Ben Zaken_2025_J.Alzheimers.Dis__13872877251387167

Background: Drug repurposing offers a rapid, cost-effective approach for discovering therapies against multiple targets.ObjectiveHere, we screen virtual ligand libraries consisting of 3468 approved drugs against 11 protein targets associated with Alzheimer's disease (AD). Methods: We employ blind molecular docking, and target amyloid-beta (Abeta), microtubule-associated protein tau (MAPT), Apolipoprotein E4 (APOE4), acetylcholinesterase (AChE), butyrylcholinesterase (BChE), amyloid-beta protein precursor (AbetaPP), beta-secretase (BACE1), brain-derived neurotrophic factor (BDNF), presenilin 1 (PSEN1) and 2 (PSEN2), and alpha-synuclein (SNCA) proteins using AutoDock Vina. Results: Notably, multitarget binding recurs among the top-10 ligands with Ergotamine and Dihydroergotamine potentially binding 8; Dutasteride 7; Drospirenone and Nilotinib 6; Adapalene and Conivaptan 5; Bromocriptine 4; and Rolapitant, Irinotecan, Plerixafor, Saquinavir, and Telmisartan 3, out of 11 protein targets. As such, we reveal potential binding sites for ergot alkaloids, steroids, retinoids, antivirals, angiotensin receptor blockers, and Neurokinin 1 (NK1) receptor antagonists on multiple AD targets. Importantly, the therapeutic potential of the top-scoring ligands is confounded by pharmacokinetics and adverse-effects. For example, poor blood-brain barrier (BBB) penetration, and vasoconstriction, discount ergot-alkaloid use in AD. Likewise, potential toxicity limits prolonged use of steroids, Nilotinib, Adapalene, and Irinotecan. Conversely, BBB penetration, neuronal protection, oral availability, anti-inflammation, and anti-hypertension, admit Angiotensin receptor blockers (ARB), (Telmisartan/Candesartan); Antidiuretic hormone (ADH) inhibitors (Conivaptan/Tolvaptan); and of the NK1 receptors antagonists (Rolapitant/Netupitant) use in AD. Conclusions: Our multitarget screening identifies selective synergistic AD modulators, such as ARB, ADH and NK1 receptor inhibitors, and simplifies drug discovery by focusing on the most promising candidates for experimental validation.