Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups

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

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

The prototypic study approach: Design type: Case-control study (rare outcome) Case definition (example): Seizures following MMR vaccination Control 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 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 exp 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 Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups

How do we create the system necessary for the optimal scientific study? Needs: System Basic science background Technology Analytic capability Scientists Efficiencies 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 setting Active identification of VAEs possible Adv: 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 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 Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups

Vaccine Safety Datalink (VSD) Patient Characteristics (Birth records) (Census) Health Outcomes (Hospital) (ER) (Clinic) Vaccination Records 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 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 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 Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups

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 Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups

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 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: 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 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 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 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 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 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/integration Technology/Analytic capability: available; needs coordinated approach Efficiencies: needs evaluation Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups

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 programs Health outcomes/disease Patient characteristics Analytic data file Highly controlled process Standardized data collection from each site Confidential and deidentified HIPAA compliant/Minimal data transfer

Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups