Dissolving microneedles in transdermal drug delivery: A critical analysis of limitations and translation challenges
Bibliographic record
Abstract
Microneedles (MNs) have emerged as an innovative approach for transdermal drug delivery, offering an efficient and minimally invasive alternative to conventional injections and oral delivery systems. While their potential has been widely recognized and extensively studied, the translation of MN technology into clinical practice remains limited. Despite the vast amount of published research, much of it involves over-complexification without addressing the core barriers to practical application. For example, dissolving/degradable MNs face key limitations such as poor drug loading capacity, low dosing consistency, and challenges in delivering effective therapeutic concentrations. These constraints restrict their utility to niche applications, such as vaccination or delivering potent drugs that require minimal doses. Additionally, the lack of standardized quality control measures, the complex manufacturing processes, and the high costs associated specifically with sterile/aseptic production further impede clinical translation. Regulatory frameworks for MNs remain vague, slowing the development of products that meet approval standards. This review critically examines the fundamental barriers to dissolving/degradable MN commercialization, as the most studied type of MN, while exploring promising strategies to overcome them. Advances in formulation science, fabrication techniques, and material engineering have demonstrated potential in enhancing drug loading efficiency and delivery consistency. Moreover, the establishment of clearer regulatory guidelines and scalable production strategies could significantly accelerate the commercialization of MN technology. By shifting focus toward pragmatic and clinically relevant solutions, this review aims to bridge the gap between research innovations and real-world applications, paving the way for broader implementation of MN technology in medicine.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".