The implications of model–informed drug discovery and development for tuberculosis

Morris Muliaditan; Geraint R. Davies; Ulrika S. H. Simonsson; Stephen H. Gillespie; Oscar Della Pasqua

doi:10.1016/j.drudis.2016.09.004

The implications of model–informed drug discovery and development for tuberculosis

Morris Muliaditan, Geraint R. Davies, Ulrika S. H. Simonsson, Stephen H. Gillespie, Oscar Della Pasqua

Research output: Contribution to journal › Article › peer-review

Abstract

Despite promising advances in the field and highly effective first-line treatment, an estimated 9.6 million people are still infected with tuberculosis (TB). Innovative methods are required to effectively transition the growing number of compounds into novel combination regimens. However, progression of compounds into patients occurs despite the lack of clear understanding of the pharmacokinetic-pharmacodynamic (PK/PD) relations. The PreDiCT-TB consortium was established in response to the existing gaps in TB drug development. The aim of the consortium is to develop new preclinical tools in concert with an in silico model-based approach, grounded in PKPD principles. Here, we highlight the potential impact of such an integrated framework on various stages in TB drug development and on the dose rationale for drug combinations.

Original language	English
Pages (from-to)	481-486
Number of pages	6
Journal	Drug Discovery Today
Volume	22
Issue number	3
Early online date	28 Sept 2016
DOIs	https://doi.org/10.1016/j.drudis.2016.09.004
Publication status	Published - Mar 2017

Keywords

PKPD
Tuberculosis
Drug development
Modelling and simulation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.drudis.2016.09.004

Gillespie_2016_DDT_Model-informedDrugDiscovery_AM
© 2016, Elsevier. This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at www.sciencedirect.com / https://dx.doi.org/10.1016/j.drudis.2016.09.004
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Cite this

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title = "The implications of model–informed drug discovery and development for tuberculosis",

abstract = "Despite promising advances in the field and highly effective first-line treatment, an estimated 9.6 million people are still infected with tuberculosis (TB). Innovative methods are required to effectively transition the growing number of compounds into novel combination regimens. However, progression of compounds into patients occurs despite the lack of clear understanding of the pharmacokinetic-pharmacodynamic (PK/PD) relations. The PreDiCT-TB consortium was established in response to the existing gaps in TB drug development. The aim of the consortium is to develop new preclinical tools in concert with an in silico model-based approach, grounded in PKPD principles. Here, we highlight the potential impact of such an integrated framework on various stages in TB drug development and on the dose rationale for drug combinations.",

keywords = "PKPD, Tuberculosis, Drug development , Modelling and simulation",

author = "Morris Muliaditan and Davies, {Geraint R.} and Simonsson, {Ulrika S. H.} and Gillespie, {Stephen H.} and Pasqua, {Oscar Della}",

note = "The research leading to these results received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n°115337, the resources of which comprise financial contributions from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies{\textquoteright} in kind contribution.",

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AU - Davies, Geraint R.

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AU - Gillespie, Stephen H.

AU - Pasqua, Oscar Della

N1 - The research leading to these results received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n°115337, the resources of which comprise financial contributions from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution.

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KW - Modelling and simulation

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The implications of model–informed drug discovery and development for tuberculosis

Abstract

Keywords

UN SDGs

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IMI PREDICT TB 100%: IMI Pre-DiCT-TB

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The implications of model–informed drug discovery and development for tuberculosis

Abstract

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Projects

IMI PREDICT TB 100%: IMI Pre-DiCT-TB

Cite this