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Physiologically Based Pharmacokinetic modelling of drugs in pregnancy: A mini‐review on availability and limitations.
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- Fundamental & Clinical Pharmacology, 2024, v. 38, n. 3, p. 402, doi. 10.1111/fcp.12967
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Physiologically Based Pharmacokinetic Modelling of UGT Substrate Drugs Lamotrigine and Raltegravir during Pregnancy.
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- Future Pharmacology, 2024, v. 4, n. 2, p. 317, doi. 10.3390/futurepharmacol4020018
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Outpatient Treatment with AZD7442 (Tixagevimab/Cilgavimab) Prevented COVID-19 Hospitalizations over 6 Months and Reduced Symptom Progression in the TACKLE Randomized Trial.
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- Infectious Diseases & Therapy, 2023, v. 12, n. 9, p. 2269, doi. 10.1007/s40121-023-00861-7
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Safety, Tolerability and Pharmacokinetics of Half-Life Extended Severe Acute Respiratory Syndrome Coronavirus 2 Neutralizing Monoclonal Antibodies AZD7442 (Tixagevimab-Cilgavimab) in Healthy Adults.
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- Journal of Infectious Diseases, 2023, v. 227, n. 10, p. 1153, doi. 10.1093/infdis/jiad014
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Application of Modelling and Simulation Approaches to Predict Pharmacokinetics of Therapeutic Monoclonal Antibodies in Pediatric Population.
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- Pharmaceutics, 2023, v. 15, n. 5, p. 1552, doi. 10.3390/pharmaceutics15051552
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Pharmacokinetics under the COVID‐19 storm.
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- British Journal of Clinical Pharmacology, 2023, v. 89, n. 1, p. 158, doi. 10.1111/bcp.14668
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Evaluating the performance of machine‐learning regression models for pharmacokinetic drug–drug interactions.
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- CPT: Pharmacometrics & Systems Pharmacology, 2023, v. 12, n. 1, p. 122, doi. 10.1002/psp4.12884
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Comparative Pharmacokinetics of Tixagevimab/Cilgavimab (AZD7442) Administered Intravenously Versus Intramuscularly in Symptomatic SARS‐CoV‐2 Infection.
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- Clinical Pharmacology & Therapeutics, 2022, v. 112, n. 6, p. 1207, doi. 10.1002/cpt.2706
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Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug–drug interaction prediction.
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- CPT: Pharmacometrics & Systems Pharmacology, 2022, v. 11, n. 12, p. 1560, doi. 10.1002/psp4.12870
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Development of and insights from systems pharmacology models of antibody‐drug conjugates.
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- CPT: Pharmacometrics & Systems Pharmacology, 2022, v. 11, n. 8, p. 967, doi. 10.1002/psp4.12833
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Harnessing Clinical Trial and Real-World Data Towards an Understanding of Sex Effects on Drug Pharmacokinetics, Pharmacodynamics and Efficacy.
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- Frontiers in Pharmacology, 2022, v. 13, p. 1, doi. 10.3389/fphar.2022.874606
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Unraveling pleiotropic effects of rifampicin by using physiologically based pharmacokinetic modeling: Assessing the induction magnitude of P‐glycoprotein–cytochrome P450 3A4 dual substrates.
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- CPT: Pharmacometrics & Systems Pharmacology, 2021, v. 10, n. 12, p. 1485, doi. 10.1002/psp4.12717
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Food constituent– and herb–drug interactions in oncology: Influence of quantitative modelling on Drug labelling.
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- British Journal of Clinical Pharmacology, 2021, v. 87, n. 10, p. 3988, doi. 10.1111/bcp.14822
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Mechanistic physiology-based pharmacokinetic modeling to elucidate vincristine-induced peripheral neuropathy following treatment with novel kinase inhibitors.
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- Cancer Chemotherapy & Pharmacology, 2021, v. 88, n. 3, p. 451, doi. 10.1007/s00280-021-04302-5
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Tumour-on-chip microfluidic platform for assessment of drug pharmacokinetics and treatment response.
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- Communications Biology, 2021, v. 4, n. 1, p. 1, doi. 10.1038/s42003-021-02526-y
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ALT neuroblastoma chemoresistance due to telomere dysfunction–induced ATM activation is reversible with ATM inhibitor AZD0156.
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- Science Translational Medicine, 2021, v. 13, n. 607, p. 1, doi. 10.1126/scitranslmed.abd5750
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Special issue on applications of modeling and simulation in pharmaceutical drug development.
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- Biopharmaceutics & Drug Disposition, 2021, v. 42, n. 4, p. 105, doi. 10.1002/bdd.2280
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Blood retinal barrier and ocular pharmacokinetics: Considerations for the development of oncology drugs.
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- Biopharmaceutics & Drug Disposition, 2021, v. 42, n. 4, p. 128, doi. 10.1002/bdd.2276
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Brain exposure of the ATM inhibitor AZD1390 in humans—a positron emission tomography study.
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- Neuro-Oncology, 2021, v. 23, n. 4, p. 687, doi. 10.1093/neuonc/noaa238
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Model‐Informed Pediatric Dose Selection for Dapagliflozin by Incorporating Developmental Changes.
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- CPT: Pharmacometrics & Systems Pharmacology, 2021, v. 10, n. 2, p. 108, doi. 10.1002/psp4.12577
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Pediatric Pharmacokinetics and Dose Predictions: A Report of a Satellite Meeting to the 10th Juvenile Toxicity Symposium.
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- CTS: Clinical & Translational Science, 2021, v. 14, n. 1, p. 29, doi. 10.1111/cts.12843
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Physiologically‐Based Pharmacokinetic Models for Evaluating Membrane Transporter Mediated Drug–Drug Interactions: Current Capabilities, Case Studies, Future Opportunities, and Recommendations.
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- Clinical Pharmacology & Therapeutics, 2020, v. 107, n. 5, p. 1082, doi. 10.1002/cpt.1693
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Discovery and pharmacological characterization of AZD3229, a potent KIT/PDGFRα inhibitor for treatment of gastrointestinal stromal tumors.
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- Science Translational Medicine, 2020, v. 12, n. 541, p. 1, doi. 10.1126/scitranslmed.aaz2481
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Physiologically Based Pharmacokinetic Modeling for Olaparib Dosing Recommendations: Bridging Formulations, Drug Interactions, and Patient Populations.
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- Clinical Pharmacology & Therapeutics, 2019, v. 105, n. 1, p. 229, doi. 10.1002/cpt.1103
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Asenapine pharmacokinetics and tolerability in a pediatric population.
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- Drug Design, Development & Therapy, 2018, v. 12, p. 2677, doi. 10.2147/DDDT.S171475
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Development, Verification, and Prediction of Osimertinib Drug–Drug Interactions Using PBPK Modeling Approach to Inform Drug Label.
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- CPT: Pharmacometrics & Systems Pharmacology, 2018, v. 7, n. 5, p. 321, doi. 10.1002/psp4.12289
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Harnessing Meta‐analysis to Refine an Oncology Patient Population for Physiology‐Based Pharmacokinetic Modeling of Drugs.
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- Clinical Pharmacology & Therapeutics, 2018, v. 103, n. 2, p. 271, doi. 10.1002/cpt.917
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Erratum to: Translational Modeling in Schizophrenia: Predicting Human Dopamine D Receptor Occupancy.
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- Pharmaceutical Research, 2016, v. 33, n. 5, p. 1305, doi. 10.1007/s11095-016-1888-2
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Translational Modeling in Schizophrenia: Predicting Human Dopamine D Receptor Occupancy.
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- Pharmaceutical Research, 2016, v. 33, n. 4, p. 1003, doi. 10.1007/s11095-015-1846-4
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Dopamine D2 Receptor Occupancy as a Predictor of Catalepsy in Rats: A Pharmacokinetic-Pharmacodynamic Modeling Approach.
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- Pharmaceutical Research, 2014, v. 31, n. 10, p. 2605, doi. 10.1007/s11095-014-1358-7
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Modelling and simulation of placebo effect: application to drug development in schizophrenia.
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- Journal of Pharmacokinetics & Pharmacodynamics, 2013, v. 40, n. 3, p. 377, doi. 10.1007/s10928-012-9296-7
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Pharmacokinetic-Pharmacodynamic Modeling of the D and 5-HT Receptor Occupancy of Risperidone and Paliperidone in Rats.
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- Pharmaceutical Research, 2012, v. 29, n. 7, p. 1932, doi. 10.1007/s11095-012-0722-8
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Modelling and Simulation of the Positive and Negative Syndrome Scale (PANSS) Time Course and Dropout Hazard in Placebo Arms of Schizophrenia Clinical Trials.
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- Clinical Pharmacokinetics, 2012, v. 51, n. 4, p. 261, doi. 10.2165/11598460-000000000-00000
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Mechanism-Based Pharmacokinetic-Pharmacodynamic Modeling of the Dopamine D Receptor Occupancy of Olanzapine in Rats.
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- Pharmaceutical Research, 2011, v. 28, n. 10, p. 2490, doi. 10.1007/s11095-011-0477-7
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Structural Models Describing Placebo Treatment Effects in Schizophrenia and Other Neuropsychiatric Disorders.
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- Clinical Pharmacokinetics, 2011, v. 50, n. 7, p. 429, doi. 10.2165/11590590-000000000-00000
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