In 2019 antimicrobial resistance (AMR) directly caused or contributed to 6.22 million deaths internationally, over 4 times the number of HIV/AIDS and malaria related deaths in the same pre-pandemic year. New methods and technologies must be developed in order to continue producing effective antibiotics at a greater rate than AMR can evolve. To meet the challenge presented by rapidly evolving pathogenic microbes, we developed an integrative approach combining computational modeling and synthetic chemistry to synthesize and analyze enhanced pleuromutilin class antibiotic derivatives. Computational modeling allowed for characterization of prokaryotic ribosomal binding site features to determine candidates for the creation of high efficacy derivatives. Redocking and fingerprint analysis identified key binding pocket nucleotides (G2044, A2045, A2430, C2431, and U2438) to target for selective drug efficacy. Molecular visualization highlighted important nucleotide-antibiotic interactions by tracking how functional group changes influenced predicted binding patterns. Libraries of triazole C22 and C20 pleuromutilin derivatives were designed and underwent unbiased computational analysis to establish initial parameters for efficacy prediction. The C22 derivatives showed reduced but comparable activity as their parent. The C20 derivatives had near complete loss of inhibitory activity against Staphylococcus aureus and Escherichia coli. Findings from computational analysis were applied when designing a library of epimerized pleuromutilin derivatives, which exhibited significant drug potency increases, reducing the minimum inhibitory concentration required to treat methicillin-resistant S. aureus (MRSA) by 4 times when compared to their parent compound. Species specificity was up to 16 times higher for S. aureus strains over E. coli when compared to the parent compound. Additional antibiotic classes were also derivatized. This coupled approach to combating AMR has already, and will continue to increase antibiotic production turnaround, reduce monetary and times costs for derivative development, leading to the creation of bidentates, and other novel molecular entities for targeting AMR.

Integrating computational and experimental chemistry: A coupled approach to establishing an in silico antibiotic efficacy prediction model

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