Impact of Demographics, Organ Impairment, Disease, Formulation, and Food on the Pharmacokinetics of the Selective $ S1P_{1} $ Receptor Modulator Ponesimod Based on 13 Clinical Studies

Background Ponesimod is a selective, orally active sphingosine-1-phosphate receptor 1 modulator currently undergoing clinical evaluation for the treatment of multiple sclerosis (MS) in phase III clinical trials. Ponesimod dose-dependently reduces peripheral blood lymphocyte counts by blocking the eg...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Lott, Dominik [verfasserIn] Lehr, Thorsten [verfasserIn] Dingemanse, Jasper [verfasserIn] Krause, Andreas [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Multiple Sclerosis Psoriasis Hepatic Impairment Fingolimod Severe Hepatic Impairment

Übergeordnetes Werk:	Enthalten in: Clinical pharmacokinetics - Berlin [u.a.] : Springer, 1976, 56(2016), 4 vom: 15. Sept., Seite 395-408
Übergeordnetes Werk:	volume:56 ; year:2016 ; number:4 ; day:15 ; month:09 ; pages:395-408

Links:	Volltext

DOI / URN:	10.1007/s40262-016-0446-8

Katalog-ID:	SPR033061572

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520			\|a Background Ponesimod is a selective, orally active sphingosine-1-phosphate receptor 1 modulator currently undergoing clinical evaluation for the treatment of multiple sclerosis (MS) in phase III clinical trials. Ponesimod dose-dependently reduces peripheral blood lymphocyte counts by blocking the egress of lymphocytes from lymphoid organs. Methods A population pharmacokinetic (PK) analysis was performed based on pooled data from 13 clinical studies. Interindividual variability (IIV) and the impact of key demographic variables and other covariates on ponesimod exposure were assessed quantitatively. Results A two-compartment model with sequential zero/first-order absorption, including lag time, intercompartmental drug flow, and first-order clearance, adequately described the PK of ponesimod. Body weight, race, MS, psoriasis, hepatic impairment, drug formulation, and food were identified to significantly affect the concentration–time profile. The inclusion of these covariates into the model explained approximately 25 % of the IIV in the PK of ponesimod. Model predictions indicated that the impact of the identified covariates on ponesimod steady-state exposure is within 20 % of exposure, and thus within the margins of the IIV, with the exception of hepatic impairment. Changes up to threefold were predicted for severe cases of liver dysfunction. Conclusion The rich data set enabled building a comprehensive population PK model that accurately predicts the concentration–time data of ponesimod. Covariates other than hepatic impairment were considered not clinically relevant and thus do not require dose adjustment. A potential dose adaptation can be conducted based on the final model.
650		4	\|a Multiple Sclerosis \|7 (dpeaa)DE-He213
650		4	\|a Psoriasis \|7 (dpeaa)DE-He213
650		4	\|a Hepatic Impairment \|7 (dpeaa)DE-He213
650		4	\|a Fingolimod \|7 (dpeaa)DE-He213
650		4	\|a Severe Hepatic Impairment \|7 (dpeaa)DE-He213
700	1		\|a Lehr, Thorsten \|e verfasserin \|4 aut
700	1		\|a Dingemanse, Jasper \|e verfasserin \|4 aut
700	1		\|a Krause, Andreas \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Clinical pharmacokinetics \|d Berlin [u.a.] : Springer, 1976 \|g 56(2016), 4 vom: 15. Sept., Seite 395-408 \|w (DE-627)327644974 \|w (DE-600)2043781-X \|x 1179-1926 \|7 nnns
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912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
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912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
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912			\|a GBV_ILN_281
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912			\|a GBV_ILN_293
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912			\|a GBV_ILN_602
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912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2107
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
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912			\|a GBV_ILN_4125
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912			\|a GBV_ILN_4246
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
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912			\|a GBV_ILN_4325
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912			\|a GBV_ILN_4333
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author_variant	d l dl t l tl j d jd a k ak
matchkey_str	article:11791926:2016----::matfeorpisraipimndsaeomltoadodnhpamckntcoteeetvsp1eetro
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bklnumber	44.00 44.40
publishDate	2016
allfields	10.1007/s40262-016-0446-8 doi (DE-627)SPR033061572 (SPR)s40262-016-0446-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl 44.40 bkl Lott, Dominik verfasserin aut Impact of Demographics, Organ Impairment, Disease, Formulation, and Food on the Pharmacokinetics of the Selective $ S1P_{1} $ Receptor Modulator Ponesimod Based on 13 Clinical Studies 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Ponesimod is a selective, orally active sphingosine-1-phosphate receptor 1 modulator currently undergoing clinical evaluation for the treatment of multiple sclerosis (MS) in phase III clinical trials. Ponesimod dose-dependently reduces peripheral blood lymphocyte counts by blocking the egress of lymphocytes from lymphoid organs. Methods A population pharmacokinetic (PK) analysis was performed based on pooled data from 13 clinical studies. Interindividual variability (IIV) and the impact of key demographic variables and other covariates on ponesimod exposure were assessed quantitatively. Results A two-compartment model with sequential zero/first-order absorption, including lag time, intercompartmental drug flow, and first-order clearance, adequately described the PK of ponesimod. Body weight, race, MS, psoriasis, hepatic impairment, drug formulation, and food were identified to significantly affect the concentration–time profile. The inclusion of these covariates into the model explained approximately 25 % of the IIV in the PK of ponesimod. Model predictions indicated that the impact of the identified covariates on ponesimod steady-state exposure is within 20 % of exposure, and thus within the margins of the IIV, with the exception of hepatic impairment. Changes up to threefold were predicted for severe cases of liver dysfunction. Conclusion The rich data set enabled building a comprehensive population PK model that accurately predicts the concentration–time data of ponesimod. Covariates other than hepatic impairment were considered not clinically relevant and thus do not require dose adjustment. A potential dose adaptation can be conducted based on the final model. Multiple Sclerosis (dpeaa)DE-He213 Psoriasis (dpeaa)DE-He213 Hepatic Impairment (dpeaa)DE-He213 Fingolimod (dpeaa)DE-He213 Severe Hepatic Impairment (dpeaa)DE-He213 Lehr, Thorsten verfasserin aut Dingemanse, Jasper verfasserin aut Krause, Andreas verfasserin aut Enthalten in Clinical pharmacokinetics Berlin [u.a.] : Springer, 1976 56(2016), 4 vom: 15. Sept., Seite 395-408 (DE-627)327644974 (DE-600)2043781-X 1179-1926 nnns volume:56 year:2016 number:4 day:15 month:09 pages:395-408 https://dx.doi.org/10.1007/s40262-016-0446-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-PHA SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.00 ASE 44.40 ASE AR 56 2016 4 15 09 395-408
spelling	10.1007/s40262-016-0446-8 doi (DE-627)SPR033061572 (SPR)s40262-016-0446-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl 44.40 bkl Lott, Dominik verfasserin aut Impact of Demographics, Organ Impairment, Disease, Formulation, and Food on the Pharmacokinetics of the Selective $ S1P_{1} $ Receptor Modulator Ponesimod Based on 13 Clinical Studies 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Ponesimod is a selective, orally active sphingosine-1-phosphate receptor 1 modulator currently undergoing clinical evaluation for the treatment of multiple sclerosis (MS) in phase III clinical trials. Ponesimod dose-dependently reduces peripheral blood lymphocyte counts by blocking the egress of lymphocytes from lymphoid organs. Methods A population pharmacokinetic (PK) analysis was performed based on pooled data from 13 clinical studies. Interindividual variability (IIV) and the impact of key demographic variables and other covariates on ponesimod exposure were assessed quantitatively. Results A two-compartment model with sequential zero/first-order absorption, including lag time, intercompartmental drug flow, and first-order clearance, adequately described the PK of ponesimod. Body weight, race, MS, psoriasis, hepatic impairment, drug formulation, and food were identified to significantly affect the concentration–time profile. The inclusion of these covariates into the model explained approximately 25 % of the IIV in the PK of ponesimod. Model predictions indicated that the impact of the identified covariates on ponesimod steady-state exposure is within 20 % of exposure, and thus within the margins of the IIV, with the exception of hepatic impairment. Changes up to threefold were predicted for severe cases of liver dysfunction. Conclusion The rich data set enabled building a comprehensive population PK model that accurately predicts the concentration–time data of ponesimod. Covariates other than hepatic impairment were considered not clinically relevant and thus do not require dose adjustment. A potential dose adaptation can be conducted based on the final model. Multiple Sclerosis (dpeaa)DE-He213 Psoriasis (dpeaa)DE-He213 Hepatic Impairment (dpeaa)DE-He213 Fingolimod (dpeaa)DE-He213 Severe Hepatic Impairment (dpeaa)DE-He213 Lehr, Thorsten verfasserin aut Dingemanse, Jasper verfasserin aut Krause, Andreas verfasserin aut Enthalten in Clinical pharmacokinetics Berlin [u.a.] : Springer, 1976 56(2016), 4 vom: 15. Sept., Seite 395-408 (DE-627)327644974 (DE-600)2043781-X 1179-1926 nnns volume:56 year:2016 number:4 day:15 month:09 pages:395-408 https://dx.doi.org/10.1007/s40262-016-0446-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-PHA SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.00 ASE 44.40 ASE AR 56 2016 4 15 09 395-408
allfields_unstemmed	10.1007/s40262-016-0446-8 doi (DE-627)SPR033061572 (SPR)s40262-016-0446-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl 44.40 bkl Lott, Dominik verfasserin aut Impact of Demographics, Organ Impairment, Disease, Formulation, and Food on the Pharmacokinetics of the Selective $ S1P_{1} $ Receptor Modulator Ponesimod Based on 13 Clinical Studies 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Ponesimod is a selective, orally active sphingosine-1-phosphate receptor 1 modulator currently undergoing clinical evaluation for the treatment of multiple sclerosis (MS) in phase III clinical trials. Ponesimod dose-dependently reduces peripheral blood lymphocyte counts by blocking the egress of lymphocytes from lymphoid organs. Methods A population pharmacokinetic (PK) analysis was performed based on pooled data from 13 clinical studies. Interindividual variability (IIV) and the impact of key demographic variables and other covariates on ponesimod exposure were assessed quantitatively. Results A two-compartment model with sequential zero/first-order absorption, including lag time, intercompartmental drug flow, and first-order clearance, adequately described the PK of ponesimod. Body weight, race, MS, psoriasis, hepatic impairment, drug formulation, and food were identified to significantly affect the concentration–time profile. The inclusion of these covariates into the model explained approximately 25 % of the IIV in the PK of ponesimod. Model predictions indicated that the impact of the identified covariates on ponesimod steady-state exposure is within 20 % of exposure, and thus within the margins of the IIV, with the exception of hepatic impairment. Changes up to threefold were predicted for severe cases of liver dysfunction. Conclusion The rich data set enabled building a comprehensive population PK model that accurately predicts the concentration–time data of ponesimod. Covariates other than hepatic impairment were considered not clinically relevant and thus do not require dose adjustment. A potential dose adaptation can be conducted based on the final model. Multiple Sclerosis (dpeaa)DE-He213 Psoriasis (dpeaa)DE-He213 Hepatic Impairment (dpeaa)DE-He213 Fingolimod (dpeaa)DE-He213 Severe Hepatic Impairment (dpeaa)DE-He213 Lehr, Thorsten verfasserin aut Dingemanse, Jasper verfasserin aut Krause, Andreas verfasserin aut Enthalten in Clinical pharmacokinetics Berlin [u.a.] : Springer, 1976 56(2016), 4 vom: 15. Sept., Seite 395-408 (DE-627)327644974 (DE-600)2043781-X 1179-1926 nnns volume:56 year:2016 number:4 day:15 month:09 pages:395-408 https://dx.doi.org/10.1007/s40262-016-0446-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-PHA SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.00 ASE 44.40 ASE AR 56 2016 4 15 09 395-408
allfieldsGer	10.1007/s40262-016-0446-8 doi (DE-627)SPR033061572 (SPR)s40262-016-0446-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl 44.40 bkl Lott, Dominik verfasserin aut Impact of Demographics, Organ Impairment, Disease, Formulation, and Food on the Pharmacokinetics of the Selective $ S1P_{1} $ Receptor Modulator Ponesimod Based on 13 Clinical Studies 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Ponesimod is a selective, orally active sphingosine-1-phosphate receptor 1 modulator currently undergoing clinical evaluation for the treatment of multiple sclerosis (MS) in phase III clinical trials. Ponesimod dose-dependently reduces peripheral blood lymphocyte counts by blocking the egress of lymphocytes from lymphoid organs. Methods A population pharmacokinetic (PK) analysis was performed based on pooled data from 13 clinical studies. Interindividual variability (IIV) and the impact of key demographic variables and other covariates on ponesimod exposure were assessed quantitatively. Results A two-compartment model with sequential zero/first-order absorption, including lag time, intercompartmental drug flow, and first-order clearance, adequately described the PK of ponesimod. Body weight, race, MS, psoriasis, hepatic impairment, drug formulation, and food were identified to significantly affect the concentration–time profile. The inclusion of these covariates into the model explained approximately 25 % of the IIV in the PK of ponesimod. Model predictions indicated that the impact of the identified covariates on ponesimod steady-state exposure is within 20 % of exposure, and thus within the margins of the IIV, with the exception of hepatic impairment. Changes up to threefold were predicted for severe cases of liver dysfunction. Conclusion The rich data set enabled building a comprehensive population PK model that accurately predicts the concentration–time data of ponesimod. Covariates other than hepatic impairment were considered not clinically relevant and thus do not require dose adjustment. A potential dose adaptation can be conducted based on the final model. Multiple Sclerosis (dpeaa)DE-He213 Psoriasis (dpeaa)DE-He213 Hepatic Impairment (dpeaa)DE-He213 Fingolimod (dpeaa)DE-He213 Severe Hepatic Impairment (dpeaa)DE-He213 Lehr, Thorsten verfasserin aut Dingemanse, Jasper verfasserin aut Krause, Andreas verfasserin aut Enthalten in Clinical pharmacokinetics Berlin [u.a.] : Springer, 1976 56(2016), 4 vom: 15. Sept., Seite 395-408 (DE-627)327644974 (DE-600)2043781-X 1179-1926 nnns volume:56 year:2016 number:4 day:15 month:09 pages:395-408 https://dx.doi.org/10.1007/s40262-016-0446-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-PHA SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.00 ASE 44.40 ASE AR 56 2016 4 15 09 395-408
allfieldsSound	10.1007/s40262-016-0446-8 doi (DE-627)SPR033061572 (SPR)s40262-016-0446-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl 44.40 bkl Lott, Dominik verfasserin aut Impact of Demographics, Organ Impairment, Disease, Formulation, and Food on the Pharmacokinetics of the Selective $ S1P_{1} $ Receptor Modulator Ponesimod Based on 13 Clinical Studies 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Ponesimod is a selective, orally active sphingosine-1-phosphate receptor 1 modulator currently undergoing clinical evaluation for the treatment of multiple sclerosis (MS) in phase III clinical trials. Ponesimod dose-dependently reduces peripheral blood lymphocyte counts by blocking the egress of lymphocytes from lymphoid organs. Methods A population pharmacokinetic (PK) analysis was performed based on pooled data from 13 clinical studies. Interindividual variability (IIV) and the impact of key demographic variables and other covariates on ponesimod exposure were assessed quantitatively. Results A two-compartment model with sequential zero/first-order absorption, including lag time, intercompartmental drug flow, and first-order clearance, adequately described the PK of ponesimod. Body weight, race, MS, psoriasis, hepatic impairment, drug formulation, and food were identified to significantly affect the concentration–time profile. The inclusion of these covariates into the model explained approximately 25 % of the IIV in the PK of ponesimod. Model predictions indicated that the impact of the identified covariates on ponesimod steady-state exposure is within 20 % of exposure, and thus within the margins of the IIV, with the exception of hepatic impairment. Changes up to threefold were predicted for severe cases of liver dysfunction. Conclusion The rich data set enabled building a comprehensive population PK model that accurately predicts the concentration–time data of ponesimod. Covariates other than hepatic impairment were considered not clinically relevant and thus do not require dose adjustment. A potential dose adaptation can be conducted based on the final model. Multiple Sclerosis (dpeaa)DE-He213 Psoriasis (dpeaa)DE-He213 Hepatic Impairment (dpeaa)DE-He213 Fingolimod (dpeaa)DE-He213 Severe Hepatic Impairment (dpeaa)DE-He213 Lehr, Thorsten verfasserin aut Dingemanse, Jasper verfasserin aut Krause, Andreas verfasserin aut Enthalten in Clinical pharmacokinetics Berlin [u.a.] : Springer, 1976 56(2016), 4 vom: 15. Sept., Seite 395-408 (DE-627)327644974 (DE-600)2043781-X 1179-1926 nnns volume:56 year:2016 number:4 day:15 month:09 pages:395-408 https://dx.doi.org/10.1007/s40262-016-0446-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-PHA SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.00 ASE 44.40 ASE AR 56 2016 4 15 09 395-408
language	English
source	Enthalten in Clinical pharmacokinetics 56(2016), 4 vom: 15. Sept., Seite 395-408 volume:56 year:2016 number:4 day:15 month:09 pages:395-408
sourceStr	Enthalten in Clinical pharmacokinetics 56(2016), 4 vom: 15. Sept., Seite 395-408 volume:56 year:2016 number:4 day:15 month:09 pages:395-408
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Multiple Sclerosis Psoriasis Hepatic Impairment Fingolimod Severe Hepatic Impairment
dewey-raw	610
isfreeaccess_bool	false
container_title	Clinical pharmacokinetics
authorswithroles_txt_mv	Lott, Dominik @@aut@@ Lehr, Thorsten @@aut@@ Dingemanse, Jasper @@aut@@ Krause, Andreas @@aut@@
publishDateDaySort_date	2016-09-15T00:00:00Z
hierarchy_top_id	327644974
dewey-sort	3610
id	SPR033061572
language_de	englisch
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Ponesimod dose-dependently reduces peripheral blood lymphocyte counts by blocking the egress of lymphocytes from lymphoid organs. Methods A population pharmacokinetic (PK) analysis was performed based on pooled data from 13 clinical studies. Interindividual variability (IIV) and the impact of key demographic variables and other covariates on ponesimod exposure were assessed quantitatively. Results A two-compartment model with sequential zero/first-order absorption, including lag time, intercompartmental drug flow, and first-order clearance, adequately described the PK of ponesimod. Body weight, race, MS, psoriasis, hepatic impairment, drug formulation, and food were identified to significantly affect the concentration–time profile. The inclusion of these covariates into the model explained approximately 25 % of the IIV in the PK of ponesimod. 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author	Lott, Dominik
spellingShingle	Lott, Dominik ddc 610 bkl 44.00 bkl 44.40 misc Multiple Sclerosis misc Psoriasis misc Hepatic Impairment misc Fingolimod misc Severe Hepatic Impairment Impact of Demographics, Organ Impairment, Disease, Formulation, and Food on the Pharmacokinetics of the Selective $ S1P_{1} $ Receptor Modulator Ponesimod Based on 13 Clinical Studies
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topic_title	610 ASE 44.00 bkl 44.40 bkl Impact of Demographics, Organ Impairment, Disease, Formulation, and Food on the Pharmacokinetics of the Selective $ S1P_{1} $ Receptor Modulator Ponesimod Based on 13 Clinical Studies Multiple Sclerosis (dpeaa)DE-He213 Psoriasis (dpeaa)DE-He213 Hepatic Impairment (dpeaa)DE-He213 Fingolimod (dpeaa)DE-He213 Severe Hepatic Impairment (dpeaa)DE-He213
topic	ddc 610 bkl 44.00 bkl 44.40 misc Multiple Sclerosis misc Psoriasis misc Hepatic Impairment misc Fingolimod misc Severe Hepatic Impairment
topic_unstemmed	ddc 610 bkl 44.00 bkl 44.40 misc Multiple Sclerosis misc Psoriasis misc Hepatic Impairment misc Fingolimod misc Severe Hepatic Impairment
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title	Impact of Demographics, Organ Impairment, Disease, Formulation, and Food on the Pharmacokinetics of the Selective $ S1P_{1} $ Receptor Modulator Ponesimod Based on 13 Clinical Studies
ctrlnum	(DE-627)SPR033061572 (SPR)s40262-016-0446-8-e
title_full	Impact of Demographics, Organ Impairment, Disease, Formulation, and Food on the Pharmacokinetics of the Selective $ S1P_{1} $ Receptor Modulator Ponesimod Based on 13 Clinical Studies
author_sort	Lott, Dominik
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author_browse	Lott, Dominik Lehr, Thorsten Dingemanse, Jasper Krause, Andreas
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title_sort	impact of demographics, organ impairment, disease, formulation, and food on the pharmacokinetics of the selective $ s1p_{1} $ receptor modulator ponesimod based on 13 clinical studies
title_auth	Impact of Demographics, Organ Impairment, Disease, Formulation, and Food on the Pharmacokinetics of the Selective $ S1P_{1} $ Receptor Modulator Ponesimod Based on 13 Clinical Studies
abstract	Background Ponesimod is a selective, orally active sphingosine-1-phosphate receptor 1 modulator currently undergoing clinical evaluation for the treatment of multiple sclerosis (MS) in phase III clinical trials. Ponesimod dose-dependently reduces peripheral blood lymphocyte counts by blocking the egress of lymphocytes from lymphoid organs. Methods A population pharmacokinetic (PK) analysis was performed based on pooled data from 13 clinical studies. Interindividual variability (IIV) and the impact of key demographic variables and other covariates on ponesimod exposure were assessed quantitatively. Results A two-compartment model with sequential zero/first-order absorption, including lag time, intercompartmental drug flow, and first-order clearance, adequately described the PK of ponesimod. Body weight, race, MS, psoriasis, hepatic impairment, drug formulation, and food were identified to significantly affect the concentration–time profile. The inclusion of these covariates into the model explained approximately 25 % of the IIV in the PK of ponesimod. Model predictions indicated that the impact of the identified covariates on ponesimod steady-state exposure is within 20 % of exposure, and thus within the margins of the IIV, with the exception of hepatic impairment. Changes up to threefold were predicted for severe cases of liver dysfunction. Conclusion The rich data set enabled building a comprehensive population PK model that accurately predicts the concentration–time data of ponesimod. Covariates other than hepatic impairment were considered not clinically relevant and thus do not require dose adjustment. A potential dose adaptation can be conducted based on the final model.
abstractGer	Background Ponesimod is a selective, orally active sphingosine-1-phosphate receptor 1 modulator currently undergoing clinical evaluation for the treatment of multiple sclerosis (MS) in phase III clinical trials. Ponesimod dose-dependently reduces peripheral blood lymphocyte counts by blocking the egress of lymphocytes from lymphoid organs. Methods A population pharmacokinetic (PK) analysis was performed based on pooled data from 13 clinical studies. Interindividual variability (IIV) and the impact of key demographic variables and other covariates on ponesimod exposure were assessed quantitatively. Results A two-compartment model with sequential zero/first-order absorption, including lag time, intercompartmental drug flow, and first-order clearance, adequately described the PK of ponesimod. Body weight, race, MS, psoriasis, hepatic impairment, drug formulation, and food were identified to significantly affect the concentration–time profile. The inclusion of these covariates into the model explained approximately 25 % of the IIV in the PK of ponesimod. Model predictions indicated that the impact of the identified covariates on ponesimod steady-state exposure is within 20 % of exposure, and thus within the margins of the IIV, with the exception of hepatic impairment. Changes up to threefold were predicted for severe cases of liver dysfunction. Conclusion The rich data set enabled building a comprehensive population PK model that accurately predicts the concentration–time data of ponesimod. Covariates other than hepatic impairment were considered not clinically relevant and thus do not require dose adjustment. A potential dose adaptation can be conducted based on the final model.
abstract_unstemmed	Background Ponesimod is a selective, orally active sphingosine-1-phosphate receptor 1 modulator currently undergoing clinical evaluation for the treatment of multiple sclerosis (MS) in phase III clinical trials. Ponesimod dose-dependently reduces peripheral blood lymphocyte counts by blocking the egress of lymphocytes from lymphoid organs. Methods A population pharmacokinetic (PK) analysis was performed based on pooled data from 13 clinical studies. Interindividual variability (IIV) and the impact of key demographic variables and other covariates on ponesimod exposure were assessed quantitatively. Results A two-compartment model with sequential zero/first-order absorption, including lag time, intercompartmental drug flow, and first-order clearance, adequately described the PK of ponesimod. Body weight, race, MS, psoriasis, hepatic impairment, drug formulation, and food were identified to significantly affect the concentration–time profile. The inclusion of these covariates into the model explained approximately 25 % of the IIV in the PK of ponesimod. Model predictions indicated that the impact of the identified covariates on ponesimod steady-state exposure is within 20 % of exposure, and thus within the margins of the IIV, with the exception of hepatic impairment. Changes up to threefold were predicted for severe cases of liver dysfunction. Conclusion The rich data set enabled building a comprehensive population PK model that accurately predicts the concentration–time data of ponesimod. Covariates other than hepatic impairment were considered not clinically relevant and thus do not require dose adjustment. A potential dose adaptation can be conducted based on the final model.
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container_issue	4
title_short	Impact of Demographics, Organ Impairment, Disease, Formulation, and Food on the Pharmacokinetics of the Selective $ S1P_{1} $ Receptor Modulator Ponesimod Based on 13 Clinical Studies
url	https://dx.doi.org/10.1007/s40262-016-0446-8
remote_bool	true
author2	Lehr, Thorsten Dingemanse, Jasper Krause, Andreas
author2Str	Lehr, Thorsten Dingemanse, Jasper Krause, Andreas
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s40262-016-0446-8
up_date	2024-07-03T16:22:54.849Z
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Ponesimod dose-dependently reduces peripheral blood lymphocyte counts by blocking the egress of lymphocytes from lymphoid organs. Methods A population pharmacokinetic (PK) analysis was performed based on pooled data from 13 clinical studies. Interindividual variability (IIV) and the impact of key demographic variables and other covariates on ponesimod exposure were assessed quantitatively. Results A two-compartment model with sequential zero/first-order absorption, including lag time, intercompartmental drug flow, and first-order clearance, adequately described the PK of ponesimod. Body weight, race, MS, psoriasis, hepatic impairment, drug formulation, and food were identified to significantly affect the concentration–time profile. The inclusion of these covariates into the model explained approximately 25 % of the IIV in the PK of ponesimod. Model predictions indicated that the impact of the identified covariates on ponesimod steady-state exposure is within 20 % of exposure, and thus within the margins of the IIV, with the exception of hepatic impairment. Changes up to threefold were predicted for severe cases of liver dysfunction. Conclusion The rich data set enabled building a comprehensive population PK model that accurately predicts the concentration–time data of ponesimod. Covariates other than hepatic impairment were considered not clinically relevant and thus do not require dose adjustment. 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Impact of Demographics, Organ Impairment, Disease, Formulation, and Food on the Pharmacokinetics of the Selective $ S1P_{1} $ Receptor Modulator Ponesimod Based on 13 Clinical Studies

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