Reflections on the Value of Systems Models for Regulation of Medical Research and Product Development
Bibliographic record
Abstract
INTRODUCTION (1) In a recent editorial in Science, (2) Bill Wulf used a framework to construct a model for in the life sciences. He defined an innovation ecology as the various interrelated institutions, laws, regulations and necessary to underwrite successful commercialization of publicly funded research through an infrastructure that entails education, research, tax policy, and intellectual property protection, among others. (3) In this formulation, private intellectual property and regulatory (IPR) rights form the linchpin between innovative publicly funded medical research, reduction to practice of basic research by firms and university technology transfer offices, product approval and marketing by government and firms as well as public consumption of approved medical products. As such, 'large scale' IPR rights-intensive translational research and technology commercialization constitute important market push and pull levers for domestic governments and provide the legal and regulatory basis for the drug development cycle writ large. Even so, and as lamented by Wulf in his editorial, a narrow size fits all IPR rights framework has the potential to stifle rather than encourage innovation. Casting the landscape as an open complex organic rather than a closed historical linear model of basic-to-applied research (4) is consistent with newer open-ended analytical models such as complex adaptive systems, (5) network dynamics (6) and dynamics. (7) These 'systems' frameworks view and model as dynamic, adaptive and indeterminate networks where the behavior of the system as a whole is governed by the ever-changing and non-linear nature of the connections between actors and institutions rather than as a predictable sum of a set of linear deterministic nodes. At the heart of the functioning of a complex adaptive system is the number and nature of the interactions between network nodes, which produce novel and ever changing properties as the layers of complexity increase. This dynamic structure-function relationship of complex is nicely summed up by the phrase more is different. (8) One implication of a view of IPR rights-intensive in the medical and life sciences is that local ecologies are collapsing globally. (9) This is due, among other things, to the global reach of patent decisions of first instance such as that in KSR International Co. v. Teleflex Inc., (10) harmonization of regulatory processes and standards, such as those relating to biomedical product approval, marketing and patenting, adoption of international IPR rights-sensitive instruments such as the WTO's Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) and, less obvious, the convergence of national science and technology (S&T) policies and normative behaviors aimed at commercialization of publicly funded medical research. Within the larger political and legal cultures of participating nations, there is an increasing space being carved out for translational research and commercialization. Indeed many nations, including Canada, are in the process of implementing strong IPR rights regimes that explicitly encompass publicly funded research efforts in order to reproduce the phenomenal success of university technology transfer and commercialization in the United States. This effort is hardly unique to Canada. Not only are other jurisdictions attempting to emulate U.S. translational research, but the United States itself, self-reflective after 25 years of Bayh-Dole, (11) is seeking to identify new and improved ways of commercializing public research in the context of its public health mandate. In the context of this debate, one hears increasingly vocal deliberation over the value of closed IPR rights models. PURPOSIVE POLICY Despite the growing visibility of network (12) and other systems theories, (13) linear models of organizations and organizational change have and continue to dominate analyses of the behavior of individuals, groups and institutions and to provide the benchmarks by which both public and private ordering are gauged. …
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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 | 0.101 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.039 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.037 | 0.034 |
| Insufficient payload (model declined to judge) | 0.011 | 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".