PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 2: Comparative assessment of prediction methods of human volume of distribution

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.

Accounting for inter-correlation between enzyme abundance: a simulation study to assess implications on global sensitivity analysis within physiologically-based pharmacokinetics

Physiologically based pharmacokinetic (PBPK) models often include several sets of correlated parameters, such as organ volumes and blood flows. Because of recent advances in proteomics, it has been demonstrated that correlations are also present between abundances of drug-metabolising enzymes in the liver. As the focus of population PBPK has shifted the emphasis from the average individual to theoretically conceivable extremes, reliable estimation of the extreme cases has become paramount. We performed a simulation study to assess the impact of the correlation between the abundances of two enzymes on the pharmacokinetics of drugs that are substrate of both, under assumptions of presence or lack of such correlations. We considered three semi-physiological models representing the cases of: (1) intravenously administered drugs metabolised by two enzymes expressed in the liver; (2) orally administered drugs metabolised by CYP3A4 expressed in the liver and gut wall; (3) intravenously administered drugs that are substrates of CYP3A4 and OATP1B1 in the liver. Finally, the impact of considering or ignoring correlation between enzymatic abundances on global sensitivity analysis (GSA) was investigated using variance based GSA on a reduced PBPK model for repaglinide, substrate of CYP3A4 and CYP2C8. Implementing such correlations can increase the confidence interval for population pharmacokinetic parameters (e.g., AUC, bioavailability) and impact the GSA results. Ignoring these correlations could lead to the generation of implausible parameters combinations and to an incorrect estimation of pharmacokinetic related parameters. Thus, known correlations should always be considered in building population PBPK models.

Prediction of Human Distribution Volumes of Compounds in Various Elimination Phases Using Physiologically Based Pharmacokinetic Modeling and Experimental Pharmacokinetics in Animals

Predicting the pharmacokinetics of compounds in humans is an important part of the drug development process. In this study, the plasma concentration profiles of 10 marketed compounds exhibiting two-phase elimination after intravenous administration in humans were evaluated in terms of distribution volumes just after intravenous administration (V 1), at steady state (V ss), and in the elimination phase (Vβ ) using physiologically based pharmacokinetic (PBPK) modeling implemented in a commercially available simulator (Simcyp). When developing human PBPK models, the insight gained from prior animal PBPK models based on nonclinical data informed the optimization of the lipophilicity input of the compounds and the selection of the appropriate mechanistic tissue partition methods. The accuracy of V 1, V ss, and Vβ values predicted that using human PBPK models developed in accordance with prior animal PBPK models was superior to using those predicted using conventional approaches, such as allometric scaling, especially for V 1 and Vβ By conventional approaches, the V 1 and Vβ values of 4-5 of 10 compounds were predicted within a 3-fold error of observed values, whereas V ss values for their majority were predicted as such. PBPK models predicted V 1, V ss, and Vβ values for almost all compounds within 3-fold errors, resulting in better predictions of plasma concentration profiles than allometric scaling. The distribution volumes predicted using human PBPK models based on prior animal PBPK modeling were more accurate than those predicted without reference to animal models. This study demonstrated that human PBPK models developed with consideration of animal PBPK models could accurately predict distribution volumes in various elimination phases.

Preclinical pharmacokinetic evaluation to facilitate repurposing of tyrosine kinase inhibitors nilotinib and imatinib as antiviral agents

Background

Several tyrosine kinase inhibitors (TKIs) developed as anti-cancer drugs, also have anti-viral activity due to their ability to disrupt productive replication and dissemination in infected cells. Consequently, such drugs are attractive candidates for “repurposing” as anti-viral agents. However, clinical evaluation of therapeutics against infectious agents associated with high mortality, but low or infrequent incidence, is often unfeasible. The United States Food and Drug Administration formulated the “Animal Rule” to facilitate use of validated animal models for conducting anti-viral efficacy studies.

Methods

To enable such efficacy studies of two clinically approved TKIs, nilotinib, and imatinib, we first conducted comprehensive pharmacokinetic (PK) studies in relevant rodent and non-rodent animal models. PK of these agents following intravenous and oral dosing were evaluated in C57BL/6 mice, prairie dogs, guinea pigs and Cynomolgus monkeys. Plasma samples were analyzed using an LC-MS/MS method. Secondarily, we evaluated the utility of allometry-based inter-species scaling derived from previously published data to predict the PK parameters, systemic clearance (CL) and the steady state volume of distribution (Vss) of these two drugs in prairie dogs, an animal model not tested thus far.

Results

Marked inter-species variability in PK parameters and resulting oral bioavailability was observed. In general, elimination half-lives of these agents in mice and guinea pigs were much shorter (1–3 h) relative to those in larger species such as prairie dogs and monkeys. The longer nilotinib elimination half-life in prairie dogs (i.v., 6.5 h and oral, 7.5 h), facilitated multiple dosing PK and safety assessment. The allometry-based predicted values of the Vss and CL were within 2.0 and 2.5-fold, respectively, of the observed values.

Conclusions

Our results suggest that prairie dogs and monkeys may be suitable rodent and non-rodent species to perform further efficacy testing of these TKIs against orthopoxvirus infections. The use of rodent models such as C57BL/6 mice and guinea pigs for assessing pre-clinical anti-viral efficacy of these two TKIs may be limited due to short elimination and/or low oral bioavailability. Allometry-based correlations, derived from existing literature data, may provide initial estimates, which may serve as a useful guide for pre-clinical PK studies in untested animal models.

Application of the Tissue Composition-Based Model to Minipig for Predicting the Volume of Distribution at Steady State and Dermis-to-Plasma Partition Coefficients of Drugs Used in the Physiologically Based Pharmacokinetics Model in Dermatology

The minipig continues to build a reputation as a viable alternative large animal model to predict humans in dermatology and toxicology studies. Therefore, it is essential to describe and predict the pharmacokinetics in that species to speed up the clinical candidate selection. Essential input parameters in whole-body physiologically based pharmacokinetic models are the tissue-to-plasma partition coefficients and the resulting volume of distribution at steady-state (V_ss). Mechanistic in vitro- and in silico-based models used for predicting these parameters of tissue distribution of drugs refer to the tissue composition-based model (TCM). Robust TCMs were initially developed for some preclinical species (e.g., rat and dog) and human; however, there is currently no model available for the minipig. Therefore, the objective of this present study was to develop a TCM for the minipig and to estimate the corresponding tissue composition data. Drug partitioning into the tissues was predominantly governed by lipid and protein binding effects in addition to drug solubilization and pH gradient effects in the aqueous phase on both sides of the biological membranes; however, some more complex tissue distribution processes such as drug binding to the collagen-laminin material in dermis and a restricted drug partitioning into membranes of tissues for compounds that are amphiphilic and contain sulfur atom(s) were also challenged. The model was validated by predicting V_ss and the dermis-to-plasma partition coefficients (K_p-dermis) of 68 drugs. The prediction of K_p-dermis was extended to humans for comparison with the minipig. The results indicate that the extended TCM provided generally good agreements with observations in the minipig showing that it is also applicable to this preclinical species. In general, up to 86% and 100% of the predicted V_ss values are respectively within 2-fold and 3-fold errors compared to the experimentally determined values, whereas these numbers are 78% and 94% for K_p-dermis when the anticipated outlier compounds are not included. Binding data to dermis are comparable between minipigs and humans. Overall, this study is a first step toward developing a mechanistic TCM for the minipig, with the aim of increasing the use of physiologically based pharmacokinetic models of drugs for that species in addition to rats, dogs, and humans because such models are used in preclinical and clinical transdermal studies.

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.

Food Effect Projections via Physiologically Based Pharmacokinetic Modeling: Predictive Case Studies

Food can alter the absorption of orally administered drugs. Biopharmaceutics physiologically based pharmacokinetic (PBPK) modeling offers the possibility to simulate a compound's pharmacokinetics under fasted or fed states. To advance the utility of PBPK modeling, with a view to regulatory impact, we have pooled our experience across 4 pharmaceutical companies to propose a general multistep PBPK workflow leveraging pre-existing clinical data for immediate-release formulations of Biopharmaceutics Classification System I and II compounds. With this strategy, we wish to promote pragmatic PBPK approaches for compounds where absorption is well understood, that is, compounds with moderate-to-high permeability that are not substrates for uptake transporters. Five case studies demonstrate how food effect can be well predicted using appropriately established and validated models. The case studies integrate solubility and dissolution data for initial model development and apply a "middle-out" validation with clinical data in one prandial state. Then, whenever possible, a validation against both fasted and fed state data is recommended before application of the models prospectively for to-be-marketed formulations. Thus, when combined with limited clinical data, PBPK models could be used to simulate outcomes for new doses, formulations, or active pharmaceutical ingredient forms, in lieu of a clinical food-effect study.

Comparing Mechanistic and Preclinical Predictions of Volume of Distribution on a Large Set of Drugs

PURPOSE

Volume of distribution at steady state (Vd_ss) is a fundamental pharmacokinetic (PK) parameter driven predominantly by passive processes and physicochemical properties of the compound. Human Vd_ss can be estimated using in silico mechanistic methods or empirically scaled from Vd_ss values obtained from preclinical species. In this study the accuracy and the complementarity of these two approaches are analyzed leveraging a large data set (over 150 marketed drugs).

METHODS

For all the drugs analyzed in this study experimental in vitro measurements of LogP, plasma protein binding and pKa are used as input for the mechanistic in silico model to predict human Vd_ss. The software used for predicting human tissue partition coefficients and Vd_ss based on the method described by Rodgers and Rowland is made available as supporting information.

RESULTS

This assessment indicates that overall the in silico mechanistic model presented by Rodgers and Rowland is comparably accurate or superior to empirical approaches based on the extrapolation of in vivo data from preclinical species.

CONCLUSIONS

These results illustrate the great potential of mechanistic in silico models to accurately predict Vd_ss in humans. This in silico method does not rely on in vivo data and is, consequently, significantly time and resource sparing. The success of this in silico model further suggests that reasonable predictability of Vd_ss in preclinical species could be obtained by a similar process.

Mechanistic investigations into the species differences in pinometostat clearance: impact of binding to alpha-1-acid glycoprotein and permeability-limited hepatic uptake

1. The plasma clearance of the first-in-class DOT1L inhibitor, EPZ-5676 (pinometostat), was shown to be markedly lower in human compared to the preclinical species, mouse, rat and dog. 2. This led to vertical allometry where various interspecies scaling methods were applied to the data, with fold-errors between 4 and 13. We had previously reported the elimination and metabolic pathways of EPZ-5676 were similar across species. Therefore, the aim of this work was to explore the mechanistic basis for the species difference in clearance for EPZ-5676, focusing on other aspects of disposition. 3. The protein binding of EPZ-5676 in human plasma demonstrated a non-linear relationship suggesting saturable binding at physiologically relevant concentrations. Saturation of protein binding was not observed in plasma from preclinical species. Kinetic determinations using purified serum albumin and alpha-1-acid glycoprotein (AAG) confirmed that EPZ-5676 is a high affinity ligand for AAG with a dissociation constant (K_d) of 0.24 μM. 4. Permeability limited uptake was also considered since hepatocyte CL_int was much lower in human relative to preclinical species. Passive unbound CL_int for EPZ-5676 was estimated using a correlation analysis of logD and data previously reported on seven drugs in sandwich cultured human hepatocytes. 5. Incorporation of AAG binding and permeability limited hepatic uptake into the well-stirred liver model gave rise to a predicted clearance for EPZ-5676 within 2-fold of the observed value of 1.4 mL min^-1kg^-1. This analysis suggests that the marked species difference in EPZ-5676 clearance is driven by high affinity binding to human AAG as well as species-specific hepatic uptake invoking the role of transporters.

Deconvolution and IVIVC: Exploring the Role of Rate-Limiting Conditions

In vitro-in vivo correlations (IVIVCs) play an important role in formulation development and drug approval. At the heart of IVIVC is deconvolution, the method of deriving an in vivo “dissolution profile” for comparison with in vitro dissolution data. IVIVCs are generally believed to be possible for highly permeable and highly soluble compounds with release/dissolution as the rate-limiting step. In this manuscript, we apply the traditional deconvolution methods, Wagner-Nelson and numerical deconvolution, to profiles simulated using a simplified small intestine absorption and transit model. Small intestinal transit, dissolution, and absorption rate constants are varied across a range of values approximately covering those observed in the literature. IVIVC plots and their corresponding correlation coefficients are analyzed for each combination of parameters to determine the applicability of the deconvolution methods under a range of rate-limiting conditions. For highly absorbed formulations, the correlation coefficients obtained during IVIVC are comparable for both methods and steadily decline with decreasing dissolution rate and increasing transit rate. The applicability of numerical deconvolution to IVIVC is not greatly affected by absorption rate, whereas the applicability of Wagner-Nelson falls when dissolution rate overcomes absorption rate and absorption becomes the rate-limiting step. The discrepancy between the expected and deconvolved input arises from the violation of a key assumption of deconvolution that the unknown input and unit impulse enter the system in the same location.

Optimization of human dose prediction by using quantitative and translational pharmacology in drug discovery

In this perspective article, we explain how quantitative and translational pharmacology, when well-implemented, is believed to lead to improved clinical candidates and drug targets that are differentiated from current treatment options. Quantitative and translational pharmacology aims to build and continuously improve the quantitative relationship between drug exposure, target engagement, efficacy, safety and its interspecies relationship at every phase of drug discovery. Drug hunters should consider and apply these concepts to develop compounds with a higher probability of interrogating the clinical biological hypothesis. We offer different approaches to set an initial effective concentration or pharmacokinetic-pharmacodynamic target in man and to predict human pharmacokinetics that determine together the predicted human dose and dose schedule. All concepts are illustrated with ample literature examples.

Human absorption, distribution, metabolism and excretion properties of drug molecules: a plethora of approaches

Human radiolabel studies are traditionally conducted to provide a definitive understanding of the human absorption, distribution, metabolism and excretion (ADME) properties of a drug. However, advances in technology over the past decade have allowed alternative methods to be employed to obtain both clinical ADME and pharmacokinetic (PK) information. These include microdose and microtracer approaches using accelerator mass spectrometry, and the identification and quantification of metabolites in samples from classical human PK studies using technologies suitable for non-radiolabelled drug molecules, namely liquid chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy. These recently developed approaches are described here together with relevant examples primarily from experiences gained in support of drug development projects at GlaxoSmithKline. The advantages of these study designs together with their limitations are described. We also discuss special considerations which should be made for a successful outcome to these new approaches and also to the more traditional human radiolabel study in order to maximize knowledge around the human ADME properties of drug molecules.

Prediction of drug disposition in diabetic patients by means of a physiologically based pharmacokinetic model

BACKGROUND AND OBJECTIVE

Accumulating evidence has shown that diabetes mellitus may affect the pharmacokinetics of some drugs, leading to alteration of pharmacodynamics and/or toxic effects. The aim of this study was to develop a novel physiologically based pharmacokinetic (PBPK) model for predicting drug pharmacokinetics in patients with type 2 diabetes mellitus quantitatively.

METHODS

Contributions of diabetes-induced alteration of physiological parameters including gastric emptying rates, intestinal transit time, drug metabolism in liver and kidney functions were incorporated into the model. Plasma concentration-time profiles and pharmacokinetic parameters of seven drugs (antipyrine, nisoldipine, repaglinide, glibenclamide, glimepiride, chlorzoxazone, and metformin) in non-diabetic and diabetic patients were predicted using the developed model. The PBPK model coupled with a Monte-Carlo simulation was also used to predict the means and variability of pharmacokinetic parameters.

RESULTS

The predicted area under the plasma concentration-time curve (AUC) and maximum (peak) concentration (C max) were reasonably consistent (<2-fold errors) with the reported values. Sensitivity analysis showed that gut transit time, hepatic enzyme activity, and renal function affected the pharmacokinetic characteristics of these drugs. Shortened gut transit time only decreased the AUC of controlled-released drugs and drugs with low absorption rates. Impairment of renal function markedly altered pharmacokinetics of drugs mainly eliminated via the kidneys.

CONCLUSION

All of these results indicate that the developed PBPK model can quantitatively predict pharmacokinetic alterations induced by diabetes.

Characterization of preclinical in vitro and in vivo pharmacokinetics properties for KBP-7018, a new tyrosine kinase inhibitor candidate for treatment of idiopathic pulmonary fibrosis

KBP-7018 is a novel selective tyrosine kinase inhibitor with potential for the treatment of idiopathic pulmonary fibrosis. The objective of this study was to characterize the preclinical pharmacokinetics of KBP-7018 in vitro and in vivo, and then to assess the likelihood of developing KBP-7018 as a clinical candidate. The systemic clearance (CL) of KBP-7018 was relatively low in rodents and monkeys with a value of less than 30% of hepatic blood flow, while it was high in dogs. The steady-state volume of distribution (V_ss) ranged from 1.51 L/kg to 4.65 L/kg across the species tested. The maximum concentration (C_max) of KBP-7018 occurred at 0.25–6 hours after oral dosing, and the bioavailability was moderate (21%–68%). The human CL (~20% of hepatic blood flow) and V_ss (1.6–5.3 L/kg) were predicted by allometric scaling method and together with the other modeling methods indicated low metabolism and acceptable half-time (4.8–19.3 hours) in vivo. Overall, the preclinical data make it amenable to further oral solid dosage from design for the upcoming Phase I trials in human.

Quantifying and Communicating Uncertainty in Preclinical Human Dose-Prediction

Human dose-prediction is fundamental for ranking lead-optimization compounds in drug discovery and to inform design of early clinical trials. This tutorial describes how uncertainty in such predictions can be quantified and efficiently communicated to facilitate decision-making. Using three drug-discovery case studies, we show how several uncertain pieces of input information can be integrated into one single uncomplicated plot with key predictions, including their uncertainties, for many compounds or for many scenarios, or both.

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

BACKGROUND

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values.

RESULTS

Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied.

CONCLUSIONS

Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

Preclinical pharmacokinetic/pharmacodynamic modeling and simulation in the pharmaceutical industry: an IQ consortium survey examining the current landscape

The application of modeling and simulation techniques is increasingly common in preclinical stages of the drug discovery and development process. A survey focusing on preclinical pharmacokinetic/pharmacodynamics (PK/PD) analysis was conducted across pharmaceutical companies that are members of the International Consortium for Quality and Innovation in Pharmaceutical Development. Based on survey responses, ~68% of companies use preclinical PK/PD analysis in all therapeutic areas indicating its broad application. An important goal of preclinical PK/PD analysis in all pharmaceutical companies is for the selection/optimization of doses and/or dose regimens, including prediction of human efficacious doses. Oncology was the therapeutic area with the most PK/PD analysis support and where it showed the most impact. Consistent use of more complex systems pharmacology models and hybrid physiologically based pharmacokinetic models with PK/PD components was less common compared to traditional PK/PD models. Preclinical PK/PD analysis is increasingly being included in regulatory submissions with ~73% of companies including these data to some degree. Most companies (~86%) have seen impact of preclinical PK/PD analyses in drug development. Finally, ~59% of pharmaceutical companies have plans to expand their PK/PD modeling groups over the next 2 years indicating continued growth. The growth of preclinical PK/PD modeling groups in pharmaceutical industry is necessary to establish required resources and skills to further expand use of preclinical PK/PD modeling in a meaningful and impactful manner.

In vitro platforms for evaluating liver toxicity

The liver is a heterogeneous organ with many vital functions, including metabolism of pharmaceutical drugs and is highly susceptible to injury from these substances. The etiology of drug-induced liver disease is still debated although generally regarded as a continuum between an activated immune response and hepatocyte metabolic dysfunction, most often resulting from an intermediate reactive metabolite. This debate stems from the fact that current animal and in vitro models provide limited physiologically relevant information, and their shortcomings have resulted in "silent" hepatotoxic drugs being introduced into clinical trials, garnering huge financial losses for drug companies through withdrawals and late stage clinical failures. As we advance our understanding into the molecular processes leading to liver injury, it is increasingly clear that (a) the pathologic lesion is not only due to liver parenchyma but is also due to the interactions between the hepatocytes and the resident liver immune cells, stellate cells, and endothelial cells; and (b) animal models do not reflect the human cell interactions. Therefore, a predictive human, in vitro model must address the interactions between the major human liver cell types and measure key determinants of injury such as the dosage and metabolism of the drug, the stress response, cholestatic effect, and the immune and fibrotic response. In this mini-review, we first discuss the current state of macro-scale in vitro liver culture systems with examples that have been commercialized. We then introduce the paradigm of microfluidic culture systems that aim to mimic the liver with physiologically relevant dimensions, cellular structure, perfusion, and mass transport by taking advantage of micro and nanofabrication technologies. We review the most prominent liver-on-a-chip platforms in terms of their physiological relevance and drug response. We conclude with a commentary on other critical advances such as the deployment of fluorescence-based biosensors to identify relevant toxicity pathways, as well as computational models to create a predictive tool.

Contribution of cytochrome P450 and UDT-glucuronosyltransferase to the metabolism of drugs containing carboxylic acid groups: risk assessment of acylglucuronides using human hepatocytes

1. In order to evaluate the inhibition activity of 1-aminobenzotriazole (ABT) and (-)-borneol (borneol) against cytochrome P450 (CYP) and UDP-glucuronosyltransferase (UGT), the substrates of these metabolic enzymes were incubated with ABT and borneol in human hepatocytes. We found that 3 mM ABT and 300 μM borneol were the most suitable experimental levels to specifically inhibit CYP and UGT. 2. Montelukast, mefenamic acid, flufenamic acid, diclofenac, tienilic acid, gemfibrozil, ibufenac and repaglinide were markedly metabolized in human hepatocytes, and the metabolism of gemfibrozil, mefenamic acid and flufenamic acid was inhibited by borneol. With regard to repaglinide, montelukast, diclofenac and tienilic acid, metabolism was inhibited by ABT. Ibufenac was partly inhibited by both inhibitors. Zomepirac, tolmetin, ibuprofen, indomethacin and levofloxacin were moderately metabolized by human hepatocytes, and the metabolism of zomepirac, ibuprofen and indomethacin was equally inhibited by both ABT and borneol. The metabolism of tolmetin was strongly inhibited by ABT, and was also inhibited weakly by borneol. Residual drugs, telmisartan, valsartan, furosemide, naproxen and probenecid were scarcely metabolized. 3. Although we attempted to predict the toxicological risks of drugs containing carboxylic groups from the combination chemical stability and CLint via UGT, the results indicated that this combination was not sufficient and that clinical daily dose is important.

Prediction of drug disposition on the basis of its chemical structure

The chemical structure of any drug determines its pharmacokinetics and pharmacodynamics. Detailed understanding of relationships between the drug chemical structure and individual disposition pathways (i.e., distribution and elimination) is required for efficient use of existing drugs and effective development of new drugs. Different approaches have been developed for this purpose, ranging from statistics-based quantitative structure-property (or structure-pharmacokinetic) relationships (QSPR) analysis to physiologically based pharmacokinetic (PBPK) models. This review critically analyzes currently available approaches for analysis and prediction of drug disposition on the basis of chemical structure. Models that can be used to predict different aspects of disposition are presented, including: (a) value of the individual pharmacokinetic parameter (e.g., clearance or volume of distribution), (b) efficiency of the specific disposition pathway (e.g., biliary drug excretion or cytochrome P450 3A4 metabolism), (c) accumulation in a specific organ or tissue (e.g., permeability of the placenta or accumulation in the brain), and (d) the whole-body disposition in the individual patients. Examples of presented pharmacological agents include "classical" low-molecular-weight compounds, biopharmaceuticals, and drugs encapsulated in specialized drug-delivery systems. The clinical efficiency of agents from all these groups can be suboptimal, because of inefficient permeability of the drug to the site of action and/or excessive accumulation in other organs and tissues. Therefore, robust and reliable approaches for chemical structure-based prediction of drug disposition are required to overcome these limitations. PBPK models are increasingly being used for prediction of drug disposition. These models can reflect the complex interplay of factors that determine drug disposition in a mechanistically correct fashion and can be combined with other approaches, for example QSPR-based prediction of drug permeability and metabolism, pharmacogenomic data and tools, pharmacokinetic-pharmacodynamic modeling approaches, etc. Moreover, the PBPK models enable detailed analysis of clinically relevant scenarios, for example the effect of the specific conditions on the time course of the analyzed drug in the individual organs and tissues, including the site of action. It is expected that further development of such combined approaches will increase their precision, enhance the effectiveness of drugs, and lead to individualized drug therapy for different patient populations (geriatric, pediatric, specific diseases, etc.).

A perspective on the prediction of drug pharmacokinetics and disposition in drug research and development

Prediction of human pharmacokinetics of new drugs, as well as other disposition attributes, has become a routine practice in drug research and development. Prior to the 1990s, drug disposition science was used in a mostly descriptive manner in the drug development phase. With the advent of in vitro methods and availability of human-derived reagents for in vitro studies, drug-disposition scientists became engaged in the compound design phase of drug discovery to optimize and predict human disposition properties prior to nomination of candidate compounds into the drug development phase. This has reaped benefits in that the attrition rate of new drug candidates in drug development for reasons of unacceptable pharmacokinetics has greatly decreased. Attributes that are predicted include clearance, volume of distribution, half-life, absorption, and drug-drug interactions. In this article, we offer our experience-based perspectives on the tools and methods of predicting human drug disposition using in vitro and animal data.

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.

Pharmacokinetics, pharmacodynamics and physiologically-based pharmacokinetic modelling of monoclonal antibodies

Development of monoclonal antibodies (mAbs) and their functional derivatives represents a growing segment of the development pipeline in the pharmaceutical industry. More than 25 mAbs and derivatives have been approved for a variety of therapeutic applications. In addition, around 500 mAbs and derivatives are currently in different stages of development. mAbs are considered to be large molecule therapeutics (in general, they are 2-3 orders of magnitude larger than small chemical molecule therapeutics), but they are not just big chemicals. These compounds demonstrate much more complex pharmacokinetic and pharmacodynamic behaviour than small molecules. Because of their large size and relatively poor membrane permeability and instability in the conditions of the gastrointestinal tract, parenteral administration is the most usual route of administration. The rate and extent of mAb distribution is very slow and depends on extravasation in tissue, distribution within the particular tissue, and degradation. Elimination primarily happens via catabolism to peptides and amino acids. Although not definitive, work has been published to define the human tissues mainly involved in the elimination of mAbs, and it seems that many cells throughout the body are involved. mAbs can be targeted against many soluble or membrane-bound targets, thus these compounds may act by a variety of mechanisms to achieve their pharmacological effect. mAbs targeting soluble antigen generally exhibit linear elimination, whereas those targeting membrane-bound antigen often exhibit non-linear elimination, mainly due to target-mediated drug disposition (TMDD). The high-affinity interaction of mAbs and their derivatives with the pharmacological target can often result in non-linear pharmacokinetics. Because of species differences (particularly due to differences in target affinity and abundance) in the pharmacokinetics and pharmacodynamics of mAbs, pharmacokinetic/pharmacodynamic modelling of mAbs has been used routinely to expedite the development of mAbs and their derivatives and has been utilized to help in the selection of appropriate dose regimens. Although modelling approaches have helped to explain variability in both pharmacokinetic and pharmacodynamic properties of these drugs, there is a clear need for more complex models to improve understanding of pharmacokinetic processes and pharmacodynamic interactions of mAbs with the immune system. There are different approaches applied to physiologically based pharmacokinetic (PBPK) modelling of mAbs and important differences between the models developed. Some key additional features that need to be accounted for in PBPK models of mAbs are neonatal Fc receptor (FcRn; an important salvage mechanism for antibodies) binding, TMDD and lymph flow. Several models have been described incorporating some or all of these features and the use of PBPK models are expected to expand over the next few years.

Microdosing and drug development: past, present and future

INTRODUCTION

Microdosing is an approach to early drug development where exploratory pharmacokinetic data are acquired in humans using inherently safe sub-pharmacologic doses of drug. The first publication of microdose data was 10 years ago and this review comprehensively explores the microdose concept from conception, over the past decade, up until the current date.

AREAS COVERED

The authors define and distinguish the concept of microdosing from similar approaches. The authors review the ability of microdosing to provide exploratory pharmacokinetics (concentration-time data) but exclude microdosing using positron emission tomography. The article provides a comprehensive review of data within the peer-reviewed literature as well as the latest applications and a look into the future, towards where microdosing may be headed.

EXPERT OPINION

Evidence so far suggests that microdosing may be a better predictive tool of human pharmacokinetics than alternative methods and combination with physiologically based modelling may lead to much more reliable predictions in the future. The concept has also been applied to drug-drug interactions, polymorphism and assessing drug concentrations over time at its site of action. Microdosing may yet have more to offer in unanticipated directions and provide benefits that have not been fully realised to date.

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

1.

RATIONALE

Tissue-to-plasma partition coefficients (Kp) that characterize the tissue distribution of a drug are important input parameters in physiologically based pharmacokinetic (PBPK) models. The aim of this study was to develop an empirically derived Kp prediction algorithm using input parameters that are available early in the investigation of a compound. 2.

METHODS

The algorithm development dataset (n = 97 compounds) was divided according to acidic/basic properties. Using multiple stepwise regression, the experimentally derived Kp values were correlated with the rat volume of distribution at steady state (Vss) and one or more physicochemical parameters (e.g. lipophilicity, degree of ionization and protein binding) to account for inter-organ variability of tissue distribution. 3.

RESULTS

Prediction equations for the value of Kp were developed for 11 tissues. Validation of this model using a test dataset (n = 20 compounds) demonstrated that 65% of the predicted Kp values were within a two-fold error deviation from the experimental values. The developed algorithms had greater prediction accuracy compared to an existing empirically derived and a mechanistic tissue-composition algorithm. 4.

CONCLUSIONS

This innovative method uses readily available input parameters with reasonable prediction accuracy and will thus enhance both the usability and the confidence in the outputs of PBPK models.

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.

Nilotinib preclinical pharmacokinetics and practical application toward clinical projections of oral absorption and systemic availability

Nilotinib is a highly potent and selective bcr-abl tyrosine kinase inhibitor used for the treatment of patients who are in the chronic and accelerated phases of Philadelphia chromosome-positive (Ph+) chronic myeloid leukemia (CML). Nilotinib preclinical data and its use for practical predictions of systemic exposure profiles and oral absorption are described. The systemic clearance (CL) of nilotinib was relatively low in rodents with a value of less than 25% of hepatic blood flow (Q(H)), while it was moderate in monkeys and dogs (CL/Q(H)  = 32-35%). The steady state volume of distribution (V(ss) ) ranged from 0.55 to 3.9 l/kg across the species tested. The maximum concentration (C(max)) of nilotinib occurred at 0.5-4 h and the bioavailability was moderate (17-44%). The plasma protein binding was high (> 97.5%) in preclinical species and humans. The human CL (~ 0.1 l/h/kg) and V(ss) (~2.0 l/kg) were best predicted by the rat-dog-human proportionality method and allometric scaling method, respectively. The human intravenous pharmacokinetic profile was projected by the Wajima 'C(ss)-MRT' method. The predicted micro-constants from human intravenous profiles were incorporated into the advanced compartmental absorption and transit model within the GastroPlus program to simulate the oral concentration-time curves in humans. Overall, the simulated oral human pharmacokinetic profiles showed good agreement with observed clinical data, and the model predicted that the C(max) , AUC, t(½) , V(z) /F and CL/F values were within 1.3-fold of the observed values. The absolute oral bioavailability of nilotinib in healthy humans was predicted to be low (< 25%).

Case studies for practical food effect assessments across BCS/BDDCS class compounds using in silico, in vitro, and preclinical in vivo data

Practical food effect predictions and assessments were described using in silico, in vitro, and/or in vivo preclinical data to anticipate food effects and Biopharmaceutics Classification System (BCS)/Biopharmaceutics Drug Disposition Classification System (BDDCS) class across drug development stages depending on available data: (1) limited in silico and in vitro data in early discovery; (2) preclinical in vivo pharmacokinetic, absorption, and metabolism data at candidate selection; and (3) physiologically based absorption modeling using biorelevant solubility and precipitation data to quantitatively predict human food effects, oral absorption, and pharmacokinetic profiles for early clinical studies. Early food effect predictions used calculated or measured physicochemical properties to establish a preliminary BCS/BDDCS class. A rat-based preclinical BCS/BDDCS classification used rat in vivo fraction absorbed and metabolism data. Biorelevant solubility and precipitation kinetic data were generated via animal pharmacokinetic studies using advanced compartmental absorption and transit (ACAT) models or in vitro methods. Predicted human plasma concentration-time profiles and the magnitude of the food effects were compared with observed clinical data for assessment of simulation accuracy. Simulations and analyses successfully identified potential food effects across BCS/BDDCS classes 1-4 compounds with an average fold error less than 1.6 in most cases. ACAT physiological absorption models accurately predicted positive food effects in human for poorly soluble bases after oral dosage forms. Integration of solubility, precipitation time, and metabolism data allowed confident identification of a compound's BCS/BDDCS class, its likely food effects, along with prediction of human exposure profiles under fast and fed conditions.

Drug delivery trends in clinical trials and translational medicine: growth in biologic molecule development and impact on rheumatoid arthritis, Crohn's disease, and colitis

There are 94,709 clinical trials across 179 countries. Approximately half (47,467) are related to the three categories within the scope of the free online resource "Drug Delivery Trends in Clinical Trials and Translational Medicine," which are (1) drug delivery technology and systems, (2) biological molecule platforms, and (3) pharmacokinetic and pharmacodynamic interactions. In this commentary, trends in biological molecule platforms and their impacts are discussed. The sales of top 15 biologic drugs have reached over $63 billion in 2010. In the past 10 years, major pharmaceutical companies have acquired biological molecule platforms and have become integrated biopharmaceutical companies, highlighting the role of biotechnology in driving new therapeutic product development. The top three products--Remicade, Enbrel, and Humira--indicated for arthritis and colitis and targeted to tumor necrosis factor-alpha (TNF-α), each generated over $6 billion in annual sales. In addition to TNF-α, biologic candidates targeted to other inflammatory molecules are in clinical development, partly driven by commercial interests and medical need. Although clinical experience indicates that all the anti-TNF-α molecular platforms are effective for rheumatoid arthritis, Crohn's disease, and colitis, whether the new agents can provide additional relief or cures remains to be seen.

Application of IVIVE and PBPK modeling in prospective prediction of clinical pharmacokinetics: strategy and approach during the drug discovery phase with four case studies

Prospective simulations of human pharmacokinetic (PK) parameters and plasma concentration-time curves using in vitro in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models are becoming a vital part of the drug discovery and development process. This paper presents a strategy to deliver prospective simulations in support of clinical candidate nomination. A three stage approach of input parameter evaluation, model selection and multiple scenario simulation is utilized to predict the key components influencing human PK; absorption, distribution and clearance. The Simcyp® simulator is used to illustrate the approach and four compounds are presented as case studies. In general, the prospective predictions captured the observed clinical data well. Predicted C(max) was within 2-fold of observed data for all compounds and AUC was within 2-fold for all compounds following a single dose and three out of four compounds following multiple doses. Similarly, t(max) was within 2-fold of observed data for all compounds. However, C(last) was less accurately captured with two of the four compounds predicting C(last) within 2-fold of observed data following a single dose. The trend in results was towards overestimation of AUC and C(last) , this was particularly apparent for compound 2 for which clearance was likely underestimated via IVIVE. The prospective approach to simulating human PK using IVIVE and PBPK modeling outlined here attempts to utilize all available in silico, in vitro and in vivo preclinical data in order to determine the most appropriate assumptions to use in prospective predictions of absorption, distribution and clearance to aid clinical candidate nomination.

Comparison of in-vivo and in-silico methods used for prediction of tissue: plasma partition coefficients in rat

OBJECTIVES

To use methods from the literature to predict rat tissue:plasma partition coefficients (Kps) and volume of distribution values. Determine which model provides the most accurate predictions to increase confidence in the use of predicted pharmacokinetic parameters in physiologically based pharmacokinetic modelling.

METHODS

Six models were used to predict Kps and four to predict V(ss) for a dataset of 81 compounds in 11 rat tissues, and the predictions were compared with experimentally derived values.

KEY FINDINGS

Kp predictions made by the Rodgers et al. model were the most accurate, with 77% within threefold of experimental values. The Poulin & Theil model was the most accurate for the prediction of V(ss) , with 87% of predictions within threefold.

CONCLUSIONS

This study has shown that in-silico models available in the literature can be used to accurately predict Kp and V(ss) in rat. The Rodgers et al. model has been shown to provide the most accurate Kp predictions, with consistent accuracy across all drug classes and tissues. It was also the most accurate V(ss) predictor when no in-vivo data were used as input. However, transporter systems and other mechanisms that are not yet fully understood need to be incorporated into these types of models in the future to further increase their applicability.