The Remarkable Potential of Precision Medicine

Precision Medicine Definition & Problem Statement

• Current disease classification relies on symptoms and organs rather than molecular mechanisms, leading to the assumption that one disease has a single uniform cause.
• Traditional research is siloized, preventing the identification of shared mechanisms across different diseases and limiting drug repurposing opportunities.
• Precision medicine is envisioned as a "Google Maps" for health, layering epidemiology, environment, microbiome, and metabolome data to create a robust, mechanistic understanding of disease.
• The current trajectory treats diseases generically, resulting in expensive treatments that often fail to hit the right patient at the right time.

Genomic Sequencing Efficiency Gains

• The Human Genome Project took 13 years and $3.8 billion to sequence the first human genome.
• Current sequencing costs are approaching $1,000 and take a few hours.
• Future projections estimate costs will drop to a few hundred dollars with processing times of 10 to 15 minutes.

Translational Research Inefficiencies & NCATS Formation

• The National Center for Advancing Translational Sciences (NCATS) was established via federal legislation with leadership from House Leader Cantor, Senate Leader Harry Reid, and the President.
• Current drug development from lab discovery to market takes 15 years, costs between $2 billion and $10 billion, and has a failure rate of 99.9%.
• There are approximately 7,000 human diseases, yet treatments exist for only about 1,000.
• At the current rate of 3 diseases moving from untreatable to treatable per year, it would take 2,000 years to address all diseases.
• NCATS aims to improve efficiency by organizing research across organ systems (e.g., identifying that "the knee bone is connected to the leg bone") rather than strictly by disease.
• A recent NCATS initiative identified a rheumatoid arthritis drug as a treatment for a form of leukemia, moving from discovery to clinical trial in under one year.

Global Investment & Data Generation

• China has committed $1.9 trillion to bioscience and related sectors to address national challenges in agriculture, air, water, defense, and energy.
• Singapore is committing nearly 40% of its equivalent of the NIH budget toward bioscience despite a population less than 1/50th the size of the United States.
• Global data generation includes 4 million Google searches and 2.46 million new Facebook posts every minute.
• Healthcare app downloads are projected to grow from 44 million in 2012 to 142 million by 2016.

Mental Health & Industrial Impact

• Mental disorders are identified as a primary target for precision medicine due to their high impact on families and low current understanding of neuronal circuitry.
• A 2007 Milken Institute report identified depression as the single highest cost to the US economy (factoring in absenteeism, presenteeism, and out-of-pocket costs), exceeding the costs of cancer or diabetes.
• Mental health treatment is seen as a potential generator of a new industry class in California.

Cystic Fibrosis Case Study (Kalydeco)

• Historically, drugs approved for general populations often failed for 96-97% of specific patient groups due to genetic mutations.
• Pfizer invested $59 million to reopen trials for a Cystic Fibrosis drug, using genetic sequencing to identify the specific subgroup that responded positively.
• The Cystic Fibrosis Foundation partnered to fund the reopening, sharing in future revenues if the data-supported approach succeeds.
• Life expectancy for patients with Cystic Fibrosis has increased from 18 years to 37 years in the last 14 years due to targeted therapies.

California's Bioscience Ecosystem

• California hosts six of the 20 leading bioscience universities globally, more than any other country outside the US and UK.
• In Q1 2014, 60% of all venture capital investment in US startups occurred in California.
• Six of the top 12 metropolitan areas in the US biotech index are located in California (specifically San Jose, San Francisco, Oakland, and San Diego).
• The state hosts eight of the 10 most profitable technology companies in the United States.

Collaborative Platforms & Stem Cell Models

• Stem cell efforts succeeded by creating a platform technology involving global training, industrial tool development, and partnerships with industries willing to take risks on cell therapy.
• Precision medicine aims to replicate this by integrating big data platforms, digital/aerospace industry partnerships, and educational programs for next-generation practitioners.
• UC San Diego and the Craig Venter Institute have formed a public-private partnership where the Institute pays $1/year to lease prime property to foster collaboration.
• The UC system represents a healthcare system of 8 to 12 million patients, offering the largest potential data harvest for precision medicine if data sharing barriers are removed.

Toxicology & Organ-on-a-Chip Technology

• Approximately 30% of drug development fails due to unanticipated toxicity, traditionally tested in animals.
• NCATS is developing "organoids" (human tissue chips) made from induced pluripotent stem cells to test drug toxicity on human tissues rather than animals.
• Future applications could involve creating patient-specific tissue chips (e.g., using DNA from Jeff Bluestone, Jerry Brown, etc.) to predict individual drug responses and side effects before administration.

Regulatory Challenges & Privacy

• HIPAA regulations, created roughly 20 years ago, currently restrict the sharing of medical data necessary for big data analytics.
• Over 70% of cancer patients express willingness to share their data if it benefits their family or others, suggesting a gap between patient desire and regulatory restriction.
• Federal legislation is described as cumbersome and slow to adapt compared to rapid technological advancement.
• Proposed solutions emphasize empowering the individual citizen to control and share their own data (e.g., via personal devices) rather than waiting for doctor-led sharing protocols.

Future Outlook (10-15 Years)

• Disease taxonomy will shift from symptom-based naming to mutation-based classification (e.g., treating a patient for a specific genetic mutation that could also cause depression or cancer).
• Specialists may treat "mechanistic pathways" (e.g., cilia function) rather than specific organ diseases like "cancer" or "diabetes."
• Treatment will become preventive, personalized, and less expensive as drug repurposing and mechanistic understanding increase.
• The transition from "small data" (clinical observation) to "big data" (genomic, environmental, lifestyle) represents an inflection point similar to shifts in consumer goods personalization.

California's Strategic Vision

• California aims to become the world headquarters for precision medicine through state-led collaboration between public universities, private institutions, and patient groups.
• The state plans to model its approach after the success of the California Institute for Regenerative Medicine (CIRM), which operated separately from federal constraints to achieve leadership.
• The vision includes creating seamless tech transfer processes to move discoveries from academia to industry without the friction of current intellectual property negotiations.