Design And In-Silico Evaluation of Pyridine-4-Carbohydrazide Derivatives for Potential Therapeutic Applications

Abstract

The emergence of drug-resistant and novel diseases underscores the urgency for innovative therapeutic interventions. Drug repositioning and computational approaches offer an efficient pathway to accelerate drug discovery and development. This study leverages these techniques in designing and evaluating derivatives based on the FDA-approved compound, pyridine-4-carbohydrazide, to assess how structural modifications impact therapeutic potential. Methods: The derivatives were designed using a chemical library of small molecules containing imine functional groups, built upon pyridine-4-carbohydrazide scaffolds (INH01-INH19). Computational tools, including Molinspiration Cheminformatics, way2drug, and the pkCSM platform, were utilized to evaluate each derivative's physicochemical properties, drug-likeness, bioactivity scores, potential biological activities, and ADME (Absorption, Distribution, Metabolism, Excretion) profiles. Results: Most derivatives demonstrated enhanced physicochemical characteristics, adhering to both Lipinski’s Rule of Five and Veber’s Rule. Bioactivity scores varied with moderate to inactive interactions across six target classes, ranked as follows: enzyme inhibitors, kinase inhibitors, G-protein-coupled receptors, protease inhibitors, nuclear receptors, and ion channel modulators. Notably, derivatives INH03, INH09, INH14, and INH19 exhibited high predicted activity in multiple therapeutic areas, indicating potential applications in antibacterial, antiviral, antiprotozoal, anti-inflammatory and anticancer treatments. Moreover, structural modifications in these derivatives positively influenced ADME profiles compared to pyridine-4-carbohydrazide, though certain compounds presented challenges, such as limited solubility, P-glycoprotein interactions and CYP450 inhibition. Conclusions: These Schiff base derivatives stand out as promising candidates for further drug development, underscoring the importance of computational strategies in optimizing drug discovery and design.