Background: Enhancing the precision of drug-drug interaction (DDI) prediction is essential for improving drug safety and efficacy. The aim is to identify the most effective fraction metabolized by CY3A4 () for improving DDI prediction using physiologically based pharmacokinetic (PBPK) models.
Research Design And Methods: The values were determined for 33 approved drugs using a human liver microsome for measurements and the ADMET Predictor software for in silico predictions. Subsequently, these values were integrated into PBPK models using the GastroPlus platform. The PBPK models, combined with a ketoconazole model, were utilized to predict AUCR (AUC/AUC), and the accuracy of these predictions was evaluated by comparison with observed AUCR.
Results: The integration of method demonstrates superior performance compared to the in silico method and of 100% method. Under the Guest-limits criteria, the integration of achieves an accuracy of 76%, while the in silico and of 100% methods achieve accuracies of 67% and 58%, respectively.
Conclusions: Our study highlights the importance of data to improve the accuracy of predicting DDIs and demonstrates the promising potential of in silico in predicting DDIs.
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http://dx.doi.org/10.1080/17425255.2023.2263358 | DOI Listing |
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the formation of amyloid-beta (Aβ) plaques and neurofibrillary tangles (NFTs) composed of tau aggregates. Research in animal models has generated hypotheses on the underlying mechanisms of the interaction between Aβ and tau pathology. In support of this interaction, results from clinical trials have shown that treatment with anti-Aβ monoclonal antibodies (mAbs) affects tau pathology.
View Article and Find Full Text PDFBackground: The therapeutic management of dementia with Lewy bodies (LBD) is a challenge given the high sensitivity to drugs in this disease. This is particularly sensitive with regard to the management of parkinsonism. In particular, treatment of motor symptoms with levodopa or dopaminergic agonists poses a risk of worsening cognitive and behavioral symptoms.
View Article and Find Full Text PDFBackground: Immunotherapy of Alzheimer's disease (AD) is a promising approach to reducing the accumulation of beta-amyloid, a critical event in the onset of the disease. Targeting the group II metabotropic glutamate receptors, mGluR2 and mGluR3, could be important in controlling Aβ production, although their respective contribution remains unclear due to the lack of selective tools.
Method: 5xFAD mice were chronically treated by a brain penetrant camelid single domain antibody (VHH or nanobody) that is an activator of mGluR2.
Alzheimers Dement
December 2024
Columbia University Irving Medical Center, New York, NY, USA.
Background: Genetic studies indicate a causal role for microglia, the innate immune cells of the central nervous system (CNS), in Alzheimer's disease (AD). Despite the progress made in identifying genetic risk factors, such as CD33, and underlying molecular changes, there are currently limited treatment options for AD. Based on the immune-inhibitory function of CD33, we hypothesize that inhibition of CD33 activation may reverse microglial suppression and restore their ability to resolve inflammatory processes and mitigate pathogenic amyloid plaques, which may be neuroprotective.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
The University of Texas Health Science Center at Houston, Houston, TX, USA.
Background: Developing drugs for treating Alzheimer's disease (AD) has been extremely challenging and costly due to limited knowledge on underlying biological mechanisms and therapeutic targets. Repurposing drugs or their combination has shown potential in accelerating drug development due to the reduced drug toxicity while targeting multiple pathologies.
Method: To address the challenge in AD drug development, we developed a multi-task machine learning pipeline to integrate a comprehensive knowledge graph on biological/pharmacological interactions and multi-level evidence on drug efficacy, to identify repurposable drugs and their combination candidates RESULT: Using the drug embedding from the heterogeneous graph representation model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, mechanistic efficacy in preclinical models, population-based treatment effect, and Phase 2/3 clinical trials.
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