Accelerating Therapeutics for Opportunities in Medicine: A Paradigm Shift in Drug Discovery.

Izumi V Hinkson,Benjamin Madej,Eric A Stahlberg

doi:10.3389/fphar.2020.00770

Izumi V Hinkson, Benjamin Madej + Show 1 more

Open Access

https://doi.org/10.3389/fphar.2020.00770

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Abstract

Conventional drug discovery is long and costly, and suffers from high attrition rates, often leaving patients with limited or expensive treatment options. Recognizing the overwhelming need to accelerate this process and increase success, the ATOM consortium was formed by government, industry, and academic partners in October 2017. ATOM applies a team science and open-source approach to foster a paradigm shift in drug discovery. ATOM is developing and validating a precompetitive, preclinical, small molecule drug discovery platform that simultaneously optimizes pharmacokinetics, toxicity, protein-ligand interactions, systems-level models, molecular design, and novel compound generation. To achieve this, the ATOM Modeling Pipeline (AMPL) has been developed to enable advanced and emerging machine learning (ML) approaches to build models from diverse historical drug discovery data. This modular pipeline has been designed to couple with a generative algorithm that optimizes multiple parameters necessary for drug discovery. ATOM's approach is to consider the full pharmacology and therapeutic window of the drug concurrently, through computationally-driven design, thereby reducing the number of molecules that are selected for experimental validation. Here, we discuss the role of collaborative efforts such as consortia and public-private partnerships in accelerating cross disciplinary innovation and the development of open-source tools for drug discovery.

Highlights

Preclinical drug discovery typically takes five and a half years and accounts for about one third of the cost of drug development (Paul et al, 2010)
Because so much of the cost of development stems from the cost of failures, approaches that improve our ability to distinguish early which molecules will succeed can have a disproportionate impact on improving the output of new medicines illustrate the potential for accelerating drug discovery through artificial intelligence (AI)-driven approaches (Ringel et al, 2013)
As an open consortium backed by major public entities, the Department of Energy, the National Cancer Institute, and the University of California Office of the President, as well as pharmaceutical leader GSK, the Accelerating Therapeutics for Opportunities in Medicine consortium (ATOM) is committed to creating new tools for drug discovery that can be shared broadly and benefit the public good

Summary

Introduction

Preclinical drug discovery typically takes five and a half years and accounts for about one third of the cost of drug development (Paul et al, 2010). ATOM is applying an integrated approach to combine capabilities such as high-performance computing, human-relevant in vitro experimentation, data-driven and mechanistic modeling, and curation of pharmacological data toward the development of a novel preclinical drug discovery and development platform. As an open consortium backed by major public entities, the Department of Energy, the National Cancer Institute, and the University of California Office of the President, as well as pharmaceutical leader GSK, the Accelerating Therapeutics for Opportunities in Medicine consortium (ATOM) is committed to creating new tools for drug discovery that can be shared broadly and benefit the public good.

Results

Conclusion