Application of proteomic data in the translation of in vitro observations to associated clinical outcomes

Sibylle Neuhoff; Matthew Harwood; Amin Rostami-Hodjegan; Brahim Achour

doi:10.1016/j.ddtec.2021.06.002

Application of proteomic data in the translation of in vitro observations to associated clinical outcomes

Sibylle Neuhoff, Matthew Harwood, Amin Rostami-Hodjegan, Brahim Achour

Pharmacy

Simcyp Ltd (A Certara Company)

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

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Abstract

Translation of information on drug exposure and effect is facilitated by in silico models that enable extrapolation of in vitro measurements to in vivo clinical outcomes. These models integrate drug-specific data with information describing physiological processes and pathological changes, including alterations to proteins involved in drug absorption, distribution and elimination. Over the past 15 years, quantitative proteomics has contributed a wealth of protein expression data, which are currently used for a variety of systems pharmacology applications, as a complement or a surrogate for activity of the corresponding proteins. In this review, we explore current and emerging applications of targeted and global (untargeted) proteomics in translational pharmacology as well as strategies for improved integration into model-based drug development.

Original language	English
Pages (from-to)	13-22
Number of pages	10
Journal	Drug Discovery Today: Technologies
Volume	39
Early online date	25 Jun 2021
DOIs	https://doi.org/10.1016/j.ddtec.2021.06.002
Publication status	E-pub ahead of print - 25 Jun 2021

Keywords

In vitro-in vivo extrapolation
Physiologically-based pharmacokinetics
Quantitative proteomics
Translational pharmacology

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10.1016/j.ddtec.2021.06.002

Neuhoff_etal_2021_Drug_Discovery_Today_Technologies_AcceptedAccepted author manuscript, 951 KB

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title = "Application of proteomic data in the translation of in vitro observations to associated clinical outcomes",

abstract = "Translation of information on drug exposure and effect is facilitated by in silico models that enable extrapolation of in vitro measurements to in vivo clinical outcomes. These models integrate drug-specific data with information describing physiological processes and pathological changes, including alterations to proteins involved in drug absorption, distribution and elimination. Over the past 15 years, quantitative proteomics has contributed a wealth of protein expression data, which are currently used for a variety of systems pharmacology applications, as a complement or a surrogate for activity of the corresponding proteins. In this review, we explore current and emerging applications of targeted and global (untargeted) proteomics in translational pharmacology as well as strategies for improved integration into model-based drug development.",

keywords = "In vitro-in vivo extrapolation, Physiologically-based pharmacokinetics, Quantitative proteomics, Translational pharmacology",

author = "Sibylle Neuhoff and Matthew Harwood and Amin Rostami-Hodjegan and Brahim Achour",

note = "Funding Information: The authors would like to thank Dr Ramakrishna Rachumallu (Certara UK Limited, Simcyp Division, Sheffield, UK) and Dr Bhagwat Prasad (Washington State University, Spokane, USA) for assistance with collating data on IVIVE scalars. A.R.-H. and B.A. are grateful for support from the Centre for Applied Pharmacokinetic Research (CAPKR) consortium (Eli Lilly, Takeda, Genentech, AbbVie, Merck, MSD, GSK and Janssen). Publisher Copyright: {\textcopyright} 2021 The Authors",

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AU - Neuhoff, Sibylle

AU - Harwood, Matthew

AU - Rostami-Hodjegan, Amin

AU - Achour, Brahim

N1 - Funding Information: The authors would like to thank Dr Ramakrishna Rachumallu (Certara UK Limited, Simcyp Division, Sheffield, UK) and Dr Bhagwat Prasad (Washington State University, Spokane, USA) for assistance with collating data on IVIVE scalars. A.R.-H. and B.A. are grateful for support from the Centre for Applied Pharmacokinetic Research (CAPKR) consortium (Eli Lilly, Takeda, Genentech, AbbVie, Merck, MSD, GSK and Janssen). Publisher Copyright: © 2021 The Authors

PY - 2021/6/25

Y1 - 2021/6/25

N2 - Translation of information on drug exposure and effect is facilitated by in silico models that enable extrapolation of in vitro measurements to in vivo clinical outcomes. These models integrate drug-specific data with information describing physiological processes and pathological changes, including alterations to proteins involved in drug absorption, distribution and elimination. Over the past 15 years, quantitative proteomics has contributed a wealth of protein expression data, which are currently used for a variety of systems pharmacology applications, as a complement or a surrogate for activity of the corresponding proteins. In this review, we explore current and emerging applications of targeted and global (untargeted) proteomics in translational pharmacology as well as strategies for improved integration into model-based drug development.

AB - Translation of information on drug exposure and effect is facilitated by in silico models that enable extrapolation of in vitro measurements to in vivo clinical outcomes. These models integrate drug-specific data with information describing physiological processes and pathological changes, including alterations to proteins involved in drug absorption, distribution and elimination. Over the past 15 years, quantitative proteomics has contributed a wealth of protein expression data, which are currently used for a variety of systems pharmacology applications, as a complement or a surrogate for activity of the corresponding proteins. In this review, we explore current and emerging applications of targeted and global (untargeted) proteomics in translational pharmacology as well as strategies for improved integration into model-based drug development.

KW - In vitro-in vivo extrapolation

KW - Physiologically-based pharmacokinetics

KW - Quantitative proteomics

KW - Translational pharmacology

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SP - 13

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JO - Drug Discovery Today: Technologies

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ER -

Application of proteomic data in the translation of in vitro observations to associated clinical outcomes

Abstract

Keywords

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