Literature DB >> 35188644

MultiCASE Platform for In Silico Toxicology.

Suman K Chakravarti1, Roustem D Saiakhov2.   

Abstract

Predictive and computational toxicology, a highly scientific and research-based field, is rapidly progressing with wider acceptance by regulatory agencies around the world. Almost every aspect of the field has seen fundamental changes during the last decade due to the availability of more data, usage, and acceptance of a variety of predictive tools and an increase in the overall awareness. Also, the influence from the recent explosive developments in the field of artificial intelligence has been significant. However, the need for sophisticated, easy to use and well-maintained software platforms for in silico toxicological assessments remains very high. The MultiCASE suite of software is one such platform that consists of an integrated collection of software programs, tools, and databases. While providing easy-to-use and highly useful tools that are relevant at present, it has always remained at the forefront of research and development by inventing new technologies and discovering new insights in the area of QSAR, artificial intelligence, and machine learning. This chapter gives the background, an overview of the software and databases involved, and a brief description of the usage methodology with the aid of examples.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  CASE Ultra; Carcinogenicity prediction; Computational toxicology; Expert review; ICH M7; META Ultra; Metabolism prediction; MultiCASE; Mutagenicity prediction; QSAR

Mesh:

Year:  2022        PMID: 35188644     DOI: 10.1007/978-1-0716-1960-5_19

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  19 in total

1.  Modeling the mouse lymphoma forward mutational assay: the Gene-Tox program database.

Authors:  S G Grant; Y P Zhang; G Klopman; H S Rosenkranz
Journal:  Mutat Res       Date:  2000-02-16       Impact factor: 2.433

2.  Searching for an enhanced predictive tool for mutagenicity.

Authors:  G Klopman; H Zhu; M A Fuller; R D Saiakhov
Journal:  SAR QSAR Environ Res       Date:  2004-08       Impact factor: 3.000

3.  Benchmark performance of MultiCASE Inc. software in Ames mutagenicity set.

Authors:  Roustem D Saiakhov; Gilles Klopman
Journal:  J Chem Inf Model       Date:  2010-09-27       Impact factor: 4.956

4.  Optimizing predictive performance of CASE Ultra expert system models using the applicability domains of individual toxicity alerts.

Authors:  Suman K Chakravarti; Roustem D Saiakhov; Gilles Klopman
Journal:  J Chem Inf Model       Date:  2012-09-18       Impact factor: 4.956

5.  Combined Use of MC4PC, MDL-QSAR, BioEpisteme, Leadscope PDM, and Derek for Windows Software to Achieve High-Performance, High-Confidence, Mode of Action-Based Predictions of Chemical Carcinogenesis in Rodents.

Authors:  Edwin J Matthews; Naomi L Kruhlak; R Daniel Benz; Joseph F Contrera; Carol A Marchant; Chihae Yang
Journal:  Toxicol Mech Methods       Date:  2008       Impact factor: 2.987

6.  In-silico screening of high production volume chemicals for mutagenicity using the MCASE QSAR expert system.

Authors:  G Klopman; S K Chakravarti; N Harris; J Ivanov; R D Saiakhov
Journal:  SAR QSAR Environ Res       Date:  2003-04       Impact factor: 3.000

7.  Transitioning to composite bacterial mutagenicity models in ICH M7 (Q)SAR analyses.

Authors:  Curran Landry; Marlene T Kim; Naomi L Kruhlak; Kevin P Cross; Roustem Saiakhov; Suman Chakravarti; Lidiya Stavitskaya
Journal:  Regul Toxicol Pharmacol       Date:  2019-10-03       Impact factor: 3.271

8.  A structural analysis of the differential cytotoxicity of chemicals in the NCI-60 cancer cell lines.

Authors:  Suman K Chakravarti; Gilles Klopman
Journal:  Bioorg Med Chem       Date:  2008-01-19       Impact factor: 3.641

9.  Improvement of quantitative structure-activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project.

Authors:  Masamitsu Honma; Airi Kitazawa; Alex Cayley; Richard V Williams; Chris Barber; Thierry Hanser; Roustem Saiakhov; Suman Chakravarti; Glenn J Myatt; Kevin P Cross; Emilio Benfenati; Giuseppa Raitano; Ovanes Mekenyan; Petko Petkov; Cecilia Bossa; Romualdo Benigni; Chiara Laura Battistelli; Alessandro Giuliani; Olga Tcheremenskaia; Christine DeMeo; Ulf Norinder; Hiromi Koga; Ciloy Jose; Nina Jeliazkova; Nikolay Kochev; Vesselina Paskaleva; Chihae Yang; Pankaj R Daga; Robert D Clark; James Rathman
Journal:  Mutagenesis       Date:  2019-03-06       Impact factor: 3.000

10.  Management of pharmaceutical ICH M7 (Q)SAR predictions - The impact of model updates.

Authors:  Catrin Hasselgren; Joel Bercu; Alex Cayley; Kevin Cross; Susanne Glowienke; Naomi Kruhlak; Wolfgang Muster; John Nicolette; M Vijayaraj Reddy; Roustem Saiakhov; Krista Dobo
Journal:  Regul Toxicol Pharmacol       Date:  2020-10-13       Impact factor: 3.271

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