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Br J Pharmacol. 2017 Dec;174(23):4362-4382. doi: 10.1111/bph.14037. Epub 2017 Nov 26.

Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway.

Author information

1
Centre for Integrative Physiology, University of Edinburgh, Edinburgh, UK.
2
Northern Ireland Centre for Stratified Medicine, University of Ulster, C-Tric, Derry, UK.
3
Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh, UK.
4
Division of Infection and Pathway Medicine, University of Edinburgh Medical School, Edinburgh, UK.
5
Centre for Synthetic and Systems Biology, CH Waddington Building, King's Buildings, Edinburgh, UK.

Abstract

BACKGROUND AND PURPOSE:

An ever-growing wealth of information on current drugs and their pharmacological effects is available from online databases. As our understanding of systems biology increases, we have the opportunity to predict, model and quantify how drug combinations can be introduced that outperform conventional single-drug therapies. Here, we explore the feasibility of such systems pharmacology approaches with an analysis of the mevalonate branch of the cholesterol biosynthesis pathway.

EXPERIMENTAL APPROACH:

Using open online resources, we assembled a computational model of the mevalonate pathway and compiled a set of inhibitors directed against targets in this pathway. We used computational optimization to identify combination and dose options that show not only maximal efficacy of inhibition on the cholesterol producing branch but also minimal impact on the geranylation branch, known to mediate the side effects of pharmaceutical treatment.

KEY RESULTS:

We describe serious impediments to systems pharmacology studies arising from limitations in the data, incomplete coverage and inconsistent reporting. By curating a more complete dataset, we demonstrate the utility of computational optimization for identifying multi-drug treatments with high efficacy and minimal off-target effects.

CONCLUSION AND IMPLICATIONS:

We suggest solutions that facilitate systems pharmacology studies, based on the introduction of standards for data capture that increase the power of experimental data. We propose a systems pharmacology workflow for the refinement of data and the generation of future therapeutic hypotheses.

PMID:
28910500
PMCID:
PMC5715582
DOI:
10.1111/bph.14037
[Indexed for MEDLINE]
Free PMC Article

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