Improved Detection of Drug-Induced Liver Injury by Integrating Predicted In Vivo and In Vitro Data.

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-08-19 Epub Date: 2024-07-09 DOI:10.1021/acs.chemrestox.4c00015
Srijit Seal, Dominic Williams, Layla Hosseini-Gerami, Manas Mahale, Anne E Carpenter, Ola Spjuth, Andreas Bender
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Abstract

Drug-induced liver injury (DILI) has been a significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. Over the last decade, the existing suite of in vitro proxy-DILI assays has generally improved at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing the in silico prediction of DILI because it allows for evaluating large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects. In this study, we aim to study ML models for DILI prediction that first predict nine proxy-DILI labels and then use them as features in addition to chemical structural features to predict DILI. The features include in vitro (e.g., mitochondrial toxicity, bile salt export pump inhibition) data, in vivo (e.g., preclinical rat hepatotoxicity studies) data, pharmacokinetic parameters of maximum concentration, structural fingerprints, and physicochemical parameters. We trained DILI-prediction models on 888 compounds from the DILI data set (composed of DILIst and DILIrank) and tested them on a held-out external test set of 223 compounds from the DILI data set. The best model, DILIPredictor, attained an AUC-PR of 0.79. This model enabled the detection of the top 25 toxic compounds (2.68 LR+, positive likelihood ratio) compared to models using only structural features (1.65 LR+ score). Using feature interpretation from DILIPredictor, we identified the chemical substructures causing DILI and differentiated cases of DILI caused by compounds in animals but not in humans. For example, DILIPredictor correctly recognized 2-butoxyethanol as nontoxic in humans despite its hepatotoxicity in mice models. Overall, the DILIPredictor model improves the detection of compounds causing DILI with an improved differentiation between animal and human sensitivity and the potential for mechanism evaluation. DILIPredictor required only chemical structures as input for prediction and is publicly available at https://broad.io/DILIPredictor for use via web interface and with all code available for download.

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通过整合体内和体外预测数据改进药物诱发肝损伤的检测。
药物诱导的肝损伤(DILI)一直是药物发现过程中面临的重大挑战,常常导致临床试验失败和不得不撤药。在过去的十年中,现有的体外替代 DILI 检测方法在鉴定具有肝毒性的化合物方面普遍有所改进。然而,人们对加强 DILI 的硅学预测相当感兴趣,因为这样可以更快、更经济地评估大量化合物,尤其是在项目的早期阶段。在本研究中,我们旨在研究用于 DILI 预测的 ML 模型,首先预测九个替代 DILI 标签,然后将它们作为化学结构特征之外的特征来预测 DILI。这些特征包括体外(如线粒体毒性、胆盐输出泵抑制)数据、体内(如临床前大鼠肝毒性研究)数据、最大浓度药代动力学参数、结构指纹和理化参数。我们对来自 DILI 数据集(由 DILIst 和 DILIrank 组成)的 888 种化合物进行了 DILI 预测模型的训练,并对来自 DILI 数据集的 223 种化合物进行了外部测试。最佳模型 DILIPredictor 的 AUC-ROC 为 0.79。与仅使用结构特征的模型(1.65 LR+ 分数)相比,该模型能够检测出前 25 种有毒化合物(2.68 LR+,正似然比)。利用 DILIPredictor 的特征解释,我们确定了导致 DILI 的化学子结构,并区分了由动物体内化合物而非人体内化合物导致的 DILI 病例。例如,尽管 2-丁氧基乙醇在小鼠模型中具有肝毒性,但 DILIPredictor 仍能正确识别出它对人类无毒。总之,DILIPredictor 模型提高了对导致 DILI 的化合物的检测能力,改进了动物和人体敏感性之间的区分,并具有机制评估的潜力。DILIPredictor 只需输入化学结构即可进行预测,可在 https://broad.io/DILIPredictor 网站上通过网络界面公开使用,所有代码均可下载。
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7.20
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4.30%
发文量
567
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