ReviewCurrent topics in medicinal chemistry2024
Microfluidics-mediated Liposomal Nanoparticles for Cancer Therapy: Recent Developments on Advanced Devices and Technologies.
Review in Current topics in medicinal chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Nanoparticle carriers in the treatment of intracellular bacterial infections: Current approaches and challenges.International journal of pharmaceutics: X · 2026Review
- Porphyrin-based MOFs for Gene Delivery in Cancer Therapy: Recent Advances and Progress.Current pharmaceutical design · 2026Review
- Advancements in lung cancer: molecular insights, innovative therapies, and future prospects.Medical oncology (Northwood, London, England) · 2025Review
- Co-Encapsulation of Multiple Antineoplastic Agents in Liposomes by Exploring Microfluidics.International journal of molecular sciences · 2025Review
- A Comparative Review of Tocosomes, Liposomes, and Nanoliposomes as Potent and Novel Nanonutraceutical Delivery Systems for Health and Biomedical Applications.Biomedicines · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Liposomes, spherical particles with phospholipid double layers, have been extensively studied over the years as a means of drug administration. Conventional manufacturing techniques like thin-film hydration and extrusion have limitations in controlling liposome size and distribution. Microfluidics enables superior tuning of parameters during the self-assembly of liposomes, producing uniform populations. This review summarizes microfluidic methods for engineering liposomes, including hydrodynamic flow focusing, jetting, micro mixing, and double emulsions. The precise control over size and lamellarity afforded by microfluidics has advantages for cancer therapy. Liposomes created through microfluidics and designed to encapsulate chemotherapy drugs have exhibited several advantageous properties in cancer treatment. They showcase enhanced permeability and retention effects, allowing them to accumulate specifically in tumor tissues passively. This passive targeting of tumors results in improved drug delivery and efficacy while reducing systemic toxicity. Promising results have been observed in pancreatic, lung, breast, and ovarian cancer models, making them a potential breakthrough in cancer therapy. Surface-modified liposomes, like antibodies or carbohydrates, also achieve active targeting. Overall, microfluidic fabrication improves reproducibility and scalability compared to traditional methods while maintaining drug loading and biological efficacy. Microfluidics-engineered liposomal formulations hold significant potential to overcome challenges in nanomedicine-based cancer treatment.
Indexed as
Identifiers
38424436What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.