Correlation-based prediction of tissue-to-plasma partition coefficients using readily available input parameters

Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans

Physiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing with in vitro or in silico methods. However, traditional PBK modelling depends on animal and human data, which limits its usefulness for non-animal methods. To address this limitation, high-throughput PBK modelling aims to rely exclusively on in vitro and in silico data for model generation. Here, we evaluate a variety of in silico tools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000 + publicly available human in vivo concentration-time profiles of 200 + compounds (IV and oral administration), as well as in silico, in vitro and in vivo determined compound-specific parameters required for the PBK modelling of these compounds. Then, we systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy against the collected in vivo concentration-time profiles. Our results show that even simple, generic high-throughput PBK modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC within tenfold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisation strategies, as well as between different compounds. Finally, we outline a strategy for high-throughput PBK modelling that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of high-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation Risk Assessment.

Reverse Iontophoresis: Noninvasive Assessment of Topical Drug Bioavailability

Assessing drug disposition in the skin after the application of a topical formulation is difficult. It is hypothesized that reverse iontophoresis (RI), which can extract charged/polar molecules for monitoring purposes, may provide a noninvasive approach for the assessment of local drug bioavailability. The passive and RI extraction of salicylic acid (SA) and nicotine (NIC) from porcine skin in vitro was assessed after a simple solution of the former and a transdermal patch of the latter had been applied for 24 and 8 h, respectively. Immediately after this "passive skin loading", the amount of drug in the stratum corneum (SC) and "viable" tissue (VT) was measured either (a) after tape-stripping and subsequent solvent extraction of both skin layers or (b) following RI extraction over 4 h. Parallel experiments were then performed in vivo in healthy volunteers; in this case, the VT was not sampled and the skin loading period for NIC was only 4 h. RI extraction of both drugs was significantly higher (in vitro and in vivo) than that achieved passively, and the cumulative RI extraction profiles as a function of time were mathematically analyzed using a straightforward compartmental model. Best-fit estimates of drug amounts in the SC and VT (ASC,0 and AVT,0, respectively) at the end of "loading" and two first-order rate constants describing transfer between the model compartments were then determined. The in vitro predictions of ASC,0 and AVT,0 were in excellent agreement with the experimental results, as was the value of the former in vivo. The rate constants derived from the in vitro and in vivo results were also similar. In summary, the results provide proof-of-concept that the RI method has the potential to noninvasively assess relevant metrics of drug bioavailability in the skin.

QSAR model to predict Kp,uu,brain with a small dataset, incorporating predicted values of related parameter

The unbound brain-to-plasma concentration ratio (K_p,uu,brain) is a parameter that indicates the extent of central nervous system penetration. Pharmaceutical companies build prediction models because many experiments are required to obtain K_p,uu,brain. However, the lack of data hinders the design of an accurate prediction model. To construct a quantitative structure-activity relationship (QSAR) model with a small dataset of K_p,uu,brain, we investigated whether the prediction accuracy could be improved by incorporating software-predicted brain penetration-related parameters (BPrPs) as explanatory variables for pharmacokinetic parameter prediction. We collected 88 compounds with experimental K_p,uu,brain from various official publications. Random forest was used as the machine learning model. First, we developed prediction models using only structural descriptors. Second, we verified the predictive accuracy of each model with the predicted values of BPrPs incorporated in various combinations. Third, the K_p,uu,brain of the in-house compounds was predicted and compared with the experimental values. The prediction accuracy was improved using five-fold cross-validation (RMSE = 0.455, r² = 0.726) by incorporating BPrPs. Additionally, this model was verified using an external in-house dataset. The result suggested that using BPrPs as explanatory variables improve the prediction accuracy of the K_p,uu,brain QSAR model when the available number of datasets is small.

Predictive Performance of Next Generation Physiologically Based Kinetic (PBK) Model Predictions in Rats Based on In Vitro and In Silico Input Data

The goal of the present study was to assess the predictive performance of a minimal generic rat physiologically based kinetic (PBK) model based on in vitro and in silico input data to predict peak plasma concentrations (Cmax) upon single oral dosing. To this purpose, a dataset was generated of 3960 Cmax predictions for 44 compounds, applying different combinations of in vitro and in silico approaches for chemical parameterization, and comparison of the predictions to reported in vivo data. Best performance was obtained when (1) the hepatic clearance was parameterized based on in vitro measured intrinsic clearance values, (2) the method of Rodgers and Rowland for calculating partition coefficients, and (3) in silico calculated fraction unbound plasma and Papp values (the latter especially for very lipophilic compounds). Based on these input data, the median Cmax of 32 compounds could be predicted within 10-fold of the observed Cmax, with 22 out of these 32 compounds being predicted within 5-fold, and 8 compounds within 2-fold. Overestimations of more than 10-fold were observed for 12 compounds, whereas no underestimations of more than 10-fold occurred. Median Cmax predictions were frequently found to be within 10-fold of the observed Cmax when the scaled unbound hepatic intrinsic clearance (Clint,u) was either higher than 20 l/h or lower than 1 l/h. Similar findings were obtained with a test set of 5 in-house BASF compounds. Overall, this study provides relevant insights in the predictive performance of a minimal PBK model based on in vitro and in silico input data.

Topical drug delivery: History, percutaneous absorption, and product development

Topical products, widely used to manage skin conditions, have evolved from simple potions to sophisticated delivery systems. Their development has been facilitated by advances in percutaneous absorption and product design based on an increasingly mechanistic understanding of drug-product-skin interactions, associated experiments, and a quality-by-design framework. Topical drug delivery involves drug transport from a product on the skin to a local target site and then clearance by diffusion, metabolism, and the dermal circulation to the rest of the body and deeper tissues. Insights have been provided by Quantitative Structure Permeability Relationships (QSPR), molecular dynamics simulations, and dermal Physiologically Based PharmacoKinetics (PBPK). Currently, generic product equivalents of reference-listed products dominate the topical delivery market. There is an increasing regulatory interest in understanding topical product delivery behavior under 'in use' conditions and predicting in vivo response for population variations in skin barrier function and response using in silico and in vitro findings.

Predicting topical drug clearance from the skin

For topical drug products that target sites of action in the viable epidermal and/or upper dermal compartment of the skin, the local concentration profiles have proven difficult to quantify because drug clearance from the viable cutaneous tissue is not well characterised. Without such knowledge, of course, it is difficult-if not impossible-to predict a priori whether and over what time frame a topical formulation will permit an effective concentration of drug within the skin 'compartment' to be achieved. Here, we test the hypothesis that valuable information about drug disposition, and specifically its clearance, in this experimentally difficult-to-access compartment (at least, in vivo) can be derived from available systemic pharmacokinetic data for drugs administered via transdermal delivery systems. A multiple regression analysis was undertaken to determine the best-fit empirical correlation relating clearance from the skin to known or easily calculable drug properties. It was possible, in this way, to demonstrate a clear relationship between drug clearance from the skin and key physical chemical properties of the drug (molecular weight, log P and topological polar surface area). It was further demonstrated that values predicted by the model correlated well with those derived from in vitro skin experiments.

Methods to Predict Volume of Distribution

PURPOSE OF REVIEW

Prior to human studies, knowledge of drug disposition in the body is useful to inform decisions on drug safety and efficacy, first in human dosing, and dosing regimen design. It is therefore of interest to develop predictive models for primary pharmacokinetic parameters, clearance, and volume of distribution. The volume of distribution of a drug is determined by the physiological properties of the body and physiochemical properties of the drug, and is used to determine secondary parameters, including the half-life. The purpose of this review is to provide an overview of current methods for the prediction of volume of distribution of drugs, discuss a comparison between the methods, and identify deficiencies in current predictive methods for future improvement.

RECENT FINDINGS

Several volumes of distribution prediction methods are discussed, including preclinical extrapolation, physiological methods, tissue composition-based models to predict tissue:plasma partition coefficients, and quantitative structure-activity relationships. Key factors that impact the prediction of volume of distribution, such as permeability, transport, and accuracy of experimental inputs, are discussed. A comparison of current methods indicates that in general, all methods predict drug volume of distribution with an absolute average fold error of 2-fold. Currently, the use of composition-based PBPK models is preferred to models requiring in vivo input.

SUMMARY

Composition-based models perfusion-limited PBPK models are commonly used at present for prediction of tissue:plasma partition coefficients and volume of distribution, respectively. A better mechanistic understanding of important drug distribution processes will result in improvements in all modeling approaches.

Rapid experimental measurements of physicochemical properties to inform models and testing

The structures and physicochemical properties of chemicals are important for determining their potential toxicological effects, toxicokinetics, and route(s) of exposure. These data are needed to prioritize the risk for thousands of environmental chemicals, but experimental values are often lacking. In an attempt to efficiently fill data gaps in physicochemical property information, we generated new data for 200 structurally diverse compounds, which were rigorously selected from the USEPA ToxCast chemical library, and whose structures are available within the Distributed Structure-Searchable Toxicity Database (DSSTox). This pilot study evaluated rapid experimental methods to determine five physicochemical properties, including the log of the octanol:water partition coefficient (known as log(Kow) or logP), vapor pressure, water solubility, Henry's law constant, and the acid dissociation constant (pKa). For most compounds, experiments were successful for at least one property; log(Kow) yielded the largest return (176 values). It was determined that 77 ToxPrint structural features were enriched in chemicals with at least one measurement failure, indicating which features may have played a role in rapid method failures. To gauge consistency with traditional measurement methods, the new measurements were compared with previous measurements (where available). Since quantitative structure-activity/property relationship (QSAR/QSPR) models are used to fill gaps in physicochemical property information, 5 suites of QSPRs were evaluated for their predictive ability and chemical coverage or applicability domain of new experimental measurements. The ability to have accurate measurements of these properties will facilitate better exposure predictions in two ways: 1) direct input of these experimental measurements into exposure models; and 2) construction of QSPRs with a wider applicability domain, as their predicted physicochemical values can be used to parameterize exposure models in the absence of experimental data.

Prediction of Tissue-to-Plasma Ratios of Basic Compounds in Mice

BACKGROUND

Majority of reported studies so far developed correlation regression equations using the rat muscle-to-plasma drug concentration ratio (Kp-muscle) to predict tissue-to-plasma drug concentration ratios (Kp-tissues). Use of regression equations derived from rat Kp-muscle may not be ideal to predict the mice tissue-Kps as there are species differences.

OBJECTIVES

(i) To develop the linear regression equations using mouse tissue-Kps; (ii) to assess the correlation between organ blood flow and/or organ weight with tissue-Kps and (iii) compare the observed tissue-Kps from mice with corresponding predicted tissue-Kps using Richter's rat-Kp specific equations.

METHOD

Disposition of 12 small molecules were investigated extensively in mouse plasma and tissues after a single oral dose administration. Linear correlation was assessed for each of the tissue with rest of the other tissues, separately for weak and strong bases.

RESULT

Newly developed regression equations using mice tissue-Kps, predicted 79% data points within twofold. As observed correlation r ² range was 0.75-0.98 between Kp-muscle and Kp-brain, -spleen, -skin, -liver, -lung, suggesting superior correlation between the tissue-Kps. Order of tissue-Kps, showed that tissue concentrations were directly proportional to the organ blood flow and inversely to the organ weight. Further, the observed tissue-Kps from mice were compared with corresponding predicted tissue-Kps using Richter's rat-Kp specific equations. Overall, 46, 54 and 63% data points were under predicted (<0.5-fold) for liver, spleen and lung, respectively. Whereas 63 and 75% data points were over predicted (>twofold) for skin and brain, respectively. These findings suggest that cross species extrapolation predictability is poor.

CONCLUSION

All these findings together suggest that mouse specific regression equations developed under controlled experimental conditions could be most appropriate for predicting mouse tissue-Kps for compounds with wide range of volume of distribution.

A Whole-Body Physiologically Based Pharmacokinetic Model for Colistin and Colistin Methanesulfonate in Rat

Colistin is a polymyxin antibiotic used to treat patients infected with multidrug-resistant Gram-negative bacteria (MDR-GNB). The objective of this work was to develop a whole-body physiologically based pharmacokinetic (WB-PBPK) model to predict tissue distribution of colistin in rat. The distribution of a drug in a tissue is commonly characterized by its tissue-to-plasma partition coefficient, K_p . Colistin and its prodrug, colistin methanesulfonate (CMS) K_p priors, were measured experimentally from rat tissue homogenates or predicted in silico. The PK parameters of both compounds were estimated fitting in vivo their plasma concentration-time profiles from six rats receiving an i.v. bolus of CMS. The variability in the data was quantified by applying a nonlinear mixed effect (NLME) modelling approach. A WB-PBPK model was developed assuming a well-stirred and perfusion-limited distribution in tissue compartments. Prior information on tissue distribution of colistin and CMS was investigated following three scenarios: K_p was estimated using in silico K_p priors (I) or K_p was estimated using experimental K_p priors (II) or K_p was fixed to the experimental values (III). The WB-PBPK model best described colistin and CMS plasma concentration-time profiles in scenario II. Colistin-predicted concentrations in kidneys in scenario II were higher than in other tissues, which was consistent with its large experimental K_p prior. This might be explained by a high affinity of colistin for renal parenchyma and active reabsorption into the proximal tubular cells. In contrast, renal accumulation of colistin was not predicted in scenario I. Colistin and CMS clearance estimates were in agreement with published values. The developed model suggests using experimental priors over in silico K_p priors for kidneys to provide a better prediction of colistin renal distribution. Such models might serve in drug development for interspecies scaling and investigate the impact of disease state on colistin disposition.

Prediction of Tumor-to-Plasma Ratios of Basic Compounds in Subcutaneous Xenograft Mouse Models

BACKGROUND

Predicting target site drug concentrations is of key importance for rank ordering compounds before proceeding to chronic pharmacodynamic models. We propose generic tumor-specific correlation-based regression equations to predict tumor-to-plasma ratios (tumor-Kps) in slow- and fast-growing xenograft mouse models.

METHODS

Disposition of 14 basic small molecules was investigated extensively in mouse plasma, tissues and tumors after a single oral dose administration. Linear correlation was assessed and compared between tumor-Kp and normal tissue-to-plasma ratio (tissue-Kps) separately for each tumor xenograft. The developed regression equations were validated by leave-one-out cross-validation (LOOCV) method.

RESULT

Both slow- and fast-growing tumor-Kps showed good correlation (r ² ≥ 0.7) with majority of the normal tissue-Kps. Substantial difference was observed in the slopes of developed equations between two xenografts, which was in line with observed difference in tumor distribution. The linear correlations between tumor-Kp and skin- or spleen-Kp were within the acceptable statistical criteria (LOOCV) across xenografts and the class of compounds evaluated. Since > 70% of tumor-Kps from the test data sets were predicted within a factor of twofold for both slow- and fast-growing xenograft mouse models, the results validate the applicability of the developed equations across xenografts.

CONCLUSION

Tumor-specific correlation-based regression equations were developed and their applicability was adequately validated across xenografts. These equations could be successfully translated to predict tumor concentrations in order to preclude experimental tumor-Kp determination.

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.

Specific Inhibition of the Distribution of Lobeglitazone to the Liver by Atorvastatin in Rats: Evidence for a Rat Organic Anion Transporting Polypeptide 1B2-Mediated Interaction in Hepatic Transport

Cytochrome P450 enzymes and human organic anion transporting polypeptide (OATP) 1B1 are reported to be involved in the pharmacokinetics of lobeglitazone (LB), a new peroxisome proliferator-activated receptor γ agonist. Atorvastatin (ATV), a substrate for CYP3A and human OATP1B1, is likely to be coadministered with LB in patients with the metabolic syndrome. We report herein on a study of potential interactions between LB and ATV in rats. When LB was administered intravenously with ATV, the systemic clearance and volume of distribution at steady state for LB remained unchanged (2.67 ± 0.63 ml/min per kg and 289 ± 20 ml/kg, respectively), compared with that of LB without ATV (2.34 ± 0.37 ml/min per kg and 271 ± 20 ml/kg, respectively). Although the tissue-to-plasma partition coefficient (K_p) of LB was not affected by ATV in most major tissues, the liver K_p for LB was decreased by ATV coadministration. Steady-state liver K_p values for three levels of LB were significantly decreased as a result of ATV coadministration. LB uptake was inhibited by ATV in rat OATP1B2-overexpressing Madin-Darby canine kidney cells and in isolated rat hepatocytes in vitro. After incorporating the kinetic parameters for the in vitro studies into a physiologically based pharmacokinetics model, the characteristics of LB distribution to the liver were consistent with the findings of the in vivo study. It thus appears that the distribution of LB to the liver is mediated by the hepatic uptake of transporters such as rat OATP1B2, and carrier-mediated transport is involved in the liver-specific drug-drug interaction between LB and ATV in vivo.

Application of a systems pharmacology model for translational prediction of hERG-mediated QTc prolongation

Drug‐induced QTc interval prolongation (ΔQTc) is a main surrogate for proarrhythmic risk assessment. A higher in vivo than in vitro potency for hERG‐mediated QTc prolongation has been suggested. Also, in vivo between‐species and patient populations’ sensitivity to drug‐induced QTc prolongation seems to differ. Here, a systems pharmacology model integrating preclinical in vitro (hERG binding) and in vivo (conscious dog ΔQTc) data of three hERG blockers (dofetilide, sotalol, moxifloxacin) was applied (1) to compare the operational efficacy of the three drugs in vivo and (2) to quantify dog–human differences in sensitivity to drug‐induced QTc prolongation (for dofetilide only). Scaling parameters for translational in vivo extrapolation of drug effects were derived based on the assumption of system‐specific myocardial ion channel densities and transduction of ion channel block: the operational efficacy (transduction of hERG block) in dogs was drug specific (1–19% hERG block corresponded to ≥10 msec ΔQTc). System‐specific maximal achievable ΔQTc was estimated to 28% from baseline in both dog and human, while %hERG block leading to half‐maximal effects was 58% lower in human, suggesting a higher contribution of hERG‐mediated potassium current to cardiac repolarization. These results suggest that differences in sensitivity to drug‐induced QTc prolongation may be well explained by drug‐ and system‐specific differences in operational efficacy (transduction of hERG block), consistent with experimental reports. The proposed scaling approach may thus assist the translational risk assessment of QTc prolongation in different species and patient populations, if mediated by the hERG channel.

Partitioning of Organic Ions to Muscle Protein: Experimental Data, Modeling, and Implications for in Vivo Distribution of Organic Ions

The in vivo partitioning behavior of ionogenic organic chemicals (IOCs) is of paramount importance for their toxicokinetics and bioaccumulation. Among other proteins, structural proteins including muscle proteins could be an important sorption phase for IOCs, because of their high quantity in the human and other animals' body and their polar nature. Binding data for IOCs to structural proteins are, however, severely limited. Therefore, in this study muscle protein-water partition coefficients (KMP/w) of 51 systematically selected organic anions and cations were determined experimentally. A comparison of the measured KMP/w with bovine serum albumin (BSA)-water partition coefficients showed that anionic chemicals sorb more strongly to BSA than to muscle protein (by up to 3.5 orders of magnitude), while cations sorb similarly to both proteins. Sorption isotherms of selected IOCs to muscle protein are linear (i.e., KMP/w is concentration independent), and KMP/w is only marginally influenced by pH value and salt concentration. Using the obtained data set of KMP/w a polyparameter linear free energy relationship (PP-LFER) model was established. The derived equation fits the data well (R(2) = 0.89, RMSE = 0.29). Finally, it was demonstrated that the in vitro measured KMP/w values of this study have the potential to be used to evaluate tissue-plasma partitioning of IOCs in vivo.

Prediction of blood:air and fat:air partition coefficients of volatile organic compounds for the interpretation of data in breath gas analysis

In this article, a database of blood:air and fat:air partition coefficients (λ b:a and λ f:a) is reported for estimating 1678 volatile organic compounds recently reported to appear in the volatilome of the healthy human. For this purpose, a quantitative structure-property relationship (QSPR) approach was applied and a novel method for Henry's law constants prediction developed. A random forest model based on Molecular Operating Environment 2D (MOE2D) descriptors based on 2619 literature-reported Henry's constant values was built. The calculated Henry's law constants correlate very well (R(2) test  =  0.967) with the available experimental data. Blood:air and fat:air partition coefficients were calculated according to the method proposed by Poulin and Krishnan using the estimated Henry's constant values. The obtained values correlate reasonably well with the experimentally determined ones for a test set of 90 VOCs (R(2)  =  0.95). The provided data aim to fill in the literature data gap and further assist the interpretation of results in studies of the human volatilome.

Analysis of the distribution of organic compounds and drugs between biological tissues in the framework of solute partitioning in aqueous two-phase systems

Distribution of organic compounds between different biological tissues may be considered in the framework of solute partitioning in aqueous two-phase systems.

Optimizing pharmacokinetic bridging studies in paediatric oncology using physiologically-based pharmacokinetic modelling: application to docetaxel

AIM

Applying physiologically-based pharmacokinetic (PBPK) modelling in paediatric cancer drug development is still challenging. We aimed to demonstrate how PBPK modelling can be applied to optimize dose and sampling times for a paediatric pharmacokinetic (PK) bridging study in oncology and to compare with the allometric scaling population PK (AS-popPK) approach, using docetaxel as an example.

METHODS

A PBPK model for docetaxel was first developed for adult cancer patients using Simcyp® and subsequently used to predict its PK profiles in children by accounting for age-dependent physiological differences. Dose (mg m(-2) ) requirements for children aged 0-18 years were calculated to achieve targeted exposure in adults. Simulated data were then analyzed using population PK modelling with MONOLIX® in order to perform design optimization with the population Fisher information matrix (PFIM). In parallel, the AS-popPK approach was performed for the comparison.

RESULTS

The PBPK model developed for docetaxel adequately predicted its PK profiles in both adult and paediatric cancer patients (predicted clearance and volume of distribution within 1.5 fold of observed data). The revised dose of docetaxel for a child over 1.5 years old was higher than the adult dose. Considering clinical constraints, the optimal design contained two groups of 15 patients, having three or four sampling times and had good predicted relative standard errors (RSE<30%) for almost all parameters. The AS-popPK approach performed reasonably well but could not predict for very young children.

CONCLUSION

This research shows the clinical utility of PBPK modelling in combination with population PK modelling and optimal design to support paediatric oncology development.

Physiologically based pharmacokinetic modeling and simulation in pediatric drug development

Increased regulatory demands for pediatric drug development research have fostered interest in the use of modeling and simulation among industry and academia. Physiologically based pharmacokinetic (PBPK) modeling offers a unique modality to incorporate multiple levels of information to estimate age-specific pharmacokinetics. This tutorial will serve to provide the reader with a basic understanding of the procedural steps to developing a pediatric PBPK model and facilitate a discussion of the advantages and limitations of this modeling technique.

Developmental pharmacokinetics in pediatric populations

Information on drug absorption and disposition in infants and children has increased considerably over the past 2 decades. However, the impact of specific age-related effects on pharmacokinetics, pharmacodynamics, and dose requirements remains poorly understood. Absorption can be affected by the differences in gastric pH and stomach emptying time that have been observed in the pediatric population. Low plasma protein concentrations and a higher body water composition can change drug distribution. Metabolic processes are often immature at birth, which can lead to a reduced clearance and a prolonged half-life for those drugs for which metabolism is a significant mechanism for elimination. Renal excretion is also reduced in neonates due to immature glomerular filtration, tubular secretion, and reabsorption. Limited data are available on the pharmacodynamic behavior of drugs in the pediatric population. Understanding these age effects provide a mechanistic way to identify initial doses for the pediatric population. The various factors that impact pharmacokinetics and pharmacodynamics mature towards adult values at different rates, thus requiring continual modification of drug dose regimens in neonates, infants, and children. In this paper, the age-related changes in drug absorption, distribution, metabolism, and elimination in infants and children are reviewed, and the age-related dosing regimens for this population are discussed.

Physiologically based pharmacokinetic prediction of telmisartan in human

A previously developed physiologically based pharmacokinetic model for hepatic transporter substrates was extended to an organic anion transporting polypeptide substrate, telmisartan. Predictions used in vitro data from sandwich culture human hepatocyte and human liver microsome assays. We have developed a novel method to calibrate partition coefficients (Kps) between nonliver tissues and plasma on the basis of published human positron emission tomography (PET) data to decrease the uncertainty in tissue distribution introduced by in silico-predicted Kps. With in vitro data-predicted hepatic clearances, published empirical scaling factors, and PET-calibrated Kps, the model could accurately recapitulate telmisartan pharmacokinetic (PK) behavior before 2.5 hours. Reasonable predictions also depend on having a model structure that can adequately describe the drug disposition pathways. We showed that the elimination phase (2.5-12 hours) of telmisartan PK could be more accurately recapitulated when enterohepatic recirculation of parent compound derived from intestinal deconjugation of glucuronide metabolite was incorporated into the model. This study demonstrated the usefulness of the previously proposed physiologically based modeling approach for purely predictive intravenous PK simulation and identified additional biologic processes that can be important in prediction.

Development of a decision tree to classify the most accurate tissue-specific tissue to plasma partition coefficient algorithm for a given compound

Physiologically based pharmacokinetic (PBPK) modeling is a tool used in drug discovery and human health risk assessment. PBPK models are mathematical representations of the anatomy, physiology and biochemistry of an organism and are used to predict a drug's pharmacokinetics in various situations. Tissue to plasma partition coefficients (Kp), key PBPK model parameters, define the steady-state concentration differential between tissue and plasma and are used to predict the volume of distribution. The experimental determination of these parameters once limited the development of PBPK models; however, in silico prediction methods were introduced to overcome this issue. The developed algorithms vary in input parameters and prediction accuracy, and none are considered standard, warranting further research. In this study, a novel decision-tree-based Kp prediction method was developed using six previously published algorithms. The aim of the developed classifier was to identify the most accurate tissue-specific Kp prediction algorithm for a new drug. A dataset consisting of 122 drugs was used to train the classifier and identify the most accurate Kp prediction algorithm for a certain physicochemical space. Three versions of tissue-specific classifiers were developed and were dependent on the necessary inputs. The use of the classifier resulted in a better prediction accuracy than that of any single Kp prediction algorithm for all tissues, the current mode of use in PBPK model building. Because built-in estimation equations for those input parameters are not necessarily available, this Kp prediction tool will provide Kp prediction when only limited input parameters are available. The presented innovative method will improve tissue distribution prediction accuracy, thus enhancing the confidence in PBPK modeling outputs.