Developing a Blockchain-Based Supply Chain System for Advanced Therapies: Protocol for a Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Advanced therapies, including cell and gene therapies, have shown therapeutic promise in curing life-threatening diseases, such as leukemia and lymphoma. However, these therapies can be complicated and expensive to deliver due to their sensitivity to environment; troublesome tissue, cell, or genetic material sourcing; and complicated regulatory requirements. OBJECTIVE: This study aims to create a novel connected supply chain logistics and manufacturing management platform based on blockchain, with cell and gene therapy as a use case. Objectives are to define the requirements and perform feasibility evaluations on the use of blockchain for standardized manufacturing and establishment of a chain of custody for the needle-to-needle delivery of autologous cell and gene therapies. A way of lowering overall regulatory compliance costs for running a network of facilities operating similar or parallel processes will be evaluated by lowering the monitoring costs through publishing zero-knowledge proofs and product release by exception. METHODS: The study will use blockchain technologies to digitally connect and integrate supply chain with manufacturing to address the security, scheduling, and communication issues between advanced therapy treatment centers and manufacturing facilities in order to realize a transparent, secure, automated, and cost-effective solution to the delivery of these life-saving therapies. An agile software development methodology will be used to develop, implement, and evaluate the system. The system will adhere to the EU and US good manufacturing practices and regulatory requirements. RESULTS: This is a proposed study protocol, and upon acceptance, grant funding will be pursued for its execution in 2021. CONCLUSIONS: The successful implementation of the integrated blockchain solution to supply chain and manufacturing of advanced therapies can push the industry standards toward a safer and more secure therapy delivery process. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/17005.
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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 | 0.047 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.078 | 0.018 |
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".