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Advanced Animal Model of Colorectal Metastasis in Liver: Imaging Techniques and Properties of Metastatic Clones
Published on: November 30, 2016
Proteomics-Informed Physiologically Based Pharmacokinetic Modeling for Aldehyde Oxidase Substrates in Colorectal
Nihan Izat1, Areti-Maria Vasilogianni1, Zubida M Al-Majdoub1
1Centre for Applied Pharmacokinetic Research, School of Health Sciences, The University of Manchester, Stopford Building, Oxford Road, Manchester M13 9PT, United Kingdom.
Abstract:
Aldehyde oxidase (AO) metabolizes a broad range of compounds, including the oxidation of anticancer kinase inhibitors. Quantitative proteomics data reported in the literature for liver microsomes from colorectal liver metastases (CRLM) indicate decreased expression of various drug-metabolizing enzymes and transporters compared to the healthy liver, although information on AO remains limited. This study aimed to investigate, for the first time, potential alterations in AO abundance in CRLM patients and prospectively predict the pharmacokinetics of six AO/dual AO-CYP substrates in CRLM patients via physiologically-based pharmacokinetic (PBPK) models. Global proteomic analysis (using high-N approach) was performed on human liver cytosols (HLCs) and microsomes (HLMs) from 16 cancerous and matched histologically normal (adjacent to the tumor) liver tissues from CRLM patients. Commercial fractions from healthy donors were analyzed as controls. After the application of physiological scaling factors (cytosolic or microsomal protein per gram of liver), a strong rank correlation (rs = 0.86, n = 24) was observed in AO abundance between donor-matching HLC and HLM samples from CRLM patients. Interindividual variability was the highest within cancerous livers (CV = 95%; n = 9). The total hepatic AO abundance (nmol/g liver) was 4- and 6.9-fold lower in cancerous tissues compared to those in histologically normal and healthy tissues, respectively. Hepatic CYP3A4 and AO abundance correlated well (rs = 0.76, n = 22) across patient samples. A substrate-specific decrease in drug clearance was predicted for all six compounds in patients with CRLM with the proteomics-informed PBPK models. The implications of varying levels of tumor burden were explored in the developed PBPK models to enable prospective PK predictions for AO substrates in the CRLM cancer population.
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