Dissecting Machine-Learning Prediction of Molecular Activity: Is an Applicability Domain Needed for Quantitative Structure-Activity Relationship Models Based on Deep Neural Networks?

Deep neural networks (DNNs) are the major drivers of recent progress in artificial intelligence. They have emerged as the machine-learning method of choice in solving image and speech recognition problems, and their potential has raised the expectation of similar breakthroughs in other fields of study. In this work, we compared three machine-learning methods-DNN, random forest (a popular conventional method), and variable nearest neighbor (arguably the simplest method)-in their ability to predict the molecular activities of 21 in vivo and in vitro data sets. Surprisingly, the overall performance of the three methods was similar. For molecules with structurally close near neighbors in the training sets, all methods gave reliable predictions, whereas for molecules increasingly dissimilar to the training molecules, all three methods gave progressively poorer predictions. For molecules sharing little to no structural similarity with the training molecules, all three methods gave a nearly constant value-approximately the average activity of all training molecules-as their predictions. The results confirm conclusions deduced from analyzing molecular applicability domains for accurate predictions, i.e., the most important determinant of the accuracy of predicting a molecule is its similarity to the training samples. This highlights the fact that even in the age of deep learning, developing a truly high-quality model relies less on the choice of machine-learning approach and more on the availability of experimental efforts to generate sufficient training data of structurally diverse compounds. The results also indicate that the distance to training molecules offers a natural and intuitive basis for defining applicability domains to flag reliable and unreliable quantitative structure-activity relationship predictions.

General Approach to Estimate Error Bars for Quantitative Structure-Activity Relationship Predictions of Molecular Activity.

Key requirements for quantitative structure-activity relationship (QSAR) models to gain acceptance by regulatory authorities include a defined domain of applicability (DA) and appropriate measures of goodness-of-fit, robustness, and predictivity. Hence, many DA metrics have been developed over the past two decades. The most intuitive are perhaps distance-to-model metrics, which are most commonly defined in terms of the mean distance between a molecule and its k nearest training samples. Detailed evaluations have shown that the variance of predictions by an ensemble of QSAR models may serve as a DA metric and can outperform distance-to-model metrics. Intriguingly, the performance of ensemble variance metric has led researchers to conclude that the error of predicting a new molecule does not depend on the input descriptors or machine-learning methods but on its distance to the training molecules. This implies that the distance to training samples may serve as the basis for developing a high-performance DA metric. In this article, we introduce a new Tanimoto distance-based DA metric called the sum of distance-weighted contributions (SDC), which takes into account contributions from all molecules in a training set. Using four acute chemical toxicity data sets of varying sizes and four other molecular property data sets, we demonstrate that SDC correlates well with the prediction error for all data sets regardless of the machine-learning methods and molecular descriptors used to build the QSAR models. Using the acute toxicity data sets, we compared the distribution of prediction errors with respect to SDC, the mean distance to k-nearest training samples, and the variance of random forest predictions. The results showed that the correlation with the prediction error was highest for SDC. We also demonstrate that SDC allows for the development of robust root mean squared error (RMSE) models and makes it possible to not only give a QSAR prediction but also provide an individual RMSE estimate for each molecule. Because SDC does not depend on a specific machine-learning method, it represents a canonical measure that can be widely used to estimate individual molecule prediction errors for any machine-learning method.

Are carfentanil and acrylfentanyl naloxone resistant?

The rapid rise in deaths since 2012 due to opioid poisoning is correlated with the proliferation of potent synthetic opioid agonists such as fentanyl, acrylfentanyl, and carfentanil. The efficacy of frontline antidotes such as naloxone in reversing such poisoning events has been questioned, and the possibility of naloxone-resistant synthetic opioids has been raised. In this manuscript, we applied in vitro techniques to establish the median effective inhibitory concentrations for fentanyl, acrylfentanyl, and carfentanil and subsequently evaluate naloxone's ability to reverse agonist-receptor interactions.

Design, synthesis, and biological evaluation of C6-difluoromethylenated epoxymorphinan Mu opioid receptor antagonists.

The recent widespread abuse of high potency synthetic opioids, such as fentanyl, presents a serious threat to individuals affected by substance use disorder. Synthetic opioids generally exhibit prolonged in vivo circulatory half-lives that can outlast the reversal effects of conventional naloxone-based overdose antidotes leading to a life-threatening relapse of opioid toxicity known as renarcotization. In this manuscript, we present our efforts to combat the threat of renarcotization by attempting to extend the half-life of traditional MOR antagonists through the design of novel, fluorinated 4,5-epoxymorphinans possessing increased lipophilicity. Analogues were prepared via a concise synthetic strategy highlighted by decarboxylative Wittig olefination of the C6 ketone to install a bioisosteric 1,1-difluoromethylene unit. C6-difluoromethylenated compounds successfully maintained in vitro potency against an EC90 challenge of fentanyl and were predicted to have enhanced circulatory half-life compared to the current standard of care, naloxone. Subsequent in vivo studies demonstrated the effective blockade of fentanyl-induced anti-nociception in mice.

L-cysteine methyl ester overcomes the deleterious effects of morphine on ventilatory parameters and arterial blood-gas chemistry in unanesthetized rats.

We are developing a series of thiolesters that produce an immediate and sustained reversal of the deleterious effects of opioids, such as morphine and fentanyl, on ventilation without diminishing the antinociceptive effects of these opioids. We report here the effects of systemic injections of L-cysteine methyl ester (L-CYSme) on morphine-induced changes in ventilatory parameters, arterial-blood gas (ABG) chemistry (pH, pCO2, pO2, sO2), Alveolar-arterial (A-a) gradient (i.e., the index of alveolar gas-exchange within the lungs), and antinociception in unanesthetized Sprague Dawley rats. The administration of morphine (10 mg/kg, IV) produced a series of deleterious effects on ventilatory parameters, including sustained decreases in tidal volume, minute ventilation, inspiratory drive and peak inspiratory flow that were accompanied by a sustained increase in end inspiratory pause. A single injection of L-CYSme (500 µmol/kg, IV) produced a rapid and long-lasting reversal of the deleterious effects of morphine on ventilatory parameters, and a second injection of L-CYSme (500 µmol/kg, IV) elicited pronounced increases in ventilatory parameters, such as minute ventilation, to values well above pre-morphine levels. L-CYSme (250 or 500 µmol/kg, IV) also produced an immediate and sustained reversal of the deleterious effects of morphine (10 mg/kg, IV) on arterial blood pH, pCO2, pO2, sO2 and A-a gradient, whereas L-cysteine (500 µmol/kg, IV) itself was inactive. L-CYSme (500 µmol/kg, IV) did not appear to modulate the sedative effects of morphine as measured by righting reflex times, but did diminish the duration, however, not the magnitude of the antinociceptive actions of morphine (5 or 10 mg/kg, IV) as determined in tail-flick latency and hindpaw-withdrawal latency assays. These findings provide evidence that L-CYSme can powerfully overcome the deleterious effects of morphine on breathing and gas-exchange in Sprague Dawley rats while not affecting the sedative or early stage antinociceptive effects of the opioid. The mechanisms by which L-CYSme interferes with the OR-induced signaling pathways that mediate the deleterious effects of morphine on ventilatory performance, and by which L-CYSme diminishes the late stage antinociceptive action of morphine remain to be determined.

Evaluation of a lateral flow immunoassay for the detection of the synthetic opioid fentanyl.

In 2017, 47,600 overdose deaths were reported to be associated with the abuse of opioids, including prescription painkillers (e.g. oxycodone), opiates (e.g. heroin), or synthetic opioids (e.g. fentanyl) within the United States. The recent spike in the presence of synthetic opioids in lots of heroin distributed on the street present specific and significant challenges to law enforcement. Synthetic opioids are extremely toxic substances, which can easily be inhaled. This type of exposure can lead to accidental overdoses by law enforcement and other first responders answering calls involving illicit drugs containing these substances. Due to this extreme toxicity, it is important for these individuals to have tools that can be easily deployed for accurate presumptive field tests. Currently, there are only a limited number of presumptive tests available for fentanyl detection. In this study, we addressed this technology gap by evaluating newly developed lateral flow immunoassays (LFIs) designed for the detection of fentanyl and its derivatives. These LFIs were evaluated for effectiveness in different biofluid matrices, following an in vivo exposure, cross-reactivity with fentanyl analogs, and in case samples. This study demonstrates that LFIs have the potential to be used by law enforcement for the detection of synthetic opioids.

Synthesis and µ-Opioid Activity of the Primary Metabolites of Carfentanil.

Carfentanil is a synthetic opioid significantly more potent than clinically prescribed fentanyl. The primary metabolites of carfentanil, generated from human liver microsomes, were structurally confirmed through chemical synthesis. The synthesized compounds were evaluated for µ-opioid receptor (MOR) functional activity. Of the six metabolites assayed, a major metabolite showed comparable activity to the parent opioid. Three other metabolites showed significant MOR functional activity. The availability of the metabolites could aid improvements in the analysis of biomedical samples obtained from suspected human exposures to carfentanil and development of treatment protocols.

Assessing Deep and Shallow Learning Methods for Quantitative Prediction of Acute Chemical Toxicity.

Animal-based methods for assessing chemical toxicity are struggling to meet testing demands. In silico approaches, including machine-learning methods, are promising alternatives. Recently, deep neural networks (DNNs) were evaluated and reported to outperform other machine-learning methods for quantitative structure-activity relationship modeling of molecular properties. However, most of the reported performance evaluations relied on global performance metrics, such as the root mean squared error (RMSE) between the predicted and experimental values of all samples, without considering the impact of sample distribution across the activity spectrum. Here, we carried out an in-depth analysis of DNN performance for quantitative prediction of acute chemical toxicity using several datasets. We found that the overall performance of DNN models on datasets of up to 30 000 compounds was similar to that of random forest (RF) models, as measured by the RMSE and correlation coefficients between the predicted and experimental results. However, our detailed analyses demonstrated that global performance metrics are inappropriate for datasets with a highly uneven sample distribution, because they show a strong bias for the most populous compounds along the toxicity spectrum. For highly toxic compounds, DNN and RF models trained on all samples performed much worse than the global performance metrics indicated. Surprisingly, our variable nearest neighbor method, which utilizes only structurally similar compounds to make predictions, performed reasonably well, suggesting that information of close near neighbors in the training sets is a key determinant of acute toxicity predictions.

Metabolism of Carfentanil, an Ultra-Potent Opioid, in Human Liver Microsomes and Human Hepatocytes by High-Resolution Mass Spectrometry.

Carfentanil is an ultra-potent synthetic opioid. No human carfentanil metabolism data are available. Reportedly, Russian police forces used carfentanil and remifentanil to resolve a hostage situation in Moscow in 2002. This alleged use prompted interest in the pharmacology and toxicology of carfentanil in humans. Our study was conducted to identify human carfentanil metabolites and to assess carfentanil's metabolic clearance, which could contribute to its acute toxicity in humans. We used Simulations Plus's ADMET Predictor™ and Molecular Discovery's MetaSite™ to predict possible metabolite formation. Both programs gave similar results that were generally good but did not capture all metabolites seen in vitro. We incubated carfentanil with human hepatocytes for up to 1 h and analyzed samples on a Sciex 3200 QTRAP mass spectrometer to measure parent compound depletion and extrapolated that to represent intrinsic clearance. Pooled primary human hepatocytes were then incubated with carfentanil up to 6 h and analyzed for metabolite identification on a Sciex 5600+ TripleTOF (QTOF) high-resolution mass spectrometer. MS and MS/MS analyses elucidated the structures of the most abundant metabolites. Twelve metabolites were identified in total. N-Dealkylation and monohydroxylation of the piperidine ring were the dominant metabolic pathways. Two N-oxide metabolites and one glucuronide metabolite were observed. Surprisingly, ester hydrolysis was not a major metabolic pathway for carfentanil. While the human liver microsomal system demonstrated rapid clearance by CYP enzymes, the hepatocyte incubations showed much slower clearance, possibly providing some insight into the long duration of carfentanil's effects.