Topological Complementarity Predicts Synergistic Drug Pairs in Branched Metabolic Networks.
Drug discovery typically ranks targets by individual sensitivity. Branched metabolic networks, in contrast, have redundant flux routes that buffer single-enzyme inhibition possibly deviating the best target from the most sensitive node. We developed a topology-driven framework for identifying optimal dual-target pairs and applied it to the E. coli folate pathway. A kinetic model of the folate pathway and its extended one-carbon pool showed that single-node inhibition is compensated by flux redistribution. Pairwise synergy scoring, a metric to measure drug-pair potency as compared to single drug, identified DHPS (folP) and DHFR (folA) as a top-ranked complementary pair. Experimental validation in E. coli MG1655 using sulfamethoxazole and trimethoprim confirmed strong dose-dependent synergy at intermediate inhibition and attenuation at saturation. Our results support topological complementarity as a key predictor of combination efficacy and provide a rationale for drug-pair design in branched biosynthetic pathways.