In silico toxicology protocols

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information

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

<div><p>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 <i>in vitro</i> data and may assist in the temporary scheduling of those substances that pose a risk to public safety.</p></div

Furanylfentanyl case study.

<p>(A) Chemical structure of the designer opioid Fu-F. (B) Binding concentration prediction of Fu-F (red). (C-D) Primary and secondary binding poses from the 10 independent docking simulations of Fu-F. In panels C and D, the key salt bridge (Asp147) and aromatic stacking interactions (His297) with Fu-F are highlighted by the red and blue mesh surfaces, respectively.</p

Fentanyl congener binding prediction and classification.

<p>Scatterplot of the experimentally determined binding affinity, K_i, with the average docking score from the molecular docking procedure of the 7 fentanyl congeners (shown as circles). (+)-Tramadol (blue), meperidine (purple), propoxyphene (green) and methadone (red) are highlighted and demonstrate the method’s ability to predict the correct binding concentration regimes of fentanyl congeners. The remaining black circles represent the three fentanyl congeners that were docked and scored. The black diamonds represent the fentanyl analogs from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197734#pone.0197734.g004" target="_blank">Fig 4</a>.</p

Decoy analysis.

<p>(A) Distribution of the decoy binding scores. The vertical red and blue lines indicate the docking score of fentanyl and carfentanil, respectively. (B) Distribution of the Tanimoto index of the fentanyl decoys with respect to fentanyl. The vertical blue line indicates the Tanimoto index of carfentanil (T_c = 0.76) with respect to fentanyl. (C) Correlation of the fentanyl derivative molecular weight with respect to the ADS. (D) Correlation of the fentanyl derivative molecular weight with respect to the experimentally determined binding affinity (K_i). (E) Correlation of the fentanyl decoy molecular weight with respect to the ADS. In panels C-E, the red and blue diamonds indicate fentanyl and carfentanil, respectively.</p

Fentanyl derivatization and the μOR.

<p>(A) Commonly-modified positions around the 4-anilidopiperidine core of fentanyl. (B) The seven-helix transmembrane domain of the μOR in complex with agonist BU72; the binding site is represented by the transparent surface.</p

Molecular docking poses and scores.

<p>(A) Docking score distribution from the 10 docking simulations of fentanyl (purple), carfentanil (green) and <i>N</i>-methyl fentanyl (blue). (B) Superimposition of the ten lowest energy fentanyl poses in the μOR binding pocket. (C) The left and right panels present the primary (7) and secondary (3) carfentanil poses from the 10 docking simulations, respectively. (D) The left and right panels present the primary (6) and secondary poses (4) of <i>N</i>-methyl fentanyl from the ten docking simulations, respectively. The six primary <i>N</i>-methyl fentanyl poses are virtually identical. In panels B-D, the negatively charged sidechain of Asp147 that forms the salt bridge with the positively charged amine of each opioid is highlighted by the red mesh surface, and the aromatic sidechain of His297 is represented by the blue mesh surface.</p

Fentanyl analog structures.

<p>Summary of the fentanyl analog chemical features assessed by molecular docking.</p

Experimentally determined and predicted binding affinity ranges of the 23 opioids docked at the μOR.

<p>The 8 fentanyl derivatives and 7 fentanyl congeners are listed first and ordered by increasing binding affinity, and the 8 morphine derivatives are listed last. The green labels indicate that the model correctly predicted the binding concentration regime, yellow indicates that the model predicted the compound to bind stronger than the measured K_i, and red indicates that the model predicted the compound to bind less strongly than the measured K_i.</p

Fentanyl analog binding prediction and classification.

<p>Scatterplot of the experimentally determined binding affinity, K_i, with the ADS from the molecular docking procedure of the 8 fentanyl analogs (shown as diamonds). The grey, shaded regions display the sub-nM, 1–100 nM, and greater than 100 nM binding concentration regimes used to classify the predicted binding strength of new drugs. <i>N</i>-methyl fentanyl (blue), fentanyl (purple), carfentanil (green) and lofentanil (red) are presented with colored diamonds to demonstrate the method’s ability to separate fentanyl analogs into the proper binding concentration regimes. The remaining black diamonds represent the 4 other fentanyl analogs that were docked and scored.</p