2024年5月4日发(作者：)

Bioinformatics Advance Access published September 6, 2005

BIOINFORMATICS

Computational methods for the design of effective therapies

against drug resistant HIV strains

Niko Beerenwinkel

1,*

, Tobias Sing

2

, Thomas Lengauer

2

, Jörg Rahnenführer

2

, Kirsten Roomp

2

,

Igor Savenkov

2

, Roman Fischer

3

, Daniel Hoffmann

3

, Joachim Selbig

4

, Klaus Korn

5

, Hauke

Walter

5

, Thomas Berg

6

, Patrick Braun

7

, Gerd Fätkenheuer

8

, Mark Oette

9

, Jürgen Rockstroh

10

,

Bernd Kupfer

11

, Rolf Kaiser

12

, Martin Däumer

12

Department of Mathematics, University of California, Berkeley, CA,

2

Max Planck Institute for Informatics, Saarbrücken,

Germany,

3

Center of Advanced European Studies and Research, Bonn, Germany,

4

Max Planck Institute of Molecular Plant

Physiology and University of Potsdam, Germany,

5

Institute of Clinical and Molecular Virology, University of Erlangen-

Nürnberg, Erlangen,, Germany,

6

Medical Laboratory, Berlin, Germany,

7

PZB, Aachen, Germany,

8

Department of Internal

Medicine, University of Cologne, Germany,

9

Department of Gastroenterology, University of Düsseldorf, Germany,

10

Department of Internal Medicine, University of Bonn, Germany,

11

Institute of Medical Microbiology and Immunology,

University of Bonn, Germany,

12

Institute of Virology, University of Cologne, Germany

Received on …; revised on …; accepted on …

Advance Access publication . . .

1

ABSTRACT

Motivation:The development of drug resistance is a major obstacle

to successful treatment of HIV infection. The extraordinary replica-

tion dynamics of HIV facilitates its escape from selective pressure

exerted by the human immune system and by combination drug

therapy. We have developed several computational methods whose

combined use can support the design of optimal antiretroviral thera-

pies based on viral genomic data.

1INTRODUCTION

Persons infected with human immunodeficiency virus type 1 (HIV-

1) are highly susceptible to develop the acquired immunodefi-

ciency syndrome (AIDS), a major global threat to human health.

HIV-1 is a retrovirus with a 9.2kbp genome coding for 15 viral

proteins. Currently, 19drugs targeting three distinct steps in the

viral replication cycle are available for antiretroviral therapy.

These drugs can be grouped into four different classes, according

to their target and mechanism of action. Nucleoside and nucleotide

analogues act as chain terminators in reverse transcription of RNA

to DNA. Non-nucleoside reverse transcriptase inhibitors bind to

and inhibit reverse transcriptase (RT), a viral enzyme that catalyzes

reverse transcription. Protease inhibitors target the HIV protease,

which is involved in maturation of released viral particles by cleav-

ing precursor proteins. Finally, entry inhibitors block the penetra-

tion of HIV virions into their target cells.

Cell entry is a complex process mediated by sequential interac-

tions of the viral proteins gp120 (envelope) and gp41 (transmem-

brane) with the cellular CD4 receptor and a coreceptor, usually

CCR5 or CXCR4, depending on the individual virion. Conse-

quently, different types of entry inhibitors have been proposed:

Fusion inhibitors prevent merging of viral and host cell membranes

by binding to the transmembrane protein gp41. In contrast, core-

*

To whom correspondence should be addressed.

ceptor antagonists bind to the host protein prior to membrane fu-

sion.

The available antiretroviral agents are applied in combination

therapies—so called highly active antiretroviral therapy (HAART),

typically comprising two nucleoside analogues and either a prote-

ase inhibitor or a non-nucleoside RT inhibitor. However, therapeu-

tic success, even of HAART, is limited. Antiretroviral therapy is

not able to eradicate HIV, and durable suppression of virus replica-

tion below detectable limits is achieved in only a fraction of pa-

tients. Drug resistance can be the cause of treatment failure and is

almost always a consequence of it (Clavel et al., 2004, DeGruttola

et al., 2000).

1.1Drug resistance

The intra-patient virus population is a highly dynamic system,

characterized by high virus production and turnover rates and a

high mutation rate. These evolutionary dynamics are the basis for a

large and diversified virus population that predisposes or quickly

generates resistance mutations. In a replicating population escape

mutants with a selective advantage under therapy become domi-

nant and lead to increased virus production and eventually to ther-

apy failure. A number of mutations in protease, RT, and gp41 have

been associated with resistance to different antiviral agents (Shafer

et al., 2000). Each drug has its own characteristic resistance profile

reflecting its chemical properties and mechanism of action. Never-

theless, cross-resistance (i.e. resistance against an unused drug) is

common between drugs from the same class. Therefore, HAART

advocates the use of two different drug classes in order to reduce

the likelihood of a mutant to resist all drugs in the combination and

to suppress viral replication more effectively (Jordan et al., 2004).

After treatment failure, the shifted population may be hit with a

new drug combination, but finding such a potent regimen is chal-

lenging. Cross-resistance severely limits the remaining treatment

options and the success of subsequent regimens is further impaired.

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winkel et al.

The interplay between development of drug resistance and insuffi-

cient suppression of virus replication can eventually lead to situa-

tions in which the currently available drugs can no longer control

replication at all. In the United States, as many as 50% of patients

receiving HAART carry a virus that is resistant to at least one of

the approved drugs (Richman et al., 2004). Furthermore, transmis-

sion of drug resistant viruses is estimated to occur in about 15%of

persons newly diagnosed with HIV infection in the US (Bennett et

al., 2005).

Because cross-resistance is frequent, treatment changes cannot

be based on the assumption that the virus will remain susceptible

to the unused drugs. Therefore, resistance testing has become an

important diagnostic tool in the management of HIV-infections

(Perrin et al., 1998). Resistance testing can be performed either by

measuring viral activity in the presence and absence of a drug

(phenotypic resistance testing), or by sequencing the viral genes

coding for the drug targets (genotypic resistance testing). Geno-

typic assays are much faster and cheaper, but sequence data pro-

vide only indirect evidence of resistance.

The Arevir project is a collaborative effort between clinicians,

virologists, and computational biologists to exploit genotype data

from genotypic resistance tests for the individual selection of op-

timal drug combinations. We have developed several computa-

tional methods for the analysis of integrated genotypic, phenotypic

and clinical data. Our goal was to provide tools for supporting

personalized genotype-driven treatment decisions.

time. We have addressed the problem of estimating evolu-

tionary pathways from sequence data.

(4)Therapy optimization. Our ultimate goal is to determine

optimal drug combinations on the basis of genotypic in-

formation. For this task, we need to estimate the in vivo ef-

fect of a drug combination on a given viral genotype and to

identify the regimen that maximizes clinical response. In

addressing these problems we make use of both the in vitro

phenotype predictions and the estimated evolutionary

pathways.

For each of the four challenges we present computational ap-

proaches and indicate the biological or clinical impact. We show

how the developed tools can be linked together in order to support

the selection of effective therapies against drug resistant HIV

strains.

2DATA MANAGEMENT

In order to meet the data integration and management challenge we

have developed the Arevir database, a secure electronic platform

for collaborative research aimed at optimizing anti-HIV therapies.

This system is designed to facilitate data exchange, improve diag-

nostics, support medical decisions, and to provide the basis for data

analysis.

2.1Database schema

1.2Challenges

The following questions have been addressed and approaches to

their solutions will be described in the following sections.

(1)Data integration. A prerequisite for any attempt to use

genotypic data in a clinical setting is to provide this infor-

mation at the right time and place. Resistance testing is of-

ten performed in specialized virological labs separated

from the clinical department. Furthermore, most clinical

data management systems are not prepared to handle se-

quence data. Thus, our first task is to collect, organize, and

integrate all relevant patient data.

(2)Phenotype prediction from genotypes. The first step in

interpreting genotypic data is to understand the effect of

single mutations and to relate mutational patterns to the in

vitro phenotype. We have addressed predicting phenotypic

drug resistance from the viral drug targets as well as pre-

diction of coreceptor usage from gp120. Both models can

augment the cheaper and faster genotypic test with a pre-

diction of the phenotype, namely the susceptibility to each

of the drugs and the coreceptor in use, respectively. This

piece of information is important for the choice of therapy.

(3)Evolution of drug resistance. Understanding the muta-

tional pathways that lead to resistant strains is important

for two reasons. First, this knowledge allows for estimating

the distance of a virus population to escape from drug pres-

sure, a quantity referred to as the genetic barrier. Second,

the prediction of mutational pathways makes it possible to

design sequences of therapies rather than one regimen at a

In managing HIV-infected patients a number of different types of

data arise, including personal patient data, therapy histories, nu-

merous virologic, immunologic and other clinical test results de-

rived from patient samples from different tissues, and sequence

data, e.g. from genotypic resistance tests. Our database schema

captures these data types in different modules, consisting of a few

tables each (Beerenwinkel, 2004a).

There is an important relationship between sequences and thera-

pies via the drug targets. The compounds making up a combination

therapy target specific viral proteins. DNA segments coding for

these proteins are sequenced in order to gain information on the

level of resistance that has been developed by the virus. Thus,

given the values of clinical markers the data model allows for ask-

ing for outcomes of therapy types versus mutational patterns

within the drug targets. This is the central question of the Arevir

project. It will be revisited in a later section.

2.2Implementation

The data model has been implemented in the open source relational

database management system MySQL. A secured client/server

architecture allows for remote access to the centralized database.

Since sensitive patient data are involved, this setting needs to meet

the security demands imposed by state and national law. In addi-

tion, we have developed a web interface to the database for clini-

cians and virologists. For these users the appropriate view on the

data is through a single patient or a single patient sample. Thus,

treating physicians as well as lab personnel get access to an inte-

grated view onto all relevant data for one patient. For example,

they can evaluate a genotypic resistance test result in the context of

the patient’s medical history and current immunologic status.

Moreover, applying the developed computational tools yields phe-

notypic interpretations of the genotypes. As of 2005, the Arevir

2