1/13 – Perišić & Beitman, Big Blue Genomics
CBQB Seminar (Virtual event)
January 13, 2025
12:00 PM - 1:00 PM
Location
Virtual (CBQB)
Calendar
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Speaker:
Ognjen Perišić, PhD
Big Blue Genomics, Founder/Researcher
Michael Beitman
Co-speaker
Vice President, Technology Investment Banking at Piper Sandler
Title: Three Pillars of Drug Discovery
Abstract: Drug development is a very expensive and time-consuming process with a high failure rate. From the early-stage drug discovery, via preclinical and clinical phases I to IV, to regulatory approval, it takes years, even decades, thousands of people, and billions of dollars to bring a single drug to market. That focuses pharma companies on the most promising targets, with rare diseases often omitted from research pipelines. Computational tools have been widely accepted as tools of choice in preclinical drug development. Classical algorithms such as molecular dynamics can offer valuable insights into the behavior of drug-target complexes, but their numerical complexity and the vastness of the search space (10^60 drug-like molecules) force researchers to use more efficient machine learning (ML) algorithms. ML, with its ability to successfully handle large amounts of multidimensional data, can be of tremendous help in that regard, but the problem with ML is that it often poorly generalizes outside of the training space. That means the right balance of classical and ML tools may offer the best pathway to efficient and effective drug design. Our aim is to adopt this approach to reduce the time to clinical trials from a decade to a year. At its core, the idea is to create a preclinical drug development platform that will be based on three pillars: a) computational chemistry, b) accelerated clinical testing, and c) personalized medicine. With computational chemistry, the aim is to use physics-based calculations together with Machine Learning protocols to achieve highly accurate drug candidate prediction and classification. To accelerate clinical testing, the aim is to leverage the wealth of available genomics data, clinical data, and rapid genome sequencing to enable the development and training of systems that can assess toxicity and predict clinical outcomes. With personalized medicine, the goal is to harness the power of rapid genome sequencing and protein modeling to accelerate drug development for rare diseases and individual patients, reducing the time-to-market for these critical treatments. This is not beyond the realm of feasibility because recent developments in the pharma field prove that computational approaches can accelerate drug development and bring compounds to clinical testing in as little as 18 months.
Date posted
Jan 3, 2025
Date updated
Jan 10, 2025