Reputation and Authority: The FDA and the Fight over U.S. Prescription Drug Importation
Bibliographic record
Abstract
There is popular and bipartisan support for legalizing the importation of lower-cost medicines from Canada to help reduce the high prescription drug costs that Americans pay. Despite the wide interest in this policy, attempts over the last sixteen years to create a formal system for large-scale prescription drug importation in the United States have failed. The Trump administration recently issued a Notice of Proposed Rulemaking to enable the legal importation of prescription drugs from Canada, but the Notice has important design flaws and seems destined to suffer a similar fate as previous efforts. In this article, we argue that prescription drug importation is a form of international regulatory engagement that can work, but not in the manner that recent congressional legislation or the Trump administration has proposed. Importation of prescription drugs, even foreign versions of already-approved drugs, requires the importing nation to accept the marketing approval standards, processes, and product-specific decisions of the exporting nation as equivalent to domestic regulation. The FDA, however, has made far fewer determinations of foreign regulatory equivalence than its counterpart regulators. As a result, the statutory requirements for the FDA maintaining direct oversight over prescription drug imports from Canada are onerous and unlikely ever to be fulfilled. Examining U.S. prescription drug importation as a form of reliance on the equivalence of foreign regulation is, as far as we can determine, a novel inquiry, and it offers useful insights. Foreign equivalence determinations have been successfully used in pharmaceutical regulation in two contexts: (1) trade initiatives and (2) circumstances in which regulatory agencies were unable to fulfill their core institutional mandates without relying on the decision-making of their foreign counterparts. The FDA has not fit neatly into either of these contexts. In contrast to many of its foreign counterparts, the FDA has consolidated authority over pharmaceutical regulation, which it sustains through its reputation among its constituents—appropriators, consumers, pharmaceutical product sponsors, and the relevant medical and scientific communities— for overseeing the safety, efficacy, and quality of medicines. The FDA has resisted risking any harm to that gatekeeper reputation that might follow from its pursuit of other policy objectives, such as lowering drug prices or facilitating trade. Furthermore, FDA officials describe themselves as “the gold standard” for drug review—more thorough and rigorous about regulation than their counterparts—and, until recently, as able to fulfill their core institutional mandates without the cooperation of foreign counterparts Based on this analysis of the political economy of pharmaceutical regulation and international regulatory cooperation at the FDA, we propose that U.S. prescription drug importation could be successfully used to address generic drug shortages, a persistent regulatory problem that the FDA has been unable to solve independently. We argue that the same analysis can help identify other circumstances when the FDA might usefully engage foreign counterparts, such as foreign manufacturer inspections for the rapidly increasing volume of U.S. drug imports, the growing complexity of global pharmaceutical supply chains, and the technological advances in personalized medicines.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.034 |
| Scholarly communication | 0.025 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.022 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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".