Precision medicine: drowning in a regulatory soup?
Bibliographic record
Abstract
As US President Barack Obama noted in his 2015 State of the Union address, precision medicine promises to deliver ‘the right treatments, at the right time, every time to the right person’ which ‘gives us one of the greatest opportunities for new medical breakthroughs that we have ever seen’. These comments were a prelude to a $215 million funding commitment by the President to his Precision Medicine Initiative, the aim of which is to ‘pioneer a new model of patient-powered research that promises to accelerate biomedical discoveries and provide clinicians with new tools, knowledge, and therapies to select which treatments will work best for which patients’. The objectives include an undertaking to modernize the current regulatory landscape.\n\nSome six months prior to this address, a group of international scholars in the disciplines of law, biomedicine, bioethics, and the social sciences met at the other end of the world in Hobart, Australia to workshop the challenges involved in formulating a coherent regulatory framework for precision medicine. The inspiration for the workshop title, Leading or Limping? Regulation of Personalized Medicine, came from a famous observation by one of Australia's most eminent High Court judges, Justice Victor Windeyer in the case of Mount Isa Mines Ltd v Pusey (1970) 125 CLR 383 at 395, where he referred to the law as ‘marching with medicine but in the rear and limping a little’. The language of personalized medicine, rather than precision medicine, was used at the workshop, because at that time it was the more common term.\n\nThe terms ‘precision’, ‘personalized’, and ‘medicine’ already hint at some of the regulatory challenges that lie ahead. ‘Precision’ implies that the product or service being offered is accurate and targeted. Like any novel area, the frontier is often filled with a variety of new players some of whom will see a huge commercial opportunity and may push the boundaries of acceptability in terms of their claims. In addition, novel risks of harm to individuals may rise or be exacerbated by the new technologies. We therefore need to be assured that appropriate regulatory requirements are in place so that precision medicine can be undertaken efficiently and safely and in a manner that facilitates the translation of research into effective therapies.\n\nLanguage that focuses attention on the ‘person’ immediately raises questions around personhood and privacy. As knowledge and understanding of personal health increases, so too do the potential threats to personal privacy. ‘Medicine’ implies that these new advances sit within the established medical care system, with all the regulatory checks and balances that go along with it. Yet, we will see in the discussion that follows that one of the features of precision medicine is the blurring of boundaries between the clinic, the laboratory, and the healthcare industry, creating new regulatory spaces. On the one hand, this raises questions about the capacity of existing regulatory structures to respond. On the other hand, it risks regulatory overlap and confusion, a veritable ‘regulatory soup’ that could drown the promised advances in precision medicine.
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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 | 0.005 | 0.001 |
| 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 | 0.001 |
| 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".