The survival of patients with glioblastoma multiforme (GBM), the most common and invasive form of malignant brain tumors, remains poor despite advances in current treatment methods including surgery, radiotherapy, and chemotherapy. Minocycline is a semi‐synthetic tetracycline derivative that has been widely used as an antibiotic and more recently, it has been utilized as an antiangiogenic factor to inhibit tumorigenesis. The objective of this study was to investigate the utilization of electrospraying process to fabricate minocycline‐loaded poly(lactic‐co‐glycolic acid) (PLGA) microparticles with high drug loading and loading efficiency and to evaluate their ability to induce cell toxicity in human glioblastoma (i.e., U87‐MG) cells. The results from this study demonstrated that solvent mixture of dicholoromethane (DCM) and methanol is the optimal solvent combination for minocycline and larger amount of methanol (i.e., 70:30) resulted in a higher drug loading. All three solvent ratios of DCM:methanol tested produced microparticles that were both spherical and smooth, all in the micron size range. The electrosprayed microparticles were able to elicit a cytotoxic response in U87‐MG glioblastoma cells at a lower concentration of drug compared to the free drug. This work provides proof of concept to the hypothesis that electrosprayed minocycline‐loaded PLGA microparticles can be a promising agent for the treatment of GBM and could have potential application for cancer therapies.
Glioblastoma Multiforme, an aggressive primary brain tumor, has a poor prognosis and no effective standard of care treatments. Most patients undergoing radiotherapy, along with Temozolomide chemotherapy, develop resistance to the drug, and recurrence of the tumor is a common issue after the treatment. We propose to model the pathways active in Glioblastoma using Boolean network techniques. The network captures the genetic interactions and possible mutations that are involved in the development of the brain tumor. The model is used to predict the theoretical efficacies of drugs for the treatment of cancer.
We use the Boolean network to rank the critical intervention points in the pathway to predict an effective therapeutic strategy for Glioblastoma. Drug repurposing helps to identify non-cancer drugs that could be effective in cancer treatment. We predict the effectiveness of drug combinations of anti-cancer and non-cancer drugs for Glioblastoma.
Given the genetic profile of a GBM tumor, the Boolean model can predict the most effective targets for treatment. We also identified two-drug combinations that could be more effective in killing GBM cells than conventional chemotherapeutic agents. The non-cancer drug Aspirin could potentially increase the cytotoxicity of TMZ in GBM patients.
- Award ID(s):
- 1917166
- NSF-PAR ID:
- 10224307
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- BMC Biomedical Engineering
- Volume:
- 3
- Issue:
- 1
- ISSN:
- 2524-4426
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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