Areti-Maria Vasilogianni 1, Constantinos Demonacos 2, Sheila Annie Peters 3,4, Jill Barber 1, Amin Rostami-Hodjegan 1,5
1 Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester (Manchester, United Kingdom), 2 Division of Pharmacy and Optometry, Faculty of Biology Medicine and Health, School of Health Science, University of Manchester (Manchester, United Kingdom), 3 Translational Quantitative Pharmacology, BioPharma, R&D Global Early Development, Merck KGaA (Darmstadt, Germany), 4 Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim (Ingelheim am Rhein, Germany), 5 Certara Predictive Technologies (CPT), Simcyp Division (Sheffield, United Kingdom)
Objectives
Small intestine cancer is rare, with a steady rise over the last decades. Because of its rarity, clinical trials are limited. On the other hand, colorectal cancer is the third most common type of cancer, with half of patients having liver metastasis. Cancer is not a unique disease and clinical trials in oncology initially recruit heterogeneous populations, without covering all types of variability. The population may not be representative, leading to variability in pharmacokinetics (PK). Physiologically-based pharmacokinetic (PBPK) models can be used as an alternative to clinical studies for dosing guidance. These models require cancer-specific systems parameters for each type of cancer, which are scarce. Example of such parameters is the abundance of drug-metabolising enzymes (DMEs) and transporters [1]. Despite the important role of the gastrointestinal tract in the PK of drugs, the abundance of important PK proteins and their alterations in small intestine and colon tumours have not been studied. This study aimed to investigate, for first time, the impact of small intestine and colon cancer on the protein abundance levels of DMEs and transporters. Subsequently, these data, in addition to other cancer-specific parameters [2,3,4], were used, for first time, to create populations for small intestine and colon cancer and applied in PBPK simulations, in order to predict PK of a wide range of drugs in these patients.
Methods
Liquid chromatography–mass spectrometry proteomics was used to quantify PK proteins in healthy, non-tumour peri-carcinomatous, and tumour small intestine and colon tissues. Their differential expression, in addition to other cancer-specific physiological parameters (hepatic expression of DMEs, microsomal protein per gram of liver, haematocrit, albumin etc.), were used to create cancer sub-populations in Simcyp. PBPK simulations were performed to assess the contribution of altered abundance of DMEs and transporters to changes in PK. The drugs in these simulations had different attributes and extraction ratios. To assess implications for PK in small intestine cancer patients, PBPK simulations were performed using the default healthy and cancer populations in Simcyp, as well as 4 new populations (different scenarios for the physiology of small intestine and liver) that were created based on the proteomics data. Simulations were performed for 68 substrates (anticancer and non-anticancer). In colon cancer, 6 new populations (different scenarios for the physiology of colon and liver) were created. Simulations were performed for budesonide previously built in Simcyp [5].
Results
DMEs and transporters were differentially expressed and their proportion varied across different groups of samples, with potential implications for drug-drug interactions. Changes in the expression of DMEs and ABC transporters in small intestine cancer tissues were incorporated for the creation of new cancer populations. PBPK models of drugs with different attributes showed differences in PK (drug clearance etc.) with cancer-specific parameters compared with default parameters. In the scenario where the whole small intestine was considered cancerous, the clearance of 4 substrates was >2-fold lower compared to the default cancer population. In the worst-case scenario, with liver being non-healthy in addition to the whole small intestine being cancerous, 19 drugs showed 2-8.4-fold decreased clearance compared to the default cancer population. In colon cancer tissues, UGTs were significantly decreased, but interestingly, CYP3A4 increased by more than 2-fold compared with healthy controls. However, P-gp and BCRP were similarly expressed. These changes, in addition to hepatic changes and other cancer-specific parameters, were incorporated in different cancer sub-populations to predict PK of budesonide. The AUC of budesonide varied across the different models created to capture PK in colon cancer patients, especially when the liver was considered as non-healthy. The luminal concentration was not affected in contrast to the enterocyte concentration (differential levels across the models).
Conclusions
Overall, this study highlights cancer-driven changes in drug metabolism and transport in small intestine and colon cancer. It also demonstrates the importance of the incorporation of population-specific abundance of PK proteins in PBPK models for the prediction of PK of non-anticancer and anticancer drugs in different groups of cancer patients. The values reported here should enable updating systems parameters within existing PBPK platforms in relation to abundance of DMEs and transporters to reflect biological values. Verification with observed clinical PK data creates the basis for what has been coined as “master files” to be used with different new drugs substrates.
References:
[1] Vasilogianni, A.-M., Achour, B., Al-Majdoub, Z. M., Peters, S. A., Barber, J., & Rostami-Hodjegan, A. (2025). The quest to define cancer-specific systems parameters for personalized dosing in oncology. Expert Opinion on Drug Metabolism & Toxicology, 21(5), 599–615. https://doi.org/10.1080/17425255.2025.2476560
[2] Vasilogianni, A.-M., Achour, B., Scotcher, D., Peters, S. A., Al-Majdoub, Z. M., Barber, J., & Rostami-Hodjegan, A. (2021). Hepatic Scaling Factors for In Vitro–In Vivo Extrapolation of Metabolic Drug Clearance in Patients with Colorectal Cancer with Liver Metastasis. Drug Metabolism and Disposition, 49(7), 563–571. https://doi.org/10.1124/dmd.121.000359
[3] Vasilogianni, A.-M., Al‐Majdoub, Z. M., Achour, B., Peters, S. A., Rostami‐Hodjegan, A., & Barber, J. (2021). Proteomics of Colorectal Cancer Liver Metastasis: a Quantitative Focus on Drug Elimination and Pharmacodynamics Effects. British Journal of Clinical Pharmacology, 88(4):1811–1823. https://doi.org/10.1111/bcp.15098
[4] Vasilogianni, A.-M., Al‐Majdoub, Z. M., Achour, B., Peters, S. A., Barber, J., & Rostami‐Hodjegan, A. (2022). Quantitative Proteomics of Hepatic Drug‐Metabolizing Enzymes and Transporters in Patients with Colorectal Cancer Metastasis. Clinical Pharmacology & Therapeutics, 112(3):699–710. https://doi.org/10.1002/cpt.2633
[5] Han, C., Sun, T., Chirumamilla, S. K., Bois, F. Y., Xu, M., & Rostami-Hodjegan, A. (2023). Understanding Discordance between In Vitro Dissolution, Local Gut and Systemic Bioequivalence of Budesonide in Healthy and Crohn’s Disease Patients through PBPK Modeling. Pharmaceutics, 15(9), 2237. https://doi.org/10.3390/pharmaceutics15092237
Reference: PAGE 34 (2026) Abstr 11953 [www.page-meeting.org/?abstract=11953]
Poster: Drug/Disease Modelling - Oncology