Mariana Guimaraes1, Diane Lefaudeux1, Susana Proença1, Stephan Schaller1, Marco Siccardi1, Pavel Balazki1
1EsqLABS GmbH
Introduction Physiologically Based Pharmacokinetic (PBPK) modeling is a powerful tool for predicting drug absorption, distribution, metabolism, and excretion (ADME). Understanding in vivo permeability remains a key step in oral drug absorption modeling. In PBPK models, intestinal permeability can be predicted from physicochemical drug properties, translated from in vitro or in vivo data. The objectives of this work are to leverage a High-Throughput PBPK (HT-PBPK) framework to systematically evaluate and refine an in vitro–in vivo extrapolation (IVIVE) approach for oral permeability. By integrating experimental data from diverse permeability assays with a PBPK model parameterized for human intestinal physiology, we aim to establish a predictive relationship between in vitro measurements and in vivo drug absorption. Methods A dataset of 30 compounds was collected. Compound files were created with data gathered from the literature, including molecular weight, lipophilicity and solubility (1,2,3). A HT-PBPK modeling strategy (R 4.4.1, with OSPsuite-R, OSPSuite.ParameterIdentification packages) was used to fit an optimal PK-SIM® (version 11) input permeability (i.e., Specific transcellular permeability (Pint)). In vivo human effective permeability (Peff) and fraction absorbed (Fabs) values were obtained from the literature (4), and in vitro apparent permeability (Papp) values were sourced from Caco-2 studies (pH 6.5 and pH 7.4) (5). Correlations were developed between Pint and Peff, and Pint and Papp. The correlations were tested for internal validation by comparing each correlations’ predicted fraction absorbed into mucosa at 4h and at 24h against measured Fabs. The predictions of fraction absorbed to the mucosa were performed with solubility collected from the literature and assuming that the formulations behave as solutions (solubility was artificially increased to 10000000 g/L). The correlations achieved were classified as poor R2 <0.5 poor, R2 0.5-0.8 medium, and R2 >0.8 optimal. Results An initial evaluation was performed by running the HT-PBPK framework and comparing the default prediction accuracy of the calculated permeability by PK-SIM® Pint,default for the 30 compounds (6). A poor correlation (R2 = 0.5) was achieved for observed vs predicted fraction Fabs, with an overall trend for underprediction of observed Fabs for the investigated dataset. An empirical correlation was achieved between Pint,default and Pint,fitted, as well as between Pint,fitted and Peff and Papp. An optimal correlation was achieved between Pint,default and Pint,fitted. Correlation between Pint,fitted and Peff and Papp ranged from medium to optimal. Initial assessment showed that internal predictability of Fabs improved compared to default predictions. Results revealed that for some compounds, e.g. Verapamil, input solubility showed a clear impact on the fraction absorbed even when a ‘dissolved’ formulation is used. Additionally, it was noted that steep differences (>10%) were observed between predicted fraction absorbed at 4h and 24h, with a trend to overestimate the role of colonic absorption. It is therefore recommended that colonic absorption is evaluated during model development on a case-by-case basis as previously suggested in the literature. Conclusion Our findings highlight the advantages of HT-PBPK in rapidly assessing multiple compounds. As further datasets in Caco-2 or other permeability inputs such as MCDK cell lines or PAMPA assays are gathered, the expansion of the applicability domain can be quickly achieved. This approach enhances the reliability of PBPK models for biopharmaceutics risk assessment, formulation optimization, and regulatory decision-making, ultimately supporting more efficient drug development.
[1] Varma et al. J Med Chem. 2010;53(3):1098-108. doi: 10.1021/jm901371v [2] www.drugback.com [3] Avdeef et al. J Med Chem. 2010 May 13;53(9):3566-84. doi: 10.1021/jm901846t [4] Lennernäs H. 2007 ;37(10-11):1015-51. doi: 10.1080/00498250701704819. PMID: 17968735. [5] Sun et al. Pharm Res. 2002;19(10):1400-16. doi: 10.1023/a:1020483911355 [6] Thelen et al. J Pharm Sci. 2011;100(12):5324-45. doi: 10.1002/jps.22726
Reference: PAGE 33 (2025) Abstr 11459 [www.page-meeting.org/?abstract=11459]
Poster: Drug/Disease Modelling - Absorption & PBPK