- Alice Zhang started Verge Genomics in 2015 with Jason Chen to combine innovation in neuroscience, machine learning and genomics and apply it to the drug discovery process.
- The vision for Verge was to become the first pharmaceutical company that automated its drug discovery engine, helping to rapidly develop multiple lifesaving treatments in diseases like Alzheimer’s disease, ALS, and Parkinson’s disease where no cure exists today.
- On Monday, the San Francisco-based company announced it had raised $US32 million in series A funding, led by DFJ, bringing its total amount raised to $US36.5 million.
The drug development process is laden with problems that make it lengthy and expensive. Right now, it takes 12 years and $US2.6 billion to get a single drug to market, with the drug discovery and development process costing $US1.4 billion.
Verge Genomics, run by 29-year-old Alice Zhang, is trying to address these problems by making drug discovery faster and cheaper.
On Monday, the San Francisco-based company announced it had raised $US32 million in series A funding, led by DFJ, bringing its total amount raised to $US36.5 million.
Zhang was three months shy of her MD and PhD graduation from University of California-Los Angeles when she left school to start Verge Genomics in 2015 with Jason Chen, who she met during the program.
“I just became very frustrated with the drug discovery process,” she said. “It’s largely a guessing game where companies are essentially brute force screening millions of drugs just to stumble across a single new drug that works.”
At the time, Zhang also recognised the advancements in neuroscience, machine learning and genomics occurring all around her. Genome sequencing had become more and more affordable, and breakthroughs in understanding how function connects with genes opened a new field of possibilities for exploring disease and health. And there was an opening for an opportunity to guesswork out of drug discovery. The vision for Verge was to become the first pharmaceutical company that automated its drug discovery engine, helping to rapidly develop multiple lifesaving treatments in diseases like Alzheimer’s disease, ALS, and Parkinson’s disease where no cure exists today.
Recently, other big pharmaceutical agencies like Pfizer and Novartis are also starting to follow suit, adapting technology to different steps of the clinical trial process. At least 18 pharmaceutical companies and more than 75 startups have been working on integrating machine learning into the drug discovery process.
Verge, 14 people large, functions at full capacity. Not only do they have computer scientists managing the front-end of machine learning, but they also have researchers working in its own in-house drug discovery and animal lab. The team is stacked with computer scientists, mathematicians, neurobiologists, as well as industry veterans and drug development veterans.
There are three main problems in drug discovery that Verge is using data and software to tackle. The first is that many diseases like Alzheimer’s disease are caused by hundreds of genes. Verge’s algorithms on human genomic data can map these genes out. The second is instead of using animal data only for pre-clinical trials, Verge uses human data from day one, which may enable greater insight into how effective the drug actually is on human cells. Drugs that work in mice often fail in humans, and that’s because mice usually serve as primary mammal model. Instead of tediously screening millions of drugs, the algorithm will computationally predict drugs that work.
Verge uses brain samples from patients that have passed away from Alzheimer’s disease or Parkinson’s disease for its human data, obtained through partnerships with over a dozen different universities, hospitals and brain banks. The company then RNA-sequences them in-house, which allows them to measure the gene expression in its most current state, and it can measure simultaneously how all of the genes in the genome are behaving. This data helps scientists figure out what’s actually causing disease in these patients and see if there are connections between genes and disease.
Verge’s scientists can make predictions about what drugs they think will work. They can take a patient’s own skin cell and turn it directly into their own brain cells in a dish. Then the predictions can be tested on these brain cells to see if they can rescue them from dysfunction or death – a basic test of drug efficacy. That validation data can feed back into the platform and continuously improve predictions over time, even across different diseases.
The Verge algorithm identifies drugable targets for treatments, then design drugs accordingly. This is done by mining through human samples to identify groups of genes that are implicated with the disease, and what crucial hub in these gene networks can turn them on or off.
The latest investment in Verge will serve to advance its ALS and Parkinson’s disease drugs. There are six drugs in development, closer to the clinical end, which are being tested to make sure they’re safe and non-toxic. The funding will also be used to expand the number of diseases Verge has in its portfolio.
Emily Melton, a partner at DFJ, told Business Insider that investment in early stage startups is largely about the team, the uniqueness of the idea and the capability and expertise of the research team. But what drew her in most was Zhang. “She was this brilliant founder, with a very organic desire to create an impact,” said Melton. “She felt like it was her calling.”
Using system learning to recognise patterns that would otherwise go undetected by the human eye can speed up the process while creating a bigger and better feedback loop, said Melton. “We’re rethinking how drug discovery is done, and we’re rethinking how therapeutics are developed.”
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