Dataset Information

Understanding effect site pharmacology of uprifosbuvir, a hepatitis C virus nucleoside inhibitor: Case study of a multidisciplinary modeling approach in drug development.

ABSTRACT: Uprifosbuvir is a uridine nucleoside monophosphate prodrug inhibitor of the hepatitis C virus NS5B RNA polymerase. To quantitatively elucidate key metabolic pathways, assess the link between unmeasurable effect site concentrations and viral load reduction, and evaluate the influence of intrinsic and extrinsic factors on pharmacokinetics and pharmacodynamics, a model-informed drug development (MIDD) framework was initiated at an early stage. Originally scoped as a modeling effort focused on minimal physiologically based pharmacokinetic and covariate analyses, this project turned into a collaborative effort focused on gaining a deeper understanding of the data from drug metabolism, biopharmaceutics, pharmacometrics, and clinical pharmacology perspectives. This article presents an example of the practical execution of a MIDD-based, cooperative multidisciplinary modeling approach, creating a model that grows along with the team's integrated knowledge. Insights gained from this process could be used in forming optimal collaborations between disciplines in drug development for other investigative compounds.

SUBMITTER: van den Berg P

PROVIDER: S-EPMC8302237 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Understanding effect site pharmacology of uprifosbuvir, a hepatitis C virus nucleoside inhibitor: Case study of a multidisciplinary modeling approach in drug development.

van den Berg Paul P Gao Wei W Ahsman Maurice J MJ Arrington Leticia L Kesisoglou Filippos F Miller Randy R Post Teun M TM Rizk Matthew L ML

CPT: pharmacometrics & systems pharmacology 20210618 7

Uprifosbuvir is a uridine nucleoside monophosphate prodrug inhibitor of the hepatitis C virus NS5B RNA polymerase. To quantitatively elucidate key metabolic pathways, assess the link between unmeasurable effect site concentrations and viral load reduction, and evaluate the influence of intrinsic and extrinsic factors on pharmacokinetics and pharmacodynamics, a model-informed drug development (MIDD) framework was initiated at an early stage. Originally scoped as a modeling effort focused on minim ...[more]

PMID: 33934558

Similar Datasets

Project description:Cancer remains a major cause of morbidity and mortality worldwide, and while current therapies, such as chemotherapy, immunotherapy, and cell therapy, have been effective in many patients, the development of novel therapeutic options remains an urgent priority. Mouse double minute 2 (MDM2) is a key regulator of the tumor suppressor protein p53, which plays a critical role in regulating cellular growth, apoptosis, and DNA repair. Consequently, MDM2 has been the subject of extensive research aimed at developing novel cancer therapies. In this study, we employed a machine learning-based approach to establish a quantitative structure-activity relationship model capable of predicting the potential in vitro efficacy of small molecules as MDM2 inhibitors. Our model was used to screen 5883 FDA-approved drugs, resulting in the identification of promising hits that were subsequently evaluated using molecular docking and molecular dynamics simulations. Two antihistamine drugs, cetirizine (CZ) and rupatadine (RP), exhibited particularly favorable results in the initial in silico analyses. To further assess their potential use as the activators of the p53 pathway, we investigated the antiproliferative capability of the abovementioned drugs on human glioblastoma and neuroblastoma cell lines. Both the compounds exhibited significant antiproliferative effects on the abovementioned cell lines in a dose-dependent manner. The half-maximal inhibitory concentration (IC50) of CZ was found to be 697.87 and 941.37 μM on U87 and SH-SY5Y cell lines, respectively, while the IC50 of RP was found to be 524.28 and 617.07 μM on the same cell lines, respectively. Further investigation by quantitative reverse transcriptase polymerase chain reaction analysis revealed that the CZ-treated cell lines upregulate the expression of the p53-regulated genes involved in cell cycle arrest, apoptosis, and DNA damage response compared to their respective vehicle controls. These findings suggest that CZ activates the p53 pathway by inhibiting MDM2. Our results provide compelling preclinical evidence supporting the potential use of CZ as a modulator of the MDM2-p53 axis and its plausible repurposing for cancer treatment.

Dataset Information

Understanding effect site pharmacology of uprifosbuvir, a hepatitis C virus nucleoside inhibitor: Case study of a multidisciplinary modeling approach in drug development.

Publications

Understanding effect site pharmacology of uprifosbuvir, a hepatitis C virus nucleoside inhibitor: Case study of a multidisciplinary modeling approach in drug development.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets