AREVIR MEETING 2013 11-12 April 2013 Odysseum, Cologne EuResist - - PowerPoint PPT Presentation

AREVIR MEETING 2013

11-12 April 2013 Odysseum, Cologne

 EuResist was launched in 2006 to apply advanced

statistics and modeling to HIV treatment

• At that time only NRTI, NNRTI and PI (plus T20) were in

clinical use

• Later, IN Inhibitors and Coreceptor Antagonists have been

incorporated into the system

 Now, HCV treatment is a reality

• EuResist database redesigned to host HCV (and HBV)

treatment data

• What is the balance between efforts/challenges and

rewards/opportunities?



Virus factors

• Viral load
• Genetic variability



Host factors

• Ethnicity and race (i. e. host genetic background such as IL28b SNPs)
• Gender & age
• Fibrosis/cirrhosis stage
• Obesity and insulin resistance
• Vitamin D levels
• …



Treatment factors

• Adherence
• Side effects
• Patient’s beliefs & social support
• Provider experience
• …

Interaction layers

OUTPUT (e.g. SVR or

• thers)

Potential benefits

 Patient tailored treatment

• Increased SVR
• Minimal toxicity

 Optimal use of available resources

THERAPY VIRUS HOST

Ge, Nature 2009

Ge, Nature 2009

Factor Associated With SVR Odds Ratio (95% CI) Baseline HCV RNA (< vs ≥ 600,000 IU/mL) 1.0 10.0 0.1 IL28B rs12979860 genotype (CC vs TT) 7.3 Baseline fibrosis (METAVIR F0-F2 vs F3-F4) Whites (n = 871) Blacks (n = 191) Hispanics (n = 75) 4.2 3.0 6.1 5.1 1.1 5.6 2.4 4.1

Time of

• bservation

Gene(s) in the Predictive Model Model P- value Sensitivity % (95% CI) Specificity % (95% CI) PPV % NPV % AUC Pre-treatment EP300, SOCS6 .001 85.7 (42.2–97.6) 83.3 (51.6–97.4) 75.0 90.9 0.881 (0.651 – 0.980) 24 hours after initiation of treatment IL1B, ADAM9 .009 71.4 (29.3–95.5) 81.8 (48.2–97.2) 71.4 81.8 0.74 (0.484 – 0.913) 7 days after initiation of treatment PRKRIR .009 100.0 (58.9–100.0) 66.7 (34.9–89.9) 63.6 100.0 0.786 (0.540 – 0.936)

Younossi ZM, et al. J Hepatol. 2008;48:S285. PPV = positive predictive value; NPV = negative predictive value; AUC = area under the curve;



IL28b [Thomas, Nature 2009; Suppiah, Nat Genet 2009; Tanaka, Nat Genet 2009]

• rs12979860
• rs8099917
• ss469415590



Low Density Lipoprotein Receptor (LDLR) [Pineda, AIDS 2011]

• rs14158



Low molecular polypeptide 7 (LMP-7) [Omran, World J Hepatol 2013]

• Codon 49



Concentrative nucleoside transporter 3 (CNT-3) [Rau, J Hepatol 2012]

• rs11854484



Other genes related to specific treatment toxicity

• e.g Inosine triphosphatase (ITPA) and anemia with RBV [Fellay, Nature 2010]

SVR = Sustained Virological Response, i.e. undetectable HCV RNA after 12 or 24 weeks following treatment completion (a consistent proxy for HCV eradication)

IFN/RBV IFN/RBV plus DAA IFN-free DAA

Dabbouseh, Nature Rev Gastroenterol Hepatol 2013



31%–33% nucleotide difference among the 6 HCV genotypes



20%–25% among HCV subtypes

 Standard of care: pegIFN plus RBV  Neither IFN nor RBV target any virus specific

function

• Selection of resistance to pegIFN/RBV in vivo has

been not documented…

• …however natural susceptibility to pegIFN/RBV is

different in the different HCV genotypes

IFN Sensitivity Determining Region NS5A aa 236-275 Core aa 70 Arg→Gln, His IFN/RBV Resistance Determining Region NS5A aa 261-306 Core aa 91 Met→Leu

The most these aa and regions diverge from genotype 1 consensus, the higher the probability to achieve a cure (SVR)

Serine Protease Helicase Serine Protease Cofactor

NS5A inhibitors

Replication complex RNA and Zn binding

5’ UTR region 9.6 kb RNA 3’ UTR region Polyprotein Polyprotein Processing

Core Envelope Glycoproteins Protease

Nucleoside analogs Non-nucleoside analogs

NS5B Polymerase inhibitors

C E2 E1 C E2 E1 p7 NS2 NS3 4A NS4B NS5A NS5B p7 NS2 NS3 4A NS4B NS5A NS5B

NS3-4A Protease inhibitors

RNA-dependent RNA polymerase

Adapted from Terrault N, 19th IAC; Washington, DC; July 22-27, 2012; Abst. WEAB0104.

1012 virions per day 104 to 107 virions per ml 10-4 to 10-5 nt changes per cycle

Population sequencing Cloning Next generation sequencing

Chevaliez, EASL 2011

Serine Protease Helicase Serine Protease Cofactor

NS5A inhibitors

Replication complex RNA and Zn binding

5’ UTR region 9.6 kb RNA 3’ UTR region Polyprotein Polyprotein Processing

Core Envelope Glycoproteins Protease

Nucleoside analogs Non-nucleoside analogs

NS5B Polymerase inhibitors

C E2 E1 C E2 E1 p7 NS2 NS3 4A NS4B NS5A NS5B p7 NS2 NS3 4A NS4B NS5A NS5B

NS3-4A Protease inhibitors

RNA-dependent RNA polymerase

Adapted from Terrault N, 19th IAC; Washington, DC; July 22-27, 2012; Abst. WEAB0104.

Sarrazin, J Hepatol 2012

NS3P/NS4A recombinants of genotypes 2a, 3a, 5a, and 6a prototype isolates or with resistance mutations showed differential sensitivity to protease inhibitors but not to interferon-alfa2. Cultures were infected with culture adapted NS3P/NS4A recombinants of the indicated genotype(isolate) or 2a(JFH1) with putative resistance mutations, 2a(JFH1)RES. Values are means of 3 replicates with standard error of mean.

TELAPREVIR BOCEPREVIR SIMEPREVIR DANOPREVIR VANIPREVIR IFN-alfa Gottwein, Gastroenterol 2011

Compared with genotype 2a, genotype 3a isolates were less sensitive to protease inhibitors. Cultures were infected with culture adapted NS3P/NS4A recombinants of the indicated 2a and 3a isolates Values are means of 3 replicates with standard error of mean.

Gottwein, Gastroenterol 2011 TELAPREVIR BOCEPREVIR SIMEPREVIR DANOPREVIR VANIPREVIR IFN-alfa

 How often NS3 inhibitor resistance mutations

do occur as dominant species in nature?

 507 patients from US,

Germany and Switzerland

 Collectively, 5.5% of the

genotype 1a and 1.4% of the genotype 1b infected patients carried at least one dominant NS3 PI resistance mutation

 No impact of NS3 PI

resistance mutations on viral load

• Suggests possible fitness

changes are adjusted by the genetic background (compensatory mutations)

Kunzen, Hepatology 2008

Study N RAVs with >1% prevalence in GT1a RAVs with >1% prevalencein GT1b Gaudieri 2009 259 54S 4.4%, 170A 5.8% 170A 7.8% Trevino 2011 55 55A 5.2%, 80K 21.1% None Suzuki 2012 307 Not included 54S 3.3% Paolucci 2012 70 54S 6.4%, 55A 3.2%, 80K 9.7% None Vicenti 2012 109 54S 3.0%, 55A 1.5%, 80K 16.4% 54S 2.4% Bartels 2012 3447 54S 3.1%, 55A 2.7%, 80K 37.6% 54S 1.9%

 Caveats:

• Different reference mutation lists used
• Different geographic regions

0,5 1 1,5 2 2,5 3 3,5 4 4,5 5 36A 36M 36G 54A 54S 54G 54C 55A 55I 155K 155T 155G 156S 156T 156V % gt1a (2266) gt1b (2056) gt2 (165) gt3 (470) gt4 (142) gt5 (26) gt6 (74)

Source: 5,199 NS3 sequences from LANL HCV db accessed 16 March 2013

All the main linear NS3i RAV are below 5%

10 20 30 40 50 60 70 80 90 100 36A 36M 36G 43S 54S 55I 80K 80R 80L 122A 122R 138T 155K 156T 156V 168A 168V 168H 168Y 168N 168P 168Q 168I 168K % gt1a (2266) gt1b (2056) gt2 (165) gt3 (470) gt4 (142) gt5 (26) gt6 (74)

Source: 5,199 NS3 sequences from LANL HCV db accessed 16 March 2013

Higher prevalence of macrocyclic NS3i RAV (including cases with RAV as consensus) No RAV for next- generation macrocyclic NS3i (e.g. MK-5172)

 Does natural variability impact on response

to NS3 inhibitors?

Vierling, AASLD 2010 Data from SPRINT-1 trial

 Putative RAVs identified from in vitro and in vivo data

• V36, Q41, F43, T54, V55, R155, A156, V158, V170, M175

 Population sequencing (~20% sensitivity on minor

species)

MSD, data on file 79 76 78 34 23 10 20 30 40 50 60 70 80 90 No RAVs Other RAVs Major RAVs IFN responders IFN nonresponders

IFN responders: >1 log VL decrease at treatment week 4 (lead-in with pegIFN/RBV only) IFN non responders: <1 log VL decrease at treatment week 4 (lead-in with pegIFN/RBV only) Major RAVs: V36M, T54AS, V55A, R155K

% SVR

65 36 75 42 10 20 30 40 50 60 70 80 Naive & relapsers (N = 1594) Prior nonresponders (N = 382) SVR % RAVs (N = 112) no RAVs (N = 1864)

Bartels, J Virol 2013

P = 0.042 P = 0.786 Pooled phase II and III TVR data analysis

Bartels, J Virol 2013

N = 6 N = 50 N = 10 Pooled phase II and III TVR data analysis

 Phase II triple-therapy study on treatment-naive  Prevalence of previously identified variants conferring reduced

susceptibility to simeprevir at baseline

• Q80K, n = 40, 10.4% (38 in GT1a, 2 in GT1b)
• R155K, n = 3, 0.8%
• D168E, n = 1, 0.3%

Lenz, AASLD 2011

20 40 60 80 100 Lack of SVR24 Virological breakthrough Relapse

Simeprevir 75 mg (n = 153)

80K no 80K 20 40 60 80 100 Lack of SVR24 Virological breakthrough Relapse

Simeprevir 150 mg (n = 156)

80K no 80K

Lenz, EASL 2012

Serine Protease Helicase Serine Protease Cofactor

NS5A inhibitors

Replication complex RNA and Zn binding

5’ UTR region 9.6 kb RNA 3’ UTR region Polyprotein Polyprotein Processing

Core Envelope Glycoproteins Protease

Nucleoside analogs Non-nucleoside analogs

NS5B Polymerase inhibitors

C E2 E1 C E2 E1 p7 NS2 NS3 4A NS4B NS5A NS5B p7 NS2 NS3 4A NS4B NS5A NS5B

NS3-4A Protease inhibitors

RNA-dependent RNA polymerase

Adapted from Terrault N, 19th IAC; Washington, DC; July 22-27, 2012; Abst. WEAB0104.

Vermehren, Best Pract Res Clin Gastroenterol 2012

Scheel, GE 2011

Natural isolates

Scheel, GE 2011

Natural isolates Both GT2a

Percent non-identity to predominant NS5A amino acid residues across GT1a (n = 548), GT1b (n = 1796), GT3 (n = 468) and GT4 (n = 40). Sequences from the European HCV database (EuHCVdb).

Hernandez, J Clin Virol 2013

Main codons involved in resistance to daclatasvir in HCV genotype 1a and 1b

Serine Protease Helicase Serine Protease Cofactor

NS5A inhibitors

Replication complex RNA and Zn binding

5’ UTR region 9.6 kb RNA 3’ UTR region Polyprotein Polyprotein Processing

Core Envelope Glycoproteins Protease

Nucleoside analogs Non-nucleoside analogs

NS5B Polymerase inhibitors

C E2 E1 C E2 E1 p7 NS2 NS3 4A NS4B NS5A NS5B p7 NS2 NS3 4A NS4B NS5A NS5B

NS3-4A Protease inhibitors

RNA-dependent RNA polymerase

Adapted from Terrault N, 19th IAC; Washington, DC; July 22-27, 2012; Abst. WEAB0104.

Waheed, Infect Genet Evol 2013

Nucleoside or nucleotide inhibitors Non-nucleoside inhibitors



Most AA substitutions well tolerated at these sites



NNIs developed thus far are specific for HCV genotype 1

• Efficacy may be better with one genotype 1 subtype than another



Genetic barrier to resistance is generally low

Sarrazin, J Hepatol 2012

Thiophene Benzofuran Benzothiadiazine

Sun, J Viral Hepatitis 2010

Sarrazin, J Hepatol 2012

 Main Sofosbuvir and Mericitabine mutation

S282T appears to be virtually absent across HCV genotypes

• Exception: Y11604 GT 4a reference sequence

 Severely impaired replication in vitro  Indeed, very hard to detect in vivo upon

nucleoside NS5B inhibitor therapy

• Exception: one patient with dual resistance to

mericitabine and danoprevir in the INFORM-SVR study

Promising acitivity across HCV genotypes

 How often do DAAs select for resistance in

vivo?

Phenotypic Resistance Profiles in Patients Who Did Not Achieve SVR with a TVR-based Regimen. Data from ADVANCE include only the T12PR arm and data from REALIZE include pooled TVR arms. Higher-level resistance (red) is defined as.25-fold increase in IC50and lower- level resistance (yellow) is defined as 3- to 25-fold increase in IC50from wild-type. Grey (n/a) indicates patients with no sequence data available due to HCV RNA levels below the LOD of the sequencing assay or lost-to-follow-up. (Kieffer, PLoS ONE 2012)

 Resistance develops as a function of

• Drug exposure

▪ Individual pk variability ▪ Adherence

• Drug potency & viral load decay rate
• Virus variability & evolution rate
• Viral load

 Do DAA resistance mutations “disappear”

following discontinuation of therapy?

There is an inverse correlation between level of resistance and in vivo fitness

HIV HCV Development of resistance Rapid (e. g. 3TC) to low/none (e. g. boosted PI) Rapid (e. g. NNI) to low/none (e. g. NI) Intra-class cross-resistance Yes Yes Natural resistance No None (NI) to rare (GT1 BVR TVR) to common (NNI) Transmitted resistance Yes Marginal? Fading resistance upon treatment discontinuation Slow/incomplete Fast to slow/complete? Baseline resistance testing Yes Marginal role? Resistance testing at failure Yes Marginal role? Importance of resistance along with novel drug development Decreasing (more potent and higher genetic barrier drugs) Decreasing (more potent and higher genetic barrier drugs)

Primary resistance (natural or transmitted) Optimal treatment start Resistance rare and

• f minor impact

Secondary resistance (acquired) Virus-tailored treatment change Treatment must be completely different anyway Fate of drug-resistant variants Possible drug recycling No drug recycling planned, different classes available

IFN

• Natural but

not acquired resistance relevant

• Genotyping

IFN plus DAA

• Natural

resistance less common but acquired more common

• Genotyping
• Sequencing?

Next gen. DAA

• Natural and

acquired resistance uncommon

• Genotyping?
• Sequencing?

 How feasible and expedite can be HCV data collection?

• Time of approval of new drugs
• Number of patients treated
• Multiple disciplines (data sources) involved

 Is DAA efficacy making models unnecessary?

• Will DAA work as well as in clinical trials?

 How much does the difference between a race (HCV)

and a marathon (HIV) impact on modeling?

• Short follow-up required

 Are efforts and rewards expected to be balanced?