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Advanced Pharmaceutical Bulletin
ISSN: 2228-5881      eISSN: 2251-7308  
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Article History
Submitted: 21 Oct 2014
Revised: 07 Nov 2014
First published online: 05 Mar 2015

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Adv Pharm Bull. 2015;5(1):51-56 doi: 10.5681/apb.2015.007
PMID:25789219        PMCID:PMC4352223

Synthesis of PCEC Copolymers with Controlled Molecular Weight Using Full Factorial Methodology

Original Research

Leila Barghi 1,2, Davoud Asgari 1, Jaleh Barar 1,3, Hadi Valizadeh 1,4 *

1 Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.
2 Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran.
3 Research Center for Pharmaceutical Nanotechnology, Tabriz University of Medical Sciences, Tabriz, Iran.
4 Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.



Abstract
Purpose: Polycaprolactone (PCL) is a biodegradable polyester and has attracted attention as a suitable carrier for development of controlled drug delivery due to its non-toxicity and biocompatibility. It has been reported that the biodegradability of PCL can be enhanced by copolymerization with PEG. Molecular weight (Mw) and CL block lengths optimization in a series of synthesized PCEC copolymers was the main purpose of this study. Methods: The composition of copolymers was designed using full factorial methodology. Molecular weight of used PEG (4 levels) and weight ratio of epsilon-caprolactone/PEG (3 levels) were selected as independent variables. The PCEC copolymers were synthesized by ring opening polymerization. Formation of copolymers was confirmed by FT-IR spectroscopy as well as H-NMR. The Mn of PCEC copolymers was calculated from HNMR spectra. The thermal behavior of copolymers was characterized on differential scanning calorimeter. Results: Molecular weight of twelve synthesized copolymers was ranged from 1782 to 9264. In order to evaluate the effect of selected variables on the copolymers composition and Mw, a mathematical model for each response parameter with p-value less than 0.001were obtained. Average percent error for prediction of total Mn of copolymers and Mn of CL blocks were 13.81% and 14.88% respectively. Conclusion: In conclusion, the proposed model is significantly valid due to obtained low percent error in Mn prediction of test sets.





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