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Goyal V, Krantz E, Simon F, Neven A, Eriksson J, Saayman A, Ibnou Zekri Lassout N, Louis M, Robinson S, Deshmukh A, Antarkar A, Ruffell C, Victor S, Chenel M, Celebic A, Caplain H, Gillon J, Ribeiro I. Bioavailability of three novel oral, sustained-release pellets, relative to an immediate-release tablet containing 500 mg flucytosine: A randomized, open-label, crossover study in healthy volunteers. Clin Transl Sci 2024; 17:e13756. [PMID: 38488418 PMCID: PMC10941517 DOI: 10.1111/cts.13756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/31/2024] [Accepted: 02/16/2024] [Indexed: 03/18/2024] Open
Abstract
The opportunistic fungal infection cryptococcal meningoencephalitis is a major cause of death among people living with HIV in sub-Saharan Africa. We report pharmacokinetic (PK) and safety data from a randomized, four-period crossover phase I trial of three sustained-release (SR) oral pellet formulations of 5-flucytosine conducted in South Africa. These formulations were developed to require less frequent administration, to provide a convenient alternative to the current immediate release (IR) formulation, A. Formulations B, C, and D were designed to release 5-flucytosine as a percentage of the nominal dose in vitro. We assessed their safety and PK profiles in a single dose (1 × 3000 mg at 0 h), relative to commercial IR tablets (Ancotil 500 mg tablets; 3 × 500 mg at 0 h and 3 × 500 mg at 6 h) in healthy, fasted participants. Forty-two healthy participants were included. All treatments were well-tolerated. The primary PK parameters, maximum observed plasma concentration (Cmax ) and area under the concentration-time profiles, were significantly lower for the SR formulations than for the IR tablets, and the geometric mean ratios fell outside the conventional bioequivalence limits. The median maximum time to Cmax was delayed for the SR pellets. Physiologically-based PK modeling indicated a twice-daily 6400 mg dose of SR formulation D in fasted condition would be optimal for further clinical development. This regimen is predicted to result in a rapid steady-state plasma exposure with effective and safe trough plasma concentration and Cmax values, within the therapeutic boundaries relative to plasma exposure after four times per day administration of IR tablets (PACTR202201760181404).
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Affiliation(s)
- Vishal Goyal
- Drugs for Neglected Diseases InitiativeNew YorkNew YorkUSA
| | | | - Francois Simon
- Drugs for Neglected Diseases InitiativeGenevaSwitzerland
| | - Anouk Neven
- Luxembourg Institute of HealthStrassenLuxembourg
| | | | | | | | - Mathieu Louis
- Drugs for Neglected Diseases InitiativeGenevaSwitzerland
| | | | | | | | | | | | | | | | - Henri Caplain
- Drugs for Neglected Diseases InitiativeGenevaSwitzerland
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Moingeon P, Chenel M, Rousseau C, Voisin E, Guedj M. Virtual patients, digital twins and causal disease models: paving the ground for in silico clinical trials. Drug Discov Today 2023; 28:103605. [PMID: 37146963 DOI: 10.1016/j.drudis.2023.103605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 03/22/2023] [Accepted: 04/27/2023] [Indexed: 05/07/2023]
Abstract
Computational models are being explored to simulate in silico the efficacy and safety of drug candidates and medical devices. Disease models that are based on patients' profiling data are being produced to represent interactomes of genes or proteins and to infer causality in the pathophysiology {AuQ: Edit OK?}, which makes it possible to mimic the impact of drugs on relevant targets. Virtual patients designed from medical records as well as digital twins were generated to simulate specific organs and to predict treatment efficacy at the individual patient level {AuQ: Edit OK?}. As the acceptance of digital evidence by regulators grows, predictive artificial intelligence (AI)-based models will support the design of confirmatory trials in humans and will accelerate the development of efficient drugs and medical devices.
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Meyer C, Dierick I, Gauderat G, Langenhorst J, Sarr C, Leroux E, Chenel M, Fouliard S, Passier P. How to Successfully Generate an Alternative Approach to a Thorough QT Study: GLPG1972 as an Example. Clin Pharmacol Ther 2023; 113:310-320. [PMID: 35355254 PMCID: PMC10083980 DOI: 10.1002/cpt.2596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 02/19/2022] [Indexed: 01/27/2023]
Abstract
During development of a drug, the requirement of evaluating the proarrhythmic risk and delayed repolarization needs to be fulfilled. Would it be possible to create an alternative to a thorough QT (TQT) study or is there a need to perform a dedicated TQT study? How is an alternative approach generated, what information is available, and which instructions are considered missing today to generate such an approach? This tutorial describes the considerations and path followed to create an early and feasible alternative to a TQT study using experience-based insights from a successful application to the US Food and Drug Administration for GLPG1972, an ADAMTS-5 inhibitor, and discusses the approach used in light of the current guidelines and literature.
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Affiliation(s)
| | | | - Glenn Gauderat
- Translational Medicine Division, Servier, Suresnes, France
| | | | | | - Emilie Leroux
- Translational Medicine Division, Servier, Suresnes, France
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Derippe T, Fouliard S, Marchiq I, Dupouy S, Almena-Carrasco M, Geronimi J, Declèves X, Chenel M, Mager DE. Mechanistic Modeling of the Interplay Between Host Immune System, IL-7 and UCART19 Allogeneic CAR-T Cells in Adult B-cell Acute Lymphoblastic Leukemia. Cancer Res Commun 2022; 2:1532-1544. [PMID: 36970053 PMCID: PMC10036133 DOI: 10.1158/2767-9764.crc-22-0176] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 09/06/2022] [Accepted: 11/03/2022] [Indexed: 06/18/2023]
Abstract
UNLABELLED Chimeric antigen receptor (CAR)-T cell therapies have shown tremendous results against various hematologic cancers. Prior to cell infusion, a host preconditioning regimen is required to achieve lymphodepletion and improve CAR-T cell pharmacokinetic exposure, leading to greater chances of therapeutic success. To better understand and quantify the impact of the preconditioning regimen, we built a population-based mechanistic pharmacokinetic-pharmacodynamic model describing the complex interplay between lymphodepletion, host immune system, homeostatic cytokines, and pharmacokinetics of UCART19, an allogeneic product developed against CD19+ B cells. Data were collected from a phase I clinical trial in adult relapsed/refractory B-cell acute lymphoblastic leukemia and revealed three different UCART19 temporal patterns: (i) expansion and persistence, (ii) transient expansion with subsequent rapid decline, and (iii) absence of observed expansion. On the basis of translational assumptions, the final model was able to capture this variability through the incorporation of IL-7 kinetics, which are thought to be increased owing to lymphodepletion, and through an elimination of UCART19 by host T cells, which is specific to the allogeneic context. Simulations from the final model recapitulated UCART19 expansion rates in the clinical trial, confirmed the need for alemtuzumab to observe UCART19 expansion (along with fludarabine cyclophosphamide), quantified the importance of allogeneic elimination, and suggested a high impact of multipotent memory T-cell subpopulations on UCART19 expansion and persistence. In addition to supporting the role of host cytokines and lymphocytes in CAR-T cell therapy, such a model could help optimizing the preconditioning regimens in future clinical trials. SIGNIFICANCE A mathematical mechanistic pharmacokinetic/pharmacodynamic model supports and captures quantitatively the beneficial impact of lymphodepleting patients before the infusion of an allogeneic CAR-T cell product. Mediation through IL-7 increase and host T lymphocytes decrease is underlined, and the model can be further used to optimize CAR-T cell therapies lymphodepletion regimen.
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Affiliation(s)
- Thibaud Derippe
- Institut de Recherches Internationales Servier, Suresnes, France
- Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, New York
- Université de Paris, Inserm, UMRS-1144, Optimisation Thérapeutique en Neuropsychopharmacologie, Paris, France
| | - Sylvain Fouliard
- Institut de Recherches Internationales Servier, Suresnes, France
| | - Ibtissam Marchiq
- Institut de Recherches Internationales Servier, Suresnes, France
| | - Sandra Dupouy
- Institut de Recherches Internationales Servier, Suresnes, France
| | | | - Julia Geronimi
- Institut de Recherches Internationales Servier, Suresnes, France
| | - Xavier Declèves
- Université de Paris, Inserm, UMRS-1144, Optimisation Thérapeutique en Neuropsychopharmacologie, Paris, France
| | - Marylore Chenel
- Institut de Recherches Internationales Servier, Suresnes, France
| | - Donald E. Mager
- Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, New York
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Tosca EM, Gauderat G, Fouliard S, Burbridge M, Chenel M, Magni P. Modeling restoration of gefitinib efficacy by co-administration of MET inhibitors in an EGFR inhibitor-resistant NSCLC xenograft model: A tumor-in-host DEB-based approach. CPT Pharmacometrics Syst Pharmacol 2021; 10:1396-1411. [PMID: 34708556 PMCID: PMC8592518 DOI: 10.1002/psp4.12710] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 08/02/2021] [Accepted: 08/17/2021] [Indexed: 12/19/2022]
Abstract
MET receptor tyrosine kinase inhibitors (TKIs) can restore sensitivity to gefitinib, a TKI targeting epidermal growth factor receptor (EGFR), and promote apoptosis in non-small cell lung cancer (NSCLC) models resistant to gefitinib treatment in vitro and in vivo. Several novel MET inhibitors are currently under study in different phases of development. In this work, a novel tumor-in-host modeling approach, based on the Dynamic Energy Budget (DEB) theory, was proposed and successfully applied to the context of poly-targeted combination therapies. The population DEB-based tumor growth inhibition (TGI) model well-described the effect of gefitinib and of two MET inhibitors, capmatinib and S49076, on both tumor growth and host body weight when administered alone or in combination in an NSCLC mice model involving the gefitinib-resistant tumor line HCC827ER1. The introduction of a synergistic effect in the combination DEB-TGI model allowed to capture gefitinib anticancer activity enhanced by the co-administered MET inhibitor, providing also a quantitative evaluation of the synergistic drug interaction. The model-based comparison of the two MET inhibitors highlighted that S49076 exhibited a greater anticancer effect as well as a greater ability in restoring sensitivity to gefitinib than the competitor capmatinib. In summary, the DEB-based tumor-in-host framework proposed here can be applied to routine combination xenograft experiments, providing an assessment of drug interactions and contributing to rank investigated compounds and to select the optimal combinations, based on both tumor and host body weight dynamics. Thus, the combination tumor-in-host DEB-TGI model can be considered a useful tool in the preclinical development and a significant advance toward better characterization of combination therapies.
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Affiliation(s)
- Elena M. Tosca
- Laboratory of Bioinformatics, Mathematical Modelling and Synthetic BiologyDepartment of ElectricalComputer and Biomedical EngineeringUniversità degli Studi di PaviaPaviaItaly
| | - Glenn Gauderat
- Clinical Pharmacokinetics and Pharmacometrics DivisionServierSuresnesFrance
| | - Sylvain Fouliard
- Clinical Pharmacokinetics and Pharmacometrics DivisionServierSuresnesFrance
| | - Mike Burbridge
- Center for Therapeutic Innovation in OncologyServierSuresnesFrance
- Present address:
Engitix therapeuticsLondonUK
| | - Marylore Chenel
- Clinical Pharmacokinetics and Pharmacometrics DivisionServierSuresnesFrance
- Present address:
Pharmetheus ABUppsalaSweden
| | - Paolo Magni
- Laboratory of Bioinformatics, Mathematical Modelling and Synthetic BiologyDepartment of ElectricalComputer and Biomedical EngineeringUniversità degli Studi di PaviaPaviaItaly
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Lang J, Vincent L, Chenel M, Ogungbenro K, Galetin A. Reduced physiologically-based pharmacokinetic model of dabigatran etexilate-dabigatran and its application for prediction of intestinal P-gp-mediated drug-drug interactions. Eur J Pharm Sci 2021; 165:105932. [PMID: 34260894 DOI: 10.1016/j.ejps.2021.105932] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 06/01/2021] [Accepted: 06/22/2021] [Indexed: 01/01/2023]
Abstract
BACKGROUND Dabigatran etexilate (DABE) has been suggested as a clinical probe for intestinal P-glycoprotein (P-gp)-mediated drug-drug interaction (DDI) studies and, as an alternative to digoxin. Clinical DDI data with various P-gp inhibitors demonstrated a dose-dependent inhibition of P-gp with DABE. The aims of this study were to develop a joint DABE (prodrug)-dabigatran reduced physiologically-based-pharmacokinetic (PBPK) model and to evaluate its ability to predict differences in P-gp DDI magnitude between a microdose and a therapeutic dose of DABE. METHODS A joint DABE-dabigatran PBPK model was developed with a mechanistic intestinal model accounting for the regional P-gp distribution in the gastrointestinal tract. Model input parameters were estimated using DABE and dabigatran pharmacokinetic (PK) clinical data obtained after administration of DABE alone or with a strong P-gp inhibitor, itraconazole, and over a wide range of DABE doses (from 375 µg to 400 mg). Subsequently, the model was used to predict extent of DDI with additional P-gp inhibitors and with different DABE doses. RESULTS The reduced DABE-dabigatran PBPK model successfully described plasma concentrations of both prodrug and metabolite following administration of DABE at different dose levels and when co-administered with itraconazole. The model was able to capture the dose dependency in P-gp mediated DDI. Predicted magnitude of itraconazole P-gp DDI was higher at the microdose (predicted vs. observed median fold-increase in AUC+inh/AUCcontrol (min-max) = 5.88 (4.29-7.93) vs. 6.92 (4.96-9.66) ) compared to the therapeutic dose (predicted median fold-increase in AUC+inh/AUCcontrol = 3.48 (2.37-4.84) ). In addition, the reduced DABE-dabigatran PBPK model predicted successfully the extent of DDI with verapamil and clarithromycin as P-gp inhibitors. Model-based simulations of dose staggering predicted the maximum inhibition of P-gp when DABE microdose was concomitantly administered with itraconazole solution; simulations also highlighted dosing intervals required to minimise the DDI risk depending on the DABE dose administered (microdose vs. therapeutic). CONCLUSIONS This study provides a modelling framework for the evaluation of P-gp inhibitory potential of new molecular entities using DABE as a clinical probe. Simulations of dose staggering and regional differences in the extent of intestinal P-gp inhibition for DABE microdose and therapeutic dose provide model-based guidance for design of prospective clinical P-gp DDI studies.
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Affiliation(s)
- Jennifer Lang
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9PT, United Kingdom
| | | | - Marylore Chenel
- Institut de Recherches Internationales Servier, Suresnes, France
| | - Kayode Ogungbenro
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9PT, United Kingdom
| | - Aleksandra Galetin
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9PT, United Kingdom.
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Simon F, Gautier-Veyret E, Truffot A, Chenel M, Payen L, Stanke-Labesque F, Tod M. Modeling Approach to Predict the Impact of Inflammation on the Pharmacokinetics of CYP2C19 and CYP3A4 Substrates. Pharm Res 2021; 38:415-428. [PMID: 33686560 DOI: 10.1007/s11095-021-03019-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 02/18/2021] [Indexed: 11/30/2022]
Abstract
PURPOSE For decades, inflammation has been considered a cause of pharmacokinetic variability, mainly in relation to the inhibitory effect of pro-inflammatory cytokines on the expression level and activity of cytochrome P450 (CYP). In vitro and clinical studies have shown that two major CYPs, CYP2C19 and CYP3A4, are both impaired. The objective of the present study was to quantify the impact of the inflammatory response on the activity of both CYPs in order to predict the pharmacokinetic profile of their substrates according to systemic C-reactive protein (CRP). METHODS The relationships between CRP concentration and both CYPs activities were estimated and validated using clinical data first on midazolam then on voriconazole. Finally, clinical data on omeprazole were used to validate the findings. For each substrate, a physiologically based pharmacokinetics model was built using a bottom-up approach, and the relationships between CRP level and CYP activities were estimated by a top-down approach. After incorporating the respective relationships, we compared the predictions and observed drug concentrations. RESULTS Changes in pharmacokinetic profiles and parameters induced by inflammation seem to be captured accurately by the models. CONCLUSIONS These findings suggest that the pharmacokinetics of CYP2C19 and CYP3A4 substrates can be predicted depending on the CRP concentration.
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Affiliation(s)
- Florian Simon
- EA3738, Faculté de médecine de Lyon-Sud, Université de Lyon 1, 69921, Université de Lyon 1, Oullins cedex, France. .,Laboratoire de biochimie-toxicologie, Centre hospitalier Lyon-Sud, Hospices civils de Lyon, Pierre Bénite, Lyon, France.
| | - Elodie Gautier-Veyret
- Laboratoire de Pharmacologie, Pharmacogenetique et Toxicologie, Centre Hospitalier Universitaire des Alpes, 38043, Grenoble, France.,University Grenoble Alpes, Inserm, CHU Grenoble Alpes, HP2, 38000, Grenoble, France
| | - Aurélie Truffot
- Laboratoire de Pharmacologie, Pharmacogenetique et Toxicologie, Centre Hospitalier Universitaire des Alpes, 38043, Grenoble, France
| | - Marylore Chenel
- Institut de recherches internationales Servier, Direction of clinical PK and pharmacometrics, Suresnes, France
| | - Léa Payen
- Laboratoire de biochimie-toxicologie, Centre hospitalier Lyon-Sud, Hospices civils de Lyon, Pierre Bénite, Lyon, France
| | - Françoise Stanke-Labesque
- Laboratoire de Pharmacologie, Pharmacogenetique et Toxicologie, Centre Hospitalier Universitaire des Alpes, 38043, Grenoble, France.,University Grenoble Alpes, Inserm, CHU Grenoble Alpes, HP2, 38000, Grenoble, France
| | - Michel Tod
- EA3738, Faculté de médecine de Lyon-Sud, Université de Lyon 1, 69921, Université de Lyon 1, Oullins cedex, France
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Lang J, Vincent L, Chenel M, Ogungbenro K, Galetin A. Impact of Hepatic CYP3A4 Ontogeny Functions on Drug–Drug Interaction Risk in Pediatric Physiologically‐Based Pharmacokinetic/Pharmacodynamic Modeling: Critical Literature Review and Ivabradine Case Study. Clin Pharmacol Ther 2020; 109:1618-1630. [DOI: 10.1002/cpt.2134] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 11/21/2020] [Indexed: 12/14/2022]
Affiliation(s)
- Jennifer Lang
- Centre for Applied Pharmacokinetic Research Division of Pharmacy and Optometry, School of Health Sciences Faculty of Biology, Medicine and Health Manchester Academic Health Science Centre University of Manchester Manchester UK
| | - Ludwig Vincent
- Centre de Pharmacocinétique et Métabolisme Technologie Servier Orléans France
| | - Marylore Chenel
- Clinical Pharmacokinetics and Pharmacometrics Institut de Recherches Internationales Servier Suresnes France
| | - Kayode Ogungbenro
- Centre for Applied Pharmacokinetic Research Division of Pharmacy and Optometry, School of Health Sciences Faculty of Biology, Medicine and Health Manchester Academic Health Science Centre University of Manchester Manchester UK
| | - Aleksandra Galetin
- Centre for Applied Pharmacokinetic Research Division of Pharmacy and Optometry, School of Health Sciences Faculty of Biology, Medicine and Health Manchester Academic Health Science Centre University of Manchester Manchester UK
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Lang J, Vincent L, Chenel M, Ogungbenro K, Galetin A. Simultaneous Ivabradine Parent-Metabolite PBPK/PD Modelling Using a Bayesian Estimation Method. AAPS J 2020; 22:129. [DOI: 10.1208/s12248-020-00502-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 08/18/2020] [Indexed: 12/14/2022]
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Simon F, Guyot L, Garcia J, Vilchez G, Bardel C, Chenel M, Tod M, Payen L. Impact of interleukin‐6 on drug transporters and permeability in the hCMEC/D3 blood–brain barrier model. Fundam Clin Pharmacol 2020; 35:397-409. [DOI: 10.1111/fcp.12596] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 07/27/2020] [Accepted: 07/30/2020] [Indexed: 12/17/2022]
Affiliation(s)
- Florian Simon
- EA3738 Faculté de médecine de Lyon‐Sud Université de Lyon 1 165 chemin du Grand Revoyet, Faculté de médecine et maïeutique Oullins France69921France
- Laboratoire de Biochimie‐Toxicologie Centre Hospitalier Lyon‐Sud Hospices civils de Lyon 165 chemin du Grand Revoyet Pierre‐Bénite France69310France
- Institut de Recherches Internationales Servier Direction of Clinical PK and Pharmacometrics 50 rue Carnot Suresnes92150France
| | - Laetitia Guyot
- Laboratoire de Biochimie‐Toxicologie Centre Hospitalier Lyon‐Sud Hospices civils de Lyon 165 chemin du Grand Revoyet Pierre‐Bénite France69310France
| | - Jessica Garcia
- Laboratoire de Biochimie‐Toxicologie Centre Hospitalier Lyon‐Sud Hospices civils de Lyon 165 chemin du Grand Revoyet Pierre‐Bénite France69310France
| | - Gaelle Vilchez
- Hospices Civils de Lyon Department of Biostatistics 162 avenue Lacassagne Lyon69424France
| | - Claire Bardel
- Hospices Civils de Lyon Department of Biostatistics 162 avenue Lacassagne Lyon69424France
| | - Marylore Chenel
- Institut de Recherches Internationales Servier Direction of Clinical PK and Pharmacometrics 50 rue Carnot Suresnes92150France
| | - Michel Tod
- EA3738 Faculté de médecine de Lyon‐Sud Université de Lyon 1 165 chemin du Grand Revoyet, Faculté de médecine et maïeutique Oullins France69921France
| | - Léa Payen
- Laboratoire de Biochimie‐Toxicologie Centre Hospitalier Lyon‐Sud Hospices civils de Lyon 165 chemin du Grand Revoyet Pierre‐Bénite France69310France
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Chelliah V, Lazarou G, Bhatnagar S, Gibbs JP, Nijsen M, Ray A, Stoll B, Thompson RA, Gulati A, Soukharev S, Yamada A, Weddell J, Sayama H, Oishi M, Wittemer-Rump S, Patel C, Niederalt C, Burghaus R, Scheerans C, Lippert J, Kabilan S, Kareva I, Belousova N, Rolfe A, Zutshi A, Chenel M, Venezia F, Fouliard S, Oberwittler H, Scholer-Dahirel A, Lelievre H, Bottino D, Collins SC, Nguyen HQ, Wang H, Yoneyama T, Zhu AZX, van der Graaf PH, Kierzek AM. Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm. Clin Pharmacol Ther 2020; 109:605-618. [PMID: 32686076 PMCID: PMC7983940 DOI: 10.1002/cpt.1987] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 07/06/2020] [Indexed: 12/12/2022]
Abstract
Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno‐oncology (IO) the aim is to direct the patient’s own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD‐L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug‐development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds’ pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.
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Affiliation(s)
| | | | | | | | | | - Avijit Ray
- Abbvie Inc., North Chicago, Illinois, USA
| | | | | | - Abhishek Gulati
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Serguei Soukharev
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Akihiro Yamada
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Jared Weddell
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Hiroyuki Sayama
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Masayo Oishi
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | | | | | | | | | | | | | | | - Irina Kareva
- EMD Serono, Merck KGaA, Billerica, Massachusetts, USA
| | | | - Alex Rolfe
- EMD Serono, Merck KGaA, Billerica, Massachusetts, USA
| | - Anup Zutshi
- EMD Serono, Merck KGaA, Billerica, Massachusetts, USA
| | | | | | | | | | | | | | - Dean Bottino
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Sabrina C Collins
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Hoa Q Nguyen
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Haiqing Wang
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Tomoki Yoneyama
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Andy Z X Zhu
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
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Simon F, Garcia J, Guyot L, Guitton J, Vilchez G, Bardel C, Chenel M, Tod M, Payen L. Impact of Interleukin-6 on Drug-Metabolizing Enzymes and Transporters in Intestinal Cells. AAPS J 2019; 22:16. [DOI: 10.1208/s12248-019-0395-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 10/10/2019] [Indexed: 01/15/2023]
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13
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Cerou M, Peigné S, Comets E, Chenel M. Application of Item Response Theory to Model Disease Progression and Agomelatine Effect in Patients with Major Depressive Disorder. AAPS J 2019; 22:4. [PMID: 31720897 DOI: 10.1208/s12248-019-0379-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 09/04/2019] [Indexed: 11/30/2022]
Abstract
INTRODUCTION In this paper, we studied the effect over time of agomelatine, an antidepressant drug administered in patient with major depressive disorder, through item response theory (IRT), taking into account a strong placebo effect and missing not at random. We also assessed the informativeness of the HAMD-17 scale's item. MATERIALS AND METHODS The data includes five phase III clinical trials sponsored by Servier Institute, totalling 1549 patients followed during a maximum of 1 year. At each observation, individual scores for the 17 items of the HAMD scale were recorded. The probability for each score was modelled with IRT. A non-linear mixed effects model was used to describe the evolution of the disease and was coupled with a time to event model to predict dropout. Clinical trial simulations were then used to compare placebo and active treatment. Informativeness of each item was evaluated using the Fisher information theory. RESULTS The best model combined an IRT model, a longitudinal model for underlying depression which describes the remission and then a possible relapse, and a hazard model for dropout depending on the evolution from baseline. The drug effect was best modelled as an effect on the remission and the relapse phases. The median predicted drop in HAMD between baseline and 6 weeks was 8.8 (90% PI, 8.3-9.2) when on placebo and 13.1 (90% PI, 12.8-13.4) when treated. Nine items were found to be the most informative. CONCLUSION The IRT framework allowed to characterise the evolution of depression with time and estimate the effect of agomelatine, as well as the link between symptoms and disease.
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Affiliation(s)
- Marc Cerou
- Université de Paris, IAME, INSERM, F-75018, Paris, France. .,Division of Clinical Pharmacokinetics and Pharmacometrics, Institut de Recherches Internationales Servier, Suresnes, France.
| | - Sophie Peigné
- Division of Clinical Pharmacokinetics and Pharmacometrics, Institut de Recherches Internationales Servier, Suresnes, France
| | - Emmanuelle Comets
- Université de Paris, IAME, INSERM, F-75018, Paris, France.,CIC 1414, INSERM, 35700, Rennes, France.,Université Rennes-1, 35700, Rennes, France
| | - Marylore Chenel
- Division of Clinical Pharmacokinetics and Pharmacometrics, Institut de Recherches Internationales Servier, Suresnes, France
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Gupta N, Bottino D, Simonsson USH, Musante CJ, Bueters T, Rieger TR, Macha S, Chenel M, Fancourt C, Kanodia J, Nayak S. Transforming Translation Through Quantitative Pharmacology for High-Impact Decision Making in Drug Discovery and Development. Clin Pharmacol Ther 2019; 107:1285-1289. [PMID: 31709519 DOI: 10.1002/cpt.1667] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 08/17/2019] [Indexed: 01/25/2023]
Affiliation(s)
- Neeraj Gupta
- Millennium Pharmaceuticals, Inc. (a wholly owned subsidiary of Takeda Pharmaceutical Company Limited), Cambridge, Massachusetts, USA
| | - Dean Bottino
- Millennium Pharmaceuticals, Inc. (a wholly owned subsidiary of Takeda Pharmaceutical Company Limited), Cambridge, Massachusetts, USA
| | | | | | | | | | | | | | | | - Jitendra Kanodia
- Theravance Biopharma US, Inc., South San Francisco, California, USA
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Pierrillas PB, Henin E, Ball K, Ogier J, Amiel M, Kraus-Berthier L, Chenel M, Bouzom F, Tod M. Prediction of Human Nonlinear Pharmacokinetics of a New Bcl-2 Inhibitor Using PBPK Modeling and Interspecies Extrapolation Strategy. Drug Metab Dispos 2019; 47:648-656. [PMID: 30940629 DOI: 10.1124/dmd.118.085605] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 03/26/2019] [Indexed: 01/05/2023] Open
Abstract
S 55746 ((S)-N-(4-hydroxyphenyl)-3-(6-(3-(morpholinomethyl)-1,2,3,4-tetrahydroisoquinoline-2-carbonyl)benzo[d][1,3]dioxol-5-yl)-N-phenyl-5,6,7,8-tetrahydroindolizine-1-carboxamide) is a new selective Bcl-2 (B-cell lymphoma 2) inhibitor developed by Servier Laboratories and used to restore apoptosis functions in cancer patients. The aim of this work was to develop a translational approach using physiologically based (PB) pharmacokinetic (PK) modeling for interspecies extrapolation to anticipate the nonlinear PK behavior of this new compound in patients. A PBPK mouse model was first built using a hybrid approach, defining scaling factors (determined from in vitro data) to correct in vitro clearance parameters and predicted Kp (partition coefficient) values. The qualification of the hybrid model using these empirically determined scaling factors was satisfactorily completed with rat and dog data, allowing extrapolation of the PBPK model to humans. Human PBPK simulations were then compared with clinical trial data from a phase 1 trial in which the drug was given orally and daily to cancer patients. Human PBPK predictions were within the 95% prediction interval for the eight dose levels, taking into account both the nonlinear dose and time dependencies occurring in S 55746 kinetics. Thus, the proposed PK interspecies extrapolation strategy, based on preclinical and in vitro information and physiologic assumptions, could be a useful tool for predicting human plasma concentrations at the early stage of drug development.
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Affiliation(s)
- Philippe B Pierrillas
- Equipe mixte de recherche 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France (P.P., E.H., M.T.); Pharmacie Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France (M.T.); Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France (P.P., F.B.); Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Suresnes, France (K.B., J.O., M.A., M.C.); and Institut de Recherches Internationales Servier, Oncology R&D Unit, Suresnes, France (L.K-B.)
| | - Emilie Henin
- Equipe mixte de recherche 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France (P.P., E.H., M.T.); Pharmacie Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France (M.T.); Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France (P.P., F.B.); Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Suresnes, France (K.B., J.O., M.A., M.C.); and Institut de Recherches Internationales Servier, Oncology R&D Unit, Suresnes, France (L.K-B.)
| | - Kathryn Ball
- Equipe mixte de recherche 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France (P.P., E.H., M.T.); Pharmacie Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France (M.T.); Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France (P.P., F.B.); Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Suresnes, France (K.B., J.O., M.A., M.C.); and Institut de Recherches Internationales Servier, Oncology R&D Unit, Suresnes, France (L.K-B.)
| | - Julien Ogier
- Equipe mixte de recherche 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France (P.P., E.H., M.T.); Pharmacie Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France (M.T.); Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France (P.P., F.B.); Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Suresnes, France (K.B., J.O., M.A., M.C.); and Institut de Recherches Internationales Servier, Oncology R&D Unit, Suresnes, France (L.K-B.)
| | - Magali Amiel
- Equipe mixte de recherche 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France (P.P., E.H., M.T.); Pharmacie Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France (M.T.); Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France (P.P., F.B.); Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Suresnes, France (K.B., J.O., M.A., M.C.); and Institut de Recherches Internationales Servier, Oncology R&D Unit, Suresnes, France (L.K-B.)
| | - Laurence Kraus-Berthier
- Equipe mixte de recherche 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France (P.P., E.H., M.T.); Pharmacie Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France (M.T.); Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France (P.P., F.B.); Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Suresnes, France (K.B., J.O., M.A., M.C.); and Institut de Recherches Internationales Servier, Oncology R&D Unit, Suresnes, France (L.K-B.)
| | - Marylore Chenel
- Equipe mixte de recherche 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France (P.P., E.H., M.T.); Pharmacie Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France (M.T.); Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France (P.P., F.B.); Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Suresnes, France (K.B., J.O., M.A., M.C.); and Institut de Recherches Internationales Servier, Oncology R&D Unit, Suresnes, France (L.K-B.)
| | - François Bouzom
- Equipe mixte de recherche 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France (P.P., E.H., M.T.); Pharmacie Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France (M.T.); Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France (P.P., F.B.); Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Suresnes, France (K.B., J.O., M.A., M.C.); and Institut de Recherches Internationales Servier, Oncology R&D Unit, Suresnes, France (L.K-B.)
| | - Michel Tod
- Equipe mixte de recherche 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France (P.P., E.H., M.T.); Pharmacie Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France (M.T.); Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France (P.P., F.B.); Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Suresnes, France (K.B., J.O., M.A., M.C.); and Institut de Recherches Internationales Servier, Oncology R&D Unit, Suresnes, France (L.K-B.)
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Pierrillas PB, Henin E, Ogier J, Kraus-Berthier L, Chenel M, Bouzom F, Tod M. Tumor Growth Inhibition Modelling Based on Receptor Occupancy and Biomarker Activity of a New Bcl-2 Inhibitor in Mice. J Pharmacol Exp Ther 2018; 367:414-424. [DOI: 10.1124/jpet.118.251694] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Accepted: 09/10/2018] [Indexed: 12/16/2022] Open
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17
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Pierrillas PB, Fouliard S, Chenel M, Hooker AC, Friberg LE, Karlsson MO. Correction to: Model-Based Adaptive Optimal Design (MBAOD) Improves Combination Dose Finding Designs: an Example in Oncology. AAPS J 2018; 20:55. [PMID: 29589158 DOI: 10.1208/s12248-018-0218-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The middle initial in the fifth author's name is incorrect in the original article. "Lena F. Friberg" should be "Lena E. Friberg". The original article was corrected.
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Affiliation(s)
- Philippe B Pierrillas
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden
| | - Sylvain Fouliard
- Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Paris, France
| | - Marylore Chenel
- Clinical Pharmacokinetics and Pharmacometrics Division, Servier, Paris, France
| | - Andrew C Hooker
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden
| | - Lena E Friberg
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden
| | - Mats O Karlsson
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden.
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18
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Pierrillas PB, Fouliard S, Chenel M, Hooker AC, Friberg LF, Karlsson MO. Model-Based Adaptive Optimal Design (MBAOD) Improves Combination Dose Finding Designs: an Example in Oncology. AAPS J 2018. [DOI: 10.1208/s12248-018-0206-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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19
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Campagne O, Delmas A, Fouliard S, Chenel M, Chichili GR, Li H, Alderson R, Scherrmann JM, Mager DE. Integrated Pharmacokinetic/Pharmacodynamic Model of a Bispecific CD3xCD123 DART Molecule in Nonhuman Primates: Evaluation of Activity and Impact of Immunogenicity. Clin Cancer Res 2018; 24:2631-2641. [PMID: 29463552 DOI: 10.1158/1078-0432.ccr-17-2265] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2017] [Revised: 10/03/2017] [Accepted: 02/15/2018] [Indexed: 11/16/2022]
Abstract
Purpose: Flotetuzumab (MGD006 or S80880) is a bispecific molecule that recognizes CD3 and CD123 membrane proteins, redirecting T cells to kill CD123-expressing cells for the treatment of acute myeloid leukemia. In this study, we developed a mathematical model to characterize MGD006 exposure-response relationships and to assess the impact of its immunogenicity in cynomolgus monkeys.Experimental Design: Thirty-two animals received multiple escalating doses (100-300-600-1,000 ng/kg/day) via intravenous infusion continuously 4 days a week. The model reflects sequential binding of MGD006 to CD3 and CD123 receptors. Formation of the MGD006/CD3 complex was connected to total T cells undergoing trafficking, whereas the formation of the trimolecular complex results in T-cell activation and clonal expansion. Activated T cells were used to drive the peripheral depletion of CD123-positive cells. Anti-drug antibody development was linked to MGD006 disposition as an elimination pathway. Model validation was tested by predicting the activity of MGD006 in eight monkeys receiving continuous 7-day infusions.Results: MGD006 disposition and total T-cell and CD123-positive cell profiles were well characterized. Anti-drug antibody development led to the suppression of T-cell trafficking but did not systematically abolish CD123-positive cell depletion. Target cell depletion could persist after drug elimination owing to the self-proliferation of activated T cells generated during the first cycles. The model was externally validated with the 7-day infusion dosing schedule.Conclusions: A translational model was developed for MGD006 that features T-cell activation and expansion as a key driver of pharmacologic activity and provides a mechanistic quantitative platform to inform dosing strategies in ongoing clinical studies. Clin Cancer Res; 24(11); 2631-41. ©2018 AACR.
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Affiliation(s)
- Olivia Campagne
- Clinical Pharmacokinetics and Pharmacometrics, Institut de Recherches Internationales Servier, Suresnes, France.,INSERM UMR-S-1144, Universités Paris Descartes-Paris Diderot, Paris, France.,Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, New York
| | - Audrey Delmas
- Clinical Pharmacokinetics and Pharmacometrics, Institut de Recherches Internationales Servier, Suresnes, France
| | - Sylvain Fouliard
- Clinical Pharmacokinetics and Pharmacometrics, Institut de Recherches Internationales Servier, Suresnes, France
| | - Marylore Chenel
- Clinical Pharmacokinetics and Pharmacometrics, Institut de Recherches Internationales Servier, Suresnes, France
| | | | - Hua Li
- MacroGenics, Inc., Rockville, Maryland
| | | | | | - Donald E Mager
- Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, New York.
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Nayak S, Sander O, Al-Huniti N, de Alwis D, Chain A, Chenel M, Sunkaraneni S, Agrawal S, Gupta N, Visser SAG. Getting Innovative Therapies Faster to Patients at the Right Dose: Impact of Quantitative Pharmacology Towards First Registration and Expanding Therapeutic Use. Clin Pharmacol Ther 2018; 103:378-383. [PMID: 29330855 PMCID: PMC5838712 DOI: 10.1002/cpt.978] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Revised: 11/28/2017] [Accepted: 12/04/2017] [Indexed: 12/16/2022]
Abstract
Quantitative pharmacology (QP) applications in translational medicine, drug‐development, and therapeutic use were crowd‐sourced by the ASCPT Impact and Influence initiative. Highlighted QP case studies demonstrated faster access to innovative therapies for patients through 1) rational dose selection for pivotal trials; 2) reduced trial‐burden for vulnerable populations; or 3) simplified posology. Critical success factors were proactive stakeholder engagement, alignment on the value of model‐informed approaches, and utilizing foundational clinical pharmacology understanding of the therapy.
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Affiliation(s)
| | | | - Nidal Al-Huniti
- Quantitative Clinical Pharmacology, AstraZeneca, Waltham, Massachusetts, USA
| | | | | | - Marylore Chenel
- Servier, Clinical Pharmacokinetics and Pharmacometrics, Suresnes, France
| | | | | | - Neeraj Gupta
- Millennium Pharmaceuticals, Inc., Cambridge, Massachusetts, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited
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Cerou M, Lavielle M, Brendel K, Chenel M, Comets E. Development and performance of npde for the evaluation of time-to-event models. Pharm Res 2018; 35:30. [DOI: 10.1007/s11095-017-2291-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Accepted: 10/23/2017] [Indexed: 01/31/2023]
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22
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Smith MK, Moodie SL, Bizzotto R, Blaudez E, Borella E, Carrara L, Chan P, Chenel M, Comets E, Gieschke R, Harling K, Harnisch L, Hartung N, Hooker AC, Karlsson MO, Kaye R, Kloft C, Kokash N, Lavielle M, Lestini G, Magni P, Mari A, Mentré F, Muselle C, Nordgren R, Nyberg HB, Parra-Guillén ZP, Pasotti L, Rode-Kristensen N, Sardu ML, Smith GR, Swat MJ, Terranova N, Yngman G, Yvon F, Holford N. Model Description Language (MDL): A Standard for Modeling and Simulation. CPT Pharmacometrics Syst Pharmacol 2017. [PMID: 28643440 PMCID: PMC5658286 DOI: 10.1002/psp4.12222] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Natallia Kokash
- Leiden University, Netherlands.,University College London, London, UK
| | | | | | | | - Andrea Mari
- CNR Institute of Neurosciences, Padova, Italy
| | | | | | | | - Henrik B Nyberg
- Uppsala Universitet, Sweden.,Mango Solutions, Chippenham, UK
| | | | | | | | - Maria L Sardu
- Merck Serono S.A., a Subsidiary of Merck KgaA, Lausanne, Switzerland
| | - Gareth R Smith
- Scientific Computing Group, Cyprotex Discovery Limited, Macclesfield, Crewe, UK
| | - Maciej J Swat
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK
| | - Nadia Terranova
- Merck Serono S.A., a Subsidiary of Merck KgaA, Lausanne, Switzerland
| | | | - Florent Yvon
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK
| | - Nick Holford
- Uppsala Universitet, Sweden.,University of Auckland, New Zealand
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Ball K, Jamier T, Parmentier Y, Denizot C, Mallier A, Chenel M. Prediction of renal transporter-mediated drug-drug interactions for a drug which is an OAT substrate and inhibitor using PBPK modelling. Eur J Pharm Sci 2017; 106:122-132. [PMID: 28552429 DOI: 10.1016/j.ejps.2017.05.055] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Revised: 05/04/2017] [Accepted: 05/23/2017] [Indexed: 01/06/2023]
Abstract
A PBPK modelling approach was used to predict organic anion transporter (OAT) mediated drug-drug interactions involving S44121, a substrate and an inhibitor of OAT1 and OAT3. Model predictions were then compared to the results of a clinical DDI study which was carried out to investigate the interaction of S44121 with probenecid, tenofovir and ciprofloxacin. PBPK models were developed and qualified using existing clinical data, and inhibition constants were determined in vitro. The model predictions for S44121 as an OAT inhibitor were similar to the results obtained from the clinical DDI study, with no interaction observed for tenofovir or ciprofloxacin in the presence of S44121. An observed AUC ratio of 2.2 was obtained for S44121 in the presence of probenecid, which was slightly higher than the model predicted AUC ratio of 1.6. A DDI study in the monkey was also carried out for the interaction between S44121 and probenecid, since the monkey has previously been reported to be a good preclinical model for OAT-mediated DDI. However, this study highlighted a species difference in the major route of S44121 elimination between monkey (mainly hepatic metabolism) and human (mainly renal excretion of unchanged drug), rendering a comparison between the two DDI studies difficult. Overall, for S44121 the PBPK modelling approach gave a better prediction of the extent of DDI than the static predictions based on inhibitor Cmax and IC50, therefore this can be considered a potentially valuable tool within drug development.
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Affiliation(s)
- Kathryn Ball
- Clinical Pharmacokinetics and Pharmacometrics Department, Institut de Recherches Internationales Servier, Suresnes, France.
| | - Tanguy Jamier
- Clinical Pharmacokinetics and Pharmacometrics Department, Institut de Recherches Internationales Servier, Suresnes, France
| | - Yannick Parmentier
- Nonclinical Pharmacokinetics and Biopharmaceutical Research Department, Technologie Servier, Orleans, France
| | - Claire Denizot
- Nonclinical Pharmacokinetics and Biopharmaceutical Research Department, Technologie Servier, Orleans, France
| | - Agnes Mallier
- Nonclinical Pharmacokinetics and Biopharmaceutical Research Department, Technologie Servier, Orleans, France
| | - Marylore Chenel
- Clinical Pharmacokinetics and Pharmacometrics Department, Institut de Recherches Internationales Servier, Suresnes, France
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Pierrillas PB, Tod M, Amiel M, Chenel M, Henin E. Improvement of Parameter Estimations in Tumor Growth Inhibition Models on Xenografted Animals: Handling Sacrifice Censoring and Error Caused by Experimental Measurement on Larger Tumor Sizes. AAPS J 2016; 18:1262-1272. [DOI: 10.1208/s12248-016-9936-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Accepted: 05/12/2016] [Indexed: 11/30/2022]
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Tessier A, Bertrand J, Chenel M, Comets E. Combined Analysis of Phase I and Phase II Data to Enhance the Power of Pharmacogenetic Tests. CPT Pharmacometrics Syst Pharmacol 2016; 5:123-31. [PMID: 27069775 PMCID: PMC4807465 DOI: 10.1002/psp4.12054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Accepted: 12/11/2015] [Indexed: 12/29/2022] Open
Abstract
We show through a simulation study how the joint analysis of data from phase I and phase II studies enhances the power of pharmacogenetic tests in pharmacokinetic (PK) studies. PK profiles were simulated under different designs along with 176 genetic markers. The null scenarios assumed no genetic effect, while under the alternative scenarios, drug clearance was associated with six genetic markers randomly sampled in each simulated dataset. We compared penalized regression Lasso and stepwise procedures to detect the associations between empirical Bayes estimates of clearance, estimated by nonlinear mixed effects models, and genetic variants. Combining data from phase I and phase II studies, even if sparse, increases the power to identify the associations between genetics and PK due to the larger sample size. Design optimization brings a further improvement, and we highlight a direct relationship between η‐shrinkage and loss of genetic signal.
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Affiliation(s)
- A Tessier
- INSERM IAME UMR 1137 Paris France; Université Paris Diderot, IAME UMR 1137, Sorbonne Paris Cité Paris France; Division of Clinical Pharmacokinetics and Pharmacometrics Institut de Recherches Internationales Servier Suresnes France
| | - J Bertrand
- University College London, Genetics Institute London UK
| | - M Chenel
- Division of Clinical Pharmacokinetics and Pharmacometrics Institut de Recherches Internationales Servier Suresnes France
| | - E Comets
- INSERM IAME UMR 1137 Paris France; Université Paris Diderot, IAME UMR 1137, Sorbonne Paris Cité Paris France; INSERM CIC 1414, Université Rennes 1 Rennes France
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Marshall SF, Burghaus R, Cosson V, Cheung SYA, Chenel M, DellaPasqua O, Frey N, Hamrén B, Harnisch L, Ivanow F, Kerbusch T, Lippert J, Milligan PA, Rohou S, Staab A, Steimer JL, Tornøe C, Visser SAG. Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation. CPT Pharmacometrics Syst Pharmacol 2016; 5:93-122. [PMID: 27069774 PMCID: PMC4809625 DOI: 10.1002/psp4.12049] [Citation(s) in RCA: 209] [Impact Index Per Article: 26.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Accepted: 10/19/2015] [Indexed: 12/11/2022]
Abstract
This document was developed to enable greater consistency in the practice, application, and documentation of Model-Informed Drug Discovery and Development (MID3) across the pharmaceutical industry. A collection of "good practice" recommendations are assembled here in order to minimize the heterogeneity in both the quality and content of MID3 implementation and documentation. The three major objectives of this white paper are to: i) inform company decision makers how the strategic integration of MID3 can benefit R&D efficiency; ii) provide MID3 analysts with sufficient material to enhance the planning, rigor, and consistency of the application of MID3; and iii) provide regulatory authorities with substrate to develop MID3 related and/or MID3 enabled guidelines.
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Affiliation(s)
| | | | - R Burghaus
- Systems Pharmacology & Medicine Bayer Pharma AG Wuppertal Germany
| | - V Cosson
- Clinical Pharmacometrics F. Hoffmann-La Roche Ltd Basel Switzerland
| | - S Y A Cheung
- Quantitative Clinical Pharmacology AstraZeneca Cambridge UK
| | - M Chenel
- Institut de Recherches Internationales Servier Suresnes France
| | - O DellaPasqua
- Clinical Pharmacology Modelling & Simulation GlaxoSmithKline R&D Ltd Uxbridge UK
| | - N Frey
- Clinical Pharmacometrics F. Hoffmann-La Roche Ltd Basel Switzerland
| | - B Hamrén
- Quantitative Clinical Pharmacology AstraZeneca Gothenburg Sweden
| | | | - F Ivanow
- Global regulatory policy & Intelligence Janssen R&D High Wycombe UK
| | - T Kerbusch
- Quantitative Pharmacology & Pharmacometrics MSD Oss Netherlands
| | - J Lippert
- Systems Pharmacology & Medicine Bayer Pharma AG Wuppertal Germany
| | | | - S Rohou
- Global Regulatory Affairs & Policy AstraZeneca Paris France
| | - A Staab
- Translational Medicine & Clinical Pharmacology Boehringer Ingelheim Pharma GmbH & Co. KG Biberach Germany
| | | | - C Tornøe
- Clinical Reporting Novo Nordisk A/S Søborg Denmark
| | - S A G Visser
- Quantitative Pharmacology & Pharmacometrics Merck & Co Kenilworth USA
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27
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Pierrillas PB, Tod M, Amiel M, Chenel M, Henin E. Improvement of Parameter Estimations in Tumor Growth Inhibition Models on Xenografted Animals: a Novel Method to Handle the Interval Censoring Caused by Measurement of Smaller Tumors. AAPS J 2016; 18:404-15. [PMID: 26757730 DOI: 10.1208/s12248-015-9862-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2015] [Accepted: 12/15/2015] [Indexed: 11/30/2022]
Abstract
The purpose of this study was to explore the interval censoring induced by caliper measurements on smaller tumors during tumor growth experiments in preclinical studies and to show its impact on parameter estimations. A new approach, the so-called interval-M3 method, is proposed to specifically handle this type of data. Thereby, the interval-M3 method was challenged with different methods (including classical methods for handling below quantification limit values) using Stochastic Simulation and Estimation process to take into account the censoring. In this way, 1000 datasets were simulated under the design of a typical of tumor growth study in xenografted mice, and then, each method was used for parameter estimation on the simulated datasets. Relative bias and relative root mean square error (relative RMSE) were consequently computed for comparison purpose. By not considering the censoring, parameter estimations appeared to be biased and particularly the cytotoxic effect parameter, k 2 , which is the parameter of interest to characterize the efficacy of a compound in oncology. The best performance was noted with the interval-M3 method which properly takes into account the interval censoring induced by caliper measurement, giving overall unbiased estimations for all parameters and especially for the antitumor effect parameter (relative bias = 0.49%, and relative RMSE = 4.06%).
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Affiliation(s)
- Philippe B Pierrillas
- EMR 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France. .,Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France.
| | - Michel Tod
- EMR 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France.,Pharmacie, Hôpital de la Croix Rousse, Hospices Civils de Lyon, Lyon, France
| | - Magali Amiel
- Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France
| | - Marylore Chenel
- Division of Clinical Pharmacokinetics and Pharmacometrics, Institut de Recherches Internationales Servier, Suresnes, France
| | - Emilie Henin
- EMR 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France
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Swat MJ, Moodie S, Wimalaratne SM, Kristensen NR, Lavielle M, Mari A, Magni P, Smith MK, Bizzotto R, Pasotti L, Mezzalana E, Comets E, Sarr C, Terranova N, Blaudez E, Chan P, Chard J, Chatel K, Chenel M, Edwards D, Franklin C, Giorgino T, Glont M, Girard P, Grenon P, Harling K, Hooker AC, Kaye R, Keizer R, Kloft C, Kok JN, Kokash N, Laibe C, Laveille C, Lestini G, Mentré F, Munafo A, Nordgren R, Nyberg HB, Parra-Guillen ZP, Plan E, Ribba B, Smith G, Trocóniz IF, Yvon F, Milligan PA, Harnisch L, Karlsson M, Hermjakob H, Le Novère N. Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development. CPT Pharmacometrics Syst Pharmacol 2015; 4:316-9. [PMID: 26225259 PMCID: PMC4505825 DOI: 10.1002/psp4.57] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Accepted: 05/06/2015] [Indexed: 12/02/2022] Open
Abstract
The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.
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Affiliation(s)
- MJ Swat
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
| | | | - SM Wimalaratne
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
| | | | | | - A Mari
- National Research Council, Institute of Biomedical EngineeringPadova, Italy
| | - P Magni
- Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di PaviaPavia, Italy
| | - MK Smith
- Global Clinical Pharmacology, PfizerSandwich, UK
| | - R Bizzotto
- INSERM, IAME, UMR 1137, Paris, France, University Paris Diderot, IAME, UMR 1137Sorbonne Paris Cité, Paris, France
| | - L Pasotti
- Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di PaviaPavia, Italy
| | - E Mezzalana
- Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di PaviaPavia, Italy
| | - E Comets
- INSERM, IAME, UMR 1137, Paris, France, University Paris Diderot, IAME, UMR 1137Sorbonne Paris Cité, Paris, France
| | - C Sarr
- Advanced Quantitative Sciences (AQS), NovartisBasel, Switzerland
| | - N Terranova
- Merck Institute for Pharmacometrics, Merck SeronoLausanne, Switzerland
| | | | - P Chan
- Global Clinical Pharmacology, PfizerSandwich, UK
| | - J Chard
- Mango SolutionsChippenham, Wiltshire, UK
| | | | - M Chenel
- SGS Exprimo NV, Mechelen, Belgium, Clinical Pharmacokinetics and Pharmacometrics, Institut de Recherches Internationales ServierSuresnes, France
| | - D Edwards
- Simcyp (a Certara company)Sheffield, UK
| | - C Franklin
- CPMS Technology and DevelopmentSouthall, UK
| | - T Giorgino
- National Research Council, Institute of Biomedical EngineeringPadova, Italy
| | - M Glont
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
| | - P Girard
- Merck Institute for Pharmacometrics, Merck SeronoLausanne, Switzerland
| | - P Grenon
- CHIME, University College LondonLondon, UK
| | - K Harling
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - AC Hooker
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - R Kaye
- Mango SolutionsChippenham, Wiltshire, UK
| | - R Keizer
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - C Kloft
- Freie Universtitaet Berlin, Germany, Institute of Pharmacy, Department of Clinical Pharmacy and BiochemistryBerlin, Germany
| | - JN Kok
- Leiden Institute of Advanced Computer Science (LIACS), Leiden UniversityLeiden, The Netherlands
| | - N Kokash
- Leiden Institute of Advanced Computer Science (LIACS), Leiden UniversityLeiden, The Netherlands
| | - C Laibe
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
| | - C Laveille
- SGS Exprimo NV, Mechelen, Belgium, Clinical Pharmacokinetics and Pharmacometrics, Institut de Recherches Internationales ServierSuresnes, France
| | - G Lestini
- INSERM, IAME, UMR 1137, Paris, France, University Paris Diderot, IAME, UMR 1137Sorbonne Paris Cité, Paris, France
| | - F Mentré
- INSERM, IAME, UMR 1137, Paris, France, University Paris Diderot, IAME, UMR 1137Sorbonne Paris Cité, Paris, France
| | - A Munafo
- Merck Institute for Pharmacometrics, Merck SeronoLausanne, Switzerland
| | - R Nordgren
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - HB Nyberg
- Mango SolutionsChippenham, Wiltshire, UK
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - ZP Parra-Guillen
- Freie Universtitaet Berlin, Germany, Institute of Pharmacy, Department of Clinical Pharmacy and BiochemistryBerlin, Germany
| | - E Plan
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - B Ribba
- Inria Grenoble - Rhône-AlpesGrenoble, France
| | - G Smith
- Scientific Computing Group, Cyprotex Discovery LimitedMacclesfield, Crewe, UK
| | - IF Trocóniz
- Department of Pharmacy and Pharmaceutical Technology, University of NavarraPamplona, Spain
| | - F Yvon
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
| | - PA Milligan
- Global Clinical Pharmacology, PfizerSandwich, UK
| | - L Harnisch
- Global Clinical Pharmacology, PfizerSandwich, UK
| | - M Karlsson
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - H Hermjakob
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
| | - N Le Novère
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
- Babraham Institute, Babraham Research CampusCambridge, UK
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Tessier A, Bertrand J, Chenel M, Comets E. Comparison of Nonlinear Mixed Effects Models and Noncompartmental Approaches in Detecting Pharmacogenetic Covariates. AAPS J 2015; 17:597-608. [PMID: 25693489 DOI: 10.1208/s12248-015-9726-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Accepted: 01/28/2015] [Indexed: 11/30/2022]
Abstract
Genetic data is now collected in many clinical trials, especially in population pharmacokinetic studies. There is no consensus on methods to test the association between pharmacokinetics and genetic covariates. We performed a simulation study inspired by real clinical trials, using the pharmacokinetics (PK) of a compound under development having a nonlinear bioavailability along with genotypes for 176 single nucleotide polymorphisms (SNPs). Scenarios included 78 subjects extensively sampled (16 observations per subject) to simulate a phase I study, or 384 subjects with the same rich design. Under the alternative hypothesis (H1), six SNPs were drawn randomly to affect the log-clearance under an additive linear model. For each scenario, 200 PK data sets were simulated under the null hypothesis (no gene effect) and H1. We compared 16 combinations of four association tests, a stepwise procedure and three penalised regressions (ridge regression, Lasso, HyperLasso), applied to four pharmacokinetic phenotypes, two observed concentrations, area under the curve estimated by noncompartmental analysis and model-based clearance. The different combinations were compared in terms of true and false positives and probability to detect the genetic effects. In presence of nonlinearity and/or variability in bioavailability, model-based phenotype allowed a higher probability to detect the SNPs than other phenotypes. In a realistic setting with a limited number of subjects, all methods showed a low ability to detect genetic effects. Ridge regression had the best probability to detect SNPs, but also a higher number of false positives. No association test showed a much higher power than the others.
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Affiliation(s)
- Adrien Tessier
- INSERM, IAME, UMR 1137, Faculté de médecine Paris Diderot Paris 7 - site Bichat, 16 rue Henri Huchard, 75018, Paris, France,
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Dumont C, Chenel M, Mentré F. Influence of covariance between random effects in design for nonlinear mixed-effect models with an illustration in pediatric pharmacokinetics. J Biopharm Stat 2014; 24:471-92. [PMID: 24697342 DOI: 10.1080/10543406.2014.888443] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Nonlinear mixed-effect models are used increasingly during drug development. For design, an alternative to simulations is based on the Fisher information matrix. Its expression was derived using a first-order approach, was then extended to include covariance and implemented into the R function PFIM. The impact of covariance on standard errors, amount of information, and optimal designs was studied. It was also shown how standard errors can be predicted analytically within the framework of rich individual data without the model. The results were illustrated by applying this extension to the design of a pharmacokinetic study of a drug in pediatric development.
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Affiliation(s)
- Cyrielle Dumont
- a Université Paris Diderot, Sorbonne Paris Cité , UMR 738, INSERM, Paris , France
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31
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Affiliation(s)
- Cyrielle Dumont
- IAME, UMR 1137, INSERM, Paris, France
- Univ Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Marylore Chenel
- Division of Clinical Pharmacokinetics, Institut de Recherches Internationales Servier, Suresnes, France
| | - France Mentré
- IAME, UMR 1137, INSERM, Paris, France
- Univ Paris Diderot, Sorbonne Paris Cité, Paris, France
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Chalret du Rieu Q, Fouliard S, White-Koning M, Kloos I, Chatelut E, Chenel M. Pharmacokinetic/Pharmacodynamic modeling of abexinostat-induced thrombocytopenia across different patient populations: application for the determination of the maximum tolerated doses in both lymphoma and solid tumour patients. Invest New Drugs 2014; 32:985-94. [PMID: 24875134 DOI: 10.1007/s10637-014-0118-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2014] [Accepted: 05/20/2014] [Indexed: 10/25/2022]
Abstract
BACKGROUND In the clinical development of oncology drugs, the recommended dose is usually determined using a 3 + 3 dose-escalation study design. However, this phase I design does not always adequately describe dose-toxicity relationships. METHODS 125 patients, with either solid tumours or lymphoma, were included in the study and 1217 platelet counts were available over three treatment cycles. The data was used to build a population pharmacokinetic/pharmacodynamic (PKPD) model using a sequential modeling approach. Model-derived Recommended Doses (MDRD) of abexinostat (a Histone Deacetylase Inhibitor) were determined from simulations of different administration schedules, and the higher bound for the probability of reaching these MDRD with a 3 + 3 design were obtained. RESULTS The PKPD model developed adequately described platelet kinetics in both patient populations with the inclusion of two platelet baseline counts and a disease progression component for patients with lymphoma. Simulation results demonstrated that abexinostat administration during the first 4 days of each week in a 3-week cycle led to a higher MDRD compared to the other administration schedules tested, with a maximum probability of 40 % of reaching these MDRDs using a 3 + 3 design. CONCLUSIONS The PKPD model was able to predict thrombocytopenia following abexinostat administration in both patient populations. A model-based approach to determine the recommended dose in phase I trials is preferable due to the imprecision of the 3 + 3 design.
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Affiliation(s)
- Quentin Chalret du Rieu
- Clinical Pharmacokinetics Department, Institut de Recherches Internationales Servier, Suresnes, France
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Badhan RKS, Chenel M, Penny JI. Development of a physiologically-based pharmacokinetic model of the rat central nervous system. Pharmaceutics 2014; 6:97-136. [PMID: 24647103 PMCID: PMC3978528 DOI: 10.3390/pharmaceutics6010097] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2013] [Revised: 02/26/2014] [Accepted: 03/06/2014] [Indexed: 01/06/2023] Open
Abstract
Central nervous system (CNS) drug disposition is dictated by a drug's physicochemical properties and its ability to permeate physiological barriers. The blood-brain barrier (BBB), blood-cerebrospinal fluid barrier and centrally located drug transporter proteins influence drug disposition within the central nervous system. Attainment of adequate brain-to-plasma and cerebrospinal fluid-to-plasma partitioning is important in determining the efficacy of centrally acting therapeutics. We have developed a physiologically-based pharmacokinetic model of the rat CNS which incorporates brain interstitial fluid (ISF), choroidal epithelial and total cerebrospinal fluid (CSF) compartments and accurately predicts CNS pharmacokinetics. The model yielded reasonable predictions of unbound brain-to-plasma partition ratio (Kpuu,brain) and CSF:plasma ratio (CSF:Plasmau) using a series of in vitro permeability and unbound fraction parameters. When using in vitro permeability data obtained from L-mdr1a cells to estimate rat in vivo permeability, the model successfully predicted, to within 4-fold, Kpuu,brain and CSF:Plasmau for 81.5% of compounds simulated. The model presented allows for simultaneous simulation and analysis of both brain biophase and CSF to accurately predict CNS pharmacokinetics from preclinical drug parameters routinely available during discovery and development pathways.
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Affiliation(s)
- Raj K Singh Badhan
- Manchester Pharmacy School, the University of Manchester, Oxford Road, Manchester, M13 9PT, UK.
| | - Marylore Chenel
- EA 3809, UFR Médecine-Pharmacie, 34 Rue du Jardin des Plantes, BP 199, 86005 Poitiers, France.
| | - Jeffrey I Penny
- Manchester Pharmacy School, the University of Manchester, Oxford Road, Manchester, M13 9PT, UK.
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Fouliard S, Robert R, Jacquet-Bescond A, du Rieu QC, Balasubramanian S, Loury D, Loriot Y, Hollebecque A, Kloos I, Soria JC, Chenel M, Depil S. Pharmacokinetic/pharmacodynamic modelling-based optimisation of administration schedule for the histone deacetylase inhibitor abexinostat (S78454/PCI-24781) in phase I. Eur J Cancer 2013; 49:2791-7. [DOI: 10.1016/j.ejca.2013.05.009] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2013] [Revised: 05/02/2013] [Accepted: 05/12/2013] [Indexed: 11/25/2022]
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Fouliard S, Chenel M, Marcucci F. Influence of the duration of intravenous drug administration on tumor uptake. Front Oncol 2013; 3:192. [PMID: 23898461 PMCID: PMC3722550 DOI: 10.3389/fonc.2013.00192] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2013] [Accepted: 07/09/2013] [Indexed: 01/04/2023] Open
Abstract
Enhancing tumor uptake of anticancer drugs is an important therapeutic goal, because insufficient drug accumulation is now considered to be an important reason for unresponsiveness or resistance to antitumor therapy. Based on a mechanistic tumor uptake model describing tumor exposure to molecules of different molecular size after bolus administration, we have investigated the influence of the duration of intravenous administration on tumor uptake. The model integrates empirical relationships between molecular size and drug disposition (capillary permeability, interstitial diffusivity, available volume fraction, and plasma clearance), together with a compartmental pharmacokinetics model and a drug/target binding model. Numerical simulations were performed using this model for protracted intravenous drug infusion, a common mode of administration of anticancer drugs. The impact of mode of administration on tumor uptake is described for a large range of molecules of different molecular size. Evaluation was performed not only for the maximal drug concentration achieved in the tumor, but also for the dynamic profile of drug concentration. It is shown that despite a lower maximal uptake for a given dose, infusion allows for a prolonged exposure of tumor tissues to both small- and large-sized molecules. Moreover, infusion may allow higher doses to be administered by reducing Cmax-linked toxicity, thereby achieving a similar maximal uptake compared to bolus administration.
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Affiliation(s)
- Sylvain Fouliard
- Clinical Pharmacokinetics Department, Institut de Recherches Internationales Servier , Suresnes , France
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Dumont C, Mentré F, Gaynor C, Brendel K, Gesson C, Chenel M. Optimal sampling times for a drug and its metabolite using SIMCYP(®) simulations as prior information. Clin Pharmacokinet 2013; 52:43-57. [PMID: 23212609 DOI: 10.1007/s40262-012-0022-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND Since 2007, it is mandatory for the pharmaceutical companies to submit a Paediatric Investigation Plan to the Paediatric Committee at the European Medicines Agency for any drug in development in adults, and it often leads to the need to conduct a pharmacokinetic study in children. Pharmacokinetic studies in children raise ethical and methodological issues. Because of limitation of sampling times, appropriate methods, such as the population approach, are necessary for analysis of the pharmacokinetic data. The choice of the pharmacokinetic sampling design has an important impact on the precision of population parameter estimates. Approaches for design evaluation and optimization based on the evaluation of the Fisher information matrix (M(F)) have been proposed and are now implemented in several software packages, such as PFIM in R. OBJECTIVES The objectives of this work were to (1) develop a joint population pharmacokinetic model to describe the pharmacokinetic characteristics of a drug S and its active metabolite in children after intravenous drug administration from simulated plasma concentration-time data produced using physiologically based pharmacokinetic (PBPK) predictions; (2) optimize the pharmacokinetic sampling times for an upcoming clinical study using a multi-response design approach, considering clinical constraints; and (3) evaluate the resulting design taking data below the lower limit of quantification (BLQ) into account. METHODS Plasma concentration-time profiles were simulated in children using a PBPK model previously developed with the software SIMCYP(®) for the parent drug and its active metabolite. Data were analysed using non-linear mixed-effect models with the software NONMEM(®), using a joint model for the parent drug and its metabolite. The population pharmacokinetic design, for the future study in 82 children from 2 to 18 years old, each receiving a single dose of the drug, was then optimized using PFIM, assuming identical times for parent and metabolite concentration measurements and considering clinical constraints. Design evaluation was based on the relative standard errors (RSEs) of the parameters of interest. In the final evaluation of the proposed design, an approach was used to assess the possible effect of BLQ concentrations on the design efficiency. This approach consists of rescaling the M(F), using, at each sampling time, the probability of observing a concentration BLQ computed from Monte-Carlo simulations. RESULTS A joint pharmacokinetic model with three compartments for the parent drug and one for its active metabolite, with random effects on four parameters, was used to fit the simulated PBPK concentration-time data. A combined error model best described the residual variability. Parameters and dose were expressed per kilogram of bodyweight. Reaching a compromise between PFIM results and clinical constraints, the optimal design was composed of four samples at 0.1, 1.8, 5 and 10 h after drug injection. This design predicted RSE lower than 30 % for the four parameters of interest. For this design, rescaling M(F) for BLQ data had very little influence on predicted RSE. CONCLUSION PFIM was a useful tool to find an optimal sampling design in children, considering clinical constraints. Even if it was not forecasted initially by the investigators, this approach showed that it was really necessary to include a late sampling time for all children. Moreover, we described an approach to evaluate designs assuming expected proportions of BLQ data are omitted.
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Affiliation(s)
- Cyrielle Dumont
- Division of Clinical Pharmacokinetics, Institut de Recherches Internationales Servier, Suresnes, France.
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37
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Chalret du Rieu Q, Fouliard S, Jacquet-Bescond A, Robert R, Kloos I, Depil S, Chatelut E, Chenel M. Application of hematological toxicity modeling in clinical development of abexinostat (S-78454, PCI-24781), a new histone deacetylase inhibitor. Pharm Res 2013; 30:2640-53. [PMID: 23737346 DOI: 10.1007/s11095-013-1089-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2013] [Accepted: 05/19/2013] [Indexed: 10/26/2022]
Abstract
PURPOSE A population pharmacokinetic/pharmacodynamic (PK/PD) model was developed to describe the thrombocytopenia (dose-limiting toxicity) of abexinostat, a new histone deacetylase inhibitor. An optimal administration schedule of the drug was determined using a simulation-based approach. METHODS Early PK and PK/PD data were analysed using a sequential population modeling approach (NONMEM 7), allowing for the description of a PK profile and platelet-count decrease after abexinostat administration with various administration schedules. Simulations of platelet count with several administration schedules over 3-week treatment cycles (ASC) and over a day (ASD) were computed to define the optimal schedule that limits the depth of thrombocytopenia. RESULTS An intermediate PK/PD model accurately described the data. The administration of abexinostat during the first 4 days of each week in a 3-week cycle resulted in fewer adverse events (with no influence of ASD on platelet count profiles), and corresponded to the optimal treatment schedule. This administration schedule was clinically evaluated in a phase I clinical trial and allowed for the definition of a new maximum tolerated dose (MTD), leading to a nearly 30% higher dose-intensity than that of another previously tested schedule. Lastly, a final model was built using all of the available data. CONCLUSIONS The final model, characterizing the dose-effect and the dose-toxicity relationships, provides a useful modeling tool for clinical drug development.
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Affiliation(s)
- Quentin Chalret du Rieu
- Clinical Pharmacokinetics Department, Institut de Recherches Internationales Servier, 50 rue Carnot, 92284, Suresnes Cedex, France
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Mentré F, Chenel M, Comets E, Grevel J, Hooker A, Karlsson MO, Lavielle M, Gueorguieva I. Current Use and Developments Needed for Optimal Design in Pharmacometrics: A Study Performed Among DDMoRe's European Federation of Pharmaceutical Industries and Associations Members. CPT Pharmacometrics Syst Pharmacol 2013; 2:e46. [PMID: 23887744 PMCID: PMC3697035 DOI: 10.1038/psp.2013.19] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2012] [Accepted: 03/13/2013] [Indexed: 01/07/2023]
Affiliation(s)
- F Mentré
- UMR 738, INSERM, University Paris Diderot, Paris, France
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Robert R, Jacquet-Bescond A, Chenel M, Fouliard S, du Rieu QC, Balasubramanian S, Loury D, Loriot Y, Kloos I, Soria JC, Depil S. Abstract A192: PK/PD modeling-based optimization of administration schedule for the histone deacetylase inhibitor (HDACi) S78454/PCI-24781 in phase I. Mol Cancer Ther 2011. [DOI: 10.1158/1535-7163.targ-11-a192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
S78454 (also called PCI-24781) is a novel, orally bioavailable, hydroxamate-based pan-HDACi currently being tested in clinical trials in the US and EU. Two single agent Phase I studies have been completed in patients with advanced solid tumors. The first study (PCYC-0402) tested 4 different administration schedules with 4-week (wk) cycles: 1) TID and 2) BID 5 days/wk 3 wks/4; 3) BID on days 1–7 & 15–21; 4) BID on days 1–5 & 15–19. The planned dose levels were 30, 45, 60, 75 and 90 mg/m2.
The second study (CL1-002) tested an additional schedule: 5) BID 14 days on/7 days off in 3-week cycles. The maximum tolerated dose (MTD), defined by dose-limiting toxicity (DLT) occurring in at least 2/3 or 2/6 patients so that further dose-escalation is not undertaken, was established at 75 mg/m2 BID and the recommended dose at 60 mg/m2 BID.
The DLT, consistently observed across all these schedules, was reversible thrombocytopenia, common to all HDACi and directly related to their mechanism of action. To address this issue, a pharmacokinetic/pharmacodynamic (PK/PD) model was used to predict the optimal administration schedule that could allow higher doses with minimal thrombocytopenia.
Platelet-time profiles from the first study (PCYC-0402) were analyzed using a mixed effect model (Sheiner et al, 1980). This approach allowed the estimation of population mean parameters and their variability by the simultaneous fit of all data records, despite their heterogeneity. This PK/PD model consisted in a previously developed PK model of S78454 used as an input into a semi-physiological platelet time-course model (Friberg et al, 2002). This model was made up of a self-renewing progenitor compartment connected to transit compartments representing maturation process, and leading to a circulating compartment. A feedback mechanism mimicked the increase in proliferation rate when platelet count falls below baseline. The model was parameterized with baseline, feedback intensity, mean maturation time of platelets, and drug concentration/effect relationship. It adequately fitted observed platelet counts after different administration schedules and showed good prediction of adverse events related to haematological toxicity. Several administration schedules were simulated using this model. The results showed that a 4 days on/ 3 days off schedule was associated with the smallest platelet decrease. Accordingly, the protocol for study CL1–002 was amended. After reaching MTD in schedule 5, subsequent cohorts received S78454 on a revised schedule of 4 days on/ 3 days off, starting at one dose level below MTD (60 mg/m2 BID). The dose escalation followed the same design as the initial schedule with the same pre-defined dose levels. As expected, the dose escalation continued for two more dose levels beyond the MTD in schedule 5. At 105 mg/m2 BID, 2/3 patients included experienced DLTs of grade 4 thrombocytopenia, and therefore the MTD in the revised schedule was established at 105 mg/m2 BID.
In conclusion, early understanding of toxicities and PK determination allowed us to build a PK/PD model of thrombocytopenia, which predicted the optimal administration schedule. This optimized schedule is planned to be used in future trials with S78454/PCI-24781. This also provides a successful example of modeling early in development that can be applied to other therapeutics.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr A192.
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Bertrand J, Comets E, Chenel M, Mentré F. Some Alternatives to Asymptotic Tests for the Analysis of Pharmacogenetic Data Using Nonlinear Mixed Effects Models. Biometrics 2011; 68:146-55. [DOI: 10.1111/j.1541-0420.2011.01665.x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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Bertrand J, Laffont CM, Mentré F, Chenel M, Comets E. Development of a complex parent-metabolite joint population pharmacokinetic model. AAPS J 2011; 13:390-404. [PMID: 21618059 DOI: 10.1208/s12248-011-9282-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2010] [Accepted: 05/10/2011] [Indexed: 11/30/2022]
Abstract
This study aimed to develop a joint population pharmacokinetic model for an antipsychotic agent in development (S33138) and its active metabolite (S35424) produced by reversible metabolism. Because such a model leads to identifiability problems and numerical difficulties, the model building was performed using the FOCE-I and the Stochastic Approximation Expectation Maximization (SAEM) estimation algorithms in NONMEM and MONOLIX, respectively. Four different structural models were compared based on Bayesian information criteria. Models were first written as ordinary differential equations systems and then in closed form (CF) to facilitate further analyses. The impact of polymorphisms on genes coding for the CYP2C19 and CYP2D6 enzymes, respectively involved in the parent drug and the metabolite elimination were investigated using permutation Wald test. The parent drug and metabolite plasma concentrations of 101 patients were analyzed on two occasions after 4 and 8 weeks of treatment at 1, 3, 6, and 24 h following daily oral administration. All configurations led to a two compartment model with back-transformation of the metabolite into the parent drug and a first-pass effect. The elimination clearance of the metabolite through other processes than back-transformation was decreased by 35% [9-53%] in CYP2D6 poor metabolizer. Permutation tests were performed to ensure the robustness of the analysis, using SAEM and CF. In conclusion, we developed a complex joint pharmacokinetic model adequately predicting the impact of CYP2D6 polymorphisms on the parent drug and its metabolite concentrations through the back-transformation mechanism.
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Affiliation(s)
- Julie Bertrand
- INSERM, UMR, Univ Paris Diderot, Sorbonne Paris Cité, UMR, France.
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Perdaems N, Blasco H, Vinson C, Chenel M, Whalley S, Cazade F, Bouzom F. Predictions of metabolic drug-drug interactions using physiologically based modelling: Two cytochrome P450 3A4 substrates coadministered with ketoconazole or verapamil. Clin Pharmacokinet 2010; 49:239-58. [PMID: 20214408 DOI: 10.2165/11318130-000000000-00000] [Citation(s) in RCA: 52] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Nowadays, evaluation of potential risk of metabolic drug-drug interactions (mDDIs) is of high importance within the pharmaceutical industry, in order to improve safety and reduce the attrition rate of new drugs. Accurate and early prediction of mDDIs has become essential for drug research and development, and in vitro experiments designed to evaluate potential mDDIs are systematically included in the drug development plan prior to clinical assessment. The aim of this study was to illustrate the value and limitations of the classical and new approaches available to predict risks of DDIs in the research and development processes. The interaction of cytochrome P450 (CYP) 3A4 inhibitors (ketoconazole and verapamil) with midazolam was predicted using the inhibitor concentration/inhibition constant ([I]/K(i)) approach, the static approach with added variability (Simcyp(R)), and whole-body physiologically based pharmacokinetic (WB-PBPK) modelling (acslXtreme(R)). Then an in-house reference drug was used to challenge the different approaches based on the midazolam experience. Predicted values (pharmacokinetic parameters, the area under the plasma concentration-time curve [AUC] ratio and plasma concentrations) were compared with observed values obtained after intravenous and oral administration in order to assess the accuracy of the prediction methods. With the [I]/K(i) approach, the interaction risk was always overpredicted for the midazolam substrate, regardless of its route of administration and the coadministered inhibitor. However, the predictions were always satisfactory (within 2-fold) for the reference drug. For the Simcyp(R) calculations, two of the three interaction results for midazolam were overpredicted, both when midazolam was given orally, whereas the prediction obtained when midazolam was administered intravenously was satisfactory. For the reference drug, all predictions could be considered satisfactory. For the WB-PBPK approach, all predictions were satisfactory, regardless of the substrate, route of administration, dose and coadministered inhibitor. DDI risk predictions are performed throughout the research and development processes and are now fully integrated into decision-making processes. The regulatory approach is useful to provide alerts, even at a very early stage of drug development. The 'steady state' approach in Simcyp(R) improves the prediction by using physiological knowledge and mechanistic assumptions. The DDI predictions are very useful, as they provide a range of AUC ratios that include individuals at the extremes of the population, in addition to the 'average tendency'. Finally, the WB-PBPK approach improves the predictions by simulating the concentration-time profiles and calculating the related pharmacokinetic parameters, taking into account the time of administration of each drug - but it requires a good understanding of the absorption, distribution, metabolism and excretion properties of the compound.
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Bertrand J, Comets E, Laffont CM, Chenel M, Mentré F. Pharmacogenetics and population pharmacokinetics: impact of the design on three tests using the SAEM algorithm. J Pharmacokinet Pharmacodyn 2009; 36:317-39. [PMID: 19562469 DOI: 10.1007/s10928-009-9124-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2009] [Accepted: 06/17/2009] [Indexed: 01/11/2023]
Abstract
Pharmacogenetics is now widely investigated and health institutions acknowledge its place in clinical pharmacokinetics. Our objective is to assess through a simulation study, the impact of design on the statistical performances of three different tests used for analysis of pharmacogenetic information with nonlinear mixed effects models: (i) an ANOVA to test the relationship between the empirical Bayes estimates of the model parameter of interest and the genetic covariate, (ii) a global Wald test to assess whether estimates for the gene effect are significant, and (iii) a likelihood ratio test (LRT) between the model with and without the genetic covariate. We use the stochastic EM algorithm (SAEM) implemented in MONOLIX 2.1 software. The simulation setting is inspired from a real pharmacokinetic study. We investigate four designs with N the number of subjects and n the number of samples per subject: (i) N = 40/n = 4, similar to the original study, (ii) N = 80/n = 2 sorted in 4 groups, a design optimized using the PFIM software, (iii) a combined design, N = 20/n = 4 plus N = 80 with only a trough concentration and (iv) N = 200/n = 4, to approach asymptotic conditions. We find that the ANOVA has a correct type I error estimate regardless of design, however the sparser design was optimized. The type I error of the Wald test and LRT are moderatly inflated in the designs far from the asymptotic (<10%). For each design, the corrected power is analogous for the three tests. Among the three designs with a total of 160 observations, the design N = 80/n = 2 optimized with PFIM provides both the lowest standard error on the effect coefficients and the best power for the Wald test and the LRT while a high shrinkage decreases the power of the ANOVA. In conclusion, a correction method should be used for model-based tests in pharmacogenetic studies with reduced sample size and/or sparse sampling and, for the same amount of samples, some designs have better power than others.
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Affiliation(s)
- Julie Bertrand
- UMR 738, INSERM, Université Paris Diderot, 75018, Paris, France.
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Chenel M, Bouzom F, Cazade F, Ogungbenro K, Aarons L, Mentré F. Drug-drug interaction predictions with PBPK models and optimal multiresponse sampling time designs: application to midazolam and a phase I compound. Part 2: clinical trial results. J Pharmacokinet Pharmacodyn 2009; 35:661-81. [PMID: 19130187 DOI: 10.1007/s10928-008-9105-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2008] [Accepted: 11/25/2008] [Indexed: 10/21/2022]
Abstract
PURPOSE To compare results of population PK analyses obtained with a full empirical design (FD) and an optimal sparse design (MD) in a Drug-Drug Interaction (DDI) study aiming to evaluate the potential CYP3A4 inhibitory effect of a drug in development, SX, on a reference substrate, midazolam (MDZ). Secondary aim was to evaluate the interaction of SX on MDZ in the in vivo study. Methods To compare designs, real data were analysed by population PK modelling technique using either FD or MD with NONMEM FOCEI for SX and with NONMEM FOCEI and MONOLIX SAEM for MDZ. When applicable a Wald test was performed to compare model parameter estimates, such as apparent clearance (CL/F), across designs. To conclude on the potential interaction of SX on MDZ PK, a Student paired test was applied to compare the individual PK parameters (i.e. log(AUC) and log(C(max))) obtained either by a non-compartmental approach (NCA) using FD or from empirical Bayes estimates (EBE) obtained after fitting the model separately on each treatment group using either FD or MD. RESULTS For SX, whatever the design, CL/F was well estimated and no statistical differences were found between CL/F estimated values obtained with FD (CL/F = 8.2 l/h) and MD (CL/F = 8.2 l/h). For MDZ, only MONOLIX was able to estimate CL/F and to provide its standard error of estimation with MD. With MONOLIX, whatever the design and the administration setting, MDZ CL/F was well estimated and there were no statistical differences between CL/F estimated values obtained with FD (72 l/h and 40 l/h for MDZ alone and for MDZ with SX, respectively) and MD (77 l/h and 45 l/h for MDZ alone and for MDZ with SX, respectively). Whatever the approach, NCA or population PK modelling, and for the latter approach, whatever the design, MD or FD, comparison tests showed that there was a statistical difference (P < 0.0001) between individual MDZ log(AUC) obtained after MDZ administration alone and co-administered with SX. Regarding C(max), there was a statistical difference (P < 0.05) between individual MDZ log(C(max)) obtained under the 2 administration settings in all cases, except with the sparse design with MONOLIX. However, the effect on C(max) was small. Finally, SX was shown to be a moderate CYP3A4 inhibitor, which at therapeutic doses increased MDZ exposure by a factor of 2 in average and almost did not affect the C(max). CONCLUSION The optimal sparse design enabled the estimation of CL/F of a CYP3A4 substrate and inhibitor when co-administered together and to show the interaction leading to the same conclusion as the full empirical design.
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Affiliation(s)
- Marylore Chenel
- Institut de Recherches Internationales Servier, 6 place des Pléiades, 92415, Courbevoie Cedex, France.
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Chenel M, Bouzom F, Aarons L, Ogungbenro K. Drug–drug interaction predictions with PBPK models and optimal multiresponse sampling time designs: application to midazolam and a phase I compound. Part 1: comparison of uniresponse and multiresponse designs using PopDes. J Pharmacokinet Pharmacodyn 2009; 35:635-59. [DOI: 10.1007/s10928-008-9104-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2008] [Accepted: 11/25/2008] [Indexed: 11/29/2022]
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Marchand S, Forsell A, Chenel M, Comets E, Lamarche I, Couet W. Norfloxacin blood-brain barrier transport in rats is not affected by probenecid coadministration. Antimicrob Agents Chemother 2006; 50:371-3. [PMID: 16377715 PMCID: PMC1346768 DOI: 10.1128/aac.50.1.371-373.2006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The effect of probenecid (PRO) on norfloxacin (NOR) blood-brain barrier transport was investigated with rats by microdialysis. Maximum brain drug concentrations were rapidly attained, and the brain penetration factor was close to 5% in the absence and presence of PRO. In conclusion, PRO has no effect on NOR blood-brain barrier transport.
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Affiliation(s)
- Sandrine Marchand
- EA 3809, Faculté de Médecine et de Pharmacie, BP 199, Poitiers, France
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Chenel M, Ogungbenro K, Duval V, Laveille C, Jochemsen R, Aarons L. Optimal Blood Sampling Time Windows for Parameter Estimation Using a Population Approach: Design of a Phase II Clinical Trial. J Pharmacokinet Pharmacodyn 2005; 32:737-56. [PMID: 16341474 DOI: 10.1007/s10928-005-0014-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2005] [Accepted: 01/20/2005] [Indexed: 10/25/2022]
Abstract
The objective of this paper is to determine optimal blood sampling time windows for the estimation of pharmacokinetic (PK) parameters by a population approach within the clinical constraints. A population PK model was developed to describe a reference phase II PK dataset. Using this model and the parameter estimates, D-optimal sampling times were determined by optimising the determinant of the population Fisher information matrix (PFIM) using PFIM_ _M 1.2 and the modified Fedorov exchange algorithm. Optimal sampling time windows were then determined by allowing the D-optimal windows design to result in a specified level of efficiency when compared to the fixed-times D-optimal design. The best results were obtained when K(a) and IIV on K(a) were fixed. Windows were determined using this approach assuming 90% level of efficiency and uniform sample distribution. Four optimal sampling time windows were determined as follow: at trough between 22 h and new drug administration; between 2 and 4 h after dose for all patients; and for 1/3 of the patients only 2 sampling time windows between 4 and 10 h after dose, equal to [4 h-5 h 05] and [9 h 10-10 h]. This work permitted the determination of an optimal design, with suitable sampling time windows which was then evaluated by simulations. The sampling time windows will be used to define the sampling schedule in a prospective phase II study.
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Affiliation(s)
- Marylore Chenel
- School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Oxford road, Manchester, M13 9PL, United Kingdom
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Marchand S, Chenel M, Lamarche I, Couet W. Pharmacokinetic modeling of free amoxicillin concentrations in rat muscle extracellular fluids determined by microdialysis. Antimicrob Agents Chemother 2005; 49:3702-6. [PMID: 16127043 PMCID: PMC1195412 DOI: 10.1128/aac.49.9.3702-3706.2005] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The aim of the present study was to investigate amoxicillin (AMX) distribution in muscle interstitial fluid by microdialysis in healthy, awake rats. Microdialysis probes were inserted into the jugular vein and hind leg muscle. Probe recoveries in each rat were determined by retrodialysis with cefadroxil. AMX was administered as a bolus dose of 50 mg.kg(-1), and microdialysis samples were collected during 180 min. Concentrations of unbound drug in blood and muscle were analyzed simultaneously by a population approach. Simulations were conducted using a hybrid, physiologically based pharmacokinetic model to investigate the potential impact of tissue blood flow on muscle AMX distribution. A two-compartment pharmacokinetic model described adequately the unbound amoxicillin concentration-time profiles in blood and muscle. Muscle AMX distribution equilibrium was rapidly achieved. Consequently, the best results were obtained by considering concentrations in muscle as part of the central compartment. The ratio of the concentration of unbound drug in muscle to that in blood (Rmodel) was estimated to 0.80 by the model, which is close to the mean value obtained by noncompartmental data analysis (Rarea= 0.86 +/- 0.29). Simulations conducted with a hybrid, physiologically based pharmacokinetic model suggest that a muscle blood flow reduction of 30% to 50%, such as could be encountered in critical care patients, has virtually no effect on muscle AMX concentration profiles. In conclusion, this study has clearly demonstrated that AMX distributes rapidly and extensively within muscle interstitial fluid, consistent with theory, and that altered muscle blood flow seems unlikely to have a major effect on these distribution characteristics.
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Chenel M, Marchand S, Dupuis A, Lamarche I, Paquereau J, Pariat C, Couet W. Simultaneous central nervous system distribution and pharmacokinetic-pharmacodynamic modelling of the electroencephalogram effect of norfloxacin administered at a convulsant dose in rats. Br J Pharmacol 2004; 142:323-30. [PMID: 15155539 PMCID: PMC1574943 DOI: 10.1038/sj.bjp.0705748] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
The objective of this study was to investigate the contribution of norfloxacin blood-brain barrier (BBB) transport to its delayed electroencephalogram (EEG) effect in rats. Norfloxacin was injected as a bolus dose of 150 mg kg(-1). Blood samples were collected for total norfloxacin plasma concentration measurements. The corresponding unbound levels were determined in brain extracellular fluid (ECF) using microdialysis. Quantitative EEG recording was conducted during 9 h post-dose. Brain ECF norfloxacin concentrations were much lower than plasma levels (AUC ratio=9.7+/-2.8%) but peaked very early, and concentration versus time profiles were parallel in both biological fluids. The best pharmacokinetic (PK) modelling was obtained by considering that ECF concentrations were part of the central compartment, with a proportionality factor. The peak of EEG effect was delayed and the effect versus plasma concentration curves exhibited a dramatic hysteresis. A PK-pharmacodynamic (PD) effect compartment model with a spline function to describe the relationship between effect and concentration at the effect site successfully described the data. Comparisons of PK-PD parameters estimated from plasma and ECF concentrations show that most of the delayed norfloxacin EEG effect is not due to BBB transport, but also that PD parameters derived from plasma data must be carefully interpreted when drug distribution at the effect site is restricted, as may often be the case for centrally acting drugs.
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Affiliation(s)
- Marylore Chenel
- EE Médicaments anti-infectieux et barrière hémato-encéphalique, PBS, Faculté de Médecine & Pharmacie, 40 avenue du Recteur Pineau, 86022 Poitiers Cedex, France
- Laboratoire de Pharmacocinétique, PBS, CHU La Milétrie, 86022 Poitiers Cedex, France
| | - Sandrine Marchand
- EE Médicaments anti-infectieux et barrière hémato-encéphalique, PBS, Faculté de Médecine & Pharmacie, 40 avenue du Recteur Pineau, 86022 Poitiers Cedex, France
| | - Antoine Dupuis
- EE Médicaments anti-infectieux et barrière hémato-encéphalique, PBS, Faculté de Médecine & Pharmacie, 40 avenue du Recteur Pineau, 86022 Poitiers Cedex, France
- Pharmacie Centrale, CHU La Milétrie, 86022 Poitiers Cedex, France
| | - Isabelle Lamarche
- EE Médicaments anti-infectieux et barrière hémato-encéphalique, PBS, Faculté de Médecine & Pharmacie, 40 avenue du Recteur Pineau, 86022 Poitiers Cedex, France
| | - Joël Paquereau
- Equipe Sommeil: Attention et Respiration, PBS, Faculté de Médecine & Pharmacie, 86022 Poitiers Cedex, France
| | - Claudine Pariat
- EE Médicaments anti-infectieux et barrière hémato-encéphalique, PBS, Faculté de Médecine & Pharmacie, 40 avenue du Recteur Pineau, 86022 Poitiers Cedex, France
| | - William Couet
- EE Médicaments anti-infectieux et barrière hémato-encéphalique, PBS, Faculté de Médecine & Pharmacie, 40 avenue du Recteur Pineau, 86022 Poitiers Cedex, France
- Author for correspondence: .
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Marchand S, Chenel M, Lamarche I, Pariat C, Couet W. Dose ranging pharmacokinetics and brain distribution of norfloxacin using microdialysis in rats. J Pharm Sci 2004; 92:2458-65. [PMID: 14603491 DOI: 10.1002/jps.10504] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The purpose of this study was to investigate the effect of dose on norfloxacin pharmacokinetics and distribution into the brain extracellular fluid (ECF), in freely moving rats. Unbound concentrations of norfloxacin in hippocampus were determined by microdialysis after an i.v. bolus dose of 12.5, 25, 50, 100, or 150 mg/kg in rats. In vivo recovery of norfloxacin was determined by retrodialysis by calibrator. Among three fluoroquinolones (enoxacin, pefloxacin, and ciprofloxacin) selected as potential calibrators, ciprofloxacin was selected as the best one. Maximum ECF brain norfloxacin concentrations are rapidly obtained but the ECFbrain/plasma areas under curves (AUC) ratios are low and independent of dose with a mean value of 8.2 +/- 5.8%. By contrast, norfloxacin systemic pharmacokinetics was nonlinear, with total plasma clearance decreasing significantly from 23.0 +/- 3.4 to 14.4 +/- 3.8 mL/min/kg when dose increased from 12.5 to 150 mg/kg.
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Affiliation(s)
- Sandrine Marchand
- Equipe "Médicaments Anti-infectieux et Barrière Hématoencéphalique," Pôle Biologie Santé, Médecine-Sud, Niveau 1, 40, Avenue du Recteur Pineau, 86022 Poitiers Cedex, France
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