Finding, and Countering, Future Resistance using Bacterial Antibiotic Adversarial Genetic Algorithm (BAAGA)

Since the beginning of antimicrobial therapy, antimicrobial resistance from bacteria has been a threat. Now, multiple companies track antibiotic resistance annually. Monitoring helps watch for new resistant mechanisms to restrict spread. This monitoring also determines if a new antimicrobial agent must be created. Proper prescription of an antimicrobial agent also plays a key role in slowing resistance. Proper prescription to a patient requires the determination of a Minimum Inhibitory Concentration (MIC). These MICs are slow to determine, which raises morbidity and mortality rates in hospital settings. Physicians cannot predict the creation of new resistance mechanisms based on the prescription of one agent over another. It is also impossible for researchers to know what agents to create in the future. Because of this, there is a race against the clock when new resistant bacteria emerge, to create appropriate antimicrobial agents to counter the new mechanisms of resistance. In this study, we propose a new simulation algorithm named Bacterial Antibiotic Adversarial Genetic Algorithm (BAAGA) that simulates the generation of new mechanisms of resistance and new antimicrobial agents in a stochastic manner. BAAGA shows the ease at which bacteria can gain resistance mechanisms and how slow the process is to find new antimicrobial agents. We also demonstrate how BAAGA can produce possible new antimicrobial agents to counter mechanisms of resistance that currently do not exist.