Cruz-Monteagudo Maykel, Cordeiro M Natália D S, Borges Fernanda
Physico-Chemical Molecular Research Unit, Department of Organic Chemistry, Faculty of Pharmacy, University of Porto, 4150-047 Porto, Portugal.
J Comput Chem. 2008 Mar;29(4):533-49. doi: 10.1002/jcc.20812.
Idiosyncratic drug toxicity (IDT), considered as a toxic host-dependent event, with an apparent lack of dose response relationship, is usually not predictable from early phases of clinical trials, representing a particularly confounding complication in drug development. Albeit a rare event (usually <1/5000), IDT is often life threatening and is one of the major reasons new drugs never reach the market or are withdrawn post marketing. Computational methodologies, like the computer-based approach proposed in the present study, can play an important role in addressing IDT in early drug discovery. We report for the first time a systematic evaluation of classification models to predict idiosyncratic hepatotoxicity based on linear discriminant analysis (LDA), artificial neural networks (ANN), and machine learning algorithms (OneR) in conjunction with a 3D molecular structure representation and feature selection methods. These modeling techniques (LDA, feature selection to prevent over-fitting and multicollinearity, ANN to capture nonlinear relationships in the data, as well as the simple OneR classifier) were found to produce QSTR models with satisfactory internal cross-validation statistics and predictivity on an external subset of chemicals. More specifically, the models reached values of accuracy/sensitivity/specificity over 84%/78%/90%, respectively in the training series along with predictivity values ranging from ca. 78 to 86% of correctly classified drugs. An LDA-based desirability analysis was carried out in order to select the levels of the predictor variables needed to trigger the more desirable drug, i.e. the drug with lower potential for idiosyncratic hepatotoxicity. Finally, two external test sets were used to evaluate the ability of the models in discriminating toxic from nontoxic structurally and pharmacologically related drugs and the ability of the best model (LDA) in detecting potential idiosyncratic hepatotoxic drugs, respectively. The computational approach proposed here can be considered as a useful tool in early IDT prognosis.
特异质药物毒性（IDT）被视为一种依赖宿主的毒性事件，明显缺乏剂量反应关系，通常在临床试验早期无法预测，是药物开发中一个特别棘手的并发症。尽管IDT是罕见事件（通常<1/5000），但往往危及生命，是新药无法上市或上市后被撤市的主要原因之一。计算方法，如本研究中提出的基于计算机的方法，在早期药物发现中应对IDT方面可发挥重要作用。我们首次报告了基于线性判别分析（LDA）、人工神经网络（ANN）和机器学习算法（OneR），结合三维分子结构表示和特征选择方法，对预测特异质肝毒性的分类模型进行的系统评估。发现这些建模技术（LDA、用于防止过拟合和多重共线性的特征选择、用于捕捉数据中非线性关系的ANN以及简单的OneR分类器）能够生成具有令人满意的内部交叉验证统计数据和对外部化学物质子集具有预测性的定量构效关系（QSTR）模型。更具体地说，在训练系列中，模型的准确率/灵敏度/特异性分别达到84%/78%/90%以上，正确分类药物的预测值范围约为78%至86%。进行了基于LDA的合意性分析，以选择触发更理想药物所需的预测变量水平，即具有较低特异质肝毒性潜力的药物。最后，分别使用两个外部测试集评估模型区分结构和药理相关的有毒和无毒药物的能力，以及最佳模型（LDA）检测潜在特异质肝毒性药物的能力。这里提出的计算方法可被视为早期IDT预后中的一种有用工具。
