In silico toxicity as a tool for harm reduction: A study of new psychoactive amphetamines and cathinones in the context of criminal science.
TL;DR: Computer-calculated toxicity values of various amphetamines and cathinones are submitted to an unsupervised multivariate analysis, namely Principal Component Analysis (PCA), and to the supervised techniques Soft Independent Modeling of Class Analogy and Partial Least Square-Discriminant Analysis to evaluate how these two NPS groups behave.
About: This article is published in Science & Justice.The article was published on 2019-05-01. It has received 3 citations till now. The article focuses on the topics: Poison control.
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TL;DR: In this article , the analytical applicability of single ion-selective membranes (ISMs) and potentiometric sensor array to distinguish and detect cathinone derivatives was demonstrated.
Abstract: This work demonstrates the analytical applicability of single ion-selective membranes (ISMs) and potentiometric sensor array to distinguish and detect cathinone derivatives. Potentiometric data from ISMs based on cation exchanger and varying content of calix[4]arene derivative were processed by principal component analysis (PCA). Such a combination of methods allowed discriminating various individual synthetic cathinones and their recognition from the mixture comprising primary amines (substituted amphetamines+aminoindane). Analytical parameters of ISM containing 1wt % of calix[4]arene derivative were sufficient to detect 1.0×10−4 mol.l−1 1-(4-fluorophenyl)-2-(ethylamino)butan-1-one and 2-(methylamino)-1-phenylbutan-1-one (buphedrone) in both model and saliva samples.
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TL;DR: Property of four diagnostic statistics of PLS-DA, namely the number of misclassifications (NMC), the Area Under the Receiver Operating Characteristic (AUROC), Q2 and Discriminant Q2 (DQ2) are discussed, seem more efficient and more reliable diagnostic statistics and should be recommended in two group discrimination metabolomic studies.
Abstract: Partial Least Squares-Discriminant Analysis (PLS-DA) is a PLS regression method with a special binary ‘dummy’ y-variable and it is commonly used for classification purposes and biomarker selection in metabolomics studies. Several statistical approaches are currently in use to validate outcomes of PLS-DA analyses e.g. double cross validation procedures or permutation testing. However, there is a great inconsistency in the optimization and the assessment of performance of PLS-DA models due to many different diagnostic statistics currently employed in metabolomics data analyses. In this paper, properties of four diagnostic statistics of PLS-DA, namely the number of misclassifications (NMC), the Area Under the Receiver Operating Characteristic (AUROC), Q
2 and Discriminant Q
2 (DQ
2) are discussed. All four diagnostic statistics are used in the optimization and the performance assessment of PLS-DA models of three different-size metabolomics data sets obtained with two different types of analytical platforms and with different levels of known differences between two groups: control and case groups. Statistical significance of obtained PLS-DA models was evaluated with permutation testing. PLS-DA models obtained with NMC and AUROC are more powerful in detecting very small differences between groups than models obtained with Q
2 and Discriminant Q
2 (DQ
2). Reproducibility of obtained PLS-DA models outcomes, models complexity and permutation test distributions are also investigated to explain this phenomenon. DQ
2 and Q
2 (in contrary to NMC and AUROC) prefer PLS-DA models with lower complexity and require higher number of permutation tests and submodels to accurately estimate statistical significance of the model performance. NMC and AUROC seem more efficient and more reliable diagnostic statistics and should be recommended in two group discrimination metabolomic studies.
602 citations
TL;DR: Designer β‐keto amphetamines (e.g. cathinones, ‘bath salts’ and ‘research chemicals’) have become popular recreational drugs, but their pharmacology is poorly characterized.
Abstract: Background and purpose: Designer beta-keto amphetamines (e.g., cathinones, "bath salts," and "research chemicals") have become popular recreational drugs, but their pharmacology is poorly characterized. Experimental approach: We determined the potencies of cathinones to inhibit dopamine (DA), noradrenaline (NA), and serotonin (5-hydroxytryptamine [5-HT]) transport into transporter-transfected human embryonic kidney 293 cells, DA and 5-HT efflux from monoamine-preloaded cells, and monoamine receptor binding affinity. Key results: Mephedrone, methylone, ethylone, butylone, and naphyrone act as nonselective monoamine uptake inhibitors, similar to cocaine. Mephedrone, methylone, ethylone, and butylone also release 5-HT, similar to 3,4-methylenedioxymethamphetamine (MDMA, ecstasy) and other entactogens. Cathinone, methcathinone, and flephedrone act as preferential DA and NA uptake inhibitors and DA releasers, similar to amphetamine and methamphetamine. Pyrovalerone and 3,4-methylenedioxypyrovalerone (MDPV) are highly potent and selective DA and NA transporter inhibitors but unlike amphetamines do not release monoamines. The non-beta-keto amphetamines are trace amine-associated receptor 1 ligands, whereas cathinones are not. All cathinones showed high blood-brain barrier permeability in an in vitro model. Mephedrone and MDPV exhibited particularly high permeability. Conclusions and implications: Cathinones have considerable pharmacological differences that form the basis for their suggested classification into three groups. The predominant action of all cathinones on the DA transporter is likely associated with a considerable risk of addiction. (c) 2012 The Authors. British Journal of Pharmacology (c) 2012 The British Pharmacological Society.
595 citations
TL;DR: Compared to conventional additive methods, XLOGP3 does not rely on an extensive classification of fragments and correction factors in order to improve accuracy and is also able to utilize the ever-increasing experimentally measured logP data more effectively.
Abstract: We have developed a new method, i.e., XLOGP3, for logP computation. XLOGP3 predicts the logP value of a query compound by using the known logP value of a reference compound as a starting point. The difference in the logP values of the query compound and the reference compound is then estimated by an additive model. The additive model implemented in XLOGP3 uses a total of 87 atom/group types and two correction factors as descriptors. It is calibrated on a training set of 8199 organic compounds with reliable logP data through a multivariate linear regression analysis. For a given query compound, the compound showing the highest structural similarity in the training set will be selected as the reference compound. Structural similarity is quantified based on topological torsion descriptors. XLOGP3 has been tested along with its predecessor, i.e., XLOGP2, as well as several popular logP methods on two independent test sets: one contains 406 small-molecule drugs approved by the FDA and the other contains 219 oligopeptides. On both test sets, XLOGP3 produces more accurate predictions than most of the other methods with average unsigned errors of 0.24-0.51 units. Compared to conventional additive methods, XLOGP3 does not rely on an extensive classification of fragments and correction factors in order to improve accuracy. It is also able to utilize the ever-increasing experimentally measured logP data more effectively.
562 citations
TL;DR: It is reported that, in DAT-KO mice, cocaine and amphetamine increase dialysate dopamine in the medial part of the nucleus accumbens, supporting the hypothesis of a primary role of nucleus accumens dopamine in drug reinforcement.
Abstract: Behavioral and biochemical studies suggest that dopamine (DA) plays a role in the reinforcing and addictive properties of drugs of abuse. Recently, this hypothesis has been challenged on the basis of the observation that, in mice genetically lacking the plasma membrane dopamine transporter [DAT-knock out (DAT-KO)], cocaine maintained its reinforcing properties of being self-administered and inducing place preference, despite the failure to increase extracellular dopamine in the dorsal striatum. Here we report that, in DAT-KO mice, cocaine and amphetamine increase dialysate dopamine in the medial part of the nucleus accumbens. Moreover, reboxetine, a specific blocker of the noradrenaline transporter, increased DA in the nucleus accumbens of DAT-KO but not of wild-type mice; in contrast, GBR 12909, a specific blocker of the dopamine transporter, increased dialysate dopamine in the nucleus accumbens of wild-type but not of DAT-KO mice. These observations provide an explanation for the persistence of cocaine reinforcement in DAT-KO mice and support the hypothesis of a primary role of nucleus accumbens dopamine in drug reinforcement.
541 citations
TL;DR: A physics-based approach is presented that shows a strong linear correlation between the computed solvation free energy in implicit solvents and the experimental log Po/w on a cleansed data set of more than 17,500 molecules.
Abstract: The n-octanol/water partition coefficient (log Po/w) is a key physicochemical parameter for drug discovery, design, and development. Here, we present a physics-based approach that shows a strong linear correlation between the computed solvation free energy in implicit solvents and the experimental log Po/w on a cleansed data set of more than 17,500 molecules. After internal validation by five-fold cross-validation and data randomization, the predictive power of the most interesting multiple linear model, based on two GB/SA parameters solely, was tested on two different external sets of molecules. On the Martel druglike test set, the predictive power of the best model (N = 706, r = 0.64, MAE = 1.18, and RMSE = 1.40) is similar to six well-established empirical methods. On the 17-drug test set, our model outperformed all compared empirical methodologies (N = 17, r = 0.94, MAE = 0.38, and RMSE = 0.52). The physical basis of our original GB/SA approach together with its predictive capacity, computational effi...
469 citations