Utilization of Genotype and Phenotype Resistance Tests in Patient Management Richard Haubrich, MD Professor of Medicine University of California San Diego.

Utilization of Genotype and Phenotype Resistance Tests in Patient Management Richard Haubrich, MD Professor of Medicine University of California San Diego

To what is Condi referring?

Case  41 y/o GWM  HIV history (all distant) –zoster –thrush –bacterial pneumonia –cutaneous KS  Medications –azithromycin –fluconazole –oxandralone –sulfamethoxazole./ trimeth

Case  CD4 = 9 (4%)  HIV RNA = 78,900  Current ARV (02- present) –ABC –3TC –TDF –SQV –RTV Past ARV –ZDV/ ddC 93-4 –d4T/ 3TC/ IDV 96-01 –ABC/ ddI/ EFV/ APV/r 01-02

When considering TDF Susceptibility 1. The current phenotype is important in deciding to use TDF 2. Past treatment history and resistance tests suggests reduced TDF activity 3. The M184V mutation re-sensitizes the virus to TDF 4. All of the above

ZDV Resistance and Resensitization 100I L74V/I

Model of NRTI Resistance Parikh et al. JID 2006; 194: 651

215Y/F + other TAMs Model of NRTI Resistance Excision Parikh et al. JID 2006; 194: 651

65R 215Y/F + other TAMs Model of NRTI Resistance Decreased Incorporation Excision Parikh et al. JID 2006; 194: 651

65R 215Y/F + other TAMs Model of NRTI Resistance Decreased Incorporation Excision Phenotypic Antagonism Counter selection Parikh et al. JID 2006; 194: 651

65R 215Y/F + other TAMs Model of NRTI Resistance Multi-NRTI Resistance Decreased Incorporation Excision Phenotypic Antagonism Counter selection Q151M Complex Parikh et al. JID 2006; 194: 651

V90IA98GL100IK101EK101PK101QK103HK103NK103S K103TV106AV106IV106MV108IE138GE138KE138QV179D V179EV179FV179GV179IY181CY181IY181VY188CY188H Y188LV189IG190AG190CG190EG190QG190SH221YP225H F227CF227LM230IM230LP236LK238NK238TY318F DUET: Identification of the ETR Resistance Mutations Of 44 NNRTI mutations studied, 26 NNRTI mutations were present in  5 patients 13 TMC125 RAMs were identified at baseline to have a significant impact on virological response ETR RAM RAM Present

Response according to number of ETR RAMs 0 1 No NNRTI mutations (reference) 2345 Number of ETR RAMs 52 1611216432199 Patients (n) Data are pooled from DUET-1 and -2; Vingerhoets J, et al. Antiviral Therapy 2007:12:S34; RAMs = resistance-associated mutations VL < 50 Overall placebo group 414

Response according to number of ETR RAMs 0 1 No NNRTI mutations (reference) 2345 Number of TMC125 RAMs 52 1611216432199 Patients (n) Data are pooled from DUET-1 and -2; Vingerhoets J, et al. Antiviral Therapy 2007:12:S34; RAMs = resistance-associated mutations VL <50 Overall placebo group 414

Response according to number of ETR RAMs 0 1 No NNRTI mutations (reference) 2345 Number of TMC125 RAMs 52 1611216432199 Patients (n) Data are pooled from DUET-1 and -2; Vingerhoets J, et al. Antiviral Therapy 2007:12:S34; RAMs = resistance-associated mutations VL <50 Overall placebo group 414 CASE = 100I; 103N

Impact of Specific Mutations *No detectable baseline NNRTI RAMs from the list of 44 No mut (ref)* G190A + 0 ETR RAMs G190A + 1 G190A + 2 G190A + 3 G190A + 4 5218462517 9 G190A 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Vingerhoets J, et al. 16 th IHDRW 2007, #32. Response relative to the subgroup with no NNRTI RAMs No mut (ref)*. 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Y181C + 0 ETR RAMs Y181C + 1 Y181C + 2 Y181C + 3 Y181C + 4 52233626178Patients (n) Y181C

Impact of Specific Mutations *No detectable baseline NNRTI RAMs from the list of 44 Vingerhoets J, et al. 16 th IHDRW 2007, #32. Response relative to the subgroup with no NNRTI RAMs No mut (ref)*. 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Y181C + 0 ETR RAMs Y181C + 1 Y181C + 2 Y181C + 3 Y181C + 4 52233626178Patients (n) Y181C

ETR: Determination of clinical cutoffs Winters B, et al. 15 th CROI, Boston 2008, #873  Cutoffs based on response rates in DUET l Clinical cutoffs (CCOs) for ETR of 1.6 and 27.6 FC defined for the vircoTYPE report (20% and 80% loss of wt ETR response)

TPV RESIST: Change in VL at Week 24 According to TPV Score Mutations Median log 10 HIV RNA change from baseline Shapiro. 12 th CROI; 2005: 104 21 mutations at 16 amino acid loci identified: 10V, 13V, 20M/R/V, 33F, 35G, 36I, 43T, 46L, 47V, 54A/M/V, 58E, 69K, 74P, 82L/T, 83D, and 84V PI Mutations: 10I, 13V, 33F, 46I, 47V, 54M, 63P, 77I, 84V, 90M

TPV RESIST: Change in VL at Week 24 According to TPV Score Mutations Median log 10 HIV RNA change from baseline Shapiro. 12 th CROI; 2005: 104 21 mutations at 16 amino acid loci identified: 10V, 13V, 20M/R/V, 33F, 35G, 36I, 43T, 46L, 47V, 54A/M/V, 58E, 69K, 74P, 82L/T, 83D, and 84V Our case = 5 TPV Mutation PI Mutations: 10I, 13V, 33F, 46I, 47V, 54M, 63P, 77I, 84V, 90M

Stanford Algorithm Protease Inhibitors ATV High-level resistance DRV Intermediate resistance FPV High-level resistance IDV High-level resistance LPV High-level resistance NFV High-level resistance SQV High-level resistance TPV High-level resistance

Stanford Algorithm ATVDRVFPVIDVLPVNFVSQVTPV M46I15612 2558 I47V5122051055 I54M101220101215105 I84V251235251235 25 L90M20615201050356 L10I22222222 L33F55505008 Total:825510974631329264

Number of mutations associated with a diminished response to DRV/r and virological response HIV RNA <50 64 50 42 22 10 42 0 20 40 60 80 100 0 (67) 1 (94) 2 (113) 3 (58) ≥4 (41) All (373) Number of DRV mutations (Number of patients) Patients with VL HIV RNA <50 copies/mL at Week 24 (%) MP de Bethune. 15 th DRW 2006, #73

Clinical Cutoffs Applying Breakpoints to a Continuum of Susceptibility Probability of response Fold change at baseline

Clinical Cutoffs Applying Breakpoints to a Continuum of Susceptibility Probability of response Fold change at baseline “Traditional” lower clinical cutoff… fold change above which the probability of clinical response begins to decrease

Clinical Cutoffs Applying Breakpoints to a Continuum of Susceptibility Probability of response Fold change at baseline “Traditional” lower clinical cutoff… fold change above which the probability of clinical response begins to decrease Upper clinical cutoff… fold change beyond which the probability of clinical response is low.

Clinical Cutoffs Applying Breakpoints to a Continuum of Susceptibility Probability of response Fold change at baseline “Traditional” lower clinical cutoff… fold change above which the probability of clinical response begins to decrease Upper clinical cutoff… fold change beyond which the probability of clinical response is low. “ Intermediate Probability of Response”

Patients With HIV-1 RNA < 50 c/mL at Week 24 (%) DUET-1 and -2: Response (< 50 c/mL) According to DRV Fold Change DRV FC 10-40DRV FC > 40 71 DRV FC < 10 56 57 30 44 2 100 75 50 25 246/ 345 200/ 358 73/12939/13231/71 1/67 0 Cahn P, et al. ICAAC 2007. Abstract H-717. Placebo + OBR ETR + OBR

Patients With HIV-1 RNA < 50 c/mL at Week 24 (%) DUET-1 and -2: Response (< 50 c/mL) According to DRV Fold Change DRV FC 10-40DRV FC > 40 71 DRV FC < 10 56 57 30 44 2 100 75 50 25 246/ 345 200/ 358 73/12939/13231/71 1/67 0 Cahn P, et al. ICAAC 2007. Abstract H-717. Placebo + OBR ETR + OBR Our case = DRV FC = 42

Continuous Phenotypic Susceptibility Score (cPSS) Activity of drug defined by relation to cut-off Maximum score = 1 for FC < Lower cut off Minimum = 0 for FC > Upper cut off For drugs where FC > lower but < upper cut off, get partial score (between 0 and 1): Score = Upper CO – patient FC Upper CO – Lower CO

Continuous Phenotypic Susceptibility Score (cPSS) Activity of drug defined by relation to cut-off Maximum score = 1 for FC < Lower cut off Minimum = 0 for FC > Upper cut off For drugs where FC > lower but < upper cut off, get partial score (between 0 and 1): Score = Upper CO – patient FC Upper CO – Lower CO LPV55 – 34= 0.46 55-9

PI Scoring for the Case DrugFCLower CO Upper CO cPSS DRV4210400 DRV4210900.6 LPV349550.46 TPV5.75280.375

CCR5 ANTAGONISTS

R5 Tropism CD4 + CCR5CD4 + CXCR4

X4 Tropism CD4 + CCR5CD4 + CXCR4

Dual Co-receptor Tropism CD4 + CCR5CD4 + CXCR4

R5/X4 (Mixed) Co-receptor Tropism CD4 + CCR5CD4 + CXCR4

HIV-1 Expression Vector: pHIVluc  U3 P A+ HIV envelope a/bc/d Indicator Gene U5 gag pol Luciferase P R  env P R A+ gp120 gp41  U3 Envelope Expression Vector: pHIVenv Tropism Assay

Transfection HIV env expression vector++ HIV genomic luc vector HIV-1 Expression Vector: pHIVluc  U3 P A+ HIV envelope a/bc/d Indicator Gene U5 gag pol Luciferase P R  env P R A+ gp120 gp41  U3 Envelope Expression Vector: pHIVenv Tropism Assay

Transfection HIV env expression vector++ HIV genomic luc vector HIV-1 Expression Vector: pHIVluc  U3 P A+ HIV envelope a/bc/d Indicator Gene U5 gag pol Luciferase P R  env P R A+ gp120 gp41  U3 Envelope Expression Vector: pHIVenv CD4 + CXCR4 + X4 Virus Tropism Assay

Transfection HIV env expression vector++ HIV genomic luc vector CD4 + CCR5 + HIV-1 Expression Vector: pHIVluc  U3 P A+ HIV envelope a/bc/d Indicator Gene U5 gag pol Luciferase P R  env P R A+ gp120 gp41  U3 Envelope Expression Vector: pHIVenv CD4 + CXCR4 + X4 Virus R5 Virus Tropism Assay

Percentage of HIV Co-receptor Usage Study/SourcePopulationNR5D/MX4 Homer cohort 1 Naive97982%18%<1% C & W cohort 2 Naive40281%19%<1% Pfizer 1026 4 Naive 142 8 85%15%<1 MOTIVATE 1 & 2 4 Experienced 256 0 56%41%3% TORO 1/2 5 Experienced61250%46%4% ACTG 5211 6 Experienced39149%47%4% 1 Brumme ZL, et al. J Infect Dis. 2005;192:466-474. 2 Moyle GJ, et al. J Infect Dis. 2005;191:866-872. 4 Coakley E, et al. 2 nd Viral Entry Workshop, Abstract 8 5 Melby et al 13 th CROI 2006 Abstract 233. & Wilkin T, et al. CROI 2006. Abstract 655.

Percentage of HIV Co-receptor Usage 1 Brumme ZL, et al. J Infect Dis. 2005;192:466-474. 2 Moyle GJ, et al. J Infect Dis. 2005;191:866-872. 4 Coakley E, et al. 2 nd Viral Entry Workshop, Abstract 8 5 Melby et al 13 th CROI 2006 Abstract 233. & Wilkin T, et al. CROI 2006. Abstract 655. PopulationCCR5 Only Naïve~80% Experienced~50%

MERIT: Week 48 virologic response by tropism  MVC (300 mg BID) vs EFV (600 mg QD) + ZDV/3TC in naïve patients (n=721)  All pts screened in as R5 tropic  25 (3.5%) changed from R5 at screening to D/M at baseline Heera J, et al. 15 th CROI, Boston, 2007, #40LB % Patients with HIV RNA <50 c/mL n= 339 / 331 11 / 14 % 68.0 7.1 54.6 69.3 0 20 40 60 80 R5D/M MVC +ZDV/3TC EFV +ZDV/3TC

Enhanced Assay Detects CXCR4 Use Prior to Standard Assay Reeves, et al. ICAAC 2007. Abstract H-1026

Possible Mechanisms of MVC Resistance Emergence of CXCR4 virusEmergence of CXCR4 virus –Pre-existing low level populations –Viral mutation from CCR5 to CXCR4 Non-competitive resistance through mutationNon-competitive resistance through mutation Failure of MVR most likely due emergence to CXCR4 or D/MFailure of MVR most likely due emergence to CXCR4 or D/M –60% of MVC –6% control Origin likely preexisting low frequency CXCR4 virus rather than tropism switchOrigin likely preexisting low frequency CXCR4 virus rather than tropism switch –detailed clonal analysis from 20 (16 MVC, 4 placebo arm) and analysis of amino acid sequence differences and phylogenetic data, suggests emergence and not tropism switch –Low level CXCR4 not detected by the tropism assay prior to treatment

-1000100200300 0 100 200 300 400 500 CXCR4-using env Clones Were Detected at Low Frequency in the Baseline Sample Time Since First Administration (Day) CXCR4-using clones detected at baseline (7%) No CCR5-tropic clones on treatment R5 DM R5 1 2 3 4 5 6 HIV-1 RNA (log 10 copies/mL) CD4 Count (cells/mcL) Patient T6 Lewis M et al. XVI IDRW, 2007, Abstract 56

-1000100200300 0 100 200 300 400 500 CXCR4-using env Clones Were Detected at Low Frequency in the Baseline Sample Time Since First Administration (Day) CXCR4-using clones detected at baseline (7%) No CCR5-tropic clones on treatment R5 DM R5 1 2 3 4 5 6 HIV-1 RNA (log 10 copies/mL) CD4 Count (cells/mcL) Patient T6 R5 tropic Dual tropic X4 tropic Non-functional clone Lewis M et al. XVI IDRW, 2007, Abstract 56

Non-competitive Inhibition & Resistance CCR5 resistance non-comp. inhibition ENVENV virus membrane CCR5 cell membrane

Non-competitive Inhibition & Resistance CCR5 resistance ENVENV non-comp. inhibition ENVENV virus membrane CCR5 cell membrane

Non-competitive Inhibition & Resistance CCR5 resistance ENVENV ENVENV non-comp. inhibition ENVENV virus membrane CCR5 cell membrane

CCR5 Resistance Mutations 8 subjects from ACTG 5211 phase IIb study with confirmed virologic failure on VCV analyzed – No tropism shifts observed – Mutations in V3 loop of gp120 identified in all patients 1 patient underwent detailed analysis of viral inhibition at various time points – Progressive susceptibility to VCV until reaching plateau – When VCV therapy was removed at Week 28, virus became susceptible to VCV again and plateau effect was reversed Tsibris AMN, et al. HIV Resistance Workshop 2007. Poster 13.

Clonal 07J Susceptibilities Following d/c of VCV, phenotypic susceptibility returned by wk 48 Tsibris, Resistance Workshop, 6/07, abstract #13

INTEGRASE INHIBITORS

Partial Analysis of Raltegravir Resistance in BENCHMRK-1 and BENCHMRK-2 Virologic failure on Raltegravir vs. placebo: 76 (16%) vs. 121 (51%) Raltegravir failure was generally associated with one of two genetic pathways: N155H or Q148K/R/H Additional mutations were observed with both pathways N155H + (E92Q,V151I, T97A, G163R, L74M) Q148K/R/H + (G140S/A, E138K) Other pathways? Y143R/C + (L74A/I, E92Q, T97A, I203M, S230R) Longitudinal analyses and associations are in progress Partial analysis based on genotyping 41 Raltegravir failures 32 with integrase changes, 9 with no consistent changes from baseline 1. Cooper D, et al. 14 th CROI, Los Angeles 2007, #105aLB; 2. Steigbigel R, et al. ibid, #105bLB

Mutation Pathways Leading to Resistance to RAL Identified  RAL failures in a phase II study analyzed  Mutations: –Near active site –Similar to mutations selected with other integrase inhibitors in cell culture  2 genetic pathways appear to confer resistance to RAL Hazuda DJ, et al. HIV Resistance Workshop 2007. Abstract 8.

Mutation Pathways Leading to Resistance to RAL Identified  RAL failures in a phase II study analyzed  Mutations: –Near active site –Similar to mutations selected with other integrase inhibitors in cell culture  2 genetic pathways appear to confer resistance to RAL Hazuda DJ, et al. HIV Resistance Workshop 2007. Abstract 8. Path 1 Path 2

Mutation Pathways Leading to Resistance to RAL Identified  RAL failures in a phase II study analyzed  Mutations: –Near active site –Similar to mutations selected with other integrase inhibitors in cell culture  2 genetic pathways appear to confer resistance to RAL Hazuda DJ, et al. HIV Resistance Workshop 2007. Abstract 8. Path 1 Path 2 Catalytic residues

How many new/ active drugs do you need? Unanswered question Multiple new agents from different classes entering into expanded access Guidelines suggest addition of at least 2 active agents cPSS may help to ‘add up’ how many drugs to include (i.e. include agents until cPSS > 2) Perhaps can leave the NRTI out? A testable hypothesis (ACTG 5241)

MOTIVATE 1 and 2: VL < 50 c/mL at Week 24 by Active Drugs in OBR No. of active drugs in OBR 012 ≥ 3 n = 0 10 20 30 40 50 60 70 80 90 100 35 515644 130134 5988104 64132121 3 18 29 9 43 19 52 53 55 61 58 Patients (%) Combined Analysis: MOTIVATE 1 & 2 Placebo + OBRMVC QD + OBRMVC BID + OBR Nelson M, et al. CROI 2007. Abstract 104aLB. Lalezari J, et al. CROI 2007. Abstract 104bLB.

Regimens with cPSS of >2.0 Resistance test cPSS >2.0 cPSS ≤2.0 Site selects regimen Observational arm Regimen + NRTIs Virologic failure Regimen without NRTIs Regimen + NRTIs Virologic failure or permanent discontinuation of NRTI strategy Randomize ACTG 5241: OPTIONS K Tashima, Chair

Regimens with cPSS of >2.0 Resistance test cPSS >2.0 cPSS ≤2.0 Site selects regimen Observational arm Regimen + NRTIs Virologic failure Regimen without NRTIs Regimen + NRTIs Virologic failure or permanent discontinuation of NRTI strategy Randomize K Tashima, Chair

Regimens with cPSS of >2.0 Resistance test cPSS >2.0 cPSS ≤2.0 Site selects regimen Observational arm Regimen + NRTIs Panel of agents T20 RAL DRV/r TPV/r ETR MVC Virologic failure Regimen without NRTIs Regimen + NRTIs Virologic failure or permanent discontinuation of NRTI strategy Randomize K Tashima, Chair

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Utilization of Genotype and Phenotype Resistance Tests in Patient Management Richard Haubrich, MD Professor of Medicine University of California San Diego.

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Utilization of Genotype and Phenotype Resistance Tests in Patient Management Richard Haubrich, MD Professor of Medicine University of California San Diego.

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