Integrated In Silico Models for the Prediction of No-Observed-(Adverse)-Effect Levels and Lowest-Observed-(Adverse)-Effect Levels in Rats for Sub-chronic Repeated-Dose Toxicity

Quantitative prediction of toxicological points of departure using two-stage machine learning models: A new approach methodology (NAM) for chemical risk assessment

Point of departure (POD) is a concept used in risk assessment to calculate the reference dose of exposure that is likely to have no appreciable risk on health. POD can be directly utilized from no observed adverse effect levels (NOAEL) which is the dose or exposure level at which there is little or no risk of adverse effects. However, NOAEL values are unavailable for most of the chemicals due to inconsistent animal toxicity data. Hence, the current study utilizes a two-stage machine learning (ML) model for predicting NOAEL values, based on data curated from diverse toxicity exposures. In the first stage, a random forest regressor is used for supervised outlier detection and removal addressing any variability in data and poor correlations. The refined data is then used for toxicity prediction using several ML models; random forest and XGBoost show relatively higher performance with an R2 value of 0.4 and 0.43, respectively, for predicting NOAEL in chronic toxicity. Similarly, feature combinations with absorption distribution metabolism and excretion (ADME) indicate better NOAEL prediction for acute toxicity. External validation is performed by predicting NOAEL values for cosmetic pigments and calculating reference doses (RfD). Notably, pigments like orange and red show higher RfD values, indicating broader safety margins. This study provides a practical framework for addressing variability and data limitations in toxicity prediction while offering insights into its applicability in risk evaluation.

Quantitative structure-activity relationships of chemical bioactivity toward proteins associated with molecular initiating events of organ-specific toxicity

The adverse outcome pathway (AOP) concept has gained attention as a way to explore the mechanism of chemical toxicity. In this study, quantitative structure-activity relationship (QSAR) models were developed to predict compound activity toward protein targets relevant to molecular initiating events (MIE) upstream of organ-specific toxicities, namely liver steatosis, cholestasis, nephrotoxicity, neural tube closure defects, and cognitive functional defects. Utilizing bioactivity data from the ChEMBL 33 database, various machine learning algorithms, chemical features and methods to assess prediction reliability were compared and applied to develop robust models to predict compound activity. The results demonstrate high predictive performance across multiple targets, with balanced accuracy exceeding 0.80 for the majority of models. Furthermore, stability checks confirmed the consistency of predictive performance across multiple training-test splits. The results obtained by using QSAR predictions to identify known markers of adversities highlighted the utility of the models for risk assessment and for prioritizing compounds for further experimental evaluation.Scientific contributionThe work describes the development of QSAR models as tools for screening chemicals with potential systemic toxicity, thus contributing to resource savings and providing indications for further better-targeted testing. This study provides advances in the field of computational modeling of MIEs and information from AOP which is still relatively young and unexplored. The comprehensive modeling procedure is highly generalizable, and offers a robust framework for predicting a wide range of toxicological endpoints.

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

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

New Theobromine Apoptotic Analogue with Anticancer Potential Targeting the EGFR Protein: Computational and In Vitro Studies

AIM

The aim of this study was to design and examine a novel epidermal growth factor receptor (EGFR) inhibitor with apoptotic properties by utilizing the essential structural characteristics of existing EGFR inhibitors as a foundation.

METHOD

The study began with the natural alkaloid theobromine and developed a new semisynthetic derivative (T-1-PMPA). Computational ADMET assessments were conducted first to evaluate its anticipated safety and general drug-likeness. Deep density functional theory (DFT) computations were initially performed to validate the three-dimensional (3D) structure and reactivity of T-1-PMPA. Molecular docking against the EGFR proteins was conducted to investigate T-1-PMPA's binding affinity and inhibitory potential. Additional molecular dynamics (MD) simulations over 200 ns along with MM-GPSA, PLIP, and principal component analysis of trajectories (PCAT) experiments were employed to verify the binding and inhibitory properties of T-1-PMPA. Afterward, T-1-PMPA was semisynthesized to validate the proposed design and in silico findings through several in vitro examinations.

RESULTS

DFT studies indicated T-1-PMPA's reactivity using electrostatic potential, global reactive indices, and total density of states. Molecular docking, MD simulations, MM-GPSA, PLIP, and ED suggested the binding and inhibitory properties of T-1-PMPA against the EGFR protein. The in silico ADMET predicted T-1-PMPA's safety and general drug-likeness. In vitro experiments demonstrated that T-1-PMPA effectively inhibited EGFRWT and EGFR790m, with IC50 values of 86 and 561 nM, respectively, compared to Erlotinib (31 and 456 nM). T-1-PMPA also showed significant suppression of the proliferation of HepG2 and MCF7 malignant cell lines, with IC50 values of 3.51 and 4.13 μM, respectively. The selectivity indices against the two cancer cell lines indicated the overall safety of T-1-PMPA. Flow cytometry confirmed the apoptotic effects of T-1-PMPA by increasing the total percentage of apoptosis to 42% compared to 31, and 3% in Erlotinib-treated and control cells, respectively. The qRT-PCR analysis further supported the apoptotic effects by revealing significant increases in the levels of Casp3 and Casp9. Additionally, T-1-PMPA controlled the levels of TNFα and IL2 by 74 and 50%, comparing Erlotinib's values (84 and 74%), respectively.

CONCLUSION

In conclusion, our study's findings suggest the potential of T-1-PMPA as a promising apoptotic anticancer lead compound targeting the EGFR.

Multi-tissue proteogenomic analysis for mechanistic toxicology studies in non-model species

New approach methodologies (NAM), including omics and in vitro approaches, are contributing to the implementation of 3R (reduction, refinement and replacement) strategies in regulatory science and risk assessment. In this study, we present an integrative transcriptomics and proteomics analysis workflow for the validation and revision of complex fish genomes and demonstrate how proteogenomics expression matrices can be used to support multi-level omics data integration in non-model species in vivo and in vitro. Using Atlantic salmon as an example, we constructed proteogenomic databases from publicly available transcriptomic data and in-house generated RNA-Seq and LC-MS/MS data. Our analysis identified ∼80,000 peptides, providing direct evidence of translation for over 40,000 RefSeq structures. The data also highlighted 183 co-located peptide groups that supported a single transcript each, and in each case, either corrected a previous annotation, supported Ensembl annotations not present in RefSeq, or identified novel previously unannotated genes. Proteogenomics data-derived expression matrices revealed distinct profiles for the different tissue types analyzed. Focusing on proteins involved in defense against xenobiotics, we detected distinct expression patterns across different salmon tissues and observed homology in the expression of chemical defense proteins between in vivo and in vitro liver systems. Our study demonstrates the potential of proteogenomic analyses in extending our understanding of complex fish genomes and provides an advanced bioinformatic toolkit to support the further development of NAMs and their application in regulatory science and (eco)toxicological studies of non-model species.

Anti-breast cancer potential of a new xanthine derivative: In silico, antiproliferative, selectivity, VEGFR-2 inhibition, apoptosis induction and migration inhibition studies

BACKGROUND

The overexpression of VEGFR-2 receptors in breast cancer provides a valuable approach to anticancer strategies. Targeting VEGFR-2, a new semisynthetic compound (T-1-MCPAB) has been designed.

METHODS

Computational methods (ADMET, toxicity, DFT, Molecular Docking, Molecular Dynamics Simulations, MM-GBSA, PLIP, and PCAT) were conducted. In addition to the semi-synthesis, in vitro studies (anti-VEGFR-2, anti-proliferative, flow cytometry, and wound scratch assay) were employed.

RESULTS

ADME and toxicity profiles of T-1-MCPAB studies indicated its overall drug-likeness showing results much better than Sorafenib. Then, T-1-MCPAB's exact 3D structure, stability, and reactivity were evoked by the DFT calculations. Molecular docking, molecular dynamics simulations, MM-GPSA, PLIP, and PCAT studies denoted the correct binding and inhibiting potential of T-1-MCPAB, towards VEGFR-2 protein. After the semisynthesis, T-1-MCPAB inhibited VEGFR-2 with an IC50 of 0.135 µM, which was comparable to sorafenib's IC50 of 0.0591 µM. T-1-MCPAB also showed a notable performance against MCF7 and T47D breast cancer cell lines with IC50 values of 30.95 µM and 63.64 µM, respectively, and had high selectivity index values of 3.7 and 1.8, respectively. Furthermore, T-1-MCPAB influenced early and late apoptosis and significantly decreased the potential of MCF7 cells to heal and migrate.

CONCLUSION

T-1-MCPAB is a promising VEGFR-2 inhibitor with potential for breast cancer treatment. Further chemical and biological studies are needed to explore its potential as a therapeutic agent.

Assessing machine learning approaches for predicting failures of investigational drug candidates during clinical trials

One of the major challenges in drug development is having acceptable levels of efficacy and safety throughout all the phases of clinical trials followed by the successful launch in the market. While there are many factors such as molecular properties, toxicity parameters, mechanism of action at the target site, etc. that regulates the therapeutic action of a compound, a holistic approach directed towards data-driven studies will invariably strengthen the predictive toxicological sciences. Our quest for the current study is to find out various reasons as to why an investigational candidate would fail in the clinical trials after multiple iterations of refinement and optimization. We have compiled a dataset that comprises of approved and withdrawn drugs as well as toxic compounds and essentially have used time-split based approach to generate the training and validation set. Five highly robust and scalable machine learning binary classifiers were used to develop the predictive models that were trained with features like molecular descriptors and fingerprints and then validated rigorously to achieve acceptable performance in terms of a set of performance metrics. The mean AUC scores for all the five classifiers with the hold-out test set were obtained in the range of 0.66-0.71. The models were further used to predict the probability score for the clinical candidate dataset. The top compounds predicted to be toxic were analyzed to estimate different dimensions of toxicity. Apparently, through this study, we propose that with the appropriate use of feature extraction and machine learning methods, one can estimate the likelihood of success or failure of investigational drugs candidates thereby opening an avenue for future trends in computational toxicological studies. The models developed in the study can be accessed at https://github.com/gnsastry/predicting_clinical_trials.git.

Predictive and explanatory themes of NOAEL through a systematic comparison of different machine learning methods and descriptors

No observed adverse effect level (NOAEL) is an identified dose level which used as a point of departure to infer a safe exposure limit of chemicals, especially in food additives and cosmetics. Recently, in silico approaches have been employed as effective alternatives to determine the toxicity endpoints of chemicals instead of animal experiments. Several acceptable models have been reported, yet assessing the risk of repeated-dose toxicity remains inadequate. This study established robust machine learning predictive models for NOAEL at different exposure durations by constructing high-quality datasets and comparing different kinds of molecular representations and algorithms. The features of molecular structures affecting NOAEL were explored using advanced cheminformatics methods, and predictive models also communicated the NOAEL between different species and exposure durations. In addition, a NOAEL prediction tool for chemical risk assessment is provided (available at: https://github.com/ifyoungnet/NOAEL). We hope this study will help researchers easily screen and evaluate the subacute and sub-chronic toxicity of disparate compounds in the development of food additives in the future.

Monte Carlo Models for Sub-Chronic Repeated-Dose Toxicity: Systemic and Organ-Specific Toxicity

The risk-characterization of chemicals requires the determination of repeated-dose toxicity (RDT). This depends on two main outcomes: the no-observed-adverse-effect level (NOAEL) and the lowest-observed-adverse-effect level (LOAEL). These endpoints are fundamental requirements in several regulatory frameworks, such as the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) and the European Regulation of 1223/2009 on cosmetics. The RDT results for the safety evaluation of chemicals are undeniably important; however, the in vivo tests are time-consuming and very expensive. The in silico models can provide useful input to investigate sub-chronic RDT. Considering the complexity of these endpoints, involving variable experimental designs, this non-testing approach is challenging and attractive. Here, we built eight in silico models for the NOAEL and LOAEL predictions, focusing on systemic and organ-specific toxicity, looking into the effects on the liver, kidney and brain. Starting with the NOAEL and LOAEL data for oral sub-chronic toxicity in rats, retrieved from public databases, we developed and validated eight quantitative structure-activity relationship (QSAR) models based on the optimal descriptors calculated by the Monte Carlo method, using the CORAL software. The results obtained with these models represent a good achievement, to exploit them in a safety assessment, considering the importance of organ-related toxicity.

In Silico Models for Repeated-Dose Toxicity (RDT): Prediction of the No Observed Adverse Effect Level (NOAEL) and Lowest Observed Adverse Effect Level (LOAEL) for Drugs

Many regulatory contexts require the evaluation of repeated-dose toxicity (RDT) studies conducted in laboratory animals. The main outcome of RDT studies is the identification of the no observed adverse effect level (NOAEL) and the lowest observed adverse effect level (LOAEL) that are normally used as point of departure for the establishment of health-based guidance values. Since in vivo RDT studies are expensive and time-consuming, in silico approaches could offer a valuable alternative. However, NOAEL and LOAEL modeling suffer some limitations since they do not refer to a single end point but to several different effects, and the doses used in experimental studies strongly influence the results. Few attempts to model NOAEL and LOAEL have been reported. The available database and models for the prediction of NOAEL and LOAEL are reviewed here.

Using VEGAHUB Within a Weight-of-Evidence Strategy

Industrial needs and regulatory requirements have played a significant role in accelerating the use of nontesting methods including in silico tools as alternatives to animal testing. The main interest is not solely on the use of in silico tools, or in read-across, but on better toxicological safety assessment of substances, and for this purpose more advanced, integrated strategies have to be implemented. VEGAHUB wants to promote this broader view, not necessarily focused on a specific approach. Applying multiple tools and complementary approaches instead of one technique may provide more elements for a more robust evaluation, but at the same time it is important to have a conceptual scheme to integrate multiple, heterogeneous lines of evidence. We will show how the user can benefit from the diversity of tools available within the platform VEGAHUB for assessing the biological properties of chemical substances on an example of (non)mutagenicity.

In Silico Methods for Environmental Risk Assessment: Principles, Tiered Approaches, Applications, and Future Perspectives

This chapter aims to introduce the reader to the basic principles of environmental risk assessment of chemicals and highlights the usefulness of tiered approaches within weight of evidence approaches in relation to problem formulation i.e., data availability, time and resource availability. In silico models are then introduced and include quantitative structure-activity relationship (QSAR) models, which support filling data gaps when no chemical property or ecotoxicological data are available. In addition, biologically-based models can be applied in more data rich situations and these include generic or species-specific models such as toxicokinetic-toxicodynamic models, dynamic energy budget models, physiologically based models, and models for ecosystem hazard assessment i.e. species sensitivity distributions and ultimately for landscape assessment i.e. landscape-based modeling approaches. Throughout this chapter, particular attention is given to provide practical examples supporting the application of such in silico models in real-world settings. Future perspectives are discussed to address environmental risk assessment in a more holistic manner particularly for relevant complex questions, such as the risk assessment of multiple stressors and the development of harmonized approaches to ultimately quantify the relative contribution and impact of single chemicals, multiple chemicals and multiple stressors on living organisms.

Defining the Human-Biota Thresholds of Toxicological Concern for Organic Chemicals in Freshwater: The Proposed Strategy of the LIFE VERMEER Project Using VEGA Tools

Several tons of chemicals are released every year into the environment and it is essential to assess the risk of adverse effects on human health and ecosystems. Risk assessment is expensive and time-consuming and only partial information is available for many compounds. A consolidated approach to overcome this limitation is the Threshold of Toxicological Concern (TTC) for assessment of the potential health impact and, more recently, eco-TTCs for the ecological aspect. The aim is to allow a safe assessment of substances with poor toxicological characterization. Only limited attempts have been made to integrate the human and ecological risk assessment procedures in a "One Health" perspective. We are proposing a strategy to define the Human-Biota TTCs (HB-TTCs) as concentrations of organic chemicals in freshwater preserving both humans and ecological receptors at the same time. Two sets of thresholds were derived: general HB-TTCs as preliminary screening levels for compounds with no eco- and toxicological information, and compound-specific HB-TTCs for chemicals with known hazard assessment, in terms of Predicted No effect Concentration (PNEC) values for freshwater ecosystems and acceptable doses for human health. The proposed strategy is based on freely available public data and tools to characterize and group chemicals according to their toxicological profiles. Five generic HB-TTCs were defined, based on the ecotoxicological profiles reflected by the Verhaar classes, and compound-specific thresholds for more than 400 organic chemicals with complete eco- and toxicological profiles. To complete the strategy, the use of in silico models is proposed to predict the required toxicological properties and suitable models already available on the VEGAHUB platform are listed.