Human total clearance values and volumes of distribution of typical human cytochrome P450 2C9/19 substrates predicted by single-species allometric scaling using pharmacokinetic data sets from common marmosets genotyped for P450 2C19

Figures & data

Figure 1. Relationship between hepatic blood flow rates and body weights in humans, marmosets, and other experimental animal species.

Table 1. Physicochemical properties, cassette-dose cocktail group numbers, LC–MS/MS conditions, and reported enzymes responsible for metabolism in humans of the 24 model compounds tested in this study.

Compound	Class/pKa^a	Cocktail group number	Ionisation mode	Parent ion, m/z	Product ion, m/z	HPLC condition^b	Enzymes responsible for metabolism in humans	References
Acetaminophen	Acid/9.5	1	[M + H]^+	152.0	93.0	(2)	UGT1A, CYP2E1	Court et al. (2013, 2017) and Ueshima et al. (1996)
Antipyrine	Neutral	4	[M + H]^+	189.0	104.0	(1)	CYP3A, CYP2Cs, CYP1A2	Engel et al. (1996)
Cilomilast	Acid/2.3	1	[M–H]^–	342.3	214.3	(1)	CYP2C8, UGT	Down et al. (2006)
Clonazepam	Neutral	3	[M + H]^+	315.9	270.0	(1)	CYP3A4/5, NAT	Toth et al. (2016)
Dapsone	Base/2.4	4	[M + H]^+	249.1	156.0	(1)	CYP3A4, CYP2C9, NAT	Gill et al. (1995)
Diclofenac	Acid/4.0	1	[M + H]^+	296.0	214.0	(1)	CYP2C9, UGT2B7, UGT1A9	Lazarska et al. (2018)
Glyburide	Acid/4.3	4	[M + H]^+	494.0	169.0	(1)	CYP3A4, CYP2C9	Zharikova et al. (2009)
Ibuprofen	Acid/4.9	2	[M–H]^–	205.2	160.9	(3)	CYP2C8, CYP2C9, UGT2B7	Buchheit et al. (2011) and Karaźniewicz-Łada et al. (2009)
Itraconazole	Base/3.9	2	[M + H]^+	705.3	392.3	(1)	CYP3A4	Peng et al. (2012)
Ketanserin	Base/7.3	2	[M + H]^+	396.2	189.1	(1)	Ketone reductase	Persson et al. (1991)
Ketoprofen	Acid/3.9	4	[M + H]^+	255.1	105.0	(1)	UGT2B7	Sabolovic et al. (2000)
Lamotrigine	Base/5.9	4	[M + H]^+	256.1	144.9	(1)	UGT1A4, UGT2B7	Inoue et al. (2016)
Midazolam	Base/6.6	1	[M + H]^+	325.9	291.1	(1)	CYP3A4	Shin et al. (2013)
Moxifloxacin	Acid/5.9; base/9.4	2	[M + H]^+	402.2	261.2	(1)	UGTs, SULT	Moise et al. (2000)
Nicardipine	Base/8.2	1	[M + H]^+	480.1	315.0	(1)	CYP3A4, CYP2C8, CYP2D6	Nakamura et al. (2005)
Pefloxacin	Acid/5.7	3	[M + H]^+	334.3	233.1	(1)	CYP1A2	Kinzig-Schippers et al. (1999)
Phenytoin	Acid/9.5	3	[M + H]^+	253.2	182.1	(1)	CYP2C9, CYP2C19	Yamamoto et al. (2015)
Probenecid	Acid/3.5	3	[M + H]^+	286.1	202.0	(1)	UGTs	Perel et al. (1970)
Repaglinide	Acid/3.7	2	[M + H]^+	453.2	230.2	(1)	CYP2C8, CYP3A4	Bidstrup et al. (2003)
Telmisartan	Acid/3.7	3	[M + H]^+	515.4	289.1	(1)	UGT1A3, UGT1A1	Yamada et al. (2011)
Tolbutamide	Acid/4.3	3	[M + H]^+	271.1	74.0	(1)	CYP2C9	Lee et al. (2003)
Verapamil	Base/9.7	2	[M + H]^+	455.2	165.2	(1)	CYP3A4	Kroemer et al. (1993)
S-Warfarin	Acid/5.6	4	[M–H]^–	307.1	160.9	(1)	CYP2C9	Kaminsky and Zhang (1997)
Zaleplon	Neutral	1	[M + H]^+	306.1	236.0	(1)	AO, P450s	Renwick et al. (1998)
Imipramine^c	–	–	[M + H]^+	281.3	86.2	–	–	–
Valsartan^d	–	–	[M–H]^–	434.1	179.0	–	–	–

AO: aldehyde oxidase; NAT: N-acetyltransferase; SULT: sulphotransferase.

^a Calculated using JChem for Excel (ChemAxon, Budapest, Hungary).

^b Formic acid aqueous solution (0.1% v/v) was used as the mobile phase in gradient systems: (1) concentrations of acetonitrile were set to 5%, 5–50%, and 50–90% during 0–0.5, 0.5–2.5, and 2.5–3.0 min, respectively, with the CAPCELL CORE ADME column; (2) concentrations of acetonitrile were set to 0% and 0–90% during 0–1.5 and 1.5–3 min, respectively, with the ACQUITY UPLC BEH C18 column; and (3) concentrations of methanol were set to 30–90% during 0–1 min in the CAPCELL PAK INERT ADME-HR column.

^c Internal standards for positive ionisation modes.

^d Internal standards for negative ionisation modes.

Table 2. Phenotypes and genotypes of polymorphic CYP2C19 for the six marmosets used in this study and their frequencies in a breeder cohort.

Phenotype	Animal number^a	Nucleotide (deduced amino acid)			Frequency^b
		Exon 1	Exon 5	Exon 9
EM	1 (M), 2 (M)	T19/T19 (Phe7/Phe7)	C761/C761 (Ser254/Ser254)	C1406/C1406 (Thr469/Thr469)	8/40
	3 (F)	T19/T19 (Phe7/Phe7)	C761/C761 (Ser254/Ser254)	T1406/T1406 (Ile469/Ile469)	0/40
PM	4 (M), 5 (M)	C19/C19 (Leu7/Leu7)	T761/T761 (Leu254/Leu254)	T1406/T1406 (Ile469/Ile469)	9/40
	6 (M)	C19/C19 (Leu7/Leu7)	T761/C761 (Leu254/Ser254)	T1406/T1406 (Ile469/Ile469)	0/40

^a M and F indicate male and female, respectively.

^b Numbers of marmosets found in Yaotsu Breeding Center of CLEA Japan Inc. The other 23 of 40 animals in this cohort had different combinations of these CYP2C19 variants.

Figure 2. Plasma concentrations of unchanged drug following intravenous administrations of 24 model drugs to extensive (open circles) and poor (solid triangles) metaboliser marmosets. Dosing levels were 0.20 mg/kg for ibuprofen, midazolam, and telmisartan, and 0.10 mg/kg for the other compounds. Asterisks denote compounds whose total clearances were significantly reduced in the poor metaboliser group compared with the extensive metaboliser group at significance levels of *p< 0.05 and **p< 0.01, respectively (unpaired t test). Data points are the means of three marmosets per group and the whiskers show SDs.

Table 3. Total clearance (CL_t) values and distribution volumes at steady state (V_ss) observed in marmosets and predicted for humans by single-species allometric scaling based on pharmacokinetic data sets in marmosets.

Chemicals	Marmoset								Human
	EM group				PM group
	Observed CLt^a, mL/min/kg	Observed Vss^a, L/kg	Predicted human CLt^b, mL/min/kg	Predicted human Vss^b, L/kg	Observed CLt^a, mL/min/kg	Observed Vss^a, L/kg	Predicted human CLt^b, mL/min/kg	Predicted human Vss^b, L/kg	Reported CLt^c, mL/min/kg	Reported Vss^c, L/kg
Acetaminophen	9.9 ± 1.4	1.4 ± 0.1	1.0	0.60	8.1 ± 1.3	1.4 ± 0.0	1.3	0.73	5.0	1.0
Antipyrine	13.0 ± 2.9	2.0 ± 0.2	1.4	0.88	10.2 ± 3.4	1.7 ± 0.1	1.6	0.86	0.64	0.77
Cilomilast	43.2 ± 5.9	0.59 ± 0.05	4.6	0.26	15.7 ± 4.9^**,d	0.45 ± 0.04	2.4	0.23	0.5	0.23
Clonazepam	14.8 ± 2.0	2.5 ± 0.9	1.6	1.09	12.1 ± 10.4	2.7 ± 1.9	1.9	1.37	0.88	2.9
Dapsone	5.3 ± 2.8	1.3 ± 0.3	0.56	0.57	3.5 ± 1.1	1.1 ± 0.1	0.54	0.56	0.48	0.83
Diclofenac	17.9 ± 3.0	0.45 ± 0.03	1.9	0.20	14.1 ± 5.1	0.42 ± 0.07	2.2	0.21	3.5	0.22
Glyburide	2.5 ± 0.2	0.19 ± 0.00	0.26	0.08	1.7 ± 0.5	0.15 ± 0.02	0.26	0.08	0.82	0.08
Ibuprofen	6.1 ± 3.1	0.32 ± 0.05	0.64	0.14	4.1 ± 0.5	0.31 ± 0.03	0.64	0.16	0.82	0.15
Itraconazole	15.8 ± 2.5	3.7 ± 0.5	1.7	1.62	17.2 ± 1.8	3.6 ± 0.1	2.7	1.83	5.1	7.4
Ketanserin	167 ± 24	5.3 ± 0.8	17.5	2.32	163 ± 30	5.0 ± 0.8	25.2	2.54	6.7	3.9
Ketoprofen	8.6 ± 4.1	1.6 ± 0.8	0.9	0.70	4.6 ± 1.6	0.75 ± 0.26	0.71	0.38	1.6	0.13
Lamotrigine	10.1 ± 1.1	2.8 ± 1.0	1.1	1.23	7.0 ± 1.6^*,d	2.4 ± 0.3	1.1	1.22	0.6	1.1
Midazolam	123 ± 15	3.1 ± 0.1	12.9	1.37	106 ± 29	3.1 ± 0.4	16.4	1.56	5.3	1.1
Moxifloxacin	19.9 ± 5.6	2.8 ± 0.5	2.1	1.23	27.6 ± 13.1	3.2 ± 0.5	4.3	1.62	2.4	1.4
Nicardipine	118 ± 30	2.7 ± 0.8	12.4	1.18	108 ± 15	2.9 ± 0.2	16.7	1.47	11	1.0
Pefloxacin	14.3 ± 4.5	5.5 ± 1.5	1.5	2.41	12.7 ± 3.5	5.2 ± 1.5	2.0	2.64	2.0	1.5
Phenytoin	6.3 ± 1.5	1.6 ± 0.2	0.66	0.70	1.6 ± 0.3^**,d	1.4 ± 0.4	0.25	0.71	0.37	0.52
Probenecid	5.3 ± 2.0	0.71 ± 0.02	0.56	0.31	3.2 ± 0.6	0.62 ± 0.13	0.5	0.31	0.25	0.13
Repaglinide	22.3 ± 2.9	2.0 ± 0.3	2.4	0.88	14.2 ± 3.6^*,d	2.0 ± 0.8	2.2	1.02	7.8	0.35
Telmisartan	63.2 ± 35.2	13.7 ± 4.3	6.7	5.99	39.6 ± 30.9	7.2 ± 2.4	6.1	3.65	8.4	5.3
Tolbutamide	0.63 ± 0.16	0.14 ± 0.01	0.07	0.06	0.26 ± 0.09^*,d	0.11 ± 0.03	0.04	0.06	0.21	0.12
Verapamil	138 ± 39	3.5 ± 0.9	14.5	1.53	131 ± 44	3.3 ± 0.9	20.2	1.67	18	3.7
S-Warfarin	1.92 ± 0.47	0.99 ± 0.16	0.2	0.43	0.37 ± 0.18^**,d	0.39 ± 0.11	0.06	0.20	0.049	0.11
Zaleplon	90.8 ± 8.6	1.5 ± 0.1	9.6	0.66	98.0 ± 32.1	1.9 ± 0.3	15.2	0.98	16	1.3

* p< 0.05.

** p< 0.01.

^a Mean ± SD from three marmosets.

^b Predicted values were calculated using scaling exponents averaged over the 24 test substances (Table 4).

^c Data are taken from Varma et al. (2010), except for V_ss of lamotrigine (Incecayir and Agabeyoglu 2006) and phenytoin (Lund et al. 1974), and CL_t/V_ss of S-warfarin (Abernethy et al. 1991).

^d Compared with the observed CL_t values in the extensive metaboliser group by the unpaired t test.

Figure 3. Relationships between observed human CL_t values and those obtained by single-species allometric scaling using data from extensive metaboliser (EM) (A), poor metaboliser (PM) (B), and mixed (C) marmoset groups with corresponding mean scaling factors. (a) Cilomilast; (b) S-warfarin; (c) repaglinide; (d) tolbutamide; (e) phenytoin; (f) ketanserin; and (g) acetaminophen. The same sets of data were scaled using the generally adopted fixed exponent of 0.75 and were plotted against observed human CL_t values (D–F). Open, grey, and solid symbols represent compounds with expected significant, minor, and no contributions of CYP2C19 to their metabolism, respectively. The solid and dashed lines represent unity and twofold differences, respectively.

Figure 4. Relationships between observed human V_ss values and those obtained by single-species allometric scaling using data from EM (A), PM (B), and mixed (C) marmoset groups with corresponding mean scaling factors. (h) Ketoprofen; (i) itraconazole. The same sets of data were scaled using a fixed exponent of 1.0 and were plotted against observed human V_ss values (D–F). The solid and dotted lines represent unity and twofold differences, respectively.

Table 4. Average absolute fold errors (AAFEs) and the percentages of CL_t and V_ss values within two- and threefold errors compared with the observed values in humans for values predicted by single-species allometric scaling in extensive (EM) and poor (PM) metaboliser marmosets.

	Human CLt from marmoset data			Human Vss from marmoset data
	EM marmoset	PM marmoset	EM + PM marmoset	EM marmoset	PM marmoset	EM + PM marmoset
Number of compounds	24	24	24 + 24	24	24	24 + 24
Average scaling exponent	0.575	0.648	0.612	0.844	0.872	0.858
Within twofold error (%)^a	54.2 (41.7)	58.3 (45.8)	50 (43.8)	70.8 (29.2)	70.8 (45.8)	70.8 (37.5)
Within threefold error (%)^a	70.8 (62.5)	70.8 (62.5)	79.2 (62.5)	87.5 (75.0)	95.8 (79.2)	91.7 (77.1)
AAFE^a	2.1 (2.9)	2.0 (2.3)	2.1 (2.6)	1.7 (2.5)	1.6 (2.1)	1.7 (2.3)

^a Values in parentheses were generated by applying the commonly used fixed scaling exponents of 0.75 and 1.0 for predicting CL_t and V_ss, respectively (Hosea et al. 2009).

Figure 5. Shown are the effects of changes to the scaling exponent for human CL_t (A) and V_ss (B) predictions on the average absolute fold error (AAFE) using data from EM (circles) and PM (triangles) marmoset groups.

Figure 6. Relationships between observed human CL_t (A–C) and V_ss (D–F) values and those extrapolated by single-species allometric scaling using data from cynomolgus or rhesus monkeys (A, D), the EM marmoset group (B, E), and the PM marmoset group (C, F). Shown are data points for the 15 of the 24 test compounds for which suitable macaque data were available. Mean scaling factors averaged over the 15 substances were used (Table 6). (1) Clonazepam; (2) dapsone; (3) telmisartan; (4) acetaminophen; (5) S-warfarin; (6) zaleplon; and (7) ketoprofen. The solid and dotted lines represent unity and twofold differences, respectively.

Table 5. Comparison of human total clearance (CL_t) values and volumes of distribution (V_ss) predicted using single-species allometric scaling from reported data in macaques and observed data in marmosets for 15 of the 24 test compounds.

Chemicals	Macaque						Marmoset^a
							EM group		PM group
	Reported CLt, mL/min/kg	Reported Vss, L/kg	Predicted human CLt^a, mL/min/kg	Predicted human Vss^a, L/kg	Monkey	Reference	Predicted human CLt, mL/min/kg	Predicted human Vss, L/kg	Predicted human CLt, mL/min/kg	Predicted human Vss, L/kg
Acetaminophen	21.1	1.2	6.3	1.0	Cynomolgus	Koyanagi et al. (2014)	1.0	0.54	1.1	0.68
Antipyrine	4.6	0.74	1.4	0.62	Cynomolgus	Lombardo et al. (2013a, 2013b)	1.3	0.80	1.4	0.81
Clonazepam	14.3	6.8	4.3	5.7	Rhesus	Lombardo et al. (2013a, 2013b)	1.5	1.0	1.6	1.3
Dapsone	5.08	1.4	1.5	1.2	Cynomolgus	Miyamoto et al. (2017)	0.53	0.52	0.47	0.52
Diclofenac	8.5	0.32	2.6	0.27	Rhesus	Lombardo et al. (2013a, 2013b)	1.8	0.18	1.9	0.20
Ibuprofen	3.85	0.44	1.16	0.37	Cynomolgus	Miyamoto et al. (2017)	0.61	0.13	0.55	0.15
Ketanserin	20.8	1.67	6.3	1.4	Cynomolgus	Miyamoto et al. (2017)	16.7	2.10	22.0	2.40
Ketoprofen	4.92	0.25	1.5	0.21	Cynomolgus	Deguchi et al. (2011)	0.86	0.64	0.62	0.36
Midazolam	13.3	1.7	4.0	1.4	Cynomolgus	Koyanagi et al. (2015)	12.3	1.2	14.3	1.5
Moxifloxacin	11.5	4.9	3.5	4.1	Rhesus	Lombardo et al. (2013a, 2013b)	2.0	1.1	3.7	1.5
Nicardipine	27.0	1.23	8.1	1.0	Rhesus	Lombardo et al. (2013a, 2013b)	11.8	1.1	14.6	1.4
Pefloxacin	11.0	0.65	3.3	0.54	Rhesus	Lombardo et al. (2013a, 2013b)	1.4	2.2	1.7	2.5
Telmisartan	2.82	1.54	0.85	1.3	Cynomolgus	Deguchi et al. (2011)	6.3	5.4	5.3	3.4
S-Warfarin	0.102	0.13	0.03	0.10	Cynomolgus	Koyanagi et al. (2015)	0.19	0.39	0.05	0.18
Zaleplon	18.4	1.24	5.5	1.0	Cynomolgus	Miyamoto et al. (2017)	9.1	0.60	13.2	0.90

^a Predicted CL_t and V_ss values were obtained using scaling exponents averaged over the 15 compounds (for which suitable macaque data were available). The values of the exponents are given in Table 6.

Table 6. Comparison of predictabilities of human CL_t and V_ss values for 15 compounds using single-species allometric scaling based on data from macaques and marmosets.

Parameter	Human CLt^a from animal data			Human Vss^a from animal data
	Macaque	Marmoset		Macaque	Marmoset
		EM	PM		EM	PM
Average scaling exponent	0.597	0.566	0.622	0.940	0.826	0.859
Within twofold error, %	66.7	66.7	66.7	66.7	73.3	86.7
Within threefold error, %	80.0	86.7	86.7	93.3	86.7	100.0
Average absolute fold error (AAFE)	1.9	1.8	1.8	1.7	1.7	1.5

The 15 test compounds for which macaque data were available were included in this analysis.

^a Individual pharmacokinetic data are listed in Table 5.

Court, M.H., et al., 2013. The UDP-glucuronosyltransferase (UGT) 1A polymorphism c.2042C>G (rs8330) is associated with increased human liver acetaminophen glucuronidation, increased UGT1A exon 5a/5b splice variant mRNA ratio, and decreased risk of unintentional acetaminophen-induced acute liver failure. The journal of pharmacology and experimental therapeutics, 345 (2), 297–307.

 PubMed Web of Science ®Google Scholar

Court, M.H., et al., 2017. Race, gender, and genetic polymorphism contribute to variability in acetaminophen pharmacokinetics, metabolism, and protein-adduct concentrations in healthy African-American and European-American volunteers. Journal of pharmacology and experimental therapeutics, 362 (3), 431–440.

 PubMed Web of Science ®Google Scholar

Ueshima, Y., et al., 1996. Acetaminophen metabolism in patients with different cytochrome P-4502E1 genotypes. Alcoholism: clinical and experimental research, 20 (1 Suppl.), 25A–28A.

 PubMed Web of Science ®Google Scholar

Engel, G., et al., 1996. Antipyrine as a probe for human oxidative drug metabolism: identification of the cytochrome P450 enzymes catalyzing 4-hydroxyantipyrine, 3-hydroxymethylantipyrine, and norantipyrine formation. Clinical pharmacology and therapeutics, 59 (6), 613–623.

 PubMed Web of Science ®Google Scholar

Down, G., et al., 2006. Clinical pharmacology of cilomilast. Clinical pharmacokinetics, 45, 217–233.

 PubMed Web of Science ®Google Scholar

Toth, K., et al., 2016. Optimization of clonazepam therapy adjusted to patient's CYP3A status and NAT2 genotype. International journal of neuropsychopharmacology, 19 (12), pyw083.

 PubMed Web of Science ®Google Scholar

Gill, H.J., Tingle, M.D., and Park, B.K., 1995. N-Hydroxylation of dapsone by multiple enzymes of cytochrome P450: implications for inhibition of haemotoxicity. British journal of clinical pharmacology, 40 (6), 531–538.

 PubMed Web of Science ®Google Scholar

Lazarska, K.E., et al., 2018. Effect of UGT2B7*2 and CYP2C8*4 polymorphisms on diclofenac metabolism. Toxicology letters, 284, 70–78.

 PubMed Web of Science ®Google Scholar

Zharikova, O.L., et al., 2009. Identification of the major human hepatic and placental enzymes responsible for the biotransformation of glyburide. Biochemical pharmacology, 78 (12), 1483–1490.

 PubMed Web of Science ®Google Scholar

Buchheit, D., et al., 2011. Production of ibuprofen acyl glucosides by human UGT2B7. Drug metabolism and disposition, 39 (12), 2174–2181.

 PubMed Web of Science ®Google Scholar

Karaźniewicz-Łada, M., Luczak, M., and Główka, F., 2009. Pharmacokinetic studies of enantiomers of ibuprofen and its chiral metabolites in humans with different variants of genes coding CYP2C8 and CYP2C9 isoenzymes. Xenobiotica, 39 (6), 476–485.

 PubMed Web of Science ®Google Scholar

Peng, C.C., et al., 2012. Stereospecific metabolism of itraconazole by CYP3A4: dioxolane ring scission of azole antifungals. Drug metabolism and disposition, 40 (3), 426–435.

 PubMed Web of Science ®Google Scholar

Persson, B., Heykants, J., and Hedner, T., 1991. Clinical pharmacokinetics of ketanserin. Clinical pharmacokinetics, 20 (4), 263–279.

 PubMed Web of Science ®Google Scholar

Sabolovic, N., et al., 2000. Nonsteroidal anti-inflammatory drugs and phenols glucuronidation in Caco-2 cells: identification of the UDP-glucuronosyltransferases UGT1A6, 1A3 and 2B7. Life sciences, 67 (2), 185–196.

 PubMed Web of Science ®Google Scholar

Inoue, K., et al., 2016. Factors that influence the pharmacokinetics of lamotrigine in Japanese patients with epilepsy. European journal of clinical pharmacology, 72 (5), 555–562.

 PubMed Web of Science ®Google Scholar

Shin, K.H., et al., 2013. Evaluation of endogenous metabolic markers of hepatic CYP3A activity using metabolic profiling and midazolam clearance. Clinical pharmacology & therapeutics, 94 (5), 601–609.

 PubMed Web of Science ®Google Scholar

Moise, P.A., Birmingham, M.C., and Schentag, J.J., 2000. Pharmacokinetics and metabolism of moxifloxacin. Drugs of today (Barcelona, Spain: 1998), 36 (4), 229–244.

 PubMed Web of Science ®Google Scholar

Nakamura, K., et al., 2005. Inhibitory effects of nicardipine to cytochrome P450 (CYP) in human liver microsomes. Biological & pharmaceutical bulletin, 28 (5), 882–885.

 PubMed Web of Science ®Google Scholar

Kinzig-Schippers, M., et al., 1999. Interaction of pefloxacin and enoxacin with the human cytochrome P450 enzyme CYP1A2. Clinical pharmacology and therapeutics, 65 (3), 262–274.

 PubMed Web of Science ®Google Scholar

Yamamoto, Y., et al., 2015. Individualized phenytoin therapy for Japanese pediatric patients with epilepsy based on CYP2C9 and CYP2C19 genotypes. Therapeutic drug monitoring, 37 (2), 229–235.

 PubMed Web of Science ®Google Scholar

Perel, J.M., et al., 1970. Identification and renal excretion of probenecid metabolites in man. Life sciences. Pt. 1: physiology and pharmacology, 9 (23), 1337–1343.

 PubMedGoogle Scholar

Bidstrup, T.B., et al., 2003. CYP2C8 and CYP3A4 are the principal enzymes involved in the human in vitro biotransformation of the insulin secretagogue repaglinide. British journal of clinical pharmacology, 56 (3), 305–314.

 PubMed Web of Science ®Google Scholar

Yamada, A., et al., 2011. The impact of pharmacogenetics of metabolic enzymes and transporters on the pharmacokinetics of telmisartan in healthy volunteers. Pharmacogenetics and genomics, 21 (9), 523–530.

 PubMed Web of Science ®Google Scholar

Lee, C.R., et al., 2003. Tolbutamide, flurbiprofen, and losartan as probes of CYP2C9 activity in humans. Journal of clinical pharmacology, 43 (1), 84–91.

 PubMed Web of Science ®Google Scholar

Kroemer, H.K., et al., 1993. Identification of P450 enzymes involved in metabolism of verapamil in humans. Naunyn-Schmiedeberg's archives of pharmacology, 348, 332–337.

 PubMed Web of Science ®Google Scholar

Kaminsky, L.S. and Zhang, Z.Y., 1997. Human P450 metabolism of warfarin. Pharmacology & therapeutics, 73 (1), 67–74.

 PubMed Web of Science ®Google Scholar

Renwick, A.B., et al., 1998. Metabolism of zaleplon by human hepatic microsomal cytochrome P450 isoforms. Xenobiotica, 28 (4), 337–348.

 PubMed Web of Science ®Google Scholar

Varma, M.V., et al., 2010. Physicochemical space for optimum oral bioavailability: contribution of human intestinal absorption and first-pass elimination. Journal of medicinal chemistry, 53 (3), 1098–1108.

 PubMed Web of Science ®Google Scholar

Incecayir, T. and Agabeyoglu, I., 2006. Bioavailability file: lamotrigine. FABAD journal of pharmaceutical sciences, 31, 163–179.

 Google Scholar

Lund, L., et al., 1974. Pharmacokinetics of single and multiple doses of phenytoin in man. European journal of clinical pharmacology, 7 (2), 81–86.

 PubMed Web of Science ®Google Scholar

Abernethy, D.R., Kaminsky, L.S., and Dickinson, T.H., 1991. Selective inhibition of warfarin metabolism by diltiazem in humans. The journal of pharmacology and experimental therapeutics, 257 (1), 411–415.

 PubMed Web of Science ®Google Scholar

Hosea, N.A., et al., 2009. Prediction of human pharmacokinetics from preclinical information: comparative accuracy of quantitative prediction approaches. Journal of clinical pharmacology, 49 (5), 513–533.

 PubMed Web of Science ®Google Scholar

Koyanagi, T., et al., 2014. Age-related pharmacokinetic changes of acetaminophen, antipyrine, diazepam, diphenhydramine, and ofloxacin in male cynomolgus monkeys and beagle dogs. Xenobiotica, 44 (10), 893–901.

 PubMed Web of Science ®Google Scholar

Lombardo, F., et al., 2013a. Comprehensive assessment of human pharmacokinetic prediction based on in vivo animal pharmacokinetic data, part 2: clearance. Journal of clinical pharmacology, 53 (2), 178–191.

 PubMed Web of Science ®Google Scholar

Lombardo, F., et al., 2013b. Comprehensive assessment of human pharmacokinetic prediction based on in vivo animal pharmacokinetic data, part 1: volume of distribution at steady state. The journal of clinical pharmacology, 53 (2), 167–177.

 PubMed Web of Science ®Google Scholar

Miyamoto, M., et al., 2017. Comparison of predictability for human pharmacokinetics parameters among monkeys, rats, and chimeric mice with humanised liver. Xenobiotica, 47 (12), 1052–1063.

 PubMed Web of Science ®Google Scholar

Deguchi, T., et al., 2011. Human pharmacokinetic prediction of UDP-glucuronosyltransferase substrates with an animal scale-up approach. Drug metabolism and disposition, 39 (5), 820–829.

 PubMed Web of Science ®Google Scholar

Koyanagi, T., et al., 2015. Age-related changes of hepatic clearances of cytochrome P450 probes, midazolam and R-/S-warfarin in combination with caffeine, omeprazole and metoprolol in cynomolgus monkeys using in vitro–in vivo correlation. Xenobiotica, 45 (4), 312–321.

 PubMed Web of Science ®Google Scholar