Conference Presentation, Panel
Precision Medicine: Accelerating Treatments From Potential to Reality
Milken InstituteMike Milken, Margaret Anderson, Pradeep Khosla, Brian Drucker, Betsy Nabel, Sam Hoggud
- The panel predicts a "golden age of medicine" driven by the transition from linear analysis to artificial intelligence and big data, aiming to accelerate treatments from potential to reality by 2030 and compress drug development timelines from 25 to 40 years down to nine months.
- Precision medicine is expected to expand beyond genomics to include proteomics and cellular networks, requiring a paradigm shift from organ-based to cellular state understanding and enabling drugs to target different cancers with identical cellular biology.
- Significant funding gaps necessitate a shift from federal reliance to private sector leadership, as the current NIH precision medicine initiative of $215 million is estimated to be 1,000 to 100,000 times smaller than required and federal funding at institutions like Mass General is projected to drop from 60-70% to 40%.
- Global bioscience leadership is anticipated to shift, with China committing $1.9 trillion over the next 10 to 12 years to potentially surpass the United States, while the UK is already home to one out of every four of the 20 leading bioscience academic universities.
- Bioscience investment is expected to extend beyond healthcare to address challenges in food security, clean water, defense, bioterrorism, energy, and the environment through digitized medical information and open data crowdsourcing.
- Future medical education will evolve to require clinicians to master data science and collaborate across disciplines, with the average age for the first independent NIH grant needing to drop from 42 to the low 30s and current training pipelines taking 15 years.
- A transition from paternalism to partnership is projected, where patients become active participants using mobile digital health devices, though this requires overcoming trust barriers regarding data security and the potential for insurance discrimination based on personal data.
- Technical challenges remain regarding data standardization, as current systems lack consistent definitions for conditions like dyspnea, creating obstacles for AI integration despite the anticipated primary value emerging from applications built on open data.
- Ethical and regulatory dilemmas are expected to arise as data assets become highly priced, potentially restricting doctor access and necessitating new intellectual property regulations and security models to prevent commercial exploitation.
- Neurological disorders are identified as a primary future investment area due to the brain representing a "big unknown," with a proposed "Apollo project" to solve biologic diseases leveraging technologies that currently exist.
- States such as Massachusetts and California may need to fund biomedical initiatives if federal trends reverse, as the doubling of the NIH budget is considered unlikely and the private sector is expected to assume greater responsibility.
- The United States risks losing research preeminence if it fails to maintain continuous funding and attract data scientists, while global benefits are projected to include saving children of presidents from life-threatening diseases that today's technology could prevent.
- A public discourse is required to encourage healthy individuals to contribute data, contrasting with the willingness of those with life-threatening diseases to share information for the benefit of themselves and others.