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. 2019 Aug 5;176(18):3712–3722. doi: 10.1111/bph.14776

Altered cytochrome 2E1 and 3A P450‐dependent drug metabolism in advanced ovarian cancer correlates to tumour‐associated inflammation

Sebastian Trousil 1, Patrizia Lee 1, Robert J Edwards 2, Lynn Maslen 1, Jingky P Lozan‐Kuehne 1, Ramya Ramaswami 1, Eric O Aboagye 1, Stephen Clarke 3, Christopher Liddle 4, Rohini Sharma 1,✉
PMCID: PMC6715602  PMID: 31236938

Abstract

Background and Purpose

Previous work has focussed on changes in drug metabolism caused by altered activity of CYP3A in the presence of inflammation and, in particular, inflammation associated with malignancy. However, drug metabolism involves a number of other P450s, and therefore, we assessed the effect of cancer‐related inflammation on multiple CYP enzymes using a validated drug cocktail.

Experimental Approach

Patients with advanced stage ovarian cancer and healthy volunteers were recruited. Participants received caffeine, chlorzoxazone, dextromethorphan, and omeprazole as in vivo probes for CYP1A2, CYP2E1, CYP2D6, CYP3A, and CYP2C19. Blood was collected for serum C‐reactive protein and cytokine analysis.

Key Results

CYP2E1 activity was markedly up‐regulated in cancer (6‐hydroxychlorzoxazone/chlorzoxazone ratio of 1.30 vs. 2.75), while CYP3A phenotypic activity was repressed in cancer (omeprazole sulfone/omeprazole ratio of 0.23 vs. 0.49). Increased activity of CYP2E1 was associated with raised serum levels of IL‐6, IL‐8, and TNF‐α. Repression of CYP3A correlated with raised levels of serum C‐reactive protein, IL‐6, IL‐8, and TNF‐α.

Conclusions and Implications

CYP enzyme activity is differentially affected by the presence of tumour‐associated inflammation, affecting particularly CYP2E1‐ and CYP3A‐mediated drug metabolism, and may have profound implications for drug development and prescribing in oncological settings.

Abbreviations

BMI

body mass index

CRP

C‐reactive protein

CYP

cytochrome P450

PK

pharmacokinetics

What is already known

  • Inflammation associated with malignancy results in repression of CYP3A mediated drug metabolism.

What this study adds

  • A significant increase in CYP2E1 activity was observed in patients with cancer that correlated inflammation.

What is the clinical significance

  • Deranged drug metabolism in the presence of cancer has implications for drug safety and development.

1. INTRODUCTION

The pharmacokinetics (PK) of anticancer drugs varies substantially between patients and is an important contributing factor to variable response and safety. Much of the inter‐patient variability in the clearance of anticancer drugs can be attributed to differences in the activities of drug‐metabolising enzymes. Cytochrome P450s (CYPs) constitute a gene superfamily of haem‐thiolate proteins that contribute to many aspects of metabolism, including the biotransformation of therapeutic drugs. The main human drug‐metabolising CYPs belong to families 1, 2, and 3, among which CYP1A2, CYP2C19, CYP2D6, CYP2E1, and CYP3A are responsible for the oxidation of over 70% of prescribed medications, including many anticancer agents (Guengerich, 1999; Scripture, Sparreboom, & Figg, 2005). Moreover, the majority of patients with a diagnosis of malignancy will be on numerous other adjunctive medications including antiemetics, anticonvulsants, and opioids, many of which are substrates for CYPs. Hence, variations in CYP activity may have profound effects on clinical outcomes.

Substantial inter‐individual variability exists in the activity of CYP enzymes, with up to 20‐fold variation in protein expression reported within the liver and, while these enzymes are distributed extrahepatically, the majority of CYPs are expressed in the liver (Abdel‐Razzak et al., 1993; Cressman, Petrovic, & Piquette‐Miller, 2012). Thus, the liver plays a significant role in the metabolism and elimination of drugs, and it would be anticipated that liver disease may have a detrimental effect on the activity of CYP enzymes. In the setting of cancer chemotherapy, this variability in drug metabolism can potentially result in either reduced response or increased toxicity (Liu et al., 2005). This is of particular concern for drugs that have a narrow therapeutic index such as cytotoxics, where toxicity can result in significant morbidity and occasionally mortality. A number of factors contribute to this variability in CYP activity including drug–drug interactions, genetic polymorphisms, and certain disease states including the presence of systemic inflammation (Frye, Schneider, Frye, & Feldman, 2002; Schuetz, 2004; Shedlofsky, Israel, McClain, Hill, & Blouin, 1994; Shedlofsky, Israel, Tosheva, & Blouin, 1997; Thummel & Wilkinson, 1998).

Systemic inflammation is a recognised hallmark of cancer and is increasingly acknowledged as an adverse predictor of outcome (Hanahan & Weinberg, 2011). Raised levels of C‐reactive protein and pro‐inflammatory cytokines in serum, especially IL‐6, are associated with poor prognosis among patients with varying tumour types including breast cancer, ovarian cancer, gastric cancer, renal cell carcinoma, and colon cancer (Blay et al., 1992; Nakashima et al., 2000; Sharma, Hook, Kumar, & Gabra, 2008; Ueda, Shimada, & Urakawa, 1994; Wu et al., 1996; Zhang & Adachi, 1999). The presence of acute inflammation also negatively affects P450 drug metabolism, in particular CYP3A drug metabolism (Aitken & Morgan, 2007; Frye et al., 2002; Mayo, Skeith, Russell, & Jamali, 2000; Shedlofsky et al., 1994; Shedlofsky et al., 1997). Repression of CYP3A enzymes has been shown to be mediated primarily by the pro‐inflammatory cytokines IL‐6, IL‐1β, and TNF‐α by binding to the nuclear pregnane X receptor (PXR; Gu et al., 2006; Kacevska et al., 2011). However, while elevated levels of both TNF‐α and IL‐6 have been shown to negatively affect CYP enzyme activity in a number of disease states, including congestive cardiac failure, rheumatoid arthritis, and infection, few studies have investigated this relationship in the presence of malignancy (Chen et al., 1994; Frye et al., 2002). Slaviero and colleagues demonstrated that raised levels of circulating IL‐6 in patients with advanced cancer correlated with reduced CYP3A activity, as measured by the erythromycin breath test. This in turn resulted reduced elimination of both docetaxel and vinorelbine. Patients with compromised drug metabolism were found to be more at risk of toxicity following the first cycle of chemotherapy using these agents (Rivory, Slaviero, & Clarke, 2002; Slaviero, Clarke, & Rivory, 2003). Despite recent recognition of the increasingly important role of cytokines in clinical manifestations of malignancy, as well as their effects on some aspects of drug metabolism, the latter remain to be fully elucidated.

The aim of the study is to assess whether patients with malignancy have altered drug metabolism mediated by five key CYPs, compared with healthy volunteers, and whether any alteration correlates with levels of circulating cytokines. This may allow further individualisation of chemotherapy dose selection in the setting of advanced malignancy.

2. METHODS

2.1. Subjects

This study was performed in compliance with the ethical principles provided by the Declaration of Helsinki and approved by the institutional review board. Written informed consent was obtained from all subjects prior to study entry. Patients were recruited from the Medical Oncology Outpatients Clinic at Hammersmith Hospital, London, UK. Patients were eligible for entry into the study if they had stage III/IV, histologically proven, epithelial ovarian cancer. Patients had to be at least 18 years of age, life expectancy of >3 months, and ECOG performance status of <2. Patients had not received chemotherapy for at least 4 weeks prior to entry to the study. Healthy volunteers were recruited following advertisement at the Hammersmith Hospital, London, UK. Volunteers were all female, of comparable for age and body mass index, and had to be at least 18 years of age. Subjects were excluded from the study if they were receiving medications known to alter the activity of CYPs under investigation and had a history of porphyria or G6PD deficiency, active uncontrolled infections, gastrointestinal disease, vaginal bleeding, haemolysis, or any significant co‐existing medical illness and psychological, familial, sociological, or geographical condition potentially hampering compliance with the study protocol and follow‐up schedule. All subjects were required to have satisfactory baseline haematological and organ function (neutrophil count >1.5 × 109·L−1, platelets >100 × 109·L−1, haemoglobin >9 g·dl−1, bilirubin <1.5 × upper limit of normal range and aspartate aminotransferase or alanine aminotransferase <2.5 × upper limit of normal range, and creatinine clearance >45 ml·min−1 as calculated by the modified Cockcroft and Gault lean body mass formula). All patients and healthy volunteers were female.

2.2. Study schema

Subjects had blood samples taken at baseline for serum CA125, C‐reactive protein (CRP), complete blood picture, electrolytes, urea, and creatinine, liver function tests, and analysis of serum cytokines. The activity of five CYP enzymes was estimated using the Pittsburgh oral drug cocktail approach, whereby mephenytoin was omitted (Frye et al., 2002; Liu et al., 2005). Each subject simultaneously received four drugs the morning after an overnight fast. Subjects were asked to refrain from any alcohol and caffeine consumption for 48 hr prior to the administration. The drugs administered were caffeine 100 mg (CYP1A2), chlorzoxazone 250 mg (CYP2E1), dextromethorphan 30 mg (CYP2D6), and omeprazole 40 mg (CYP2C19 and CYP3A). Each drug is specific for the relevant CYP enzyme stated. Blood samples (10 ml) were collected at baseline, 4 and 8 hr after cocktail administration. All urine output from 0 to 8 hr was collected. Phenotypic measurements of CYP activity were determined from the fractional metabolic clearance of the metabolite of interest. Thus, these phenotypic measurements serve as estimates of enzyme activity. No interaction has been shown previously with the simultaneous administration of these probe drugs. A week following the administration of the metabolic cocktail, patients received chemotherapy of the treating physician's choice. Toxicity data were recorded following the first cycle of chemotherapy using NCI CTC Version 2.

2.3. Analytical techniques

The concentrations of the following drugs and their metabolites were measured by LC–MS in plasma: caffeine and paraxanthine, chlorzoxazone and 6‐hydroxychlorzoxazone, omeprazole and 5‐hydroxyomeprazole, omeprazole sulfone, and urinary dextromethorphan and dextrorphan. Parent drugs and metabolites were analysed as previously described (Frye, Matzke, Adedoyin, Porter, & Branch, 1997; Gonzalez et al., 2002; Loos et al., 2011; Nolin, Gastonguay, Bies, Matzke, & Frye, 2003; Oh, Park, Shinde, Shin, & Kim, 2012). Calibrated standard curves were generated for each parent compound or metabolite to derive sample concentrations. Analysis was performed by York Bioanalytical Solutions, UK.

2.3.1. Measurement of cytokines

Circulating levels of IL‐1β, IL‐6, IL‐8, and TNF‐α were determined using a multiplex bead‐based array according to manufacturer's instruction (Luminex, R&D Systems, Minneapolis).

2.4. Data and statistical analysis

The data and statistical analysis comply with the recommendations of the British Journal of Pharmacology on experimental design and analysis in pharmacology. A minimum of 18 controls and 18 patients were recruited. This was based on an assumption of at least a 1.5‐fold difference in the mean enzyme activity of any one P450 between the two groups (healthy subjects or cancer patients; Abdel‐Razzak et al., 1993; Aitken, Richardson, & Morgan, 2006; Charles et al., 2006; Kehrer, Mathijssen, Verweij, de Bruijn, & Sparreboom, 2002). With a level of significance of 5%, a sample size of 18 per group gives a power of 80% to detect statistically significant difference in mean enzyme activity between the two groups. Four extra subjects per arm were recruited to take into account any drop‐outs.

The sample size for comparing means of two independent groups was calculated using IBM SPSS SamplePower (Version 3). Wilcoxon signed‐rank test was used to assess the association of variables between patients and volunteers. ANOVA was used for assessment of associations with categories and continuous variables. The relationship between phenotypic indexes and toxicity was assessed by Spearman rank correlation. Data were analysed using either GraphPad Prism Version 5.0 or SPSS Version 21 (GraphPad Prism, RRID:SCR_002798; SPSS, RRID:SCR_002865). A P value of <.05 was taken to be significant.

2.5. Nomenclature of targets and ligands

Key protein targets and ligands in this article are hyperlinked to corresponding entries in http://www.guidetopharmacology.org, the common portal for data from the IUPHAR/BPS Guide to PHARMACOLOGY (Harding et al., 2018), and are permanently archived in the Concise Guide to PHARMACOLOGY 2017/18 (Alexander, Cidlowski et al., 2017; Alexander, Fabbro et al., 2017).

3. RESULTS

3.1. Demographics

Forty‐three subjects were recruited to the study (21 healthy volunteers and 22 patients). The median age of the healthy volunteers was 53 (range: 41–70), while that of the patients was 65 (range: 47–85). Significant differences in clinical parameters were observed between the two study groups including CRP, serum alanine aminotransferase, bilirubin, alkaline phosphatase, and albumin (Table 1). All patients with ovarian cancer had stage IV disease. Of these, eight had serous histology, 11 endometrioid, and three had mucinous tumours. When considering previous therapy, 14 patients had received one previous line of therapy, seven patients had two previous line of therapy, and one patient had three previous chemotherapeutic regimens. Two patients had a history of diabetes. No subjects had a history of excessive alcohol intake.

Table 1.

Demographic details of enrolled patients and healthy volunteers

Characteristics Volunteers (n = 21) Patients (n = 22) P value
Age (median, range) 52.9 (41.3–69.7) 64.6 (46.9–85.3) .01
BMI (median, range) 24.9 (21.4–36.2) 25.5 (19.7–45.2) .39
AST, U·L−1 (median, range) 23 (13–29) 36 (10–54) .16
ALT, U·L−1 (median, range) 17.5 (7–35) 15.5 (8–76) <.01
Bilirubin, μmol·L−1 (median, range) 8 (4–18) 7 (3–15) .05
ALP, IU·L−1 (median, range) 63.5 (39–112) 95 (60–496) <.01
Albumin, g·L−1 (median, range) 41 (38–45) 37 (13–46) .01
CRP, mg·L−1 (median, range) 10 (0–14) 28 (0–148) .036
Serum CA125, U·ml−1 (median, range) — 292 (18–9275)
Chemotherapy administered
Single‐agent carboplatin 8
Carboplatin/paclitaxel 5
Carboplatin/gemcitabine 3
Liposomal doxorubicin 5
Paclitaxel 1
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Abbreviations: ALP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; CRP, C‐reactive protein.

3.2. Significant alternation in drug metabolism is observed in patients with malignancy compared with healthy volunteers

The activities of CYP1A2, CYP2C19, CYP2E1, CYP2D6, and CYP3A were determined, and the differential activities between healthy volunteers and patients with cancer were assessed (Figure 1a–e). Of key interest, there was a threefold increase in activity of CYP2E1 in cancer patients compared with healthy volunteers (Figure 1a). Moreover, healthy volunteers had a relatively narrow range of activity, 1.35 ± 0.37 (mean ± SD), while there was a wide range of CYP2E1 activity in the cancer cohort, 4.43 ± 3.48. When considering the entire study cohort, subjects can be considered as having normal CYP2E1 metabolic activity (range: 0.57–1.35, n = 9), high activity (range: 1.36–4.42, n = 21), and super metabolic activity (>4.43, n = 6). When compared with the remaining cohort, the high and super metabolisers all had cancer, lower albumin, increasing age and lower body mass index (BMI); all comparisons were significant.

Figure 1.

Figure 1

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Metabolic phenotypes of (a) CYP3A, (b) CYP2E1, (c) CYP1A2, (d) CYP2C19, and (e) CYP2D6 metabolism in healthy volunteers (n = 21) and ovarian cancer patients (n = 22). Individual data points are shown as well as medians. *P < .05, significantly different as indicated

When considering CYP3A, activity was 42% lower in the patient population (Figure 1b). Importantly, no relationship was observed between serum CA125, a marker of cancer severity, and CYP activity. No other demographic factors were correlated to CYP3A activity. A significant relationship was observed between CYP2E1 activity and CYP3A (r = .36, Figure 2). No other significant alterations in metabolic phenotype were observed.

Figure 2.

Figure 2

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Relationship between CYP2E1 activity and CYP3A activity

In terms of worst grade toxicity experienced by patients during cycle 1 of chemotherapy, 14 patients (64%) had experienced no toxicities, five patients (23%) had experienced grade 1 toxicity, two patients (9%) had experienced grade 2 toxicity, and one patient (5%) had experienced grade 3 toxicity. The relationship between worst grade toxicity experienced was assessed for the metabolism indexes of each enzyme. No association between grade of toxicity and CYP activity was observed (Figure S1).

3.3. Alterations in drug metabolism correlate with the presence of inflammation

Ovarian cancer patients had higher levels of serum CRP compared with healthy volunteers (median values of 182 mg·l−1 in patients and 17 mg·l−1 in healthy volunteers). The presence of raised CRP was significantly associated with reduced CYP3A activity such that subjects with a raised CRP (>100 mg·l−1) had a CYP3A ratio of 0.22, compared with 0.52 in those with normal CRP. A significant relationship was also observed between raised CRP levels and increased CYP2E1 metabolic activity (r = .44). No other associations between CRP and P450 activity was noted.

We investigated the levels of circulating cytokines in patients and healthy volunteers at baseline to ascertain which cytokines may be associated with the observed differences in the metabolic phenotype. A significant difference in the circulating levels of serum IL‐6 (median, 12.89 and 37.33 pg·ml−1), IL‐8 (median 25.62 And 71.61 pg·ml−1), and TNF‐α (median 27.71 and 45.42 pg·ml−1) was observed in patients with ovarian cancer, compared with healthy volunteers (Figure 3a–c). No association was noted between the levels of circulating IL‐1β and the presence of malignancy (Figure 3d).

Figure 3.

Figure 3

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Serum inflammation markers are increased in ovarian cancer patients. Significantly raised levels of circulating serum (a) IL‐6, (b) IL‐8, and (c) TNF‐α were seen in cancer patients (n = 22) compared with healthy volunteers (n = 21). (d) No difference in IL‐β was observed, *P < .05, significantly different from Normal

We then assessed the relationship between metabolic phenotype and levels of circulating inflammatory cytokines. Elevated levels of serum IL‐6, IL‐8 and TNF‐α were significantly associated with increasing CYP2E1 activity (Figure 4a–c). A significant association was also observed between all the pro‐inflammatory cytokines and CYP3A (Figure 5a–d).

Figure 4.

Figure 4

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Relationship between serum inflammation markers and CYP2E1 metabolism. Scatter plots showing relationship between CYP2E1 activity and (a) IL‐6, (b) IL‐8, and (c) TNF‐α. Individual data points are shown

Figure 5.

Figure 5

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Relationship between serum inflammation markers and CYP3A metabolism. Scatter plots showing relationship between CYP3A activity and (a) IL‐1β, (b) IL‐6, (c) IL‐8, and (d) TNF‐α. Individual data points are shown

4. DISCUSSION

The primary aim of this study was to assess the effects of the pro‐inflammatory state associated with malignancy on CYP‐mediated drug metabolism. We report a marked, multi‐fold, up‐regulation in CYP2E1 activity and repression of CYP3A in patients with ovarian cancer compared with healthy volunteers, which was associated with raised serum CRP in the cancer cohort. No change in activity of the other CYPs investigated was noted. The effects of inflammation on metabolism have been investigated in patients with a number of malignancies receiving differing chemotherapeutic regimens, as well as in non‐malignant, inflammatory conditions, such that the alteration in drug metabolism is related to the presence of inflammation per se rather than the presence of any given tumour type (Alexandre et al., 2007; Chen et al., 1994; Frye et al., 2002; Harvey & Morgan, 2014; Rivory et al., 2002; Slaviero et al., 2003; Terada, Noda, & Inui, 2015). While repression of CYP3A has been previously documented, to the best of our knowledge, no study has documented up‐regulation of CYP2E1 activity in association with advanced malignancy, an important and novel finding given the role of CYP2E1 in drug metabolism (Alexandre et al., 2007; Kacevska et al., 2013; Rivory et al., 2002).

CYP2E1 is a membrane‐bound protein that is highly expressed in the liver where it plays a key role in the metabolism of toxins and carcinogens (Gonzalez, 2007). CYP2E1 metabolises 5% of prescribed medications, but of these, acetaminophen (paracetamol) and zopiclone are important substrates for CYP2E1, both of which are used extensively in patients with malignancy, as an analgesic and a sedative respectively (Lieber, 1997). Up‐regulation of CYP2E1 may render both medications ineffective, a key concern in a cancer population where symptom control is paramount. Importantly, acetaminophen is metabolised predominantly by CYP2E1 to form the toxic metabolite NAPQ1 that undergoes rapid conjunction. An increase in CYP2E1 activity of up to 10‐fold, as seen in the super metabolisers, may result in rapid clearance of the drug, rendering it ineffective and potentially increase the risk of acetaminophen‐induced liver toxicity as metabolism is driven towards the formation of NAPQ1.

Mechanistically, elevation of CYP2E1 activity in pro‐inflammatory conditions, as observed in patients with cancer, is generally supported by preclinical studies. For instance, inflammation‐induced expression of CYP2E1 is mediated by the IL‐6‐induced binding of STAT3 to the promoter region of CY2E1 (Patel et al., 2014; Tindberg, Baldwin, Cross, & Ingelman‐Sundberg, 1996), although some other preclinical studies have shown repression of CYP2E1 activity in the presence of inflammation (Abdel‐Razzak et al., 1993; Hakkola, Hu, & Ingelman‐Sundberg, 2003). The later studies considered changes in CYP2E1 activity in primary hepatocytes, while the papers by Patel and Tindberg used colorectal cancer cells and astrocytes, suggesting that the differences in CYP2E1 activity can be attributed to the tissue type studied. The regulation of CYP2E1 is complex with many different mechanisms reported at both the transcriptional and translational levels, which may also have a differential effect on CYP2E1 activity. For example, constitutive hepatic expression of CYP2E1 is transcriptionally regulated by liver‐enriched transcription factors, while pathophysiological conditions affect RNA stability, and exogenous compounds regulate CYP2E1 expression at a post‐translational level (Roberts, Song, Soh, Park, & Shoaf, 1995; Ueno & Gonzalez, 1990). Of particular interest, CYP2E1 expression is induced by both starvation and diabetes (Hong, Pan, Gonzalez, Gelboin, & Yang, 1987; Lorr, Miller, Chung, & Yang, 1984). In an animal model of starvation, a significant increase in chlorzoxazone metabolism was reported, up to threefold in fasted rats (Wan, Ernstgard, Song, & Shoaf, 2006). Patients with advanced cancer nutrition and energy homeostasis are often greatly perturbed, contributing to the clinical picture of cancer cachexia (Sadeghi et al., 2018). Subjects in our study were given the metabolic cocktail after an overnight fast, and it is possible that in patients with cancer, baseline energy metabolism may have been more perturbed by this fast, compared with healthy volunteers. We did observe a relationship between BMI and CYP2E1 activity, but there was no difference in BMI between healthy volunteers and cancer patients, suggesting that BMI per se was unlikely to account for the dramatic increase in CYP2E1 activity observed in the cancer population.

Preclinical work conducted by our group and others suggest that pro‐inflammatory cytokines, in particular IL‐6, interact with PXR, which in turn represses the activity of CYP3A (Kacevska et al., 2011; Pascussi et al., 2000; Teng & Piquette‐Miller, 2005; Yang et al., 2010). However, other indirect mechanisms of P450 regulation may be hypothesised, which would explain the differential change in P450 activity in response to inflammation observed in this study. Multiple P450s co‐localise on the membrane of the endoplasmic reticulum where they share the common redox partners, NADPH–cytochrome P450 reductase and cytochrome b 5. In particular, it has been shown that CYP2E1 and CYP3A4/5 oligomerise to form complexes that can alter the activity of each individual P450 whereby an association of CYP3A with CYP2E1 causes activation of CYP2E1 (Davydov, Davydova, Sineva, & Halpert, 2015). We did observe an inverse relationship between CYP3A and CYP2E1 activity that may support such a hypothesis of interplay between the two enzymes. Moreover, it may also be possible that inflammatory mediators may affect the interaction between individual P450s, NADPH–cytochrome P450 reductase and cytochrome b 5, thus changing the activity of P450s studied.

There are a number of limitations in our study. Firstly, we did not investigate the relationship between PK profile of the chemotherapeutic agents administered and CYP activity. This is of particular importance in the area of drug development, where the presence of cancer‐associated inflammation and altered hepatic drug metabolism can alter the PK profile of novel agents (Morgan, 2009). Recognising this, Schwenger and colleagues performed a meta‐analysis of all studies that compared differences in drug metabolism in healthy volunteers and patients with cancer to construct a computational model that predicts differences in PK in cancer patients (Schwenger et al., 2018). The modelling suggests that reducing the activity of CYP1A2, CYP2C19, and CYP3A4 by 20–30% in a virtual oncology population accurately predicted the PK profiles of a number of P450s substrates and a subset of oncology compounds. Changes in the activity of drug transporters did not affect PK. The model is a promising step forward in drug development process particularly in predicting PK in cancer patients.

A further limitation is the population studied. While matched for gender, there are differences in other demographic data, in particular age, which is known to affect CYP activity (Bebia et al., 2004). We noted that age affected CYP2E1 activity, which was reported to be higher in post‐menopausal women, a finding consistent with that of Bebia et al. (2004). Given that the ovarian cancer population studied were older and all rendered post‐menopausal by virtue of treatment received, this may have affected the CYP2E1 activity observed. However, the magnitude of increase in activity is much greater than that reported and is unlikely to be due to age alone. Further limitation is the lack of genotyping for CYP2D6 and CYP2C19, although the effect of genetic polymorphisms is unlikely to influence the findings.

Despite the clear urgency to assess the metabolic phenotype before administering chemotherapy and to individualise dosing in order to maximise efficacy and reduce toxicity, novel markers of metabolism that are easy to use in the clinical setting are still lacking. A number of groups have investigated simpler alternatives including the erythromycin breath test. However, these still remain cumbersome (Baker et al., 2004). Clayton and colleagues illustrated that metabolomics could predict acetaminophen toxicity and, while no study has investigated the utility of metabolomics in prediction of CYP activity, this may open a new avenue of research (Clayton et al., 2006).

In conclusion, we investigated the effects of cancer on the metabolic phenotype and its association with therapy‐related toxicity. We have shown that there is marked increase in the activity of CYP2E1 and repression of CYP3A, which is associated with raised pro‐inflammatory cytokines. This may have implications for prescribing medications that are substrates for these P450s, especially in the palliative setting.

4.1. Additional information

Ethics approval and consent to participate: The study was approved by the Charing Cross Research Ethics Committee, Ethics Number 06/Q0411/157. All subjects gave informed consent prior to study enrolment. The study was performed in accordance with the Declaration of Helsinki.

CONFLICT OF INTEREST

The authors declare no conflicts of interest.

AUTHOR CONTRIBUTIONS

S.T. contributed in all the study‐related procedures, experimental sample preparation, data analysis, and manuscript preparation; P.L. in the experimental sample preparation, data analysis, and manuscript preparation; R.J.E. in the experimental design, data interpretation, statistics, and manuscript preparation; L.M. in the regulatory submissions, protocol writing, experimental sample preparation, and manuscript preparation; R.R. in the statistical analysis and manuscript preparation; E.O.A. in the data interpretation and manuscript preparation; S.C. in the study design, data interpretation, and manuscript preparation; C.L. in the study design, data interpretation, and manuscript preparation; and R.S. in the study concept, protocol preparation, regulatory submission, all study‐related procedures, sample preparation, and manuscript submission.

DECLARATION OF TRANSPARENCY AND SCIENTIFIC RIGOUR

This Declaration acknowledges that this paper adheres to the principles for transparent reporting and scientific rigour of preclinical research as stated in the BJP guidelines for Design & Analysis and as recommended by funding agencies, publishers, and other organisations engaged with supporting research.

Supporting information

Figure S1.

Metabolic phenotypes of CYP3A (A), CYP2E1 (B), CYP1A2 (C), CYP2C19 (D) and CYP2D6 (E) metabolism according to grade of toxicity experienced by patients receiving chemotherapy (n = 22). Toxicity is graded according to NCI CTC version 2. Box and whisker plots are shown indicating the range of metabolic activity and median.

Click here for additional data file. (97.9KB, tif)

ACKNOWLEDGEMENTS

This article presents independent research funded by the Cancer Institute NSW, Australia. This research was funded by the Cancer Institute NSW, Australia, and supported by the NIHR Imperial Biomedical Research Centre and Clinical Research Facility. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the DHSC.

This work was supported by an unrestricted educational grant from the Cancer Institute NSW, Sydney, Australia, to R.S.

Trousil S, Lee P, Edwards RJ, et al. Altered cytochrome 2E1 and 3A P450‐dependent drug metabolism in advanced ovarian cancer correlates to tumour‐associated inflammation. Br J Pharmacol. 2019;176:3712–3722. 10.1111/bph.14776

Sebastian Trousil and Patrizia Lee contributed equally to this work.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1.

Metabolic phenotypes of CYP3A (A), CYP2E1 (B), CYP1A2 (C), CYP2C19 (D) and CYP2D6 (E) metabolism according to grade of toxicity experienced by patients receiving chemotherapy (n = 22). Toxicity is graded according to NCI CTC version 2. Box and whisker plots are shown indicating the range of metabolic activity and median.

Click here for additional data file. (97.9KB, tif)

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