Nanomedicine-based and unmodified drug interventions
to address
COVID-19 have evolved over the course of the pandemic as more information
is gleaned and virus variants continue to emerge. For example, some
early therapies (e.g., antibodies) have experienced markedly decreased
efficacy. Due to a growing concern of future drug resistant variants,
current drug development strategies are seeking to find effective
drug combinations. In this study, we used IDentif.AI, an artificial
intelligence-derived platform, to investigate the drug–drug
and drug–dose interaction space of six promising experimental
or currently deployed therapies at various concentrations: EIDD-1931,
YH-53, nirmatrelvir, AT-511, favipiravir, and auranofin. The drugs
were tested in vitro against a live B.1.1.529 (Omicron)
virus first in monotherapy and then in 50 strategic combinations designed
to interrogate the interaction space of 729 possible combinations.
Key findings and interactions were then further explored and validated
in an additional experimental round using an expanded concentration
range. Overall, we found that few of the tested drugs showed moderate
efficacy as monotherapies in the actionable concentration range, but
combinatorial drug testing revealed significant dose-dependent drug–drug
interactions, specifically between EIDD-1931 and YH-53, as well as
nirmatrelvir and YH-53. Checkerboard validation analysis confirmed
these synergistic interactions and also identified an interaction
between EIDD-1931 and favipiravir in an expanded range. Based on the
platform nature of IDentif.AI, these findings may support further
explorations of the dose-dependent drug interactions between different
drug classes in further pre-clinical and clinical trials as possible
combinatorial therapies consisting of unmodified and nanomedicine-enabled
drugs, to combat current and future COVID-19 strains and other emerging
pathogens.