scholarly journals Comparative Assessment of In Vitro and In Silico Methods for Aerodynamic Characterization of Powders for Inhalation

Pharmaceutics ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1831
Author(s):  
Jelisaveta Ignjatović ◽  
Tijana Šušteršič ◽  
Aleksandar Bodić ◽  
Sandra Cvijić ◽  
Jelena Đuriš ◽  
...  
Keyword(s):  
In Silico ◽  
Aerodynamic Performance ◽  
Dry Powders ◽  
In Vitro Methods ◽  
Discrete Phase Modeling ◽  
Discrete Phase ◽  
Computational Fluid Dynamics Cfd ◽  
In Silico Methods

In vitro assessment of dry powders for inhalation (DPIs) aerodynamic performance is an inevitable test in DPI development. However, contemporary trends in drug development also implicate the use of in silico methods, e.g., computational fluid dynamics (CFD) coupled with discrete phase modeling (DPM). The aim of this study was to compare the designed CFD-DPM outcomes with the results of three in vitro methods for aerodynamic assessment of solid lipid microparticle DPIs. The model was able to simulate particle-to-wall sticking and estimate fractions of particles that stick or bounce off the inhaler’s wall; however, we observed notable differences between the in silico and in vitro results. The predicted emitted fractions (EFs) were comparable to the in vitro determined EFs, whereas the predicted fine particle fractions (FPFs) were generally lower than the corresponding in vitro values. In addition, CFD-DPM predicted higher mass median aerodynamic diameter (MMAD) in comparison to the in vitro values. The outcomes of different in vitro methods also diverged, implying that these methods are not interchangeable. Overall, our results support the utility of CFD-DPM in the DPI development, but highlight the need for additional improvements in these models to capture all the key processes influencing aerodynamic performance of specific DPIs.

COMPUTATIONAL VS. IN VITRO APPROACH TO PREDICT AERODYNAMIC PERFORMANCE OF DRY POWDERS FOR INHALATION

2021 ◽  
Author(s):  
Jelisaveta Ignjatović ◽  
◽  
Tijana Šušteršič ◽  
Sandra Cvijić ◽  
Aleksandar Bodić ◽  
...  
Keyword(s):  
Computational Models ◽  
Aerodynamic Performance ◽  
The Other ◽  
Geometric Standard Deviation ◽  
Dry Powders ◽  
Aerodynamic Properties ◽  
Discrete Phase ◽  
Predicted Values ◽  
Next Generation Impactor

Computational fluid dynamics (CFD) coupled with discrete phase modeling (DPM) appeared as an alternative approach to the commonly used in vitro methods for the assessment of dry powders for inhalation (DPI) aerodynamic properties. The aim of this study was to compare the parameters that describe DPI aerodynamic performance, obtained computationally by CFD-DPM and in vitro by next generation impactor (NGI). The analyzed parameters included: emitted fraction (EF), fine particle fraction (FPF), mass median aerodynamic diameter (MMAD) and geometric standard deviation (GSD). The results showed that CFD-DPM simulated EF values were generally comparable to the NGI obtained values, but there were some differences between the results obtained by these two methods. On the other hand, CFD-DPM predicted MMAD values were almost twice bigger than the NGI determined values, while the predicted GSD values were lower than NGI obtained values. In addition, CFD-DPM predicted values indicated larger differences between MMAD for different formulations in comparison to the NGI results. The largest difference between CFD-DPM and NGI results was observed for FPF values. Namely, CFD-DPM predicted FPF values were markedly lower than the NGI determined values for four of five tested formulations. Overall, although the designed CFD-DPM model and NGI measurements provided comparable data on the DPI EF values, the other relevant parameters obtained by these two approaches largely diverged indicating the need for further refinement of computational models to fully capture DPI aerodynamic performance.

IN VITRO/IN SILICO CHARACTERIZATION OF ACTIVE AND PASSIVE STRESSES IN CARDIAC MUSCLE

2012 ◽  
Vol 10 (2) ◽  
pp. 171-188 ◽  
Author(s):  
Markus Boel ◽  
Oscar J. Abilez ◽  
Ahmed N Assar ◽  
Christopher K. Zarins ◽  
Ellen Kuhl
Keyword(s):  
Cardiac Muscle ◽  
In Silico ◽  
In Silico Characterization

Elucidation of Phosphodiesterase-1 Inhibitory Effect of Some Selected Natural Polyphenolics Using In Vitro and In Silico Methods

2016 ◽  
Vol 17 (4) ◽  
pp. 412-417 ◽  
Author(s):  
Abdur Rauf ◽  
Ilkay Erdogan Orhan ◽  
Abdulselam Ertas ◽  
Hamdi Temel ◽  
Taibi Ben Hadda ◽  
...  
Keyword(s):  
In Silico ◽  
Inhibitory Effect ◽  
In Silico Methods

In-Vitro and In-Silico Characterization of Xylose Reductase from Emericella nidulans

2019 ◽  
Vol 13 (2) ◽  
pp. 159-170 ◽  
Author(s):  
Vishal Ahuja ◽  
Aashima Sharma ◽  
Ranju Kumari Rathour ◽  
Vaishali Sharma ◽  
Nidhi Rana ◽  
...  
Keyword(s):  
Production Process ◽  
In Silico ◽  
Xylose Reductase ◽  
In Silico Analysis ◽  
Emericella Nidulans ◽  
Higher Temperature ◽  
In Silico Characterization ◽  
Silico Analysis

Background: Lignocellulosic residues generated by various anthropogenic activities can be a potential raw material for many commercial products such as biofuels, organic acids and nutraceuticals including xylitol. Xylitol is a low-calorie nutritive sweetener for diabetic patients. Microbial production of xylitol can be helpful in overcoming the drawbacks of traditional chemical production process and lowring cost of production. Objective: Designing efficient production process needs the characterization of required enzyme/s. Hence current work was focused on in-vitro and in-silico characterization of xylose reductase from Emericella nidulans. Methods: Xylose reductase from one of the hyper-producer isolates, Emericella nidulans Xlt-11 was used for in-vitro characterization. For in-silico characterization, XR sequence (Accession No: Q5BGA7) was used. Results: Xylose reductase from various microorganisms has been studied but the quest for better enzymes, their stability at higher temperature and pH still continues. Xylose reductase from Emericella nidulans Xlt-11 was found NADH dependent and utilizes xylose as its sole substrate for xylitol production. In comparison to whole cells, enzyme exhibited higher enzyme activity at lower cofactor concentration and could tolerate higher substrate concentration. Thermal deactivation profile showed that whole cell catalysts were more stable than enzyme at higher temperature. In-silico analysis of XR sequence from Emericella nidulans (Accession No: Q5BGA7) suggested that the structure was dominated by random coiling. Enzyme sequences have conserved active site with net negative charge and PI value in acidic pH range. Conclusion: Current investigation supported the enzyme’s specific application i.e. bioconversion of xylose to xylitol due to its higher selectivity. In-silico analysis may provide significant structural and physiological information for modifications and improved stability.

scholarly journals Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2505
Author(s):  
Raheem Remtulla ◽  
Sanjoy Kumar Das ◽  
Leonard A. Levin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
In Silico ◽  
Disulfide Bonds ◽  
Oral Absorption ◽  
In Vivo Studies ◽  
Drug Candidates ◽  
In Silico Methods

Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated in the downstream effects of axonal injury. A difficulty in using standard in silico methods for studying these drugs is that most computational tools are not designed for borane-containing compounds. Using in silico and machine learning methodologies, the absorption-distribution properties of these unique compounds were assessed. Features examined with in silico methods included cellular permeability, octanol-water partition coefficient, blood-brain barrier permeability, oral absorption and serum protein binding. The resultant neural networks demonstrated an appropriate level of accuracy and were comparable to existing in silico methodologies. Specifically, they were able to reliably predict pharmacokinetic features of known boron-containing compounds. These methods predicted that phosphine-borane compounds and their metabolites meet the necessary pharmacokinetic features for orally active drug candidates. This study showed that the combination of standard in silico predictive and machine learning models with neural networks is effective in predicting pharmacokinetic features of novel boron-containing compounds as neuroprotective drugs.

scholarly journals Glucocorticoid resistance conferring mutation in the C-terminus of GR alters the receptor conformational dynamics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anna Kaziales ◽  
Florian Rührnößl ◽  
Klaus Richter
Keyword(s):  
Glucocorticoid Receptor ◽  
Steroid Hormones ◽  
In Silico ◽  
Conformational Dynamics ◽  
Glucocorticoid Resistance ◽  
Physiological Processes ◽  
C Terminus ◽  
Hsp90 Chaperone ◽  
In Silico Methods

AbstractThe glucocorticoid receptor is a key regulator of essential physiological processes, which under the control of the Hsp90 chaperone machinery, binds to steroid hormones and steroid-like molecules and in a rather complicated and elusive response, regulates a set of glucocorticoid responsive genes. We here examine a human glucocorticoid receptor variant, harboring a point mutation in the last C-terminal residues, L773P, that was associated to Primary Generalized Glucocorticoid Resistance, a condition originating from decreased affinity to hormone, impairing one or multiple aspects of GR action. Using in vitro and in silico methods, we assign the conformational consequences of this mutation to particular GR elements and report on the altered receptor properties regarding its binding to dexamethasone, a NCOA-2 coactivator-derived peptide, DNA, and importantly, its interaction with the chaperone machinery of Hsp90.

scholarly journals Comparative In Vitro and In Silico Analyses of Variants in Splicing Regions of BRCA1 and BRCA2 Genes and Characterization of Novel Pathogenic Mutations

PLoS ONE ◽  
2013 ◽  
Vol 8 (2) ◽  
pp. e57173 ◽  
Author(s):  
Mara Colombo ◽  
Giovanna De Vecchi ◽  
Laura Caleca ◽  
Claudia Foglia ◽  
Carla B. Ripamonti ◽  
...  
Keyword(s):  
In Silico ◽  
Brca1 And Brca2 ◽  
Pathogenic Mutations ◽  
Brca1 And Brca2 Genes ◽  
In Silico Analyses
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