A Toxicity Prediction Tool for Potential Agonist/Antagonist Activities in Molecular Initiating Events Based on Chemical Structures

Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions

Perfluorinated and perfluoroalkyl substances (PFASs), encompassing a vast array of isomeric chemicals, are recognized as typical emerging contaminants with direct or potential impacts on human health and the ecological environment. With the complex and elusive toxicological profiles of PFASs, machine learning (ML) has been increasingly employed in their toxicity studies due to its proficiency in prediction and data analytics. This integration is poised to become a predominant trend in environmental toxicology, propelled by the swift advancements in computational technology. This review diligently examines the literature to encapsulate the varied objectives of employing ML in the toxicity studies of PFASs: (1) Utilizing ML to establish Quantitative Structure-Activity Relationship (QSAR) models for PFASs with diverse toxicity endpoints, facilitating the targeted toxicity prediction of unidentified PFASs; (2) Investigating and substantiating the Adverse Outcome Pathway (AOP) through the synergy of ML and traditional toxicological methods, with this refining the toxicity assessment framework for PFASs; (3) Dissecting and elucidating the features of established ML models to advance Open Research into the toxicity of PFASs, with a primary focus on determinants and mechanisms. The discourse extends to an in-depth examination of ML studies, segregating findings based on their distinct application trajectories. Given that ML represents a nascent paradigm within PFASs research, this review delineates the collective challenges encountered in the ML-mediated study of PFAS toxicity and proffers strategic guidance for ensuing investigations.

A Chemical Structure and Machine Learning Approach to Assess the Potential Bioactivity of Endogenous Metabolites and Their Association with Early Childhood Systemic Inflammation

Metabolomics has gained much attention due to its potential to reveal molecular disease mechanisms and present viable biomarkers. This work uses a panel of untargeted serum metabolomes from 602 children from the COPSAC2010 mother-child cohort. The annotated part of the metabolome consists of 517 chemical compounds curated using automated procedures. We created a filtering method for the quantified metabolites using predicted quantitative structure-bioactivity relationships for the Tox21 database on nuclear receptors and stress response in cell lines. The metabolites measured in the children's serums are predicted to affect specific targeted models, known for their significance in inflammation, immune function, and health outcomes. The targets from Tox21 have been used as targets with quantitative structure-activity relationships (QSARs). They were trained for ~7000 structures, saved as models, and then applied to the annotated metabolites to predict their potential bioactivities. The models were selected based on strict accuracy criteria surpassing random effects. After application, 52 metabolites showed potential bioactivity based on structural similarity with known active compounds from the Tox21 set. The filtered compounds were subsequently used and weighted by their bioactive potential to show an association with early childhood hs-CRP levels at six months in a linear model supporting a physiological adverse effect on systemic low-grade inflammation.

Efficiency of pharmaceutical toxicity prediction in computational toxicology

The adverse effects and toxicity of chemical substances pose substantial challenges in drug discovery and environmental science. Their management, most especially in the early development stage, is crucial in preventing costly failures in clinical trials. Predictive methodologies, such as computational toxicology, offer an effective means of managing risks, particularly for new compounds with insufficient post-marketing surveillance and those lacking information on adverse effects. Computational approaches have become increasingly important in environmental science, in which the sheer number and diversity of chemicals present similar challenges to toxicity control. Traditional animal-based evaluation methods are resource intensive, time consuming, and ethically problematic, making them unsuitable for use in assessing the vast compound range. It is an urgent task for the academic community to minimize the risks associated with drug discovery and environmental exposure. This study focuses on systems used to predict toxicity from chemical structure information and outlines the prediction accuracy and systems developed in Japan.

Predictive Models Based on Molecular Images and Molecular Descriptors for Drug Screening

Various toxicity and pharmacokinetic evaluations as screening experiments are needed at the drug discovery stage. Currently, to reduce the use of animal experiments and developmental expenses, the development of high-performance predictive models based on quantitative structure-activity relationship analysis is desired. From these evaluation targets, we selected 50% lethal dose (LD50), blood-brain barrier penetration (BBBP), and the clearance (CL) pathway for this investigation and constructed predictive models for each target using 636-11,886 compounds. First, we constructed predictive models using the DeepSnap-deep learning (DL) method and images of compounds as features. The calculated area under the curve (AUC) and balanced accuracy (BAC) were, respectively, 0.887 and 0.818 for LD50, 0.893 and 0.824 for BBBP, and 0.883 and 0.763 for the CL pathway. Next, molecular descriptors (MDs) of compounds were calculated using Molecular Operating Environment, alvaDesc, and ADMET Predictor to construct predictive models using the MD-based method. Using these MDs, we constructed predictive models using DataRobot. The calculated AUC and BAC were, respectively, 0.931 and 0.805 for LD50, 0.919 and 0.849 for BBBP, and 0.900 and 0.807 for the CL pathway. In this investigation, we constructed predictive models combining the DeepSnap-DL and MD-based methods. In ensemble models using the mean predictive probability of the DeepSnap-DL and MD-based methods, the calculated AUC and BAC were, respectively, 0.942 and 0.842 for LD50, 0.936 and 0.853 for BBBP, and 0.908 and 0.832 for the CL pathway, with improved predictive performance observed for all variables compared with either single method alone. Moreover, in consensus models that adopted only compounds for which the results of the two methods agreed, the calculated BAC for LD50, BBBP, and the CL pathway were 0.916, 0.918, and 0.847, respectively, indicating higher predictive performance than the ensemble models for all three variables. The predictive models combining the DeepSnap-DL and MD-based methods displayed high predictive performance for LD50, BBBP, and the CL pathway. Therefore, the application of this approach to prediction targets in various drug discovery screenings is expected to accelerate drug discovery.

Analysis of structure-activity and structure-mechanism relationships among thyroid stimulating hormone receptor binding chemicals by leveraging the ToxCast library

The thyroid stimulating hormone receptor (TSHR) is crucial in thyroid hormone production in humans, and dysregulation in TSHR activation can lead to adverse health effects such as hypothyroidism and Graves' disease. Further, animal studies have shown that binding of endocrine disrupting chemicals (EDCs) with TSHR can lead to developmental toxicity. Hence, several such chemicals have been screened for their adverse physiological effects in human cell lines via high-throughput assays in the ToxCast project. The invaluable data generated by the ToxCast project has enabled the development of toxicity predictors, but they can be limited in their predictive ability due to the heterogeneity in structure-activity relationships among chemicals. Here, we systematically investigated the heterogeneity in structure-activity as well as structure-mechanism relationships among the TSHR binding chemicals from ToxCast. By employing a structure-activity similarity (SAS) map, we identified 79 activity cliffs among 509 chemicals in TSHR agonist dataset and 69 activity cliffs among 650 chemicals in the TSHR antagonist dataset. Further, by using the matched molecular pair (MMP) approach, we find that the resultant activity cliffs (MMP-cliffs) are a subset of activity cliffs identified via the SAS map approach. Subsequently, by leveraging ToxCast mechanism of action (MOA) annotations for chemicals common to both TSHR agonist and TSHR antagonist datasets, we identified 3 chemical pairs as strong MOA-cliffs and 19 chemical pairs as weak MOA-cliffs. In conclusion, the insights from this systematic investigation of the TSHR binding chemicals are likely to inform ongoing efforts towards development of better predictive toxicity models for characterization of the chemical exposome.

A Comparative Study of Tumor-Specificity and Neurotoxicity between 3-Styrylchromones and Anti-Cancer Drugs

Background. Many anti-cancer drugs used in clinical practice cause adverse events such as oral mucositis, neurotoxicity, and extravascular leakage. We have reported that two 3-styrylchromone derivatives, 7-methoxy-3-[(1E)-2-phenylethenyl]-4H-1-benzopyran-4-one (Compound A) and 3-[(1E)-2-(4-hydroxyphenyl)ethenyl]-7-methoxy-4H-1-benzopyran-4-one (Compound B), showed the highest tumor-specificity against human oral squamous cell carcinoma (OSCC) cell lines among 291 related compounds. After confirming their superiority by comparing their tumor specificity with newly synthesized 65 derivatives, we investigated the neurotoxicity of these compounds in comparison with four popular anti-cancer drugs. Methods: Tumor-specificity (TS_M, TS_E, TS_N) was evaluated as the ratio of mean CC₅₀ for human normal oral mesenchymal (gingival fibroblast, pulp cell), oral epithelial cells (gingival epithelial progenitor), and neuronal cells (PC-12, SH-SY5Y, LY-PPB6, differentiated PC-12) to OSCC cells (Ca9-22, HSC-2), respectively. Results: Compounds A and B showed one order of magnitude higher TS_M than newly synthesized derivatives, confirming its prominent tumor-specificity. Docetaxel showed one order of magnitude higher TS_M, but two orders of magnitude lower TS_E than Compounds A and B. Compounds A and B showed higher TS_M, TS_E, and TS_N values than doxorubicin, 5-FU, and cisplatin, damaging OSCC cells at concentrations that do not affect the viability of normal epithelial and neuronal cells. QSAR prediction based on the Tox21 database suggested that Compounds A and B may inhibit the signaling pathway of estrogen-related receptors.

A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae

Introduction

Anthracnose of banana caused by Colletotrichum species is one of the most serious post-harvest diseases, which can cause significant yield losses. Clarifying the infection mechanism of the fungi using non-destructive methods is crucial for timely discriminating infected bananas and taking preventive and control measures.

Methods

This study presented an approach for tracking growth and identifying different infection stages of the C. musae in bananas using Vis/NIR spectroscopy. A total of 330 banana reflectance spectra were collected over ten consecutive days after inoculation, with a sampling rate of 24 h. The four-class and five-class discriminant patterns were designed to examine the capability of NIR spectra in discriminating bananas infected at different levels (control, acceptable, moldy, and highly moldy), and different time at early stage (control and days 1-4). Three traditional feature extraction methods, i.e. PC loading coefficient (PCA), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), combining with two machine learning methods, i.e. partial least squares discriminant analysis (PLSDA) and support vector machine (SVM), were employed to build discriminant models. One-dimensional convolutional neural network (1D-CNN) without manually extracted feature parameters was also introduced for comparison.

Results

The PCA-SVM and·SPA-SVM models had good performance with identification accuracies of 93.98% and 91.57%, 94.47% and 89.47% in validation sets for the four- and five-class patterns, respectively. While the 1D-CNN models performed the best, achieving an accuracy of 95.18% and 97.37% for identifying infected bananas at different levels and time, respectively.

Discussion

These results indicate the feasibility of identifying banana fruit infected with C. musae using Vis/NIR spectra, and the resolution can be accurate to one day.

Machine Learning Model for Screening Thyroid Stimulating Hormone Receptor Agonists Based on Updated Datasets and Improved Applicability Domain Metrics

Machine learning (ML) models for screening endocrine-disrupting chemicals (EDCs), such as thyroid stimulating hormone receptor (TSHR) agonists, are essential for sound management of chemicals. Previous models for screening TSHR agonists were built on imbalanced datasets and lacked applicability domain (AD) characterization essential for regulatory application. Herein, an updated TSHR agonist dataset was built, for which the ratio of active to inactive compounds greatly increased to 1:2.6, and chemical spaces of structure-activity landscapes (SALs) were enhanced. Resulting models based on 7 molecular representations and 4 ML algorithms were proven to outperform previous ones. Weighted similarity density (ρ_s) and weighted inconsistency of activities (I_A) were proposed to characterize the SALs, and a state-of-the-art AD characterization methodology AD_SAL{ρ_s, I_A} was established. An optimal classifier developed with PubChem fingerprints and the random forest algorithm, coupled with AD_SAL{ρ_s ≥ 0.15, I_A ≤ 0.65}, exhibited good performance on the validation set with the area under the receiver operating characteristic curve being 0.984 and balanced accuracy being 0.941 and identified 90 TSHR agonist classes that could not be found previously. The classifier together with the AD_SAL{ρ_s, I_A} may serve as efficient tools for screening EDCs, and the AD characterization methodology may be applied to other ML models.

A method for in vitro data and structure curation to optimize for QSAR modelling of minimum absolute potency levels and a comparative use case

Large screening programs such as the US Tox21 are releasing experimental in vitro results for many endpoints of relevance for human health. In (Q)SAR modelling, it is essential to clearly define the endpoint (OECD QSAR Validation Principle 1) and extract the most robust data points according to the definition. We have developed a comprehensive data curation procedure to interpret in vitro experimental data sets for (Q)SAR development, with modules for selecting actives according to quality of curve fittings, magnitude of activity and 'absolute' potency cut-offs, requiring non-cytotoxicity at activity concentration; extracting only very robust inactives; selecting only substances tested in high purity; and accounting for assay signal interference. A structure curation procedure with uniform representation of tautomeric classes of substances is also developed. The detailed method and a use case of modelling Tox21 data for an estrogen receptor α agonism assay with and without use of the method is presented.

Integrative Ligand-Based Pharmacophore Modeling, Virtual Screening, and Molecular Docking Simulation Approaches Identified Potential Lead Compounds against Pancreatic Cancer by Targeting FAK1

Pancreatic cancer is a very deadly disease with a 5-year survival rate, making it one of the leading causes of cancer-related deaths globally. Focal adhesion kinase 1 (FAK1) is a ubiquitously expressed protein in pancreatic cancer. FAK, a tyrosine kinase that is overexpressed in cancer cells, is crucial for the development of tumors into malignant phenotypes. FAK functions in response to extracellular signals by triggering transmembrane receptor signaling, which enhances focal adhesion turnover, cell adhesion, cell migration, and gene expression. The ligand-based drug design approach was used to identify potential compounds against the target protein, which included molecular docking: ADME (absorption, distribution, metabolism, and excretion), toxicity, molecular dynamics (MD) simulation, and molecular mechanics generalized born surface area (MM-GBSA). Following the retrieval of twenty hits, four compounds were selected for further evaluation based on a molecular docking approach. Three newly discovered compounds, including PubChem CID24601203, CID1893370, and CID16355541, with binding scores of -10.4, -10.1, and -9.7 kcal/mol, respectively, may serve as lead compounds for the treatment of pancreatic cancer associated with FAK1. The ADME (absorption, distribution, metabolism, and excretion) and toxicity analyses demonstrated that the compounds were effective and nontoxic. However, further wet laboratory investigations are required to evaluate the activity of the drugs against the cancer.

Nuclear Receptor and Stress Response Pathways Associated with Antineoplastic Agent-Induced Diarrhea

In severe cases, antineoplastic agent-induced diarrhea may be life-threatening; therefore, it is necessary to determine the mechanism of toxicity and identify the optimal management. The mechanism of antineoplastic agent-induced diarrhea is still unclear but is often considered to be multifactorial. The aim of this study was to determine the molecular initiating event (MIE), which is the initial interaction between molecules and biomolecules or biosystems, and to evaluate the MIE specific to antineoplastic agents that induce diarrhea. We detected diarrhea-inducing drug signals based on adjusted odds ratios using the Food and Drug Administration Adverse Event Reporting System. We then used the quantitative structure-activity relationship platform of Toxicity Predictor to identify potential MIEs that are specific to diarrhea-inducing antineoplastic agents. We found that progesterone receptor antagonists were potential MIEs associated with diarrhea. The findings of this study may help improve the prediction and management of antineoplastic agent-induced diarrhea.

Application of ToxCast/Tox21 data for toxicity mechanism-based evaluation and prioritization of environmental chemicals: Perspective and limitations

In response to the need to minimize the use of experimental animals, new approach methodologies (NAMs) using advanced technology have emerged in the 21st century. ToxCast/Tox21 aims to evaluate the adverse effects of chemicals quickly and efficiently using a high-throughput screening and to transform the paradigm of toxicity assessment into mechanism-based toxicity prediction. The ToxCast/Tox21 database, which contains extensive data from over 1400 assays with numerous biological targets and activity data for over 9000 chemicals, can be used for various purposes in the field of chemical prioritization and toxicity prediction. In this study, an overview of the database was explored to aid mechanism-based chemical prioritization and toxicity prediction. Implications for the utilization of the ToxCast/Tox21 database in chemical prioritization and toxicity prediction were derived. The research trends in ToxCast/Tox21 assay data were reviewed in the context of toxicity mechanism identification, chemical priority, environmental monitoring, assay development, and toxicity prediction. Finally, the potential applications and limitations of using ToxCast/Tox21 assay data in chemical risk assessment were discussed. The analysis of the toxicity mechanism-based assays of ToxCast/Tox21 will help in chemical prioritization and regulatory applications without the use of laboratory animals.

Application of machine learning to predict the inhibitory activity of organic chemicals on thyroid stimulating hormone receptor

With the promotion of carbon neutrality, it is also important to synchronously promote the assessment and sustainable management of chemicals so as to protect public health. Humans and animals are possibly exposed to endocrine disruptors that have inhibitory effects on thyroid stimulating hormone receptor (TSHR). As such, it is important to identify chemicals that inhibit TSHR and to develop models to predict their inhibitory activity. In this study, 5952 compounds derived from a cyclic adenosine monophosphate (cAMP) analysis, a key signaling pathway in thyrocytes, were used to establish a binary classification model comparing methods that included random forest (RF), extreme gradient boosting (XGB), and logistic regression (LR). The prediction model based on RF showed the highest identification accuracy for revealing chemicals that may inhibit TSHR. For the RF model, recall was calculated at 0.89, balance accuracy was 0.85, and its receiver operating characteristic (ROC) curve-area under (AUC) was 0.92, indicating that the model had very high predictive capacity. The lowest CDocker energy (CE) and CDocker interaction energy (CIE) for chemicals and TSHR were determined and were subsequently introduced into the predictive model as descriptors. A regression model, extreme gradient boosting-Regression (XGBR), was successfully established yielding an R² = 0.65 to predict inhibitory activity for active compounds. Parameters that included dissociation characteristics, molecular structure, and binding energy were all key factors in the predictive model. We demonstrate that QSAR models are useful approaches, not only for identifying chemicals that inhibit TSHR, but for predicting inhibitory activity of active compounds.

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications

Recently, research on the development of artificial intelligence (AI)-based computational toxicology models that predict toxicity without the use of animal testing has emerged because of the rapid development of computer technology. Various computational toxicology techniques that predict toxicity based on the structure of chemical substances are gaining attention, including the quantitative structure-activity relationship. To understand the recent development of these models, we analyzed the databases, molecular descriptors, fingerprints, and algorithms considered in recent studies. Based on a selection of 96 papers published since 2014, we found that AI models have been developed to predict approximately 30 different toxicity end points using more than 20 toxicity databases. For model development, molecular access system and extended-connectivity fingerprints are the most commonly used molecular descriptors. The most used algorithm among the machine learning techniques is the random forest, while the most used algorithm among the deep learning techniques is a deep neural network. The use of AI technology in the development of toxicity prediction models is a new concept that will aid in achieving a scientific accord and meet regulatory applications. The comprehensive overview provided in this study will provide a useful guide for the further development and application of toxicity prediction models.

Development of in silico prediction models for drug-induced liver malignant tumors based on the activity of molecular initiating events: Biologically interpretable features

Liver malignant tumors (LMTs) have recently been reported as severe and life-threatening adverse drug events associated with drug-induced liver injury (DILI). DILIs are the most common adverse drug event and can cause the withdrawal of medicinal products or major regulatory action. To reduce the attrition rate and cost of drug discovery, various quantitative structure-toxicity relationship models have been proposed to predict the probability of a DILI based on the chemical structure of a drug. However, there are many unresolved issues regarding the predictors of LMT-inducing drugs, and biologically interpretable prediction models for LMT have not been developed. Here, we constructed prediction models for whether a drug is LMT-inducing based on the activity of molecular initiating events (MIEs), which are biologically interpretable features and are defined as the initial interaction between a molecule and biosystem. We then constructed five machine learning models (i.e., LightGBM, XGBoost, random forest, neural network, and support vector machine) and evaluated their predictive performances. LightGBM achieved the best performance among the tested models. The MIEs making the highest contribution to the model construction for drug-induced LMT were inducement of Enhanced Level of Genome Instability Gene 1 (human ATAD5), nuclear factor-κ B, and activation of thyrotropin-releasing hormone receptor. These results support the previous literature and can be related to the mechanism onset of drug-induced LMT. Our findings may provide useful knowledge for drug development, research, and regulatory decision-making and will contribute to building more accurate and meaningful DILI prediction models by increasing understanding of biological predictors.

Tumor-Specificity, Neurotoxicity, and Possible Involvement of the Nuclear Receptor Response Pathway of 4,6,8-Trimethyl Azulene Amide Derivatives

Background: Very few papers covering the anticancer activity of azulenes have been reported, as compared with those of antibacterial and anti-inflammatory activity. This led us to investigate the antitumor potential of fifteen 4,6,8-trimethyl azulene amide derivatives against oral malignant cells. Methods: 4,6,8-Trimethyl azulene amide derivatives were newly synthesized. Anticancer activity was evaluated by tumor-specificity against four human oral squamous cell carcinoma (OSCC) cell lines over three normal oral cells. Neurotoxicity was evaluated by cytotoxicity against three neuronal cell lines over normal oral cells. Apoptosis induction was evaluated by Western blot and cell cycle analyses. Results: Among fifteen derivatives, compounds 7, 9, and 15 showed the highest anticancer activity, and relatively lower neurotoxicity than doxorubicin, 5-fluorouracil (5-FU), and melphalan. They induced the accumulation of a comparable amount of a subG₁ population, but slightly lower extent of caspase activation, as compared with actinomycin D, used as an apoptosis inducer. The quantitative structure–activity relationship analysis suggests the significant correlation of tumor-specificity with a 3D shape of molecules, and possible involvement of inflammation and hormone receptor response pathways. Conclusions: Compounds 7 and 15 can be potential candidates of a lead compound for developing novel anticancer drugs.

Analysis of Factors Associated with Hiccups Using the FAERS Database

In this study, we used the large number of cases in the FDA adverse-event reporting system (FAERS) database to investigate risk factors for drug-induced hiccups and to explore the relationship between hiccups and gender. From 11,810,863 adverse drug reactions reported between the first quarter of 2004 and the first quarter of 2020, we extracted only those in which side effects occurred between the beginning and end of drug administration. Our sample included 1454 adverse reactions for hiccups, with 1159 involving males and 257 involving females (the gender in 38 reports was unknown). We performed univariate analyses of the presence or absence of hiccups for each drug and performed multivariate analysis by adding patient information. The multivariate analysis showed nicotine products to be key suspect drugs for both men and women. For males, the risk factors for hiccups included older age, lower body weight, nicotine, and 14 other drugs. For females, only nicotine and three other drugs were extracted as independent risk factors. Using FAERS, we were thus able to extract new suspect drugs for drug-induced hiccups. Furthermore, this is the first report of a gender-specific analysis of risk factors for hiccups that provides novel insights into drug-induced hiccups, and it suggests that the mechanism responsible is strongly related to gender. Thus, this study can contribute to elucidating the mechanism underlying this phenomenon.

Development of a novel noninvasive quantitative method to monitor Siraitia grosvenorii cell growth and browning degree using an integrated computer-aided vision technology and machine learning

The rapid, accurate and noninvasive detection of biomass and plant cell browning can provide timely feedback on cell growth in plant cell culture. In this study, Siraitia grosvenorii suspension cells were taken as an example, a phenotype analysis platform was successfully developed to predict the biomass and the degree of cell browning based on the color changes of cells in computer-aided vision technology. First, a self-made laboratory system was established to obtain images. Then, matrices were prepared from digital images by a self-developed high-throughput image processing tool. Finally, classification models were used to judge different cell types, and then a semi-supervised classification to predict different degrees of cell browning. Meanwhile, regression models were developed to predict the plant cell mass. All models were verified with a good agreement by biological experiments. Therefore, this method can be applied for low-cost biomass estimation and browning degree quantification in plant cell culture.

Molecular Initiating Events Associated with Drug-Induced Liver Malignant Tumors: An Integrated Study of the FDA Adverse Event Reporting System and Toxicity Predictions

Liver malignant tumors (LMTs) represent a serious adverse drug event associated with drug-induced liver injury. Increases in endocrine-disrupting chemicals (EDCs) have attracted attention in recent years, due to their liver function-inhibiting abilities. Exposure to EDCs can induce nonalcoholic fatty liver disease and nonalcoholic steatohepatitis, which are major etiologies of LMTs, through interaction with nuclear receptors (NR) and stress response pathways (SRs). Therefore, exposure to potential EDC drugs could be associated with drug-induced LMTs. However, the drug classes associated with LMTs and the molecular initiating events (MIEs) that are specific to these drugs are not well understood. In this study, using the Food and Drug Administration Adverse Event Reporting System, we detected LMT-inducing drug signals based on adjusted odds ratios. Furthermore, based on the hypothesis that drug-induced LMTs are triggered by NR and SR modulation of potential EDCs, we used the quantitative structure-activity relationship platform for toxicity prediction to identify potential MIEs that are specific to LMT-inducing drug classes. Events related to cell proliferation and apoptosis, DNA damage, and lipid accumulation were identified as potential MIEs, and their relevance to LMTs was supported by the literature. The findings of this study may contribute to drug development and research, as well as regulatory decision making.

Predictive Capability of QSAR Models Based on the CompTox Zebrafish Embryo Assays: An Imbalanced Classification Problem

The CompTox Chemistry Dashboard (ToxCast) contains one of the largest public databases on Zebrafish (Danio rerio) developmental toxicity. The data consists of 19 toxicological endpoints on unique 1018 compounds measured in relatively low concentration ranges. The endpoints are related to developmental effects occurring in dechorionated zebrafish embryos for 120 hours post fertilization and monitored via gross malformations and mortality. We report the predictive capability of 209 quantitative structure-activity relationship (QSAR) models developed by machine learning methods using penalization techniques and diverse model quality metrics to cope with the imbalanced endpoints. All these QSAR models were generated to test how the imbalanced classification (toxic or non-toxic) endpoints could be predicted regardless which of three algorithms is used: logistic regression, multi-layer perceptron, or random forests. Additionally, QSAR toxicity models are developed starting from sets of classical molecular descriptors, structural fingerprints and their combinations. Only 8 out of 209 models passed the 0.20 Matthew's correlation coefficient value defined a priori as a threshold for acceptable model quality on the test sets. The best models were obtained for endpoints mortality (MORT), ActivityScore and JAW (deformation). The low predictability of the QSAR model developed from the zebrafish embryotoxicity data in the database is mainly due to a higher sensitivity of 19 measurements of endpoints carried out on dechorionated embryos at low concentrations.

Molecular Determinants of the Kinetic Binding Properties of Antihistamines at the Histamine H1 Receptors

The binding affinity of ligands for their receptors is determined by their kinetic and thermodynamic binding properties. Kinetic analyses of the rate constants of association and dissociation (k_on and k_off, respectively) of antihistamines have suggested that second-generation antihistamines have a long duration of action owing to the long residence time (1/k_off) at the H₁ receptors. In this study, we examined the relationship between the kinetic and thermodynamic binding properties of antihistamines, followed by an evaluation of the structural determinants responsible for their kinetic binding properties using quantitative structure-activity relationship (QSAR) analyses. We found that whereas the binding enthalpy and entropy might contribute to the increase and decrease, respectively, in the k_off values, there was no significant relationship with the k_on values. QSAR analyses indicated that k_on and k_off values could be determined by the descriptors FASA_H (water-accessible surface area of all hydrophobic atoms divided by total water-accessible surface area) and vsurf_CW2 (a 3D molecular field descriptor weighted by capacity factor 2, the ratio of the hydrophilic surface to the total molecular surface), respectively. These findings provide further insight into the mechanisms by which the kinetic binding properties of antihistamines are regulated by their thermodynamic binding forces and physicochemical properties.