A comprehensive analysis of the role of correction factors in the allometric predictivity of clearance from rat, dog, and monkey to humans

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

A data-driven approach was adopted to derive new one- and two-species-based methods for predicting human drug clearance (CL) using CL data from rat, dog, or monkey (n = 102). The new one-species methods were developed as CL(human)/kg = 0.152 x CL(rat)/kg, CL(human)/kg = 0.410 x CL(dog)/kg, and CL(human)/kg = 0.407 x 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) . W (human)(0.628) (referred to as rat-dog method) and CL(human) = a(rat-monkey) . 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.

Equation chapter 1 section 1A new method for predicting human hepatic clearance fromin Vitro experimental data using molecular descriptors

The present study demonstrated that the information of molecular descriptors of drugs increases the accuracy of predicting human in vivo hepatic clearance from in vitro experimental data in humans and rats. A new method uses not only the experimental data but also the information of molecular descriptors. Predictions for the datasets from hepatocyte experiments and microsome experiments were made by the present method, and the prediction accuracy was compared with those of the previous methods, such as methods using in vitro-in vivo scaling factor and multiple linear regression analysis, that use only the experimental data. Results showed that the present method was the most accurate prediction model with the lowest prediction errors and the strongest correlations. These results suggest that the information of molecular descriptors is significant for predicting the human in vivo pharmacokinetic parameters from in vitro experimental data. This study also demonstrated that in vitro experimental data in humans and rats were important information for predicting human in vivo hepatic clearance, and the additional rat in vivo data were not significant for prediction with the information of molecular descriptors. These results imply that the present method can be useful for high-throughput drug candidate screening by reducing the time and cost in the early stage of the drug discovery process.

A global examination of allometric scaling for predicting human drug clearance and the prediction of large vertical allometry**This work was presented at the American Association of Pharmaceutical Scientists Annual meeting, Salt Lake City, USA, Oct. 26, 2003

Allometrically scaled data sets (138 compounds) used for predicting human clearance were obtained from the literature. Our analyses of these data have led to four observations. (1) The current data do not provide strong evidence that systemic clearance (CL(s); n = 102) is more predictable than apparent oral clearance (CL(po); n = 24), but caution needs to be applied because of potential CL(po) prediction error caused by differences in bioavailability across species. (2) CL(s) of proteins (n = 10) can be more accurately predicted than that of non-protein chemicals (n = 102). (3) CL(s) is more predictable for compounds eliminated by renal or biliary excretion (n = 33) than by metabolism (n = 57). (4) CL(s) predictability for hepatically eliminated compounds followed the order: high CL (n = 11) > intermediate CL (n = 17) > low CL (n = 29). All examples of large vertical allometry (% error of prediction greater than 1000%) occurred only when predicting human CL(s) of drugs having very low CL(s). A qualitative analysis revealed the application of two potential rules for predicting the occurrence of large vertical allometry: (1) ratio of unbound fraction of drug in plasma (f(u)) between rats and humans greater than 5; (2) C logP greater than 2. Metabolic elimination could also serve as an additional indicator for expecting large vertical allometry.

EXTRAPOLATION OF PRECLINICAL PHARMACOKINETICS AND MOLECULAR FEATURE ANALYSIS OF “DISCOVERY-LIKE” MOLECULES TO PREDICT HUMAN PHARMACOKINETICS

The prediction of human pharmacokinetics from preclinical species is an integral component of drug discovery. Recent studies with a 103-compound dataset suggested that scaling from monkey pharmacokinetic data tended to be the most accurate method for predicting human clearance. Additionally, interrogation of the two-dimensional molecular properties of these molecules produced a set of associations which predict the likely extrapolative outcome (success or failure) of preclinical data to project human pharmacokinetics. However, a limitation of the previous analyses was the relative paucity of data for typical "discovery-like" molecules (molecular weight >300 and/or clogP >3). The objective of this investigation was to generate preclinical data required for extension of this dataset for additional discovery-like molecules and determine whether the aforementioned findings continue to apply for these molecules. In vivo nonrodent intravenous pharmacokinetic data were generated for 13 molecules, and data for 8 additional molecules were obtained from the literature. Additionally, the various scaling methodologies and molecular features analysis were applied to this new dataset to predict human pharmacokinetics. Whereas the predictive accuracies demonstrated across all of the various methodologies were lower for this higher clearance compound dataset, scaling from monkey liver blood flow continued to be an accurate methodology, and human volume of distribution was similarly well predicted regardless of scaling methodology. Lastly, application of the molecular feature associations, particularly data-dependent associations, afforded an improved predictivity compared with the liver blood flow scaling approaches, and provides insight into the extrapolation of high clearance compounds in the preclinical species to human.

truPK – human pharmacokinetic models for quantitative ADME prediction

The use of in silico prediction of absorption, distribution, metabolism and excretion (ADME) properties is gaining acceptance as a useful assessment tool for early identification of likely drug candidate failures. However, it has been difficult to locate reliable models for the prediction of human pharmacokinetics (PK) in silico Currently available methods for estimating ADME and toxicity properties, such as in vitro and animal models, are not very predictive of what is observed in the clinic. Existing in silico ADME prediction tools concentrate on physicochemical properties, such as solubility, log P, rule-of-five compliance, Caco-2 permeability, blood-brain barrier and so on, or only classify drug-like candidates as 'poor', 'medium' or 'good' for a PK parameter, without ascribing values. Although physiology-based pharmacokinetic -models can predict ADME properties, they rely on using various measured properties as input for better accuracy. Strand Genomics has developed a tool, truPK, that predicts the properties of a molecule (bioavailability, protein binding, volume of distribution, elimination half-life and absorption rate) that affect its dose and dose frequency in humans. truPK's five models built using sophisticated machine methods have predicted with > 75% accuracies in external validation sets.

Extrapolation of human pharmacokinetic parameters from rat, dog, and monkey data: Molecular properties associated with extrapolative success or failure

Human pharmacokinetic parameters are often predicted prior to clinical study from in vivo preclinical pharmacokinetic data. Recent data suggest that extrapolation of monkey pharmacokinetic data tends to be the most accurate method for predicting human clearance. In this study, the molecular features of a 103-compound dataset were analyzed to determine whether calculated physiochemical properties may be used to predict the extrapolative success or failure of rat, dog, and monkey data to project human pharmacokinetic parameters. Molecular properties (molecular weight, molar refractivity, log of the octanol-water partition coefficient, polar surface area, hydrogen bond donor/acceptor count, and rotatable bond count) were calculated, and relationships were sought for each preclinical species between extrapolative outcome for human clearance, distributional volume, and mean residence time, and each molecular feature or combination of features. The findings indicated that calculated molecular properties may be used both to predict extrapolative outcome for human pharmacokinetic properties from preclinical animal data, and to prospectively aid in the selection of an appropriate preclinical species in which to generate preclinical data to more reliably project human clearance. These observations demonstrate the utility of a combined computational and in vivo animal testing approach to projecting human pharmacokinetic parameters.

A novel model for prediction of human drug clearance by allometric scaling

Sixty-one sets of clearance (CL) values in animal species were allometrically scaled for predicting human clearance. Unbound fractions (f(u)) of drug in plasma in rats and humans were obtained from the literature. A model was developed to predict human CL: CL=33.35 ml/min x (a/Rf(u))(0.770), where Rf(u) is the f(u) ratio between rats and humans and a is the coefficient obtained from allometric scaling. The new model was compared with simple allometric scaling and the "rule of exponents" (ROE). Results indicated that the new model provided better predictability for human values of CL than did ROE. It is especially significant that for the first time the proposed model improves the prediction of CL for drugs illustrating large vertical allometry.

A UNIFIED MODEL FOR PREDICTING HUMAN HEPATIC, METABOLIC CLEARANCE FROM IN VITRO INTRINSIC CLEARANCE DATA IN HEPATOCYTES AND MICROSOMES

The aim of this study was to evaluate a unified method for predicting human in vivo intrinsic clearance (CL(int, in vivo)) and hepatic clearance (CL(h)) from in vitro data in hepatocytes and microsomes by applying the unbound fraction in blood (fu(b)) and in vitro incubations (fu(inc)). Human CL(int, in vivo) was projected using in vitro data together with biological scaling factors and compared with the unbound intrinsic clearance (CL(int, ub, in vivo)) estimated from clinical data using liver models with and without the various fu terms. For incubations conducted with fetal calf serum (n=14), the observed CL(int, in vivo) was modeled well assuming fu(inc) and fu(b) were equivalent. CL(int, ub, in vivo) was predicted best using both fu(b) and fu(inc) for other hepatocyte data (n=56; r(2)=0.78, p=3.3 x 10(-19), average fold error=5.2). A similar model for CL(int, ub, in vivo) was established for microsomal data (n=37; r(2)=0.77, p=1.2 x 10(-12), average fold error=6.1). Using the model for CL(int, ub, in vivo) (including a further empirical scaling factor), the CL(h) in humans was also calculated according to the well stirred liver model for the most extensive dataset. CL(int, in vivo) and CL(h) were both predicted well using in vitro human data from several laboratories for acidic, basic, and neutral drugs. The direct use of this model using only in vitro human data to predict the metabolic component of CL(h) is attractive, as it does not require extra information from preclinical studies in animals.

A mathematical description of the functionality of correction factors used in allometry for predicting human drug clearance

The functionality of the correction factors, maximum life-span potential (MLP), and brain weight (BrW) used in allometry is mathematically described. Correction by MLP or BrW is equivalent to a multiplication of some constants by the predicted values in humans from simple allometry, but they have no relationship to measured pharmacokinetic parameters in the animal species. The values of these constants (F(MLP) or F(BrW)) were calculated for some commonly used combinations of animal species. For all combinations of animal species, the value of F(BrW) is always greater than that of F(MLP) with a fold-increase of about 1.3 to 1.9. Different combinations of species give different values of F(BrW) and F(MLP). In addition, the role of correction factors (MLP and BrW) or the "rule of exponents" (ROE) was evaluated. An intrinsic defect in using correction factors or ROE was revealed; different study designs will produce significantly different prediction results. However, ROE may still serve as a useful practical approach in predicting human CL since it was derived from real observations and has been applied to many examples.

Application of simple mathematical expressions to relate the half-lives of xenobiotics in rats to values in humans

INTRODUCTION

Previous publications from GlaxoSmithKline and University of Toledo laboratories convey our independent attempts to predict the half-lives of xenobiotics in humans using data obtained from rats. The present investigation was conducted to compare the performance of our published models against a common dataset obtained by merging the two sets of rat versus human half-life (hHL) data previously used by each laboratory.

METHODS

After combining data, mathematical analyses were undertaken by deploying both of our previous models, namely the use of an empirical algorithm based on a best-fit model and the use of rat-to-human liver blood flow ratios as a half-life correction factor. Both qualitative and quantitative analyses were performed, as well as evaluation of the impact of molecular properties on predictability.

RESULTS

The merged dataset was remarkably diverse with respect to physiochemical and pharmacokinetic (PK) properties. Application of both models revealed similar predictability, depending upon the measure of stipulated accuracy. Certain molecular features, particularly rotatable bond count and pK(a), appeared to influence the accuracy of prediction.

DISCUSSION

This collaborative effort has resulted in an improved understanding and appreciation of the value of rats to serve as a surrogate for the prediction of xenobiotic half-lives in humans when clinical pharmacokinetic studies are not possible or practicable.