Exscientia Case Study

Overview and Origin

• Company Name: Exscientia

• Incorporated in 2012

• Founded by Andrew Hopkins

• The idea came about when Andrew was a PhD student in the 1990's researching HIV drugs and thought of how much more efficient it owuld be if there was technology to automate the testing and discovery of new molecules that can be combined into pharmaceutical drugs.

• Exscientia went public on October 1, 2021 on the NASDAQ stock exchange under the ticker symbole EXAI, having rasied over 672.7 million. They have a current market cap of 770.88 million with an enterprise value of 328.35 million.

Business Activities

• Exscientia is working to reduce the time it takes to discover, design and develope new pharmacautical drugs. They also incorporate target identification and patient selection based on DNA samples to provide drug therapies. In the words of the founder "Our mission is to design the best drugs for as many patients as possible by thinking about how we can automate the creation of drugs and their development."

• The companies's intended customers are the congomerates whom they can license their drugs to with the production capacity and quaity control standards already in place. Their secondary market is consumers/patients who seek targeted treatment. Their focus is oncology and immunology, targeting cancer, blood, and inflammatory disease.

• The main advantage that Exscientia has is their infrastcure and partnerships cultivated over years. They were the first to bring AI dveloped drugs to clinical trials and have been refining the process for the past decade, including the building of automated labs. Key partnerships with large investors like The Bill and Melinda Gates Foundation are also an advantage given their connection to medical industries all around the world. Their partnership with Sanofi is worth up to 5.2 bllion, ensuring a steady flow of funds for continued operations, expansion, and research and devlopment for years to come.

• Their primary technology is AI software intergrated into their automated lab, what they call their precision medicine platform. This platofrm uses AI to examine live patient tissue on the level of a single cell.

Landscape

• Exscientia is in the medical/healthcare and are considered a biotech company.

• The major trend in the space ten years ago was to use machine learning to map the human genome and find biomarkers relevant to diseases and even aging. Today the technonogy can take those biomarkers and test newly designed drugs against them using small samples of subject DNA, doing in weeks or days what used to take month or years. The trend now is about creation and discovery of new molecules, treatments, and preventions.

• There are a number of competitors in this field: Relay Theraputics, Insistro, Deep Genomics, Atomwise, Valo Health, Recursion Pharmaceutials

Results

• Exscientia had the largest IPO for a company of its kind at the time and the impact was to get more of the incumbents and traditional tech companies to renew their interest in the space as their own AI technology develops and branches into different fields.

• One of the core metrics used by companies in this field is getting their drugs to clinical trials. Exscientia was the first AI drug company to get three drugs to trial and has 4 drugs in their pipeline right now, two of which have reached stage 1 clinical trials.

• Exscientia's performance is in line with its competitors

Recommendations

• I would advise this company to add consumer accessible blood and saliva tests that can be offered in doctors offices or sent away for in the mail, similar to genetic testing companies like 23AndMe, and to leverage the use of their auromated lab infrastructure for people to take, test, and receive results with little third party intervention.

• Assuming privacy permissions are granted, this could significantly increase the data set that can be used for training their models as well as opening a new revenue stream to support the data collection. This would also increase the canidate pool for patient selection of new designer drugs.

• Predictive analytics would useful here, measuring known genetic markers of disease against a patient's medical history, machine learning to help predict patient responses to treatments, and AI for disease modeling based on current, high quality data.

• This technology is appropriate because the trend is moving away from treatment alone once a disease is found and into early or even pre-diagnosis and prevention.

• Sources:

  https://www.labiotech.eu/trends-news/exscientia-drug-discovery-ai/#:~:text=Exscientia%27s%20total%20winnings%20from%20the,2.5B%20(%242.9B) https://stockanalysis.com/stocks/exai/ https://www.cbinsights.com/company/exscientia https://ourworldindata.org/cancer#:~:text=The%20map%20shows%20that%20we,countries%20shown%20in%20light%20yellow https://www.exscientia.ai/pipeline https://pubs.acs.org/doi/10.1021/acs.jcim.2c00258 https://exscientia.cdn.prismic.io/exscientia/ea1ecbda-7d03-447b-becd-541e005f8129_Tse+et+al+2021.pdf https://exscientia.cdn.prismic.io/exscientia/2060d8b0-d8df-40d4-b689-0fab25939edb_Imrie+et+al+2020.pdf https://www.ukri.org/who-we-are/how-we-are-doing/research-outcomes-and-impact/bbsrc/exscientia-a-clinical-pipeline-for-ai-designed-drug-candidates/ https://www.liebertpub.com/doi/10.1089/genbio.2022.29035.aho