Benson2017 - Systems Pharmacology Multidrug (cholesterol biosynthesis pathway)

BensonWattersonetal_SystemsPharmacology_Multidrug

This model is described in the article:

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

Benson H, Watterson S, Sharman J,
Mpamhanga C, Parton A, Southan C, Harmar A, Ghazal P.
Br. J. Pharmacol. 2017 Sep; :
Abstract:

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.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
optimisation 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.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.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 work-flow for the refinement of data and the
generation of future therapeutic hypotheses.

This model is hosted on
BioModels Database
and identified by:
MODEL1506220000.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.

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