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Application of statistical design to evaluate critical process parameters and optimize formulation technique of polymeric nanoparticles.

ABSTRACT: In advanced medication, drug-loaded polymeric nanoparticles (NPs) appeared as a novel drug delivery system with lots of advantages over conventional medicines. Despite all the advantages, NPs do not gain popularity for manufacturing hurdles. The study focused on the formulation difficulties and implementation of statistical design to establish an effective model for manufacturing NPs. In this study, physico-chemical properties of the drug and polymer (PLGA) were incorporated to understand the mechanistic insights of nanoformulations. Primarily, the process controlling parameters were screened by Plackett-Burman design and the critical process parameters (Cpp) were further fabricated by Box-Behnken design (BBD). The TLM-PLGA-NPs (telmisartan loaded PLGA NPs) exhibited particle size, encapsulation efficiency and zeta potential of 232.4 nm, 79.21% and -9.92 mV respectively. The NPs represented drug loading of 76.31%. Korsmeyer-Peppas model (R 2 = 0.925) appeared to be the best fitted model for in vitro release kinetics of NPs. The model identified Fickian diffusion of TLM from the polymeric nanoparticles. The ANOVA results of variables indicate that BBD is a suitable model for the development of polymeric NPs. The study successfully identified and evaluated the correlation of significant parameters that were directly or indirectly influencing the formulations which deliberately produce desired nanoparticles with the help of statistical design.

SUBMITTER: Sarkar P

PROVIDER: S-EPMC6689589 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Application of statistical design to evaluate critical process parameters and optimize formulation technique of polymeric nanoparticles.

Sarkar Pradipta P Bhattacharya Saswati S Pal Tapan Kumar TK

Royal Society open science 20190724 7

In advanced medication, drug-loaded polymeric nanoparticles (NPs) appeared as a novel drug delivery system with lots of advantages over conventional medicines. Despite all the advantages, NPs do not gain popularity for manufacturing hurdles. The study focused on the formulation difficulties and implementation of statistical design to establish an effective model for manufacturing NPs. In this study, physico-chemical properties of the drug and polymer (PLGA) were incorporated to understand the me ...[more]

PMID: 31417765

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Project description:Micelles composed of block copolymers of poly(ethylene glycol)- b-poly( N-2-benzoyloxypropyl methacrylamide) (mPEG- b-p(HPMA-Bz)) have shown great promise as drug-delivery carriers due to their excellent stability and high loading capacity. In the present study, parameters influencing micelle size were investigated to tailor sizes in the range of 25-100 nm. Micelles were prepared by a nanoprecipitation method, and their size was modulated by the block copolymer properties such as molecular weight, their hydrophilic-to-hydrophobic ratio, homopolymer content, as well as formulation and processing parameters. It was shown that the micelles have a core-shell structure using a combination of dynamic light scattering and transmission electron microscopy analysis. By varying the degree of polymerization of the hydrophobic block ( NB) between 68 and 10, at a fixed hydrophilic block mPEG5k ( NA = 114), it was shown that the hydrophobic core of the micelle was collapsed following the power law of ( NB × Nagg)1/3. Further, the calculated brush height was similar for all the micelles examined (10 nm), indicating that crew-cut micelles were made. Both addition of homopolymer and preparation of micelles at lower concentrations or lower rates of addition of the organic solvent to the aqueous phase increased the size of micelles due to partitioning of the hydrophobic homopolymer chains to the core of the micelles and lower nucleation rates, respectively. Furthermore, it was shown that by using different solvents, the size of the micelles substantially changed. The use of acetone, acetonitrile, ethanol, tetrahydrofuran, and dioxane resulted in micelles in the size range of 45-60 nm after removal of the organic solvents. The use of dimethylformamide and dimethylsulfoxide led to markedly larger sizes of 75 and 180 nm, respectively. In conclusion, the results show that by modulating polymer properties and processing conditions, micelles with tailorable sizes can be obtained.

Project description:Adipose-derived mesenchymal stromal cells (MSC(AT)) display immunomodulatory and angiogenic properties, but an improved understanding of quantitative critical quality attributes (CQAs) that inform basal MSC(AT) fitness ranges for immunomodulatory and/or angiogenic applications is urgently needed for effective clinical translation. We constructed an in vitro matrix of multivariate readouts to identify putative CQAs that were sensitive enough to discriminate between specific critical processing parameters (CPPs) chosen for their ability to enhance MSC immunomodulatory and angiogenic potencies, with consideration for donor heterogeneity. We compared 3D aggregate culture conditions (3D normoxic, 3D-N) and 2D hypoxic (2D-H) culture as non-genetic CPP conditions that augment immunomodulatory and angiogenic fitness of MSC(AT). We measured multivariate panels of curated genes, soluble factors, and morphometric features for MSC(AT) cultured under varying CPP and licensing conditions, and we benchmarked these against two functional and therapeutically relevant anchor assays – in vitro monocyte/macrophage (MΦ) polarization and in vitro angiogenesis. Our results showed that varying CPP conditions was the primary driver of MSC(AT) immunomodulatory fitness; 3D-N conditions induced greater MSC(AT)-mediated MΦ polarization toward inflammation-resolving subtypes. In contrast, donor heterogeneity was the primary driver of MSC(AT) angiogenic fitness. Our analysis further revealed panels of putative CQAs with minimum and maximum values that consisted of twenty MSC(AT) characteristics that informed immunomodulatory fitness ranges, and ten MSC(AT) characteristics that informed angiogenic fitness ranges. Interestingly, many of the putative CQAs consisted of angiogenic genes or soluble factors that were inversely correlated with immunomodulatory functions (THBS1, CCN2, EDN1, PDGFA, VEGFA, EDIL3, ANGPT1, and ANG genes), and positively correlated to angiogenic functions (VEGF protein), respectively. We applied desirability analysis to empirically rank the putative CQAs for MSC(AT) under varying CPP conditions and donors to numerically identify the desirable CPP conditions or donors with maximal MSC(AT) immunomodulatory and/or angiogenic fitness. Taken together, our approach enabled combinatorial analysis of the matrix of multivariate readouts to provide putative quantitative CQAs that were sensitive to variations in select CPPs that enhance MSC immunomodulatory/angiogenic potency, and donor heterogeneity. These putative CQAs may be used to prospectively screen potent MSC(AT) donors or cell culture conditions to optimize for desired basal MSC(AT) immunomodulatory or angiogenic fitness.

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Application of statistical design to evaluate critical process parameters and optimize formulation technique of polymeric nanoparticles.

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Application of statistical design to evaluate critical process parameters and optimize formulation technique of polymeric nanoparticles.

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