Regulatory Pathway for Companion Diagnostics in France
In France, companion diagnostics (CDx) are subject to a thorough regulatory and reimbursement process, primarily led by the Haute Autorité de Santé (HAS). The approval of CDx is closely tied to their corresponding therapies, particularly in oncology and precision medicine. The process involves a detailed clinical and economic evaluation before the CDx can be reimbursed under the national healthcare system.
1. HAS (Haute Autorité de Santé) Evaluation
Once a companion diagnostic is developed, it must undergo a rigorous evaluation by HAS. This includes an assessment of its clinical utility, cost-effectiveness, and budget impact.
- Clinical Utility: HAS evaluates how the CDx improves patient outcomes. The diagnostic must demonstrate clear benefits, such as guiding therapeutic decisions more effectively and reducing unnecessary treatments.
2. Cost-Effectiveness and Health Economics
Cost-effectiveness analysis (CEA) is a crucial part of the evaluation for companion diagnostics in France. This method compares the health benefits of using the CDx (in terms of improved life expectancy or quality-adjusted life years, QALYs) against the cost of implementing the test in routine healthcare practice.
Key Elements of Cost-Effectiveness Analysis (CEA):
- Modelling Clinical Outcomes: Economic models are created to project how the CDx will affect patient outcomes. For example, a model for HER2 testing in breast cancer would forecast how many patients would benefit from trastuzumab based on HER2 status, and the survival benefits associated with the test.
- Incremental Cost-Effectiveness Ratio (ICER): HAS evaluates the ICER, which reflects the cost required to gain an additional QALY by using the CDx. In France, treatments or diagnostics that cost less than €50,000 per QALY are typically considered cost-effective.
Example: If a HER2 test costs €10,000 but saves €50,000 by preventing ineffective treatments and improving patient survival, it would be deemed cost-effective.
In health economics, a diagnostic test like the HER2 companion diagnostic is considered cost-effective if the financial investment in the test results in improved health outcomes and reduced costs elsewhere in the healthcare system. To explain this scientifically, we can look at the following concepts:
Cost of the Test
The HER2 test is used to determine whether a patient’s breast cancer is HER2-positive, meaning their cancer cells have high levels of HER2 protein. This test typically involves immunohistochemistry (IHC) or fluorescence in situ hybridisation (FISH), which detects HER2 gene amplification.
Let us assume the HER2 test costs €10,000 per patient, which includes the testing procedure and associated medical services (e.g., tissue biopsy, lab work, interpretation of results).
Benefits of the Test
The benefit of HER2 testing is identifying patients who can benefit from HER2-targeted therapy like trastuzumab (Herceptin), which has been shown to significantly improve survival in HER2-positive patients. These therapies are more effective than standard treatments (e.g., chemotherapy) for patients with HER2-positive breast cancer.
Avoiding Ineffective Treatment
Without HER2 testing, all patients might receive standard chemotherapy, which is less effective in HER2-positive patients. Administering chemotherapy to patients who would benefit more from targeted therapy would result in suboptimal outcomes. In contrast, if HER2 testing is conducted, patients who are HER2-positive will receive the correct targeted therapy, improving their prognosis.
Cost Savings from Preventing Ineffective Treatments
Chemotherapy Costs: If a HER2-negative patient receives chemotherapy, the average cost of a course of treatment could be substantial, ranging from €20,000 to €30,000. However, for HER2-positive patients, chemotherapy might not yield significant benefits, and using trastuzumab instead could dramatically improve survival.
By using HER2 testing, only the patients who would benefit from trastuzumab receive it. This prevents unnecessary chemotherapy in patients for whom it is less effective, potentially saving costs associated with chemotherapy side effects, hospitalisations, and follow-up treatments.
Let us say by avoiding unnecessary chemotherapy and reducing hospitalisation, €50,000 in costs is saved for each HER2-positive patient who avoids ineffective chemotherapy.
Improved Survival and Quality of Life
In addition to cost savings, HER2-positive patients who are identified through testing and treated with trastuzumab experience improved survival and quality-adjusted life years (QALYs). These gains in survival and quality of life represent significant health benefits, which are quantified in cost-effectiveness analysis.
In scientific terms, the cost-effectiveness of HER2 testing can be quantified using the Incremental Cost-Effectiveness Ratio (ICER):
ICER = (Cost of Intervention (HER2 Testing) – Cost of Standard Care) / QALYs gained by HER2 Testing
Numerator: In this example, the numerator is the cost difference between HER2 testing (€10,000) and the savings from avoiding ineffective chemotherapy (€50,000), giving a net cost of -€40,000 (savings).
Denominator: The denominator is the number of quality-adjusted life years (QALYs) gained by identifying HER2-positive patients and treating them appropriately with trastuzumab, which improves their survival and quality of life compared to standard chemotherapy.
Example Calculation:
Assume that HER2 testing and trastuzumab treatment result in an additional 0.8 QALYs per patient compared to chemotherapy.
The cost savings are €50,000 (due to avoiding ineffective chemotherapy), while the cost of the test is €10,000.
ICER Calculation:
ICER = (10,000 – 50,000) / 0.8 = -€50,000
In this example, the negative ICER means that HER2 testing not only improves survival (gains in QALYs) but also reduces healthcare costs (by avoiding chemotherapy and associated hospitalisations). This makes the test not just cost-effective, but also cost-saving in this scenario, which is the optimal outcome in health economics.
Real-World Example of Cost-Effectiveness: EGFR Testing in Lung Cancer
A cost-effectiveness study was conducted to assess the value of EGFR mutation testing for non-small cell lung cancer (NSCLC). The test identified patients eligible for EGFR-targeted therapies, such as gefitinib. By optimising treatment choices, survival rates improved, and unnecessary chemotherapy was avoided, leading to significant cost savings. HAS validated the economic model and confirmed its cost-effectiveness based on this data.
3. Budget Impact Analysis
While cost-effectiveness analysis assesses value, Budget Impact Analysis (BIA) examines the financial implications of adopting the CDx in the healthcare system. This method provides a broader view of how the diagnostic impacts national healthcare spending.
Steps in Budget Impact Analysis:
- Population Impact: BIA estimates how many patients are likely to need the CDx and the associated treatment. For example, in the case of HER2 testing, it would predict the number of breast cancer patients requiring the test in a given year.
- Cost Modelling: The analysis considers direct costs (the test itself, associated drugs) and indirect costs (hospital stays, adverse reactions) to determine the overall financial burden.
Real-World Example of Budget Impact: BRCA Testing for Ovarian Cancer
For BRCA testing in ovarian cancer, the budget impact model evaluated the roll-out of BRCA testing alongside PARP inhibitors like olaparib. Though the upfront cost of both the test and the drug was high, the analysis predicted long-term savings due to reduced chemotherapy and hospital admissions. Over 5 years, BRCA testing was shown to streamline patient care and reduce overall treatment costs.
Scientific Principles Behind Budget Impact Analysis (BIA)
BIA operates by combining epidemiological, clinical, and economic data to provide a robust estimate of the financial implications of introducing a companion diagnostic into a healthcare system. Below is an expanded, scientific approach to each of the key elements of the BIA process.
Population Impact: Epidemiological Modelling
Population impact modelling forms the basis of any BIA by estimating the number of patients likely to be tested and treated using a companion diagnostic. This step relies on epidemiological data, which includes the prevalence and incidence of the condition, and the percentage of patients eligible for the diagnostic and associated therapies.
Scientific Approach to Population Impact:
– Incidence Data: For ovarian cancer, data from national cancer registries (e.g., the French National Cancer Institute) are used to estimate the number of new cases diagnosed annually. In our BRCA testing example, suppose there are 10,000 new cases of ovarian cancer in France each year.
– Genetic Stratification: Studies indicate that around 20% of ovarian cancer cases are associated with BRCA mutations, which are actionable with treatments like PARP inhibitors. This means that the target population for BRCA testing is 2,000 patients per year.
Direct and Indirect Costs: Cost Modelling
Cost modelling is where health economics and clinical data converge. The objective is to estimate all direct and indirect costs associated with the diagnostic and treatment pathway. In BIA, these costs are broken down as follows:
Direct Costs:
– Cost of BRCA Testing: The actual cost of the diagnostic procedure (BRCA genetic test), including laboratory work, technician fees, sample collection, and interpretation of results. Suppose the direct cost of the BRCA test is €5,000 per patient.
– Cost of Treatment: The cost of administering the treatment indicated by the BRCA test. In our case, this is olaparib, a PARP inhibitor. This includes drug acquisition, administration, and follow-up care. The cost of olaparib is approximately €40,000 per patient per year.
– Diagnostic Process-Related Costs: Follow-up testing or biopsies that might be required based on initial test results, and costs for genetic counselling and oncology follow-up visits.
Indirect Costs:
– Hospitalisation and Adverse Events: Chemotherapy, which is the standard treatment in ovarian cancer, can lead to serious side effects such as neutropenia, infections, and the need for hospitalisation. By using a targeted therapy like olaparib, fewer patients are hospitalised due to severe side effects, leading to significant savings.
– Survival and Productivity Gains: Patients who survive longer and experience better quality of life due to targeted therapies may contribute more productively to society. This aspect can be modelled using human capital theory, which estimates the economic value of an individual’s productivity based on their ability to work or contribute socially.
Budget Impact Modelling: Dynamic Economic Models
The BIA model uses a dynamic economic modelling approach to capture the full impact of the intervention over time. In contrast to cost-effectiveness analysis, which focuses on the value per patient, BIA evaluates the overall cost burden on the healthcare system.
Scientific Methods in BIA Modelling:
– Markov Models: These are frequently used to simulate patient flow through different health states (e.g., disease-free, relapse, progression, survival) over time. Each state incurs specific costs, and patients move between states based on probability transitions derived from clinical trials and real-world data.
– Monte Carlo Simulations: These are used to account for uncertainty in key parameters, such as the effectiveness of olaparib, the reduction in hospitalisation rates, or the annual incidence of ovarian cancer. Monte Carlo simulations can run thousands of iterations to estimate a range of potential budget impacts, providing confidence intervals around the BIA estimates.
Estimating the Time Horizon: Multiyear Projections
In health economics, it is important to project costs and savings over a defined period, typically 3 to 5 years for BIAs. The goal is to evaluate both the short-term costs (e.g., the initial high cost of testing and drug therapy) and the long-term savings (e.g., fewer hospitalisations, and improved survival).
Discounting Future Costs: Economic models apply a discount rate (commonly 3-5%) to account for the fact that future costs and savings have less value compared to present-day money. Discounting helps adjust for inflation and other financial factors.
Sensitivity Analysis and Real-World Adjustments
Sensitivity analysis is a critical part of scientific BIA, as it examines how variations in key parameters (e.g., drug cost, hospitalisation rates, testing uptake) affect the overall financial estimates.
Types of Sensitivity Analyses:
– One-Way Sensitivity Analysis: In this method, one parameter (e.g., drug cost) is varied while holding all other factors constant, to see how much it influences the budget impact.
– Probabilistic Sensitivity Analysis (PSA): Multiple parameters are varied simultaneously based on probability distributions. PSA helps quantify the overall uncertainty in the BIA model and provides a range of potential outcomes.
Real-World Example: BRCA Testing for Ovarian Cancer in France
Population Impact: 2,000 patients eligible for BRCA testing annually in France (based on incidence
and BRCA mutation prevalence). Direct Costs: €5,000 per patient for BRCA testing, €40,000 per patient per year for olaparib treatment.
Indirect Savings: Reduction in hospitalisations, with savings of €8,000,000 per year.
Net Budget Impact Over 5 Years: Total Direct Costs: €450,000,000 over 5 years. Total Savings: €40,000,000 from reduced hospitalisations. Net Budget Impact: €410,000,000 over 5 years.
Conclusion
A scientific BIA uses epidemiological modelling, cost calculations, dynamic economic simulations, and sensitivity analyses to offer a comprehensive estimate of the financial implications of introducing a CDx like BRCA testing into a national healthcare system. The balance between upfront costs (testing and targeted treatment) and long-term savings (fewer hospitalisations, improved survival, reduced adverse effects) is essential in understanding the overall budgetary impact.
4. Social Utility and Public Health Impact
In France, the broader social utility of companion diagnostics is also considered. HAS evaluates how the diagnostic contributes to public health, addresses healthcare disparities, and improves quality of life.
Key Elements of Social Utility Analysis:
- Health Equity: CDx that help reduce health inequalities, by giving access to precision medicine for underserved populations, are seen as having high social utility.
- Public Health Impact: Diagnostics that reduce overall disease burden or mortality rates at the population level are considered highly valuable in terms of social utility.
Real-World Example of Social Utility: PD-L1 Testing in Lung Cancer
PD-L1 testing helps identify NSCLC patients who would benefit from immunotherapy. This not only improves survival rates but also reduces side effects compared to chemotherapy, thus increasing quality of life. The test enabled better targeting of immunotherapy, reducing disparities in access to treatment and improving patient outcomes across different socio-economic groups.
5. Forfait Innovation: Early Access Scheme
The Forfait Innovation pathway is a distinctive feature of the French system that allows for temporary reimbursement of highly innovative diagnostics, such as CDx. This scheme accelerates access to promising technologies by enabling their use while further clinical and economic data are gathered.
Forfait Innovation Process:
- Temporary Reimbursement: Typically lasts 1 to 3 years, allowing real-world data collection on the CDx.
- Post-Study Assessment: After the temporary period, HAS re-evaluates the diagnostic using newly collected data. If the CDx demonstrates sustained clinical and economic benefits, it can be permanently added to the NABM for full reimbursement.
Real-World Example Timelines:
- HER2 Testing for Trastuzumab in Breast Cancer
- HAS Evaluation: 9 months.
- NABM Listing: 9 months after HAS approval.
- Total Timeline: 18 months from submission to reimbursement.
- EGFR Testing for Lung Cancer
- HAS Evaluation: 12 months.
- NABM Listing: 7 months after approval.
- Total Timeline: Approximately 19 months from initial submission to full reimbursement.
- BRCA Testing for PARP Inhibitors in Ovarian Cancer
- HAS Evaluation: 9 months (initial evaluation).
- Forfait Innovation: 2 years of temporary reimbursement.
- Permanent Reimbursement: 9 months after further data review.
- Total Timeline: Approximately 36 months from initial submission to permanent reimbursement.
Conclusion
Companion diagnostics in France are evaluated through a highly structured process that integrates clinical utility, cost-effectiveness, budget impact, and social utility assessments. This ensures that only the most clinically valuable and economically sustainable diagnostics are reimbursed. Real-world examples like HER2, EGFR, and BRCA testing highlight the rigorous but supportive nature of the French system, with pathways like Forfait Innovation enabling early access to innovative technologies.
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