Overcoming drug resistance through in silico prediction

TECHNOLOGIES

DRUG DISCOVERY

TODAY

Overcoming drug resistance throughin silico predictionPablo Carbonell1,2,3, Jean-Yves Trosset4,*1University Evry, iSSB, F-91000 Evry, France2CNRS, iSSB, F-91000 Evry, France3Research Program on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, IMIM (Hospital del Mar

Medical Research Institute), Dr. Aiguader, 88 Barcelona, Spain4Sup’Biotech, BIRL, F-94800 Villejuif, France

Drug Discovery Today: Technologies Vol. 11, 2014

Editors-in-Chief

Kelvin Lam – Simplex Pharma Advisors, Inc., Arlington, MA, USA

Henk Timmerman – Vrije Universiteit, The Netherlands

Drug resistance

Prediction tools are commonly used in pre-clinical

research to assist target selection, to optimize drug

potency or to predict the pharmacological profile of

drug candidates. In silico prediction and overcoming

drug resistance is a new opportunity that creates a high

interest in pharmaceutical research. This review pre-

sents two main in silico strategies to meet this chal-

lenge: a structure-based approach to study the

influence of mutations on the drug-target interaction

and a system-biology approach to identify resistance

pathways for a given drug. In silico screening of syner-

gies between therapeutic and resistant pathways

through biological network analysis is an example of

technique to escape drug resistance. Structure-based

drug design and in silico system biology are comple-

mentary approaches to reach few objectives at once:

increase efficiency, reduce toxicity and overcoming

drug resistance.

Introduction

Drug resistance is a major cause of treatment failure in patients.

Not only the development of acquired resistance may render

current treatments inefficient but can also influence the action

of other drugs by cross-resistance mechanisms [1]. Spread of

*Corresponding author.: J.-Y. Trosset([email protected]), ([email protected])

1740-6749/$ � 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ddtec.2014

Section editors:Jurgen Moll – Boehringer-Ingelheim, Vienna, Austria.Gemma Texido – Nerviano Medical Sciences S.r.l,Nerviano, Italy.

resistant pathogens through the population can also reduce

the accessibility of successful treatments [2]. Predicting drug

resistance is therefore a major need in pharmaceutical

research. Many drug resistance mechanisms may be involved

at the same time but each one of them needs to be addressed

individually [3,4]. In silico data analysis and simulations might

explain certain mechanisms and suggest new strategies for

overcoming drug resistance.

In this review, we will present two main in silico techniques

to help predicting drug resistance: a structure-based approach

issued from the field of drug design to infer potential muta-

tions on the drug target and a network-based approach from

systems biology to study the interactions between functional

and resistance pathways. The investigation of drug resistance

is also an opportunity to design more selective therapies.

Exploiting synergies between targeted and resistance path-

way has gained increasing interest for the development of

drug combinations that could be efficient on resistant tumors

[5]. This aspect will be highlighted in the third section on

overcoming drug resistance.

Structure-based modeling approaches to drug

resistance

The most direct way for a cell or an organism to counteract

the action of a drug is to mutate its primary target. The

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Drug Discovery Today: Technologies | Drug resistance Vol. 11, 2014

resistant mutations aim at lowering the relative binding

affinity of the drug with respect to the natural substrate.

Sequence analysis in relation with experimental phenotype

is a starting point to predict the effect of resistant mutations

[6,7]. Such a predictive model of drug resistance has been

applied for HIV-1 protein inhibitors, assuming that each

mutation contributes linearly and independently to drug

resistance [8]. Other models relied on Bayesian [9] or Neural

Networks [10] using an initial input of observed resistance

mutations. Resistance mechanisms involving metabolizing

enzyme or drug efflux pumps can be studied in silico using

Quantitative Structure–Property Relationship (QSPR)-based

models. They have been proposed, for example, to predict

substrates on protein efflux pumps [11] or on drug metaboliz-

ing enzymes [12].

Structural analysis of protein–ligand complexes gives addi-

tional insights to understand the effect of mutations on the

target and especially the influence on drug binding [13,14].

Mutated side chains that directly interact with the drug can be

identified using an automatic in silico scanning strategy. Frey

et al. developed an algorithm, called K* that scanned all twenty

residue amino-acids that are in close vicinity of the drug [15].

This method takes into account the flexibility of the side chains

in the vicinity of the mutated side chain(s) but not the overall

relaxation of the protein backbone which is kept fixed in this

original algorithm. Flexibility of the protein and more pre-

cisely the global effect of a mutation on the dynamics of a

protein can be studied by molecular dynamics simulations

(MD) [16]. Computational mutation scanning (CMS) devel-

oped by Hao et al. implemented a MD free energy perturbation

together with a molecular mechanics-Poisson Boltzmann sur-

face area (MM-PBSA) technique to estimate the relative free

energy shift caused by a group of mutations for 6 HIV-1

protease inhibitors [17]. A total of 77 drug–mutant combina-

tions have been studied and the correlation coefficients of the

linear fit between calculated and experimental relative free

energy difference range between 0.55 and 0.81 for a given

inhibitor. The limits of the methods derived from molecular

mechanics reside in the quality of the force field. Prediction of

the correct protein mutant necessitates an accurate estimation

of the relative free energy difference of protein conformers that

are induced by each mutation as well as the relative binding

free energy difference between the drug and the substrate in

the folded and unfolded state for both wild type and mutated

state of the protein. Recent development of high resolution

protein force field is an attempt to meet this goal [18]. If

resistant mutations remain difficult to predict from quantita-

tive simulation, computer-based analysis of target structures

can give insights on possible mutations that can destabilize

the ligand. The gate keeper mutant T315I of ABL kinase is a

good example of a side chain mutation that disrupts the

binding of imatinib, a drug used in the treatment of Chronic

Myeloid Leukemia (CML) [19]. Fig. 1 shows an example of

102 www.drugdiscoverytoday.com

structure-based in silico analysis of the impact of a mutation on

target and its influence on drug binding.

Long range charged residue mutations can also destabilize

the drug by modifying the electronic distribution of the

ligand or by inducing conformational changes on the protein

binding site. The three mutants of the activation loop of

kinases, V600E of B-Raf, Y823D of c-Kit and H396R of Bcr-Abl,

are examples of charged mutated side chains that induce a

conformational change of the enzyme which stabilize the

active state of the enzyme [19–21]. If such a type of resistant

mutations is difficult to predict, strategies to overcome resis-

tance exist in that case. This involves the discovery of alter-

native resistance pathways that are re-activated by the loss of

gain of function of a given enzyme.

Network biology modeling approaches to drug

resistance

Development of indirect drug resistance mechanisms is often

a result of a systems-level cellular response taking advantage

of pathway redundancy [22,23], with feedback and cross-talk

synergies that render the drug less efficient [24]. In recent

years, modeling drug resistance through network analysis of

regulatory and metabolic pathways has increased [25–28].

Moreover, drug–target networks can be combined with bio-

chemical, biological or medical data to predict relations

between targets [29,30]. Common network representations

to analyze such relationships include Bayesian networks,

where each node represents a random variable and each edge

corresponds to conditional probabilities between nodes. For

instance, a Bayesian network was used to combine proteome

and transcriptome profiles to model Staphylococus aureus drug

resistance [31]. Boolean networks are also widely used, where

each node has two states (active/inactive). Regulatory inter-

actions were modeled by using Boolean logic on a protein

interaction network to define potential therapeutic strategies

for trastuzumab resistant breast cancer [32]. Another example

is the transcriptional network to model the stress response

induced by the fungicide mancozeb [33].

In the case of antibiotic resistance, simulation of resistance

transmission in bacterial populations has shown the need of

targeting the susceptible population as soon as possible with

potent antimicrobials [34]. Functional metagenomics can be

used to screen for antibiotic resistance genes in the environ-

ment, which generally are characterized by their evolvability.

For instance, beta-lactamases are known for their extended

substrate promiscuity and the presence of mutations were

shown to inactivate not only beta-lactams but also other

antibiotics, such as cephalosporins. [35]. Identification of

enzymes that could inactivate the antibiotic can be done by

in silico analyses of metabolic networks [35]. In this direction,

constraint-based analysis [36] in reconstructed metabolic

models of resistant strains can help to understand antibiotic

resistance mechanisms as well as ways to overcoming it,

Vol. 11, 2014 Drug Discovery Today: Technologies | Drug resistance

Mutation Databases.Ex: KD4v Missense Databasedecrypthon.igbmc.fr/kd4v

Visualize selected mutation in the protein 3D structure if available (leftpanel). Analysis consequence of mutation on drug binding (right panel).

Comparisonmutant protein

versus wild type

Glivec/Gleevec(green) superimposed with Danusertib (red) which is a new kinaseinhibitor escaping resistance mutation T315I in ABL kinase (right panel). Highlight ofthe gate keeper mutated residue T315I (ILE in orange) with imatinib (green)superimposed on danusertib (orange) with molecular surface of the ATP binding siteof Abl kinase mutant T315I (left panel).

I-315

Drug Discovery Today: Technologies

Figure 1. Comparison of mutant versus wild type protein. Structure of the complex imatinib and wild type Abl kinase is taken from PDB 3K5V. The

structure of the complex danusertib with kinase Abl T315I mutant is taken from 2V7A. Images were created by ICM MolSoft Viewer (MolSoft Inc.).

Molecular surface of protein binding site in left bottom panel is derived from LZM (ThistleSoft Inc.).

especially in cases where the antibiotic targets a metabolic

reaction like cytochromes and other target-modifying

enzymes [37]. Generally, upon inhibition of a protein in a

given pathway, metabolic adjustments occur to minimize the

effect of inhibition on the particular protein [38]. Analysis of

these adjustments would provide at least a partial explanation

of resistance mechanisms, because processes such as gene

regulation are not generally modeled extensively in such

reconstructed in silico models [37].

Constraint-based analyses have been carried out on several

antibiotic-resistant pathogens. For instance, on the metabo-

lism of Mycobacterium tuberculosis [39], a pipeline protocol

(‘targetTB’) was proposed based on protein interactions with

the aim to determine proteins that are crucial for survival

deletions [40]. Two genome-scale metabolic models were

available, one from McFadden and co-workers [39], including

739 metabolites and 726 genes, and the other from Duarte

et al. [41] including 939 reactions, 828 metabolites and 661

genes. This pipeline integrated flux balance analysis of the

metabolic network with gene essentiality data including

sequence and structural analysis of the proteins.

A similar constraint-based flux analysis based on gene and

metabolite essentiality was performed on the metabolic net-

work of Acinetobacter baumannii AYE, a drug-resistant strain

involved in nosocomial infections [42]. Drug–target candi-

dates were obtained by this means and essential metabolites

were selected based on their number of consuming reactions.

Targeting the production of these metabolites should more

efficiently disrupt the metabolic system and delay pathogen’s

resistance through endogenous mutations [43]. A constraint-

based analysis was also performed for Salmonella typhimurium

LT2 (iRR1083) to predict metabolic pathways that are prob-

ably operational during infection [44]. The reconstructed

model included reactions linked to resistance mechanisms,

like drug efflux, proton pumps, and inhibitory effects of

antibiotics on bacterial metabolism. The model was validated

with experimental data with an accuracy of 80% for growth

and virulence phenotypes, constituting thus a promising

model for testing hypothesis about therapeutic interventions

overcoming resistance.

Metabolic network analysis was used as well to study drug

resistance of cancer cells. Genetic alterations that cause

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tumor development directly affect cellular energy metabo-

lism allowing unregulated proliferation in a hypoxic envir-

onment which is contributing to the development of drug

resistance [45]. However, such adaptive metabolic mechan-

isms were constrained by the availability of precursor sub-

strates and cofactors, which are shared with other

interconnecting pathways, representing vulnerabilities to

network perturbations and therefore targets that could lead

to efficient treatment of cancer drug resistance [45]. Several

metabolic models of human metabolism useful to under-

stand cancer cell metabolism and resistance have been pro-

posed [46,47]. In that way, the core metabolism of cancer cells

was modeled to identify metabolic alterations involved in

initiation and sustainment of cancer phenotypes [48].

Many anticancer drugs activate caspases via the mitochon-

drial apoptosis pathway, which triggers a bioenergetics crisis

by the release of cytochrome-c resulting in apoptosis. An in

silico model was developed to study the temporal behavior of

the bioenergetics state variables leading to resistance to these

processes in cancer cells [49]. The study showed that recovery

from bioenergetics crisis is possible through an increase in

cytosolic ATP production by an elevated rate of glycolysis,

suggesting therapeutic co-targets that could overcome resis-

tance triggered by bioenergetics rescue mechanisms.

In silico strategies to overcoming resistance

In silico strategies to overcoming drug resistance rely on the

structural and network models that were developed to under-

stand the various underlying mechanisms. The goal is to

lower the risk of appearance of major resistance mechanisms.

Escaping one of them might however create a selective pres-

sure favoring the emergence of an alternative mechanism,

Molecular surface of protein binding siteof 1CDK around ATP (natural substrate).

Molecular surface of proof 1STC around staurosp

(a) (b)

Figure 2. The minimal surface envelop around natural substrate. Molecular sur

wild type CAMP-dependent protein kinase (PDB: 1CDK), (b) around inhibitor s

1STC) and (c) superposition of staurosporine on minimal surface envelop. Th

potential to conformer adjustment of the side chains in the protein binding sit

(ThisthtleSoft Inc.).


but it is hoped that at least the appearance of drug resistance

will be delayed by this mean.

From structural-based analysis, two strategies can be used

to overcome the resistant mutations on the primary target

binding site: the minimal substrate envelop hypothesis, and

the focus on backbone protein atoms [50]. In the minimal

envelop hypothesis with drug mimicking the natural sub-

strate, the idea is that mutations affecting the drug will also

affect the substrate. This old strategy has led to successful

drugs before the appearance of computer-based drug design.

The challenge here is to achieve drug selectivity and to escape

the chemical space covered by patents. Fortunately, this

minimal substrate envelop can often be extended due to

the presence of regions in the binding site that is surrounded

by backbone protein atoms (Fig. 2). These regions are a priori

not affected by resistance mutations. This does of course not

consider the global flexibility of the protein [51]. To discri-

minate between regions of the binding site that might be

influenced by protein conformational change or polymorph-

ism, we can compare the structures of various members of a

protein family [52]. Regions of protein binding site that are

present in all members of the protein family are likely to be

stable and less influenced by protein dynamics or side chain

residues variability. Those regions are privileged targeting

areas in drug design to minimize the influence of resistant

mutation(s) on drug binding.

Instead of affecting the drug target, resistance mutation

may activate parallel resistant pathways as it is often observed

in drug induced resistance in tumors [53,54]. Activation of

survival or tumor growth pathways may render anti-cancer

drugs obsolete. An example of cross-talk between a targeted

and a resistance pathway can be found in colon cancer cell

tein binding siteorine (inhibitor)

Molecular surface of protein binding siteof 1CDK around ATP (natural substrate) super-posed with protein binding site of 1STC.

(c)


face of the protein receptor (a) around natural substrate ATP (orange) in

taurosporine (green) in wild type cAMP-dependent protein kinase (PDB:

e difference of the two molecular surfaces of panels (a) and (b) mark a

e. Surfaces are generated by LZM from FLO-QXP molecular package


wt cells: viable Resistancecells : viable

Targeted gene Bon wt cells: viable

Targeted gene Bon resistant cells: lethal

A, a,B B A, B a, B


Figure 3. Synthetic lethality chemical screen on drug induced resistant cell lines. Mutation on gene A (a) and drug targeting gene B on wild type (wt) cell is

viable. Targeting gene B on resistant cells is lethal due to synergies between gene A and B (adapted from [5]).

Gene U

Gene X∗

Gene Y

Gene ZGene W

Resistantmutation


Figure 4. Genetic suppressor chemical screening strategies. The

phenotype due to a mutation in gene X can be restored by action of a

drug on upstream or downstream regulating gene products. There

are two cases: either X* is a reduced/loss-of-function mutation or X*

is a gain-of-function mutation. In the first case, the phenotype can be

restored by inhibiting Y (or eventually U). For example, this is the case

when X inhibits Y and Y induces cell growth (e.g. mTOR inhibitors on

PTEN� tumors). A phenotype caused by a reduced-function-mutation

or negative regulation of gene X, could also be restored by activating

W or Z (e.g. activation of caspase-8-mediated apoptosis due to

negative regulation of the mTOR pathway). If X* is a gain-of-function

mutation, the phenotype can be restored by inhibiting Z (or W). For

example, overexpressed oncogene Myc (symbolized by gain-of-

function mutant X*) up-regulates downstream glutaminase

(symbolized by gene Z), a key gatekeeper target for glutamine

addiction in cancer cells. Glutamine withdrawal triggers apoptosis of

Myc-overexpressing cells [57]. Likewise, inhibitors of glutaminase

(gene W) preferentially slowed growth of glioblastoma cells

expressing mutants of iso-citrate deshydrogenase 1 (symbolized by

gain-of-function mutant X*) [58].

Figure adapted from URL: http://www.wormbook.org.

lines containing the hyper-active mutant oncogene BRAF

(V600E). Inhibition of BRAF (V600E) by inhibitors such as

PXL-4032 induces a rapid feedback activation of EGFR which

supports continued cell proliferation [55]. These two genes

develop therefore a synthetic lethal relationship in which the

inhibition of one protein would be compensated by the

inhibition of the other. Targeting the two proteins simulta-

neously is a powerful strategy that has shown great promise in

anti-cancer therapy. Fig. 3 presents an experimental screen-

ing strategy to search for synthetic lethal co-targets that

emerge in resistance cell lines. Synthetic lethal targets have

no effect when targeted alone but only in combination (e.g.

BRAF and EGFR in colon cancer cells). Other types of syner-

gies exist. The effect of the first mutation can be relieved by a

second mutation in another gene of the genome (extragenic

suppressor) or in the same gene (intragenic suppressor). An

example of extragenic suppression is a mutation in tumor

that introduces a loss-of-function of a tumor suppressor gene

like PTEN that activate cell proliferation. Inhibition of a

downstream target like mTOR overcomes the effect of the

resistant mutation on PTEN. It has been shown that mTOR

inhibitors have higher sensitivity in PTEN�/� resistant tumors

[56]. Strategies for genetic suppressor screens are illustrated in

Fig. 4. In silico analysis of synthetic lethality has recently been

carried out to reveal genetic interactions not only between

pathways but within the same pathway [59]. Synthetic lethal

targets can also be screened in silico by knocking out virtually

all pairs of nodes in the metabolic and/or signaling network

and evaluating the effect on the fitness function. Biological

network analysis can help in selecting co-targets that are

connected to different pathways of resistance [40] or neigh-

bors of central nodes/edges of resistance-related proteins [60].

Examples of network-based analysis for targets include the

ones carried out for Plasmodium falciparum metabolic network

[61,62] and Campylobacter jejuni [63]. A study of flux distribu-

tion in the metabolic network of Escherichia coli was performed

to identify targets that were predicted to increase reactive

oxygen species (ROS), enhancing in that way synergistic effects

between antibiotics [64]. Constraint-based analyses were also

used in a model of Pseudomonas aeruginosa to target genes

associated with biofilm-formation [65,66], which plays a key

role in antibiotic resistance or in the characterization of anti-

biotic biosynthesis pathways that could counteract bacterial

resistance [67]. Similarly, a model of cancer metabolism was


http://www.wormbook.org/


used to predict 52 drug targets, of which 40% were already

targeted by known anticancer drugs [68,69]. In another recent

study, the effect of aspirin resistance was evaluated using

constraint-based modeling of the platelet metabolism. Time

course metabolomics and fluxomics data from aspirin-sensi-

tive and aspirin-resistant platelets were incorporated as con-

straints into two in silico models of the platelet metabolic

network. The analysis of the shifts of metabolic fluxes between

these two models provided key elements to understand aspirin

resistance [70] and propose strategies to overcome these resis-

tance mechanisms.

Conclusion

In silico simulation is an ideal tool for understanding the

principles underlying cross-talks between targeted and drug

resistance pathways. Constraint-based analysis of metabolic

networks can provide insights into the effects of loss-of-

function or gain-of-function of a given enzyme on global

cell metabolism and predict the emergence of new alternative

resistance pathways. Moreover, other types of biological net-

work analysis can be carried out to highlight synergies

between cell signaling co-targets. This involves usually tar-

gets from epigenetic code and/or kinases and phosphatases,

for instance in the synthetic lethal relationship between

BRAF mutant and EGFR in colon cancer cells. Synthetic

lethality screening is a powerful approach to overcome drug

resistance in cancer or parasitic diseases. Search for synthetic

lethal partners in other organisms can be done by in silico

analysis of experimental synthetic lethality screening carried

out on yeast for example [59]. Structure-based principles also

exist to lower the risk of emergence of resistance mutation on

the drug target itself. Focusing drug interaction with back-

bone protein atoms is an example of such principles. Over-

coming drug resistance as a strategy in early stage of drug

discovery will not only lower the risk of resistance but also

might provide better combined therapies that take into

account the synergies between resistance mechanisms and

targeted pathways.

Conflict of interest

The authors declare no conflict of interest.

Acknowledgements

The authors thank F. Delfaud and V. Gerutz for helpful dis-

cussion and suggestions. Pablo Carbonell is supported by

Genopole through an ATIGE Grant by PRES UniverSud Paris,

by Agence Nationale de la Recherche and by UPFellows pro-

gram with the support of the Marie Curie COFUND program.

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Overcoming drug resistance through in silico prediction