Optimal experimental design for discriminating between microbial growth models as function of suboptimal temperature: From in silico to in vivo

The present study focused to optimize dual coated multiparticulates using Box-Behnken Experimental Design and in-silico simulation using GastroPlusTM software. The optimized formulations (OB1 and OB2) were comparatively evaluated for particle size, morphological, in vitro drug release, and in vivo permeation studies. In silico simulation study predicted the in vivo performance of the optimized formulation based on in-vitro data. Results suggested that optimized formulation was obtained using maximum content of Eudragit FS30D and minimum drying time (2 min). In vitro data corroborated that curcumin release was completely protected from premature drug release in the proximal part of gastro intestinal tract and successfully released to the colon (95%) which was closely predicted (90.1 %) by GastroPlusTM simulation technique. Finally, confocal laser scanning microscopy confirmed the in-vitro findings wherein maximum intensity was observed with OB1 treated group suggesting successful delivery of OB1 to the colon for enhanced absorption as predicted in regional absorption profile in ascending colon (30.9%) and caecum (23.2%). Limited drug absorption was predicted in small intestine (1.5-8.7%). The successful outcomes of the research work minimized the release of curcumin in the upper gastric tract and the maximized drug access to the colon (pH 7.4) as prime concern.

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On-Line Optimal Input Design Increases the Efficiency and Accuracy of the Modelling of an Inducible Synthetic Promoter

Processes ◽

10.3390/pr6090148 ◽

2018 ◽

Vol 6 (9) ◽

pp. 148 ◽

Cited By ~ 14

Author(s):

Lucia Bandiera ◽

Zhaozheng Hou ◽

Varun Kothamachu ◽

Eva Balsa-Canto ◽

Peter Swain ◽

...

Keyword(s):

Experimental Design ◽

A Priori ◽

Inducible Promoter ◽

Optimal Experimental Design ◽

Parameter Inference ◽

Synthetic Gene Circuits ◽

Input Design ◽

On Line

Synthetic biology seeks to design biological parts and circuits that implement new functions in cells. Major accomplishments have been reported in this field, yet predicting a priori the in vivo behaviour of synthetic gene circuits is major a challenge. Mathematical models offer a means to address this bottleneck. However, in biology, modelling is perceived as an expensive, time-consuming task. Indeed, the quality of predictions depends on the accuracy of parameters, which are traditionally inferred from poorly informative data. How much can parameter accuracy be improved by using model-based optimal experimental design (MBOED)? To tackle this question, we considered an inducible promoter in the yeast S. cerevisiae. Using in vivo data, we re-fit a dynamic model for this component and then compared the performance of standard (e.g., step inputs) and optimally designed experiments for parameter inference. We found that MBOED improves the quality of model calibration by ∼60%. Results further improve up to 84 % when considering on-line optimal experimental design (OED). Our in silico results suggest that MBOED provides a significant advantage in the identification of models of biological parts and should thus be integrated into their characterisation.

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