Physiologically based pharmacokinetic (PBPK) modeling of disposition of epiroprim in humans

A New Version of the Tissue Composition-Based Model for Improving the Mechanism-Based Prediction of Volume of Distribution at Steady-State for Neutral Drugs

In-vitro models are available in the literature for predicting the volume of distribution at steady-state (Vdss) of drugs. The mechanistic model refers to the tissue composition-based model (TCM), which includes important factors that govern Vdss such as drug physiochemistry and physiological data. The recognized TCM published by Rodgers and Rowland (TCM-RR) and a subsequent adjustment made by Simulations Plus Inc. (TCM-SP) have been shown to be generally less accurate with neutral compared to ionized drugs. Therefore, improving these models for neutral drugs becomes necessary. The objective of this study was to propose a new TCM for improving the prediction of Vdss for neutral drugs. The new TCM included two modifications of the published models (i) accentuate the effect of the blood-to-plasma ratio (BPR) that should cover permeated molecules across the biomembranes, which is lacking in these models for neutral compounds, and (ii) use a different approach to estimate the binding in tissues. The new TCM was validated with a large dataset of 202 commercial and proprietary compounds including preclinical and clinical data. All scenario datasets were predicted more accurately with the TCM-New, whereas all statistical parameters indicate that the TCM-New showed significant improvements in terms of accuracy over the TCM-RR and TCM-SP. Predictions of Vdss were frequently more accurate for the TCM-new with 83% within twofold error versus only 50% for the TCM-RR. And more than 95% of the predictions were within threefold error and patient interindividual differences can be predicted with the TCM-New, greatly exceeding the accuracy of the published models. Overall, the new TCM incorporating BPR significantly improved the Vdss predictions in animals and humans for neutral drugs, and, hence, has the potential to better support the drug discovery and facilitate the first-in-human predictions.

Reconstruction of exposure to methylene diphenyl-4,4'-diisocyanate (MDI) aerosol using computational fluid dynamics, physiologically based toxicokinetics and statistical modeling

OBJECTIVES

This study employed computational fluid dynamics (CFD), physiologically based toxicokinetics (PBTK), and statistical modeling to reconstruct exposure to methylene diphenyl-4,4'-diisocyanate (MDI) aerosol. By utilizing a validated CFD model, human respiratory deposition of MDI aerosol in different workload conditions was investigated, while a PBTK model was calibrated using experimental rat data. Biomonitoring data and Markov Chain Monte Carlo (MCMC) simulation were utilized for exposure assessment.

RESULTS

Deposition fraction of MDI in the respiratory tract at the light, moderate, and heavy activity were 0.038, 0.079, and 0.153, respectively. Converged MCMC results as the posterior means and prior values were obtained for several PBTK model parameters. In our study, we calibrated a rat model to investigate the transport, absorption, and elimination of 4,4'-MDI via inhalation exposure. The calibration process successfully captured experimental data in the lungs, liver, blood, and kidneys, allowing for a reasonable representation of MDI distribution within the rat model. Our calibrated model also represents MDI dynamics in the bloodstream, facilitating the assessment of bioavailability. For human exposure, we validated the model for recent and long-term MDI exposure using data from relevant studies.

CONCLUSION

Our computational models provide reasonable insights into MDI exposure, contributing to informed risk assessment and the development of effective exposure reduction strategies.

Investigation of simplified physiologically-based pharmacokinetic models in rat and human

Whole-body physiologically-based pharmacokinetic (PBPK) models have many applications in drug research and development. It is often necessary to inform these models with animal or clinical data, updating model parameters, and making the model more predictive for future applications. This provides an opportunity and a challenge given the large number of parameters of such models. The aim of this work was to propose new mechanistic model structures with reduced complexity allowing for parameter optimization. These models were evaluated for their ability to estimate realistic values for unbound tissue to plasma partition coefficients (Kpu) and simulate observed pharmacokinetic (PK) data. Two approaches are presented: using either established kinetic lumping methods based on tissue time constants (drug-dependent) or a novel clustering analysis to identify tissues sharing common Kpu values or Kpu scalars based on similarities of tissue composition (drug-independent). PBPK models derived from these approaches were assessed using PK data of diazepam in rats and humans. Although the clustering analysis produced minor differences in tissue grouping depending on the method used, two larger groups of tissues emerged. One including the kidneys, liver, spleen, gut, heart, and lungs, and another including bone, brain, muscle, and pancreas whereas adipose and skin were generally considered distinct. Overall, a subdivision into four tissue groups appeared most physiologically relevant in terms of tissue composition. Several models were found to have similar abilities to describe diazepam i.v. data as empirical models. Comparability of estimated Kpus to experimental Kpu values for diazepam was one criterion for selecting the appropriate PK model structure.

Prediction of human pharmacokinetics for low-clearance compounds using pharmacokinetic data from chimeric mice with humanized livers

Development of low-clearance (CL) compounds that can be slowly metabolized is a major goal in the pharmaceutical industry. However, the pursuit of low intrinsic CL (CL_int ) often leads to significant challenges in evaluating the pharmacokinetics of such compounds. Although in vitro-in vivo extrapolation is widely used to predict human CL, its application has been limited for low-CL_int compounds because of the low turnover of parent compounds in metabolic stability assays. To address this issue, we focused on chimeric mice with humanized livers (PXB-mice), which have been increasingly reported to accurately predict human CL in recent years. The predictive accuracy for nine low-CL_int compounds with no significant turnover in a human hepatocyte assay was investigated using PXB-mouse methods such as single-species allometric scaling (PXB-SSS) approach and a novel physiologically based scaling (PXB-PBS) approach that assumes that the CL_int per hepatocyte is equal between humans and PXB-mice. The percentages of compounds with predicted CL within 2- and 3-fold ranges of the observed CL for low-CL_int compounds were 89% and 100%, respectively, for both PXB-SSS and PXB-PBS approaches. Moreover, the predicted CL was mostly consistent among the methods. Conversely, percentages of compounds with predicted CL within 2- and 3-fold ranges of the observed CL for low-CL_int compounds were 50% and 63%, respectively for multispecies allometric scaling (MA). Overall, these PXB-mouse methods were much more accurate than conventional MA approaches, suggesting that PXB-mice are useful tool for predicting the human CL of low-CL_int compounds that are slowly metabolized.

Tissue and interspecies comparison of catechol-O-methyltransferase mediated catalysis of 6-O-methylation of esculetin to scopoletin and its inhibition by entacapone and tolcapone

Catechol-O-methyltransferase (COMT) methylates both endogenous and exogenous catechol compounds to inactive and safe metabolites. We first optimised conditions for a convenient and sensitive continuous fluorescence-based 6-O-methylation assay of esculetin, which we used for investigating the COMT activity in human, mouse, rat, dog, rabbit, and sheep liver cytosols and microsomes and in ten different rat tissues. Furthermore, we compared the inhibition potencies and mechanisms of two clinically used COMT inhibitors, entacapone and tolcapone, in these species. In most tissues, the COMT activity was at least three times higher in cytosol than in microsomes. In the rat, the highest COMT activity was found in the liver, followed by kidney, ileum, thymus, spleen, lung, pancreas, heart, brain, and finally, skeletal muscle. Entacapone and tolcapone were characterised as highly potent mixed type tight-binding inhibitors. The competitive inhibition type dominated over the uncompetitive inhibition with entacapone, whereas uncompetitive inhibition dominated with tolcapone. Rats, dogs, pigs, and sheep are high COMT activity species, in contrast to humans, mice, and rabbits; COMT activity is highest in the liver. Both entacapone and tolcapone are potent COMT inhibitors, but their inhibition mechanisms differ.

Discovery and Characterization of AZD6738, a Potent Inhibitor of Ataxia Telangiectasia Mutated and Rad3 Related (ATR) Kinase with Application as an Anticancer Agent

The kinase ataxia telangiectasia mutated and rad3 related (ATR) is a key regulator of the DNA-damage response and the apical kinase which orchestrates the cellular processes that repair stalled replication forks (replication stress) and associated DNA double-strand breaks. Inhibition of repair pathways mediated by ATR in a context where alternative pathways are less active is expected to aid clinical response by increasing replication stress. Here we describe the development of the clinical candidate 2 (AZD6738), a potent and selective sulfoximine morpholinopyrimidine ATR inhibitor with excellent preclinical physicochemical and pharmacokinetic (PK) characteristics. Compound 2 was developed improving aqueous solubility and eliminating CYP3A4 time-dependent inhibition starting from the earlier described inhibitor 1 (AZ20). The clinical candidate 2 has favorable human PK suitable for once or twice daily dosing and achieves biologically effective exposure at moderate doses. Compound 2 is currently being tested in multiple phase I/II trials as an anticancer agent.

Physiologically based pharmacokinetic modeling of tea catechin mixture in rats and humans

Although green tea (Camellia sinensis) (GT) contains a large number of polyphenolic compounds with anti-oxidative and anti-proliferative activities, little is known of the pharmacokinetics and tissue dose of tea catechins (TCs) as a chemical mixture in humans. The objectives of this study were to develop and validate a physiologically based pharmacokinetic (PBPK) model of tea catechin mixture (TCM) in rats and humans, and to predict an integrated or total concentration of TCM in the plasma of humans after consuming GT or Polyphenon E (PE). To this end, a PBPK model of epigallocatechin gallate (EGCg) consisting of 13 first-order, blood flow-limited tissue compartments was first developed in rats. The rat model was scaled up to humans by replacing its physiological parameters, pharmacokinetic parameters and tissue/blood partition coefficients (PCs) with human-specific values. Both rat and human EGCg models were then extrapolated to other TCs by substituting its physicochemical parameters, pharmacokinetic parameters, and PCs with catechin-specific values. Finally, a PBPK model of TCM was constructed by linking three rat (or human) tea catechin models together without including a description for pharmacokinetic interaction between the TCs. The mixture PBPK model accurately predicted the pharmacokinetic behaviors of three individual TCs in the plasma of rats and humans after GT or PE consumption. Model-predicted total TCM concentration in the plasma was linearly related to the dose consumed by humans. The mixture PBPK model is able to translate an external dose of TCM into internal target tissue doses for future safety assessment and dose-response analysis studies in humans. The modeling framework as described in this paper is also applicable to the bioactive chemical in other plant-based health products.

Prediction of Deoxypodophyllotoxin Disposition in Mouse, Rat, Monkey, and Dog by Physiologically Based Pharmacokinetic Model and the Extrapolation to Human

Deoxypodophyllotoxin (DPT) is a potential anti-tumor candidate prior to its clinical phase. The aim of the study was to develop a physiologically based pharmacokinetic (PBPK) model consisting of 13 tissue compartments to predict DPT disposition in mouse, rat, monkey, and dog based on in vitro and in silico inputs. Since large interspecies difference was found in unbound fraction of DPT in plasma, we assumed that K_t:pl,u (unbound tissue-to-plasma concentration ratio) was identical across species. The predictions of our model were then validated by in vivo data of corresponding preclinical species, along with visual predictive checks. Reasonable matches were found between observed and predicted plasma concentrations and pharmacokinetic parameters in all four animal species. The prediction in the related seven tissues of mouse was also desirable. We also attempted to predict human pharmacokinetic profile by both the developed PBPK model and interspecies allometric scaling across mouse, rat and monkey, while dog was excluded from the scaling. The two approaches reached similar results. We hope the study will help in the efficacy and safety assessment of DPT in future clinical studies and provide a reference to the preclinical screening of similar compounds by PBPK model.

Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine

Personalized medicine strives to deliver the 'right drug at the right dose' by considering inter-person variability, one of the causes for therapeutic failure in specialized populations of patients. Physiologically-based pharmacokinetic (PBPK) modeling is a key tool in the advancement of personalized medicine to evaluate complex clinical scenarios, making use of physiological information as well as physicochemical data to simulate various physiological states to predict the distribution of pharmacokinetic responses. The increased dependency on PBPK models to address regulatory questions is aligned with the ability of PBPK models to minimize ethical and technical difficulties associated with pharmacokinetic and toxicology experiments for special patient populations. Subpopulation modeling can be achieved through an iterative and integrative approach using an adopt, adapt, develop, assess, amend, and deliver methodology. PBPK modeling has two valuable applications in personalized medicine: (1) determining the importance of certain subpopulations within a distribution of pharmacokinetic responses for a given drug formulation and (2) establishing the formulation design space needed to attain a targeted drug plasma concentration profile. This review article focuses on model development for physiological differences associated with sex (male vs. female), age (pediatric vs. young adults vs. elderly), disease state (healthy vs. unhealthy), and temporal variation (influence of biological rhythms), connecting them to drug product formulation development within the quality by design framework. Although PBPK modeling has come a long way, there is still a lengthy road before it can be fully accepted by pharmacologists, clinicians, and the broader industry.

Lipophilicity and biomimetic properties measured by HPLC to support drug discovery

HPLC methods that use chromatographic retention times for gaining information about the properties of compounds for the purpose of designing drug molecules are reviewed. Properties, such as lipophilicity, protein binding, phospholipid binding, and acid/base character can be incorporated in the design of molecules with the right biological distribution and pharmacokinetic profile to become an effective drug. Standardization of various methodologies is suggested in order to obtain data suitable for inter-laboratory comparison. The published HPLC methods for lipophilicity, acid/base character, protein and phospholipid binding are critically reviewed and compared with each other using the solvation equation approach. One of the most important discussion points is how these data can be used in models and how they can influence the drug discovery process. Therefore, the published models for volume of distribution, unbound volume of distribution and drug efficiency are also discussed. The general relationships between the chemical structure and biomimetic HPLC properties are described in view of ranking and selecting putative drug molecules.

Evaluation of biocompatibility using in vitro methods: interpretation and limitations

The in vitro biocompatibility of novel materials has to be proven before a material can be used as component of a medical device. This must be done in cell culture tests according to internationally recognized standard protocols. Subsequently, preclinical and clinical tests must be performed to verify the safety of the new material and device. The present chapter focuses on the first step, the in vitro testing according to ISO 10993-5, and critically discusses its limited significance. Alternative strategies and a brief overview of activities to improve the current in vitro tests are presented in the concluding section.

Development of a hybrid physiologically based pharmacokinetic model with drug-specific scaling factors in rat to improve prediction of human pharmacokinetics

Accurate prediction of pharmacokinetics (PK) in humans has been a vital part of drug discovery. The aims of this study are to verify the usefulness of scaling factors for clearance (CL) and apparent volume of distribution at the steady state (Vss ) estimated from the difference between observed and predicted PK profiles in rats for human PK prediction, and to develop a novel hybrid physiologically based pharmacokinetic (PBPK) model with the two scaling factors. The human prediction accuracies for CL with in vitro-in vivo extrapolation and Vss with a tissue composition model were improved by using rat-scaling factors. This improvement was explainable by data that the scaling factors for CL and Vss in rats were correlated with those in humans. The predictability of plasma concentration-time profiles by the hybrid PBPK model incorporating two scaling factors was compared mainly with that by the conventional PBPK model. The hybrid PBPK model yielded higher prediction accuracy for plasma concentrations than the conventional method. Furthermore, we proposed a tiered approach using the three prediction methods, including the hybrid Dedrick approach, that were previously reported (Sayama H, Komura H, Kogayu M. 2013. Drug Metab Dispos 41:498-507), taking the available information in the individual stages of drug discovery and development into consideration.

A physiologically based pharmacokinetic model of rifampin in mice

One problem associated with regimen-based development of antituberculosis (anti-TB) drugs is the difficulty of a systematic and thorough in vivo evaluation of the large number of possible regimens that arise from consideration of multiple drugs tested together. A mathematical model capable of simulating the pharmacokinetics and pharmacodynamics of experimental combination chemotherapy of TB offers a way to mitigate this problem by extending the use of available data to investigate regimens that are not initially tested. In order to increase the available mathematical tools needed to support such a model for preclinical anti-TB drug development, we constructed a preliminary whole-body physiologically based pharmacokinetic (PBPK) model of rifampin in mice, using data from the literature. Interindividual variability was approximated using Monte Carlo (MC) simulation with assigned probability distributions for the model parameters. An MC sensitivity analysis was also performed to determine correlations between model parameters and plasma concentration to inform future model development. Model predictions for rifampin concentrations in plasma, liver, kidneys, and lungs, following oral administration, were generally in agreement with published experimental data from multiple studies. Sensitive model parameters included those descriptive of oral absorption, total clearance, and partitioning of rifampin between blood and muscle. This PBPK model can serve as a starting point for the integration of rifampin pharmacokinetics in mice into a larger mathematical framework, including the immune response to Mycobacterium tuberculosis infection, and pharmacokinetic models for other anti-TB drugs.

A semiphysiologically based pharmacokinetic modeling approach to predict the dose-exposure relationship of an antiparasitic prodrug/active metabolite pair

Dose selection during antiparasitic drug development in animal models and humans traditionally has relied on correlations between plasma concentrations obtained at or below maximally tolerated doses that are efficacious. The objective of this study was to improve the understanding of the relationship between dose and plasma/tissue exposure of the model antiparasitic agent, pafuramidine, using a semiphysiologically based pharmacokinetic (semi-PBPK) modeling approach. Preclinical and clinical data generated during the development of pafuramidine, a prodrug of the active metabolite, furamidine, were used. A whole-body semi-PBPK model for rats was developed based on a whole-liver PBPK model using rat isolated perfused liver data. A whole-body semi-PBPK model for humans was developed on the basis of the whole-body rat model. Scaling factors were calculated using metabolic and transport clearance data generated from rat and human sandwich-cultured hepatocytes. Both whole-body models described pafuramidine and furamidine disposition in plasma and predicted furamidine tissue (liver and kidney) exposure and excretion profiles (biliary and renal). The whole-body models predicted that the intestine contributes significantly (30-40%) to presystemic furamidine formation in both rats and humans. The predicted terminal elimination half-life of furamidine in plasma was 3- to 4-fold longer than that of pafuramidine in rats (170 versus 47 h) and humans (64 versus 19 h). The dose-plasma/tissue exposure relationship for the prodrug/active metabolite pair was determined using the whole-body models. The human model proposed a dose regimen of pafuramidine (40 mg once daily) based on a predefined efficacy-safety index. A similar approach could be used to guide dose-ranging studies in humans for next-in-class compounds.

Impact of intestinal glucuronidation on the pharmacokinetics of raloxifene

Raloxifene is extensively glucuronidated in humans, effectively reducing its oral bioavailability (2%). It was also reported to be glucuronidated in preclinical animals, but its effects on the oral bioavailability have not been fully elucidated. In the present study, raloxifene and its glucuronides in the portal and systemic blood were monitored in Gunn rats deficient in UDP-glucuronosyltransferase (UGT) 1A, Eisai hyperbilirubinemic rats (EHBRs), which hereditarily lack multidrug resistance-associated protein (MRP) 2, and wild-type rats after oral administration. The in vitro-in vivo correlation (IVIVC) of four UGT substrates (raloxifene, biochanin A, gemfibrozil, and mycophenolic acid) in rats was also evaluated. In Gunn rats, the product of fraction absorbed and intestinal availability and hepatic availability of raloxifene were 0.63 and 0.43, respectively; these values were twice those observed in wild-type Wistar rats, indicating that raloxifene was glucuronidated in both the liver and intestine. The ratio of glucuronides to unchanged drug in systemic blood was substantially higher in EHBRs (129-fold) than in the wild-type Sprague-Dawley rats (10-fold), suggesting the excretion of raloxifene glucuronides caused by MRP2. The IVIVC of the other UGT substrates in rats displayed a good relationship, but the oral clearance values of raloxifene and biochanin A, which were extensively glucuronidated by rat intestinal microsomes, were higher than the predicted clearances using rat liver microsomes, suggesting that intestinal metabolism may be a great contributor to the first-pass effect. Therefore, evaluation of intestinal and hepatic glucuronidation for new chemical entities is important to improve their pharmacokinetic profiles.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 3: comparative assessement of prediction methods of human clearance

The objective of this study was to evaluate the performance of various allometric and in vitro-in vivo extrapolation (IVIVE) methodologies with and without plasma protein binding corrections for the prediction of human intravenous (i.v.) clearance (CL). The objective was also to evaluate the IVIVE prediction methods with animal data. Methodologies were selected from the literature. Pharmaceutical Research and Manufacturers of America member companies contributed blinded datasets from preclinical and clinical studies for 108 compounds, among which 19 drugs had i.v. clinical pharmacokinetics data and were used in the analysis. In vivo and in vitro preclinical data were used to predict CL by 29 different methods. For many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. In addition, 66 methods of predicting oral (p.o.) area under the curve (AUCp.o. ) were evaluated for 107 compounds using rational combinations of i.v. CL and bioavailability (F), and direct scaling of observed p.o. CL from preclinical species. Various statistical and outlier techniques were employed to assess the predictability of each method. Across methods, the maximum success rate in predicting human CL for the 19 drugs was 100%, 94%, and 78% of the compounds with predictions falling within 10-fold, threefold, and twofold error, respectively, of the observed CL. In general, in vivo methods performed slightly better than IVIVE methods (at least in terms of measures of correlation and global concordance), with the fu intercept method and two-species-based allometry (rat-dog) being the best performing methods. IVIVE methods using microsomes (incorporating both plasma and microsomal binding) and hepatocytes (not incorporating binding) resulted in 75% and 78%, respectively, of the predictions falling within twofold error. IVIVE methods using other combinations of binding assumptions were much less accurate. The results for prediction of AUCp.o. were consistent with i.v. CL. However, the greatest challenge to successful prediction of human p.o. CL is the estimate of F in human. Overall, the results of this initiative confirmed predictive performance of common methodologies used to predict human CL.

Translational considerations for cancer nanomedicine

There are many important considerations during preclinical development of cancer nanomedicines, including: 1) unique aspects of animal study design; 2) the difficulties in evaluating biological potency, especially for complex formulations; 3) the importance of analytical methods that can determine platform stability in vivo, and differentiate bound and free active pharmaceutical ingredient (API) in biological matrices; and 4) the appropriateness of current dose scaling techniques for estimation of clinical first-in-man dose from preclinical data. Biologics share many commonalities with nanotechnology products with regard to complexity and biological attributes, and can, in some cases, provide context for dealing with these preclinical issues. In other instances, such as the case of in vivo stability analysis, new approaches are required. This paper will discuss the significance of these preclinical issues, and present examples of current methods and best practices for addressing them. Where possible, these recommendations are justified using the existing regulatory guidance literature.

Lumping of physiologically-based pharmacokinetic models and a mechanistic derivation of classical compartmental models

In drug discovery and development, classical compartment models and physiologically based pharmacokinetic (PBPK) models are successfully used to analyze and predict the pharmacokinetics of drugs. So far, however, both approaches are used exclusively or in parallel, with little to no cross-fertilization. An approach that directly links classical compartment and PBPK models is highly desirable. We derived a new mechanistic lumping approach for reducing the complexity of PBPK models and establishing a direct link to classical compartment models. The proposed method has several advantages over existing methods: Perfusion and permeability rate limited models can be lumped; the lumped model allows for predicting the original organ concentrations; and the volume of distribution at steady state is preserved by the lumping method. To inform classical compartmental model development, we introduced the concept of a minimal lumped model that allows for prediction of the venous plasma concentration with as few compartments as possible. The minimal lumped parameter values may serve as initial values for any subsequent parameter estimation process. Applying our lumping method to 25 diverse drugs, we identified characteristic features of lumped models for moderate-to-strong bases, weak bases and acids. We observed that for acids with high protein binding, the lumped model comprised only a single compartment. The proposed lumping approach established for the first time a direct derivation of simple compartment models from PBPK models and enables a mechanistic interpretation of classical compartment models.

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

Methods for prediction of hepatic clearance (CLH) in man have been evaluated. A physiologically-based in-vitro to in-vivo (PB-IVIV) method with human unbound fraction in blood (fu,bl) and hepatocyte intrinsic clearance (CLint)-data has a good rationale and appears to give the best predictions (maximum ∼2-fold errors; < 25% errors for half of CL-predictions; appropriate ranking). Inclusion of an empirical scaling factor is, however, needed, and reasons include the use of cryopreserved hepatocytes with low activity, and inappropriate CLint- and fu,bl-estimation methods. Thus, an improvement of this methodology is possible and required. Neglect of fu,bl or incorporation of incubation binding does not seem appropriate. When microsome CLint-data are used with this approach, the CLH is underpredicted by 5- to 9-fold on average, and a 106-fold underprediction (attrition potential) has been observed. The poor performance could probably be related to permeation, binding and low metabolic activity. Inclusion of scaling factors and neglect of fu,bl for basic and neutral compounds improve microsome predictions. The performance is, however, still not satisfactory. Allometry incorrectly assumes that the determinants for CLH relate to body weight and overpredicts human liver blood flow rate. Consequently, allometric methods have poor predictability. Simple allometry has an average overprediction potential, > 2-fold errors for ∼1/3 of predictions, and 140-fold underprediction to 5800-fold overprediction (potential safety risk) range. In-silico methodologies are available, but these need further development. Acceptable prediction errors for compounds with low and high CLH should be ∼50 and ∼10%, respectively. In conclusion, it is recommended that PB-IVIV with human hepatocyte CLint and fu,bl is applied and improved, limits for acceptable errors are decreased, and that animal CLH-studies and allometry are avoided.

Physiologically based pharmacokinetics (PBPK)

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

Practical anticipation of human efficacious doses and pharmacokinetics using in vitro and preclinical in vivo data

Accurate predictions of human pharmacokinetic and pharmacodynamic (PK/PD) profiles are critical in early drug development, as safe, efficacious, and "developable" dosing regimens of promising compounds have to be identified. While advantages of successful integration of preclinical PK/PD data in the "anticipation" of human doses (AHD) have been recognized, pharmaceutical scientists have faced difficulties with practical implementation, especially for PK/PD profile projections of compounds with challenging absorption, distribution, metabolism, excretion and formulation properties. In this article, practical projection approaches for formulation-dependent human PK/PD parameters and profiles of Biopharmaceutics Classification System classes I-IV drugs based on preclinical data are described. Case examples for "AHD" demonstrate the utility of preclinical and clinical PK/PD modeling for formulation risk identification, lead candidate differentiation, and prediction of clinical outcome. The application of allometric scaling methods and physiologically based pharmacokinetic approaches for clearance or volume of distribution projections is described using GastroPlus. Methods to enhance prediction confidence such as in vitro-in vivo extrapolations in clearance predictions using in vitro microsomal data are discussed. Examples for integration of clinical PK/PD and formulation data from frontrunner compounds via "reverse pharmacology strategies" that minimize uncertainty with PK/PD predictions are included. The use of integrated softwares such as GastroPlus in combination with established PK projection methods allow the projection of formulation-dependent preclinical and human PK/PD profiles required for compound differentiation and development risk assessments.

Assessing drug distribution in tissues expressing P-glycoprotein through physiologically based pharmacokinetic modeling: model structure and parameters determination

Background

The expression and activity of P-glycoproteins due to genetic or environmental factors may have a significant impact on drug disposition, drug effectiveness or drug toxicity. Hence, characterization of drug disposition over a wide range of conditions of these membrane transporters activities is required to better characterize drug pharmacokinetics and pharmacodynamics. This work aims to improve our understanding of the impact of P-gp activity modulation on tissue distribution of P-gp substrate.

Methods

A PBPK model was developed in order to examine activity and expression of P-gp transporters in mouse brain and heart. Drug distribution in these tissues was first represented by a well-stirred (WS) model and then refined by a mechanistic transport-based (MTB) model that includes P-gp mediated transport of the drug. To estimate transport-related parameters, we developed an original three-step procedure that allowed extrapolation of in vitro measurements of drug permeability to the in vivo situation. The model simulations were compared to a limited set of data in order to assess the model ability to reproduce the important information of drug distributions in the considered tissues.

Results

This PBPK model brings insights into the mechanism of drug distribution in non eliminating tissues expressing P-gp. The MTB model accounts for the main transport mechanisms involved in drug distribution in heart and brain. It points out to the protective role of P-gp at the blood-brain barrier and represents thus a noticeable improvement over the WS model.

Conclusion

Being built prior to in vivo data, this approach brings an interesting alternative to fitting procedures, and could be adapted to different drugs and transporters.

The physiological based model is novel and unique and brought effective information on drug transporters.

Physiologically based pharmacokinetics of matrine in the rat after oral administration of pure chemical and ACAPHA

ACAPHA, a botanical drug for the treatment of human esophageal cancer in China, is under investigation as a lung cancer chemoprevention agent at the BC Cancer Agency (Vancouver, BC, Canada). Little or no information is available on the pharmacokinetics of ACAPHA in animals. The objectives of this study were as follows: to examine the disposition kinetics of matrine, a bioactive marker of ACAPHA in the rat; to develop a physiologically based pharmacokinetic (PBPK) model for pure matrine; and to characterize the absorption and clearance of crude matrine in ACAPHA-treated rats using the PBPK model. Pure matrine (15 mg/kg) or crude matrine in the form of ACAPHA (0.38 or 3.8 g/kg) was administered to the rat by gavages. The rats were sacrificed at different time points postdosing. Blood and major organs were removed from the rat, extracted with toluene/butanol, and quantified for matrine using gas chromatography-mass spectrometry. An 11-compartment, flow-limited PBPK model of matrine was developed. The PBPK model was able to simulate closely the empirical data of rats treated with pure matrine. Because the absorption and clearance of crude matrine in ACAPHA-treated rats could not be parameterized a priori, they were estimated by fitting the experimental data to the PBPK model. Results of the study show that pure matrine is absorbed and eliminated by the rat at faster rates than crude matrine. Moreover, the ACAPHA matrix may change the pharmacokinetics of matrine in the rat significantly. The PBPK model is a valuable tool to gain insights into the disposition kinetics of a botanical drug.

Whole body physiologically-based pharmacokinetic models: their use in clinical drug development

BACKGROUND

Whole-body physiologically-based pharmacokinetic (WB-PBPK) models mathematically describe an organism as a closed circulatory system consisting of compartments that represent the organs important for compound absorption, distribution, metabolism and elimination.

OBJECTIVES

To review the current state of WB-PBPK model use in the clinical phases of drug development.

METHODS

A qualitative description of the WB-PBPK model structure is included along with a review of the varying methods available for input parameterisation. Current and potential WB-PBPK model application in clinical development is discussed.

CONCLUSIONS

This modelling tool is at present used for small and large molecule drug development primarily as a means to scale pharmacokinetics from animals to humans based on physiology. The pharmaceutical industry is active in employing these models to clinical drug development although the applications in use now are narrow in comparison to the potential. Expanded integration of WB-PBPK models into the drug development process will only be achieved with staff training, managerial will, success stories and regulatory agency openness.

On the volume of distribution at steady state and its relationship with two-compartmental models

The volume of distribution at steady state is considered to be one of the primary pharmacokinetic measurements obtained from in vivo experiments. This quantity is quite commonly calculated using moments of the observed concentration curve, the process being referred to as noncompartmental analysis. In this paper the underlying assumptions of noncompartmental analysis are analysed with regard to the observed behaviour of models with two compartments: one of which has elimination from the central compartment, the other from the peripheral tissue compartment. This is in order to clarify the relationship between volume of distribution and clearance. It is shown that these two models are indistinguishable from measurements in blood. Furthermore relationships between the parameter values for the two models are given so that they produce the same observed profile. Expressions are derived in a novel way that relates the volume of distribution to these model rate constants. The definitions of clearance and volume of distribution at steady state are investigated using several different mathematical techniques, demonstrating the consistency of the derived expressions. It is shown, in a manner that the authors believe is a new approach, that when the assumption of central elimination does not apply, noncompartmental analysis will under estimate the volume of distribution, whereas clearance remains unchanged. This is compared quantitatively with respect to the volume predicted where central elimination holds, and is a result of an extended mean residence time.

Whole-body physiologically based pharmacokinetic models

This review summarizes the most recent developments in and applications of physiologically based pharmacokinetic (PBPK) modeling methodology originating from both the pharmaceutical and environmental toxicology areas. It focuses on works published in the last 5 years, although older seminal papers have also been referenced. After a brief introduction to the field and several essential definitions, the main body of the text is structured to follow the major steps of a typical PBPK modeling exercise. Various applications of the methodology are briefly described. The major future trends and perspectives are outlined. The main conclusion from the review of the available literature is that PBPK modeling, despite its obvious potential and recent incremental developments, has not taken the place it deserves, especially in pharmaceutical and drug development sciences.

Quantitative structure-pharmacokinetic/pharmacodynamic relationships

Quantitative structure-activity relationships have long been considered a vital component of drug discovery and development, providing insight into the role of molecular properties in the biological activity of similar and unrelated compounds. Recognition that in vitro bioassay and/or pre-clinical activity are insufficient for anticipating which compounds are suitable leads for further development has shifted the focus toward integrated pharmacokinetic (PK) and pharmacodynamic (PD) processes. Over the last decade, considerable progress has been made in constructing empirical and mechanistic quantitative structure-PK relationships (QSPKR), as well as diverse mechanism-based pharmacodynamic models of drug effects. In this review, traditional and contemporary approaches to developing QSPKR models are discussed, along with selected examples of attempts to couple QSPKR and pharmacodynamic models to anticipate the intensity and time-course of the pharmacological effects of new or related compounds, or quantitative structure-pharmacodynamic relationships modeling. Such models are in accordance with the goals of systems biology and the ideal of designing drugs and delivery systems from first principles.

The virtual laboratory approach to pharmacokinetics: design principles and concepts

Modeling and simulation in pharmacokinetics has turned into the focus of pharmaceutical companies, driven by the emerging consensus that in silico predictions, combined with in vitro data, have the potential to significantly increase insight into pharmacokinetic processes. To support in silico methodology adequately, software tools need to be user-friendly and, at the same time, flexible. In brief, the software has to allow the modeling of ideas that go beyond the current knowledge--in the form of a virtual laboratory. In this review, we present and discuss the necessary design principles and concepts required to do this. They have been implemented in the software package MEDICI-PK, demonstrating its feasibility and advantages.

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

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

A Novel Strategy for Physiologically Based Predictions of Human Pharmacokinetics

BACKGROUND

The major aim of this study was to develop a strategy for predicting human pharmacokinetics using physiologically based pharmacokinetic (PBPK) modelling. This was compared with allometry (of plasma concentration-time profiles using the Dedrick approach), in order to determine the best approaches and strategies for the prediction of human pharmacokinetics.

METHODS

PBPK and Dedrick predictions were made for 19 F. Hoffmann-La Roche compounds. A strategy for the prediction of human pharmacokinetics using PBPK modelling was proposed in this study. Predicted values (pharmacokinetic parameters, plasma concentrations) were compared with observed values obtained after intravenous and oral administration in order to assess the accuracy of the prediction methods.

RESULTS

By following the proposed strategy for PBPK, a prediction would have been made prospectively for approximately 70% of the compounds. The prediction accuracy for these compounds in terms of the percentage of compounds with an average-fold error of <2-fold was 83%, 50%, 75%, 67%, 92% and 100% for apparent oral clearance (CL/F), apparent volume of distribution during terminal phase after oral administration (V(z)/F), terminal elimination half-life (t(1/2)), peak plasma concentration (C(max)), area under the plasma concentration-time curve (AUC) and time to reach C(max) (t(max)), respectively. For the other 30% compounds, unacceptable prediction accuracy was obtained in animals; therefore, a prospective prediction of human pharmacokinetics would not have been made using PBPK. For these compounds, prediction accuracy was also poor using the Dedrick approach. In the majority of cases, PBPK gave more accurate predictions of pharmacokinetic parameters and plasma concentration-time profiles than the Dedrick approach.

CONCLUSIONS

Based on the dataset evaluated in this study, PBPK gave reasonable predictions of human pharmacokinetics using preclinical data and is the recommended approach in the majority of cases. In addition, PBPK modelling is a useful tool to gain insights into the properties of a compound. Thus, PBPK can guide experimental efforts to obtain the relevant information necessary to understand the compound's properties before entry into human, ultimately resulting in a higher level of prediction accuracy.

Prediction of human pharmacokinetic profile in animal scale up based on normalizing time course profiles

The aim of the present study was to develop a method for predicting the concentration-time profile in humans based on pharmacokinetic data for animals. The method is based on the assumptions that concentration-time profiles of a drug are similar among species and "normalized curves" from a variety of animal species including humans can be superimposed. Normalized curves are obtained by normalizing the time axis with the MRT (mean residence time) and the concentration axis with dose/Vdss, where Vdss is the volume of distribution at steady state. The concentration-time profile in humans after intravenous injection can be simulated using the normalized curve for an animal and the predicted values of clearance (CL) and Vdss for humans. Although the general idea of our method is similar to the Dedrick plots, ours is superior in that it enables the use of predicted CL and Vdss values from any method. Our method was applied to some drugs using actual published data sets, and the assumption of the similarity of concentration-time profiles among species was found to be acceptable for these drugs. The results for the prediction of concentration-time profiles for humans were also acceptable. This method can be applied to any drug on the assumption that normalized curves from a variety of species can be superimposed.