The Impact of Recent Generic Drug Price Policies on Pharmaceutical Innovation: A Theoretical Rationale and Proposal of a Method Supporting Innovation in Areas of Unmet Medical Need
Bibliographic record
Abstract
New discoveries are a critical priority for the pharmaceutical industry. However, the use of fixed incremental cost-effectiveness (ICER) thresholds for health technology assessment (HTA) may compromise incentives to innovate and affect future treatment options. This paper highlights the impact of generic drug price policies on pharmaceutical innovation in the context of fixed ICER thresholds and proposes a new consideration for the cost-effectiveness analysis (CEA). There is a direct causal relationship between HTA and the market price of a drug; in jurisdictions where HTA agencies apply fixed ICER thresholds as an important reimbursement listing criterion, the incremental cost of a new drug is expected to be proportional to its incremental benefit over the comparator. However, the comparator price is subject to market forces or sudden policies and may change markedly affecting the cost-effectiveness assessment (e.g. where the comparator patent has expired). Since recent generic price regulations increased the price gap between drugs' generic and patented versions, it is harder to achieve a sufficient level of incremental benefits in order to offset incremental prices of new treatments. Consequently, even promising drugs may have challenges to show attractive ICERs and research and development (R&D) investments may become unattractive in certain disease area. In order to promote innovation in therapeutic fields with unmet medical needs, a compromise would be to include the comparator's patented price in the CEA instead of the generic drug. By identifying the relevant disease areas, decision makers and HTA authorities could therefore convey the importance of investing in these therapeutic areas to manufacturers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".