Predicting opioid receptor binding affinity of pharmacologically unclassified designer substances using molecular docking

• Published in: PloS one Vol. 13; no. 5; p. e0197734
• Main Authors: Ellis, Christopher R., Kruhlak, Naomi L., Kim, Marlene T., Hawkins, Edward G., Stavitskaya, Lidiya
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.05.2018; Public Library of Science (PLoS)
• Subjects: 60 APPLIED LIFE SCIENCES; Affinity; Analgesics; Analogs; Analysis; Binding; Binding Sites; Biology and Life Sciences; Care and treatment; Computer applications; Correlation analysis; Derivatives; Dosage and administration; Drug abuse; Drug overdose; Fentanyl; Fentanyl - analogs & derivatives; Fentanyl - chemistry; Fentanyl - metabolism; Food; Health risks; Humans; Kinetics; Mathematical models; Medicine and Health Sciences; Molecular chains; Molecular docking; Molecular Docking Simulation; Narcotics; Opioid receptors (type mu); Opioids; Pain; Pharmacology; Physical Sciences; Protein Binding; Protein Structure, Tertiary; Public health; Public safety; Receptors, Opioid, mu - chemistry; Receptors, Opioid, mu - metabolism; Risk factors; Safety; Safety and security measures; Schedules; Science & Technology - Other Topics; Substance abuse; Substance abuse treatment; Thermodynamics; Canada; United States > US; Maryland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0197734

Opioids represent a highly-abused and highly potent class of drugs that have become a significant threat to public safety. Often there are little to no pharmacological and toxicological data available for new, illicitly used and abused opioids, and this has resulted in a growing number of serious adverse events, including death. The large influx of new synthetic opioids permeating the street-drug market, including fentanyl and fentanyl analogs, has generated the need for a fast and effective method to evaluate the risk a substance poses to public safety. In response, the US FDA's Center for Drug Evaluation and Research (CDER) has developed a rapidly-deployable, multi-pronged computational approach to assess a drug's risk to public health. A key component of this approach is a molecular docking model to predict the binding affinity of biologically uncharacterized fentanyl analogs to the mu opioid receptor. The model was validated by correlating the docking scores of structurally diverse opioids with experimentally determined binding affinities. Fentanyl derivatives with sub-nanomolar binding affinity at the mu receptor (e.g. carfentanil and lofentanil) have significantly lower binding scores, while less potent fentanyl derivatives have increased binding scores. The strong correlation between the binding scores and the experimental binding affinities suggests that this approach can be used to accurately predict the binding strength of newly identified fentanyl analogs at the mu receptor in the absence of in vitro data and may assist in the temporary scheduling of those substances that pose a risk to public safety.