Non-invasive imaging of amyloid beta (Aβ) and tau fibrils in the brain may support an early and precise diagnosis of Alzheimer’s disease. Molecular imaging technologies involving radionuclides such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT) against beta amyloid plaques and tau fibrils are among emerging research areas in the field of medicinal chemistry. In the current study, we have developed partial least square (PLS) regression-based two-dimensional quantitative structure-activity relationship (2D-QSAR) models using datasets of 38 PET and 73 SPECT imaging agents targeted against Aβ protein and 31 imaging agents (both PET and SPECT) targeted against tau protein. Following the strict Organization for Economic Co-operation and Development (OECD) guidelines, we have strived to select significant descriptors from the large initial pool of descriptors using multilayered variable selection strategy using the double cross-validation (DCV) method followed by the best subset selection (BSS) method prior to the development of the final PLS models. The developed models showed significant statistical performance and reliability. Molecular docking studies have been performed 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, we have also designed some imaging agents based on the information provided by the models developed and some of them are predicted to be similar to or more active than the most active imaging agents present in the original dataset.

中文翻译：

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变量选择的多层策略在 PET 和 SPECT 显像剂作为阿尔茨海默病诊断剂的 QSAR 建模中的应用

大脑中淀粉样蛋白 β (Aβ) 和 tau 纤维的非侵入性成像可能支持阿尔茨海默病的早期和精确诊断。涉及放射性核素的分子成像技术，如针对 β 淀粉样斑块和 tau 纤维的正电子发射断层扫描 (PET) 和单光子发射计算机断层扫描 (SPECT)，属于药物化学领域的新兴研究领域。在目前的研究中，我们开发了基于偏最小二乘 (PLS) 回归的二维定量构效关系 (2D-QSAR) 模型，使用 38 种 PET 和 73 种 SPECT 显像剂的数据集，针对 Aβ 蛋白和 31 种显像剂。 PET 和 SPECT）均针对 tau 蛋白。遵循严格的经济合作与发展组织 (OECD) 指导方针，在开发最终 PLS 模型之前，我们努力使用双重交叉验证 (DCV) 方法和最佳子集选择 (BSS) 方法，使用多层变量选择策略从大型初始描述符池中选择重要的描述符。开发的模型显示出显着的统计性能和可靠性。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，然后将结果与从 QSAR 模型获得的结构特征相关联。此外，我们还根据开发的模型提供的信息设计了一些显像剂，其中一些被预测为与原始数据集中存在的最活跃的显像剂相似或更活跃。