SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data

Phytochemical constituents analysis in laminaria digitata for Alzheimer's disease: molecular docking and in-silico toxicity approach

Alzheimer's disease (AD) is a common brain disease associated with cognitive impairment and dementia. donepezil, an acetylcholinesterase (AChE) inhibitor drug as a commercial AD drug represents a non-cost-effective treatment with the toxic effects reported. As the prevalence of AD increases, the development of effective therapeutic treatments is urgently required. Laminaria digitata is a brown seaweed claimed to be able to prevent and treat neurodegenerative diseases. Therefore, this study measured and compared the binding affinity and toxicity of seven common phytoconstituents in Laminaria digitata against acetylcholinesterase (AChE) with those of donepezil using a molecular docking approach. The binding free energy values of donepezil, dieckol, eckol, fucodiphlorethol G, 7-Phloroecol, laminaran, alginic acid, and fucoidan with acetylcholinesterase (AChE) were -12.3, -13.5, -10.5, -8,7, -9.7, -8.0, -10.3, and -7.4 kcal/mol. All ligands constantly interacted with the AChE amino acid residues, namely Tyr124. Dieckol, with the strongest and most stable interaction, is classified as class IV toxicity, with an LD50 value of 866 mg/kg. It has aryl hydrocarbon receptor (AhR) and mitochondrial membrane potential (MMP) toxicity at certain doses. Theoretically, based on Lipinski's rule, dieckol is likely to have poor absorption and permeation properties; therefore, several considerations during the drug discovery process are needed.

Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction

The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.

Safety Assessment of Streptococcus salivarius UBSS-01 in Rats and Double-Blind Placebo-Controlled Study in Healthy Individuals

Streptococcus salivarius is a common, harmless, and prevalent member of the oral microbiota in humans. In the present study, the safety of S. salivarius UBSS-01 was evaluated using in silico methods and preclinical and clinical studies. In an acute toxicity study, rats were administered with 5 g/kg (500 × 109 CFU) S. salivarius UBSS-01. The changes in phenotypic behaviors and hematological, biochemical, electrolytes, and urine analyses were monitored. No toxicity was observed at 14 days post-treatment. The no observable effects limit (NOEL) of S. salivarius UBSS-01 was >5 g/kg in rats. In a 28-day repeat dose toxicity study, rats were administered S. salivarius UBSS-01 once daily at doses of 0.1, 0.5, and 1 g/kg (10, 50, and 100 billion CFU/kg, respectively) body weight. S. salivarius UBSS-01 did not influence any of the hematology parameters and clinical chemistry parameters in plasma and serum samples after 28-day repeated administration. No structural abnormality was observed in the histological examination of organs. Whole genome analysis revealed the absence of virulence factors or genes that may transmit antibiotic resistance. In the double-blind study with 60 human participants (aged 18-60 years), consumption of S. salivarius UBSS-01 for 30 days was found to be safe and results were comparable with placebo treatment These findings indicate that S. salivarius UBSS-01 may be safe for human consumption.

Identification and characterization of domain-specific inhibitors of DENV NS3 and NS5 proteins by in silico screening methods

The dengue virus (DENV) infects approximately 400 million people annually worldwide causing significant morbidity and mortality. Despite advances in understanding the virus life cycle and infectivity, no specific treatment for this disease exists due to the lack of therapeutic drugs. In addition, vaccines available currently are ineffective with severe side effects. Therefore, there is an urgent need for developing therapeutics suitable for effective management of DENV infection. In this study, we adopted a drug repurposing strategy to identify new therapeutic use of existing FDA approved drug molecules to target DENV2 non-structural proteins NS3 and NS5 using computational approaches. We used Drugbank database molecules for virtual screening and multiple docking analysis against a total of four domains, the NS3 protease and helicase domains and NS5 MTase and RdRp domains. Subsequently, MD simulations and MM-PBSA analysis were performed to validate the intrinsic atomic interactions and the binding affinities. Furthermore, the internal dynamics in all four protein domains, in presence of drug molecule binding were assessed using essential dynamics and free energy landscape analyses, which were further coupled with conformational dynamics-based clustering studies and cross-correlation analysis to map the regions that exhibit these structural variations. Our comprehensive analysis identified tolcapone, cefprozil, delavirdine and indinavir as potential inhibitors of NS5 MTase, NS5 RdRp, NS3 protease and NS3 helicase functions, respectively. These high-confidence candidate molecules will be useful for developing effective anti-DENV therapy to combat dengue infection.Communicated by Ramaswamy H. Sarma.

Inhibitory potential of furanocoumarins against cyclin dependent kinase 4 using integrated docking, molecular dynamics and ONIOM methods

Cyclin Dependent Kinase 4 (CDK4) is vital in the process of cell-cycle and serves as a G1 phase checkpoint in cell division. Selective antagonists of CDK4 which are in use as clinical chemotherapeutics cause various side-effects in patients. Furanocoumarins induce anti-cancerous effects in a range of human tumours. Therefore, targeting these compounds against CDK4 is anticipated to enhance therapeutic effectiveness. This work intended to explore the CDK4 inhibitory potential of 50 furanocoumarin molecules, using a comprehensive approach that integrates the processes of docking, drug-likeness, pharmacokinetic analysis, molecular dynamics simulations and ONIOM (Our own N-layered Integrated molecular Orbital and Molecular mechanics) methods. The top five best docked compounds obtained from docking studies were screened for subsequent analysis. The molecules displayed good pharmacokinetic properties and no toxicity. Epoxybergamottin, dihydroxybergamottin and notopterol were found to inhabit the ATP-binding zone of CDK4 with substantial stability and negative binding free energy forming hydrogen bonds with key catalytic residues of the protein. Notopterol exhibiting the highest binding energy was subjected to ONIOM calculations wherein the hydrogen bonding interactions were retained with significant negative interaction energy. Hence, through these series of computerised methods, notopterol was screened as a potent CDK4 inhibitor and can act as a starting point in successive processes of drug design.Communicated by Ramaswamy H. Sarma.

An integrated assessment of ecological and human health risks of per- and polyfluoroalkyl substances through toxicity prediction approaches

Per- and polyfluoroalkyl substances (PFAS) are also known as "forever chemicals" due to their persistence and ubiquitous environmental distribution. This review aims to summarize the global PFAS distribution in surface water and identify its ecological and human risks through integrated assessment. Moreover, it provides a holistic insight into the studies highlighting the human biomonitoring and toxicological screening of PFAS in freshwater and marine species using quantitative structure-activity relationship (QSAR) based models. Literature showed that PFOA and PFOS were the most prevalent chemicals found in surface water. The highest PFAS levels were reported in the US, China, and Australia. The TEST model showed relatively low LC50 of PFDA and PFOS for Pimephales promelas (0.36 and 0.91 mg/L) and high bioaccumulation factors (518 and 921), revealing an elevated associated toxicity. The risk quotients (RQs) values for P. promelas and Daphnia magna were found to be 269 and 23.7 for PFOS. Studies confirmed that long-chain PFAS such as PFOS and PFOA undergo bioaccumulation in aquatic organisms and induce toxicological effects such as oxidative stress, transgenerational epigenetic effects, disturbed genetic and enzymatic responses, perturbed immune system, hepatotoxicity, neurobehavioral toxicity, altered genetic and enzymatic responses, and metabolism abnormalities. Human biomonitoring studies found the highest PFOS, PFOA, and PFHxS levels in urine, cerebrospinal fluid, and serum samples. Further, long-chain PFOA and PFOS exposure create severe health implications such as hyperuricemia, reduced birth weight, and immunotoxicity in humans. Molecular docking analysis revealed that short-chain PFBS (-11.84 Kcal/mol) and long-chain PFUnDA (-10.53 Kcal/mol) displayed the strongest binding interactions with human serum albumin protein. Lastly, research challenges and future perspectives for PFAS toxicological implications were also discussed, which helps to mitigate associated pollution and ecological risks.

Application of QSAR models for acute toxicity of tetrazole compounds administrated orally and intraperitoneally in rat and mouse

Tetrazoles and their derivatives possess various biological activities, such as antibacterial, anti-fungal, and other activities. However, these compounds may induce specific cumulative and toxic effects in living organisms. Therefore, quantitative structure-activity relationship (QSAR) models were constructed to study the acute oral toxicity of tetrazoles in rats and mice. The toxicity data of 111 tetrazole compounds were collected using the ChemIDplus, ChEMBL and ECHA databases as response variables, while the PaDEL-descriptor generated the 2D descriptors as independent variables. The models were developed and validated following the OECD guidelines by the DTC-QSAR tool. Three QSAR models were successfully established for the oral routes of rat and mouse and the intraperitoneal route of mouse, respectively. The scatter plots showed high consistency between the training and test data sets. All the models successfully met the external and internal validation criteria. Most of the descriptors kept in the final models exhibited positive correlations with toxicity, whereas only 6 descriptors exhibited negative associations. Several chemicals were identified as response or structural outliers, based on the standardized residuals and leverage values. In conclusion, the findings of this investigation demonstrate that the proposed QSAR models hold promise in forecasting the acute toxicity of recently developed or synthesized tetrazole compounds, thereby mitigating potential risks to human health and the environment.

Pharmacokinetic correlation of structurally modified chalcone derivatives as promising leads to treat tuberculosis

In this study, we evaluated the potential of curated structurally modified chalcone derivatives as anti-tuberculosis (TB) agents through computer-aided drug design. Compounds from the flavonoid family known as chalcones were identified by the chemical group 1,3-diaryl-2-propen-1-one. After a search of the literature, 14 outstanding structurally modified chalcones were selected and evaluated for inhibitory activity against Mycobacterium tuberculosis H37Rv targets. The therapeutic potential of the chalcones was directly based on the drug-likeness and pharmacokinetic properties of the synthesized compounds. Prompt drug selection and personalized therapy are required to prevent TB from progressing and spreading to others. Pharmacokinetic parameters helps in the identification of lead molecule, at the earlier stages of drug development.

Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives

Toxicity prediction is a critical step in the drug discovery process that helps identify and prioritize compounds with the greatest potential for safe and effective use in humans, while also reducing the risk of costly late-stage failures. It is estimated that over 30% of drug candidates are discarded owing to toxicity. Recently, artificial intelligence (AI) has been used to improve drug toxicity prediction as it provides more accurate and efficient methods for identifying the potentially toxic effects of new compounds before they are tested in human clinical trials, thus saving time and money. In this review, we present an overview of recent advances in AI-based drug toxicity prediction, including the use of various machine learning algorithms and deep learning architectures, of six major toxicity properties and Tox21 assay end points. Additionally, we provide a list of public data sources and useful toxicity prediction tools for the research community and highlight the challenges that must be addressed to enhance model performance. Finally, we discuss future perspectives for AI-based drug toxicity prediction. This review can aid researchers in understanding toxicity prediction and pave the way for new methods of drug discovery.

Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations

Explainable machine learning for molecular toxicity prediction is a promising approach for efficient drug development and chemical safety. A predictive ML model of toxicity can reduce experimental cost and time while mitigating ethical concerns by significantly reducing animal and clinical testing. Herein, we use a deep learning framework for simultaneously modeling in vitro, in vivo, and clinical toxicity data. Two different molecular input representations are used; Morgan fingerprints and pre-trained SMILES embeddings. A multi-task deep learning model accurately predicts toxicity for all endpoints, including clinical, as indicated by the area under the Receiver Operator Characteristic curve and balanced accuracy. In particular, pre-trained molecular SMILES embeddings as input to the multi-task model improved clinical toxicity predictions compared to existing models in MoleculeNet benchmark. Additionally, our multitask approach is comprehensive in the sense that it is comparable to state-of-the-art approaches for specific endpoints in in vitro, in vivo and clinical platforms. Through both the multi-task model and transfer learning, we were able to indicate the minimal need of in vivo data for clinical toxicity predictions. To provide confidence and explain the model's predictions, we adapt a post-hoc contrastive explanation method that returns pertinent positive and negative features, which correspond well to known mutagenic and reactive toxicophores, such as unsubstituted bonded heteroatoms, aromatic amines, and Michael receptors. Furthermore, toxicophore recovery by pertinent feature analysis captures more of the in vitro (53%) and in vivo (56%), rather than of the clinical (8%), endpoints, and indeed uncovers a preference in known toxicophore data towards in vitro and in vivo experimental data. To our knowledge, this is the first contrastive explanation, using both present and absent substructures, for predictions of clinical and in vivo molecular toxicity.

Quantifying Functional-Group-like Structural Fragments in Molecules and Its Applications in Drug Design

A functional group in a molecule is a structural fragment consisting of a few atoms or a single atom that imparts reactivity to a molecule. Hence, defining functional groups is crucial in chemistry to predict the properties and reactivities of molecules. However, there is no established method in the literature for defining functional groups based on reactivity parameters. In this work, we addressed this issue by designing a set of predefined structural fragments along with reactivity parameters like electron conjugation and ring strain. This approach uses bond orders and atom connectivities to quantify the presence of these fragments within an organic molecule based on a given input molecular coordinate. To assess the effectiveness of this approach, we performed a case study to show the benefits of using these newly designed structural fragments instead of traditional fingerprint-based methods for grouping potential COX1/COX2 inhibitors by screening an approved drug library against aspirin molecule. The structural fragment-based model for ternary classification of rat oral LD₅₀ of chemicals showed performance similar to the fingerprint-based models. In evaluating the regression model performance for aqueous solubility, log(S), predictions, our approach outperformed the fingerprint-based model.

Molecular docking analysis of novel quercetin derivatives for combating SARS-CoV-2

Quercetin belongs to the flavonoid family, which is one of the most frequent types of plant phenolics. This flavonoid compound is a natural substance having a number of pharmacological effects, including anticancer and antioxidant capabilities, as well as being a strong inhibitor of various toxicologically important enzymes. We discuss the potential of newly recently synthesized quercetin-based derivatives to inhibit SARS-CoV-2 protein. ADMET analysis indicated that all of the studied compounds had low toxicities and good absorption and solubility properties. The molecular docking results revealed that the propensity for binding to SARS-CoV-2 main protease is extraordinary. The results are remarkable not only for the binding energy values, which outperform several compounds in prior studies, but also for the number of hydrogen bonds formed. Compound 7a was capable of forming 10 strong hydrogen bonds as well as interact to the protein receptor with a binding energy of -7.79 kcal/mol. Therefore, these compounds should be highlighted in further experimental studies in the context of treating SARS-CoV-2 infection and its effects.

PredAOT: a computational framework for prediction of acute oral toxicity based on multiple random forest models

BACKGROUND

Acute oral toxicity of drug candidates can lead to drug development failure; thus, predicting the acute oral toxicity of small compounds is important for successful drug development. However, evaluation of the acute oral toxicity of small compounds considered in the early stages of drug discovery is limited because of cost and time. Here, we developed a computational framework, PredAOT, that predicts the acute oral toxicity of small compounds in mice and rats.

METHODS

PredAOT is based on multiple random forest models for the accurate prediction of acute oral toxicity. A total of 6226 and 6238 compounds evaluated in mice and rats, respectively, were used to train the models.

RESULTS

PredAOT has the advantage of predicting acute oral toxicity in mice and rats simultaneously, and its prediction performance is similar to or better than that of existing tools.

CONCLUSION

PredAOT will be a useful tool for the quick and accurate prediction of the acute oral toxicity of small compounds in mice and rats during drug development.

Molecular Screening of Bioactive Compounds of Garlic for Therapeutic Effects against COVID-19

An outbreak of pneumonia occurred on December 2019 in Wuhan, China, which caused a serious public health emergency by spreading around the globe. Globally, natural products are being focused on more than synthetic ones. So, keeping that in view, the current study was conducted to discover potential antiviral compounds from Allium sativum. Twenty-five phytocompounds of this plant were selected from the literature and databases including 3-(Allylsulphinyl)-L-alanine, Allicin, Diallyl sulfide, Diallyl disulfide, Diallyl trisulfide, Glutathione, L-Cysteine, S-allyl-mercapto-glutathione, Quercetin, Myricetin, Thiocysteine, Gamma-glutamyl-Lcysteine, Gamma-glutamylallyl-cysteine, Fructan, Lauricacid, Linoleicacid, Allixin, Ajoene, Diazinon Kaempferol, Levamisole, Caffeicacid, Ethyl linoleate, Scutellarein, and S-allylcysteine methyl-ester. Virtual screening of these selected ligands was carried out against drug target 3CL protease by CB-dock. Pharmacokinetic and pharmacodynamic properties defined the final destiny of compounds as drug or non-drug molecules. The best five compounds screened were Allicin, Diallyl Sulfide, Diallyl Disulfide, Diallyl Trisulfide, Ajoene, and Levamisole, which showed themselves as hit compounds. Further refining by screening filters represented Levamisole as a lead compound. All the interaction visualization analysis studies were performed using the PyMol molecular visualization tool and LigPlot+. Conclusively, Levamisole was screened as a likely antiviral compound which might be a drug candidate to treat SARS-CoV-2 in the future. Nevertheless, further research needs to be carried out to study their potential medicinal use.

Ensemble multiclassification model for aquatic toxicity of organic compounds

With environmental pollution becoming increasingly serious, organic compounds have become the main hazard of environmental pollution and exert substantial negative impacts on aquatic organisms. In research pertaining to the acute toxicity of organic compounds, traditional biological experimental methods are time-consuming and expensive. In addition, computer-aided binary classification models cannot accurately classify acute toxicity. Therefore, the multiclassication model is necessary for more accurate classification of acute toxicity. In this study, median lethal concentrations of 373 organic compounds in the environmental toxicology datasets ECOTOX and EAT5 were used. These chemicals were classified into four categories based on the European Economic Community criteria. Then the random forest, support vector machine, extreme gradient boosting, adaptive gradient boosting, and C5.0 decision tree algorithms and eight molecular fingerprints were used to build a multiclassification base model for the acute toxicity of organic compounds. The base models were repeated 100 times with fivefold cross-validation and external validation. The ensemble model was obtained by the voting method. The best base classifier was ExtendFP-C5.0, which had an accuracy, sensitivity and specificity values of 87.30%, 87.32% and 95.76% for external validation, and the voting ensemble model performance of 96.92%, 96.93% and 98.97%, respectively. The ensemble model achieved a higher accuracy than previously reported studies. Our study will help to further classify the acute toxicity of organic compounds to aquatic organisms and predict the hazard classes of organic compounds.

Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs)

Recent years have seen a substantial growth in the adoption of machine learning approaches for the purposes of quantitative structure-activity relationship (QSAR) development. Such a trend has coincided with desire to see a shifting in the focus of methodology employed within chemical safety assessment: away from traditional reliance upon animal-intensive in vivo protocols, and towards increased application of in silico (or computational) predictive toxicology. With QSAR central amongst techniques applied in this area, the emergence of algorithms trained through machine learning with the objective of toxicity estimation has, quite naturally, arisen. On account of the pattern-recognition capabilities of the underlying methods, the statistical power of the ensuing models is potentially considerable-appropriate for the handling even of vast, heterogeneous datasets. However, such potency comes at a price: this manifesting as the general practical deficits observed with respect to the reproducibility, interpretability and generalisability of the resulting tools. Unsurprisingly, these elements have served to hinder broader uptake (most notably within a regulatory setting). Areas of uncertainty liable to accompany (and hence detract from applicability of) toxicological QSAR have previously been highlighted, accompanied by the forwarding of suggestions for "best practice" aimed at mitigation of their influence. However, the scope of such exercises has remained limited to "classical" QSAR-that conducted through use of linear regression and related techniques, with the adoption of comparatively few features or descriptors. Accordingly, the intention of this study has been to extend the remit of best practice guidance, so as to address concerns specific to employment of machine learning within the field. In doing so, the impact of strategies aimed at enhancing the transparency (feature importance, feature reduction), generalisability (cross-validation) and predictive power (hyperparameter optimisation) of algorithms, trained upon real toxicity data through six common learning approaches, is evaluated.

Design, synthesis and biological evaluation of new eugenol derivatives containing 1,3,4-oxadiazole as novel inhibitors of thymidylate synthase

We report the preparation and cytotoxicity of two new eugenol derivatives that contain 1,3,4-oxadiazole, as novel inhibitors of thymidylate synthase; these derivatives are shown to be promising chemotherapeutic agents.

Predicting chemical structure using reinforcement learning with a stack-augmented conditional variational autoencoder

In this paper, a reinforcement learning model is proposed that can maximize the predicted binding affinity between a generated molecule and target proteins. The model used to generate molecules in the proposed model was the Stacked Conditional Variation AutoEncoder (Stack-CVAE), which acts as an agent in reinforcement learning so that the resulting chemical formulas have the desired chemical properties and show high binding affinity with specific target proteins. We generated 1000 chemical formulas using the chemical properties of sorafenib and the three target kinases of sorafenib. Then, we confirmed that Stack-CVAE generates more of the valid and unique chemical compounds that have the desired chemical properties and predicted binding affinity better than other generative models. More detailed analysis for 100 of the top scoring molecules show that they are novel ones not found in existing chemical databases. Moreover, they reveal significantly higher predicted binding affinity score for Raf kinases than for other kinases. Furthermore, they are highly druggable and synthesizable.

Enabling data-limited chemical bioactivity predictions through deep neural network transfer learning

The main limitation in developing deep neural network (DNN) models to predict bioactivity properties of chemicals is the lack of sufficient assay data to train the network's classification layers. Focusing on feedforward DNNs that use atom- and bond-based structural fingerprints as input, we examined whether layers of a fully trained DNN based on large amounts of data to predict one property could be used to develop DNNs to predict other related or unrelated properties based on limited amounts of data. Hence, we assessed if and under what conditions the dense layers of a pre-trained DNN could be transferred and used for the development of another DNN associated with limited training data. We carried out a quantitative study employing more than 400 pairs of assay datasets, where we used fully trained layers from a large dataset to augment the training of a small dataset. We found that the higher the correlation r between two assay datasets, the more efficient the transfer learning is in reducing prediction errors associated with the smaller dataset DNN predictions. The reduction in mean squared prediction errors ranged from 10 to 20% for every 0.1 increase in r² between the datasets, with the bulk of the error reductions associated with transfers of the first dense layer. Transfer of other dense layers did not result in additional benefits, suggesting that deeper, dense layers conveyed more specialized and assay-specific information. Importantly, depending on the dataset correlation, training sample size could be reduced by up to tenfold without any loss of prediction accuracy.

Computational study of nitro-benzylidene phenazine as dengue virus-2 NS2B-NS3 protease inhibitor

According to the World Health Organisation (WHO), as of week 23 of 2022, there were more than 1,311 cases of dengue in Malaysia, with 13 deaths reported. Furthermore, there was an increase of 65.7% during the same period in 2021. Despite the increase in cumulative dengue incidence, there is no effective antiviral drug available for dengue treatment. This work aimed to evaluate several nitro-benzylidene phenazine compounds, especially those that contain 4-hydroxy-3,5-bis((2-(4-nitrophenyl)hydrazinylidene)-methyl)benzoate through pharmacophore queries selection method as potential dengue virus 2 (DENV2) NS2B-NS3 protease inhibitors. Herein, molecular docking was employed to correlate the energies of selected hits' free binding and their binding affinities. Pan assay interference compounds (PAINS) filter was also adopted to identify and assess the drug-likeness, toxicity, mutagenicity potentials, and pharmacokinetic profiles to select hit compounds that can be considered as lead DENV2 NS2B-NS3 protease inhibitors. Molecular dynamics assessment of two nitro-benzylidene phenazine derivatives bearing dinitro and hydroxy groups at the benzylidene ring showed their stability at the main binding pocket of DENV2 protease, where their MM-PBSA binding energies were between -22.53 and -17.01 kcal/mol. This work reports those two nitro-benzylidene phenazine derivatives as hits with 52-55% efficiency as antiviral candidates. Therefore, further optimisation is required to minimise the lead compounds' toxicity and mutagenicity.

Principles and Procedures for Assessment of Acute Toxicity Incorporating In Silico Methods

Acute toxicity in silico models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an in silico analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including in silico methods and in vitro or in vivo experiments. In silico methods that can assist the prediction of in vivo outcomes (i.e., LD50) are analyzed concluding that predictions obtained using in silico approaches are now well-suited for reliably supporting assessment of LD50-based acute toxicity for the purpose of GHS classification. A general overview is provided of the endpoints from in vitro studies commonly evaluated for predicting acute toxicity (e.g., cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of in vitro data allow for a shift away from assessments solely based on endpoints such as LD50, to mechanism-based endpoints that can be accurately assessed in vitro or by using in silico prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how in silico approaches support the assessment of acute toxicity.

Machine learning and artificial intelligence in toxicological sciences

Machine learning and artificial intelligence approaches have revolutionized multiple disciplines, including toxicology. This review summarizes representative recent applications of machine learning and artificial intelligence approaches in different areas of toxicology, including physiologically based pharmacokinetic (PBPK) modeling, quantitative structure-activity relationship modeling for toxicity prediction, adverse outcome pathway analysis, high-throughput screening, toxicogenomics, big data and toxicological databases. By leveraging machine learning and artificial intelligence approaches, now it is possible to develop PBPK models for hundreds of chemicals efficiently, to create in silico models to predict toxicity for a large number of chemicals with similar accuracies compared to in vivo animal experiments, and to analyze a large amount of different types of data (toxicogenomics, high-content image data, etc.) to generate new insights into toxicity mechanisms rapidly, which was impossible by manual approaches in the past. To continue advancing the field of toxicological sciences, several challenges should be considered: (1) not all machine learning models are equally useful for a particular type of toxicology data, and thus it is important to test different methods to determine the optimal approach; (2) current toxicity prediction is mainly on bioactivity classification (yes/no), so additional studies are needed to predict the intensity of effect or dose-response relationship; (3) as more data become available, it is crucial to perform rigorous data quality check and develop infrastructure to store, share, analyze, evaluate, and manage big data; and (4) it is important to convert machine learning models to user-friendly interfaces to facilitate their applications by both computational and bench scientists.

Direct Prediction of Physicochemical Properties and Toxicities of Chemicals from Analytical Descriptors by GC-MS

With advances in machine learning (ML) techniques, the quantitative structure–activity relationship (QSAR) approach is becoming popular for evaluating chemicals. However, the QSAR approach requires that the chemical structure of the target compound is known and that it should be convertible to molecular descriptors. These requirements lead to limitations in predicting the properties and toxicities of chemicals distributed in the environment as in the PubChem database; the structural information on only 14% of compounds is available. This study proposes a new ML-based QSAR approach that can predict the properties and toxicities of compounds using analytical descriptors of mass spectrum and retention index obtained via gas chromatography–mass spectrometry without requiring exact structural information. The model was developed based on the XGBoost ML method. The root-mean-square errors (RMSEs) for log K_o-w, log (molecular weight), melting point, boiling point, log (vapor pressure), log (water solubility), log (LD₅₀) (rat, oral), and log (LD₅₀) (mouse, oral) are 0.97, 0.052, 51, 23, 0.74, 1.1, 0.74, and 0.6, respectively. The model performed well on a chemical standard mixture measurement, with similar results to those of model validation. It also performed well on a measurement of contaminated oil with spectral deconvolution. These results indicate that the model is suitable for investigating unknown-structured chemicals detected in measurements. Any online user can execute the model through a web application named Detective-QSAR (http://www.mixture-platform.net/Detective_QSAR_Med_Open/). The analytical descriptor-based approach is expected to create new opportunities for the evaluation of unknown chemicals around us.

Predicting chemical hazard across taxa through machine learning

We applied machine learning methods to predict chemical hazards focusing on fish acute toxicity across taxa. We analyzed the relevance of taxonomy and experimental setup, showing that taking them into account can lead to considerable improvements in the classification performance. We quantified the gain obtained throught the introduction of taxonomic and experimental information, compared to classification based on chemical information alone. We used our approach with standard machine learning models (K-nearest neighbors, random forests and deep neural networks), as well as the recently proposed Read-Across Structure Activity Relationship (RASAR) models, which were very successful in predicting chemical hazards to mammals based on chemical similarity. We were able to obtain accuracies of over 93% on datasets where, due to noise in the data, the maximum achievable accuracy was expected to be below 96%. The best performances were obtained by random forests and RASAR models. We analyzed metrics to compare our results with animal test reproducibility, and despite most of our models "outperform animal test reproducibility" as measured through recently proposed metrics, we showed that the comparison between machine learning performance and animal test reproducibility should be addressed with particular care. While we focused on fish mortality, our approach, provided that the right data is available, is valid for any combination of chemicals, effects and taxa.

Prediction of the Neurotoxic Potential of Chemicals Based on Modelling of Molecular Initiating Events Upstream of the Adverse Outcome Pathways of (Developmental) Neurotoxicity

Developmental and adult/ageing neurotoxicity is an area needing alternative methods for chemical risk assessment. The formulation of a strategy to screen large numbers of chemicals is highly relevant due to potential exposure to compounds that may have long-term adverse health consequences on the nervous system, leading to neurodegeneration. Adverse Outcome Pathways (AOPs) provide information on relevant molecular initiating events (MIEs) and key events (KEs) that could inform the development of computational alternatives for these complex effects. We propose a screening method integrating multiple Quantitative Structure–Activity Relationship (QSAR) models. The MIEs of existing AOP networks of developmental and adult/ageing neurotoxicity were modelled to predict neurotoxicity. Random Forests were used to model each MIE. Predictions returned by single models were integrated and evaluated for their capability to predict neurotoxicity. Specifically, MIE predictions were used within various types of classifiers and compared with other reference standards (chemical descriptors and structural fingerprints) to benchmark their predictive capability. Overall, classifiers based on MIE predictions returned predictive performances comparable to those based on chemical descriptors and structural fingerprints. The integrated computational approach described here will be beneficial for large-scale screening and prioritisation of chemicals as a function of their potential to cause long-term neurotoxic effects.

Mapping Mechanistic Pathways of Acute Oral Systemic Toxicity Using Chemical Structure and Bioactivity Measurements

Regulatory agencies around the world have committed to reducing or eliminating animal testing for establishing chemical safety. Adverse outcome pathways can facilitate replacement by providing a mechanistic framework for identifying the appropriate non-animal methods and connecting them to apical adverse outcomes. This study separated 11,992 chemicals with curated rat oral acute toxicity information into clusters of structurally similar compounds. Each cluster was then assigned one or more ToxCast/Tox21 assays by looking for the minimum number of assays required to record at least one positive hit call below cytotoxicity for all acutely toxic chemicals in the cluster. When structural information is used to select assays for testing, none of the chemicals required more than four assays and 98% required two assays or less. Both the structure-based clusters and activity from the associated assays were significantly associated with the GHS toxicity classification of the chemicals, which suggests that a combination of bioactivity and structural information could be as reproducible as traditional in vivo studies. Predictivity is improved when the in vitro assay directly corresponds to the mechanism of toxicity, but many indirect assays showed promise as well. Given the lower cost of in vitro testing, a small assay battery including both general cytotoxicity assays and two or more orthogonal assays targeting the toxicological mechanism could be used to improve performance further. This approach illustrates the promise of combining existing in silico approaches, such as the Collaborative Acute Toxicity Modeling Suite (CATMoS), with structure-based bioactivity information as part of an efficient tiered testing strategy that can reduce or eliminate animal testing for acute oral toxicity.

The use of Bayesian methodology in the development and validation of a tiered assessment approach towards prediction of rat acute oral toxicity

There exists consensus that the traditional means by which safety of chemicals is assessed—namely through reliance upon apical outcomes obtained following in vivo testing—is increasingly unfit for purpose. Whilst efforts in development of suitable alternatives continue, few have achieved levels of robustness required for regulatory acceptance. An array of “new approach methodologies” (NAM) for determining toxic effect, spanning in vitro and in silico spheres, have by now emerged. It has been suggested, intuitively, that combining data obtained from across these sources might serve to enhance overall confidence in derived judgment. This concept may be formalised in the “tiered assessment” approach, whereby evidence gathered through a sequential NAM testing strategy is exploited so to infer the properties of a compound of interest. Our intention has been to provide an illustration of how such a scheme might be developed and applied within a practical setting—adopting for this purpose the endpoint of rat acute oral lethality. Bayesian statistical inference is drawn upon to enable quantification of degree of confidence that a substance might ultimately belong to one of five LD50-associated toxicity categories. Informing this is evidence acquired both from existing in silico and in vitro resources, alongside a purposely-constructed random forest model and structural alert set. Results indicate that the combination of in silico methodologies provides moderately conservative estimations of hazard, conducive for application in safety assessment, and for which levels of certainty are defined. Accordingly, scope for potential extension of approach to further toxicological endpoints is demonstrated.

In Silico Models for Predicting Acute Systemic Toxicity

In this chapter, we give a brief overview of the regulatory requirements for acute systemic toxicity information in the European Union, and we review structure-based computational models that are available and potentially useful in the assessment of acute systemic toxicity. Emphasis is placed on quantitative structure-activity relationship (QSAR) models implemented by means of a range of software tools. The most recently published literature models for acute systemic toxicity are also discussed, and perspectives for future developments in this field are offered.

Uncertainty-Informed Deep Transfer Learning of Perfluoroalkyl and Polyfluoroalkyl Substance Toxicity

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) pose a significant hazard because of their widespread industrial uses, environmental persistence, and bioaccumulation. A growing, increasingly diverse inventory of PFAS, including 8163 chemicals, has recently been updated by the U.S. Environmental Protection Agency. However, with the exception of a handful of well-studied examples, little is known about their human toxicity potential because of the substantial resources required for in vivo toxicity experiments. We tackle the problem of expensive in vivo experiments by evaluating multiple machine learning (ML) methods, including random forests, deep neural networks (DNN), graph convolutional networks, and Gaussian processes, for predicting acute toxicity (e.g., median lethal dose, or LD₅₀) of PFAS compounds. To address the scarcity of toxicity information for PFAS, publicly available datasets of oral rat LD₅₀ for all organic compounds are aggregated and used to develop state-of-the-art ML source models for transfer learning. A total of 519 fluorinated compounds containing two or more C-F bonds with known toxicity are used for knowledge transfer to ensembles of the best-performing source model, DNN, to generate the target models for the PFAS domain with access to uncertainty. This study predicts toxicity for PFAS with a defined chemical structure. To further inform prediction confidence, the transfer-learned model is embedded within a SelectiveNet architecture, where the model is allowed to identify regions of prediction with greater confidence and abstain from those with high uncertainty using a calibrated cutoff rate.

Mussel Shells, a Valuable Calcium Resource for the Pharmaceutical Industry

(1) Background: The mussel (Mytilus edulis, Mytilus galloprovincialis) is the most widespread lamellibranch mollusk, being fished on all coasts of the European seas. Mussels are also widely grown in Japan, China, and Spain, especially for food purposes. This paper shows an original technique for mussel shell processing for preparation of calcium salts, such as calcium levulinate. This process involves synthesis of calcium levulinate by treatment of Mytilus galloprovincialis shells with levulinic acid. The advantage of mussel shell utilization results in more straightforward qualitative composition. Thus, the weight of the mineral component lies with calcium carbonate, which can be used for extraction of pharmaceutical preparations. (2) Methods: Shell powder was first deproteinized by calcination, then the mineral part was treated with levulinic acid. The problem of shells generally resulting from the industrialization of marine molluscs creates enough shortcomings, if one only mentions storage and handling. One of the solutions proposed by us is the capitalization of calcium from shells in the pharmaceutical industry. (3) Results: The toxicity of calcium levulinate synthesized from the mussel shells was evaluated by the method known in the scientific literature as the Constantinescu phytobiological method (using wheat kernels, Triticum vulgare Mill). Acute toxicity of calcium levulinate was evaluated; the experiments showed the low toxicity of calcium levulinate. (4) Conclusion: The experimental results highlighted calcium as the predominant element in the composition of mussel shells, which strengthens the argument of capitalizing the shells as an important natural source of calcium.

2-Phenoxyacetamide derivatives as SARS-CoV-2 main protease inhibitor: In silico studies

2-Phenoxyacetamide group has been identified as one of markers in the discovery and development of SARS-CoV-2 antiviral agent through its main protease (M^pro) inhibition pathway. This study aims to study a series of 2-phenoxyacetamide derivatives using in silico method toward SARS-CoV-2 M^pro as the protein target. The study was initiated by employing structure-based pharmacophore to virtually screen and to select the ligands, which have the best fit score (hits) along with the common pharmacophore features being matched. The result shows that from the 11 ligands designed, four ligands are selected as the hits by demonstrating fit score in the range of 56.20 to 65.53 to the pharmacophore model, employing hydrogen bond acceptor (HBA) and hydrophobic (H) as the common features. The hits were then docked into the binding site of the M^pro to see the binding mode of the corresponding hits as well as its affinity. The docking results free energy of binding (ΔG_bind) of the hits are in agreement with the pharmacophore fit score, in the range of −6.83 to −7.20 kcal/ mol. To gain the information of the hits as a potential drug to be developed, the in silico study was further proceed by predicting the mutagenic potency, toxicity and pharmacokinetic profiles. Based on the efficiency percentage, all hits meet the criteria as drug candidates by showing 84–88% leading to a conclusion that 2-phenoxyacetamide derivatives are beneficial to be marked as the lead compound for SARS-CoV-2 M^pro inhibitor.

The effect of noise on the predictive limit of QSAR models

A key challenge in the field of Quantitative Structure Activity Relationships (QSAR) is how to effectively treat experimental error in the training and evaluation of computational models. It is often assumed in the field of QSAR that models cannot produce predictions which are more accurate than their training data. Additionally, it is implicitly assumed, by necessity, that data points in test sets or validation sets do not contain error, and that each data point is a population mean. This work proposes the hypothesis that QSAR models can make predictions which are more accurate than their training data and that the error-free test set assumption leads to a significant misevaluation of model performance. This work used 8 datasets with six different common QSAR endpoints, because different endpoints should have different amounts of experimental error associated with varying complexity of the measurements. Up to 15 levels of simulated Gaussian distributed random error was added to the datasets, and models were built on the error laden datasets using five different algorithms. The models were trained on the error laden data, evaluated on error-laden test sets, and evaluated on error-free test sets. The results show that for each level of added error, the RMSE for evaluation on the error free test sets was always better. The results support the hypothesis that, at least under the conditions of Gaussian distributed random error, QSAR models can make predictions which are more accurate than their training data, and that the evaluation of models on error laden test and validation sets may give a flawed measure of model performance. These results have implications for how QSAR models are evaluated, especially for disciplines where experimental error is very large, such as in computational toxicology.

A risk classification strategy for migrants of food contact material combined with three (Q)SAR tools in silico

The chemical constituents in food contact materials (FCMs) may transfer into food during the contact, which may pose potential risk to humans. So, it is important to evaluate the safety of FCMs. Due to the advantages of cost-effectiveness and high throughput, (Q)SAR tools have been gradually used for risk assessment. In this work, a risk classification strategy for migrants of food contact materials combined with three (Q)SAR tools was developed based on a single endpoint (Mutagenicity) assessment and risk matrix approach, respectively. 419 migrants existing in a self-built toxicology database beneficial from Python crawler technology were evaluated. 5 toxic hazard ranks and 4 risk ranks were obtained for single endpoint assessment and risk matrix respectively, with 21 substances assigned as Toxic hazard Class I and 43 substances assigned as RISK Ⅰ which need the highest safety concern. Besides, for the Toxic hazard Class I substances assessed by the single endpoint, 19 of them were confirmed experimentally, and all of them were overlapped in the RISK Ⅰ substances, which suggests the effectiveness and reliability of this strategy.

Chemometric QSAR modeling of acute oral toxicity of Polycyclic Aromatic Hydrocarbons (PAHs) to rat using simple 2D descriptors and interspecies toxicity modeling with mouse

The information of the acute oral toxicity for most polycyclic aromatic hydrocarbons (PAHs) in mammals are lacking due to limited experimental resources, leading to a need to develop reliable in silico methods to evaluate the toxicity endpoint. In this study, we developed the quantitative structure-activity relationship (QSAR) models by genetic algorithm (GA) and multiple linear regression (MLR) for the rat acute oral toxicity (LD₅₀) of PAHs following the strict validation principles of QSAR modeling recommended by OECD. The best QSAR model comprised eight simple 2D descriptors with definite physicochemical meaning, which showed that maximum atom-type electrotopological state, van der Waals surface area, mean atomic van der Waals volume, and total number of bonds are main influencing factors for the toxicity endpoint. A true external set (554 compounds) without rat acute oral toxicity values, and 22 limit test compounds, were firstly predicted along with reliability assessment. We also compared our proposed model with the OPERA predictions and recently published literature to prove the prediction reliability. Furthermore, the interspecies toxicity (iST) models of PAHs between rat and mouse were also established, validated and employed for filling data gap. Overall, our developed models should be applicable to new or untested or not yet synthesized PAHs falling within the applicability domain (AD) of the models for rapid acute oral toxicity prediction, thus being important for environmental or personal exposure risk assessment under regulatory frameworks.

FP-ADMET: a compendium of fingerprint-based ADMET prediction models

MOTIVATION

The absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs plays a key role in determining which among the potential candidates are to be prioritized. In silico approaches based on machine learning methods are becoming increasing popular, but are nonetheless limited by the availability of data. With a view to making both data and models available to the scientific community, we have developed FPADMET which is a repository of molecular fingerprint-based predictive models for ADMET properties. In this article, we have examined the efficacy of fingerprint-based machine learning models for a large number of ADMET-related properties. The predictive ability of a set of 20 different binary fingerprints (based on substructure keys, atom pairs, local path environments, as well as custom fingerprints such as all-shortest paths) for over 50 ADMET and ADMET-related endpoints have been evaluated as part of the study. We find that for a majority of the properties, fingerprint-based random forest models yield comparable or better performance compared with traditional 2D/3D molecular descriptors.

AVAILABILITY

The models are made available as part of open access software that can be downloaded from https://gitlab.com/vishsoft/fpadmet .

A study on catalytic and non-catalytic sites of H5N1 and H1N1 neuraminidase as the target for chalcone inhibitors

The H1N1 pandemic in 2009 and the H5N1 outbreak in 2005 have shocked the world as millions of people were infected and hundreds of thousands died due to the infections by the influenza virus. Oseltamivir, the most common drug to block the viral life cycle by inhibiting neuraminidase (NA) enzyme, has been less effective in some resistant cases due to the virus mutation. Presently, the binding of 10 chalcone derivatives towards H5N1 and H1N1 NAs in the non-catalytic and catalytic sites was studied using molecular docking. The in silico study was also conducted for its drug-like likeness such as Lipinski Rule, mutagenicity, toxicity and pharmacokinetic profiles. The result demonstrates that two chalcones (1c and 2b) have the potential for future NA inhibitor development. Compound 1c inhibits H5N1 NA and H1N1 NA with IC₅₀ of 27.63 µM and 28.11 µM, respectively, whereas compound 2b inhibits NAs with IC₅₀ of 87.54 µM and 73.17 µM for H5N1 and H1N1, respectively. The in silico drug-like likeness prediction reveals that 1c is 62% better than 2b (58%) in meeting the criteria. The results suggested that 1c and 2b have potencies to be developed as non-competitive inhibitors of neuraminidase for the future development of anti-influenza drugs.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s13765-021-00639-w.

Quantitative Structure-Activity Relationship Modeling of the Amplex Ultrared Assay to Predict Thyroperoxidase Inhibitory Activity

The thyroid system plays a major role in the regulation of several physiological processes. The dysregulation of the thyroid system caused by the interference of xenobiotics and contaminants may bring to pathologies like hyper- and hypothyroidism and it has been recently correlated with adverse outcomes leading to cancer, obesity, diabetes and neurodevelopmental disorders. Thyroid disruption can occur at several levels. For example, the inhibition of thyroperoxidase (TPO) enzyme, which catalyses the synthesis of thyroid hormones, may cause dysfunctions related to hypothyroidism. The inhibition of the TPO enzyme can occur as a consequence of prolonged exposure to chemical compounds, for this reason it is of utmost importance to identify alternative methods to evaluate the large amount of pollutants and other chemicals that may pose a potential hazard to the human health. In this work, quantitative structure-activity relationship (QSAR) models to predict the TPO inhibitory potential of chemicals are presented. Models are developed by means of several machine learning and data selection approaches, and are based on data obtained in vitro with the Amplex UltraRed-thyroperoxidase (AUR-TPO) assay. Balancing methods and feature selection are applied during model development. Models are rigorously evaluated through internal and external validation. Based on validation results, two models based on Balanced Random Forest (BRF) and K-Nearest Neighbours (KNN) algorithms were selected for a further validation phase, that leads predictive performance (BA = 0.76-0.78 on external data) that is comparable with the reported experimental variability of the AUR-TPO assay (BA ∼0.70). Finally, a consensus between the two models was proposed (BA = 0.82). Based on the predictive performance, these models can be considered suitable for toxicity screening of environmental chemicals.

Machine learning models for classification tasks related to drug safety

In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015-2021). The study focuses only on classification models with large datasets (i.e. more than a thousand compounds). A comprehensive literature search and meta-analysis was carried out for nine different targets: hERG-mediated cardiotoxicity, blood-brain barrier penetration, permeability glycoprotein (P-gp) substrate/inhibitor, cytochrome P450 enzyme family, acute oral toxicity, mutagenicity, carcinogenicity, respiratory toxicity and irritation/corrosion. The comparison of the best classification models was targeted to reveal the differences between machine learning algorithms and modeling types, endpoint-specific performances, dataset sizes and the different validation protocols. Based on the evaluation of the data, we can say that tree-based algorithms are (still) dominating the field, with consensus modeling being an increasing trend in drug safety predictions. Although one can already find classification models with great performances to hERG-mediated cardiotoxicity and the isoenzymes of the cytochrome P450 enzyme family, these targets are still central to ADMET-related research efforts.

Automated read-across workflow for predicting acute oral toxicity: I. The decision scheme in the QSAR toolbox

A decision-scheme outlining the steps for identifying the appropriate chemical category and subsequently appropriate tested source analog(s) for data gap filling of a target chemical by read-across is described. The primary features used in the grouping of the target chemical with source analogues within a database of 10,039 discrete organic substances include reactivity mechanisms associated with protein interactions and specific-acute-oral-toxicity-related mechanisms (e.g., mitochondrial uncoupling). Additionally, the grouping of chemicals making use of the in vivo rat metabolic simulator and neutral hydrolysis. Subsequently, a series of structure-based profilers are used to narrow the group to the most similar analogues. The scheme is implemented in the OECD QSAR Toolbox, so it automatically predicts acute oral toxicity as the rat oral LD50 value in log [1/mol/kg]. It was demonstrated that due to the inherent variability in experimental data, classification distribution should be employed as more adequate in comparison to the exact classification. It was proved that the predictions falling in the adjacent GSH categories to the experimentally-stated ones are acceptable given the variation in experimental data. The model performance estimated by adjacent accuracy was found to be 0.89 and 0.54 while based on R². The mechanistic and predictive coverages were >0.85.

In-silico evaluation of Malawi essential medicines and reactive metabolites for potential drug-induced toxicities

BACKGROUND

Drug-induced toxicity is one of the problems that have negatively impacted on the well-being of populations throughout the world, including Malawi. It results in unnecessary hospitalizations, retarding the development of the country. This study assessed the Malawi Essential Medicines List (MEML) for structural alerts and reactive metabolites with the potential for drug-induced toxicities.

METHODS

This in-silico screening study used StopTox, ToxAlerts and LD-50 values toxicity models to assess the MEML drugs. A total of 296 drugs qualified for the analysis (those that had defined chemical structures) and were screened in each software programme. Each model had its own toxicity endpoints and the models were compared for consensus of their results.

RESULTS

In the StopTox model, 86% of the drugs had potential to cause at least one toxicity including 55% that had the potential of causing eye irritation and corrosion. In ToxAlerts, 90% of the drugs had the potential of causing at least one toxicity and 72% were found to be potentially reactive, unstable and toxic. In LD-50, 70% of the drugs were potentially toxic. Model consensus evaluation results showed that the highest consensus was observed between ToxAlerts and StopTox (80%). The overall consensus amongst the three models was 57% and statistically significant (p < 0.05).

CONCLUSIONS

A large number of drugs had the potential to cause various systemic toxicities. But the results need to be interpreted cautiously since the clinical translation of QSAR-based predictions depends on many factors. In addition, inconsistencies have been reported between screening results amongst different models.

GHOST: Adjusting the Decision Threshold to Handle Imbalanced Data in Machine Learning

Machine learning classifiers trained on class imbalanced data are prone to overpredict the majority class. This leads to a larger misclassification rate for the minority class, which in many real-world applications is the class of interest. For binary data, the classification threshold is set by default to 0.5 which, however, is often not ideal for imbalanced data. Adjusting the decision threshold is a good strategy to deal with the class imbalance problem. In this work, we present two different automated procedures for the selection of the optimal decision threshold for imbalanced classification. A major advantage of our procedures is that they do not require retraining of the machine learning models or resampling of the training data. The first approach is specific for random forest (RF), while the second approach, named GHOST, can be potentially applied to any machine learning classifier. We tested these procedures on 138 public drug discovery data sets containing structure-activity data for a variety of pharmaceutical targets. We show that both thresholding methods improve significantly the performance of RF. We tested the use of GHOST with four different classifiers in combination with two molecular descriptors, and we found that most classifiers benefit from threshold optimization. GHOST also outperformed other strategies, including random undersampling and conformal prediction. Finally, we show that our thresholding procedures can be effectively applied to real-world drug discovery projects, where the imbalance and characteristics of the data vary greatly between the training and test sets.

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.

CATMoS: Collaborative Acute Toxicity Modeling Suite

BACKGROUND

Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals.

OBJECTIVES

The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg).

METHODS

An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches.

RESULTS

The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results.

DISCUSSION

CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

Prediction of Human Lethal Doses and Concentrations of MEIC Chemicals from Rodent LD50 Values: An Attempt to Make Some Reparation

The prediction of human toxicities from animal toxicity tests is often poor, and is now discouraged and in some cases banned, especially those involving the LD₅₀ test. However, there is a vast number of historical LD₅₀ data in both public and in-house repositories that are being put to little use. This study examined the correlations between human lethality (doses and concentrations) of 36 MEIC chemicals and the median values of a large number of mouse and rat LD₅₀ values obtained for four different routes of administration. The best correlations were found with mouse and rat intraperitoneal LD₅₀ values (r² = 0.838 and 0.810 for human lethal dose, and r² = 0.753 and 0.785 for human lethal concentration). The results show that excellent prediction of human lethal dose and concentration can be made, for this series of chemicals at least, by using uncurated rodent LD₅₀ values, thus offering some reparation for the millions of rodent lives sacrificed in LD₅₀ testing.

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.

In Silico Assessment of Acute Oral Toxicity for Mixtures

While exposure of humans to environmental hazards often occurs with complex chemical mixtures, the majority of existing toxicity data are for single compounds. The Globally Harmonized System of chemical classification (GHS) developed by the Organization for Economic Cooperation and Development uses the additivity formula for acute oral toxicity classification of mixtures, which is based on the acute toxicity estimate of individual ingredients. We evaluated the prediction of GHS category classifications for mixtures using toxicological data collected in the Integrated Chemical Environment (ICE) developed by the National Toxicology Program (United States Department of Health and Human Services). The ICE database contains in vivo acute oral toxicity data for ∼10,000 chemicals and for 582 mixtures with one or multiple active ingredients. By using the available experimental data for individual ingredients, we were able to calculate a GHS category for only half of the mixtures. To expand a set of components with acute oral toxicity data, we used the Collaborative Acute Toxicity Modeling Suite (CATMoS) implemented in the Open Structure-Activity/Property Relationship App to make predictions for active ingredients without available experimental data. As a result, we were able to make predictions for 503 mixtures/formulations with 72% accuracy for the GHS classification. For 186 mixtures with two or more active ingredients, the accuracy rate was 76%. The structure-based analysis of the misclassified mixtures did not reveal any specific structural features associated with the mispredictions. Our results demonstrate that CATMoS together with an additivity formula can be used to predict the GHS category for chemical mixtures.

In vivo toxicity of nitroaromatic compounds to rats: QSTR modelling and interspecies toxicity relationship with mouse

Nitroaromatic compounds (NACs) in the environment can cause serious public health and environmental problems due to their potential toxicity. This study established quantitative structure-toxicity relationship (QSTR) models for the acute oral toxicity of NACs towards rats following the stringent OECD principles for QSTR modelling. All models were assessed by various internationally accepted validation metrics and the OECD criteria. The best QSTR model contains seven simple and interpretable 2D descriptors with defined physicochemical meaning. Mechanistic interpretation indicated that van der Waals surface area, presence of C-F at topological distance 6, heteroatom content and frequency of C-N at topological distance 9 are main factors responsible for the toxicity of NACs. This proposed model was successfully applied to a true external set (295 compounds), and prediction reliability was analysed and discussed. Moreover, the rat-mouse and mouse-rat interspecies quantitative toxicity-toxicity relationship (iQTTR) models were also constructed, validated and employed in toxicity prediction for true external sets consisting of 67 and 265 compounds, respectively. These models showed good external predictivity that can be used to rapidly predict the rat oral acute toxicity of new or untested NACs falling within the applicability domain of the models, thus being beneficial in environmental risk assessment and regulatory purposes.

An evaluation of the performance of selected (Q)SARs/expert systems for predicting acute oral toxicity

Multiple US agencies use acute oral toxicity data in a variety of regulatory contexts. One of the ad-hoc groups that the US Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) established to implement the ICCVAM Strategic Roadmap was the Acute Toxicity Workgroup (ATWG) to support the development, acceptance, and actualisation of new approach methodologies (NAMs). One of the ATWG charges was to evaluate in vitro and in silico methods for predicting rat acute systemic toxicity. Collaboratively, the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) and the US Environmental Protection Agency (US EPA) collected a large body of rat oral acute toxicity data (~16,713 studies for 11,992 substances) to serve as a reference set to evaluate the performance and coverage of new and existing models as well as build understanding of the inherent variability of the animal data. Here, we focus on evaluating in silico models for predicting the Lethal Dose (LD50) as implemented within two expert systems, TIMES and TEST. The performance and coverage were evaluated against the reference dataset. The performance of both models were similar, but TEST was able to make predictions for more chemicals than TIMES. The subset of the data with multiple (>3) LD50 values was used to evaluate the variability in data and served as a benchmark to compare model performance. Enrichment analysis was conducted using ToxPrint chemical fingerprints to identify the types of chemicals where predictions lay outside the upper 95% confidence interval. Overall, TEST and TIMES models performed similarly but had different chemical features associated with low accuracy predictions, reaffirming that these models are complementary and both worth evaluation when seeking to predict rat LD50 values.