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Individualization of piperacillin dosage based on therapeutic drug monitoring with or without model-informed precision dosing: a scenario analysis.
J Antimicrob Chemother
January 2025
Service of Clinical Pharmacology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 17, 1011, Lausanne, Switzerland.
Background: Model-informed precision dosing (MIPD) combines population pharmacokinetic knowledge with therapeutic drug monitoring (TDM) to optimize dosage adjustment. It could improve target concentration attainment over empirical TDM, still widely practised for broad-spectrum antibiotics.
Objectives: To evaluate the respective performance of TDM and MIPD in achieving target piperacillin exposure.
Implementation of model-informed precision dosing for tamoxifen therapy in patients with breast cancer: A prospective intervention study.
Breast
January 2025
Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands.
Tamoxifen is an estrogen-receptor (ER) antagonist, used as adjuvant treatment of ER-positive breast cancer. It is converted by CYP2D6 into endoxifen, its most active metabolite. Patients with endoxifen plasma concentrations <16 nM face a higher risk of recurrence.
View Article and Find Full Text PDFNavigating Recent Changes in Dosing Information: Dynamics of FDA-Approved Monoclonal Antibodies in Post-Marketing Realities.
Clin Transl Sci
January 2025
College of Pharmacy, Daegu Catholic University, Gyeongsan, Korea.
Monoclonal antibodies (mAbs) are critical components in the therapeutic landscape, but their dosing strategies often evolve post-approval as new data emerge. This review evaluates post-marketing label changes in dosing information for FDA-approved mAbs from January 2015 to September 2024, with a focus on both initial and extended indications. We systematically analyzed dosing modifications, categorizing them into six predefined groups: Dose increases or decreases, inclusion of new patient populations by body weight or age, shifts from body weight-based dosing to fixed regimens, and adjustments in infusion rates.
View Article and Find Full Text PDFIntegrating Model-Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation.
Clin Transl Sci
January 2025
Global Biometrics and Data Management, Pfizer Research and Development, New York, New York, USA.
The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug-target interactions from big data, enabling more accurate predictions and novel hypothesis generation.
View Article and Find Full Text PDFPhysiologically based Pharmacokinetic/Pharmacodynamic Modeling (PBPK/PD) of Famotidine in Pregnancy.
J Clin Pharmacol
January 2025
Bayer HealthCare SAS, Lille, France, on behalf of:, Model-Informed Drug Development, Research and Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany.
Famotidine, a H-receptor antagonist, is commonly used to treat heartburn and gastroesophageal reflux disease during pregnancy. However, information on the pharmacokinetics (PK) of famotidine in pregnant patients is limited since pregnant patients are usually excluded from clinical trials. This study aimed to develop and evaluate a physiologically based pharmacokinetic (PBPK) model for famotidine in non-pregnant and pregnant populations, and to combine it with a pharmacodynamic (PD) model to predict the effect of famotidine on intragastric pH.
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