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Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway
Author(s) -
Benson Helen E,
Watterson Steven,
Sharman Joanna L,
Mpamhanga Chido P,
Parton Andrew,
Southan Christopher,
Harmar Anthony J,
Ghazal Peter
Publication year - 2017
Publication title -
british journal of pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.432
H-Index - 211
eISSN - 1476-5381
pISSN - 0007-1188
DOI - 10.1111/bph.14037
Subject(s) - workflow , systems pharmacology , systems biology , drug discovery , computer science , drug , computational biology , drug development , mevalonate pathway , key (lock) , pharmacology , computational model , clinical pharmacology , set (abstract data type) , bioinformatics , medicine , biology , artificial intelligence , biosynthesis , database , biochemistry , computer security , programming language , enzyme
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.