A two-stage, numerical deconvolution approach was employed to develop level A in vitro-in vivo correlations using data for three formulations of an extended-release oral dosage form. The in vitro dissolution data for all formulations exhibited near-complete dissolution within the time frame of the test. The pharmacokinetic concentration-time profiles for 16 subjects in a cross-over study demonstrated notably limited bioavailability for the slowest formulation. These data were used as the basis for the IVIVC model development. Two models were identified that satisfied the nominal requirements for a conclusive internal predictability of the IVIVC, provided that all three formulations were used as internal datasets. These were a simple linear model with absorption cut-off and a piecewise-linear variable absorption scale model. A subsequent cross-validation of the models' robustness indicated that neither model predicted satisfactorily the pharmacokinetic characteristics of all formulations in a conclusive manner. The piecewise-linear variable absorption scale model provided the most accurate results, particularly with respect to the prediction of the slowest formulation's pharmacokinetic metrics. But this latter model also involved additional free parameters compared with the simple linear model with absorption cut-off. It is argued that more complex IVIVC models with extra parameterization require comprehensive validation to ascertain the accuracy and robustness of the model. In order to achieve this, it is necessary to ensure a complete suite of supporting datasets for internal and external validation, irrespective of the mathematical approach used subsequently to develop the IVIVC.
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http://dx.doi.org/10.1002/bdd.1820 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Centre for Tuberculosis Research, Tuberculosis Research Laboratory, Translational Health Science and Technology Institute, National Capital Region Biotech Science Cluster 3rd Milestone, Faridabad, Haryana 121001, India.
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January 2025
National Forensic Sciences University, Gandhinagar 382007, Gujarat, India. Electronic address:
Cancer-associated cachexia (CAC) is a debilitating condition, observed in patients with advanced stages of cancer. It is marked by ongoing weight loss, weakness, and nutritional impairment. Lower tolerance of chemotherapeutic agents and radiation therapy makes it difficult to treat CAC.
View Article and Find Full Text PDFFuture Med Chem
January 2025
Department of Chemistry, Kurukshetra University, Kurukshetra, India.
The escalating cases of type II diabetes combined with adverse side effects of current antidiabetic drugs spurred the advancement of innovative approaches for the management of postprandial glucose levels. α-Amylase is an endoamylase responsible for the breakdown of internal α-1,4-glycosidic linkages in dietary starch, producing oligosaccharides. Subsequently, α-glucosidase degraded these oligosaccharides to monosaccharides, which are absorbed into the bloodstream and become available to the body.
View Article and Find Full Text PDFJ Phys Chem B
January 2025
Department of Physical Chemistry, Sciences II, University of Geneva, 30 Quai Ernest Ansermet, Geneva 1211, Switzerland.
The formation of protein condensates (droplets) via liquid-liquid phase separation (LLPS) is a commonly observed phenomenon in vitro. Changing the environmental properties with cosolutes, molecular crowders, protein partners, temperature, pressure, etc. has been shown to favor or disfavor the formation of protein droplets by fine-tuning the water-water, water-protein, and protein-protein interactions.
View Article and Find Full Text PDFJ Cell Mol Med
January 2025
Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia.
This study explores novel therapeutic avenues for diabetes, a global health concern marked by elevated blood glucose levels. We investigated the anti-diabetic potential of Gymnema Sylvestre's bioactive compounds, including Gymnemic acid I, Stigmasterol, Deacylgymnemic acid, Beta-Amyrin acetate, Longispinogenin, Gymnemic acid II, Gymnemic acid, Gymnemic acid X, Gymnemaside VI, Phytic acid and Gymnemic acid X. Employing network pharmacology, molecular docking and molecular dynamics (MD), we elucidated the potential mechanism of action.
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