Literature DB >> 16815770

A model validation and consensus building environment.

T Abshear1, G M Banik, M L D'Souza, K Nedwed, C Peng.   

Abstract

Over half of the failures in drug development are due to problems with the absorption, distribution, metabolism, excretion, and toxicity, or ADME/Tox properties of a candidate compound. The utilization of in silico tools to predict ADME/Tox and physicochemical properties holds great potential for reducing the attrition rate in drug research and development, as this technology can prioritize candidate compounds in the pharmaceutical R&D pipeline. However, a major concern surrounding the use of in silico ADME/Tox technology is the reliability of the property predictions. Bio-Rad Laboratories, Inc. has created a computational environment that addresses these concerns. This environment is referred to as KnowItAll. Within this platform are encoded a number of ADME/Tox predictors, the ability to validate these predictors with/without in-house data and models, as well as build a 'consensus' model that may be a much better model than any of the individual predictive model. The KnowItAll system can handle two types of predictions: real number and categorical classification.

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Year:  2006        PMID: 16815770     DOI: 10.1080/10659360600787551

Source DB:  PubMed          Journal:  SAR QSAR Environ Res        ISSN: 1026-776X            Impact factor:   3.000


  5 in total

1.  Structure Activity Relationships (SARs) Using a Structurally Diverse Drug Database: Validating Success of Predictor Tools.

Authors:  Malcolm J D'Souza; Fumie Koyoshi; Lynn M Everett
Journal:  Pharm Rev       Date:  2009 Sep-Oct

Review 2.  Advances in computationally modeling human oral bioavailability.

Authors:  Junmei Wang; Tingjun Hou
Journal:  Adv Drug Deliv Rev       Date:  2015-01-09       Impact factor: 15.470

3.  Extracting Relevant Information from FDA Drug Files to Create a Structurally Diverse Drug Database Using KnowItAll®

Authors:  Malcolm J D'Souza; Fumie Koyoshi
Journal:  Pharm Rev       Date:  2009-05-08

Review 4.  Applicability of predictive toxicology methods for monoclonal antibody therapeutics: status Quo and scope.

Authors:  Arathi Kizhedath; Simon Wilkinson; Jarka Glassey
Journal:  Arch Toxicol       Date:  2016-10-20       Impact factor: 5.153

5.  Pushing the limits of solubility prediction via quality-oriented data selection.

Authors:  Murat Cihan Sorkun; J M Vianney A Koelman; Süleyman Er
Journal:  iScience       Date:  2020-12-17
  5 in total

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