Addressing antimicrobial resistance with the IDentif.AI platform: Rapidly optimizing clinically actionable combination therapy regimens against nontuberculous mycobacteria
Abstract
Background: Current standard of care (SOC) regimens against nontuberculous mycobacteria (NTM) usually result in unsatisfactory therapeutic responses, primarily due to multi-drug resistance and antibiotic susceptibility-guided therapies.In the midst of rising incidences in NTM infections, strategies to develop NTM-specific treatments have been explored and validated.Methods: To provide an alternative approach to address NTM-specific treatment, IDentif.AI was harnessed to rapidly optimize and design effective combination therapy regimens against Mycobacterium abscessus (M.abscessus), the highly resistant and rapid growth species of NTM.IDentif.AI interrogated the drug interaction space from a pool of 6 antibiotics, and pinpointed multiple clinically actionable drug combinations.IDentif.AI-pinpointed actionable combinations were experimentally validated and their interactions were assessed using Bliss independence model and diagonal measurement of n-way drug interactions.Results: Notably, IDentfi.AI-designed 3-and 4-drug combinations demonstrated greater %Inhibition efficacy than the SOC regimens.The platform also pinpointed two unique drug interactions (Levofloxacin (LVX)/Rifabutin (RFB) and LVX/Meropenem (MEM)) that may serve as the backbone of potential 3-and 4-drug combinations like LVX/MEM/RFB, which exhibited 58.33±4.99%Inhibition efficacy against M. abscessus.Further analysis of LVX/RFB via Bliss independence model pointed to dose-dependent synergistic interactions in…
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