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Challenges in DesigningPharmacogenomics Clinical Trials

Joseph Levy, PhDTeva Pharmaceutical Industries

MedicReS 2013 Istanbul

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The present

One Size fits all

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The Future

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Outline

PGx short overview Response related research questions Clinical trial designs Challenges

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What is Pharmacogenomics?

Pharmacogenomics (PGx) is the study of how genes affect the way our bodies respond to a medicine.

An inter-disciplinary science involving Biology and Genetics Medicine Pharmacology Statistics

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Goals of PGx research

Achieving better safety profile and mitigating potential risks

Better understanding of MoA Identify disease genetics Investigating the contribution of genetic variation to inter-

individual variability in response to drug treatment Promoting an optimal drug response for the individual

patient

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The Human Genome

46 chromosomes – 23 pairs ~2 meters of DNA ~3.4B DNA bases ~25000 genes ~26M variable sites (0.7%) ~10M SNPs

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Types of PGx studies

Studies to investigate genotype-trait association within a population of unrelated individuals:

Exploratory studies Gnome-wide association studies (GWA/GWAS) Whole Genome Sequencing (WGS) studies

Prospective studies Candidate polymorphism studies Candidate gene studies

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GWA and WGS studies

Consider large set of SNP and possibly other variants Aim to identify association between variants and phenotype Less hypothesis driven Nee to handle multiplicity testing issues, but… Type II error (False Negative) is more important than Type I

error (False Positive)

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Candidate gene/polymorphism studies

Consider polymorphism(s) within a gene or multiple genes There may be a priori hypothesis about functionality Functionality: the given variants influence the disease trait

directly Non-functionality: the given variants may be associated to a

functional variant within the gene(s) This association is called Linkage Disequilibrium In any case, the candidate gene/polymorphism is pre-

specified

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Response related research questions

1. Does the treatment effect of the drug vary between subjects with different genotypes?

2. What is the benefit of the drug over placebo or an standard treatment for patients with a particular genotype?

3. What is the risk-benefit ratio of genotype-guided treatment over standard care?

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Subgroups analysis design

Randomization

OUTCOME

Drug

Pbo/Std

Genotypying

Wildtype

Variant

Genotypying

Wildtype

Variant

OUTCOME

Double Blind

Follow-up

Answers first research question

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FORTE GWA study The FORTE study compared Glatiramer Acetate 20 mg and

40 mg doses in RRMS patients. 731 subjects consented to provide DBA samples Response classification was available for 599 Caucasian

subjects Extreme phenotype approach: 52 super-responders, 61

non-responders 1M SNPs considered 31 SNPs were identified significantly associated with

SR/NR 2 relevant pathways that merge into a unique functional

gene network were identified Predictive model for response: a 87.9 % sensitivity, 92.7 %

specificity

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Tamoxifen therapy for breast cancer

Clinical trial performed in Sweden, 1996 Four treatment groups, following a modified radical

mastectomy : Adjuvant chemotherapy Adjuvant chemotherapy + tamoxifen Radiotherapy Radiotherapy + tamoxifen

679 postmenopausal breast cancer patients randomized 226 fresh frozen tumor tissues were available 7 years later Relationship of CYP2D6 and SULT1A1 genotypes and

benefit from tamoxifen therapy was observed

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Subgroups analysis design advantages

Can piggyback on Phase II or III trial Appropriate for GWA/WGS exploratory studies Can be performed in prospective/retrospective fashion Simple, fast, relatively inexpensive Can test many genetic markers in one study Small chance of bias even where there are many genetic

subgroups Provides efficient assessment of relative treatment efficacy

in each genotype subgroup and in the whole group

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Subgroups analysis design disadvantages

Possible confounding bias Possible selection bias:

Some subjects may not consent If DNA is not collected at baseline, clinical outcome may affect

decision to consent

Dependency of genotype distribution in the original study population

Size of subgroups can’t be controlled Possible imbalance in baseline characteristics/ prognostic factors Statistical power issues

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Enrichment design

Genotypying

Wildtype

Variant

Excess of Wildtype

Randomization

Drug

Pbo/Std

Randomization

Drug

Pbo/Std

OUTCOME

Double Blind

Follow-up

Answers second research question

Sometimes off-study

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Impact of CYP2D6 on venlafaxine and desvenlafaxine PK

14 healthy volunteers 7 CYP2D6 poor metabolizers 7 CYP2D6 extensive metabolizers CYP2D6 intermediate and ultra-rapid metabolizers were

excluded An excess of extensive metabolizers was also excluded

Randomized sequence crossover study Endpoints – AUC and Cmax Conclusions

No impact of CYP2D6 polymorphisms on exposure to desvenlafaxine

CYP2D6 polymorphisms impacts exposure to venlafaxine

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Anti-HER2 monoclonal antibody plus chemotherapy for metastatic breast cancer

Genotyping at screening – 469 women that that over-expressed HER2 were randomized

HER2 is over-rexpressed in 25-30% percent of breast cancers ⇒ sample size without enrichment would be 1550-1900

Randomization stratified by past treatmenat by an anthracycline: Standard chemotherapy (an anthracycline and

cyclophosphamide or paclitaxel) Standard chemotherapy + tratuzumab

Conclusion: Trastuzumab increases the clinical benefit of first-line chemotherapy in metastatic breast cancer that overexpresses HER2

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Enrichment design advantages

Genotype strata can be balanced Can select subjects with genotypes between which the

largest difference in treatment effect is expected (instead of all genotypes)

Sample size and power calculation takes final analysis into account

Selective consenting bias is avoided Opens door for possible adaptations

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Enrichment design disadvantages

“Tailored” study for particular genetic hypothesis Appropriate for contexts where there is such a strong

biological basis for believing that “wildtype subjects” will not benefit from the new drug

Efficient only when prevalence of variant is high and the effectiveness in variant population is high compared to the wildtype population

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Genotype guided design

Randomization

Drug

Pbo/Std

Genotypying

Wildtype

VariantOUTCOME

Double Blind

Follow-up

Answers third research question

Exclusion or alternative treatment

Drug

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Sensitivity to primary chemotherapy with paclitaxel in breast cancer

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The Tarceva Italian Lung Optimization study

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Genotype guided design advantages

Assesses the added value of the PGx-based treatment over the current use of the drug and the corresponding costs

Economic advantage of limiting the number of potentially expensive DNA genotypings

Can be mimicked by a “regular” clinical trial, by comparing the whole treatment arm to its subgroup of “variant subjects” (statistical analysis is computationally intensive)

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Genotype guided design disadvantages

Inefficient in terms of sample size A positive study cannot distinguish between a successful

treatment selection strategy and a situation in which the experimental drug is better than the control therapy for all patients

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More to consider

Adaptations: randomization, patient enrollment, enrichment, sample size re-estimation, group sequential design

Combine general efficacy endpoint and PGx related endpoint in a single study – need to consider multiplicity issues

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References

van der Baan, F. H., Klungel, O. H., Egberts, A. C., Leufkens, H. G., Grobbee, D. E., Roes, K. C., & Knol, M. J. (2011). Pharmacogenetics in randomized controlled trials: considerations for trial design. Pharmacogenomics, 12(10), 1485-1492.

Farina, G., Longo, F., Martelli, O., Pavese, I., Mancuso, A., Moscetti, L., ... & Scanni, A. (2011). Rationale for Treatment and Study Design of TAILOR: A Randomized Phase III Trial of Second-line Erlotinib Versus Docetaxel in the Treatment of Patients Affected by Advanced Non–Small-Cell Lung Cancer With the Absence of Epidermal Growth Factor Receptor Mutations. Clinical lung cancer, 12(2), 138-141.

Freidlin, B., McShane, L. M., & Korn, E. L. (2010). Randomized clinical trials with biomarkers: design issues. Journal of the National Cancer Institute, 102(3), 152-160.

Ito, Y., Nagasaki, K., Miki, Y., Iwase, T., Akiyama, F., Matsuura, M., ... & Hatake, K. (2011). Prospective randomized phase II study determines the clinical usefulness of genetic biomarkers for sensitivity to primary chemotherapy with paclitaxel in breast cancer. Cancer science, 102(1), 130-136.

Macciardi, F., Cohen, J., Comabella Lopez, M., Comi, G., Cutter, G., Eyal, E., ... & Wolinsky, J. (2012, April). A Genetic Model To Predict Response to Glatiramer Acetate Developed from a Genome Wide Association Study (GWAS). In NEUROLOGY (Vol. 78).

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References (Cont.) Mandrekar, S. J., & Sargent, D. J. (2009). Clinical trial designs for predictive biomarker

validation: one size does not fit all. Journal of biopharmaceutical statistics, 19(3), 530-542. Preskorn, S., Patroneva, A., Silman, H., Jiang, Q., Isler, J. A., Burczynski, M. E., Saeeduddin

A., Jeffrey P., & Nichols, A. I. (2009). Comparison of the pharmacokinetics of venlafaxineextended release and desvenlafaxine in extensive and poor cytochrome P450 2D6 metabolizers. Journal of clinical psychopharmacology, 29(1), 39-43.

Simon, R. (2012). Clinical trials for predictive medicine. Statistics in Medicine, 31(25), 3031-3040

Slamon, D. J., Leyland-Jones, B., Shak, S., Fuchs, H., Paton, V., Bajamonde, A., ... & Norton, L. (2001). Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. New England Journal of Medicine, 344(11), 783-792.

Trepicchio, W. L., Essayan, D., Hall, S. T., Schechter, G., Tezak, Z., Wang, S. J., Weinreich D., & Simon, R. (2006). Designing prospective clinical pharmacogenomic (PG) trials: meeting report on drug development strategies to enhance therapeutic decision making. The pharmacogenomics journal, 6(2), 89-94.

Wegman, P., Vainikka, L., Stal, O., Nordenskjold, B., Skoog, L., Rutqvist, L. E., & Wingren, S. (2005). Genotype of metabolic enzymes and the benefit of tamoxifen in postmenopausal breast cancer patients. Breast Cancer Res, 7(3), R284-R290.

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Backup Slides

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SNPs

SNP - Single Nucleotide Polymorphism

Most common type of genetic variation

Each SNP represent a difference in a single DNA base

The SNP in the picture is CT or AC or AG or GT –they are all the same

SNP can have 3 possible values: AA, Aa or aa