Recent Progress in Hydrogel-Based Bioinks for 3D Bioprinting: A Patent Landscape Analysis and Technology Updates
Bibliographic record
Abstract
Hydrogel-based bioinks have emerged as a critical component in the field of three-dimensional (3D) bioprinting, with numerous polymers being explored and utilized for this purpose. The high volume of patent applications reflects a competitive and dynamic research environment, where various entities are actively developing new formulations and applications for hydrogel-based bioinks. As this field continues to evolve, tracking these trends is essential for understanding the future direction of the technology and identifying key innovations and players in the industry. This study reveals substantial growth in the patent landscape for hydrogel-based bioinks in 3D bioprinting, with 173 patent documents published between 2013 and 2024. The marked increase in patent filings, particularly from 2018 onwards, underscores the growing recognition of the technologys potential in diverse applications, including tissue engineering and regenerative medicine. Although patent applications have outpaced granted patents, the steady rise in granted patents indicates the fields maturation and the transition of innovations from concept to legally protected technologies. The leading patent applicants in this domain include both industry leaders and academic institutions. Companies such as Organovo INC and Cellink AB are driving innovation through extensive patent activity, while academic institutions and foundations also make significant contributions, highlighting a robust ecosystem where industrial and academic research propel the technology forward. The global distribution of intellectual property filings in this field is broad, with significant activity in the United States, Europe, and Asia. This diversity in patenting jurisdictions reflects the global interest in advancing bioprinting technologies, particularly for healthcare applications. Patent classifications for hydrogel-based bioinks in 3D bioprinting illustrate the convergence of materials science, biotechnology, and advanced manufacturing. These classifications highlight the diverse applications of bioinks, ranging from tissue regeneration and stem cell therapy to the development of medical devices and multifunctional bioactive materials based on polymers.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".