基于化学结构的药物主要靶点和非靶点的计算机预测回顾性分析

IF 3.1 Q2 TOXICOLOGY
Takafumi Takai, Brandon D Jeffy, Swathi Prabhu, Jennifer D Cohen
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引用次数: 0

摘要

在药物发现过程的早期阶段,在考虑潜在的安全风险时,评估候选药物的脱靶药理学非常重要。这种脱靶负荷最常用的评估方法是使用与明确定义的毒理学事件密切相关的体外药理学分析小组。除了体外小组外,初步的计算机评估正在成为一种有价值的方法,以支持识别潜在的脱靶点,甚至在合成化学材料之前。为了确定计算机靶标分析的实用性,我们根据94种化合物的内部数据集和相关的体外面板数据(包括结合抑制和功能性激动作用/拮抗作用)评估了专有计算机预测工具的预测性能。在测试的化合物中,主要目标的预测灵敏度为35%。然而,对于未在化学文摘服务登记处报告的化合物子集,预测主要目标的灵敏度下降到16%。对于所有测试化合物的已知脱靶点,结合分析的敏感性值为16%,功能分析的敏感性值为23%。为了更好地理解计算机脱靶预测的适用性,我们进行了体外结合试验,以评估另外五个由计算机预测但未被我们的标准脱靶结合或功能面板覆盖的脱靶。尽管通过这项活动没有发现新的脱靶点,但随着技术的发展,计算机预测可以提供有价值的见解,以确定体内研究中化合物引起的靶器官毒性的潜在脱靶和机制见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Retrospective Analysis of Chemical Structure-Based in silico Prediction of Primary Drug Target and Off-Targets

In early phases of the drug discovery process, evaluating the off-target pharmacology of a candidate drug is important when considering potential safety risks. Such off-target liabilities are most commonly evaluated using panels of in vitro pharmacology assays with strong association to well-defined toxicological events. In addition to in vitro panels, preliminary in silico evaluation is emerging as a valuable approach to support identification of potential off-target hits, even prior to synthesis of chemical material. To ascertain the utility of in silico target profiling, the predictive performance of a proprietary in silico predictive tool was evaluated against an in-house data set of 94 compounds with associated in vitro panel data, including binding inhibition and functional agonism/antagonism. Of the compounds tested, the primary target was predicted with 35% sensitivity. However, the sensitivity to predict the primary target decreased to 16% for a subset of compounds not reported within the Chemical Abstracts Service registry. For the known off-target hits for all tested compounds, the value of sensitivity was 16% for binding assays and 23% for functional assays. To better understand the applicability of the in silico off-target prediction, we performed in vitro binding assays, to evaluate five additional off-targets that were predicted by in silico but not covered by our standard off-target binding or functional panels. Although no new off-target hit was identified through this campaign, as technologies evolve, the in silico predictions could provide valuable insights to identify potential off-targets and mechanistic insights on target organ toxicities caused by compounds in in vivo studies.

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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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