Physiologically Based Pharmacokinetic (PBPK) Modeling of Pharmaceutical Nanoparticles

With the great interests in the discovery and development of drug products containing nanoparticles, there is a great demand of quantitative tools for assessing quality, safety, and efficacy of these products. Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches provide excellent tools for describing and predicting in vivo absorption, distribution, metabolism, and excretion (ADME) of nanoparticles administered through various routes. PBPK modeling of nanoparticles is an emerging field, and more than 20 PBPK models of nanoparticles used in pharmaceutical products have been published in the past decade. This review provides an overview of the ADME characteristics of nanoparticles and how these ADME processes are described in PBPK models. Recent advances in PBPK modeling of pharmaceutical nanoparticles are summarized. The major challenges in model development and validation and possible solutions are also discussed.     

Using Physiologically Based Pharmacokinetic (PBPK) Modelling to Gain Insights into the Effect of Physiological Factors on Oral Absorption in Paediatric Populations

Paediatric pharmaceutics has become an important topic, but currently, there is an incomplete knowledge of paediatric gastrointestinal physiology and adequate biopharmaceutical tools still have to be developed. The present study aimed to increase the understanding of oral drug absorption in paediatric populations by using physiologically based pharmacokinetic (PBPK) modelling and in vitro dissolution testing. The oral absorption of two model compounds, sotalol and paracetamol, was studied by collection of reported pharmacokinetic profiles from adult and paediatric subjects. A PBPK model based on input parameters collected from the literature was first developed and validated in adults before being extrapolated to paediatric age groups. The accuracy of the model simulations was assessed by comparison to the observed pharmacokinetic profiles, and in the case of discrepancy, further investigations were made via parameter sensitivity analysis and in vitro dissolution testing. The PBPK models accurately predicted sotalol and paracetamol exposure in adult populations. An accurate simulation was also obtained after model extrapolation to children older than 2 years of age. However, the simulation in infants and newborns resulted in a discrepancy, which was further analysed. Dissolution testing suggested no significant difference in the drug release rate between paediatric and adult age groups. In contrast, mean gastric emptying time seemed to be underestimated in infants and newborns, and optimisation of this input parameter improved the prediction of the model. Considering age-specific differences in gastrointestinal tract physiology should improve prediction of drug absorption in paediatric patients.     

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.     

Exploration of the Plausible Mechanism of Ethambutol Induced Ocular Toxicity by Using Proteomics Informed Physiologically Based Pharmacokinetic (PBPK) Modeling

Pharmaceutical Research - Ethambutol (EMB) is a first-line anti-tubercular drug that is known to cause optic neuropathy. The exact mechanism of its eye toxicity is unknown; however, proposition is...     

Physiologically Based Pharmacokinetic (PBPK) Modeling of Everolimus (RAD001) in Rats Involving Non-Linear Tissue Uptake

Everolimus is a novel macrolide immunosuppressant developed for the prophylaxis of allogeneic renal or cardiac transplant rejection. Treatments with immunosuppressants are often associated with organ toxicity that is linked to high organ exposure. Therefore, gaining insight into the pharmacokinetics of everolimus in various organs is highly desirable especially those organs of therapeutic interest or those that pose safety concerns. The aim of this work was to characterize the disposition kinetics of everolimus in rats by physiologically based pharmacokinetic (PBPK) modeling. Blood and tissue samples were collected from male Wistar rats over 24 hr following intravenous (iv) bolus and iv infusion of 1 mg/kg and 10 mg/kg/2 hr of everolimus. Further blood samples were collected between 1 and 170 hr from a third group of rats, which received iv infusion of 1 mg/kg/2 hr of everolimus. Drug concentrations in blood and tissues were determined by a liquid chromatography reverse dilution method. Distribution of everolimus between blood fractions was determined in vitro at 37°C. The results of the study demonstrated that everolimus exhibited moderate non-linear binding to red blood cells. Also, the tissue-to-blood concentration ratio decreased in all tissues as blood concentration increased. A PBPK model involving non-linear tissue binding was able to successfully describe the observed data in blood and all the organs investigated. The highest binding potential was observed in thymus, lungs, and spleen with the greatest tissue affinity observed in thymus, skin, and muscle as compared to other tissues. Everolimus exhibited a high clearance rate that was limited to the hepatic blood flow (47.2 ml/min/kg). The PBPK model was also able to predict the venous blood concentration reasonably well following oral administration. The oral bioavailability value, as estimated with the PBPK, was 12% and was similar to the value obtained by non-compartmental analysis. In conclusion, A PBPK model has been developed that successfully predicts the time course of everolimus in blood and a variety of organs. This model takes into account the non- linear binding of everolimus to red blood cells and tissues. This model may be used to predict everolimus concentration–time course in organs from other species including humans.     

Physiologically Based Oral Absorption Modelling to Study Gut-Level Drug Interactions

Physiologically based oral absorption models are in silico tools primarily used to guide formulation development and project the clinical performance of formulation variants. This commentary briefly discusses additional oral absorption model applications, focusing on gut-level drug interactions. Gut-level drug interactions can involve drug degradation, metabolic enzymes, transporters, gastrointestinal motility modulators, acid-reducing agents, and food. The growth in publications reporting physiologically based oral absorption model utilization and successful pharmacokinetic prediction (e.g., after acid-reducing agents or food coadministration) indicate that oral absorption models have achieved a level of maturity within the industry particularly over the past 15 years. Provided appropriate data and model validation, oral absorption modeling/simulation may serve as a surrogate for clinical studies by providing both mechanistic and quantitative understanding of oral delivery considerations on pharmacokinetics.     

Re-writing Oral Pharmacokinetics Using Physiologically Based Finite Time Pharmacokinetic (PBFTPK) Models

Pharmaceutical Research - To develop physiologically based finite time pharmacokinetic (PBFTPK) models for the analysis of oral pharmacokinetic data. The models are based on the passive drug...     

A Physiologically Based Pharmacokinetic (PBPK) Model for Predicting the Efficacy of Drug Overdose Treatment With Liposomes in Man

Physiologically based pharmacokinetic (PBPK) models were developed for design and optimization of liposome therapy for treatment of overdoses of tricyclic antidepressants and local anesthetics. In vitro drug-binding data for pegylated, anionic liposomes and published mechanistic equations for partition coefficients were used to develop the models. The models were proven reliable through comparisons to intravenous data. The liposomes were predicted to be highly effective at treating amitriptyline overdoses, with reductions in the area under the concentration versus time curves (AUC) of 64% for the heart and brain. Peak heart and brain drug concentrations were predicted to drop by 20%. Bupivacaine AUC and peak concentration reductions were lower at 15.4% and 17.3%, respectively, for the heart and brain. The predicted pharmacokinetic profiles following liposome administration agreed well with data from clinical studies where protein fragments were administered to patients for overdose treatment. Published data on local cardiac function were used to relate the predicted concentrations in the body to local pharmacodynamic effects in the heart. While the results offer encouragement for future liposome therapies geared toward overdose, it is imperative to point out that animal experiments and phase I clinical trials are the next steps to ensuring the efficacy of the treatment.     

Physiologically Based Absorption Modelling to Predict the Impact of Drug Properties on Pharmacokinetics of Bitopertin

Bitopertin (RG1678) is a glycine reuptake inhibitor in phase 3 trials for treatment of schizophrenia. Its clinical oral pharmacokinetics is sensitive to changes in drug substance particle size and dosage form. Physiologically based pharmacokinetic (PBPK) absorption model simulations of the impact of changes in particle size and dosage form (either capsules, tablets, or an aqueous suspension) on oral pharmacokinetics was verified by comparison to measured plasma concentrations. Then, a model parameter sensitivity analysis was applied to set limits on the particle sizes included in tablets for the market. The model was also used to explore the in vitro to in vivo correlation. Simulated changes in oral pharmacokinetics caused by differences in particle size and dosage form were confirmed in two separate relative bioavailability studies. Model parameter sensitivity analyses predicted that AUCinf was hardly reduced as long as particle diameter (D50) remained smaller than 30 μm, and >20% reduced Cmax is anticipated only when particle diameter exceeds 15 μm. An exploration of the sensitivity to the presence of larger particles within a polydisperse distribution showed that simulated Cmax is again more affected than AUC but is less than 20% reduced as long as D50 is less than 8 μm and D90 is smaller than 56 μm. PBPK absorption modelling can contribute to a quality by design (QbD) approach for clinical formulation development and support the setting of biorelevant specifications for release of the product.     

Physiologically Based Pharmacokinetic (PBPK) Modeling of Lornoxicam: Exploration of doses for CYP2C9 Genotypes and Patients with Cirrhosis

Lornoxicam physiologically based pharmacokinetic (PBPK) models were developed and validated on the basis of clinical pharmacokinetic results obtained by considering CYP2C9 genetic polymorphisms in healthy adult populations. PBPK models were extended to predict lornoxicam pharmacokinetics for patients with cirrhosis by quantitatively examining the pathophysiological information associated with cirrhosis. The predicted plasma exposure to lornoxicam was approximately 1.12–2.83 times higher in the CYP2C9*1/*3 and *1/*13 groups than in the CYP2C9*1/*1 group of healthy adult populations and patients with cirrhosis. The predicted plasma exposure to lornoxicam was approximately 1.28–3.61 times higher in patients with cirrhosis than in healthy adult populations. If the relationship between lornoxicam exposure in plasma and drug efficacy was proportional, then the proposed adjusted doses of lornoxicam for each group varied from 1.25 mg to 8 mg. As the severity of cirrhosis increased, or when the CYP2C9 genotype was *1 heterozygous, the dose adjustment range of lornoxicam increased. Therefore, the effect of pathophysiological factors (cirrhosis severity) on the pharmacokinetics of lornoxicam might be more important than that of CYP2C9 genetic factors. These results could be useful for broadening the scope of clinical application of lornoxicam by enabling dose selection based on CYP2C9 genotypes and liver cirrhosis degree.     

The Quantitative Prediction of CYP-mediated Drug Interaction by Physiologically Based Pharmacokinetic Modeling

PURPOSE: The objective is to confirm if the prediction of the drug-drug interaction using a physiologically based pharmacokinetic (PBPK) model is more accurate. In vivo Ki values were estimated using PBPK model to confirm whether in vitro Ki values are suitable. METHOD: The plasma concentration-time profiles for the substrate with coadministration of an inhibitor were collected from the literature and were fitted to the PBPK model to estimate the in vivo Ki values. The AUC ratios predicted by the PBPK model using in vivo Ki values were compared with those by the conventional method assuming constant inhibitor concentration. RESULTS: The in vivo Ki values of 11 inhibitors were estimated. When the in vivo Ki values became relatively lower, the in vitro Ki values were overestimated. This discrepancy between in vitro and in vivo Ki values became larger with an increase in lipophilicity. The prediction from the PBPK model involving the time profile of the inhibitor concentration was more accurate than the prediction by the conventional methods. CONCLUSION: A discrepancy between the in vivo and in vitro Ki values was observed. The prediction using in vivo Ki values and the PBPK model was more accurate than the conventional methods.     

Mechanistic Physiologically Based Pharmacokinetic (PBPK) Model of the Heart Accounting for Inter-Individual Variability: Development and Performance Verification

Modern model-based approaches to cardiac safety and efficacy assessment require accurate drug concentration-effect relationship establishment. Thus, knowledge of the active concentration of drugs in heart tissue is desirable along with inter-subject variability influence estimation. To that end, we developed a mechanistic physiologically based pharmacokinetic model of the heart. The models were described with literature-derived parameters and written in R, v.3.4.0. Five parameters were estimated. The model was fitted to amitriptyline and nortriptyline concentrations after an intravenous infusion of amitriptyline. The cardiac model consisted of 5 compartments representing the pericardial fluid, heart extracellular water, and epicardial intracellular, midmyocardial intracellular, and endocardial intracellular fluids. Drug cardiac metabolism, passive diffusion, active efflux, and uptake were included in the model as mechanisms involved in the drug disposition within the heart. The model accounted for inter-individual variability. The estimates of optimized parameters were within physiological ranges. The model performance was verified by simulating 5 clinical studies of amitriptyline intravenous infusion, and the simulated pharmacokinetic profiles agreed with clinical data. The results support the model feasibility. The proposed structure can be tested with the goal of improving the patient-specific model-based cardiac safety assessment and offers a framework for predicting cardiac concentrations of various xenobiotics.     

Investigating the Critical Variables of Azithromycin Oral Absorption Using In Vitro Tests and PBPK Modeling

Azithromycin is an antibiotic listed in the essential list of medicines for adults and pediatrics. Conflicting evidence has been found regarding azithromycin classification according to the Biopharmaceutics classification system (BCS). The purpose of this study was to identify the critical variables that influence the oral absorption of azithromycin in adults and pediatrics.Azithromycin solubility and dissolution studies (oral suspension) were performed in buffers and biorelevant media simulating the fasted and fed gastrointestinal tract. A PBPK model was developed for azithromycin for healthy adult volunteers and pediatrics (Simcyp® v18.2) informed by in vitro solubility and dissolution studies to predict drug performance after administration of azithromycin as an oral suspension.The developed PBPK model predicted azithromycin plasma concentrations-time profiles after administration of an oral suspension to adults and pediatrics. Sensitivity analysis of solubility vs dose suggests that absorption is independent of solubility within the therapeutic dose range in both adults and pediatrics. The developed PBPK model for adults and pediatrics was consistent with the mechanism of permeation through the intestinal membrane (passive and active processes) being the rate-limiting step of azithromycin's absorption.The physiologically based approach proposed was shown to be useful to determine the factors controlling drug absorption in adults and pediatrics.     

Predicting Nonlinear Pharmacokinetics of Omeprazole Enantiomers and Racemic Drug Using Physiologically Based Pharmacokinetic Modeling and Simulation: Application to Predict Drug/Genetic Interactions

Purpose

The objective of this study is to develop a physiologically-based pharmacokinetic (PBPK) model for each omeprazole enantiomer that accounts for nonlinear PK of the two enantiomers as well as omeprazole racemic drug.

Methods

By integrating in vitro, in silico and human PK data, we first developed PBPK models for each enantiomer. Simulation of racemic omeprazole PK was accomplished by combining enantiomer models that allow mutual drug interactions to occur.

Results

The established PBPK models for the first time satisfactorily predicted the nonlinear PK of esomeprazole, R-omeprazole and the racemic drug. The modeling exercises revealed that the strong time-dependent inhibition of CYP2C19 by esomeprazole greatly altered the R-omeprazole PK following administration of racemic omeprazole as in contrast to R-omeprazole given alone. When PBPK models incorporated both autoinhibition of each enantiomer and mutual interactions, the ratios between predicted and observed AUC following single and multiple dosing of omeprazole were 0.97 and 0.94, respectively.

Conclusions

PBPK models of omeprazole enantiomers and racemic drug were developed. These models can be utilized to assess CYP2C19-mediated drug and genetic interaction potential for omeprazole and esomeprazole.     

Investigation of Clinical Pharmacokinetic Variability of an Opioid Antagonist Through Physiologically Based Absorption Modeling

Identifying the source of inter- and/or intrasubject variability in pharmacokinetics (PK) provides fundamental information in understanding the pharmacokinetics-pharmaco- dynamics relationship of a drug and project its efficacy and safety in clinical populations. This identification process can be challenging given that a large number ofpotential causes could lead to PK variability. Here we present an integrated approach of physiologically based absorption modeling to investigate the root cause of unexpectedly high PK variability of a Phase I clinical trial drug. LY2196044 exhibited high intersubject variability in the absorption phase of plasma concentration-time profiles in humans. This could not be explained by in vitro measurements of drug properties and excellent bioavailability with low variability observed in preclinical species. GastroPlus? modeling suggested that the compound’s optimal solubility and permeability characteristics would enable rapid and complete absorption in preclinical species and in humans. However, simulations of human plasma concentration-time profiles indicated that despite sufficient solubility and rapid dissolution of LY2196044 in humans, permeability and/or transit in the gastrointestinal (GI) tract may have been negatively affected. It was concluded that clinical PK variability was potentially due to the drug’s antagonism on opioid receptors that affected its transit and absorption in the GI tract.     

Physiologically Based Pharmacokinetic Modeling of a Homologous Series of Barbiturates in the Rat: A Sensitivity Analysis

Sensitivity analysis studies the effects of the inherent variability and uncertainty in model parameters on the model outputs and may be a useful tool at all stages of the pharmacokinetic modeling process. The present study examined the sensitivity of a whole-body physiologically based pharmacokinetic (PBPK) model for the distribution kinetics of nine 5-n-alkyl-5-ethyl barbituric acids in arterial blood and 14 tissues (lung, liver, kidney, stomach, pancreas, spleen, gut, muscle, adipose, skin, bone, heart, brain, testes) after iv bolus administration to rats. The aims were to obtain new insights into the model used, to rank the model parameters involved according to their impact on the model outputs and to study the changes in the sensitivity induced by the increase in the lipophilicity of the homologues on ascending the series. Two approaches for sensitivity analysis have been implemented. The first, based on the Matrix Perturbation Theory, uses a sensitivity index defined as the normalized sensitivity of the 2-norm of the model compartmental matrix to perturbations in its entries. The second approach uses the traditional definition of the normalized sensitivity function as the relative change in a model state (a tissue concentration) corresponding to a relative change in a model parameter. Autosensitivity has been defined as sensitivity of a state to any of its parameters; cross-sensitivity as the sensitivity of a state to any other states' parameters. Using the two approaches, the sensitivity of representative tissue concentrations (lung, liver, kidney, stomach, gut, adipose, heart, and brain) to the following model parameters: tissue-to-unbound plasma partition coefficients, tissue blood flows, unbound renal and intrinsic hepatic clearance, permeability surface area product of the brain, have been analyzed. Both the tissues and the parameters were ranked according to their sensitivity and impact. The following general conclusions were drawn: (i) the overall sensitivity of the system to all parameters involved is small due to the weak connectivity of the system structure; (ii) the time course of both the auto- and cross-sensitivity functions for all tissues depends on the dynamics of the tissues themselves, e.g., the higher the perfusion of a tissue, the higher are both its cross-sensitivity to other tissues' parameters and the cross-sensitivities of other tissues to its parameters; and (iii) with a few exceptions, there is not a marked influence of the lipophilicity of the homologues on either the pattern or the values of the sensitivity functions. The estimates of the sensitivity and the subsequent tissue and parameter rankings may be extended to other drugs, sharing the same common structure of the whole body PBPK model, and having similar model parameters. Results show also that the computationally simple Matrix Perturbation Analysis should be used only when an initial idea about the sensitivity of a system is required. If comprehensive information regarding the sensitivity is needed, the numerically expensive Direct Sensitivity Analysis should be used.     

Reducing Whole Body Physiologically Based Pharmacokinetic Models Using Global Sensitivity Analysis: Diazepam Case Study

Abtract There are situations in drug development where one may wish to reduce the dimensionality and complexity of whole body physiologically based pharmacokinetic models. A technique for formal reduction of such models, based on global sensitivity analysis, is suggested. Using this approach mean and variance of tissue(s) and/or blood concentrations are preserved in the reduced models. Extended Fourier amplitude sensitivity test (FAST), a global sensitivity technique, takes a sampling approach, acknowledging parameter variability and uncertainty, to calculate the impact of parameters on concentration variance. We used existing literature rules for formal model reduction to identify all possible smaller dimensionally models. To discriminate among those competing mechanistic models extended FAST was used, whereby we treated model structural uncertainty as another factor contributing to the overall uncertainty. A previously developed 14 compartment whole body physiologically based model for diazepam disposition in rat was reduced to three alternative reduced models, with preserved arterial mean and variance concentration profiles.     

Physiologically Based Pharmacokinetic Modeling of Drug Disposition in Rat and Human: A Fuzzy Arithmetic Approach

PURPOSE: Probabilistic methods are insufficient for dealing with the vagueness inherent in human judgment of minimal data available during early drug development. We sought to use fuzzy set theory as a basis for quantifying and propagating vague judgment in a physiologically based pharmacokinetic (PBPK) model for diazepam disposition. MATERIALS AND METHODS: First, using diazepam distribution data in rat tissues and fuzzy regression, we estimated fuzzy rat tissue-to-plasma partition coefficients (Kp's). We scaled the coefficients prior to human PBPK modeling. Next, we constructed the fuzzy set of hepatic intrinsic clearance (CLint) by integrating CLint values measured in vitro from human hepatocytes. Finally, we used these parameters, and other physiological and biochemical information, to predict human diazepam disposition. We compared the simulated plasma kinetics with published concentration-time profiles. RESULTS: We successfully identified rat Kp's by fuzzy regression. The predicted rat tissue concentration-time contours enveloped the animal tissue distribution data. For the human PBPK model, the mean in vivo plasma concentrations were contained in the simulated concentration-time envelopes. CONCLUSIONS: We present a novel computational approach for handling information paucity in PBPK models using fuzzy arithmetic. Our methodology can model the vagueness associated with human perception and interpretation of minimal drug discovery data.     

Physiologically Based Pharmacokinetic Modeling of Rosuvastatin to Predict Transporter-Mediated Drug-Drug Interactions

Pharmaceutical Research - To build a physiologically based pharmacokinetic (PBPK) model of the clinical OATP1B1/OATP1B3/BCRP victim drug rosuvastatin for the investigation and prediction of its...     

Sensitivity Analysis of Pharmacokinetic and Pharmacodynamic Systems: I. A Structural Approach to Sensitivity Analysis of Physiologically Based Pharmacokinetic Models

Based on a frequency response approach to the sensitivity analysis of pharmacokinetic models, the concept of structural sensitivity is introduced. The core of this concept is the factorization of the system sensitivity into two multipliers. The first one, called structural sensitivity index, has an analytical form, which depends solely on the structure and connectivity of the system and does not depend on the drug administered or the factor perturbed. The second multiplier, the parameter sensitivity index, depends on the drug properties, the tissue of interest and the parameter perturbed, but is largely independent of the structure of the system. The structural and parametric sensitivity indices can be evaluated and analyzed separately. The most important feature of the proposed approach is that the conclusions drawn from the analysis of the structural sensitivity index are valid across all mammalian species, as the latter share a common anatomical and physiological structure. The concept of structural sensitivity is illustrated on the commonly used structure of the whole body physiologically based pharmacokinetic models by showing that the factorization of the sensitivity carried out arises naturally from the mechanism of the distribution of perturbations throughout the organism. The concept of structural sensitivity has interesting practical implications. It enables the formal proof of relationships and facts that have been observed previously. Moreover, the conclusions drawn introduce in fact a ranking of the tissues or subsystems with respect to their impact on the model outputs. From this ranking, direct recommendations regarding the design of experiments for whole-body physiologically based pharmacokinetic models are derived.