Machine Learning‐Led Optimization of Combination Therapy: Confronting the Public Health Threat of Extensively Drug Resistant Gram‐Negative Bacteria

Developing optimized regimens for combination antibiotic therapy is challenging and often performed empirically over many clinical studies. Novel implementation of a hybrid machine-learning pharmacokinetic/pharmacodynamic/toxicodynamic (ML-PK/PD/TD) approach optimizes combination therapy using human PK/TD data along with in vitro PD data. This study utilized human population PK (PopPK) of aztreonam, ceftazidime/avibactam, and polymyxin B along with in vitro PDs from the Hollow Fiber Infection Model (HFIM) to derive optimal multi-drug regimens de novo through implementation of a genetic algorithm (GA). The mechanism-based PD model was constructed based on 7-day HFIM experiments across 4 clinical, extensively drug resistant Klebsiella pneumoniae isolates. GA-led optimization was performed using 13 different fitness functions to compare the effects of different efficacy (60%, 70%, 80%, or 90% of simulated subjects achieving bacterial counts of 10