Synthesis, Biological Evaluation and Machine Learning Prediction Model for Fluorinated <i>Cinchona</i> Alkaloid-Based Derivatives as Cholinesterase Inhibitors

Abstract

A series of 46 Cinchona alkaloid derivatives that differ in positions of fluorine atom(s) in the molecule were synthesized and tested as human acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibitors. All tested compounds reversibly inhibited AChE and BChE in the nanomolar to micromolar range; for AChE, the determined enzyme-inhibitor dissociation constants (Ki) ranged from 3.9–80 µM, and 0.075–19 µM for BChE. The most potent AChE inhibitor was N-(para-fluorobenzyl)cinchoninium bromide, while N-(meta-fluorobenzyl)cinchonidinium bromide was the most potent BChE inhibitor with Ki constant in the nanomolar range. Generally, compounds were non-selective or BChE selective cholinesterase inhibitors, where N-(meta-fluorobenzyl)cinchonidinium bromide was the most selective showing 533 times higher preference for BChE. In silico study revealed that twenty-six compounds should be able to cross the blood-brain barrier by passive transport. An extensive machine learning procedure was utilized for the creation of multivariate linear regression models of AChE and BChE inhibition. The best possible models with predicted R2 (CD-derivatives) of 0.9932 and R2(CN-derivatives) of 0.9879 were calculated and cross-validated. From these data, a smart guided search for new potential leads can be performed. These results pointed out that quaternary Cinchona alkaloids are the promising structural base for further development as selective BChE inhibitors which can be used in the central nervous system.

Keywords

• <i>Cinchona</i> alkaloid derivatives
• cholinesterase inhibitors
• multivariate linear regression models