Project IDentif.AI: Harnessing Artificial Intelligence to Rapidly Optimize Combination Therapy Development for Infectious Disease Intervention

Abstract

In 2019/2020, the emergence of coronavirus disease 2019 (COVID-19) resulted in rapid increases in infection rates as well as patient mortality. Treatment options addressing COVID-19 included drug repurposing, investigational therapies such as remdesivir, and vaccine development. Combination therapy based on drug repurposing is among the most widely pursued of these efforts. Multi-drug regimens are traditionally designed by selecting drugs based on their mechanism of action. This is followed by dose-finding to achieve drug synergy. This approach is widely-used for drug development and repurposing. Realizing synergistic combinations, however, is a substantially different outcome compared to globally optimizing combination therapy, which realizes the best possible treatment outcome by a set of candidate therapies and doses toward a disease indication. To address this challenge, the results of Project IDentif.AI (Identifying Infectious Disease Combination Therapy with Artificial Intelligence) are reported. An AI-based platform is used to interrogate a massive 12 drug/dose parameter space, rapidly identifying actionable combination therapies that optimally inhibit A549 lung cell infection by vesicular stomatitis virus within three days of project start. Importantly, a sevenfold difference in efficacy is observed between the top-ranked combination being optimally and sub-optimally dosed, demonstrating the critical importance of ideal drug and dose identification. This platform is disease indication and disease mechanism-agnostic, and potentially applicable to the systematic N-of-1 and population-wide design of highly efficacious and tolerable clinical regimens. This work also discusses key factors ranging from healthcare economics to global health policy that may serve to drive the broader deployment of this platform to address COVID-19 and future pandemics.

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

“…An earlier version of IDentif.AI was previously rapidly developed as a proof of concept strategy to pinpoint an optimal combination for vesicular stomatitis virus (VSV). [7] Here, we report a clinically-actionable IDentif.AI with a streamlined workflow that incorporates clinically-relevant dose design, an AI-based strategy that prospectively and experimentally crowdsources the patient-derived live SARS-CoV-2 virus to drive the optimization process, as well as a follow-on validation process that has resulted in a ranked list of drug combinations that are simultaneously optimized for drug composition and the dose of each respective therapy. This has resulted in results that broadly and independently align with clinical trial outcomes without requiring any data from these studies, thereby resulting in a platform that can be used as a first-line approach towards clinical decision support and therapeutic guidance with any number of additional drug options to address the COVID-19 pandemic as well as future outbreaks.…”

“…Instead, it uses an orthogonally-designed set of calibrating regimens and in vitro experimentation to simultaneously identify effective drugs, their unpredictable interactions and corresponding, clinically relevant doses that optimize treatment outcomes from prohibitively large drug-dose parameter spaces that cannot be reconciled by brute force drug screening. [7,11] In effect, IDentif.AI leverages these calibrating regimens to crowdsource SARS-CoV-2 live virus responses to experimentally drive the efficacy towards an optimal outcome. An earlier version of IDentif.AI was previously rapidly developed as a proof of concept strategy to pinpoint an optimal combination for vesicular stomatitis virus (VSV).…”

“…Instead, it uses an orthogonally designed set of calibrating regimens and in vitro experimentation to simultaneously identify effective drugs, their unpredictable interactions and corresponding, clinically relevant doses that optimize treatment outcomes from prohibitively large drug-dose parameter spaces that cannot be reconciled by brute force drug screening. 7,11 In effect, IDentif.…”

Applications of artificial intelligence in battling against covid-19: A literature review

“…Achieving this combination, however, is a challenge. To manage this, an AI based platform is proposed in [567] to analyze a 12 drug/doze parameter space in order to identify therapies that inhibit lung cell infection. Predicting interactions among heterogenous graph structured data has application in recommendation system and drug discovery and repurposing drugs for novel diseases.…”

Addressing COVID‐19 Drug Development with Artificial Intelligence

“…Among all of the strategies that are being used, however, AI‐based platforms have emerged as promising solutions, particularly when they are capable of rapidly optimizing drug combinations without complex disease mechanism data. Project IDentif.AI, a recently developed strategy, does not require big data‐based training sets to rapidly pinpoint suitable drug combinations from extraordinarily large parameter spaces . Project IDentif.AI is a neural network‐based approach based on a quadratic correlation between inputs defined by drugs and their respective doses and outputs defined by treatment efficacy and safety.…”