Overcoming Multidrug-Resistance in Bacteria with a Two-Step Process to Repurpose and Recombine Established Drugs

The
emergence and ongoing spread of multidrug-resistant (MDR) bacteria
is a major global public health threat. MDR has extensively combated
the potency of antibiotics. Development of new antibiotics requires
several years with prohibitive cost that will not last. An alternative
solution is to recombine failed antibiotics, which has been proven
to be not only cost-effective, but also potent. However, selection
of the optimal combinations of these chemicals through conventional
trial-and-error methods is challenging and slow, since M candidates
with N doses lead to NM possible combinations. Herein,
we present a artificial intelligence (AI) guided chemical combination
optimization technique, namely Streamlined Rapid Identification of
Combinatorial Therapies (STRICT), which is phenotype based and can
efficiently learn and identify the optimal drug-combinations with
minimal experimental efforts. With the guidance of STRICT, we successfully
identified potent combinations of five antibiotics from 26 antibiotics
that are individually ineffective at inhibiting an artificially induced
strain of MDR bacteria. Rather than examine millions of tests, STRICT
accomplished this task with only 120 carefully selected tests. Our
results indicate that STRICT is a powerful platform to identify efficacious
multiantibiotic combinations for the treatment of MDR bacteria. The
AI-guided platform introduced here is an effective tool for drug repurposing,
beneficial toward large-scale drug screening for other disease models,
and also has a broad application in chemical combination optimization
to deliver a desired end point for a complex system.