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Adv Drug Deliv Rev. 2015 Jun 23;86:101-11. doi: 10.1016/j.addr.2015.03.005. Epub 2015 Mar 18.

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

Author information

1
School of Pharmacy, Faculty of Science and Engineering, University of Wolverhampton, City Campus, Wulfruna Street, WV1 1SB, England, United Kingdom; School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England, United Kingdom. Electronic address: m.hewitt@wlv.ac.uk.
2
School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England, United Kingdom. Electronic address: c.m.ellison@ljmu.ac.uk.
3
School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England, United Kingdom. Electronic address: m.t.cronin@ljmu.ac.uk.
4
Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, IMIM (Hospital del Mar Medical Research Institute), Dr. Aiguader 88, E-08003 Barcelona, Spain. Electronic address: manuel.pastor@upf.edu.
5
Bayer HealthCare, Bayer Pharma AG, Investigational Toxicology, Müllerstraße 178, 13352 Berlin, Germany. Electronic address: thomas.steger-hartmann@bayer.com.
6
Chemical Sciences, Computational Chemistry, GlaxoSmithKline, Stevenage, SG1 2NY, England, United Kingdom. Electronic address: jordi.4.munoz-muriedas@gsk.com.
7
Biochemical & Cellular Toxicology, Discovery Investigative Safety - PreClinical Safety, Novartis Pharma AG, Werk Klybeck, Postfach, CH-4002 Basel, Switzerland. Electronic address: francois.pognan@novartis.com.
8
School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England, United Kingdom. Electronic address: j.madden@ljmu.ac.uk.

Abstract

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.

KEYWORDS:

Good computer modelling practice; Model reliability; Peer-verification; QSAR; Toxicity prediction; Validation

PMID:
25794480
DOI:
10.1016/j.addr.2015.03.005
[Indexed for MEDLINE]

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