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      Vasilogianni, Areti-Maria; orcid: 0000-0001-6665-6115; Achour, Brahim; orcid: 0000-0002-2595-5626; Scotcher, Daniel; orcid: 0000-0001-9144-3824; Peters, Sheila Annie; Al-Majdoub, Zubida M; orcid: 0000-0002-1497-3140; Barber, Jill; orcid: 0000-0002-5424-0291; Rostami-Hodjegan, Amin; orcid: 0000-0003-3917-844X; email: amin.rostami@manchester.ac.uk (2021-05-12)
      linked with physiologically based pharmacokinetic (PBPK) modelling is used to predict the fates of drugs in patients. Ideally, the IVIVE-PBPK models should incorporate "systems" information accounting for characteristics of the specific target population. There is a paucity of such scaling factors in cancer, particularly microsomal protein per gram of liver (MPPGL) and cytosolic protein per gram of liver (CPPGL). In this study, cancerous and histologically normal liver tissue from 16 patients with colorectal liver metastasis (CRLM) were fractionated to microsomes and cytosol. Protein content was measured in homogenates, microsomes and cytosol. The loss of microsomal protein during fractionation was accounted for using corrections based on NADPH cytochrome P450 reductase activity in different matrices. MPPGL was significantly lower in cancerous tissue (24.8 {plus minus} 9.8 mg/g) than histologically normal tissue (39.0 {plus minus} 13.8 mg/g). CPPGL in cancerous tissue was 42.1 {plus minus} 12.9 mg/g compared with 56.2 {plus minus} 16.9 mg/g in normal tissue. No correlations between demographics (sex, age and BMI) and MPPGL or CPPGL were apparent in the data. The generated scaling factors together with assumptions regarding the relative volumes of cancerous versus non-cancerous tissue were used to simulate plasma exposure of drugs with different extraction ratios. The PBPK simulations revealed a substantial difference in drug exposure (AUC), up to 3.3-fold, when using typical scaling factors (healthy population) instead of disease-related parameters in cancer population. These indicate the importance of using population-specific scalars in IVIVE-PBPK for different disease states. Accuracy in predicting the fate of drugs from in vitro data using IVIVE-PBPK depends on using correct scaling factors. The values for two of such scalars, namely microsomal and cytosolic protein per gram of liver, is not known in cancer patients. This study presents, for the first time, scaling factors from cancerous and matched histologically normal livers. PBPK simulations of various metabolically cleared drugs demonstrate the necessity of population-specific scaling for model-informed precision dosing in oncology. [Abstract copyright: Copyright © 2020 American Society for Pharmacology and Experimental Therapeutics.]
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      El-Khateeb, Eman; orcid: 0000-0002-8365-6528; email: eman.elkhateeb@manchester.ac.uk; Al-Majdoub, Zubida M; orcid: 0000-0002-1497-3140; Rostami-Hodjegan, Amin; orcid: 0000-0003-3917-844X; Barber, Jill; orcid: 0000-0002-5424-0291; Achour, Brahim; orcid: 0000-0002-2595-5626 (2021-05-27)
      Model-based assessment of the effects of liver disease on drug pharmacokinetics requires quantification of changes in enzymes and transporters responsible for drug metabolism and disposition. Different proteomic methods are currently used for protein quantification in tissues and systems, each with specific procedures and requirements. The outcome of quantitative proteomic assays from four different methods (one targeted and three label-free), applied to the same sample set, were compared in this study. Three pooled cirrhotic liver microsomal samples, corresponding to cirrhosis with non-alcoholic fatty liver disease, biliary disease or cancer, and a control microsomal pool, were analyzed using QconCAT-based targeted proteomics, the total protein approach (TPA), high three (Hi3) ion intensity approach, and intensity-based absolute quantification (iBAQ), to determine the absolute and relative abundance in disease compared with control. The relative abundance data provided a 'disease perturbation factor' (DPF) for each target protein. Absolute and relative abundances generated by standard-based label-free methods (iBAQ and Hi3) showed good agreement with targeted proteomics (limited bias and scatter) but TPA (standard-free method) over-estimated absolute abundances by approximately 2 fold. DPF was consistent between different proteomic methods but varied between enzymes and transporters, indicating discordance of effects of cirrhosis on various ADME proteins. DPF ranged from no change (e.g. for UGT1A6 in NAFLD group) to less than 0.3 (e.g. CES1 in cirrhosis of biliary origin). This study demonstrated that relative changes in enzymes and transporters (DPF) are independent of the quantitative proteomic methods used. Standard-based label-free methods such as high three ion intensity (Hi3) and intensity-based absolute quantification (iBAQ) methods, were less biased and more precise than the total protein approach (TPA), when compared with targeted data. The DPF reconciled differences across proteomic methods observed with absolute levels. Using this approach, differences were revealed in the expression of enzymes/transporters in cirrhosis associated with different etiologies. [Abstract copyright: Copyright © 2020 American Society for Pharmacology and Experimental Therapeutics.]
    • Review article: time to revisit Child‐Pugh score as the basis for predicting drug clearance in hepatic impairment

      El‐Khateeb, Eman; orcid: 0000-0002-8365-6528; Darwich, Adam S.; orcid: 0000-0001-8218-4306; Achour, Brahim; orcid: 0000-0002-2595-5626; Athwal, Varinder; orcid: 0000-0002-1684-721X; email: varinder.athwal@manchester.ac.uk; Rostami‐Hodjegan, Amin; orcid: 0000-0003-3917-844X; email: amin.rostami@manchester.ac.uk (2021-07-04)
      Summary: Background: Prescription information for many drugs entering the market lacks dosage guidance for hepatic impairment. Dedicated studies for assessing the fate of drugs in hepatic impairment commonly stratify patients using Child‐Pugh score. Child‐Pugh is a prognostic clinical score with limitations in reflecting the liver's metabolic capacity. Aims: To demonstrate the need for better drug dosing approaches in hepatic impairment, summarise the current status, identify knowledge gaps related to drug kinetic parameters in hepatic impairment, propose solutions for predicting the liver disease impact on drug exposure and discuss barriers to dosing guidance in those patients. Methods: Relevant reports on dosage adjustment in hepatic impairment were analysed concerning the prediction of the impairment impact on drug kinetics using physiologically‐based pharmacokinetic (PBPK) modelling. Results: PBPK models are suggested as a potential framework to understand drug clearance changes in hepatic impairment. Quantifying changes in abundance and activity of drug‐metabolising enzymes and transporters, understanding the impact of shunting, and accounting for interindividual variations in drug absorption could help in extending the success of these models in hepatically‐impaired populations. These variables might not correlate with Child‐Pugh score as a whole. Therefore, new metabolic activity markers, imaging techniques and other scoring systems are proposed to either support or substitute Child‐Pugh score. Conclusions: Many physiological changes in hepatic impairment determining the fate of drugs do not necessarily correlate with Child‐Pugh score. Quantifying these changes in individual patients is essential in future hepatic impairment studies. Further studies assessing Child‐Pugh alternatives are recommended to allow better prediction of drug exposure.