PDF(Pubmed)
Abstract：
Computational toxicology models have been successfully implemented to prioritize and screen chemicals. There are numerous in silico (quantitative) structure-activity relationship ([Q]SAR) models for the prediction of a range of human-relevant toxicological endpoints, but for a given endpoint and chemical, not all predictions are identical due to differences in their training sets, algorithms, and methodology. This poses an issue for high-throughput screening of a large chemical inventory as it necessitates several models to cover diverse chemistries but will then generate data conflicts. To address this challenge, we developed a consensus modeling strategy to combine predictions obtained from different existing in silico (Q)SAR models into a single predictive value while also expanding chemical space coverage. This study developed consensus models for nine toxicological endpoints relating to estrogen receptor (ER) and androgen receptor (AR) interactions (i.e., binding, agonism, and antagonism) and genotoxicity (i.e., bacterial mutation, in vitro chromosomal aberration, and in vivo micronucleus). Consensus models were created by combining different (Q)SAR models using various weighting schemes. As a multi-objective optimization problem, there is no single best consensus model, and therefore, Pareto fronts were determined for each endpoint to identify the consensus models that optimize the multiple-criterion decisions simultaneously. Accordingly, this work presents sets of solutions for each endpoint that contain the optimal combination, regardless of the trade-off, with the results demonstrating that the consensus models improved both the predictive power and chemical space coverage. These solutions were further analyzed to find trends between the best consensus models and their components. Here, we demonstrate the development of a flexible and adaptable approach for in silico consensus modeling and its application across nine toxicological endpoints related to ER activity, AR activity, and genotoxicity. These consensus models are developed to be integrated into a larger multi-tier NAM-based framework to prioritize chemicals for further investigation and support the transition to a non-animal approach to risk assessment in Canada.
摘要：
计算毒理学模型已成功实施，以确定化学品的优先级和筛选。有许多计算机（定量）结构-活性关系（[Q]SAR）模型用于预测一系列与人类相关的毒理学终点，但是对于给定的终点和化学物质，由于训练集的差异，并非所有预测都是相同的，算法，和方法论。这对大型化学品库存的高通量筛选提出了问题，因为它需要几种模型来覆盖不同的化学物质，但随后会产生数据冲突。为了应对这一挑战，我们开发了一种共识建模策略，将从不同的现有计算机（Q）SAR模型中获得的预测结果合并为单个预测值，同时还扩大了化学空间覆盖范围。这项研究开发了与雌激素受体（ER）和雄激素受体（AR）相互作用相关的9个毒理学终点的共识模型（即，绑定,激动,和拮抗作用)和遗传毒性(即，细菌突变,体外染色体畸变，和体内微核)。通过使用各种加权方案组合不同的(Q)SAR模型来创建一致性模型。作为一个多目标优化问题,没有单一的最佳共识模型，因此,为每个终点确定帕累托前沿，以确定同时优化多标准决策的共识模型。因此，这项工作为每个包含最优组合的端点提供了一组解决方案，不管权衡，结果表明，共识模型提高了预测能力和化学空间覆盖率。进一步分析这些解决方案，以发现最佳共识模型及其组件之间的趋势。这里,我们展示了一种灵活和适应性的方法的开发，用于计算机共识建模及其在与ER活性相关的九个毒理学终点的应用，AR活动，和遗传毒性。这些共识模型被开发为整合到一个更大的基于NAM的多层框架中，以优先考虑化学品进行进一步调查，并支持加拿大向非动物方法进行风险评估的过渡。
