Algorithm and clinical validation M. Obermeier 4/2007 - - PowerPoint PPT Presentation

• M. Obermeier 4/2007

Algorithm and clinical validation

• M. Obermeier 4/2007

Interpretation-systems

Free available: Rule based:

• ANRS
• HIVdb
• REGA

Truly bioinformatics based:

• geno2pheno[resistance]
• geno2pheno[THEO]

• M. Obermeier 4/2007

Interpretations-Systeme

commercial: Rule based :

• TrueGene HIV-1
• ViroSeq

Truly bioinformatics based:

• Virtual phenotype

• M. Obermeier 4/2007

Yet another algorithm?

German initiative for standardization predict clinical success not phenotype Integration of combination therapies

(resensitising effects)

Rule based systems can be faster

adapted to new drugs and easier updated than bioinformatic approaches (need for data!)

• M. Obermeier 4/2007

HIV-GRADE base

Experts opinion literature genotype-phenotype correlations genotype-virtual phenotype correlations (geno2pheno) different databases consisting of treatment, genotype

and clinical outcome

Scientific board meeting twice a year actual algorithm version 04-2007

• M. Obermeier 4/2007

Special characteristics of HIV-GRADE

Explicit results for resensitising effects

_SP-nomenclature (selective pressure)

Results for boosted and non-boosted PIs 5 level classification: Hypersusceptible Susceptible Limited susceptibility Intermediate resistance Resistance

• M. Obermeier 4/2007

Basis of the HIV-GRADE internet-tool

HIV-Alg module from Stanford-HIVdb PERL-source-code is freely available Software is in development since

1999

Algorithm Specification Interface (ASI)

• M. Obermeier 4/2007

send sequences to geno2pheno

geno2pheno report

Workflow

sequences mutation-lists

identify genes alignment on Consensus B Sequence extraction of mutations

rule-based analysis detailled output batch output

• M. Obermeier 4/2007

Number of rules

ANRS 89 REGA 82 HIVDB 18 scoring-rules (+60 comments) GRADE 303

• M. Obermeier 4/2007

HIV-GRADE TDF

Resistance Intermediate Limited susceptibility 65R 69 ins (41L or 210W) + 2

• ut of (67N, 70R,

219Q/E) 41L + 210W + 215 F/Y 4 out of (41L, 67N, 70R, 210W, 215 F/Y, 219Q/E) 2 out of (41L, 210W, 215 F/Y) 67N + 70R+ 219Q/E 70E 41L 215F/Y 2 out of (67N, 70R, 219Q/E) 151M

• M. Obermeier 4/2007

HIV-GRADE DRV

Resistance Intermediate Limited susceptibility 5 out of (11I, 32I, 33F, 47V, 50V(x2), 54M (x2), 54L, 73S, 76V (x2), 84V, 89V) 4 out of (11I, 32I, 33F, 47V, 50V(x2), 54M (x2), 54L, 73S, 76V (x2), 84V, 89V) 3 out of (11I, 32I, 33F, 47V, 50V(x2), 54M (x2), 54L, 73S, 76V (x2), 84V, 89V)

• M. Obermeier 4/2007

Algorithm Specification Interface (ASI)

Rule-based algorithms can be

described using xml-syntax

Xml-Files available for Stanford-

HIVdb, ANRS and REGA HIV-GRADE can be described in a compatible format.

• M. Obermeier 4/2007

Rule example

<RULE> <CONDITION> EXCLUDE 65R AND (SELECT ATLEAST 2 AND NOTMORETHAN 2 FROM (74V,181C,184V)) AND (SELECT ATLEAST 5 FROM (41L,67N,70R,210W,215FY,219QE)) AND (SELECT ATLEAST 2 AND NOTMORETHAN 2 FROM (41L,210W,215YF)) </CONDITION> <ACTIONS> <LEVEL>5</LEVEL> </ACTIONS> </RULE>

• M. Obermeier 4/2007

Sequence entry form

• M. Obermeier 4/2007

Mutation list entry form

• M. Obermeier 4/2007

Internet Tool output

HIV-1 subtype included sequences common informations all mutations resistance associated mutations drugs all scored mutations comments scored mut results

• M. Obermeier 4/2007

www.hiv-grade.de

Clinical Validation

n=365 5 centers Active drug score from 0 (R) to 1 (S) (ADS) Inclusion criteria Treatment failure Genotype Treatment before and after change VL 0-12 weeks before change VL change 8-16 weeks after change in

treatment

• M. Obermeier 4/2007

Treatment

50 100 150 200 250 300 AZT ddI ddC d4T 3TC ABC TDF FTC NVP DLV EFV SQV RTV IDV NFV APV LPV_r ATV TPV_r T20 r

drug

number

before change after change

• M. Obermeier 4/2007

Active drug score (ADS)

transformation of a qualitative

statement into a quantitative factor

Resistant => 0 Intermediate => 0.33 limited susceptibility => 0.66 Susceptible => 1 Hypersusceptible => 1.33 Sum of all given drugs

• M. Obermeier 4/2007

Clinical validation

GRADE HIVDB REGA ANRS False positive rate True positive rate

• M. Obermeier 4/2007

Simple linear regression

• Simple model:

b ADS a VL VL

change before change after

+ Δ = * ) log(

_ _

• M. Obermeier 4/2007

simple linear regression HIV-GRADE

• 1

1 2 3

• 2

2 4

ΔADS

ΔLOGVL

R2=0.12

• M. Obermeier 4/2007

simple linear regression Stanford HIVdb

• 2
• 1

1 2 3

• 2

2 4

R2=0.13 ΔADS

ΔLOGVL

• M. Obermeier 4/2007

simple linear regression ANRS

• 2
• 1

1 2 3 4

• 2

2 4

ΔADS

ΔLOGVL

R2=0.07

• M. Obermeier 4/2007

simple linear regression REGA

• 2
• 1

1 2 3

• 2

2 4

ΔADS

ΔLOGVL

R2=0.13

• M. Obermeier 4/2007

Multiple linear regression

b ADS a VL VL

drugs v drug drug change before change after

+ Δ = ∑

= # 1 _ _

* ) log(

ν ν

• M. Obermeier 4/2007

Multiple linear regression results

In this cohort none of the algorithms

can correctly predict the resistance against ABC.

HIVdb and GRADE are good in

predicting APV resistance (whereas ANRS is not)

• M. Obermeier 4/2007

simple linear regression HIV-GRADE (w/o ABC)

• 1

1 2 3

• 2
• 1

1 2 3 4

ΔADS

ΔLOGVL

R2=0.17

• M. Obermeier 4/2007

Multiple regression HIV-GRADE (w/o ABC)

• 1.0
• 0.5

0.0 0.5 1.0 1.5 2.0

• 2
• 1

1 2 3 4

ΔADS

ΔLOGVL

R2=0.28

• M. Obermeier 4/2007

Correction of ADS with VL before treatment change

1 2 3 4 5

• 2

2 4

ΔLOGVL

ΔADSVLco R2=0.36

HIV-GRADE association

Thomas Berg, Medizinisches Labor Dr. Berg, Berlin Patrick Braun, PZB, Aachen Martin Däumer, Institut für Virologie, Köln Josef Eberle, Pettenkofer-Institut, München Robert Ehret, PZB Aachen Rolf Kaiser, Institut für Virologie, Köln Nils Kleinkauf, Charité, Berlin Klaus Korn, NRZ für Retroviren, Erlangen Harm Müller, Fenner-Labor, Hamburg Martin Stürmer, Institut für Medizinische Virologie, Frankfurt Hauke Walter, NRZ für Retroviren, Erlangen