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Chemometric modeling of PET imaging agents for diagnosis of Parkinson's disease: a QSAR approach

机译：宠物成像剂的化学计量模型诊断帕金森病的诊断：QSAR方法

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Recently, adenosine A(2A) receptor antagonists have been identified as an interesting drug target for the treatment of Parkinson's disease (PD). Radiolabelled molecular imaging technologies such as positron emission tomography (PET) have emerged in the research field of medicinal chemistry as a diagnostic tool for PD. In the current study, we have performed quantitative structure-activity relationship (QSAR) analysis of 35 xanthine ligand PET tracers as A(2A)R (adenosine receptors) antagonists in order to determine their structural features required to have binding affinity and selectivity towards A(2A)R. The division of the dataset into training and test sets was done using a random method, while the feature selection for the binding affinity was done using Genetic Algorithm (GA). The best model with five descriptors was obtained using the spline option in the GA run. QSAR models with four descriptors were also developed for A(2A)R selectivity, where significant descriptors were selected from the large pool of descriptors using stepwise regression method followed by Best Subset Selection (BSS) method. Furthermore, to improve the quality of the external predictions, we used the "Intelligent Consensus Predictor" tool (). Both the models showed robustness in terms of statistical parameters. Molecular docking studies have been carried out to understand the molecular interactions between the ligand and receptor, and the results are then correlated with the structural features obtained from the QSAR models. Furthermore, the information derived from the newly found descriptors gives an insight for the development of new candidate PET tracers for the use in PD.
机译：最近，腺苷A（2a）受体拮抗剂已被鉴定为治疗帕金森病（Pd）的有趣药物靶标。在药用化学研究领域中出现了正电子发射断层扫描（PET）的放射性标记分子成像技术作为PD的诊断工具。在目前的研究中，我们已经进行了35黄嘌呤配体PET示踪剂的定量结构 - 活性关系（QSAR）分析作为（2A）R（腺苷受体）拮抗剂，以确定其具有结合亲和力和选择性的结构特征（2a）r。 DataSet将数据集分为训练和测试集的使用随机方法完成，而使用遗传算法（GA）进行结合亲和力的特征选择。使用GA运行中的样条键选项获得了具有五个描述符的最佳模型。还为（2A）R选择性开发了具有四个描述符的QSAR模型，其中使用逐步回归方法从大型描述符中选择了重要描述符，然后是最佳子集选择（BSS）方法。此外，为了提高外部预测的质量，我们使用了“智能共识预测仪”工具（）。两种模型都表现出统计参数方面的鲁棒性。已经进行了分子对接研究以了解配体和受体之间的分子相互作用，然后与从QSAR模型获得的结构特征相关。此外，来自新发现的描述符的信息介绍了用于在PD中使用的新候选宠物示踪剂的开展。

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