A conceptual model and practical guidance for the development, administration, and evaluation of individualized therapies
Bibliographic record
Abstract
Bespoke therapies represent a promising tool to address a diverse range of genetic and acquired conditions, offering new hope where conventional treatments have fallen short. With the rapid rise of bespoke therapies, profound ethical and regulatory challenges emerge, making it crucial to establish a comprehensive framework that ensures these treatments reach clinical settings and meet patients' needs as quickly as possible while protecting all parties involved. Although current guidelines are continually evolving to address the range of ethical tensions raised by these therapies, several gaps remain. A significant unresolved question is determining where personalized interventions fall on the research-care continuum and understanding the institutional, regulatory, and ethical implications when custom therapies are classified as research, care, or a mix of both. To address these questions, we introduce a conceptual model alongside practical guidance for the development, administration, and evaluation of individualized therapies, using CRISPR/Cas9-based interventions for Duchenne Muscular Dystrophy as a case study. We argue that the goals of an intervention should be as individualized as the bespoke product itself, tailored to the specifics of each case. Rather than attempting to pinpoint the exact location of an intervention on the continuum, which may be hard to operationalize and have limited utility, our approach focuses on the practical details of how such interventions are administered and the individual component parts of an intervention. It advocates for transparent discussions among all partners to anticipate and adjust various components/parameters along the process of administering individualized interventions. Our paper highlights the most critical of these parameters in (1) the planning and development of individualized therapies in laboratory settings, (2) their regulatory oversight, and (3) evaluation. By discussing these stages and parameters in detail, we aim to provide guidance on how to navigate the ethical complexities inherent to individualized interventions and offer a preliminary framework for balancing the interplay between research objectives and patient care needs. Acknowledging that the scientific rigor and adequacy of any new model must be evaluated, we also identify the types of evidence that are required to validate that our model effectively meets individual and societal needs.
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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 | 0.039 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.017 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".