www.hhs.govnvponvacdocumentsDavisNVACISOfeb06.ppt

Document Sample
www.hhs.govnvponvacdocumentsDavisNVACISOfeb06.ppt Powered By Docstoc
					          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
  New/future field of vaccine genomics
  Some examples
  Future opportunities
       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
       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
         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
       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^    
                                    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
Whooping Cough Notifications and Vaccine 
  Coverage, England and Wales 1960-93
   Cases                                                 91%
   200,000                  81%


  150,000                 DTP Vaccine 
                            uptake

  100,000
                                            30%

   50,000


           0           
              1960          1970                1980   1990
                                         Year
       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
                                  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
                                    
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’
                   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 
                     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
•   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
             Pharmacogenomics vs. ‘Vaccine-genomics’

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
Goals:

To understand the genetic variations that predispose children,
adolescents or adults to vaccine adverse events or vaccine failure
The typical study approach:



 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
How would results be applied?


 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 do we create the system necessary for the optimal scientific
study?


  Needs:

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


 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?


 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
              Vaccine Safety Datalink (VSD)




                      Health                     Patient
Vaccination
                     Outcomes                 Characteristics
 Records               (Hospital)               (Birth records)
                         (ER)                      (Census)
                        (Clinic)




                  VSD Linked
               Analysis Database
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?
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?


 Needs:

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


 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?


 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?

 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?

 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?


 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?

 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?

 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?

 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
    Vaccine Safety Case Study:
Rheumatoid Arthritis and Hepatitis B 
             Vaccine 
       Rheumatoid Arthritis: Background

n Chronic autoimmune disease
n Population prevalence of 1-2% worldwide

n Over 7 million affected in U.S.

n <50% 5 year survival rate among most severely 

  affected
    Why study genetics & vaccination with regard 
              to rheumatoid arthritis?

n RA: HLA DR4 associated with increased 
  susceptibility to disease
n Hepatitis B infection: HLA DR3 associated with 

  altered susceptibility
n Reports of RA among HB vaccine recipients: HLA-

  DR types that either increase RA susceptibility or 
  HBV response
                      Study Question

n   Are there specific genes that predispose to 
    Rheumatoid arthritis following hepatitis B 
    vaccination?
                      Study design options:

n   Cohort: Are rates of RA increased among vaccine recipients relative to 
    non-HBV recipients?
      n Specifically, are rates of RA particularly increased among persons 

        with specific genetic polymorphisms (ie of HLA DR4) compared to 
        those persons without such polymorphisms?
n   Case-control: Is HBV receipt over-represented among subjects with RA 
    compared to controls?
      n Specifically, is combination of HBV and certain polymorphisms over

        -represented among cases compared with controls?
        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 to
                    determine if chart review is adequate to satisfy
                               1987 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)
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
Power calculations for gene-environment study of RA, 
hepatitis B vaccination, and HLA-polymorphisms

If:
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
Opportunities and Obligations: Vaccine Safety in the Genomics Era




 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


 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 
                     Conclusions
• 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 
    Opportunities and Obligations: Vaccine Safety in the 
                       Genomics Era 
                 Challenges 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
 Opportunities and Obligations: Vaccine Safety in the 
                    Genomics Era 
                Vision 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 
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 et al
It is known that the risk for febrile seizures is increased after
MMR vaccination.

Is this increase even higher among particular people:
          personal or family history of seizures
          perinatal factors
          socioeconomic status.

The latter 2 are environment, but the first (family history) gives
some glimpse of genetic risk factors that might interact with
environment (MMR vaccination)
 Setting:

• Population-based cohort study of all children born in Denmark 
  1/1/91 – 12/31/98 and who survived to 3 months of age: total of 
  537,171 children 

• Unique personal ID allowed link to mother and father
• (similar database construction underway within VSD)
Vaccination:
• National Board of Health maintains ‘registry’ of 
  MMR vaccinations administered in Denmark
• Collected info on vaccinations given 1/1/91 – 
  12/31/99
• Jeryl-Lynn strain used/studied
• (recommended) Age of vaccine: 15 months
• Exact date of vaccine not available: imputed day of 
  week as Wednesday.
Outcome ascertainment:

•   Incidence of first febrile seizure, recurrent febrile seizures, and subsequent 
    epilepsy, gathered from National Hospital Registry which includes 
    inpatient, outpatient and emergency department visits.

•   For febrile seizures, excluded children with history of non-febrile seizures, 
    cerebral palsy, head trauma, intracranial tumors, meningitis and encephalitis 
    (this clarifies the ‘phenotype’ of febrile seizure).

•   Used ICD-10 coding schema
Evaluation of high risk strata (effect modifiers):


• Personal history of febrile seizures

• Family history 

• Birth characteristics and sociodemographic factors
Statistical Analysis:



• Poisson regression. Entered at 3 mo of age. Censored at first 
  diagnosis of FS, epilepsy, death, emigration, 
  enceph/mening/head injury, or age 5 years or 12/31/99



• MMR vaccination: analyzed as time varying covariate
Subgroup analysis: tested for statistical interaction


•   Question: Is the RR for children with family history (FH) of epilepsy higher 
    than RR for children without family history of epilepsy

    Risk after MMR vaccination among children with FH
    Risk not after MMR vacc among children with FH
    Vs
    Risk after MMR vaccination among children without FH
    Risk not after MMR vacc among children without FH
Results

  
• 439,251 children (82%) received MMR vaccination 
• 17,986 children developed febrile seizures at least once
• RR for FS in the 2 wks after MMR vaccination:  2.75 (95% CI 
   2.55-2.97)
• RR did not vary significantly in the subgroups of children:
   – family history of seizures
   – perinatal factors
   – socioeconomic status
Subgroup analysis, continued

•   RR was higher among the subgroup of children with
    family history of epilepsy
                   RR of 4 following vaccination among subjects 
                   with + family history
                   RR of 2.7 after vaccination among subjects 
                   with – family history
However, story is different when evaluated using risk 
difference metric:



• Among children with family history of epilepsy, 
  vaccination adds 3.4 additional seizures per 1000 
  persons vaccinated
      (Due to higher RR (4) acting on a lower baseline 
      risk among non-vaccinated children (compared to 
      children with personal history of FS)
Significant findings:


•   When viewed on a multiplicative scale, there is only interaction noted 
    among children with a family history of epilepsy (RR of 4 following 
    vaccination among those with family history of epilepsy vs RR of ~2.7 
    among those without family history of epilepsy)

•   However, viewed on an additive scale, the message is much different: 
     – 1.6 additional seizures per 1000 children overall
     – 19.5 additional seizures per 1000 among children with personal history
     – 3.4 additional seizures per 1000 among children with FH epilepsy

•   Additive scale is influenced by baseline risk, and give a more clinically 
    relevant picture of the risk to the particular person
      Example of rapid response to emerging public health 
•
                                          question
    New conjugate meningococcal vaccination licensed in January 2005

     –   Recommended for universal use among adolescents in February 2005

           •   Distribution commenced in March 2005.  


•   By December, 2005, 7 reports of Guillain-Barre Syndrome within six weeks following Menactra administration 
    had been received by the VAERS passive reporting system.  

     –   All cases were among 17-19 year olds, and occurred 11-31 days following vaccination.  

•   Quickly mobilized VSD and studied 110,000 vaccinees.  

     –   Comparison with background rates indicated that the number of reported cases was ~ ‘expected’

     –   MMWR publication notified public and medical community
     –   But did not result in additional reports

•   Result:
     –   Vaccine remained on market
     –   Planning for further studied underway
                        Vision into the Future 
Three major goals

1) Establish broad input into the research agenda for Immunization Safety 
   Office
   – External advisory panel comprised of wide range of stakeholders including federal 
     agencies, major medical organizations, FDA, CMS, NIH, DoD, parental groups to help 
     shape vaccine safety research plan  

2) Active surveillance of new vaccines
   – Weekly updates from VSD and potentially other MCOs
   – ‘Real-time’ assessments of vaccine safety
                          Vision into the Future 

3) Bring vaccine safety research into the genomics era

   – Personalized medicine & personalized drug delivery under intense study by 
     NIH/NHGRI/pharmaceutical industry

   – Understand which people/subgroups are at increased risk for:
       • Vaccine adverse events
       • Vaccine failure
The Road to a Public Health Approach to Pharmacogenomics will be a
                     long and raucous journey
                            Immunization Safety Office
Vaccine Safety Datalink (VSD) project
     –   Collaborative project with comprehensive medical and immunization histories of over 7.5 million people. 
     –   Studies health problems among vaccinated people compared with unvaccinated people.  
     –   Currently using in-depth evaluations to study the safety of thimerosal in vaccines, and risk for autism following vaccination.

Vaccine Adverse Event Reporting System (VAERS) 
     –   An early-warning passive surveillance system to detect problems related to vaccines.  

Clinical Immunization Safety Assessment (CISA) Network
     –   Provides in-depth, standardized clinical evaluations for individuals with unusual or severe vaccine adverse events to 
         understand virologic, immunologic and genetic markers for post-vaccination adverse events. 

Brighton Collaboration
     –   A global collaboration to standardize case definitions and the study of vaccine reactions, providing a common “vocabulary” 
         for vaccine safety research,

Vaccine Acceptance and Risk Perception (VARP) 
     –   Scientific study  of interventions that increase vaccine acceptance

Vaccine Technology 
     –   Development of safer vaccines and delivery (needle-free jet injectors) 
         Importance of Vaccine Safety
n   Higher standard of safety expected of vaccines
      –"First do no harm" (primum non nocere)
      –Moral duty: public health clinical medicine
      –Vaccinees generally healthy (vs. ill for drugs)
      –Vaccinations universally recommended or mandated
n   Lower risk tolerance = search for rare reactions
      –vaccine < 1/100,000 vs. drug: 1/1 - 1/1000
n   Studies of rare events:
      –More costly and difficult
      –Less likely to be definitive
      –Has, up until now, largely ignored issues of ‘subgroup
       susceptibility’
         Lines of Evidence Suggesting Causality

n   Temporal association
      –illness follows exposure
      –cases cluster within definable time after vaccination
n   Biologic plausibility
      –animal models
      –tissue culture or other models
n   Specificity
      –unique clinical picture
      –unique laboratory result
n   Epidemiologic evidence
      –risk of illness > expected by chance
               IOM Vaccine Safety Report, 1991-94: Conclusions

    Pert      Rub       DT/Td/T        Meas Mump               Polio     Hep B          Hib
Categ 1 Autism                                       Neuropathy Transverse
No                                                   Residual    myelitis (IPV)
Evidence                                              seizure   Thrombocytopenia
                                                                Anaphylaxis
Categ 3 Infantile spasms      Encephalopathy                                       Early onset Hib
Favors Hypsarrythmia          Infantile spasm (DT)                                    (conjugate)
Rejection Reye syndrome       SIDS (DT)
          SIDS

Categ 4 Acute   Chronic      GBS           Anaphylaxis          GBS (OPV)*           Early onset
Favors encepha- arthritis                                                             Hib 18m
Accept lopathy                                                                     (unconjugated
        Shock                                                                         PRP)

Categ 5 Anaphylaxis Acute      Anaphylaxis Thrombocytopenia      Polio (OPV) Anaphylaxis
Establish Protracted arthritis             Anaphylaxis (MMR)    Death (OPV)
Causal     Crying                          Death
Traditional Vaccine Safety Studies: Pre-Licensure
 n   Laboratory
 n   Animals
 n   Humans
       –Phases I: gross toxicity (N: ~ 10)
       –Phase II: dosing range/ reactogenicity (N: 10-100)
       –Phase III: efficacy (+ preliminary safety) (N: 1000-10,000)
       –Advantages:
           èClose, detailed follow-up

           èRandomized, placebo-controls => causality assessment easy

       –Disadvantage:
           èPoorly detected reactions: rare, delayed onset, subpopulations

           èNo standard case definition for "safety"
Traditional Vaccine Safety Studies: Post-Licensure

n   Traditional tools
      –passive surveillance (spontaneous reporting system)
      –ad hoc controlled epidemiologic studies
n   New tools
      –Phase IV trials "linked" to licensure of new vaccine
          èLarge-Linked Database (LLDB) in HMO population

          èN ~10,000

      –pre-organized LLDB's
          èongoing safety monitoring

          ècontrolled epidemiologic studies

      –"enhanced" passive surveillance as "registry"
Traditional Passive Surveillance (e.g., VAERS) 

 n   Strengths
       –National in scope
       –Timeliness
       –Relative low cost


 n   Weaknesses
      –Under-, biased reporting
      –Complexity
         èmultiple "exposures" + "outcomes"

         èdetect "new" + change "known" AE's

         èmix of causal and coincidental events

      –Generally unable to assess causality
    Institute of Medicine (IOM) Reports on
    Vaccine Safety
n   "Many gaps and limitations" in current knowledge + research 
    capacity:
      – Infrastructure for vaccine safety surveillance inadequate

      – Needed: Population laboratory under active surveillance
                        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
     – HealthPartners, Minneapolis
     – Harvard Pilgrim Health Plan, Boston
     – Kaiser Colorado, Denver
     – Marshfield Clinic, Wisconsin
•   Total over 6 million members
    Advantages of HMOs for Health 
              Research
• Identifiable (large) population
    – incidence rates and attributable risks
•   Computerized data bases
•   Cost data
•   Integrated systems
•   Infrastructure
        VSD Analytic Approach
• Screening analyses (automated data)
  – preliminary assessment of vaccine-outcome 
    associations
• In-depth studies (chart reviews, interviews)
  – validate outcomes (and dates)
  – verify vaccination history (and dates)
  – additional risk factor or clinical information
    Selected Findings from VSD 
              Studies
• No increased risk of chronic arthropathy among 
  women receiving rubella vaccine
• No increased risk of aseptic meningitis after 
  Jeryl-Lynn mumps vaccine (in U.S. MMR)
• Risk of clinical events after 2nd MMR greater 
  at 10-12 than at 4-6 years of age
      Selected Current VSD Studies
•   Neonatal and infant mortality
•   Wheezing and asthma
•   Timing of vaccination and type 1 diabetes
•   MMR vaccine and IBD
•   Hepatitis vaccination and risk of MS
•   Possible sequelae of thimerosal in vaccines
•   Rotavirus vaccine and intussusception
      Diversity of Research Projects
• VSD database and infrastructure allow a wide 
  range of studies in addition to vaccine safety
  –   vaccination coverage
  –   cost studies of different policies or strategies
  –   clinical trials
  –   descriptive epidemiology
                Research in MCOs: 
                     Caveats
• Identifiable (large) population
   – enrollment and disenrollment
• Computerized data bases
   – not developed for research
   – Dynamic
• Cost data
   – may not be generalizable
• Integrated systems
   – not true of many health plans
• Infrastructure
   – not in place in all health plans
   – research infrastructure often not present
     Research in Managed Care 
     Organizations: Conclusions
• Managed care is the dominant health care 
  delivery system in the U.S.
• MCO’s provide great opportunities for 
  population-based health research
• Immunization research in MCO’s allows timely 
  and efficient monitoring of vaccine safety
      The Public Health Approach to Pharmacogenomics




Goal: Personalized delivery of therapeutics that accounts for the genetic
  variation of the patient
       The Public Health Approach to Pharmacogenomics


Goal: Personalized delivery of therapeutics that accounts for the genetic variation
  of the patient

Thesis:
   Gene-based diagnostic tests are very powerful
        Have distinctive risk/benefit profiles
        May have unintended effects

   Therefore, the default for gene-based diagnostic tests and for pharmacogenetics
   should be:
        RCTS
        Good observational studies

   A requirement linked to licensure
The Public Health Approach to determine the real world effectiveness of
pharmacogenomics and monitor its applications


Proper Public Health Use of Genetic Tests and Pharmacogenomics

How do we get from here to here?

Identification of                                      Appropriate use of
gene-disease                                           genetic testing
association


Not all outcomes research means the same thing…



Evidence                   Integrating                 Surveillance
Of                         Evidence
Effectiveness
The Public Health Approach to determine the real world effectiveness of
pharmacogenomics and monitor its applications


Evidence               Integrating              Surveillance
Of                                   Evidence
Effectiveness

What is evidence?
The basic science approach:
•   Variations in cyp450 polymorphisms are common
•   Medications for cardiovascular disease and depression are among the most comonly used, and are a
    significant cost driver
•   Use of these medications may produce adverse effects and/or difficulties in obtaining proper
    therapeutic index.
•   Polymorphisms of the cyp450 pathway plays a role in responsivenes

•   The basic science approach addresses the evidence about how cyp450 and medications interact to
    affect responsiveness
The Public Health Approach to determine the real world effectiveness of
pharmacogenomics and monitor its applications


Evidence            Integrating                   Surveillance
Of                            Evidence
Effectiveness

What is evidence?
The public health approach:
• Polymorphisms of the cyp450 pathway plays a role in responsiveness

•   Do these polymorphisms affect measurable clinical outcomes?
     – Increased morbidity/mortality?
     – Increased costs of health care?
     – Decreased quality of life?

•   The public health approach asks
     – Given that cyp450 pathway and medications work together to affect responsiveness,
       does it matter?
     – Is there a better way to deliver rx for some people?
The Public Health Approach to determine the real world effectiveness of
pharmacogenomics and monitor its applications


Evidence                        Integrating                    Surveillance
Of                              Evidence
Effectiveness

The public health approach:


•   In addition to wanting to know what happens at the ‘overall population’ level,
    the PH approach also wants to know what happens in these subpopulations:
     –   With drug interactionsi.e. CV and other medications
     –   Elderly                          i.e. diminished cardiac function
     –   Pediatrics                       i.e. different disease?
     –   Different ethnic groups          i.e. gene-gene-drug interactions
The Public Health Approach to determine the real world effectiveness of
pharmacogenomics and monitor its applications


Evidence                   Integrating                 Surveillance
Of                         Evidence
Effectiveness

The public health approach:

How would we go about collecting information on measurable clinical outcomes
  (morbidity/mortality) in a diverse population (elderly, children, different
  ethnicities)?

•   Observational studies
•   Randomized clinical trials (RCTs)
•   Large practical trials (PCTs)
Public Health Approach to the real world effectiveness of pharmacogenomics



Evidence            Integrating                   Surveillance
Of                             Evidence
Effectiveness
Observational (cohort or case-control) studies

          Cyp450 ++
                                          Depression
                               (Rate of) good outcome       (Rate of) bad outcome
             (%) anti-dep +                a                           b
             (%) anti-dep -                c                           d

          Cyp450 --
                                          Depression
                               (Rate of) good outcome       (Rate of) bad outcome
             (%) anti-dep +                a                           b
             (%) anti-dep -                c                           d
Public Health Approach to the real world effectiveness of pharmacogenomics



Observational (cohort or case-control) studies

          Cyp450 ++
                                           Depression
                                (Rate of) good outcome           (Rate of) bad outcome
              (%) anti-dep +                a                               b
              (%) anti-dep -                c                               d

          Cyp450 --
                                           Depression
                                (Rate of) good outcome           (Rate of) bad outcome
              (%) anti-dep +                a                               b
              (%) anti-dep -                c                               d

Advantage :           Data is easily available (relatively)
                      Comparison by gene group is relatively unbiased

Disadvantage:         Sample size limitations when stratifying additionally by elderly,
                      by children, by other medications, by ethnic groups, etc.
Public Health Approach to the real world effectiveness of pharmacogenomics



Evidence               Integrating                   Surveillance
Of                               Evidence
Effectiveness

Randomized Clinical Trials allow you to enroll based on gene status
       cyp450 ++                            Asthma
                                  (Rate of) good outcome       (Rate of) bad outcome
                (%) anti-depl +               a                           b
                (%) anti-dep -                c                           d
        cyp450 --                           Asthma
                                  (Rate of) good outcome       (Rate of) bad outcome
                (%) anti-depl +               a                           b
                (%) anti-dep -                c                           d
Public Health Approach to the real world effectiveness of pharmacogenomics



Evidence             Integrating                    Surveillance
Of                             Evidence
Effectiveness

Randomized Clinical Trials allow enrollment based on gene status

Problems with generalizability and sample size requirements has led to concept of Large
   Practical Clinical Trials

Objective: To enroll many (>100,000) people in trial randomized at patient (or
   clinic/provider) level

Will allow for head to head comparisons of commonly used medications

For pharmacogenomics, can study not only
         “does statin A work better than statin B”, but also

          “are there haplotypic ‘groups’ whereby statin A works best for haplotypic
          group A, while statin B works best for haplotypic group B”?
Public Health Approach to the real world effectiveness of pharmacogenomics



Evidence             Integrating                     Surveillance
Of                             Evidence
Effectiveness

Large Practical Clinical Trials

Head to head comparisons of commonly used medications
   Can study not only “does statin A work better than statin B”, but also
          “are there haplotypic ‘groups’ whereby statin A works best for haplotypic
          group A, while statin B works best for haplotypic group B”?

Utilizing the natural experiments among large numbers:
    Can also study these genetic differences in drug effectiveness among risk groups (elderly,
    pediatrics, etc)
    Can look at interactions with other genes, other medications

Advantage: studies looks at drug, gene and system effects

Diadvantage: very expensive to do properly, even with observational data
Public Health Approach to the real world effectiveness of
pharmacogenomics


Evidence                         Integrating             Surveillance
Of                               Evidence
Effectiveness

•   RCTs or quasi-experimental designs
•   What type of system is necessary to get evidence integrated into
    practice?


NEEDS:
Network of ResearchersOrganizations         IRBs         Data



Clinical researchers              MCOs
Health care researchers BCBS/United          Standards   EMR development
Biostatisticians                  Medicare/aid
                                  VA
Public Health Approach to the real world effectiveness of
pharmacogenomics


Evidence                         Integrating                        Surveillance
Of                               Evidence
Effectiveness

•   Safety

    Vaccine model:
           VAERS reporting
           VSD (population & denominator based collaborative project)
           Future:   registry
                     buccal swabs for DNA
                     candidate gene generation

    Pharmaceutical model
          AERS reporting
          CERT and other population based collaborative projects
          Future?: registry
                     buccal swabs for DNA
                     candidate gene generation
                Vision into the Future 
    Vaccine safety research in the Era of Genomics 

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 
How do we create the system necessary for the optimal scientific
study?

Needs:

System
Basic science background
Technology
Analytic capability
Scientists
Efficiencies

				
DOCUMENT INFO
Shared By:
Categories:
Tags:
Stats:
views:0
posted:7/28/2013
language:English
pages:97