Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models

The use of in silico tools within the drug development process to predict a wide range of properties including absorption, distribution, metabolism, elimination and toxicity has become increasingly important due to changes in legislation and both ethical and economic drivers to reduce animal testing...

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Veröffentlicht in:Advanced drug delivery reviews 2015-06, Vol.86, p.101-111
Hauptverfasser: Hewitt, Mark, Ellison, Claire M., Cronin, Mark T.D., Pastor, Manuel, Steger-Hartmann, Thomas, Munoz-Muriendas, Jordi, Pognan, Francois, Madden, Judith C.
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container_end_page 111
container_issue
container_start_page 101
container_title Advanced drug delivery reviews
container_volume 86
creator Hewitt, Mark
Ellison, Claire M.
Cronin, Mark T.D.
Pastor, Manuel
Steger-Hartmann, Thomas
Munoz-Muriendas, Jordi
Pognan, Francois
Madden, Judith C.
description The use of in silico tools within the drug development process to predict a wide range of properties including absorption, distribution, metabolism, elimination and toxicity has become increasingly important due to changes in legislation and both ethical and economic drivers to reduce animal testing. Whilst in silico tools have been used for decades there remains reluctance to accept predictions based on these methods particularly in regulatory settings. This apprehension arises in part due to lack of confidence in the reliability, robustness and applicability of the models. To address this issue we propose a scheme for the verification of in silico models that enables end users and modellers to assess the scientific validity of models in accordance with the principles of good computer modelling practice. We report here the implementation of the scheme within the Innovative Medicines Initiative project “eTOX” (electronic toxicity) and its application to the in silico models developed within the frame of this project. [Display omitted]
doi_str_mv 10.1016/j.addr.2015.03.005
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source MEDLINE; Elsevier ScienceDirect Journals
subjects Computer Simulation
Good computer modelling practice
Humans
Model reliability
Models, Theoretical
Peer-verification
Pilot Projects
QSAR
Reproducibility of Results
Toxicity prediction
Validation
title Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models
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