Characterisation of intravenous pharmacokinetics in Göttingen minipig and clearance prediction using established in vitro to in vivo extrapolation methodologies

Figures & data

Figure 1. Correlation between predicted blood CL_hep,met scaled from in vitro data using IVIVE Methods 1–3 and the derived in vivo blood CL_hep,met for reference compounds (n = 33). Panel A presents the IVIVE of CL from the WSM where all binding parameters are included (IVIVE Method 1); panel B where fu_inc is excluded based on the assumption fu_inc = 1 (IVIVE Method 2); and panel C where all binding parameters are excluded based on the assumption fu_b/fu_inc cancel out (IVIVE Method 3). The solid line represents line of unity and dotted line represents line of best fit from linear regression. The dot-dashed line represents the liver blood flow limitation set in minipig (38.9 mL/min/kg). Closed and open circles represent reference compounds included or excluded from linear regression analysis and statistical analysis (Table 4) of the IVIVE of CL_hep,met.

Figure 2. Correlation between log transformed predicted in vivo blood CL_int scaled from in vitro data using Methods 4 and 5 and the derived in vivo blood CL_int for selected reference compounds (n = 24, listed in Figure 6). Panels A–C present the IVIVE of CL_int using the WSM with all measured in vitro variables grouped together on the X-axis (Sohlenius-Sternbeck et al. 2012); the corresponding regression corrected IVIVE plot of predicted and derived in vivo CL_int; the corresponding regression corrected IVIVE plot of predicted and derived in vivo blood CL_hep,met respectively (IVIVE Method 4). Panels D–F present the IVIVE of CL_int traditionally established with unbound in vitro CL_int correlated with in vivo CL_int corrected with fu_b; the corresponding regression corrected IVIVE plot of predicted and derived in vivo CL_int,ub; the corresponding regression corrected IVIVE plot of predicted and derived in vivo blood CL_hep,met, respectively (IVIVE Method 5).

Figure 3. Total plasma and blood concentration-time profiles (mean ± stdev, n = 3) of reference compounds (n = 20) administered by discrete or cassette IV bolus injection to female Göttingen minipigs. *N-des.cloz is an abbreviation for N-desmethylclozapine.

Table 1. In vivo PK parameters determined in female Göttingen minipigs for reference compounds (n = 20).

Ion class	Cassette or discrete dosing	Compound		Plasma T1/2	Plasma AUClast*	Plasma Vss	Plasma CLtotal	Derived blood CLhep,met
			Dose (mg/kg)	(h) Mean ± SD	(h*ng/ml) Mean ± SD	(L/kg) Mean ± SD	(mL/min/kg) Mean ± SD	(mL/min/kg) Mean	Rb Ratio
Neutral	Cassette 1	Antipyrine	1.0	1.6 ± 0.7	1645 ± 59	0.84 ± 0.12	10 ± 0.3	9.4^a	1.04^d
	Cassette 5	Buspirone	0.5	3.0 ± 0.1	509 ± 69	0.99 ± 0.17	17 ± 2	23	0.74
	Cassette 3	Carbamazepine	0.5	2.1 ± 0.3	1120 ± 150	1.11 ± 0.14	7.4 ± 1.1	6.8	1.08
	Cassette 3	Diazepam	0.5	5.1 ± 0.4	936 ± 83	1.96 ± 0.07	8.8 ± 0.7	12	0.71
	Cassette 4	Way-100635	0.1	3.5 ± 0.7	58 ± 7	2.44 ± 0.69	29 ± 3	31	0.93
Basic	Discrete	Altanserin	0.1	4.7 ± 0.3	98 ± 3	1.99 ± 0.06	17 ± 0.6	17	1.02^e
	Cassette 1	Atenolol	0.3	4.4 ± 1.6	471 ± 30	2.09 ± 0.68	11 ± 1	6.2^b	1.07^d
	Discrete	Bupropion	0.1	3.6 ± 0.2	58 ± 4	4.12 ± 0.29	28 ± 2	31	0.90
	Cassette 5	Cimetidine	0.5	1.4 ± 0.1	453 ± 82	0.92 ± 0.15	19 ± 3	15^c	1.00
	Cassette 2	Citalopram	0.5	2.2 ± 0.02	219 ± 40	4.36 ± 1.00	39 ± 8	27	1.42
	Cassette 4	Diphenhydramine	0.2	1.9 ± 0.1	82 ± 17	3.34 ± 0.83	42 ± 9	48	0.86
	Cassette 4	Doxepin	0.1	2.4 ± 0.2	43 ± 11	4.51 ± 1.38	40 ± 10	45	0.89
	Cassette 1	Fluoxetine	0.3	3.3 ± 1.6	94 ± 12	13.40 ± 1.99	47 ± 10	47	1.00^f
	Discrete	Fluoxetine	1.0	3.2 ± 0.5	400 ± 60	8.93 ± 2.21	42 ± 7	42	1.00^f
	Cassette 1	Metoclopramide	0.3	8.5 ± 12.1	134 ± 17	8.14 ± 7.05	36 ± 6	36	1.00^f
	Cassette 2	N-desmethylclozapine	0.5	4.6 ± 0.1	357 ± 107	8.91 ± 3.37	25 ± 9	18	1.34
	Cassette 5	Propranolol	0.1	1.7 ± 0.3	121 ± 32	1.45 ± 0.65	14 ± 4	20	0.74
	Discrete	Risperidone	0.1	0.9 ± 0.03	86 ± 7	1.02 ± 0.18	19 ± 1	26	0.75
	Discrete	Verapamil	0.2	2.8 ± 0.2	188 ± 25	2.08 ± 0.35	18 ± 2	28	0.63
Acidic	Cassette 2	Indomethacin	0.38	5.5 ± 0.6	309 ± 62	3.18 ± 0.31	19 ± 4	29	0.67
Zwitterionic	Cassette 2	Gabapentin	0.5	4.0 ± 0.1	4241 ± 335	0.58 ± 0.04	2.0 ± 0.2	2.6	0.78

^a Fraction of dose excreted unchanged in urine (Fe) in female Göttingen Minipig was 1.8% (Lignet et al. 2016).

^b Fe in female Göttingen Minipig was 37% (Lignet et al. 2016).

^c Fe in female Göttingen Minipig was 21% (Lignet et al. 2016).

^d In vitro R_b determined in NIBS pigs in study by Yoshimatsu (Yoshimatsu et al. 2016).

^e Data are based on blood and plasma AUC_1–12 h due to insufficient matrices.

^f Measured in vitro or in vivo R_b not available therefore the R_b value is estimated according to ion class whereby a value of 0.57 is used for acids and all other ion classes are set to 1.00.

* AUC_{%-extrapolated} <5% for all compounds except for indomethacin, metoclopramide, and fluoxetine (cassette dosed) where AUC_{%-extrapolated} <15%.

Table 2. Published minipig in vitro and in vivo IV PK data for reference compounds (n = 19).

Ion class	Drug	Dose (mg/kg)	Plasma T1/2 (h) Mean ± SD	Plasma Vss (L/kg) Mean (± SD)	Plasma CLtotal (ml/min/kg) Mean (± SD)	Fe in urine (%)	Rb Ratio	Fup (%)	Fuinc (%)	CLint,app μL/min/10^6 cells Mean (± SD)	Breed (gender)	Source of in vivo PK data	In vivo data source used for IVIVE
Neutral	Acetaminophen	10^a	–	–	6.3 ± 1.0^a	–	–	45^a	–	–	Göttingen (female)	a	a
		0.1^b	–	0.5^b	3.2^b	–	1.12^b	–	–	–	NIBS (male)	b
	Antipyrine	10^c	1.7 ± 0.4^c	0.7 ± 0.2^c	4.8 ± 1.1^c	1.8 ± 1.5^c	0.79^c	98^c	79^c	0.7 ± 2.4^c	Göttingen (female)*	c	d
		10^c	9.3 ± 5.8^c	0.8 ± 0.3^c	1.5 ± 0.9^c	2.7 ± 2.6^c	0.79^c	98^c	79^c	0.7 ± 2.4^c	Göttingen (male)**	c
		0.1^b	–	0.7^b	3.9^b	–	1.04^b	63^b	–	–	NIBS (male)	b
		1.0^d	1.6 ± 0.7^d	0.8 ± 0.1^d	10 ± 3^d	–	–	99^d	97^d	<3.0^d	Göttingen (female)	d
		0.1^h	–	0.43 ± 0.1^h	1.5 ± 1.1^h	–	0.82^h	94^h	–	–	Göttingen (male)	h
	Diazepam	1.0^c	1.9 ± 0.4^c	0.9 ± 0.3^c	9.9 ± 1.5^c	0.1 ± 0.2^c	0.60^c	3.7^c	47^c	4.05 ± 0.75^c	Göttingen (female)	c	d
		1.0^c	1.5 ± 0.5^c	0.8 ± 0.3^c	9.3 ± 0.7	–	–	–	–	–	Göttingen (male)	c
		0.5^d	5.1 ± 0.4^d	2.0 ± 0.1^d	8.8 ± 0.7^d	–	0.71^d	5.4^d	91^d	13.5^d	Göttingen (female)	d
		0.1^h	–	1.3 ± 0.3^h	7.9 ± 2.0^h	–	0.72^h	10^h	–	–	Göttingen (male)	h
	Felodipine	0.1^b	–	2.2^b	14.5^b	–	0.78^b	0.38^b	–	–	NIBS (male)	b	b
	Griseofulvin	–	–	–	12.1^e	–	–	–	–	–	Göttingen (NA)	e	e
	Hydrochlorothiazide	4.0^c	5.9 ± 1.2^c	3.6 ± 0.8^c	11.8 ± 1.9^c	38.8 ± 11.8^c	0.80^c	52^c	79^c	1.2 ± 1.7^c	Göttingen (female)	c	c
		4.0^c	4.5 ± 0.6^c	2.8 ± 0.8^c	11.8 ± 1.5^c	–	–	–	–	–	Göttingen (male)	c
	Midazolam	1.0^c	1.0 ± 0.2^c	1.0 ± 0.2^c	15 ± 1.9^c	–	0.61^c	6.9^c	16^c	10.8 ± 2.2^c	Göttingen (female)	c	c
		1.0^c	0.9 ± 0.5^c	1.5 ± 0.6^c	29 ± 6.1^c	–	–	–	–	–	Göttingen (male)	c
	Theophylline	1.0^c	8.2 ± 0.5^c	0.9 ± 0.1^c	1.4 ± 0.1^c	13.6 ± 4.1^c	0.83^c	82^c	72^c	0.95 ± 0.95^c	Göttingen (female)	c	c
		1.0^c	16 ± 3.1^c	1.0 ± 0.1^c	0.9 ± 0.3^c	–	–	–	–	–	Göttingen (male)	c
Basic	Atenolol	1.0^c	3.5 ± 1.7^c	1.5 ± 0.1^c	10.1 ± 2.0^c	37.1 ± 11.6^c	1.1^c	94^c	100^c	1.6 ± 1.8^c	Göttingen (female)	c	d
		1.0^c	5.3 ± 0.3^c	1.5 ± 0.1^c	5.8 ± 1.7^c	–	–	–	–	–	Göttingen (male)	c
		0.1^b	–	1.4^b	3.5^b	–	1.1^b	82^b	–	–	NIBS (male)	b
		0.3^d	4.4 ± 1.6^d	2.1 ± 0.7^d	11 ± 1^d	–	–	98^d	100^d	1.9^d	Göttingen (female)	d
	Buprenorphine	0.02^b	–	3.4^b	17.1^b	–	0.75^b	1.5^b	–	–	NIBS (male)	b	b
	Cimetidine	1.0^c	0.7 ± 0.2^c	1.6 ± 0.3^c	43 ± 8.2^c	20.7 ± 8.2^c	0.93^c	70^c	86^c	5.65 ± 0.05^c	Göttingen (female)	c	d
		1.0^c	0.8 ± 0.0^c	1.3 ± 0.1^c	32 ± 2.2^c	–	–	–	–	–	Göttingen (male)	c
		0.5	1.4 ± 0.1^d	0.9 ± 0.2^d	19 ± 3^d	–	1.00^d	86^d	100^d	3.78^d	Göttingen (female)	d
	Lidocaine	0.1^b	–	1.9^b	12.8^b	–	0.63^b	60^b	–	–	NIBS (male)	b	b
	Propranolol	0.1^b	–	1.9^b	11.1^b	–	0.99^b	18^b	–	–	NIBS (male)	b	d
		0.1^d	–	1.5 ± 0.7^d	14 ± 4^d	–	0.74^d	25^d	39^d	≫300^d	Göttingen (female)	d
	Raloxifene	0.1^b	–	3.84^b	21.5^b	–	1.04^b	0.88^b	–	–	NIBS (male)	b	b
	Verapamil	0.2^f	–	–	34.2^f	–	–	25^f	–	–	Göttingen (male)	f	d
		0.2^d	2.8 ± 0.2^d	2.1 ± 0.4^d	18 ± 2^d	–	0.63^d	13^d	29^d	–	Göttingen (female)	d
Acid	Diclofenac	0.1^b	–	0.14^b	1.2^b	–	0.58^b	0.21^b	–	–	NIBS (male)	b
		0.1^h	–	0.18 ± 0.01^h	5.2 ± 2.4^h	–	0.65^h	–	–	–	Göttingen (male)	h	h
	Ketoprofen	0.1^b	–	0.18^b	0.9^b	–	0.67^b	0.69^b	–	–	NIBS (male)	b	h
		0.1^h	–	0.19 ± 0.07^h	1.7 ± 0.8^h	–	0.79^h	1.0^h	–	–	Göttingen (male)	h
Zwitterionic	Fexofenadine	0.1^b	–	0.63^b	4.7^b	–	0.68^b	44^b	–	–	NIBS (male)	b	b
	Moxifloxacin	2.8^g	5.7 ± 1.0^g	3.8 ± 1.2^g	10.8^g	13.8^g	–	63^g	–	0.65 ± 1.39^g	Göttingen (female)	g	g
		0.1^h	–	1.3 ± 0.12^h	3.6 ± 0.6^h	–	1.14^h	69^h	–	–	Göttingen (male)	h

^a Suenderhauf et al. (2014), ^bYoshimatsu et al. (2016), ^cLignet et al. (2016), ^dInternal data, ^eSuenderhauf and Parrott (2013), ^fPatel et al. (2017), ^gSiefert et al. (1999), ^hDing et al. (2021).

^* In vivo data generated in female animals, in vitro data (R_b, fu_p, fu_inc, and hepatocyte CL_int,app) were measured in mixed gender pooled matrix or source not stated. **In vivo data generated in male animals, in vitro data (R_b, fu_p, fu_inc, and hepatocyte CL_int,app) were either measured in mixed gender pooled matrix or source not stated. –: No data available.

Table 3. In silico predictions and in vitro properties determined in Göttingen minipig for the complete reference set (n = 33 compounds).

		Log DpH7.4		HLM Fuinc		Fup	Hepatocyte CLint,app
Ion class	Reference compound	Measured	Predicted^a	Measured Mean ± SD % free	Predicted^a % free	Measured Mean ± SD % free	Measured Mean (μL/min/10^6 cells)	Human clearance route^i
Neutral	Acetaminophen ^♦	0.51^b	0.50	100 ± 4.8	91.5	69.8 ± 1.5	<3.0	CYP2E1, UGT
	Antipyrine ^♦	0.34^b	0.59	97.4 ± 7.7	91.1	98.5 ± 5.0	<3.0	CYP450’s including CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C18 and CYP3A4
	Buspirone ^♦	–	1.77	68.7 ± 3.1	73.5	32.0 ± 0.9	294	CYP3A4 > CYP3A5, CYP2D6
	Carbamazepine ^♦	1.78^b	2.40	80.7 ± 3.6	69.8	41.7 ± 4.5	<3.0	CYP3A4, CYP2C8, CYP1A2, UGT2B7
	Diazepam ^♦	2.25^c	2.80	90.7 ± 6.4	73.6	5.4 ± 0.5	13.5	CYP2C19 > CYP3A4, CYP1A2
	Felodipine	–	5.03	0.57 ± 0.03	6.3	0.04 ± 0.01^g	71.5	CYP450’s including CYP3A4
	Griseofulvin ^♦	2.18^b	2.51	66.3 ± 7.6	67.2	17.4 ± 2.8	5.19	Metabolism and excretion
	Hydrochlorothiazide ^♦	–0.12^b	–0.07	96.4 ± 5.0	93.3	60.7 ± 4.1	<3.0	Renal excretion
	Midazolam ^♦	–	3.56	44.4 ± 1.8	37.2	8.0 ± 0.5	64.8 ± 3.1**	CYP3A4 ≫ CYP3A5, UGT1A4
	Theophylline	–0.05^b	–0.14	97.2 ± 2.4	94.2	80.1 ± 0.7	<3.0	CYP1A2
	Way-100635 ^♦	NA	3.50	NA*	35.7	22.0 ± 4.1	>300 (319*)	–
Basic	Altanserin ^♦	–	2.79	15.8 ± 2.5	45.7	1.5 ± 0.4	19.9	–
	Atenolol ^♦	−1.29^b	−1.41	>99	90.1	97.6 ± 6.6	<3 (1.93*)	CYP450 insignificant, renal excretion
	Buprenorphine ^♦	–	4.23	39.6 ± 1.5	9.8	8.2 ± 0.6	84.8 ± 11.3**	CYP3A4, CYP2C8, UGT1A1
	Bupropion ^♦	2.61^b	2.51	62.6 ± 4.5	48.0	63.8 ± 6,6	10.7	CYP2B6, reductase
	Cimetidine ^♦	0.35^b	–1.09	>99	88.0	85.5 ± 6.4	3.78	CYP450, UGT/SULT, renal excretion
	Citalopram ^♦	–	1.95	43.2 ± 3.5	40.2	54.3 ± 2.9	16.0	CYP2C19, CYP3A4 and CYP2D6 and renal excretion
	Diphenhydramine	1.52^c	2.08	49.5 ± 1.9	49.2	47.3 ± 3.8	61.1	CYP450 ≫ UGT
	Doxepin	2.20^d	2.69	20.0 ± 1.2	30.2	25.3 ± 0.8	197	CYP450’s including CYP2D6^c
	Fluoxetine	1.83^b	2.03	1.4 ± 1.1	30.0	10.7 ± 2.0	38.2	CYP2D6
	Lidocaine	–	1.72	88.1 ± 1.2	82.2	62.7 ± 8.2	65.50	CYP1A2 > CYP3A4
	Metoclopramide ^♦	0.64^e	0.65	76.4 ± 2.0	84.2	81.0 ± 0.4	43.4	CYP2D6 > other CYP450’s
	N-Desmethylclozapine ^♦	–	2.17	12.5 ± 0.5	50.0	18.7 ± 3.6	17.7	–
	Propranolol	1.26^b	0.95	39.2 ± 0.7	59.8	24.7 ± 0.9	≫300^h	CYP2D6 > CYP1A2, CYP2C19, UGT
	Raloxifene ^♦	–	4.58	1.68 ± 0.16	5.3	0.6 ± 0.1	59.7 ± 10.5**	UGT1A9, UGT1A1
	Risperidone ^♦	2.04^e	1.84	62.0 ± 6.6	61.9	29.7 ± 3.2	54.0	CYP2D6, CYP3A4
	Verapamil ^♦	2.57^c	3.35	28.7 ± 1.4	22.3	12.7 ± 0.7	83.8	CYP3A4 ≫ CYP2C9
Acid	Diclofenac ^♦	1.15^b	1.17	76.8 ± 15.8	73.7	0.3 ± 0.1^f	9.10 ± 1.5**	CYP2C9 > CYP3A4, UGT
	Indomethacin	1.00^b	0.88	64.3 ± 4.0	79.6	3.0 ± 0.7	20.1	CYP450 and renal excretion^c
	Ketoprofen ^♦	–1.51^b	–0.004	100 ± 15.3	90.2	0.2 ± 0.02	<3 (2.20*)	CYP2C9, UGT2B7
Zwitter-ionic	Fexofenadine ^♦	–	3.07	71.2 ± 3.0	51.6	13.0 ± 0.7	3.73 ± 0.40**	biliary ≫ renal excretion, CYP3A4 minor component
	Gabapentin	−2.00^b	−1.29	93.5 ± 8.7	95.2	97.0^g	<3.0	Renal excretion
	Moxifloxacin ^♦	–	0.07	89.8 ± 5.3	92.5	68.9 ± 3.2	<3.0	UGT, SULT as well as renal and biliary excretion

^a In silico prediction from ADMET_™ Predictor software version 10.3.

^b Literature value (Zhu et al. 2002).

^c Literature value (Austin et al. 2002).

^dLiterature value (Unger and Chiang 1981).

^e Literature value (Lombardo et al. 2001).

^f Measured in diluted (10%) plasma due to high binding.

^g Value from literature. Tested in human plasma (Obach et al. 2008).

^h The in vitro hepatocyte CL_int,app value for propranolol could not be determined with confidence as the CL_int,app exceeded the assay sensitivity range of 300 μL/min/10⁶ cells

^I Literature details in Supplementals, Worksheet Tab 1.

* Value in parenthesis is used in IVIVE.

**These compounds were tested in duplicates on two separate occasions. Data are shown as mean ± SD for the full dataset.

–: No data available.

♦ Recommended reference set (n = 24) for establishing ‘regression offset’ for IVIVE methods 4 and 5. Prediction performance of IVIVE methodologies 1–5 (Tables 4 and 5) was based on the same reference set (n = 24).

Figure 4. Comparison of the key parameters used in the WSM for prediction of CL_hep,met and in vivo CL_int for each reference compound (n = 33). The dynamic range and spread of values measured in vitro (fu_p, fu_inc, CL_int,app) and in vivo (plasma CL_total) are presented in panels A–D, respectively. Closed symbols represent reference compounds recommended for establishing the regression offset.

Table 4. Statistical analyses for IVIVE methodologies 1–5 used to predict minipig in vivo blood CL_hep,met via the WSM.

Statistics n = 24	Method 1	Method 2	Method 3	Method 4	Method 5
Comment	All binding parameters included; empirical scaler excluded (Equation (8)(8) $$\mathrm{Method}\mathrm{ }1:\mathit{Predicted}\mathrm{ }\mathit{blood}\mathrm{ }{CL}_{\mathit{hep},\mathrm{ }\mathit{met}}=\frac{Q_h·{CL}_{\mathit{int},\mathit{app}}/{fu}_{\mathit{inc}}·\mathit{PSF}·{fu}_b}{Q_h\mathrm{ }+\mathrm{ }({CL}_{\mathit{int},\mathit{app}}/{fu}_{\mathit{inc}}·\mathit{PSF}·{fu}_b)}$$(8) )	Assumed fuinc = 1, empirical scaler excluded (Equation (9)(9) $$\mathrm{Method}\mathrm{ }2:\mathrm{ }\mathit{Predicted}\mathrm{ }\mathit{blood}\mathrm{ }{CL}_{\mathit{hep},\mathrm{ }\mathit{met}}=\frac{Q_h·{CL}_{\mathit{int},\mathit{app}}·\mathit{PSF}·{fu}_b}{Q_h+{CL}_{\mathit{int},\mathit{app}}·\mathit{PSF}·{fu}_b}$$(9) )	Assumed fub/fuinc = 1, empirical scaler excluded (Equation (10)(10) $$\mathrm{Method}\mathrm{ }3:\mathrm{ }\mathit{Predicted}\mathrm{ }\mathit{blood}\mathrm{ }{CL}_{\mathit{hep},\mathit{met}}=\frac{Q_h·{CL}_{\mathit{int},\mathit{app}}·\mathit{PSF}}{Q_h+{CL}_{\mathit{int},\mathit{app}}·\mathit{PSF}}$$(10) )	All binding parameters included, incorporated empirical scaler based on a regression offset derived from Equations (11)–(14)	All binding parameters included, incorporated empirical scaler based on regression offset derived from Equation (15)–(18)
Within 2-fold (%)	58	38	83	96	83
Within 3-fold (%)	75	67	100	100	92
Within 5-fold (%)	88	75	100	100	92
AFE	2.5	4.0	1.1	0.9	1.1
AAFE	2.7	4.1	1.4	1.4	1.9
RMSE	4.9	7.0	1.5	1.5	2.8
R^2	0.69	0.51	0.65	0.70	0.61

Table 5. Statistical analyses for IVIVE methodologies 4–5 used to predict minipig in vivo blood CL_int via the WSM.

Statistics n = 24	Method 4	Method 5	Method 4	Method 5
Comment	Excludes empirical scaler based on regression offset	Excludes empirical scaler based on regression offset	Includes empirical scaler based on regression offset derived from Equations (11)–(14)	Includes empirical scaler based on regression offset derived from Equations. (15)–(18)
Within 2-fold (%)	29	25	67	46
Within 3-fold (%)	46	50	88	79
Within 5-fold (%)	67	67	96	83
AFE	3.1	3.1	1.0	1.0
AAFE	4.2	4.2	1.9	2.9
RMSE	6.8	6.8	2.3	3.9
R^2	0.61	0.52	0.61	0.52

Figure 5. Prediction of CL_hep,met using recommended IVIVE Method 4 (note for method 5 the IVIVE process is established in the same way but using CL_int,ub and Equations (14)–(17)). Step 1: the lab-specific ‘regression offset’ is established using the recommended reference set highlighted in Panel 2. Step 2: to assess IVIVE of CL_hep.met for a new compound, in vitro data is generated in the same lab-specific assays used in step 1 then scaled using the lab-specific regression offset (slope and intercept) for comparison with the in vivo CL_hep,met.

Figure 6. A comparison of the predicted blood CL_hep,met from IVIVE Methods 4 and 5 is presented. The solid lines represent line of unity and dotted lines represent line of best fit from linear regression. Closed circles represent recommended reference compounds to establish the empirical regression correction and subsequently used in the statistical analyses (Table 5) of the IVIVE of predicted in vivo CL_int and predicted CL_hep,met.

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