Literature DB >> 32166946

Going All In: A Strategic Investment in In Silico Toxicology.

Jakub Kostal1, Adelina Voutchkova-Kostal1.   

Abstract

As vast numbers of new chemicals are introduced to market annually, we are faced with the grand challenge of protecting humans and the environment while minimizing economically and ethically costly animal testing. In silico models promise to be the solution we seek, but we find ourselves at crossroads of future development efforts that would ensure standalone applicability and reliability of these tools. A conscientious effort that prioritizes experimental testing to support the needs of in silico models (versus regulatory needs) is called for to achieve this goal. Using economic analogy in the title of this work, we argue that a prudent investment is to go all-in to support in silico model development, rather than gamble our future by keeping the status quo of a "balanced portfolio" of testing approaches. We discuss two paths to future in silico toxicology-one based on big-data statistics ("broadsword"), and the other based on direct modeling of molecular interactions ("scalpel")-and offer rationale that the latter approach is more transparent, is better aligned with our quest for fundamental knowledge, and has a greater potential to succeed if we are willing to transform our toxicity-testing paradigm.

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Year:  2020        PMID: 32166946     DOI: 10.1021/acs.chemrestox.9b00497

Source DB:  PubMed          Journal:  Chem Res Toxicol        ISSN: 0893-228X            Impact factor:   3.739


  4 in total

1.  An Evaluation of the Occupational Health Hazards of Peptide Couplers.

Authors:  Jessica C Graham; Alejandra Trejo-Martin; Martyn L Chilton; Jakub Kostal; Joel Bercu; Gregory L Beutner; Uma S Bruen; David G Dolan; Stephen Gomez; Jedd Hillegass; John Nicolette; Matthew Schmitz
Journal:  Chem Res Toxicol       Date:  2022-05-09       Impact factor: 3.973

2.  Structure-to-process design framework for developing safer pesticides.

Authors:  Jessica M Lewer; Zachary R Stickelman; Jessica H Huang; John F Peloquin; Jakub Kostal
Journal:  Sci Adv       Date:  2022-03-30       Impact factor: 14.136

3.  Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach.

Authors:  Heather L Ciallella; Daniel P Russo; Lauren M Aleksunes; Fabian A Grimm; Hao Zhu
Journal:  Environ Sci Technol       Date:  2021-07-25       Impact factor: 11.357

4.  Toxicology Advances for 21st Century Chemical Pollution.

Authors:  Bryan W Brooks; Tara Sabo-Attwood; Kyungho Choi; Sujin Kim; Jakub Kostal; Carlie A LaLone; Laura M Langan; Luigi Margiotta-Casaluci; Jing You; Xiaowei Zhang
Journal:  One Earth       Date:  2020-04-24
  4 in total

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