An in vitro evaluation of antitumor activity of sirolimus-encapsulated liposomes in breast cancer cells

PEGylated liposomes enhance the effect of cytotoxic drug: A review

Cancer is a second leading disease-causing death worldwide that will continuously grow as much as 70% in the next 20 years. Chemotherapy is still becoming a choice for cancer treatment despite its severity of side effects and low success rate due to ineffective delivery of the chemodrugs. Since it was introduced in 1960, significant progress has been achieved in the use of liposomes in drug delivery. The study aims to review relevant literatures on role of PEGylated liposome in enhancing cytotoxic activity of several agents. A systematic literature on the use of PEGylated liposomes in anticancer research via Scopus, Google scholar and PubMed databases was conducted for studies published from 2000 to 2022. A total of 15 articles were selected and reviewed from 312 articles identified covering a variety of anticancer treatments by using PEGylated liposomes. PEGylated liposome which is purposed to achieve steric equilibrium is one of enhanced strategies to deliver anticancer drugs. It has been shown that some improvement of delivery and protection form a harsh gastric environment of several anticancer drugs when they are formulated in a PEGylated liposome. One of the successful drugs that has been clinically used is Doxil®, followed by some other drugs in the pipeline Various drugs (compounds) had been used to enhance the efficacy of PEGylated liposomes for targeted cancer cells in vitro and in vivo. In conclusion, PEGylated liposomes enhance drug activities and have great potential to become efficient anticancer delivery to follow Doxil® in the clinical setting.

Exploring the Phosphatidylcholine in Inflammatory Bowel Disease: Potential Mechanisms and Therapeutic Interventions

BACKGROUND

Inflammatory bowel disease (IBD) is a significant health problem with an increasing financial burden worldwide. Although various treatment strategies have been used, the results were not satisfactory. More and more researches have proved that the application of phosphatidylcholine (PC) may become an alternative therapy for IBD.

OBJECTIVE

This review aims to provide an overview of the possible mechanisms of PC and promote the potential application of PC for IBD therapy further.

METHODS

A comprehensive literature search was performed in PubMed with the following keywords: 'phosphatidylcholine', 'inflammatory bowel disease', 'Crohn's disease', 'inflammation', 'ulcerative colitis', 'therapy', 'nanomedicines', 'PKCζ', 'lysophosphatidylcholine', 'microbiota' and 'drug carrier'. The logical operators "AND" and "OR" were applied to combine different sets of the search results.

RESULTS

Studies suggested that PC displays a significant effect in the treatment of IBD by modulating gut barrier function, remodeling gut microbiota structure, regulating polarization of macrophages, and reducing the inflammatory response. PC has also been exploited as a drug carrier for anticancer or anti-inflammation agents in multiple forms, which implies that PC has immense potential for IBD therapy.

CONCLUSION

PC has shown promising potential as a new therapeutic agent or a drug carrier, with a novel, stable, prolonged mechanism of action in treating IBD. However, more high-quality basic and clinical studies are needed to confirm this.

Computationally repurposing drugs for breast cancer subtypes using a network-based approach

‘De novo’ drug discovery is costly, slow, and with high risk. Repurposing known drugs for treatment of other diseases offers a fast, low-cost/risk and highly-efficient method toward development of efficacious treatments. The emergence of large-scale heterogeneous biomolecular networks, molecular, chemical and bioactivity data, and genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called ‘in silico’ drug repurposing, i.e., computational drug repurposing (CDR). The aim of CDR is to discover new indications for an existing drug (drug-centric) or to identify effective drugs for a disease (disease-centric). Both drug-centric and disease-centric approaches have the common challenge of either assessing the similarity or connections between drugs and diseases. However, traditional CDR is fraught with many challenges due to the underlying complex pharmacology and biology of diseases, genes, and drugs, as well as the complexity of their associations. As such, capturing highly non-linear associations among drugs, genes, diseases by most existing CDR methods has been challenging. We propose a network-based integration approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes. Indeed, further clinical analysis is needed to confirm the therapeutic effects of identified drugs on each breast cancer subtype.