The importance of drug safety and tolerability in the development of new immunosuppressive therapy for transplant recipients: The Transplant Therapeutics Consortium’s position statement
Bibliographic record
Abstract
The Transplant Therapeutics Consortium (TTC) is a public-private partnership between the US Food and Drug Administration and the transplantation community including the transplantation societies and members of the biopharmaceutical industry. The TTC was formed to accelerate the process of developing new medical products for transplant patients. The initial goals of this collaboration are the following: (a) To define which aspects of the kidney transplant drug-development process have clear needs for improvement from an industry and regulatory perspective; (b) to define which of the unmet needs in the process could be positively impacted through the development of specific drug-development tools based on available data; and (c) to determine the most appropriate pathway to achieve regulatory acceptance of the proposed process-accelerating tools. The TTC has identified 2 major areas of emphasis: new biomarkers or endpoints for determining the efficacy of new therapies and new tools to assess the safety or tolerability of new therapies. This article presents the rationale and planned approach to develop new tools to assess safety and tolerability of therapies for transplant patients. We also discuss how similar efforts might support the continued development of patient-reported outcome measures in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.170 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.042 | 0.057 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".