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用于预测药物化合物急性口服毒性的计算模型的性能、可靠性和潜在应用。

The performance, reliability and potential application of in silico models for predicting the acute oral toxicity of pharmaceutical compounds.

机构信息

Bristol Myers Squibb, 1 Squibb Drive, New Brunswick, NJ, 08903, USA.

Bristol Myers Squibb, 1 Squibb Drive, New Brunswick, NJ, 08903, USA.

出版信息

Regul Toxicol Pharmacol. 2021 Feb;119:104816. doi: 10.1016/j.yrtph.2020.104816. Epub 2020 Nov 6.

Abstract

Acute oral toxicity (AOT) information is utilized to categorize compounds according to the severity of their hazard and used to inform risk assessments for human health and the environment. Given the wealth of historical AOT information and technological advances, in silico models are being created and evaluated as potential tools to predict the AOT of compounds and reduce reliance on animal testing. Utilizing a historical database of AOT data on 371 Bristol Myers Squibb pharmaceutical compounds (PCs) (195 pharmaceutical intermediates and 176 active pharmaceutical ingredients), we evaluated two pioneering in silico AOT programs: the Leadscope Acute Oral Toxicity Model Suite and the Collaborative Acute Toxicity Modeling Suite. These models demonstrated a high degree of agreement with the in vivo results as well as a high level of sensitivity. We found that these models can be effectively utilized to identify PCs which are of low acute oral toxicity (LD > 2000 mg/kg), PCs which should not be classified as Dangerous Goods (LD > 300 mg/kg), and can assist in identifying a starting dose for in vivo AOT studies. This manuscript provides an evaluation of the performance of these in silico models and proposes use cases where these in silico models can be most confidently and effectively employed.

摘要

急性口服毒性 (AOT) 信息用于根据化合物危害的严重程度对其进行分类,并用于为人类健康和环境风险评估提供信息。鉴于丰富的历史 AOT 信息和技术进步,正在创建和评估基于计算机的模型,作为预测化合物 AOT 并减少对动物测试依赖的潜在工具。我们利用 Bristol Myers Squibb 制药化合物 (PC) 的历史 AOT 数据库(371 个数据点)(195 个制药中间体和 176 个活性药物成分),评估了两种开创性的基于计算机的 AOT 程序:Leadscope 急性口服毒性模型套件和协作性急性毒性建模套件。这些模型与体内结果高度一致,并且具有很高的灵敏度。我们发现这些模型可有效地用于识别急性口服毒性较低的 PC(LD>2000mg/kg)、不应被归类为危险货物的 PC(LD>300mg/kg),并有助于确定体内 AOT 研究的起始剂量。本文对这些基于计算机的模型的性能进行了评估,并提出了可以最有信心和有效地使用这些基于计算机的模型的用例。

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