Liposomal antimicrobials in the fight against bacterial and fungal pathogens: Clinical successes and development challenges
Bibliographic record
Abstract
Bacterial, fungal, and protozoan infections pose a rapidly escalating threat to global health, exacerbated by the rise in antimicrobial resistance. Current therapies against microbial pathogens are limited by high systemic toxicity and poor drug solubility. Liposomal formulations (spherical vesicles composed of lipid bilayers) have demonstrated remarkable clinical potential in addressing these concerns, as evidenced by the marketed products AmBisome® and Arikayce®. These products, which deliver amphotericin B via parenteral injection and amikacin via inhalation, exemplify how liposomes effectively mitigate drug-associated toxicity, enhance therapeutic efficacy, and overcome the biological barriers inherent to infection sites, including complex microbial biofilms, mucosal interfaces, or the blood-brain barrier. Complementary insights from anticancer research indicate that strategic manipulation of liposomal composition and structure can enhance their therapeutic potential. Adjustments in lipid charge, fluidity, and PEGylation, in particular, highlight their versatility and broad applicability for antimicrobial drug delivery. Liposomal antimicrobials can modulate pharmacokinetic profiles, achieve targeted release at sites of infection, and increase local drug concentrations, which are key advantages over conventional treatments. Despite these therapeutic advances, successful clinical translation and widespread adoption of liposomal antimicrobials remain highly dependent on overcoming existing technological and manufacturing challenges. This review emphasises the need for a paradigm shift within liposomal antimicrobial development, encouraging progression from initial research and development toward scalable, reproducible, and economically viable commercial manufacturing platforms. This transition is essential not only for ensuring the global accessibility and affordability of existing therapies but also for expanding the development of clinically relevant liposomal antimicrobial nanomedicines.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".