Table II.

Approaches for Human Clearance Prediction

Method	Equation	Data required	Dataset	AFE or APE	<twofold
Simple allometry (SA) (3,6,11)	where a and b are the coefficient and exponent of the allometric equation and W is body weight	CL and body weight in at least two animal species	n = 60 (10)	3.23	81%
			n = 102 (11)	2.65	54%
			n = 26 (11)	N.A.	46%
			n = 50 (9)	1.41	53%
			n = 102 (9)	1.28	54%
			n = 103 (42)	N.A.	53%
			n = 24 (37)	N.A.	17%
			n = 22 (63)	2.03	68%
			n = 45 (40)	N.A.	71%
Allometric scaling of unbound CL (38)	Unbound CL = CL/f u, p	CL, body weight, and plasma unbound (f u, p) in at least two animal species	n = 24 (37)	N.A.	62%
	Unbound CL = a × (W)b		n = 12 (12)	1.79	69%
	where f u, p is the unbound fraction in plasma		n = 20 (38)	2.7	55%
Two term power equation (39)	or where a is the coefficient and α and β are the exponents of the allometric equation. W and BrW are body weight and brain weight	CL and body weight in at least three animal species	N.A.	N.A	N.A.
Rule of exponents (RoE) (3)	If 0.55 < b <0.70, CL = a × (W)b	CL, body weight, and brain weight in at least two animal species	All categories
	If 0.71 < b < 0.99, CLhuman = a × (MLPanimal × CLanimal)b / MLPhuman		n = 60 (10)	1.85	81%
	If b ≥ 1, CLhuman = a × (BrWanimal × CLanimal)b / 1.53		n = 102 (11)	2.25	54%
	If b > 1.3, CL will be overpredicted		n = 103 (42)	N.A.	49%
	If b < 0.55, CL will be underpredicted		n = 24 (37)	N.A.	37%
			n = 45 (40)	N.A.	93%
Allometric scaling for renally and biliarily excreted drugs (7,44)	For renal secreted drugs:	CL, body weight, GFR, kidney blood flow, kidney weight, bile flow, and UDPGT activity in at least two animal species	Renal-secreted drugs
	For biliary excreted drugs: or		n = 10 (43)	N.A.	70%
			Biliary excreted drugs, n = 8 (44)	N.A.	50%
Allometric scaling after normalization by CLint, in vitro (8,45)		CL and body weight in at least two animal species, human and animal microsomal or hepatocyte K m, V max, or concentrations	Extensively metabolized compounds
	or		n = 10 (8)	N.A.	80%
			n = 11 (14)	1.6	82%
Multi-exponential Allometry (MA) (9,48)	where a and b are the coefficient and exponent of SA	CL and body weight in at least two animal species	All categories
			n = 50 (9)	1.19	76%
			n = 102 (9)	1.39	54%
			n = 45 (48)	N.A.	89%
Liver blood flow method (LBF) (11,49)		Human and animal liver blood flow, animal CL	All categories	Rat 2.57	Rat, 45%
			n = 102 (11)	Dog 2.79	Dog, 50%
				Monkey 1.89	Monkey, 70%
			n = 103 (49)	N.A.	Rat, dog, 66%
				N.A.	Monkey, 72%
			n = 103 (42)	N.A.	Rat, 40%
				N.A.	Dog, 44%
				N.A	Monkey, 68%
Scaling from one or two animal species (11)		CL in at least one species of rat, dog, or monkey	All categories		Rat, 58%
					Dog, 33%
			n = 26 (11)	N.A.	Monkey, 44%
					Rat–dog, 55%
					Rat–monkey, 78%
Vertical allometry (3,6) and f u corrected intercept method (FCIM) (10,40)	Vertical allometry (VA) criteria: ClogP > 2; f u, rat/f u, human > 5; metabolic elimination	ClogP, elimination route, CL, and body weight in at least two animal species, plasma unbound fraction in rats and humans	All categories
	FCIM approach: where a is the coefficient of SA and Rf u is the ratio of unbound fraction in plasma between rats and human		n = 60 (10)	78	90%
			n = 24 (37)	N.A.	50%
			n = 40 (40)	N.A.	89%
In vitro–in vivo extrapolation (IVIVE) using physiological scaling factors (12,23,60)		Drug disappearance rate in human microsomes or hepatocytes	All categories
	Well-stirred model		n = 33 (13)	6.17	15%
	Parallel tube model		n = 22 (63)	2.01	64%
	Dispersion model D N is the dispersion number		n = 7 (12)	1.95	86%
			n = 29 (65)	2.28	79%
IVIVE using a drug-specific scaling factor (13,60,61)		Drug disappearance rate in human and animal microsomes or hepatocytes, animal hepatic clearance, the fraction unbound in animal plasma, the animal blood-to-plasma concentration ratio	All categories
			n = 33 (13)	2.33	39%
			n = 13 (60)	1.57	69%
	where PB-SF is the physiologically based scaling factor. The ratio of animal in vivo and in vitro CLint is the drug-specific scaling factor			1.68	77%
IVIVE using an empirical scaling factor (13,62–64)	, where SF is the empirical scaling factor, determined by the nonlinear iterative least squares regression between CLint, in vivo and CLint, in vitro	Drug disappearance rate in human and rat microsomes or hepatocytes, a dataset of human CLint, in vivo and CLint, in vitro	All categories
			n = 33 (13)	1.00	46%
			n = 22 (63)	1.64	64%
			n = 52 (64)	1.8–2.2	62–76%
IVIVE with protein binding correction or at the presence of human plasma (67,70)	Well-stirred model	Drug disappearance rate in human microsomes or hepatocytes, the fraction unbound in human plasma (f u, p), and liver microsomes (f u, micro), or hepatocytes (f u, hepa)	All categories
	Hallifax equation:		n = 7 (12)	9.28	57%
			n = 29 (65)	f u, p 4.39	45%
				f u, p, f u, m 2.31	62%
			n = 8 (97)	N.A.	50%
			n = 7 (67)	N.A.	86%
			n = 10 (70)	N.A.	80%
IVIVE using recombinant P450 enzymes (68)	Relative abundance approachRelative activity approach	Intrinsic activity of each recombinant CYP enzymes for each test compound, relative abundance, or activity of CYP enzymes	Benzodiazepines
			n = 5 (69)	N.A.	80%
			n = 72 (68)	N.A.	44%
			n = 15 (98)	N.A.	93%
Physiologically based approach for renal clearance prediction (34)		GFR, f u, p, CLsecr, and P e	N.A.	N.A.	N.A.
Computational approaches: multiple linear regression (MLR) (72), partial least squares (PLS) (73), artificial neural network (ANN) (63,97), and k-nearest neighbors approach (kNN) (74)	Multivariate linear regression analysis	Molecular weight and hydrogen bond acceptor number of the test compound, CL in dogs and rats	All categories
			n = 68 (72)	N.A.	74%
			n = 50 (71)	1.28	78%
			Extensively metabolized compounds n = 22 (63)	1.81	68%