Validation of quantitative structure-activity relationship models to predict water-solubility of organic compounds

Risk mitigation strategy and mechanism analysis of neonicotinoid pesticides on earthworms

Neonicotinoid insecticides (NNIs) are a new class of widely used insecticides with certain risks to non-target organisms, like earthworms. The gray correlation method was used to calculate the comprehensive risk effect value of acute toxicity (LC50) and bioaccumulation (logKow) of NNIs on earthworms. A comprehensive effects three-dimensional quantitative structure-activity relationship (3D-QSAR) model was constructed, using NNIs molecular structures and the comprehensive effect value as the independent and dependent variables, respectively. One of the representatives guadipyr (GUA) was selected as the template molecule for the molecular design and modification. A total of 63 NNIs alternatives were designed with a reduced comprehensive value higher than 10%, and as high as 42%. After screening, 15 NNIs alternatives were screened with decreased acute toxicity to earthworms, bioaccumulation effects and improved functional property. The calculated primary acute risk quotient of earthworms shows that the designed NNIs alternatives have lower earthworm risks (reduction of 70.48-99.99%). Results also found that the electronic, geometric and topological parameters of NNIs are the key descriptors that affect NNIs alternatives' toxicity. The number of hydrophobic interaction amino acid residues in NNIs molecules also contributes to the acute toxicity and the bioaccumulation of NNIs alternatives on earthworms. This study aims to design and screen functionally improved and environmentally friendly NNIs alternatives that have low risk to earthworms and provide theoretical methods and new ideas for the risk control and development of pesticides represented by NNIs.

Prediction model for milk transfer of drugs by primarily evaluating the area under the curve using QSAR/QSPR

PURPOSE

Information on milk transferability of drugs is important for patients who wish to breastfeed. The purpose of this study is to develop a prediction model for milk-to-plasma drug concentration ratio based on area under the curve (M/P_AUC). The quantitative structure-activity/property relationship (QSAR/QSPR) approach was used to predict compounds involved in active transport during milk transfer.

METHODS

We collected M/P ratio data from literature, which were curated and divided into M/P_AUC ≥ 1 and M/P_AUC < 1. Using the ADMET Predictor® and ADMET Modeler™, we constructed two types of binary classification models: an artificial neural network (ANN) and a support vector machine (SVM).

RESULTS

M/P ratios of 403 compounds were collected, M/P_AUC data were obtained for 173 compounds, while 230 compounds only had M/P_non-AUC values reported. The models were constructed using 129 of the 173 compounds, excluding colostrum data. The sensitivity of the ANN model was 0.969 for the training set and 0.833 for the test set, while the sensitivity of the SVM model was 0.971 for the training set and 0.667 for the test set. The contribution of the charge-based descriptor was high in both models.

CONCLUSIONS

We built a M/P_AUC prediction model using QSAR/QSPR. These predictive models can play an auxiliary role in evaluating the milk transferability of drugs.

Modulating physicochemical properties of tetrahydropyridine-2-amine BACE1 inhibitors with electron-withdrawing groups: A systematic study

A common challenge for medicinal chemists is to reduce the pK_a of strongly basic groups' conjugate acids into a range that preserves the desired effects, usually potency and/or solubility, but avoids undesired effects like high volume of distribution (V_d), limited membrane permeation, and off-target binding to, notably, the hERG channel and monoamine receptors. We faced this challenge with a 3,4,5,6-tetrahydropyridine-2-amine scaffold harboring an amidine, a key structural component of potential inhibitors of BACE1, the rate-limiting enzyme in the production of Aβ species that make up amyloid plaques in Alzheimer's disease. In our endeavor to balance potency with desirable properties to achieve brain penetration, we introduced a diverse set of groups in beta position of the amidine that modulate logD, PSA and pK_a. Given the synthetic challenge to prepare these highly functionalized warheads, we first developed a design flow including predicted physicochemical parameters which allowed us to select only the most promising candidates for synthesis. For this we evaluated a set of commercial packages to predict physicochemical properties, which can guide medicinal chemists in their endeavors to modulate pK_a values of amidine and amine bases.

In silico predictions of absorption of MDI substances after dermal or inhalation exposures to support a category based read-across assessment

Methylenediphenyl diisocyanate (MDI) substances used polyurethane production can range from their simplest monomeric forms (e.g., 4,4'-MDI) to mixtures of the monomers with various homologues, homopolymer, and prepolymer derivatives. The relative dermal or inhalation absorption of 39 constituents of these substances in human were predicted using the GastroPlus® program. Predicted dermal uptake and absorption of the three MDI monomers from an acetone vehicle was 84-86% and 1.4-1.5%, respectively, with lower uptake and absorption predicted for the higher MW analogs. Lower absorption was predicted from exposures in a more lipophilic vehicle (1-octanol). Modeled inhalation exposures afforded the highest pulmonary absorption for the MDI monomers (38-54%), with 3-27% for the MW range of 381-751, and <0.1% for the remaining, higher MW derivatives. Predicted oral absorption, representing mucociliary transport, ranged from 5 to 10% for the MDI monomers, 10-25% for constituents of MW 381-751, and ≤3% for constituents with MW > 900. These in silico evaluations should be useful in category-based, worst-case, Read-Across assessments for MDI monomers and modified MDI substances for potential systemic effects. Predictions of appreciable mucociliary transport may also be useful to address data gaps in oral toxicity testing for this category of compounds.

Comparison of Predictions by BCS, rDCS and Machine Learning for the Effect of Food on Oral Drug Absorption Based on Features Calculated In silico

In this study, observed food effects of 473 drugs were categorized into positive, negative, or no effects and compared with the predictions made by machine learning (ML), the Biopharmaceutics Classification System (BCS) and refined Developability Classification System (rDCS). All methods used primarily in silico estimates for prediction, and for ML, four algorithms were evaluated using nested cross-validation to select important information from 371 features calculated based on the chemical structure. Approximately 18 features, including estimated solubility in biorelevant media, were selected as important, and the random forest classifier was the best among four algorithms with 36.6% error rate (ER) and 10.8% opposite prediction rate (OPR). The prediction by rDCS utilizing solubility in a biorelevant medium was somewhat inferior, but not by much; 41.0% ER and 11.4% OPR. Compared with these two methods, the prediction by BCS was inferior; 54.5% ER and 21.4% OPR. ER was improved modestly by using measured features instead of in silico estimates when BCS was applied to a subset of 151 drugs (46.4% from 55.0%). ML and rDCS predicted the food effects of the same subset using in silico estimates with ERs of 37.7% and 42.4%, respectively, suggesting that the predictions by ML and rDCS using in silico features are similar or more accurate than those by BCS using measured features. These results suggest that ML was useful in revealing essential features from complex information and, together with rDCS, is effective in predicting food effects during drug development, including early drug discovery.

Liver toxicity of anthraquinones: A combined in vitro cytotoxicity and in silico reverse dosimetry evaluation

Anthraquinones are found in a variety of consumer products such as dietary supplements, traditional Chinese medicines, and drugs. Along with their widespread use, potential safety concerns have emerged, especially liver toxicity. Therefore, there is a need to conduct rapid and inexpensive safety assessment for anthraquinones due to a lack of animal and human toxicological data. Here, a combined in vitro cytotoxicity and in silico reverse dosimetry approach was adopted to consider the potential human liver toxicity of 16 anthraquinones and derivatives. First, cytotoxicity (EC₅₀) in two human liver cell lines (HepG2/C3A and HuH-7) was measured under two conditions (single and repeated dosing, 72 h). Second, toxic doses (D_tox) required to yield plasma steady-state concentrations (C_ss) equal to in vitro EC₅₀ values were predicted by reverse dosimetry simulation using a PBPK model. Finally, D_tox was compared to literature-derived estimated daily intake (EDI) of anthraquinones to assess safety. Among the 16 anthraquinones, rhein was identified as a potential hepatotoxicant due to a combination of cytotoxicity, plasma concentration, and daily intake level. These in vitro and in silico findings provide preliminary data and guidance for further animal and clinical studies to confirm liver toxicity of anthraquinones.

QSPR studies on water solubility, octanol-water partition coefficient and vapour pressure of pesticides

The assessment of the environmental fate and (eco)toxicological effects of pesticide compounds is of crucial importance. The present review is focused on Quantitative Structure-Property Relationships (QSPR) applications on three environmentally relevant physicochemical properties of pesticides, which can be used for assessing their environmental partition and transport, as well as exposure potential namely water solubility, octanol-water partition coefficient and vapour pressure. This article revises various interesting QSPR applications with special emphasis on studies developed during the 2009-2019 period.

Conformation-independent quantitative structure-property relationships study on water solubility of pesticides

Water solubility is a key physicochemical parameter in pesticide control and regulation, although sometimes its experimental determination is not an easy task. In this study, we present Quantitative Structure-Property Relationships (QSPRs) for predicting the water solubility at 20 °C of 1211 approved heterogeneous pesticide compounds, collected from the online Pesticides Properties Data Base (PPDB). Validated and generally applicable Multivariable Linear Regression (MLR) models were established, including molecular descriptors carrying constitutional and topological aspects of the analyzed compounds. The most representative descriptors were selected from the exploration of a large number of about 18,000 structural variables. A hybrid approach that involves a molecular descriptor, a fingerprint, and a flexible descriptor showed the best predictive performance.

Aqueous Drug Solubility: What Do We Measure, Calculate and QSPR Predict?

Detailed critical analysis of publications devoted to QSPR of aqueous solubility is presented in the review with discussion of four types of aqueous solubility (three different thermodynamic solubilities with unknown solute structure, intrinsic solubility, solubility in physiological media at pH=7.4 and kinetic solubility), variety of molecular descriptors (from topological to quantum chemical), traditional statistical and machine learning methods as well as original QSPR models.