Liver microsomal protein content and activity in patients with hepatocellular carcinoma and cirrhosis: implications for the in vivo prediction of individual hepatic clearance

Introduction

Hepatocellular carcinoma (HCC) stands as the predominant form of primary liver cancer and constitutes a significant contributor to cancer-related mortality globally (1). The widely accepted multistage developmental model of HCC involves the progression from hepatitis to cirrhosis and ultimately to liver cancer. The liver serves as the principal organ for metabolic clearance in humans, making it a primary target for studies aimed at drug optimization and safety assessments (2). Research indicates that approximately more than 80% of patients with primary liver cancer have accompanying cirrhosis induced by hepatitis B virus (HBV) and/or hepatitis C virus (HCV) (3), which can lead to hepatic metabolic disorders. However, there is no a suitable parameter to predict the hepatic metabolism of patients with hepatocellular carcinoma and cirrhosis (HCCC) to guide the use of medication.

The cytochrome P450 (CYP) superfamily comprises a significant collection of enzymes predominantly located within the endoplasmic reticulum, with a primary expression in the liver (4). These enzymes play a crucial role in the metabolism of approximately 90% of pharmaceuticals used in clinical settings and are also involved in the metabolic activation of various chemical compounds (5-7). Advances in tissue homogenate methodologies and differential centrifugation techniques have enabled the isolation of microsomal vesicles derived from the endoplasmic reticulum (8-10). The microsomal protein contains most of the CYP content of the liver. Hence, the amount of microsomal protein is a critical impact factor in assessing CYP metabolic activity.

The investigation of drug-metabolizing enzymes is conducted using two primary approaches: in vivo and in vitro. In vivo study provides a comprehensive view through the use of pharmacokinetics, which rather than focusing on a single metabolic process, can offer insights into drug absorption, distribution, metabolism, and excretion. Meanwhile, human liver microsomes (HLMs) are extensively used for in vitro assessments of drug metabolic stability due to their ability to be consistently reproduced, their suitability for long-term storage, and their thorough characterization of optimal incubation conditions.

Traditionally, the evaluation of CYP metabolic activity in vitro has been conducted using a metric based on the per milligram of microsomal protein level (CL_M). However, this approach has limitations, particularly regarding the variability of metabolic activity among individuals, as it overlooks the significant individual differences in the quantity of microsomal protein per gram of liver (MPPGL) (11,12). In our published study (13), a bottom-up approach was used to predict pharmacokinetics in a large number of samples from patients with HCC. In that study, the change of CL_M values for 10 CYP isoforms were not consistent with those of the in vivo hepatic clearance (CL_H). Therefore, we postulated that assessing CYP activity based on the clearance of liver tissue (CL_L) might provide a more accurate representation of in vitro CYP metabolism and could more effectively account for the individual variability in CYP activity, as it incorporates the individual differences in MPPGL.

For accurately ascertaining the values of CL_L and CL_H, the quantification of MPPGL emerged as a pivotal determinant. MPPGL, which serves as a microsomal scaling factor for human liver, is essential for predicting in vivo hepatic clearance based on in vitro data and can additionally facilitate the investigation of interliver variability associated with this parameter, particularly in light of the observed underestimation stemming from microsomal parameters (14,15). The numerous studies that have generated MPPGL values from various cohorts have employed different liver tissue sources, use a diversity of correction methodologies for accounting for microsomal protein losses, and operated with relatively limited sample sizes, leading to discrepancies in the mean values reported (11,14,16-18). Our previous study was the first to complete an absolute quantification of MPPGL across a substantial collection of normal liver samples and to successfully extrapolate the in vivo clearance of tolbutamide based on microsomal protein content (19). However, there are a lack of data regarding the microsomal protein in patients with HCCC, which impedes accurate determination of CL_L and CL_H values in this patient population.

Therefore, in this study, we determined the content of liver microsomal protein in the liver tissues of patients with HCCC (n=48) and assessed the in vitro metabolic activity of 10 types of CYP. Furthermore, the activity of CYPs in CL_L and predicted hepatic CL_H were extrapolated. Our objective was to delineate the most effective approach for assessing the activity of drug-metabolizing enzymes. We hope this data will inform and enhance trial design for population pharmacokinetic studies and improve the clinical management of pharmacotherapy in patients with HCCC. We present this article in accordance with the MDAR reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2024-963/rc).

Methods

Materials and chemicals

All probe substrates and several metabolites, including phenacetin, acetaminophen, coumarin, bupropion, paclitaxel, tolbutamide, omeprazole, dextromethorphan, chlorzoxazone, and midazolam, were acquired from the National Institute for the Food and Drug Control in China. The metabolites, namely 7'-hydroxycoumarin, hydroxybupropion, 6-hydroxypaclitaxel, 4'-hydroxytolbutamide, 5'-hydroxyomeprazole, O-demethylation dextrorphan, 6-hydroxychlorzoxazone, and 1'-hydroxymidazolam, were sourced from Sigma-Aldrich (St. Louis, MO, USA). The reduced form of nicotinamide adenine dinucleotide phosphate (NADPH) was procured from Roche Co., Ltd (Basel, Switzerland). Additionally, all solvents used for high-performance liquid chromatography (HPLC) were obtained from Siyou Chemical Reagent Co. (Tianjin, China).

Samples of HLMs

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Medical Ethics Committee of Zhengzhou University (No. ZZUIRB2022-152). Informed consent was collected from all participating patients.

In this study, liver tissues were collected from individuals of the Chinese Han population who underwent partial liver resection in Henan Provincial People’s Hospital or Henan Provincial Tumor Hospital between March 2012 and July 2014. Types of surgery included hepatic cancer resection and hepatic hemangioma resection. All patients received a liver function test, imageological examination (computer tomography or ultrasonography), and HBV and HCV virology examination. Patients who were HBV- or HCV-positive were included in the histopathological examination for cirrhosis. Patients with hepatic hemangioma and negative for serum HBV and HCV indicators and with normal liver function were enrolled as the control group. Patients with hepatic cancer and positive for serum HBV and/or HCV indicators and with cirrhosis according to histopathological examination were enrolled in the HCCC group. Detailed information was well documented and consisted of information on sex, age, height, weight, smoking behaviors, alcohol use, habitual medication prior to surgery, history of allergies, pathological evaluations, imaging examination, and laboratory assessments (which included, but were not restricted to, outcomes from standard blood examinations, hepatic function assessments, and renal function evaluations).

Liver tissue samples were promptly preserved in liquid nitrogen within a half-hour after resection. HLMs were isolated using the hypothermal differential centrifugation technique, as previously reported (19). The thawed liver tissues were placed on ice and measured for weight. The samples were meticulously homogenized in an ice-cold solution of 0.05 M of Tris-HCl (PH =7.0) with a glass homogenizer, supplemented with 1.12% weight-in-volume KCl and 1.12% volume-per-volume EDTA (10 mL of buffer per gram of the liver). Following homogenization, 0.5 mL of this mixture was set aside for analysis of porphyrin oxidation-reduction (POR) activity, while the remaining homogenate underwent centrifugation at 9,000 ×g for 20 minutes at 4 ℃. The supernatant was subsequently collected and subjected to ultracentrifugation at 100,000 ×g for 1 hour at 4 ℃ with an Optima L-100K ultracentrifuge (Beckman Coulter, Brea, CA, USA). The microsomal pellets obtained were then resuspended in a 0.15 M of Tris-HCl buffer (PH =7.6) and centrifuged for an additional hour at 100,000 ×g at 4 ℃. The final microsomal pellets were reconstituted in 0.25 M of sucrose at a ratio of 2 mL per gram of the original sample. The homogenate and the microsomal suspension were subsequently frozen in liquid nitrogen and stored at –80 ℃. The protein concentrations of the microsomes were quantified using the Bradford assay method (20).

Determination of MPPGL in HLMs

The quantification of MPPGL was performed in accordance with a previously described methodology (19). The assay was carried out in a total volume of 200 µL, which comprised 5 µg of the microsomal protein, 0.2 mM of horse cytochrome c, and 0.3 M of potassium phosphate buffer (PH =7.7). The reaction commenced upon the introduction of 20 µL of NADPH (10 mM) into the 200 µL assay mixture, resulting in a final volume of 220 µL. The rate of cytochrome c reduction was assessed by monitoring the increase in absorbance at 550 nm, attributable to the reduced form of cytochrome c, both prior to and following the addition of NADPH (0–5 minutes). This measurement was conducted using a BioTek Synergy H1MD multimode microplate reader (Agilent Technologies, Santa Clara, CA, USA) in kinetic mode. The MPPGL concentrations were subsequently calculated via the following equation:

MPPGL=rateofreductionhomogenate(nM/min⁡/gliver)rateofreductionmicrosome(nM/min⁡/mgmicrosomalprotein)[1]

Measurement of CYP enzyme activity in liver microsomes

The metric for clearance, expressed as in per milligrams of microsomal protein, was designated as CL_M, which signifies the clearance mediated by CYP in liver microsomes. The enzymatic activities of ten distinct CYP isoforms, specifically CYP3A4/5, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP1A2, were evaluated via probe substrates in HLMs. Metabolites served as indicators to evaluate the metabolic rates of these CYPs. The respective activities of CYP3A4/5, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP1A2 were assessed through specific reactions, including midazolam 1-hydroxylation, coumarin 7-hydroxylation, bupropion 4-hydroxylation, paclitaxel 6-hydroxylation, tolbutamide 4-hydroxylation, omeprazole 5-hydroxylation, dextromethorphan O-demethylation, chlorzoxazone 6-hydroxylation, and phenacetin O-deethylation.

For the assessment of biotransformation, seven to eight substrate concentrations were scrutinized. The incubation mixtures comprised HLMs, 1 mM of NADPH, various substrate concentrations, and 100 mM of phosphate buffer (PH =7.4). These mixtures were preincubated at 37 ℃ for 5 min. Reaction was terminated via the addition of 20 µL of ice-cold acetonitrile, 10 µL of perchloric acid, or 1 mL of ethylacetate.

The analysis of all metabolites was conducted with HPLC. A range of metabolites was successfully separated, including acetaminophen, hydroxybupropion, 4'-hydroxytolbutamide, 6-hydroxypaclitaxel, 5'-hydroxyomeprazole, 6-hydroxychlorzoxazone, and 1'-hydroxymidazolam, employing HPLC with ultraviolet detection (HPLC-UV). Additionally, the O-demethylation products dextrorphan and 7'-hydroxycoumarin were isolated through HPLC with fluorescence detection (HPLC-FLD). The Michaelis-Menten constant (K_m) and the maximum reaction rate (V_max) for each CYP enzyme were ascertained via nonlinear regression analyses facilitated by GraphPad Prism 6.0 (GraphPad Software, La Jolla, CA, USA). Furthermore, the intrinsic clearance in HLMs (CL_M) was computed by determining the ratio of V_max to K_m.

CYP enzyme activity at the liver tissue level

Clearance determined based according to per gram of liver tissue was denoted as the CL_L. For patients with HCCC and control participants, the CYP activity at the liver tissue level was extrapolated from the activity of microsome clearance through an equation.

The CL_L value representing CYP-mediated clearance in liver tissues was scaled as follows:

CLL=CLM×MPPGL[2]

In vivo clearance

The methodology employed for patients with HCCC and the controls involved a systematic bottom-up approach that employed in vitro clearance data to infer in vivo clearance. This process incorporated several equations as outlined in previous research (13).

First, the clearance associated with CYPs in the whole liver (CL_WL) was calculated as follows:

CLWL=CLL×LW/BW[3]

In this equation, LW represents the weight of the liver, while BW denotes the body weight of the patient. We derived LW from the actual body weight of each patient by calculating the liver volume (LV) and multiplying it by the liver density as follows: LV (mL) = 12.5 × BW (kg) + 536.4. The liver density was maintained at 1.001 g/mL. Given the presence of cirrhosis, the LV for patients with HCCC was adjusted by applying a correction factor, specifically the reciprocal of the ratio of normal LV (coefficient =1.12).

Subsequently, the in vivo clearance for CYP (CL_H) was assessed using the well-stirred model as follows:

CLH=QH×CLWL×fu,p/RBQH+CLWL×fu,p/RB[4]

In this equation, QH is defined as 24.5% of the cardiac output (CO), with CO values derived from a cohort of normal Han Chinese males and females. The blood-to-plasma concentration ratio (R_B) and the fraction unbound in plasma (f_{u, p}) were sourced from a variety of published studies (21,22).

Statistical analyses

The Shapiro-Wilk test for normality was employed to assess the distribution characteristics of CL_M, CL_L, CL_H, K_m, and V_max pertaining to each CYP enzyme, along with the distribution patterns of MPPGL and the total CYP content within the HLM samples. For data that did not conform to a normal distribution, the median and interquartile range (IQR) were calculated to represent central tendency and variability, respectively. Outliers and extreme values were identified based on their distance from the IQR, specifically values that fell between 1.5 to 3 box lengths and those exceeding 3 box lengths from either the upper or lower bounds of the IQR. All identified outliers and extreme values were incorporated into the subsequent analyses. Pairwise comparisons were conducted via the Mann-Whitney test, with a significance threshold set at P<0.05 (two-tailed). Furthermore, a nonparametric Spearman rank correlation analysis was used to determine the correlation coefficient (r). The graphical representations in this study were created with GraphPad Prism version 5.0 software, Photoshop CC 2014 (Adobe, San Jose, CA, USA), and PowerPoint 2016 (Microsoft, Redmond, WA, USA).

Results

Patients

All patients were administered standard anesthetics and were not previously exposed to agents that could induce or inhibit the CYP enzymes in the study. The clinical characteristics of patients are summarized in Table 1. The mean age of the control participants was 52.06±11.03 years, which was older than that of the patients with HCCC (45.19±7.58 years). The majority of patients in both groups were between 20 and 60 years old. In comparison to the control group, the HCCC group had a higher proportion of males and higher rates of smoking and alcohol consumption.

Changes in MPPGL content

The median concentration of MPPGL was 28.35 mg/g in the patients with HCCC, with the range being 12.56–52.64 mg/g (Figure 1). Furthermore, the MPPGL concentrations at the 2.5th and the 97.5th percentiles were 13.20 and 52.48 mg/g, respectively, representing a fourfold variation. MPPGL levels in patients with HCCC were nonnormally distributed and significantly lower than those observed in the control group (median 37.65, range, 9.90–116.50 mg/g in the control group; P=0.008; Figure 1).

Figure 1 MPPGL content in samples from controls (n=68) and from those with HCCC (n=48). The horizontal lines represent the median with interquartile range. MPPGL, microsomal protein per gram of liver; HCCC, hepatocellular carcinoma and cirrhosis.

The effect of the demographic factors on MPPGL content

A nonparametric test for two independent samples was then conducted to clarify the relationship between demographic variables and MPPGL content. MPPGL content did not demonstrate any significant correlation with age (P>0.05), sex (P>0.05), or smoking and alcohol consumption (P>0.05) regardless of whether the participants were controls or patients with HCCC (Table 2).

Enzyme activity at the microsome level

Kinetic parameters, including K_m, V_max, and CL_M, for the 10 CYPs, including CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4/5, were assessed in the liver microsomes of patients with HCCC and in control participants.

In comparison to those in the control group, the CL_M values for CYP1A2, CYP2C8, and CYP2C19 were significantly decreased (P<0.001, P<0.001, and P=0.003, respectively) while those for CYP2D6 and CYP2E1 were significantly increased (P<0.001 and P<0.001, respectively) in patients with HCCC. However, the CL_M values of CYP2A6, CYP2B6, CYP2C9, and CYP3A4/5 were not significantly changed between the two groups (P>0.05) (Figure 2).

Figure 2 The activity of the 10 CYPs in the HLMs of controls (n=68) and those of patients with HCCC (n=48). Enzyme activity is expressed as the median value in the column graph. The black horizontal lines indicate the interquartile range. The red bars represent the data of the controls. The blue bars represent the data of patients with HCCC. *, P<0.05; **, P<0.01, between controls and patients with HCCC. K_m, Michaelis-Menten constant; V_max, maximum reaction rate; CL_M, clearance at microsomal protein level; CYP, cytochrome P450; HLMs, human liver microsomes; HCCC, hepatocellular carcinoma and cirrhosis.

Enzyme activity at the liver tissue level

According to the MPPGL content, individual activity according to the measure of per gram of liver tissue (CL_L) for CYPs was determined by multiplying each individual MPPGL and the respective individual CL_M for the CYPs.

The CL_L values for CYP1A2, CYP2A6, CYP2B6, CYP2C8, and CYP2C19 in patients with HCCC were significantly lower than those in controls (P<0.001, 0.006, P=0.02, P<0.001, and P<0.001, respectively). The CL_L values for CYP2D6 and CYP2E1 were significantly higher in patients with HCCC than in controls (P=0.01 and P<0.001, respectively). The CL_L values of CYP2C9 and CYP3A4/5 were not significantly different between the two groups (P>0.05) (Figure 3).

Figure 3 The activity of the 10 CYPs at liver tissue level in control samples (n=68) and HCCC samples (n=48). Enzyme activity is expressed as the median value in the column graph. The black horizontal lines indicate the interquartile range. The red bars represent the data of the controls. The blue bars represent the data of patients with HCCC. *, P<0.05; **, P<0.01, between controls and patients with HCCC. CL_L, clearance at liver tissues level; CYP, cytochrome P450; HCCC, hepatocellular carcinoma and cirrhosis.

The association between CL_L and CL_M in patients with HCCC

To clarify the relationships between CL_L and CL_M for the 10 CYPs, Spearman correlation analysis was conducted. A significant correlation was observed between enzyme activity derived from microsomes and that from liver tissues across the 10 CYPs (all P values <0.001; Figure 4). The r values ranged from 0.6364 to 0.8536. The highest correlation between CL_M and CL_L was found for CYP2B6 (r=0.8536; P<0.001; Figure 4).

Figure 4 The linear correlations between CYP activity based on liver tissue (CL_L) and CYP activity based on microsomes (CL_M) in patients with HCCC (n=48). r, spearman rank correlation coefficient; CL_L, clearance at liver tissues level; CL_M, clearance at microsomal protein level; CYP, cytochrome P450; HCCC, hepatocellular carcinoma and cirrhosis.

In vivo metabolic activity

The in vitro-in vivo extrapolation (IVIVE) technique was used to estimate the in vivo hepatic clearance (CL_H) of CYPs in both control participants and patients with HCCC. The CL_H values of CYP1A2, CYP2A6, CYP2B6, CYP2C8, and CYP2C19 were significantly lower in patients with HCCC compared to those in the control group (P<0.001, P<0.001, P=0.002, P<0.001, P<0.001, respectively; Figure 5). The CL_H value of CYP2E1 was significantly higher in patients with HCCC than in control group (P=0.001; Figure 5). However, the CL_H values of CYP2C9, CYP2D6, and CYP3A4/5 were not significantly different between the two groups (P>0.05; Figure 5). Of note, the CL_H value of CYP2C8 in patients with HCCC was 86.12% lower than that of controls (52.61 vs. 378.95 mL/min; P<0.001; Figure 5).

Figure 5 In vivo metabolic activity for the 10 CYPs in the control samples (n=68) and HCCC samples (n=48). Enzyme activity is expressed as the median value in the column graph. The black horizontal lines indicate the interquartile range. The red bars represent the data of the controls. The blue bars represent the data of patients with HCCC. **, P<0.01 between controls and patients with HCCC. CL_H, hepatic clearance; CYP, cytochrome P450; HCCC, hepatocellular carcinoma and cirrhosis.

The association between CL_H and CL_M in patients with HCCC

There were significant correlations between the in vivo metabolic activity and enzyme activity based on microsomes for 10 CYPs (all P values <0.001; Figure 6). The r values ranged from 0.6329 to 0.8860, and the most pronounced correlation was between CL_H and CL_M for CYP2B6 (r=0.8860; P<0.001; Figure 6).

Figure 6 The linear correlations between CYP activity based on microsomes (CL_M) and in vivo CYP activity (CL_H) in patients with HCCC (n=48). CL_H, hepatic clearance; CL_M, clearance at microsomal protein level; r, spearman rank correlation coefficient; CYP, cytochrome P450; HCCC, hepatocellular carcinoma and cirrhosis.

The association between CL_H and CL_L in patients with HCCC

A highly significant correlation was observed between in vivo metabolic activity and enzyme activity based on liver tissues for the 10 CYPs (all P values <0.001; Figure 7). The r values ranged from 0.9223 to 0.9946, and the most pronounced correlation was between CL_H and CL_L for CYP2C8 (r=0.9946; P<0.0001; Figure 7).

Figure 7 The linear correlations between CYP activity in vivo (CL_H) and CYP activity based on liver tissue (CL_L) in patients with HCCC (n=48). CL_H, hepatic clearance; CL_L, clearance at liver tissues level; r, spearman rank correlation coefficient; CYP, cytochrome P450; HCCC, hepatocellular carcinoma and cirrhosis.

Statistical differences between r values

Among the 10 CYPs, the median Spearman rank correlation coefficient between CL_H and CL_M was 0.7880±0.079 while that between CL_H and CL_L was 0.9868±0.022. Comparative analysis of these two data sets revealed a statistically significant difference between the two groups (P<0.001; Figure 8).

Figure 8 The spearman rank correlation coefficient of CL_H/CL_M and CL_H/CL_L. Data are shown as bar graphs representing medians. The blue bar represents the spearman rank correlation coefficient (r value) between CL_H and CL_M for 10 CYPs. The red bar represents the r value between CL_H and CL_L for 10 CYPs. The horizontal lines indicate the interquartile range. CL_H, hepatic clearance; CL_M, clearance at microsomal protein level; CL_L, clearance at liver tissues level; CYP, cytochrome P450.

Discussion

In this study, we examined the real individual differences in liver microsomal proteins and the in vitro and in vivo clearance of 10 CYPs at different levels, including liver microsomes and liver tissues, in 48 patients with HCCC. The microsomes protein content was significantly decreased in patients with HCCC compared to that in control participants. According to MPPGL and CL_M, the CYP activity based on liver tissue and in vivo activity were predicted. For patients with HCCC, there were various activity alterations for different CYPs at the microsome level, liver tissue level, and in vivo. In contrast to that of CL_M, the variation tendency of CL_L for the 10 CYP isoforms was closer to that of CL_H. Moreover, there was a highly significant correlation between CL_L and CL_H for the 10 CYPs.

Our result showed that the median MPPGL was 28.35 (range, 12.56–52.64) mg/g for patients with HCCC and 37.65 (range, 9.90–116.50) mg/g for controls, representing a statistically significant difference. Furthermore, we examined the influence of various demographic factors on MPPGL, such as age, sex, smoking status, and alcohol consumption. Our findings revealed that these factors did not exhibit a significant impact on MPPGL content. Previous research has also demonstrated that MPPGL is not associated with sex, smoking, or alcohol use (12). However, a modest yet statistically significant inverse correlation was reported between age and MPPGL (12). These previous studies used a variety of tissue types and employed different correction techniques to address microsomal protein losses, which may explain the observed age differences (12,22).

In our study, all samples were from patients with HCC and confirmed to be cirrhosis from HBV or HCV infection. In a recent study, the MPPGL values were 26.8 mg/g in patients with nonalcoholic fatty liver disease, 27.4 mg/g in those with nonalcoholic steatohepatitis, and 24.3 mg/g in those with nonalcoholic steatohepatitis and cirrhosis, suggesting that different degrees of hepatic metabolic status affect MPPGL levels (15). In a study that examined four patients with pediatric biliary atresia, the reported minimum MPPGL value was 18.73 mg/g (23), while in another study, the maximum MPPGL value was 77 mg/g of four liver samples from liver bank (17). A review of literature indicated the mean value of human MPPGL is 32 mg/g (12). This variation may be attributed to the differing correction methodologies employed to address the losses of MPPGL, as well as the relatively limited sample sizes used in the research. Moreover, the medical history and detailed information of these patients were lacking, and thus the impact of diseases on MPPGL could not be verified.

In our previous study, the median MPPGL in patients with HCC was 28.85 (range, 7.60–93.60) mg/g, representing an approximate 12-fold interindividual variation, with some of these liver specimens being cirrhosis and others being fibrosis (13). In the present study, the MPPGL value of patient with HCCC was in good agreement with the previously reported result. However, the interindividual variation of MPPGL for patients with HCCC was obviously reduced (4-fold vs. 12-fold). The possible reason for this is that the influence of fibrosis on microsomes protein content is less than that of cirrhosis. Cirrhosis is a critical stage during the development of HCC, and it is thus necessary to assess the effect of cirrhosis on microsomes protein and enzyme metabolism. Wilson et al. (11) determined the geometric mean of MPPGL to be 33 (range, 26–54) mg/g of the human liver from HCC or metastatic tumor (n=20). Unfortunately, the nature of these liver specimens was not determined. Our MPPGL results from patients with HCCC, which were significantly decreased and had less interindividual variation, suggest an overall decreased microsome protein content in cirrhotic tissue.

The alterations in the clearance for CYPs in patients with HCCC have not previously been reported. We found that MPPGL was decreased in patients with HCCC as compared to controls. We further sought to determine whether CYP activity corresponds to the decrease in MPPGL. We found that the changes in enzymatic activities in CL_M were different for the 10 CYPs. Multiple previous investigations have established that the enzymatic activities of CYP3A, CYP2A6, CYP2D6, CYP2C19, CYP2E1, and CYP1A2 are generally reduced in the context of liver diseases (24-30). In our study, we found an opposite pattern of alteration in the CL_M values for CYP2D6 and CYP2E1, which were significantly increased in patients with HCCC. The observed discrepancies may partially stem from the previous studies mainly focusing on patients with uncomplicated cirrhosis who lacked concurrent HCC.

Pertaining to the interindividual variability of MPPGL, it is worth noting certain differences in the activity of some CYPs. Specifically, the activity of CYP2A6 and CYP2B6 in CL_M were not significantly changed in patients with HCCC as compared to controls, while the activity of these two enzymes in CL_L were significantly decreased. In addition, the activity of CYP2D6 in CL_M and CL_L was significantly increased in patients with HCCC as compared to controls but was not significantly changed in CL_H. This alteration may be attributable to several factors, including functional LV, hepatic blood flow, plasma protein binding, and MPPGL. Furthermore, a substantial elevation in the activity of CYP2E1 was observed at a variety levels (CL_M, CL_L, and CL_H) in patients with HCCC, suggesting that the liver may be converting certain procarcinogens, such as nitrosamines, into potent carcinogenic compounds.

To identify a more suitable parameter for depicting the in vivo clearance in patients with HCCC, we conducted a correlation analysis among CL_M, CL_L, and CL_H. Our findings revealed a significant correlation between CL_L and CL_M across the 10 CYPs, with correlation coefficients ranging from 0.6364 to 0.8536. Additionally, a noteworthy correlation was observed between CL_H and CL_M for the same 10 CYPs, with r values spanning from 0.6329 to 0.8860. Importantly, the strongest correlation was identified between CL_H and CL_L for the same 10 CYPs, with r values ranging from 0.9223 to 0.9946. Consequently, it can be inferred that CL_L is likely a more appropriate representation of the in vitro metabolism for CYPs and could facilitate the evaluation of individual variations in CYP activity. This observation may explain by the fluctuations in liver microsomal protein content, which could impact enzyme activity. In addition, the most pronounced correlation was identified between CL_H and CL_L for CYP2C8 (r=0.9946; P<0.001). A significant reduction in CYP2C8 activity was observed in patients with HCCC, suggesting a diminished metabolic processing of carcinogens. These observations may provide valuable insights for future investigations into the mechanisms underlying hepatocarcinogenesis in patients with HCCC.

Our study has certain limitations. First, the scaling factors obtained from liver microsomes, as well as fresh or cryopreserved hepatocytes, can potentially be utilized to predict the in vivo metabolic drug clearance (12). In our study, we have only determined the MPPGL value in patients with HCCC. However, it should be noted that the hepatocellularity per gram of liver is another crucial factor that needs to be considered, since hepatocytes are the main sites of drug metabolism (11). Second, we employed standard incubations in buffer, which may lead to an underestimation of in vivo clearance. A previous study has shown that adding plasma can improve the correlation between in vitro metabolic studies of highly protein-bound molecules and their actual in vivo metabolism when using rat liver microsomes (31). Further exploration is required for different conditions applicable to drugs of diverse pharmacological types to better understand how these variations impact drug metabolism and clearance, thereby improving the accuracy of prediction.

Conclusions

MPPGL contents were significant decreased and may influence the activities of CYPs to a varying extent in patients with HCCC. The interindividual variation of MPPGL may explain the changes in CL_M, CL_L, and CL_H. Furthermore, we estimated the hepatic clearance in vivo and identified CL_L as a suitable approach to accurately depicting liver metabolism. These findings are expected provide further significant insights that may assist clinicians in modifying their drug prescriptions for a diverse array of medications administered to patients with HCCC.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2024-963/rc

Data Sharing Statement: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2024-963/dss

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2024-963/prf

Funding: This work was supported by the National Natural Science Foundation of China (No. 81473279), the Science and Technology Innovative Scholar Program of Henan Province (No. 134200510019), and the Scientific and Technical Innovation Team of Zhengzhou City (No. 131PCXTD604).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2024-963/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Medical Ethics Committee of Zhengzhou University (No. ZZUIRB2022-152). Informed consent was collected from all participating patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: J. Gray)