Computer Optimization of Biodegradable Nanoparticles Fabricated by Dispersion Polymerization

Emmanuel O. Akala, Simeon Adesina and Oluwaseun Ogunwuyi
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Emmanuel O. Akala: Center for Drug Research and Development, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, 2300 4th Street NW, Washington, DC 20059, USA
Simeon Adesina: Center for Drug Research and Development, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, 2300 4th Street NW, Washington, DC 20059, USA
Oluwaseun Ogunwuyi: Center for Drug Research and Development, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, 2300 4th Street NW, Washington, DC 20059, USA

IJERPH, 2015, vol. 13, issue 1, 1-17

Abstract: Quality by design (QbD) in the pharmaceutical industry involves designing and developing drug formulations and manufacturing processes which ensure predefined drug product specifications. QbD helps to understand how process and formulation variables affect product characteristics and subsequent optimization of these variables vis-à-vis final specifications. Statistical design of experiments (DoE) identifies important parameters in a pharmaceutical dosage form design followed by optimizing the parameters with respect to certain specifications. DoE establishes in mathematical form the relationships between critical process parameters together with critical material attributes and critical quality attributes. We focused on the fabrication of biodegradable nanoparticles by dispersion polymerization. Aided by a statistical software, d -optimal mixture design was used to vary the components (crosslinker, initiator, stabilizer, and macromonomers) to obtain twenty nanoparticle formulations (PLLA-based nanoparticles) and thirty formulations (poly-?-caprolactone-based nanoparticles). Scheffe polynomial models were generated to predict particle size (nm), zeta potential, and yield (%) as functions of the composition of the formulations. Simultaneous optimizations were carried out on the response variables. Solutions were returned from simultaneous optimization of the response variables for component combinations to (1) minimize nanoparticle size; (2) maximize the surface negative zeta potential; and (3) maximize percent yield to make the nanoparticle fabrication an economic proposition.

Keywords: nanoparticles; quality by design (QbD); d -optimal mixture design; statistical design of experiments; computer optimization; dispersion polymerization (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2015
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