IMI – Oral Biopharmaceutics Tools project – Evaluation of Bottom-up PBPK Prediction Success Part 2: An Introduction to the Simulation Exercise and Overview of Results

Alison Margolskee; Adam Darwich; Leon Aarons; Aleksandra Galetin; Amin Rostami-Hochaghan; Sara Carlert; Maria Hammarberg; Constanze Hilgendorf; Pernilla Johansson; Eva Karlsson; Donal Murphy; Christer Tannergren; Helena Thorn; Mohammed Yasin; Florent Mazuir; Olivier Nicolas; Sergej Ramusovic; Christine Xu; Shriram Pathak; Timo Korjamo; Johanna Laru; Jussi Malkki; Sari Pappinen; Johanna Tuunainen; Jennifer Dressman; Simone Hansmann; Edmund Kostewicz; Handan He; Tycho Heimbach; Fan Wu; Carolin Hoft; Loic Laplanche; Yan Pang; Michael B. Bolger; Eva Huehn; Viera Lukacova; James M. Mullin; Ke X. Szeto; Chester Costales; Jian Lin; Mark McAllister; Sweta Modi; Charles Rotter; Manthena Varma; Mei Wong; Amitava Mitra; Jan Bevernage; Jeike Biewenga; Achiel Van Peer; Richard Lloyd; Carole Shardlow; Peter Langguth; Irina Mishenzon; Mai Anh Nguyen; Jonathan Brown; Hans Lennernas; Bertil Abrahamsson

doi:10.1016/j.ejps.2016.10.036

IMI – Oral Biopharmaceutics Tools project – Evaluation of Bottom-up PBPK Prediction Success Part 2: An Introduction to the Simulation Exercise and Overview of Results

Alison Margolskee, Adam Darwich, Leon Aarons, Aleksandra Galetin, Amin Rostami-Hochaghan, Sara Carlert, Maria Hammarberg, Constanze Hilgendorf, Pernilla Johansson, Eva Karlsson, Donal Murphy, Christer Tannergren, Helena Thorn, Mohammed Yasin, Florent Mazuir, Olivier Nicolas, Sergej Ramusovic, Christine Xu, Shriram Pathak, Timo KorjamoJohanna Laru, Jussi Malkki, Sari Pappinen, Johanna Tuunainen, Jennifer Dressman, Simone Hansmann, Edmund Kostewicz, Handan He, Tycho Heimbach, Fan Wu, Carolin Hoft, Loic Laplanche, Yan Pang, Michael B. Bolger, Eva Huehn, Viera Lukacova, James M. Mullin, Ke X. Szeto, Chester Costales, Jian Lin, Mark McAllister, Sweta Modi, Charles Rotter, Manthena Varma, Mei Wong, Amitava Mitra, Jan Bevernage, Jeike Biewenga, Achiel Van Peer, Richard Lloyd, Carole Shardlow, Peter Langguth, Irina Mishenzon, Mai Anh Nguyen, Jonathan Brown, Hans Lennernas, Bertil Abrahamsson

Pharmacy

Research output: Contribution to journal › Article › peer-review

956 Downloads (Pure)

Abstract

Orally administered drugs are subject to a number of barriers impacting bioavailability (Foral), causing challenges during drug and formulation development. Physiologically-based pharmacokinetic (PBPK) modelling can help during drug and formulation development by providing quantitative predictions through a systems approach. The performance of three available PBPK software packages (GI-Sim, Simcyp®, and GastroPlus™) were evaluated by comparing simulated and observed pharmacokinetic (PK) parameters.

Since the availability of input parameters was heterogeneous and highly variable, caution is required when interpreting the results of this exercise. Additionally, this prospective simulation exercise may not be representative of prospective modelling in industry, as API information was limited to sparse details.

43 active pharmaceutical ingredients (APIs) from the OrBiTo database were selected for the exercise. Over 4000 simulation output files were generated, representing over 2550 study arm-institution-software combinations and approximately 600 human clinical study arms simulated with overlap. 84% of the simulated study arms represented administration of immediate release formulations, 11% prolonged or delayed release, and 5% intravenous (i.v.).

Higher percentages of i.v. predicted area under the curve (AUC) were within two-fold of observed (52.9%) compared to per oral (p.o.) (37.2%), however, Foral and relative AUC (Frel) between p.o. formulations and solutions were generally well predicted (64.7% and 75.0%). Predictive performance declined progressing from i.v. to solution and immediate release tablet, indicating the compounding error with each layer of complexity. Overall performance was comparable to previous large-scale evaluations.

A general overprediction of AUC was observed with average fold error (AFE) of 1.56 over all simulations. AFE ranged from 0.0361 to 64.0 across the 43 APIs, with 25 showing overpredictions. Discrepancies between software packages were observed for a few APIs, the largest being 606, 171, and 81.7-fold differences in AFE between SimCYP and GI-Sim, however average performance was relatively consistent across the three software platforms.

Original language	English
Journal	European Journal of Pharmaceutical Sciences
Early online date	2 Nov 2016
DOIs	https://doi.org/10.1016/j.ejps.2016.10.036
Publication status	Published - 2016

Access to Document

10.1016/j.ejps.2016.10.036

Margolskee 2016 - IMI ORBITO 2Accepted author manuscript, 1.54 MB

Cite this

Margolskee, A., Darwich, A., Aarons, L., Galetin, A., Rostami-Hochaghan, A., Carlert, S., Hammarberg, M., Hilgendorf, C., Johansson, P., Karlsson, E., Murphy, D., Tannergren, C., Thorn, H., Yasin, M., Mazuir, F., Nicolas, O., Ramusovic, S., Xu, C., Pathak, S., ... Abrahamsson, B. (2016). IMI – Oral Biopharmaceutics Tools project – Evaluation of Bottom-up PBPK Prediction Success Part 2: An Introduction to the Simulation Exercise and Overview of Results. European Journal of Pharmaceutical Sciences. https://doi.org/10.1016/j.ejps.2016.10.036

@article{05f2fecf24444a658d2a30fe1a239b63,

title = "IMI – Oral Biopharmaceutics Tools project – Evaluation of Bottom-up PBPK Prediction Success Part 2: An Introduction to the Simulation Exercise and Overview of Results",

abstract = "Orally administered drugs are subject to a number of barriers impacting bioavailability (Foral), causing challenges during drug and formulation development. Physiologically-based pharmacokinetic (PBPK) modelling can help during drug and formulation development by providing quantitative predictions through a systems approach. The performance of three available PBPK software packages (GI-Sim, Simcyp{\textregistered}, and GastroPlus{\texttrademark}) were evaluated by comparing simulated and observed pharmacokinetic (PK) parameters.Since the availability of input parameters was heterogeneous and highly variable, caution is required when interpreting the results of this exercise. Additionally, this prospective simulation exercise may not be representative of prospective modelling in industry, as API information was limited to sparse details.43 active pharmaceutical ingredients (APIs) from the OrBiTo database were selected for the exercise. Over 4000 simulation output files were generated, representing over 2550 study arm-institution-software combinations and approximately 600 human clinical study arms simulated with overlap. 84% of the simulated study arms represented administration of immediate release formulations, 11% prolonged or delayed release, and 5% intravenous (i.v.).Higher percentages of i.v. predicted area under the curve (AUC) were within two-fold of observed (52.9%) compared to per oral (p.o.) (37.2%), however, Foral and relative AUC (Frel) between p.o. formulations and solutions were generally well predicted (64.7% and 75.0%). Predictive performance declined progressing from i.v. to solution and immediate release tablet, indicating the compounding error with each layer of complexity. Overall performance was comparable to previous large-scale evaluations.A general overprediction of AUC was observed with average fold error (AFE) of 1.56 over all simulations. AFE ranged from 0.0361 to 64.0 across the 43 APIs, with 25 showing overpredictions. Discrepancies between software packages were observed for a few APIs, the largest being 606, 171, and 81.7-fold differences in AFE between SimCYP and GI-Sim, however average performance was relatively consistent across the three software platforms.",

author = "Alison Margolskee and Adam Darwich and Leon Aarons and Aleksandra Galetin and Amin Rostami-Hochaghan and Sara Carlert and Maria Hammarberg and Constanze Hilgendorf and Pernilla Johansson and Eva Karlsson and Donal Murphy and Christer Tannergren and Helena Thorn and Mohammed Yasin and Florent Mazuir and Olivier Nicolas and Sergej Ramusovic and Christine Xu and Shriram Pathak and Timo Korjamo and Johanna Laru and Jussi Malkki and Sari Pappinen and Johanna Tuunainen and Jennifer Dressman and Simone Hansmann and Edmund Kostewicz and Handan He and Tycho Heimbach and Fan Wu and Carolin Hoft and Loic Laplanche and Yan Pang and Bolger, {Michael B.} and Eva Huehn and Viera Lukacova and Mullin, {James M.} and Szeto, {Ke X.} and Chester Costales and Jian Lin and Mark McAllister and Sweta Modi and Charles Rotter and Manthena Varma and Mei Wong and Amitava Mitra and Jan Bevernage and Jeike Biewenga and {Van Peer}, Achiel and Richard Lloyd and Carole Shardlow and Peter Langguth and Irina Mishenzon and Nguyen, {Mai Anh} and Jonathan Brown and Hans Lennernas and Bertil Abrahamsson",

year = "2016",

doi = "10.1016/j.ejps.2016.10.036",

language = "English",

journal = "European Journal of Pharmaceutical Sciences",

issn = "0928-0987",

publisher = "Elsevier BV",

}

Margolskee, A, Darwich, A, Aarons, L , Galetin, A , Rostami-Hochaghan, A, Carlert, S, Hammarberg, M, Hilgendorf, C, Johansson, P, Karlsson, E, Murphy, D, Tannergren, C, Thorn, H, Yasin, M, Mazuir, F, Nicolas, O, Ramusovic, S, Xu, C, Pathak, S, Korjamo, T, Laru, J, Malkki, J, Pappinen, S, Tuunainen, J, Dressman, J, Hansmann, S, Kostewicz, E, He, H, Heimbach, T, Wu, F, Hoft, C, Laplanche, L, Pang, Y, Bolger, MB, Huehn, E, Lukacova, V, Mullin, JM, Szeto, KX, Costales, C, Lin, J, McAllister, M, Modi, S, Rotter, C, Varma, M, Wong, M, Mitra, A, Bevernage, J, Biewenga, J, Van Peer, A, Lloyd, R, Shardlow, C, Langguth, P, Mishenzon, I, Nguyen, MA, Brown, J, Lennernas, H & Abrahamsson, B 2016, 'IMI – Oral Biopharmaceutics Tools project – Evaluation of Bottom-up PBPK Prediction Success Part 2: An Introduction to the Simulation Exercise and Overview of Results', European Journal of Pharmaceutical Sciences. https://doi.org/10.1016/j.ejps.2016.10.036

TY - JOUR

T1 - IMI – Oral Biopharmaceutics Tools project – Evaluation of Bottom-up PBPK Prediction Success Part 2: An Introduction to the Simulation Exercise and Overview of Results

AU - Margolskee, Alison

AU - Darwich, Adam

AU - Aarons, Leon

AU - Galetin, Aleksandra

AU - Rostami-Hochaghan, Amin

AU - Carlert, Sara

AU - Hammarberg, Maria

AU - Hilgendorf, Constanze

AU - Johansson, Pernilla

AU - Karlsson, Eva

AU - Murphy, Donal

AU - Tannergren, Christer

AU - Thorn, Helena

AU - Yasin, Mohammed

AU - Mazuir, Florent

AU - Nicolas, Olivier

AU - Ramusovic, Sergej

AU - Xu, Christine

AU - Pathak, Shriram

AU - Korjamo, Timo

AU - Laru, Johanna

AU - Malkki, Jussi

AU - Pappinen, Sari

AU - Tuunainen, Johanna

AU - Dressman, Jennifer

AU - Hansmann, Simone

AU - Kostewicz, Edmund

AU - He, Handan

AU - Heimbach, Tycho

AU - Wu, Fan

AU - Hoft, Carolin

AU - Laplanche, Loic

AU - Pang, Yan

AU - Bolger, Michael B.

AU - Huehn, Eva

AU - Lukacova, Viera

AU - Mullin, James M.

AU - Szeto, Ke X.

AU - Costales, Chester

AU - Lin, Jian

AU - McAllister, Mark

AU - Modi, Sweta

AU - Rotter, Charles

AU - Varma, Manthena

AU - Wong, Mei

AU - Mitra, Amitava

AU - Bevernage, Jan

AU - Biewenga, Jeike

AU - Van Peer, Achiel

AU - Lloyd, Richard

AU - Shardlow, Carole

AU - Langguth, Peter

AU - Mishenzon, Irina

AU - Nguyen, Mai Anh

AU - Brown, Jonathan

AU - Lennernas, Hans

AU - Abrahamsson, Bertil

PY - 2016

Y1 - 2016

N2 - Orally administered drugs are subject to a number of barriers impacting bioavailability (Foral), causing challenges during drug and formulation development. Physiologically-based pharmacokinetic (PBPK) modelling can help during drug and formulation development by providing quantitative predictions through a systems approach. The performance of three available PBPK software packages (GI-Sim, Simcyp®, and GastroPlus™) were evaluated by comparing simulated and observed pharmacokinetic (PK) parameters.Since the availability of input parameters was heterogeneous and highly variable, caution is required when interpreting the results of this exercise. Additionally, this prospective simulation exercise may not be representative of prospective modelling in industry, as API information was limited to sparse details.43 active pharmaceutical ingredients (APIs) from the OrBiTo database were selected for the exercise. Over 4000 simulation output files were generated, representing over 2550 study arm-institution-software combinations and approximately 600 human clinical study arms simulated with overlap. 84% of the simulated study arms represented administration of immediate release formulations, 11% prolonged or delayed release, and 5% intravenous (i.v.).Higher percentages of i.v. predicted area under the curve (AUC) were within two-fold of observed (52.9%) compared to per oral (p.o.) (37.2%), however, Foral and relative AUC (Frel) between p.o. formulations and solutions were generally well predicted (64.7% and 75.0%). Predictive performance declined progressing from i.v. to solution and immediate release tablet, indicating the compounding error with each layer of complexity. Overall performance was comparable to previous large-scale evaluations.A general overprediction of AUC was observed with average fold error (AFE) of 1.56 over all simulations. AFE ranged from 0.0361 to 64.0 across the 43 APIs, with 25 showing overpredictions. Discrepancies between software packages were observed for a few APIs, the largest being 606, 171, and 81.7-fold differences in AFE between SimCYP and GI-Sim, however average performance was relatively consistent across the three software platforms.

AB - Orally administered drugs are subject to a number of barriers impacting bioavailability (Foral), causing challenges during drug and formulation development. Physiologically-based pharmacokinetic (PBPK) modelling can help during drug and formulation development by providing quantitative predictions through a systems approach. The performance of three available PBPK software packages (GI-Sim, Simcyp®, and GastroPlus™) were evaluated by comparing simulated and observed pharmacokinetic (PK) parameters.Since the availability of input parameters was heterogeneous and highly variable, caution is required when interpreting the results of this exercise. Additionally, this prospective simulation exercise may not be representative of prospective modelling in industry, as API information was limited to sparse details.43 active pharmaceutical ingredients (APIs) from the OrBiTo database were selected for the exercise. Over 4000 simulation output files were generated, representing over 2550 study arm-institution-software combinations and approximately 600 human clinical study arms simulated with overlap. 84% of the simulated study arms represented administration of immediate release formulations, 11% prolonged or delayed release, and 5% intravenous (i.v.).Higher percentages of i.v. predicted area under the curve (AUC) were within two-fold of observed (52.9%) compared to per oral (p.o.) (37.2%), however, Foral and relative AUC (Frel) between p.o. formulations and solutions were generally well predicted (64.7% and 75.0%). Predictive performance declined progressing from i.v. to solution and immediate release tablet, indicating the compounding error with each layer of complexity. Overall performance was comparable to previous large-scale evaluations.A general overprediction of AUC was observed with average fold error (AFE) of 1.56 over all simulations. AFE ranged from 0.0361 to 64.0 across the 43 APIs, with 25 showing overpredictions. Discrepancies between software packages were observed for a few APIs, the largest being 606, 171, and 81.7-fold differences in AFE between SimCYP and GI-Sim, however average performance was relatively consistent across the three software platforms.

U2 - 10.1016/j.ejps.2016.10.036

DO - 10.1016/j.ejps.2016.10.036

M3 - Article

SN - 0928-0987

JO - European Journal of Pharmaceutical Sciences

JF - European Journal of Pharmaceutical Sciences

ER -

IMI – Oral Biopharmaceutics Tools project – Evaluation of Bottom-up PBPK Prediction Success Part 2: An Introduction to the Simulation Exercise and Overview of Results

Abstract

Access to Document

Fingerprint

Cite this