Biology is transitioning from a 'science of discovery' to an 'engineering' discipline, a shift attributed to the capabilities of AI and machine learning. Vijay Pande, who previously managed a $4 billion biotech practice at a16z and now co-leads VZVC, highlighted how AI allows computers to comprehend intricate biological systems. This understanding can help identify drug targets for specific diseases, aid in drug creation, and support clinical trials.
Despite these advancements, clinical trials remain a significant financial hurdle. Pande noted that while the time and cost to reach trials have decreased with AI, running a single trial can still cost hundreds of millions of dollars. The probability of a drug successfully progressing from the first to the third trial is only 20%, with failures often stemming from animal models not accurately predicting human responses. AI models are expected to offer better predictive capabilities than animal models.
Pande also discussed the potential for 'precision medicine', where treatments are tailored to individuals rather than based on population averages. This approach aims to ensure the first drug prescribed is the correct one. The shift towards this personalised medicine is supported by advances in measuring various biological factors beyond genomics, such as proteomics, combined with automation in robotic measurements that integrate well with AI.
A challenge in AI-driven biotech is the lack of readily available biological data, unlike text data that can be scraped from the internet. However, Pande believes a trend towards building 'atlases of biological information', often in the form of foundation models, could facilitate greater data sharing. This could lead to a scenario where AI acts as a comprehensive specialist, potentially seeing connections that individual human doctors might miss, similar to having a team of top doctors collaborating.