Department of Chemistry, The George Washington University, 800 22nd Street NW, Washington, D.C. 20052-0066, United States.
Chem Res Toxicol. 2020 Apr 20;33(4):880-888. doi: 10.1021/acs.chemrestox.9b00497. Epub 2020 Mar 19.
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. 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 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 model development, rather than gamble our future by keeping the of a "balanced portfolio" of testing approaches. We discuss two paths to future 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.
每年都有大量的新化学物质推向市场，我们面临着保护人类和环境的巨大挑战，同时尽量减少经济和伦理上代价高昂的动物测试。替代模型有望成为我们所寻求的解决方案，但我们发现自己正处于未来发展努力的十字路口，这些努力将确保这些工具的独立适用性和可靠性。需要认真努力，优先进行实验测试，以支持替代模型的需求（而不是监管需求），以实现这一目标。在本文的标题中使用经济类比，我们认为，谨慎的投资是全力支持替代模型的发展，而不是通过保留“测试方法的平衡投资组合”来冒险我们的未来。我们讨论了未来毒理学的两种途径——一种基于大数据统计（“大刀阔斧”），另一种基于分子相互作用的直接建模（“手术刀”）——并提出了这样的理由，即后者方法更透明，更符合我们对基础知识的追求，如果我们愿意改变我们的毒性测试范式，成功的潜力更大。
