hiv drug resistance analysis tool

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HIV drug resistance analysis tool

based on process algebraLuciano Vieira de Arajo

Dept of Bioinformatics

University of So PauloRua do Mato, 1010 055080-090

+55-11-30916172

[email protected]

Ester C. SabinoFundao Pr-Sangue

University of So Paulo, BrazilAv.Dr.Enas de Carvalho

Aguiar,155+55-11-30615544-ext221

[email protected]

J oo Eduardo FerreiraDept. of Computer Science

University of So PauloRua do Mato, 1010 055080-090

+55-11-30916172

[email protected]

ABSTRACTThe increasing number of drugs used in HIV patient treatment andthe mutations associated with drug resistance make the inferenceof drug resistance a complex task that demands computationalsystems. Furthermore, the software development/update cangenerate an extra level of complexity in the process drug

resistance analysis. An alternative to handle the complexity ofdrug resistance and software development is to use a formalrepresentation of involved processes, such as process algebra.This allows mathematical reasoning about the analysis process, a

precise description of system behavior, more advancedcomputational approaches, as concurrent/parallel execution and(semi) automatic software development. The first contribution ofthis research is a mapping of drug resistance algorithms rules intoexpressions of process algebra which facilitates the computationalmanipulation of theses rules. The second contribution is theHIVdag (HIV Drug Analysis Generator) system. This softwaresupports the definition, generation and analyses of genotypic drugresistance tests based on process algebra expressions. Therefore,the users can easily create/update their own drug resistancealgorithms any time and independent of software development.

Categories and Subject DescriptorsJ.3 [Computer Applications]: LIFE AND MEDICAL SCIENCES,Biology and genetics, Medical information systems

General TermsAlgorithms, Management, Design, Experimentation, Languages.

KeywordsProcess Algebra, Drug Resistance, HIV, NPDL, MutationAnalysis, Genotypic Drug Resistance Testing.

1. INTRODUCTIONThe drug resistance can be understood as a decrease of virussusceptibility to drug used in the patient treatment. It plays animportant role in the HIV patients treatment. There are differentinitiatives to maintain updated sources of information about drugresistance mutations, such as HIV Sequence Database at LosAlamos National Laboratories (www.hiv.lanl.gov) and DrugResistance Summary section of the Stanford University(hivdb.stanford.edu). These drug resistance informationrepositories have been used as reference to several groups todevelop their own drug resistance interpretation algorithms. Inspite of using the same source of information, each group has adifferent interpretation of drug resistance mutations. As result,some papers have reported discordance between the most useddrug resistance interpretation algorithms [7, 8, 11]. As long as,there isnt a consensus about genotypic drug resistance testing, weneed resources to easily develop, update and compare thealgorithms generated for different groups.

In this paper, we present HIVdag software that offers a quick,precise and flexible generation of Drug Resistance TestsSoftware. It is obtained through a mapping of drug resistance ruleinto expressions of process algebra and the management ofexpressions execution using a process definition language, called

NPDL.

This paper is organized as follows. Section 2 summarizesrelated work. Section 3 describes the drug resistanceinterpretation rules. Section 4 presents a synthesis of ProcessAlgebra and NPDL. Section 5 describes mapping of drugresistance interpretation rules into process algebra expressions. Insection 6, we present our software HIVdag. Section 7 presents theconclusion.

2. RELATED WORK

The most used and publicly available HIV Genotypic drugresistance testing are: HIVdb [8], created by the StanfordUniversity; ANRS [7], developed by Agence Nationale deRecherches sur le Sida; Rega [11], developed by Rega Instituteand the HIV Genotyping Test Brazilian InterpretationAlgorithm [1], maintained by the Brazilian Ministry of HealthRENAGENO Expert Committee. This last system has beenwidely used, in Brazil, as part of treatment offered by BrazilianMinistry of Health and it is also used to describe the drugresistance profile of Brazilian HIV patient. All of them are rule-

based systems that report level of drug resistance to each analyzed

Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and thatcopies bear this notice and the full citation on the first page. To copyotherwise, or republish, to post on servers or to redistribute to lists,requires prior specific permission and/or a fee.SAC08, March 16-20, 2008, Fortaleza, Cear, Brazil.Copyright 2008 ACM 978-1-59593-753-7/08/0003$5.00.

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drug. More details about drug resistance interpretation rules arepresented in Section 3.

The ANRS, Rega and Brazilian Interpretation Algorithm arebased on rules consisted of boolean expression that analyze thepresence or absence of sets of specific mutations and report threelevels of drug resistance: susceptible, intermediate, and resistant.As well, the HIVdb is a rule-based system. However, HIVdb rules

attribute a drug penalty score to a total drug score. Thus, the drugresistance is reported according to the total drug score into fivelevels: susceptible, potential low-level resistance, low-levelresistance, intermediate resistance, and high-level resistance.Another difference between them is the interpretation ofmutations and drug resistance. Consequently, these softwaregenerate different results and motivate the comparison of their

behavior, as in [8, 10].All these algorithms were manually developed without special

computer techniques and using program languages like Perl.Thus, each one is bound to limitations, such as: codificationerrors, lack of tests and time to development/update. Thechallenge is to avoid manual develop and be able to not onlycompare the algorithms results but also to understand and toexplore the differences.

One initiative of automate code generation of genotypic drugresistance system was the ASI [4] - Algorithm SpecificationInterface, developed by University of Stanford. The ASI is aspecific interface and compiler to generate 3 drug resistancealgorithms: HIVdb, ANRS, Rega and variations of thesesalgorithms. In the ASI, the rules are defined using XML format.After that, the compiler generates the algorithm code using theXML file. This approach is an informal way to generate code, thatdescribes the rules used, but it doesnt describe the system

behavior.Furthermore, an extension of algorithms to include newparameters, such as virus subtype and used drugs can demand alarge system restructure.

The Figure 1 shows a rule of Rega Algorithm in XML formatwhich is a representative sample of other algorithms such as

ANRS, HIVdb, Rega. The XML file contains the resistance level,set of rules of each drug and the classification table of total

penalty score, when representing HIVdb algorithms. In spite offlexibility provide by XML approach, it is not a clear formalmodel to support the correctness representation and executioncontrol of drug resistance rules.

Rega v7.1.1

7.1.1

1Susceptible GSS 1S

. . .

6Resistant GSS 0R

. . .

. . .

AZT

SELECT ATLEAST 1 FROM (151M,69i)

6

. . .

. . .

Figure 1: Sample of HIVdb drug resistance rule in XML format[4]

Some works [10,11,13] have indicated that HIV drug resistanceresearch can be improved using information about of both virusand patient treatment. In addition, the HIV integratedenvironments, as DBCollHIV[1], have provided integrated dataabout laboratorial exams, drug treatment history andepidemiology and bioinformatics tool to analyze it, such as:Brazilian Interpretation Algorithm, subtype tool, etc. This scenedemands software to support the improvement of drug resistanceresearch.

3. DRUG RESISTANCE INTERPRETATIONRULES

The actual drug resistances rules are based-on mutations analysiswhich is carrying out with the verification if the virus has or notmutations associated with drug resistance. Thus, mutations areessential part of drug resistance rules. At the rules, mutations arerepresented using numbers and letters; where numbers representthe mutations genome position and letters indicate the mutateamino acid founded at the genome position. For instance, 41Lindicates a mutate amino acid L at genome position 41. Some

positions have more than one amino acid associated withresistance which is indicating for a list of letters delimited for bars(/), as in 181C/I/L.

In order to understand the structure of drug resistance rules, wepresent one representative sample of Brazilian Algorithm, ANRSand HIVdb algorithms, as follow:

Brazilian Algorithm sample of rules to drug called DLV

- Presence of 1 or more from (100I , 181C/I/L, 188L, 230L, 236L)and absence of 190A indicates Resistant

This rule means that if the list of virus mutations has thepresence of 1 or more mutations of set (100I, 181C/I/L, 188L,230L, 236L) and the absence of mutation 190A, the virus isreported as resistant to drug DLV.

The next example is an ANRS rule sample of rules to drugcalled DDI.

- Exclude 70R AND Exclude 184VI AND Select Atleast 2 From

(41L,69D,74V,215FY,219QE)

This rule indicates that the presence of at least two mutations ofset (41L,69D,74V,215FY,219QE) in the absence of mutations70R and 184V/I reports the virus as resistant to DDI.

The last example is a part of HIVdb rule to drug called AZT.

- 116Y =>10,

151L => 20,

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The first line of this rule indicates that mutation 116Y attributespenalty score of 10 to AZT score. And the second line shows thatthe presence of mutation at position 151L scores 20 points to AZTscore. The special feature of HIVdb is the classification of finalscore into a resistance level, as follow: -Infinite to 10=>susceptible, 10 to 15=> potential low-level resistance, 15 TO30=> low-level resistance, 30 to 60=> intermediate resistance, 60

to +infinite => high-level resistance.The rules of HIVdb, Brazilian Algorithms, ANRS and Vega

share the same structure. Each algorithm contains a set of rules toeach drug and the rules evaluate the association between mutationand resistance levels/penalty score.

4. SUMMARY OF PROCESS ALGEBRA

AND NPDL

Process Algebra (PA) is a framework for formal reasoning aboutprocesses. It is useful to detect undesirable features and toformally derive desirable features of a system specification [5].The PA uses the algebraic expressions to represent systems

behavior. These algebraic expressions are formed by atomicactions and binary operators, where the atomic action representsan indivisible task to be executed and the binary operatorindicates the execution order of the actions. TheNavigation Plan

Definition Language (NPDL) [3] is a process definition languagebased on process algebra. The NPDL implements not onlyoperator of PA but also some extended operators that facilitate the

process definition. The complete list of operators of PA andNPDL can be found at [3] and [5] respectively. In this section, wepresent only the operators used to represent drug resistance rules,as namely: (1) Alternative composition + which, in the termt1+t2, defines the process that executes eithert1 ort2, but only oneat a time; (2) Sequential composition which, in the term t1.t2,defines the process that executes t2 after the finish of execution oft1; (3) Parallel composition || which, in the term t1 || t2, defines

the process that executes t1 and t2 at the same time; (4)conditional execution %r which, in the term % rt1, defines the

process that executes t1, only if the rule rreturn a true value. Thecomplementary behavior is represented by %!r", where theoperator %!, in the term %!r t1, represent the process thatexecutes t1, only if rule rreturn a false value. In some cases, theoperator % can be associated with a silent action which is anaction that does not perform a task; it only indicates that theexpression evaluation can continue.

As a example of NPDL expression, we present: P = %r (A .B) + %!r (C||D). In this expression, P represents the process thatevaluates the result of rule r to decide which actions must beexecuted. Ifrreturns the true value, the action B is executed afterthe finish of A execution. If a false value is returned, the actions Cand D can be executed simultaneously.

5. MAPPING OF DRUG RESISTANCE

RULES INTO PROCESS ALGEBRA

EXPRESSIONS.

In summarized way, we can consider that the rules of theanalyzed algorithms possess the same structure. That is, each

algorithm is composed of several rules while each rule representsa set of drug resistance mutation associated with a drug mutationlevel or a penalty score. In order to map rules of drug resistanceinto expressions of PA, we must represent the drug resistancerules as atomic actions and operators that indicate the executionorder of these actions. An atomic action can be understood as

program/method that carries out a specific indivisible task. In the

case of drug resistance, we define some atomic action.Let MS (MutationSearch) be a rule (i.e., a Boolean function)

action that receives, as parameter: a list of mutations to becompared with virus mutations list, a pair of integer valuesrepresenting minimum and maximum values of mutationsmatches to be considered. The MS execution generates aBoolean value, true or false. The true value indicates that thequantity of mutations matches is delimited by minimum andmaximum values received as parameter. Otherwise, MS returnsfalse. Moreover, if the parameter maximum is 0 (zero), itindicates that mutations must be absence of virus to returntrue. If the maximum value is null indicates unlimited numberof matches.

Let SS (SetScore) be an atomic action that increases the drug

total score penalty using integer number received as parameter.

Let SC (ScoreClassification) be an atomic action that receivesas parameter an integer number representing the total penaltyscore and return its respective drug resistance level.

Let RS (ResultsSynchronization) be an atomic action thatsynchronizes the results of different rules. It retrieves the rulesresults and returns the most significant result or the resistancelevel associated with the final drug score.

Let SRL (SetResistanceLevel) be an atomic action that sets thedrug resistance level.

Let RE (ResultsEquivalence) be an atomic action that receivesas parameter the equivalence relation between algorithmsresults.

Let ARC (AlgorithmsResultComparison) be an atomic actionthat retrieves and compares the results of each executedalgorithm.

Let GO (Go ON) be an atomic action that simulates thebehavior of silent action of PA.

Considering the list of NPDL operators {, +, ||, %r} and theatomic actions {MS,SS,SC,RS,SRL,RE,ARC,GO}, we mapsamples of drug resistance rules of Brazilian Algorithm, such as:one set of drug rules, the algorithms and the comparison betweenalgorithms.

The first mapping example is the set of rules that analyses thedrug resistance level of drug called DLV. This analysis iscarrying out using 4 rules, namely:

DLV1 = Presence of 1 or more from (100I, 181C/I/L, 188L,

230L, 236L) AND absence of190A reports Resistant level.

DLV2= Presence of 1 or more from (225H, 227L) OR Presence of

1 or more from (106A, 103N) reports Intermediate level.

DLV3= Presence of 1 or more from (106A/M, 103N/H/T/S V)

AND absence of (190A,225H,227L) reports Resistant level.

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DLV4= Presence of 1 or more from (190E) reports Resistant

level.

In order to represent the evaluation of presence or absence ofsome virus mutations, we use the ruleMS. The set of mutation to

be sought and the minimum and maximum of mutations matches

to be considered by MS are respectively represented asparameters ofMS. To verify the result ofMS execution, we usethe operator %, e.g. %MS. However, the operator % releases theexecution of associated action. Particularly, theMS execution thatreturns a true value indicates that the rule verification mustcontinue. Thus, the action associated with the blue operator will

be a silent actionGO which only indicates that the verification ofthe rule must continue. The connectors operators OR and ANDare represented by the alternative composition + and sequentialcomposition ,respectively. At last, the attribution of resistancelevel to drug is represented by the atomic action SRL and theresistance level to be attributed is represented as parameter ofSRL. Thereby, the first rule of DLV is mapped as:

DLV1 = %MS(100I,181C/I/L,188L,230L,236L;1;null) GO

%MS(190A;0;0) GO

SRL(Resistant)This expression indicates two sequential executions of MS

followed by the execution of SRL. The first execution ofMSseeks for the presence of mutation 100I,181C/I/L,188L,230L,236L. If a minimum of one mutation, without maximumlimitation, is found the MSreturns a true value. Other wise, MSreturns a false value. After that, the operator % evaluates the MSreturn. If the returned value is true, the silent action GO indicatesthat the verification must continue and the operator releases thesecond execution ofMS. In case of false value, the rule executionis finished, because the first rule condition was not satisfied. Ifthe second MS execution is released, it will seek for the presenceof mutation 190A, if a minimum and maximum of zero mutationsare found, this execution of MS is finished, and the true value is

returned. At the end of second MS execution the operator %evaluates the MS return. If the returned value is true, theexecution of SRL is released. Otherwise, the rule execution isfinished, once the second rule condition was not satisfied.Considering that, the execution of SRL is released, the analysis

process represented by DLV1 is set as resistance level is set asResistant and it is finished.

Follow the describe process, the others three rule of DLV aremapped as:

DLV2 = ( %MS(225H, 227L; 1; null)GO + %MS(106A, 103N; 1 ;null)

GO ) SRL(Intermediate)

DLV3 = %MS(106A/M, 103N/H/T/S/V;1; null) GO %MS(190A, 225H,

227L; 0; 0)GO SRL(Intermediate)

DLV4 = %SM(190E; 1; null)GO SRL(Resistant)

After the mapping of the four DLV rules, we must define theprocess to represent the complete analysis of DLV, as DLV =(DLV1||DLV2||DLV3||DLV4) RS. This expression indicates thatrules can be executed simultaneously followed by asynchronization of the results. The version 4 of BrazilianInterpretation Algorithm analyses twenty one drug rules and it can

be represented in process algebra as BAI_V4 =(ABC||DDI||3TC||D4T||TDF||DDC||AZT||

AZT_3TC||DLV||EFV||NVP||APV||IDV||LPV/r||NFV||RTV||SQV||ATV||

APV_r||SQV_r||IDV_r).

Thereby, each algorithm rule can be executed in parallelpathway; where each pathway analyzes one drug with its set ofrules. And each drug rule have its actions executed according withthe operators definition.

The mapping of ANRS and REGA algorithms follow the same

steps used to mapping Brazilian Algorithm rules. However, themapping of HIVdb rules needs the actions SS(SetScore) andSC(ScoreClassification) to mapping of score attribution andclassification of drug resistance rule according the drug score,respectively. Follow, we present part of XML that defines theHIVdb rules to DLV drug.

DLV

SCORE FROM (98G => 5,

100I => 40, 100V => 10,

...

318F => 50

)

Using the actions SS and SC, this rule is mapped as:

DLV = (%MS(98G;1;null) GO SS(5) + %MS(100I;1;null) GO

SS(40) + %MS(100V;1;null) GO SS(10) + %MS(101E/P;1;null) GO

SS(5)+ + %MS(318F;1;null) GO SS(50) ) SC

After the successful execution of each MS action, the action SSadds the indicated score to the total drug score. Then, at the endof expression, the action SC attributes the drug resistance level

based on the total drug score.As presented, the atomic actions can be composed using PA

operators to form a process that represents one drug resistancerule. After that, several drug resistance rules can be combined intoa process representing the drug resistance analysis of the drug. Atlast, the processes of each drug are joined using de PA operatorsinto a main process to define a complete drug resistancealgorithm.

The comparison between algorithms comprehends theconcurrent execution of chosen algorithms and synchronized bythe action ARC that compares the algorithms results, as in:

P = (HIVdb||ANRS||Brazilian_Algorithms)ARC

Not only mutations can be analyzed in this process but also

others parameters, such as: treatment adherence, drugs used intreatment, time of drug treatment and laboratorial exams results.Each of these parameters can be encapsulated into an atomicaction or into a process and be introduced in the analysis processof drug and/or algorithm.

6. HIVdag softwareWe have developed HIVdag. It is a software for creation,execution and comparison of HIV Genotypic Drug ResistanceTests. The HIVdag is based on concepts of process algebra which

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formally describes system behavior and uses NPDL for managethe execution of PA expressions. It was developed using Ruby onRails (www.rubyonrails.org), PostgreSQL database(www.postgresql.org) and a web services integrated to NPDL

interpreter called NavigationPlanTool[3].

The HIVdag offers user friendly interfaces to define thealgorithms rules, Figure 2. At the first part of interface, the userinforms the algorithm of reference, drug of reference, result type(Resistance Level or Score) and rule result according with resulttype selected. At the second part, the user defines the mutationsto be analyzed and their associations. It is made using thefollowing options: occurrence type (presence of or absence of),Min Mutation and Max Mutation representing the minimum andthe maximum number of mutations matches to be considered and

the set of mutations to be searched. Each part of rule can becomposed using parenthesis and logical operators (AND,OR).Using this interface, the user doesnt need know about processalgebra or NPDL because the rules definition is made in thesimilar way of user understand it. After the definition of all rules,

the algorithm is automatically generated and released to use. The

rule represented at Figure 2 is the rule to drug DLV referents toBrazilian Algorithms Version 4, which is automatically mappedto the following NPDL expression, DLV1 = (( %MS(106A/M, 1, null)GO + %MS(103N/ H/T/S/V,1,null) GO) %MS(190A, 225H, 227L, 0,0)

GO) SRL(R)

The analysis result can be visualized in a web page, as in (http://clinmaldb.usp.br:8083/hiv/resistencia/resistencia.html),export to file in XML/CSV Format or integrated to DBCollHIV asin the bottom of Figure 3, where the drug and its respective

Figure 2 HIVdag interface to drug resistance rules definition

Figure 3. Sample of HIVdag integrated into DBCollHIV

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resistance level are reported.

The HIVdag also supports analysis of a large set of patient andgenerates a drug resistance profile of them, as in Figure 4. Thistype of report is useful to measure the impact of new algorithmversions on a group of patients in treatment and to help in thedecision process of drug acquisition, which is important to HIVgovernment drug distribution programs, as in Brazil.

Figure 4. HIVdag drug resistance profile report, resulted from[2]

7. CONCLUSION

In this paper, we presented an alternative for mapping ofgenotypic drug resistance algorithms rules using expressions of

process algebra. The algebraic representation of drug resistancerules allows a formal reasoning about the rules and provides thecorrectness in automatic generation of software for drug

resistance analyses. To complete this approach, we develop theHIVdag software that uses the process algebra expressions todefines, generates, executes and compares genotypic drugresistance test software. This work offers to the researcher asimple, fast and precise way to explore the differentinterpretations for mutations associated to drug resistance withoutworry about software development.

Our ongoing researches include a new interface to allowadvanced users to add and analyze new parameters into the drugresistance rules, for instance: virus subtype, used drugs by

patients and laboratorial exams results.

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DOI: 10.1093/jac/dkh021

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