Finite Element Method (FEM) Modeling of Freeze-drying: Monitoring Pharmaceutical Product Robustness During Lyophilization

Lyophilization is an approach commonly undertaken to formulate drugs that are unstable to be commercialized as ready to use (RTU) solutions. One of the important aspects of commercializing a lyophilized product is to transfer the process parameters that are developed in lab scale lyophilizer to commercial scale without a loss in product quality. This process is often accomplished by costly engineering runs or through an iterative process at the commercial scale. Here, we are highlighting a combination of computational and experimental approach to predict commercial process parameters for the primary drying phase of lyophilization. Heat and mass transfer coefficients are determined experimentally either by manometric temperature measurement (MTM) or sublimation tests and used as inputs for the finite element model (FEM)-based software called PASSAGE, which computes various primary drying parameters such as primary drying time and product temperature. The heat and mass transfer coefficients will vary at different lyophilization scales; hence, we present an approach to use appropriate factors while scaling-up from lab scale to commercial scale. As a result, one can predict commercial scale primary drying time based on these parameters. Additionally, the model-based approach presented in this study provides a process to monitor pharmaceutical product robustness and accidental process deviations during Lyophilization to support commercial supply chain continuity. The approach presented here provides a robust lyophilization scale-up strategy; and because of the simple and minimalistic approach, it will also be less capital intensive path with minimal use of expensive drug substance/active material.     

Complete sequence and organisation of the <Emphasis Type="Italic">Jatropha curcas</Emphasis> (Euphorbiaceae) chloroplast genome

Jatropha curcas is an important non-edible oil seed tree species and is considered a promising source of biodiesel. The complete nucleotide sequence of J. curcas chloroplast genome (cpDNA) was determined by pyrosequencing and gaps filled by Sanger sequencing. The cpDNA is a circular molecule of 163,856 bp in length and codes for 110 distinct genes (78 protein coding, four rRNA and 28 distinct tRNA). Genome organisation and arrangement are similar to the reported angiosperm chloroplast genome. However, in Jatropha, the infA and the rps16 genes are non-functional. The inverted repeat (IR) boundary is within the rpl2 gene, and the 13 nucleotides at the ends of the two duplicate genes are different. Repeat analysis suggests the presence of 72 repeat regions (>30 bp) apart from the IR; of these, 48 were direct and 24 were palindromic repeats. Phylogenetic analysis of 81 protein coding chloroplast genes from 65 taxa by maximum parsimony, maximum likelihood and minimum evolution analyses at 100 bootstraps provide strong support for the placement of inaperturate crotonoids of which Jatropha is a member as sister to articulated crotonoids of which Manihot is a member.     

Microarray Based Gene Expression Analysis of Murine Brown and Subcutaneous Adipose Tissue: Significance with Human

BackgroundTwo types of adipose tissues, white (WAT) and brown (BAT) are found in mammals. Increasingly novel strategies are being proposed for the treatment of obesity and its associated complications by altering amount and/or activity of BAT using mouse models.ConclusionOur study provides evidence for inter species (mouse vs human) differences in differential gene expression between sWAT and BAT. Critical understanding of this data may help in development of novel ways to engineer one form of adipose tissue to another using murine model with focus on human.     

BP-ANN for Fitting the Temperature-Germination Model and Its Application in Predicting Sowing Time and Region for Bermudagrass

Temperature is one of the most significant environmental factors that affects germination of grass seeds. Reliable prediction of the optimal temperature for seed germination is crucial for determining the suitable regions and favorable sowing timing for turf grass cultivation. In this study, a back-propagation-artificial-neural-network-aided dual quintic equation (BP-ANN-QE) model was developed to improve the prediction of the optimal temperature for seed germination. This BP-ANN-QE model was used to determine optimal sowing times and suitable regions for three Cynodon dactylon cultivars (C. dactylon, ‘Savannah’ and ‘Princess VII’). Prediction of the optimal temperature for these seeds was based on comprehensive germination tests using 36 day/night (high/low) temperature regimes (both ranging from 5/5 to 40/40°C with 5°C increments). Seed germination data from these temperature regimes were used to construct temperature-germination correlation models for estimating germination percentage with confidence intervals. Our tests revealed that the optimal high/low temperature regimes required for all the three bermudagrass cultivars are 30/5, 30/10, 35/5, 35/10, 35/15, 35/20, 40/15 and 40/20°C; constant temperatures ranging from 5 to 40°C inhibited the germination of all three cultivars. While comparing different simulating methods, including DQEM, Bisquare ANN-QE, and BP-ANN-QE in establishing temperature based germination percentage rules, we found that the R² values of germination prediction function could be significantly improved from about 0.6940–0.8177 (DQEM approach) to 0.9439–0.9813 (BP-ANN-QE). These results indicated that our BP-ANN-QE model has better performance than the rests of the compared models. Furthermore, data of the national temperature grids generated from monthly-average temperature for 25 years were fit into these functions and we were able to map the germination percentage of these C. dactylon cultivars in the national scale of China, and suggested the optimum sowing regions and times for them.     

Effect of TiO2 nanoparticles on oxidative damage and antioxidant defense systems in chickpea seedlings during cold stress

The effects of TiO₂ nanoparticles (NPs) on physiologo-biochemical responses were studied in two chickpea (Cicer arietimun L.) genotypes differing in cold sensitivity (tolerant Sel11439 and sensitive ILC533) during cold stress (CS). The results showed that hydrogen peroxide and MDA contents and electrolyte leakage index (ELI) increased under CS conditions in both genotypes and that these damage indices were higher in ILC533 than in Sel11439 plants. In plants treated with TiO₂ NPs, a decreased H₂O₂ level was accompanied by a decrease in the MDA content and ELI compared to control plants, and these changes occurred more effectively in Sel11439 than in ILC533 plants. The antioxidant enzymes were more effective in cell protection against CS in Sel11439 plants compared to ILC533 plants, as well as in plants treated with TiO₂ NPs compared to control plants. The lipoxygenase activity was induced efficiently only in Sel11439 plants treated with TiO₂ NPs during CS, probably indicating its role in stress response (which was confirmed by measuring allen oxide synthase activity). TiO₂ NPs caused stability of chlorophyll and carotenoid contents during CS. Results suggest that TiO₂ NPs confer an increased tolerance of chickpea plants to CS, decreasing the level of injuries and increasing the capacity of defense systems.