Metabolically re-modeling the drug pipelineruppin/drug_remodelling_review.pdf · Metabolically 1...

Metabolically re-modeling the drug pipelineMatthew A Oberhardt1,2,3, Keren Yizhak1,2,3 and Eytan Ruppin1,2

Available online at www.sciencedirect.com

Costs for drug development have soared, exposing a clear

need for new R&D strategies. Systems biology has meanwhile

emerged as an attractive vehicle for integrating omics data and

other post-genomic technologies into the drug pipeline. One of

the emerging areas of computational systems biology is

constraint-based modeling (CBM), which uses genome-scale

metabolic models (GSMMs) as platforms for integrating and

interpreting diverse omics datasets. Here we review current

uses of GSMMs in drug discovery, focusing on prediction of

novel drug targets and promising lead compounds. We then

expand our discussion to prediction of toxicity and selectivity of

potential drug targets. We discuss successes as well as

limitations of GSMMs in these areas. Finally, we suggest new

ways in which GSMMs may contribute to drug discovery,

offering our vision of how GSMMs may re-model the drug

pipeline in years to come.

Addresses1 School of Computer Sciences, Tel Aviv University, Tel Aviv 69978,

Israel2 The Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978,

Israel

Corresponding authors: Oberhardt, Matthew A ([email protected]),

Yizhak, Keren ([email protected]) and Ruppin, Eytan

([email protected])3 These authors contributed equally to this study.

Current Opinion in Pharmacology 2013, 13:778–785

This review comes from a themed issue on New technologies

Edited by Andreas Gescher and Marco Gobbi

For a complete overview see the Issue and the Editorial

Available online 31st May 2013

1471-4892/$ – see front matter, # 2013 Elsevier Ltd. All rights

reserved.

http://dx.doi.org/10.1016/j.coph.2013.05.006

IntroductionDespite huge advances in biology in the last few decades,

the resources pumped into drug development become

ever larger and yet pipelines are not producing more

drugs [1–3]. Industry analysts have blamed this in part

on stagnation in R&D due to risk aversion in Big Pharma

[4], and yet with strains of bacteria emerging that are

resistant to nearly every existing antibiotic [5] and many

of the most devastating diseases in the developed and

developing world still lacking cures, new drugs are des-

perately needed. A major pitfall of traditional approaches

(such as the ‘one drug, one target’ paradigm) is their

inability to account for the full complexity of biological

systems [6]. With many systems biology techniques now

being developed specifically to navigate this complexity,


a synthesis between the traditional drug pipeline and

systems methods may soon become not an R&D luxury,

but an industry necessity [4].

Systems biology addresses biological questions not from

the perspective of individual protein or gene actors, but

rather taking into account full interacting cellular net-

works. Modern omics technologies, as well as huge

advances in computational capabilities, have made sys-

tems biology both appropriate and crucial for fully utiliz-

ing the resources for biological discovery that are available

today. One of the most influential paradigms within

computational systems biology is constraint-based mod-

eling (CBM), which focuses on building and analyzing

genome-scale metabolic models (GSMMs). CBM

imposes context-specific constraints on a space of possible

metabolic behaviors and allows prediction of numerous

metabolic phenotypes, including growth rates, nutrient

uptake rates, gene essentiality and more (Figure 1) [7].

The CBM field has been rapidly expanding, and today

dozens of manually curated GSMMs are available in

addition to over a hundred bacterial GSMMs built

through an automated system called SEED [8]. In

2007, two genome-scale models of human metabolism

were published [9,10], followed by a new version that has

been published very recently [Recon2 [11�]]. While these

models are generic and thus not specific to any mature

cell-type or tissue-type, they have successfully served as a

basis for context-specific models of human tissues [12–22]. GSMMs have been used in areas including metabolic

flux analysis [23,24], studies of network evolution [25],

and metabolic engineering [26,27]. These uses and many

others have been thoroughly reviewed elsewhere [28,29].

The increasing availability of high-quality GSMMs pre-

sents new opportunities in drug development and design

both for combating pathogens and for treating human

disorders, which together form the focus of this review.

Determining the best drug targetsTraditional drug development left the determination of

targets for a second step following the discovery of lead

compounds with potential therapeutic efficacy (i.e. phe-

notypic screens), whereas the genomics era brought a

surge of interest in the reverse ‘target-based’ approach,

that is, determining good potential gene or protein targets

first, and then screening for suitable small molecules to hit

them [30]. Despite great interest in what seemed a more

rational approach to drug development, target based

screens have under-performed phenotypic screening in

drug development [30,31]. One of the reasons cited for

this failure is not target based screening itself, but the

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Metabolically re-modeling the drug pipeline Oberhardt, Yizhak and Ruppin 779

Figure 1

Lipids

AminoAcids

Nucleotide s

Cellula rBiomas s

‘Om ics ’ da ta

INPUT 3

Growth mediu m

INPUT 1

High

Low

Moderate

Glucos e Sulfat e

Genome-Scal e Metabol ic Mod el

Byprodu ctsecretion rates

OUTPUT 1

Growth rate andgene esse ntia lit y

X

OUTPUT 2

A

B

C

D

E

F

A

B

C

D

E

F

Prediction of flu xrates at wil d-typeand pertu rbat ion

states

X

OUTPUT 3Substrate upta kerat e

10gGlu cos e

1gSulfate

..

..

INPUT 2

Current Opinion in Pharmacology

Inputs and outputs to GSMMs. A genome-scale metabolic model (GSMM) integrates information on compounds present in the growth medium,

substrate uptake rates (if known), and information on the internal state of a cell (such as gene knockouts or large-scale omics datasets) to predict the

steady state fluxes and activities of all reconstructed pathways in metabolism. Outputs of a GSMM analysis typically include growth yields (or growth

rates if substrate uptake data is available), gene essentiality information, and prediction of wild-type or perturbed flux distributions across the whole

metabolic network.

need to integrate new technologies in order to predict

better targets [32]. There has been a major focus in the

GSMM community on using models to predict drug

targets, as GSMMs can directly predict the effects of

perturbations such as gene knockouts on growth and thus

lend themselves naturally to this task. Therefore, we

focus here on the ways GSMMs have been used to predict

novel drug targets. Drugs serve two major purposes:

either to kill or to functionally alter a target tissue or

cell. Here, we review GSMM methods for both of these

aims.

Drugs to kill

Selective killing of certain target cells while sparing

healthy human cells is the fundamental goal of many

drugs. This goal is common, for example, in targeting

both bacterial pathogens as well as cancer. About a third

of current antibiotics target metabolic genes (derived

from DrugBank: [33]), and metabolism comprises a large

potential pool for more targets. Because of their basic

structure and capabilities, GSMMs are a natural choice for

rational drug target development.


GSMMs have been recognized for quite a while as

vehicles to predict lethal metabolic drug targets in patho-

gens [34]. However, despite many proofs-of-principle

such as accurate prediction of essential genes using flux

balance analysis (FBA) (e.g. [35]) and ample predictions

of possible targets in over a decade of use, these efforts

have led to few promising new drug leads, and to our

knowledge, no new drugs. Part of the reason for this is that

many key factors essential in a good drug target have been

out of the scope of GSMMs — for example, the ‘drugg-

ability’ of a target, which can involve its cellular localiz-

ation, its three-dimensional structure, and its potential

binding with known classes of inhibitors, among other

factors [32,36].

More recently, merging druggability information with

GSMM-based essentiality predictions has shown tremen-

dous potential. A recent study aimed to repurpose already

FDA-approved drugs to kill the deadly parasite, Leishma-nia major, combining GSMM predictions of gene essenti-

ality and synthetic lethality (i.e. lethality of two genes in

combination) along with ‘druggability’ scores and known


780 New technologies

Figure 2

Eicosanoid pathwaymetabolites identified

as cancer specificmetabolic features;

Agren et al. [16]

Novel antibiotic leadcompounds against E.

coli and S. aureusdeveloped to target FAbiosynthesis pathway;

Shen et al. [38]

Suggested role of ACL inproducing acetylcholinein Alzheimer disease;

Lewis et al. [21]

hypertensive side effectcaused by the drugtorcetrapib linked toreduced secretion of

PGI2 in kidney; Chang etal. [20]

Glucose

G6P

F6P

FBP

G3P

DHAP

3PG

2PG

PEP

Lactate Ac-CoA

To PentosePhosphate Pathway

R5PNucleotideSynthesis

TCACycle

Glycolysis

Serine

Glycine

DHF

mTHF

THF

dUMP

dTMP

Amino acidSynthesis

3PHP

NAD NADH

SerineP-Serine

HemeHeme

Bilirubin

Heme Biosynthesis

Succinyl-CoA

Citrate

Citrate Ac-CoA

Fatty Acid Oxidation

Folate Metabolism

Serine Biosynthesis

NADH

OxidativePhosphorylation

ATP

Acyl-CoAAcyl-CoA

Fatty Acid Biosynthesis

ArachidonicAcid

HPETE PGG2

Leukotrines PGI2

EicosanoidMetabolism

HMOX

HMOX identified andvalidated as synthetic lethalwith FH in renal cell cancer;

Frezza et al. [46]

PDH

ACLLipid

Synthesis

Halofantrine, a malarial drug associatedwith the vacuolar ATP synthase catalyticsubunit A, found to be active against L.

major; Chavali et al. [37]

DHFR

DHFR, a knownchemotherapeutic,identified as cancer

target; Folger et al. [15]

Chemical analogs toFolate metabolism

intermediates found aspromising antibiotic

leads in V. vulnificus;Kim et al. [45]

Pyruvate

1,3BPG

Brain GSMM identifiedknown altered activityof PDH in Alzheimerdisease; Lewis et al.

[21]

Human targets Bacterial targets

Key:


Drug target related predictions using GSMMs. A map of metabolism is shown, with descriptions of some of the highest confidence drug targets (or

lead molecules) that have been identified in GSMM studies. Human (blue, solid border) and bacterial (orange with dashed border) targets are both

shown.

chemical-protein interactions [37�]. This led to successful

in vitro killing of L. major by Halofantrine, an antimalarial

agent, as well as synergistic killing by four repurposed

drug pairings (Figure 2). Such repurposing efforts, especi-

ally when aimed at neglected diseases (of which L. majorinfection is one), may have enormous impact.

Recent literature includes several other mergers of

GSMM predictions with structural or druggability data

in order to discover new drugs. In a study of Escherichia coliand Staphylococcus aureus, predicted enzyme targets in the

FASII pathway were virtually screened for binding

against a library of small molecules. Out of 41 predicted

inhibitors, eight proved strongly and nine weakly active in

extracted enzyme assays [38]. Another study of the same

two organisms also used GSMMs alongside protein-mol-

ecule binding predictions to identify drugs leads that

target the histidine biosynthesis pathway, which showed

marked in vitro efficacy [39]. Some recent efforts have


also brought the focus from genes to metabolites, analyz-

ing ‘choke points’ (i.e. reactions uniquely consuming or

producing a metabolite) [40,41] or metabolite essentiality

[42,43]. Metabolite-centric methods have the advantage

that small molecules can be sought based directly on their

similarity to the essential metabolite, and will often have

the potential to hit multiple enzymatic targets, a desirable

drug attribute [6]. This line of reasoning led to identifi-

cation of chorismate as a prime drug target for four

prominent pathogens [44]. Chorismate is an important

biochemical intermediate in plants and microorganisms

and a precursor of metabolites such as aromatic amino

acids, salicylic acid, and more. Importantly, there is no

significant homology between genes that encode for

reactions that utilize chorismate and the human genome,

suggesting that blocking this compound might affect the

pathogen and not the human host. A later study from

the same group screened live cells of Vibrio vulnificuswith 352 analogs of five promising target metabolites,



as identified through metabolite-centric GSMM

methods. This screen yielded a small molecule inhibitor

with bacteriocidal properties in the range of currently

used antibiotics [45��].

These studies demonstrate how combining GSMM-

based target predictions with information on druggability

and enzyme/metabolite structural analysis can lead to

novel drug candidates. These pipelines are new, but as

they mature we may begin to see their effects in the drug

industry. So far, attempts to use GSMMs to develop lethal

drugs have focused mostly on microbial pathogens, but

there is increased focus and interest in turning GSMM

methods toward cancer. The power of GSMM in this

arena was recently shown in a study of the role of

fumarase, a TCA-cycle enzyme whose loss in the germ-

line can cause leiomyomatosis and renal carcinoma [46].

In the study, a kidney cancer GSMM was used to predict

what metabolic mechanisms these cells might use to

survive without fumarase. A novel pathway was predicted

and then confirmed in vitro in immortalized kidney cancer

cells. This in turn led to promising new drug targets in the

haem oxygenase pathway that selectively killed cancer

cells that were fumarase negative while sparing fumarase

positive cells (Figure 2), a major step forward in selective

targeting of this type of cancer.

Drugs to alter

While most drugs targeting microbes aim to kill the target

cells, the majority of human-cell targeted drugs obviously

aim not to kill the cells but rather to alter their function in

a therapeutic manner. For example, 16 of the 20 most

prescribed therapeutic drug classes in the US in 2011 aim

to alter the functions of human cells (derived from: [47]).

GSMMs are attractive tools for developing new drugs in

this area, as they provide a large-scale, high confidence

vehicle for predicting genome-wide effects of a pertur-

bation. Hundreds of past studies have explored the

effects of drugs or metabolic alterations (such as gene

knockouts or changes in environmental conditions) on

microbes, providing proofs of principle for many types of

uses of GSMMs in this arena (see, e.g. [48]). In an out-

standing example, a GSMM of E. coli was recently used to

build a range of ensemble models that account for pro-

duction of reactive oxygen species (ROS) [49]. Gene

deletions were then predicted that increase endogenous

production of ROS, and knockout of these genes in vitrowas shown to increase susceptibility of E. coli to oxidants

and to antibiotics. This study shows the power of GSMMs

in developing adjuvant therapies to microbial pathogens,

and more broadly, shows how prediction of metabolic

alterations can suggest new targets for drugs.

Until very recently, usage of GSMMs to develop drugs

that alter cells’ state has been limited because of the lack

of a GSMM for human cells. However, with the recent

introduction of human metabolic reconstructions ([9] and


a recent update [11�]), methods to explore drug-related

alterations in human metabolism are emerging. Generally

speaking, many of the techniques mentioned in the ToKill section can also be applied to altering metabolism,

but rather than inhibiting production of cellular biomass

(and hence killing a cell), the goal is to inhibit the

production of a metabolite known to have systemic

impact on disease (e.g. cholesterol). Conversely, our lab

recently developed a new GSMM method to address the

problem of restoring diminished functions to cells. The

method, which identifies gene targets most likely to

globally transform a diseased metabolic state toward a

healthy one, identified novel lifespan extending genes,

which were then successfully experimentally validated in

yeast. In a promising follow-up, the method identified the

eicosanoids pathway, a known age related pathway, as a

high-priority target for age-reversal in mammals (Yizhak

et al., personal communication).

Despite these few examples, this area of drug develop-

ment using GSMMs is still relatively underdeveloped.

Potential future applications include developing adjuvant

therapies (as mentioned above), reducing the metastatic

potential of cancer, and even predicting dietary interven-

tions that may augment or replace a certain drug [50].

Using GSMMs to predict drug toxicity andselectivityDespite extensive advancements in drug discovery, drug

toxicity and selectivity remain major drivers of failures

and cost during the drug development process. These

failures result from unexpected negative systems-level

effects of hitting the appropriate target, as well as to off-

targeting in both primary and secondary tissues. Early

pre-clinical elimination or modification of troublesome

lead compounds is hence an important task that may

significantly bolster drug industry R&D efficiency.

GSMMs fit naturally into this scheme, as they are well

suited to predicting system-wide effects of metabolic

perturbations. Importantly, these effects can be evaluated

at multiple levels that include both diseased and healthy

tissues, and thus can give valuable insight into selective

targeting.

The prediction of ligand binding sites using protein

structures is a crucial step toward identifying drug side-

effects. This type of structural analysis can identify as-yet

uncharacterized drug targets that may participate directly

in drug response. While ligand binding predictions go

beyond GSMM-based methods, the integration of both

approaches is complementary and hence of much value.

Such a combined approach was recently applied [20] to

investigate the hypertensive side effect of the cholesteryl

ester transfer protein inhibitor torcetrapib in the context

of human renal function. Focusing on 41 predicted off-

targets of torcetrapib, the authors used a renal metabolic

model to show that the inhibition of PTGIS precludes the



secretion of PGI2. This metabolite has been linked to

blood pressure and is thus expected to have a hyperten-

sion effect. As mentioned earlier, GSMMs have also been

used alongside ligand binding models in the prediction of

novel antimicrobial compounds [38,39], an approach that

shows marked promise.

Many drugs work to inhibit a certain enzyme or function of

the cell, but some drugs are rather designed to stimulate or

trigger a response. Clearly, inhibitory and stimulatory

responses should be modeled differently. Our lab has

recently developed CBM-based method termed EDGE

that is designed to address this challenge (Wagner et al.,personal communication). Given a species metabolic

model, EDGE works to systematically predict whether

the over-activation of a metabolic enzyme is expected to be

toxic to the organism or not. As an initial proof of concept,

EDGE was shown to successfully detect genes whose

expression is toxic in microorganisms, both in rich and

in poor media. EDGE therefore may help predict unin-

tended drug side effects in future studies in humans.

The capacity of a drug to produce the desired effect only

at a selected target cell or tissue is another major chal-

lenge in drug design. This problem is encountered at

various levels: first, antibiotics that are designed to kill

only a certain spectrum of bacteria (bacteria versus bac-

teria); second, antibiotics that should spare the host

human cells (bacteria versus human); and third, drugs

that must target diseased cells while keeping healthy

tissues in our body intact (human versus human). The

recent development of GSMMs, both for hundreds of

bacterial species and for numerous human tissues, pro-

vides for the first time an opportunity to address these

issues. GSMMs have been used to target specific bacterial

spectra [44], and reducing likely toxicity by filtering out

gene or metabolite targets present in humans is standard

practice in predicting novel antibacterial drug targets

[45��]. Furthermore, GSMMs have been used to study

host–pathogen interactions, resulting in a joint Mycobac-terium tuberculosis — alveolar macrophage model [51] that

displayed higher accuracy than the non-joined model in

predicting gene essentiality. Although the tuberculosis

study did not focus on drug development, the growth of

M. tuberculosis is both so sluggish and so interconnected

with its macrophage host that such models may be

required in order to boost both effectiveness and selec-

tivity of future drugs.

The issue of using GSMMs to develop selective drug

targets has already been initially addressed in the context

of cancer. An attempt to accomplish this goal was taken by

Folger et al. [15] who built a metabolic model of cancer

metabolism and identified drug targets that can poten-

tially affect cell proliferation. In parallel, the effect of

these targets on the production of ATP in a generic non-

proliferating human cell was examined, resulting in a set


of predictions that is highly enriched with FDA-approved

metabolic anticancer drugs (Figure 2). As mentioned

previously, a GSMM of the kidney cancer was used to

determine new drug targets in the haem pathway that are

selective for certain cancer cells, relying on utilizing

synthetic lethality [46]. Finally, Facchetti et al. [52]

developed a novel method for identifying drug targets

by inhibiting a predefined objective function such as cell

growth in one cell type while preserving the metabolism

of another. This analysis correctly identified known che-

motherapeutic agents, lending a strong proof-of-principle

to its use in novel drug discovery. Since these studies

have been published, multiple GSMMs of human tissues

have been reconstructed. Utilizing them, we can there-

fore expect a more thorough analysis of drug selectivity in

a broad span of human metabolic disorders in the near

future.

Future directionsGSMMs lend themselves naturally to the early stages

of drug development, most notably the determination of

new targets for target-based screens. However, as direct

drivers of innovation and as scaffolds for interpretation of

complex large-scale datasets, the potential of GSMMs in

drug development is yet largely untapped. We put some

current and projected future uses in Figure 3.

One of the great strengths of GSMMs is their ability to

integrate high throughput data to, for example, model

complex lifestyles of pathogens in vivo or in situ. As such,

GSMMs have been used to explore the progression of a

chronic cystic fibrosis lung infection [53], lifecycle stages

of the parasites causing Chagas and Malaria [54,55], and

features of other complex diseases [51,56]. Large efforts

such as the ENCODE project [57] have recently revealed

many new areas of biology, such as genome-wide human

DNA methylations, copy number variations, and SNPs

[58], which may also soon be integrated with GSMMs

following along the lines of recent multi-system modeling

efforts [59,60�]. GSMMs provide a solid framework for

contextualizing such data, and may be integrated into

future drug development pipelines in ways analogous to

how physiologically based pharmacokinetic (PBPK)

models already have been [61].

Additionally, GSMMs may be important for developing

entirely new classes of drugs, such as probiotic treatments

in which engineered bacteria outcompete pathogens in

the human gut. Such ideas are not so outlandish con-

sidering, for example, the significantly higher cure rates of

Clostridium difficile infections via fecal transplant (a

probiotic treatment) than via antibiotics [62]. GSMMs

are natural platforms for use in developing such probio-

tics, as they can integrate with 16s rRNA or metagenomic

data to produce predictive community models [63,64],

and can also aid in engineering of specific probiotic strains

[65].



Figure 3

Develop ment& clini cal trial sNovel screens? Drug

UsageResista nce

Future GSMM possibili ties

Designingprobiotics orother novel

lead pipelines

Repurposingdrugs

Reducingtoxicity-baseddrug failures

Finding new/ better drug

targets

Targetingcertain lifestages ofparasites

Reducingbacterial

resistance atpopulation level

Reducing cancer/bacterial resistance

in patientsDeveloping

personalizedmedicines

Determiningmechanisms/targets of lead

compounds

Developingphenotypic

assays

Curre nt GSMM application sor proofs of prin cipl e

DrugPip elin e

Development& clinical trialsNovel screens? Drug

UsageResistance

Repurposingdrugs

Reducingtoxicity-baseddrug failures

/ better drugtargets

Rb

res

Reducing cancer/bacterial resistance

in patientsDeveloping

personalizedmedicines

Determiningmechanisms/targets of lead

Developing

Target based screens

Phenotypic screens


Current and future uses of GSMMs for drug discovery. GSMMs have potential to aid in many areas of the drug discovery process, although most

studies to date focus on determining better drug targets for target based screens, and a few other select applications (top, green). The drug pipeline is

portrayed along with current (green) and future potential (orange) applications of GSMMs toward drug discovery.

As mentioned before, GSMMs can also be used to suggest

new ways to repurpose existing drugs [37,66]. Drug

repurposing sits far later in the drug development pipe-

line than target discovery, the prediction of toxicity, and

most of the other applications previously discussed (see

Figure 3), and thus represents a break from the emphasis

on the very early stages of drug development.

Many other portions of drug development have not even

seen a preliminary GSMM study. This is especially

striking in phenotypic screening, as despite clear poten-

tial, GSMMs have never (to our knowledge) been used in

either developing phenotypic screens or in interpreting

the mechanisms of lead compounds from them, even in

proof of principle. Concerns that arise later in the life-

times of drugs, such as the common occurrence of resist-

ance in bacteria and cancer, also have not yet been

addressed using GSMMs, despite the demonstrated use-

fulness of GSMMs in examining important resistance

factors such as gene promiscuity [67] and cellular evol-

ution [68,69]. Finally, GSMM methods subtle enough to

predict differential phenotypes of a tissue across many

humans, a requirement for personalized medicine, are

only just now being developed (Yizhak et al., personal

communication). Such methods will also help in


developing drugs that target certain tissues over others.

GSMMs are obviously not magic bullets, but once they

are noticed and adopted by the pharmaceutical industry,

they will undoubtedly add value and insight that would

be difficult to gain otherwise.

AcknowledgementsKY is partially supported by a fellowship from the Edmond J. SafraBioinformatics Center at Tel-Aviv University and is grateful to the AzrieliFoundation for the award of an Azrieli Fellowship. MO gratefullyacknowledges support from the Whitaker Foundation and from the DanDavid Foundation. ER’s research is supported by grants from the IsraeliScience Foundation (ISF), the Microme project (EU FP7), and by theIsraeli Centers of Research Excellence, Gene Regulation in ComplexHuman Disease Center (41/11).

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