Sulfotransferase-mediated phase II drug metabolism prediction of substrates and sites using accessibility and reactivity-based algorithms.

IF 2.8 4区 医学 Q3 CHEMISTRY, MEDICINAL
Molecular Informatics Pub Date : 2024-10-01 Epub Date: 2024-08-07 DOI:10.1002/minf.202400008
Shivam Kumar Vyas, Avik Das, Upadhyayula Suryanarayana Murty, Vaibhav A Dixit
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引用次数: 0

Abstract

Sulphotransferases (SULTs) are a major phase II metabolic enzyme class contributing ~20 % to the Phase II metabolism of FDA-approved drugs. Ignoring the potential for SULT-mediated metabolism leaves a strong potential for drug-drug interactions, often causing late-stage drug discovery failures or black-boxed warnings on FDA labels. The existing models use only accessibility descriptors and machine learning (ML) methods for class and site of sulfonation (SOS) predictions for SULT. In this study, a variety of accessibility, reactivity, and hybrid models and algorithms have been developed to make accurate substrate and SOS predictions. Unlike the literature models, reactivity parameters for the aliphatic or aromatic hydroxyl groups (R/Ar-O-H), the Bond Dissociation Energy (BDE) gave accurate models with a True Positive Rate (TPR)=0.84 for SOS predictions. We offer mechanistic insights to explain these novel findings that are not recognized in the literature. The accessibility parameters like the ratio of Chemgauss4 Score (CGS) and Molecular Weight (MW) CGS/MW and distance from cofactor (Dis) were essential for class predictions and showed TPR=0.72. Substrates consistently had lower BDE, Dis, and CGS/MW than non-substrates. Hybrid models also performed acceptablely for SOS predictions. Using the best models, Algorithms gave an acceptable performance in class prediction: TPR=0.62, False Positive Rate (FPR)=0.24, Balanced accuracy (BA)=0.69, and SOS prediction: TPR=0.98, FPR=0.60, and BA=0.69. A rule-based method was added to improve the predictive performance, which improved the algorithm TPR, FPR, and BA. Validation using an external dataset of drug-like compounds gave class prediction: TPR=0.67, FPR=0.00, and SOS prediction: TPR=0.80 and FPR=0.44 for the best Algorithm. Comparisons with standard ML models also show that our algorithm shows higher predictive performance for classification on external datasets. Overall, these models and algorithms (SOS predictor) give accurate substrate class and site (SOS) predictions for SULT-mediated Phase II metabolism and will be valuable to the drug discovery community in academia and industry. The SOS predictor is freely available for academic/non-profit research via the GitHub link.

利用基于可及性和反应性的算法预测硫代转氨酶介导的 II 期药物代谢底物和位点。
磺基转移酶(SULTs)是一种主要的 II 期代谢酶,在 FDA 批准药物的 II 期代谢中占约 20%。忽视 SULT 介导的潜在代谢可能会导致药物间的强烈相互作用,这往往会导致后期药物发现的失败或 FDA 标签上的黑框警告。现有模型仅使用可及性描述符和机器学习(ML)方法来预测 SULT 的类别和磺化位点(SOS)。本研究开发了多种可及性、反应性和混合模型及算法,以准确预测底物和 SOS。与文献模型不同的是,脂肪族或芳香族羟基(R/Ar-O-H)的反应性参数、键离解能(BDE)给出了准确的模型,SOS 预测的真阳性率(TPR)=0.84。我们从机理角度解释了这些在文献中未得到认可的新发现。可及性参数,如 Chemgauss4 Score(CGS)与分子量(MW)之比 CGS/MW 以及与辅助因子的距离(Dis),对于类别预测至关重要,其 TPR=0.72。底物的 BDE、Dis 和 CGS/MW 始终低于非底物。混合模型在 SOS 预测方面的表现也可以接受。使用最佳模型,算法在类别预测方面的表现可以接受:TPR=0.62,误报率(FPR)=0.24,平衡准确率(BA)=0.69,SOS 预测:SOS预测:TPR=0.98,FPR=0.60,BA=0.69。为提高预测性能,增加了基于规则的方法,从而提高了算法的 TPR、FPR 和 BA。使用外部类药物数据集进行验证后,得出了类预测结果:TPR=0.67, FPR=0.00, SOS 预测:最佳算法的 TPR=0.80 和 FPR=0.44。与标准 ML 模型的比较也表明,我们的算法对外部数据集的分类具有更高的预测性能。总之,这些模型和算法(SOS 预测器)能为 SULT 介导的第二阶段代谢提供准确的底物类别和位点(SOS)预测,对学术界和工业界的药物发现界很有价值。SOS 预测器可通过 GitHub 链接免费提供给学术/非营利研究使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Molecular Informatics
Molecular Informatics CHEMISTRY, MEDICINAL-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.30
自引率
2.80%
发文量
70
审稿时长
3 months
期刊介绍: Molecular Informatics is a peer-reviewed, international forum for publication of high-quality, interdisciplinary research on all molecular aspects of bio/cheminformatics and computer-assisted molecular design. Molecular Informatics succeeded QSAR & Combinatorial Science in 2010. Molecular Informatics presents methodological innovations that will lead to a deeper understanding of ligand-receptor interactions, macromolecular complexes, molecular networks, design concepts and processes that demonstrate how ideas and design concepts lead to molecules with a desired structure or function, preferably including experimental validation. The journal''s scope includes but is not limited to the fields of drug discovery and chemical biology, protein and nucleic acid engineering and design, the design of nanomolecular structures, strategies for modeling of macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, computer-assisted screening strategies, as well as novel technologies for the de novo design of biologically active molecules. As a unique feature Molecular Informatics publishes so-called "Methods Corner" review-type articles which feature important technological concepts and advances within the scope of the journal.
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