Computational Systems Pharmacology-Target Mapping for Fentanyl-Laced Cocaine Overdose.

Journal: ACS chemical neuroscience
PMID:

Abstract

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.

Authors

  • Jin Cheng
    School of Medical Technology, Qiqihar Medical University, Qiqihar, 161006, Heilongjiang, China.
  • Siyi Wang
    Department of Otolaryngology, Head and Neck Surgery, Affiliated Hospital of Southwest Medical University Luzhou, Sichuan, China.
  • Weiwei Lin
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, Drug Discovery Institute, Departments of Computational Biology and Structural Biology, School of Medicine , University of Pittsburgh , Pittsburgh , Pennsylvania 15261 , United States.
  • Nan Wu
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, Drug Discovery Institute, Departments of Computational Biology and Structural Biology, School of Medicine , University of Pittsburgh , Pittsburgh , Pennsylvania 15261 , United States.
  • Yuanqiang Wang
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, Drug Discovery Institute, Departments of Computational Biology and Structural Biology, School of Medicine , University of Pittsburgh , Pittsburgh , Pennsylvania 15261 , United States.
  • Maozi Chen
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, Drug Discovery Institute, Departments of Computational Biology and Structural Biology, School of Medicine , University of Pittsburgh , Pittsburgh , Pennsylvania 15261 , United States.
  • Xiang-Qun Xie
  • Zhiwei Feng
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; NIDA National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA.