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.
The opioid epidemic currently plaguing the United States has been exacerbated by an alarming rise in fatal overdoses as a result of the proliferated abuse of synthetic mu opioid receptor (MOR) agonists, such as fentanyl and its related analogues. Attempts to manage this crisis have focused primarily on widespread distribution of the clinically approved opioid reversal agent naloxone (Narcan); however, due to the intrinsic metabolic lability of naloxone, these measures have demonstrated limited effectiveness against synthetic opioid toxicity. This work reports a novel polymer-based strategy to create a long-acting formulation of naloxone with the potential to address this critical issue by utilizing covalent nanoparticle (cNP) drug delivery technology.
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.
In the United States in 2016, 64,000 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). 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
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 valueapproximately the average activity of all training moleculesas 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.
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