The Correction Factors Do Help in Improving the Prediction of Human Clearance from Animal Data

Mahmood, Iftekhar

doi:10.1002/jps.20299

Cited by 12 publications

(9 citation statements)

References 43 publications

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“…Lavé & Coassolo (1998) have commented on this approach. Mahmood (2005) disagreed with the conclusion by Nagilla & Ward (2004) that empirical correction factors do not improve the allometric predictions. This is despite the facts that the simple allometric approach had a higher percentage of predictions within 2-fold than the MLP and brain weight based methods, and that the maximum observed overpredictions and underpredictions for standard, MLP and brain weight methods were approximately 70-and 140-fold, 25-and 250-fold, and 30-and 220-fold, respectively.…”

Section: Allometric Scaling Without Correction For Interspecies CL Inmentioning

confidence: 63%

Prediction of human pharmacokinetics—evaluation of methods for prediction of hepatic metabolic clearance

Fagerholm

2007

Journal of Pharmacy and Pharmacology

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Methods for prediction of hepatic clearance (CL(H)) in man have been evaluated. A physiologically-based in-vitro to in-vivo (PB-IVIV) method with human unbound fraction in blood (f(u, bl)) and hepatocyte intrinsic clearance (CL(int))-data has a good rationale and appears to give the best predictions (maximum approximately 2-fold errors; < 25% errors for half of CL-predictions; appropriate ranking). Inclusion of an empirical scaling factor is, however, needed, and reasons include the use of cryo-preserved hepatocytes with low activity, and inappropriate CL(int)- and f(u, bl)-estimation methods. Thus, an improvement of this methodology is possible and required. Neglect of f(u, bl) or incorporation of incubation binding does not seem appropriate. When microsome CL(int)-data are used with this approach, the CL(H) is underpredicted by 5- to 9-fold on average, and a 106-fold underprediction (attrition potential) has been observed. The poor performance could probably be related to permeation, binding and low metabolic activity. Inclusion of scaling factors and neglect of f(u, bl) for basic and neutral compounds improve microsome predictions. The performance is, however, still not satisfactory. Allometry incorrectly assumes that the determinants for CL(H) relate to body weight and overpredicts human liver blood flow rate. Consequently, allometric methods have poor predictability. Simple allometry has an average overprediction potential, > 2-fold errors for approximately 1/3 of predictions, and 140-fold underprediction to 5800-fold overprediction (potential safety risk) range. In-silico methodologies are available, but these need further development. Acceptable prediction errors for compounds with low and high CL(H) should be approximately 50 and approximately 10%, respectively. In conclusion, it is recommended that PB-IVIV with human hepatocyte CL(int) and f(u, bl) is applied and improved, limits for acceptable errors are decreased, and that animal CL(H)-studies and allometry are avoided.

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Section: Allometric Scaling Without Correction For Interspecies CL Inmentioning

confidence: 63%

Prediction of human pharmacokinetics—evaluation of methods for prediction of hepatic metabolic clearance

Fagerholm

2007

Journal of Pharmacy and Pharmacology

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“…Furthermore, they proposed that the monkey liver blood flow approach was superior to the rule of exponents. This controversy is currently not resolved (Mahmood, 2005;Nagilla and Ward, 2005).The coefficients (a) of the power function have been considered important in determining the magnitude of CL, because the exponents (b) have been shown to be relatively constant, with a typical value close to 0.75 (Boxenbaum, 1982). Based upon analysis of more than 60 drugs, we have observed that the water-octanol partition coefficient (log P) and the ratio of unbound fraction (f u ) in plasma between rats and humans (Rf u ) may provide simple rules for anticipating the occurrence of large vertical allometry.…”

mentioning

confidence: 98%

mentioning

confidence: 99%

A Novel Model for Prediction of Human Drug Clearance by Allometric Scaling

Tang

Mayersohn²

2005

Drug Metab Dispos

127

114

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Allometric scaling is widely used in predicting human clearance (CL) based on animal data. Since prediction errors are commonly observed in the practical application of this approach, various modifications to allometric scaling have been proposed. These modifications include in vitro metabolic data (Lave et al., 1997), correction by either maximum life-span potential (MLP) or brain weight (BrW) (Mahmood and Balian, 1996b), the "rule of exponents" (ROE) (Mahmood and Balian, 1996a), and scaling unbound CL (Feng et al., 2000). Correction by in vitro metabolic data was successful in predicting human CL of 10 extensively metabolized drugs (Lave et al., 1997). Based on a data analysis of 16 drugs, however, Mahmood (2002) concluded that the use of in vitro data obtained from liver microsomes to predict hepatic CL in humans did not provide reliable predictions. In addition, in vitro metabolic corrections cannot be applied to compounds eliminated by excretion. Scaling unbound CL across animal species improved the prediction for certain compounds (Feng et al., 2000); however, it failed to predict well for a few compounds with large vertical allometry such as diazepam and valproate. Recently, Mahmood (2000) suggested that unbound CL cannot be predicted any better than total clearance. Corrections either with MLP or BrW have been shown to be inappropriate if they are used indiscriminately, which led to the idea of ROE. This rule provides selection criteria for use of MLP or BrW, based on the values of the exponents obtained from simple allometry (Mahmood and Balian, 1996a). Although ROE has been shown to improve the prediction significantly compared with simple allometry, this method is still not satisfactory in predicting large vertical allometry. More recent studies (Nagilla and Ward, 2004) found that the corrections using MLP or BrW or the rule of exponents in allometric scaling did not result in significant improvements in predictions of human CL. Furthermore, they proposed that the monkey liver blood flow approach was superior to the rule of exponents. This controversy is currently not resolved (Mahmood, 2005;Nagilla and Ward, 2005).The coefficients (a) of the power function have been considered important in determining the magnitude of CL, because the exponents (b) have been shown to be relatively constant, with a typical value close to 0.75 (Boxenbaum, 1982). Based upon analysis of more than 60 drugs, we have observed that the water-octanol partition coefficient (log P) and the ratio of unbound fraction (f u ) in plasma between rats and humans (Rf u ) may provide simple rules for anticipating the occurrence of large vertical allometry. Based upon these findings, therefore, we attempted to develop a new model for predicting human CL. Materials and MethodsA literature search was performed to obtain animal data for allometric scaling of systemic CL (CL used in this article refers to systemic CL) and f u ratio in rats and humans. Only data sets including at least three animal species were used for scaling. Coefficien...

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“…Furthermore, they recommended that the monkey LBF method be used for the most accurate and reliable estimation of human clearance based on the results they obtained with various methods including three-species allometry. The controversy concerning which animal species to use, which criteria to use for assessing prediction performance, and issues of data quality, has been discussed recently in a commentary by Mahmood (2005a) and correspondence by Nagilla and Ward (2005).…”

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confidence: 99%

Interspecies Prediction of Human Drug Clearance Based on Scaling Data from One or Two Animal Species

Tang¹,

et al. 2007

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ABSTRACT:A data-driven approach was adopted to derive new one-and twospecies-based methods for predicting human drug clearance (CL) using CL data from rat, dog, or monkey (n ‫؍‬ 102). The new onespecies methods were developed as CL human /kg ‫؍‬ 0.152 ⅐ CL rat /kg, CL human /kg ‫؍‬ 0.410 ⅐ CL dog /kg, and CL human /kg ‫؍‬ 0.407 ⅐ CL monkey / kg, referred to as the rat, dog, and monkey methods, respectively. The coefficient of the monkey method (0.407) was similar to that of the monkey liver blood flow (LBF) method (0.467), whereas the coefficients of the rat method (0.152) and dog method (0.410) were considerably different from those of the LBF methods (rat, 0.247; dog, 0.700). The new rat and dog methods appeared to perform better than the corresponding LBF methods, whereas the monkey method and the monkey LBF method showed improved predictability compared with the rat and dog one-species-based methods and the allometrically based "rule of exponents" (ROE). The new two-species methods were developed as CL human ‫؍‬ a rat-dog ⅐ W human 0.628 (referred to as rat-dog method) and CL human ‫؍‬ a rat-monkey ⅐ W human 0.650 (referred to as rat-monkey method), where a rat-dog and a rat-monkey are the coefficients obtained allometrically from the corresponding two species. The predictive performance of the two-species methods was comparable with that of the three-species-based ROE. Twenty-six Wyeth compounds having data from mouse, rat, dog, monkey, and human were used to test these methods. The results showed that the rat, dog, monkey, rat-dog, and rat-monkey methods provided improved predictions for the majority of the compounds compared with those for the ROE, suggesting that the use of three or more species in an allometrically based approach may not be necessary for the prediction of human exposure.

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The Correction Factors Do Help in Improving the Prediction of Human Clearance from Animal Data

Cited by 12 publications

References 43 publications

Prediction of human pharmacokinetics—evaluation of methods for prediction of hepatic metabolic clearance

Prediction of human pharmacokinetics—evaluation of methods for prediction of hepatic metabolic clearance

A Novel Model for Prediction of Human Drug Clearance by Allometric Scaling

Interspecies Prediction of Human Drug Clearance Based on Scaling Data from One or Two Animal Species

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