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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.

Kratom is a botanical substance that is marketed and promoted in the US for pharmaceutical opioid indications despite having no US Food and Drug Administration approved uses. Kratom contains over forty alkaloids including two partial agonists at the mu opioid receptor, mitragynine and 7-hydroxymitragynine, that have been subjected to the FDA's scientific and medical evaluation. However, pharmacological and toxicological data for the remaining alkaloids are limited. Therefore, we applied the Public Health Assessment via Structural Evaluation (PHASE) protocol to generate in silico binding profiles for 25 kratom alkaloids to facilitate the risk evaluation of kratom. PHASE demonstrates that kratom alkaloids share structural features with controlled opioids, indicates that several alkaloids bind to the opioid, adrenergic, and serotonin receptors, and suggests that mitragynine and 7-hydroxymitragynine are the strongest binders at the mu opioid receptor. Subsequently, the in silico binding profiles of a subset of the alkaloids were experimentally verified at the opioid, adrenergic, and serotonin receptors using radioligand binding assays. The verified binding profiles demonstrate the ability of PHASE to identify potential safety signals and provide a tool for prioritizing experimental evaluation of high-risk compounds.

Assessing drug permeability across the blood-brain barrier (BBB) is important when evaluating the abuse potential of new pharmaceuticals as well as developing novel therapeutics that target central nervous system disorders. One of the gold-standard in vivo methods for determining BBB permeability is rodent log BB; however, like most in vivo methods, it is time-consuming and expensive. In the present study, two statistical-based quantitative structure-activity relationship (QSAR) models were developed to predict BBB permeability of drugs based on their chemical structure. The in vivo BBB permeability data were harvested for 921 compounds from publicly available literature, non-proprietary drug approval packages, and University of Washington’s Drug Interaction Database. The cross-validation performance statistics for the BBB models ranged from 82 to 85% in sensitivity and 80–83% in negative predictivity. Additionally, the performance of newly developed models was assessed using an external validation set comprised of 83 chemicals. Overall, performance of individual models ranged from 70 to 75% in sensitivity, 70–72% in negative predictivity, and 78–86% in coverage. The predictive performance was further improved to 93% in coverage by combining predictions across the two software programs. These new models can be rapidly deployed to predict blood brain barrier permeability of pharmaceutical candidates and reduce the use of experimental animals.

The 2020 FDA drug-drug interaction (DDI) guidance includes a consideration for metabolites with structural alerts for potential mechanism-based inhibition (MBI) and describes how this information may be used to determine whether in vitro studies need to be conducted to evaluate the inhibitory potential of a metabolite on CYP enzymes. To facilitate identification of structural alerts, an extensive literature search was performed and alerts for mechanism-based inhibition of cytochrome P450 enzymes (CYP) were collected. Furthermore, five quantitative structure-activity relationship (QSAR) models were developed to predict not only time-dependent inhibition of CYP3A4, an enzyme that metabolizes approximately 50% of all marketed drugs, but also reversible inhibition of 3A4, 2C9, 2C19 and 2D6. The non-proprietary training database for the QSAR models contains data for 10,129 chemicals harvested from FDA drug approval packages and published literature. The cross-validation performance statistics for the new CYP QSAR models range from 78% to 84% sensitivity and 79%–84% normalized negative predictivity. Additionally, the performance of the newly developed QSAR models was assessed using external validation sets. Overall performance statistics showed up to 75% in sensitivity and up to 80% in normalized negative predictivity. The newly developed models will provide a faster and more effective evaluation of potential drug-drug interaction caused by metabolites.

The U.S. Food and Drug Administration (FDA) Division of Applied Regulatory Science (DARS) moves new science into the drug review process and addresses emergent regulatory and public health questions for the Agency. By forming interdisciplinary teams, DARS conducts mission-critical research to provide answers to scientific questions and solutions to regulatory challenges. Staffed by experts across the translational research spectrum, DARS forms synergies by pulling together scientists and experts from diverse backgrounds to collaborate in tackling some of the most complex challenges facing FDA. This includes (but is not limited to) assessing the systemic absorption of sunscreens, evaluating whether certain drugs can convert to carcinogens in people, studying drug interactions with opioids, optimizing opioid antagonist dosing in community settings, removing barriers to biosimilar and generic drug development, and advancing therapeutic development for rare diseases. FDA tasks DARS with wide ranging issues that encompass regulatory science; DARS, in turn, helps the Agency solve these challenges. The impact of DARS research is felt by patients, the pharmaceutical industry, and fellow regulators. This article reviews applied research projects and initiatives led by DARS and conducts a deeper dive into select examples illustrating the impactful work of the Division.

Opioids are small-molecule agonists of -opioid receptor ( OR), while reversal agents such as naloxone are antagonists of OR. Here we developed machine learning (ML) models to classify the intrinsic activities of ligands at the human OR based on the SMILE strings and two-dimensional molecular descriptors. We first manually curated a database of 983 small molecules with measured values at the human OR. Analysis of the chemical space allowed identification of dominant scaffolds and structurally similar agonists and antagonists. Decision tree models and directed message passing neural networks (MPNNs) were then trained to classify agonistic and antagonistic ligands. The hold-out test AUCs (areas under the receiver operator curves) of the extra-tree (ET) and MPNN models are 91.5±3.9% and 91.8± 4.4%, respectively. To overcome the challenge of small dataset, a student-teacher learning method called tri-training with disagreement was tested using an unlabeled dataset comprised of 15,816 ligands of human, mouse, or rat OR, OR, or OR. We found that the tri-training scheme was able to increase the hold-out AUC of MPNN to as high as 95.7%. Our work demonstrates the feasibility of developing ML models to accurately predict the intrinsic activities of OR ligands, even with limited data. We envisage potential applications of these models in evaluating uncharacterized substances for public safety risks and discovering new therapeutic agents to counteract opioid overdoses.

The μ opioid agonist morphine is the standard for severe pain management. Despite the ability of morphine to treat severe pain, there are significant side effects which often cause undermedication in clinical settings. Such effects are respiratory depression, tolerance, constipation, and dependence. Accordingly, investigation of novel classes of opioid analgesics would provide great therapeutic benefits. 14-Phenylpropyloxymorphinans are agonists that exhibit extreme potency at mu receptors, suggesting that the 14-phenylpropyloxy group has a major effect on receptor binding and is responsible for the dramatic increase in potency. Our hypothesis is that both a basic amine and a phenylpropyloxy group alone are required for opioid activity, and the aromatic A-ring, that was historically considered essential, is not required. By removing the A-ring, this allows the skeleton to adopt an alternate binding mode with the receptor, thereby potentially causing alternate receptor trafficking events and post-receptor mechanisms, all of which are involved in the development of tolerance. During initial studies, a conformationally sampled pharmacophore approach was utilized to confirm that the aromatic moiety in the novel series does not mimic the A-ring. In order to further substantiate our hypothesis, a series of phenylpropyloxyethylamines and cinnamyloxyethylamines were synthesized, and analyzed for opioid receptor binding affinity. Opioid binding studies showed that the optimal N-substituent is the N-phenethyl, specifically analog 2-(cinnamyloxy)-N-methyl-N-phenethylethanamine which has an affinity of 1680 nM for μ opioid receptors. Subsequently, rings B, C, and D from the morphine skeleton were systematically re-introduced as ring-constrained analogs. Binding studies showed that the B-ring analog containing a N,N-dimethyl substituent produced the highest affinity of 2340 nM, while the C- and D-ring analogs were fully inactive. Furthermore, by combining the B-ring with the optimal N-substituent, phenethyl, we were able to achieve 1640 nM affinity at μ. Moreover, upon introduction of an indole group into the C-ring analog, N,N-dimethyl-1-(3-(3-phenylpropoxy)-2,3,4,9-tetrahydro-1H-carbazol-3-yl)methanamine, the affinity was increased to 1110 nM, which represents a viable lead compound for optimization studies.

Nitazenes are a class of novel synthetic opioids with exceptionally high potency. Currently, an experimental structure of OR-opioid receptor ( OR) in complex with a nitazene is lacking. Here we used a suite of computational tools, including consensus docking, conventional molecular dynamics (MD) and metadynamics simulations, to investigate the OR binding modes of nitro-containing meto-, eto-, proto-, buto-, and isotonitazenes and nitro-less analogs, metodes-, etodes-, and protodesnitazenes. Docking generated three binding modes, whereby the nitro-substituted or unsubstituted benzimidazole group extends into SP1 (subpocket 1 between transmembrane helix or TM 2 and 3), SP2 (subpocket 2 between TM1, TM2, and TM7) or SP3 (subpocket 3 between TM5 and TM6). Simulations suggest that etonitazene and likely also other nitazenes favor the SP2-binding mode. Comparison to the experimental structures of OR in complex with BU72, fentanyl, and mitragynine pseudoindoxyl (MP) allows us to propose a putative model for OR-ligand recognition in which ligand can access hydrophobic SP1 or hydrophilic SP2, mediated by the conformational change of Gln124 . Interestingly, in addition to water-mediated hydrogen bonds, the nitro group in nitazenes forms a -hole interaction with the conserved Tyr75 . Our computational analysis provides new insights into the mechanism of OR-opioid recognition, paving the way for investigations of the structure-activity relationships of nitazenes.

The drug overdose crisis in the United States is driven largely by the ultrapotent synthetic (UPS) opioid fentanyl; however, fentanyl's extreme potency is poorly understood. Here we used state-of-the-art molecular dynamics simulations and experiments to test a hypothesis that fentanyl's extreme potency is driven by its ability to partition into the plasma membrane, creating a drug reservoir near the receptor. The estimated effective permeability of fentanyl at pH 7.5 is on the order of 10 -7 cm/s - at least two orders of magnitude faster than morphine. In contrast, isotonitazene (a newly emerged UPS opioid) and naloxone (an antagonist) effectively do not partition into the membrane under the same conditions. The simulations captured the proton-coupled permeation processes, challenging the long-standing pH-partition hypothesis. Subsequent reporter cell line experiments demonstrated that cells exposed to fentanyl, but not morphine, reactivated the receptor even after washout and addition of naloxone. Immobilized affinity membrane chromatography confirmed that fentanyl has significantly higher phospholipid affinity than morphine. Our findings strongly support the drug depot hypothesis and highlight the importance of membrane-dependent opioid pharmacology for understanding toxicity and guiding the design of more effective antagonists. The simulation methodology enables detailed analysis of membrane permeation by ionizable inhibitors, supporting ADME optimization in drug development.The drug overdose crisis in the United States is driven largely by the ultrapotent synthetic (UPS) opioid fentanyl; however, fentanyl's extreme potency is poorly understood. Here we used state-of-the-art molecular dynamics simulations and experiments to test a hypothesis that fentanyl's extreme potency is driven by its ability to partition into the plasma membrane, creating a drug reservoir near the receptor. The estimated effective permeability of fentanyl at pH 7.5 is on the order of 10 -7 cm/s - at least two orders of magnitude faster than morphine. In contrast, isotonitazene (a newly emerged UPS opioid) and naloxone (an antagonist) effectively do not partition into the membrane under the same conditions. The simulations captured the proton-coupled permeation processes, challenging the long-standing pH-partition hypothesis. Subsequent reporter cell line experiments demonstrated that cells exposed to fentanyl, but not morphine, reactivated the receptor even after washout and addition of naloxone. Immobilized affinity membrane chromatography confirmed that fentanyl has significantly higher phospholipid affinity than morphine. Our findings strongly support the drug depot hypothesis and highlight the importance of membrane-dependent opioid pharmacology for understanding toxicity and guiding the design of more effective antagonists. The simulation methodology enables detailed analysis of membrane permeation by ionizable inhibitors, supporting ADME optimization in drug development.

Nitazenes are driving a wave of overdose deaths in the United States and Europe and often require additional doses of naloxone to reverse. To understand the molecular basis, we conducted a joint experimental and simulation study of three common nitazenes, eto-, etodes-, and protonitazene. Radioligand experiments demonstrated that all three nitazenes display higher receptor affinity and longer dissociation half-lives than fentanyl. Notably, protonitazene dissociates slower than carfentanil and its displacement requires fourfold higher antagonist concentrations. The observed trend in nitazene half-lives is recapitulated by molecular dynamics simulations, which suggest that kinetics is controlled by specific interactions with two receptor subpockets. A newly published cryo-EM structure of fluetonitazene- OR complex confirms the predicted interactions, including a -hole bond between the nitro group and Tyr , a residue recently shown to modulate OR signaling bias. Our findings suggest slow receptor dissociation as a key factor challenging overdose reversal. The mechanistic insights have implications for understanding opioid toxicity and designing more effective countermeasures.