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Edinburgh Research Explorer
Is systems pharmacology ready to impact upon
therapydevelopment?
Citation for published version:Benson, H, Watterson, S, Sharman,
J, Mpamhanga, C, Parton, A, Southan, C, Harmar, A & Ghazal,
P2017, 'Is systems pharmacology ready to impact upon therapy
development? A study on the cholesterolbiosynthesis pathway'
British Journal of Pharmacology, vol. 174, no. 23. DOI:
10.1111/bph.14037
Digital Object Identifier (DOI):10.1111/bph.14037
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RESEARCH PAPER
Is systems pharmacology ready to impactupon therapy development?
A study on thecholesterol biosynthesis pathway
Correspondence Steven Watterson, Northern Ireland Centre for
Stratified Medicine, University of Ulster, C-Tric, Altnagelvin
HospitalCampus, Derry BT47 6SB, UK. E-mail:
[email protected]
Received 23 September 2016; Revised 10 August 2017; Accepted 30
August 2017
Helen E Benson1,*,†, Steven Watterson2,* , Joanna L Sharman1 ,
Chido P Mpamhanga3,‡, Andrew Parton2,Christopher Southan1, Anthony
J Harmar3 and Peter Ghazal4,5
1Centre for Integrative Physiology, University of Edinburgh,
Edinburgh, UK, 2Northern Ireland Centre for Stratified Medicine,
University of Ulster,
C-Tric, Derry, UK, 3Centre for Cardiovascular Science,
University of Edinburgh, The Queen’s Medical Research Institute,
Edinburgh, UK, 4Division
of Infection and Pathway Medicine, University of Edinburgh
Medical School, Edinburgh, UK, and 5Centre for Synthetic and
Systems Biology, CH
Waddington Building, King’s Buildings, Edinburgh, UK
*Joint first authors.†Current address: TB Section, Respiratory
Disease Department, National Infection Service, Public Health
England, 61 Colindale Avenue,London NW9 5EQ, UK.‡Current address:
LifeArc, Accelerator Building, SBC Open Innovation Campus,
Stevenage SG1 2FX, UK.
BACKGROUND AND PURPOSEAn 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
drugcombinations can be introduced that outperform conventional
single-drug therapies. Here, we explore the feasibility of
suchsystems pharmacology approaches with an analysis of the
mevalonate branch of the cholesterol biosynthesis pathway.
EXPERIMENTAL APPROACHUsing open online resources, we assembled a
computational model of the mevalonate pathway and compiled a set of
inhibitorsdirected against targets in this pathway. We used
computational optimization to identify combination and dose options
thatshow not only maximal efficacy of inhibition on the cholesterol
producing branch but also minimal impact on the geranylationbranch,
known to mediate the side effects of pharmaceutical treatment.
KEY RESULTSWe describe serious impediments to systems
pharmacology studies arising from limitations in the data,
incomplete coverage andinconsistent reporting. By curating a more
complete dataset, we demonstrate the utility of computational
optimization foridentifying multi-drug treatments with high
efficacy and minimal off-target effects.
CONCLUSION AND IMPLICATIONSWe suggest solutions that facilitate
systems pharmacology studies, based on the introduction of
standards for data capture thatincrease the power of experimental
data. We propose a systems pharmacology workflow for the refinement
of data and thegeneration of future therapeutic hypotheses.
AbbreviationsAPI, Application Programme Interface; BPS, British
Pharmacological Society; BRENDA, Braunschweig Enzyme Database;CID,
compound identifier; FDA, US Food and Drug Administration; FDFT1,
farnesyl-diphosphate farnesyl transferase 1;
This is an open access article under the terms of the Creative
Commons Attribution License, which permits use, distribution and
reproduction in any medium,provided the original work is properly
cited.
BJP British Journal ofPharmacologyBritish Journal of
Pharmacology (2017) 174 4362–4382 4362
DOI:10.1111/bph.14037 © 2017 The Authors. British Journal of
Pharmacologypublished by John Wiley & Sons Ltd on behalf of
British Pharmacological Society.
http://orcid.org/0000-0002-2750-6410http://orcid.org/0000-0002-5275-6446http://creativecommons.org/licenses/by/4.0/
-
GtoPdb, Guide to Pharmacology Database; HMGCR,
hydroxymethylglutaryl-coa reductase;
HMGCS1,hydroxymethylglutaryl-CoA synthase; HPC, high-performance
computing; KEGG, Kyoto Encyclopedia of Genes and Ge-nomes; IUBMB,
International Union of Biochemistry and Molecular Biology; IUPHAR,
International Union of Basic andClinical Pharmacology; n2s,
name-to-structure; ODE, ordinary differential equation; SBGN,
Systems Biology GraphicalNotation; SBGN-ML, Systems Biology
Graphical Notation Markup Language; SBML, Systems Biology Markup
Language
Introduction
The expansion of available genomic and proteomic data
hasenhanced our understanding of biomolecular interactionnetworks.
Consequently, the development of systems biologyapproaches has
enabled us to better understand how cellularbehaviour emerges from
these networks (Boran and Iyengar,2010a). Systems-level approaches
have been used to predictthe on- and off-target impacts of an
intervention (Boran andIyengar, 2010b) and to identify the most
sensitive compo-nents in pathways that suggest candidate drug
targets(Benson et al., 2013). They also have the untapped
potentialto suggest therapies comprising combinations of
drugschosen to strategically reprogram biomolecular
interactionnetworks in order to drive the system from a diseased to
ahealthy state (Zhao et al., 2013; van Hasselt and van derGraaf,
2015; Watterson and Ghazal, 2010). This approach,known as systems
pharmacology (Boran and Iyengar, 2010b;Westerhoff et al., 2015), is
underpinned by the expansion inpathway, pharmacology and medicinal
chemistry databases.
For example, WikiPathways held 804 human
pathways(http://www.wikipathways.org/index.php/WikiPathways:Statistics)
with 253 added in 2015 (Kutmon et al., 2016).Kyoto Encyclopedia of
Genes and Genomes (KEGG) PATHWAYholds 518 pathway maps (Kanehisa et
al., 2017) (http://www.kegg.jp/kegg/docs/statistics.html). Reactome
currently holds2148 human pathways involving 10684 proteins and
isoforms(http://reactome.org/stats.html) (Croft et al., 2014;
Fabregatet al., 2016). ChEMBL version 23 (Gaulton et al., 2016)
includes14675320 bioactivities, and the International Union of
Basicand Clinical Pharmacology/British Pharmacological
Society(IUPHAR/BPS) Guide to Pharmacology (GtoPdb) contains15281
curated interactions in its 2017.5 release (Southan et al.,2016).
In 2016, the Food and Drug Administration (FDA) newdrug approvals
fell to 22, following 45 approvals in 2015 (USFood and Drug
Administration, 2016a; US Food and DrugAdministration, 2016bb).
According to DrugBank release 5.0,their distinctmolecular count of
approved small-molecule drugsis 2037 (Law et al., 2013).
As this catalogue of pharmacological interactions growsand our
understanding of pathway systems expands, it willbe advantageous to
integrate these resources in order to de-vise new potential
therapies. Drug combination-based inter-ventions represent an
opportunity for therapy developmentthat can yield one-size-fits-all
or personalized/stratified thera-pies, and they can target pathways
precisely rather thanperturbing entire networks. Two US National
Institute forHealth workshop white papers have made a strong case
forsystems pharmacology (Sorger et al., 2011) as a way to
reduceattrition in therapy, to stimulate drug development, to
bridgethe gap between network biology and translational medicineand
to enhance industrial–academic collaborations. Systemspharmacology
is also likely to impact upon genomic
medicine (Westerhoff et al., 2015), Systems Pathology, Sys-tems
Biology and Pharmacometrics (van der Greef andMcBurney, 2005;
Vicini and van der Graaf, 2013) and thetools that could contribute
to systems pharmacology havebeen described (Lehár et al., 2007;
Berger and Iyengar, 2009;Kell and Goodacre, 2014).
Previous work under the domain of systems pharmacologyhas
primarily focussed on pharmacokinetic–pharmacodynamicmodelling
(Darwich et al., 2017). Industry has evaluated systemspharmacology
as a tool to inform trial design in areas ofcardiovascular disease,
endocrinology, neurogenerative disease,respiratory disease,
oncology and infectious disease (Visser et al.,2014) and to inform
regulatory development (Visser et al., 2014;Peterson andRiggs,
2015). There have a been a number of specificstudies of nerve
growth factor (Benson et al., 2013), coagulation(Wajima et al.,
2009), innate immunity (Madrasi et al., 2014), can-cer (Abaan et
al., 2013) and atherosclerosis (Pichardo–Almarzaet al., 2015).
However, whilst there is much enthusiasm for systemspharmacology
as a tool to improve the efficacy and safety ofthe drug development
pipeline (van der Graaf and Benson,2011; Rostami-Hodjegan, 2012;
Trame et al., 2016), the prac-tical challenges of systematically
amalgamating pharmacol-ogy and pathway biology in a coherent
framework have notbeen adequately addressed.
Here, we describe a systems pharmacology study of thecholesterol
biosynthesis pathway, detailing the barriers toprogress that we
encountered and suggesting solutions tothese impediments, before
proposing amodel of how systemspharmacology studies could be
conducted in future. In par-ticular, we build a dynamic ordinary
differential equation(ODE) model of the pathway, which we
parameterize as faras possible from the literature. We identify
relevant pharma-cological agents that act on this pathway and
parameterizethem as far as possible from the literature and online
data-bases. We then use computational optimization techniquesto
identify the drug combinations that are most effective
atsuppressing the outputs of the pathway that lead to choles-terol
production and that minimize off-target effects. In com-pleting our
analysis, we identify many of the problems thatprevent this type of
work being undertaken routinely, andwe suggest solutions that would
enable systems pharmacol-ogy to make a regular contribution to
therapy development.
As explored in previous studies (Mazein et al., 2011;
2013;Watterson et al., 2013; Bhattacharya et al., 2014; Caspi et
al.,2016), the cholesterol biosynthesis pathway is critical to
bothcardiovascular health (Lewington et al., 2007; Hendersonet al.,
2016; Parton et al., 2016) and innate immunity (Blancet al., 2011;
Lu et al., 2015; Robertson et al., 2016). As the tar-get of the
statin class of drug, we would expect this pathwayto be amongst the
most thoroughly characterized, and forthis reason, we have chosen
it for our feasibility study of sys-tems pharmacology. For
simplicity, we have focused on the
The feasibility of systems pharmacology BJP
British Journal of Pharmacology (2017) 174 4362–4382 4363
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segment of the pathway that transforms acetyl-CoA tosqualene and
that forks to produce geranylgeranyl-di-phosphate. As a precursor
to cholesterol, we would expectsqualene synthesis to track
cholesterol synthesis and so weuse this as a proxy. The branch of
the pathway that producesgeranylgeranyl-diphosphate has been shown
tomediate boththe innate immune response (Blanc et al., 2011) and
themyopathy side-effects associated with statin treatment(Wagner et
al., 2011). Any intervention that demonstrates aminimal impact on
this branch will avoid one of thesignificant side effects
associated with standard cholesterollowering treatments.
Methods
Pathway productionWe started from the representations available
in KEGG(Kanehisa et al., 2014), MetaCyc (Caspi et al., 2016) and
theGtoPdb (Southan et al., 2016) taking these resources to
berepresentative of the community of online pathway data-bases. We
reviewed the primary literature to establish thestructure of the
mevalonate portion of the cholesterolbiosynthesis pathway, in
particular the enzymes involvedin the pathway, the reactions they
catalyse, their subcellularlocalization, the species in which they
were identified andany known isoforms.
Diagrams of the pathway were created using the SystemsBiology
Graphical Notation (SBGN) (Le Novère et al., 2009),the yEd diagram
software (yWorks GmbH, http://www.yworks.com/products/yed) and the
SBGN-ED add-on toVANTED (Czauderna et al., 2010). From these
diagrams, webuilt kinetic models as systems of ODEs.
The ODE model of this pathway was built usingMichaelis–Menten
kinetics to describe each step except theinteractions consuming
isopentenyl diphosphate andproducing geranylgeranyl diphosphate and
pre-squalenediphosphate. These steps were described using mass
actionkinetics in order to simplify the process of calculating
thesteady state of the model and hence the steady state behav-iour
of the pathway. Mass action kinetics were justified bythe
expectation that the pathway interactions would operatefar from
substrate saturation making the dynamics robustagainst small
fluctuations in enzyme concentration. Mass ac-tion rate constants
were calculated from the Kcat, Km and Kiparameters as described
elsewhere (Watterson et al., 2013)and enzyme concentrations were
taken from experimentallymeasured values (Watterson et al.,
2013).
The pathway map and the associated mathematicalmodel are
available from the Supporting Information FilesS1 and S2 as Systems
Biology Graphical Notation MarkupLanguage (SBGN-ML) (Van Iersel et
al., 2012) and SystemsBiology Markup Language (SBML) files (Hucka
et al., 2003)respectively.
Pathway parameterizationWe identified the kinetic parameters
that quantify each reac-tion unambiguously (Km and Kcat) using the
BraunschweigEnzyme Database (BRENDA) (Chang et al., 2015) and
verifiedthe values described against those in the primary
literature. In
many instances, enzymes were associated with multiplekinetic
parameter sets. We selected kinetic parameters basedupon the
following criteria: (i) specificity to the wild-typeenzyme in one
of the three main mammalian model species:human, mouse or rat; (ii)
sourced from a primary literaturereference describing in vivo or in
vitro experimental data asopposed to computationally derived
structural modellingdata; and (iii) sourced from a reference that
could be accessedand therefore verified. For many enzymes, this
yielded arange of values for each parameter, and where this was
thecase, we used the mean of the values obtained.
Inhibitor listInhibitor compounds not already indexed in GtoPdb
wereidentified for each reaction from ChEMBL and BRENDA,databases
that we took to be representative of the commu-nity of target
databases. We included a compound in ourset if it met three
criteria: (i) the enzyme used in the assaywas wild-type from one of
the three main mammalianmodel species: human, mouse or rat; (ii) an
experimentallydetermined reaction-specific inhibition constant (Ki)
wasreported; and (iii) the assay conditions were
reported.Crucially, all data were checked against the primary
litera-ture references. Where this yielded a range of
inhibitionconstants for nominally identical compounds, the
mostpotent Ki values were used.
We verified the correct chemical structures of the inhibi-tors
by cross-referencing the original references against theonline
chemical databases PubChem (Kim et al., 2016) andChemSpider (Pence
andWilliams, 2010). The actual chemicalstructures of the marketed
statin drugs were established bychecking the FDA labels and the
international non-proprietary name-assigned structures on the World
HealthOrganization MedNet site
(https://mednet-communities.net/inn). Comparison of unique
structural identifiers allowedus to identify duplicates within the
ChEMBL, BRENDA andliterature-derived dataset, and to establish
whether the chem-ical structure reported in a given reference
matched themarketed drug or research compound structures.
Curated content describing the enzymes in this pathway,their
substrates and small molecule inhibitors was used toconsolidate and
expand GtoPdb using the same approachand guidelines as described
elsewhere (Pawson et al., 2014).The enzymes, list of inhibitors and
kinetic parameters arenow all updated in the July 2016.3 release of
GtoPdb.
Hypothesis generationWe combined ODE kinetic models, the pathway
parametersand the inhibitor parameters to create a model
describingthe dynamics of the mevalonate pathway. We sought
toidentify the drug combination that would best suppressthe
production of squalene as a precursor for cholesterol,but would
also maintain production of geranylgeranyl-diphosphate at the same
levels as in the absence of any in-hibitors, thereby eliminating a
significant side-effect oftreatment. Firstly, we identified the
steady-state activity ofthe pathway in the absence of any
inhibitors. Then we usedcomputational optimization to identify the
drug combina-tion that, at steady state, minimized squalene
production,but left geranylgeranyl diphosphate production the
sameas in the absence of inhibitors.
BJP H Benson et al.
4364 British Journal of Pharmacology (2017) 174 4362–4382
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This was implemented using the Genetic Algorithm func-tion
available on Matlab (MathWorks, http://www.mathworks.com) in
parallel with a population size of 200and a function tolerance of
10�6. Matlab was chosen as themodelling platform for its
flexibility, stability and compre-hensive libraries. The genetic
algorithm started with one in-stance of a set of drug
concentrations where each drug wasassigned a concentration equal to
its Ki. A 199 further in-stances of sets of drug concentrations
were automaticallygenerated from this instance by adding Gaussian
noise tothe concentration of each drug (with standard deviation
1,the default setting). These 200 instances comprised the
firstgeneration of candidate interventions. All instances of setsof
concentrations were evaluated for their efficacy at sup-pressing
squalene synthesis whilst maintaining geranylger-anyl diphosphate
production. Two hundred new instanceswere created as a second
generation of candidate interven-tions from the two most effective
instances of the first gener-ation and with the addition of
Gaussian noise. The 200 newinstances were then themselves evaluated
with the two mosteffective instances used to generate a further 200
new in-stances, the third generation. This process was repeated
untilwe arrived at instances from which no improvement in effi-cacy
could be found for 20 consecutive generations, at whichpoint we
interpreted the best performing instance identifiedthus far as
optimal.
Nomenclature of targets and ligandsKey protein targets and
ligands in this article are hyperlinkedto corresponding entries in
http://www.guidetopharma-cology.org, the common portal for data
from theIUPHAR/BPS Guide to PHARMACOLOGY (Southan et al.,2016), and
are permanently archived in the Concise Guideto PHARMACOLOGY
2015/16 (Alexander et al., 2015).
Results
Pathway productionWe produced the model of the mevalonate arm of
the choles-terol biosynthesis pathway shown in Figure 1 in SBGN
nota-tion, describing the sequence of metabolic steps that leadfrom
acetyl-CoA and acetoacetyl-CoA consumption tosqualene and
geranylgeranyl diphosphate production. Thispathway comprises 12
steps (see Table 1), involving 10 en-zymes and 14 metabolites.
The parameters required for the resulting ODE model areshown in
Table 1. After pooling results across mouse, humanand rat models,
we were able to obtain experimental valuesfor only 12 out of the 24
required parameters. Across the stud-ies reported, pH values ranged
from 7.0 to 8.0 and tempera-tures ranged from 25°C to 37°C,
although in some studies,neither pH nor temperature values were
given. When verifiedagainst the primary references, we found that
one parametervalue obtained from BRENDA was missing from the
literaturereference provided, suggesting that it had been
misattributed[Kcat = 0.023/s for hydroxymethylglutaryl-Coa
reduc-tase (HMGCR)]. A second parameter had been transcribed(for
diphosphomevalonate decarboxylase) where theliterature source
contradicted itself, specifying Km = 10 μM
in the abstract and Km = 10 mM in the manuscript.
Becausecomputational hypothesis generation is highly sensitive
tothe values of the parameters, ambiguous or inaccuratereporting
can have a significant impact on any predictionsmade.
Substrates were reported in varying levels of structural
de-tail. Common names were used that could refer to
multipleexplicit forms of a chemical structure. However,
variationsin the chirality and chemical structure can significantly
affectsubstrate affinity. The relative enzyme concentrations
hadbeen inferred previously (Watterson et al., 2013) and arelisted
in Table 2.
Figure 1The mevalonate arm of the cholesterol biosynthesis
pathway.
The feasibility of systems pharmacology BJP
British Journal of Pharmacology (2017) 174 4362–4382 4365
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Supporting Information Table S1 compares representa-tions of the
cholesterol biosynthesis pathway across themainpublicly available
pathway and chemical databases. Itincludes a summary of
cross-referencing between databaseswith standard identifiers for
unambiguous representation,which will be essential for future
cross-platforminteroperability.
InhibitorsThe inhibitors obtained from GtoPdb, BRENDA and
theliterature, together with their inhibition constants (Ki),
arelisted in Table 3. Six of the 10 enzyme targets had
quantifiedparameters in humans. It was necessary to include two
inhib-itors that had been only reported for rat enzymes
[L-659,699for hydroxymethylglutaryl-CoA synthase (HMGCS1)and
3-hydroxy-3-methyl-6-phosphohexanoic acid forphosphomevalonate
kinase] in order to maximizecoverage of the pathway. Two enzyme
paralogues(isopentenyl diphosphate Δ-isomerases 1 and 2) hadno
reported inhibitors with available Ki values, representinga region
of the pathway that cannot currently be modulatedin our modelling
process. This can be contrasted with theenzymes HMGCR and farnesyl
diphosphate synthase,each of which had an extensive list of
inhibitors. Inhibitionconstants could be obtained for 8 of the 10
enzymes in thepathway. Where reported, these values came from
studiesconducted across a range of pH levels from 6.8 to 7.5
andtemperatures from 25°C to 37°C.
Both explicit structure and name-to-structure (n2s)ambiguities
existed around the reporting of inhibitor enti-ties. In some cases,
the common or trade name of a com-pound was used, without
specification of the exactchemical structure and stereochemistry.
In other cases, wefound a different n2s assignment across different
databaseresources or indeed within the same resource. For
example,under the HMGCS1 entry of BRENDA, the same inhibitoris
listed twice as L-659,699 and
(E,E)-11-[3-(hydroxy-methyl)-4-oxo-2-oxytanyl]-3,5,7-trimethyl-2,4-undecadienenoic
acid.
Several results recorded in ChEMBL were transcribedagainst the
incorrect drug target. Three inhibitors listedagainst the enzyme
HMGCS1 describe results obtained fromexperiments with HMGCR
(Balasubramanian et al., 1989).There were also cases where the
incorrect species had been re-corded. For example, the compound
with ChEMBL IDCHEMBL88601 cited in one study (Procopiou et al.,
1994)(ChEMBL document ID CHEMBL1151052) is listed againstthe human
squalene synthase (FDFT1) enzyme, whilst infact, the paper
describes results for the yeast Candida albicansand rats.
Hypothesis generationIn order to complete the gaps in the
available parameter sets,we proceeded by assuming that where
parameters were takenfrom separate studies, the same metabolite
chemicalstructures were referenced. For all the unknown
parameters,we substituted a single representative value, obtained
byaveraging across all known corresponding parameters.
Calculating the steady-state behaviour of the pathway inthe
absence of inhibitors yielded the profile of flux shownon the left
of Figure 2A, which we take to be wild-type
behaviour. Using computational optimization, we identifiedthe
following drug combination that produced the steady-state profile
of flux shown in the middle of Figure 2A and inFigure 2B: L-659,699
= 0.0294 nM, rosuvastatin = 2.60 nM,farnesyl thiodiphosphate =
0.0340 nM, cinnamicacid = 0.00104 nM, 6-fluoromevalonate
5-diphosphate = 0.0213 nM, zoledronic acid = 9.97 nM,BPH-628 = 5.86
nM; zaragozic acid A = 0.755 nM (seeTable 3 and Supporting
Information Tables S2 and S3). Here,the production of squalene, a
precursor of cholesterol, isheavily suppressed and the production
of geranylgeranyldiphosphate is maintained at wild-type levels. In
Figure 2B,we see specifically the flux at endpoints of the two
pathwaybranches. With this drug combination, the flux
fromgeranylgeranyl diphosphate → protein prenylation is thesame
between the wild-type (inhibitor free) case and theoptimal
multi-drug intervention case. However, the flux fromsqualene →
cholesterol synthesis has been significantlysuppressed.
In Figure 2A, B, we compare the flux profiles for wild-type and
optimal multi-drug interventions to the casewhere rosuvastatin, a
type of statin, is applied alone. Thisinhibitor targets the
interaction catalysed by HMGCR, andwe chose a concentration
sufficient to suppress the rate ofsqualene formation and
consumption to the same extentas the multi-drug intervention. As
can be seen in Figure 2B, rosuvastatin intervention impacts upon
both branchesof the pathway, suppressing geranylgeranyl
diphosphateformation and protein prenylation as an off-target
effectof treatment.
Interestingly, a concentration of 362 nM rosuvastatin
wasrequired to achieve the same level of squalene suppression asthe
multi-drug intervention. The greatest individual drugconcentration
required in the optimal multi-drug interven-tion was 9.97 nM, and
the total combined concentrationwas 19.3 nM, a dramatically lower
dosage.
The value of drug combinations can also be seen inSupporting
Information Figure S1 where we consider the im-pact of pairs of
drugs (Lehár et al., 2007). Here, we see thatdrug pairs with
targets above the fork inhibit the flux throughboth pathway
endpoints (Supporting Information FigureS1A, B). Drug pairs with
targets above and below the forktogether inhibit the flux through
the cholesterol synthesiz-ing branch (Supporting Information Figure
S1C, D). How-ever, drug pairs with targets above and below the fork
athigh doses can have a low impact on the flux through theprotein
prenylation branch (Supporting Information FigureS1E, F).
Critically, Supporting Information Figure S1B, Eshows that
concentrations can be selected that significantlysuppress the
cholesterol synthesizing branch but that donot suppress the protein
prenylation branch. The resultsdemonstrate comparable impact to the
multi-drug interven-tion described above, but at higher individual
and combinedconcentrations.
In order to identify the optimal multi-drug combination,it was
necessary to use a high-performance computing(HPC) platform.
However, the HPC demands were modest.Using an eight-node desktop
computer running MATLAB inparallel, we can see that the score (a
dimensionless value,greater than or equal to zero, that quantifies
how effectivelythe best performing multi-drug intervention
identified
BJP H Benson et al.
4366 British Journal of Pharmacology (2017) 174 4362–4382
http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=5886http://www.guidetopharmacology.org/GRAC/ObjectDisplayForward?objectId=638http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=3202http://www.guidetopharmacology.org/GRAC/ObjectDisplayForward?objectId=641http://www.guidetopharmacology.org/GRAC/ObjectDisplayForward?objectId=646http://www.guidetopharmacology.org/GRAC/ObjectDisplayForward?objectId=646http://www.guidetopharmacology.org/GRAC/ObjectDisplayForward?objectId=647http://www.guidetopharmacology.org/GRAC/ObjectDisplayForward?objectId=644http://www.guidetopharmacology.org/GRAC/ObjectDisplayForward?objectId=645http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=2954http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=3216http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=3203http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=3203http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=3205http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=3205http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=3177http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=3188http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=3057
-
Table 1A list of the enzymes of the mevalonate branch of the
cholesterol synthesis pathway, with gene and protein identifiers
and EC numbers
E.Cnumber
Enzyme/GtoPdbtarget ID
UniProtID
HGNCID
IUBMBenzymeapproved name
Reactioncatalysed
Km(mM)/PMID
Reportedsubstrate/GtoPdb ligand ID
Kcat(s-1)/PMID
2.3.3.10 HMGCS1/638
Q01581 5007 Hydroxymethylglutaryl-CoA synthase
Acetyl CoA + H2O+ acetoacetylCoA =
(S)-3-hydroxy-3-methylglutaryl-CoA +coenzyme A
0.009/6118268
Acetyl-CoA/3038
–
– – – – – 0.2/6118268 Acetyl-CoA/3038
–
– – – – – 0.073/19706283
Acetyl-CoA/3038
–
– – – – – 0.076/19706283
Acetyl-CoA/3038
–
– – – – – 0.084/19706283
Acetyl-CoA/3038
–
– – – – – 0.029/7913309
Acetyl-CoA/3038
–
1.1.1.34 HMGCR/639
P04035 5006 Hydroxymethylglutaryl-CoA reductase(NAPDH)
(S)-3-Hydroxy-3-methylglutaryl-CoA + 2 NADPH =mevaldyl CoA+
2NADP+
0.006/4985697
3-Hydroxy-3-methylglutaryl-CoA/3040
–
– – – – – 0.012/4985697
3-Hydroxy-3-methylglutaryl-CoA/3040
–
– – – – Mevaldyl CoA+ 2NADP+ = (R)-mevalonate +coenzymeA + 2
NADP+
0.01/10392455
3-Hydroxy-3-methylglutaryl-CoA/3040
–
– – – – – 0.014/10392455
3-Hydroxy-3-methylglutaryl-CoA/3040
–
– – – – – 0.015/10392455
3-Hydroxy-3-methylglutaryl-CoA/3040
–
– – – – – 0.019/10392455
3-Hydroxy-3-methylglutaryl-CoA/3040
–
– – – – – 0.024/10392455
3-Hydroxy-3-methylglutaryl-CoA/3040
–
– – – – – 0.07/16128575
3-Hydroxy-3-methylglutaryl-CoA/3040
–
– – – – – 0.6/�� 3-Hydroxy-3-methylglutaryl-CoA/3040
–
– – – – – 0.068/18446881
Hydroxymethylglutaryl-CoA
0.023/18446881
– – – – – 0.004/7077140
S-3-Hydroxy-3-methylglutaryl-CoA/3040
–
continues
The feasibility of systems pharmacology BJP
British Journal of Pharmacology (2017) 174 4362–4382 4367
-
Table 1(Continued)
E.Cnumber
Reactioncatalysed Organism
Reportedconditions
MeanKm(mM)
SubstitutedmeanKm
SubstitutedmeanKcat
2.3.3.10 Acetyl CoA + H2O+ acetoacetylCoA =
(S)-3-hydroxy-3-methylglutaryl-CoA +coenzyme A
Rattusnorvegicus
absence ofacetoacetyl-CoA, hydrolysisreaction
0.0785 – 6.651575
– Rattusnorvegicus
0.01 Macetoacetyl-CoA
– – –
– Homosapiens
– – – –
– Homosapiens
– – – –
– Homosapiens
– – – –
– Homo sapiens – – – –
1.1.1.34 (S)-3-Hydroxy-3-methylglutaryl-CoA + 2 NADPH =mevaldyl
CoA+ 2NADP+
Rattusnorvegicus
Only oneenantiomer
0.0765 – 0.0023
– Rattus norvegicus – – – –
Mevaldyl CoA+ 2NADP+ = (R)-mevalonate +coenzymeA + 2 NADP+
Mus musculus Enzyme fromtumour
– – –
– Mus musculus Enzyme from liverand tumour
– – –
– Mus musculus Enzyme from liver,implanted tumour
– – –
– Mus musculus Enzyme from liver,implanted tumour
– – –
– Mus musculus Enzymefrom liver
– – –
– Homo sapiens – – – –
– Homo sapiens pH 7.5/Tempnot specified
– – –
– Rattus norvegicus – – – –
– Rattus norvegicus – – – –
2.7.1.36 ATP + (R)-mevalonate= ADP +(R)-5-phosphomevalonate
Rattusnorvegicus
pH 7.5/25C 0.0337 – –
– Rattusnorvegicus
pH 7.5/34C – – –
– Homo sapiens pH 7.5/30C – – –
– Homo sapiens pH 7.0/30C – – –
2.7.4.2 ATP + (R)-5-phosphomevalonate =
Homosapiens
pH 7.0/30C 0.034 – 6.651575
continues
BJP H Benson et al.
4368 British Journal of Pharmacology (2017) 174 4362–4382
-
Table 1 (Continued)
E.Cnumber
Enzyme/GtoPdbtarget ID
UniProtID
HGNCID
IUBMBenzymeapproved name
Reactioncatalysed
Km(mM)/PMID
Reportedsubstrate/GtoPdb ligand ID
Kcat(s-1)/PMID
2.7.1.36 MVK/640 Q03426 7530 Mevalonatekinase
ATP + (R)-mevalonate= ADP +(R)-5-phosphomevalonate
0.035/14680942
(RS)-mevalonate/3056
–
– – – – – 0.035/17964869
(RS)-mevalonate/3056
21.9/18302342
– – – – – 0.0408/18302342
(RS)-mevalonate/3056
–
– – – – – 0.024/9325256
Mevalonate/3056
–
2.7.4.2 PMVK/641 Q15126 9141 Phosphomevalonatekinase
ATP + (R)-5-phosphomevalonate =ADP +
(R)-5-diphosphomevalonate
0.034/17902708
(R)-5-Phosphomevalonate/3046
–
4.1.1.33 MVD/642 P53602 7529
Diphosphomevalonatedecarboxylase
ATP + (R)-5-diphosphomevalonate =ADP + phosphate+
isopentenyldiphosphate + CO2
0.02/8744421
5-Diphosphomevalonate/3055
–
– – – – – 0.0289/18823933
5-Diphosphomevalonate/3055
4.5/18823933
– – – – – 0.036/16626865
5-Diphosphomevalonate/3055
–
– – – – – 0.036/17888661
5-Diphosphomevalonate/3055
–
– – – – – 0.01/11913522
Mevalonatediphosphate/3055
–
5.3.3.2 IDI1 andIDI2*/646& 647
Q13907/Q9BXS1
5387/23487
Isopentenyl-diphosphatedelta isomerase
Isopentenyldiphosphate =dimethylallyldiphosphate
0.0228/17202134
Isopentenyldiphosphate/3048
–
– – – – – 0.033/8806705
Isopentenyldiphosphate/3048
–
2.5.1.1 FDPS/644 P14324 3631 Farnesyldiphosphatesynthase
Dimethylallyldiphosphate +isopentenyldiphosphate= diphosphate
+geranyl diphosphate
– – –
2.5.1.10 – – – – Geranyl diphosphate+ isopentenyldiphosphate
=diphosphate +trans,trans-farnesyldiphosphate
– – –
2.5.1.1 GGPS1/643 O95749 4249 Farnesyltranstransferase
Dimethylallyldiphosphate +isopentenyldiphosphate= diphosphate+
geranyldiphosphate
– – –
The feasibility of systems pharmacology BJP
British Journal of Pharmacology (2017) 174 4362–4382 4369
-
Table 1 (Continued)
E.Cnumber
Reactioncatalysed Organism
Reportedconditions
MeanKm(mM)
SubstitutedmeanKm
SubstitutedmeanKcat
ADP + (R)-5-diphosphomevalonate
4.1.1.33 ATP + (R)-5-diphosphomevalonate =ADP + phosphate+
isopentenyldiphosphate + CO2
Rattus norvegicus – 0.0262 – –
– Homo sapiens 30C – – –
– Rattus norvegicus – – – –
– Rattus norvegicus – – – –
– Mus musculus pH 7.2 – – –
5.3.3.2 Isopentenyldiphosphate =dimethylallyldiphosphate
Homo sapiens pH 8.0 0.0279 – 6.651575
– Homo sapiens – – – –
2.5.1.1 Dimethylallyldiphosphate +isopentenyldiphosphate=
diphosphate +geranyl diphosphate
– – – 0.0351375 6.651575
2.5.1.10 Geranyl diphosphate+ isopentenyldiphosphate
=diphosphate +trans,trans-farnesyldiphosphate
– – – 0.0351375 6.651575
2.5.1.29 Trans,trans-farnesyldiphosphate +isopentenyldiphosphate
=diphosphate +geranylgeranyldiphosphate
Rattus norvegicus pH 7.0/37C 0.0027 – –
– Homo sapiens pH 7.7/37C – – –
– Rattus norvegicus pH 7.0/37C – – –
– Homo sapiens pH 7.7/37C – – –
2.5.1.21 2 Trans,trans-farnesyldiphosphate =diphosphate
+presqualenediphosphate
Homo sapiens – 0.0016 – 6.651575
Presqualenediphosphate+ NAD(P)H + H+
= trans-squalene +diphosphate+ NAD(P)+
Rattus norvegicus – – – –
BJP H Benson et al.
4370 British Journal of Pharmacology (2017) 174 4362–4382
-
achieves our objective, with zero indicating success)converges
rapidly on an effective drug combination. Itsuccessfully identified
an optimal combination in 46 minand achieved an approximately
optimal solution in less than10 min.
The results of our curation of the pathway and the inhib-itors
that target it are available in GtoPdb at
http://www.guidetopharmacology.org/GRAC/FamilyDisplayForward?familyId=104,
an example of which is shown in SupportingInformation Figure
S2.
The model produced is available from http://biomodels.org
(Chelliah et al., 2013) (ID: MODEL1506220000).
Discussion
The importance of systems pharmacologyMulti-drug interventions.
Multi-drug approaches are alreadyemployed in areas including HIV
and oncology (Petrelli andGiordano, 2008; Thakur and Marchand,
2012). However, theexisting interventions have typically been
developedheuristically, rather than through systematic studies of
thepathways involved, requiring significant domain expertise
andsubjective judgement. Systems pharmacology introducesobjective
metrics that have the potential to transform therapy
Table 1 (Continued)
E.Cnumber
Enzyme/GtoPdbtarget ID
UniProtID
HGNCID
IUBMBenzymeapproved name
Reactioncatalysed
Km(mM)/PMID
Reportedsubstrate/GtoPdb ligand ID
Kcat(s-1)/PMID
2.5.1.10 – – – – Geranyl diphosphate+ isopentenyldiphosphate
=diphosphate +trans,trans-farnesyldiphosphate
– – –
2.5.1.29 – – – – Trans,trans-farnesyldiphosphate
+isopentenyldiphosphate =diphosphate +geranylgeranyldiphosphate
0.0029/17846065
Isopentenyldiphosphate/3048
–
– – – – – 0.003/16698791
Isopentenyldiphosphate/3048
–
– – – – – 0.00071/17846065
Trans,trans-farnesyldiphosphate/3050
–
– – – – – 0.0042/16698791
Trans,trans-farnesyldiphosphate/3050
0.204/16698791
2.5.1.21 FDFT1/645 P37268 3629
Farnesyl-diphosphatefarnesyltransferase 1
2 Trans,trans-farnesyldiphosphate =diphosphate
+presqualenediphosphate
0.0023/9473303
Farnesyldiphosphate/2910
–
– – – – Presqualenediphosphate+ NAD(P)H + H+
= trans-squalene +diphosphate+ NAD(P)+
0.001/1569107
Trans-farnesyldiphosphate/3050
–
Reported substrates, kinetic values and details of the
experimental studies from which they were obtained, along with
references are recorded.Please note that ligands outlined in the
table are listed using the nomenclature from the original
literature. Where the reference did not specify theisomer used
experimentally, it was assumed the racemate was used.FDPS, farnesyl
diphosphate synthase; IDI1, isopentenyl diphosphate delta isomerase
1; IDI2, isopentenyl diphosphate delta isomerase 2;
MVD,diphosphomevalonate decarboxylase; MVK, mevalonate kinase;
PMVK, phosphomevalonate kinase.
The feasibility of systems pharmacology BJP
British Journal of Pharmacology (2017) 174 4362–4382 4371
http://www.guidetopharmacology.org/GRAC/FamilyDisplayForward?familyId=104http://www.guidetopharmacology.org/GRAC/FamilyDisplayForward?familyId=104http://www.guidetopharmacology.org/GRAC/FamilyDisplayForward?familyId=104http://biomodels.orghttp://biomodels.org
-
development, yielding therapeutichypothesesmore rapidly
andcost-effectively.
Many diseases are multifactorial in nature, involvingmultiple
pathways in their pathology. Effective futuretherapies will likely
employ multi-drug approaches thattargetmultiple points in the
network of pathways responsible(i.e. polypharmacology). Promiscuous
drugs can beincorporated advantageously into the generation of
thesehypothetical interventions, provided that their
interactionsare known and parameterized.
Multi-drug approaches can minimize the pleotropiceffects of an
intervention. As we demonstrated for statins,where a single drug
intervention suppressed the output of apathway to the same extent
as multiple drugs targeting thesame pathway, not only was the dose
of each of the multipledrugs significantly lower than the dose of
the single drugbut also the combined dose of all of the multiple
drugs wassignificantly lower than the dose of the single drug.
Thisintrinsically reduces the risk from off-target or
pleotropiceffects for each drug.
The systems pharmacology approach allows us to pre-dict
multi-drug strategies that may be optimal to treat adisease and can
be used as a prioritization triage for futuredrug development. It
can support personalized and strati-fied medicine, where we adapt
the parameter sets of theunderlying models of pathway activity to
represent an in-dividual (for personalized medicine) or a
subpopulation(for stratified medicine) and we develop interventions
thatare customized to be optimal for the patient or patientgroup. A
challenge lies in developing optimized therapiesso that they
preferentially target key tissues. Pathwaymodels and
pharmacological interactions can be madetissue specific by
generating a new parameter set for eachtissue. Hypothesis
generation would then use optimizationto determine an intervention
that impacted upon a keypathway in a key tissue, leaving other
pathways unchangedacross all tissues and with a minimal impact on
the keypathway in non-targeted tissues.
Drug development. Fewmulti-drug treatmentsmake it throughthe
development process. The number of combinationaltherapies listed in
the Therapeutic Target Database at the timeof writing is 115 (Qin
et al., 2014). A combination therapy,LCZ696, with the brand name
Entresto, was approved in 2015and is in Phase III of clinical
trials for the treatment ofcardiovascular disorders. Establishing
drug combinations usinga conventional drug development pipeline
creates significantchallenges as development essentially replicates
the single drugdevelopment process multiple times. Systems
pharmacology istherefore critical to expanding the range of
multi-druginterventions available in a cost-effective manner.
Although itmay add extra steps to the preclinical stages of the
drugdevelopment process, it could have a significant positiveimpact
on the cost-efficiency associated with each success byreducing the
attrition rate in the later stages of the pipeline(Bowes et al.,
2012).
Integrating our understanding of pharmacology and sys-tems
biology will also enable us to make better predictionsof the
behaviour of individual drugs. For example, squalenesynthase
(FDFT1) has been investigated as a potential drugtarget that lies
downstream of HMGCR, the target for statins,in the cholesterol
biosynthesis pathway (see Figure 1). FDFT1catalyses an interaction
after the fork to geranylgeranyl-diphosphate production, and it has
been speculated thatsqualene synthase inhibitors might suppress
cholesterolproduction without impacting on the
geranylgeranyl-diphosphate producing branch, in contrast to statin
treat-ment. However, squalene synthase inhibitors typically haveKi
values orders of magnitude greater than the typical Ki forstatins
(See Table 3). As a result, squalene synthase
inhibitorconcentrations are required to be orders of magnitude
greaterthan statin concentrations to suppress the
correspondingenzyme activity comparably. Such high concentrations
riskunforeseen off-target effects, making squalene
synthaseinhibitors a higher risk drug to develop.
Systems level analysis. At the heart of systems pharmacologyis
the growing recognition that we will only be able to
trulyunderstand the best ways to therapeutically intervene
inphysiological function by considering biology at a systemslevel.
The network of interactions that mediatephysiological function is a
dynamical system, and just ashealth and disease are different
dynamical states of cells,tissues and organs, they also describe
different dynamicalstates of the networks (Ahn et al., 2006). In a
networkcontext, dynamical states can comprise a single
stableconfiguration of the whole network or a sequence
ofconfigurations that repeat cyclically and stably. However, itis
the configuration (species concentrations, distributionsand
structural conformations) of the network as a whole, orat least of
critical subnetworks, that relate to phenotype,rather than any
single component of the network (Lewisand Glass, 1991).
Small networks often yield dynamics that are intuitiveand
predictable. However, as networks become larger andricher in
structure, novel and often counter-intuitivedynamics can emerge and
it will only be once we are ableto build high-confidence models at
this scale that the fullpotential of systems level studies will be
realized (Aderem,2005). Building high confidence networks at this
scale is
Table 2Normalized enzyme levels
Enzyme Level
HMGCS1 1441
HMGCR 258
MVK 76
PMVK 874
MVD 111
IDI1 2707
IDI2 –
FDPS 7029
GGPS1 86
FDFT1 3425
FDPS, farnesyl diphosphate synthase; IDI1, isopentenyl
diphosphatedelta isomerase 1; IDI2, isopentenyl diphosphate delta
isomerase 2;MVD, diphosphomevalonate decarboxylase; MVK, mevalonate
ki-nase; PMVK, phosphomevalonate kinase.
BJP H Benson et al.
4372 British Journal of Pharmacology (2017) 174 4362–4382
-
Table
3List
ofinhibitors
forea
chof
theen
zymes
inthemev
alon
atebran
chof
thech
olesterolsyn
thesispa
thway
withK i
values
andreferenc
es
E.C
num
ber
Enzy
me
Inhib
itor
name/
Gto
Pdb
ligandID
InChi
key
Appro
ved
dru
g?
Org
anism
KiorIC
50
(nM)/
PMID
Rep
orted
conditions
Multidru
gco
nce
ntration
(nM)
2.3.3.10
HMGCS1
L-65
9699/58
86
ODCZJZWSX
PVLA
W-
KXCGKLM
DSA
-NNo
Rattus
norveg
icus
Ki=53
.7/
7913
309
–0.215
1.1.1.34
HMGCR
Rosuva
statin/2
954
BPRH
UIZQVS
MCRT
-YX
WZHEE
RSA-N
Yes
Homosapien
sKi
=2.3/
1612
857
5pH6.8,3
7C
5.48
Rosuva
statin/2
954
BPRH
UIZQVS
MCRT
-YX
WZHEE
RSA-N
Yes
Homosapien
sKi
=3.5/
1277
315
0–
–
Rosuva
statin/2
954
BPRH
UIZQVS
MCRT
-YX
WZHEE
RSA-N
Yes
Homosapien
sKi
=0.9/
1568
689
8–
–
Cerivastatin/29
50SE
ERZIQ
QUAZTO
L-ANMDKA
QQSA
-NYe
sHomosapien
sKi
=5.7/
1612
857
5–
–
Cerivastatin/29
50SE
ERZIQ
QUAZTO
L-ANMDKA
QQSA
-NYe
sHomosapien
sKi
=10
/12
77315
0–
–
Atorvastatin/
2949
XUKU
URH
RXDUEB
C-
KAYW
LYCHSA
-NYe
sHomosapien
sKi
=8/
1277
315
0–
–
Atorvastatin/
2949
XUKU
URH
RXDUEB
C-
KAYW
LYCHSA
-NYe
sHomosapien
sKi
=14
/16
12857
5–
–
Lova
statin/2739
PCZOHLX
UXFIOCF-
BXMDZJJM
SA-N
Yes
Homosapien
sKi
=0.6/
doi:1
0.102
1/np
5006
1a020
–
Lova
statin/2739
PCZOHLX
UXFIOCF-
BXMDZJJM
SA-N
Yes
Homosapien
sKi
=0.6/
6933
445
––
Simva
statin/2
955
RYMZZMVNJRMUDD-
HGQWONQES
A-N
Yes
Homosapien
sKi
=2.6/
1568
689
8–
–
CHEM
BL393
12/7991
VWKZ
OIO
UHUHQKZ-
HZPD
HXFC
SA-N
No
Homosapien
sKi
=3/
doi:1
0.101
6/S0
960-894X
(01)
8078
8-5
–
CHEM
BL391
02/7993
XKZCNQAYF
RBCKR-
HNNXBM
FYSA
-NNo
Homosapien
sKi
=16
/doi:1
0.101
6/S0
960-894X
(01)
8078
8-5
–
Fluv
astatin/29
51FJLG
EFLZ
QAZZCD-
MCBH
FWOFS
A-N
Yes
Homosapien
sKi
=27
5/
1612
857
5–
–
2.7.1.36
MVK
Farnesyl
thiodiph
ospha
te/321
6DRA
DWUUFB
CYM
DM-
UHFFFA
OYS
A-L
No
Homosapien
sKi
=29
/14
67922
5pH7.5,3
0C
0.050
0
2.7.4.2
PMVK
Cinnam
icac
id/3
203
WBYW
AXJH
AXSJNI-
VOTS
OKGWSA
-NNo
Rattus
norveg
icus
Ki=24
800
00/
2260
78
pH7.4,3
7C
0.024
0
Isoferulic
acid/798
0**
QURC
VMIEKC
OAJU-
HWKA
NZRO
SA-N
No
Rattus
norveg
icus
Ki=38
500
00/
2260
78
pH7.4,3
7C
–
XRC
IRZGXKW
CWNQ-
UHFFFA
OYS
A-N
No
Rattus
norveg
icus
Ki=14
500
0/
doi:1
0.102
1/ja004
93a0
44–
continue
s
The feasibility of systems pharmacology BJP
British Journal of Pharmacology (2017) 174 4362–4382 4373
-
Table
3(C
ontinue
d)
E.C
num
ber
Enzy
me
Inhib
itor
name/
Gto
Pdb
ligandID
InChi
key
Appro
ved
dru
g?
Org
anism
KiorIC
50
(nM)/
PMID
Rep
orted
conditions
Multidru
gco
nce
ntration
(nM)
3-hyd
roxy
-3-m
ethyl-
6-pho
spho
hexa
noic
acid/3
202
P-co
umaric
acid/5
787
NGSW
KAQJJW
ESNS-
ZZXKWVIFSA
-NNo
Rattus
norveg
icus
Ki=23
900
00/
2260
78
pH7.4,3
7C
–
4.1.1.33
MVD
6-fluo
romev
alon
ate5-
diph
ospha
te/3205
GLN
COGHKIHKS
A-
UHFFFA
OYS
A-N
No
Homo
sapien
sKi
=62
/18
82393
3pH7.0,3
0C
0.050
0
2-fluo
romev
alon
ate5-
diph
ospha
te/3204
WPX
HWHACORB
SDS-
UHFFFA
OYS
A-N
No
Rattus
norveg
icus
Ki=30
20/
1662
686
5pH7.5,2
5C
–
Diphospho
glycolyl
proline/320
6CDFD
GXYB
ANXCPC
-UHFFFA
OYS
A-N
No
Homosapien
sKi
=23
00/
1882
393
3–
–
CHEM
BL116
0330
/79
94
YERU
UUBB
RAPJND-
UHFFFA
OYS
A-N
No
Homosapien
sKi
=75
0/
doi:1
0.101
6/096
0-89
4X(96)00
374-5
–
CHEM
BL116
0328
/79
96
YIGLD
WRZ
XXHIG
Z-
ZCFIWIBFS
A-N
No
Homo
sapien
sKi
=37
/doi:1
0.101
6/096
0-89
4X(96)00
374-5
–
P0-geran
yl2-
fluo
romev
alon
ate
5-dipho
sphate/32
07
ACYP
MTK
DKJZHBJ-
MDWZMJQ
ESA-N
No
Homo
sapien
sKi
=41
69/
1882
393
3–
–
P0-geran
yl3,5,
9-trihyd
roxy
-3-methy
lnon
anate9-
diph
ospha
te/5621
PMUQIJK
CGIYWGT-
GZTJUZNOSA
-NNo
Rattus
norveg
icus
Ki=64
57/
1652
425
6–
–
5.3.3.2
IDI1
––
––
––
–
5.3.3.2
IDI2
––
––
––
–
2.5.1.1,2
.5.1.10
FDPS
Zoledron
icacid/3177
XRA
SPMIURG
NCCH-
UHFFFA
OYS
A-N
Yes
Homosapien
sKi
=0.07/
1832
789
9–
12.7
Zoledron
icacid/3177
VWKZ
OIO
UHUHQKZ-
HZPD
HXFC
SA-N
Yes
Homosapien
sKi
=85
/18
32789
9–
–
Risedronate/317
6IID
JRNMFW
XDHID
-UHFFFA
OYS
A-N
Yes
Homosapien
sKi
=0.36/
1832
789
9–
–
Risedronate/317
6IID
JRNMFW
XDHID
-UHFFFA
OYS
A-N
Yes
Homosapien
sKi
=81
/18
32789
9–
–
NE5
8062/316
6XUCBN
FJYK
WKAMN-
UHFFFA
OYS
A-N
No
Homosapien
sKi
=1/
1832
789
9–
–
NE9
7220/317
1NAIJO
BGUXRH
QJW
-UHFFFA
OYS
A-N
No
Homosapien
sKi
=1.09/
1832
789
9–
–
NE9
7220/317
1NAIJO
BGUXRH
QJW
-UHFFFA
OYS
A-N
No
Homosapien
sKi
=12
/18
32789
9–
–
continue
s
BJP H Benson et al.
4374 British Journal of Pharmacology (2017) 174 4362–4382
-
Table
3(C
ontinue
d)
E.C
num
ber
Enzy
me
Inhib
itor
name/
Gto
Pdb
ligandID
InChi
key
Appro
ved
dru
g?
Org
anism
KiorIC
50
(nM)/
PMID
Rep
orted
conditions
Multidru
gco
nce
ntration
(nM)
NE5
8018/316
8XXNASZ
AYA
NFLID
-UHFFFA
OYS
A-N
No
Homosapien
sKi
=0.74
/1832
789
9–
–
NE5
8018/316
8XXNASZ
AYA
NFLID
-UHFFFA
OYS
A-N
No
Homosapien
sKi
=59
/18
32789
9–
–
2.5.1.1,
2.5.1.10,
2.5.1.29
GGPS
1BP
H-628/318
8MPB
UFK
ZCEB
TBSK
-UHFFFA
OYS
A-N
No
Homosapien
sKi
=20
/17
53589
5–
13.5
BPH-608/797
7YX
QQNSY
ZOQHKHD-
UHFFFA
OYS
A-N
No
Homosapien
sKi
=60
/17
53589
5–
–
BPH-675/797
5MZVW
VRVN
MXTD
AK-
UHFFFA
OYS
A-N
No
Homosapien
sKi
=70
/17
53589
5–
–
BPH-629/797
6BY
VXAUZOTG
ITQZ-
UHFFFA
OYS
A-N
No
Homosapien
sKi
=11
0/
1753
589
5–
–
BPH-676/797
8NWIARQ
RYIRVY
CM-
UHFFFA
OYS
A-N
No
Homosapien
sKi
=11
0/
1753
589
5–
–
2.5.1.21
FDFT
1Zarag
ozicacid
A/3
057
DFK
DOZMCHOGOBR
-NCSQ
YGPN
SA-N
No
Homosapien
sKi
=0.25/
7864
626
pH7.4,3
7C
4.79
CHEM
BL243
62/
3105
FBPJEW
KDFU
WVKV
-UHFFFA
OYS
A-N
No
Homosapien
sKi
=43
/doi:1
0.101
6/S0
960-
894X
(97)00
053-X
–
CHEM
BL120
8103
/31
20
HGDWHTA
SNMRJMP-
UHFFFA
OYS
A-N
No
Homosapien
sKi
=30
0/
1945
609
9Re
combinan
ten
zyme
expressed
inEsch
erichiacoli
–
CHEM
BL120
7858
/31
27
AGJZDRX
KAQZWEP
-UHFFFA
OYS
A-N
No
Homosapien
sKi
=52
0/
1945
609
9Re
combinan
ten
zyme
expressed
inE.
coli
–
BPH-830/312
1GNET
VUVZ
FYJATO
-UHFFFA
OYS
A-N
No
Homosapien
sKi
=53
0/
1945
609
9Re
combinan
ten
zyme
expressed
inE.
coli
–
SQ-109
/7997
JFIBVD
BTCDTB
RH-
REZTV
BANSA
-NNo
Homosapien
sKi
=74
0/
2248
671
0Re
combinan
ten
zyme
expressed
inE.
coli
–
[1-(Hyd
roxy
carbam
oyl)-
4-(3-phen
oxy
phe
nyl)
butyl]p
hospho
nate/3120
HGDWHTA
SNMRJMP-
UHFFFA
OYS
A-N
No
Homosapien
sKi
=30
2/
1945
609
9–
–
Compou
nd13
[PMID
:19
45609
9]/312
7AGJZDRX
KAQZWEP
-UHFFFA
OYS
A-N
No
Homosapien
sKi
=52
5/
1945
609
9–
–
(1-M
ethyl-1-{[3-
(3-phen
oxyp
hen
yl)
prop
yl]carbam
oyl}
ethyl)pho
sphon
ate/312
7
AGJZDRX
KAQZWEP
-UHFFFA
OYS
A-N
No
Homosapien
sKi
=52
5/
1945
609
9–
–
**Den
otesinteractionno
tlistedon
GtoPd
b.Th
esereac
tions
wereselected
from
either
BREN
DAor
ChEM
BLto
complete
theda
tasetrequiredforthemodellin
gprocess.
FDPS
,farne
syld
iphospha
tesynthase;
IDI1,isope
nten
yldipho
spha
tede
ltaisom
erase1;
IDI2,isopen
teny
ldipho
sphatedelta
isom
erase2;
MVD,d
ipho
sphom
evalona
tede
carboxy
lase;M
VK,
mev
alon
ate
kina
se;P
MVK
,phosph
omev
alon
atekina
se.
The feasibility of systems pharmacology BJP
British Journal of Pharmacology (2017) 174 4362–4382 4375
-
inherently challenging as we see here. Coherently
andunambiguously parameterizing all the interactions of anetwork is
a significant logistical challenge. However, wehave also seen that
doing so enables us to identify andaddress the side-effects of
treatment whilst the therapy isbeing designed, rather than
retroactively. Hence, systems-level approaches are well suited to
pharmacologicalapplications.
Current impediments to systems pharmacology
Problem 1: lack of systematic recordingThe absence of systematic
and rigorous descriptions of me-tabolites and pharmaceutical
compounds poses a significantchallenge. Example 1, fluvastatin
consists of two
enantiomers, represented by PubChem compound identifiers(CIDs)
1548972 and 446155, with the 3R, 5S enantiomer(CID 446155) being
significantly more pharmacologically ac-tive than the other (Di
Pietro et al., 2006; Boralli et al., 2009).Commercial preparations
used in vitro often vary in theirstereochemical composition, with
both enantiomersavailable individually, as well as in a racemic
mixture. How-ever, the authors did not always specify the
stereochemicalcomposition used despite this necessarily impacting
uponthe inhibition constant, Ki, reported. Example 2, mevalonateis
a metabolite that occurs naturally in mammals as the (R)-isomer
form. Sigma-Aldrich currently refers to its marketedversion as
‘(RS)-mevalonic acid’. However, in one study(Potter and Miziorko,
1997), the metabolite is obtained fromthe supplier Sigma-Aldrich,
and it is recorded on BRENDA
Figure 2(A) The profile of flux through the pathway shown in
Figure 1 described as a cone plot for the three scenarios:
wild-type (treatment free), opti-mized multi-drug intervention and
single-drug statin-like intervention. Cone size and colour both
represent flux level. We show only the flux lead-ing to cholesterol
synthesis [the flux to protein prenylation is presented in (B)].
Interactions are numbered by their product: (1:
3-hydroxy-3-methylglutaryl-CoA; 2: melvaldyl-CoA, 3: mevalonate, 4:
mevalonate-P, 5: mevalonate diphosphate, 6: isopentenyl
diphosphate, 7: dimethylallyldiphosphate, 8: geranyl diphosphate,
9: farnesyl diphosphate, 10: presqualene diphosphate, 11: squalene,
12: cholesterol synthesis). (B) The fluxthrough the endpoints of
the two branches for the three scenarios: wild-type, optimized
multi-drug intervention and single-drug statin-likeintervention.
Flux through the squalene/cholesterol synthesis branch is shown in
blue. Flux through the geranylgeranyl-PP/protein prenylationbranch
is shown in red. The statin concentration has been selected to
ensure that the flux through the cholesterol synthesis branch is
the sameas in the multi-drug intervention. (C) Convergence on the
optimal multi-drug intervention that suppresses cholesterol
synthesis whilst minimizingoff target effects, shown against time
and against generations of the genetic algorithm.
BJP H Benson et al.
4376 British Journal of Pharmacology (2017) 174 4362–4382
http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=2951http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=3042http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=3042
-
under the general name ‘mevalonic acid’ without unambigu-ous
chemical identifiers such as the Simplified Molecular-Input
Line-Entry System or the International Chemical Iden-tifier. The
isomer form affects the parameterization of the me-tabolite. Hence,
this ambiguity creates potential inaccuracyin any resulting
modelling.
Problem 2: reporting of the wrong dataWe found cases of
incorrect or incomplete kinetic data reportedin the primary
literature that undermined the ability to modelinteractions. Vmax
values were regularly reported instead of Kcatvalues where Vmax is
related to Kcat by Vmax = Kcat × (enzymeconcentration). For a Vmax
value to be reusable in subsequentstudies, the enzyme concentration
must also be reportedalongside it. However, we regularly found this
not to be thecase, making most reported Vmax values unusable.
Similarly, inhibitors were frequently parameterized byIC50
values instead of Ki values, where Ki and IC50 are relatedby Ki =
IC50/(1 + S/Km) and S is the substrate concentration.For IC50
values to be reusable in future studies, the
substrateconcentrations must also be reported. Here, again, we
foundregular omissions that rendered most reported IC50
valuesunusable.
Solution (1 and 2): introduce data capture standards that
facilitateunambiguous reconstruction of the results
withoutoptimization. Reporting must include clear and
thoroughdescriptions of experimental configurations andunambiguous
identification of chemical structures throughthe use of
comprehensive and standard nomenclature. Pastexperience has shown
that effective standards can bedeveloped through open community
exercises (e.g. SBMLand SBGN). The necessity for appropriate
standards hasbeen recognized previously by the chemical biology
andpharmacometric communities (Oprea et al., 2011; Swatet al.,
2015).
Standards are already employed widely across the life sci-ences,
frequently building upon ontologies (controlled vo-cabularies of
biological/chemical entities and concepts). TheInternational Union
of Pure and Applied Chemistry, the In-ternational Union of
Biochemistry and Molecular Biology(IUBMB) Joint Commission on
Biochemical Nomenclatureand the Nomenclature Committee of IUBMB
have providedguidelines on biochemical descriptions and enzyme
classifi-cation. A library of ontologies for the life sciences has
alsobeen proposed by the Open Biomedical Ontologies Foundry(Smith
et al., 2007). Standards and guidelines also exist forreporting
biomedical studies, including the minimum infor-mation (MI)
standards overseen by the Minimum Informa-tion for Biological and
Biomedical Investigations Foundrywho include the Standards for
Reporting Enzymology DataCommission (Gardossi et al., 2010). The MI
standards of di-rect relevance include the ‘minimum information
about abioactive entity’ (Orchard et al., 2011), the ‘minimum
infor-mation about a proteomic experiment’ (Taylor et al., 2007)and
the ‘minimum information about a molecular interac-tion experiment’
(Orchard et al., 2007).
Problem 3: curation errorsOnline databases can contain errors.
We have identified caseswhere the incorrect structures, enzyme
targets, species and
parameter values had been recorded. Errors were at low
fre-quency, but some would undermine systems
pharmacologyapproaches, and these fell into two groups: errors that
derivedfrom mistakes in the literature itself, such as from
mis-interpretation of data, and errors that derived from the
incor-rect transcribing from the literature to the database. The
for-mer derive from verbatim acceptance of results frommanuscripts
following author error. The latter errors can beintroduced by
databases themselves, either from semi-automated triage tools or
inadvertent curator mistakes, andthis can be associated with a lack
of clarity in the originaldocument. In the present study and for
the GtoPdb, wereviewed the primary literature when expanding our
datasetsand re-curated existing database coverage.
Solution 3: quality control in curation of results. Using teams
ofcurators to validate each other’s work can reduce errors. Thiscan
be arranged systematically into error-identifying or
error-correcting curation quality control programmes. In an
error-identifying programme, each result is independentlycurated
twice and where disagreements are identified, thedata is reviewed.
Such approaches have been discussedwithin the International Society
for Biocuration (Bateman,2010). However, the funding limitations of
most publicdatabases preclude this degree of validation. In an
error-correcting programme, each result would be
independentlycurated three times and where a disagreement is found,
theconsensus would be accepted automatically as correct.
Systems pharmacology for the futureA workflow for future studies
and hypothesis generation. Withan adequate set of standards and a
well-characterizedexperimental system, it should be possible to
developintervention hypotheses that can be tested to inform
futuretherapy development and to contribute to iterativerefinement
of databases. To make this a consistent, highconfidence process, it
would be advantageous to work inone experimental system. Such an
experimental systemcould be in vivo or in vitro. However, an in
vitro model wouldoffer more control and consistency. Such an in
vitro systemwould serve as a first approximation to in vivo
physiologyand would contribute to determining how in
vitroparameters are mapped to in vivo parameters in order
tomaximize their value. An advantage of using an in vitrosystem is
that it would lend itself to automated hypothesisgeneration and
testing and it could be used tosystematically search for new
protein–protein anddrug–target interactions. It has been suggested
that artificialintelligence methods would be suitable for this
purpose inthe laboratory (King et al., 2004). Automation would
bothminimize the time required for study and reduce the risk
ofmisreporting or mis-curating the results.
Our current systems-level understanding has grown to ascale
where manual manipulation is no longer feasible.Standards such as
SBML, SBGN and SBGN-ML and reposito-ries such as BioModels have
been developed partially to ad-dress this and automated model
development allows the fullvalue of databases to be realized
(Swainston et al., 2011).Open Pharmacological Concept Triple Store
(Williamset al., 2012) is a consortium responsible for a number
ofpharmacological and life science databases whose aims
The feasibility of systems pharmacology BJP
British Journal of Pharmacology (2017) 174 4362–4382 4377
-
include the improvement of data availability through theuse of
data standards, the incorporation of contextual datathrough
semantic web standards and the cross-platformlinkage of datasets
through an identity mapping service. De-veloping multi-drug
hypotheses is a challenge that growsexponentially with the number
of drugs and interactionsconsidered. HPC resources are likely to be
essential for thisdevelopment.
The following workflow would enable the process to beautomated
(see Figure 3).
(I) Pharmacological literature seeds databases of
pharma-cological interactions.
(II) Pharmacological and chemical databases containingsufficient
information for experimental results to bereproduced accurately.
Database Application ProgramInterfaces (APIs) facilitate extraction
of results for hy-pothesis generation.
(III) Interaction literature seeds databases of
biologicalpathways.
(IV) Pathway databases containing sufficient informationfor
experimental results to be reproduced accurately.Database APIs
facilitate extraction of results for hypoth-esis generation.
(V) Hypothesis generation for single drug and
multi-druginterventions using data obtained through APIs fromthe
pharmacological and pathway databases.
(VI) Hypothesis testing. Success yields a candidate therapyand
provides validation of the database. Failure initiatesfurther
exploration of the underlying interactions thatin turn refine the
databases.
(VII) Candidate Intervention. Following success, the groupof
compounds enters an optimization pipeline thatreduces them to a
minimal set of lead compounds forpreclinical testing to establish
their efficacy and safety.
ConclusionThe growth in our understanding of
pharmacologicalinteractions and the continuing development of our
abilityto computationally model pathway biology will
increasinglyenable us to explore drug combinations that target
multiplepoints on multiple pathways to reprogram system
levelbehaviour. In this way, systems pharmacology may lead tomore
effective therapies with fewer side effects. Here, weexplored this
approach for the mevalonate arm of thecholesterol biosynthesis
pathway, and in doing so, we iden-tify many of the current barriers
to progress.
We attempted to build a systems pharmacology model ofthe
mevalonate arm of the cholesterol biosynthesis pathway,but gaps and
inconsistencies in the data prevented us fromachieving this to a
high level of confidence. In particular, we
Figure 3The proposed systems pharmacology workflow.
BJP H Benson et al.
4378 British Journal of Pharmacology (2017) 174 4362–4382
-
found the lack of comprehensive and systematic
parameteriza-tions, experimental variation, ambiguity in structural
detailand inappropriate and inaccurate reporting from the
primaryliterature to be obstacles. That this should be the case for
a path-way of such high biomedical and commercial significance
wasunexpected. For this reason, our best current
parameterizationrepresents a patchwork of values taken from
multiple speciesand experimental configurations. Nonetheless, by
completinggaps in our knowledge with representative values, we were
ableto demonstrate subtle reprogramming of pathway dynamicsthat may
contribute significantly to drug development. Wepropose that these
obstacles can be removed through theadoption of standards and
quality control.
Although we focused on the mevalonate arm of choles-terol
biosynthesis, this approach could be applied to anypathway of
interest for which targets and ligands are known.However, before
this can happen at a general level, both thecomputational biology
and the pharmacology communitiesmust collaborate to remove the
current barriers to progress.
Acknowledgements
Initial calculations of optimal multi-drug interventions
werecompleted using the supercomputing cluster made availableby the
Intelligent Systems Research Centre at the Universityof Ulster. We
gratefully acknowledge the teams and fundersthat support the range
of external database resources used,without which this work would
not have been possible. Anyobservations of error are meant to form
part of a constructivediscussion rather than criticism. We are
indebted to the lateProf Anthony Harmar (dedication below) for his
engagementand enthusiasm during the early phases of this
project.
The IUPHAR/BPS Guide to PHARMACOLOGY databaseis funded by the
International Union of Basic and ClinicalPharmacology, the British
Pharmacological Society andWellcome Trust Biomedical Resources
grant 099156/Z/12/Z(H.B., J.S. and C.S.). This work was in part
supported by agrant awarded to Professor Tony Bjourson from
EuropeanUnion Regional Development Fund (ERDF) EU
SustainableCompetitiveness Programme for N. Ireland;
NorthernIreland Public Health Agency (HSC R&D); and
UlsterUniversity.
This paper is dedicated to the memory of Prof Anthony(Tony) J.
Harmar, Emeritus Professor of Pharmacology, Univer-sity of
Edinburgh.
Author contributionsThis work was conceived by H.B., S.W., P.G.
and A.H. Theanalysis and data compilation was undertaken by H.B.,
S.W., J.S. and A.P. The manuscript was written by H.B., S.W.,J.S.,
C.M., A.P., C.S. and P.G.
Conflict of interestH.B., J.S., C.M., C.S. and A.H. have served
as curators for theIUPHAR/BPS GtoPdb.
Declaration of transparency andscientific rigourThis Declaration
acknowledges that this paper adheres to theprinciples for
transparent reporting and scientific rigour ofpreclinical research
recommended by funding agencies,publishers and other organisations
engaged with supportingresearch.
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