Computational guided identification of novel anti-mycobacterial agent proved by in-vitro and in-vivo validation.

Background: An upsurge of antibiotic resistant bacteria such as Mycobacterium tuberculosis is recorded on daily bases as a result of many factors including: the daily antibiotics exploitation, failure to follow lengthy complex drug regimen, and ongoing bacterial mutation. TB treatment protocol is usually a lengthy and expensive one that is composed of 4 or even 5 drugs that have multiple substantial side effects. Traditional drug discovery methodologies are usually lengthy multifaceted process complicated with unpredictable outcomes in terms of efficacy and safety, hence there is an urge to find innovative drug discovery method that can produce multiple novel potential antimycobacterial agents that are safe and effective both in-vitro and in-vivo.

Results: The obtained results illustrated that maleic acid represented a potential drug with minimum inhibitory concentration of 312 µg/ml and an identical minimum bactericidal concentration against Mycobacterium tuberculosis. Its IC50 was measured to be 374.44 mg/ml with SI of 1200. Preliminary testing showed that maleic acid can be considered as a possible histidinol-phosphate aminotransferase inhibitor with a high binding affinity (-5.0475 kcal/mol) and promising molecular dynamics. Maleic acid combination with rifampicin had ƩFIC of 0.375 which indicated synergistic activity between them. It efficiently produced 3 ± 0.3009 log₁₀ CFU reduction of infected mice lungs compared to control group and illustrated superior preservation of lung tissue and structure on histological screening level.

Conclusion: After careful filtration processes, computational guided scavenge of online protein databases for potential druggable targets represents a promising pathway for identification of novel antimycobacterial agents. One of the promising identified agents was maleic acid which can act as an alternative/additional drug for combating tuberculosis infection.