Field Of Predictive Toxicology Research Articles

Enhanced identification of endocrine disruptors through integration of science-based regulatory practices and innovative methodologies: The MERLON Project

The prevalence of hormone-related health issues caused by exposure to endocrine disrupting chemicals (EDCs) is a significant, and increasing, societal challenge. Declining fertility rates together with rising incidence rates of reproductive disorders and other endocrine-related diseases underscores the urgency in taking more action. Addressing the growing threat of EDCs in our environment demands robust and reliable test methods to assess a broad variety of endpoints relevant for endocrine disruption. EDCs also require effective regulatory frameworks, especially as the current move towards greater reliance on non-animal methods in chemical testing puts to test the current paradigm for EDC identification, which requires that an adverse effect is observed in an intact organism. Although great advances have been made in the field of predictive toxicology, disruption to the endocrine system and subsequent adverse health effects may prove particularly difficult to predict without traditional animal models. The MERLON project seeks to expedite progress by integrating multispecies molecular research, new approach methodologies (NAMs), human clinical epidemiology, and systems biology to furnish mechanistic insights and explore ways forward for NAM-based identification of EDCs. The focus is on sexual development and function, from foetal sex differentiation of the reproductive system through mini-puberty and puberty to sexual maturity. The project aims are geared towards closing existing knowledge gaps in understanding the effects of EDCs on human health to ultimately support effective regulation of EDCs in the European Union and beyond.

Possibilities of predictive toxicology in assessing the potential hepatotoxicity of medicinal substances

Currently, one of the "gold" standards for determining the potential toxicity of drugs is highly informative preventive screening (HCS). This method has become a powerful tool for the evaluation of molecular, cellular and tissue toxicity, especially in the field of predictive toxicology, aimed at predicting unwanted drug-related substances. It is critical for the pharmaceutical industry to identify and subsequently mitigate potential safety risks early in the R&D process and thus reduce toxicity and the safety-related shrinkage of drug development programs. In this article, we review some of the most common used HCS assays for predictive toxicology in the pharmaceutical industry. PubMed, Elsevier Science (Scopus), and Clarivate Analytics (Web of Science) research databases were searched for original research to identify the hallmarks of the HCS methodology. Hepatotoxicity is one of the most common causes of safety and toxicity related downsizing of potential drugs in the pharmaceutical industry, as well as drug withdrawals. Hepatotoxic substances are associated with xenobiotics, drugs, substances of natural origin and chemical agents that constitute an important cause of hepatic action. A single classification based on the mechanisms of toxicity makes it possible to distinguish between intrinsic hepatotoxicity (preliminary and dose-dependent) and idiosyncratic hepatotoxicity (invisible, non-mediated immune and metabolic reactions). HCS analyzes are not intended to replace animal testing because they cannot capture the full complexity of an organism. However, they can reduce the amount of animal testing by early detection of hazards and complement animal testing. Through early identification of safety risks and optimization of chemistry series across relevant safety endpoints, HCS assays for predictive toxicology hold the promise of enhancing the successful selection of new drug candidates with satisfactory toxicological properties.

Combining In Vivo Data with In Silico Predictions for Modeling Hepatic Steatosis by Using Stratified Bagging and Conformal Prediction.

Hepatic steatosis (fatty liver) is a severe liver disease induced by the excessive accumulation of fatty acids in hepatocytes. In this study, we developed reliable in silico models for predicting hepatic steatosis on the basis of an in vivo data set of 1041 compounds measured in rodent studies with repeated oral exposure. The imbalanced nature of the data set (1:8, with the “steatotic” compounds belonging to the minority class) required the use of meta-classifiers—bagging with stratified under-sampling and Mondrian conformal prediction—on top of the base classifier random forest. One major goal was the investigation of the influence of different descriptor combinations on model performance (tested by predicting an external validation set): physicochemical descriptors (RDKit), ToxPrint features, as well as predictions from in silico nuclear receptor and transporter models. All models based upon descriptor combinations including physicochemical features led to reasonable balanced accuracies (BAs between 0.65 and 0.69 for the respective models). Combining physicochemical features with transporter predictions and further with ToxPrint features gave the best performing model (BAs up to 0.7 and efficiencies of 0.82). Whereas both meta-classifiers proved useful for this highly imbalanced toxicity data set, the conformal prediction framework also guarantees the error level and thus might be favored for future studies in the field of predictive toxicology.

SEURAT: Safety Evaluation Ultimately Replacing Animal Testing--recommendations for future research in the field of predictive toxicology.

The development of non-animal methodology to evaluate the potential for a chemical to cause systemic toxicity is one of the grand challenges of modern science. The European research programme SEURAT is active in this field and will conclude its first phase, SEURAT-1, in December 2015. Drawing on the experience gained in SEURAT-1 and appreciating international advancement in both basic and regulatory science, we reflect here on how SEURAT should evolve and propose that further research and development should be directed along two complementary and interconnecting work streams. The first work stream would focus on developing new 'paradigm' approaches for regulatory science. The goal here is the identification of 'critical biological targets' relevant for toxicity and to test their suitability to be used as anchors for predicting toxicity. The second work stream would focus on integration and application of new approach methods for hazard (and risk) assessment within the current regulatory 'paradigm', aiming for acceptance of animal-free testing strategies by regulatory authorities (i.e. translating scientific achievements into regulation). Components for both work streams are discussed and may provide a structure for a future research programme in the field of predictive toxicology.

QSAR DataBank - an approach for the digital organization and archiving of QSAR model information.

BackgroundResearch efforts in the field of descriptive and predictive Quantitative Structure-Activity Relationships or Quantitative Structure–Property Relationships produce around one thousand scientific publications annually. All the materials and results are mainly communicated using printed media. The printed media in its present form have obvious limitations when they come to effectively representing mathematical models, including complex and non-linear, and large bodies of associated numerical chemical data. It is not supportive of secondary information extraction or reuse efforts while in silico studies poses additional requirements for accessibility, transparency and reproducibility of the research. This gap can and should be bridged by introducing domain-specific digital data exchange standards and tools. The current publication presents a formal specification of the quantitative structure-activity relationship data organization and archival format called the QSAR DataBank (QsarDB for shorter, or QDB for shortest).ResultsThe article describes QsarDB data schema, which formalizes QSAR concepts (objects and relationships between them) and QsarDB data format, which formalizes their presentation for computer systems. The utility and benefits of QsarDB have been thoroughly tested by solving everyday QSAR and predictive modeling problems, with examples in the field of predictive toxicology, and can be applied for a wide variety of other endpoints. The work is accompanied with open source reference implementation and tools.ConclusionsThe proposed open data, open source, and open standards design is open to public and proprietary extensions on many levels. Selected use cases exemplify the benefits of the proposed QsarDB data format. General ideas for future development are discussed.

4th Annual predictive toxicology summit 2012

Introduction: This meeting report presents a brief summary on the 4th Annual Predictive Toxicology Summit 2012, which was held on 15 – 16 February 2012 in London.Areas covered: The majority of presentations came from global pharmaceutical companies, although small and medium enterprise (SME) and academic researchers were represented too. Major regulatory bodies were also present. The article highlights the summit, which was considered a good learning opportunity to catch up on the recent advances in predictive toxicology.Expert opinion: Predictive toxicology has become more and more important due to social and economic pressure and scientific reasons. Technological developments are rapid, but there is a gulf between the technology developers and the pharmaceutical end users; hence, early engagement is desirable. Stem cell-derived cell-based assays as well as three-dimensional in vitro tissue/organ model development are within the reach now, but a lot needs to be done to optimise and validate the developed protocols and products. The field of predictive toxicology needs fundamental research of interdisciplinary nature, which requires much needed trained personnel and funding.