Predictive Toxicology Research Articles

ToxRefDB v2.1: update to curated in vivo study data in the Toxicity Reference Database.

The Toxicity Reference Database (ToxRefDB) contains in vivo study data from over 5,900 guideline or guideline-like studies for over 1,100 chemicals. The database includes information regarding study design, chemical treatment, dosing, treatment group parameters, treatment-related (significantly different from control) and critical (adverse) effects, guided by a controlled effect vocabulary, as well as endpoint testing status according to health effects guideline requirements. ToxRefDB v2.1 is an update to address a compilation error found in ToxRefDB v2.0 that resulted in some effects being inadvertently omitted from the database. Though effect data has been recovered, no new studies were added. The recovered data improves the utility of ToxRefDB as a resource for curated legacy in vivo information, which enhances scientific confidence in vitro high-throughput screening of chemicals and supports retrospective and predictive toxicology applications for which outcomes in traditional regulatory toxicology studies serve as reference information.

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.

VenomPred 2.0: A Novel In Silico Platform for an Extended and Human Interpretable Toxicological Profiling of Small Molecules.

The application of artificial intelligence and machine learning (ML) methods is becoming increasingly popular in computational toxicology and drug design; it is considered as a promising solution for assessing the safety profile of compounds, particularly in lead optimization and ADMET studies, and to meet the principles of the 3Rs, which calls for the replacement, reduction, and refinement of animal testing. In this context, we herein present the development of VenomPred 2.0 (http://www.mmvsl.it/wp/venompred2/), the new and improved version of our free of charge web tool for toxicological predictions, which now represents a powerful web-based platform for multifaceted and human-interpretable in silico toxicity profiling of chemicals. VenomPred 2.0 presents an extended set of toxicity endpoints (androgenicity, skin irritation, eye irritation, and acute oral toxicity, in addition to the already available carcinogenicity, mutagenicity, hepatotoxicity, and estrogenicity) that can be evaluated through an exhaustive consensus prediction strategy based on multiple ML models. Moreover, we also implemented a new utility based on the Shapley Additive exPlanations (SHAP) method that allows human interpretable toxicological profiling of small molecules, highlighting the features that strongly contribute to the toxicological predictions in order to derive structural toxicophores.

Toxicology of carbon nanomaterials in the Caenorhabditis elegans model: current status, characterization, and perspectives for testing harmonization

Carbon nanomaterials are promising advanced materials for novel technologies. Therefore, biosafety studies are mandatory to support their safe development, uses, and disposal in sustainable innovation. Traditional toxicological assays are typically expensive, time-consuming, and have low throughput; they have been replaced by new approach methodologies (NAMs) focused on in vitro, in chemico and in silico approaches, along with alternative models. Caenorhabditis elegans has emerged as a complete model organism for predictive toxicology due to its transparent body, short reproductive and life cycles, and fully sequenced genome with high homology with the human genome. In this review, we discuss the current status, state-of-the-art characterization techniques, and scientific gaps in nanotoxicity studies involving the carbon nanomaterials and the C. elegans model considering the last two decades of research. Moreover, we show the existing supportive tools to evaluate the internalization and biodistribution of carbon nanomaterials in C. elegans and discuss their advantages and limitations. Methodological and experimental gaps must still be discussed with the scientific community; hence, we bring this discussion to light and point out future orientations and perspectives. This review will contribute for guiding the research with C. elegans and harmonization of assays/protocols linked to computational tools and nanoinformatics approaches during the development of carbon nanomaterials.

In silico molecular docking, ADME study and synthesis of new 1,3-diazetidin-2-one derivatives with high anti-proliferative activity

Background: Cancer and inflammation are strongly connected; tumor growth and spread are also greatly influenced by inflammation. Nitrogen-based heterocycle analogs are excellent suppliers of pharmaceuticals. Quaternary rings play a bigger role in drug development as bioactive scaffolds. For improved tolerance and synergistic benefits, heterocyclic nitrogen rings are present in many anticancer medications. Understanding how to bind to the EGFR and its prospective impacts on cancer cells, expect to construct new heterocyclic compounds that may help produce potent anticancer medicines with a high safety profile. Methods: Novel 1,3-diazetidin-2-one derivatives were designed, synthesized from mefenamic acid, and their cytotoxic activity against a lung cancer cell line (A549) was initially tested in vitro. These compounds were anchored to the crystal structure of the epidermal growth factor receptor (PDB code 1M17) in a molecular docking study to determine their binding affinity at the active site. The newly synthesized derivatives were verified and confirmed by elemental analysis and spectroscopic data (FT-IR, 1H-NMR, and 13C-NMR). In addition, physicochemical, drug-like, and toxicological predictions were performed for these derivatives. Results: Based on a molecular docking study, all compounds (M4a-e) demonstrated superior PLPfitness (84.70, 85.89, 91.90, 88.61, and 92.77, respectively) to erlotinib (76.20). The anti-proliferation evaluation of the A549 cell line revealed that compounds M4c and M4e had exceptional and promising anti-proliferative activity on this cell line to treat lung cancer, with IC50 values of 1.75 µm and 2.05 µm at 72 hours, respectively, making them significantly more active than the reference erlotinib, which had an IC50 value of 11.5 µm at 72 hours. Conclusions: The cytotoxicity investigation and the molecular docking study showed a robust association with the novel compounds (M4a-e). Suggest a comprehensive pharmacological survey to understand how these newly created chemicals combat cancer fully.

LP-36: The Clariant CompTox Suite – Towards implementation of predictive toxicology approaches to support discovery of chemical alternatives that are safe and sustainable by design

LP-36: The Clariant CompTox Suite – Towards implementation of predictive toxicology approaches to support discovery of chemical alternatives that are safe and sustainable by design

S16-01: Performance Assessment and Utilisation of a Human Liver-Chip for Predictive Toxicology

S16-01: Performance Assessment and Utilisation of a Human Liver-Chip for Predictive Toxicology

P06-26: Using the KNIME Analytics Platform for the analysis of different combination of evidence approaches with in silico toxicological predictions from VEGA and OECD (Q)SAR Toolbox

P06-26: Using the KNIME Analytics Platform for the analysis of different combination of evidence approaches with in silico toxicological predictions from VEGA and OECD (Q)SAR Toolbox

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.

Computational investigation of four isoquinoline alkaloids against polycystic ovarian syndrome

Hyperandrogenism, insulin resistance, and estrogen dominance are the prime defining traits of women with polycystic ovarian syndrome which disrupts hormonal, adrenal, or ovarian functions resulting in impaired folliculogenesis and excess androgen production. The purpose of this study is to identify an appropriate bioactive antagonistic ligand from isoquinoline alkaloids [palmatine (PAL), jatrorrhizine (JAT), magnoflorine (MAG) and berberine (BBR)] from stems of Tinospora cordifolia. Phytocomponents inhibit/prevent androgenic, estrogenic, and steroidogenic receptors, insulin binding, and resultant hyperandrogenism. Intending to develop new inhibitors for human androgen receptor (1E3G), insulin receptor (3EKK), estrogen receptor beta (1U3S), and human steroidogenic cytochromeP450 17A1 (6WR0), here we report the docking studies by employing a flexible ligand docking approach using AutodockVina 4.2.6. ADMET screened swissADME and toxicological predictions to identify novel and potent inhibitors against PCOS. Binding affinity was obtained using Schrodinger. Two ligands, mainly BER (−8.23) and PAL (−6.71) showed the best docking score against androgen receptors. A molecular docking study reveals that compounds BBR and PAL were found to be tight binder at the active site of IE3G. Molecular dynamics results suggest that BBR and PAL showed good binding stability of active site residues. The present study corroborates the molecular dynamics of the compound BBR and PAL, potent Inhibitors of IE3G, having therapeutic potential for PCOS. We project that this study’s findings will be helpful in drug development efforts targeting PCOS. Hence isoquinoline alkaloids (BER& PAL) have potential roles against androgen receptors, and in specific PCOS, scientific evaluation has been put forth based on virtual screening. Communicated by Ramaswamy H. Sarma

From modeling dose-response relationships to improved performance of decision-tree classifiers for predictive toxicology of nanomaterials

The development and application of predictive models towards toxicity of engineered nanomaterials is still far from being satisfactory. One promising contribution to confront this challenge is to effectively augment the performance of machine learning classifiers by progressing the approach towards balancing experimental toxicity data. We propose an improved balancing methodology by fitting the in-vitro toxicological dose-response datasets of engineered nanomaterials to three, four, and five, free parameter dose-response models. The four-free parameter model displays the best fit (in terms of adjusted R2) for most of the examined data. The fitted curve yields, in each case, a continuous sequence of data points, which extends the restricted experimental data and generates additional fitted data points for the minority class, leading to the formation of balanced data for predicting the nanoparticle’s toxicology by decision tree classifiers. The ability to best predict the experimental toxicity data, by applying the decision tree model, was tested by forming three versions of the same experimental data: the imbalanced raw experimental data, the balanced data by applying the common Synthetic Minority Oversampling Technique, and by using the approach of Balanced Fitted Dose-Response method, introduced in the present study. We demonstrate that our approach provides improved performance of decision trees in predicting nanoparticles’ toxicity, a method that pertains also to chemical toxicity, central in health and environmental research.

Pharmacokinetic and toxicological prediction of the chemical constituents of the essential oil of the leaves of Croton heliotropiifolius Kunth

ABSTRACT The development of new drugs through studies of candidate molecules is a complex undertaking; however, computational or in silico approaches aimed at optimizing molecules with greater development potential are being utilized for predictions of pharmacokinetic properties such as absorption, distribution, metabolism and excretion (ADME) as well as toxicological parameters. The objective of this study was to examine in silico and in vivo pharmacokinetic and toxicological properties of the chemical constituents present in the essential oil of Croton heliotropiifolius Kunth leaves. The following Pubchem platform as well as Software SwissADME and PreADMET software were employed for in silico studies while micronucleus (MN) testing for in vivo determination of mutagenicity, using Swiss adult male Mus musculus mice. In silico findings demonstrated that all chemical constituents presented (1) high oral absorption (2) medium cellular permeability and (3) high blood brain permeability. As for toxicity, these chemical constituents exhibited low to medium risk of occurrence of cytotoxicity. Regarding in vivo evaluation, peripheral blood samples obtained from animals tested with the oil showed no significant differences in number of MN compared to negative controls. Data indicate that further investigations are necessary to corroborate the findings of this study. Our data suggest that essential oil extracted from Croton heliotropiifolius Kunth leaves may serve as a candidate for new drug development.

Drug toxicity: Advanced treatments, innovative discoveries and monitoring rise to the challenge between prevention and therapy

Pharmacology and pharmaceutical science driven by medical progress have excessively evolve the past decades. Although, toxicity caused by drugs remains a major consequence in healthcare. Modern technologies and conscientious research and studies have formed new strategies in facing drug toxicity. Toxicology prediction, the use of toxicityrelated biomarkers and genome study contribute effectively in prevention and treatment as far as it concerns drug poising. Furthermore, specifically designed technologies such as nanotechnology offer various therapy capacities. Safety and quality of treatments and new protocols are evaluated by the Food and Drug Administration (FDA), providing qualification and credibility

In Silico Study of Novel Cyclodextrin Inclusion Complexes of Polycaprolactone and Its Correlation with Skin Regeneration.

Three novel biomaterials obtained via inclusion complexes of β-cyclodextrin, 6-deoxi-6-amino-β-cyclodextrin and epithelial growth factor grafted to 6-deoxi-6-amino-β-cyclodextrin with polycaprolactone. Furthermore, some physicochemical, toxicological and absorption properties were predicted using bioinformatics tools. The electronic, geometrical and spectroscopical calculated properties agree with the properties obtained via experimental methods, explaining the behaviors observed in each case. The interaction energy was obtained, and its values were -60.6, -20.9 and -17.1 kcal/mol for β-cyclodextrin/polycaprolactone followed by the 6-amino-β-cyclodextrin-polycaprolactone complex and finally the complex of epithelial growth factor anchored to 6-deoxy-6-amino-β-cyclodextrin/polycaprolactone. Additionally, the dipolar moments were calculated, achieving values of 3.2688, 5.9249 and 5.0998 Debye, respectively, and in addition the experimental wettability behavior of the studied materials has also been explained. It is important to note that the toxicological predictions suggested no mutagenic, tumorigenic or reproductive effects; moreover, an anti-inflammatory effect has been shown. Finally, the improvement in the cicatricial effect of the novel materials has been conveniently explained by comparing the poly-caprolactone data obtained in the experimental assessments.

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.

The problem of validation of toxicological models in pharmacy

Animal toxicity testing is widely used in the chemical, pharmaceutical and research industries as a part of preclinical laboratory testing of substances to ensure that chemicals are safe for humans and animals. Under preclinical conditions, the safe and effective dose, acceptable dose, and exposure threshold of a substance can be established using mammals and rodents. There are enough materials on the introduction of biomodels in experiments. To summarize this knowledge, a polythematic abstract-bibliographic base was analyzed. The main attention was paid to such scientometric databases as: Web of Science Core Collection, Medline, PubMed, RSCI, as well as eLIBRARY.ru portal data for the last 10 years. An assessment of the statistical data and publication activity of the authors was made for the query modeling and ethics in veterinary medicine and medicine.The aim of the study was to analyze modern models and prospects for using new biomodels for experiments in pharmacology and toxicology.Having established protocols is important to ensure consistency between different studies within the same model, ensuring reproducibility and repeatability in experiments.Based on the analysis of literature sources over the past 10 years, it can be concluded that despite the large number of regulatory framework and transgenic laboratory animals with embedded human target genes in their genome, it is not possible to obtain an identity. Probably, the main bias in predictive toxicology will be made on neural networks and computer replacement of existing biomodels. Since, despite their relative accuracy, maintenance and feeding are costly, and the field of using animals in experiments is increasingly criticized and requires new approaches.

Using chemical and biological data to predict drug toxicity.

Various sources of information can be used to better understand and predict compound activity and safety-related endpoints, including biological data such as gene expression and cell morphology. In this review, we first introduce types of chemical, in vitro and in vivo information that can be used to describe compounds and adverse effects. We then explore how compound descriptors based on chemical structure or biological perturbation response can be used to predict safety-related endpoints, and how especially biological data can help us to better understand adverse effects mechanistically. Overall, the described applications demonstrate how large-scale biological information presents new opportunities to anticipate and understand the biological effects of compounds, and how this can support predictive toxicology and drug discovery projects.

Reference compounds for characterizing cellular injury in high-content cellular morphology assays

Robust, generalizable approaches to identify compounds efficiently with undesirable mechanisms of action in complex cellular assays remain elusive. Such a process would be useful for hit triage during high-throughput screening and, ultimately, predictive toxicology during drug development. Here we generate cell painting and cellular health profiles for 218 prototypical cytotoxic and nuisance compounds in U-2 OS cells in a concentration-response format. A diversity of compounds that cause cellular damage produces bioactive cell painting morphologies, including cytoskeletal poisons, genotoxins, nonspecific electrophiles, and redox-active compounds. Further, we show that lower quality lysine acetyltransferase inhibitors and nonspecific electrophiles can be distinguished from more selective counterparts. We propose that the purposeful inclusion of cytotoxic and nuisance reference compounds such as those profiled in this resource will help with assay optimization and compound prioritization in complex cellular assays like cell painting.

Gaseous products generated from polyethylene and polyethylene terephthalate during ultraviolet irradiation: Mechanism, pathway and toxicological analyses

The generation of various degradation products from microplastics (MPs) has been confirmed under ultraviolet (UV) irradiation. The gaseous products, primarily volatile organic compounds (VOCs), are usually overlooked, leading to potential unknown risks to humans and the environment. In this study, the generation of VOCs from polyethylene (PE) and polyethylene terephthalate (PET) under UV-A (365 nm) and UV-C (254 nm) irradiation in water matrixes were compared. More than 50 different VOCs were identified. For PE, UV-A-derived VOCs mainly included alkenes and alkanes. On this basis, UV-C-derived VOCs included various oxygen-containing organics, such as alcohols, aldehydes, ketones, carboxylic acid and even lactones. For PET, both UV-A and UV-C irradiation induced the generation of alkenes, alkanes, esters, phenols, etc., and the differences between these two reactions were insignificant. Toxicological prioritization prediction revealed that these VOCs have diverse toxicological profiles. The VOCs with the highest potential toxicity were dimethyl phthalate (CAS: 131–11-3) from PE and 4-acetylbenzoate (3609-53-8) from PET. Furthermore, some alkane and alcohol products also presented high potential toxicity. The quantitative results indicated that the yield of these toxic VOCs from PE could reach 102 μg g−1 under UV-C treatment. The degradation mechanisms of MPs included direct scission by UV irradiation and indirect oxidation induced by diverse activated radicals. The former mechanism was dominant in UV-A degradation, while UV-C included both mechanisms. Both mechanisms contributed to the generation of VOCs. Generally, MPs-derived VOCs can be released from water to the air after UV irradiation, posing a potential risk to ecosystems and human beings, especially for UV-C disinfection indoors in water treatments.

Antiparkinsonian Potential of Natural Products: Molecular Docking, Prediction of Pharmacokinetic and Toxicological Properties

Background: Parkinson's disease (PD) is a neurodegenerative pathology common in the elderly, and it may be related to several factors, such as excessive and continuous use of drugs, alcoholism, cerebral ischemia, among others. Emphasizing that there is still no cure for PD, current pharmacological treatment aims to restore reduced dopaminergic activity in the central nervous system and manage symptoms. However, due to the different side effects caused by antiparkinsonian drugs, their use is recommended just when symptoms are considerably impairing professional performance or the patient's daily tasks. Therefore, studies for the development of new drugs have been conducted, and natural products are gaining importance due to the possibility of discovering new bioactive molecular. In this sense, this research aimed to perform the in silico study of molecules of natural origin for the treatment of PD. Methods: A search for molecules from medicinal plants was carried out, they underwent a pharmacokinetic and toxicological prediction with subsequent molecular docking study, being coupled with the MAO-B enzyme and the dopamine receptor 2. Results: The ginsenosides compounds present an unfavorable pharmacokinetic pattern, which can be explained by their molecular mass, while the other molecules present average patterns, with the exception of kavains, which obtained very satisfactory results. When it comes to toxicity, the molecules curcumin, dihydrokavain, vitexin, kavain and tetrahydrocurcumin did not exhibit any toxic alert. As for the molecular docking study, the compound curcumin stood out with a considerable number of interactions at many amino acid residues relevant to antiparkinsonian activity, both in the MAO-B enzyme and in the D2 receptor. Conclusion: Of 25 molecules of natural products, 3 are good candidates for studies of oral drugs, owing to their excellent pharmacokinetic profile and low probability of being toxic. The curcumin molecule has a great notoriety, as it obtained relevant interactions with the two proteins studied in molecular docking, speciallyin the MAO-B enzyme.