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

Application of the Population Pharmacokinetics Model-Based Approach to the Prediction of Drug-Drug Interaction between Rivaroxaban and Carbamazepine in Humans

Rivaroxaban (RIV) is one of the direct oral anticoagulants used to prevent and treat venous and arterial thromboembolic events. Considering the therapeutic indications, RIV is likely to be concomitantly administered with various other drugs. Among these is carbamazepine (CBZ), one of the recommended first-line options to control seizures and epilepsy. RIV is a strong substrate of cytochrome P450 (CYP) enzymes and Pgp/BCRP efflux transporters. Meanwhile, CBZ is well known as a strong inducer of these enzymes and transporters. Therefore, drug-drug interaction (DDI) between CBZ and RIV is expected. This study aimed to predict the DDI profile of CBZ and RIV in humans by using a population pharmacokinetics (PK) model-based approach. We previously investigated the population PK parameters of RIV administered alone or with CBZ in rats. In this study, those parameters were extrapolated from rats to humans by using simple allometry and liver blood flow scaling, and then applied to back-simulate the PK profiles of RIV in humans (20 mg RIV per day) used alone or with CBZ (900 mg CBZ per day). Results showed that CBZ significantly reduced RIV exposure. The AUC_inf and C_max of RIV decreased by 52.3% and 41.0%, respectively, following the first RIV dose, and by 68.5% and 49.8% at the steady state. Therefore, the co-administration of CBZ and RIV warrants caution. Further studies investigating the extent of DDIs between these drugs should be conducted in humans to fully understand their safety and effects.

Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies

Physiologically based pharmacokinetic modelling is well established in the pharmaceutical industry and is accepted by regulatory agencies for the prediction of drug-drug interactions. However, physiologically based pharmacokinetic modelling is valuable to address a much wider range of pharmaceutical applications, and new regulatory impact is expected as its full power is leveraged. As one example, physiologically based pharmacokinetic modelling is already routinely used during drug discovery for in-vitro to in-vivo translation and pharmacokinetic modelling in preclinical species, and this leads to the application of verified models for first-in-human pharmacokinetic predictions. A consistent cross-industry strategy in this application area would increase confidence in the approach and facilitate further learning. With this in mind, this article aims to enhance a previously published first-in-human physiologically based pharmacokinetic model-building strategy. Based on the experience of scientists from multiple companies participating in the GastroPlus™ User Group Steering Committee, new Absorption, Distribution, Metabolism and Excretion knowledge is integrated and decision trees proposed for each essential component of a first-in-human prediction. We have reviewed many relevant scientific publications to identify new findings and highlight gaps that need to be addressed. Finally, four industry case studies for more challenging compounds illustrate and highlight key components of the strategy.

Application of an Inter-Species Extrapolation Method for the Prediction of Drug Interactions between Propolis and Duloxetine in Humans

Duloxetine (DLX) is a potent drug investigated for the treatment of depression and urinary incontinence. DLX is extensively metabolized in the liver by two P450 isozymes, CYP2D6 and CYP1A2. Propolis (PPL) is one of the popular functional foods known to have effects on activities of CYPs, including CYP1A2. Due to the high probability of using DLX and PPL simultaneously, the present study was designed to investigate the potent effect of PPL on pharmacokinetics (PKs) of DLX after co-administration in humans. A PK study was first conducted in 18 rats (n = 6/group), in which the plasma concentration of DLX and its major metabolite 4-hydroxy duloxetine (4-HD) with or without administration of PPL was recorded. Population PKs and potential effects of PPL were then analyzed using NONMEM software. Lastly, these results were extrapolated from rats to humans using the allometric scaling and the liver blood flow method. PPL (15,000 mg/day) exerts a statistically significant increase in DLX exposures at steady state, with a 20.2% and 24.6% increase in DLX C m a x , s s and the same 28.0% increase in DLX A U C s s when DLX (40 or 60 mg) was administered once or twice daily, respectively. In conclusion, safety issues are required to be attended to when individuals simultaneously use DLX and PPL at high doses, and the possibility of interactions between DLX and PPL might be noted.

Research tools for extrapolating the disposition and pharmacokinetics of nanomaterials from preclinical animals to humans

A critical step in the translational science of nanomaterials from preclinical animal studies to humans is the comprehensive investigation of their disposition (or ADME) and pharmacokinetic behaviours. Disposition and pharmacokinetic data are ideally collected in different animal species (rodent and nonrodent), at different dose levels, and following multiple administrations. These data are used to assess the systemic exposure and effect to nanomaterials, primary determinants of their potential toxicity and therapeutic efficacy. At toxic doses in animal models, pharmacokinetic (termed toxicokinetic) data are related to toxicologic findings that inform the design of nonclinical toxicity studies and contribute to the determination of the maximum recommended starting dose in clinical phase 1 trials. Nanomaterials present a unique challenge for disposition and pharmacokinetic investigations owing to their prolonged circulation times, nonlinear pharmacokinetic profiles, and their extensive distribution into tissues. Predictive relationships between nanomaterial physicochemical properties and behaviours in vivo are lacking and are confounded by anatomical, physiological, and immunological differences amongst preclinical animal models and humans. These challenges are poorly understood and frequently overlooked by investigators, leading to inaccurate assumptions of disposition, pharmacokinetic, and toxicokinetics profiles across species that can have profoundly detrimental impacts for nonclinical toxicity studies and clinical phase 1 trials. Herein are highlighted two research tools for analysing and interpreting disposition and pharmacokinetic data from multiple species and for extrapolating this data accurately in humans. Empirical methodologies and mechanistic mathematical modelling approaches are discussed with emphasis placed on important considerations and caveats for representing nanomaterials, such as the importance of integrating physiological variables associated with the mononuclear phagocyte system (MPS) into extrapolation methods for nanomaterials. The application of these tools will be examined in recent examples of investigational and clinically approved nanomaterials. Finally, strategies for applying these extrapolation tools in a complementary manner to perform dose predictions and in silico toxicity assessments in humans will be explained. A greater familiarity with the available tools and prior experiences of extrapolating nanomaterial disposition and pharmacokinetics from preclinical animal models to humans will hopefully result in a more straightforward roadmap for the clinical translation of promising nanomaterials.

Pharmacological Optimization for Successful Traumatic Brain Injury Drug Development

The purpose of this review is to highlight the pharmacological barrier to drug development for traumatic brain injury (TBI) and to discuss best practice strategies to overcome such barriers. Specifically, this article will review the pharmacological considerations of moving from the disease target "hit" to the "lead" compound with drug-like and central nervous system (CNS) penetrant properties. In vitro assessment of drug-like properties will be detailed, followed by pre-clinical studies to ensure adequate pharmacokinetic and pharmacodynamic characteristics of response. The importance of biomarker development and utilization in both pre-clinical and clinical studies will be detailed, along with the importance of identifying diagnostic, pharmacodynamic/response, and prognostic biomarkers of injury type or severity, drug target engagement, and disease progression. This review will detail the important considerations in determining in vivo pre-clinical dose selection, as well as cross-species and human equivalent dose selection. Specific use of allometric scaling, pharmacokinetic and pharmacodynamic criteria, as well as incorporation of biomarker assessments in human dose selection for clinical trial design will also be discussed. The overarching goal of this review is to detail the pharmacological considerations in the drug development process as a method to improve both pre-clinical and clinical study design as we evaluate novel therapies to improve outcomes in patients with TBI.

Interpretation of Non-Clinical Data for Prediction of Human Pharmacokinetic Parameters: In Vitro-In Vivo Extrapolation and Allometric Scaling

Extrapolation of pharmacokinetic (PK) parameters from in vitro or in vivo animal to human is one of the main tasks in the drug development process. Translational approaches provide evidence for go or no-go decision-making during drug discovery and the development process, and the prediction of human PKs prior to the first-in-human clinical trials. In vitro-in vivo extrapolation and allometric scaling are the choice of method for projection to human situations. Although these methods are useful tools for the estimation of PK parameters, it is a challenge to apply these methods since underlying biochemical, mathematical, physiological, and background knowledge of PKs are required. In addition, it is difficult to select an appropriate methodology depending on the data available. Therefore, this review covers the principles of PK parameters pertaining to the clearance, volume of distribution, elimination half-life, absorption rate constant, and prediction method from the original idea to recently developed models in order to introduce optimal models for the prediction of PK parameters.

Design and Conduct Considerations for First-in-Human Trials

A milestone step in translational science to transform basic scientific discoveries into therapeutic applications is the advancement of a drug candidate from preclinical studies to initial human testing. First-in-human (FIH) trials serve as the link to advance new promising drug candidates and are conducted primarily to determine the safe dose range for further clinical development. Cross-functional collaboration is essential to ensure efficient and successful FIH trials. The aim of this publication is to serve as a tutorial for conducting FIH trials for both small molecule and biological drug candidates with topics covering regulatory requirements, preclinical safety testing, study design considerations, safety monitoring, biomarker assessment, and global considerations. An emphasis is placed on FIH trial design considerations, including starting dose selection, study size and population, dose escalation scheme, and implementation of adaptive designs. In light of the recent revision of the European Medicines Agency (EMA) guideline on FIH trials to promote safety and mitigate risk, we also discuss new measures introduced in the guideline that impact FIH trial design.

In vitro and in vivo pharmacokinetic characterization of mavacamten, a first-in-class small molecule allosteric modulator of beta cardiac myosin

Mavacamten is a small molecule modulator of cardiac myosin designed as an orally administered drug for the treatment of patients with hypertrophic cardiomyopathy. The current study objectives were to assess the preclinical pharmacokinetics of mavacamten for the prediction of human dosing and to establish the potential need for clinical pharmacokinetic studies characterizing drug-drug interaction potential. Mavacamten does not inhibit CYP enzymes, but at high concentrations relative to anticipated therapeutic concentrations induces CYP2B6 and CYP3A4 enzymes in vitro. Mavacamten showed high permeability and low efflux transport across Caco-2 cell membranes. In human hepatocytes, mavacamten was not a substrate for drug transporters OATP, OCT and NTCP. Mavacamten was determined to have minimal drug-drug interaction risk. In vitro mavacamten metabolite profiles included phase I- and phase II-mediated metabolism cross-species. Major pathways included aromatic hydroxylation (M1), aliphatic hydroxylation (M2); N-dealkylation (M6), and glucuronidation of the M1-metabolite (M4). Reaction phenotyping revealed CYPs 2C19 and 3A4/3A5 predominating. Mavacamten demonstrated low clearance, high volume of distribution, long terminal elimination half-life and excellent oral bioavailability cross-species. Simple four-species allometric scaling led to predicted plasma clearance, volume of distribution and half-life of 0.51 mL/min/kg, 9.5 L/kg and 9 days, respectively, in human.

Efficient Generation of Cynomolgus Monkey Induced Pluripotent Stem Cell-Derived Intestinal Organoids with Pharmacokinetic Functions

In preclinical studies, the cynomolgus monkey (CM) model is frequently used to predict the pharmacokinetics of drugs in the human small intestine, because of its evolutionary closeness to humans. Intestinal organoids that mimic the intestinal tissue have attracted attention in regenerative medicine and drug development. In this study, we generated intestinal organoids from CM induced pluripotent stem (CMiPS) cells and analyzed their pharmacokinetic functions. CMiPS cells were induced into the hindgut; then, the cells were seeded on microfabricated culture vessel plates to form spheroids. The resulting floating spheroids were differentiated into intestinal organoids in a medium containing small-molecule compounds. The mRNA expression of intestinal markers and pharmacokinetic-related genes was markedly increased in the presence of small-molecule compounds. The organoids possessed a polarized epithelium and contained various cells constituting small intestinal tissues. The intestinal organoids formed functional tight junctions and expressed drug transporter proteins. In addition, in the organoids generated, cytochrome P450 3A8 (CYP3A8) activity was inhibited by the specific inhibitor ketoconazole and was induced by rifampicin. Therefore, in the present work, we successfully generated intestinal organoids, with pharmacokinetic functions, from CMiPS cells using small-molecule compounds.

Interspecies scaling of excretory amounts using allometry - retrospective analysis with rifapentine, aztreonam, carumonam, pefloxacin, miloxacin, trovafloxacin, doripenem, imipenem, cefozopran, ceftazidime, linezolid for urinary excretion and rifapentine, cabotegravir, and dolutegravir for fecal excretion

1. Interspecies allometry scaling for prediction of human excretory amounts in urine or feces was performed for numerous antibacterials. Antibacterials used for urinary scaling were: rifapentine, pefloxacin, trovafloxacin (Gr1/low; <10%); miloxacin, linezolid, PNU-142300 (Gr2/medium; 10-40%); aztreonam, carumonam, cefozopran, doripenem, imipenem, and ceftazidime (Gr3/high; >50%). Rifapentine, cabotegravir, and dolutegravir was used for fecal scaling (high; >50%). 2. The employment of allometry equation: Y = aW(b) enabled scaling of urine/fecal amounts from animal species. Corresponding predicted amounts were converted into % recovery by considering the respective human dose. Comparison of predicted/observed values enabled fold difference and error calculations (mean absolute error [MAE] and root mean square error [RMSE]). Comparisons were made for urinary/fecal data; and qualitative assessment was made amongst Gr1/Gr2/Gr3 for urine. 3. Average correlation coefficient for the allometry scaling was >0.995. Excretory amount predictions were largely within 0.75- to 1.5-fold differences. Average MAE and RMSE were within ±22% and 23%, respectively. Although robust predictions were achieved for higher urinary/fecal excretion (>50%), interspecies scaling was applicable for low/medium excretory drugs. 4. Based on the data, interspecies scaling of urine or fecal excretory amounts may be potentially used as a tool to understand the significance of either urinary or fecal routes of elimination in humans in early development.

The precautionary principle and chemicals management: The example of perfluoroalkyl acids in groundwater

Already in the late 1990s microgram-per-liter levels of perfluorooctane sulfonate (PFOS) were measured in water samples from areas where fire-fighting foams were used or spilled. Despite these early warnings, the problems of groundwater, and thus drinking water, contaminated with perfluoroalkyl and polyfluoroalkyl substances (PFASs) including PFOS are only beginning to be addressed. It is clear that this PFAS contamination is poorly reversible and that the societal costs of clean-up will be high. This inability to reverse exposure in a reasonable timeframe is a major motivation for application of the precautionary principle in chemicals management. We conclude that exposure can be poorly reversible; 1) due to slow elimination kinetics in organisms, or 2) due to poorly reversible environmental contamination that leads to continuous exposure. In the second case, which is relevant for contaminated groundwater, the reversibility of exposure is not related to the magnitude of a chemical's bioaccumulation potential. We argue therefore that all PFASs entering groundwater, irrespective of their perfluoroalkyl chain length and bioaccumulation potential, will result in poorly reversible exposures and risks as well as further clean-up costs for society. To protect groundwater resources for future generations, society should consider a precautionary approach to chemicals management and prevent the use and release of highly persistent and mobile chemicals such as PFASs.

Prediction of human clearance based on animal data and molecular properties

Human clearance is often predicted prior to clinical study from in vivo preclinical data by virtue of interspecies allometric scaling methods. The aims of this study were to determine the important molecular descriptors for the extrapolation of animal data to human clearance and further to build a model to predict human clearance by combination of animal data and the selected molecular descriptors. These important molecular descriptors selected by genetic algorithm (GA) were from five classes: quantum mechanical, shadow indices, E-state keys, molecular properties, and molecular property counts. Although the data set contained many outliers determined by the conventional Mahmood method, the variation of most outliers was reduced significantly by our final support vector machine (SVM) model. The values of cross-validated correlation coefficient and root-mean-squared error (RMSE) for leave-one-out cross-validation (LOOCV) of the final SVM model were 0.783 and 0.305, respectively. Meanwhile, the reliability and consistency of the final model were also validated by an external test set. In conclusion, the SVM model based on the molecular descriptors selected by GA and animal data achieved better prediction performance than the Mahmood method. This approach can be applied as an improved interspecies allometric scaling method in drug research and development.

Metabolic scaling in animals: methods, empirical results, and theoretical explanations

Life on earth spans a size range of around 21 orders of magnitude across species and can span a range of more than 6 orders of magnitude within species of animal. The effect of size on physiology is, therefore, enormous and is typically expressed by how physiological phenomena scale with mass(b). When b ≠ 1 a trait does not vary in direct proportion to mass and is said to scale allometrically. The study of allometric scaling goes back to at least the time of Galileo Galilei, and published scaling relationships are now available for hundreds of traits. Here, the methods of scaling analysis are reviewed, using examples for a range of traits with an emphasis on those related to metabolism in animals. Where necessary, new relationships have been generated from published data using modern phylogenetically informed techniques. During recent decades one of the most controversial scaling relationships has been that between metabolic rate and body mass and a number of explanations have been proposed for the scaling of this trait. Examples of these mechanistic explanations for metabolic scaling are reviewed, and suggestions made for comparing between them. Finally, the conceptual links between metabolic scaling and ecological patterns are examined, emphasizing the distinction between (1) the hypothesis that size- and temperature-dependent variation among species and individuals in metabolic rate influences ecological processes at levels of organization from individuals to the biosphere and (2) mechanistic explanations for metabolic rate that may explain the size- and temperature-dependence of this trait.

Predicting pediatric age-matched weight and body mass index

The empirical scaling from adult to pediatric using allometric size adjustments based on body weight continued to be the mainstream method for pediatric dose selection. Due to the flexibility of a polynomial function to conform to the data trend, an empirical function for simulating age-matched weight and body mass index by gender in the pediatric population is developed by using a polynomial function and a constant coefficient to describe the interindividual variability in weight. A polynomial of up to fifth order sufficiently described the pediatric data from the Center for Disease Control (CDC) and the World Health Organization (WHO). The coefficients of variation to describe the variability were within 17%. The percentages of the CDC simulated weights for pediatrics between 0 and 5 years that fell outside the WHO 90% and 95% confidence boundaries were well within the expected percentage values, indicating that the CDC dataset can be used to substitute for the WHO dataset for the purpose of pediatric drug development. To illustrate the utility of this empirical function, the CDC-based age-matched weights were simulated and were used in the prediction of the concentration-time profiles of tenofovir in children based on a population pharmacokinetic model whose parameters were allometrically scaled. We have shown that the resulting 95% prediction interval of tenofovir in newborn to 5 years of age was almost identical whether the weights were simulated based on WHO or CDC dataset. The approach is simple and is broadly applicable in adjusting for pediatric dosages using allometry.

The application of allometric scaling principles to predict pharmacokinetic parameters across species

INTRODUCTION

Interspecies allometric scaling provides a simple and fast option to interpolate or extrapolate drug dose or pharmacokinetic parameters to a species of interest. Over the years, new scaling methods have been developed in order to improve the performance of these predictions. It is critical to choose appropriate allometric scaling approach(es) to analyze the available pharmacokinetic data.

AREAS COVERED

This review provides updated information on the latest allometric scaling methods developed for the most frequently interpolated or extrapolated pharmacokinetic parameters. The different degrees of success and advantages/disadvantages of different methods are compared and contrasted. The pitfalls that affect the accuracy of prediction and the solutions to avoid the risk of prediction errors are discussed. The application of allometric scaling in veterinary medicine is presented.

EXPERT OPINION

Although interspecies allometric scaling needs further refinements and has limitations, it is still a potential tool and rational option for the estimate of pharmacokinetic parameters in species for which there are no data available or to better interpret preclinical efficacy and safety trials. Allometric scaling can offer insight into possible mechanisms of species-dependent drug disposition.

Bioaccumulation of perfluoroalkyl acids in dairy cows in a naturally contaminated environment

Beef and dairy products may be important vectors of human exposure to perfluoroalkyl acids (PFAAs), but the understanding of how PFAAs are accumulated and transferred through agricultural food chains is very limited. Here, the bioaccumulation of PFAAs in dairy cows receiving naturally contaminated feed and drinking water was investigated by conducting a mass balance of PFAAs for a herd of dairy cows in a barn on a typical Swedish dairy farm. It was assumed that the cows were able to reach steady state with their dietary intake of PFAAs. Perfluorooctane sulfonic acid (PFOS) and perfluoroalkyl carboxylic acids (PFCAs) with 8 to 12 carbons were detected in cow tissue samples (liver, muscle, and blood) at concentrations up to 130 ng kg(-1). Mass balance calculations demonstrated an agreement between total intake and excretion within a factor of 1.5 and consumption of silage was identified as the dominant intake pathway for all PFAAs. Biomagnification factors (BMFs) were highly tissue and homologue specific. While BMFs of PFOS and PFCAs with 9 and 10 fluorinated carbons in liver ranged from 10 to 20, perfluorooctanoic acid (PFOA) was not biomagnified (BMF<1) in any of the investigated tissues. Biotransfer factors (BTFs; defined as the concentration in tissue divided by the total daily intake) were calculated for muscle and milk. Log BTFs ranged from -1.95 to -1.15 day kg(-1) with the highest BTF observed for PFOS in muscle. Overall, the results of this study suggest that long-chain PFAAs have a relatively high potential for transfer to milk and beef from the diet of dairy cows. However, a low input of PFAAs to terrestrial systems via atmospheric deposition and low bioavailability of PFAAs in soil limits the amount of PFAAs that enter terrestrial agricultural food chains in background contaminated environments and makes this pathway less important than aquatic exposure pathways. The BTFs estimated here provide a useful tool for predicting human exposure to PFAAs via milk and beef under different contamination scenarios.

Human microdose evaluation of the novel EP1 receptor antagonist GSK269984A

AIM

The primary objective was to evaluate the pharmacokinetics (PK) of the novel EP(1) antagonist GSK269984A in human volunteers after a single oral and intravenous (i.v.) microdose (100 µg).

METHOD

GSK269984A was administered to two groups of healthy human volunteers as a single oral (n= 5) or i.v. (n= 5) microdose (100 µg). Blood samples were collected for up to 24 h and the parent drug concentrations were measured in separated plasma using a validated high pressure liquid chromatography-tandem mass spectrometry method following solid phase extraction.

RESULTS

Following the i.v. microdose, the geometric mean values for clearance (CL), steady-state volume of distribution (V(ss) ) and terminal elimination half-life (t(1/2) ) of GSK269984A were 9.8 l h(-1) , 62.8 l and 8.2 h. C(max) and AUC(0,∞) were 3.2 ng ml(-1) and 10.2 ng ml(-1)  h, respectively; the corresponding oral parameters were 1.8 ng ml(-1) and 9.8 ng ml(-1)  h, respectively. Absolute oral bioavailability was estimated to be 95%. These data were inconsistent with predictions of human PK based on allometric scaling of in vivo PK data from three pre-clinical species (rat, dog and monkey).

CONCLUSION

For drug development programmes characterized by inconsistencies between pre-clinical in vitro metabolic and in vivo PK data, and where uncertainty exists with respect to allometric predictions of the human PK profile, these data support the early application of a human microdose study to facilitate the selection of compounds for further clinical development.

Applications of human pharmacokinetic prediction in first-in-human dose estimation

Quantitative estimations of first-in-human (FIH) doses are critical for phase I clinical trials in drug development. Human pharmacokinetic (PK) prediction methods have been developed to project the human clearance (CL) and bioavailability with reasonable accuracy, which facilitates estimation of a safe yet efficacious FIH dose. However, the FIH dose estimation is still very challenging and complex. The aim of this article is to review the common approaches for FIH dose estimation with an emphasis on PK-guided estimation. We discuss 5 methods for FIH dose estimation, 17 approaches for the prediction of human CL, 6 methods for the prediction of bioavailability, and 3 tools for the prediction of PK profiles. This review may serve as a practical protocol for PK- or pharmacokinetic/pharmacodynamic-guided estimation of the FIH dose.

Approaches for predicting human pharmacokinetics using interspecies pharmacokinetic scaling

Reliably predicting pharmacokinetic behavior in humans from preclinical data is an important aspect of drug development. The most widely used technique in this regard is allometric scaling. In this review, various approaches developed for predicting pharmacokinetic parameters in humans using interspecies scaling are introduced and discussed. Methods to predict plasma concentration-time profiles in humans after intravenous and oral administration are also reviewed. The reliable prediction of human pharmacokinetics with regard to investigational drugs is aimed, ultimately, at selecting the first in-human dose with which to begin clinical studies. Approaches for the selection of the first in-human dose are also reviewed. Although there have been many trials to compare and optimize interspecies scaling methods, no firm conclusions have been reached. Because interspecies scaling methods are still highly empirical, further effort is needed to improve the reliability of predicting human pharmacokinetics by interspecies scaling.

Interspecies scaling and prediction of human clearance: comparison of small- and macro-molecule drugs

Human clearance prediction for small- and macro-molecule drugs was evaluated and compared using various scaling methods and statistical analysis. Human clearance is generally well predicted using single or multiple species simple allometry for macro- and small-molecule drugs excreted renally. The prediction error is higher for hepatically eliminated small-molecules using single or multiple species simple allometry scaling, and it appears that the prediction error is mainly associated with drugs with low hepatic extraction ratio (Eh). The error in human clearance prediction for hepatically eliminated small-molecules was reduced using scaling methods with a correction of maximum life span (MLP) or brain weight (BRW). Human clearance of both small- and macro-molecule drugs is well predicted using the monkey liver blood flow method. Predictions using liver blood flow from other species did not work as well, especially for the small-molecule drugs.

Comparison of the in vivo distribution of four different annexin a5 adducts in rhesus monkeys

Annexin A5 has been used for the detection of apoptotic cells, due to its ability to bind to phosphatidylserine (PS). Four different labeled Annexin A5 adducts were evaluated in rhesus monkey, with radiolabeling achieved via 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA). Of these adducts differing conjugation methods were employed which resulted in nonspecific radiolabeling (AxA5-I), or site-specific radiolabeling (AxA5-II). A nonbinding variant of Annexin A5 was also evaluated (AxA5-II_NBV), conjugation here was site specific. The fourth adduct examined had both specific and nonspecific conjugation techniques employed (AxA5-II_mDOTA). Blood clearance for each adduct was comparable, while appreciable uptake was observed in kidney, liver, and spleen. Significant differences in uptake of AxA5-I and AxA5-II were observed, as well as between AxA5-II and AxA5-II_NBV. No difference between AxA5-II and AxA5-II_mDOTA was observed, suggesting that conjugating DOTA nonspecifically did not affect the in vivo biodistribution of Annexin A5.

Intravenous prediction of human pharmacokinetic parameters for ketorolac, a non-steroidal anti-inflammatory agent, using allometry approach

The intravenous pharmacokinetics data of ketorolac in mice, rats, rabbits, dogs and monkeys were assembled from literature. The relationship between the main pharmacokinetic parameters [viz., volume of distribution (V (d)) and clearance (CL)] and body weight was studied across five mammalian species, using double-logarithmic plots to predict the human pharmacokinetic parameters of CL and V (d) using simple allometry or with correction factors [maximum life span potential (MLP), brain weight, CF1 (bile flow/liver weight) and CF2 (bile flow/body weight)]. The metabolism pattern, biotransformation pathways and the predominant urinary excretion of parent and the formed metabolites of ketorolac were found to be similar amongst mice, rats, rabbits, dogs, monkeys and humans, facilitating the scaling process. The human parameter value for V (d) was predicted by simple allometric equation: 0.2481W(1.0549) (r (2) = 0.9217). The predicted V (d) value (21.92 L) is close to the reported value (17.5 L), whereas the CL was predicted by simple allometric approach or with standard correction factors viz., MLP, brain weight, CF1 and CF2. Best proximity CL value was obtained with MLP having allometric equation: 0.7126W(1.3264) (r (2) = 0.9640). The outcome of this exercise suggests that allometric scaling with suitable correction factors could potentially be used to predict the human pharmacokinetic parameters of drugs belonging to non-steroidal anti-inflammatory drugs retrospectively.

Comparative assessment of empirical and physiological approaches on predicting human clearances

The empirical and physiological predictive approaches to human clearance were evaluated using preclinical in vitro and in vivo data of various datasets to establish a methodology for the prediction of clearance. Among the examined empirical approaches, an allometric scaling method with the rule of exponent (ROE), based on the exponent in simple allometry, provided better prediction. The effect of lipophilicity (clog P) and clearance on the predictivity was investigated using the ROE method. High predictivity was found for a low lipophilic compound with clog P < 0 and for a compound with moderate or high clearance. As a physiological approach, the in vitro-in vivo scaling method using metabolic stability in liver microsomes and hepatocytes was evaluated, and the predictivity taking the plasma protein binding and the nonspecific binding in incubation into consideration was compared with the ROE method. The two methods appeared to show comparable predictivity, although the in vitro-in vivo scaling was conducted under limited conditions like the use of physiological scaling factor and lipophilicity-derived nonspecific binding data. The ROE method could be an alternative predictor of the human clearance of compounds to which a physiological approach cannot be applied, in addition to low lipophilic compounds, with acceptable accuracy.

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

Methods for prediction of hepatic clearance (CLH) in man have been evaluated. A physiologically-based in-vitro to in-vivo (PB-IVIV) method with human unbound fraction in blood (fu,bl) and hepatocyte intrinsic clearance (CLint)-data has a good rationale and appears to give the best predictions (maximum ∼2-fold errors; &lt; 25% errors for half of CL-predictions; appropriate ranking). Inclusion of an empirical scaling factor is, however, needed, and reasons include the use of cryopreserved hepatocytes with low activity, and inappropriate CLint- and fu,bl-estimation methods. Thus, an improvement of this methodology is possible and required. Neglect of fu,bl or incorporation of incubation binding does not seem appropriate. When microsome CLint-data are used with this approach, the CLH 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 fu,bl for basic and neutral compounds improve microsome predictions. The performance is, however, still not satisfactory. Allometry incorrectly assumes that the determinants for CLH relate to body weight and overpredicts human liver blood flow rate. Consequently, allometric methods have poor predictability. Simple allometry has an average overprediction potential, &gt; 2-fold errors for ∼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 CLH should be ∼50 and ∼10%, respectively. In conclusion, it is recommended that PB-IVIV with human hepatocyte CLint and fu,bl is applied and improved, limits for acceptable errors are decreased, and that animal CLH-studies and allometry are avoided.

The best model for humans is human -- how to accelerate early drug development safely

Traditionally, the choice of which candidate compounds to take forward into development has been based on pre-clinical data. However, lack of predictivity of the human clinical situation in the models used has led to poor decision-making, and the later in the development process that such mistakes are realised, the more costly and time-consuming it is to correct them. Furthermore, compounds that may have made perfectly good drugs, have been dropped due to poor pharmacokinetics in animal models. Accelerator mass spectrometry (AMS) is an ultra-sensitive detection technique that can be used to quantify carbon-14. By administering very small amounts of (14)C-labelled compounds, AMS can be used to obtain human clinical data very early in the drug development process. Such studies: a) can be helpful in understanding human pharmacokinetics using microdosing; b) can provide early human metabolism information, to validate the choice of animal species used in pre-clinical safety testing and identify unique or disproportionate human metabolites during Phase 1; and c) can provide fundamental human pharmacokinetic data, including absolute bioavailability, by facilitating a scientifically optimal and cost-effective study design. The provision of these clinical insights at the earliest possible opportunity can lead to improved decision-making, and therefore can reduce the time and cost involved in the drug development process.

Physiologically based pharmacokinetics (PBPK)

Allometric scaling is widely used to predict human pharmacokinetic parameters from preclinical species, and many different approaches have been proposed over the years to improve its predictive performance. Nevertheless, prediction errors are commonly observed in the practical application of simple allometry, for example, in cases where the hepatic metabolic clearance is mainly determined by enzyme activities, which do not scale allometrically across species. Therefore, if good correlation was noted for some drugs, poor correlation was observed for others, highlighting the need for other conceptual approaches. Physiologically based pharmacokinetic (PBPK) models are now a well-established approach to conduct extrapolations across species and to generate simulations of pharmacokinetic profiles under various physiological conditions. While conventional pharmacokinetic models are defined by drug-related data themselves, PBPK models have richer information content and integrate information from various sources, including drug-dependent, physiological, and biological parameters as they vary in between species, subjects, or with age and disease state. Therefore, the biological and mechanistic bases of PBPK models allow the extrapolation of the kinetic behavior of drugs with regard to dose, route, and species. In addition, by providing a link between tissue concentrations and toxicological or pharmacological effects, PBPK modeling represents a framework for mechanistic pharmacokinetic-pharmacodynamic models.

Interspecies scaling of therapeutic monoclonal antibodies: initial look

The authors evaluated interspecies scaling for the prediction of human clearance of 18 therapeutic monoclonal antibodies (mAbs). Human and monkey/chimpanzee data of 14 mAbs were classified based on the targeted antigens (soluble or membrane bound). Simple allometry and/or a time-invariant method (elementary Dedrick plot) were performed. Results indicate that human clearance might be accurately predicted from monkey data for mAbs targeting soluble receptors or membrane-bound receptors with limited tissue distribution using simplified allometry. The optimal exponents were estimated to be 0.85 or 0.90. If nonlinearity is anticipated at the human efficacious dose, pharmacokinetic parameters obtained at high doses in animals might not be sufficient for full pharmacokinetic characterization and prediction. Using prespecified criteria, including predicted human clearance (< or = or > 10 mL/d/kg), simplified allometric scaling might be helpful in predicting the effect of receptor-mediated clearance for mAbs targeting membrane-bound antigens. Furthermore, simplified allometry and an elementary Dedrick plot provide similar results in predicted clearance. Given the significant advantages offered by simplified allometry, it should be used when data are available from only 1 species. When reasonable data from > or =3 species are available, traditional allometry should be explored. Overall, clearance prediction is useful for human dose prediction in drug discovery and development.

Prediction of human pharmacokinetics from preclinical information: comparative accuracy of quantitative prediction approaches

Quantitative prediction of human pharmacokinetics is critical in assessing the viability of drug candidates and in determining first-in-human dosing. Numerous prediction methodologies, incorporating both in vitro and preclinical in vivo data, have been developed in recent years, each with advantages and disadvantages. However, the lack of a comprehensive data set, both preclinical and clinical, has limited efforts to evaluate the optimal strategy (or strategies) that results in quantitative predictions of human pharmacokinetics. To address this issue, the authors conducted a retrospective analysis using 50 proprietary compounds for which in vitro, preclinical pharmacokinetic data and oral single-dose human pharmacokinetic data were available. Five predictive strategies, involving either allometry or use of unbound intrinsic clearance from microsomes or hepatocytes, were then compared for their ability to predict human oral clearance, half-life through predictions of systemic clearance, volume of distribution, and bioavailability. Use of a single-species scaling approach with rat, dog, or monkey was as accurate as or more accurate than using multiple-species allometry. For those compounds cleared almost exclusively by P450-mediated pathways, scaling from human liver microsomes was as predictive as single-species scaling of clearance based on data from rat, dog, or monkey. These data suggest that use of predictive methods involving either single-species in vivo data or in vitro human liver microsomes can quantitatively predict human in vivo pharmacokinetics and suggest the possibility of streamlining the predictive methodology through use of a single species or use only of human in vitro microsomal preparations.

Comparative evaluation of oral systemic exposure of 56 xenobiotics in rat, dog, monkey and human

The prediction of human pharmacokinetics is often based on in vivo preclinical pharmacokinetic data. However, to date, no clear guidance has been available about the relative ability of the major preclinical species to estimate human oral exposure. The study was conducted to survey the literature on oral pharmacokinetic parameters in rat, dog, monkey and human, and to compare various methods for prediction of oral exposure in humans. Fifty-six non-peptide xenobiotics were identified with oral pharmacokinetic data in rat, dog, monkey and human, and comparison of the data from each species to humans was conducted along with an evaluation of the molecular features of these compounds. Monkey liver blood flow-based oral exposure was qualitatively and quantitatively more predictive of human oral exposure than rat or dog. Furthermore, generation of data in three versus two preclinical species did not always improve human predictivity. The use of molecular properties did not substantially improve the prediction of human oral exposure compared with the prediction from monkey alone. These observations confirm the continued importance of non-human primates in preclinical pharmacokinetics, and also have implications for pharmacokinetic lead optimization and for prediction of human pharmacokinetic parameters from in vivo preclinical data.

Prediction of metabolic drug clearance in humans: in vitro-in vivo extrapolation vs allometric scaling

Previously in vitro-in vivo extrapolation (IVIVE) with the Simcyp Clearance and Interaction Simulator has been used to predict the clearance of 15 clinically used drugs in humans. The criteria for the selection of the drugs were that they are used as probes for the activity of specific cytochromes P450 (CYPs) or have a single CYP isoform as the major or sole contributor to their metabolism and that they do not exhibit non-linear kinetics in vivo. Where data were available for the clearance of the drugs in at least three animal species, the predictions from IVIVE have now been compared with those based on allometric scaling (AS). Adequate data were available for estimating oral clearance (CLp.o.) in 9 cases (alprazolam, sildenafil, caffeine, clozapine, cyclosporine, dextromethorphan, midazolam, omeprazole and tolbutamide) and intravenous clearance in 6 cases (CLi.v.) (cyclosporine, diclofenac, midazolam, omeprazole, theophylline and tolterodine). AS predictions were based on five different methods: (1) simple allometry (clearance versus body weight); (2) correction for maximum life-span potential (CL x MLP); (3) correction for brain weight (CL x BrW); (4) the use of body surface area; and (5) the rule of exponents. A prediction accuracy was indicated by mean-fold error and the Pearson product moment correlation coefficient. Predictions were considered successful if the mean-fold error was <or=2. IVIVE predictions were accurate in 14 of 15 cases (mean-fold error range: 1.02-4.00). All five AS methods were accurate in 13, 11, 10, 10 and 14 cases, respectively. However, in some cases the error of AS exceeded fivefold. On the basis of the current results, IVIVE is more reliable than AS in predicting human clearance values for drugs mainly metabolized by CYP450 enzymes. This suggests that the place of AS methods in pre-clinical drug development warrants further scrutiny.

Mechanism-based concepts of size and maturity in pharmacokinetics

Growth and development can be investigated using readily observable demographic factors such as weight and age. Size is the primary covariate and can be referenced to a 70-kg person with allometry using a coefficient of 0.75 for clearance and 1 for volume. The use of these coefficients is supported by fractal geometric concepts and observations from diverse areas in biology. Fat free mass (FFM) might be expected to do better than total body weight when there are wide variations in fat affecting body composition. Clearance pathways develop in the fetus before birth. The use of postnatal age as a descriptor of maturation is unsatisfactory because birth may occur prematurely; therefore postmenstrual age is a superior predictor of elimination function. A sigmoid E(max) model (Hill equation) describes gradual maturation of clearance in early life leading to a mature adult clearance achieved at a later age.

Projection of Exposure and Efficacious Dose Prior to First-in-Human Studies: How Successful Have We Been?

PURPOSE

Preclinical and clinical data for 35 proprietary Bristol-Myers Squibb discovery compounds (years 1997 to 2005) were collected and analyzed. In each case, exposure and efficacy in human subjects were projected at the time of nomination (for development) prior to first-in-human dosing.

MATERIALS AND METHODS

Projections of area under the plasma concentration-time curve (AUC) in humans involved the use of one or more methods: (1) allometric scaling of animal pharmacokinetic data; (2) clearance projection employing in vitro data (liver microsomes and hepatocytes); (3) chimpanzee as an animal model; (4) the species-invariant time method; and (5) the Css-mean residence time or "Css-MRT" method. Whenever possible, prior clinical experience with lead compounds enabled the selection of the most appropriate method(s). Multiple approaches were also available at the time of the human efficacious dose projections: (1) efficacious exposure from animal efficacy models; (2) in vitro potency; and (3) prior experience with clinical leads.

RESULTS

Over the 8 year period described, AUC in humans was projected within 2-fold (20 out of 35 compounds; 57%), greater than 2-fold to 4-fold (11 out of 35 compounds; 32%), and greater than 4-fold (4 out of 35 compounds; 11%) of the observed value. At the time of writing, clinical efficacy data were available for 10 compounds only. In this instance, the efficacious doses were also projected within 2-fold (7 out of 10 compounds; 70%), greater than 2-fold to 4-fold (2 out of 10 compounds; 20%), and greater than 4-fold (1 out of 10 compounds; 10%) of the actual clinical dose.

CONCLUSION

Overall, it was possible to project human exposure and efficacious dose within 4-fold of observed clinical values for about 90% of the compounds.

Utility of the Coefficient of Determination (r2) in Assessing the Accuracy of Interspecies Allometric Predictions: Illumination or Illusion?

The appropriateness of relying on the coefficient of determination (r(2)) as a statistical metric for judging the predictability of human clearance (CL) based on interspecies animal data was assessed. An explicit mathematical expression was derived for r(2) as a function of species body weight and the corresponding measured value of CL. The derived mathematical function demonstrated that r(2) is numerically large in most instances. Simulations using random CL generated from a common combination of species of mouse, rat, and monkey resulted in an r(2) of 0.75 as the minimum, and 0.95 and 0.98 at 50th and 75th percentiles, respectively, given that total CL values increase with increasing species body weight. Analysis of literature data also indicated that the prediction accuracy of human CL was not correlated with values of r(2). Therefore, it is concluded that r(2) is a limited statistical measure when assessing allometric scaling for the purpose of predicting human CL.