advances in pharmacogenomics and population-based identification of "at-risk" groups
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Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups. Robert L. Davis, MD, MPH Visiting Scientist Immunization Safety Office Centers for Disease Control and Prevention Senior Investigator Center for Health Studies Group Health Cooperative Seattle, Washington. - PowerPoint PPT PresentationTRANSCRIPT
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
Robert L. Davis, MD, MPHVisiting Scientist
Immunization Safety OfficeCenters for Disease Control and Prevention
Senior InvestigatorCenter for Health StudiesGroup Health Cooperative
Seattle, Washington
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
Goals:
To understand the genetic variations that predispose children, adolescents or adults to vaccine adverse events or vaccine failure
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
The prototypic study approach:
Design type: Case-control study (rare outcome)
Case definition (example): Seizures following MMR vaccinationControl definition (ex): 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
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
The prototypic study application:
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 populationHow many people need to be identified & excluded from vaccination to prevent one seizure?
Quantify risks and benefits of excluding children/adults from vaccinationMay be different depending on vaccine, outcome, likelihood of exp to wild type
disease, presence of herd immunity, etcEx: MMR and seizures Smallpox vaccine and myocarditis
Study/identify risk minimization processesEx: tylenol to prevent febrile seizures; vaccinating at different ages; not vaccinating, etc
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
How do we create the system necessary for the optimal scientific study?
Needs:
SystemBasic science backgroundTechnologyAnalytic capabilityScientistsEfficiencies
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
How do we create the system necessary for the optimal scientific study?
Needs:
SystemBasic science backgroundTechnologyAnalytic capabilityScientistsEfficiencies
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
How do we create the system necessary for the optimal scientific study?
System needs:
Need 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 settingActive identification of VAEs possibleAdv: full spectrum of VAE
unbiased ascertainment
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
How do we create the system necessary for the optimal scientific study?
Systems: Vaccine Safety DatalinkBegan in 1991 as a collaborative project between CDC and four HMOs:
Group Health Cooperative, Seattle, WANorthwest Kaiser Permanente, Portland, ORNorthern California Kaiser Permanente, OaklandSouth California Kaiser Permanente, Los Angeles
Expanded in 2000 to include four more HMOs:Harvard Pilgrim Health Care, Boston, MAHealthPartners, Minneapolis, MNKaiser Permanente Colorado, Denver, COMarshfield Clinic, Marshfield, WI
Total over 10 million members
Vaccine Safety Datalink (VSD)
VaccinationRecords
HealthOutcomes
(Hospital)(ER)
(Clinic)
PatientCharacteristics
(Birth records)(Census)
VSD LinkedAnalysis Database
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
How do we create the system necessary for the optimal scientific study?
Needs:
SystemBasic science backgroundTechnologyAnalytic capabilityScientistsOther: Efficiencies
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
How do we create the system necessary for the optimal scientific study?
Needs:
Basic science background
Understanding of pathways involved in potential VAEsBasic disease pathogenesisInflammation pathwaysImmune 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
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
How do we create the system necessary for the optimal scientific study?
Needs:
SystemBasic science backgroundTechnologyAnalytic capabilityScientistsOther: Efficiencies
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
How do we create the system necessary for the optimal scientific study?
Needs:TechnologyAnalytic capability
Technology:Use of 500K chips for SNP analysis becoming more routineCould 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 ProjectWill need attention in any future endeavors for vaccine genomics
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
How do we create the system necessary for the optimal scientific study?
Needs:TechnologyAnalytic 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 analysis3. Need advanced/new biostatistical algorithms for fitting models4. Almost guaranteed to find more false than true positives5. Individual SNPs might not be as important or illuminating as haplotypes
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
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 training in biostatistical genetics and genetic epidemiology
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
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
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
How do we create the system necessary for the optimal scientific study?
Needs:
SystemBasic science backgroundTechnologyAnalytic capabilityScientistsOther: Efficiencies
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
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 eventsCollaborative network with organizations/populations of interestHistorically: infectious disease specialists; epidemiologists
For basic science/gene pathways:Immunologists/infectious disease specialistsGeneticists
For analysis:Collaboration with partners with capabilities to run samplesBiostatisticians/genetic epidemiologists to analyze data
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
How do we create the system necessary for the optimal scientific study?
Needs:
SystemBasic science backgroundTechnologyAnalytic capabilityScientistsOther: Efficiencies
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
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
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
Vision for the Future:
Screen VSD data-sets yearly Identify subjects/collect specimens on cases
q yr: febrile seizures; severe limb swellingq 5 yrs: arthritis; prolonged crying; q 10 yrs: encephalopathy; GBS; anaphylaxisw/high profile situations: ie intussusception;GBS
Run genome-scans (500K chips or higher) on cases Compare with standard age, HMO, race matched controls
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
Vision for the Future:Screen VSD data-sets yearly
Identify subjects/collect specimens on casesq yr: febrile seizures; severe limb swellingq 5 yrs: arthritis; prolonged crying; q 10 yrs: encephalopathy; GBS; anaphylaxisw/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_ genesGenerate new set(s) of potential candidate genes/pathways for next iteration
Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups
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/integrationTechnology/Analytic capability: available; needs coordinated approachEfficiencies: needs evaluation
VSD Study Types
Age specific• Children
– Seizures (primarily febrile) after DTP and MMR
• Adolescents– Safety of new meningococcal conjugate vaccine
• Adults– Autoimmune thyroid disease– Multiple sclerosis after hepatitis B
• Elderly– Flu vaccine safety and efficacy
VSD Data management
Source Data: Data Center:Vaccination SAS programsHealth outcomes/diseasePatient characteristics
Analytic data file
Highly controlled processStandardized data collection from each site
Confidential and deidentifiedHIPAA compliant/Minimal data transfer