Effects of Cimetidine and Dolutegravir on the Endogenous Drug–Drug Interaction Biomarkers for Organic Cation Transporter 2 and Multidrug and Toxin Extrusion Protein 1 in Healthy Volunteers

Abstract

This study was designed to assess the quantitative performance of endogenous drug–drug interaction (DDI) biomarkers (N1‐methylnicotinamide (1‐NMN), N1‐methyladenosine (m¹A), and creatinine) for the organic cation transporters, OCT2 and MATE1/2K in the kidney. Ten healthy volunteers received cimetidine (400 and 800 mg, single dose) or dolutegravir (50 mg, twice a day) together with metformin (500 mg). Cimetidine and dolutegravir were considered to act mainly as MATE1/2K and OCT2 inhibitors, respectively. The renal clearance (CL_r) of metformin was decreased by 15.5% and 42.5% by cimetidine 400 and 800 mg, and by 26.8% and 56.9% by dolutegravir first and fifth doses, respectively. CL_r ratio (CL_rR) of 1‐NMN were 0.93 and 0.64 for cimetidine 400 and 800 mg, and 0.87 and 0.47 for dolutegravir first and fifth doses, respectively. CL_rR of m¹A was less than that of 1‐NMN: 1.0 and 0.80 for cimetidine 400 and 800 mg, and 0.77 and 0.71 for dolutegravir first and fifth doses, respectively. CL_r of creatinine was significantly decreased only by cimetidine 800 mg. Individual CL_rR of 1‐NMN and m¹A showed a positive correlation with the corresponding CL_rR of metformin with r ² of 0.58 and 0.55, respectively. When evaluated individually, m¹A showed a better correlation during cimetidine periods (r ² 0.64) than 1‐NMN (r ² 0.36), but vice versa during dolutegravir periods (r ² 1‐NMN, 0.80; m¹A, 0.32). These results suggest that 1‐NMN and m¹A might be more promising than creatinine as endogenous biomarkers for quantitatively assessing the DDI potential of investigational drugs for OCT2 and MATE1/2K based on their CL_rR change.

Study Highlights.

• WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?

The prediction of drug‐transporter‐mediated drug–drug interactions (DDI) is an indispensable issue in drug development. This endogenous biomarker strategy is considered promising for translating preclinical DDI data to humans. However, clinical data supporting their application in drug development remains insufficient for kidney transporters.

• WHAT QUESTION DID THIS STUDY ADDRESS?

This study addressed the dependence of the pharmacokinetic parameter changes of three endogenous biomarkers, 1‐NMN, m¹A, and creatinine, on the systemic exposure to the perpetrator drugs (cimetidine and dolutegravir) with different inhibition potencies against OCT2 and MATE1/2K to elucidate their pros and cons.

• WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE?

This study validated the mechanism of DDI between metformin and dolutegravir. Based on this knowledge, we demonstrated performance of 1‐NMN and m¹A as the endogenous quantitative biomarkers for OCT2 and MATE1/2K in the kidneys, and reported point of caution that affects detection sensitivity.

• HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE?

The data presented in this study aid design of the future clinical study to investigate inhibition of OCT2 and MATE1/2K in drug development, and gaining understanding the mechanism underlying the drug–drug interactions. These data also provide the basis for regulatory science.

Pharmacokinetic drug–drug interactions (DDIs) caused by the inhibition of drug transporters pose a significant risk of adverse reactions. To avoid DDIs in clinical settings, it is essential to gather information about the extent and duration of the inhibition of major drug transporters during the development of drug candidates. This risk assessment was conducted in accordance with regulatory guidelines for both preclinical and clinical evaluation. Endogenous biomarkers have recently been proposed as a means of detecting transporter inhibition during drug development. ¹ , ² , ³ , ⁴ These biomarkers act as substrates of drug transporters that are synthesized in the body, absorbed from the gut as nutrients, or generated as metabolites by the gut microbiota and subsequently absorbed into the bloodstream. They are eliminated from the systemic circulation by hepatic and renal transporters. The inhibition of drug transporters alters the pharmacokinetic parameters of these biomarkers, and upon the decline of inhibitor concentrations, the parameters return to their baseline levels. Endogenous biomarkers can assess the strength and duration of transporter inhibition after administration of investigational drugs, thereby eliminating the need for probe drug administration and offering more opportunities to investigate the DDI potential of investigational drugs during clinical development.

In the kidney, three transporters, organic cation transporter 2 (OCT2) and multidrug and toxin extrusion protein 1 (MATE1) and MATE2‐K, play crucial roles in the secretion of water‐soluble cationic drugs such as metformin, which is often recommended and utilized as a probe drug in clinical DDI studies. ⁵ The inhibition of OCT2, MATE1, and MATE2‐K can lead to a reduction in the urinary excretion of their substrate drugs and prolong elimination half‐life, potentiating drug action in the body. ¹ , ⁶ Perpetrator drugs known to cause DDIs with metformin include cimetidine, ⁷ , ⁸ pyrimethamine, ⁹ , ¹⁰ , ¹¹ trimethoprim, ¹² and vandetanib. ¹³ Dolutegravir (50 mg, QD or BID), which is used to treat Human Immunodeficiency Virus (HIV) infection, has been found to increase the area under the concentration‐time curve (AUC) of metformin in both HIV‐infected patients and healthy subjects ¹⁴ ; however, its effect on the renal clearance (CL_r) of metformin has yet to be determined. It is important to note that inhibition of OCT2 and MATE1/2‐K can also cause a mild increase in serum creatinine, which has led to the consideration of using endogenous biomarkers for detecting OCT2/MATEs‐mediated DDIs in humans. ¹⁵ , ¹⁶ , ¹⁷ , ¹⁸ In addition to creatinine, two metabolites, N1‐methyl nicotinamide (1‐NMN) and N1‐methyladenosine (m¹A), have been proposed as endogenous probes for OCT2 and MATE1/2‐K in the kidneys. ¹ , ⁴ , ¹⁹ Unlike creatinine, these metabolites undergo significant tubular secretion in the kidney. Our previous study demonstrated that pyrimethamine, a potent MATE inhibitor, decreased the CL_r of these metabolites in a dose‐dependent manner, and the CL_r ratio correlated well with the CL_r of metformin. ¹⁹ Additionally, these biomarkers were analyzed in subjects who received other MATE1/2‐K inhibitors, such as trimethoprim and cimetidine. ¹² , ²⁰ , ²¹ However, the effects of OCT2 inhibition have not yet been reported.

This study aimed to investigate the influence of two different drugs, cimetidine and dolutegravir, on the potential utility of m¹A and 1‐NMN compared to creatinine in healthy participants. Although cimetidine is a more potent inhibitor of MATE1/2‐K than OCT2, ²² inhibition of OCT2 and MATE1/2‐K by dolutegravir is controversial, and dolutegravir may inhibit both transporters. ²³ , ²⁴ , ²⁵ Cimetidine has a short elimination half‐life, which means that multiple doses do not increase systemic exposure in the body, and the effect of DDI on the CL_r of metformin depends on the time interval for urine collection after administration. ⁷ Considering its elimination half‐life (13–14 hour), ²⁶ dolutegravir, on the other hand, accumulates in a twice‐a‐day regimen, leading to higher inhibition of OCT2 and MATE1/2‐K. In this study, we compared the impact of these two drugs on CL_r and AUC of m¹A and 1‐NMN with the goal of validating their utility as DDI biomarkers in healthy participants.

METHODS

Chemicals

All compounds used in the experiments were commercially available. All other chemicals used for the analyses were commercially available analytical‐grade products ( Supporting Information ).

Clinical study design

This was an open‐label, dual‐sequence, 4‐period crossover study involving 10 healthy Japanese male participants (Figure S1 ). The dosing and sampling schedules are described in the Supplementary Methods .

Dosing schedule

Metformin (METGLUCO® tablet, 500 mg) was orally administered at 10 am during each period. Cimetidine (Tagamet® tablet, 400 mg) was given 1 hour before metformin administration during periods 2 and 3. Dolutegravir (Tivicay® tablet, 50 mg) was administered twice daily during period 4. Metformin was given 1 hour after the first and fifth doses of dolutegravir.

Blood and urine sampling schedule

Blood samples were collected before drug administration and at 0.5, 1, 2, 4, 6, 8, 12, and 24 hours after metformin administration. Plasma specimens were prepared by centrifugation (1,710 g for 10 minutes at 4°C). Urine samples were collected at 0–4, 4–8, 8–12, and 12–24 hours after metformin administration. The samples were stored at −80°C for LC–MS/MS analysis.

Quantification of test compounds by liquid chromatography with tandem mass spectrometry

All samples were pretreated as described previously ¹⁹ and measured using a QTRAP5500 system (AB SCIEX, Toronto, Canada) equipped with a Nexera X2 LC system (Shimadzu, Kyoto, Japan) operated in the electrospray ionization mode. The conditions of liquid chromatography and tandem mass spectrometry are summarized in the Supplementary Methods.

Statistical analysis

Natural log‐transformed values of all parameters were analyzed by cohort using a mixed effect model with treatment as a fixed effect and subject as a random effect. The AUC and CL_r were log‐transformed, arithmetic means and 90% confidence intervals (CIs) were calculated, and an exponential transformation was performed to obtain the geometric mean. The arithmetic means and 90% CIs of the difference between the inhibitor group and the control (metformin alone) group were calculated from the log‐transformed AUC and CL_r and then exponentially transformed into the geometric mean ratio. The GMR of the control group was based on the baseline data. The correlation of the CL_r ratio was analyzed using linear regression. Plasma concentrations are presented as the mean ± SEM.

RESULT

Inhibition of OCT2 and MATE1/2‐K by dolutegravir

Using HEK293 cells stably expressing OCT2 and MATE1/2‐K, the K _i values of dolutegravir for OCT2 and MATE transporters were determined. The average values from two or three experiments were calculated for OCT2 and MATE1, while the value from a single experiment was used for MATE2‐K. The K _i values were 0.29 ± 0.04 μM for OCT2, 0.83 μM (0.95 and 0.71 μM from individual experiments) for MATE1, and 2.97 ± 0.40 μM (estimated value ± computer‐calculated SEM) for MATE2‐K (Figure 1 ). The geometric means of the reported K _i values for dolutegravir, as obtained from the DIDB® (CERTARA, Radnor, PA, https://www.druginteractionsolutions.org/), were 2.12 μM for OCT2, 6.42 μM for MATE1, and 19.4 μM for MATE2‐K.

Figure 1.

Effect of dolutegravir on OCT2‐, MATE1‐, and MATE2‐K mediated uptake of metformin. The uptake of metformin (10 μM) in the absence and presence of dolutegravir (0.1–30 μM for OCT2, and 0.3–100 μM for MATE1) was determined for 2 and 5 minutes at 37°C in OCT2‐, MATE1‐, and MATE2‐K‐HEK, respectively. The K _i values of dolutegravir were determined by non‐linear regression analysis, assuming that IC50 can approximate K _i when the substrate concentrations were sufficiently lower than the K_m values. The rigid lines represent fitted lines. The previously determined K _i values were also plotted for comparison. They were obtained from the DIDB® (CERTARA, Radnor, PA). The dotted and dashed lines represent the C _max,u of dolutegravir at fifth and first doses, respectively.

Participant demographics

Ten healthy adults were enrolled in the study. They ranged in age from 20 to 37 years, in body mass index from 19.8 to 23.6 kg/m², in height from 158.6 to 181.1 cm, and in weight from 54.1 to 72.9 kg. Vomiting was reported as an adverse event in one subject who received 800 mg of cimetidine (Period 2). As this was judged to have no causal relationship with the study drug, this subject continued to participate until the end of this period. Just before the day of visiting the clinics for Period 4, another participant suffered from a fever and did not participate in the study.

Plasma concentration‐time profiles of OCT2 and MATE1/2‐K inhibitors

Cimetidine was rapidly eliminated from the body after administration and plasma concentrations increased in a dose‐dependent manner (Figure 2 a ). The half‐life at the terminal phase (T _1/2) was 3.00 ± 0.27 and 3.03 ± 0.12 hours for the 400 and 800 mg doses, respectively. The maximum plasma concentrations (C _max) of cimetidine were 11.8 (30.0) and 25.7 (34.5) μM (geometric mean and geometric %CV) at doses of 400 and 800 mg, respectively. Higher plasma concentrations of dolutegravir were achieved after repeated dosing every 12 hours (BID, Figure 2 a ). The C _max of dolutegravir up to 12 hours after metformin administration were 7.07 (36.5) and 21.7 (18.6) μM (geometric mean and geometric %CV) at first and fifth doses, respectively. The half‐life after T _max was 13.0 ± 1.1 and 16.6 ± 1.7 hours for the first and fifth doses, respectively. The AUC_inf extrapolated to infinity at first dose was 141 (46.1) μM*hour, while the AUC from 0 to 12 hours at fifth dose was 209 (19.9) μM*hour (geometric mean and geometric %CV).

Figure 2.

Plasma concentration time profiles of each drug and X _urine and CL_r of metformin. (a) Plasma concentrations of cimetidine (400 and 800 mg, po) and dolutegravir (50 mg BID) were shown. Time 0 represented the time at which metformin was administered. Cimetidine or dolutegravir was administered 1 hour before the administration of metformin. The plasma concentrations of dolutegravir at 12, 24, 48, and 60 hours represent the trough concentrations, and the last sampling time point was 72 hours. Each symbol and vertical bar represents the arithmetic mean and SEM. (b) Plasma concentration‐time profile of metformin with or without cimetidine or dolutegravir‐treatments are shown. Each symbol and error bar represents the arithmetic mean and SEM, respectively. Amounts of urinary excretion (X _urine) at each urine collection interval were shown. Each bar and vertical bar represents the mean and SEM. Box‐and‐whisker plots of the CL_r of metformin is shown. The subject numbers: n = 10 control and cimetidine‐treated group; n = 9 dolutegravir‐treated group.

Effect of OCT2 and MATE1/2‐K inhibitors on the pharmacokinetics of metformin

The plasma concentrations and urinary excretion of metformin were measured in the absence and presence of each inhibitor (Table 1 ). Maximum plasma concentrations (C _max) of metformin were increased 2.1‐ and 2.7‐times (both with P < 0.001) by the co‐administration of cimetidine 400 and 800 mg, respectively, and 1.6‐ and 2.3‐times (P < 0.01 and P < 0.001) by first and fifth doses of dolutegravir, respectively (Figure 2 b ). Consequently, AUC of metformin were increased 1.7 and 2.2‐times (both with P < 0.001) by the cimetidine treatment, and 1.8‐ and 3.0‐times (both with P < 0.001) by the dolutegravir treatment, respectively (Table 1 ). CL_r of metformin was decreased by 15.5% and 42.5% (P = 0.0519 and P < 0.001) by cimetidine 400 and 800 mg, respectively, and by 26.8% and 56.9% (P < 0.01 and P < 0.001) by dolutegravir treatment, respectively (Figure 2 b ; Table 1 ).

Table 1.

Summary of pharmacokinetic parameters of metformin given alone or with cimetidine or dolutegravir

	C max (μM)	T max (hour)	AUCinf a (μM × hour)	feb	T 1/2 (hour)	CL/F c (mL/minute)	CLr d (mL/minute)	F e
Control	10.4 (18.7)	3.00 (0.33)	71.1 (16.0)	0.649 (13.7)	3.94 (0.13)	907 (16.0)	468 (8.26)	0.662 (13.3)
Cimetidine (400 mg)	21.5 (28.2)***	2.80 (0.33) ns	122 (17.5)***	0.940 (13.9)***	3.83 (0.11) ns	531 (17.5)***	396 (18.4) ns (P = 0.516)	0.957 (13.6)***
Cimetidine (800 mg)	28.0 (45.2)***	2.80 (0.33) ns	157 (34.8)***	0.826 (22.1)**	3.68 (0.15) ns	411 (34.8)***	269 (29.9)***	0.840 (21.9)*
Dolutegravir 50 mg (first)	17.0 (34.1)**	2.67 (0.33) ns	128 (23.6)***	0.828 (20.6)**	4.50 (0.19)**	505 (23.6)***	339 (17.1)**	0.859 (19.4)**
Dolutegravir 50 mg (fifth)	23.8 (20.5)***	3.33 (0.33) ns	221 (17.2)***	0.844 (20.7)**	4.60 (0.14)***	292 (17.2)***	199 (11.3)***	0.874 (19.9)**

Each parameter was shown as geometric mean (geometric %CV); T _max and T _1/2 were shown as mean (SEM).

Parameters other than T _max and T _1/2 were logarithmically transformed and subjected to statistical analysis. *P < 0.05, ** P < 0.01, ***P < 0.001 (Dunnett's comparison vs. the control group).

Parameters: C _max, maximum plasma concentration; T _max, time to achieve the maximum concentration; AUC_inf, AUC from time 0 to infinity; fe, fraction recovered in the urine; T _1/2, half‐life at the terminal phase; CL/F, oral clearance; CLr, renal clearance; F, bioavailability.

^a AUC_inf was calculated as AUC_0–24hour + C_24hour/(ln2/T _1/2).

^b fe was calculated as X _{urine,0–24hour}/Dose.

^c CL/F was calculated as Dose/AUC_inf.

^d CLr was calculated as X _{urine, 0–24hour}/AUC_0–24hour.

^e F was calculated as CLr/(CL/F).

Effect of OCT2 and MATE1/2‐K inhibitors on the plasma concentrations and urinary excretion of endogenous substrates

Plasma concentrations and urinary excretion of three endogenous substrates, 1‐NMN, m¹A, and creatinine, were measured to examine the effect of the inhibitors on plasma concentrations and CL_r. The plasma concentrations of 1‐NMN showed diurnal variations and decreased from morning to night (10  ^pm , the last sampling time) (Figure 3 a ). Cimetidine treatment accelerated the decline of the plasma concentrations of 1‐NMN, whereas dolutegravir delayed this elimination. However, both drugs decreased the CL_r of 1‐NMN. Cimetidine and dolutegravir caused the accumulation of m¹A in the plasma and decreased CL_r (Figure 3 b ). Repeated doses of dolutegravir (fifth dose) had a greater impact on the plasma concentrations of both 1‐NMN and m¹A than a single dose of dolutegravir (first dose). The AUC of creatinine from 4 to 6 hours post‐administration standardized by the creatinine concentration before administration showed a significant increase with P < 0.01 in all groups (Dunnett's comparison vs. the control group). The effect of cimetidine and dolutegravir on the plasma concentration of creatinine appeared to be evident for 4–6 hours post‐administration, but a reduction in CL_r creatinine was observed only with cimetidine 800 mg (Figure 3 c ).

Figure 3.

Effects of cimetidine and dolutegravir on the plasma concentrations, X _urine and CL_r of 1‐NMN (a), m¹A (b) and creatinine (c). Plasma concentration‐time profiles of metformin with or without cimetidine or dolutegravir were shown. Each symbol and error bar represents the mean and SEM, respectively. Amounts of urinary excretion (X _urine) at each urine collection interval were shown. Each bar and vertical bar represents the mean and SEM. Box‐and‐whisker plots of the CL_r of metformin is shown. The subject numbers: n = 10 control and cimetidine‐treated group; n = 9 dolutegravir‐treated group.

Effect of OCT2 and MATE1/2‐K inhibitor on AUC ratio (AUCR) and CL_r ratio (CL_rR) of metformin and endogenous substrates

The GMR and 90% confidence intervals for the AUCR and CL_rR of metformin and endogenous compounds were summarized in Table 2 . Cimetidine and dolutegravir increased metformin AUCR and decreased CL_rR in a systemic, exposure‐dependent manner (Figure 4 ). For 1‐NMN, a decrease in AUCR was observed with cimetidine treatment, whereas AUCR increased with repeated doses of dolutegravir. The CL_r of m¹A was also decreased by cimetidine and dolutegravir, accompanied by a slight increase in AUCR to 1.16 and 1.25 by cimetidine 400 and 800 mg, and to 1.22 and 1.40 by first and fifth doses of dolutegravir. m¹A could not capture transporter inhibition by cimetidine (400 mg). The AUCR of creatinine slightly exceeded 1, ranged from 1.11 to 1.17, for cimetidine and dolutegravir; however, the CL_rR was below 1 only for cimetidine 800 mg.

Table 2.

Geometric mean and geometric mean ration (90% CI) for metformin and endogenous biomarkers

	Metformin		1‐NMN		m1A		Creatinine
Geometric mean	AUC0–24hour (μM·hour)	CLr (mL/minute)	AUC0–8hour (μM·hour)	CLr (mL/minute)	AUC0–8hour (μM·hour)	CLr (mL/minute)	AUC0–8hour (μM·hour)	CLr (mL/minute)
Baseline	N/A	N/A	0.834	310	0.822	188	641	116
Control	69.7	468	0.843	278	0.791	198	600	120
Cimetidine 400 mg	119	396	0.609	258	0.918	198	669	123
Cimetidine 800 mg	154	269	0.572	178	0.984	159	689	110
Dolutegravir (first)	123	339	0.994	234	0.977	151	712	131
Dolutegravir (fifth)	213	199	1.39	125	1.12	140	691	132
Control vs. baseline
GMR	N/A	N/A	1.01	0.89	0.96	1.05	0.94	1.04
90% CI	N/A	N/A	0.85, 1.20	0.81, 0.99	0.95, 0.98	1.00, 1.11	0.92, 0.95	0.99, 1.09
Cimetidine 400 mg vs. control
GMR	1.71	0.85	0.72	0.93	1.16	1.00	1.11	1.02
90% CI	1.57, 1.88	0.77, 0.93	0.59, 0.89	0.84, 1.03	1.13, 1.19	0.93, 1.06	1.09, 1.14	0.95, 1.09
Cimetidine 800 mg vs. control
GMR	2.22	0.57	0.68	0.64	1.25	0.80	1.15	0.91
90% CI	1.94, 2.52	0.50, 0.67	0.57, 0.81	0.55, 0.74	1.23, 1.27	0.75, 0.85	1.10, 1.19	0.86, 0.97
Dolutegravir (first) vs. control
GMR	1.75	0.73	1.12	0.87	1.22	0.77	1.17	1.09
90% CI	1.55, 1.96	0.66, 0.81	0.91, 1.37	0.76, 1.00	1.19, 1.25	0.70, 0.84	1.13, 1.20	1.00, 1.18
Dolutegravir (fifth) vs. control
GMR	3.02	0.43	1.57	0.47	1.40	0.71	1.13	1.10
90% CI	2.72, 3.36	0.40, 0.46	1.25, 1.96	0.39, 0.55	1.36, 1.45	0.65, 0.77	1.10, 1.17	1.01, 1.20

1‐NMN, N¹‐methylnicotinamide; AUC, area under the plasma concentration–time curve; CI, confidence interval; CL_r, renal clearance; GMR, geometric mean ratio; m¹A, N¹‐methyladenosine; N/A, not applicable.

Figure 4.

AUC ratio and CL_r ratio of metformin and the endogenous substrates. The geometric mean ratios of AUC and CL_r with 90% confidence intervals were shown.

Time dependence in calculating GMR of AUC and CL_r of metformin

AUC and CL_r were calculated at four intervals from 0 to 4, 8, 12, and 24 hours after metformin administration to examine their impact on AUCR and CL_rR (Figure S2 ). Consistent with a previous report, ⁵ the CL_rR of metformin varied depending on the collection time interval by 8 hours, but it was not apparent for AUCR in the cimetidine‐treated phase. In the dolutegravir‐treated phase, there was no obvious difference in the calculated time interval. For the endogenous biomarkers m¹A and 1‐NMN, analysis was performed for up to 8 hours.

Correlation between CL_r of metformin and endogenous biomarkers

The correlation between the CL_r ratios of endogenous biomarkers and metformin in each participant was examined (Figure 5 a ). When the correlation coefficients were calculated for the cimetidine and dolutegravir groups, 1‐NMN (r ² = 0.58, P < 0.001) and m¹A (r ² = 0.55, P < 0.001) were positively correlated with metformin (Figure S2 ). When the coefficients were calculated separately for the cimetidine and dolutegravir groups, m¹A showed a better correlation for the cimetidine group (r ² = 0.64, P < 0.001) and a weak correlation for the dolutegravir group (r ² = 0.32, P = 0.015). In contrast, 1‐NMN showed a weak correlation with metformin in the cimetidine group (r ² = 0.36, P = 0.005), and a strong correlation with metformin in the dolutegravir group (r ² = 0.80, P < 0.001). Creatinine levels did not correlate with metformin (r ² = 0.02, P = 0.398).

Figure 5.

Correlation CL_rR (a) and AUCR (b) between metformin and endogenous biomarkers. The correlation of CL_rR (a) and AUCR (b) among the test compounds were shown with correlation coefficients and P values. Each symbol represents an individual data point.

Correlation between AUC of metformin and endogenous biomarkers

The correlation between the AUCR of metformin and endogenous biomarkers was evaluated. A positive correlation was observed for m¹A (r ² = 0.60, P < 0.001) (Figure 5 b ), although the dynamic range was narrower for m¹A than for metformin. There was a moderate correlation for 1‐NMN (r ² = 0.19, P = 0.006) and no correlation for creatinine (r ² = 0.01, P = 0.598).

DISCUSSION

In this study, we evaluated the utility of 1‐NMN, m¹A and creatinine as endogenous biomarkers in assessing DDIs mediated by OCT2 and MATE1/2‐K, using cimetidine and dolutegravir. Cimetidine is a moderate inhibitor of MATE1/2‐K with a shorter elimination half‐life than pyrimethamine, ²² and dolutegravir is a likely OCT2 and MATE1/2‐K inhibitor at clinical doses. ²³ , ²⁵ Dolutegravir accumulates in the body at repeated doses. According to the static approach using C _max,u, both drugs raised the flag to trigger the subsequent clinical investigation (Table S1 ).

We confirmed the systemic exposure‐dependent effects of cimetidine and dolutegravir on the AUC and CL_r values of metformin. Consistent with previous reports, ⁷ , ⁸ , ¹⁴ both drugs increased the metformin AUC in an exposure‐dependent manner (Figure 2 ). Similar to cimetidine, the decline in CL_r is a part of the DDI mechanism caused by dolutegravir, which occurred at a single dose (Figure 2 ). Notably, repeated doses of dolutegravir potentiated its effect on the CL_r of metformin (Figure 2 ). As OCT2 and MATE1/2K mediate the uptake and subsequent efflux, respectively, in the overall tubular secretion of metformin, it is not possible to identify the responsible transporter simply based on the CL_r values of metformin. Comparison of C _max,u with K _{i,in vitro} suggests that the major transporter inhibited was MATE1/2‐K for cimetidine and both of OCT2 and MATE1/2‐K for dolutegravir, which inhibition is stronger in OCT2 than in MATE1/2‐K (Table S1 ). We considered that the intracellular concentration of dolutegravir in the proximal tubules would not exceed the unbound plasma concentrations (as detailed in the Supporting Information ), and the inhibition of MATE1/2‐K was evaluated based solely on plasma data. Recently, we demonstrated the importance of Mate1 in the elimination of various drugs in mouse kidneys. ²⁷ , ²⁸ Considering the narrow substrate specificity of OCT2 compared to that of MATE1/2‐K (Figures S3 and S4 ), MATE1/2‐K inhibitors may cause DDI with a wider range of drugs than OCT2 inhibitors in clinical settings. A clinical approach to support in vitro inhibition data, for instance, a probe cocktail strategy including MATE1 selective substrate needs to be established. The DDI mechanisms that affect metformin AUC may involve an increase in the fraction absorbed (F) without a clear dose‐dependence for cimetidine (Table 1 ). This effect was not observed in a previous study that used cimetidine as the perpetrator drug. ⁷ There is no explanation to account for this discrepancy. In addition, the elimination half‐life of metformin was not prolonged as expected from the decreased CL_r for cimetidine (Figure S5 ). Cimetidine, but not dolutegravir, may also decrease the volume of distribution of metformin in the body. Although the tissue‐to‐plasma ratio is small (0.8), the major distribution organ of metformin in the whole body is the skeletal muscle, ²⁹ where MATE1 mRNA is detected. ³⁰ Inhibition of MATE1 may result in a reduction in the distribution of metformin to this organ, thereby offsetting its effect on the elimination rate constant due to CL_r. This hypothesis is further supported by the observed effect with another MATE1 inhibitor, pyrimethamine (as shown in Figure S5 ).

The performance of the endogenous biomarkers was assessed in terms of their correlation with the CL_rR of metformin and sensitivity to transporter inhibition. Using all the test data, the CL_rR of 1‐NMN and m¹A was positively correlated with the CL_rR of metformin (Figure 5 ). When evaluating the correlation coefficient for each inhibitor, it was found that the CL_rR of 1‐NMN showed a better correlation for the dolutegravir period than for the cimetidine period, whereas the CL_rR of m¹A showed the opposite trend (Figure S6 ). The sensitivity of endogenous biomarkers to cimetidine and dolutegravir depends on the contribution of OCT2 and MATE1/2‐K to urinary excretion. In the kidneys, compounds with low plasma protein binding undergo glomerular filtration (GFR), which is not influenced by transporters. Based on the absolute values of CL_r, GFR likely makes a greater contribution to the urinary excretion of m¹A than does 1‐NMN. In addition, the clearance for tubular secretion, which was estimated by subtracting creatinine CL_r observed from CL_r at fifth dose of dolutegravir, remained at 5.6% and 29% for 1‐NMN and m¹A, respectively. These factors may narrow the dynamic range of the CL_rR for m¹A in response to OCT2 inhibition. However, the correlation of CL_rR was better for m¹A than for 1‐NMN when cimetidine was used as the perpetrator (Figure S6 ). In our previous study, in which pyrimethamine was used as the MATE perpetrator, the correlation was slightly better for m¹A (r ² = 0.65 vs. r ² = 0.53 for m¹A and 1‐NMN). ¹⁰ These results suggest that m¹A might be a better MATE1/2‐K probe than 1‐NMN. Given that the coefficient of determination (r ²) is approximately 0.6, indicating a moderate correlation, it suggests that confidently predicting the DDI impact for MATE1 and MATE2‐K based on the CL_rR of the endogenous biomarkers remains challenging. While detecting the presence or absence of transporter inhibition may be feasible, it is premature to substitute DDI studies using probe drugs with endogenous biomarkers at this stage. Unlike m¹A and 1‐NMN, creatinine could not capture transporter inhibition by cimetidine and dolutegravir. The performance of creatinine remains controversial across studies, ¹⁵ , ²¹ , ³¹ presumably because of the low contribution of transporters to urinary excretion.

There was a sharp contrast in the dependence of the sensitivity of endogenous biomarkers on time interval between cimetidine and dolutegravir (Figure S2 ). As reported ⁵ previously, the short duration of the inhibitory effect of cimetidine on the urinary excretion of metformin is due to its short plasma half‐life. The time‐interval dependence was more remarkable for m¹A and 1‐NMN than for metformin. Prolonging the time interval used to calculate CL_r to 8 hours or later no longer detected an effect of cimetidine (400 mg) on the CL_r of these metabolites (Figure S2 ). This difference between metformin and endogenous biomarkers is due to the first being administered in a single dose (probe drugs) while the latter are constantly synthesized throughout the study period to be eliminated into urine (endogenous biomarkers). Unlike probe drugs, the time interval used for urine collection is an apparently critical factor affecting the sensitivity of endogenous biomarkers to drug transporter inhibition. Indeed, the correlation coefficient of CL_r (0–4 hours) improved to 0.83 for m¹A (Figure S6 ). These properties of endogenous biomarkers offer an advantage over probe drugs in understanding the duration of drug transporter inhibition by the investigated drugs.

The plasma concentrations of 1‐NMN and m¹A before metformin administration increased with repeated doses of dolutegravir (Figure S7 ). Thus, 1‐NMN and m¹A could be plasma biomarkers for detecting OCT2 inhibition when perpetrator compounds are administered at multiple doses. Interestingly, this study elucidated that cimetidine and dolutegravir had opposite effects on the variation in the AUC of 1‐NMN following administration of the inhibitors. Despite the reduction in CL_r, AUC decreased when cimetidine was administered at either 400 or 800 mg but increased with dolutegravir (Figure 4 ). Such a reduction in the AUC of 1‐NMN despite decreased CL_r was noted with other MATE1/2‐K inhibitors, such as cimetidine, ²¹ trimethoprim, ³² and pyrimethamine. ¹⁰ , ³³ Empirically, the AUC change of 1‐NMN from the baseline may be able to discriminate MATE1/2‐K inhibition from OCT2 inhibition to validate in vitro inhibition data. To validate this hypothesis, elucidation of the underlying mechanism is essential. This study eliminated the possibility that cimetidine altered the plasma concentration of nicotinamide, the precursor of 1‐NMN (Figure S8 ), and that cimetidine and dolutegravir inhibited the activity of nicotinamide N‐methyltransferase (NNMT) (Figure S9 ). Additionally, the plasma concentrations of 1‐NMN showed diurnal variations (Figure 3 a ). The circadian rhythm of plasma NMN might be due to the diurnal variation of nicotinamide phosphoribosyltransferase (NAMPT), ³⁴ which converts nicotinamide to nicotinamide mononucleotide, a precursor of NAD. Monitoring the CL_r of carnitine and its derivatives may offer another empirical strategy to support MATE1/2‐K inhibition because the CL_r of these metabolites was significantly decreased after pyrimethamine ³⁵ and cimetidine ²¹ administration. Further efforts are required to establish a strategy to determine the DDI risk of investigative drugs for OCT2/MATE1/2‐K‐mediated DDI using multiple endogenous compounds.

In this study, we also investigated the potential DDI risk of cimetidine and dolutegravir for multispecific organic anion transporters (OAT1/3) in the kidney ¹ by measuring the CL_rR of pyridoxic acid (PDA), an endogenous biomarker of OAT1/3 proposed based on probenecid studies. ³⁶ While there was no change in CL_r of PDA with cimetidine, there was a significant decrease in CL_r of PDA at fifth dose of dolutegravir, but the magnitude of inhibition was at most 21% (Figure S10 ), eliminating the possibility of OAT1/3‐mediated DDI and their non‐specific effect on kidney function.

Currently, the International Transporter Consortium classifies 1‐NMN as “Tier2” and m¹A as “other” type of biomarkers to assess the inhibition of OCT2/MATE1/MATE2‐K. ¹ This study reported unique characteristics of the pharmacokinetic parameter changes of 1‐NMN and m¹A caused by the inhibition of either OCT2 or MATE1/2‐K. Additional studies using different OCT2/MATE1/2‐K inhibitors and drugs with negative inhibition potentials are necessary to understand whether these biomarkers can be classified as “Tier1” biomarkers to improve decision‐making regarding DDI risk assessment. Their application requires calculation of CL_r place orders to design the protocol of clinical studies, addition of the control period, and collection of natural urine at regular intervals. Finally, we cannot exclude the possibility of false‐negative judgment because of the low time resolution, due to limitations in the collection time interval for urine, to capture the drug transporter inhibition compared to OATP1B endogenous biomarkers. ³⁷ , ³⁸ Although we could not find a candidate compound whose plasma concentration can serve as a biomarker by metabolomic analysis in mice, ¹⁹ continuous effort to identify plasma endogenous DDI biomarkers for renal transporters is necessary.

In conclusion, 1‐NMN and m¹A are more promising biomarkers for assessing OCT2‐ and MATE1/2‐K‐mediated DDIs than creatinine for predominantly secreted drugs. Specifically, 1‐NMN may be more effective for detecting OCT2 inhibition, while m¹A may be better suited for identifying MATE1/2‐K inhibition.

FUNDING

This study was funded and supported by Asahi Kasei Pharma, Genentech, Gilead Sciences, GlaxoSmithKline, Incyte Research Institute, Merck & Co., Inc., Rahway, NJ, USA, Ono Pharmaceuticals, Takeda Pharmaceuticals for the clinical study. This study was partially supported by Grants‐in‐Aid for Scientific Research (B) (23H02646) to HK.

CONFLICT OF INTEREST

Hideki Hirabayashi is employed by Takeda pharmaceutical company and hold common stocks of Takeda pharmaceutical company. All other authors declared no competing interests for this work.

ETHICS STATEMENT

The study was conducted in accordance with the Clinical Trials Act of Japan. The study protocol was reviewed by the Certified Review Board, the Graduate School of Medicine, The University of Tokyo (CRB3180024). This study was registered as a specified clinical trial in the Japan Registry of Clinical Trials (jRCTs031210406). Written informed consent was obtained from all the participants prior to their inclusion in the study.

AUTHOR CONTRIBUTIONS

T.Ko., K.T., M.J.Z., K.Y., X.C., Y.S., H.K. wrote the manuscript. T.Ko., K.T., M.J.Z., K.Y., X.C., H.H., J.M., K.R., T.T., Y.Y., Y.L., K.M., Ke.F., Y.S., H.K. designed the research. T.Ko., Ka.F., Y.T., T.Ki., Ke.F., H.K. performed the research. T.Ko., T.F., Y.T., T.Ki., K.M., Y.S., H.K. analyzed the data.

PRESENTATION

ISSX 2024 Workshop Virtual Transporters, February 28, 2024, online; ISSX/JSSX 2024 at Hilton Hawaiian Village Waikiki Beach Resort in Honolulu, Hawaii, September 15–18, 2024.

Supporting information

Data S1.