospitals and sending the samples abroad for analysis, 54gene launched its own genetics sequencing and microarray lab in Lagos last September. The company did this in partnership with U.S.-based biotech company Illumina.

Speaking with TechCrunch, Ene-Obong says in addition to the genotyping capabilities offered, the lab also provides whole-genome sequencing (WGS) and whole-exome sequencing (WES).

Not to bore you with the jargon but here’s why this is important. Genotyping tends to show only 0.02% of an individual’s DNA; however, WGS can show almost 100% of the same person’s DNA.

For WES, although it represents only 1.5% of the human genome, it shows approximately 85% of known disease-related variants.

With these three in place, the company can advance genomics research and expand its ability to help scientists and researchers in Africa.

Unlike fintech and other fast-moving sectors like e-commerce, innovation in healthtech takes some time to take shape finally. 54gene is one of the few startups in the sector and even in Africa to have moved from seed stage to Series B in under two years.

It’s this sort of frightening speed that makes one wonder what the company is doing right. So I ask the CEO whether the company is indeed seeing significant progress in advancing African genomics; he answers in the affirmative.

“Though the arc of conducting early research through drug approval can be long in biotech, we have taken the approach to building the backbone that is needed for short-term successes to long-term gains that provide better healthcare delivery and treatment outcomes from diseases,” he added.

In addition to setting its first lab, the CEO says the company increasing its biobanking capacity by 5x and is counts that as a major success.

During its last raise, 54gene had a biobank capacity for 60,000 samples. If Ene-Obong comments are anything to go by, the two-year-old company currently has a biobank with over 300,000 samples, close to its longer-term aim to manage up to 500,000.

Another one is the recruitment and training of talent to generate and process data needed to produce insights for the company’s drug discovery efforts.

Nigeria has a dearth of experienced clinicians and with the remaining few leaving in droves, it is not hard to see why it is a win for the company. Knowing this, 54gene plans to use part of the new funding to recruit and train more professionals

Other use of funding will expand its capabilities in sequencing, target identification and validation, and precision medicine clinical trials. Also of great importance is its expansion across the African continent.

To aid this expansion, 54gene will have to carry out partnerships. A recent one occurred between the company and the Tanzania Human Genetics Organization and Ene-Obong says 54gene is in varying stages of conversations with more partners. However, he was tight-lipped on who they might be.

“We are excited about our Africa-first approach which will see us expand to countries within East and West Africa in the coming year,” he added.

54gene made some hires to this end: Michelle Ephraim, Colm O’Dushlaine, Peter Fekkes, Teresia Bost, Jude Uzonwanne — all of who have decades of experience working with companies like Leica Biosystems, Regeneron Genetic Center, Novartis, Celgene, and the Bill and Melinda Gates Foundation

Pan-African venture capital firm Cathay AfricInvest Innovation Fund led this round. Lead investor from the company’s Series A funding, Adjuvant Capital invested once again with participation from other VCs including KdT Ventures, Plexo Capital, Endeavor Capital, and Ingressive Capital.

#54gene, #africa, #biotech, #drug-discovery, #funding, #genomics, #precision-medicine, #science, #tc, #whole-genome-sequencing

Shares of protein discovery platform Absci pop in market debut

Absci Corp., a Vancouver company behind a multi-faceted drug development platform, went public on Thursday. It’s another sign of snowballing interest in new approaches to drug development – a traditionally risky business. 

Absci focuses on speeding drug development in the preclinical stages. The company has developed and acquired a handful of tools that can predict drug candidates, identify potential therapeutic targets, and test therapeutic proteins on billions of cells and identify which ones are worth pursuing. 

“We are offering a fully-integrated end-to-end solution for pharmaceutical drug development,” Absci founder Sean McClain tells TechCrunch. “Think of this as the Google index search for protein drug discovery and biomanufacturing.” 

The IPO was initially priced at $16 per share, with a pre-money valuation of about $1.5 billion, per S-1 filings. The company is offering 12.5 million shares of common stock, with plans to raise $200 million. However, Absci stock has already ballooned to $21 per share as of writing. Common stock is trading under the ticker “ABSI.” 

The company has elected to go public now, McClain says, to increase the company’s ability to attract and retain new talent. “As we continue to rapidly grow and scale, we need access to the best talent, and the IPO gives us amazing visibility for talent acquisition and retention,” says McClain.

Absci was founded in 2011 with a focus on manufacturing proteins in E.Coli. By 2018, the company had launched its first commercial product called SoluPro – a biogeneered E.Coli system that can build complex proteins. In 2019, the company scaled this process up by implementing a “protein printing” platform.

Since its founding Absci has grown to 170 employees and raised $230 million – the most recent influx was a $125 million crossover financing round closed in June 2020 led by Casdin Capital and Redmile Group. But this year, two major acquisitions have rounded out Absci’s offerings from protein manufacturing and testing to AI-enabled drug development. 

In January 2021, Absci acquired Denovium, a company using deep learning AI to categorize and predict the behavior of proteins. Denovium’s “engine” had been trained on more than 100 million proteins. In June, the company also acquired Totient, a biotech company that analyzes the immune system’s response to certain diseases. At the time of Totient’s acquisition, the company had already reconstructed 4,500 antibodies gleaned from immune system data from 50,000 patients. 

Absci already had protein manufacturing, evaluation and screening capabilities, but the Totient acquisition allowed it to identify potential targets for new drugs. The Denovium acquisition added an AI-based engine to aid in protein discovery. 

“What we’re doing is now feeding [our own data] into deep learning models and so that is why we acquired Denovium. Prior to Totient we were doing drug discovery and cell line development. This [acquisition] allows us to go fully integrated where we can now do target discovery as well,” McClain says. 

These two acquisitions place Absci into a particularly active niche in the drug development world. 

To start with, there’s been some noteworthy fiscal interest in developing new approaches to drug development, even after decades of low returns on drug R&D. In the first half of 2021, Evaluate reported that new drug developers raised about $9 billion in IPOs on Western exchanges. This is despite the fact that drug development is traditionally high risk. R&D returns for biopharmaceuticals hit a record low of 1.6 percent in 2019, and have rebounded to only about 2.5 percent, a Deloitte 2021 report notes. 

Within the world of drug development, we’ve seen AI play an increasingly large role. That same Deloitte report notes that “most biopharma companies are attempting to integrate AI into drug discovery, and development processes.” And, drug discovery projects received the greatest amount of AI investment dollars in 2020, according to Stanford University’s Artificial Intelligence Index annual report

More recently, the outlook on the use of AI in drug development has been bolstered by companies that have moved a candidate through the stages of pre-clinical development. 

In June, Insilico Medicine, a Hong Kong-based startup, announced that it had brought an A.I-identified drug candidate for idiopathic pulmonary fibrosis through the preclinical testing stages – a feat that helped close a $255 million Series C round. Founder Alexander Zharaonkov told TechCrunch the PI drug would begin a clinical trial on the drug late this year or early next year. 

With a hand in AI and in protein manufacturing, Absci has already positioned itself in a crowded, but hype-filled space. But going forward, the company will still have to work out the details of its business model.  

Absci is pursuing a partnership business model with drug manufacturers. This means that the company doesn’t have plans to run clinical trials of its own. Rather, it expects to earn revenue through “milestone payments” (conditional upon reaching certain stages of the drug development process) or, if drugs are approved, royalties on sales. 

This does offer some advantages, says McClain. The company is able to sidestep the risk of drug candidates failing after millions of R&D cash is poured into testing and can invest in developing “hundreds” of drug candidates at once. 

At this point, Absci does have nine currently “active programs” with drugmakers. The company’s cell line manufacturing platforms are in use in drug testing programs at eight biopharma companies, including Merck, Astellas, and Alpha Cancer technologies (the rest are undisclosed). Five of these projects are in the preclinical stage, one is in Phase 1 clinical trials, one is in a Phase 3 clinical trial, and the last is focused on animal health, per the company’s S-1 filing. 

One company, Astellas, is currently using Absci’s discovery platforms. But McClain notes that Absci has only just rolled out its drug discovery capabilities this year. 

However, none of these partners have formally licensed any of Absci’s platforms for clinical or commercial use. McClain notes that the nine active programs have milestones and royalty “potentials” associated with them. 

The company does have some ground to make up when it comes to profitability. So far this year, Absci has generated about $4.8 million in total revenue – up from about $2.1 million in 2019. Still, the costs have remained high, and S-1 filings note that the company has incurred net losses in the past two years. In 2019, the company reported $6.6 million in net losses in 2019 and $14.4 million in net losses in 2020. 

The company’s S-1 chalks up these losses to expenditures related to cost of research and development, establishing an intellectual property portfolio, hiring personnel, raising capital and providing support for these activities. 

Absci has recently completed the construction of a 77,000 square foot facility, notes McClain. So going forward the company does foresee the potential to increase the scale of its operations. 

In the immediate future, the company plans to use money raised from the IPO to grow the number of programs using Absci’s technology, invest in R&D and continue to refine the company’s new AI-based products. 

 

#ai, #artificial-intelligence, #biotech, #drug-development, #drug-discovery, #tc, #therapeutics

A.I. drug discovery platform Insilico Medicine announces $255 million in Series C funding

Insilico Medicine, an A.I-based platform for drug development and discovery announced $255 million in Series C financing on Tuesday. The massive round is reflective of a recent breakthrough for the company: proof that it’s A.I based platform can create a new target for a disease, develop a bespoke molecule to address it, and begin the clinical trial process. 

It’s also yet another indicator that A.I and drug discovery continues to be especially attractive for investors. 

Insilico Medicine is a Hong Kong-based company founded in 2014 around one central premise: that A.I assisted systems can identify novel drug targets for untreated diseases, assist in the development of new treatments, and eventually predict how well those treatments may perform in clinical trials. Previously, the company had raised $51.3 million in funding, according to Crunchbase

Insilico Medicine’s aim to use A.I to drive drug development isn’t particularly new, but there is some data to suggest that the company might actually accomplish that gauntlet of discovery all the way through trial prediction. In 2020, the company identified a novel drug target for idiopathic pulmonary fibrosis, a disease in which tiny air sacs in the lungs become scarred, which makes breathing laborious. 

Two A.I-based platforms first identified 20 potential targets, narrowed it down to one, and then designed a small molecule treatment that showed promise in animal studies. The company is currently filing an investigational new drug application with the FDA and will begin human dosing this year, with aims to begin a clinical trial late this year or early next year. 

The focus here isn’t on the drug, though, it’s on the process. This project condensed the process of preclinical drug development that typically takes multiple years and hundreds of millions of dollars into just 18 months, for a total cost of about $2.6 million. Still, founder Alex Zhavoronkov doesn’t think that Insilico Medicine’s strengths lie primarily in accelerating preclinical drug development or reducing costs: its main appeal is in eliminating an element of guesswork in drug discovery, he suggests. 

“Currently we have 16 therapeutic assets, not just IPF,” he says. “It definitely raised some eyebrows.” 

“It’s about the probability of success,” he continues. “So the probability of success of connecting the right target to the right disease with a great molecule is very, very low. The fact that we managed to do it in IPF and other diseases I can’t talk about yet – it increases confidence in A.I in general.” 

Bolstered partially by the proof-of-concept developed by the IPF project and enthusiasm around A.I based drug development, Insilico Medicine attracted a long list of investors in this most recent round. 

The round is led by Warburg Pincus, but also includes investment from Qiming Venture Partners, Pavilion Capital, Eight Roads Ventures, Lilly Asia Ventures, Sinovation Ventures, BOLD Capital Partners, Formic Ventures, Baidu Ventures, and new investors. Those include CPE, OrbiMed, Mirae Asset Capital, B Capital Group, Deerfield Management, Maison Capital, Lake Bleu Capital, President International Development Corporation, Sequoia Capital China and Sage Partners. 

This current round was oversubscribed four-fold, according to Zhavoronkov. 

A 2018 study of 63 drugs approved by the FDA between 2009 and 2018 found that the median capitalized research and development investment needed to bring a drug to market was $985 million, which also includes the cost of failed clinical trials. 

Those costs and the low likelihood of getting a drug approved has initially slowed the process of drug development. R&D returns for biopharmaceuticals hit a low of 1.6 percent in 2019, and bounced back to a measly 2.5 percent in 2020 according to a 2021 Deloitte report

Ideally, Zhavoronkov imagines an A.I-based platform trained on rich data that can cut down on the amount of failed trials. There are two major pieces of that puzzle: PandaOmics, an A.I platform that can identify those targets; and Chemistry 42, a platform that can manufacture a molecule to bind to that target.

“We have a tool, which incorporates more than 60 philosophies for target discovery,” he says. 

“You are betting something that is novel, but at the same time you have some pockets of evidence that strengthen your hypothesis. That’s what our A.I does very well.” 

Although the IPF project has not been fully published in a peer-reviewed journal, a similar project published in Nature Biotechnology was. In that paper, Insilco’s deep learning model was able to identify potential compounds in just 21 days

The IPF project is a scale-up of this idea. Zhavoronkov doesn’t just want to identify molecules for known targets, he wants to find new ones and shepherd them all the way through clinical trials. And, indeed, also to continue to collect data during those clinical trials that might improve future drug discovery projects. 

“So far nobody has challenged us to solve a disease in partnership” he says. “If that happens, I’ll be a very happy man.” 

That said, Insilico Medicine’s approach to novel target discovery has been used piecemeal, too. For instance, Insilico Medicine has collaborated with Pfizer on novel target discovery, and Johnson and Johnson on small molecule design and done both with Taisho Pharmaceuticals. Today, the company also announced a new partnership with Teva Branded Pharmaceutical Products R&D, Inc. Teva will aim to use PandaOmics to identify new drug targets.

That said, it’s not just Insilico Medicine raking in money and partnerships. The whole field of A.I-based novel targets has been experiencing significant hype.

In 2019 Nature noted that at least 20 partnerships between major drug companies and A.I drug discovery tech companies had been reported. In 2020, investment in A.I companies pursuing drug development increased to $13.9 billion, a four-fold increase from 2019, per Stanford University’s Artificial Intelligence Index annual report. R&D cost 

Drug discovery projects received the greatest amount of private A.I investment in 2020, a trend that can partially be attributed to the pandemic’s need for rapid drug development. However, the roots of the hype predate Covid-19. 

Zhavorokov is aware that A.I based drug development is riding a bit of a hype wave right now. “Companies without substantial evidence supporting their A.I powered drug discovery claims manage to raise very quickly,” he notes. 

Insilico Medicine, he says, can distinguish itself based on the quality of its investors. “Our investors don’t gamble,” he says. 

But like so many other A.I-based drug discovery platforms, we’ll have to see whether they make it through the clinical trial churn. 

 

#ai, #artificial-intelligence, #clinical-trials, #drug-discovery, #machine-learning, #tc

“Alzheimer’s is open for business:” Controversial FDA approval could pave the way for future drugs

On Monday, a 17-year drought in the world of Alzheimer’s drugs ended with the FDA approval of Biogen’s Aduhlem (aducanumab). The controversy behind the FDA’s decision was considerable, but it doesn’t seem to be spooking drug developers who are now narrowing in on the degenerative brain disease. 

In a nutshell, the approval of Aduhelm came after conflicting results from clinical trials. In November 2020 an independent FDA advisory board did not recommend that the agency endorse the drug, but in June, the agency approved the drug anyway via an Accelerated Approval Program. 

Aduhelm is now the first novel treatment to address one underlying cause of Alzheimer’s – beta-amyloid plaques that accumulate in the brain. 

The drug received support from patient and industry groups (the FDA also noted that the “need for treatment is urgent”, in a statement explaining the agency’s choice). Still, there have been a number of doctors who have expressed concern. One member of the expert committee that voted not to recommend the approval of Aduhelm back in November has resigned since the announcement.

However, the inconsistency of the science and highly public debate around the approval of Aduhelm doesn’t seem to have halted enthusiasm within the pharmaceutical industry. Rather, it may signal a new wave of additional treatments in the next few years, which will piggyback off of the approval of Aduhelm (however controversial that approval may be).

“This is great news for investors and for drug companies that are working towards new drugs,” says Alison Ward, a research scientist at the USC Schaeffer Center for Health Policy and Economics. 

Historically there have been a few factors that have made the development of a drug for Alzheimer’s an uphill battle. 

The first, is a 17-year history of failure to bring a drug through clinical trials. Even Biogen’s clinical trials for Aduhelm were halted in 2019 because it wasn’t clear that they would reach their clinical endpoints (effectively, the target outcomes of the trial). In fact, Aduhlem was approved based on a “surrogate endpoint,” the decline of beta-amyloid, not the primary endpoint, cognitive function. 

Trials for Alzheimer’s drugs have also historically been expensive. A 2018 paper in Alzheimer’s and Dementia: Translational Research and Clinical Interventions (a journal run by the Alzheimer’s Association) estimated that the cost of developing an Alzheimer’s drug was about $5.6 billion. By comparison, the mean investment needed to bring a new drug to market is about $1.3 billion according to analysis of SEC filings for companies that applied for FDA approval between 2009 and 2018 (though the median cost was about $985 million). Older estimates have put the costs of bringing a drug to market at $2.8 billion

For Alzheimer’s specifically, Phase 3 trials are still largely sponsored by industry, but over the past five years, trials sponsored solely by the industry have decreased. Government grants and funding via public-private partnerships have made up an increasing share of available funds.

Martin Tolar, the founder CEO of Alzheon, another company pursuing an oral treatment for Alzheimer’s (currently in a phase 3 clinical trial), says that attracting other forms of funding was a challenge. 

“It was impossible to finance anything,” he says. “It was impossible to get Wall Street interested because everything was failing one after the other after the other.” 

He expects this recent approval of Aduhelm to change that outlook considerably. Already, we are seeing some increased interest in companies already in phase 3 clinical trials: After the FDA announcement, shares of Eli Lilly, also running a phase 3 clinical trial, surged by 10 percent

“I’ve had probably hundreds of discussions, of calls, from bankers, investors, collaborators, pharma, you name it,” Tolar says. “Alzheimer’s is open for business.”

With renewed interest and what appears like a pathway to approval at the FDA, the environment for the next generation of Alzheimer’s drugs seems to be ripening. Right now, there are about 130 phase 3 clinical trials on Alzheimer’s drugs that are either completed, active or recruiting. 

Tolar sees the FDA decision, based on imperfect data, as a “signal of urgency” to approve new treatments that are imminent arrivals. 

As Ward pointed out in a white paper on in-class drug innovation, “follow on” drugs go on to become leaders in the industry, especially if they demonstrate better safety or efficacy than the drug that was first to market.  That, the paper argues, suggests drug approval may “pave the way” for more effective drugs in the future. 

In the case of Alzheimer’s, it might not be one drug that dominates, even as more get approved, she notes. Rather, a cadre of new, approved drugs may go on to compliment one another.

“The way that the medical community is thinking of AD [Alzheimer’s Disease] now is that it’s likely going to be a combination of drugs or a cocktail of drugs that comes together to have true success at delaying progression,” she says.  

“If we’re looking to treat AD with a cocktail of drugs, history suggests it’s individually approved drugs that come together to make those drug cocktails.” 

There are still some potential pitfalls for future drugs to consider. One argument is that with an approved drug available, it may be more difficult to recruit participants in clinical trials, slowing the pace of drug discovery. In that respect, Ward argues that this will ultimately be dwarfed by patients who will now look into a potential diagnosis for Alzheimer’s now that there’s something to treat it with. 

There’s also the fact that Aduhelm’s costs are high (about $56,000 for a year’s supply, the brunt of which will be borne by Medicare), and the data remains questionable. Those factors may push patients towards other drugs, even if they’re in clinical trials. 

Additionally, there is the question of how well Aduhelm actually performs during the critical followup study mandated by the FDA as a condition of the drug’s approval. Whether Aduhelm can truly slow cognitive decline, as well as help address levels of beta-amyloid from the brain, remains questionable based on current data. 

Still, Tolar doesn’t see the results of that study as particularly relevant because the industry will have moved on. Biogen CEO Michel Vounatsos has said it may not share results of this trial for as many as nine years, though he noted the company would try to deliver data sooner. 

 “There will be better drugs by then,” Tolar predicts. 

Tolar’s phase 3 clinical trial just began dosing this week, and is scheduled to end by 2024

Biogen and Esai will likely also have another drug ready for evaluation by then, as two phase 3 clinical trials for another beta-amyloid antibody treatment called lecanemab are scheduled for completion by 2024 and 2027

The signal sent by Monday’s approval may be a pathway for future drugs, rather than an end itself. The data is imperfect, the costs high, and the controversy considerable, but the band-aid has been ripped off. 

#dementia, #drug-development, #drug-discovery, #health, #life-sciences, #tc, #therapeutics

After contentious debate, FDA approves first Alzheimer’s drug since 2003

On Monday, the US Food and Drug Administration granted approval to a keenly-watched Alzheimer’s drug, aducanumab, developed by the drugmaker Biogen. The decision to approve the drug, which was once abandoned as a failure, has been the subject of debate within the scientific and regulatory community for months.

Aducanumab, which will be marketed as Aduhelm, is the first novel Alzheimer’s treatment to be approved since 2003, the FDA noted in a press release. Aducanumab is also the first novel treatment designed to address one of several proposed underlying causes of Alzheimer’s: the buildup of beta-amyloid plaques in the brain that disrupt the communication of neurons. 

Critically, the drug received a conditional form of FDA approval called the ‘Accelerated Approval Program.’ The accelerated approval pathway is designed to provide early access to drugs for serious conditions if they address markers of disease – even when the FDA has misgivings about the overall results of clinical trials. Because of this, Biogen will still have to conduct a post-approval confirmatory trial of aducanumab. 

If the drug does not work as intended, we can take steps to remove it from the market. But hopefully, we will see further evidence of benefit in the clinical trial and as greater numbers of people receive Aduhelm,” the FDA statement reads. 

TechCrunch has contacted Biogen for comment on the upcoming confirmatory trial, and will update this story with Biogen’s response. 

The use of the accelerated approval pathway is clearly intended to address lingering controversies that have plagued aducanumab in the months leading up to the FDA’s ruling. 

In early-stage trials, there were promising signs that aducanumab might slow cognitive decline, a major Alzheimer’s symptom. In a 2016 trial published in the journal Nature, 125 patients with mild or moderate Alzheimer’s who received monthly infusions of the drug saw levels of plaques decrease, as did symptoms of cognitive decline. 

The decline of the plaques in the brain were “robust and unquestionable” as one Lancet Neurology paper puts it, but the clinical findings were more modest – it wasn’t clear exactly how much people’s cognitive ability benefitted from the treatment. 

These early trials eventually led the FDA to allow the drug to skip phase 2 clinical trials, which are designed to identify dosages of the drug, and proceed directly to phase 3 clinical trials. This move was criticized by some physicians. 

Those phase 3 clinical trials, called ENGAGE and EMERGE, have become the center of tension. Both trials tested monthly intravenous injections of the drug on about 1600 patients with early Alzheimer’s. In 2019, both trials were halted because the drug didn’t appear to be slowing cognitive decline, the primary endpoint of the trials. 

Additional data analyzed in late 2019 from the EMERGE trial suggested that the drug was linked with a 23 percent less cognitive decline, compared to a placebo. There were side effects: namely swelling and inflammation of the brain. This was seen in about 40 percent of Phase 3 trial participants, though most were symptomatic and most of those with symptoms (headache, nausea, visual disturbances) resolved after 4-16 weeks. 

Still, even the new data wasn’t enough to convince an independent FDA advisory committee, who, in November 2020 did not endorse approval of the drug. 

On Monday, The FDA, argued that the drug’s effects on beta-amyloid plaques were strong enough to suggest that benefit outweighed the risk. Critically, the FDA did not comment on the strength of clinical outcomes – in short, the agency is basing this approval on the drug’s ability to address beta-amyloid plaques, not how well each patient cognitive function responds to the drug. The followup study will need to address that outcome directly. 

Still, about 6 million people have Alzheimer’s in the US, and patient organizations have rallied in response to this drug. The Alzheimer’s Association has hailed the drug as a “victory for people living with Alzheimer’s.” 

Ahead of the FDA’s decision on Monday, it was clear that, should aducanumab be approved, it would soon become a “blockbuster drug.” The financial picture around the drug seems to support that idea. 

Trading of Biogen shares were initially halted, but have since jumped 40 percent today, following the announcement. Shares of Eisai Co. Ltd, a Japanese company working with Biogen jumped over 46 percent in the first three hours following the FDA’s approval. 

Certainly, Biogen was banking on this approval as a long-term strategy. In an April 2021, earnings presentation, the company estimated that there were 600 sites ready to launch the treatment post-approval. Biogen has also submitted marketing authorization applications for aducanumab in Brazil, Canada, Switzerland and Australia. On June 7, the company announced that a year’s supply of the drug would cost $56,000

In the wider world of Alzheimer’s drugs, it’s possible other companies may see this approval as proof-of-concept for other drugs targeting beta amyloid plaques. 

In an editorial that accompanied the 2016 Nature paper on aducanumab, Eric Reiman, executive director of Banner Alzheimer’s Institute, argued that scientific confirmation that beta-amyloid-targeted treatment slows cognitive decline would be a “game changer.” The aducanumab trials have been likened to a test of this idea. Speaking to The Financial Times, Howard Filit, founding executive director of the Alzheimer’s Drug Discovery Foundation, called aducanumab “the first rigorous test of the beta-amyloid hypothesis.”

In that sense, conditional approval may indicate that the FDA is sympathetic to this form of Alzheimer’s treatment. 

There’s at least one more beta-amyloid targeted drug from a major drugmaker (Eli Lilly) clinical trials. We may see some more of them emerge soon, provided that Biogen’s confirmatory study of aducanumab doesn’t prompt the FDA to withdraw approval. 

 

#alzheimers, #drug-discovery, #life-sciences, #pharmaceuticals, #tc

Engine Biosciences expands its digital drug discovery pipeline with $43M round A

Drug discovery is a large and growing field, in which are to be found both ambitious startups and billion-dollar big pharma incumbents. Engine Biosciences is one of the former, a Singaporean outfit with with an expert founding crew and a different approach to the business of finding new therapeutics, and it just raised $43 million to keep growing.

Digital drug discovery in general means large-scale analysis of biological data like genes, gene expression, protein structures, binding sites, things like that. Where it has hit a wall in the past is not on the digital side, where any number of likely molecules or processes can be generated, but on the next step, when those notions need to be tested in vitro. So a new crop of biotech companies have worked to integrate these aspects.

Engine does so with a pair of tools it has dubbed NetMAPPR and CombiGEM. NetMAPPR is a huge sort of search engine for genes and gene interactions, taking especial note of “errors” that could provide a foothold for a molecule or treatment. CombiGEM is like a mass genetic testing process that can look into thousands of gene combinations and edits on diseased cells simultaneously, providing quick experimental confirmation of the targets and effects proposed by the digital side. The company is focused on anti-cancer drugs but is looking into other fields as they become viable.

Jeffrey Lu, Co-Founder and CEO, Engine Biosciences

Image Credits: Engine Biosciences

The focus on gene interactions sets their approach apart, said co-founder and CEO Jeffrey Lu.

“Gene interactions are relevant to all diseases, and in cancers, where we focus, a proven approach for effective precision medicines,” he explained. “For example, there are four approved drugs targeting the PARP enzyme in the context of mutation in the BRCA gene that is changing cancer treatment for millions of people. The fundamental principle of this precision medicine is based on understanding the gene interaction between BRCA and PARP.”

The company raised a $10M seed in 2018, and has been doing its thing ever since — but it needs more money if it’s going to bring some of these things to market.

“We already have chemical compounds directed towards the novel biology we have uncovered,” said Lu. “These are effectively prototype drugs, which are showing anti-cancer effects in diseased cells. We need to refine and optimize these prototypes to a suitable candidate to enter the clinic for testing in humans.”

Right now they’re working with other companies to do the next step up from automated testing, which is to say animal testing, to clear the way for human trials.

The CombiGEM experiments — hundreds of thousands of them — produce a large amount of data as well, and they’re sharing and collaborating on that front with several medical centers throughout Asia. “We have built what we believe to be the largest data compendium related to gene interactions in the context of cancer disease relevance,” said Lu, adding that this is crucial to the success of the machine learning algorithms they employ to predict biological processes.

The $43M round was led by Polaris Partners, with participation by newcomers Invus and a long list of existing investors. The money will go towards the requisite testing and paperwork involved in bringing a new drug to market based on promising leads.

“We have small molecule compounds for our lead cancer programs with data from in vitro (in cancer cells) experiments. We are refining the chemistry and expanding studies this year,” said Lu. “Next year, we anticipate having our first drug candidate enter the late preclinical phase of development and regulatory work for an IND (investigational new drug) filing with the FDA, and starting the clinical trials in 2023.”

It’s a long road to human trials, let alone widespread use, but that’s the risk any drug discovery startup takes. The carrot dangling in front of them is not just the possibility of a product that could generate billions in income, but perhaps save the lives of countless cancer patients awaiting novel therapies.

#artificial-intelligence, #biotech, #drug-discovery, #engine-biosciences, #funding, #fundings-exits, #recent-funding, #science, #startups, #tc

With new Partner Colin Hanna, and Shikha Ahluwalia as Associate, Balderton puts down roots in Berlin

As of now, one fo the UK’s biggest and most active tech VCs has a new partner. Principal Colin Hanna has spearheaded several of Balderton’s deals in the past couple of years, and has now been appointed a Partner. But there’s a twist to this plot. He will be officially based in Berlin (where he’s lived since 2019), thus giving the VC a more powerful reach, being based, as it is, solely in London.

Hanna said: “Having been with Balderton for five years, I am humbled to now call my mentors my Partners. I look forward to strengthening Balderton’s unique approach from Berlin as we engineer serendipity for European founders with planet-scale ambition.”

Bernard Liautaud, Managing Partner of Balderton commented: “We are delighted to announce Colin’s promotion to Partner. Since he joined Balderton in 2016, Colin has had a significant impact on both Balderton and our portfolio… Colin has strengthened our position in DACH by establishing our permanent presence in Berlin and bringing in Shikha Ahluwalia, whom we are delighted to have. In addition, he was instrumental in the definition of the Balderton Sustainable Future Goals. We have no doubt Colin will be highly successful in his new role.”

The story does not end there, however. Joining him will be tech entrepreneur and founder Shikha Ahluwalia as an Associate covering the DACH region.

co-founded SBL, the D2C women’s fashion e-commerce company in India. Prior to that she was had a tech advisory boutique, and was previously with JP Morgan’s Investment Banking Division in London.

Balderton has 10 current investments across DACH including Contentful, Infarm, SOPHiA Genetics, McMakler, Demodesk, and vivenu.

Ahluwalia commented: “Over the past few years, I have seen the DACH start-up ecosystem evolve rapidly. We at Balderton believe the next European giant will be a technology company and know that the DACH ecosystem plays a significant role in helping form category-leading technology companies. As a former founder myself, I have first-hand experience with the unique challenges of running young businesses. I am excited to contribute and support founders on their own journey as part of Balderton Capital.”

Speaking to me over an interview Hanna said: “Shikha’s hiring deepens our commitment to the local Berlin ecosystem and to the DACH region more broadly. We have been actively supporting Founders in Germany for more than a decade.”

After spending his childhood in Jakarta and Hong Kong, and picking up a degree in Political Economy, Hanna has carved out a career in venture investing – at Balderton since July 4, 2016 – looking at it through the prism on the rise of urban living, grassroots-driven technologies like open source and crypto, and the political ramifications of technology.

He sits on the Board of companies like e-bikes startup VanMoof, Finoa (a crypto custodian), Rahko (quantum computing drug discovery, and helped lead on investments into Traefik and Luno and Vivenu).

One these you might pick up from all those is that they err towards the ‘purpose-driven’ side of the equation.

He told me: “I believe the next generation of Founders, particularly in Europe, care more about just their bank accounts and want to build companies that generate impact and are not afraid to take a view on how they want the world to change. Measuring this is a challenge and something we are trying to do with our SFGs at Balderton which I helped spearhead. I believe that when companies like Coinbase and others go “apolitical” they commit themselves to defending the structural status quo rather than becoming agents of deliberate change.”

“My point about purpose driven companies is that when I think when employees want to work with companies believe in their values and you try to tell them those aren’t important, that could be viewed as political. I don’t think we should be we should be muffling the employees.”

Does he think Coinbase, and also recent more recently Basecamp / 37 Signals were wrong to so-called ‘depoliticize’ their businesses?

“I think, I think every CEO is free to run their company how they see fit. But I think that that poses challenges for them on the talent side. I understand, as an American, how charged and how destructive the political climate became, and so I can really understand and empathize why certain choices were made at that time, because you get to a point where that where the conversation becomes toxic… I hope that the steps that they’ve taken, don’t strangle dialogue and conversation that’s constructive about how we want to make an impact and change the world, either as individuals or with the companies we work for,” he said.

Hanna also told me that he think VCs should be wary that the shift to remote will make it easier to invest more widely. “You have to more background checks on founders now, and things like that. But is it a ‘little bit’ more dangerous or is it ‘50% more dangerous’ the fact that people aren’t meeting up in person?”

#balderton, #balderton-capital, #basecamp, #berlin, #bernard-liautaud, #ceo, #coinbase, #colin-hanna, #companies, #drug-discovery, #europe, #finance, #germany, #india, #jakarta, #jp-morgan, #london, #managing-partner, #sophia-genetics, #startup-company, #tc, #technology, #united-kingdom, #vanmoof

Longevity startup Gero AI has a mobile API for quantifying health changes

Sensor data from smartphones and wearables can meaningfully predict an individual’s ‘biological age’ and resilience to stress, according to Gero AI.

The ‘longevity’ startup — which condenses its mission to the pithy goal of “hacking complex diseases and aging with Gero AI” — has developed an AI model to predict morbidity risk using ‘digital biomarkers’ that are based on identifying patterns in step-counter sensor data which tracks mobile users’ physical activity.

A simple measure of ‘steps’ isn’t nuanced enough on its own to predict individual health, is the contention. Gero’s AI has been trained on large amounts of biological data to spots patterns that can be linked to morbidity risk. It also measures how quickly a personal recovers from a biological stress — another biomarker that’s been linked to lifespan; i.e. the faster the body recovers from stress, the better the individual’s overall health prognosis.

A research paper Gero has had published in the peer-reviewed biomedical journal Aging explains how it trained deep neural networks to predict morbidity risk from mobile device sensor data — and was able to demonstrate that its biological age acceleration model was comparable to models based on blood test results.

Another paper, due to be published in the journal Nature Communications later this month, will go into detail on its device-derived measurement of biological resilience.

The Singapore-based startup, which has research roots in Russia — founded back in 2015 by a Russian scientist with a background in theoretical physics — has raised a total of $5 million in seed funding to date (in two tranches).

Backers come from both the biotech and the AI fields, per co-founder Peter Fedichev. Its investors include Belarus-based AI-focused early stage fund, Bulba Ventures (Yury Melnichek). On the pharma side, it has backing from some (unnamed) private individuals with links to Russian drug development firm, Valenta. (The pharma company itself is not an investor).

Fedichev is a theoretical physicist by training who, after his PhD and some ten years in academia, moved into biotech to work on molecular modelling and machine learning for drug discovery — where he got interested in the problem of ageing and decided to start the company.

As well as conducting its own biological research into longevity (studying mice and nematodes), it’s focused on developing an AI model for predicting the biological age and resilience to stress of humans — via sensor data captured by mobile devices.

“Health of course is much more than one number,” emphasizes Fedichev. “We should not have illusions about that. But if you are going to condense human health to one number then, for a lot of people, the biological age is the best number. It tells you — essentially — how toxic is your lifestyle… The more biological age you have relative to your chronological age years — that’s called biological acceleration — the more are your chances to get chronic disease, to get seasonal infectious diseases or also develop complications from those seasonal diseases.”

Gero has recently launched a (paid, for now) API, called GeroSense, that’s aimed at health and fitness apps so they can tap up its AI modelling to offer their users an individual assessment of biological age and resilience (aka recovery rate from stress back to that individual’s baseline).

Early partners are other longevity-focused companies, AgelessRx and Humanity Inc. But the idea is to get the model widely embedded into fitness apps where it will be able to send a steady stream of longitudinal activity data back to Gero, to further feed its AI’s predictive capabilities and support the wider research mission — where it hopes to progress anti-ageing drug discovery, working in partnerships with pharmaceutical companies.

The carrot for the fitness providers to embed the API is to offer their users a fun and potentially valuable feature: A personalized health measurement so they can track positive (or negative) biological changes — helping them quantify the value of whatever fitness service they’re using.

“Every health and wellness provider — maybe even a gym — can put into their app for example… and this thing can rank all their classes in the gym, all their systems in the gym, for their value for different kinds of users,” explains Fedichev.

“We developed these capabilities because we need to understand how ageing works in humans, not in mice. Once we developed it we’re using it in our sophisticated genetic research in order to find genes — we are testing them in the laboratory — but, this technology, the measurement of ageing from continuous signals like wearable devices, is a good trick on its own. So that’s why we announced this GeroSense project,” he goes on.

“Ageing is this gradual decline of your functional abilities which is bad but you can go to the gym and potentially improve them. But the problem is you’re losing this resilience. Which means that when you’re [biologically] stressed you cannot get back to the norm as quickly as possible. So we report this resilience. So when people start losing this resilience it means that they’re not robust anymore and the same level of stress as in their 20s would get them [knocked off] the rails.

“We believe this loss of resilience is one of the key ageing phenotypes because it tells you that you’re vulnerable for future diseases even before those diseases set in.”

“In-house everything is ageing. We are totally committed to ageing: Measurement and intervention,” adds Fedichev. “We want to building something like an operating system for longevity and wellness.”

Gero is also generating some revenue from two pilots with “top range” insurance companies — which Fedichev says it’s essentially running as a proof of business model at this stage. He also mentions an early pilot with Pepsi Co.

He sketches a link between how it hopes to work with insurance companies in the area of health outcomes with how Elon Musk is offering insurance products to owners of its sensor-laden Teslas, based on what it knows about how they drive — because both are putting sensor data in the driving seat, if you’ll pardon the pun. (“Essentially we are trying to do to humans what Elon Musk is trying to do to cars,” is how he puts it.)

But the nearer term plan is to raise more funding — and potentially switch to offering the API for free to really scale up the data capture potential.

Zooming out for a little context, it’s been almost a decade since Google-backed Calico launched with the moonshot mission of ‘fixing death’. Since then a small but growing field of ‘longevity’ startups has sprung up, conducting research into extending (in the first instance) human lifespan. (Ending death is, clearly, the moonshot atop the moonshot.) 

Death is still with us, of course, but the business of identifying possible drugs and therapeutics to stave off the grim reaper’s knock continues picking up pace — attracting a growing volume of investor dollars.

The trend is being fuelled by health and biological data becoming ever more plentiful and accessible, thanks to open research data initiatives and the proliferation of digital devices and services for tracking health, set alongside promising developments in the fast-evolving field of machine learning in areas like predictive healthcare and drug discovery.

Longevity has also seen a bit of an upsurge in interest in recent times as the coronavirus pandemic has concentrated minds on health and wellness, generally — and, well, mortality specifically.

Nonetheless, it remains a complex, multi-disciplinary business. Some of these biotech moonshots are focused on bioengineering and gene-editing — pushing for disease diagnosis and/or drug discovery.

Plenty are also — like Gero —  trying to use AI and big data analysis to better understand and counteract biological ageing, bringing together experts in physics, maths and biological science to hunt for biomarkers to further research aimed at combating age-related disease and deterioration.

Another recent example is AI startup Deep Longevity, which came out of stealth last summer — as a spinout from AI drug discovery startup Insilico Medicine — touting an AI ‘longevity as a service’ system which it claims can predict an individual’s biological age “significantly more accurately than conventional methods” (and which it also hopes will help scientists to unpick which “biological culprits drive aging-related diseases”, as it put it).

Gero AI is taking a different tack toward the same overarching goal — by honing in on data generated by activity sensors embedded into the everyday mobile devices people carry with them (or wear) as a proxy signal for studying their biology.

The advantage being that it doesn’t require a person to undergo regular (invasive) blood tests to get an ongoing measure of their own health. Instead our personal device can generate proxy signals for biological study passively — at vast scale and low cost. So the promise of Gero’s ‘digital biomarkers’ is they could democratize access to individual health prediction.

And while billionaires like Peter Thiel can afford to shell out for bespoke medical monitoring and interventions to try to stay one step ahead of death, such high end services simply won’t scale to the rest of us.

If its digital biomarkers live up to Gero’s claims, its approach could, at the least, help steer millions towards healthier lifestyles, while also generating rich data for longevity R&D — and to support the development of drugs that could extend human lifespan (albeit what such life-extending pills might cost is a whole other matter).

The insurance industry is naturally interested — with the potential for such tools to be used to nudge individuals towards healthier lifestyles and thereby reduce payout costs.

For individuals who are motivated to improve their health themselves, Fedichev says the issue now is it’s extremely hard for people to know exactly which lifestyle changes or interventions are best suited to their particular biology.

For example fasting has been shown in some studies to help combat biological ageing. But he notes that the approach may not be effective for everyone. The same may be true of other activities that are accepted to be generally beneficial for health (like exercise or eating or avoiding certain foods).

Again those rules of thumb may have a lot of nuance, depending on an individual’s particular biology. And scientific research is, inevitably, limited by access to funding. (Research can thus tend to focus on certain groups to the exclusion of others — e.g. men rather than women; or the young rather than middle aged.)

This is why Fedichev believes there’s a lot of value in creating a measure than can address health-related knowledge gaps at essentially no individual cost.

Gero has used longitudinal data from the UK’s biobank, one of its research partners, to verify its model’s measurements of biological age and resilience. But of course it hopes to go further — as it ingests more data. 

“Technically it’s not properly different what we are doing — it just happens that we can do it now because there are such efforts like UK biobank. Government money and also some industry sponsors money, maybe for the first time in the history of humanity, we have this situation where we have electronic medical records, genetics, wearable devices from hundreds of thousands of people, so it just became possible. It’s the convergence of several developments — technological but also what I would call ‘social technologies’ [like the UK biobank],” he tells TechCrunch.

“Imagine that for every diet, for every training routine, meditation… in order to make sure that we can actually optimize lifestyles — understand which things work, which do not [for each person] or maybe some experimental drugs which are already proved [to] extend lifespan in animals are working, maybe we can do something different.”

“When we will have 1M tracks [half a year’s worth of data on 1M individuals] we will combine that with genetics and solve ageing,” he adds, with entrepreneurial flourish. “The ambitious version of this plan is we’ll get this million tracks by the end of the year.”

Fitness and health apps are an obvious target partner for data-loving longevity researchers — but you can imagine it’ll be a mutual attraction. One side can bring the users, the other a halo of credibility comprised of deep tech and hard science.

“We expect that these [apps] will get lots of people and we will be able to analyze those people for them as a fun feature first, for their users. But in the background we will build the best model of human ageing,” Fedichev continues, predicting that scoring the effect of different fitness and wellness treatments will be “the next frontier” for wellness and health (Or, more pithily: “Wellness and health has to become digital and quantitive.”)

“What we are doing is we are bringing physicists into the analysis of human data. Since recently we have lots of biobanks, we have lots of signals — including from available devices which produce something like a few years’ long windows on the human ageing process. So it’s a dynamical system — like weather prediction or financial market predictions,” he also tells us.

“We cannot own the treatments because we cannot patent them but maybe we can own the personalization — the AI that personalized those treatments for you.”

From a startup perspective, one thing looks crystal clear: Personalization is here for the long haul.

 

#ageing, #api, #apps, #artificial-intelligence, #biotech, #deep-neural-networks, #drug-development, #drug-discovery, #elon-musk, #gero-ai, #google, #health, #humanity-inc, #insilico-medicine, #insurance, #longevity, #machine-learning, #mobile, #mobile-devices, #peter-thiel, #russia, #science, #smartphones, #tc, #wearable-devices

The health data transparency movement is birthing a new generation of startups

In the early 2000s, Jeff Bezos gave a seminal TED Talk titled “The Electricity Metaphor for the Web’s Future.” In it, he argued that the internet will enable innovation on the same scale that electricity did.

We are at a similar inflection point in healthcare, with the recent movement toward data transparency birthing a new generation of innovation and startups.

Those who follow the space closely may have noticed that there are twin struggles taking place: a push for more transparency on provider and payer data, including anonymous patient data, and another for strict privacy protection for personal patient data. What’s the main difference?

This sector is still somewhat nascent — we are in the first wave of innovation, with much more to come.

Anonymized data is much more freely available, while personal data is being locked even tighter (as it should be) due to regulations like GDPR, CCPA and their equivalents around the world.

The former trend is enabling a host of new vendors and services that will ultimately make healthcare better and more transparent for all of us.

These new companies could not have existed five years ago. The Affordable Care Act was the first step toward making anonymized data more available. It required healthcare institutions (such as hospitals and healthcare systems) to publish data on costs and outcomes. This included the release of detailed data on providers.

Later legislation required biotech and pharma companies to disclose monies paid to research partners. And every physician in the U.S. is now required to be in the National Practitioner Identifier (NPI), a comprehensive public database of providers.

All of this allowed the creation of new types of companies that give both patients and providers more control over their data. Here are some key examples of how.

Allowing patients to access all their own health data in one place

This is a key capability of patients’ newly found access to health data. Think of how often, as a patient, providers aren’t aware of treatment or a test you’ve had elsewhere. Often you end up repeating a test because a provider doesn’t have a record of a test conducted elsewhere.

#artificial-intelligence, #cloud-computing, #column, #drug-discovery, #ec-column, #ec-consumer-health, #ec-market-map, #enterprise, #food-and-drug-administration, #health, #health-systems, #healthcare, #healthcare-data, #machine-learning, #startups, #united-states

Could Valo Health become one of Flagship Pioneering’s biggest companies yet?

The investment firm Flagship Pioneering has incubated a lot of life sciences companies since it was founded in 2000. In fact, while a general partner with Flagship Pioneering over the last 15 years, David Berry has started more than 30 companies, five of which trade publicly right now: Seres Therapeutics, Sensen Bio, Evelo Biosciences, T2 Biosystems, and Axcella Health.

Berry is often a company’s first CEO, then transitions out of the company within 18 months. But he has no plans to leave his post as CEO of Valo Health, a three-year-old, Boston-based, 110-person drug discovery company that Berry and Flagship seem to think could become one of the firm’s most important companies yet. That’s notable, considering that Flagship incubated 11-year-old Moderna, which currently boasts a $50 billion market cap thanks in large part its coronavirus vaccine.

Perhaps it’s no surprise, given Berry’s and Flagship’s track record that Valo has attracted believers. Notably, today it is announcing a fresh $110 million in extended Series B financing from Koch Disruptive Industries that brings the round total to $300 million and the overall amount the young company has raised to more than $450 million.

Still, given that there are hundreds of drug discovery companies in the world seizing on the latest advancements in AI, machine learning and computation, it’s easy to wonder what’s so special about this one. We got Berry’s take during a chat with him yesterday, parts of which we are featuring below edited for length and clarity.

TC: Valo is trying to accelerate the creation of drugs, and it has a computational platform called Opal to do it faster and more effectively than many rivals. Is there a way to make it clearer to outsiders why this platform is so unique? 

DB: First, from day one, we were operating on a different scale [than past Flagship Pioneering companies]. Typically, when you look at Flagship companies, there’s an [exclusive] initial commitment by Flagship of plus or minus $50 million. But because of the scale of the opportunity that we saw ahead of us with Valo, we actually started out by bringing in external financing partners as part of a Series A that was right around $100 million.

[Also unique is the] breadth of what we’re trying to achieve through our systematic approach to R&D, as opposed to a targeted approach to thinking about it. There’s been an historical challenge in life sciences in that companies are primarily viewed based on what their lead therapeutic asset looks like. But if you have the potential to change the scope, the scale, the potential, the speed, the probability of success, [and] the cost of developing drugs, you’re not going to look like a typical therapeutics company.

TC: So your focus on multiple therapeutic areas at once — oncology, neurodegenerative, and cardiovascular diseases — is a distinguishing element of the company. How are you tackling so much simultaneously?

DB: The legacy biopharma model is basically this point-to-point system [where up to 15 groups] do some work, and then they basically take the result of it and they throw it over a wall to another group that has its own framework. The model is intrinsically disintegrated. They use mice. They use cell lines. They use extracted organs. And those just don’t represent what a full, intact living human actually looks like, and they don’t reflect what the disease looks like in the context of that human.

What we’re doing is what I would call that next transformation . . enabled by high-quality human-centric data [that we analyze] in an end-to-end, but componentized manner. What I mean by that is we’ve created a single underlying architecture so that we’re using the same species, we’re using the same decision-making criteria. we’re using the same KPIs throughout the entirety of the R&D cascade, [and] we’re using the same bases of the core computation. We’re using the same self-reinforcing model to learn as we go. We have a local expression, because we have to perform a certain set of tasks in order to comply with the regulatory environment. But by doing it in this way as we do those tasks, we’re learning a lot more and we’re keeping that human centricity, so when we uncover for example, a new target in cardiovascular disease or neurodegenerative disease, it’s based on our human data. It’s not based on a dog model or mouse model or something along those lines. It’s not based on cells adapted to plastic in a lab.

TC: Where is that human data coming from? Is the data you’re feeing into Opal somehow better or different than what others are using?

DB: We haven’t we haven’t yet disclosed where our datasets are coming from, but we have reason to believe that the scale and quality of the data sets are substantially high. We have not seen data sets that compare in scope and size. We have announced one subset of our data lake, but I would call it a small subset through a data partnership we announced earlier. [Editor’s note: this is with a company called Global Genomics Group, which gives Valo access to a cardio-metabolic dataset.]

TC: You’ve been at this for a few years. Have you had any major breakthroughs?

DB: I believe what we’ve done over the last two years is build an incredibly strong technology basis and foundation [for] transformation. We’ve announced four therapeutic programs that we’ve launched thus far, and each represents not only something where we’ve been able to develop a therapeutic candidate in very short periods of time, but we’ve also been able to overcome issues that were historical barriers things that made developing those sorts of candidates much more difficult, and we were able to overcome those barriers in weeks.

TC: Can you elaborate on one of those therapies to underscore your point?

DB: One of the programs we announced is called NAMPT. What was really interesting about it is it’s a very powerful cancer target. The downside of it is it’s known to cause a very particular toxicological effect — it causes retinal toxicity — and what we wanted to figure out was whether we could get the benefit of the molecule by targeting the target but avoiding getting that molecule into the retina, which required a very specific design. Long story short, in a couple of weeks, we were able to achieve a molecule that had enough differentiation between the blood in the eye that it shouldn’t have any substantial effects.

TC: Are any of these four candidates heading into the market any time soon?

DB: I would love them to be in the market soon but they’re not yet there. We are expecting that with the financing in hand, we should ultimately have molecules in clinical trials, and ultimately we’re very excited to be able to transition some of the drugs that we’re developing into [viable offerings in the market].

TC: Would you sell then sell these to a big pharma company, or would Valo be marketing these itself?

DB: Both are viable potential paths. Because we’re developing a number of different therapeutics, it gives us flexibility in the way we think about our ultimate business model.

#biotech, #drug-discovery, #flagship-pioneering, #machine-learning, #medicine, #moderna, #recent-funding, #startups, #tc, #venture-capital

Nanome raises $3 million to help scientists get up close with molecular structures in VR

Discovery and research of new molecular compounds is an expensive business, with development costs exceeding $10 billion per substance in some cases. Part of that comes from the need to closely examine every relevant molecule, studying its chemical composition and interactions as well as its physical structure at the atomic level. Despite advances in software to help model these compounds and molecules, there are still challenges in fully understanding their shapes through a two-dimensional computer screen.

San Diego-based startup Nanome uses virtual reality to solve that problem. The idea for Nanome came out of CEO and Founder Steve McCloskey’s time in the nanoengineering program at UC San Diego, where he saw a need for a better understanding of three-dimensional molecular structures.

“Understanding structure empowers our users to understand how their designs function,” he wrote in an email. “Yet, the R&D process for drug discovery relies on 2D monitors, keyboard, and mouse, which limits the understanding of complex 3D structures or interactions and contributes to massive R&D costs averaging $2.5B per drug.”

Nanome recently closed a funding round led by Bullpen Capital for $3 million to establish new business partnerships, build up the company’s brand, and expand their science and engineering team. “Nanome is reimagining the way we interact with science at a time when innovation in collaboration is more important than ever before,” said Bullpen Capital General Partner Ann Lai in a press release. Formic Ventures, led by Oculus co-founder Michael Antonov, also took part in the round.

McCloskey thinks that Nanome’s platform has become even more relevant during the COVID-19 pandemic, as researchers might be forced to work remotely on occasion, limiting their access to in-lab technology and software.

“Nanome helps scientists get on the same page quicker,” he wrote in an email. “Traditionally scientists working with molecules use screenshots or screen sharing, and rely on the mouse cursor and Zoom to communicate their insights and ask for feedback from other team members.” Nanome streamlines this process by bringing researchers to the same virtual reality space to work on molecule development together.

So far, Nanome has worked largely on projects with companies in the food and beverage industry, as well as another to develop more sustainable batteries. But they have plans to use this new funding to expand into pharmaceutical chemistry, synthetic biology, and even education. Their next product update will feature what McCloskey calls ‘Spatial Recording,’ that will allow users to record their work for later review – basically a screen recording but with a VR experience. “This is not only an amazing feature for asynchronous collaboration among researchers, it is also useful for producing lectures and lessons,” he wrote in an email.

#ann-lai, #biotech, #bullpen-capital, #chemistry, #drug-discovery, #funding, #health, #oculus, #pharmaceutics, #recent-funding, #san-diego, #science, #startup, #startups, #virtual-reality

K Health expands into virtual childcare and raises $132 million at a $1.5 billion valuation

K Health, the virtual health care provider that uses machine learning to lower the cost of care by providing the bulk of the company’s health assessments, is launching new tools for childcare on the heels of raising cash that values the company at $1.5 billion.

The $132 million round raised in December will help the company expand and help pay for upgrades including an integration with most electronic health records — an integration that’s expected by the second quarter.

Throughout 2020 K Health has leveraged its position operating at the intersection of machine learning and consumer healthcare to raised $222 million in a single year.

This appetite from investors shows how large the opportunity is in consumer healthcare as companies look to use technology to make care more affordable.

For K Health, that means a monthly subscription to its service of $9 for unlimited access to the service and physicians on the platform, as well as a $19 per-month virtual mental health offering and a $19 fee for a one-time urgent care consultation.

To patients and investors the pitch is that the data K Health has managed to acquire through partnerships with organizations like the Israel health maintenance organization Maccabi Healthcare Services, which gave up decades of anonymized data on patients and health outcomes to train K Health’s predictive algorithm, can assess patients and aid the in diagnoses for the company’s doctors.

In theory that means the company’s service essentially acts as a virtual primary care physician, holding a wealth of patient information that, when taken together, might be able to spot underlying medical conditions faster or provide a more holistic view into patient care.

For pharmaceutical companies that could mean insights into population health that could be potentially profitable avenues for drug discovery.

In practice, patients get what they pay for.

The company’s mental health offering uses medical doctors who are not licensed psychiatrists to perform their evaluations and assessments, according to one provider on the platform, which can lead to interactions with untrained physicians that can cause more harm than good.

While company chief executive Allon Bloch is likely correct in his assessment that most services can be performed remotely (Bloch puts the figure at 90%), they should be performed remotely by professionals who have the necessary training.

There are limits to how much heavy lifting an algorithm or a generalist should do when it comes to healthcare, and it appears that K Health wants to push those limits.

“Drug referrals, acute issues, prevention issues, most of those can be done remotely,” Bloch said. “There’s an opportunity to do much better and potentially cheaper. 

K Health has already seen hundreds of thousands of patients either through its urgent care offering or its subscription service and generated tens of millions in revenue in 2020, according to Bloch. He declined to disclose how many patients used the urgent care service vs. the monthly subscription offering.

Telemedicine companies, like other companies providing services remotely, have thrived during the pandemic. Teladoc and Amwell, two of the early pioneers in virtual medicine have seen their share prices soar. Companies like Hims, that provide prescriptions for elective conditions that aren’t necessarily covered by health, special purpose acquisition companies at valuations of $1.6 billion.

Backing K Health are a group of investors led by GGV Capital and Valor Equity Partners. Kaiser Permanente’s pension fund and the investment offices of the owners of 3G Capital (the Brazilian investment firm that owns Burger King and Kraft Heinz), along with 14W, Max Ventures, Pico Partners, Marcy Venture Partners, Primary Venture Partners and BoxGroup, also participated in the round. 

Organizations working with the company include Maccabi Healthcare; the Mayo Clinic, which is investigating virtual care models with the company; and Anthem, which has white labeled the K Health service and provides it to some of the insurer’s millions of members.

#articles, #boxgroup, #burger-king, #drug-discovery, #ggv-capital, #health, #healthcare, #healthcare-industry, #israel, #k-health, #kaiser-permanente, #kraft, #machine-learning, #max-ventures, #mayo-clinic, #pharmaceutical, #primary-care, #primary-venture-partners, #tc, #technology, #teladoc-health, #telehealth, #telemedicine, #valor-equity-partners

Genesis Therapeutics raises $52M A round for its AI-focused drug discovery mission

Sifting through the trillions of molecules out there that might have powerful medicinal effects is a daunting task, but the solution biotech has found is to work smarter, not harder. Genesis Therapeutics has a new simulation approach and cross-disciplinary team that has clearly made an impression: the company just raised a $52 million A round.

Genesis competed in the Startup Battlefield at Disrupt last year, impressing judges with its potential, and obviously others saw it as well — in particular Rock Springs Capital, which led the round.

Over the last few years many companies have been formed in the drug discovery space, powered by increased computing and simulation power that lets them determine the potential of molecules in treating certain diseases. At least that’s the theory. The reality is a bit messier, and while these companies can narrow the search, they can’t just say “here, a cure for Parkinson’s.”

Founder Evan Feinberg got into the field when an illness he inherited made traditional lab work, as an intern at a big pharma company, difficult for him. The computational side of the field, however, was more accessible and ended up absorbing him entirely.

He had dabbled in the area before and arrived at what he feels is a breakthrough in how molecules are represented digitally. Machine learning has, of course, accelerated work in many fields, biochemistry among them, but he felt that the potential of the technology had not been tapped.

“I think initially the attempts were to kind of cut and paste deep learning techniques, and represent molecules a lot like images, and classify them — like you’d say, this is a cat picture or this is not a cat picture,” he explained in an interview. “We represent the molecules more naturally: as graphs. A set of nodes or vertices, those are atoms, and things that connect them, those are bonds. But we’re representing them not just as bond or no bond, but with multiple contact types between atoms, spatial distances, more complex features.”

The resulting representation is richer and more complex, a more complete picture of a molecule than you’d get from its chemical formula or a stick diagram showing the different structures and bonds. Because in the world of biochemistry, nothing is as simple as a diagram. Every molecule exists as a complicated, shifting 3D shape or conformation where important aspects like the distance between two carbon formations or bonding sites is subject to many factors. Genesis attempts to model as many of those factors as it can.

“Step one is the representation,” he said, “but the logical next step is, how does one leverage that representation to learn a function that takes an input and outputs a number, like binding affinity or solubility, or a vector that predicts multiple properties at once?”

That’s the work they’ve focused on as a company — not just creating a better model molecule, but being able to put a theoretical molecule into simulation and say, it will do this, it won’t do this, it has this quality but not that one.

Some of this work may be done in partnerships, such as the one Genesis has struck up with Genentech, but the teams could very well find drug candidates independent of those, and for that reason the company is also establishing an internal development process.

The $52M infusion ought to do a lot to push that forward, Feinberg wrote in an email:

“These funds allow us to execute on a number of critical objectives, most importantly further pioneering AI technologies for drug development and advancing our therapeutics pipeline. We will be hiring more top notch AI researchers, software engineers, medicinal chemists and biotech talent, as well as building our own research labs.”

Other companies are doing simulations as well and barking up the same tree, but Feinberg says Genesis has at least two legs up on them, despite the competition raising hundreds of millions and existing for years.

“We’re the only company in the space that’s working at the intersection of modern deep neural network approaches and biophysical simulation — conformational change of ligands and proteins,” he said. “And we’re bringing this super technical platform to experts who have taken FDA-approved drugs to market. We’ve seen tremendous value creation just from that — the chemists inform the AI too.”

The recent breakthrough of AlphaFold, which is performing the complex task of simulation protein folding far faster than any previous system, is as exciting to Feinberg as to everyone else in the field.

“As scientists, we are incredibly excited by recent progress in protein structure prediction. It is an important basic science advance that will ultimately have important downstream benefits to the development of novel therapeutics,” he wrote. “Since our Dynamic PotentialNet technology is unique in how it leverages 3D structural information of proteins, computational protein folding — similar to recent progress in cryo-EM — is a nice complementary tailwind for the Genesis AI Platform. We applaud all efforts to make protein structure more accessible such that therapeutics can be more easily developed for patients of all conditions.”

Also participating in the funding round were T. Rowe Price Associates, Andreessen Horowitz (who led the seed round), Menlo Ventures, and Radical Ventures.

#artificial-intelligence, #biotech, #drug-discovery, #funding, #fundings-exits, #genesis-therapeutics, #health, #recent-funding, #startups, #tc

Alphabet’s DeepMind achieves historic new milestone in AI-based protein structure prediction

DeepMind, the AI technology company that’s part of Google parent Alphabet, has achieved a significant breakthrough in AI-based protein structure prediction. The company announced today that its AlphaFold system has officially solved a protein folding grand challenge that has flummoxed the scientific community for 50 years. The advance inn DeepMind’s AlphaFold capabilities could lead to a significant leap forward in areas like our understanding of disease, as well as future drug discovery and development.

The test that AlphaFold passed essentially shows that the AI can correctly figure out, to a very high degree of accuracy (accurate to within the width of an atom, in fact), the structure of proteins in just days – a very complex task that is crucial to figuring out how diseases can be best treated, as well as solving other big problems like working out how best to break down ecologically dangerous material like toxic waste. You may have heard of ‘Folding@Home,’ the program that allows people to contribute their own home computing (and formerly, game console) processing power to protein folding experiments. That massive global crowdsourcing effort was necessary because using traditional methods, portion folding prediction takes years and is extremely expensive in terms of straight cost, and computing resources.

DeepMind’s approach involves using an “Attentionb-basd neural network system” (basically a neural network that can focus on specific inputs in order to increase efficiency). It’s able to continually refine its own predictive graph of possible protein folding outcomes based on their folding history, and provide highly accurate predictions as a result.

How proteins fold – or go from being a random string of amino acids when originally created, to a complex 3D structure in their final stable form – is key to understanding how diseases are transmitted, as well as how common conditions like allergies work. If you understand the folding process, you can potentially alter it, halting an infection’s progress mid-stride, or conversely, correct mistakes in folding that can lead to neurodegenerative and cognitive disorders.

DeepMind’s technological leap could make accurately predicting these folds a much less time- and resource-consuming process, which could dramatically change the pace at which our understanding of diseases and therapeutics progresses. This could come in handy to address major global threats including future potential pandemics like the COVID-19 crisis we’re currently enduring, by predicting viral protein structures to a high degree of accuracy early in the appearance fo any new future threats like SARS-CoV-2, thus speeding up the development of potential effective treatments and vaccines.

#alphafold, #artificial-intelligence, #biotech, #computing, #deepmind, #drug-discovery, #foldinghome, #google, #health, #neural-network, #protein-folding, #science, #tc, #technology

Daphne Koller: ‘Digital biology is an incredible place to be right now’

Working at the intersection of biology and computing may be the most exciting new spot for technologists at the moment.

That’s the word from Daphne Koller, the founder and chief executive officer of Insitro — the biotech company that’s raised over $243 million in the two short years since it launched.

Speaking at our virtual TechCrunch Disrupt conference, Koller, a serial entrepreneur who previously co-founded Coursera and briefly served as the chief computing officer for the Alphabet subsidiary focused on human health, Calico, views digital biology as the next big technological revolution.

“Digital biology is an incredible place to be right now,” Koller said in an interview.

It’s certainly been an incredible opportunity for Koller whose work now spans the development of treatments for potential neurological diseases and a nearer term research and development effort on hepatitis with Gilead Pharmaceuticals.

Koller’s Insitro takes its name and inspiration from the combination of two different practices in biological research — the in vitro experiments that are done on living samples in labs and the in silico experiments that are done on the computer.

By synthesizing these two disciplines Koller’s company flips the process of drug discovery on its head, the company is designed sift through massive amounts of data to search for patterns in the expression of certain conditions. Once those patterns are determined, the company can examine the pathways or mechanisms associated with that expression to determine targets for potential therapies.

Then Insitro will pursue the development of novel molecules that can be used to intervene and either reverse or stop the progression of an illness by stopping the biological mechanisms associated with it.

“We now have massive amounts of data that is truly relevant to human disease,” Koller said. “Machine learning has given us a bunch of tools to really make sense of data.”

The company can identify new patient segments, new interventions new drugs that may modulate the expression of those conditions. “We view ourselves as being on the first phase of a very long journey using machine learning,” said Koller.

Take the company’s work on hepatitis in conjunction with Gilead. There, Koller and her team were able to take a small, high-quality dataset from Gilead’s trials and identify how a disease progressed by looking at the patient data from different points in time. Looking at the progression allowed the company to identify drivers that facilitated the progression of fibrosis that causes tissue damage. Now the company is using those targets as a starting point to find modifiers that could slow down the progression of the disease. 

It comes down to using computers to understand the biology, new biotechnology to model that biology in a Petri dish, and from the different models determine the interventions that will make a difference, Koller said.

“What we’re trying to do is so different and so out of alignment with how these [pharmaceutical] companies do their work,” Koller said. “It’s trying to shift the trajectory of these companies of hundreds or thousands of people and shift the culture to a tech culture that is going to be really a challenge.”

It’s the main reason Koller launched her own company rather than joining a big pharma play, and it’s a classic example of the innovator’s dilemma and the disruptive power of technology laid out in the theories of Clayton Christensen that give the Disrupt conference its name.

“[It’s] the notion of the innovator’s dilemma and coming in with a mindset that says we’re going to do this a completely different way,” said Koller. “The drug discovery effort is becoming increasingly expensive and increasingly prone to failure and if we do this in a different way will it enable us to generate better outcomes.”

#chief-executive-officer, #computing, #coursera, #daphne-koller, #disease, #drug-discovery, #health, #illness, #insitro, #machine-learning, #medicine, #serial-entrepreneur, #tc

MedTech startup uMotif raises £5m from AlbionVC, as COVID-19 accelerates remote clinical studies

couMotif has an app that allows patients to monitor themselves for treatments or drug trials which then feeds into a platform allowing a much faster approach to clinical studies. It’s now raised £5 million in a Series A investment round led by existing UK investor AlbionVC, with participation from Oslo-based DNV-GL and existing angel investors. This latest round takes it to a total funding size of £7.5m.

The platform is sold into life sciences companies which are gradually replacing centralized studies where patients have to go to a site, such as a hospital, to submit their data. The trend has obviously been catalyzed by Covid-19. The platform is now used by studies taking place in 26 countries from clinical to real-world settings, and across more than 25 therapeutic areas – from dermatology and rare disease to oncology and cardiology. The largest study involved over 13,000 participants tracking their pain levels and the weather. This was featured on the BBC and published in Nature.

Its competitors are almost entirely US-based and include organizations such as SnapIOT, Medable, and ClinicalInk as well as other large platform companies.

“We’re excited to help our customers implement patient-centered research designs by using the uMotif platform to capture high-quality data,” siad Bruce Hellman, CEO and Co-Founder of uMotif in a statement. “This new funding will rapidly accelerate our development and will ultimately help our customers to get new therapies to patients faster”.

Dr. Andrew Elder, deputy managing partner at AlbionVC says: “Now more than ever, having access to reliable patient data during clinical trials is crucial. uMotif’s platform is built with patients in mind; designed to help academics, researchers and healthcare professionals to capture the best quality data in a way that suits the participants. It’s a win-win for all stakeholders and the platform has the potential and momentum to revolutionize the speed and efficiency with which therapies can reach and help millions of patients.”

#covid, #drug-discovery, #europe, #health, #medical-research, #oslo, #pain, #tc, #united-kingdom

Atomwise’s machine learning-based drug discovery service raises $123 million

With a slew of partnerships with large pharmaceutical companies under its belt and the successful spin out of at least one new company, Atomwise has already proved the value of its machine learning platform for discovering and commercializing potential small molecule therapies for a host of conditions.

Now the company has raised $123 million in new funding to accelerate its business.

“Scaling the technology and scaling the team and scaling what we’ve been doing with it,” says chief executive officer Abe Heifets when asked about what comes next for the eight year old business.

Atomwise has already signed contracts worth $5.5 billion with corporate partners that include Eli Lilly & Co., Bayer, Hansoh Pharmaceuticals, and Bridge Biotherapeutics. Smaller, earlier stage companies like StemoniX and SEngine Precision Medicine are also using Atomwise’s tech.

Now the company will look to capture more of the value of drug discovery for itself, looking to develop and commercialize its discoveries by taking over more of the development process and working with manufacturers at a later stage, according to Heifets.

Atomwise tipped its new strategy last year when it announced a partnership with Velocity Drug Development and a $14.5 million investment to create x-37, a spinoff that’s developing small molecule therapies for endodermal cancers, which include cancers of the liver, pancreas, colon, stomach, and bladder.

“We have something like 750 projects running today around the world,” says Heifets. “These comprise more than 600 unique targets and that’s with a vast range of partnerships.”

The power of Atomwise’s drug discovery platform is its ability to harness machine learning to structure new proteins that have never existed — and ensure that they’re able to reach precise target receptors to accomplish a desired task.

Here, the x-37 spinoff is especially illustrative. One line of research the company is conducting into molecules that can target the PIM3 protein receptor. If a drug can block PIM3, it can kill cancerous endodermal cells, according to Heifets. However, if the molecules bind to another, similar target, PIM1, the therapy can cause heart attacks and kill patients.

“This is a challenge and empirically was considered undruggable,” says Heifets. Atomwise’s company screened 11 billion potential molecules against the targets to come up with 500 potential therapies. They’re now working on refining the therapy to bring something to market.

And x-37 is only one of the companies that Atomwise has created to commercialize various new molecules. There’s also Atropos Therapeutics, Theia Biosciences and vAIrus.

Atomwise is far from the only company to think that the application of machine learning technologies to drug discovery is a winning combination. Menten.ai is a company that’s taken the new technology developments one step further and added quantum computing to the mix to come up with new drugs.

“The market opportunity we’re going after is four times the value of the entire pharma industry today,” said Heifets. “Here’s what that’s about. There’s 20,000 human genes and only 4% have ever been drugs. Another 16% have been evidenced. But the opportunity of drugging the undruggable is way bigger than the entire pharma industry.”

Unlocking that opportunity is going to take lots of capital. That’s why B Capital and Sanabil Investments combined to lead Atomwise’s Series B round. It’s also why companies like DCVC, BV, Tencent, Y Combinator, Dolby Ventures, AME Cloud Ventures and two, undisclosed, insurance companies have invested in the company’s latest round.

 with a goal to commercialize high potential candidates through the drug development process. The company plans to continue to expand its work with corporate partners, which currently include major players in the biopharma space including Eli Lilly and Company, Bayer, Hansoh Pharmaceuticals, and Bridge Biotherapeutics, as well as emerging biotechnology companies like StemoniX and SEngine Precision Medicine. Atomwise has signed approximately $5.5 billion in deal value with corporate partners to date.

To date, Atomwise has worked with 750 academic research collaborations addressing over 600 disease targets, to model and screen over 16 billion new molecules for virtual screening. These molecules have generated 17 pending patent applications and several peer-reviewed publications. There are 285 active drug discovery partnerships with researchers at top universities around the world, and recently announced 15 research collaborations with global universities to explore broad-spectrum therapies for COVID-19, targeting 15 unique and novel mechanisms of action.

“New technologies are enabling better and faster R&D for the life science industry,” said Raj Ganguly, co-Founder and Managing Partner at B Capital Group . “The advancements Atomwise has made with its computational drug discovery platform have effectively cut months or even years off of the R&D lifecycle. More importantly, however, they are solving biology problems previously believed to be unsolvable by researchers and delivering that capability to everyone from academics to big pharma. We’re excited to continue to partner with the Atomwise team on its mission to develop new, more effective therapies.”

For lead investor, B Capital, the Atomwise investment is part of a thesis around lowering the cost of care and improving outcomes.

“Companies like Atomwise that are improving the cost curve are in the same vein of bringing therapies to market faster and cheaper. Which means you can improve access and improve costs and address things like rare diseases,” said Adam Seabrook, a principal at B Capital focused on healthcare.

#ame-cloud-ventures, #articles, #atomwise, #b-capital-group, #bayer, #biotechnology, #chief-executive-officer, #disease, #drug-development, #drug-discovery, #health, #healthcare, #insurance, #life-sciences, #machine-learning, #partner, #quantum-computing, #raj-ganguly, #series-b, #tc, #tencent, #y-combinator

Use AI responsibly to uplift historically disenfranchised people during COVID-19

One of the most distressing aspects of the ongoing pandemic is that COVID-19 is having a disproportionate impact on communities of color and lower-income Americans due to structural factors rooted in history and long-standing societal biases.

Those most at risk during this pandemic are 24 million of the lowest-income workers; the people who have less job security and can’t work from home. In fact, only 9.2% of the bottom 25% have the ability to work from home. Compare that to the 61.5% of the top 25% and the disparity is staggering. Additionally, people in these jobs typically do not have the financial security to avoid public interaction by stockpiling food and household goods, buying groceries online or avoiding public transit. They cannot self-isolate. They need to venture out far more than other groups, heightening their risk of infection.

The historically disadvantaged will also be hit the hardest by the economic impacts of the pandemic. They are overrepresented in the industries experiencing the worst downturn. The issues were atrocious prior to COVID-19, with the typical Black and Latinx households having a net worth of just $17,100 and $20,765, respectively, compared with the $171,000 held by the typical white household. An extended health and economic crisis will only exacerbate these already extreme disparities.

AI as a beacon of hope

A rare encouraging aspect of the ongoing pandemic response is the use of cutting-edge technology — especially AI — to address everything from supply chains to early-stage vaccine research.

The potential of human + AI exceeds the potential of humans working alone by far, but there are tremendous risks that require careful consideration. AI requires massive amounts of data, but ingrained in that data are the societal imperfections and inequities that have given rise to disproportionate health and financial impacts in the first place.

In short, we cannot use a tool until we know it works and understand the potential for unintended consequences. Some health groups hurried to repurpose existing AI models to help track patients and manage the supply of beds, ventilators and other equipment in their hospitals. Researchers have tried to develop AI models from scratch to focus on the unique effects of COVID-19, but many of those tools have struggled with bias and accuracy issues. Balancing the instinct to “help now” and the risks of “unforeseen consequences” amidst the high stakes of the COVID-19 pandemic is why the responsible use of AI is more important now than ever.

4 ways to purposefully and responsibly use AI to combat COVID-19

1. Avoid delegating to algorithms that run critical systems

Think of an AI system designed to distribute ventilators and medical equipment to hospitals with the objective of maximizing survival rates. Disadvantaged populations have higher comorbidities and thus may be less likely to receive supplies if the system is not properly designed. If these preexisting prejudices are not accounted for when designing the AI system, then well-intentioned efforts could result in directing supplies away from especially vulnerable communities.

Artificial intelligence is also being used to improve supply chains across all sectors. The Joint Artificial Intelligence Center is prototyping AI that can track data on ventilators, PPE, medical supplies and food. When the goal is to anticipate panic-buying and ensure health care professionals have access to the equipment they need, this is a responsible use of AI.

As these examples illustrate, we can quickly arrive at a problematic use case when decision-making authority is delegated to an algorithm. The best and most responsible use of AI is maximizing efficiency to ensure the necessary supplies get to those truly in need. Previous failures in AI show the need for healthy skepticism when delegating authority on potentially life-and-death decisions to an algorithm.

2. Be wary of disproportional impacts and singling out specific communities

Think of an AI system that uses mobility data to detect localized communities that are violating stay-at-home orders and route police for additional enforcement. Disadvantaged populations do not have the economic means to stockpile food and other supplies, or order delivery, forcing them to go outside.  As we mentioned earlier, being overrepresented in frontline sectors means leaving the home more frequently. In addition, individuals and families experiencing homelessness could be targeted for violating stay-at-home enforcement. In New York City, police enforcement of stay-at-home directives has disproportionately targeted Black and Latinx residents. Here is where responsible AI steps in. AI systems should be designed not to punish these populations with police enforcement, but rather help identify the root causes and route additional food and resources. This is not a panacea, but will avoid exacerbating existing challenges.

Israel has already demonstrated that this model works. In mid-March it passed an emergency law enabling the use of mobile data to pinpoint the infected as well as those they had come in contact with. Maccabi Healthcare Services are using AI to ID its most at-risk customers and prioritize them for testing. This is a fantastic example of adopting previously responsible and successful AI by adapting an existing system that was built and trained to identify people most at risk for flu, using millions of records from over 27 years.

3. Establish AI that is human-centric with privacy by design and native controls

Think of an AI system that uses mobile phone apps to track infections and trace contacts in an effort to curb new infections. Minority and economically disadvantaged populations have lower rates of smartphone ownership than other groups. AI systems should take these considerations into account to avoid design bias. This will ensure adequate protections for vulnerable populations, but also improve the overall efficacy of the system since these individuals may have high human contact in their jobs. Ensuring appropriate track and trace takes place within these populations is critically important.

In the U.S., MIT researchers are developing Private Automatic Contact Tracing (PACT), which uses Bluetooth communications for contact tracing while also preserving individual privacy. If you test positive and inform the app, everyone who has been in close proximity to you in the last 14 days gets a notification. Anonymity and privacy are the biggest keys to responsible use of AI to curb the spread of COVID-19.

In India the government’s Bridge to Health app uses a phone’s Bluetooth and location data to let users know if they have been near a person with COVID-19. But, again, privacy and anonymity are the keys to responsible and ethical use of AI.

This is a place where the true power of human + AI shines through. As these apps are rolled out, it is important that they are paired with human-based track and trace to account for disadvantaged populations. AI allows automating and scaling track and trace for most of the population; humans ensure we help the most vulnerable.

4. Validate systems and base decisions on sanitized representative data

Think of an AI system that helps doctors make rapid decisions on which patients to treat and how to treat them in an overburdened health care system. One such system developed in Wuhan identified biomarkers that correlate with higher survival rates to help doctors pinpoint which patients likely need critical care and which can avoid the hospital altogether.

The University of Chicago Medical Center is working to upgrade an existing AI system called eCART. The system will be enhanced for COVID to use more than 100 variables to predict the need for intubation eight hours in advance. While eight hours may not seem like much, it provides doctors an opportunity to take action before a patient’s condition deteriorates.

But, the samples and data sets systems like these rely on could have the potential to produce unreliable outcomes or ones that reinforce existing biases. If the AI is trained on observations of largely white individuals — as was the case with data in the International Cancer Genome Consortium — how willing would you be to delegate life-and-death health care decisions for a nonwhite patient? These are issues that require careful consideration and demonstrate why it is so important to validate not only the systems themselves, but also the data on which they rely.

Questions we must ask

As companies, researchers and governments increasingly leverage AI, a parallel discussion around responsible AI is necessary to ensure benefits are maximized while harmful consequences are minimized. We need better guidelines and assessments of AI around fairness, trustworthiness, bias and ethics.

There are dozens of dimensions we should evaluate every use case against to ensure it is developed in a responsible manner. But, these four simple questions provide a great framework to start a discussion between AI system developers and policy makers who may be considering deploying an AI solution to combat COVID-19.

  • What are the consequences if the system makes a mistake? Can we redesign the system to minimize this?
  • Can we clearly explain how the AI system produced specific outcomes in a way that is understandable to the general public?
  • What are potential sources of bias — data, human and design — and how can they be minimized?
  • What steps can be taken to protect the privacy of individuals?

When to use AI solutions and tools

Each of these questions will apply in different ways to particular use cases. A natural language processing (NLP) system sifting through tens of thousands of scientific papers that might focus the search for a COVID-19 vaccine poses no direct threat of harm to individuals and performs a task faster that an army of research assistants ever could. Case in point, in April at Harvard the Harvard T.H. Chan School of Public Health and the Human Vaccines Project announced the Human Immunomics Initiative to leverage AI models to accelerate vaccine development.

This is a global effort with scientists around the world working together to expedite drug discovery processes to defeat COVID-19 through the use of AI. From the aforementioned work in the U.S. all the way to Australia where Oracle cloud technology and vaccine technology developed by Vaxine is being leveraged by Flinders University to develop promising vaccine candidates, we can see AI being used for its most ethical purpose, saving human lives.

Another use case is the omnipresent issue facing us during this pandemic: dissemination of misinformation across the planet. Imagine trying to manually filter the posts of the 1.7 billion daily Facebook users every day and scan for misinformation about COVID-19. This is an ideal project for human + AI — with humans confirming cases of misinformation flagged by AI.

This use case is relatively low risk, but its ultimate success depends on human oversight and engagement. That’s even more so the case in the high-risk use cases that are grabbing headlines amidst the COVID-19 pandemic. Human + AI is not just a safeguard against a system gone off the rails, it’s critical to AI delivering meaningful and impactful results as illustrated through earlier examples.

We need to classify use cases into three buckets to guide our decision making:

1. Red

  • Use case represents a decision that should not be delegated to an AI system.
  • Using an AI system to decide which patients receive medical treatment during a crisis. This is a case where humans should ultimately be making decisions because of their impact on life-and-death decisions. This has already been recognized by the medical community, where ethical frameworks have been developed to support these very types of decisions.

2. Yellow

  • Use case could be deployed responsibly, but it depends upon the design and execution.
  • Using an AI system to monitor adherence to quarantine policies. This is a case where use cases may be acceptable depending on design and deployment of systems. For example, using the system to deploy police to neighborhoods to “crackdown” on individuals not adhering to quarantine policies would be problematic. But deploying police to these neighborhoods to understand why quarantine is being broken so policy makers can better address citizen needs would be legitimate — provided privacy of individuals is protected.

3. Green

  • Use case is low risk and the benefits far outweigh the risks.
  • Content filtering on social media platforms to ensure malicious and misleading information regarding COVID-19 is not shared widely.

We must ask our four questions and deliberately analyze the answers we find. We can then responsibly and confidently decide which bucket to put the project into and move forward in a responsible and ethical manner.

A recent U.S. example

We recently created Lighthouse, a new dynamic navigation cockpit that helps organizations capture a holistic picture of the ongoing crisis. These “lighthouses” are being used to illuminate the multiple dimensions of the situation. For example, we recently partnered with an American city to develop a tool that predicted disruptions in the food supply chain. One data source was based on declines in foot traffic in and around distribution centers. Without accessing any personally identifiable information (PII) — and therefore preserving individual privacy — it shows which parts of the city were most likely to suffer shortages, enabling leaders to respond preemptively and prevent an even worse public health crisis.

This is an easily duplicated process that other organizations can follow to create and implement responsible AI to help the historically disenfranchised navigate and thrive during the age of COVID-19.

Moving forward

When confronting the ethical dilemmas presented by crises like COVID-19, enterprises and organizations equipped with responsible AI programs will be best positioned to offer solutions that protect the most vulnerable and historically disenfranchised groups by respecting privacy, eliminating historical bias and preserving trust. In the rush to “help now,” we cannot throw responsible AI out the window. In fact, in the age of COVID-19, it is more important than ever before to understand the unintended consequences and long-term effects of the AI systems we create.

#artificial-intelligence, #column, #coronavirus, #covid-19, #drug-discovery, #food-supply-chain, #health, #israel, #opinion, #social-media-platforms

Bit Bio’s “enter button for the keyboard to the software of life” nabs the company $41.5 million

Bit Bio, the new startup which pitches itself as the “enter button for the keyboard to the software of life” only needed three weeks to raise its latest $41.5 million round of funding.

Originally known as Elpis Biotechnology and named for the Greek goddess of hope, the Cambridge, England-based company was founded by Mark Kotter in 2016 to commercialize technology that can reduce the cost and increase the production capacity for human cell lines. These cells can be used in targeted gene therapies and as a method to accelerate drug discovery at pharmaceutical companies.

The company’s goal is to be able to reproduce every human cell type.

“We’re just at a very crucial time in biology and medicine and the bottleneck that has become really clear is a scalable source of robust human cells,” said Kotter. “For drug discovery this is important. When you look at failure rates in clinical trials they’re at an all time high… that’s in direct contradiction to the massive advancements in biotechnology in research and the field.”

In the seventeen years since scientists completely mapped the human genome, and eight years since scientists began using the gene editing technology known as CRISPR to edit genetic material, there’s been an explosion of treatments based on individual patient’s genetic material and new drugs developed to more precisely target the mechanisms that pathogens use to spread through organisms.

These treatments and the small molecule drugs being created to stop the spread of pathogens or reduce the effects of disease require significant testing before coming to market — and Bit Bio’s founder thinks his company can both reduce the time to market and offer new treatments for patients.

It’s a thesis that had investors like the famous serial biotech entrepreneur, Richard Klausner, who served as the former director of the National Cancer Institute and founder of revolutionary biotech companies like Lyell Immunopharma, Juno, and Grail, leaping at the chance to invest in Bit Bio’s business, according to Kotter.

Joining Klausner are the famous biotech investment firms Foresite Capital, Blueyard Capital and Arch Venture Partners.

“Bit Bio is based on beautiful science. The company’s technology has the potential to bring the long-awaited precision and reliability of engineering to the application of stem cells,” said Klausner in a statement. “Bit Bio’s approach represents a paradigm shift in biology that will enable a new generation of cell therapies, improving the lives of millions.”

Photo: Andrew Brookes/Getty Images

Kotter’s own path to develop the technology which lies at the heart of Bit Bio’s business began a decade ago in a laboratory in Cambridge University. It was there that he began research building on the revolutionary discoveries of Shinya Yamanaka, which enabled scientists to transform human adult cells into embryonic stem cells.

“What we did is what Yamanaka did. We turned everything upside down. We want to know how each cell is defined… and once we know that we can flip the switch,” said Kotter. “We find out which transcription factors code for a single cell and we turn it on.”

Kotter said the technology is like uploading a new program into the embryonic stem cell.

Although the company is still in its early days, it has managed to attract a few key customers and launch a sister company based on the technology. That company, Meatable, is using the same process to make lab-grown pork.

Meatable is the earliest claimant to a commercially viable, patented process for manufacturing meat cells without the need to kill an animal as a prerequisite for cell differentiation and growth.

Other companies have relied on fetal bovine serum or Chinese hamster ovaries to stimulate cell division and production, but Meatable says it has developed a process where it can sample tissue from an animal, revert that tissue to a pluripotent stem cell, then culture that cell sample into muscle and fat to produce the pork products that palates around the world crave.

“We know which DNA sequence is responsible for moving an early-stage cell to a muscle cell,” says Meatable chief executive Krijn De Nood.

If that sounds similar to Bit Bio, that’s because it’s the same tech — just used to make animal instead of human cells.

Image: PASIEKA/SCIENCE PHOTO LIBRARY/Getty Images

If Meatable is one way to commercialize the cell differentiation technology, Bit Bio’s partnership with the drug development company Charles River Laboratories is another.

“We actually do have a revenue generating business side using human cells for research and drug discovery. We have a partnership with Charles River Laboratories the large preclinical contract research organization,” Kotter said. “That partnership is where we have given early access to our technology to Charles River… They have their own usual business clients who want them to help with their drug discovery. The big bottleneck at the moment is access to human cells.”

Drug trials fail because the treatments developed either are toxic or don’t work in humans. The difference is that most experiments to prove how effective the treatments are rely on animal testing before making the leap to human trials, Kotter said.

The company is also preparing to develop its own cell therapies, according to Kotter. There, the biggest selling point is the increased precision that  Bit Bio can bring to precision medicine, said Kotter. “If you look at these cell therapies at the moment you get mixed bags of cells. There are some that work and some that have dangerous side effects. We think we can be precise [and] safety is the biggest thing at this point.”

The company claims that it can produce cell lines in less than a week with 100 percent purity, versus the mixed bags from other companies cell cultures.

“Our moonshot goal is to develop a platform capable of producing every human cell type. This is possible once we understand the genes governing human cell behaviour, which ultimately form the ‘operating system of life’,” Kotter said in a statement. “This will unlock a new generation of cell and tissue therapies for tackling cancer, neurodegenerative disorders and autoimmune diseases and accelerate the development of effective drugs for a range of conditions. The support of leading deep tech and biotech investors will catalyse this unique convergence of biology and engineering.”

 

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Emerging from stealth, Octant is bringing the tools of synthetic biology to large scale drug discovery

Octant, a company backed by Andreessen Horowitz just now unveiling itself publicly to the world, is using the tools of synthetic biology to buck the latest trends in drug discovery.

As the pharmaceuticals industry turns its attention to precision medicine — the search for ever more tailored treatments for specific diseases using genetic engineering — Octant is using the same technologies to engage in drug discovery and diagnostics on a mass scale.

The company’s technology genetically engineers DNA to act as an identifier for the most common drug receptors inside the human genome. Basically, it’s creating QR codes that can flag and identify how different protein receptors in cells respond to chemicals. These are the biological sensors which help control everything from immune responses to the senses of sight and smell, the firing of neurons; even the release of hormones and communications between cells in the body are regulated.

“Our discovery platform was designed to map and measure the interconnected relationships between chemicals, multiple drug receptor pathways and diseases, enabling us to engineer multi-targeted drugs in a more rational way, across a wide spectrum of targets,” said Sri Kosuri, Octant’s co-founder and chief executive officer, in a statement.

Octant’s work is based on a technology first developed at the University of California Los Angeles by Kosuri and a team of researchers, which slashed the cost of making genetic sequences to $2 per gene from $50 to $100 per gene.

“Our method gives any lab that wants the power to build its own DNA sequences,” Kosuri said in a 2018 statement. “This is the first time that, without a million dollars, an average lab can make 10,000 genes from scratch.”

Joining Kosuri in launching Octant is Ramsey Homsany, a longtime friend of Kosuri’s, and a former executive at Google and Dropbox . Homsany happened to have a background in molecular biology from school, and when Kosuri would talk about the implications of the technology he developed, the two men knew they needed to for a company.

“We use these new tools to know which bar code is going with which construct or genetic variant or pathway that we’re working with [and] all of that fits into a single well,” said Kosuri. “What you can do on top of that is small molecule screening… we can do that with thousands of different wells at a time. So we can build these maps between chemicals and targets and pathways that are essential to drug development.”

Before coming to UCLA, Kosuri had a long history with companies developing products based on synthetic biology on both the coasts. Through some initial work that he’d done in the early days of the biofuel boom in 2007, Kosuri was connected with Flagship Ventures, and the imminent Harvard-based synthetic biologist George Church . He also served as a scientific advisor to Gen9, a company acquired by the multi-billion dollar synthetic biology powerhouse, Ginkgo Bioworks.

“Some of the most valuable drugs in history work on complex sets of drug targets, which is why Octant’s focus on polypharmacology is so compelling,” said Jason Kelly, the co-founder and CEO of Gingko Bioworks, and a member of the Octant board, in a statement. “Octant is engineering a lot of luck and cost out of the drug discovery equation with its novel platform and unique big data biology insights, which will drive the company’s internal development programs as well as potential partnerships.”

The new technology arrives at a unique moment in the industry where pharmaceutical companies are moving to target treatments for diseases that are tied to specific mutations, rather than look at treatments for more common disease problems, said Homsany.

“People are dropping common disease problems,” he said. “The biggest players are dropping these cases and it seems like that just didn’t make sense to us. So we thought about how would a company take these new technologies and apply them in a way that could solve some of this.”

One reason for the industry’s turn away from the big diseases that affect large swaths of the population is that new therapies are emerging to treat these conditions which don’t rely on drugs. While they wouldn’t get into specifics, Octant co-founders are pursuing treatments for what Kosuri said were conditions “in the metabolic space” and in the “neuropsychiatric space”.

Helping them pursue those targets, since Octant is very much a drug development company, is $20 million in financing from investors led by Andreessen Horowitz .

“Drug discovery remains a process of trial and error. Using its deep expertise in synthetic biology, the Octant team has engineered human cells that provide real-time, precise and complete readouts of the complex interactions and effects that drug molecules have within living cells,” said Jorge Conde, general partner at Andreessen Horowitz, and member of the Octant board of directors. “By querying biology at this unprecedented scale, Octant has the potential to systematically create exhaustive maps of drug targets and corresponding, novel treatments for our most intractable diseases.”

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