Pharmacoeconomics & Health Outcomes

Pharmacoeconomics & Health Outcomes

Pharmacoeconomics & Clinical Trials Pharmacogenomics

Leon E. Cosler, R.Ph., Ph.D. Associate Professor of Pharmacoeconomics Albany College of Pharmacy

Road Map

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PE in the clinical trial process

- Unique characteristics to consider - Ch. 11

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PE and the impact of Pharmacogenomics

- Key definitions and applications - The costs & effectiveness issues - Ch. 10

Clinical Trials & Drug Marketing

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Safety & Efficacy • 2+ clinical trials • (economics and outcomes not addressed)

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FDAMA 1997 • Sect. 114 standards for economic info. • New: “competent & reliable evidence” • OLD: “… from well controlled clinical trials..” • “widely accepted by experts” • specific standards never published

Important criteria to consider in RCTs 1. Benchmarks 3. Study design 4. Data Management 5. Analysis / Interpretation 7. Reporting Results

Important PE criteria 1. Benchmarks • • •

Baseline epidemiology needed Natural course of disease Burden on society -

• • •

Economic burden may be different

Factors which affect QOL Factors which affect patient satisfaction Baseline resource use needed

Important PE criteria 2. Study design • •

Need to plan along with RCT design PE objectives stated early in design -

• • •

Primary goal or secondary “ad-hoc” Pivotal Phase III vs Phase IV

Multiple perspectives sometimes needed Resource use inside RCT artificial * QOL indicators may not be generalizeable either…

Important PE criteria 2. Study design • • •

Sample sizes may need to be adjusted Clinical parameters may be insufficient QOL instruments selected > General +/- Dx specific often used

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Respondent burden a consideration

Important PE criteria 3. Data Management • •

Most RCT data collected from providers Some PE data needed from patients > caregivers and / or parents

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Where to survey? > office or at home? > “team” answers

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Frequency > baseline and final > more frequently

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Methods for handling missing data

Important PE criteria 4. Analysis / Interpretation • • • • •

Pre-specified analysis vs post-hoc ITT vs per-protocol Confounding variables Sensitivity analyses Multi-national studies > Can’t combine economic or HRQOL data

Important PE criteria 5. Reporting Results • • • • • • •

k.i.s.s. Summary results not individual Pts. Address dropouts and effects Sensitivity and robustness of findings Pre-specified vs ad-hoc ITT vs per-protocol Multi-national

Pharmacogenomics

Key Terms

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Pharmacogenetics

- How genetic differences influence Pt. responses to Rxs

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Pharmacogenomics

- Applying genetic traits for Dxs and Rx -

metabolism to Tx management Assembling a comprehensive list of SNPs SNPs predict Rx efficacy & toxicity

Why do we care?

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ADRs ~ 5th leading cause of death in the U.S. ~ 70K to 100K preventable deaths per year ~ 2 million hospitalizations per year $1.6B to $4.2B annually to resolve

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Differential Rx efficacy

- 30% of schizophrenics non-responsive - Interferon B only helps 1/3 of MS Pts - Chemotherapy responses vary widely

Why do we care?

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Wide dose variations

- propranolol doses may vary by 40x - Warfarin doses may vary by 20x - Simvastin dose-dependent • (~6% no response)

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Narrow Tx window Rxs

- gentamicin - digoxin - cyclospirine

What do we know?

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Genetic differences explain metabolism PM

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(normal / wild type genotype) EM

PM (Poor Metabolizers) • Prolonged Rx effects • Toxic ADR

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UEM • No Tx effects at normal doses • Toxicity from excessive metabolites

UEM

Warfarin Pharmacogenomics • • •

Polymorphisms in CYP2C9 influence drug metabolism Polymorphisms in VKORC1 affect response Should genotype testing be performed to individualize warfarin dosing?

How are costs of care affected? •

Increased

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Cost of testing Cost of add’t appts False + and Low prevalence Rx R&D • Old Rxs • New biologics

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Decreased

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More drugs approved? Targeted population Fewer Tx failures Fewer ADRs * Fewer labs needed? 1-time genetic profile

What about Effectiveness? •

Increased

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Target responsive Pts High Sensitivity High Specificity Strong relationships with phenotypes

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Decreased

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Less effective Txs ? Weak relationship fit phenotypes

What about the cost-effectiveness?

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When it’s less expensive • and maintains or increases effectiveness

OR

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If its more expensive… • and the gain in effectiveness is worthwhile

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Check out Table 2. Chapter 10

Impact on Clinical Trials

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Positives: • More drugs may make it through • Smaller sample sizes • Faster & cheaper…

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Negatives • Patients excluded from trials • Results generalizeability • New drugs with smaller potential markets

Implications

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Improved treatment outcomes • Maximize Rx effects • Reduce / eliminate ADRs • Improved Pt. adherence?

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Reduce unnecessary Txs and tests • $750M based on False + PSA test • unnecessary biopsies

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Ethical issues • Racial stereotyping? • Should payers have access to this data?

Drugs and the Human Genome Project

“In the future we may all carry a 'gene chip assay report' that contains our unique genetic profile that would be consulted before drugs are prescribed…”

That’s (almost) all for today… !