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Commercializing the Next Generation of Molecular Diagnostics and .

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Show Notes

Slide 1 :

March 13, 2008 MSH-OTTIL Commercializing the Next Generation of Molecular Diagnostics and Therapeutics ~ Addressing the Gap in Biomarker Validation ~ Presented by The Office of Technology Transfer & Industrial Liaison

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March 13, 2008 MSH-OTTIL The Agendia MammaPrint Success Story… 2002 – Discovery of 70 gene signature (117 patients) 2002 – Duplication of results (in another sample set: 295 patients) 2006 – Assay performance 2006 – Optimized array format: reproducibility; back to original sample set 2006 – External confirmation (307 patients, 5 hospitals) 2007 – Approval by FDA

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March 13, 2008 MSH-OTTIL Recently published . . .

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March 13, 2008 MSH-OTTIL EGAPP to Release Evaluations of Three New Genetic Tests By Year End [March 11, 2008] BETHESDA, Md. -- The Evaluation of Genomic Applications in Practice and Prevention group will release three new sets of recommendations later this year about gene expression profiling in breast cancer, genetic testing for lynch syndrome in colorectal cancer patients, and testing for UGT1A1 in colorectal cancer patients treated with irinotecan. These three evaluations are currently in draft form, according to Alfred Berg, chair of the EGAPP Working Group, who spoke here yesterday at a conference sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases. Of the three forthcoming recommendations, the one investigating gene expression profiling in breast cancer is the furthest along. "We found limited evidence of analytic validity, limited evidence of clinical validity. Of course no direct evidence, meaning controlled trials testing clinical outcomes or clinical utility. There are mixed estimates of cost-effectiveness," said Berg, who also chairs the University of Washington's Department of Family Medicine.

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March 13, 2008 MSH-OTTIL Although AHRQ found no evidence suggesting that genomic tests for ovarian cancer have adverse effects beyond those common to other ovarian cancer tests, which primarily include the risks of diagnosis for false-positive results and the risks of delayed or inappropriate treatment of false-negative results, "model simulations suggest that annual screening, even with a highly sensitive test, will not reduce ovarian cancer mortality by more than 50 percent." According to Berg, the majority of the genomic technologies evaluated by EGAPP have been found lacking in evidence to support their clinical utility and validity. With these three recommendations, EGAPP will outline its investigational methodology, particularly its system for determining the clinical outcomes of these assays, "in great detail," he added. Prior to its assessment of SSRI genetic testing, AHRQ released its report on ovarian cancer detection and management, in which it evaluated tests for single gene products, genetic variations affecting risk of ovarian cancer, gene expression, and proteomics for CA-125 and BRCA1/2.

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March 13, 2008 MSH-OTTIL Predictive genomic profiling used to produce personalized nutrition and other lifestyle health recommendations is currently offered directly to consumers. By examining previous meta-analyses and HuGE reviews, we assessed the scientific evidence supporting the purported gene-disease associations for genes included in genomic profiles offered online. We identified seven companies that offer predictive genomic profiling. We searched PubMed for meta-analyses and HuGE reviews of studies of gene-disease associations published from 2000 through June 2007 in which the genotypes of people with a disease were compared with those of a healthy or general-population control group. The seven companies tested at least 69 different polymorphisms in 56 genes. Of the 56 genes tested, 24 (43%) were not reviewed in meta-analyses. For the remaining 32 genes, we found 260 meta-analyses that examined 160 unique polymorphism-disease associations, of which only 60 (38%) were found to be statistically significant. Even the 60 significant associations, which involved 29 different polymorphisms and 28 different diseases, were generally modest, with synthetic odds ratios ranging from 0.54 to 0.88 for protective variants and from 1.04 to 3.2 for risk variants. Furthermore, genes in cardiogenomic profiles were more frequently associated with noncardiovascular diseases than with cardiovascular diseases, and though two of the five genes of the osteogenomic profiles did show significant associations with disease, the associations were not with bone diseases. There is insufficient scientific evidence to conclude that genomic profiles are useful in measuring genetic risk for common diseases or in developing personalized diet and lifestyle recommendations for disease prevention. A Critical Appraisal of the Scientific Basis of Commercial Genomic Profiles Used to Assess Health Risks and Personalize Health Interventions Marta Gwinn Linda A. Bradley, Ben A. Oostra Cornelia M. van Duijn, Muin J. Khoury The American Journal of Human Genetics, Volume 82, Issue 3, 593-599, 3 March 2008

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March 13, 2008 MSH-OTTIL

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March 13, 2008 MSH-OTTIL

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March 13, 2008 MSH-OTTIL Evaluation Process of a Biomarker Test Model for evaluating data on emerging tests Steps: collection, interpretation, reporting data that allow access to reliable information End-result: decision making By-product: identification of gaps in knowledge

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March 13, 2008 MSH-OTTIL The Evaluation Process for Biomarker Testing Disorder & Setting Analytical Validity Clinical Validity Clinical Utility Ethical, Social, Legal implications

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March 13, 2008 MSH-OTTIL Disorder/Setting Targeted Questions for Comprehensive Assessment of a Biomarker Test 1. What is the specific clinical disorder to be studied? 2. What are the clinical findings defining this disorder? 3. What is the clinical setting in which the test is to be performed? 4. What DNA test(s) are associated with this disorder? 5. Are preliminary screening questions employed? 6. Is it a stand-alone test or is it one of a series of tests? 7. If it is part of a series of screening tests, are all tests performed in all instances (parallel) or are only some tests performed on the basis of other results (series)?

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March 13, 2008 MSH-OTTIL 8. Is the test qualitative or quantitative? 9. How often is the test positive when a mutation is present? 10. How often is the test negative when a mutation is not present? 11. Is an internal QC program defined and externally monitored? 12. Have repeated measurements been made on specimens? 13. What is the within- and between-laboratory precision? 14. If appropriate, how is confirmatory testing performed to resolve false positive results in a timely manner? 15. What range of patient specimens have been tested? 16. How often does the test fail to give a useable result? 17. How similar are results obtained in multiple laboratories using the same, or different technology? Analytic Validity

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March 13, 2008 MSH-OTTIL Clinical Validity 18. How often is the test positive when the disorder is present? 19. How often is the test negative when a disorder is not present? 20. Are there methods to resolve clinical false positive results in a timely manner? 21. What is the prevalence of the disorder in this setting? 22. Has the test been adequately validated on all populations to which it may be offered? 23. What are the positive and negative predictive values? 24. What are the genotype/phenotype relationships? 25. What are the genetic, environmental or other modifiers?

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March 13, 2008 MSH-OTTIL Clinical Utility 26. What is the natural history of the disorder? 27. What is the impact of a positive (or negative) test on patient care? 28. If applicable, are diagnostic tests available? 29. Is there an effective remedy, acceptable action, or other measurable benefit? 30. Is there general access to that remedy or action? 31. Is the test being offered to a socially vulnerable population? 32. What quality assurance measures are in place? 33. What are the results of pilot trials? 34. What health risks can be identified for follow-up testing and/or intervention? 35. What are the financial costs associated with testing? 36. What are the economic benefits associated with actions resulting from testing? 37. What facilities/personnel are available or easily put in place? 38. What educational materials have been developed and validated and which of these are available? 39. Are there informed consent requirements? 40. What methods exist for long term monitoring? 41. What guidelines have been developed for evaluating program performance?

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March 13, 2008 MSH-OTTIL 42. What is known about stigmatization, discrimination, privacy/confidentiality and personal/family social issues? 43. Are there legal issues regarding consent, ownership of data and/or samples, patents, licensing, proprietary testing, obligation to disclose, or reporting requirements? 44. What safeguards have been described and are these safeguards in place and effective? ELSI

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March 13, 2008 MSH-OTTIL The Commercialization Process

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March 13, 2008 MSH-OTTIL The Commercialization Process Many steps are needed in order to move from the lab to the market Discovery Scientific expertise and appropriate technology platforms Collaboration among scientists, clinicians, statisticians Access to samples Discovery based on multiple technologies, access to samples, scientific/technical expertise, multi-disciplinary teams Confirmation/validation Technical validation Clinical validation Assay development Based on access to, or development of assay systems, reagents, technology platforms Regulatory approval Implementation/marketing Clinical labs, commercial partners, strategic business decisions

Slide 18 :

March 13, 2008 MSH-OTTIL

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March 13, 2008 MSH-OTTIL Later-Stage Programs = Higher Value Discovery Validation Approval Marketing Value Risk

Slide 20 :

March 13, 2008 MSH-OTTIL Biomarker Research and Development Discovery Validation Qualification Regulation

Slide 21 :

March 13, 2008 MSH-OTTIL Validation is the process of assessing the assay, or measurement performance characteristics Purpose: Reliable biomarker data that meets the experiment or study objective Qualification is the evidentiary process of linking a biomarker with biology and clinical endpoints Purpose: to generate reliable biomarker data that is scientifically and clinically meaningful How Can You Add Value to your Biomarker? Validation and Qualification

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March 13, 2008 MSH-OTTIL Phases of Qualification

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March 13, 2008 MSH-OTTIL

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March 13, 2008 MSH-OTTIL Sample size increases with parameters measured Measurement of thousands of bioanalytical variables is not restrictive for sample size Unpaired comparisons, e.g. disease vs healthy Bonferroni methods – control for study-wise false positives Total P (false positives) < 0.05, power = 90% Effect size (Mean difference/SD) N (Samples per group) Variables 1 10 100 1000 5000

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March 13, 2008 MSH-OTTIL

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March 13, 2008 MSH-OTTIL Case Study: Development of a Diagnostic Test for Pre-Term Labour

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March 13, 2008 MSH-OTTIL Discovery Microarray-based gene expression profiling

Slide 28 :

March 13, 2008 MSH-OTTIL Early validation Extension of earlier work to include 200 additional patients (duplication study) Affymetrix microarrays From Gene to Protein 200 serum samples from same prospective cohort Proteomic analysis (MALDI-TOFF) Previous gene findings will direct protein search Identification of a protein signature Duplication study: reproducibility in different sample set Develop immunoassay Prospective longitudinal study with IA format Seek commercial partners (Atlas Genetics and others) PTL Panel

Slide 29 :

March 13, 2008 MSH-OTTIL Discovery Duplication (retrospective longitudinal) Replication Prospective Screening Independent Validation Sample no. Biomarker 40 40 200 Genomic profile Proteomic profile Platform UHN arrays MS Affy arrays MS Affy arrays MS Focus array format Protein IA >200 >200 Study Genomic profile Proteomic profile Gene signature Protein markers Gene signature Protein markers Gene signature Protein marker Focus array format Protein IA

Slide 30 :

March 13, 2008 MSH-OTTIL Slides All slides are available online at: www.mshri.on.ca/techtransfer/seminars.html

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