Co-developing a drug with a diagnostic to create a stratified medicine presents challenges for product developers, regulators, payers and clinicians. With the aim of developing a shared framework and tools for understanding the impact of these challenges, here we present an analysis using data and modelling from case studies in oncology and Alzheimer's disease. Key findings are summarized below.
A prospective stratified development and market-based approach created positive net economic value for the drug developer in all three case studies examined.
A stratification approach following an all-comers trial does not always create value owing to the time delays and increased costs it incurs.
Three key factors from a stratified medicine approach serve as the most crucial determinants of the potential economic value for a developer: the therapeutic effect, predictive biomarker prevalence and the clinical performance of the companion diagnostic.
Relatively small study sizes (< 300 patients) are required to discover predictive biomarkers with similar performance characteristics of the KRAS mutational status (for epidermal growth factor receptor inhibitors) or HER2 gene expression level (for trastuzumab).
Co-developing a stratified medicine presents multiple challenges, including an inherent timing mismatch, because the science underlying the predictive biomarker usually trails that of the therapeutic, and a relatively low economic value is typically associated with the companion diagnostic.
Multiple variable simulations demonstrate that developmental, regulatory and commercial factors are frequently multiplicative rather than additive, resulting in complementary virtuous or spiralling negative cascades of the economic value for the developer of the therapeutic.
This article illustrates how such analyses can aid the coordination of diagnostic and drug development, and the selection of optimal development and commercialization strategies. It also illustrates the interplay of key factors on the economic feasibility of a stratified medicine, which may have important implications for public policy makers.
Co-developing a drug with a diagnostic to create a stratified medicine — a therapy that is targeted to a specific patient population on the basis of a clinical characteristic such as a biomarker that predicts treatment response — presents challenges for product developers, regulators, payers and physicians. With the aim of developing a shared framework and tools for addressing these challenges, here we present an analysis using data from case studies in oncology and Alzheimer's disease, coupled with integrated computational modelling of clinical outcomes and developer economic value, to quantify the effects of decisions related to key issues such as the design of clinical trials. This illustrates how such analyses can aid the coordination of diagnostic and drug development, and the selection of optimal development and commercialization strategies. It also illustrates the impact of the interplay of these factors on the economic feasibility of stratified medicine, which has important implications for public policy makers.
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Trusheim, M., Berndt, E. & Douglas, F. Stratified medicine: strategic and economic implications of combining drugs and clinical biomarkers. Nature Rev. Drug Discov. 6, 287–293 (2007).
Hu, S. X. et al. Pharmacogenomics and personalized medicine: mapping of future value creation. BioTechniques 39, S1–S6 (2005).
Aspinall, M. G. & Hamermesh, R. G. Realizing the promise of personalized medicine. Harvard Bus. Rev. 85, 108–117 (2007).
Phillips, K. A. et al. Diagnostics and biomarker development: priming the pipeline. Nature Rev. Drug Discov. 5, 463–469 (2006).
US Food and Drug Administration. Table of Pharmacogenomic Biomarkers in Drug Labels. FDA website [online], (2011).
Loupakis, F. et al. EGF-receptor targeting with monoclonal antibodies in colorectal carcinomas: rationale for a pharmacogenomic approach. Pharmacogenomics 9, 55–69 (2008).
Hughes, B. Developing tools for stratified medicine. Nature Rev. Drug Discov. 8, 919–920 (2009).
Hamburg, M. A. & Collins, F. S. The path to personalized medicine. N. Eng. J. Med. 363, 301–304 (2010).
Jensen, E. V. et al. Estrogen receptors and breast cancer response to adrenalectomy. Natl Cancer Inst. Monogr. 34, 55–70 (1971).
Sawyers, C. L. The cancer biomarker problem. Nature 452, 548–552 (2008).
Hudis, C. A. Trastuzumab — mechanism of action and use in clinical practice. N. Engl. J. Med. 357, 39–51 (2007).
Slamon, D. J. et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N. Engl. J. Med. 344, 783–792 (2001).
Vogel, C. L. & Franco, S. X. Clinical experience with trastuzumab (Herceptin). Breast J. 9, 452–462 (2003).
US Food and Drug Administration. FDA labeling information — Trastuzumab. FDA website [online], (2008).
Romond, E. H. et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N. Engl. J. Med. 353, 1673–1684 (2005).
Piccart-Gebhart, M. J. Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer. N. Engl. J. Med. 353, 1659–1672 (2005).
US Food and Drug Administration. FDA labeling information — Panitumumab. FDA website [online], (2009).
Cunningham, D. et al. Cetuximab monotherapy and cetuximab plus irinotecan in irinotecan-refractory metastatic colorectal cancer. N. Engl. J. Med. 351, 337–345 (2004).
Adams, R. & Maughan, T. Predicting response to epidermal growth factor receptor-targeted therapy in colorectal cancer. Expert Rev. Anticancer Ther. 7, 503–518 (2007).
Chung, K. Y. et al. Cetuximab shows activity in colorectal cancer patients with tumors that do not express the epidermal growth factor receptor by immunohistochemistry. J. Clin. Oncol. 23, 1803–1810 (2005).
Mitchell, E. P. et al. Panitumumab activity in metastatic colorectal cancer (mCRC) patients (pts) with low or negative tumor epidermal growth factor receptor (EGFr) levels: an updated analysis. J. Clin. Oncol. Abstr. 25, 4082 (2007).
Amado, R. G. et al. Wild-type KRAS is required for panitumumab efficacy in patients with metastatic colorectal cancer. J. Clin. Oncol. 26, 1626–1634 (2008).
Davis, J. C. et al. The microeconomics of personalized medicine: today's challenge and tomorrow's promise. Nature Rev. Drug. Discov. 8, 279–286 (2009).
Jackson, C. & Snyder, P. Electroencephalography and event-related potentials as biomarkers of mild cognitive impairment and mild Alzheimer's disease. Alzheimers Dement. 4, S137–S143 (2008).
Alzheimer's Association. 2010 Alzheimer's Disease Facts and Figures. Alzheimers Dement. 6, 158–194 (2010).
Geldmacher, D. S. Treatment guidelines for Alzheimer's disease: redefining perceptions in primary care. Prim. Care Companion J. Clin. Psychiatry 9, 113–121 (2007).
Rockwood, K. et al. The clinical meaningfulness of ADAS-cog changes in Alzheimer's disease patients treated with donepezil in an open-label trial. BMC Neurol. 30, 26 (2007).
Dorfman, N. The Age of Alzheimer's. Medical Marketing & Media [online], (2006).
Mattson, N. et al. CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA 302, 385–393 (2009).
Blennow, K. & Zetterberg, H. Use of CSF biomarkers in Alzheimer's disease clinical trials. J. Nutr. Health Aging 13, 358–361 (2009).
Thai, L. J. et al. The role of biomarkers in clinical trials for Alzheimer's Disease. Alzheimer Dis. Assoc. Disord. 20, 6–15 (2006).
Salloway, S. et al. A phase 2 multiple ascending dose trial of bapineuzumab in mild to moderate Alzheimer disease. Neurology 73, 2061–2070 (2009).
Risner, M. et al. Efficacy of rosiglitazone in a genetically defined population with mild-to-moderate Alzheimer's disease. Pharmacogenomics J. 6, 246–254 (2006).
Slamon, D. J. et al. Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science 235, 177–182 (1987).
Turner, N., Tutt, A. & Ashworth, A. Hallmarks of “BRCAness” in sporadic cancers. Nature Rev. Cancer 4, 1–6 (2004).
Soda, M. et al. Identification of the transforming EML4–ALK fusion gene in non-small cell lung cancer. Nature 448, 561–566 (2007).
Chiarle, R. et al. The anaplastic lymphoma kinase in the pathogenesis of cancer. Nature Rev. Cancer 8, 11–23 (2008).
Kwak, E. L. et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N. Engl. J. Med. 363, 1693–1703 (2010).
Butrynski. et al. Crizotinib in ALK-rearranged inflammatory myofibroblastic tumor. N. Engl. J. Med. 363, 1727–1733 (2010).
US Food and Drug Administration. FDA labeling information — Daptomycin. FDA website [online], (2010).
Mermel, L. A. et al. Clinical practice guidelines for the diagnosis and management of intravascular catheter-related infection: 2009 Update by the Infectious Diseases Society of America. Clin. Infect. Dis. 49, 1–45 (2009).
Pratilas, C. A. & Solit, D. B. Targeting the mitogen-activated protein kinase pathway: physiological feedback and drug response. Clin. Cancer Res. 16, 3329–3334 (2010).
Solit, D. B. et al. BRAF mutation predicts sensitivity to MEK inhibitors. Nature 439, 358–362 (2006).
Kwak, E. L. et al. Clinical activity of the oral ALK inhibitor PF-02341066 in ALK-positive patients with non-small cell lung cancer (NSCLC). J. Clin. Oncol. Abstr. 28, 3 (2010).
McDermott, U. et al. Genomic alterations of anaplastic lymphoma kinase may sensitize tumors to anaplastic lymphoma kinase inhibitors. Cancer Res. 68, 3389–3395 (2008).
Fine, B. M. & Amler, L. Predictive biomarkers in the development of oncology drugs: a therapeutic industry perspective. Clin. Pharmacol. Ther. 85, 535–538 (2009).
Taube, S. et al. A perspective on challenges and issues in biomarker development and drug and biomarker codevelopment. J. Natl Cancer Inst. 101, 1453–1463 (2009).
Mandrekar, S. J. & Sargent, D. J. Clinical trial designs for predictive biomarker validation: theoretical considerations and practical challenges. J. Clin. Oncol. 27, 4027–4034 (2009).
Simon, R. The use of genomics in clinical trial design. Clin. Cancer Res. 14, 5984–5993 (2008).
Peeters, M. et al. Use of massively parallel, next-generation sequencing to identify gene mutations beyond KRAS that predict response to panitumumab in a randomized, phase 3, monotherapy study of metastatic colorectal cancer (mCRC). in Proceedings of the 101st Annual Meeting of the American Association for Cancer Research (17–21 Apr 2010; Washington DC; Abstract LB-174).
Nixon, R. M. et al. The rheumatoid arthritis drug development model: a case study in Bayesian clinical trial simulation. Pharm. Stat. 8, 371–389 (2009).
Simon, R. M., Paik, S & Hayes, D. F. Use of archived specimens in evaluation of prognostic and predictive biomarkers. J. Natl Cancer Inst. 101 1446–1452 (2009).
Raponi, M. et al. KRAS mutations predict response to EGFR inhibitors. Curr. Opin. Pharmacol. 8, 413–418 (2008).
The authors would like to thank L. Surh, now retired from GlaxoSmithKline, who helped guide and review their efforts. The authors would also like to thank I. Saulea and D. Smith, both of SDG Life Sciences, for their support in the analysis of the trastuzumab case study.
Some authors, as noted by their affiliations, are employed by pharmaceutical developers, diagnostic developers or by consulting firms that have such firms as clients. The authors who are affiliated with the Massachusetts Institute of Technology have received funding support from pharmaceutical firms for other work, but not this manuscript.
Modelling tools used (PDF 1767 kb)
Herceptin adjuvant therapy economic modeling input assumptions and sources used in IMS and MIT model comparison (PDF 318 kb)
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- Stratified medicine
A therapeutic combined with a companion diagnostic that targets a patient subpopulation for treatment.
- Companion diagnostic
A predictive biomarker that is developed into a regulatory approved and/or a commercially available diagnostic test, and may be included in a drug label.
- Predictive biomarker
A baseline characteristic that categorizes patients by their likelihood of responding to a particular treatment. It may predict a favourable response or an unfavourable response (for example, an adverse event).
An oncogene that is implicated in several types of cancer. Patients with mutations in KRAS are likely to be poor responders to EGFR inhibitors such as cetuximab and panitumumab.
- Monte Carlo simulation
A stochastic modelling approach in which values for some variables are independently selected at random from defined distributions. After many iterations, distributions for resulting variables — such as the net present value — are created.
- Net present value
(NPV). The sum of the discounted cash flow from a multiyear activity. A positive value indicates a value-adding investment.
- Expected net present value
(eNPV). The risk-adjusted net present value accounting for the probability of technical and regulatory success.
Also known as ERBB2. HER2 is the target of the monoclonal antibody drug trastuzumab, which is indicated for patients with breast cancer on the basis of tumour HER2 expression status as assessed by companion diagnostics.
- Epidermal growth factor receptor
(EGFR). A member of the human epidermal growth factor receptor family of cell-surface receptor tyrosine kinases. EGFR has an important role in cell growth, proliferation and survival. It is the target of the monoclonal antibody drugs cetuximab and panitumumab.
- Time to progression
An end point of a cancer trial. Defined as the time from randomization to objective tumour progression, not including deaths. Taken from the US Food and Drug Administration Guidance: Clinical Trial Endpoints for the Approval of Cancer Drugs and Biologics, May 2007.
- Overall response rate
Also known as the objective response rate. The proportion of patients with a reduction in tumour size of a predefined amount and for a minimum time period. Taken from the US Food and Drug Administration Guidance: Clinical Trial Endpoints for the Approval of Cancer Drugs and Biologics, May 2007.
- Deterministic waterfall analysis
A multivariable sensitivity exercise in which selected variables are sequentially changed, with the incremental impact of each variable shown as a step from the original case to the final case in which all sensitivity variables have been changed.
The Alzheimer's Disease Assessment Scale-cognitive subscale. Consists of 11 tasks that evaluate memory, language, attention and other cognitive abilities affected by the disease.
- Apolipoprotein E
(APOE). A major component of chylomicron. The APOE4 allele has been associated with a higher risk and increased severity of Alzheimer's disease. APOE3 is the normal allele, and the APOE2 allele may have some protective effects with regard to Alzheimer's disease.
The amyloid-β protein fragment that is a major component of the plaques that are a characteristic hallmark of Alzheimer's disease.
- Magnetic resonance imaging
A medical imaging technology that uses high magnetic fields and radio frequencies to create images of internal body structures.
- Positron emission tomography
A medical imaging technique that produces three-dimensional images of internal body structures by detecting γ-rays emitted by a radionucleotide-tagged biologically active molecule. Images can be sequenced to create a time series of moving images. This technique often uses a glucose analogue to measure relative metabolism rates.
- Mini-mental state examination
Also known as the Folstein test, this is a brief questionnaire that is used to assess cognitive impairment.
- Probability of technical success
The likelihood that a candidate product will meet its developer-prescribed efficacy end point and safety criteria.
- Probability of regulatory success
The likelihood that a candidate product will be approved by a regulatory agency, given its technical performance.
- Probability of technical and regulatory success
(PTRS). A probability value calculated by multiplying the probability of regulatory success with the probability of technical success.
- Isoquant NPV curve
A contour line on a two-dimensional chart to show the constant value of a third dimension, in this case the net present value (NPV).
- Poly(ADP-ribose) polymerase inhibitors
Drugs that interfere with poly(ADP-ribose) polymerase, a protein that is involved in DNA repair and apoptosis.
The gene encoding breast cancer type 1 susceptibility protein. BRCA1 and BRCA2 genes are implicated as predictors of breast and ovarian cancer.
- Anaplastic lymphoma kinase
(ALK). Also known as ALK tyrosine kinase receptor or CD246 antigen. Direct mutations in the ALK gene or fusion of the ALK sequence with other genes are implicated in some forms of cancer.
A term used to describe patients who express the anaplastic lymphoma kinase (ALK) gene fused with the echinoderm microtubule-associated protein-like 4 (EML4) gene.
A gene that encodes the serine/threonine protein kinase BRAF protein. Inherited mutations in this gene can cause birth defects, whereas spontaneous mutations can cause cancer. The BRAF gene has a role in regulating mitogen-activated protein kinase kinase (MEK).
Mitogen-activated protein kinase kinase.
- Positive predictive values
The proportion of patients who test positive who are truly positive.
- Probability of success
The probability that a clinical trial design will detect a therapeutic effect of a given size. Also referred to as the power of the clinical trial design.
Single nucleotide polymorphism.
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Trusheim, M., Burgess, B., Hu, S. et al. Quantifying factors for the success of stratified medicine. Nat Rev Drug Discov 10, 817–833 (2011). https://doi.org/10.1038/nrd3557
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