Commercial Opportunities and Ethical Pitfalls in Personalized Medicine: A Myriad of Reasons to Revisit the Myriad Genetics Saga
Bibliographic record
Abstract
In 1996, the US-based biotechnology company Myriad Genetics began offering genetic diagnostic tests for mutations in the genes BRCA1 and BRCA2, which are linked to hereditary breast and ovarian cancer. Since that time, Myriad has been a forerunner in the field of personalized medicine through the use of effective commercialization strategies which have been emulated by other commercial biotechnology companies. Myriad’s strategies include patent acquisition and active enforcement, direct-to-consumer advertising, diversification, and trade secrets. These business models have raised substantial ethical controversy and criticism, often related to the company’s focus on market dominance and the potential conflict between private sector profitability and the promotion of public health. However, these strategies have enabled Myriad to survive the economic challenges that have affected the biotechnology sector and to become financially successful in the field of personalized medicine. Our critical assessment of the legal, economic and ethical aspects of Myriad’s practices over this period allows the identification of the company’s more effective business models. It also discusses of the consequences of implementing economically viable models without first carrying out broader reflection on the socio-cultural, ethical and political contexts in which they would apply. Keywords: BRCA genes, breast and ovarian cancer, commercialization, direct-to-consumer advertising, gene patents, genetic diagnostic tests, myriad genetics, trade secrets.
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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 | 0.052 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.063 |
| Scholarly communication | 0.029 | 0.031 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.031 | 0.045 |
| Insufficient payload (model declined to judge) | 0.004 | 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".