Literature DB >> 31654368

Drug Exposure to Establish Pharmacokinetic-Response Relationships in Oncology.

Belén P Solans1,2, María Jesús Garrido3,4, Iñaki F Trocóniz5,6.   

Abstract

In the oncology field, understanding the relationship between the dose administered and the exerted effect is particularly important because of the narrow therapeutic index associated with anti-cancer drugs and the high interpatient variability. Therefore, in this review, we provide a critical perspective of the different methods of characterising treatment exposure in the oncology setting. The increasing number of modelling applications in oncology reflects the applicability and the impact of pharmacometrics on all phases of the drug development process and patient management as well. Pharmacometric modelling is a worthy component within the current paradigm of model-based drug development, but pharmacometric modelling techniques are also accessible for the clinician in the optimisation of current oncology therapies. Consequently, the application of population models in a hospital setting by generating close collaborations between physicians and pharmacometricians is highly recommended, providing a systematic means of developing and assessing model-based metrics as 'drivers' for various responses to treatments, which can then be evaluated as predictors for treatment success. Characterising the key determinants of variability in exposure is of particular importance for anticancer agents, as efficacy and toxicity are associated with exposure. We present the different strategies to describe and predict drug exposure that can be applied depending on the data available, with the objective of obtaining the most useful information in the patients' favour throughout the full drug cycle. Therefore, the objective of the present article is to review the different approaches used to characterise a patient's exposure to oncology drugs, which will result in a better understanding of the time course of the response and the magnitude of interpatient variability.

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Year:  2020        PMID: 31654368     DOI: 10.1007/s40262-019-00828-3

Source DB:  PubMed          Journal:  Clin Pharmacokinet        ISSN: 0312-5963            Impact factor:   6.447


  130 in total

1.  Customized in silico population mimics actual population in docetaxel population pharmacokinetic analysis.

Authors:  Susan F Hudachek; Daniel L Gustafson
Journal:  J Pharm Sci       Date:  2010-08-27       Impact factor: 3.534

2.  Assessing the impact of the addition of dendritic cell vaccination to neoadjuvant chemotherapy in breast cancer patients: A model-based characterization approach.

Authors:  Belén P Solans; Ascensión López-Díaz de Cerio; Arlette Elizalde; Luis Javier Pina; Susana Inogés; Jaime Espinós; Esteban Salgado; Luis Daniel Mejías; Iñaki F Trocóniz; Marta Santisteban
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3.  The Long Neglected Player: Modeling Tumor Uptake to Guide Optimal Dosing.

Authors:  Leire Ruiz-Cerdá; Eduardo Asín-Prieto; Zinnia P Parra-Guillen; Iñaki F Trocóniz
Journal:  Clin Cancer Res       Date:  2018-03-26       Impact factor: 12.531

4.  Semimechanistic cell-cycle type-based pharmacokinetic/pharmacodynamic model of chemotherapy-induced neutropenic effects of diflomotecan under different dosing schedules.

Authors:  Víctor Mangas-Sanjuan; Núria Buil-Bruna; María J Garrido; Elena Soto; Iñaki F Trocóniz
Journal:  J Pharmacol Exp Ther       Date:  2015-05-06       Impact factor: 4.030

5.  Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation.

Authors:  S F Marshall; R Burghaus; V Cosson; S Y A Cheung; M Chenel; O DellaPasqua; N Frey; B Hamrén; L Harnisch; F Ivanow; T Kerbusch; J Lippert; P A Milligan; S Rohou; A Staab; J L Steimer; C Tornøe; S A G Visser
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2016-03-14

6.  A Whole-Body Physiologically Based Pharmacokinetic Model of Gefitinib in Mice and Scale-Up to Humans.

Authors:  Youwei Bi; Jiexin Deng; Daryl J Murry; Guohua An
Journal:  AAPS J       Date:  2015-11-11       Impact factor: 4.009

7.  Model-Based Characterization of the Pharmacokinetics of Pembrolizumab: A Humanized Anti-PD-1 Monoclonal Antibody in Advanced Solid Tumors.

Authors:  M Ahamadi; T Freshwater; M Prohn; C H Li; D P de Alwis; R de Greef; J Elassaiss-Schaap; A Kondic; J A Stone
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2016-11-14

8.  Phase I study of tomuzotuximab, a glycoengineered therapeutic antibody against the epidermal growth factor receptor, in patients with advanced carcinomas.

Authors:  Walter Fiedler; Sara Cresta; Henning Schulze-Bergkamen; Sara De Dosso; Jens Weidmann; Anna Tessari; Hans Baumeister; Antje Danielczyk; Bruno Dietrich; Steffen Goletz; Alfredo Zurlo; Marc Salzberg; Cristiana Sessa; Luca Gianni
Journal:  ESMO Open       Date:  2018-02-01

Review 9.  Clinical Pharmacokinetics and Pharmacodynamics of Immune Checkpoint Inhibitors.

Authors:  Maddalena Centanni; Dirk Jan A R Moes; Iñaki F Trocóniz; Joseph Ciccolini; J G Coen van Hasselt
Journal:  Clin Pharmacokinet       Date:  2019-07       Impact factor: 6.447

10.  Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development.

Authors:  Hm Jones; K Rowland-Yeo
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2013-08-14
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