IDentif.AI: Rapidly optimizing combination therapy design against severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) with digital drug development

Abstract

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to multiple drug repurposing clinical trials that have yielded largely uncertain outcomes. To overcome this challenge, we used IDentif.AI, a platform that pairs experimental validation with artificial intelligence (AI) and digital drug development to rapidly pinpoint unpredictable drug interactions and optimize infectious disease combination therapy design with clinically relevant dosages. IDentif.AI was paired with a 12-drug candidate therapy set representing over 530,000 drug combinations against the SARS-CoV-2 live virus collected from a patient sample. IDentif.AI pinpointed the optimal combination as remdesivir, ritonavir, and lopinavir, which was experimentally validated to mediate a 6.5-fold enhanced efficacy over remdesivir alone. Additionally, it showed hydroxychloroquine and azithromycin to be relatively ineffective. The study was completed within 2 weeks, with a three-order of magnitude reduction in the number of tests needed. IDentif.AI independently mirrored clinical trial outcomes to date without any data from these trials. The robustness of this digital drug development approach paired with in vitro experimentation and AI-driven optimization suggests that IDentif.AI may be clinically actionable toward current and future outbreaks.

Keywords: COVID‐19; SARS‐CoV‐2; artificial intelligence; combinatory treatment; digital medicine; drug development; drug interactions.

FIGURE 1

Project IDentif.AI workflow. Project IDentif.AI has four phases: (1) clinically relevant concentrations are established for each drug based on dose–response curves and maximal plasma concentration (C_max) of clinically administered dosages, (2) combination therapies determined with an orthogonal array composite (OACD) design are experimentally tested in an in vitro, cellular infectious disease (ID) model, (3) IDentif.AI analysis of the drug dose parameter space identifies drug–drug interactions and ranks optimal, clinically relevant drug‐dosage combinations, and (4) biological validation of clinically relevant combinations designed by IDentif.AI‐designed or already in trials

FIGURE 2

Experimental validation of the IDentif.AI‐designed at clinically relevant doses. (a) %Inhibition of the optimal IDentif.AI combination of RDV/LPV/RTV (green), IDentif.AI designed combinations (blue), and regimens in clinical trials (red). (b) %Cytotoxicity of Vero E6 (gray), AC16 (orange), and THLE‐2 (purple), of the optimal IDentif.AI combination of RDV/LPV/RTV, IDentif.AI designed combinations, and regimens in clinical trials. Data are shown as mean ± propagated SD; N = 3. Kruskal‐Wallis test detected statistically significant differences at p<0.001 for the %Inhibiton and the %Cytotoxicity groups, but the pairwise comparisons with Dunn's post hoc did not detect any statistically significant differences

FIGURE 3

Antiviral drug interactions determined from the IDentif.AI analysis of inhibition in vitro experiments at orthogonal array composite design (OACD) concentration Levels 1 and 2. (a) IDentif.AI determined synergistic interaction between lopinavir and ritonavir (RTV). (b) IDentif.AI determined an antagonistic interaction between RTV and oseltamivir phosphate. (c,d) Synergistic interaction between remdesivir and lopinavir (c) was boosted by the presence of RTV (d)