Trusheim MR, Berndt ER, Douglas FL. Stratified medicine: strategic and economic implications of combining drugs and clinical biomarkers. Nat Rev Drug Discov 6: 287-293

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Nature Reviews Drug Discovery (Impact Factor: 41.91). 05/2007; 6(4):287-93. DOI: 10.1038/nrd2251
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The potential to use biomarkers for identifying patients that are more likely to benefit or experience an adverse reaction in response to a given therapy, and thereby better match patients with therapies, is anticipated to have a major effect on both clinical practice and the development of new drugs and diagnostics. In this article, we consider current and emerging examples in which therapies are matched with specific patient population characteristics using clinical biomarkers - which we call stratified medicine - and discuss the implications of this approach to future product development strategies and market structures.

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    • "Contract grant sponsors: Leeds CR-UK Centre Award (to G.R.T.); Yorkshire Cancer Research Program (to P.Q.); Leeds University Scholarship (to K.M.S.). Grant number C37059/A11941 from Cancer Research UK to PAC patient [Trusheim et al., 2007] "
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    ABSTRACT: Most cancers arise and evolve as a consequence of somatic mutations. These mutations influence tumor behavior and clinical outcome. Consequently, there is considerable interest in identifying somatic variants within specific genes (such as BRAF, KRAS and EGFR) so that chemotherapy can be tailored to the patient's tumor genotype rather than using a generic treatment based on histological diagnosis alone. Owing to the heterogeneous nature of tumors, a somatic mutation may be present in only a subset of cells, necessitating the use of quantitative techniques to detect rare variants. The highly quantitative nature of next-generation sequencing (NGS), together with the ability to multiplex numerous samples, makes NGS an attractive choice with which to screen for somatic variants. However, the large volumes of sequence data present significant difficulties when applying NGS for the detection of somatic mutations. To alleviate this, we have developed methodologies including a set of data analysis programs, which allow the rapid screening of multiple formalin-fixed, paraffin-embedded samples for the presence of specified somatic variants using unaligned Illumina NGS data.Laboratory Investigation advance online publication, 28 July 2014; doi:10.1038/labinvest.2014.96.
    Laboratory Investigation 07/2014; 94(10). DOI:10.1038/labinvest.2014.96 · 3.68 Impact Factor
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    • "Matching patients to treatments using particular patient characteristics (such as genetic polymorphisms or symptom profile) is called stratified or personalised medicine. Its benefits have been demonstrated with a number of anticancer medications (Trusheim et al., 2007). Psychiatrists commonly select antidepressants based on symptom profile (Zimmerman et al., 2004), but due to an absence of established clinical moderators (Simon and Perlis, 2010), there is limited evidence to inform this choice. "
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    ABSTRACT: Background Using data from the GenPod trial this study investigates: (i) if depressed individuals with multiple physical symptoms have a poorer response to antidepressants before and after adjustment for baseline Beck Depression Inventory II (BDI-II); and (ii) if reboxetine is more effective than citalopram in depression with multiple physical symptoms. Methods Linear regression models were used to estimate differences in mean BDI-II score at 6 and 12 weeks. Results Before adjusting for baseline BDI-II, the difference in mean BDI-II score between no and multiple physical symptoms was 4.5 (95% CI 1.87, 7.14) at 6 weeks, 4.51 (95% CI 1.60, 7.42) at 12 weeks. After adjustment for baseline BDI-II, there was no evidence of a difference in outcome according to physical symptoms with a difference in mean BDI-II of 2.17 (95% CI −0.39, 4.73) at 6 weeks and 2.43 (95% CI −0.46, 5.32) at 12 weeks. There was no evidence that reboxetine was more effective than citalopram in those with multiple physical symptoms at 6 (P=0.18) or 12 weeks (P=0.24). Limitations Differential non-adherence between treatment arms has the potential to bias estimates of treatment efficacy. Conclusion Multiple physical symptoms predict response to antidepressants, but not after adjustment for baseline depression severity. Physical symptoms could be a marker of severe depression rather than an independent prognostic factor and depression should be considered in patients with multiple physical symptoms. Treatment with reboxetine conferred no advantage over citalopram in those with physical symptoms, and it is less well tolerated.
    Journal of Affective Disorders 07/2014; 163:40–46. DOI:10.1016/j.jad.2014.03.051 · 3.38 Impact Factor
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    • "As Redekop and Mladsi note, most economists would be quick to point out that all medical treatments should be personalized in the sense that the physician (the agent) should advise the patient (the principal) taking into account patient preferences, the evidence base that supports the likely benefits and risks of different treatment choices, and the cost to the payer and to the patient. The important difference in PM is the use of a biomarker-based diagnostic test [3] to further define and identify a subgroup of patients for whom the treatment performs better— in terms of either cost-effectiveness or benefit-risk balance. Thus, we restrict our use of the term PM to refer to this biomarkerbased stratification. "
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    ABSTRACT: The preceding articles in this volume have identified and discussed a wide range of methodological and practical issues in the development of personalized medicine. This concluding article uses the resulting insights to identify implications for the economic incentives for evidence generation. It argues that promoting an efficient path to personalized medicine is going to require appropriate incentives for evidence generation including: 1) a greater willingness on the part of payers to accept prices that reflect value; 2) consideration of some form of intellectual property protection (e.g., data exclusivity) for diagnostics to incentivize generation of evidence of clinical utility; 3) realistic expectations around the standards for evidence; and 4) public investment in evidence collection to complement the efforts of payers and manufacturers. It concludes that such incentives could build and maintain a balance among: 1) realistic thresholds for evidence and the need for payers to have confidence in the clinical utility of the drugs and tests they use; 2) payment for value, with prices that ensure cost-effectiveness for health systems; and 3) levels of intellectual property protection for evidence generation that provide a return for those financing research and development, while encouraging competition to produce both better and more efficient tests.
    Value in Health 09/2013; 16(6):S39-S43. DOI:10.1016/j.jval.2013.06.003 · 3.28 Impact Factor
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