Affordability and availability of off-patent drugs in the United States—the case for importing from abroad: observational study
Bibliographic record
Abstract
OBJECTIVES: To evaluate whether off-patent prescription drugs at risk of sudden price increases or shortages in the United States are available from independent manufacturers approved in other well regulated settings around the world. DESIGN: Observational study. SETTING: Off-patent drugs in the USA and approved by the Food and Drug Administration, up to 10 April 2017. STUDY COHORT: Novel tablet or capsule prescription drugs approved by the FDA since 1939 that were no longer protected by patents or other market exclusivity and had up to three generic versions. MAIN OUTCOME MEASURES: Number of additional manufacturers that had obtained approval from any of seven non-US regulators with similar standards (European Medicines Agency (European Union), HealthCanada (Canada), Therapeutic Goods Association (Australia), Medsafe (New Zealand), Swissmedic (Switzerland), Medicines Control Council (South Africa), and the Israel Health Ministry). Association with drug characteristics including US orphan drug designation for drugs treating rare diseases, World Health Organization essential medicine designation, treatment area, drug product complexity (that is, with attributes that could complicate establishing bioequivalence or manufacturing), and total Medicaid spending in 2015. RESULTS: Of 170 eligible study drugs, more than half (109, 64%) had at least one manufacturer approved by a non-US regulator and 32 (19%) had four or more. Among 44 (26%) drugs with no FDA approved generic versions, 21 (48%) were available from at least one manufacturer approved by one of the seven non-US regulators, and two (5%) by four or more manufacturers. Across all drugs and regulators (including the FDA), 66 (39%) drugs were available from four or more total manufacturers. Of 109 drugs with at least one non-US regulator approved manufacturer, 12 (11%) were approved for patients with rare diseases and 29 (27%) were WHO designated essential medicines; only 12 (11%) were complex products that might be more complicated to import. The highest numbers of drugs were indicated for treating cardiovascular diseases, diabetes, or hyperlipidemia (19, 17%); psychiatric disease (16, 15%); and infectious diseases (15, 14%). In 2015, Medicaid alone spent nearly US$700m (£508m; €570m) on generic drugs without adequate US competition that could have had a manufacturer approved by non-US peer regulatory agencies. CONCLUSION: In this study, more than half the off-patent drugs with no generic competition in the USA had at least one independent manufacturer approved by a non-US peer regulatory agency; slightly fewer than half had four or more total manufacturers. Facilitating US patient access to such manufacturers could help sustain affordable access to essential off-patent drugs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".