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author:("hamid, mehrad")
1.  Biodegradable m-PEG/PCL Core-Shell Micelles: Preparation and Characterization as a Sustained Release Formulation for Curcumin 
Advanced Pharmaceutical Bulletin  2014;4(Suppl 2):501-510.
Purpose: Among the potent anticancer agents, curcumin is known as a very efficacious against many different types of cancer cells, but its clinical applications has been limited because of hydrophobicity, low gastrointestinal absorption, poor bioavailability and rapid metabolism. In this way, a novel micellar delivery system with mPEG–PCL was synthesized and the release profile of the curcumin from the drug-loaded micelles was evaluated.
Methods: In this study, curcumin was encapsulated within monomethoxypoly(ethylene glycol)-poly(ε-caprolactone) (mPEG-PCL) micelles through a single-step nano-precipitation method, leading to creation of curcumin-loaded mPEG-PCL (Cur/mPEG-PCL) micelles. Di-block mPEG-PCL copolymers were synthesized and used to prepare micelles. mPEG-PCL copolymer was characterized in vitro by HNMR, FTIR, DSC and GPC techniques. Then, mPEG–PCL copolymers with curcumin were self-assembled into micelles in aqueous solution. The resulting micelles were characterized further by various techniques such as dynamic light scattering (DLS) and atomic force microscopy (AFM).
Results: The findings showed the successful formation of smooth and spherical curcumin-loaded micelles. The encapsulation efficiency of curcumin was 88 ± 3.32%. The results of AFM revealed that the micelles have spherical shapes with size of 73.8 nm. The release behavior of curcumin from micelles was compared in different media. In vitro release of curcumin from curcumin-entrapped micelles was followed remarkably sustained profile. The sustained release of drug was hypothetically due to the entrapment of curcumin in core of micelles.
Conclusion: The results indicate the successful formulation of curcumin loaded m-PEG/PCL micelles. From the results, iIt can be concluded that curcumin m-PEG-PCL micelles may be considered as an effective treatment strategy for cancer in the future.
PMCID: PMC4312397
mPEG-PCL; Micelles; Curcumin; Drug delivery
2.  The Anticancer Agent Prodigiosin Is Not a Multidrug Resistance Protein Substrate 
DNA and Cell Biology  2013;32(3):90-97.
The brilliant red pigments prodiginines are natural secondary metabolites that are produced by select species of Gram-negative and Gram-positive bacteria. These molecules have received significant attention due to their reported antibacterial, antifungal, immunosuppressive, and anticancer activities. In this study, a Serratia marcescens SER1 strain was isolated and verified using 16s rDNA. The prodigiosin was purified using silica chromatography and was analyzed by 1H-NMR spectroscopy. The cell cytotoxic effects of the purified prodigiosin on multiple drug resistant cell lines that overexpress MDR1, BCRP, or MRP2 pumps were analyzed. Prodigiosin had nearly identical cytotoxic effects on the resistant cells in comparison to their parental lines. In agreement with the same prodigiosin cytotoxicity, FACS analysis of prodigiosin accumulation and efflux in MDR overexpressing cell lines also indicated that this pro-apoptotic agent operates independently of the presence of the MDR1, BCRP, or MRP transporter and may be a potential treatment for malignant cancer cells that overexpress multidrug resistance transporters.
The anticancer agent prodigiosin is not a multidrug resistance protein substrate. Prodigiosin, produced by Serratia marcescens, has cytotoxic effects on cells irrespective of whether the cells express MDR1, MRP2, or BCRP pumps of the multidrug resistance families. Therefore this drug may be potentially useful as a treatment for malignancies that overexpress the MDR pumps.
PMCID: PMC3589871  PMID: 23373476
3.  Modeling of thermodynamic and physico-chemical properties of coumarins bioactivity against Candida albicans using a Levenberg–Marquardt neural network 
In recent years, due to vital need for novel fungicidal agents, investigation on natural antifungal resources has been increased. The special features exhibited by neural network classifiers make them suitable for handling complex problems like analyzing different properties of candidate compounds in computer-aided drug design. In this study, by using a Levenberg–Marquardt (LM) neural network (the fastest of the training algorithms), the relation between some important thermodynamic and physico-chemical properties of coumarin compounds and their biological activities (tested against Candida albicans) has been evaluated. A set of already reported antifungal bioactive coumarin and some well-known physical descriptors have been selected and using LM training algorithm the best architecture of neural model has been designed for forecasting the new bioactive compounds.
PMCID: PMC3170013  PMID: 21918627
Levenberg/Marquardt algorithm; coumarin; neural network

Results 1-3 (3)