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Trajet d'une expatriée : de la phylogénie du VIH au traitement de la grippe, et de Paris à San Francisco Colombe Chappey DEA 1986, PhD 1992.

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Presentation on theme: "Trajet d'une expatriée : de la phylogénie du VIH au traitement de la grippe, et de Paris à San Francisco Colombe Chappey DEA 1986, PhD 1992."— Presentation transcript:

1 Trajet d'une expatriée : de la phylogénie du VIH au traitement de la grippe, et de Paris à San Francisco Colombe Chappey DEA 1986, PhD 1992

2 Mod é lisation DEA 1986 Essais Cliniques Bioinformatique Reconnaissance de Formes (These 92) Epid é miologie Moleculaire Statistiques Cliniques Analyse dimages Programmation (Computer Science) Transmission de la grippe Personalized Health Care (Soins personnalisés) Analyse Exploratoire Bio-marqueurs predictifs

3 Mod é lisation DEA 1986 Essais Cliniques Bioinformatique Reconnaissance de Formes (These 92) Epid é miologie Moleculaire VIH Statistiques Cliniques Analyse dimages Programmation (Computer Science) Transmission de la grippe Personalized Health Care (Soins personnalisés) Analyse Exploratoire Bio-marqueurs predictifs Au cours de mon trajet…

4 Partager mon experience Transitions – –de la recherche publique en France aux Etat-Unis – –De lAcademic au privé – –de la petite Biotech a la grosse Pharma Données: Explosion des données genetiques disponibles – –Nouvelles technologies de sequencages Limportance du to think outside the box (en dehors de sa bulle) – –Position unique du bioinformaticien/biostatisticien entre données et idées Opportunities is often missed because it is dressed in overalls and looks like work (Thomas Edison}

5 Reconnaissance de motifs appliquée a la comparaison de sequences biologiques …A G G T T G C… …A G G T C… Comparaison de séquences nucleiques/proteines -> Alignement des éléments/motifs en commun -> pondérer les différences/mutations et les insertions/deletions

6 Comparaison de séquences biologiques de Virus dimmunodéficience Comparaison de – –9 séquences de VIH type 1 – –1 séquences VIH type 2 – –5 séquences de VIS Le nombre de sequences de VIH a tres vite augmente. Certaines séquences sont plus similaires que dautres 1988

7 MASH : Algorithme dalignement de plusieurs s é quences Chappey C, Danckaert A, Dessen P, Hazout S. MASH an interactive multiple alignment and consensus sequence construction. Comp. Applic. Biosci. 1991; 7:

8 Applications Chappey C, Danckaert A, Dessen P, Hazout S. MASH an interactive multiple alignment and consensus sequence construction. Comp. Applic. Biosci. 1991; 7: Homogénéité et hétérogénéité par region Distance entre séquences Classification time

9 Cas du Dentiste

10 Prediction de Structure/function de la Proteine dEnveloppe du VIF Pancino G, Chappey C, Saurin W, Sonigo P. B epitopes and selection pressures in feline immunodeficiency virus envelope glycoproteins. J. Virol. 1993; 67: Pancino G, Fossati I, Chappey C, Castelot S, Hurtrel B, Moraillon A, Klatzmann D, Sonigo P. Structure and variations of feline immunodeficiency virus envelope glycoproteins. Virology 1993; 192: Profile of structural constraints= based on quantification of amino acid replacements Selection for change = Profile of the ratio of nonsynonymous to synonymous change proportions (nsi/si, si)

11 Bilan des années de These (+) Tremplin pour les collaborations Institut Pasteur, France Agence Nationale Recherche Sida (ANRS) Institut Cochin de Genetique Moleculaire (ICGM) HIV database de Los Alamos National Laboratory, NM (+) Publications# Méthodes2 Application du logiciel dalignement – –Human immunodeficiency virus type 1 4 – –Transmission HIV mother-infant5 – –Simian / human T-cell lymphotropic virus type 1 3 – –Simian immunodeficiency virus 1 – –Feline immunodeficiency virus FIV2 (-) Occasions manquées Commercialisation du logiciel dalignment (alors que CLUSTAL…) Analyses non-publiées

12 National Center Biotechnology Information (GenBank) National Institutes of Health

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14 Histoire de GenBank et NCBI BLAST (Basic Local Alignment Search Tool) international computer database of nucleic acid sequence data – Los Alamos Natl Lab, NM (NSF) 1979 Wilbur and Lipman Algorithme de recherche de similarites entre sequences GenBank demenage a NIH Human EST Human Genome

15 Programmation dun outil dannotation et de Soumission de Séquences Biologiques a GenBank La publication de nouvelles séquences biologiques nécessite de les rendre publiques -Avant, elles etaient publier dans les journaux scientifiques -Avec GenBank, elles sont envoyées par au service qui faisait les annotations et leur associait un numéro dAcces (Accession Number) -Besoin doutil informatique permettant aux biologistes dannotater leur séquences avant de les envoyer -Types de séquences -Gene codant (CD) -> simple soumission -EST (Expressed Segment T) -> soumission en batch -Population de Séquences -> soumission des séquences alignées

16 Sequin: Soumission de Sequence aux DB genetiques 1995

17 Editeur dAnnotation de Sequences

18 Editeur dAnnotation de Sequences Alignees Wheeler DL, Chappey C, Lash AE, Leipe DD, Madden TL, Schuler GD, Tatusova TA, Rapp BA. Database resources of the National Center for Biotechnology Information.Nucleic Acids Res Jan 1;28(1):10-4.

19 PopSet de GenBank

20 CN3D Viewer de Structure de Protéines Wang Y, Geer LY, Chappey C, Kans JA, Bryant SH. Cn3D: sequence and structure views for entrez. Trends Biochem Sci Jun;25(6): Marchler-Bauer A, Addess KJ, Chappey C, Geer L, Madej T, Matsuo Y, Wang Y, Bryant SH. MMDB: Entrez's 3D structure database. Nucleic Acids Res. 1999;27(1):240-3.

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22 Bilan des années NIH (+) Acquisition de connaissances dans un institut de renommée internationale Data format: ASN-1 (Abstract Syntax Notation One) – –Format de répresentation de données ISO permettant linteroperabilité entre plateformes et représentation de données hétérogenes. – –Convertie en XML Programmer en C/C++, Web server, Travailler dans le milieu academic américain – –Données et programmes sont disponibles au public (QC) ftp.ncbi.nih.gov (-) Occasion manquée (ou non) lopportunité de travailler sur le Génome Humain

23 1998 NCBI - Whats Next? Phénotype: caractères observables d'un organisme – –Gene expression profiling: (par Microarray Affymetrix, Stanford) sur RNA, comparaison de lexpression de génes, dans différents types cellulaires (traités non- traités…) – –SNPs / DeCode… – –HIV Drug Resistance Database in Stanford Données cliniques: occurrence et évolution de maladies – –dbGaP: SNPs et maladies genetiques – –Allele mutants et (partial) resistance a linfection par le VIH – –Reponse clinique aux antiviraux et la presence de virus resistance

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25 ViroLogic Inc Mission: "The right therapy to the right patient at the right time. ~10 antiviraux anti-VIH Business Model simple: Hopital + Laboratoire dAnalyses DB Algorithm Patient Resistance Report ~100 employes, 80 dans la laboratoire danalyse, 20 dans la recherche, ladministration…

26 Test de Résistance du VIH aux antiviraux 2 approches : Phénotype-Génotype Translation Polyprotein Test de Genotype determine la sequence de la proteine cible de lantiviral Un algorithme reconnait les mutations cles qui diminue la function de la proteine Test de Phenotype teste la capacite de chaque antiviraux de diminuer la FONCTION de la protein virale cible de lantivirale. Clivage Processing Folding

27 Database de ViroLogic Génotype Phénotype IC50 fold change Response Clinique Reduction de la charge virale Small studies (n ~ 100s) clinical cut-off pour le phenotype Small studies (n ~ 100s) PT-GT database (n > 100,000) Identification de mutation associees a la resistance du VIH aux antiviraux

28 codon 184 R(=A/G)TG -> M/V Calling Bases and Mixtures from Raw Sequence (ABI Chomatogram) Data

29 Zolopa, A. R. et. al. Ann Intern Med 1999;131: Fréquences des Mutations par Réponse virologic apres 2 semaines

30 Régles dinterprétation du Genotype Resistance Collaborative Group (DeGruttola et al., 2000) Initially used in GeneSeq assay, with some modifications Expert Consensus, derived for meta-analysis (not intended for clinical use) UK Drug Resistance Database (2006) Stanford (R. Shafer), HIVResistance.com Comprehensive, updated frequently, good notes International AIDS Society IAS (Hirsch et al., JAMA 2000; 2008 updates) Expert consensus; updated frequentlyhttp://iasusa.org

31 Interprétation du Génotype viral V82A V32I L90M A71V I47V I84V V82F M46I G48V D30N I50V I54V N88S. |. |. |. |. |. |. |. |. | Wild-type: PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF Patient PQIALWQRPLVTIKIGGQLKEALLDTGADNTILEEMNLPGRWKPKMVGGIGGFVKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF V32I I47V D30N T4A I54V Patient virus genotype Drug Resistance associated Mutations (RAMs) Regles dinterprétatio n du Génotype D30N Resistance to NPV I47V, I54V Intermediate resistance to fAMP, TPV

32 How are Drug Resistance Mutations Identified? In vitro selection, clinical studies, site-directed mutagenesis BUT…In vitro selection, clinical studies, site-directed mutagenesis BUT… Drug resistance mutations identified during drug development (esp. in vitro) may not be the most relevant mutations in clinical settingsDrug resistance mutations identified during drug development (esp. in vitro) may not be the most relevant mutations in clinical settings Mutations that are sufficient to cause drug resistance may not be necessary to effect drug resistanceMutations that are sufficient to cause drug resistance may not be necessary to effect drug resistance Cross-resistance due to mutations selected by related drugsCross-resistance due to mutations selected by related drugs

33 Mesure de Résistance Phenotypique IC50: Concentration of drug required to inhibit viral replication by 50%. Fold Change = _IC50 patient_ IC50 reference Reference: wild-type reference strain NL4-3 Chappey 02/23/09 % inhibition Log concentration of drug

34 Analysis Univariée des mutations Wild-type Mutant, mixed Mutant - Fishers Exact test with the Benjamini correction for multiple tests (for each mutation) -Wilcoxon–Mann–Whitney test For comparison of median FC To determine which mutations are associated with High or Low TPV IC50 Fold Change

35 Variabilité de la résistance au SQV des virus avec L90M 1639 samples, excludes those with >1 mutation at 30, 48, 50, 82, 84 or mixtures at these positions; 35% are <2.5, 69% are <10

36 TPV Susceptibility in Groups of Samples categorized by the TPV Mutation Score N: R 2 =0.51 (Total=1411) lower clinical cutoff

37 PT-GT Discordances N: (Total=1411) lower clinical cutoff At a mutation score cutoff of 4 total discordance was 18.1% PT-R GT-S PT-S GT-R PT-R GT-R PT-S GT-S

38 Performance of the New Tipranavir Mutation Score Validation Dataset (Total=1845) N: At a mutation score cutoff of 4 total discordance was 16%

39 39 Trade off between Model Complexity, Predictive accuracy and Biological Descriptive Meaning Increasing Biological Descriptive Meaning Model Predictive Accuracy Model complexity Genotype Rules ML Regression SVM Genotype Rules and Mutation Score MLR: Multiple Linear Regression SVM: Non-linear Support Vector Machine Neural Network

40 De la bulle des Dot-Com … aux Subprimes Chart of NASDAQ closing values from 1994 to 2008 March 10, 2000 Introduction en bourse licenciement #1 NIH Grant 400K NIH Grant 400K Grant 2m licenciement #2 Embauche 2009

41 Small Business Innovation Research Grants NIH GrantsTitleDatesResume$ SBIR Phase I HIV Phenotype/Genotype Database Resources Aug – July 2005 This grant supported the development of a relational database populated with phenotypic and genotypic drug resistance data collected from a large number (>80,000) of HIV-1 patient isolates. Statistical and analytical query tools were developed to derive highly accurate genotypic-phenotypic correlations SBIR Phase I HIV-1 Envelope phenotype/genotype database resources. May 2004 – Apr The goal of the project was to create, populate and exploit an HIV-1 envelope database comprised of high quality data derived from genotypic and phenotypic assays recently developed at Monogram Biosciences to characterize and evaluate entry inhibitors and vaccines SBIR Phase II The Development of a Web-based Data Retrieval System for HIV Therapy Guidance June 2007 – May 2010 The goal of the project was to implement a web-based database retrieval system to search the Monogram HIV drug resistance database to support clinical management of HIV/AIDS patients and development of novel therapeutics

42 Bilan (+) Organisation du travail dans un societe privee – –Respect des délais – –Coaching des collaborateurs – –Concrétisation de projets i.e. rédiger des projets aboutissant a un financement, et donc a une réalité (!) Application des connaissances acquises – –Utilisation de R, Perl … (-) Occasions manquées – –Insuffisante priorité accordée a ma carriere au sein de la société (a la rue vs. promue)

43 Genentech Roche Senior Biostatistician Genentech : employes – –Produits : les anticorps therapeutiques – – founded tablets ® 2010 Protropin ® 1990 Actimmune

44 Page 20 Histoire de la collaboration entre Genentech et Roche At the Roche Institute of Molecular Biology a pure interferon alfa is isolated. Roche Nutley and Genentech start work on a joint project to produce a genetically engineered version of the substance. Genentech and Roche complete a $2.1 billion merger, and Genentech continues to trade on the NYSE. Roche exercises its option to cause Genentech to redeem its outstanding special common shares not owned by Roche. Roche announces its intent to publicly sell up to 19 percent of Genentech shares and continue Genentech as a publicly traded company on the NYSE (symbol: DNA) with independent directors. Roche signs license agreement to sell Genentechs products in ex-U.S. markets. Roche and Genentech announce that they have signed a merger agreement, and Genentech becomes a wholly owned member of the Roche Group. Pour maladies virales -HIV: Saquinavir SQV -HCV: Inhibiteurs de polymerase et de protease en Phase 2 -Grippe: Tamiflu (post- marketing)

45 Personalized Health Care - Are We there Yet?

46 46 What is our role as Statisticians? How/when do we get involved? The Drug/Diagnostic Co-development Establish Dx hypothesis Identify Dx marker candidates Preclinical validation Develop clinical Dx Strategy (DxST) Develop in house assays in Ph I Assess need for Dx Initiate selected programs Phase I/II/III Developmental Research Early stage research Late stage research Dx Biomarker validation Develop validated Dx assay with partner Phase III strategy and implementation Risk mitigation plans Research/Research Dx Development Dx/PDB Companion Dx Drug Companion Dx Test + Mark Lackner

47 Ce qui me reste a faire… Epouser un milliardaire americain – –George Soros – –Warren Buffet – –Donald Trump Monter une start-up Biotech – –Et la revendre a Pfizer pour 18 mds dEuros – –Ensuite racheter lUPMC Chirurgie esthetique GIS

48 ArcGIS – Epidemie de grippe

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50

51 Back-up Slides

52 52 The Drug/Diagnostic Co-development Establish Dx hypothesis Identify Dx marker candidates Preclinical validation Develop clinical Dx Strategy (DxST) Develop in house assays in Ph I Assess need for Dx Initiate selected programs Phase I/II/III Developmental Research Early stage research Late stage research Dx Biomarker validation Develop validated Dx assay with partner Phase III strategy and implementation Risk mitigation plans Research/Research Dx Development Dx/PDB Companion Dx Drug Companion Dx Test + Mark Lackner

53 Virus susceptibility to antiretroviral drugs allows for the control of the infection HIV resistance: occurs when HIV changes or mutates so it can escape the effect of an antiretroviral drug -> choosing an ART regimen in light of resistant HIV -> resistance testing Antiviral drug susceptibility correlates with virologic outcome Deeks S. JID, 1999;179:1375–81

54 Agenda Phenotype (PT) and genotype (GT) assays require bioinformatics- based interpretation algorithms to interpret a patient virus as resistant (R) or susceptible (S) to a drugPhenotype (PT) and genotype (GT) assays require bioinformatics- based interpretation algorithms to interpret a patient virus as resistant (R) or susceptible (S) to a drug Phenotype assay measure of the ability of a virus to replicate in presence of a drugPhenotype assay measure of the ability of a virus to replicate in presence of a drug –Cut-offs are used to categorize the PT measure as drug Resistant or Susceptible Genotype assayGenotype assay –provides the list of mutations present in a virus pool and differing from the wild-type drug-sensitive virus –An algorithm is used to recognize the key mutations associated with resistance from patient-specfic polymorphism

55 Application using RESIST trial for tipranavir TPV Boehringer Ingelheim Protease Inhibitor Aptivus® (tipranavir)Boehringer Ingelheim Protease Inhibitor Aptivus® (tipranavir) The RESIST trial evaluated Aptivus® (tipranavir) in treatment- experienced HIV-1 infected patientsThe RESIST trial evaluated Aptivus® (tipranavir) in treatment- experienced HIV-1 infected patients Baseline samples selected were:Baseline samples selected were: 1.The study regimen did not include enfuvirtide 2.Where the study PI/r was not a continuation of the prestudy PI/r Endpoint: Viral Load reduction at week 4Endpoint: Viral Load reduction at week 4

56 Phenotype Assay: Technical Process 1.Isolating the viral RNA for Protease and Reverse Transcriptase 2. Constructing the test vector 3. Producing and testing the virus PR Patient-Derived Segment Indicator Gene RTIN LUCIFERASE RESISTANCE TEST VECTOR DNA Petropoulos CJ, ANTIMICROBIAL AGENTS AND CHEMOTHERAPY, Apr. 2000, p. 920–928

57 Phenotype Resistance Interpretation Clinical cut-off -drug level at which a patients probability of treatment failure increases. -Based on outcome data from clinical trials. Biological cut-off -based on natural variability of wild-type viruses from treatment-naïve HIV-1 infected patients - 99th percentile of the IC50 FC distribution -Requires a large number of wild-type samples. Assay/technical cut-off -Based on assay variability with repeated testing of patient samples Clinical Relevance Highest Moderate

58 HIGHLY CONFIDENTIAL -- NOT FOR DISTRIBUTION 58

59 Conclusion 1 2 week process that may fail in case of viruses with low replication capacity2 week process that may fail in case of viruses with low replication capacity PT may not capture the resistance in case of minor populations of resistant variants that are selected by the drug pressurePT may not capture the resistance in case of minor populations of resistant variants that are selected by the drug pressure Phenotypic Cutoffs caveatsPhenotypic Cutoffs caveats –Biological cutoffs are assay specific –Clinical cutoffs are method dependent

60 Genotype assay and Rule-based interpretation PROTEASE (1-99) and REVERSE TRANSCRIPTASE (1-305)PROTEASE (1-99) and REVERSE TRANSCRIPTASE (1-305) Validated for samples with viral loads 500 copies/mLValidated for samples with viral loads 500 copies/mL Use of multiple primers : Redundancy of 2 to 5 sequence fragmentsUse of multiple primers : Redundancy of 2 to 5 sequence fragments Detects all mutations and mixtures from co-existing populations of virus (as minor as 10-30%)Detects all mutations and mixtures from co-existing populations of virus (as minor as 10-30%) Clone ID Virus tropism Peptide sequence E04_101157_c07 R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGDIRQAHC E04_101157_c08 R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGDIRQAHC E04_101157_c09 X4 CTRPSNHTRKRVTLGPSRVYYTTGEITGDIRRAHC E04_101157_c13 X4 CTRPSNHTRKRVTLGPSRVYYTTGEITGDIRRAHC E04_101157_c19 X4 CTRPSNHTRKRVTLGPSRVYYTTGEITGDIRRAHC E04_101157_c21 R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGDIRQAHC E04_101157_c23 R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGDIRQAHC E04_101157_c25 R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGNIRQAHC E04_101157_c26 R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGDIRQAHC E04_101157_c30 X4 CTRPSNHTRKRVTLGPSRVYYTTGEITGDIRRAHC E04_101157_c34 R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGDIRQAHC Patient virus population (quasispecies)

61 HIGHLY CONFIDENTIAL -- NOT FOR DISTRIBUTION 61

62 HIGHLY CONFIDENTIAL -- NOT FOR DISTRIBUTION 62

63 HIGHLY CONFIDENTIAL -- NOT FOR DISTRIBUTION 63

64 HIGHLY CONFIDENTIAL -- NOT FOR DISTRIBUTION 64

65 HIGHLY CONFIDENTIAL -- NOT FOR DISTRIBUTION 65

66 HIGHLY CONFIDENTIAL -- NOT FOR DISTRIBUTION 66

67 Conclusions 2 - Genotype algorithms evolve over time with increased clinical experience and more clinical data on cross-resistance and reverse susceptibility -Use of large database combining phenotype and genotype results to generate more accurate genotype interpretive algorithms -Minimizing PT-GT Discordance : tradeoff between false negatives (PT-S GT-R) and the false positives (PT-R GT-S) -PT-R GT-S -New mutations -Cross-resistance -PT-S GT-R -Suppression of resistance or re-sensitization -Presence of mixtures -Use of more complex prediction models yield to more accurate algorithms but with less biological descriptive meaning

68 Monogram Technologies for Resistance Testing GeneSeq Sequencing Resistance Mutations Prediction of Drug Susceptibility Rules for genotype Interpretation PhenoSense Recombinant Virus Transfection Measure of Drug Susceptibility Infection Patient virus PR-RT DNA RT-PCR Vector Assembly Categorization of Drug Susceptibility Categorize R if FC > cut-off S if FC < cut-off PR Patient-Derived Segment Indicator Gene RTIN LUCIFERASE RESISTANCE TEST VECTOR DNA Pheno-Geno Database

69 Discussion Interpretation of phenotypic (cutoffs) and genotypic (algorithms) resistance assays is an evolving scienceInterpretation of phenotypic (cutoffs) and genotypic (algorithms) resistance assays is an evolving science Large databases of phenotypic and genotypic information are essential tools to understand and improve discordance ratesLarge databases of phenotypic and genotypic information are essential tools to understand and improve discordance rates The use of both types of assay in many cases provides the most complete picture of an individual patients virus resistance profileThe use of both types of assay in many cases provides the most complete picture of an individual patients virus resistance profile

70 Acknowledgements Genotypic testing Phenotypic testing Treatment rounds Utility Increasing Genetic Complexity All my colleagues at Monogram Biosciences (Clinical Reference Laboratory and Research and Development)All my colleagues at Monogram Biosciences (Clinical Reference Laboratory and Research and Development) And my collaborators (Steve Deeks, UCSF, Andy Zolopa, Stanford, Sebastian Bonhoeffer, Swizerland, R. Shafer,Stanford..)And my collaborators (Steve Deeks, UCSF, Andy Zolopa, Stanford, Sebastian Bonhoeffer, Swizerland, R. Shafer,Stanford..)

71 -Biological cut-off: based on natural variability of wild-type viruses from treatment-naïve HIV-1 infected patients (infected by patient who is also drug naïve) -When the treatment history is not known, wild-type virus WT is defined by the absence of any drug-selected mutation in PR or RT: -PR: 23, 24, 30, 32, 33F, 46, 47, 48, 50, 54, 82 (not 82I), 84, 90 -RT: 41, 65, 67, 69 (incl. ins.), 70, 74, 75, 100, 101E or P, 103N or S, 106A or M, 151, 181, 184, 190, 210, 215F or Y, 219, 225, 227, 230, 236 Biological Cut-off: Definition

72 Biological Cut-off for TPV TPV fold change N=2848, no PI or RTI recognized resistance mutations Natural Variation of TPV FC Among Wild-type Samples 99 th percentile = 2.1

73 Genotype Interpretation for Tipranavir (TPV) TPV susceptibility based on genotype uses an algorithm that counts mutations associated with reduced in vitro susceptibility or in vivo virological response. The TPV mutation score was derived from analysis of a limited number of patient samples collected during phase 2 and 3 clinical trials and considers the following mutations: L10V, I13V, K20M, R, or V, L33F, E35G, M36I, K43T, M46L, I47V, I54A, M, or V, Q58E, H69K, T74P, V82L or T, N83D, I84V 1. Kohlbrenner et al., HIV DART, 2004

74 Mutations Associated with PT-R GT-S MutationN mutOdds ratioP-Value I54A* A71L V11L V82T I47V1222.8< G73T L89V I84V3562.2< V32I M36L I D60E K55R L90M7871.3< M46I L10I *underlined mutations in existing TPV mutation score the ratio of % H samples with the mutation to % L samples with the mutation

75 (N= 176) Phenotype-Clinical: Week 4 HIV-1 VL Change vs. Baseline IC50 Fold Change to TPV

76 Probability of response Fold Change Lower clinical cutoff: The IC50 fold change at which the HIV RNA response first begins to decline Upper clinical cutoff: The fold change above which a clinically meaningful HIV RNA response (>0.3 log 10 ) is unlikely Zone of Intermediate Response Clinical Cutoffs: Definitions

77 Clinical Cutoffs: Methods Lower clinical cut-off Comparison of HIV RNA responses between two adjacent groups across a moving IC50 FC cut-off (Kruskal-Wallis test) Upper clinical cut-off 1.Phenotypic susceptibility scoring to account for background effect 2.Define the HIV RNA change attributable to the PI/r 3.Define the fold change associated with an HIV RNA reduction of -0.3 log 10 copies/mL Chappey 02/23/09

78 LCCO: First difference from reference Expanding Window method

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84 LCCO: First difference from reference Fixed Window Method

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86 LCCO: First difference from reference Fixed Window method

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91 TPV fold change N=2848, no PI or RTI recognized resistance mutations Comparing LCCO with the Biological Cut-off Natural Variation of TPV FC Among Wild-type Samples 99 th percentile = 2.1 LCCO = 1.5 In order to minimize misclassification of wildtype isolates as resistant a TPV/r LCO at 2.0 was chosen

92 Clinical Cutoffs: Methods Lower clinical cut-off Comparison of HIV RNA responses between two adjacent groups across a moving IC50 FC cut-off (Kruskal-Wallis test) Upper clinical cut-off 1.Phenotypic susceptibility scoring (PSS) to account for background effect 2.Define the HIV RNA change attributable to the PI/r 3.Define the fold change associated with an HIV RNA reduction of -0.3 log 10 copies/mL Chappey 02/23/09

93 Adjust HIV RNA change attributable to TPV/r % HIV RNA reduction attributable to each drug: TPV 50% PSS=0 UCCO Determination: Calculate the proportion of HIV RNA change attributed to PI/r PSS=1 2 NRTI TPV/r 2 NRTI TPV/r TPV 100%

94 Phenotypic Susceptibility Scoring (PSS)

95 Scatter plots of drug susceptibility versus week 4 HIV RNA change TPV FC (log 10 ) versus unadjusted Week 4 HIV-1 RNA (log 10 ) change, N=176, (R²=0.22, p<0.0001) - 0.3log 10 c/mL Regimen phenotypic susceptibility score (PSS) versus HIV RNA change (R²=0.19, p<0.0001)

96 TPV FC versus Adjusted Week 4 HIV RNA Change

97 Adjusted Week 4 HIV RNA outcomes by TPV susceptibility category

98 What is our role as Statisticians? How/when do we get involved?

99 What is Our Responsibility We are strategic partners – –PHC strategy is part of the Development Plan Embrace the PHC strategy Engage the DST in strategic/prioritization/timelines discussions related to PHC – –Raise the right issues – –Plan for resources Work with DST and your manager Network with the Biomarker Experts/Dx sub-teams – –Be proactive/Stay informed Get Involved!

100 100 What is our role as Statisticians? How/when do we get involved? The Drug/Diagnostic Co-development Establish Dx hypothesis Identify Dx marker candidates Preclinical validation Develop clinical Dx Strategy (DxST) Develop in house assays in Ph I Assess need for Dx Initiate selected programs Phase I/II/III Developmental Research Early stage research Late stage research Dx Biomarker validation Develop validated Dx assay with partner Phase III strategy and implementation Risk mitigation plans Research/Research Dx Development Dx/PDB Companion Dx Drug Companion Dx Test + Mark Lackner

101 PHC strategy Development Strategy PHC Strategy Strong Dx hypothesis No activity in Dx- Strong Dx hypothesis Some activity in Dx- No strong Dx hypothesis Exploratory Stage Development Strategy Patient selection through all phases of development Complex, larger phase IIs with stratification Complex phase IIIs No selection or stratification Possible data mining trap

102 Impact on components of CDP Target product profile – – Parallel development of companion diagnostic Phase I trials – – Selection for quick signal seeking Phase II trials – – Complex issues become more complex – – More unknowns, more questions to answer Phase III trials – – Clinical Validation of Dx – – Design depends on Phase II outcome Selection, stratification or all-comers

103 Phase II Considerations Objective: simultaneous Rx/Dx evaluation Scientific rationale and pre-clinical data - main determinants of the scenario prior to Phase II Statistical considerations – – Co-primary endpoints – – Value added and feasibility of stratification – – Defining cut-offs for continuous biomarker – – Go/No Go decision algorithm Dedicated studies to investigate assay or biomarker properties – –Reproducibility, prevalence, prognostic value

104 Phase III Considerations Study Objective – –Assess/determine risk/benefit – –Clinical Validation of Dx Implementation issues – –Analytically validate Dx assay before applying it to specimens in pivotal trials – –Accruing / prospective stratification based on non-final assay – can result in discordance Analysis method – –Test two hypothesis, All comers Dx positive subgroup Appropriately control for type I error – –Clearly define your decision tree – there are no freebies

105 End of Phase III Decision Criteria Phase III outcome Not statistically significant in all comers Statistically significant in all comers Statistically significant in Dx+ group SELECTION CLAIM All comers claim if no diff. b/w Dx- & Dx+ groups Greater benefit claim if clinically meaningful diff. b/w Dx- & Dx+ Selection Claim if no improvement in Dx- group

106 Old Drugs – New Tests Biomarker not known at the time of study initiation Data not analyzed with that biomarker as part of the hypothesis New scientific advancements/new technologies Biomarker discovery – generation of new hypotheses Prospective-Retrospective Study Exploratory Analysis

107 Prospective/Retrospective Study Completed or post-interim-analysis trial – –Patient samples collected prior to treatment initiation – –Clinical outcomes data unblinded and analyzed without the biomarker data – –Diagnostic hypothesis/analysis plan - prospectively specified – –Analysis is retrospective

108 Components of good biomarker analysis plan Role of randomization - fairness of comparison Marker availability – impact of convenience samples – –Bias due to missing data Marker performance – –Marker performance and prevalence may explain study to study heterogeneity Statistical control of false positive conclusions – – –How many hypothesis – –How many outcomes Model selection – –Over-fitting can lead to bias Validation methods – –Data to generate the hypothesis vs. data to confirm the hypothesis

109 Summary Companion diagnostics are at the heart of personalized health care – –Predictive claims rely on understanding the effect of the drug in biomarker positive and negative patients – –Optimal approach: Adequate and well-controlled trials, prospectively designed to assess risk/benefit in biomarker subgroups – –Late emergence of critical biomarkers for existing drugs - revision of drugs use As strategic partners, we need to be involved in all stages of the co-development process


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