IPRF Reflection Paper on Biodistribution
Bibliographic record
Abstract
In a session held on May 15, 2015 during the 18th Annual American Society of Gene and Cell Therapy (ASGCT) Meeting, members of the International Pharmaceutical Regulators Forum Gene Therapy Working Group (IPRF GTWG) presented the current expectations of various international regulatory authorities for preclinical-nonclinical biodistribution (BD) studies for gene therapy products (excluding genetically modified cells) (http://www.iprp.global/working-group/gene-therapy). This session was a significant accomplishment because it allowed regulators from across the globe to have an open dialog on an issue facing stakeholders in this complex and fast-moving field. A summary of these presentations and the panel discussion was subsequently published as a meeting report.1Huang Y. Havert M. Gavin D. Serabian M. Ong L.L. McIntyre M.C. Ferry N. Kume A. Petrin D. Fu Y.-H. et al.Biodistribution studies: understanding international expectations.Mol. Ther. Methods Clin. Dev. 2016; 3: 16022Abstract Full Text Full Text PDF Scopus (4) Google Scholar This meeting report noted that regulators had similar perspectives worldwide on the definition of BD and the importance of collecting BD data. The regulators recognized that this agreement was a critical first step, but more work was needed to achieve a consensus position on the regulatory approach to BD assessment. A key effort for the IPRF GTWG since the 2015 ASGCT meeting has been continued dialog to develop consensus on regulatory expectations for the design and timing of BD studies and scientific considerations related to BD data analysis. To this end, a draft consensus position document was developed through subsequent IPRF GTWG meetings and finalized in a face-to-face meeting in May 2017. This document was then distributed to all members of the IPRF GTWG for additional comment and approval before acceptance by the IPRF Management Committee. We are now pleased to report that, in July 2018, the document was posted on the IPRF website as a Reflection Paper (http://development.iprp.backend.dev6.penceo.com/sites/default/files/2018-09/IPRP_GTWG_ReflectionPaper_BD_Final_2018_0713.pdf). Completion of the Reflection Paper is an important bookend to the 2015 ASGCT meeting. It represents a key milestone on a topic that was originally initiated to expedite the development of promising gene therapy products. Although this Reflection Paper is not a substitute for formal guidance from any regulatory authority, the scientific considerations outlined in this paper can be a starting point for discussion and will therefore likely have an impact on product development programs in this area. The BD Reflection Paper is also the first official document produced by the IPRF GTWG and is noteworthy as the first international document with regulatory consensus on a gene therapy topic since 2009 (http://www.ich.org/products/consideration-documents.html). As the interest in gene therapy products to treat a myriad of serious medical conditions continues to grow, the IPRF is poised to contribute a key role in this effort. (IPRF changed names in January 2018 and is now referred to as The International Pharmaceutical Regulators Programme [IPRP].)
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.056 | 0.051 |
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".