Personalized Medicine, Bioethics and Social Responsibilities: Re-thinking the Pharmaceutical Industry to Remedy Inequities in Patient Care and International Health
Bibliographic record
Abstract
US Senator Barack Obama recently proposed the Genomics and Personalized Medicine Act of 2006, which should it be enacted, would establish a Genomics and Personalized Medicine Interagency Working Group to coordinate personalized medicine efforts, fund genomics research to improve drug safety and establish a US Biobanking Research Initiative similar to efforts deployed in other countries. But what impact could personalized medicine have on the drug development process, the pharmaceutical industry and international health, including that in developing countries? Can personalized medicines support innovation, sustainability and growth in the pharmaceutical industry and also respond to changing world realities, emerging public demands for safer and more efficacious medicines and equitable access to pharmaceuticals? The present paper examines these socio-ethical and science policy questions by first elucidating their intrinsic and often complex interactions with other economic and policy issues (and the often divergent interests of stakeholders). We then present some examples from other industries (e.g., the case of hybrid cars and attendant growth of consumer interest and confidence in high quality sustainable products), with a view to identifying the factors that might contribute to a successful integration of pharmacogenomics and related biomarker technologies in patient care, international health and public policy. In particular, we propose ways to integrate the concept of sustainability into corporate and investor models of pharmaceutical industry development. While the power of pharmacogenomics to serve as a driver for the pharmaceutical industry remains to be evaluated, we submit that biomedical innovation and economic prosperity can co-exist with ethical drug development and the sustainable commercialization of customized drug therapies. Keywords: Pharmacogenomics, personalized medicine, bioethics, social responsibility, sustainability, international health, economics, inequity, distributive justice, business ethics
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".