Quantitative structure-activity relationship of organophosphate compounds based on molecular interaction fields descriptors

Vapor Pressure and Toxicity Prediction for Novichok Agent Candidates Using Machine Learning Model: Preparation for Unascertained Nerve Agents after Chemical Weapons Convention Schedule 1 Update

The recent terrorist attacks using Novichok agents and subsequent operations have necessitated an understanding of its physicochemical properties, such as vapor pressure and toxicity, as well as unascertained nerve agent structures. To prevent continued threats from new types of nerve agents, the organization for the prohibition of chemical weapons (OPCW) updated the chemical weapons convention (CWC) schedule 1 list. However, this information is vague and may encompass more than 10 000 possible chemical structures, which makes it almost impossible to synthesize and measure their properties and toxicity. To assist this effort, we successfully developed machine learning (ML) models to predict the vapor pressure to help with escape and removal operations. The model shows robust and high-accuracy performance with promising features for predicting vapor pressure when applied to Novichok materials and accurate predictions with reasonable errors. The ML classification model was successfully built for the swallow globally harmonized system class of organophosphorus compounds (OP) for toxicity predictions. The tuned ML model was used to predict the toxicity of Novichok agents, as described in the CWC list. Although its accuracy and linearity can be improved, this ML model is expected to be a firm basis for developing more accurate models for predicting the vapor pressure and toxicity of nerve agents in the future to help handle future terror attacks with unknown nerve agents.

Ensemble machine learning to evaluate the in vivo acute oral toxicity and in vitro human acetylcholinesterase inhibitory activity of organophosphates

Organophosphates (OPs) are hazardous chemicals widely used in industry and agriculture. Distribution of their residues in nature causes serious risks to humans, animals, and plants. To reduce hazards from OPs, quantitative structure-activity relationship (QSAR) models for predicting their acute oral toxicity in rats and mice and inhibition constants concerning human acetylcholinesterase were developed according to the bioactivity data of 456 unique OPs. Based on robust, two-dimensional molecular descriptors and quantum chemical descriptors, which accurately reflect OP electronic structures and reactivities, the influences of eight machine-learning algorithms on the prediction performance of the QSAR models were explored, and consensus QSAR models were constructed. Several strict model validation indices and the results of applicability domain evaluations show that the established consensus QSAR models exhibit good robustness, practical prediction abilities, and wide application scopes. Poor correlation was observed between acute oral toxicity at the mammalian level and the inhibition constants at the molecular level, indicating that the acute toxicity of OPs cannot be evaluated only by the experimental data of enzyme inhibitory activity, their toxicokinetic characteristics must also be considered. The constructed QSAR models described herein provide rapid, theoretical assessment of the bioactivity of unstudied or unknown OPs, as well as guidance for making decisions regarding their regulation.

Quantitative structure-toxicity relationship model for acute toxicity of organophosphates via multiple administration routes in rats and mice

Organophosphates (OPs) are highly toxic compounds, with widespread application in agricultural and chemical industries, whose introduction into the environment poses serious hazards to humans and ecological systems. To assess and ultimately mitigate these hazards, this study predicted the acute toxicity of OPs according to their chemical structure and administration route. The acute toxicity data of 161 OPs in two species via six different administration routes were manually collected and used to develop a series of quantitative structure-toxicity relationship (QSTR) models with robust and practical predictive abilities. The random forest algorithm was used to develop the models, employing both quantum chemical and two-dimensional descriptors according to OECD guidelines. Correlation results and feature similarities indicated that whereas acute toxicity data from rats and mice via the same administration route were combinable for modeling, data from different routes were not. Six QSTR models for each route in a single species and two QSTR models for a single route in the two species were constructed, achieving practical predictive performance. Despite significant variances in their datasets, the prediction models could predict the acute toxicity of novel or unknown OPs, realize rapid assessment, and provide guidance for regulatory decisions to reduce the hazards of OPs.

QSAR model for pesticides toxicity to Rainbow Trout based on "ideal correlations"

Pesticides have an impact on the aquatic environment, with ecological effects. The regulation of this impact is of key importance. One of the components of the planning of agricultural and industrial activities is the development of databases and models in order to identify substances that may cause damage. In this study, a quantitative structure-activity relationship (QSAR) approach was established for the prediction of acute toxicity toward rainbow trout of various pesticides. The so-called index of ideality of correlation is the main component of this approach. The validation of this approach has been carried out with three random splits into the training and validation sets. The range of statistical quality of models obtained here for the validation set is R² = [0.81-0.86] and RMSE = [0.55-0.65].

Formulation and Evaluation of Fast-Disintegrating Sublingual Tablets of Atropine Sulfate: the Effect of Tablet Dimensions and Drug Load on Tablet Characteristics

In this study, we formulated and evaluated the effects of tablet dimensions and drug load on the characteristics of atropine sulfate (AS) fast-disintegrating sublingual tablets (FDSTs). We aim to develop AS FDSTs as an alternative non-invasive and portable dosage form for the emergency treatment of organophosphate (OP) toxicity. AS autoinjector, AtroPen®, is the only self-administered dosage form available as an antidote for-out-of-hospital emergency use, but it is associated with several limitations and drawbacks. Seven FDST formulations of two tablet sizes, 150 mg (A) and 50 mg (B), and of several AS loads, 0 mg (A1, B1), 2 mg (A2, B2), 4 mg (B3), and 8 mg (B4a, B4b), were formulated and manufactured by direct compression. AS FDST characteristics were evaluated using USP and non-USP tests. Results were statistically compared at p < 0.05. All FDSTs passed the USP content uniformity and friability tests, disintegrated and released AS in ≤30 and 60 s. B1 and B2 were significantly harder than A1 and A2. Water uptake of A1 was significantly the highest. However, B1 and B2 had shorter disintegration and wetting times and higher amounts of AS dissolved than did A1 and A2 (p < 0.05). Increasing AS negatively affected FDST tensile strength (p < 0.05 for B4a) and water uptake (p < 0.05 for B3, B4a and B4b), however, without affecting AS dissolution. Formulation of AS up to 16% into smaller FDSTs was successful. Smaller FDSTs were harder and disintegrated more quickly. These AS FDSTS have the potential for further in vivo testing to evaluate their OP antidote potential.

A Sequential Algorithm for Multiblock Orthogonal Projections to Latent Structures

Methods of multiblock bilinear factorizations have increased in popularity in chemistry and biology as recent increases in the availability of information-rich spectroscopic platforms has made collecting multiple spectroscopic observations per sample a practicable possibility. Of the existing multiblock methods, consensus PCA (CPCA-W) and multiblock PLS (MB-PLS) have been shown to bear desirable qualities for multivariate modeling, most notably their computability from single-block PCA and PLS factorizations. While MB-PLS is a powerful extension to the nonlinear iterative partial least squares (NIPALS) framework, it still spreads predictive information across multiple components when response-uncorrelated variation exists in the data. The OnPLS extension to O2PLS provides a means of simultaneously extracting predictive and uncorrelated variation from a set of matrices, but is more suited to unsupervised data discovery than regression. We describe the union of NIPALS MB-PLS with an orthogonal signal correction (OSC) filter, called MB-OPLS, and illustrate its equivalence to single-block OPLS for regression and discriminant analysis.

Analysis of organophosphate-Zn metalloporphyrin interactions via UV-vis spectroscopy and molecular modeling

UV-vis absorption spectra of zinc tetraphenylporphine (ZnTPP) on interaction with six organophosphorus (OP) compounds in cyclohexane were compared using ab initio methods and the molecular and solvation ligand descriptors π(*), Vx, and σ. OPs with polarizable hydrocarbon substituents in the homologous series tri-ethyl, -pentyl, -octyl, and -phenyl phosphates and the toxicologically relevant methyl paraoxon (1a-e) each gave a red shift in the Soret band (λsor) of ZnTPP in the range of 8-10 nm. Sensitivity as ΔAsor-b/Δug OP for the spectral band of the ligand bound ZnTPP (λsor-b) decreased with increasing extent of alkyl and aromatic substitution. Calculated and combined energies for OP and ZnTPP examined as a function of distance (Å) between ligand and porphyrin center suggest increased steric crowding with increasing Vx, and aromatic content of the OP. Spectrally fitted K1:1 and ΔAsor-b/ug OP each vary exponentially with Vx/σ. Lack of a red shift in λsor-b where ZnTPP was titrated with the toxic diethyl chlorophosphate (1g) is consistent with a model in which the magnitude of ΔEsor is proportional to the donor capacity of the phosphoryl-O ligand.