Ameji John - Department of Chemistry, Federal University Lokoja, Nigeria
Adamu Uzairu - Department of Chemistry, Ahmadu Bello University, Zaria
Gideon Shallangwa - Department of Chemistry, Ahmadu Bello University
Sani UBA - Department of chemistry, Ahmadu Bello University Zaria, Nigeria

The rising mortality and morbidity associated with Salmonella typhimurium induced salmonellosis aggravated by the emergence of multi-drug resistant strains of this pathogenic bacterium has made continuous search for novel antibiotics a necessity. In our quest to design newer drug candidates, a series of cephalosporin analogues with significant bioactivities against the aforementioned bacterium were optimized using Density Functional Theorem (DFT) and subjected to Quantitative Structure-Activity Relationship modelling using Multi-linear Regression to harness the dominant descriptors of the antimicrobial properties of the compounds. The validated model (R۲ = ۰.۹۲, R۲Adj = ۰.۹۱, Q۲LOO = ۰.۸۸, R۲pred = ۰.۷۱, LOF = ۰.۰۰۰۱) reveals the predominance of SHBint۳, SpMin۶_Bhe, and L۲u descriptors. Molecular docking simulation of the compounds guided by the model led to the design of two novel ligands, L۱ and L۲ with binding affinity of -۷.۷ kcal/mol and -۸.۳ kcal/mol, respectively against the PBP۶ protein target of the bacterium. These values are higher than the -۶.۷ kcal/mol obtained for cefuroxime (R) antibiotic used as standard for comparison against the same protein target. The newly designed ligands exhibit excellent pharmacokinetic and toxicological profiles as well positive drug-likeness. Using the generated model, Minimum Inhibitory Concentration values of ۰.۰۳۸µg/mL and ۰.۰۵۱µg/mL were predicted for L۱ and L۲, respectively. The novel ligands displayed a balance of potency, binding affinity, pharmacokinetic and toxicological profiles as well as unique mechanisms of interactions with the PBP۶ target of Salmonella typhimurium. We therefore recommend their synthesis, biological evaluation and clinical trial.

pharmacokinetic, Drug-likeness, Descriptor, QSAR, Salmonellosis