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Predictive Toxicology From Vision to Reality, Volume 64

  • Erscheinungsdatum: 15.10.2014
  • Verlag: Wiley-VCH
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Predictive Toxicology

Tailored to the needs of drug developers, this one-stop reference for medicinal chemists covers all the latest developments in the field of predictive toxicology and its applications in safety assessment. With a keen emphasis on novel approaches, the topics have been tackled by selected expert scientists, who are familiar with the theoretical scientific background as well as with the practical application of current methods. Emerging technologies in toxicity assessment are introduced and evaluated in terms of their predictive power, with separate sections on computer predictions and simulation methods, novel in vitro systems including those employing stem cells, toxicogenomics and novel biomarkers. In each case, the most promising methods are discussed and compared to classical in vitro and in vivo toxicology assays. Finally, an outlook section discusses such forward-looking topics as immunotoxicology assessment and novel regulatory requirements. With its wealth of methodological knowledge and its critical evaluation of modern approaches, this is a valuable guide for toxicologists working in pharmaceutical development, as well as in safety assessment and the regulation of drugs and chemicals. Friedlieb Pfannkuch graduated as a physician from the Free University of Berlin, Germany and is Professor at the University of Basel, Switzerland. He has more than 27 years of experience in non-clinical safety assessment for all phases of drug development. During his career he was head of experimental toxicology at Ciba-Geigy in Basel, head of the non-clinical safety section at Yamanouchi Europe in the Netherlands, responsible for non-clinical nutrition safety at Roche Vitamins, and from 2003 until his retirement in 2011 he was a senior scientist in the global non-clinical drug safety department of F. Hoffmann-La Roche in Basel. He has contributed to international pharmaceutical consortia, such as toxicity testing of alternatives to CFCs propellants - IPACT, the ILSI task force on Food Safety in Europe and to working groups of the International Conference on Harmonization - ICH. In the period from 2004-2009 he was the responsible manager of the European Commission's Research Framework Program 6 Project InnoMed: 'Predictive Toxicology - PredTox'. Laura Suter-Dick graduated as a biologist from the University of Buenos Aires (Argentina) and subsequently received her PhD from the Free University of Berlin (Germany). She has nearly 20 years of experience within the pharmaceutical industry, mainly in the field of toxicology. During her career in the pharmaceutical industry she worked as a scientist in the reproductive toxicology at Sandoz in Basel. She specialized in molecular toxicology (toxicogenomics) and in vitro assays at F. Hoffmann- La Roche Ltd., where she led the mechanistic toxicology. She has recently been appointed Professor for Molecular Toxicology at the Life Sciences School of the University of Applied Sciences and Arts Northwestern Switzerland. She acts as an external expert in several scientific panels and is also a Board Member of ESTIV (European Society of Toxicology in vitro).


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Predictive Toxicology

Introduction to Predictive Toxicology Tools and Methods

Laura Suter-Dick and Friedlieb Pfannkuch
1.1 Computational Tools and Bioinformatics

1.1.1 In Silico Prediction Tools

Computational tools are used in many life sciences research areas, including toxicity prediction. They take advantage of complex mathematical models to predict the effects caused by a given compound on an organism. Due to the complexity of the possible interactions between a treatment and a patient and the diversity of possible outcomes, models are applied to well-defined and specific fields, such as DNA damaging potential, estimation of the necessary dose to elicit an effect in a patient, or identification of relevant gene expression changes.

In silico tools make use of information regarding chemical structures and the immense data legacy that allows inferring interactions between chemical structures, physicochemical properties, and biological processes. These methods are farthest away from traditional animal studies, since they rely on existing databases rather than on generating experimental animal data.

Due to the complexity of this task, there are a fairly small number of endpoints that can be predicted with commonly employed in silico tools such as DEREK, VITIC, and M-Case with acceptable accuracy. In order to improve the current models and to expand to additional prediction algorithms, further validation and extension of the underlying databases is ongoing.

Similarly, modeling and simulation (M&S) can generate mathematical models able to simulate and therefore predict how a compound will behave in humans before clinical data become available. In the field of nonclinical safety, complex models allow for a prediction of the effect of an organism on a compound (pharmacokinetic models) as well as, to some extent, pharmacodynamic extrapolations, based on data generated in animal models as well as in in vitro human systems.
1.1.2 Bioinformatics

In addition to the in silico and modeling tools described above, the dramatically increasing amount of toxicologically relevant data needs to be appropriately monitored and collected. All "new" technologies produce very high volumes of data and thus having and using bioinformatics tools that can collect data from diverse sources and mine them to detect relevant patterns of change is vital. For this purpose, large databases are necessary, along with bioinformatics tools that can deal with diverse data types, multivariate analysis, and supervised and unsupervised discrimination algorithms. These tools take advantage of advanced statistics, combined with the large data sets stored in the databases generated using technologies such as omics or high-content imaging.
1.2 Omics Technologies

The omics technologies arose with the advent of advanced molecular biology techniques able to determine changes in the whole transcriptome, proteome, or metabolome. These powerful techniques were considered the ultimate holistic approach to tackle many biological questions, among them toxicological assessment. Several companies have invested in these areas of toxicological research.
1.2.1 Toxicogenomics (Transcriptomics)

Toxicogenomics is the more widespread of the omics technologies. Predictive approaches are based on databases with compounds (toxic/nontoxic) generated by (pharmaceutical) companies as well as by commercial vendors in the 1990s. All share the same focus of investigation: target organ toxicity to the liver and the kidney.

In addition, gene expression data are often the basis for mechanistic understanding of biological processes in several fields, including toxicology, pharmacology, and disease phenotype. Thus, transcriptomic data can be used as a merely predictive tool, as a mechanistic tool, or as a combinatio

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