Use of Design-of-Experiment principles to develop a dose-response function for colour photographs

Computer Optimization of Stealth Biodegradable Polymeric Dual-loaded Nanoparticles for Cancer Therapy Using Central Composite Face-centered Design

BACKGROUND

Combination chemotherapy capable of overcoming cancer drug resistance can be facilitated by nanotechnology.

OBJECTIVE

Synthesis, characterization, statistical experimental design, analysis and optimization of stealth pH-sensitive polymeric nanoparticles suitable as a platform for simultaneous delivery of paclitaxel and 17-AAG in breast cancer therapy were investigated.

METHODS

An acetal crosslinker and a poly(ɛ)caprolactone macromonomer were synthesized and characterized. The statistical experimental design used was the response surface method (RSM). We used the central composite face-centered design (CCF) in three independent factors and seventeen runs. Nanoparticles were fabricated by dispersion polymerization techniques. Response variables evaluated were: particle size, drug loading, encapsulation efficiency, and in vitro availability.

RESULTS

Scanning electron micrographs showed the formation of spherical nanoparticles. Computer software was used for the analysis of variance with a 95% confidence level and Q2 (goodness of prediction) to select an appropriate model for each of the response variables. Each term in each of the models was tested for the significance of the regression coefficients. The computer software optimizer was used for optimization to select factor combination to minimize particle size, time (h) for maximum release of paclitaxel and 17-AAG, to maximize paclitaxel and 17-AAG loading efficiency and to maximize paclitaxel and 17-AAG encapsulation efficiency.

CONCLUSION

The optimization was successful, as shown by the validation data which lie within the confidence intervals of predicted values of the response variables. The selected factor combination is suitable for the in vivo evaluation of the nanoparticles loaded with paclitaxel and 17-AAG.

Different methodologies for sustainability of optimization techniques used in submerged and solid state fermentation

Optimization techniques are considered as a part of nature's way of adjusting to the changes happening around it. There are different factors that establish the optimum working condition or the production of any value-added product. A model is accepted for a particular process after its sustainability has been verified on a statistical and analytical level. Optimization techniques can be divided into categories as statistical, nature inspired and artificial neural network each with its own benefits and usage in particular cases. A brief introduction about subcategories of different techniques that are available and their computational effectivity will be discussed. The main focus of the study revolves around the applicability of these techniques to any particular operation such as submerged fermentation (SmF) and solid state fermentation (SSF), their ability to produce secondary metabolites and the usefulness in the laboratory and industrial level. Primary studies to determine the enzyme activity of different microorganisms such as bacteria, fungi and yeast will also be discussed. l-Asparaginase, the most commonly used drugs in the treatment of acute lymphoblastic leukemia (ALL) shall be considered as an example, a short discussion on models used in the production by the processes of SmF and SSF will be discussed to understand the optimization techniques that are being dealt. It is expected that this discussion would help in determining the proper technique that can be used in running any optimization process for different purposes, and would help in making these processes less time-consuming with better output.