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.