Rao Mohan, Nassiri Vahid, Srivastava Sanjay, Yang Amy, Brar Satjit, McDuffie Eric, Sachs Clifford
Preclinical and Clinical Pharmacology and Chemistry, Neurocrine Biosciences, San Diego, CA 92130, USA.
Open Analytics NV, Jupiterstraat 20, 2600 Antwerp, Belgium.
Pharmaceuticals (Basel). 2024 Nov 19;17(11):1550. doi: 10.3390/ph17111550.
BACKGROUND/OBJECTIVES: Drug-Induced Kidney Injury (DIKI) presents a significant challenge in drug development, often leading to clinical-stage failures. The early prediction of DIKI risk can improve drug safety and development efficiency. Existing models tend to focus on physicochemical properties alone, often overlooking drug-target interactions crucial for DIKI. This study introduces an AI/ML (artificial intelligence/machine learning) model that integrates both physicochemical properties and off-target interactions to enhance DIKI prediction.
We compiled a dataset of 360 FDA-classified compounds (231 non-nephrotoxic and 129 nephrotoxic) and predicted 6064 off-target interactions, 59% of which were validated in vitro. We also calculated 55 physicochemical properties for these compounds. Machine learning (ML) models were developed using four algorithms: Ridge Logistic Regression (RLR), Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN). These models were then combined into an ensemble model for enhanced performance.
The ensemble model achieved an ROC-AUC of 0.86, with a sensitivity and specificity of 0.79 and 0.78, respectively. The key predictive features included 38 off-target interactions and physicochemical properties such as the number of metabolites, polar surface area (PSA), pKa, and fraction of Sp3-hybridized carbons (fsp3). These features effectively distinguished DIKI from non-DIKI compounds.
The integrated model, which combines both physicochemical properties and off-target interaction data, significantly improved DIKI prediction accuracy compared to models that rely on either data type alone. This AI/ML model provides a promising early screening tool for identifying compounds with lower DIKI risk, facilitating safer drug development.
背景/目的：药物性肾损伤（DIKI）在药物研发中是一项重大挑战，常常导致临床阶段的失败。DIKI风险的早期预测能够提高药物安全性和研发效率。现有模型往往仅关注物理化学性质，常常忽略对DIKI至关重要的药物-靶点相互作用。本研究引入了一种人工智能/机器学习（AI/ML）模型，该模型整合了物理化学性质和脱靶相互作用，以增强DIKI预测能力。
我们汇编了一个包含360种经美国食品药品监督管理局（FDA）分类的化合物的数据集（231种非肾毒性化合物和129种肾毒性化合物），并预测了6064种脱靶相互作用，其中59%在体外得到验证。我们还计算了这些化合物的55种物理化学性质。使用四种算法开发了机器学习（ML）模型：岭逻辑回归（RLR）、支持向量机（SVM）、随机森林（RF）和神经网络（NN）。然后将这些模型组合成一个集成模型以提高性能。
集成模型的受试者工作特征曲线下面积（ROC-AUC）为0.86，灵敏度和特异性分别为0.79和0.78。关键预测特征包括38种脱靶相互作用以及物理化学性质，如代谢物数量、极性表面积（PSA）、pKa和Sp3杂化碳的比例（fsp3）。这些特征有效地将DIKI化合物与非DIKI化合物区分开来。
与仅依赖单一数据类型的模型相比，结合物理化学性质和脱靶相互作用数据的集成模型显著提高了DIKI预测准确性。这种AI/ML模型为识别具有较低DIKI风险的化合物提供了一种有前景的早期筛选工具，有助于更安全的药物研发。
