A Vision for Revolutionizing Precision Medicine
Cypress HomeCare Solutions is home to Health Futures – Taking Stock in You Radio show, powered by Money Radio 1510AM and 105.3FM. Each week, the show provides listeners with expert advice on how to live a longer, healthier, and happier life.
On Friday, June 23rd, 2023 we had the pleasure of having Dr. Rui Chang, an expert in predictive network modeling, on the show to discuss his recent breakthrough methods of researching Alzheimer’s.
To listen to the full hour-long episode, click here.
Insights from Dr. Rui Chang into Leveraging AI to Revolutionize Precision Medicine
Bob Roth recently hosted Dr. Rui Chang, an associate professor of neurology at the University of Arizona, on his radio show Health Futures. Dr. Chang shared fascinating insights into how artificial intelligence and machine learning can revolutionize precision medicine, especially for complex diseases like Alzheimer’s.
Dr. Chang’s Diverse Background
Dr. Chang has a diverse background spanning computer science, biomedical engineering, and neurology. Originally from China, he earned degrees in Germany and did postdoctoral research at UC San Diego applying machine learning to stem cell biology. He served on the faculty at Mt. Sinai in New York before joining the University of Arizona. This diverse training equips him to bridge the gap between medicine and technology.
The AI Revolution in Healthcare
While AI has been an “underdog” technology for decades, we’re now in an era of explosive growth. Vast amounts of biomedical big data have been generated, far beyond humans’ capacity to analyze alone. AI’s ability to discern subtle patterns within massive datasets makes it an extremely valuable medical tool.
Dr. Chang explained that AI refers to a broad field encompassing diverse statistical and machine learning algorithms applied to big data problems. These models find associations between inputs and outputs, yielding new predictions and hypotheses.
AI Applied to Complex Diseases
In medicine, AI can integrate complex omics data – genomics, proteomics, metabolomics – to model biological systems. It unravels how genes regulate each other in network models. This helps identify root upstream causes of disease, not just downstream effects.
For example, amyloid beta plaques and tau tangles are late-stage impacts in Alzheimer’s disease. The real culprits driving neurodegeneration lie further upstream. AI modeling can nominate master regulator genes and proteins that may be promising treatment targets.
Dr. Chang has applied his AI algorithms to Alzheimer’s, identifying thousands of candidate targets and potential drugs, including repurposed approved therapies. His lab tests predictions experimentally to see which hold up biologically. He also explores AI applications for aging, cancer, autoimmune disorders, and more.
Opportunities and Limitations of AI
While AI rapidly generates hypotheses, human expertise is still essential. In a recent collaboration, Dr. Chang’s AI nominated 19 potential Alzheimer’s targets. Experiments validated 10, showing AI’s promise but also the need for biological follow-up.
Dr. Chang emphasized AI should be viewed as a “friendly filter” – an aid, not a replacement for human intelligence. Thoughtful oversight matters, as the Boeing 737 MAX autopilot issue demonstrated. Like other technologies, AI’s impacts depend on human wisdom in application.
The Future Is Collaborative
Moving forward, Dr. Chang believes multidisciplinary collaboration will be key. AI guides experimental designers to high-probability candidates. Scientific teams then iteratively refine algorithms based on results.
Dr. Chang suggested AI can accelerate breakthroughs in our most complex and urgent healthcare challenges. But realizing its full potential requires partnerships spanning computing, engineering, biology, genomics, medicine and more.
Research Study with Contributions by Dr. Chang
A recent study with contributions from Dr. Rui Chang demonstrates the power of using advanced artificial intelligence to unravel the complex metabolic changes underlying Alzheimer’s disease. Working with over 1000 blood samples from the Alzheimer’s Disease Neuroimaging Initiative, Dr. Chang applied predictive network modeling to construct patient-specific models of metabolic pathways in Alzheimer’s. His AI algorithms were able to integrate multi-omics data and identify key drivers and signatures of disease progression based on sex and APOE genotype.
The models revealed Alzheimer’s fundamentally disrupts lipid metabolism, especially of phospholipids like phosphatidylcholines. However, the specific pattern differed between males and females. Alzheimer’s males showed disruptions in amino acid metabolism, while females had stronger lipid changes. When further sub-dividing groups by APOE genotype, a key Alzheimer’s risk gene, Dr. Chang found APOE4 overrides sex differences. Both APOE4 males and females demonstrated a phosphatidylcholine-dominant profile. Non-APOE4 males retained disruptions in amino acids like branched-chain amino acids.
Dr. Chang’s AI models also nominated potential drivers of disease, like the metabolite alpha-aminoadipic acid in males. Many of these are upstream of visible pathology, representing early disease mechanisms. His network models significantly outperformed conventional methods in identifying metabolite panels that predicted Alzheimer’s diagnosis and correlated with cognitive decline.
Overall, this research demonstrates artificial intelligence’s unique ability to map the system-wide metabolic failures underlying Alzheimer’s in an individualized way. While limited to partial metabolite coverage, Dr. Chang’s innovative application of AI and machine learning points to the future potential of multi-omics profiling to enable personalized precision medicine. His work provides a powerful proof of concept for this approach in Alzheimer’s and neurodegeneration and has made headlines around the globe.
Dr. Chang’s Private Sector Initiative
Tackling health challenges from one angle is a full-time pursuit for some people, but not Dr. Chang. In addition to his full time responsibilities as the the current Associate Director of The Center for Innovation in Brain Sciences (CIBS) at University of Arizona, Dr. Chang also founded a company based in Tucson, AZ called Path Biotech.
- Path BioTech was founded in 2021 based on Dr. Chang’s AI research to advance predicted drug candidates into clinical trials.
- The company is also developing natural products and neutraceuticals that may provide therapeutic benefits.
- The name Path BioTech refers to using AI approaches to map biological pathways implicated in diseases like Alzheimer’s and identify targeted treatment paths.
- Dr. Chang mentioned Path BioTech has validated around 200 compounds predicted by his AI systems so far, with over half matching real biological activities related to disease modulation.
- The company aims to accelerate getting AI-predicted therapies to patients faster, as well as making natural product treatments more accessible.
So in summary, Path BioTech is a biotech company established by Dr. Rui Chang to translate his lab’s AI discoveries into potential new precision medicine solutions for patients. The name reflects their mission of illuminating biological pathways for targeted therapies using artificial intelligence.
Insights for Healthy Longevity
For individuals and families, the future of healthcare lies in optimizing wellness proactively, not just treating disease reactively. Precision prevention tailored to our genes, environment and lifestyle will transform public health.
Understanding healthy longevity factors like diet, exercise, social connection, stress reduction, and sound sleep is vital. While genetics play a role, adherence to healthy behaviors and environments may determine up to 75% of outcomes. Monitoring subtle changes with smart technologies can also facilitate early, personalized interventions.
By integrating big data analytics with holistic lifestyle medicine, the healthcare revolution promises greatly enhanced prospects for healthspan – not just lifespan. AI opens exciting possibilities, but realizing them demands open minds, multidisciplinary teamwork, and most importantly, compassion.
Frequently Asked Questions (FAQ)
How can AI be applied responsibly to improve patient care while protecting privacy?
- Use de-identified data and strict access controls
- Provide transparency about how AI is used
- Involve ethics oversight groups
- Allow patients to opt out of data sharing
What solutions seem most promising for making precision medicine approaches accessible and affordable?
- Tiered/selective analysis to right-size to individual needs
- Efforts to reduce genomic sequencing costs
- Partnering with community resources and clinics
- Hybrid telehealth and in-person care models
How can multidisciplinary teams best collaborate to advance AI innovation in medicine?
- Foster regular communication and idea exchange
- Engage stakeholders early and often
- Clarify roles while encouraging synergistic thinking
- Share data and workflows using collaborative platforms
- Make space for both scientific creativity and constructive critique
What healthy lifestyle changes do emerging insights from AI modeling suggest could prevent or slow disease?
- Increased physical activity and metabolic health
- Stress reduction and resilience building
- Brain-stimulating cognitive engagement
- Dietary optimization for microbiome
- Proactive supplement regimens based on genetics
How can community education foster a greater understanding of AI’s benefits versus risks in healthcare?
- Outreach on media channels people already use
- Public talks and demonstrations
- K-12 curriculum and career exposure
- Transparent discussion of tough topics
- Highlight real-world benefits and limitations
What safeguards and oversight processes are needed to ensure AI ethics in medicine?
- External auditing
- Patient advocate involvement
- Reviews for avoiding bias and misuse
- Provider training on appropriate usage
- Continuous risk identification and mitigation
- Regulatory bodies to enforce standards
How can AI augment clinicians’ abilities to provide personalized, proactive care for patients?
- Risk assessment and early diagnostics
- Optimized treatment recommendations
- Monitoring response to therapies
- Predicting future health trajectories
- Freeing doctors to focus on building relationships
What role might at-home sensing technologies play in creating big datasets for preventive AI modeling?
- Providing real-world lifestyle and environmental data
- Enabling continuous, passive monitoring
- Broadening data diversity and inclusion
- Combining self-tracking with clinical measures
- Surfacing early individual variations from norms