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

Maternal Ezetimibe Concentrations Measured in Breast Milk and Its Use in Breastfeeding Infant Exposure Predictions

BACKGROUND

Lactating mothers taking ezetimibe, an antihyperlipidemic agent, may be hesitant to breastfeed despite the known benefit of breastfeeding to both mother and infant. Currently, no data exist on the presence or concentration of ezetimibe and its main active metabolite, ezetimibe-glucuronide (EZE-glucuronide), in human breast milk.

METHODS

Voluntary breast milk samples containing ezetimibe and EZE-glucuronide were attained from lactating mothers taking ezetimibe as part of their treatment. An assay was developed and validated to measure ezetimibe and EZE-glucuronide concentrations in breast milk. A workflow that utilized a developed and evaluated pediatric physiologically based pharmacokinetic (PBPK) model, the measured concentrations in milk, and weight-normalized breast milk intake volumes was applied to predict infant exposures and determine the upper area under the curve ratio (UAR).

RESULTS

Fifteen breast milk samples from two maternal-infant pairs were collected. The developed liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay showed an analytical range of 0.039-5.0 ng/mL and 0.39-50.0 ng/mL for ezetimibe and EZE-glucuronide, respectively. The measured concentrations in the breast milk samples were 0.17-1.02 ng/mL and 0.42-2.65 ng/mL of ezetimibe and EZE-glucuronide, respectively. The evaluated pediatric PBPK model demonstrated minimal exposure overlap in adult therapeutic dose and breastfed infant simulated area under the concentration-time curve from time zero to 24 h (AUC24). Calculated UAR across infant age groups ranged from 0.0015 to 0.0026.

CONCLUSIONS

PBPK model-predicted ezetimibe and EZE-glucuronide exposures and UAR suggest that breastfeeding infants would receive non-therapeutic exposures. Future work should involve a 'mother-infant pair study' to ascertain breastfed infant plasma ezetimibe and EZE-glucuronide concentrations to confirm the findings of this work.

How to select the initial dose for a pediatric study?

In typical clinical development programs, a new drug is first developed for the adult use. Drugs are often approved for adult use or in the process of obtaining approval in adults in the target indication before pediatric development is initiated. In designing the first pediatric clinical trial, one of the challenges is to select the initial dose to be tested. The ICH E11 R1 guidance advises that chronologic age alone may not always be the most appropriate categorical determinant to define developmental subgroups in pediatric studies. In this manuscript, the approaches to utilize available data in adults related to those factors beyond age to inform the starting dose selection in pediatric drug development are discussed. Practical considerations and approaches are provided for informing pediatric starting dose. Additional considerations to use pre-clinical information are provided in the case when adult information is limited or not available.

Drug Design and Success of Prospective Mouse In Vitro-In Vivo Extrapolation (IVIVE) for Predictions of Plasma Clearance (CLp) from Hepatocyte Intrinsic Clearance (CLint)

Hepatocyte intrinsic clearance (CL_int) and methods of in vitro-in vivo extrapolation (IVIVE) are often used to predict plasma clearance (CL_p) in drug discovery. While the prediction success of this approach is dependent on the chemotype, specific molecular properties and drug design features that govern these outcomes are poorly understood. To address this challenge, we investigated the success of prospective mouse CL_p IVIVE across 2142 chemically diverse compounds. Dilution scaling, which assumes that the free fraction in hepatocyte incubations (f_u,inc) is governed by binding to the 10% of serum in the incubation medium, was used as our default CL_p IVIVE approach. Results show that predictions of CL_p are better for smaller (molecular weight (MW) < 500 Da), less polar (total polar surface area (TPSA) < 100 Ų, hydrogen bond donor (HBD) ≤1, hydrogen bond acceptor (HBA) ≤ 6), lipophilic (log D > 3), and neutral compounds, with low HBD count playing the key role. If compounds are classified according to their chemical space, predictions were good for compounds resembling central nervous system (CNS) drugs [average absolute fold error (AAFE) of 2.05, average fold error (AFE) of 0.90], moderate for classical druglike compounds (according to Lipinski, Veber, and Ghose guidelines; AAFE of 2.55; AFE of 0.68), and poor for nonclassical "beyond the rule of 5" compounds (AAFE of 3.31; AFE of 0.41). From the perspective of measured druglike properties, predictions of CL_p were better for compounds with moderate-to-high hepatocyte CL_int (>10 μL/min/10⁶ cells), high passive cellular permeability (P_app > 100 nm/s), and moderate observed CL_p (5-50 mL/min/kg). Influences of plasma protein binding (f_u,p) and P-glycoprotein (Pgp) apical efflux ratio (AP-ER) were less pronounced. If the extended clearance classification system (ECCS) is applied, predictions were good for class 2 (P_app > 50 nm/s; neutral or basic; AAFE of 2.35; AFE of 0.70) and acceptable for class 1A compounds (AAFE of 2.98; AFE of 0.70). Classes 1B, 3 A/B, and 4 showed poor outcomes (AAFE > 3.80; AFE < 0.60). Functional groups trending toward weaker CL_p IVIVE were esters, carbamates, sulfonamides, carboxylic acids, ketones, primary and secondary amines, primary alcohols, oxetanes, and compounds liable to aldehyde oxidase metabolism, likely due to multifactorial reasons. Multivariate analysis showed that multiple properties are relevant, combining together to define the overall success of CL_p IVIVE. Our results indicate that the current practice of prospective CL_p IVIVE is suitable only for CNS-like compounds and well-behaved classical druglike space (e.g., high permeability or ECCS class 2) without challenging functional groups. Unfortunately, based on existing mouse data, prospective CL_p IVIVE for complex and nonclassical chemotypes is poor and hardly better than random guessing. This is likely due to complexities such as extrahepatic metabolism and transporter-mediated disposition which are poorly captured by this methodology. With small-molecule drug discovery increasingly evolving toward nonclassical and complex chemotypes, existing CL_p IVIVE methodology will require improvement. While empirical correction factors may bridge the gap in the near future, improved and new in vitro assays, data integration models, and machine learning (ML) methods are increasingly needed to address this challenge and reduce the number of nonclinical pharmacokinetic (PK) studies.

Inferring Therapeutic Targets in Candida albicans and Possible Inhibition through Natural Products: A Binding and Physiological Based Pharmacokinetics Snapshot

Despite being responsible for invasive infections, fungal pathogens have been underrepresented in computer aided therapeutic target mining and drug design. Excess of Candida albicans causes candidiasis, causative of thrush and vaginal infection due to off-balance. In this study, we attempted to mine drug targets (n = 46) using a subtractive proteomic approach in this pathogenic yeast and screen natural products with inhibition potential against fructose-bisphosphate aldolase (FBA) of the C. albicans. The top compound selected on the basis of best docking score from traditional Indian medicine/Ayurvedic library was (4-Hydroxybenzyl)thiocarbamic acid, from the ZINC FBA inhibitor library was ZINC13507461 (IUPAC name: [(2R)-2-hydroxy-3-phosphonooxypropyl] (9E,12E)-octadeca-9,12-dienoate), and from traditional Tibetan medicine/Sowa rigpa was Chelerythrine (IUPAC name: 1,2-Dimethoxy-12-methyl-9H-[1,3]benzodioxolo[5,6-c]phenanthridin-12-ium), compared to the control (2E)-1-(4-nitrophenyl)-2-[(4-nitrophenyl)methylidene]hydrazine. No Ames toxicity was predicted for prioritized compounds while control depicted this toxicity. (4-Hydroxybenzyl)thiocarbamic acid showed hepatotoxicity, while Chelerythrine depicted hERG inhibition, which can lead to QT syndrome, so we recommend ZINC13507461 for further testing in lab. Pharmacological based pharmacokinetic modeling revealed that it has low bioavailability and hence, absorption in healthy state. In cirrhosis and renal impairment, absorption and plasma accumulation increased so we recommend further investigation into this occurrence and recommend high dosage in further tests to increase bioavailability.

Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data

Pharmacokinetic research plays an important role in the development of new drugs. Accurate predictions of human pharmacokinetic parameters are essential for the success of clinical trials. Clearance (CL) and volume of distribution (Vd) are important factors for evaluating pharmacokinetic properties, and many previous studies have attempted to use computational methods to extrapolate these values from nonclinical laboratory animal models to human subjects. However, it is difficult to obtain sufficient, comprehensive experimental data from these animal models, and many studies are missing critical values. This means that studies using nonclinical data as explanatory variables can only apply a small number of compounds to their model training. In this study, we perform missing-value imputation and feature selection on nonclinical data to increase the number of training compounds and nonclinical datasets available for these kinds of studies. We could obtain novel models for total body clearance (CL_tot) and steady-state Vd (Vd_ss) (CL_tot: geometric mean fold error [GMFE], 1.92; percentage within 2-fold error, 66.5%; Vd_ss: GMFE, 1.64; percentage within 2-fold error, 71.1%). These accuracies were comparable to the conventional animal scale-up models. Then, this method differs from animal scale-up methods because it does not require animal experiments, which continue to become more strictly regulated as time passes.

Model-Informed Drug Development in Pediatric Dose Selection

Model-informed drug development (MIDD) has been a powerful and efficient tool applied widely in pediatric drug development due to its ability to integrate and leverage existing knowledge from different sources to narrow knowledge gaps. The dose selection is the most common MIDD application in regulatory submission related to pediatric drug development. This article aims to give an overview of the 3 broad categories of use of MIDD in pediatric dose selection: leveraging from adults to pediatric patients, leveraging from animals to pediatric patients, and integrating mechanism in infants and neonates. Population pharmacokinetic analyses with allometric scaling can reasonably predict the clearance in pediatric patients aged >5 years. A mechanistic-based approach, such as physiologically based pharmacokinetic accounting for ontogeny, or an allometric model with age-dependent exponent, can be applied to select the dose in pediatric patients aged ≤2 years. The exposure-response relationship from adults or from other drugs in the same class may be useful in aiding the pediatric dose selection and benefit-risk assessment. Increasing application and understanding of use of MIDD have contributed greatly to several policy developments in the pediatric field. With the increasing efforts of MIDD under the Prescription Drug User Fee Act VI, bigger impacts of MIDD approaches in pediatric dose selection can be expected. Due to the complexity of model-based analyses, early engagement between drug developers and regulatory agencies to discuss MIDD issues is highly encouraged, as it is expected to increase the efficiency and reduce the uncertainty.

Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning

Research into pharmacokinetics plays an important role in the development process of new drugs. Accurately predicting human pharmacokinetic parameters from preclinical data can increase the success rate of clinical trials. Since clearance (CL) which indicates the capacity of the entire body to process a drug is one of the most important parameters, many methods have been developed. However, there are still rooms to be improved for practical use in drug discovery research; "improving CL prediction accuracy" and "understanding the chemical structure of compounds in terms of pharmacokinetics". To improve those, this research proposes a multimodal learning method based on deep learning that takes not only the chemical structure of a drug but also rat CL as inputs. Good results were obtained compared with the conventional animal scale-up method; the geometric mean fold error was 2.68 and the proportion of compounds with prediction errors of 2-fold or less was 48.5%. Furthermore, it was found to be possible to infer the partial structure useful for CL prediction by a structure contributing factor inference method. The validity of these results of structural interpretation of metabolic stability was confirmed by chemists.

In vitro and in vivo human metabolism and pharmacokinetics of S- and R-praziquantel

Racemic praziquantel (PZQ) is the drug of choice for the treatment of schistosomiasis. R-Praziquantel (R-PZQ) has been shown as the therapeutic form, whereas S-PZQ is less efficacious and responsible for the bitter taste of the tablet. This study aimed at investigating the metabolism of R- and S-PZQ as this could have implications on efficacy and safety of racemate and R-PZQ specific formulations under development. In vitro CYP reaction phenotyping assay using 10 recombinant CYP (rCYP) isoenzymes showed hepatic CYP1A2, 2C19, 2D6, 3A4, and 3A5 were the major enzymes involved in metabolism of PZQ. Enzyme kinetic studies were performed by substrate depletion and metabolite formation methods, by incubating PZQ and its R- or S-enantiomers in human liver microsomes (HLM) and the rCYP enzymes. The effect of selective CYP inhibitors on PZQ metabolism was assessed in HLM. CYP1A2, 2C19, and 3A4 exhibited different catalytic activity toward PZQ, R- and S-enantiomers. Metabolism of R-PZQ was mainly catalyzed by CYP1A2 and CYP2C19, whereas metabolism of S-PZQ was mainly by CYP2C19 and CYP3A4. Based on metabolic CLint obtained through formation of hydroxylated metabolites, CYP3A4 was estimated to contribute 89.88% to metabolism of S-PZQ using SIMCYP® IVIVE prediction. Reanalysis of samples from a human PZQ-ketoconazole (KTZ) drug-drug interaction pharmacokinetic study confirmed these findings in that KTZ, a potent inhibitor of CYP3A, selectively increased area under the curve of S-PZQ by 68% and that of R-PZQ by just 9%. Knowledge of enantioselective metabolism will enable better understanding of variable efficacy of PZQ in patients and the R-PZQ formulation under development.

Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay

The in vitro-in vivo extrapolation (IVIVE) approach for predicting total plasma clearance (CL_tot) has been widely used to rank order compounds early in discovery. More recently, a computational machine learning approach utilizing physicochemical descriptors and fingerprints calculated from chemical structure information has emerged, enabling virtual predictions even earlier in discovery. Previously, this approach focused more on in vitro intrinsic clearance (CL_int) prediction. Herein, we directly compare these two approaches for predicting CL_tot in rats. A structurally diverse set of 1114 compounds with known in vivo CL_tot, in vitro CL_int, and plasma protein binding was used as the basis for this evaluation. The machine learning models were assessed by validation approaches using the time- and cluster-split training and test sets, and five-fold cross validation. Assessed by five-fold validation, the random forest regression (RF) and radial basis function (RBF) models demonstrated better prediction performance in eight attempted machine learning models. The CL_tot values predicted by the RF and RBF models were within two-fold of the observed values for 67.7 and 71.9% of cluster-split test set compounds, respectively, while the predictivity was worse in the time-split dataset. The predictivity of both models tended to be improved by incorporating in vitro parameters, unbound fraction in plasma (f_u,p), and CL_int. CL_tot prediction utilizing in vitro CL_int and the well-stirred model, correcting for the fraction unbound in blood, was substantially worse compared to machine learning approaches for the same cluster-split test set. The reason that CL_tot is underestimated by IVIVE is not fully explained by considering the calculated microsomal unbound fraction (cf_u,mic), extended clearance classification system (ECCS), and omitting high clearance compounds in excess of hepatic blood flow. The analysis suggests that in silico machine learning models may have the power to reduce reliance on or replace in vitro and in vivo studies for chemical structure optimization in early drug discovery.

Physiologically Based Pharmacokinetic and Pharmacodynamic Analysis Enabled by Microfluidically Linked Organs-on-Chips

Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches are beginning to be integrated into drug development and approval processes because they enable key pharmacokinetic (PK) parameters to be predicted from in vitro data. However, these approaches are hampered by many limitations, including an inability to incorporate organ-specific differentials in drug clearance, distribution, and absorption that result from differences in cell uptake, transport, and metabolism. Moreover, such approaches are generally unable to provide insight into pharmacodynamic (PD) parameters. Recent development of microfluidic Organ-on-a-Chip (Organ Chip) cell culture devices that recapitulate tissue-tissue interfaces, vascular perfusion, and organ-level functionality offer the ability to overcome these limitations when multiple Organ Chips are linked via their endothelium-lined vascular channels. Here, we discuss successes and challenges in the use of existing culture models and vascularized Organ Chips for PBPK and PD modeling of human drug responses, as well as in vitro to in vivo extrapolation (IVIVE) of these results, and how these approaches might advance drug development and regulatory review processes in the future.

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.

Use of generally recognized as safe or dietary compounds to inhibit buprenorphine metabolism: potential to improve buprenorphine oral bioavailability

The present study evaluated the potential of five generally recognized as safe (GRAS) or dietary compounds (α-mangostin, chrysin, ginger extract, pterostilbene and silybin) to inhibit oxidative (CYP) and conjugative (UGT) metabolism using pooled human intestinal and liver microsomes. Buprenorphine was chosen as the model substrate as it is extensively metabolized by CYPs to norbuprenorphine and by UGTs to buprenorphine glucuronide. Chrysin, ginger extract, α-mangostin, pterostilbene and silybin were tested for their inhibition of the formation of norbuprenorphine or buprenorphine glucuronide in both intestinal and liver microsomes. Pterostilbene was the most potent inhibitor of norbuprenorphine formation in both intestinal and liver microsomes, with IC50 values of 1.3 and 0.8 μM, respectively, while α-mangostin and silybin most potently inhibited buprenorphine glucuronide formation. The equipotent combination of pterostilbene and ginger extract additively inhibited both pathways in intestinal microsomes. Since pterostilbene and ginger extract showed potent CYP and/or UGT inhibition of buprenorphine metabolism, their equipotent combination was tested to assess the presence of synergistic inhibition. However, because the combination showed additive inhibition, it was not used while performing IVIVE analysis. Based on quantitative in vitro-in vivo extrapolation, pterostilbene (21 mg oral dose) appeared to be most effective in improving the mean predicted Foral and AUC∞ PO of buprenorphine from 3 ± 2% and 340 ± 330 ng*min/ml to 75 ± 8% and 36,000 ± 25,000 ng*min/ml, respectively. At a 10-fold lower dose of pterostilbene, the predicted buprenorphine Foral approximated sublingual bioavailability (~35%) and showed a 2-4 fold reduction in the variability around the predicted AUC∞ PO of buprenorphine. These results demonstrate the feasibility of using various GRAS/dietary compounds to inhibit substantially the metabolism by CYP and UGT enzymes to achieve higher and less variable oral bioavailability. This inhibitor strategy may be useful for drugs suffering from low and variable oral bioavailability due to extensive presystemic oxidative and/or conjugative metabolism.

Assessing CYP2C19 Ontogeny in Neonates and Infants Using Physiologically Based Pharmacokinetic Models: Impact of Enzyme Maturation Versus Inhibition

The objective of this study was to develop pediatric physiologically based pharmacokinetic (PBPK) models for pantoprazole and esomeprazole. Pediatric PBPK models were developed by Simcyp version 15 by incorporating cytochrome P450 (CYP)2C19 maturation and auto-inhibition. The predicted-to-observed pantoprazole clearance (CL) ratio ranged from 0.96-1.35 in children 1-17 years of age and 0.43-0.70 in term infants. The predicted-to-observed esomeprazole CL ratio ranged from 1.08-1.50 for children 6-17 years of age, and 0.15-0.33 for infants. The prediction was markedly improved by assuming no auto-inhibition of esomeprazole in infants in the PBPK model. Our results suggested that the CYP2C19 auto-inhibition model was appropriate for esomeprazole in adults and older children but could not be directly extended to infants. A better understanding of the complex interplay of enzyme maturation, inhibition, and compensatory mechanisms for CYP2C19 is necessary for PBPK modeling in infants.

Misconceptions and issues regarding allometric scaling during the drug development process

INTRODUCTION

Allometry is the study of size and its consequences. The simple hypothesis of allometric scaling is that all physiological parameters are proportional to body size or body mass. This review examines the development of theory-based allometry or fixed exponents (0.75 and 1.0 for basal metabolic rate and volume, respectively) and the evidence for or against the theory. The main focus of this report is to show the readers that there is enough evidence from experimental data that negate the concept of theory-based allometry in biology, physiology, and pharmacokinetics. Areas covered: In this review, the history of the development of theoretical allometry and the strong evidence against theory-based allometry demonstrated by experimental data is provided. During drug development, allometry is applied to both inter-species (from animals to humans) and intra-species (adults to children) scaling. These two forms of allometric scaling in the context of theory-based allometry are discussed and provide insight on scientific progress that refute theory-based allometry. Expert opinion: Theory-based allometry is a mere theory and experimental data and real-life observations strongly negate the existence of such a theory. Pharmacostatistical and physiological models based on theory-based allometry can be misleading and incorrect because the theory-based allometric exponent 0.75 is not universal. The exponents of allometry are data dependent and are not fixed in the universe.

Evaluation and calibration of high-throughput predictions of chemical distribution to tissues

Toxicokinetics (TK) provides critical information for integrating chemical toxicity and exposure assessments in order to determine potential chemical risk (i.e., the margin between toxic doses and plausible exposures). For thousands of chemicals that are present in our environment, in vivo TK data are lacking. The publicly available R package "httk" (version 1.8, named for "high throughput TK") draws from a database of in vitro data and physico-chemical properties in order to run physiologically-based TK (PBTK) models for 553 compounds. The PBTK model parameters include tissue:plasma partition coefficients (Kp) which the httk software predicts using the model of Schmitt (Toxicol In Vitro 22 (2):457-467, 2008). In this paper we evaluated and modified httk predictions, and quantified confidence using in vivo literature data. We used 964 rat Kp measured by in vivo experiments for 143 compounds. Initially, predicted Kp were significantly larger than measured Kp for many lipophilic compounds (log10 octanol:water partition coefficient > 3). Hence the approach for predicting Kp was revised to account for possible deficiencies in the in vitro protein binding assay, and the method for predicting membrane affinity was revised. These changes yielded improvements ranging from a factor of 10 to nearly a factor of 10,000 for 83 Kp across 23 compounds with only 3 Kp worsening by more than a factor of 10. The vast majority (92%) of Kp were predicted within a factor of 10 of the measured value (overall root mean squared error of 0.59 on log10-transformed scale). After applying the adjustments, regressions were performed to calibrate and evaluate the predictions for 12 tissues. Predictions for some tissues (e.g., spleen, bone, gut, lung) were observed to be better than predictions for other tissues (e.g., skin, brain, fat), indicating that confidence in the application of in silico tools to predict chemical partitioning varies depending upon the tissues involved. Our calibrated model was then evaluated using a second data set of human in vivo measurements of volume of distribution (Vss) for 498 compounds reviewed by Obach et al. (Drug Metab Dispos 36(7):1385-1405, 2008). We found that calibration of the model improved performance: a regression of the measured values as a function of the predictions has a slope of 1.03, intercept of - 0.04, and R2 of 0.43. Through careful evaluation of predictive methods for chemical partitioning into tissues, we have improved and calibrated these methods and quantified confidence for TK predictions in humans and rats.

A Liver-Centric Multiscale Modeling Framework for Xenobiotics

We describe a multi-scale, liver-centric in silico modeling framework for acetaminophen pharmacology and metabolism. We focus on a computational model to characterize whole body uptake and clearance, liver transport and phase I and phase II metabolism. We do this by incorporating sub-models that span three scales; Physiologically Based Pharmacokinetic (PBPK) modeling of acetaminophen uptake and distribution at the whole body level, cell and blood flow modeling at the tissue/organ level and metabolism at the sub-cellular level. We have used standard modeling modalities at each of the three scales. In particular, we have used the Systems Biology Markup Language (SBML) to create both the whole-body and sub-cellular scales. Our modeling approach allows us to run the individual sub-models separately and allows us to easily exchange models at a particular scale without the need to extensively rework the sub-models at other scales. In addition, the use of SBML greatly facilitates the inclusion of biological annotations directly in the model code. The model was calibrated using human in vivo data for acetaminophen and its sulfate and glucuronate metabolites. We then carried out extensive parameter sensitivity studies including the pairwise interaction of parameters. We also simulated population variation of exposure and sensitivity to acetaminophen. Our modeling framework can be extended to the prediction of liver toxicity following acetaminophen overdose, or used as a general purpose pharmacokinetic model for xenobiotics.

Application of physiologically based pharmacokinetic modeling in the prediction of pharmacokinetics of bicyclol controlled-release formulation in human

Physiologically based pharmacokinetic (PBPK) modeling can assist in formulation development. Bicyclol is a novel anti-hepatitis drug. A bilayer osmotic pump table of bicyclol is being developed. PBPK models for bicyclol immediate-release (IR) and controlled-release (CR) tablets in beagle dog, as well as PBPK model for IR tablets in human were constructed. These models incorporated physicochemical properties and in vitro preclinical data. Parameter sensitivity analysis was performed for the effects of solubility and dissolution on pharmacokinetic (PK) parameters. Models were refined by comparing simulated results to experimental measurements. Furthermore, the clinical PK for bicyclol CR tablets was predicted using the in vivo dissolution profile by deconvolution of the mean PK profile of CR tablets in dogs. In summary, the present study described a strategy employing PBPK models to evaluate the effects of formulation factors on PK profiles and predict the performance of bicyclol CR tablets in human.

Reprint of PopGen: A virtual human population generator

The risk assessment of environmental chemicals and drugs is moving towards a paradigm shift in approach which seeks the full replacement animal testing with high throughput, mechanistic, in vitro systems. This new vision will be reliant on the measurement in vitro, of concentration-dependent responses where prolonged excessive perturbations of specific biochemical pathways are likely to lead to adverse health effects in an intact organism. Such an approach requires a framework, into which disparate data generated using in vitro, in silico and in chemico systems, can be integrated and utilised for quantitative in vitro-to-in vivo extrapolation (QIVIVE), ultimately to the human population level. Physiologically based pharmacokinetic (PBPK) models are ideally suited for this and are obligatory in order to translate in vitro concentration-response relationships to an exposure or dose, route and duration regime in people. In this report we describe PopGen a virtual human population generator which is a user friendly, open access web-based application for the prediction of realistic anatomical, physiological and phase 1 metabolic variation in a wide range of healthy human populations. We demonstrate how PopGen can be used for QIVIVE by providing input to a PBPK model, at an appropriate level of detail, to reconstruct exposure from human biomonitoring data. We discuss how the process of exposure reconstruction from blood biomarkers, in general, is analogous to exposure or dose reconstruction from concentration-response measurements made in proposed in vitro cell based systems which are assumed to be surrogates for target organs.

Challenges in conducting clinical trials in children: approaches for improving performance

Recent legislative changes in both Europe and the USA have increased the responsibility of drug developers to purposefully study the agents they market in children so that specific dosing recommendations can be made to assist clinicians in their use. Typically, clinicians use empiricalor experiential-based rationales for selecting the dose to use in children, generally in a manner that attempts to achieve the same dose-exposure or pharmacokinetic profile in children as in adults. However, whether this approach achieves the necessary dose exposure or exposure effect needed may not be systematically explored during off-label use. This creates the opportunity for under- or over-exposure in children, particularly in very young children (i.e., less than 2 years old) where a combination of factors during development can effect both pharmacokinetics and pharmacodynamics. The ethical, physiological and statistical differences of studying new therapeutic agents in children present economic challenges that may create unintended incentives - both positive and negative - for any individual developer who tries to meet the requirements of new legislation to study pharmaceutical agents in children. There should be a continued emphasis in academic clinical pharmacology programs towards creative methods and approaches to better understand these differences in children compared with adults. The ability to use information from knowledge obtained from adult studies, from preclinical studies, from studies of compounds with similar chemistry or pharmacology, or from known physiological differences between children and adults is essential to choosing a suitable dose for children and achieving these regulatory aims.

PopGen: A virtual human population generator

The risk assessment of environmental chemicals and drugs is moving towards a paradigm shift in approach which seeks the full replacement animal testing with high throughput, mechanistic, in vitro systems. This new vision will be reliant on the measurement in vitro, of concentration-dependent responses where prolonged excessive perturbations of specific biochemical pathways are likely to lead to adverse health effects in an intact organism. Such an approach requires a framework, into which disparate data generated using in vitro, in silico and in chemico systems, can be integrated and utilised for quantitative in vitro-to-in vivo extrapolation (QIVIVE), ultimately to the human population level. Physiologically based pharmacokinetic (PBPK) models are ideally suited for this and are obligatory in order to translate in vitro concentration-response relationships to an exposure or dose, route and duration regime in people. In this report we describe PopGen, a virtual human population generator which is a user friendly, open access web-based application for the prediction of realistic anatomical, physiological and phase 1 metabolic variation in a wide range of healthy human populations. We demonstrate how PopGen can be used for QIVIVE by providing input to a PBPK model, at an appropriate level of detail, to reconstruct exposure from human biomonitoring data. We discuss how the process of exposure reconstruction from blood biomarkers, in general, is analogous to exposure or dose reconstruction from concentration-response measurements made in proposed in vitro cell based systems which are assumed to be surrogates for target organs.

Dose selection based on physiologically based pharmacokinetic (PBPK) approaches

Physiologically based pharmacokinetic (PBPK) models are built using differential equations to describe the physiology/anatomy of different biological systems. Readily available in vitro and in vivo preclinical data can be incorporated into these models to not only estimate pharmacokinetic (PK) parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. They provide a mechanistic framework to understand and extrapolate PK and dose across in vitro and in vivo systems and across different species, populations and disease states. Using small molecule and large molecule examples from the literature and our own company, we have shown how PBPK techniques can be utilised for human PK and dose prediction. Such approaches have the potential to increase efficiency, reduce the need for animal studies, replace clinical trials and increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however some limitations need to be addressed to realise its application and utility more broadly.

Applications of minimal physiologically-based pharmacokinetic models

Conventional mammillary models are frequently used for pharmacokinetic (PK) analysis when only blood or plasma data are available. Such models depend on the quality of the drug disposition data and have vague biological features. An alternative minimal-physiologically-based PK (minimal-PBPK) modeling approach is proposed which inherits and lumps major physiologic attributes from whole-body PBPK models. The body and model are represented as actual blood and tissue (usually total body weight) volumes, fractions (f ( d )) of cardiac output with Fick's Law of Perfusion, tissue/blood partitioning (K ( p )), and systemic or intrinsic clearance. Analyzing only blood or plasma concentrations versus time, the minimal-PBPK models parsimoniously generate physiologically-relevant PK parameters which are more easily interpreted than those from mammillary models. The minimal-PBPK models were applied to four types of therapeutic agents and conditions. The models well captured the human PK profiles of 22 selected beta-lactam antibiotics allowing comparison of fitted and calculated K ( p ) values. Adding a classical hepatic compartment with hepatic blood flow allowed joint fitting of oral and intravenous (IV) data for four hepatic elimination drugs (dihydrocodeine, verapamil, repaglinide, midazolam) providing separate estimates of hepatic intrinsic clearance, non-hepatic clearance, and pre-hepatic bioavailability. The basic model was integrated with allometric scaling principles to simultaneously describe moxifloxacin PK in five species with common K ( p ) and f ( d ) values. A basic model assigning clearance to the tissue compartment well characterized plasma concentrations of six monoclonal antibodies in human subjects, providing good concordance of predictions with expected tissue kinetics. The proposed minimal-PBPK modeling approach offers an alternative and more rational basis for assessing PK than compartmental models.

MEGen: A Physiologically Based Pharmacokinetic Model Generator

Physiologically based pharmacokinetic models are being used in an increasing number of different areas. However, they are perceived as complex, data hungry, resource intensive, and time consuming. In addition, model validation and verification are hindered by the relative complexity of the equations. To begin to address these issues a web application called MEGen for the rapid construction and documentation of bespoke deterministic PBPK model code is under development. MEGen comprises a parameter database and a model code generator that produces code for use in several commercial software packages and one that is freely available. Here we present an overview of the current capabilities of MEGen, and discuss future developments.

Physiologically based pharmacokinetic modeling: methodology, applications, and limitations with a focus on its role in pediatric drug development

The concept of physiologically based pharmacokinetic (PBPK) modeling was introduced years ago, but it has not been practiced significantly. However, interest in and implementation of this modeling technique have grown, as evidenced by the increased number of publications in this field. This paper demonstrates briefly the methodology, applications, and limitations of PBPK modeling with special attention given to discuss the use of PBPK models in pediatric drug development and some examples described in detail. Although PBPK models do have some limitations, the potential benefit from PBPK modeling technique is huge. PBPK models can be applied to investigate drug pharmacokinetics under different physiological and pathological conditions or in different age groups, to support decision-making during drug discovery, to provide, perhaps most important, data that can save time and resources, especially in early drug development phases and in pediatric clinical trials, and potentially to help clinical trials become more “confirmatory” rather than “exploratory”.

Knowledge-driven approaches for the guidance of first-in-children dosing

Pediatric pharmacokinetic and pediatric safety and efficacy studies are, in most cases, a mandatory activity during the drug development process in North America and Europe. Pharmacokinetic modeling in anticipation of the pediatric clinical trial should take a data or knowledge-driven approach by employing either top-down or bottom-up approaches to assessing differential age-related dosing. These two approaches depend on different starting information and are likely to be used in conjunction with each other for the purposes of defining pediatric dosing guidelines. This review primarily focuses on the available bottom-up, mechanistic models for predicting age-dependent drug absorption, distribution and elimination, and their integration through the whole-body physiologically based pharmacokinetic (PBPK) model. The bottom-up approach incorporating adult and pediatric whole-body PBPK models for optimizing age-related dosing is detailed for a drug currently undergoing pediatric development.

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.

Incorporating human dosimetry and exposure into high-throughput in vitro toxicity screening

Many chemicals in commerce today have undergone limited or no safety testing. To reduce the number of untested chemicals and prioritize limited testing resources, several governmental programs are using high-throughput in vitro screens for assessing chemical effects across multiple cellular pathways. In this study, metabolic clearance and plasma protein binding were experimentally measured for 35 ToxCast phase I chemicals. The experimental data were used to parameterize a population-based in vitro-to-in vivo extrapolation model for estimating the human oral equivalent dose necessary to produce a steady-state in vivo concentration equivalent to in vitro AC(50) (concentration at 50% of maximum activity) and LEC (lowest effective concentration) values from the ToxCast data. For 23 of the 35 chemicals, the range of oral equivalent doses for up to 398 ToxCast assays was compared with chronic aggregate human oral exposure estimates in order to assess whether significant in vitro bioactivity occurred within the range of maximum expected human oral exposure. Only 2 of the 35 chemicals, triclosan and pyrithiobac-sodium, had overlapping oral equivalent doses and estimated human oral exposures. Ranking by the potencies of the AC(50) and LEC values, these two chemicals would not have been at the top of a prioritization list. Integrating both dosimetry and human exposure information with the high-throughput toxicity screening efforts provides a better basis for making informed decisions on chemical testing priorities and regulatory attention. Importantly, these tools are necessary to move beyond hazard rankings to estimates of possible in vivo responses based on in vitro screens.

The Simcyp population-based ADME simulator

The Simcyp population-based absorption, distribution, metabolism and excretion simulator is a platform and database for 'bottom-up' mechanistic modelling and simulation of the processes of oral absorption, tissue distribution, metabolism and excretion of drugs and drug candidates in healthy and disease populations. It combines experimental data generated routinely during preclinical drug discovery and development from in vitro enzyme and cellular systems and relevant physicochemical attributes of compound and dosage form with demographic, physiological and genetic information on different patient populations. The mechanistic approach implemented in the Simcyp Simulator allows simulation of complex absorption, distribution, metabolism and excretion outcomes, particularly those involving multiple drug interactions, parent drug and metabolite profiles and time- and dose-dependent phenomena such as auto-induction and auto-inhibition.This review describes the framework and organisation of the simulator and how it combines the different categories of information.

Biokinetic modeling and in vitro-in vivo extrapolations

The introduction of in vitro methodologies in the toxicological risk assessment process requires a number of prerequisites regarding both the toxicodynamics and the biokinetics of the compounds under study. In vitro systems will need to be relevant for measuring those structural and physiological changes that are good indicators for adverse effects. Furthermore, the dose metric found to have an effect in the in vitro system should be relevant. One element in defining the appropriate dose metric is related to the kinetic behavior of the compound in the in vitro system: binding to proteins, binding to plastic, evaporation, and the interaction between the culture medium and the cells. Ways to measure and model "in vitro biokinetics" are described. Second, the appropriate dose metric in vitro, e.g., the effective concentration, will need to be extrapolated to relevant in vivo exposure scenarios. The application of physiologically based biokinetic modelling is essential in such extrapolations. The parameters needed to build these models often can be estimated based on nonanimal data, namely chemical properties (QSARs) and in vitro experiments.

Application of toxicokinetics to improve chemical risk assessment: implications for the use of animals

While toxicokinetics has become an integral part of pharmaceutical safety assessment over the last two decades, its use in the chemical industry is relatively new. However, it is recognised as a potentially important tool in human health risk assessment and recent initiatives have advocated greater application of toxicokinetics as part of an improved assessment strategy for crop protection chemicals that could offer greater efficiency, use fewer animals and provide better data for risk assessment purposes. To explore the potential scientific and animal welfare benefits of increased use of toxicokinetic data across the chemical industry, an international workshop was held in 2008. Experts from a wide range of chemical industry sectors, including industrial chemicals, agrochemicals and consumer products, participated in the meeting as well as representatives from relevant regulatory authorities. Pharmaceutical industry experts were also invited, in order to share experiences from the extensive use of toxicokinetics in drug development. Given that increased generation of toxicokinetic data could potentially result in an increased number of animals undergoing testing, technologies and strategies to reduce and refine animal use for this purpose were also considered. This paper outlines and expands upon the key themes that emerged from the workshop.

Interspecies scaling of oleanolic acid in mice, rats, rabbits and dogs and prediction of human pharmacokinetics

This study was conducted to predict the pharmacokinetics of oleanolic acid in humans based on animal data by allometry and several species-invariant time methods. Oleanolic acid was injected intravenously to mice, rats, rabbit and dogs (dose 1 mg/kg). The serum concentration-time profiles of oleanolic acid were best described by bi-exponential equation in all animal species. The average Cl, V ( ss ) and t ( 1/2 ) were 0.065 L/h, 0.019 L and 28.7 min in mice, 0.47 +/- 0.06 L/h, 0.117 +/- 0.029 L and 29.7 +/- 12.2 min in rats, 2.77 +/- 0.88 L/h, 1.83 +/- 0.60 L and 84.4 +/- 16.9 min in rabbits and 14.0 +/- 0.7 L/h, 9.2 +/- 10.1 L and 54.5 +/- 57.2 min in dogs, respectively. Based on animal data, human pharmacokinetic parameters of Cl, V ( ss ) and t (1/2) were predicted by simple allometry. In addition, actual concentration-time profiles obtained from animals were transformed to human profiles by species-invariant times of kallynochron, apolysichron and dienetichron. The predicted human pharmacokinetic parameters of Cl, V ( ss ) and t (1/2) by using simple allometry and species-invariant time transformation method ranged from 48.3-97.2 L/h, 49.1-92.9 L and 45.6-187.2 min, respectively. Those predicted parameters of oleanolic acid may be useful in designing dosing schedules of oleanolic acid in future clinical studies.

Predicting pharmacokinetics of drugs using physiologically based modeling--application to food effects

Our knowledge of the major mechanisms underlying the effect of food on drug absorption allows reliable qualitative prediction based on biopharmaceutical properties, which can be assessed during the pre-clinical phase of drug discovery. Furthermore, several recent examples have shown that physiologically based absorption models incorporating biorelevant drug solubility measurements can provide quite accurate quantitative prediction of food effect. However, many molecules currently in development have distinctly sub-optimal biopharmaceutical properties, making the quantitative prediction of food effect for different formulations from in vitro data very challenging. If such drugs reach clinical development and show undesirable variability when dosed with food, improved formulation can help to reduce the food effect and carefully designed in vivo studies in dogs can be a useful guide to clinical formulation development. Even so, such in vivo studies provide limited throughput for screening, and food effects seen in dog cannot always be directly translated to human. This paper describes how physiologically based absorption modeling can play a role in the prediction of food effect by integrating the data generated during pre-clinical and clinical research and development. Such data include physicochemical and in vitro drug properties, biorelevant solubility and dissolution, and in vivo pre-clinical and clinical pharmacokinetic data. Some background to current physiological absorption models of human and dog is given, and refinements to models of in vivo drug solubility and dissolution are described. These are illustrated with examples using GastroPlus to simulate the food effect in dog and human for different formulations of two marketed drugs.