Geriatric drug delivery ― barriers, current technologies and the road ahead
Bibliographic record
Abstract
The geriatric population encompasses the largest part of the health care system worldwide. Chronic medical conditions are highly prevalent in the elderly, consequently, due to their complex health needs, there is a significant rate of multi-drug therapy. Despite the high numbers of medications prescribed, geriatric patients face several barriers when it comes to successful drug delivery including alterations in cognitive and physical function. The current review highlights the impact of chronic diseases on the ageing population along with how changes in drug pharmacokinetics could impact drug efficacy and safety. Also discussed are applications of administration routes in the geriatric population and complications that could arise. A focus is placed on the traditional and upcoming drug delivery advancements being employed in seniors with a focus addressing obstacles faced by this patient category. Nanomedicines, three-dimensional printing, long-acting formulations, transdermal systems, orally disintegrating tablets, and shape/taste modification technologies are discussed. Several barriers to drug delivery in the elderly have been identified in literature and directions for future studies should focus on addressing these gaps for geriatric drug formulation development including personalised medicine, insights into novel drug delivery systems like nanomedicines, methods for decreasing pill burden and shape/size modifications.ARTICLE HIGHLIGHTSTypically, senior citizens take more medications than any other patient population, yet most drug delivery technologies are not tailored to address the specific cognitive and physical barriers that these individuals encounter.The safety of drug delivery systems in the elderly patients should be prioritised with considerations on changes in pharmacokinetics with age, use of non-toxic excipients, and selecting drugs with minimal off-target side effects.Several commercialised and upcoming drug delivery technologies have begun to address the current limitations that the ageing population faces.Future research should focus on applying novel strategies like 3D printing, personalised medicine, and long-acting formulations to improve drug delivery to elderly patients.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".