Antibiotic resistance: bioinformatics-based understanding as a functional strategy for drug design

Abstract

The use of antibiotics to manage infectious diseases dates back to ancient civilization, but the lack of a clear distinction between the therapeutic and toxic dose has been a major challenge. This precipitates the notion that antibiotic resistance was from time immemorial, principally because of a lack of adequate knowledge of therapeutic doses and continuous exposure of these bacteria to suboptimal plasma concentration of antibiotics. With the discovery of penicillin by Alexander Fleming in 1924, a milestone in bacterial infections' treatment was achieved. This forms the foundation for the modern era of antibiotic drugs. Antibiotics such as penicillins, cephalosporins, quinolones, tetracycline, macrolides, sulphonamides, aminoglycosides and glycopeptides are the mainstay in managing severe bacterial infections, but resistant strains of bacteria have emerged and hampered the progress of research in this field. Recently, new approaches to research involving bacteria resistance to antibiotics have appeared; these involve combining the molecular understanding of bacteria systems with the knowledge of bioinformatics. Consequently, many molecules have been developed to curb resistance associated with different bacterial infections. However, because of increased emphasis on the clinical relevance of antibiotics, the synergy between in silico study and in vivo study is well cemented and this facilitates the discovery of potent antibiotics. In this review, we seek to give an overview of earlier reviews and molecular and structural understanding of bacteria resistance to antibiotics, while focusing on the recent bioinformatics approach to antibacterial drug discovery.

Fig. 1. Evolution of antibiotic resistance in bacteria cell.

Fig. 2. Nomenclatures of different types of aminoglycoside-modifying enzymes.

Fig. 3. Crystal structure of rifampicin monooxygenase (RIFMO) with mutant residues.

Fig. 4. Important pharmacodynamics parameters describing the efficacy of different antibiotics. AUC, area under the concentration–time curve; C_max, maximum concentration; C_min, minimum concentration; MIC, minimum inhibitory concentration; PAE, postantibiotic effect; T, time.