Unilaterally Altering the Bargain: TRIPS, Section 107A, and the Regulatory Review Exception under Indian Patent Law
Bibliographic record
Abstract
In 2003, Section 107A of the Patents Act wrote a regulatory review exception into Indian law. Its beholden purpose is to enable patent-protected pharmaceutical products to be brought to Indian markets quicker and in large numbers. Emboldened by a WTO decision brake-testing a similar Canadian statute, Indian pharmaceutical manufacturers have consistently claimed Section 107A benefits from domestic courts. Lately, the economic trail of these benefits has grown to implicate entities abroad. Pushback to this trend from patentees has duly arrived. It has culminated in a stack of Delhi High Court decisions in Bayer v. Union of India. Bayer writes three precepts into Indian law. First, it stipulates that, so long as the purpose of Section 107A is met, courts will not concern themselves with the geographical incidence of the underlying transaction. Second, it prescribes a checklist to assist domestic courts in assessing whether Section 107A is, indeed, satisfied. Finally, it sets out that commercial animus is the bright line dividing permissible transactions from impermissible ones. As this goes to press, less than six years have passed since Bayer. Yet, even in this short time, each of the Bayer precepts has been systemically annihilated by later courts. As a result, Section 107A law sits nervously: unsure whether to stick with the Bayer ideals or twist in favour of a more chaotic exercise of the TRIPS flexibilities that birthed Section 107A in the first place. In this essay, we narrate how and why this state of affairs has come to be. We side, on balance, with respecting outcomes that predictably interpret Section 107A and its attendant economic considerations. We conclude that, if push comes to shove, Indian courts ought to follow the lead of the WTO Canada decision over two decades ago.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.037 |
| Scholarly communication | 0.026 | 0.012 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.018 | 0.024 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".