Evolutionary accumulation modeling in AMR: machine learning to infer and predict evolutionary dynamics of multi-drug resistance

Abstract

Can we understand and predict the evolutionary pathways by which bacteria acquire multidrug resistance (MDR)? These questions have substantial potential impact in basic biology and in applied approaches to address the global health challenge of antimicrobial resistance (AMR). In this minireview, we discuss how a class of machine-learning approaches called evolutionary accumulation modeling (EvAM) may help reveal these dynamics using genetic and/or phenotypic AMR data sets, without requiring longitudinal sampling. These approaches are well-established in cancer progression and evolutionary biology but currently less used in AMR research. We discuss how EvAM can learn the evolutionary pathways by which drug resistances and other AMR features (for example, mutations driving these resistances) are acquired as pathogens evolve, predict next evolutionary steps, identify influences between AMR features, and explore differences in MDR evolution between regions, demographics, and more. We demonstrate a case study from the literature on MDR evolution in Mycobacterium tuberculosis and discuss the strengths and weaknesses of these approaches, providing links to some approaches for implementationThis work was supported by the Trond Mohn Foundation (project HyperEvol under grant agreement No. TMS2021TMT09 to I.G.J.) through the Centre for Antimicrobial Resistance in Western Norway (CAMRIA) (TMS2020TMT11). This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 805046 [EvoConBiO] to I.G.J). This research was supported by grant PID2019-111256RB-I00 funded by MCIN/AEI/10.13039/501100011033 to Ramon Diaz-Uriart