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Published in: Clinical Pharmacokinetics 8/2013

01-08-2013 | Original Research Article

Physiologically Based Pharmacokinetic Modelling to Predict Single- and Multiple-Dose Human Pharmacokinetics of Bitopertin

Authors: Neil Parrott, Dominik Hainzl, Daniela Alberati, Carsten Hofmann, Richard Robson, Bruno Boutouyrie, Meret Martin-Facklam

Published in: Clinical Pharmacokinetics | Issue 8/2013

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Abstract

Background

Bitopertin (RG1678) is a glycine reuptake inhibitor currently in phase 3 trials for treatment of schizophrenia. This paper describes the use of physiologically based pharmacokinetic (PBPK) modelling and preclinical data to gain insights into and predict bitopertin clinical pharmacokinetics.

Methods

Simulations of pharmacokinetics were initiated early in the drug discovery stage by integrating physicochemical properties and in vitro measurements into a PBPK rat model. Comparison of pharmacokinetics predicted by PBPK modelling with those measured after intravenous and oral dosing in rats and monkeys showed a good match and thus increased confidence that a similar approach could be applied for human prediction. After comparison of predicted plasma concentrations with those measured after single oral doses in the first clinical study, the human model was refined and then applied to simulate multiple-dose pharmacokinetics.

Results

Clinical plasma concentrations measured were in good agreement with PBPK predictions. Predicted area under the plasma concentration–time curve (AUC) was within twofold of the observed mean values for all dose levels. Maximum plasma concentration (C max) at higher doses was well predicted but approximately twofold below observed values at the lower doses. A slightly less than dose-proportional increase in both AUC and C max was observed, and model simulations indicated that when the dose exceeded 50 mg, solubility limited the fraction of dose absorbed. Refinement of the absorption model with additional solubility and permeability measurements further improved the match of simulations to observed single-dose data. Simulated multiple-dose pharmacokinetics with the refined model were in good agreement with observed data.

Conclusions

Clinical pharmacokinetics of bitopertin can be well simulated with a mechanistic PBPK model. This model supports further clinical development and provides a valuable repository for pharmacokinetic knowledge gained about the molecule.
Appendix
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Literature
1.
2.
go back to reference Tandon R, Nasrallah HA, Keshavan MS. Schizophrenia: “Just the Facts” 4: clinical features and conceptualization. Schizophr Res. 2009;110(1–3):1–23.PubMedCrossRef Tandon R, Nasrallah HA, Keshavan MS. Schizophrenia: “Just the Facts” 4: clinical features and conceptualization. Schizophr Res. 2009;110(1–3):1–23.PubMedCrossRef
3.
go back to reference Thomas SP, Nandhra HS, Singh SP. Pharmacologic treatment of first-episode schizophrenia: a review of the literature. Prim Care Companion CNS Disord. 2012;14(1):PCC.11r01198. Thomas SP, Nandhra HS, Singh SP. Pharmacologic treatment of first-episode schizophrenia: a review of the literature. Prim Care Companion CNS Disord. 2012;14(1):PCC.11r01198.
4.
go back to reference Miyamoto S, Miyake N, Jarskog LF, et al. Pharmacological treatment of schizophrenia: a critical review of the pharmacology and clinical effects of current and future therapeutic agents. Mol Psychiatry. 2012;17(12):1206–27. Miyamoto S, Miyake N, Jarskog LF, et al. Pharmacological treatment of schizophrenia: a critical review of the pharmacology and clinical effects of current and future therapeutic agents. Mol Psychiatry. 2012;17(12):1206–27.
5.
go back to reference Javitt DC. Glutamate and schizophrenia: phencyclidine, N-methyl-d-aspartate receptors, and dopamine–glutamate interactions. Int Rev Neurobiol. 2007;78:69–108.PubMedCrossRef Javitt DC. Glutamate and schizophrenia: phencyclidine, N-methyl-d-aspartate receptors, and dopamine–glutamate interactions. Int Rev Neurobiol. 2007;78:69–108.PubMedCrossRef
6.
go back to reference Javitt DC. Glycine transport inhibitors for the treatment of schizophrenia: symptom and disease modification. Curr Opin Drug Discov Devel. 2009;12(4):468–78.PubMed Javitt DC. Glycine transport inhibitors for the treatment of schizophrenia: symptom and disease modification. Curr Opin Drug Discov Devel. 2009;12(4):468–78.PubMed
7.
go back to reference Pinard E, Alanine A, Alberati D, et al. Selective GlyT1 Inhibitors: discovery of [4-(3-fluoro-5-trifluoromethylpyridin-2-yl)piperazin-1-yl][5-methanesulfonyl-2-((S)-2,2,2-trifluoro-1-methylethoxy)phenyl]methanone (RG1678), a promising novel medicine to treat schizophrenia. J Med Chem. 2012;53(12):4603–14.CrossRef Pinard E, Alanine A, Alberati D, et al. Selective GlyT1 Inhibitors: discovery of [4-(3-fluoro-5-trifluoromethylpyridin-2-yl)piperazin-1-yl][5-methanesulfonyl-2-((S)-2,2,2-trifluoro-1-methylethoxy)phenyl]methanone (RG1678), a promising novel medicine to treat schizophrenia. J Med Chem. 2012;53(12):4603–14.CrossRef
8.
go back to reference Alberati D, Moreau JL, Lengyel J, et al. Glycine reuptake inhibitor RG1678: a pharmacologic characterization of an investigational agent for the treatment of schizophrenia. Neuropharmacology. 2012;62(2):1152–61.PubMedCrossRef Alberati D, Moreau JL, Lengyel J, et al. Glycine reuptake inhibitor RG1678: a pharmacologic characterization of an investigational agent for the treatment of schizophrenia. Neuropharmacology. 2012;62(2):1152–61.PubMedCrossRef
9.
go back to reference Rowland M, Peck C, Tucker G. Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol. 2011;51:45–73.PubMedCrossRef Rowland M, Peck C, Tucker G. Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol. 2011;51:45–73.PubMedCrossRef
10.
go back to reference Parrott N, Jones H, Paquereau N, et al. Application of full physiological models for pharmaceutical drug candidate selection and extrapolation of pharmacokinetics to man. Basic Clin Pharmacol Toxicol. 2005;96(3):193–9.PubMedCrossRef Parrott N, Jones H, Paquereau N, et al. Application of full physiological models for pharmaceutical drug candidate selection and extrapolation of pharmacokinetics to man. Basic Clin Pharmacol Toxicol. 2005;96(3):193–9.PubMedCrossRef
11.
go back to reference Parrott N, Paquereau N, Coassolo P, et al. An evaluation of the utility of physiologically based models of pharmacokinetics in early drug discovery. J Pharm Sci. 2005;94(10):2327–43.PubMedCrossRef Parrott N, Paquereau N, Coassolo P, et al. An evaluation of the utility of physiologically based models of pharmacokinetics in early drug discovery. J Pharm Sci. 2005;94(10):2327–43.PubMedCrossRef
12.
go back to reference Jones HM, Parrott N, Jorga K, et al. A novel strategy for physiologically based predictions of human pharmacokinetics. Clin Pharmacokinet. 2006;45(5):511–42.PubMedCrossRef Jones HM, Parrott N, Jorga K, et al. A novel strategy for physiologically based predictions of human pharmacokinetics. Clin Pharmacokinet. 2006;45(5):511–42.PubMedCrossRef
13.
go back to reference Jones HM, Gardner IB, Collard WT, et al. Simulation of human intravenous and oral pharmacokinetics of 21 diverse compounds using physiologically based pharmacokinetic modelling. Clin Pharmacokinet. 2011;50(5):331–47.PubMedCrossRef Jones HM, Gardner IB, Collard WT, et al. Simulation of human intravenous and oral pharmacokinetics of 21 diverse compounds using physiologically based pharmacokinetic modelling. Clin Pharmacokinet. 2011;50(5):331–47.PubMedCrossRef
14.
go back to reference Bjorkman S. Prediction of drug disposition in infants and children by means of physiologically based pharmacokinetic (PBPK) modelling: theophylline and midazolam as model drugs. Br J Clin Pharmacol. 2005;59(6):691–704.PubMedCrossRef Bjorkman S. Prediction of drug disposition in infants and children by means of physiologically based pharmacokinetic (PBPK) modelling: theophylline and midazolam as model drugs. Br J Clin Pharmacol. 2005;59(6):691–704.PubMedCrossRef
15.
go back to reference Parrott N, Davies B, Hoffmann G, et al. Development of a physiologically based model for oseltamivir and simulation of pharmacokinetics in neonates and infants. Clin Pharmacokinet. 2011;50(9):613–23. Parrott N, Davies B, Hoffmann G, et al. Development of a physiologically based model for oseltamivir and simulation of pharmacokinetics in neonates and infants. Clin Pharmacokinet. 2011;50(9):613–23.
16.
go back to reference Edginton AN, Willmann S. Physiology-based simulations of a pathological condition: prediction of pharmacokinetics in patients with liver cirrhosis. Clin Pharmacokinet. 2008;47(11):743–52.PubMedCrossRef Edginton AN, Willmann S. Physiology-based simulations of a pathological condition: prediction of pharmacokinetics in patients with liver cirrhosis. Clin Pharmacokinet. 2008;47(11):743–52.PubMedCrossRef
17.
go back to reference Rowland Yeo K, Aarabi M, Jamei M, et al. Modeling and predicting drug pharmacokinetics in patients with renal impairment. Expert Rev Clin Pharmacol. 2011:4(2):261–74. Rowland Yeo K, Aarabi M, Jamei M, et al. Modeling and predicting drug pharmacokinetics in patients with renal impairment. Expert Rev Clin Pharmacol. 2011:4(2):261–74.
18.
go back to reference Inoue S, Howgate EM, Rowland Yeo K, et al. Prediction of in vivo drug clearance from in vitro data. II: potential inter-ethnic differences. Xenobiotica. 2006;36(6):499–513.PubMedCrossRef Inoue S, Howgate EM, Rowland Yeo K, et al. Prediction of in vivo drug clearance from in vitro data. II: potential inter-ethnic differences. Xenobiotica. 2006;36(6):499–513.PubMedCrossRef
19.
go back to reference Cubitt HE, Yeo KR, Howgate EM, et al. Sources of interindividual variability in IVIVE of clearance: an investigation into the prediction of benzodiazepine clearance using a mechanistic population-based pharmacokinetic model. Xenobiotica. 2011;41(8):623–38.PubMedCrossRef Cubitt HE, Yeo KR, Howgate EM, et al. Sources of interindividual variability in IVIVE of clearance: an investigation into the prediction of benzodiazepine clearance using a mechanistic population-based pharmacokinetic model. Xenobiotica. 2011;41(8):623–38.PubMedCrossRef
20.
go back to reference Kansy M, Avdeef A, Fischer H. Advances in screening for membrane permeability: high solution PAMPA for medicinal chemists. Drug Discov Today Technol. 2004;1(4):349–55.CrossRef Kansy M, Avdeef A, Fischer H. Advances in screening for membrane permeability: high solution PAMPA for medicinal chemists. Drug Discov Today Technol. 2004;1(4):349–55.CrossRef
21.
go back to reference Alsenz J, Haenel E. Development of a 7-day, 96-well Caco-2 permeability assay with high-throughput direct UV compound analysis. Pharm Res. 2003;20(12):1961–9.PubMedCrossRef Alsenz J, Haenel E. Development of a 7-day, 96-well Caco-2 permeability assay with high-throughput direct UV compound analysis. Pharm Res. 2003;20(12):1961–9.PubMedCrossRef
22.
go back to reference Parrott N, Lave T. Applications of physiologically based absorption models in drug discovery and development. Mol Pharm. 2008;5(5):760–75. Parrott N, Lave T. Applications of physiologically based absorption models in drug discovery and development. Mol Pharm. 2008;5(5):760–75.
23.
go back to reference Galia E, Nicolaides E, Horter D, et al. Evaluation of various dissolution media for predicting in vivo performance of class I and class II drugs. Pharm Res. 1998;15(5):698–705.PubMedCrossRef Galia E, Nicolaides E, Horter D, et al. Evaluation of various dissolution media for predicting in vivo performance of class I and class II drugs. Pharm Res. 1998;15(5):698–705.PubMedCrossRef
24.
go back to reference Poulin P, Theil FP. Prediction of pharmacokinetics prior to in vivo studies II. Generic physiologically based pharmacokinetic models of drug disposition. J Pharm Sci. 2002;91(5):1358–70.PubMedCrossRef Poulin P, Theil FP. Prediction of pharmacokinetics prior to in vivo studies II. Generic physiologically based pharmacokinetic models of drug disposition. J Pharm Sci. 2002;91(5):1358–70.PubMedCrossRef
25.
go back to reference Simulations Plus, Inc. Gastroplus 8.0 User Manual. Lancaster (CA): Simulations Plus, Inc.; 2012. Simulations Plus, Inc. Gastroplus 8.0 User Manual. Lancaster (CA): Simulations Plus, Inc.; 2012.
26.
go back to reference Zuegge J, Schneider G, Coassolo P, et al. Prediction of hepatic metabolic clearance: comparison and assessment of prediction models. Clin Pharmacokinet. 2001;40(7):553–63.PubMedCrossRef Zuegge J, Schneider G, Coassolo P, et al. Prediction of hepatic metabolic clearance: comparison and assessment of prediction models. Clin Pharmacokinet. 2001;40(7):553–63.PubMedCrossRef
27.
go back to reference Poulin P, Theil FP. A priori prediction of tissue:plasma partition coefficients of drugs to facilitate the use of physiologically-based pharmacokinetic models in drug discovery. J Pharm Sci. 2000;89(1):16–35.PubMedCrossRef Poulin P, Theil FP. A priori prediction of tissue:plasma partition coefficients of drugs to facilitate the use of physiologically-based pharmacokinetic models in drug discovery. J Pharm Sci. 2000;89(1):16–35.PubMedCrossRef
28.
go back to reference Agoram B, Woltosz WS, Bolger MB. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv Drug Deliv Rev. 2001;50(Suppl. 1):S41–67.PubMedCrossRef Agoram B, Woltosz WS, Bolger MB. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv Drug Deliv Rev. 2001;50(Suppl. 1):S41–67.PubMedCrossRef
29.
go back to reference Davies B, Morris T. Physiological parameters in laboratory animals and humans. Pharm Res. 1993;10(7):1093–5.PubMedCrossRef Davies B, Morris T. Physiological parameters in laboratory animals and humans. Pharm Res. 1993;10(7):1093–5.PubMedCrossRef
30.
go back to reference Dressman JB, Amidon GL, Reppas C, et al. Dissolution testing as a prognostic tool for oral drug absorption: immediate release dosage forms. Pharm Res. 1998;15(1):11–22.PubMedCrossRef Dressman JB, Amidon GL, Reppas C, et al. Dissolution testing as a prognostic tool for oral drug absorption: immediate release dosage forms. Pharm Res. 1998;15(1):11–22.PubMedCrossRef
31.
go back to reference de Zwart LL, Rompelberg CJ, Sips AJ, et al. Anatomical and physiological differences between various species used in studies on the pharmacokinetics and toxicology of xenobiotics. A review of literature (online). Rijksinstitute voor Volksgezondheit en Milieu. 1999; RIVM report 623860 010. http://www.rivm.nl/bibliotheek/rapporten/623860010.html. Accessed 27 Sep 2012. de Zwart LL, Rompelberg CJ, Sips AJ, et al. Anatomical and physiological differences between various species used in studies on the pharmacokinetics and toxicology of xenobiotics. A review of literature (online). Rijksinstitute voor Volksgezondheit en Milieu. 1999; RIVM report 623860 010. http://​www.​rivm.​nl/​bibliotheek/​rapporten/​623860010.​html. Accessed 27 Sep 2012.
33.
go back to reference Poulin P, Theil FP. Prediction of pharmacokinetics prior to in vivo studies. I. Mechanism-based prediction of volume of distribution. J Pharm Sci. 2002;91(1):129–56.PubMedCrossRef Poulin P, Theil FP. Prediction of pharmacokinetics prior to in vivo studies. I. Mechanism-based prediction of volume of distribution. J Pharm Sci. 2002;91(1):129–56.PubMedCrossRef
34.
go back to reference Jones HM, Parrott N, Ohlenbusch G, et al. Predicting pharmacokinetic food effects using biorelevant solubility media and physiologically based modelling. Clin Pharmacokinet. 2006;45(12):1213–26.PubMedCrossRef Jones HM, Parrott N, Ohlenbusch G, et al. Predicting pharmacokinetic food effects using biorelevant solubility media and physiologically based modelling. Clin Pharmacokinet. 2006;45(12):1213–26.PubMedCrossRef
35.
go back to reference Pajouhesh H, Lenz GR. Medicinal chemical properties of successful central nervous system drugs. NeuroRX. 2005;2(4):541–53.PubMedCrossRef Pajouhesh H, Lenz GR. Medicinal chemical properties of successful central nervous system drugs. NeuroRX. 2005;2(4):541–53.PubMedCrossRef
36.
go back to reference Berezhkovskiy LM. Volume of distribution at steady state for a linear pharmacokinetic system with peripheral elimination. J Pharm Sci. 2004;93(6):1628–40.PubMedCrossRef Berezhkovskiy LM. Volume of distribution at steady state for a linear pharmacokinetic system with peripheral elimination. J Pharm Sci. 2004;93(6):1628–40.PubMedCrossRef
37.
go back to reference Rodgers T, Rowland M. Physiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. J Pharm Sci. 2006;95(6):1238–57.PubMedCrossRef Rodgers T, Rowland M. Physiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. J Pharm Sci. 2006;95(6):1238–57.PubMedCrossRef
38.
go back to reference Parrott N, Lavé T. Prediction of intestinal absorption: comparative assessment of GASTROPLUS and IDEA. Eur J Pharm Sci. 2002;17:51–61.PubMedCrossRef Parrott N, Lavé T. Prediction of intestinal absorption: comparative assessment of GASTROPLUS and IDEA. Eur J Pharm Sci. 2002;17:51–61.PubMedCrossRef
39.
go back to reference Sutton SC. Role of physiological intestinal water in oral absorption. AAPS. 2009;11(2):277–85.CrossRef Sutton SC. Role of physiological intestinal water in oral absorption. AAPS. 2009;11(2):277–85.CrossRef
40.
go back to reference Hofmann C, Banken B, Hahn M, et al. Evaluation of the effects of bitopertin (RG1678) on cardiac repolarization: a thorough QTc study in healthy male volunteers. Clin Ther (in press). Hofmann C, Banken B, Hahn M, et al. Evaluation of the effects of bitopertin (RG1678) on cardiac repolarization: a thorough QTc study in healthy male volunteers. Clin Ther (in press).
41.
go back to reference Zhao P, Rowland M, Huang SM. Best practice in the use of physiologically based pharmacokinetic modeling and simulation to address clinical pharmacology regulatory questions. Clin Pharmacol Ther. 2012;92(1):17–20.PubMedCrossRef Zhao P, Rowland M, Huang SM. Best practice in the use of physiologically based pharmacokinetic modeling and simulation to address clinical pharmacology regulatory questions. Clin Pharmacol Ther. 2012;92(1):17–20.PubMedCrossRef
42.
go back to reference Parrott N, Lukacova V, Fraczkiewicz G, et al. Predicting pharmacokinetics of drugs using physiologically based modeling—application to food effects. AAPS. 2009;11(1):45–53.CrossRef Parrott N, Lukacova V, Fraczkiewicz G, et al. Predicting pharmacokinetics of drugs using physiologically based modeling—application to food effects. AAPS. 2009;11(1):45–53.CrossRef
43.
go back to reference Jamei M, Dickinson GL, Rostami-Hodjegan A. A framework for assessing inter-individual variability in pharmacokinetics using virtual human populations and integrating general knowledge of physical chemistry, biology, anatomy, physiology and genetics: a tale of ‘bottom-up’ vs ‘top-down’ recognition of covariates. Drug Metab Pharmacokinet. 2009;24(1):53–75.PubMedCrossRef Jamei M, Dickinson GL, Rostami-Hodjegan A. A framework for assessing inter-individual variability in pharmacokinetics using virtual human populations and integrating general knowledge of physical chemistry, biology, anatomy, physiology and genetics: a tale of ‘bottom-up’ vs ‘top-down’ recognition of covariates. Drug Metab Pharmacokinet. 2009;24(1):53–75.PubMedCrossRef
44.
go back to reference Heikkinen AT, Baneyx G, Caruso A, et al. Application of PBPK modeling to predict human intestinal metabolism of CYP3A substrates—an evaluation and case study using GastroPlus™. Eur J Pharm Sci. 2012;47:375–86.PubMedCrossRef Heikkinen AT, Baneyx G, Caruso A, et al. Application of PBPK modeling to predict human intestinal metabolism of CYP3A substrates—an evaluation and case study using GastroPlus™. Eur J Pharm Sci. 2012;47:375–86.PubMedCrossRef
46.
go back to reference De Buck SS, Sinha VK, Fenu LA, et al. The prediction of drug metabolism, tissue distribution, and bioavailability of 50 structurally diverse compounds in rat using mechanism-based absorption, distribution, and metabolism prediction tools. Drug Metab Dispos. 2007;35(4):649–59.PubMedCrossRef De Buck SS, Sinha VK, Fenu LA, et al. The prediction of drug metabolism, tissue distribution, and bioavailability of 50 structurally diverse compounds in rat using mechanism-based absorption, distribution, and metabolism prediction tools. Drug Metab Dispos. 2007;35(4):649–59.PubMedCrossRef
47.
go back to reference Jones HM, Gardner IB, Collard WT, et al. Simulation of human intravenous and oral pharmacokinetics of 21 diverse compounds using physiologically based pharmacokinetic modelling. Clin Pharmacokinet. 2011;50(5):331–47. Jones HM, Gardner IB, Collard WT, et al. Simulation of human intravenous and oral pharmacokinetics of 21 diverse compounds using physiologically based pharmacokinetic modelling. Clin Pharmacokinet. 2011;50(5):331–47.
48.
go back to reference Heikkinen AT, Friedlein A, Lamerz J, et al. Mass Spectrometry based quantification of CYP enzymes to establish in vitro-in vivo scaling factors for intestinal and hepatic metabolism in beagle dog. Pharm Res. 2012;29(7):1832–42. doi:10.1007/s11095-012-0707-7.PubMedCrossRef Heikkinen AT, Friedlein A, Lamerz J, et al. Mass Spectrometry based quantification of CYP enzymes to establish in vitro-in vivo scaling factors for intestinal and hepatic metabolism in beagle dog. Pharm Res. 2012;29(7):1832–42. doi:10.​1007/​s11095-012-0707-7.PubMedCrossRef
49.
go back to reference Rostami-Hodjegan A, Tucker GT. Simulation and prediction of in vivo drug metabolism in human populations from in vitro data. Nat Rev Drug Discov. 2007;6(2):140–8.PubMedCrossRef Rostami-Hodjegan A, Tucker GT. Simulation and prediction of in vivo drug metabolism in human populations from in vitro data. Nat Rev Drug Discov. 2007;6(2):140–8.PubMedCrossRef
50.
go back to reference Baneyx G, Fukushima Y, Parrott N. Use of physiologically based pharmacokinetic modeling for assessment of drug–drug interactions. Future Med Chem. 2012;4(5):681–93.PubMedCrossRef Baneyx G, Fukushima Y, Parrott N. Use of physiologically based pharmacokinetic modeling for assessment of drug–drug interactions. Future Med Chem. 2012;4(5):681–93.PubMedCrossRef
51.
go back to reference Howgate EM, Rowland-Yeo K, Proctor NJ, et al. Prediction of in vivo drug clearance from in vitro data. I: impact of inter-individual variability. Xenobiotica. 2006;36(6):473–97.PubMedCrossRef Howgate EM, Rowland-Yeo K, Proctor NJ, et al. Prediction of in vivo drug clearance from in vitro data. I: impact of inter-individual variability. Xenobiotica. 2006;36(6):473–97.PubMedCrossRef
52.
go back to reference Johnson TN, Rostami-Hodjegan A, Tucker GT. Prediction of the clearance of eleven drugs and associated variability in neonates, infants and children. Clin Pharmacokinet. 2006;45(9):931–56.PubMedCrossRef Johnson TN, Rostami-Hodjegan A, Tucker GT. Prediction of the clearance of eleven drugs and associated variability in neonates, infants and children. Clin Pharmacokinet. 2006;45(9):931–56.PubMedCrossRef
Metadata
Title
Physiologically Based Pharmacokinetic Modelling to Predict Single- and Multiple-Dose Human Pharmacokinetics of Bitopertin
Authors
Neil Parrott
Dominik Hainzl
Daniela Alberati
Carsten Hofmann
Richard Robson
Bruno Boutouyrie
Meret Martin-Facklam
Publication date
01-08-2013
Publisher
Springer International Publishing
Published in
Clinical Pharmacokinetics / Issue 8/2013
Print ISSN: 0312-5963
Electronic ISSN: 1179-1926
DOI
https://doi.org/10.1007/s40262-013-0061-x

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