Therapeutic Potential of 3D-Printed Alloys as Drug-Eluting Implants: Current Progress
Bibliographic record
Abstract
In physiological environments, several metallic alloys, including titanium, stainless steel, cobalt–chromium, and emerging biodegradable systems such as magnesium (Mg), zinc (Zn), and iron (Fe), offer mechanical properties and biocompatibility suitable for load-bearing implants. With the rapid advancement of 3D printing technologies, these alloys can now be fabricated into patient-specific, complex geometries that enhance both structural performance and functional integration. Beyond serving as structural supports, 3D-printed alloys are increasingly engineered as localized drug-delivery platforms to release anti-inflammatory, antibacterial, anticancer, and osteogenic agents at the implant–tissue interface, addressing the dual clinical needs of site-specific therapy and mechanical stabilization. Nevertheless, this field remains underexplored because studies differ widely in alloy chemistry, surface topography, porosity, coating strategy, drug-loading methods, and release profiles, as well as in how material degradation or passivation interacts with pharmacokinetics. For the first time, this review consolidates drug-loading and elution strategies across 3D-printed alloy platforms, compares therapeutic categories in relation to alloy and coating types, and critically evaluates how the surface microstructure or alloy geometry influences release behavior.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".