The choice of excipients constitutes a major part of preformulation and formulation studies during the preparation of pharmaceutical dosage forms. The physical, mechanical, and chemical properties of excipients affect various formulation parameters, such as disintegration, dissolution, and shelf life, and significantly influence the final product. Therefore, several studies have been performed to evaluate the effect of drug-excipient interactions on the overall formulation. This article reviews the information available on the physical and chemical instabilities of excipients and their incompatibilities with the active pharmaceutical ingredient in solid oral dosage forms, during various drug-manufacturing processes. The impact of these interactions on the drug formulation process has been discussed in detail. Examples of various excipients used in solid oral dosage forms have been included to elaborate on different drug-excipient interactions.
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http://dx.doi.org/10.1208/s12249-017-0864-4 | DOI Listing |
In mammals, X-linked dosage compensation involves two processes: X-chromosome inactivation (XCI) to balance X chromosome dosage between males and females, and hyperactivation of the remaining X chromosome (Xa-hyperactivation) to achieve X-autosome balance in both sexes. Studies of both processes have largely focused on coding genes and have not accounted for transposable elements (TEs) which comprise 50% of the X-chromosome, despite TEs being suspected to have numerous epigenetic functions. This oversight is due in part to the technical challenge of capturing repeat RNAs, bioinformatically aligning them, and determining allelic origin.
View Article and Find Full Text PDFEClinicalMedicine
November 2024
Division of Gastroenterology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Biotherapeutics are among the therapeutics that have revolutionized standard inflammatory bowel disease (IBD) treatment, which was previously limited to mesalamine, 5-aminosalicylic acid, corticosteroids, and classical immunosuppressants. Self-administrable biotherapeutics for IBD would enable home-based treatment and reduce the burden on medical infrastructure. Self-administration is made possible through subcutaneous injectable, oral, and rectal dosage forms.
View Article and Find Full Text PDFBMC Womens Health
January 2025
Department of Obstetrics and Gynecology, University Clinic of Bern, Friedbuehlstrasse 19, Bern, 3010, Switzerland.
Background: Bacterial vaginosis (BV) is a prevalent vaginal condition among reproductive-age women, characterized by off-white, thin vaginal discharge with a fishy odor. It increases susceptibility to sexually transmitted diseases (STDs) and pelvic inflammatory disease (PID). BV involves a shift in vaginal microbiota, with reduced lactobacilli and increased anaerobic bacteria.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P.O. Box 2457, 11451, Riyadh, Saudi Arabia.
This study focuses on the use of machine learning (ML) models to predict the biodistribution of nanoparticles in various organs, using a dataset derived from research on nanoparticle behavior for cancer treatment. The dataset includes both categorical and numerical variables related to nanoparticle properties, with a focus on their distribution across organs such as the tumor, heart, liver, spleen, lung, and kidney tissues. In order to address the complex and non-linear nature of the data, three machine learning models were utilized: Bayesian Ridge Regression (BRR), Kernel Ridge Regression (KRR), and K-Nearest Neighbors (KNN).
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January 2025
Scientific Affairs Department, Al-Mustaqbal University, Babylon, 51001, Iraq.
This study investigates the application of various neural network-based models for predicting temperature distribution in freeze drying process of biopharmaceuticals. For heat-sensitive biopharmaceutical products, freeze drying is preferred to prevent degradation of pharmaceutical compounds. The modeling framework is based on CFD (Computational Fluid Dynamics) and machine learning (ML).
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