Computational Studies toward the Identification of CB2R-M1R Dual Modulators

The complex and multifactorial nature of different neurodegenerative disorders hampers the capacity to identify effective treatments. Therefore, instead of relying solely on monotherapies or combination therapies, which typically come with dosing complications and limited synergy, multitarget-directed ligand strategies have emerged as one of the most dynamic and promising approaches to improve outcomes for such diseases. This study sought to identify dual modulators that specifically target cannabinoid receptor type 2 (CB2R) and muscarinic acetylcholine receptor subtype 1 (M1R), two receptors involved in various physiological and neurological processes and frequently implicated in disorders like Alzheimer’s, Parkinson’s, and chronic pain. Herein, we utilized a comprehensive computational pipeline starting with a network pharmacology analysis to map the pharmacological landscape of the dual-targeted ligands. Thereafter, molecular descriptors were employed to uncover structural similarities between CB2R agonists and M1R-positive allosteric modulators. Promising candidates were further evaluated for their binding affinities to the corresponding receptors by molecular docking studies. Collectively, these integrated computational approaches yielded a shortlist of chemotypes with the potential for dual regulation of CB2R and M1R. These findings provide a computational foundation and potential chemical starting points for future experimental studies aimed at exploring CB2R–M1R dual modulation in intricate neurodegenerative disorders and related conditions.各类神经退行性疾病兼具复杂性与多因素致病的特征，极大制约了有效治疗方案的研发进程。因此，相较于通常存在给药复杂度高、协同作用有限等弊端的单一疗法或联合疗法，多靶点配体策略已成为改善此类疾病预后最具活力与前景的途径之一。本研究旨在筛选可同时靶向cannabinoid receptor type 2（CB2R）与毒蕈碱型乙酰胆碱受体亚型1（M1R）的双重调节剂；这两类受体参与多种生理及神经过程，并常与阿尔茨海默病、帕金森病及慢性疼痛等疾病的病理进程密切相关。本研究采用一套完整的计算分析流程：首先通过网络药理学（network pharmacology）分析勾勒出双靶点配体的药理特征图谱；随后借助分子描述符（molecular descriptors）分析，揭示CB2R激动剂与M1R正向变构调节剂（positive allosteric modulators）之间的结构相似性。针对筛选出的潜在候选化合物，本研究进一步通过分子对接（molecular docking）实验评估其与对应受体的结合亲和力。综上，这套整合式计算策略筛选得到了一系列具备双重调控CB2R与M1R潜力的化学型（chemotypes）化合物候选清单。本研究结果为后续探索CB2R-M1R双重调控在复杂神经退行性疾病及相关病症中的应用提供了计算层面的理论基础与潜在的化学起始原料。