In silico toxicology: computational methods for the prediction of chemical toxicity

Pharmacophore-based virtual screening, molecular docking and molecular dynamics studies for the discovery of novel neuraminidase inhibitors

The in silico evaluation of 27 p-aminosalicylic acid derivatives, also referred to as neuraminidase inhibitors was the focus of the current study. To search and predict new potential neuraminidase inhibitors, this study was based on the ligand-based pharmacophore modeling, 3D QSAR, molecular docking, ADMET and MD simulation studies. The data was generated from recently reported inhibitors and divided into two groups, one of these group has 17 compounds for training and the second group has 10 compounds for testing purpose. The generated pharmacophore has known as ADDPR_4 was found statistically significant 3D-QSAR model owing the high trust scores (R2 = 0.974, Q2 = 0.905, RMSE = 0.23). Morever external validation was also employed to evaluate the prediction capacity of the built pharmacophore model (R2pred = 0.905). In addition, in silico ADMET, analyses were employed to evaluate the obtained hits for drug likeness properties. The stability of formed complexes was further evaluated using molecular dynamics. Top two hits showed stable complexes with Neuraminidase based on calculated total binding energy by MM-PBSA.Communicated by Ramaswamy H. Sarma.

A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling

In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure-Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R2train = 0.93, R2ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S-P fragments, ionization potential, and presence of C-N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.

In-silico green toxicology approach toward discovering safer ligands for development of safe-by-design metal-organic frameworks

A vast variety of chemical compounds have been fabricated and commercialized, they not only result in industrial exposure during manufacturing and usage, but also have environmental impacts throughout their whole life cycle. Consequently, attempts to assess the risk of chemicals in terms of toxicology have never ceased. In-silico toxicology, also known as predictive toxicology, has advanced significantly over the last decade as a result of the drawbacks of experimental investigations. In this study, ProTox-III was applied to predict the toxicity of the ligands used for metal-organic framework (MOF) design and synthesis. Initially, 35 ligands, that have been frequently utilized for MOF synthesis and fabrication, were selected. Subsequently, canonical simplified molecular-input line-entry system (SMILES) of ligands were extracted from the PUBCHEM database and inserted into the ProTox-III online server. Ultimately, webserver outputs including LD50 and the probability of toxicological endpoints (cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, and ecotoxicity) were obtained and organized. According to retrieved LD50 data, the safest ligand was 5-hydroxyisophthalic. In contrast, the most hazardous ligand was 5-chlorobenzimidazole, with an LD50 of 8 mg/kg. Among evaluated endpoints, ecotoxicity was the most active and was detected in several imidazolate ligands. This data can open new horizons in design and development of green MOFs.

The state of the art in secondary pharmacology and its impact on the safety of new medicines

Secondary pharmacology screening of investigational small-molecule drugs for potentially adverse off-target activities has become standard practice in pharmaceutical research and development, and regulatory agencies are increasingly requesting data on activity against targets with recognized adverse effect relationships. However, the screening strategies and target panels used by pharmaceutical companies may vary substantially. To help identify commonalities and differences, as well as to highlight opportunities for further optimization of secondary pharmacology assessment, we conducted a broad-ranging survey across 18 companies under the auspices of the DruSafe leadership group of the International Consortium for Innovation and Quality in Pharmaceutical Development. Based on our analysis of this survey and discussions and additional research within the group, we present here an overview of the current state of the art in secondary pharmacology screening. We discuss best practices, including additional safety-associated targets not covered by most current screening panels, and present approaches for interpreting and reporting off-target activities. We also provide an assessment of the safety impact of secondary pharmacology screening, and a perspective on opportunities and challenges in this rapidly developing field.

Acute and sub-chronic toxicity study of novel polyherbal formulation in non-alcoholic fatty liver using Wistar rats

Aim: This study assessed the acute and sub-chronic toxicity of a novel polyherbal formulation tablet in Wistar rats Materials & methods: Acute toxicity and sub-chronic toxicity was assessed following OECD (Organisation for the Economic Co-operation and Development) guidelines based on 423 and 408. Results & conclusion: No mortality and toxicity showed in rats during acute toxicity. The LD50 of the extract was at 2000 mg/kg. In sub-chronic study, both sex rats were orally administered at 250, 500,1000 and 2000 mg/kg for 90 days and revealed no significant difference (p < 0.05) in hematological and other parameters compared with the control. Histopathology study did not reveal morphological alteration. The No observed adverse effect level of the tablet was observed until 2000 mg/kg.

Exploring Curcumin-Loaded Lipid-Based Nanomedicine as Efficient Targeted Therapy for Alzheimer's Diseases

Alzheimer's disease (AD) is a neurological condition currently with 47 million people suffering from it globally. AD might have many reasons such as genetic issues, environmental factors, and Aβ accumulation, which is the biomarker of the disease. Since the primary reason is unknown, there is no targeted treatment at the moment, but ongoing research aims to slow its progression by managing amyloid-beta peptide production rather than symptomatic improvement. Since phytochemicals have been demonstrated to possess antioxidant, anti-inflammatory, and neuroprotective properties, they may target multiple pathological factors and can reduce the risk of the disease. Curcumin, as a phytochemical found in turmeric known for its antioxidant, free radical scavenging properties, and as an antiamyloid in treating AD, has come under investigation. Although its low bioavailability limits its efficacy, a prominent drug delivery system (DDS) is desired to overcome it. Hence, the potency of lipid-based nanoparticles encapsulating curcumin (LNPs-CUR) is considered in this study as a promising DDS. In vivo studies in animal models indicate LNPs-CUR effectively slow amyloid plaque formation, leading to cognitive enhancement and reduced toxicity compared to free CUR. However, a deeper understanding of CUR's pharmacokinetics and safety profile is crucial before LNPs-CUR can be considered as a medicine. Future investigations may explore the combination of NPs with other therapeutic agents to increase their efficacy in AD cases. This review provides the current position of CUR in the AD therapy paradigm, the DDS suggestions for CUR, and the previous research from the point of analytical view focused on the advantages and challenges.

Prioritizing of potential environmental exposure carcinogens beyond IARC group 1-2B based on weight of evidence (WoE) approach

Environmental exposures are the main cause of cancer, and their carcinogenicity has not been fully evaluated, identifying potential carcinogens that have not been evaluated is critical for safety. This study is the first to propose a weight of evidence (WoE) approach based on computational methods to prioritize potential carcinogens. Computational methods such as read across, structural alert, (Quantitative) structure-activity relationship and chemical-disease association were evaluated and integrated. Four different WoE approach was evaluated, compared to the best single method, the WoE-1 approach gained 0.21 and 0.39 improvement in the area under the receiver operating characteristic curve (AUC) and Matthew's correlation coefficient (MCC) value, respectively. The evaluation of 681 environmental exposures beyond IARC list 1-2B prioritized 52 chemicals of high carcinogenic concern, of which 21 compounds were known carcinogens or suspected carcinogens, and eight compounds were identified as potential carcinogens for the first time. This study illustrated that the WoE approach can effectively complement different computational methods, and can be used to prioritize chemicals of carcinogenic concern.

Chloropicrin induced ocular injury: Biomarkers, potential mechanisms, and treatments

Ocular tissue, especially the cornea, is overly sensitive to chemical exposures. The availability and adoption of chemical threat agent chloropicrin (CP) is growing in the United States as a pesticide and fumigant; thereby increasing the risk of its use in warfare, terrorist attacks and non-intentional exposure. Exposure to CP results in immediate ocular, respiratory, and dermal injury; however, we lack knowledge on its mechanism of toxicity as well as of its breakdown products like chlorine and phosgene, and effective therapies are elusive. Herein, we have reviewed the recent findings on exposure route, toxicity and likely mechanisms of CP induced ocular toxicity based on other vesicating chemical warfare agents that cause ocular injury. We have focused on the implication of their toxicity and mechanistic outcomes in the ocular tissue, especially the cornea, which could be useful in the development of broad-spectrum effective therapeutic options. We have discussed on the potential countermeasures, overall hallmarks and challenges involved in studying ocular injuries from chemical threat agent exposures. Finally, we reviewed useful available technologies and methods that can assist in the identification of effective medical countermeasures for chemical threat agents related ocular injuries.

Expression analysis and mapping of Viral-Host Protein interactions of Poxviridae suggests a lead candidate molecule targeting Mpox

BACKGROUND

Monkeypox (Mpox) is an important human pathogen without etiological treatment. A viral-host interactome study may advance our understanding of molecular pathogenesis and lead to the discovery of suitable therapeutic targets.

METHODS

GEO Expression datasets characterizing mRNA profile changes in different host responses to poxviruses were analyzed for shared pathway identification, and then, the Protein-protein interaction (PPI) maps were built. The viral gene expression datasets of Monkeypox virus (MPXV) and Vaccinia virus (VACV) were used to identify the significant viral genes and further investigated for their binding to the library of targeting molecules.

RESULTS

Infection with MPXV interferes with various cellular pathways, including interleukin and MAPK signaling. While most host differentially expressed genes (DEGs) are predominantly downregulated upon infection, marked enrichments in histone modifiers and immune-related genes were observed. PPI analysis revealed a set of novel virus-specific protein interactions for the genes in the above functional clusters. The viral DEGs exhibited variable expression patterns in three studied cell types: primary human monocytes, primary human fibroblast, and HeLa, resulting in 118 commonly deregulated proteins. Poxvirus proteins C6R derived protein K7 and K7R of MPXV and VACV were prioritized as targets for potential therapeutic interventions based on their histone-regulating and immunosuppressive properties. In the computational docking and Molecular Dynamics (MD) experiments, these proteins were shown to bind the candidate small molecule S3I-201, which was further prioritized for lead development.

RESULTS

MPXV circumvents cellular antiviral defenses by engaging histone modification and immune evasion strategies. C6R-derived protein K7 binding candidate molecule S3I-201 is a priority promising candidate for treating Mpox.

Is read-across for chemicals comparable to medical device equivalence and where to use it for conformity assessment?

Novel medical devices must conform to medical device regulation (MDR) for European market entry. Likewise, chemicals must comply with the Registration, Evaluation, Authorization and Restriction of Chemicals (REACh) regulation. Both pose regulatory challenges for manufacturers, but concordantly provide an approach for transferring data from an already registered device or compound to the one undergoing accreditation. This is called equivalence for medical devices and read-across for chemicals. Although read-across is not explicitly prohibited in the process of medical device accreditation, it is usually not performed due to a lack of guidance and acceptance criteria from the authorities. Nonetheless, a scientifically justified read-across of material-based endpoints, as well as toxicological assessment of chemical aspects, such as extractables and leachables, can prevent failure of MDR device equivalence if data is lacking. Further, read-across, if applied correctly can facilitate the standard MDR conformity assessment. The need for read-across within medical device registration should let authorities to reconsider device accreditation and the formulation of respective guidance documents. Acceptance criteria like in the European Chemicals Agency (ECHA) read-across assessment framework (RAAF) are needed. This can reduce the impact of the MDR and help with keeping high European innovation device rate, beneficial for medical device patients.

In vitro and in silico studies of the potential cytotoxic, antioxidant, and HMG CoA reductase inhibitory effects of chitin from Indonesia mangrove crab (Scylla serrata) shells

This study aimed to characterize chitin extracted from Indonesia mangrove crab (Scylla serrata) shells, as well as to assess its in vitro cytotoxic, antioxidant, and HMG CoA reductase inhibitory potentials. In silico molecular docking, molecular dynamic, and ADMET prediction analyses were also carried out. Chitin was extracted from mangrove crab shells using deproteination and demineralization processes, Scanning Electron Microscopy (SEM) and Fourier Transform Infrared (FTIR) characterization are then performed. The MTT method was further tested in a study of cell viability, while in vitro method was used to assess HMG CoA reductase inhibitory and antioxidant activities. The extracted chitin was found to have a moderate level of cytotoxic and antioxidant activities. In vitro studies showed that it has an IC50 of 36,65 ± 0,082 μg/mL as an HMG CoA reductase inhibitor, and decreased enzyme activity by 68.733 % at 100 μg/mL as a concentration. Furthermore, in the in silico study, chitin showed a strong affinity to several targets, including HMG CoA reductase, HMG synthase, LDL receptor, PPAR-alfa, and HCAR-2 with binding energies of -5.7; -5.8; -3.6; -5.6; -4.6 kcal/mol, respectively. Based on the ADMET properties, it had non-toxic molecules, which were absorbed and distributed across the blood-brain barrier. The molecular dynamics (MD) simulation also showed that it remained stable in the active sites of HMG CoA reductase receptor for 100 ns. These results indicated that chitin from Indonesian mangrove crab shells can be used to develop more potent HMG CoA reductase inhibitor with antioxidant and cytotoxic activities for effective dyslipidemia therapy.

Alternatives of Animal Models for Biomedical Research: a Comprehensive Review of Modern Approaches

Biomedical research has long relied on animal models to unravel the intricacies of human physiology and pathology. However, concerns surrounding ethics, expenses, and inherent species differences have catalyzed the exploration of alternative avenues. The contemporary alternatives to traditional animal models in biomedical research delve into three main categories of alternative approaches: in vitro models, in vertebrate models, and in silico models. This unique approach to artificial intelligence and machine learning has been a keen interest to be used in different biomedical research. The main goal of this review is to serve as a guide to researchers seeking novel avenues for their investigations and underscores the importance of considering alternative models in the pursuit of scientific knowledge and medical breakthroughs, including showcasing the broad spectrum of modern approaches that are revolutionizing biomedical research and leading the way toward a more ethical, efficient, and innovative future. Models can insight into cellular processes, developmental biology, drug interaction, assessing toxicology, and understanding molecular mechanisms.

ProTox 3.0: a webserver for the prediction of toxicity of chemicals

Interaction with chemicals, present in drugs, food, environments, and consumer goods, is an integral part of our everyday life. However, depending on the amount and duration, such interactions can also result in adverse effects. With the increase in computational methods, the in silico methods can offer significant benefits to both regulatory needs and requirements for risk assessments and the pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox 3.0, which incorporates molecular similarity and machine-learning models for the prediction of 61 toxicity endpoints such as acute toxicity, organ toxicity, clinical toxicity, molecular-initiating events (MOE), adverse outcomes (Tox21) pathways, several other toxicological endpoints and toxicity off-targets. All the ProTox 3.0 models are validated on independent external sets and have shown strong performance. ProTox envisages itself as a complete, freely available computational platform for in silico toxicity prediction for toxicologists, regulatory agencies, computational chemists, and medicinal chemists. The ProTox 3.0 webserver is free and open to all users, and there is no login requirement and can be accessed via https://tox.charite.de. The web server takes a 2D chemical structure as input and reports the toxicological profile of the compound for each endpoint with a confidence score and overall toxicity radar plot and network plot.

Benchmarking of Small Molecule Feature Representations for hERG, Nav1.5, and Cav1.2 Cardiotoxicity Prediction

In the field of drug discovery, there is a substantial challenge in seeking out chemical structures that possess desirable pharmacological, toxicological, and pharmacokinetic properties. Complications arise when drugs interfere with the functioning of cardiac ion channels, leading to serious cardiovascular consequences. The discontinuation and removal of numerous approved drugs from the market or at late development stages in the pipeline due to such inhibitory effects further highlight the urgency of addressing this issue. Consequently, the early prediction of potential blockers targeting cardiac ion channels during the drug discovery process is of paramount importance. This study introduces a deep learning framework that computationally determines the cardiotoxicity associated with the voltage-gated potassium channel (hERG), the voltage-gated calcium channel (Cav1.2), and the voltage-gated sodium channel (Nav1.5) for drug candidates. The predictive capabilities of three feature representations─molecular fingerprints, descriptors, and graph-based numerical representations─are rigorously benchmarked. Additionally, a novel training and evaluation data set framework is presented, enabling predictive model training of drug off-target cardiotoxicity using a comprehensive and large curated data set covering these three cardiac ion channels. To facilitate these predictions, a robust and comprehensive small molecule cardiotoxicity prediction tool named CToxPred has been developed. It is made available as open source under the permissive MIT license at https://github.com/issararab/CToxPred.

Computational screening of natural MtbDXR inhibitors for novel anti-tuberculosis compound discovery

DXR (1-deoxy-d-xylulose-5-phosphate reductoisomerase) is an essential enzyme in the Methylerythritol 4-phosphate (MEP) pathway, which is used by M. tuberculosis and a few other pathogens. This essential enzyme in the isoprenoid synthesis pathway has been previously reported as an important target for antibiotic drug design. However, till now, there is no record of any drug-like safe molecule to inhibit MtbDXR. Numerous plant species have been traditionally used for tuberculosis therapies. In this study, we selected six plant species with anti-tubercular properties. The chemoinformatic screening was performed on 352 phytochemicals from those plants against the MtbDXR protein. After molecular docking analysis, we filtered the top five compounds, CID: 5280443 (Apigenin), CID: 3220 (Emodin), CID: 5280863 (Kaempferol), CID: 5280445 (Luteolin), and CID: 6101979 (beta-Hydroxychalcone), based on binding affinity. Molecular dynamics simulations disclosed the stability of the compounds at the active site of the proteins. Finally, in silico ADME and toxicity evaluations confirmed the compounds to be effective and safe for oral administration. Thus, our findings identified three drug-like safe molecules- Apigenin, Kaempferol, and beta-Hydroxychalcone, that showed good stability in the protein's active site. The results of this computational approach may act as an initial instruction for future in vitro and in vivo testing to identify natural drug-like compounds to treat tuberculosis.Communicated by Ramaswamy H. Sarma.

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches. The paper opens with an overview of well-known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI-based models for drug-target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines. To fully realize the promise of AI in drug discovery, the review acknowledges the difficulties that come with its uses in this field and advocates for more study and development.

In-silico identification and exploration of small molecule coumarin-1,2,3-triazole hybrids as potential EGFR inhibitors for targeting lung cancer

Globally, lung cancer is a significant public health concern due to its role as the leading cause of cancer-related mortalities. The promising target of EGFR for lung cancer treatment has been identified, providing a potential avenue for more effective therapies. The purpose of the study was to design a library of 1843 coumarin-1,2,3-triazole hybrids and screen them based on a designed pharmacophore to identify potential inhibitors targeting EGFR in lung cancer with minimum or no side effects. Pharmacophore-based screening was carried out and 60 hits were obtained. To gain a better understanding of the binding interactions between the compounds and the targeted receptor, molecular docking was conducted on the 60 screened compounds. In-silico ADME and toxicity studies were also conducted to assess the drug-likeness and safety of the identified compounds. The results indicated that coumarin-1,2,3-triazole hybrids COUM-0849, COUM-0935, COUM-0414, COUM-1335, COUM-0276, and COUM-0484 exhibit dock score of - 10.2, - 10.2, - 10.1, - 10.1, - 10, - 10 while reference molecule - 7.9 kcal/mol for EGFR (PDB ID: 4HJO) respectively. The molecular docking and molecular dynamics simulations revealed that the identified compounds formed stable interactions with the active site of EGFR, indicating their potential as inhibitors. The in-silico ADME and toxicity studies showed that the compounds had favorable drug-likeness properties and low toxicity, further supporting their potential as therapeutic agents. Finally, we performed DFT studies on the best-selected ligands to gain further insights into their electronic properties. The findings of this study provide important insights into the potential of coumarin-1,2,3-triazole hybrids as promising EGFR inhibitors for the management of lung cancer.

Pharmaceuticals and Personal Care Products in the Aquatic Environment: How Can Regions at Risk be Identified in the Future?

Pharmaceuticals and personal care products (PPCPs) are an indispensable component of a healthy society. However, they are well-established environmental contaminants, and many can elicit biological disruption in exposed organisms. It is now a decade since the landmark review covering the top 20 questions on PPCPs in the environment (Boxall et al., 2012). In the present study we discuss key research priorities for the next 10 years with a focus on how regions where PPCPs pose the greatest risk to environmental and human health, either now or in the future, can be identified. Specifically, we discuss why this problem is of importance and review our current understanding of PPCPs in the aquatic environment. Foci include PPCP occurrence and what drives their environmental emission as well as our ability to both quantify and model their distribution. We highlight critical areas for future research including the involvement of citizen science for environmental monitoring and using modeling techniques to bridge the gap between research capacity and needs. Because prioritization of regions in need of environmental monitoring is needed to assess future/current risks, we also propose four criteria with which this may be achieved. By applying these criteria to available monitoring data, we narrow the focus on where monitoring efforts for PPCPs are most urgent. Specifically, we highlight 19 cities across Africa, Central America, the Caribbean, and Asia as priorities for future environmental monitoring and risk characterization and define four priority research questions for the next 10 years. Environ Toxicol Chem 2024;43:575-588. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

First report on pesticide sub-chronic and chronic toxicities against dogs using QSAR and chemical read-across

Excessive use of chemicals is the outcome of the industrialization of agricultural sectors which leads to disturbance of ecological balance. Various agrochemicals are widely used in agricultural fields, urban green areas, and to protect from various pest-associated diseases. Due to their long-term health and environmental hazards, chronic toxicity assessment is crucial. Since in vivo and in vitro toxicity assessments are costly, lengthy, and require a large number of animal experiments, in silico toxicity approaches are better alternatives to save time, cost, and animal experimentation. We have developed the first regression-based 2D-QSAR models using different sub-chronic and chronic toxicity data of pesticides against dogs employing 2D descriptors. From the statistical results (n train = 53 - 62 ,   r 2  = 0.614 to 0.754, Q L O O 2  = 0.501 to 0.703 and   Q F 1 2  = 0.531 to 0.718, Q F 2 2 = 0.523 - 0.713 ), it was concluded that the models are robust, reliable, interpretable, and predictive. Similarity-based read-across algorithm was also used to improve the predictivity (Q F 1 2 = 0.595 - 0.813 , Q F 2 2 = 0.573 - 0.809 ) of the models. 5132 chemicals obtained from the CPDat and 1694 pesticides obtained from the PPDB database were also screened using the developed models, and their predictivity and reliability were checked. Thus, these models will be helpful for eco-toxicological data-gap filling, toxicity prediction of untested pesticides, and development of novel, safer & eco-friendly pesticides.

Developmental neurotoxicity (DNT) QSAR combination prediction model establishment and structural characteristics interpretation

With the incidence of neurodevelopmental disorders on the rise, it is imperative to screen and evaluate developmental neurotoxicity (DNT) compounds from a large number of environmental chemicals and understand their mechanisms. In this study, DNT qualitative structure-activity relationship (QSAR) study was carried out for the first time based on DNT data of mammals and structural characterization of DNT compounds was preliminarily illustrated. Five different classification algorithms and two feature selection methods were used to construct prediction models. The best model had good predictive ability on the external test set, but a small application domain (AD). Through combining of three different models, both MCC and AD values were improved. Furthermore, electronical properties, van der Waals volume-related properties and S, Cl or P containing substructure were found to be associated with DNT through modeling descriptors analysis and structure alerts (SAs) identification. This study lays a foundation for further DNT prediction of environmental exposures in human and contributes to the understanding of DNT mechanism.

In silico study of antibacterial tyrosyl-tRNA synthetase and toxicity of main phytoconstituents from three active essential oils

The misuse and overuse of antibiotics have resulted in antibiotic resistance. However, there are alternative approaches that could either substitute antibiotics or enhance their effectiveness without harmful side effects. One such approach is the use of terpene-rich essential oils. In this study, we aimed to demonstrate the antibacterial activity of the main components of three plant essential oils, namely Anthemis punctata, Anthemis pedunculata and Daucus crinitus. Specifically, we targeted bacterial tyrosyl-tRNA synthetase, an enzyme that plays a critical role in bacterial protein synthesis. To investigate how the phytocompounds interact with the enzyme's active sites, we employed a molecular docking study using Autodock Software Tools 1.5.7. Our findings revealed that all 28 phytocompounds bound to the enzyme's active sites with binding energies ranging from -6.96 to -4.03 kcal/mol. These results suggest that terpene-rich essential oils could be a potential source of novel antimicrobial agents.Communicated by Ramaswamy H. Sarma.

Machine Learning Reinforced Genetic Algorithm for Massive Targeted Discovery of Selectively Cytotoxic Inorganic Nanoparticles

Nanoparticles (NPs) have been employed as drug delivery systems (DDSs) for several decades, primarily as passive carriers, with limited selectivity. However, recent publications have shed light on the emerging phenomenon of NPs exhibiting selective cytotoxicity against cancer cell lines, attributable to distinct metabolic disparities between healthy and pathological cells. This study revisits the concept of NPs selective cytotoxicity, and for the first time proposes a high-throughput in silico screening approach to massive targeted discovery of selectively cytotoxic inorganic NPs. In the first step, this work trains a gradient boosting regression model to predict viability of NP-treated cell lines. The model achieves mean cross-validation (CV) Q2 = 0.80 and root mean square error (RMSE) of 13.6. In the second step, this work develops a machine learning (ML) reinforced genetic algorithm (GA), capable of screening >14 900 candidates/min, to identify the best-performing selectively cytotoxic NPs. As proof-of-concept, DDS candidates for the treatment of liver cancer are screened on HepG2 and hepatocytes cell lines resulting in Ag NPs with selective toxicity score of 42%. This approach opens the door for clinical translation of NPs, expanding their therapeutic application to a wider range of chemical space of NPs and living organisms such as bacteria and fungi.

Predictive binary mixture toxicity modeling of fluoroquinolones (FQs) and the projection of toxicity of hypothetical binary FQ mixtures: a combination of 2D-QSAR and machine-learning approaches

All sorts of chemicals get degraded under various environmental stresses, and the degradates coexist with the parent compounds as mixtures in the environment. Antibiotics emerge as an additional concern due to the bioactive nature of both the parent compound and degradation products and their combined exposure to the environment. Therefore, environmental risk assessment of antibiotics and their degradation products is very much necessary. In this direction, we made use of in silico new approach methodologies (NAMs) and machine-learning algorithms. In this study, we have developed a robust and predictive mixture-quantitative structure-activity relationship (QSAR) model with promising quality and predictability (internal: MAETrain = 0.085, QLOO2 = 0.849, external: MAETest = 0.090, and QF12 = 0.859) for predicting the toxicity of the mixtures of a class of antibiotics and their degradation products. To obtain the predictive model, toxicity data of 78 binary fluoroquinolone mixtures in E. coli (endpoint: log 1/IC50 in molar) have been utilized. We have used only 0D-2D descriptors to efficiently encode the structural features of mixture components without any additional complexities. The optimization of the class of mixture descriptors has been performed in this study by using three different mixing rules (linear combination of molecular contributions, the squared molecular contributions, and the norm of molecular contributions). Different machine-learning approaches namely, random forest (RF), ada boost, gradient boost (GB), extreme gradient boost (XGB), support vector machine (SVM), linear support vector machine (LSVM), and ridge regression (RR) have been employed here apart from the conventional partial least squares (PLS) regression to optimize the modeling approach. A rigorous validation protocol has been used for assessing the goodness-of-fit, robustness, and external predictability of the models. Finally, the toxicity of possible untested mixtures of different photodegradation products of fluoroquinolones has been predicted using the best model reported in this study.

Application of interpretable machine learning models to improve the prediction performance of ionic liquids toxicity

With the wide application prospect of ionic liquids (ILs) as solvent in the future industry, in order to promote green and sustainable chemical engineering, the toxicity problem of common concern has been systematically modeled. Machine learning has promoted the development of chemical property prediction model with its powerful data processing ability. Two typical ensemble learning models, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), were used to model the toxicity of ILs to Vibrio fischeri in this work. The model's hyperparameters were fine-tuned using Bayesian optimization, and its robustness was enhanced through the 5-fold cross validation. The results of the model comparison showed that the XGBoost model exhibited good generalization ability. In addition, the SHapley Additive exPlanations (SHAP) method was used to explain the model in more detail and the XGBoost model was used to supplement the toxicity value matrix of 1590 ILs.

Novel Flurbiprofen Derivatives as Antioxidant and Anti-Inflammatory Agents: Synthesis, In Silico, and In Vitro Biological Evaluation

In this study, we present the synthesis of five novel compounds by combining flurbiprofen with various substituted 2-phenethylamines. The synthesized derivatives underwent comprehensive characterization using techniques such as 1H- and 13C-NMR spectroscopy, UV-Vis spectroscopy, and high-resolution mass spectrometry (HRMS). Detailed HRMS analysis was performed for each of these newly created molecules. The biological activities of these compounds were assessed through in vitro experiments to evaluate their potential as anti-inflammatory and antioxidant agents. Furthermore, the lipophilicity of these derivatives was determined, both theoretically using the cLogP method and experimentally through partition coefficient (RM) measurements. To gain insights into their binding affinity, we conducted an in silico analysis of the compounds' interactions with human serum albumin (HSA) using molecular docking studies. Our findings reveal that all of the newly synthesized compounds exhibit significant anti-inflammatory and antioxidant activities, with results statistically comparable to the reference compounds. Molecular docking studies further explain the observed in vitro results, shedding light on the molecular mechanisms behind their biological activities. Using in silico method, toxicity was calculated, resulting in LD50 values. Depending on the administration route, the novel flurbiprofen derivatives show lower toxicity compared to the standard flurbiprofen.

Synthesis, Characterizations, and Quantum Chemical Investigations on Imidazo[1,2-a]pyrimidine-Schiff Base Derivative: (E)-2-Phenyl-N-(thiophen-2-ylmethylene)imidazo[1,2-a]pyrimidin-3-amine

In this study, (E)-2-phenyl-N-(thiophen-2-ylmethylene)imidazo[1,2-a]pyrimidin-3-amine (3) is synthesized, and detailed spectral characterizations using 1H NMR, 13C NMR, mass, and Fourier transform infrared (FT-IR) spectroscopy were performed. The optimized geometry was computed using the density functional theory method at the B3LYP/6-311++G(d,p) basis set. The theoretical FT-IR and NMR (1H and 13C) analysis are agreed to validate the structural assignment made for (3). Frontier molecular orbitals, molecular electrostatic potential, Mulliken atomic charge, electron localization function, localized orbital locator, natural bond orbital, nonlinear optical, Fukui functions, and quantum theory of atoms in molecules analyses are undertaken and meticulously interpreted, providing profound insights into the molecular nature and behaviors. In addition, ADMET and drug-likeness studies were carried out and investigated. Furthermore, molecular docking and molecular dynamics simulations have been studied, indicating that this is an ideal molecule to develop as a potential vascular endothelial growth factor receptor-2 inhibitor.

Identification of Antagonistic Action of Pyrrolizidine Alkaloids in Muscarinic Acetylcholine Receptor M1 by Computational Target Prediction Analysis

Pyrrolizidine alkaloids (PAs) are one of the largest distributed classes of toxins in nature. They have a wide range of toxicity, such as hepatotoxicity, pulmonary toxicity, neuronal toxicity, and carcinogenesis. Yet, biological targets responsible for these effects are not well addressed. Using methods of computational biology for target identification, we tested more than 200 PAs. We used a machine-learning approach that applies structural similarity for target identification, ChemMapper, and SwissTargetPrediction. The predicted targets with high probabilities were muscarinic acetylcholine receptor M1. The predicted interactions between these two targets and PAs were further studied by molecular docking-based binding energies using AutoDock and VinaLC, which revealed good binding affinities. The PAs are bound to the same binding pocket as pirenzepine, a known M1 antagonist. These results were confirmed by in vitro assays showing that PAs increased the levels of intracellular calcium. We conclude that PAs are potential acetylcholine receptor M1 antagonists. This elucidates for the first time the serious neuro-oncological toxicities exerted by PA consumption.

Identifying novel therapeutic inhibitors to target FMS-like tyrosine kinase-3 (FLT3) against acute myeloid leukemia: a molecular docking, molecular dynamics, and DFT study

Around 30% of acute myeloid leukemia (AML) patients have triggering mutations in Feline McDonough Sarcoma (FMS)-like tyrosine kinase 3 (FLT3), which has been suggested as a possible therapeutic candidate for AML therapy. Many tyrosine kinase inhibitors are available and have a wide variety of applications in the treatment of cancer by inhibiting subsequent steps of cell proliferation. Therefore, our study aims to identify effective antileukemic agents against FLT3 gene. Initially, well-known antileukemic drug candidates have been chosen to generate a structure-based pharmacophore model to assist the virtual screening of 217,77,093 compounds from the Zinc database. The final hits compounds were retrieved and evaluated by docking against the target protein, where the top four compounds have been selected for the analysis of ADMET. Based on the density functional theory (DFT), the geometry optimization, frontier molecular orbital (FMO), HOMO-LUMO, and global reactivity descriptor values have been evaluated that confirming a satisfactory profile and reactivity order for the selected candidates. In comparison to control compounds, the docking results revealed that the four compounds had substantial binding energies (-11.1 to -11.5 kcal/mol) with FLT3. The physicochemical and ADMET (adsorption, distribution, metabolism, excretion, toxicity) prediction results corresponded to the bioactive and safe candidates. Molecular dynamics (MD) confirmed the better binding affinity and stability compared to gilteritinib as a potential FLT3 inhibitor. In this study, a computational approach has been performed that found a better docking and dynamics score against target proteins, indicating potent and safe antileukemic agents, furthermore in-vivo and in-vitro investigations are recommended.Communicated by Ramaswamy H. Sarma.

Hydroxamic acid derivatives as selective HDAC3 inhibitors: computer-aided drug design strategies

Histone deacetylases (HDACs) are critical epigenetic drug targets that have gained significant attention in the scientific community for the treatment of cancer. The currently marketed HDAC inhibitors lack selectivity for the various HDAC isoenzymes. Here, we describe our protocol for the discovery of novel potential hydroxamic acid based HDAC3 inhibitors through pharmacophore modeling, virtual screening, docking, molecular dynamics (MD) simulation and toxicity studies. The ten pharmacophore hypotheses were established, and their reliability was validated by different ROC (receiving operator curve) analysis. Among them, the best model (Hypothesis 9 or RRRA) was employed for searching SCHEMBL, ZINC and MolPort database to screen out hit molecules as selective HDAC3 inhibitors, followed by different docking stages. MD simulation (50 ns) and MMGBSA study were performed to study the stability of ligand binding modes and with the help of trajectory analysis, to calculate the ligand-receptor complex RMSD (root-mean-square deviation), RMSF (root-mean-square fluctuation) and H-bond distance, etc. Finally, in-silico toxicity studies were performed on top screened molecules and compared with reference drug SAHA and established structure-activity relationship (SAR). The results indicated that compound 31, with high inhibitory potency and less toxicity (probability value 0.418), is suitable for further experimental analysis.Communicated by Ramaswamy H. Sarma.

Where developmental toxicity meets explainable artificial intelligence: state-of-the-art and perspectives

INTRODUCTION

The application of Artificial Intelligence (AI) to predictive toxicology is rapidly increasing, particularly aiming to develop non-testing methods that effectively address ethical concerns and reduce economic costs. In this context, Developmental Toxicity (Dev Tox) stands as a key human health endpoint, especially significant for safeguarding maternal and child well-being.

AREAS COVERED

This review outlines the existing methods employed in Dev Tox predictions and underscores the benefits of utilizing New Approach Methodologies (NAMs), specifically focusing on eXplainable Artificial Intelligence (XAI), which proves highly efficient in constructing reliable and transparent models aligned with recommendations from international regulatory bodies.

EXPERT OPINION

The limited availability of high-quality data and the absence of dependable Dev Tox methodologies render XAI an appealing avenue for systematically developing interpretable and transparent models, which hold immense potential for both scientific evaluations and regulatory decision-making.

Inhibition potential of natural flavonoids against selected omicron (B.1.19) mutations in the spike receptor binding domain of SARS-CoV-2: a molecular modeling approach

The omicron (B.1.19) variant of contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is considered a variant of concern (VOC) due to its increased transmissibility and highly infectious nature. The spike receptor-binding domain (RBD) is a hotspot of mutations and is regarded as a prominent target for screening drug candidates owing to its crucial role in viral entry and immune evasion. To date, no effective therapy or antivirals have been reported; therefore, there is an urgent need for rapid screening of antivirals. An extensive molecular modelling study has been performed with the primary goal to assess the inhibition potential of natural flavonoids as inhibitors against RBD from a manually curated library. Out of 40 natural flavonoids, five natural flavonoids, namely tomentin A (-8.7 kcal/mol), tomentin C (-8.6 kcal/mol), hyperoside (-8.4 kcal/mol), catechin gallate (-8.3 kcal/mol), and corylifol A (-8.2 kcal/mol), have been considered as the top-ranked compounds based on their binding affinity and molecular interaction profiling. The state-of-the-art molecular dynamics (MD) simulations of these top-ranked compounds in complex with RBD exhibited stable dynamics and structural compactness patterns on 200 nanoseconds. Additionally, complexes of these molecules demonstrated favorable free binding energies and affirmed the docking and simulation results. Moreover, the post-simulation validation of these interacted flavonoids using principal component analysis (PCA) revealed stable interaction patterns with RBD. The integrated results suggest that tomentin A, tomentin C, hyperoside, catechin gallate, and corylifol A might be effective against the emerging variants of SARS-CoV-2 and should be further evaluated using in-vitro and in-vivo experiments.Communicated by Ramaswamy H. Sarma.

Use of in silico and in vitro methods as a potential new approach methodologies (NAMs) for (photo)mutagenicity and phototoxicity risk assessment of agrochemicals

The increased use of agrochemicals raises concerns about environmental, animal, and mainly human toxicology. The development of New Approach Methodologies (NAMs) for toxicological risk assessment including new in vitro tests and in silico protocols is encouraged. Although agrochemical mutagenicity testing is well established, a complementary alternative approach may contribute to increasing reliability, with the consequent reduction of false-positive results that lead to unnecessary use of animals in follow-up in vivo testing. Additionally, it is unreasonable to underestimate the phototoxic effects of an accidental dermal exposure to agrochemicals during agricultural work or domestic application in the absence of adequate personal protection equipment, especially in terms of photomutagenicity. In this scenario, we addressed the integration of in vitro and in silico techniques as NAMs to assess the mutagenic and phototoxic potential of agrochemicals. In the present study we used the yno1 S. cerevisiae strain as a biomodel for in vitro assessment of agrochemical mutagenicity, both in the absence and in the presence of simulated sunlight. In parallel, in silico predictions were performed using a combination of expert rule-based and statistical-based models to assess gene mutations and phototoxicity. None of the tested agrochemicals showed mutagenic potential in the two proposed approaches. The Gly and 2,4D herbicides were photomutagenic in the in vitro yeast test despite the negative in silico prediction of phototoxicity. Herein, we demonstrated a novel experimental approach combining both in silico and in vitro experiments to address the complementary investigation of the phototoxicity and (photo)mutagenicity of agrochemicals. These findings shed light on the importance of investigating and reconsidering the photosafety assessment of these products, using not only photocytotoxicity assays but also photomutagenicity assays, which should be encouraged.

TICBase: Integrated Resource for Data on Drug and Environmental Chemical Interactions with Mammalian Drug Transporters

Environmental health science seeks to predict how environmental toxins, chemical toxicants, and prescription drugs accumulate and interact within the body. Xenobiotic transporters of the ATP-binding cassette (ABC) and solute carrier (SLC) superfamilies are major determinants of the uptake and disposition of xenobiotics across the kingdoms of life. The goal of this study was to integrate drug and environmental chemical interactions of mammalian ABC and SLC proteins in a centralized, integrative database. We built upon an existing publicly accessible platform-the "TransPortal"-which was updated with novel data and searchable features on transporter-interfering chemicals from manually curated literature data. The integrated resource TransPortal-TICBase (https://transportal.compbio.ucsf.edu) now contains information on 46 different mammalian xenobiotic transporters of the ABC- and SLC-type superfamilies, including 13 newly added rodent and 2 additional human drug transporters, 126 clinical drug-drug interactions, and a more than quadrupled expansion of the initial in vitro chemical interaction data from 1,402 to 6,296 total interactions. Based on our updated database, environmental interference with major human and rodent drug transporters occurs across the ABC- and SLC-type superfamilies, with kinetics indicating that some chemicals, such as the ionic liquid 1-hexylpyridinium chloride and the antiseptic chlorhexidine, can act as strong inhibitors with potencies similar or even higher than pharmacological model inhibitors. The new integrated web portal serves as a central repository of current and emerging data for interactions of prescription drugs and environmental chemicals with human drug transporters. This archive has important implications for predicting adverse drug-drug and drug-environmental chemical interactions and can serve as a reference website for the broader scientific community of clinicians and researchers.

Mining toxicogenomic data for dose-responsive pathways: implications in advancing next-generation risk assessment

Introduction: While targeted investigation of key toxicity pathways has been instrumental for biomarker discovery, unbiased and holistic analysis of transcriptomic data provides a complementary systems-level perspective. However, in a systematic context, this approach has yet to receive comprehensive and methodical implementation. Methods: Here, we took an integrated bioinformatic approach by re-analyzing publicly available MCF7 cell TempO-seq data for 44 ToxCast chemicals using an alternative pipeline to demonstrate the power of this approach. The original study has focused on analyzing the gene signature approach and comparing them to in vitro biological pathway altering concentrations determined from ToxCast HTS assays. Our workflow, in comparison, involves sequential differential expression, gene set enrichment, benchmark dose modeling, and identification of commonly perturbed pathways by network visualization. Results: Using this approach, we identified dose-responsive molecular changes, biological pathways, and points of departure in an untargeted manner. Critically, benchmark dose modeling based on pathways recapitulated points of departure for apical endpoints, while also revealing additional perturbed mechanisms missed by single endpoint analyses. Discussion: This systems-toxicology approach provides multifaceted insights into the complex effects of chemical exposures. Our work highlights the importance of unbiased data-driven techniques, alongside targeted methods, for comprehensively evaluating molecular initiating events, dose-response relationships, and toxicity pathways. Overall, integrating omics assays with robust bioinformatics holds promise for improving chemical risk assessment and advancing new approach methodologies (NAMs).

Harnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Per- and Polyfluoroalkyl Substances (PFASs)

Many per- and polyfluoroalkyl substances (PFASs) pose significant health hazards due to their bioactive and persistent bioaccumulative properties. However, assessing the bioactivities of PFASs is both time-consuming and costly due to the sheer number and expense of in vivo and in vitro biological experiments. To this end, we harnessed new unsupervised/semi-supervised machine learning models to automatically predict bioactivities of PFASs in various human biological targets, including enzymes, genes, proteins, and cell lines. Our semi-supervised metric learning models were used to predict the bioactivity of PFASs found in the recent Organisation of Economic Co-operation and Development (OECD) report list, which contains 4730 PFASs used in a broad range of industries and consumers. Our work provides the first semi-supervised machine learning study of structure-activity relationships for predicting possible bioactivities in a variety of PFAS species.

Usage of model combination in computational toxicology

New Approach Methodologies (NAMs) have ushered in a new era in the field of toxicology, aiming to replace animal testing. However, despite these advancements, they are not exempt from the inherent complexities associated with the study's endpoint. In this review, we have identified three major groups of complexities: mechanistic, chemical space, and methodological. The mechanistic complexity arises from interconnected biological processes within a network that are challenging to model in a single step. In the second group, chemical space complexity exhibits significant dissimilarity between compounds in the training and test series. The third group encompasses algorithmic and molecular descriptor limitations and typical class imbalance problems. To address these complexities, this work provides a guide to the usage of a combination of predictive Quantitative Structure-Activity Relationship (QSAR) models, known as metamodels. This combination of low-level models (LLMs) enables a more precise approach to the problem by focusing on different sub-mechanisms or sub-processes. For mechanistic complexity, multiple Molecular Initiating Events (MIEs) or levels of information are combined to form a mechanistic-based metamodel. Regarding the complexity arising from chemical space, two types of approaches were reviewed to construct a fragment-based chemical space metamodel: those with and without structure sharing. Metamodels with structure sharing utilize unsupervised strategies to identify data patterns and build low-level models for each cluster, which are then combined. For situations without structure sharing due to pharmaceutical industry intellectual property, the use of prediction sharing, and federated learning approaches have been reviewed. Lastly, to tackle methodological complexity, various algorithms are combined to overcome their limitations, diverse descriptors are employed to enhance problem definition and balanced dataset combinations are used to address class imbalance issues (methodological-based metamodels). Remarkably, metamodels consistently outperformed classical QSAR models across all cases, highlighting the importance of alternatives to classical QSAR models when faced with such complexities.

Review of machine learning and deep learning models for toxicity prediction

The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.

In silico simulations and molecular descriptors to predict in vitro transactivation potencies of Baikal seal estrogen receptors by environmental contaminants

Baikal seals (Pusa sibirica) are vulnerable to high levels of organic pollutants. Here, we evaluated the transactivation potencies of bisphenols (BPs) and hydroxylated polychlorinated biphenyls (OH-PCBs) via the Baikal seal estrogen receptor α and β (bsERα and bsERβ) using in vitro and in silico approaches. In vitro reporter gene assays showed that most BPs and OH-PCBs exhibited estrogenic activity with bsER sub-type-specific potency. Among the BPs tested, bisphenol AF showed the lowest EC50 for both bsERs. 4'-OH-CB50 and 4'-OH-CB30 showed the lowest EC50 among OH-PCBs tested for bsERα and bsERβ, respectively. 4-((4-Isopropoxyphenyl)-sulfonyl)phenol, 4'-OH-CB72, and 4'-OH-CB121 showed weak bsERα-specific transactivation. Only 4-OH-CB107 did not affect both bsERs. In silico docking simulations revealed the binding affinities of these chemicals to bsERs and partially explained the in vitro results. Using the in silico simulations and molecular descriptors as explanatory variables and the in vitro results as objective variables, the quantitative structure-activity relationship (QSAR) models constructed for classification and regression accurately separated bsER-active compounds from non-active compounds and predicted the in vitro bsERα- and bsERβ-transactivation potencies, respectively. The QSAR models also suggested that chemical polarity, van der Waals surface area, bridging atom structure, position of the phenolic-OH group, and ligand interactions with key residues of the ligand binding pocket are critical variables to account for the bsER transactivation potency of the test compounds. We also succeeded in constructing computational models for predicting in vitro transactivation potencies of mouse ERs in the same manner, demonstrating the applicability of our approach independent of species-specific responses.

Identification of potential flavonoid compounds as antibacterial therapeutics against Klebsiella pneumoniae infection using structure-based virtual screening and molecular dynamics simulation

Klebsiella pneumoniae, which is among the top three pathogens on WHO's priority list, is one of the gram-negative bacteria that doctors and researchers around the world have fought for decades. Capsular polysaccharide (CPS) protein is extensively recognized as an important K. pneumoniae virulence factor. Thus, CPS has become the most characterized target for the discovery of novel drug candidates. The ineffectiveness of currently existing antibiotics urges the search for potent antimicrobial compounds. Flavonoids are a group of plant metabolites that have antibacterial potential and can enhance the present medications to elicit improved results against diverse diseases without adverse reactions. Henceforth, the present study aims to illustrate the inhibitory potential of flavonoids with varying pharmacological properties, targeting the CPS protein of K. pneumoniae by in silico approaches. The flavonoid compounds (n = 169) were retrieved from the PubChem database and screened using the structure-based virtual screening approach. Compounds with the highest binding score were estimated through their pharmacokinetic effects by ADMET descriptors. Finally, four potential inhibitors with PubChem CID: (4301534, 5213, 5481948, and 637080) were selected after molecular docking and drug-likeness analysis. All four lead compounds were employed for the MDS analysis of a 100 ns time period. Various studies were undertaken to assess the stability of the protein-ligand complexes. The binding free energy was computed using MM-PBSA, and the outcomes indicated that the molecules are having stable interactions with the binding site of the target protein. The results revealed that all four compounds can be employed as potential therapeutics against K. pneumoniae.

DCAMCP: A deep learning model based on capsule network and attention mechanism for molecular carcinogenicity prediction

The carcinogenicity of drugs can have a serious impact on human health, so carcinogenicity testing of new compounds is very necessary before being put on the market. Currently, many methods have been used to predict the carcinogenicity of compounds. However, most methods have limited predictive power and there is still much room for improvement. In this study, we construct a deep learning model based on capsule network and attention mechanism named DCAMCP to discriminate between carcinogenic and non-carcinogenic compounds. We train the DCAMCP on a dataset containing 1564 different compounds through their molecular fingerprints and molecular graph features. The trained model is validated by fivefold cross-validation and external validation. DCAMCP achieves an average accuracy (ACC) of 0.718 ± 0.009, sensitivity (SE) of 0.721 ± 0.006, specificity (SP) of 0.715 ± 0.014 and area under the receiver-operating characteristic curve (AUC) of 0.793 ± 0.012. Meanwhile, comparable results can be achieved on an external validation dataset containing 100 compounds, with an ACC of 0.750, SE of 0.778, SP of 0.727 and AUC of 0.811, which demonstrate the reliability of DCAMCP. The results indicate that our model has made progress in cancer risk assessment and could be used as an efficient tool in drug design.

Compositional profiling and molecular docking studies of Eucalyptus polybrachtea essential oil against mucormycosis and aspergillosis

Essential oil (EO) from Eucalyptus polybrachtea is used as complementary and traditional medicine worldwide. The present study aimed at compositional profiling of EO and molecular docking of EO's bioactive compound 1,8 cineole against fungal enzymes involved in the riboflavin synthesis pathway, namely riboflavin synthase (RS), riboflavin biosynthesis protein RibD domain-containing protein (RibD), and 3,4-dihydroxy-2-butanone 4-phosphate synthase (DBPS) as apposite sites for drug designing against aspergillosis and mucormycosis, and in vitro confirmation. The compositional profile of EO was completed by GC-FID analysis. For molecular docking, the Patchdock tool was used. The ligand-enzyme 3-D interactions were examined, and ADMET properties (absorption, distribution, metabolism, excretion, and toxicity) were calculated. GC-FID discovered the occurrence of 1,8 cineole as a major component in EO, which was subsequently used for docking analysis. The docking analysis revealed that 1,8 cineole actively bound to RS, RibD, and DBPS fungal enzymes. The results of the docking studies demonstrated that the ligand 1,8 cineole exhibited H-bond and hydrophobic interactions with RS, RibD, and DBPS fungal enzymes. 1,8 cineole obeyed Lpinsky's rule and exhibited adequate bioactivity. Wet-lab authentication was achieved by using three fungal strains: Aspergillus niger, Aspergillus oryzae, and Mucor sp. Wet lab results indicated that EO was able to inhibit fungal growth.

Making the Case for Quantum Mechanics in Predictive Toxicology─Nearly 100 Years Too Late?

The use of quantum mechanics (QM) has long been the norm to study covalent-binding phenomena in chemistry and biochemistry. The pharmaceutical industry leverages QM models explicitly in covalent drug discovery and implicitly to characterize short-range interactions in noncovalent binding. Predictive toxicology has resisted widespread adoption of QM, including in the pharmaceutical industry, despite its obvious relevance to the metabolic processes in the upstream of adverse outcome pathways and advances in both QM methods and computational resources, which support fit-for-purpose applications in reasonable timeframes. Here, we make the case for embracing QM as an indispensable part of a toxicologist's toolkit. We argue that QM provides the necessary orthogonality to alert-based expert systems and traditional QSARs, consistent with calls for animal-free integrated testing strategies for safety assessments of commercial chemicals. We outline existing roadblocks to this transition, including the need to train model developers in QM and the shift toward service-based toxicity models that utilize high-performance computing clusters. Lastly, we describe recent examples of successful implementations of QM in hazard assessments and propose how in silico toxicology can be further advanced by integrating QM with artificial intelligence.

Pharmacoinformatics and Breed-Based De Novo Hybridization Studies to Develop New Neuraminidase Inhibitors as Potential Anti-Influenza Agents

Influenza represents a profoundly transmissible viral ailment primarily afflicting the respiratory system. Neuraminidase inhibitors constitute a class of antiviral therapeutics employed in the management of influenza. These inhibitors impede the liberation of the viral neuraminidase protein, thereby impeding viral dissemination from the infected cell to host cells. As such, neuraminidase has emerged as a pivotal target for mitigating influenza and its associated complications. Here, we apply a de novo hybridization approach based on a breed-centric methodology to elucidate novel neuraminidase inhibitors. The breed technique amalgamates established ligand frameworks with the shared target, neuraminidase, resulting in innovative inhibitor constructs. Molecular docking analysis revealed that the seven synthesized breed molecules (designated Breeds 1-7) formed more robust complexes with the neuraminidase receptor than conventional clinical neuraminidase inhibitors such as zanamivir, oseltamivir, and peramivir. Pharmacokinetic evaluations of the seven breed molecules (Breeds 1-7) demonstrated favorable bioavailability and optimal permeability, all falling within the specified parameters for human application. Molecular dynamics simulations spanning 100 nanoseconds corroborated the stability of these breed molecules within the active site of neuraminidase, shedding light on their structural dynamics. Binding energy assessments, which were conducted through MM-PBSA analysis, substantiated the enduring complexes formed by the seven types of molecules and the neuraminidase receptor. Last, the investigation employed a reaction-based enumeration technique to ascertain the synthetic pathways for the synthesis of the seven breed molecules.

A review on potential approach for in silico toxicity analysis of respirable fraction of ambient particulate matter

Epidemiological and toxicological studies have shown the adverse effect of ambient particulate matter (PM) on respiratory and cardiovascular systems inside the human body. Various cellular and acellular assays in literature use indicators like ROS generation, cell inflammation, mutagenicity, etc., to assess PM toxicity and associated health effects. The presence of toxic compounds in respirable PM needs detailed studies for proper understanding of absorption, distribution, metabolism, and excretion mechanisms inside the body as it is difficult to accurately imitate or simulate these mechanisms in lab or animal models. The leaching kinetics of the lung fluid, PM composition, retention time, body temperature, etc., are hard to mimic in an artificial experimental setup. Moreover, the PM size fraction also plays an important role. For example, the ultrafine particles may directly enter systemic circulations while coarser PM10 may be trapped and deposited in the tracheo-bronchial region. Hence, interpretation of these results in toxicity models should be done judiciously. Computational models predicting PM toxicity are rare in the literature. The variable composition of PM and lack of proper understanding for their synergistic role inside the body are prime reasons behind it. This review explores different possibilities of in silico modeling and suggests possible approaches for the risk assessment of PM particles. The toxicity testing approach for engineered nanomaterials, drugs, food industries, etc., have also been investigated for application in computing PM toxicity.

Selected phytochemicals of Momordica charantia L. as potential anti-DENV-2 through the docking, DFT and molecular dynamic simulation

Dengue fever is now one of the major global health concerns particularly for tropical and sub-tropical countries. However, there has been no FDA approved medication to treat dengue fever. Researchers are looking into DENV NS5 RdRp protease as a potential therapeutic target for discovering effective anti-dengue agents. The aim of this study to discover dengue virus inhibitor from a set of five compounds from Momordica charantia L. using a series of in-silico approaches. The compounds were docked into the active area of the DENV-2 NS5 RdRp protease to obtain the hit compounds. The successful compounds underwent additional testing for a study on drug-likeness similarity. Our study obtained Momordicoside-I as a lead compound which was further exposed to the Cytochrome P450 (CYP450) toxicity analysis to determine the toxicity based on docking scores and drug-likeness studies. Moreover, DFT studies were carried out to calculate the thermodynamic, molecular orbital and electrostatic potential properties for the lead compound. Moreover, the lead compound was next subjected to molecular dynamic simulation for 200 ns in order to confirm the stability of the docked complex and the binding posture discovered during docking experiment. Overall, the lead compound has demonstrated good medication like qualities, non-toxicity, and significant binding affinity towards the DENV-2 RdRp enzyme.Communicated by Ramaswamy H. Sarma.

In-silico design, pharmacophore-based screening, and molecular docking studies reveal that benzimidazole-1,2,3-triazole hybrids as novel EGFR inhibitors targeting lung cancer

Lung cancer is a complex and heterogeneous disease, which has been associated with various molecular alterations, including the overexpression and mutations of the epidermal growth factor receptor (EGFR). In this study, designed a library of 1843 benzimidazole-1,2,3-triazole hybrids and carried out pharmacophore-based screening to identify potential EGFR inhibitors. The 164 compounds were further evaluated using molecular docking and molecular dynamics simulations to understand the binding interactions between the compounds and the receptor. In-si-lico ADME and toxicity studies were also conducted to assess the drug-likeness and safety of the identified compounds. The results of this study indicate that benzimidazole-1,2,3-triazole hybrids BENZI-0660, BENZI-0125, BENZI-0279, BENZI-0415, BENZI-0437, and BENZI-1110 exhibit dock scores of -9.7, -9.6, -9.6, -9.6, -9.6, -9.6 while referencing molecule -7.9 kcal/mol for EGFR (PDB ID: 4HJO), respectively. The molecular docking and molecular dynamics simulations revealed that the identified compounds formed stable interactions with the active site of EGFR, indicating their potential as inhibitors. The in-silico ADME and toxicity studies showed that the compounds had favorable drug-likeness properties and low toxicity, further supporting their potential as therapeutic agents. Finally, performed DFT studies on the best-selected ligands to gain further insights into their electronic properties. The findings of this study provide important insights into the potential of benzimidazole-1,2,3-triazole hybrids as promising EGFR inhibitors for the treatment of lung cancer. This research opens up a new avenue for the discovery and development of potent and selective EGFR inhibitors for the treatment of lung cancer.Communicated by Ramaswamy H. Sarma.

Identifying Differences in the Performance of Machine Learning Models for Off-Targets Trained on Publicly Available and Proprietary Data Sets

Each year, publicly available databases are updated with new compounds from different research institutions. Positive experimental outcomes are more likely to be reported; therefore, they account for a considerable fraction of these entries. Established publicly available databases such as ChEMBL allow researchers to use information without constrictions and create predictive tools for a broad spectrum of applications in the field of toxicology. Therefore, we investigated the distribution of positive and nonpositive entries within ChEMBL for a set of off-targets and its impact on the performance of classification models when applied to pharmaceutical industry data sets. Results indicate that models trained on publicly available data tend to overpredict positives, and models based on industry data sets predict negatives more often than those built using publicly available data sets. This is strengthened even further by the visualization of the prediction space for a set of 10,000 compounds, which makes it possible to identify regions in the chemical space where predictions converge. Finally, we highlight the utilization of these models for consensus modeling for potential adverse events prediction.

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

In-silico identification of small molecule benzofuran-1,2,3-triazole hybrids as potential inhibitors targeting EGFR in lung cancer via ligand-based pharmacophore modeling and molecular docking studies

Lung cancer is one of the most common and deadly types of cancer worldwide, and the epidermal growth factor receptor (EGFR) has emerged as a promising therapeutic target for the treatment of this disease. In this study, we designed a library of 1840 benzofuran-1,2,3-triazole hybrids and conducted pharmacophore-based screening to identify potential EGFR inhibitors. The 20 identified compounds were further evaluated using molecular docking and molecular dynamics simulations to understand their binding interactions with the EGFR receptor. In-silico ADME and toxicity studies were also performed to assess their drug-likeness and safety profiles. The results of this study showed the benzofuran-1,2,3-triazole hybrids BENZ-0454, BENZ-0143, BENZ-1292, BENZ-0335, BENZ-0332, and BENZ-1070 dock score of - 10.2, - 10, - 9.9, - 9.8, - 9.7, - 9.6, while reference molecule - 7.9 kcal/mol for EGFR (PDB ID: 4HJO) respectively. The molecular docking and molecular dynamics simulations revealed that the identified compounds formed stable interactions with the active site of the receptor, indicating their potential as inhibitors. The in-silico ADME and toxicity studies suggested that the compounds had good pharmacokinetic and safety profiles, further supporting their potential as therapeutic agents. Finally, performed DFT studies on the best-selected ligands to gain further insights into their electronic properties. The findings of this study provide important insights into the potential of benzofuran-1,2,3-triazole hybrids as promising EGFR inhibitors for the treatment of lung cancer. Overall, this study provides a valuable starting point for the development of novel EGFR inhibitors with improved efficacy and safety profiles.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00157-1.