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Opportunities and Obligations: Vaccine Safety in the Genomics Era

Opportunities and Obligations: Vaccine Safety in the Genomics Era. Robert Davis, MD, MPH Director Immunization Safety Office CDC. Opportunities and Obligations: Vaccine Safety in the Genomics Era. History of vaccine safety issues Vaccine safety infrastructure

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Opportunities and Obligations: Vaccine Safety in the Genomics Era

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  1. Opportunities and Obligations: Vaccine Safety in the Genomics Era Robert Davis, MD, MPH Director Immunization Safety Office CDC

  2. Opportunities and Obligations: Vaccine Safety in the Genomics Era • History of vaccine safety issues • Vaccine safety infrastructure • New/future field of vaccine genomics • Some examples • Future opportunities

  3. Disease Pre-vaccine Era* Year 1999** % change Diphtheria 206,939 1921 1 -99.99 Measles 894,134 1941 86 -99.99 Mumps 152,209 1968 352 -99.76 Pertussis 265,269 1934 6,031 -97.63 Polio (wild) 21,269 1952 0 -100.00 Rubella 57,686 1969 238 -99.58 Cong. Rubella Synd. 20,000+ (1964-5) 3 -99.98 Tetanus 1,560+ 1948 33 -97.88 Invasive Hib Disease 20,000+ 1984 33 -99.83

  4. Disease Pre-vaccine Era* Year 1999** % change Diphtheria 206,939 1921 1 -99.99 Measles 894,134 1941 86 -99.99 Mumps 152,209 1968 352 -99.76 Pertussis 265,269 1934 6,031 -97.63 Polio (wild) 21,269 1952 0 -100.00 Rubella 57,686 1969 238 -99.58 Cong. Rubella Synd. 20,000+ (1964-5) 3 -99.98 Tetanus 1,560+ 1948 33 -97.88 Invasive Hib Disease 20,000+ 1984 33 -99.83

  5. Disease Pre-vaccine Era* Year 1999** % change Diphtheria 206,939 1921 1 -99.99 Measles 894,134 1941 86 -99.99 Mumps 152,209 1968 352 -99.76 Pertussis 265,269 1934 6,031 -97.63 Polio (wild) 21,269 1952 0 -100.00 Rubella 57,686 1969 238 -99.58 Cong. Rubella Synd. 20,000+ (1964-5) 3 -99.98 Tetanus 1,560+ 1948 33 -97.88 Invasive Hib Disease 20,000+ 1984 33 -99.83

  6. Disease Pre-vaccine Era* Year 1999** % change Diphtheria 206,939 1921 1 -99.99 Measles 894,134 1941 86 -99.99 Mumps 152,209 1968 352 -99.76 Pertussis 265,269 1934 6,031 -97.63 Polio (wild) 21,269 1952 0 -100.00 Rubella 57,686 1969 238 -99.58 Cong. Rubella Synd. 20,000+ (1964-5) 3 -99.98 Tetanus 1,560+ 1948 33 -97.88 Invasive Hib Disease 20,000+ 1984 33 -99.83 Vaccine Adverse Events 0 11,827^

  7. But……. No vaccine is 100 percent safe. • As more people are vaccinated, diseases decrease or even disappear • But real - and perceived - vaccine side effects increase. Public concern about the safety of vaccines • Decreased vaccination levels • Disease epidemics Alternatively, high profile disasters shake public confidence in vaccine (and drug) safety • Swine flu vaccine campaign & GBS • Rotavirus vaccine & intussusception • Vioxx & myocardial infarction • Lead to increased development costs, regulatory burden, and increased disease burden

  8. Cases 91% 81% 200,000 DTP Vaccine uptake 150,000 100,000 30% 50,000 0 1960 1970 1980 1990 Year Whooping Cough Notifications and Vaccine Coverage, England and Wales 1960-93

  9. Recent research supports safety of vaccine policy No increased risk for autism after MMR vaccine No increased risk for autism among children receiving high doses of thimerosal No increased risk for multiple sclerosis or optic neuritis after hepatitis B vaccine No increased risk for inflammatory bowel disease after MMR or MCV

  10. Recent Research But, many parents think we are asking the wrong questions • They don’t really care about population based studies _if_ they think they are somehow at ‘risk’ • Recent finding Jan 2006 of ‘autism gene’ from Utah study • Likely no single gene dictates autism • More likely that each gene - individually - raises the risk • Parents want to know ‘If my child has one or more risk genes, will the vaccines trigger autism (or some other problem)’ • This is a very good question

  11. Parents want to know ‘If my child has one or more risk genes, will the vaccines trigger autism (or some other problem)’ This fundamental idea – will my genes modify the effect of an exposure - is the biggest question today in medication safety as well as in pharmaceutical development ‘Are there some drugs that work better/are safer in some people’

  12. Personalized Genetic Medicine Personalized medicine & personalized drug delivery under intense study by NIH/NHGRI/pharmaceutical industry • Efficacy: • Beta blockers work better among carriers of specific genes (which also differ by race) • Safety: • Albuterol may not work - and may even be harmful - among asthmatics with specific genetic variations

  13. Personalized Genetic Medicine • Diagnostics • The AmpliChip CYP450 test: • first microarray-based pharmacogenomic test in clinical setting. • provides information on enzyme activity of the CYPC19 and CYP2D6 genes - genes that play particularly important roles in the metabolism of a large number of widely prescribed medications • more accurate dosing; safer dosing

  14. Personalized medicine & personalized drug delivery under intense study by NIH/NHGRI/pharmaceutical industry Efficacy: • Beta blockers work better among carriers of specific genes (which also differ by race) Safety: • Albuterol appears to work less well, and may even be harmful, among asthmatics with specific genetic variations at the pnp gene Diagnositcs • AmpliChip CYP450 test • For vaccine: Personalized approach is to understand which people are at risk for: • Vaccine adverse events • Vaccine failure

  15. Pharmacogenomics: Personalized therapies for acute/chronic conditions Typically, response to medication is observed, or can be measured Side effects and adverse medication events common – medication discontinuation, illness, lawsuits; large incentive for personalizing delivery Vaccine-genomics: Personalized vaccines to improve safety profile or vaccine responsiveness Vaccine response rarely measured in real world. Serious side effects or vaccine AE very rare. Little economic incentive for manufacturers to lead way for personalizing delivery Pharmacogenomics vs. ‘Vaccine-genomics’

  16. Goals: To understand the genetic variations that predispose children, adolescents or adults to vaccine adverse events or vaccine failure

  17. Case-control study (rare outcome) Cases: children with seizures following MMR vaccination Controls: children vaccinated with MMR who did not experience seizures Assess genetic differences between cases and controls, using either ‘candidate’ genes or ‘whole genome’ approach Optimally: identify a single polymorphism or group of polymorphisms very common in cases, uncommon in controls The typical study approach:

  18. If able to identify a single polymorphism or group of polymorphisms very common in cases yet uncommon in controls (ie high RR for disease): Assess predictive power of polymorphism(s) when applied to population How many people need to be identified & excluded from vaccination to prevent one seizure? Quantify risks and benefits of excluding children/adults from vaccination May be different depending on vaccine, outcome, likelihood of exposure to wild type disease, presence of herd immunity, etc Ex: MMR and seizures Smallpox vaccine and myocarditis Study/identify risk minimization processes Ex: tylenol to prevent febrile seizures; vaccinating at different ages; not vaccinating, etc How would results be applied?

  19. Needs: System Basic science background Technology Analytic capability Scientists Efficiencies How do we create the system necessary for the optimal scientific study?

  20. System needs: Need to have capacity to ascertain rare events after vaccination On the order of 1/1000 to 1/10,000 (or even rarer) Cannot be done with premarketing or even postmarketing clinical trials Option 1: VAERS (Vaccine Adverse Events Reporting System) Passively reported VAE Option 2: Population based setting Active identification of VAEs possible Advantage: full spectrum of VAE unbiased ascertainment How do we create the system necessary for the optimal scientific study?

  21. Systems: Vaccine Safety Datalink • Began in 1991 as a collaborative project between CDC and four HMOs: • Group Health Cooperative, Seattle, WA • Northwest Kaiser Permanente, Portland, OR • Northern California Kaiser Permanente, Oakland • South California Kaiser Permanente, Los Angeles • Expanded in 2000 to include four more HMOs: • Harvard Pilgrim Health Care, Boston, MA • HealthPartners, Minneapolis, MN • Kaiser Permanente Colorado, Denver, CO • Marshfield Clinic, Marshfield, WI • Total over 10 million members How do we create the system necessary for the optimal scientific study?

  22. Vaccine Safety Datalink (VSD) Patient Characteristics (Birth records) (Census) Health Outcomes (Hospital) (ER) (Clinic) Vaccination Records VSD Linked Analysis Database

  23. Needs: System Basic science background Technology Analytic capability Scientists Other: Efficiencies How do we create the system necessary for the optimal scientific study?

  24. Needs: Basic science background Understanding of pathways involved in potential VAEs Basic disease pathogenesis Inflammation pathways Immune response pathways Used to identify potential candidate genes and candidate gene pathways For many (if not most) of VAEs, this is currently unknown Distinct from medication AE related (for ex) to cyp450 pathway How do we create the system necessary for the optimal scientific study?

  25. Needs: System Basic science background Technology Analytic capability Scientists Other: Efficiencies How do we create the system necessary for the optimal scientific study?

  26. Needs: Technology Analytic capability Technology: Use of 500K chips for SNP analysis becoming more routine Could partner with producers of chips (Affy; Illumina etc) for cost, individualized production etc Specimen collection: typically blood samples – (buccal swabs or other in future offer possibility of ‘remote’/streamlined collection of specimens from case/family) Data tracking one of major challenges of Human Genome Project Will need attention in any future endeavors for vaccine genomics How do we create the system necessary for the optimal scientific study?

  27. Needs: Technology Analytic capability 500K chips give information on 500,000 single nucleotide polymorphisms Challenges: ‘typical’ logistic regression analysis has 10-100 covariates (not 500K) 1. Running chips is a specialized ‘knowledge/capability’ 2. Need mainframe computers for data storage and analysis 3. Need advanced/new biostatistical algorithms for fitting models 4. Almost guaranteed to find more false than true positives 5. Individual SNPs might not be as important or illuminating as haplotypes How do we create the system necessary for the optimal scientific study?

  28. Needs: Analytic capability 1. Running chips is a specialized ‘knowledge/skill’ 2. Need mainframe computers for data storage and analysis Need to create this capability (ie within CDC) or collaborate with academic partners 3. Need advanced/new biostatistical algorithms for fitting models Needs specialized collaborations with biostatistical genetics and genetic epidemiology How do we create the system necessary for the optimal scientific study?

  29. Needs: Analytic capability 4. Almost guaranteed to find more false than true positives For candidate genes: can use standard approach For non-candidate genes: (a) assess strength and consistency of association; (b) assess biologic plausibility (if possible) (c) replicate, replicate, replicate 5. Individual SNPs might not be as important or illuminating as haplotypes How do we create the system necessary for the optimal scientific study?

  30. Needs: For identification of cases, selection of controls, and enrollment Knowledge of vaccine/schedule/adverse events Collaborative network with organizations/populations of interest Historically: infectious disease specialists; epidemiologists For basic science/gene pathways: Immunologists/infectious disease specialists Geneticists For analysis: Collaboration with partners with capabilities to run samples Biostatisticians/genetic epidemiologists to analyze data How do we create the system necessary for the optimal scientific study?

  31. How do we create the system necessary for the optimal scientific study? Needs: System Basic science background Technology Analytic capability Scientists Other: Efficiencies How do we create the system necessary for the optimal scientific study?

  32. Needs: Efficiencies Consider moving away from specific control groups. Option: genotype 1000 people from each HMO and use that as a standard control group for every study Expensive to begin with, but saves cost savings and more efficient in the long run How do we create the system necessary for the optimal scientific study?

  33. How do we create the system necessary for the optimal scientific study? Presently: System: exists in integrated fashion (VSD) Basic science background/scientific expertise: needs concentration/integration Technology/Analytic capability: available; needs coordinated approach Efficiencies: needs evaluation How do we create the system necessary for the optimal scientific study?

  34. Vaccine Safety Case Study:Rheumatoid Arthritis and Hepatitis B Vaccine

  35. Rheumatoid Arthritis: Background • Chronic autoimmune disease • Population prevalence of 1-2% worldwide • Over 7 million affected in U.S. • <50% 5 year survival rate among most severely affected

  36. Why study genetics & vaccination with regard to rheumatoid arthritis? • RA: HLA DR4 associated with increased susceptibility to disease • Hepatitis B infection: HLA DR3 associated with altered susceptibility • Reports of RA among HB vaccine recipients: HLA-DR types that either increase RA susceptibility or HBV response

  37. Study Question • Are there specific genes that predispose to Rheumatoid arthritis following hepatitis B vaccination?

  38. Study design options: • Cohort: Are rates of RA increased among vaccine recipients relative to non-HBV recipients? • Specifically, are rates of RA particularly increased among persons with specific genetic polymorphisms (ie of HLA DR4) compared to those persons without such polymorphisms? • Case-control: Is HBV receipt over-represented among subjects with RA compared to controls? • Specifically, is combination of HBV and certain polymorphisms over-represented among cases compared with controls?

  39. Flow chart for case identification, sample collection, and data analysis.All persons ages 15 to 59 with continuous HMO membership from 1/1/95 to 12/31/99|Computer Definition of Possible RA Cases|Chart Review of Possible RA Cases|Rheumatologist Review of Selected Cases todetermine if chart review is adequate to satisfy1987 ACR criteria for RA|Data analysis (initial Kaiser retrospective study)Obtain permission from NCK personal physicians to contact RA patients|Pts. Invited to Enroll in RA-HBV Genetic Studies|Pts. Consented for Study, Blood Drawn|Blood Shipped, Fed Express, o/n to Atlanta|HLA and Hepatitis Antibody Testing|Data Analysis(genetic case-only substudy)

  40. In a separate study of RA, Celera Diagnostics: identified & replicated ~ 22 SNPs in RA patient samples includes R620W missense SNP in PTPN22* VSD study has assessed the frequency of these gene SNPs among cases in addition to the HLA DR4 polymorphisms listed previously *Begovich et al. 2004 AJHG 75:330

  41. Power calculations for gene-environment study of RA, hepatitis B vaccination, and HLA-polymorphismsIf:Genetic risk (HLA-DR4) OR = 5 (conservative)Environmental risk (HBV) OR = 2 (likely over-estimate)And:If DR4 prevalence is ~15% (NCK population of caucasians, NA and AA)Will have 80% power, alpha = 0.05 to detect interaction of 10 HB coverage Sample size needed 2% ~ 200 5% ~100 10% ~50

  42. Screen VSD data-sets yearly Identify subjects/collect specimens on cases q yr: febrile seizures; severe limb swelling q 5 yrs: arthritis; prolonged crying; q 10 yrs: encephalopathy; GBS; anaphylaxis w/high profile situations: ie intussusception;GBS Run genome-scans (500K chips or higher) on cases Compare with standard age, HMO, race matched controls Opportunities and Obligations: Vaccine Safety in the Genomics Era

  43. Vision for the Future: Screen VSD data-sets yearly Identify subjects/collect specimens on cases q yr: febrile seizures; severe limb swelling q 5 yrs: arthritis; prolonged crying; q 10 yrs: encephalopathy; GBS; anaphylaxis w/high profile situations: ie intussusception;GBS Run genome-scans (500K chips or higher) on cases Compare with standard age, HMO, race matched controls Assess findings for _candidate_ genes Generate new set(s) of potential candidate genes/pathways for next iteration Opportunities and Obligations: Vaccine Safety in the Genomics Era

  44. Opportunities and Obligations: Vaccine Safety in the Genomics EraConclusions • Study of vaccine genomics just beginning to get underway • Evaluations of gene-environment interactions (HLA-HBV and RA; MMR and FH epilepsy and febrile seizures) can be wrapped into large database infrastructure (US VSD; Scandinavian population-based studies) • Other studies not discussed today (ie HLA control of antibody response to specific vaccination) can be accomplished within the venue of prelicensure clinical trials

  45. Opportunities and Obligations: Vaccine Safety in the Genomics EraChallenges on the horizon • New vaccines • Rotavirus • HPV • Acellular pertussis • MMR-V, and many more • Increased focus on adolescents and adults (meningococcal; varicella; etc) • Different diseases/potential adverse events (ie autoimmune) • Increasingly packed schedule • Relatively unknown safety profile

  46. Opportunities and Obligations: Vaccine Safety in the Genomics EraVision into the Future CDC has a critical role for integrating genomics into vaccine safety • Infrastructure (collaborations) • Only CDC – with VSD and VAERS – able to identify subjects with rare AEs • Scientists with the expertise in understanding adverse events Forge collaborations with genomics community • Begin to understand how genetic variation underlies VAE • Understand how to identify people at increased risk, and devise alternate immunization strategies

  47. Is there evidence from the literature that interactions between vaccination and ‘subgroups’ exist?“MMR Vaccination and Febrile Seizures. Evaluation of Susceptible Subgroups and Long-term Prognosis” JAMA Vol. 292 No. 3, July 21, 2004 Vestergaard, Hviid, Madsen etal

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