Challenging the Norm: A Multidisciplinary Perspective on Intravenous to Subcutaneous Bridging Strategies for Biologics
Bibliographic record
Abstract
The transition from intravenous (i.v.) to subcutaneous (s.c.) administration of biologics is a critical strategy in drug development aimed at improving patient convenience, compliance, and therapeutic outcomes. Focusing on the increasing role of model-informed drug development (MIDD) in the acceleration of this transition, an in-depth overview of the essential clinical pharmacology, and regulatory considerations for successful i.v. to s.c. bridging for biologics after the i.v. formulation has been approved are presented. Considerations encompass multiple aspects beginning with adequate pharmacokinetic (PK) and pharmacodynamic (i.e., exposure-response) evaluations which play a vital role in establishing comparability between the i.v. and s.c. routes of administrations. Selected key recommendations and points to consider include: (i) PK characterization of the s.c. formulation, supported by the increasing preclinical understanding of the s.c. absorption, and robust PK study design and analyses in humans; (ii) a thorough characterization of the exposure-response profiles including important metrics of exposure for both efficacy and safety; (iii) comparability studies designed to meet regulatory considerations and support approval of the s.c. formulation, including noninferiority studies with PK and/or efficacy and safety as primary end points; and (iv) comprehensive safety package addressing assessments of immunogenicity and patients' safety profile with the new route of administration. Recommendations for successful bridging strategies are evolving and MIDD approaches have been used successfully to accelerate the transition to s.c. dosing, ultimately leading to improved patient experiences, adherence, and clinical outcomes.
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 | 0.059 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.016 | 0.031 |
| Insufficient payload (model declined to judge) | 0.006 | 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".