Summary

Dr. Duxin Sun is the Associate Dean for Research, the Charles Walgreen Jr. Professor of Pharmacy and Pharmaceutical Sciences in the College of Pharmacy at the University of Michigan. He serves as the Director of the Pharmacokinetics (PK) Core. Dr. Sun also has a joint appointment in the Chemical Biology program, the Interdisciplinary Medicinal Chemistry program, and University of Michigan's Comprehensive Cancer Center.

Dr. Sun’s research interests focus on drug development, cancer nanomedicine, cancer vaccine, and pharmacokinetics. Dr. Sun established the STAR system (Structure-Tissue/Cell Selectivity-Activity Relationship) to enhance drug development success rate by addressing the 90% failuar rate. He designed albumin based nanomedicines to enhance clinical efficacy of immuno-oncology drugs by targeting immune cells in the lymphatic system and tumors. He also developed SARS-CoV-2 B epitope-guided neoantigen peptide or mRNA vaccines to enhance their efficacy by activating CD4/CD8 T cell immunity through B cell-mediated antigen presentation. 

Dr. Sun earned his BS in Pharmacy, MS in Pharmacology, and PhD in Pharmaceutical Sciences, and has also received training in Molecular Biology as a visiting scientist. With research experience in both academia and the pharmaceutical industry, Dr. Sun has published over 280 papers, and has mentored 40 PhD students and 75 postdoctoral fellows/visiting scientists. Dr. Sun is an elected Fellow of both the American Association for the Advancement of Science (AAAS) and the American Association of Pharmaceutical Scientists (AAPS). He has served on the FDA Pharmaceutical Science and Clinical Pharmacology Advisory Committee and participated in study sections for the NIH and FDA.

Dr. Sun's administrative Specialist is Erika Zucal - [email protected].

Feature Story
Can machine learning overcome the 95% failure rate and reality that only 30% of approved cancer drugs meaningfully extend patient survival?

This perspective discussed the following questions: Why does 95% of cancer drug development fail despite significant improvement at each step of the process using hundreds of helpful strategies in the past 30 years? Why do only 30% approved cancer drugs meaningfully extend patient survival by more than 2.5 months, while the average cost for a cancer drug regimen per patient in the U.S. ranges from $170,000 to $277,000? Are current strategies to improve each step of drug development process, including AI- or machine learning (ML)-driven approaches, falling into “survivorship bias” trap by overly focusing on many less critical issues but overlooking root causes of key aspects? Is it practical to add more criteria to the already lengthy and costly drug development process, which takes 10-15 years and costs $1-2 billions per one approved drug? Can application of AI/ML methodology in the current drug development process improve efficiency, boost success rates, and enhance drug’s efficacy? What are the root causes of these challenges, and what should be the future research priorities to find potential solutions?

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Responsibilities

  • Associate Dean for Research: UM Pharmacy Professor Research Outreach (UM Pharmacy PRO)

Research Interests

Selected Publications