Henrik Cordes* & Hermann Rapp"
*Sanofi-Aventis Deutschland GmbH, R&D, Frankfurt, Germany ; " Boehringer Ingelheim Pharma GmbH & Co. KG, DDS, Biberach an der Riß, Germany
Introduction: During drug discovery and development, understanding and describing the mechanistic drivers of pharmacokinetics (PK) for new molecular entity is crucial to enable human PK and clinical dose estimations. Here, the identification of absorption, distribution, metabolism, and excretion (ADME) processes is a first step in drug discovery to set the foundation for a mechanistic PK understanding that drive drug-target and drug-body interactions. Sufficient knowledge on ADME processes enables inter-species and human PK estimations beyond simple allometric scaling of PK parameters [1]. The widely used physiologically based pharmacokinetic (PBPK) modeling framework explicitly considers ADME processes in their respective organs, that in their combination, are driving the observed plasma PK [2]. Here, gene expression information, when integrated into PBPK models is used as surrogate for protein abundance and activity [3]. Notably, the absolute and relative expression of ADME genes and their orthologs counterparts in non-human species, can significantly differ between species or even strains affecting both, the pharmacokinetics behavior, and the pharmacodynamics effects. Accounting for these differences reduces the uncertainty inherent to inter-species extrapolations, especially from the pre-clinical into the human or patient situation. Objectives: – Enable user friendly integration of gene expression information into PBPK modeling workflow – Provide a community standard for ADME and target gene expression to increase model comparability based on curated healthy normal data – Establish an automated workflow that enables a reproducible generation of whole-body gene expression databases for pre-clinical, animal health species and humans Methods: Cran R programing / scripting with major libraries: – biomaRt [4] was used for adding gene naming, IDs, annotations, and mapping of orthologs to human genes, – BgeeDB [5] was used to access the gene expression values, special (tissue) sample location, and sample ontology (development state and/or age) – RSQLite [6] was used to store the annotated and reformatted data into the final species-specific database Results: We established an automated workflow that generates whole body gene expression databases for 17 species including humans, relevant for drug development, animal health, nutritional sciences, and toxicology. Exclusively, RNA-Seq data from healthy, normal, and untreated primary tissue samples was used to provide a comparable reference of normal gene expression as basis for ADME and target gene expression. The databases are interoperable with the popular open-source software PK-Sim [7]. Conclusions: By providing a seamless access to a central source containing curated, healthy, and normal gene expression data, we seek to provide a community standard for ADME and target gene expression information to increase comparability and interoperability between published PBPK models, and to increasing the transparency about used data with respect to regulatory bodies.
References:
1. Kuepfer, L. Prospects and limitations of physiologically-based pharmacokinetic modelling for cross-species extrapolation. Svu-international J Vet Sci 2, 45–51 (2019).
2. Kuepfer, L. et al. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model. Cpt Pharmacometrics Syst Pharmacol 5, 516–531 (2016).
3. Meyer, M., Schneckener, S., Ludewig, B., Kuepfer, L. & Lippert, J. Using expression data for quantification of active processes in physiologically based pharmacokinetic modeling. Drug Metab Dispos 40, 892–901 (2012).
4. Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc 4, 1184–1191 (2009).
5. Bastian, F. B. et al. The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals. Nucleic Acids Res 49, gkaa793- (2020).
6. Müller K, Wickham H, James DA, Falcon S (2022). RSQLite: SQLite Interface for R. https://rsqlite.r-dbi.org, https://github.com/r-dbi/RSQLite.
7. Lippert, J. et al. Open Systems Pharmacology Community—An Open Access, Open Source, Open Science Approach to Modeling and Simulation in Pharmaceutical Sciences. Cpt Pharmacometrics Syst Pharmacol 8, 878–882 (2019).
Reference: PAGE 30 (2022) Abstr 10116 [www.page-meeting.org/?abstract=10116]
Poster: Methodology - Other topics