Rational Clinical Dose Selection of Adeno‐Associated Virus‐Mediated Gene Therapy Based on Allometric Principles
Bibliographic record
Abstract
One of the challenges in translational medicine is to select first-in-human doses of investigational drugs based on findings in preclinical studies. Despite substantial progress in the optimization of recombinant adeno-associated virus (AAV) vectors of in vivo gene therapy for treating various diseases, there remain significant limitations to the use of preclinical data to guide dose selection in clinical trials. Here we introduce a novel concept of gene efficiency factor (GEF) to describe the efficiency of the gene transfer system and describe and apply the concept of GEF in AAV-mediated in vivo gene transfer systems. We explore the utility of allometric scaling to translate GEF across species using AAV-mediated in vivo factor IX (FIX) gene therapy for hemophilia B and to demonstrate the use of GEF in predicting efficacious AAV vector doses in humans. We show for the first time that an allometric relationship exists for GEF of AAV-mediated in vivo gene therapy. Furthermore, we demonstrate the feasibility of using the allometric relationship of GEF to select efficacious first-in-human doses of virus-mediated invivo gene therapy. Based on our findings, allometry of GEF can be used to translate biological efficiency from animal studies to clinical studies and provide a rational basis of setting first-in-human doses for new virus-mediated invivo gene therapy products.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".