A Hard Pill to Swallow: A Critical Look at Eli Lilly & Co.'s NAFTA Challenge of the Canadian Patent Regime, and Its Potential Side Effects
Bibliographic record
Abstract
On September 12, 2013, Eli Lilly & Co., in filing its Notice of Arbitration with the North American Free Trade Agreement against the Government of Canada, became the first private investor to contest a national patent regime through arbitral means. In the Notice, Lilly alleges that Canada violated NAFTA Articles 1110 (covering expropriation) and 1105 (covering mini- mum standards of treatment) by allowing Canada Federal Courts to unlawfully invalidate two of Lilly’s patents, CA 2,041,113 and CA 2,209,735, protecting the compounds comprising Zyprexa and Strattera, respectively, through application of its controversial “promise doctrine.” Whether arbitration through NAFTA is an appropriate way to contest a NAFTA member state’s patent regime is hotly contested. But Canada Federal Courts have invalidated an increasing number of pharmaceutical patents for failing to meet the promise doctrine, leaving pharmaceutical companies in Canada uncertain as to the extent of their patent rights. Still, NAFTA allowing pharmaceuti- cal companies to successfully dispute national patent laws could have significant future consequences. This note outlines the circumstances surrounding Lilly’s dispute, analyzes the dispute’s viability, and explores various potential implications of the dispute going forward.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.040 | 0.028 |
| Scholarly communication | 0.027 | 0.012 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.022 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 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".