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Going All In: A Strategic Investment in In Silico Toxicology.
Chemical Research in Toxicology ( IF 3.7 ) Pub Date : 2020-03-19 , DOI: 10.1021/acs.chemrestox.9b00497 Jakub Kostal 1 , Adelina Voutchkova-Kostal 1
Chemical Research in Toxicology ( IF 3.7 ) Pub Date : 2020-03-19 , DOI: 10.1021/acs.chemrestox.9b00497 Jakub Kostal 1 , Adelina Voutchkova-Kostal 1
Affiliation
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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.
中文翻译：
全力以赴：对硅毒理学的战略投资。
由于每年都会有大量新化学药品投放市场，因此我们面临着保护人类和环境同时最大程度地减少经济上和道德上昂贵的动物试验的巨大挑战。计算机模型有望成为我们寻求的解决方案，但我们发现自己处于未来开发工作的十字路口，这些工作将确保这些工具的独立适用性和可靠性。为了实现此目标，需要做出认真的努力，优先考虑实验性测试以支持计算机模型的需求（相对于监管需求）。使用本文的标题中的经济类比，我们认为，谨慎的投资应全力以赴，以支持计算机模型的开发，而不是通过保持测试方法的“平衡组合”的现状来赌博我们的未来。
更新日期：2020-04-23
中文翻译：
全力以赴：对硅毒理学的战略投资。
由于每年都会有大量新化学药品投放市场，因此我们面临着保护人类和环境同时最大程度地减少经济上和道德上昂贵的动物试验的巨大挑战。计算机模型有望成为我们寻求的解决方案，但我们发现自己处于未来开发工作的十字路口，这些工作将确保这些工具的独立适用性和可靠性。为了实现此目标，需要做出认真的努力，优先考虑实验性测试以支持计算机模型的需求（相对于监管需求）。使用本文的标题中的经济类比，我们认为，谨慎的投资应全力以赴，以支持计算机模型的开发，而不是通过保持测试方法的“平衡组合”的现状来赌博我们的未来。
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