Evolutionary druggability for low-dimensional fitness landscapes toward new metrics for antimicrobial applications

• Published in: eLife Vol. 12
• Main Authors: Guerrero, Rafael F, Dorji, Tandin, Harris, Ra'Mal M, Shoulders, Matthew D, Ogbunugafor, C Brandon
• Format: Journal Article
• Language: English
• Published: England eLife Sciences Publications Ltd 04.06.2024; eLife Sciences Publications, Ltd
• Subjects: Alleles; Anti-Bacterial Agents - pharmacology; Antimicrobial agents; beta-Lactamases - genetics; beta-Lactamases - metabolism; beta-Lactams - pharmacology; Drug development; Drug interaction; Drug resistance; druggability; Evolution, Molecular; Evolutionary Biology; evolutionary genetics; Genetic diversity; Genetic Fitness; Genotype & phenotype; Mutation; Pathogens; Therapeutic targets; Topography; Viral infections; β Lactamase; β-Lactam antibiotics; druggability; evolutionary biology; drug resistance; E. coli; evolutionary genetics
• DOI: 10.7554/eLife.88480

The term 'druggability' describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variant's sensitivity across a breadth of drugs in a panel, or a given drug's range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16 β-lactamase alleles and 7 β-lactam drugs, we introduce two metrics that capture (i) the average susceptibility of an allelic variant of a drug target to any available drug in a given panel (' ), and (ii) the average applicability of a drug (or mixture) across allelic variants of a drug target (' '). Finally, we (iii) disentangle the quality and magnitude of interactions between loci in the drug target and the seven drug environments in terms of their mutation by mutation by environment (G x G x E) interactions, offering mechanistic insight into the variant variability and drug applicability metrics. Summarizing, we propose that our framework can be applied to other datasets and pathogen-drug systems to understand which pathogen variants in a clinical setting are the most concerning (low variant vulnerability), and which drugs in a panel are most likely to be effective in an infection defined by standing genetic variation in the pathogen drug target (high drug applicability).