Development of a Simultaneous Liquid Chromatography-Tandem Mass Spectrometry Analytical Method for Urinary Endogenous Substrates and Metabolites for Predicting Cytochrome P450 3A4 Activity

Abstract

CYP3A4, which contributes to the metabolism of more than 30% of clinically used drugs, exhibits high variation in its activity; therefore, predicting CYP3A4 activity before drug treatment is vital for determining the optimal dosage for each patient. We aimed to develop and validate an LC-tandem mass spectrometry (LC-MS/MS) method that simultaneously measures the levels of CYP3A4 activity-related predictive biomarkers (6β-hydroxycortisol (6β-OHC), cortisol (C), 1β-hydroxydeoxycholic acid (1β-OHDCA), and deoxycholic acid (DCA)). Chromatographic separation was achieved using a YMC-Triart C18 column and a gradient flow of the mobile phase comprising deionized water/25% ammonia solution (100 : 0.1, v/v) and methanol/acetonitrile/25% ammonia solution (50 : 50 : 0.1, v/v/v). Selective reaction monitoring in the negative-ion mode was used for MS/MS, and run times of 33 min were used. All analytes showed high linearity in the range of 3–3000 ng/mL. Additionally, their concentrations in urine samples derived from volunteers were analyzed via treatment with deconjugation enzymes, ignoring inter-individual differences in the variation of other enzymatic activities. Our method satisfied the analytical validation criteria under clinical conditions. Moreover, the concentrations of each analyte were quantified within the range of calibration curves for all urine samples. The conjugated forms of each analyte were hydrolyzed to accurately examine CYP3A4 activity. Non-invasive urine sampling employed herein is an effective alternative to invasive plasma sampling. The analytically validated simultaneous quantification method developed in this study can be used to predict CYP3A4 activity in precision medicine and investigate the potential clinical applications of CYP3A4 biomarkers (6β-OHC/C and 1β-OHDCA/DCA ratios).

INTRODUCTION

In personalized medicine, clinical tests, such as cancer genome sequencing and selection of optimal drugs and dosing, and genetic tests for pharmacogenetic polymorphisms, play an important role in considering inter-individual differences in therapeutic effects and adverse drug reactions.^1–6) Cancer genomic medicine has been developed and clinically implemented in recent decades, and therapeutic drug monitoring is performed for dose adjustment.^7,8) However, severe adverse drug responses, owing to inappropriate dosing, can lead to the termination of drug therapy; therefore, it is necessary to perform appropriate dosing before drug therapy.

Over 90% of clinically used drugs, including anticancer drugs, are metabolized by CYP enzymes, which are mainly expressed in the liver.^9,10) In particular, CYP3A4 is one of the major drug-metabolizing enzymes involved in the activity of 30–50% of clinically used drugs; therefore, variation in CYP3A4 activity affects drug response.^11,12) According to previous reports, including those of the U.S. Food and Drug Administration (FDA), inter-individual variability is often caused by CYP3A4 inducers and inhibitors, food–drug and drug–drug interactions, and genetic polymorphisms.^13–17) Additionally, CYP3A4 expression should be considered when evaluating the wide range of their inter-individual differences.¹⁵⁾ In this context, an effective method for evaluating CYP3A4 activity is required to predict and design dosing algorithms because various adverse events are caused by these variations in clinical practice.

Micro-dosing tests can be performed to evaluate in vivo CYP3A4 activity; however, the burden for the patient remains owing to the use of a compound and not a drug.¹⁸⁾ Endogenous biomarkers present in the plasma or urine for CYP3A4 activity prediction may help resolve these issues. To date, the concentration ratios of 4β-hydroxycholesterol to cholesterol, 6β-hydroxycortisol (6β-OHC) to cortisol (C), and 1β-hydroxydeoxycholic acid (1β-OHDCA) to deoxycholic acid (DCA) in the plasma or urine have been correlated with the concentration of drugs metabolized by CYP3A4.^19–28) Notably, 1β-OHDCA has been reported as a CYP3A4-specific biomarker, although CYP3A4 and CYP3A5 often exhibit an overlap in substrate specificity.²²⁾ As 4β-hydroxycholesterol has a relatively long half-life (approximately 17 d) compared with that of other biomarkers,²⁹⁾ 6β-OHC and 1β-OHDCA may represent better indicators of the actual CYP3A4 activity at the time of sample collection. Moreover, the evaluation of CYP3A4 activity using urine samples will have the advantage of being a non-invasive and simple collection method compared to the evaluation using plasma. Notably, there are no reports on the simultaneous analysis of several CYP3A4 substrates and metabolites; therefore, comparing biomarker usability for predicting CYP3A4 activity is difficult.

In this study, we validated an LC-tandem mass spectrometry (LC-MS/MS) method for predicting CYP3A4 activity by quantifying the urinary concentrations of each analyte (Fig. 1). In addition, biomarker concentration ratios (6β-OHC/C and 1β-OHDCA/DCA) in the urine samples of 18 volunteers were evaluated to assess inter-individual differences in their concentrations. This approach incorporated hydrolysis reactions to determine the actual CYP3A4 activity because C, DCA, and their metabolites are mainly excreted in the urine as conjugates.²²⁾

Fig. 1. Chemical Structures of Analytes Analyzed in This Study

A, cortisol; B, 6β-hydroxycortisol; C, deoxycholic acid; D, 1β-hydroxydeoxycholic acid.

MATERIALS AND METHODS

Chemicals, Reagents, and Participants

Reagents were purchased from the following sources: 6β-OHC, 6β-hydroxycortisol-²H₄, and 1β-OHDCA (Santa Cruz Biotechnology, Dallas, TX, U.S.A.); C, DCA, deoxycholic acid-²H₄, cholic acid-²H₄, glycodeoxycholic acid, 25% ammonia solution, sulfatase from Clostridium perfringens, and choloylglycine hydrolase from C. perfringens (Sigma-Aldrich, St. Louis, MO, U.S.A.); cortisol-¹³C₃ (Cambridge Isotope Laboratories, Tewksbury, MA, U.S.A.); cholic acid (FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan); ursodeoxycholic acid and taurodeoxycholic acid (Nacalai Tesque, Kyoto, Japan); deoxycholic acid 3-sulfate sodium salt (IsoSciences LLC, Ambler, PA, U.S.A.); and β-glucuronidase/arylsulfatase (Roche, Basel, Switzerland). Deoxycholic acid 3-glucuronide was synthesized according to a previously described protocol.³⁰⁾ Urine samples from 18 volunteers were collected, stored at −80 °C, and analyzed. The work was performed in accordance with the ethical principles for medical research outlined in the Declaration of Helsinki 1964 and per subsequent revisions (https://www.wma.net/ [accessed Jan. 19th, 2022]). The participants provided written informed consent, and the study protocols were approved by the Ethics Review Board of Tohoku University Hospital (Approval No. 2021-1-928). All other chemicals and reagents were of the highest commercially available quality.

Calibration Curve

Stock solutions of all analytes and the internal standard (IS) were dissolved at 10–1000 µg/mL concentration and stored at −20 °C. For clinical applications, sample preparation for calibration curves was performed based on a previously reported method with several modifications.^23,24) Each calibration standard (CS) was prepared by diluting the stock solutions to 3, 10, 30, 100, 300, 1000, and 3000 ng/mL in water/ethanol (1 : 1, v/v). After adding a 100 µL aliquot of CS, samples were added to 900 µL of acetonitrile containing 2 ng cortisol-¹³C₃, 25 ng 6β-hydroxycortisol-²H₄, 10 ng deoxycholic acid-²H₄, and 20 ng cholic acid-²H₄ as ISs; the mixture was vacuum-dried at 40 °C, and the dried powder was dissolved in 100 µL of 0.1 M Tris–HCl (pH 5.0) followed by the addition of 300 µL acetonitrile. The mixture was vacuum-dried at 40 °C and the dried powder was dissolved in 50 µL of water/methanol/acetonitrile (2 : 1 : 1, v/v/v). The peak area ratio of the analyte and the IS was plotted against the standard concentration. Calibration curves were generated using the least-squares method with 1/x weighting.

LC-MS/MS Conditions

Each compound was measured in the negative-ion detection mode using the selected reaction monitoring (SRM) mode. A Nexera ultra-HPLC system (Shimadzu, Kyoto, Japan) and a QTRAP 6500 system (Sciex, Framingham, MA, U.S.A.) were combined and used as an LC/MS/MS system. The injection volume was set to 5 µL for LC-MS/MS analysis. Chromatographic separation was performed using a YMC-Triart C18 column (2.1 mm i.d. × 150 mm, 3 µm; YMC, Kyoto, Japan) maintained at 40 °C. The mobile phases were prepared using deionized water/25% ammonia solution (100 : 0.1, v/v) as eluent A and methanol/acetonitrile/25% ammonia solution (50 : 50 : 0.1, v/v/v) as eluent B. The gradient program was as follows: elution was initiated using 5% B for 3 min, followed by a linear gradient of 20% B from 3–15 min. A linear gradient to 95% B from 15–25 min was held at 95% B for 3 min and then immediately returned to the initial conditions, which were maintained for 5 min until the end of the run at a flow rate of 0.4 mL/min. Quantification was performed using the Analyst ver. 1.6.2 or SCIEX OS-Q (Sciex) software, in which ion SRM transitions were monitored (Table 1). The optimized mass spectrometry conditions were as follows: curtain gas, 10 psi; collision gas, 12 psi; ion transfer voltage, −3000 V; temperature, 350 °C; ion source gas 1, 80 psi; and ion source gas 2, 50 psi.

Table 1. Optimized Selecting Reaction Monitoring Parameters for Each Analyte

	m/z	DP (V)	EP (V)	CE (V)	CXP (V)
Cortisol	361.1→331.2	−40	−14	−15	−15
6β-Hydroxycortisol	377.1→347.3	−100	−6	−15	−21
Deoxycholic acid	391.2→345.3	−160	−6	−50	−24
1β-Hydroxydeoxycholic acid	407.2→343.0	−120	−10	−45	−21
Cortisol-13C3	364.1→334.4	−100	−6	−20	−12
6β-Hydroxycortisol-2H4	381.1→351.3	−40	−4	−20	−15
Deoxycholic acid-2H4	395.2→349.4	−160	−8	−35	−15
Cholic acid-2H4	411.2→347.5	−60	−2	−50	−9

m/z, mass-to-charge ratio; DP, declustering potential; EP, entrance potential; CE, collision energy; CXP, collision cell exit potential.

Validation

For quality control (QC), mixed solutions of 3, 7, 200, and 2400 ng/mL were prepared as lower limit of quantification (LLOQ), low-quality control (LQC), middle-quality control (MQC), and high-quality control (HQC) samples, respectively. To determine the matrix effects, 100 µL of MQC solution or water/ethanol (1 : 1, v/v) was dried under a stream of nitrogen gas. Subsequently, 100 µL of water/ethanol (1 : 1, v/v) or urine from six different volunteers was added and mixed with 900 µL of IS mixture (“Calibration Curve”). The specimens were analyzed using the procedure described in “Calibration Curve” and “LC-MS/MS Conditions.” The matrix factor (MF) for each analyte was calculated as follows: IS-corrected MF was calculated to consider the MF of the IS ratio, according to previously reported procedures.^31,32)

  

  

Intra- and inter-assay precision and accuracy were evaluated by measuring QC samples at five different concentrations (blank, 0 ng/µL; LLOQ, 3 ng/µL; LQC, 7 ng/µL; MQC, 200 ng/µL; and HQC, 2400 ng/µL). Briefly, 100 µL of each QC solution was dried under a stream of nitrogen gas, and then 100 µL of urine was added and mixed with 900 µL of the IS mixture (“Calibration Curve”). The specimens were analyzed using the procedure described above. The recovery and relative standard deviation percentages of each analyte were calculated based on relative error (RE) and coefficient of variation (CV), respectively. Blank urine was used as a reference for calculation because the analytes were endogenous compounds.

  

  

The stability of each analyte in urine was assessed after incubation at room temperature (25 °C) for 8 and 24 h, at 4 °C for 8 and 48 h, and at −80 °C for 28 d; after freeze-thaw cycles (repeated three times); and after incubation at 48 h in an autosampler. Briefly, 100 µL of QC solutions (LQC and HQC) were dried under a stream of nitrogen gas, and then urine was added and stored under these conditions. The specimens were analyzed using the procedure described above. The quantitative values were calculated as the recovery compared to the data for samples prepared immediately.

Ratios of Metabolite to the CYP3A4 Substrate

Eighteen urine samples were obtained from volunteers in the morning. Sample preparation for urine analysis was performed based on a previously reported method, with several modifications.^23,24) A 100 µL aliquot of urine was added to 900 µL methanol containing the IS mixture (see above) for deproteinization. After the mixture was centrifuged at 15000 × g for 10 min at 4 °C, the supernatant was vacuum-dried at 40 °C, and the dried powder was dissolved in 100 µL of 0.1 M Tris–HCl (pH 5.0) containing hydrolysis enzymes (5 U choloylglycine hydrolase and 10 µL β-glucuronidase/arylsulfatase). The mixture was incubated overnight at 37 °C with shaking at 1500 rpm. The hydrolysis reaction was quenched using 300 µL acetonitrile, followed by centrifugation at 15000 × g for 10 min at 4 °C. The supernatant was vacuum-dried at 40 °C, and dissolved in 50 µL water/methanol/acetonitrile (2 : 1 : 1, v/v/v). Five microliters of samples were analyzed using the LC-MS/MS method described in “LC-MS/MS Conditions,” and the ratio of the level of metabolites (6β-OHC and 1β-OHDCA) to that of CYP3A4 substrates (C and DCA), respectively, was calculated.

Additionally, four compounds (deoxycholic acid 3-sulfate, taurodeoxycholic acid, glycodeoxycholic acid, and deoxycholic acid 3-glucuronide) were hydrolyzed to confirm that the reaction was successful. Briefly, 100 µL of 0.1 M Tris–HCl (pH 5.0) containing hydrolysis enzymes (5 U choloylglycine hydrolase and 10 µL β-glucuronidase/arylsulfatase) and 1 µg of each compound (deoxycholic acid 3-sulfate, taurodeoxycholic acid, glycodeoxycholic acid, and deoxycholic acid 3-glucuronide) were incubated overnight at 37 °C with shaking at 1500 rpm. The hydrolysis reaction was quenched using 300 µL acetonitrile, followed by centrifugation at 15000 × g for 10 min at 4 °C. The supernatant was vacuum-dried at 40 °C, and dissolved in 50 µL water/methanol/acetonitrile (2 : 1 : 1, v/v/v). To verify whether conjugated DCAs were present, a previously described method was applied.³³⁾

RESULTS

All analytes and ISs were successfully separated and exhibited elution profiles with sharp peaks. In contrast, no signal peak was observed for the blank injection under optimized LC-MS/MS conditions (Fig. 2). Additionally, the peaks were separated from those of the urinary contaminants.

Fig. 2. Representative Selected Reaction Monitoring Chromatograms of (A) Calibration Standard Sample, (B) Urine Sample, and (C) Urine Sample Spiked with Quality Control Solution

Relevant parameters for transitions of analytes are listed in Table 1. C, cortisol; 6β-OHC, 6β-hydroxycortisol; DCA, deoxycholic acid; 1β-OHDCA, 1β-hydroxydeoxycholic acid; CA, cholic acid.

The calibration curves exhibited a linear range of 3–3000 ng/mL, with R² values of 0.9987–0.9996, and below 15% of the nominal concentration in each CS sample (Supplementary Table 1). For the IS-corrected MF, the RE values ranged from −12.00 to 2.09%; the MFs of 6β-OHC and C tended to increase (Table 2). Similarly, the CV values of IS-corrected MF were within ±10.15%. Intra- and inter-day accuracy and precision were determined at the LLOQ, LQC, MQC, and HQC levels (Table 3). Although endogenous 6β-OHC was abundant compared with LLOQ concentrations, the RE and CV values of LLOQ samples ranged within ±20%. Moreover, both RE and CV values of LQC, MQC, and HQC samples ranged within ±15%. The stability test showed that the concentrations of 6β-OHC, 1β-DCA, and DCA in urine remained unaltered under all the tested conditions; however, the C concentration in urine decreased under the thawing conditions (Table 4).

Table 2. Matrix Factor of Analytes and Internal Standards

Analyte	IS	MF		IS-corrected MF
		RE (%)	CV (%)	RE (%)	CV (%)
Cortisol	Cortisol-13C3	43.50	6.70	2.09	4.59
6β-Hydroxycortisol	6β-Hydroxycortisol-2H4	81.87	24.69	−12.00	10.15
Deoxycholic acid	Deoxycholic acid-2H4	−0.27	8.24	−11.72	3.75
1β-Hydroxydeoxycholic acid	Cholic acid-2H4	0.57	4.56	−4.24	3.16
Cortisol-13C3		40.70	6.86	—	—
6β-Hydroxycortisol-2H4		105.80	21.00	—	—
Deoxycholic acid-2H4		13.13	9.14	—	—
Cholic acid-2H4		5.12	5.71	—	—

Data represent the RE and CV values of samples prepared independently using urine from six different volunteers. IS, internal standard; MF, matrix factor; RE, relative error; CV, coefficient of variation.

Table 3. Assay Performance for Each Analyte

Analyte	RE (%)				CV (%)
	LLOQ	LQC	MQC	HQC	LLOQ	LQC	MQC	HQC
Intra-day assay
Cortisol	5.56	9.76	6.19	−4.49	5.15	3.81	4.35	3.87
6β-Hydroxycortisol	14.44	14.29	12.96	−9.78	15.49	14.79	6.17	3.41
Deoxycholic acid	4.44	8.33	13.65	9.68	7.18	3.48	4.34	3.98
1β-Hydroxydeoxycholic acid	6.67	13.57	10.63	0.65	3.42	4.55	2.63	3.41
Inter-day assay
Cortisol	4.44	4.29	3.45	−7.94	3.69	7.12	3.75	4.67
6β-Hydroxycortisol	14.44	8.57	14.18	−6.22	19.83	13.35	1.84	7.44
Deoxycholic acid	5.56	14.29	10.10	9.34	7.95	1.25	4.30	4.05
1β-Hydroxydeoxycholic acid	8.89	14.76	14.83	−0.43	9.35	8.47	8.20	14.89

Data represent RE and CV values for the Intra-day (N = 6) and Inter-day (N = 3) assays. RE, relative error; CV, coefficient of variation; LLOQ, 3 ng/mL; LQC, 7 ng/mL; MQC, 200 ng/mL; HQC, 2400 ng/mL.

Table 4. Stability under Different Conditions

Analyte	Freeze and Thaw		r.t. for 8 h		r.t. for 24 h		4 °C for 8 h		4 °C for 48 h		–80 °C for 28 d		Autosampler for 48 h
	RE (%)	CV (%)	RE (%)	CV (%)	RE (%)	CV (%)	RE (%)	CV (%)	RE (%)	CV (%)	RE (%)	CV (%)	RE (%)	CV (%)
LQC
Cortisol	−4.85	7.03	2.41	12.92	−33.22	4.60	−9.74	9.07	−48.58	14.52	−11.15	12.42	−6.74	7.78
6β-Hydroxycortisol	−1.91	8.17	1.71	4.79	8.34	12.60	10.34	9.90	−2.52	6.63	9.54	9.48	9.04	4.06
Deoxycholic acid	−3.37	5.42	−0.36	5.56	14.21	10.39	−1.10	1.84	14.30	4.89	5.09	4.38	1.66	6.62
1β-Hydroxydeoxycholic acid	0.91	6.77	−4.23	5.15	7.34	2.86	4.32	3.71	−0.71	6.89	−13.22	9.03	8.23	5.47
HQC
Cortisol	−4.50	1.65	−3.21	6.99	−21.51	5.90	−0.86	3.07	−33.42	1.90	−11.15	5.52	−8.91	4.19
6β-Hydroxycortisol	3.68	10.11	1.54	5.45	14.65	7.78	0.51	3.17	12.30	2.61	12.12	7.45	11.61	1.94
Deoxycholic acid	−1.54	3.09	2.88	2.49	1.03	5.15	1.20	2.78	8.88	0.90	2.97	5.67	−2.87	1.41
1β-Hydroxydeoxycholic acid	−3.43	2.92	6.86	1.81	−5.89	2.72	11.24	3.61	4.91	7.33	−0.05	2.69	13.51	3.66

Data represent RE and CV values for samples prepared independently in triplicate. r.t., room temperature; RE, relative error; CV, coefficient of variation; LQC, 7 ng/mL; HQC, 2400 ng/mL.

The concentrations of almost all analytes in the 18 urine samples varied in the calibration curves (Supplementary Table 2). 6β-OHC and 1β-DCA concentrations were correlated with each substrate concentration (Figs. 3A, B), although the concentration ratio in certain participants deviated from the straight line. Moreover, the biomarker concentration ratios (6β-OHC/C and 1β-OHDCA/DCA) among the participants were not strongly correlated (Fig. 3C).

Fig. 3. Correlations among Biomarker Concentrations in Urine (A: C and 6β-OHC; B: DCA and 1β-OHDCA)

(C) Correlation between the ratios of 6β-OHC/C and 1β-OHDCA/DCA. C, cortisol; 6β-OHC, 6β-hydroxycortisol; DCA, deoxycholic acid; 1β-OHDCA, 1β-hydroxydeoxycholic acid.

References containing the four analytes (deoxycholic acid 3-sulfate, taurodeoxycholic acid, glycodeoxycholic acid, and deoxycholic acid 3-glucuronide) were prepared and subjected to enzymatic deconjugation. Compared to those in Fig. 4A, the peak areas of the four analytes in Fig. 4B were decreased by >90%, and a conspicuous peak derived from DCA was observed. Furthermore, signal peaks corresponding to the four conjugated analytes in urine samples were not observed (Fig. 4C).

Fig. 4. Representative Selected Reaction Monitoring Chromatograms of (A) Unhydrolyzed Urine Sample Spiked with 1 µg of Deoxycholic Acid 3-Sulfate, Taurodeoxycholic Acid, Glycodeoxycholic Acid, and Deoxycholic Acid 3-Glucuronide; (B) Hydrolyzed Urine Sample Spiked with 1 µg of Deoxycholic Acid 3-Sulfate, Taurodeoxycholic Acid, Glycodeoxycholic Acid, and Deoxycholic Acid 3-Glucuronide; and (C) Hydrolyzed Urine Sample

Relevant parameters for transitions of analytes are listed in Supplementary Table 3. DCA, deoxycholic acid; DCA-3S, deoxycholic acid 3-sulfate; TDCA, taurodeoxycholic acid; GDCA, glycodeoxycholic acid; DCA-3G, deoxycholic acid 3-glucuronide. These analytes were measured using previously reported methods.^33,46)

DISCUSSION

Personalized medicine for improving treatment efficiency and preventing adverse drug reactions is required worldwide.^1,2) Inter-individual differences in CYP3A4 activity represent a critical factor associated with treatment efficiency and adverse drug reaction because CYP3A4 contributes to the metabolism of more than 30% of clinically used drugs, including several anti-cancer drugs, such as cabozantinib, docetaxel, gefitinib, and imatinib.^11,12,34) Previously, severe toxicity caused by docetaxel in transplant patients carrying genetic polymorphisms was reported.³⁵⁾ Loss-of-function allelic variants have been characterized by assessing enzymatic activities for 40 CYP3A4 variant enzymes, and structurally important sites in CYP3A4 have been revealed.³⁶⁾ In contrast, based on a few case reports, CYP3A4 drug–drug interactions are caused by increased drug concentration.^37,38) Overall, determining CYP3A4 activity during drug therapy will play an important role in personalized medicine because various factors potentially affect CYP3A4 activity. To date, several biomarkers of CYP3A4 activity have been identified in plasma and urine; however, the most suitable CYP3A4 biomarkers for evaluating its activity have not been conclusively identified.^19–28) Therefore, a clinically available high-throughput method is required to investigate the utility of CYP3A4 biomarkers. Here, an LC-MS/MS method for predicting CYP3A4 activity by measuring 6β-OHC, C, 1β-OHDCA, and DCA levels was validated and applied using human urine samples.

C and DCA are metabolized by CYP3A4 to 6β-OHC and 1β-OHDCA, respectively, whereas their compounds are mainly conjugated by phase II enzymes, such as sulfatase and glucuronidase.^22,39–41) Although measurements of all free and conjugated analytes are suitable for predicting CYP3A4 activity, certain reference standards, including conjugated hydroxylated compounds, are not available. Hayes et al. suggested that deconjugation enzyme reactions indicate CYP3A4 activity because only non-conjugated DCA and 1β-OHDCA have been quantified.²²⁾ Moreover, cortisol glucuronide and cortisol sulfate are present in human urine; however, the ratio of their concentrations to that of free C varies with age.³⁹⁾ Furthermore, phase II enzyme activity shows inter-individual differences. Therefore, we chose deconjugation enzyme reactions during preprocessing for LC-MS/MS analysis, and all analytes were quantified in human urine samples. DCA 3-sulfate cannot be metabolized by sulfatase owing to insufficient reaction buffer conditions and pH for mediating deconjugated reactions.^23,24) More importantly, phosphate buffer competitively inhibits sulfatase based on the structural similarity of phosphates with sulfates.⁴²⁾ We considered several factors, such as pH, enzyme amounts, and reaction buffer, and could successfully establish a protocol for deconjugation reaction.

Recently, several studies have reported simultaneous analyses of various steroid hormones and bile acids; however, minor compounds, such as 6β-OHC and 1β-OHDCA, have not been included.^43–46) In contrast, the 6β-OHC/C and 1β-OHDCA/DCA ratios in urine have been used as endogenous biomarkers for predicting CYP3A4 activity in previous studies that have adapted negative-ion detection modes for DCA and 1β-OHDCA.^{19,21–26,43)} C and 6β-OHC can be detected in both the positive- and negative-ion modes, although 6β-OHC requires derivatization owing to the low sensitivity of LC-MS/MS and GC/MS.^26,27,43) Therefore, studies on 6β-OHC detection have primarily focused on the establishment of a validated method. Recently, the negative-ion detection mode was used for 6β-OHC quantification.^27,28) Moreover, various compounds, including hormones, have been added to an aqueous ammonium solution as a basic condition, resulting in an ethylene-crosslinked hybrid silica gel column that has been used as the analytical separation column.^46–49) Overall, the LC-MS/MS conditions for the simultaneous analysis of endogenous urinary CYP3A4 biomarkers were successfully optimized.

The bioanalytical method validation was performed considering the principles and criteria prescribed in the U.S. FDA guidelines (https://www.gmp-compliance.org [accessed Sep 6th, 2022]). The RE and CV values of 6β-OHC MF were relatively higher because only two urine samples showed ion enhancement compared with the standard solution. Inter-individual differences in urine matrix may affect the MF because some cases showed avoidance of ion suppression for 6β-OHC from urine and others did not.^50,51) Although several analytes affected matrix effects based on the characteristics of each analyte and inter-individual differences in human urine specimens, a complex matrix, REs, and CVs for IS-corrected MFs were within ±15%, which satisfy the acceptance criteria of the U.S. FDA guidelines. Because the background subtraction method was applied for the quantification of endogenous analytes, the increase in background peak area after spiking with LLOQ samples might be slightly higher than the reproducibility limits.⁵²⁾ Using urine samples containing endogenous analytes at lower concentrations, the intra- and inter-day accuracy and precision were within ±15% (±20% for LLOQ). As for the stability tests, the REs and CVs for C were over ±15% at room temperature for 24 h and at 4 °C for 48 h, whereas other analytes showed good stability. In clinical practice, urine samples can be collected and stored during working hours in a day (generally 8 h). C was stable at room temperature and at 4 °C for 8 h; therefore, the quantitative method in this study was validated for clinical application.

Finally, we assessed the quantitative application of the method for human urine samples obtained from 18 volunteers. In previous studies, the concentration ratios of plasma 6β-OHC/C and urine 1β-OHDCA/DCA have been claimed to represent endogenous CYP3A4 biomarkers.^19,21–28) In the present study, we evaluated the correlation between 6β-OHC/C and 1β-OHDCA/DCA concentration ratios in the urine. However, no strong correlation was observed between each ratio due to various possible factors, in addition to the sample size limitation. DCA 1β-hydroxylation is specifically catalyzed by CYP3A4, whereas C 6β-hydroxylation is catalyzed by CYP3A4 and CYP3A5, which may explain this discrepancy.²²⁾

In summary, we established an analytically validated simultaneous quantification method for investigating the correlation between urinary biomarkers, and applied it to human urine samples. Analytical validation tests satisfied the clinically applicable conditions, and analyte concentrations in all urine samples were within the concentration range used for generating the calibration curves. However, this method requires an overnight deconjugation procedure, and thus it would be necessary to devise operations, such as previous day tests, for daily applications in clinical settings. The simultaneous quantification method developed in this study is highly efficient in comparison to other methods that measure CYP3A4 biomarkers individually. Additionally, conjugated forms of each analyte were hydrolyzed to accurately examine CYP3A4 activity. Although several compounds are defined as biomarkers that reflect CYP3A4 activity, individual biomarkers have never been compared to determine which among them is the best. Combinational biomarker applications may be suitable for individual CYP3A4 activity predictions. Further research using larger sample sizes and urine sampling from patients receiving CYP3A4-metabolized drugs is essential to draw a definitive conclusion. More importantly, non-invasive urine sampling employed in this study is an effective alternative to invasive plasma sampling; therefore, the analytically validated simultaneous quantification method developed in this study can be used to predict CYP3A4 activity in precision medicine and to investigate the potential clinical applications of CYP3A4 biomarkers. In the future, this validated LC-MS/MS method can be used in clinical practice as an approved method for CYP3A4 activity prediction.

Acknowledgments

We are grateful to all the volunteers who provided urine samples. This study was supported by a Grant from the Japan Society for the Promotion of Science (Grant No. 22K15333 to M.K.).

Conflict of Interest

The authors declare no conflict of interest.

Supplementary Materials

This article contains supplementary materials.

REFERENCES

• 1) Venne J, Busshoff U, Poschadel S, Menschel R, Evangelatos N, Vysyaraju K, Brand A. International consortium for personalized medicine: an international survey about the future of personalized medicine. Per. Med., 17, 89–100 (2020).
• 2) Esplin ED, Oei L, Snyder MP. Personalized sequencing and the future of medicine: discovery, diagnosis and defeat of disease. Pharmacogenomics, 15, 1771–1790 (2014).
• 3) Malone ER, Oliva M, Sabatini PJB, Stockley TL, Siu LL. Molecular profiling for precision cancer therapies. Genome Med., 12, 8 (2020).
• 4) Li J, Zhang L, Zhou H, Stoneking M, Tang K. Global patterns of genetic diversity and signals of natural selection for human ADME genes. Hum. Mol. Genet., 20, 528–540 (2011).
• 5) Marques SC, Ikediobi ON. The clinical application of UGT1A1 pharmacogenetic testing: gene-environment interactions. Hum. Genomics, 4, 238–249 (2010).
• 6) Kumondai M, Ito A, Hishinuma E, Kikuchi A, Saito T, Takahashi M, Tsukada C, Saito S, Yasuda J, Nagasaki M, Minegishi N, Yamamoto M, Kaneko A, Teramoto I, Kimura M, Hirasawa N, Hiratsuka M. Development and application of a rapid and sensitive genotyping method for pharmacogene variants using the single-stranded tag hybridization chromatographic printed-array strip (STH-PAS). Drug Metab. Pharmacokinet., 33, 258–263 (2018).
• 7) Verheijen RB, Yu H, Schellens JHM, Beijnen JH, Steeghs N, Huitema ADR. Practical recommendations for therapeutic drug monitoring of kinase inhibitors in oncology. Clin. Pharmacol. Ther., 102, 765–776 (2017).
• 8) Mueller-Schoell A, Groenland SL, Scherf-Clavel O, van Dyk M, Huisinga W, Michelet R, Jaehde U, Steeghs N, Huitema ADR, Kloft C. Therapeutic drug monitoring of oral targeted antineoplastic drugs. Eur. J. Clin. Pharmacol., 77, 441–464 (2021).
• 9) Lynch T, Price A. The effect of cytochrome P450 metabolism on drug response, interactions, and adverse effects. Am. Fam. Physician, 76, 391–396 (2007).
• 10) Fujita K. Cytochrome P450 and anticancer drugs. Curr. Drug Metab., 7, 23–37 (2006).
• 11) Zanger UM, Schwab M. Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacol. Ther., 138, 103–141 (2013).
• 12) Jackson KD, Durandis R, Vergne MJ. Role of cytochrome P450 enzymes in the metabolic activation of tyrosine kinase inhibitors. Int. J. Mol. Sci., 19, 2367 (2018).
• 13) Westlind A, Löfberg L, Tindberg N, Andersson TB, Ingelman-Sundberg M. Interindividual differences in hepatic expression of CYP3A4: relationship to genetic polymorphism in the 5′-upstream regulatory region. Biochem. Biophys. Res. Commun., 259, 201–205 (1999).
• 14) Huang SM, Strong JM, Zhang L, et al. New era in drug interaction evaluation: US Food and Drug Administration update on CYP enzymes, transporters, and the guidance process. J. Clin. Pharmacol., 48, 662–670 (2008).
• 15) Elens L, van Gelder T, Hesselink DA, Haufroid V, van Schaik RH. CYP3A4*22: promising newly identified CYP3A4 variant allele for personalizing pharmacotherapy. Pharmacogenomics, 14, 47–62 (2013).
• 16) Ishiguro A, Sato R, Nagai N. Development of a new Japanese guideline on drug interaction for drug development and appropriate provision of information. Drug Metab. Pharmacokinet., 35, 12–17 (2020).
• 17) Hendriks DFG, Vorrink SU, Smutny T, Sim SC, Nordling Å, Ullah S, Kumondai M, Jones BC, Johansson I, Andersson TB, Lauschke VM, Ingelman-Sundberg M. Clinically relevant cytochrome P450 3A4 induction mechanisms and drug screening in three-dimensional spheroid cultures of primary human hepatocytes. Clin. Pharmacol. Ther., 108, 844–855 (2020).
• 18) Hohmann N, Kocheise F, Carls A, Burhenne J, Haefeli WE, Mikus G. Midazolam microdose to determine systemic and pre-systemic metabolic CYP3A activity in humans. Br. J. Clin. Pharmacol., 79, 278–285 (2015).
• 19) Joellenbeck L, Qian Z, Zarba A, Groopman JD. Urinary 6-beta-hydroxycortisol cortisol ratios measured by high-performance liquid-chromatography for use as a biomarker for the human cytochrome-P-450 3a4. Cancer Epidemiol Biomarkers Prev., 1, 567–572 (1992).
• 20) Diczfalusy U, Nylén H, Elander P, Bertilsson L. 4β-Hydroxycholesterol, an endogenous marker of CYP3A4/5 activity in humans. Br. J. Clin. Pharmacol., 71, 183–189 (2011).
• 21) Rais N, Hussain A, Chawla YK, Kohli KK. Association between urinary 6β-hydroxycortisol/cortisol ratio and CYP3A5 genotypes in a normotensive population. Exp. Ther. Med., 5, 527–532 (2013).
• 22) Hayes MA, Li XQ, Grönberg G, Diczfalusy U, Andersson TB. CYP3A specifically catalyzes 1β-hydroxylation of deoxycholic acid: characterization and enzymatic synthesis of a potential novel urinary biomarker for CYP3A activity. Drug Metab. Dispos., 44, 1480–1489 (2016).
• 23) Li XQ, Thelingwani RS, Bertilsson L, Diczfalusy U, Andersson TB, Masimirembwa C. Evaluation of 1β-hydroxylation of deoxycholic acid as a non-invasive urinary biomarker of CYP3A activity in the assessment of inhibition-based drug-drug interaction in healthy volunteers. J. Pers. Med., 11, 457 (2021).
• 24) Magliocco G, Desmeules J, Bosilkovska M, Thomas A, Daali Y. The 1β-hydroxy-deoxycholic acid to deoxycholic acid urinary metabolic ratio: toward a phenotyping of CYP3A using an endogenous marker? J. Pers. Med., 11, 150 (2021).
• 25) Bergström H, Lindahl A, Warnqvist A, Diczfalusy U, Ekström L, Björkhem-Bergman L. Studies on CYP3A activity during the menstrual cycle as measured by urinary 6beta-hydroxycortisol/cortisol. Pharmacol. Res. Perspect., 9, e00884 (2021).
• 26) Hirano R, Yokokawa A, Furuta T, Shibasaki H. Sensitive and simultaneous quantitation of 6β-hydroxycortisol and cortisol in human plasma by LC-MS/MS coupled with stable isotope dilution method. J. Mass Spectrom., 53, 665–674 (2018).
• 27) Hirano R, Yokokawa A, Furihata T, Shibasaki H. Dried blood spots analysis of 6beta-hydroxycortisol and cortisol using liquid chromatography/tandem mass spectrometry for calculating 6beta-hydroxycortisol to cortisol ratio. J. Mass Spectrom., 56, e4790 (2021).
• 28) Benzi JRL, Moreira FL, Marques MP, Duarte G, Suarez-Kurtz G, Lanchote VL. A background subtraction approach for determination of endogenous cortisol and 6beta-hydroxycortisol in urine by UPLC-MS/MS with application in a within-day variability study in HIV-infected pregnant women. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci., 1144, 122074 (2020).
• 29) Diczfalusy U, Kanebratt KP, Bredberg E, Andersson TB, Böttiger Y, Bertilsson L. 4β-Hydroxycholesterol as an endogenous marker for CYP3A4/5 activity. Stability and half-life of elimination after induction with rifampicin. Br. J. Clin. Pharmacol., 67, 38–43 (2009).
• 30) Goto J, Miura H, Inada M, Nambara T, Nagakura T, Suzuki H. Studies on steroids: CCXXXVIII. Determination of bile acids in liver tissue by gas chromatography-mass spectrometry with negative ion chemical ionization detection. J. Chromatogr., 452, 119–129 (1988).
• 31) Maekawa M, Jinnoh I, Narita A, Iida T, Saigusa D, Iwahori A, Nittono H, Okuyama T, Eto Y, Ohno K, Clayton PT, Yamaguchi H, Mano N. Investigation of diagnostic performance of five urinary cholesterol metabolites for Niemann–Pick disease type C. J. Lipid Res., 60, 2074–2081 (2019).
• 32) Iwahori A, Maekawa M, Narita A, Kato A, Sato T, Ogura J, Sato Y, Kikuchi M, Noguchi A, Higaki K, Okuyama T, Takahashi T, Eto Y, Mano N. Development of a diagnostic screening strategy for Niemann–Pick diseases based on simultaneous liquid chromatography-tandem mass spectrometry analyses of n-palmitoyl-o-phosphocholine-serine and sphingosylphosphorylcholine. Biol. Pharm. Bull., 43, 1398–1406 (2020).
• 33) Suga T, Yamaguchi H, Ogura J, Shoji S, Maekawa M, Mano N. Altered bile acid composition and disposition in a mouse model of non-alcoholic steatohepatitis. Toxicol. Appl. Pharmacol., 379, 114664 (2019).
• 34) Marre F, Sanderink GJ, de Sousa G, Gaillard C, Martinet M, Rahmani R. Hepatic biotransformation of docetaxel (Taxotere) in vitro: involvement of the CYP3A subfamily in humans. Cancer Res., 56, 1296–1302 (1996).
• 35) Powell NR, Shugg T, Ly RC, Albany C, Radovich M, Schneider BP, Skaar TC. Life-threatening docetaxel toxicity in a patient with reduced-function CYP3A variants: a case report. Front. Oncol., 11, 809527 (2022).
• 36) Kumondai M, Gutiérrez Rico EMG, Hishinuma E, Ueda A, Saito S, Saigusa D, Tadaka S, Kinoshita K, Nakayoshi T, Oda A, Abe A, Maekawa M, Mano N, Hirasawa N, Hiratsuka M. Functional characterization of 40 CYP3A4 variants by assessing midazolam 1′-hydroxylation and testosterone 6β-hydroxylation. Drug Metab. Dispos., 49, 212–220 (2021).
• 37) Abu Mellal A, Hussain N, Said ASA. The clinical significance of statins-macrolides interaction: comprehensive review of in vivo studies, case reports, and population studies. Ther. Clin. Risk Manag., 15, 921–936 (2019).
• 38) Skov K, Falskov B, Jensen EA, Dorff MH. Supratheraputic rivaroxaban levels: a persistent drug–drug interaction after discontinuation of amiodarone. Basic Clin. Pharmacol. Toxicol., 127, 351–353 (2020).
• 39) Krasowski MD, Drees D, Morris CS, Maakestad J, Blau JL, Ekins S. Cross-reactivity of steroid hormone immunoassays: clinical significance and two-dimensional molecular similarity prediction. BMC Clin. Pathol., 14, 33 (2014).
• 40) Wang R, Hartmann MF, Wudy SA. Targeted LC-MS/MS analysis of steroid glucuronides in human urine. J. Steroid Biochem. Mol. Biol., 205, 105774 (2021).
• 41) Bathena SP, Thakare R, Gautam N, Mukherjee S, Olivera M, Meza J, Alnouti Y. Urinary bile acids as biomarkers for liver diseases I. Stability of the baseline profile in healthy subjects. Toxicol. Sci., 143, 296–307 (2015).
• 42) Haning RV Jr, Hackett RJ, Canick JA. Steroid sulfatase in the human ovary and placenta: enzyme kinetics and phosphate inhibition. J. Steroid Biochem. Mol. Biol., 41, 161–165 (1992).
• 43) Gouveia MJ, Brindley PJ, Santos LL, Correia da Costa JM, Gomes P, Vale N. Mass spectrometry techniques in the survey of steroid metabolites as potential disease biomarkers: a review. Metabolism, 62, 1206–1217 (2013).
• 44) Gómez C, Stücheli S, Kratschmar DV, Bouitbir J, Odermatt A. Development and validation of a highly sensitive LC-MS/MS method for the analysis of bile acids in serum, plasma, and liver tissue samples. Metabolites, 10, 282 (2020).
• 45) Babu AF, Koistinen VM, Turunen S, Solano-Aguilar G, Urban JF Jr, Zarei I, Hanhineva K. Identification and distribution of sterols, bile acids, and acylcarnitines by LC-MS/MS in humans, mice, and pigs-a qualitative analysis. Metabolites, 12, 49 (2022).
• 46) Shoji S, Maekawa M, Ogura J, Sato T, Mano N. Identification cholesterol metabolites altered before the onset of nonalcoholic steatohepatitis by targeted metabolomics. Biochim. Biophys. Acta Mol. Cell Biol. Lipids, 1867, 159135 (2022).
• 47) Peng L, Farkas T. Analysis of basic compounds by reversed-phase liquid chromatography-electrospray mass spectrometry in high-pH mobile phases. J. Chromatogr. A, 1179, 131–144 (2008).
• 48) Ozaki H, Nakano Y, Sakamaki H, Yamanaka H, Nakai M. Basic eluent for rapid and comprehensive analysis of fatty acid isomers using reversed-phase high performance liquid chromatography/Fourier transform mass spectrometry. J. Chromatogr. A, 1585, 113–120 (2019).
• 49) Abe A, Maekawa M, Sato T, Sato Y, Kumondai M, Takahashi H, Kikuchi M, Higaki K, Ogura J, Mano N. Metabolic alteration analysis of steroid hormones in Niemann–Pick disease type C model cell using liquid chromatography/tandem mass spectrometry. Int. J. Mol. Sci., 23, 4459 (2022).
• 50) Wang Y, Fujioka N, Xing C. Quantitative profiling of cortisol metabolites in human urine by high-resolution accurate-mass MS. Bioanalysis, 10, 2015–2026 (2018).
• 51) Arioli F, Gamberini MC, Pavlovic R, Di Cesare F, Draghi S, Bussei G, Mungiguerra F, Casati A, Fidani M. Quantification of cortisol and its metabolites in human urine by LC-MSⁿ: applications in clinical diagnosis and anti-doping control. Anal. Bioanal. Chem., 414, 6841–6853 (2022).
• 52) Thakare R, Chhonker YS, Gautam N, Alamoudi JA, Alnouti Y. Quantitative analysis of endogenous compounds. J. Pharm. Biomed. Anal., 128, 426–437 (2016).