The United States of America is fighting against one of its worst-ever drug crises. Over 900 people a week die from opioid- or heroin-related overdoses, while millions more suffer from opioid prescription addiction. Recently, drug overdoses caused by fentanyl-laced cocaine specifically are on the rise. Due to drug synergy and an increase in side effects, polydrug addiction can cause more risk than addiction to a single drug. In the present work, we systematically analyzed the overdose and addiction mechanism of cocaine and fentanyl. First, we applied our established chemogenomics knowledgebase and machine-learning-based methods to map out the potential and known proteins, transporters, and metabolic enzymes and the potential therapeutic target(s) for cocaine and fentanyl. Sequentially, we looked into the detail of (1) the addiction to cocaine and fentanyl by binding to the dopamine transporter and the μ opioid receptor (DAT and μOR, respectively), (2) the potential drug-drug interaction of cocaine and fentanyl via p-glycoprotein (P-gp) efflux, (3) the metabolism of cocaine and fentanyl in CYP3A4, and (4) the physiologically based pharmacokinetic (PBPK) model for two drugs and their drug-drug interaction at the absorption, distribution, metabolism, and excretion (ADME) level. Finally, we looked into the detail of JWH133, an agonist of cannabinoid 2-receptor (CB2) with potential as a therapy for cocaine and fentanyl overdose. All these results provide a better understanding of fentanyl and cocaine polydrug addiction and future drug abuse prevention.
Keywords: cocaine; computational systems pharmacology-target mapping; fentanyl; fentanyl-laced cocaine; overdose; polydrug addiction.