Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: building model credibility

AbstractImaging clinical trials can be burdensome and often delay patient access to novel, high-quality medical devices. Tools for in silico imaging trials have significantly improved in sophistication and availability. Here, I describe some of the principal advantages of in silico imaging trials and enumerate five lessons learned during the design and execution of the first all-in silico virtual imaging clinical trial for regulatory evaluation (the VICTRE study).

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Role of In-silico modeling in Drug Development for Inhalation Treatment

Journal of Molecular Pharmaceutics & Organic Process Research ◽

10.4172/2329-9053.1000106 ◽

2013 ◽

Vol 01 (02) ◽

Cited By ~ 1

Author(s):

Shengqian Wu Sharareh Salar-Behzadi

Keyword(s):

Drug Development ◽

In Silico ◽

In Silico Modeling

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In silico structural characterization of protein targets for drug development against Trypanosoma cruzi

Journal of Molecular Modeling ◽

10.1007/s00894-016-3115-9 ◽

2016 ◽

Vol 22 (10) ◽

Cited By ~ 5

Author(s):

Carlyle Ribeiro Lima ◽

Nicolas Carels ◽

Ana Carolina Ramos Guimaraes ◽

Pierre Tufféry ◽

Philippe Derreumaux

Keyword(s):

Trypanosoma Cruzi ◽

Drug Development ◽

Structural Characterization ◽

In Silico ◽

Protein Targets

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An integrated dataset for in silico drug discovery

Journal of Integrative Bioinformatics ◽

10.1515/jib-2010-116 ◽

2010 ◽

Vol 7 (3) ◽

Cited By ~ 13

Author(s):

Simon J Cockell ◽

Jochen Weile ◽

Phillip Lord ◽

Claire Wipat ◽

Dmytro Andriychenko ◽

...

Keyword(s):

Systems Biology ◽

Drug Discovery ◽

Drug Development ◽

Data Integration ◽

In Silico ◽

Drug Repositioning ◽

Integrated Systems ◽

Integration Platform ◽

Treatment Indications ◽

New Treatment

SummaryDrug development is expensive and prone to failure. It is potentially much less risky and expensive to reuse a drug developed for one condition for treating a second disease, than it is to develop an entirely new compound. Systematic approaches to drug repositioning are needed to increase throughput and find candidates more reliably. Here we address this need with an integrated systems biology dataset, developed using the Ondex data integration platform, for the in silico discovery of new drug repositioning candidates. We demonstrate that the information in this dataset allows known repositioning examples to be discovered. We also propose a means of automating the search for new treatment indications of existing compounds.

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In Silico Disease Models of Breast Cancer

Omics Approaches in Breast Cancer ◽

10.1007/978-81-322-0843-3_16 ◽

2014 ◽

pp. 315-331

Author(s):

Anjana Munshi ◽

Vandana Sharma

Keyword(s):

Breast Cancer ◽

In Silico ◽

Disease Models

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The 18th FRAME Annual Lecture, October 2019: Human In Silico Trials in Pharmacology

Alternatives to Laboratory Animals ◽

10.1177/0261192919896356 ◽

2019 ◽

Vol 47 (5-6) ◽

pp. 221-227

Author(s):

Blanca Rodriguez

Keyword(s):

Drug Development ◽

In Silico ◽

Drug Testing ◽

Development Process ◽

Computer Modelling ◽

Current Knowledge ◽

Magnetic Resonance Images ◽

Population Heterogeneity ◽

Drug Development Process

Safety and efficacy testing is a crucial part of the drug development process, and several different methods are used to obtain the necessary data (e.g. in vitro testing, animal trials and clinical trials). Our group has been investigating the potential of modelling and simulation as an alternative approach to some of the methods used for testing drugs for cardiac effects. To achieve our goal of developing and promoting novel approaches in drug development, we formed multidisciplinary collaborations that included clinicians, computer scientists and biologists. Our in silico models are based on human data (e.g. magnetic resonance images, electrocardiogram) and on current knowledge of human electrophysiology, thus generating predictions that are directly applicable to humans. Such models are a particularly powerful tool because they encompass different sources of population heterogeneity, which is crucial for drug testing and for assessing how interindividual variability might affect clinical endpoints. Our group has shown that computer modelling can be used to predict the effects of a test drug in a virtual population or in combination with machine learning to predict different phenotypes when a drug is given to a diseased population. Furthermore, our user-friendly drug testing software is freely available and is being adopted by industry in their drug development process. We have been engaging with industry and regulators to show that our models can contribute to the replacement of animals in drug development. Our ambition is to generate models for simulation of different diseases and therapies for investigations from subcellular to whole organ.

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