Development of computational and AI/ML tools to accelerate discovery of vaccines and antibodies against nitazenes and fentanyl analogs

Abstract. This HEAL project will employ computational, machine learning (ML) and Artificial Intelligence (AI) tools to accelerate the discovery of vaccines and antibodies against highly toxic ultrapotent synthetic opioids (UPSO). United States, Canada, and Europe have registered dramatic increases in fatal drug poisoning due to the widespread availability of fentanyl, fentanyl analogs, emerging compounds of the nitazene class, and their mixtures. Because of their potency, ease of illicit synthesis, and widespread availability, UPSO are fueling the ongoing overdose crisis. Beyond their impact on individuals with Opioid Use Disorder (OUD), these compounds could be involved in mass casualty incidents (MCI) or deliberately deployed in chemical attacks. Current FDA- approved countermeasures consist of opioid receptor antagonists, which may not always be sufficient to reverse overdoses involving UPSO or drug mixtures containing UPSO. To address this public health threat, our team has developed vaccines and monoclonal antibodies (mAb) against a series of UPSO. Vaccine-elicited antibodies and mAbs selectively bind the target drug in serum, reduce distribution of the unbound (free) drug to the brain, and prevent or reverse drug-induced effects. Based on their pre-clinical profile, anti-UPSO vaccines and mAbs have the potential to counteract overdose toxicity and poisoning in both civilian and defense scenarios. Due to their selectivity, vaccines and mAbs could be combined with existing treatments to increase survival. Because of the speed with which new and emerging UPSO can enter the drug supply, new tools to optimize and streamline the process of vaccine and antibody discovery are needed to rapidly address these threats by accelerating their translation into the product development space. To address this challenge, this project will employ state-of-the- art structure- and computational-guided platforms and AI/ML tools to accelerate discovery of vaccines and mAb against emerging UPSOs such as nitazenes. Specifically, AIM1 will focus on discovery of novel conjugate vaccines against nitazenes paired with computational methods to identify correlates of vaccine efficacy, AIM2 will focus on isolation of mAb against nitazenes and other UPSO accelerated by structure-informed design, and AIM3 will focus on evaluation of vaccines and mAb in vivo in rats challenged with nitazenes, fentanyl analogs, and their mixtures. Completion of this research proposal will lead to development and validation of computational methods to rapidly isolate vaccines and mAb against novel and rising UPSO, and other chemicals of concern.