Podcast, Interview
a16z Podcast | Taking the Pulse on Bio
- The biological sector is projected to undergo a continued transition from empirical science to an engineering discipline, utilizing machine learning and AI to interpret genomic data, identify "killer cancer" through blood analysis, and develop therapeutics that bypass human understanding of disease mechanisms.
- Diagnostic applications are anticipated to generate actionable insights with superior accuracy and continuous learning capabilities, while the combination of AI and sensors is expected to create new diagnostic areas capable of scaling across disease types via repeated engineering processes.
- Cell engineering is predicted to evolve engineered cells into "intelligent drugs" that navigate the body, act on disease states, and self-terminate upon resolution, alongside CRISPR modalities that will accelerate and diversify as biology becomes more engineering-focused.
- Digital health therapies are expected to scale efficiently through A-B testing in digital environments, allowing for constant efficacy improvements without toxicity, while the industry increasingly targets opportunities that minimize science risk in favor of engineering and scale risks.
- Data network effects are forecasted to establish non-expiring barriers to entry stronger than patents, enabling sustained company growth over decades, while traditional high science-risk therapeutic companies are expected to decline in favor of engineering-based approaches.
- Regulatory landscapes are expected to evolve with record numbers of generic drug approvals, decreased regulatory risk for effective powerful therapies, and a shift away from animal models toward more predictive "organs on chips" or "humans on chips."
- Clinical trial metrics are projected to improve dramatically, with potential 96% reductions in recruitment costs and time facilitated by social networks and technology, alongside lower failure rates resulting from more predictive and targeted testing paradigms.
- The industry will likely shift reimbursement models from selling to self-insured employers to direct engagement with insurance plans, CMS, Medicare, and Medicaid to access the mass market, while non-therapeutic CRISPR applications are expected to emerge for diagnostics and drug discovery platforms.
- Fundamental infrastructure improvements include anticipated direct communication between major electronic health record systems like Epic and Cerner, and biology is expected to impact diverse sectors including energy, textiles, food, and data storage.
- Founders for emerging companies will ideally possess deep expertise in both biology and computer science, mirroring the advancement of electronic design automation through the literal engineering of biological circuits.