Materials and Chemicals.

All chemicals were purchased from Sigma-Aldrich (Poole, Dorset, UK) unless otherwise stated. EDTA-free protease inhibitor cocktail was obtained from Roche Applied Sciences (Mannheim, Germany).

Liver Samples.

Matched cancerous and histologically normal liver tissue specimens from 16 adult patients with CRLM were obtained opportunistically after hepatectomy from the Manchester University National Health Service Foundation Trust (MFT) Biobank, Manchester, UK. The study was covered under the MFT Biobank generic ethics approval (NRES 14/NW/1260 and 19/NW/0644). Among the 16 donors, seven were female and nine were male, and their ages ranged from 34 to 85 years. The body mass index (BMI) of the patients ranged from 21.6 to 36.3 kg/m². Supplemental Table 1 presents demographic and clinical details of the donors.

Preparation of Human Liver Microsomal and Cytosolic Fractions.

Microsomal and cytosolic fractions were generated from liver tissue using differential centrifugation as previously described (Achour et al., 2017). Liver tissue was homogenized by a mechanical homogenizer (Thermo Fisher Scientific, UK) in homogenization buffer (150 mM KCl, 2 mM EDTA, 50 mM Tris, 1 mM dithiothreitol, and EDTA-free protease inhibitor cocktail, pH 7.4) at 10 ml for each gram of liver tissue. The homogenate was centrifuged at 10,000 g for 20 minutes at 4°C using an Optima L-100 ultracentrifuge (Beckman Coulter, Fullerton, CA). The first pellet (cell debris) was stored at −80°C, and then the supernatant was further centrifuged at 100,000 g for 75 minutes at 4°C. The cytosol (the supernatant) was stored at −80°C, and the pellet (microsomes) was resuspended in 1 ml of storage buffer (0.25 M potassium phosphate, pH 7.25) per gram of liver tissue and stored at −80°C.

Measurement of Total Protein Content in Homogenates and Fractions.

The protein content of liver homogenates, microsomes, and cytosolic samples was measured using bicinchoninic acid protein assay (Pierce Microplate BCA protein assay Kit – Reducing Agent Compatible) in triplicate. Absorbance was measured at 562 nm using a SpectraMax 190 platereader (Molecular Devices, Sunnyvale, CA) with bovine serum albumin used as calibration standard. For the homogenates and the cytosolic samples that contained dithiothreitol, a compatibility reagent solution was used. The CPPGL and the homogenate protein per gram of liver (HomPPGL) were calculated based on the total protein content, and no further correction for loss was required.

Measurement of NADPH Cytochrome P450 Reductase Activity.

In the current study, NADPH P450 reductase activity was used to account for microsomal membrane loss during fractionation. The activity of NADPH P450 reductase was measured in homogenates and microsomes from the same liver samples to estimate loss of microsomal protein during fractionation. The protocol was adapted from methods by Guengerich et al. (2009) and Achour et al. (2011). In a 1-ml cuvette, oxidized equine cytochrome c (0.5 mM, 80 µL) was mixed with potassium phosphate buffer (0.25 M, 980 µl, pH 7.25), KCN (1 mM, 10 µl), and homogenates (10 µl, equivalent to 1 mg of tissue) or 1:10 diluted microsomes (10 µl, equivalent to 1 mg of tissue). The absorbance of the mixtures was measured using a Jenway 7315 UV-visible spectrophotometer (Thermo Fisher Scientific) at 550 nm in kinetic mode. The absorbance was monitored for 2 minutes to establish the baseline, followed by addition of reduced NADPH solution (10 mM, 10 µl) to start the reaction of cytochrome c reduction, which was monitored for 5 minutes.

The slope of the initial linear phase of the reaction is directly proportional to the amount of cytochrome P450 reductase in the sample. The enzyme activity (units/mg liver tissue) was calculated using eq. 1, and fractional loss of microsomal protein was estimated based on the ratio of activity in microsomes relative to the homogenate from the same liver sample (eq. 2) using the ratio of the slope from the microsomal fraction (1 mg of tissue) to the slope from the homogenate (1 mg of total protein) for each individual. The ratios also allowed calculation of microsomal membrane enrichment. MPPGL for each sample was corrected using the recovery factors according to eq. 3 (Barter et al., 2008). Recovery factor is equal to 1-fractional loss of microsomal protein. where ΔA550/min represents the rate of change in the absorbance at 550 nm, dil represents the dilution factor of the original enzyme sample, total volume is the volume of the reaction mixture (ml), 21.1 is the extinction coefficient for reduced cytochrome c (mM⁻¹ cm⁻¹), and V represents the volume of the enzyme sample (ml) corresponding to 1 mg of liver tissue.

Statistical Data Analysis.

Statistical data analysis was performed, and graphs were generated using GraphPad Prism 8.1.2 (La Jolla, CA). The data are presented as mean ± S.D. CV was used to describe variability in datasets, and the Kolmogorov-Smirnov test was used to assess the normality of distribution of the datasets. Several datasets did not follow normal distribution, and therefore nonparametric statistical tests for differences were used. Differences in HomPPGL, uncorrected MPPGL, and CPPGL values between histologically normal and matched cancerous tissues were assessed using Wilcoxon test. Differences in MPPGL values between histologically normal and matched cancerous tissues were assessed using Mann-Whitney test. This test was also used for the assessment of the effect of hepatic lobe of origin and sex of donors on MPPGL and CPPGL in normal and cancerous tissues. For the assessment of the effect of BMI and age on MPPGL and CPPGL, Spearman correlation and linear regression analysis were used. In each of the above cases, the probability cut-off value for statistical significance was set at 0.05.

Physiologically Based Pharmacokinetic Simulations.

The effect of using the generated scaling factors in a cancer population was assessed using PBPK modeling on Simcyp V18 Release 1 (Certara, Sheffield, UK) in healthy and cancer populations. For the assessment of effects of MPPGL changes on simulated plasma drug exposure, four cytochrome P450 substrates with different hepatic extraction ratios (see Table 1) were used: alfentanil (predominantly metabolized by CYP3A4), alprazolam (predominantly metabolized by CYP3A4 and CYP3A5), midazolam (predominantly metabolized by CYP3A4 and CYP3A5), and desipramine (predominantly metabolized by CYP2D6). P450 isoforms are responsible for the metabolism of the majority of clinically used drugs in all fields of treatment (anticancer and non–anti-cancer drugs), with CYP3A4/5 being the most prevalent, followed by CYP2D6. For this reason, we used CYP3A4, CYP3A5, and CYP2D6 enzymes for our simulations. The compound files were not changed from those provided within the Simcyp simulator. PBPK simulations were performed using system parameters already available on the simulator for healthy (“Sim-Healthy Volunteers”) and cancer (“Sim-Cancer”) virtual populations, without or with inclusion of MPPGL data measured in current study. The effects of MPPGL changes in cancer on drug exposure after oral administration were assessed using four different MPPGL models:

MPPGL model 1 (Healthy; healthy population): the default MPPGL in Simcyp was used for the healthy population; mean MPPGL was 39.8 mg/g tissue (defined by the Simcyp model), (eq. 4, Barter et al., 2008).

For eq. 4, the CV% is 26.9.

MPPGL model 2 (Cancer-D; cancer-default population): the default MPPGL in Simcyp was used for the cancer population; mean MPPGL was 39.8 mg/g tissue (defined by the Simcyp model), (eq. 4, Barter et al., 2008).

These two models were used to assess any effects on drug exposure between healthy and cancer populations without changing MPPGL values. The key differences in the systems parameters between Healthy and Cancer-D involve age distribution, hematocrit, α1-acid glycoprotein, and albumin levels.

MPPGL model 3 (New Cancer-ALN; new cancer population-assuming liver is obtained from patients with cancer, but entire liver tissue is histologically normal, therefore ALN is defined as “all liver normal”): experimentally derived MPPGL in histologically normal tissue was used for the cancer population; mean MPPGL was 39 mg/g tissue, (eq. 5, adapted from Barter et al., 2008 with revised baseline).

For eq. 5, CV% is 35.36.

Model 3 assumes that the whole liver remains histologically normal, and this implies the maximum metabolic capacity of microsomal enzymes. CV% used for this model is experimentally derived based on MPPGL in histologically normal tissue.

MPPGL model 4 (New Cancer-ALC; new cancer population-assuming liver is obtained from patients with cancer, and entire liver tissue is histologically cancerous, therefore ALC is defined as “all liver cancerous”): experimentally derived MPPGL in cancerous tissue was used for the cancer population; mean MPPGL was 24.8 mg/g tissue, (eq. 6, adapted from Barter et al., 2008 with revised baseline).

For eq. 6, CV% is 39.7.

Model 4 assumes that the whole liver is cancerous, and this implies the minimum metabolic capacity of microsomal enzymes. It also assumes that the liver mass does not change, and that each pmol of enzyme has the same activity, irrespective of disease state. CV% used for this model is experimentally derived based on MPPGL in cancerous tissue.

The size of the liver being normal is important, as this will define how much of the liver will be fully functional. If a proportion of liver is not normal, this may lead to decreased scaled intrinsic clearance, with effect on clearance being compound dependent. In cases of a surgical resection, clearance should be calculated based on healthy MPPGL and remnant liver size. Although surgical resection is the ideal solution for patients with CRLM, this is not feasible for many patients that have to live with a liver with histologically normal and cancerous parts, with unchangeable liver size. Therefore, metabolic capacities of enzymes come from two different sources: histologically normal and cancerous liver (relative contributions of normal and cancerous parts are unknown in the current study). In this case, it is more appropriate to use MPPGL for histologically normal tissue with the weight of the liver being histologically normal and MPPGL for cancerous tissue with the weight of the liver being cancerous.

For eq. 4, the age was plotted against MPPGL values for Healthy population; for both observed and predicted (Barter et al., 2008) values (Supplemental Fig. 1). For each model and for each drug, a generic trial design was used, with the following characteristics. The age range of the cancerous donors is 34–85 and the age range in the virtual population is 20–50 years old, which consists a limitation of our study. However, this limitation wouldn’t have any effect on the final observations, as the age range is kept consistent in all the models, and additionally, age-dependent MPPGL in cancer samples was not apparent (Fig. 4D). The mean (for all 100 virtual subjects) systemic concentration-time profiles were plotted, and the area under the curve from time 0 to infinity (AUC_0-inf) and C_max values were compared across the four MPPGL methods/models (Table 1). Parameters used for PBPK simulations are listed in Supplemental Table 2. Oral doses for alfentanil, alprazolam, midazolam, and desipramine are 0.043 mg/kg, 0.5 mg, 5 mg, and 50 mg, respectively.

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Fig. 1.

Total protein content (mg per gram of liver) in homogenates [HomPPGL (A)], microsomal fractions [MPPGL before correction for losses (B)] and cytosolic fractions [CPPGL (C)] from histologically normal and matched tumor samples (n = 16). The MPPGL values in (B) are not corrected for loss of membrane protein. Blue and red symbols represent normal and cancer samples. The lines represent means, error bars represent S.D.s, and percentages represent CV. The asterisks (****) represent statistical differences with P < 0.0001 between histologically normal and cancerous tissues. Wilcoxon test was used for comparison of HomPPGL, uncorrected MPPGL, and CPPGL between matched cancerous and histologically normal samples.

Lack of differences in CPPGL between normal and cancerous tissue (see Results) meant that significant effects on the clearance and systemic concentrations of drugs metabolized by cytosolic enzymes were not expected. Therefore, no PBPK simulations were performed to assess possible effects on pharmacokinetics of drugs metabolized by cytosolic enzymes.