Davis, C. and Abraham, J.Unhealthy Pharmaceutical Regulation: Innovation, Politics and Promissory Science. Basingstoke: Palgrave Macmillan. 2013. 336pp £65.00 (hbk) ISBN: 978–0‐230–00866–3
Bibliographic record
Abstract
This is the first international comparison of the world's two largest pharmaceutical regulatory agencies (that is, the American Food and Drug Administration (FDA) and the European Medicines Agency (EMA) and the neoliberal deregulatory reforms that have been taking place across these institutions over the past 30 years. The general question driving the book is the extent to which those reforms have been in the interest of public health, which is answered through further questions such as: ‘What is the relationship between deregulation, innovation and the availability of valuable therapies for patients? What role is played in shaping regulatory decision-making by public expectations and the assertions of what some scholars call “promissory science”? Has pharmaceutical regulation and innovation during the neo-liberal era been an unwarranted misadventure or even misdirection so far as health is concerned, or are they on the right track?’ (p.3). Permit marketing approval of new drugs intended to treat serious or life-threatening illnesses that appear to provide meaningful therapeutic benefits to patients compared with existing treatment on the basis of adequate and well-controlled clinical trials establishing that the drug product has an effect on a surrogate endpoint that is ‘reasonably likely’ to predict clinical benefit. (p.56) The 1992 Prescription Drug User Fee Act that instituted fees to be paid by companies submitting new drug applications (and for each existing product on the market and each manufacturing plant in operation) that could be used only for the FDA's drug review process (p.59). The power of this book rests in the authors’ ability to track how those particular neoliberal policies in the FDA and EMA influenced the (de)regulation of a set of specific drugs, and to assess the impact that this neoliberal approach has had on public health. The placebo standard and regulatory reliance on some ‘established’ surrogate measures of drug efficacy, such as blood-glucose control, needed to be challenged in ways contrary to the commercial interests of the pharmaceutical industry by prolonging approval times and increasing the risk of non-approval. (p.153) The case implies that requiring companies to provide better and more targeted evidence about efficacy, if appropriate, is much more likely to be in patients’ interests, even if that process takes longer initially. (p.263) The decision by EU regulators to withdraw those drugs from the market was more in the interest of public health than risk managing them on the market, as the FDA chose to do. (p.264) This extremely well-researched book serves the dual purpose of collating the decades of work carried out by Davis and Abraham and laying the foundation for a new social science discipline concerned with pharmaceuticals and public health policy. The book outlines how a collection of theoretical tools, that is, neoliberal theory, capture theory, corporate bias theory, disease-politics theory (hard and soft versions) and expectations/marketing theory could be used by such a discipline. The final chapter reviews their applicability across the drug case studies, providing lessons and arguments for changes needed in the regulation of pharmaceutical in the EU and the USA. As a consequence this book is highly relevant and recommended for anyone interested in pharmaceuticals, whether an academic, clinician, scientific or biomedical researcher, regulator or policymaker, and it may be of particular interest to patients. The book is part of the Health, Technology, and Society series published by Palgrave that includes a number of other high quality and award-winning contributions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.022 | 0.012 |
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".