Abstract: The current paradigm of drug discovery, iterative guesswork, is slow and expensive. In contrast, state of the art tools make it possible to identify the cause of a genetic disease (for example, single nucleotide variations) in a matter of hours for patients, at clinical care facilities with access to rapid whole-genome sequencing (rWGS). However, due to the complexity of the drug development process, clinicians are essentially powerless to act on this diagnostics information with precision therapeutics that address the genetic cause of the disease. In this talk, we will argue that drug discovery can be supplanted by drug engineering, provided we choose the right material, the right purpose-built datasets, the right pharmacology assays and computational tools. We argue that the right material is nucleic acids (Oligonucleotide-based medicines). Also, we argue for a systematic and efficient approach for mapping the design space of nucleic acid sequence/chemistry to the safety pharmacology and efficacy required for creating medicines. We will then discuss our highly interdisciplinary work on constructing this sequence-function relationship, and how we are using this paradigm to engineer personalized medicines at an unprecedented speed and low cost. In this discussion we will advocate how our physics training is powerful in solving the problems of drug design through the application of basic notions and tools of condensed matter physics and the physics of complex systems—effective theories, Design of Experiments (DoE), numerical analysis, and statistical/machine learning, dynamical systems etc. For students, we want to convince you that there are worthy, challenging, and highly impactful problems for you to solve in both industry and academia.
David Pekker Bio: David Pekker obtained his PhD in condensed matter theory from UIUC in 2007, where he studied superconducting quantum devices with Paul Goldbart. He went on to do two postdocs: one at Harvard with Eugene Demler and one at Caltech with Gil Refael. In 2014 David became an Assistant Professor of Physics at the University of Pittsburgh. Starting in summer of 2021, David took a leave of absence from his teaching responsibilities in order to join Creyon Bio. Today, David continues to run his research group at Pitt. Simultaneously, he is working to advance the state of the art in computational chemistry as the director of theory at Creyon Bio.
Swagatam (Swag) Mukhopadhyay Bio: Swagatam Mukhopadhyay is a theoretical physicist by training with a decade of experience and leadership in quantitative thinking in biology. He has extensive research experience, both in academia and industry, applying/advancing computational methods in biological datasets (numerical optimization, signal processing, biophysical/systems modeling, information theory, machine learning/AI and NGS technologies). After earning his doctorate degree in Condensed Matter Theory at University of Illinois at Urbana-Champaign, he did a post-doctoral fellowship within the physics department at University of California, Santa Barbara on glassy and disordered media. He got fascinated by biological complexity during that time, and moved to Rutgers University in a yeast biology lab, where he worked at the crossroads of theory and experiments on epigenetic gene silencing and higher-order chromatin organization, in collaboration with biologists at Princeton. He joined Cold Spring Harbor Lab and worked as a Computational Scientist in the field of Autism Genomics, computational neuroanatomy, and RNAi. After a brief stint in investigating the role of non-coding variants in large-scale human genomics (at Human Longevity Inc.), he joined the functional genomics team at Ionis Pharmaceuticals where he optimized and helped create some of the high-throughput assays used to understand both ASO pharmacology and disease models. Swag was instrumental in demonstrating the value of using quantitative methods to unravel ASO sequence-function relationships that helped explain oligonucleotide chemistry experimental observations. His work also led to dramatic improvements in lead-identification screening efficiencies.
