A physiologic model for simulating gastrointestinal flow and drug absorption in rats

Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically-Based Pharmacokinetics, and First-Principles Models

Oral drug absorption is the primary route for drug administration. However, this process hinges on multiple factors, including the drug's physicochemical properties, formulation characteristics, and gastrointestinal physiology. Given its intricacy and the exorbitant costs associated with experimentation, the trial-and-error method proves prohibitively expensive. Theoretical models have emerged as a cost-effective alternative by assimilating data from diverse experiments and theoretical considerations. These models fall into three categories: (i) data-driven models, encompassing classical pharmacokinetics, quantitative-structure models (QSAR), and machine/deep learning; (ii) mechanism-based models, which include quasi-equilibrium, steady-state, and physiologically-based pharmacokinetics models; and (iii) first principles models, including molecular dynamics and continuum models. This review provides an overview of recent modeling endeavors across these categories while evaluating their respective advantages and limitations. Additionally, a primer on partial differential equations and their numerical solutions is included in the appendix, recognizing their utility in modeling physiological systems despite their mathematical complexity limiting widespread application in this field.

Predictions of Bedaquiline Central Nervous System Exposure in Patients with Tuberculosis Meningitis Using Physiologically based Pharmacokinetic Modeling

BACKGROUND AND OBJECTIVE

The use of bedaquiline as a treatment option for drug-resistant tuberculosis meningitis (TBM) is of interest to address the increased prevalence of resistance to first-line antibiotics. To this end, we describe a whole-body physiologically based pharmacokinetic (PBPK) model for bedaquiline to predict central nervous system (CNS) exposure.

METHODS

A whole-body PBPK model was developed for bedaquiline and its metabolite, M2. The model included compartments for brain and cerebrospinal fluid (CSF). Model predictions were evaluated by comparison to plasma PK time profiles following different dosing regimens and sparse CSF concentrations data from patients. Simulations were then conducted to compare CNS and lung exposures to plasma exposure at clinically relevant dosing schedules.

RESULTS

The model appropriately described the observed plasma and CSF bedaquiline and M2 concentrations from patients with pulmonary tuberculosis (TB). The model predicted a high impact of tissue binding on target site drug concentrations in CNS. Predicted unbound exposures within brain interstitial exposures were comparable with unbound vascular plasma and unbound lung exposures. However, unbound brain intracellular exposures were predicted to be 7% of unbound vascular plasma and unbound lung intracellular exposures.

CONCLUSIONS

The whole-body PBPK model for bedaquiline and M2 predicted unbound concentrations in brain to be significantly lower than the unbound concentrations in the lung at clinically relevant doses. Our findings suggest that bedaquiline may result in relatively inferior efficacy against drug-resistant TBM when compared with efficacy against drug-resistant pulmonary TB.

Model-based optimization of controlled release formulation of levodopa for Parkinson's disease

Levodopa is currently the standard of care treatment for Parkinson's disease, but chronic therapy has been linked to motor complications. Designing a controlled release formulation (CRF) that maintains sustained and constant blood concentrations may reduce these complications. Still, it is challenging due to levodopa's pharmacokinetic properties and the notion that it is absorbed only in the upper small intestine (i.e., exhibits an "absorption window"). We created and validated a physiologically based mathematical model to aid the development of such a formulation. Analysis of experimental results using the model revealed that levodopa is well absorbed throughout the entire small intestine (i.e., no "absorption window") and that levodopa in the stomach causes fluctuations during the first 3 h after administration. Based on these insights, we developed guidelines for an improved CRF for various stages of Parkinson's disease. Such a formulation is expected to produce steady concentrations and prolong therapeutic duration compared to a common CRF with a smaller dose per day and a lower overall dose of levodopa, thereby improving patient compliance with the dosage regime.

The Rat Continuous Intestine Model Predicts the Impact of Particle Size and Transporters on the Oral Absorption of Glyburide

Oral drug absorption is known to be impacted by the physicochemical properties of drugs, properties of oral formulations, and physiological characteristics of the intestine. The goal of the present study was to develop a mathematical model to predict the impact of particle size, feeding time, and intestinal transporter activity on oral absorption. A previously published rat continuous intestine absorption model was extended for solid drug absorption. The impact of active pharmaceutical ingredient particle size was evaluated with glyburide (GLY) as a model drug. Two particle size suspensions of glyburide were prepared with average particle sizes of 42.7 and 4.1 μm. Each suspension was dosed as a single oral gavage to male Sprague Dawley rats, and concentration-time (C-t) profiles of glyburide were measured with liquid chromatography coupled with tandem mass spectrometry. A continuous rat intestine absorption model was extended to include drug dissolution and was used to predict the absorption kinetics of GLY depending on particle size. Additional literature datasets of rat GLY formulations with particle sizes ranging from 0.25 to 4.0 μm were used for model predictions. The model predicted reasonably well the absorption profiles of GLY based on varying particle size and varying feeding time. The model predicted inhibition of intestinal uptake or efflux transporters depending on the datasets. The three datasets used formulations with different excipients, which may impact the transporter activity. Model simulations indicated that the model provides a facile framework to predict the impact of transporter inhibition on drug C-t profiles. Model simulations can also be conducted to evaluate the impact of an altered intestinal lumen environment. In conclusion, the rat continuous intestine absorption model may provide a useful tool to predict the impact of varying drug formulations on rat oral absorption profiles.

Opportunities and challenges of physiologically based pharmacokinetic modeling in drug delivery

Physiologically based pharmacokinetic (PBPK) modeling is an important in silico tool to bridge drug properties and in vivo PK behaviors during drug development. Over the recent decade, the PBPK method has been largely applied to drug delivery systems (DDS), including oral, inhaled, transdermal, ophthalmic, and complex injectable products. The related therapeutic agents have included small-molecule drugs, therapeutic proteins, nucleic acids, and even cells. Simulation results have provided important insights into PK behaviors of new dosage forms, which strongly support drug regulation. In this review, we comprehensively summarize recent progress in PBPK applications in drug delivery, which shows large opportunities for facilitating drug development. In addition, we discuss the challenges of applying this methodology from a practical viewpoint.

Lilly Absorption Modeling Platform: A Tool for Early Absorption Assessment

Oral drug absorption modeling has developed at a rapid pace in the 40 years or so since the first ideas for mathematical approaches to oral absorption were introduced. The success of compartmental approaches accelerated the uptake of absorption modeling, and over the last 20 years, work on absorption modeling has shifted almost exclusively to the compartmental framework. This report describes a new noncompartmental absorption modeling framework, the Lilly Absorption Modeling Platform (LAMP). LAMP connects a well-mixed stomach to a continuous tube model of the small intestine with plug flow. Within the continuous tube framework, the model includes intestinal mixing and a novel highly tunable precipitation model that can describe a combination of rapid nucleation and slow growth. The framework is designed to balance speed, consistency, and ease of use with a minimum of model complexity to capture the essential features of gastrointestinal (GI) physiology and critical elements of the oral absorption process. The model was validated based on predictions of the fraction absorbed and the maximum absorbable dose for a set of Eli Lilly and Company clinical compounds.

Hepatectomy-Induced Alterations in Hepatic Perfusion and Function - Toward Multi-Scale Computational Modeling for a Better Prediction of Post-hepatectomy Liver Function

Liver resection causes marked perfusion alterations in the liver remnant both on the organ scale (vascular anatomy) and on the microscale (sinusoidal blood flow on tissue level). These changes in perfusion affect hepatic functions via direct alterations in blood supply and drainage, followed by indirect changes of biomechanical tissue properties and cellular function. Changes in blood flow impose compression, tension and shear forces on the liver tissue. These forces are perceived by mechanosensors on parenchymal and non-parenchymal cells of the liver and regulate cell-cell and cell-matrix interactions as well as cellular signaling and metabolism. These interactions are key players in tissue growth and remodeling, a prerequisite to restore tissue function after PHx. Their dysregulation is associated with metabolic impairment of the liver eventually leading to liver failure, a serious post-hepatectomy complication with high morbidity and mortality. Though certain links are known, the overall functional change after liver surgery is not understood due to complex feedback loops, non-linearities, spatial heterogeneities and different time-scales of events. Computational modeling is a unique approach to gain a better understanding of complex biomedical systems. This approach allows (i) integration of heterogeneous data and knowledge on multiple scales into a consistent view of how perfusion is related to hepatic function; (ii) testing and generating hypotheses based on predictive models, which must be validated experimentally and clinically. In the long term, computational modeling will (iii) support surgical planning by predicting surgery-induced perfusion perturbations and their functional (metabolic) consequences; and thereby (iv) allow minimizing surgical risks for the individual patient. Here, we review the alterations of hepatic perfusion, biomechanical properties and function associated with hepatectomy. Specifically, we provide an overview over the clinical problem, preoperative diagnostics, functional imaging approaches, experimental approaches in animal models, mechanoperception in the liver and impact on cellular metabolism, omics approaches with a focus on transcriptomics, data integration and uncertainty analysis, and computational modeling on multiple scales. Finally, we provide a perspective on how multi-scale computational models, which couple perfusion changes to hepatic function, could become part of clinical workflows to predict and optimize patient outcome after complex liver surgery.

Assessing the impacts on fetal dosimetry of the modelling of the placental transfers of xenobiotics in a pregnancy physiologically based pharmacokinetic model

The developmental origin of health and diseases theory supports the critical role of the fetal exposure to children's health. We developed a physiologically based pharmacokinetic model for human pregnancy (pPBPK) to simulate the maternal and fetal dosimetry throughout pregnancy. Four models of the placental exchanges of chemicals were assessed on ten chemicals for which maternal and fetal data were available. These models were calibrated using non-animal methods: in vitro (InV) or ex vivo (ExV) data, a semi-empirical relationship (SE), or the limitation by the placental perfusion (PL). They did not impact the maternal pharmacokinetics but provided different profiles in the fetus. The PL and InV models performed well even if the PL model overpredicted the fetal exposure for some substances. The SE and ExV models showed the lowest global performance and the SE model a tendency to underprediction. The comparison of the profiles showed that the PL model predicted an increase in the fetal exposure with the pregnancy age, whereas the ExV model predicted a decrease. For the SE and InV models, a small decrease was predicted during the second trimester. All models but the ExV one, presented the highest fetal exposure at the end of the third trimester. Global sensitivity analyses highlighted the predominant influence of the placental transfers on the fetal exposure, as well as the metabolic clearance and the fraction unbound. Finally, the four transfer models could be considered depending on the framework of the use of the pPBPK model and the availability of data or resources to inform their parametrization.

A workflow to build PBTK models for novel species

Physiology-based pharmacokinetic and toxicokinetic (PBPK/TK) models allow us to simulate the concentration of xenobiotica in the plasma and different tissues of an organism. PBPK/TK models are therefore routinely used in many fields of life sciences to simulate the physiological concentration of exogenous compounds in plasma and tissues. The application of PBTK models in ecotoxicology, however, is currently hampered by the limited availability of models for focal species. Here, we present a best practice workflow that describes how to build PBTK models for novel species. To this end, we extrapolated eight previously established rabbit models for several drugs to six additional mammalian species (human, beagle, rat, monkey, mouse, and minipig). We used established PBTK models for these species to account for the species-specific physiology. The parameter sensitivity in the resulting 56 PBTK models was systematically assessed to rank the relevance of the parameters on overall model performance. Interestingly, more than 80% of the 609 considered model parameters showed a negligible sensitivity throughout all models. Only approximately 5% of all parameters had a high sensitivity in at least one of the PBTK models. This approach allowed us to rank the relevance of the various parameters on overall model performance. We used this information to formulate a best practice guideline for the efficient development of PBTK models for novel animal species. We believe that the workflow proposed in this study will significantly support the development of PBTK models for new animal species in the future.

The Segregated Intestinal Flow Model (SFM) for Drug Absorption and Drug Metabolism: Implications on Intestinal and Liver Metabolism and Drug-Drug Interactions

The properties of the segregated flow model (SFM), which considers split intestinal flow patterns perfusing an active enterocyte region that houses enzymes and transporters (<20% of the total intestinal blood flow) and an inactive serosal region (>80%), were compared to those of the traditional model (TM), wherein 100% of the flow perfuses the non-segregated intestine tissue. The appropriateness of the SFM model is important in terms of drug absorption and intestinal and liver drug metabolism. Model behaviors were examined with respect to intestinally (M1) versus hepatically (M2) formed metabolites and the availabilities in the intestine (F_I) and liver (F_H) and the route of drug administration. The %contribution of the intestine to total first-pass metabolism bears a reciprocal relation to that for the liver, since the intestine, a gateway tissue, regulates the flow of substrate to the liver. The SFM predicts the highest and lowest M1 formed with oral (po) and intravenous (iv) dosing, respectively, whereas the extent of M1 formation is similar for the drug administered po or iv according to the TM, and these values sit intermediate those of the SFM. The SFM is significant, as this drug metabolism model explains route-dependent intestinal metabolism, describing a higher extent of intestinal metabolism with po versus the much reduced or absence of intestinal metabolism with iv dosing. A similar pattern exists for drug–drug interactions (DDIs). The inhibitor or inducer exerts its greatest effect on victim drugs when both inhibitor/inducer and drug are given po. With po dosing, more drug or inhibitor/inducer is brought into the intestine for DDIs. The bypass of flow and drug to the enterocyte region of the intestine after intravenous administration adds complications to in vitro–in vivo extrapolations (IVIVE).

Physiology of the Neonatal Gastrointestinal System Relevant to the Disposition of Orally Administered Medications

A thorough knowledge of the newborn (age, birth to 1 month postpartum) infant's gastrointestinal tract (GIT) is critical to the evaluation of the absorption, distribution, metabolism, and excretion (ADME) of orally administered drugs in this population. Developmental changes in the GIT during the newborn period are important for nutrient uptake as well as the disposition of orally administered medications. Some aspects of gastrointestinal function do not mature until driven by increased dietary complexity and nutritional demands later in the postnatal period. The functionalities present at birth, and subsequent maturation, can also impact the ADME parameters of orally administered compounds. This review will examine some specific contributors to the ADME processes in human neonates, as well as what is currently understood about the drivers for their maturation. Key species differences will be highlighted, with a focus on laboratory animals used in juvenile toxicity studies. Because of the gaps and inconsistencies in our knowledge, we will also highlight areas where additional study is warranted to better inform the appropriate use of medicines specifically intended for neonates.

Development and validation of a physiology-based model for the prediction of pharmacokinetics/toxicokinetics in rabbits

The environmental fates of pharmaceuticals and the effects of crop protection products on non-target species are subjects that are undergoing intense review. Since measuring the concentrations and effects of xenobiotics on all affected species under all conceivable scenarios is not feasible, standard laboratory animals such as rabbits are tested, and the observed adverse effects are translated to focal species for environmental risk assessments. In that respect, mathematical modelling is becoming increasingly important for evaluating the consequences of pesticides in untested scenarios. In particular, physiologically based pharmacokinetic/toxicokinetic (PBPK/TK) modelling is a well-established methodology used to predict tissue concentrations based on the absorption, distribution, metabolism and excretion of drugs and toxicants. In the present work, a rabbit PBPK/TK model is developed and evaluated with data available from the literature. The model predictions include scenarios of both intravenous (i.v.) and oral (p.o.) administration of small and large compounds. The presented rabbit PBPK/TK model predicts the pharmacokinetics (Cmax, AUC) of the tested compounds with an average 1.7-fold error. This result indicates a good predictive capacity of the model, which enables its use for risk assessment modelling and simulations.

Continuous Intestinal Absorption Model Based on the Convection-Diffusion Equation

Prediction of the rate and extent of drug absorption upon oral dosing needs models that capture the complexities of both the drug molecule and intestinal physiology. We report here the development of a continuous intestinal absorption model based on the convection-diffusion equation. The model includes explicit enterocyte apical membrane and intracellular lipid radial compartments along the length of the intestine. Physiologic functions along length x are built into the model and include velocity, diffusion, surface areas, and pH of the intestine. Also included are expression levels of the intestinal active uptake transporter OATP2B1 and efflux transporter P-gp. Oral dosing of solution as well as solid (with a dissolution function) was modeled for several drugs. The fraction absorbed (FA) and concentration-time (C-t) profiles were predicted and compared with clinical data. Overall, FA was well predicted upon oral (n = 21) or colonic dosing (n = 11), with four outliers. The overall accuracy (prediction of the correct bin) was 81% with outliers and 90% without outliers. Of the nine solution dosing data sets, six drugs were very well predicted with an exposure overlap coefficient (EOC) > 0.9 and predicted C_max and T_max values similar to those observed. Of the six solid dose formulations evaluated, the EOC values were > 0.9 for all drugs except budesonide. The observed precipitation of nifedipine at high doses was predicted by the model. Most of the poor predictions were for drugs that are known to be transporter substrates. As proof of concept, incorporating OATP2B1 and P-gp markedly improved the EOC and predicted C_max and T_max for fexofenadine. Finally, the continuous intestinal model accurately recapitulated the known relationships between drug absorption and permeability, solubility, and particle size. Together, these results indicate that this preliminary intestinal absorption model offers a simple and straightforward framework to build in complexities such as drug permeability, lipid partitioning, solubility, metabolism, and transport for improved prediction of the rate and extent of drug absorption.

Physiologically based pharmacokinetic and pharmacodynamic modeling in cancer drug development: status, potential and gaps

INTRODUCTION

Modeling and simulation have become important means of answering questions relevant to the development of a drug, making it possible to assess risks early and to reduce costs. Physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models contribute to a comprehensive understanding of the drug, covering specific questions from early discovery through lifecycle management stages. As for other disease areas, in oncology, PBPK and PD models are important topics that remain to be addressed.

AREAS COVERED

This review describes current PBPK and PD approaches, their applicability in drug development in general and specifically in the area of oncology. It discusses the current status and then focuses on key challenges and the potential for future use. It provides cases in which modeling currently cannot answer the questions and assesses the requirements to close gaps for PBPK/PD in oncology.

EXPERT OPINION

PBPK/PD models have led to improvements in identifying risks and reducing costs during the drug development process. Nevertheless, there is a lot of potential, where more rigorous integration of biological knowledge and specific experimental design would result in a more comprehensive biological picture. Ideally, such approaches would reveal the extent to which preclinical work can be extrapolated to clinical settings, thus enabling reliable prediction and, ultimately, reducing failed trials in clinical oncology.

Bayesian Population Physiologically-Based Pharmacokinetic (PBPK) Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations

Interindividual variability in anatomical and physiological properties results in significant differences in drug pharmacokinetics. The consideration of such pharmacokinetic variability supports optimal drug efficacy and safety for each single individual, e.g. by identification of individual-specific dosings. One clear objective in clinical drug development is therefore a thorough characterization of the physiological sources of interindividual variability. In this work, we present a Bayesian population physiologically-based pharmacokinetic (PBPK) approach for the mechanistically and physiologically realistic identification of interindividual variability. The consideration of a generic and highly detailed mechanistic PBPK model structure enables the integration of large amounts of prior physiological knowledge, which is then updated with new experimental data in a Bayesian framework. A covariate model integrates known relationships of physiological parameters to age, gender and body height. We further provide a framework for estimation of the a posteriori parameter dependency structure at the population level. The approach is demonstrated considering a cohort of healthy individuals and theophylline as an application example. The variability and co-variability of physiological parameters are specified within the population; respectively. Significant correlations are identified between population parameters and are applied for individual- and population-specific visual predictive checks of the pharmacokinetic behavior, which leads to improved results compared to present population approaches. In the future, the integration of a generic PBPK model into an hierarchical approach allows for extrapolations to other populations or drugs, while the Bayesian paradigm allows for an iterative application of the approach and thereby a continuous updating of physiological knowledge with new data. This will facilitate decision making e.g. from preclinical to clinical development or extrapolation of PK behavior from healthy to clinically significant populations.

A new physiologically based pharmacokinetic model for the prediction of gastrointestinal drug absorption: translocation model

This study aimed to construct a new local pharmacokinetic model of gastrointestinal absorption, the translocation model (TLM), using an anatomically relevant, minimally segmented structure to explain linear and nonlinear intestinal absorption, metabolism, and transport. The TLM was based on the concept of a single absorption site that flexibly moves, expands, and shrinks along with the length of the gastrointestinal tract after the intake of an oral dose. The structure of the small intestine is continuous, and various time- and location-dependent issues are freely incorporated in the analysis. Since the model has only one absorption site, understanding and modification of factors affecting absorption are simple. The absorption site is composed of four compartments: solid drug in the lumen, solution drug in the lumen, concentration in the enterocytes, and concentration in the lamina propria. The lamina propria includes the blood capillaries. Blood flow in the absorption site of the lamina propria appropriately accounts for the absorption. In the TLM, the permeability of the apical membrane and that of the basolateral membrane are distinct. By considering plicate, villi, and microvilli expansions of the surface area, the apparent permeability measured in Caco-2 experiments was converted to the effective permeability in vivo. The intestinal availability, bioavailability, and dose product of intestinal availability and absorption rate relationship of the model drugs were well explained using the TLM. The TLM would be a useful tool for the consideration of local pharmacokinetics in the gastrointestinal tract in various situations.

Physiology-based pharmacokinetic modeling: ready to be used

Physiology-based pharmacokinetic (PBPK) modeling is well recognized as a technology for mechanistically simulating and predicting the fate of substances in a mammalian body. Today, the demand for this methodology is higher than ever. The pharma industry and regulatory agencies are looking for new methods, which help to speed up and increase the efficiency of the development process for new drugs. Implementing PBPK modeling in the drug research and development workflow contributes significantly to reach this goal.:

A novel suspension formulation enhances intestinal absorption of macromolecules via transient and reversible transport mechanisms

PURPOSE

Medium chain fatty acid salts promote absorption by increasing paracellular permeability of the intestinal epithelium. Novel oily suspension (OS) formulation disperses a powder containing sodium caprylate and macromolecules such as octreotide or fluorescent dextran (FD). Formulation safety, macromolecule absorption and pharmacokinetic (PK)/pharmacodynamic (PD) were evaluated.

METHODS

Octreotide/OS toxicity was evaluated in monkeys following 9 months of daily oral enteric-coated capsule administration. The OS permeation effect was also assessed in rats, using FD/OS and octreotide/OS preparations. Octreotide/OS effects on circulating growth hormone (GH) levels were also measured.

RESULTS

Safety assessment of octreotide/OS in monkeys after 9 months showed minor drug-related findings, comparable to the injectable octreotide. Octreotide exposure levels were similar across the treatment periods. In rats, OS facilitated FD permeation up to 70 kDa in a reversible, spatial and dose-dependent manner, independent of the intestinal dosing site. Following OS administration, the staining pattern of the tight-junction protein, ZO-1, changed transiently, and a paracellular penetration marker, LC-biotin, permeated between adjacent epithelial cells. Enteral octreotide/OS absorption was dose-dependent and suppressed rat GH levels.

CONCLUSIONS

Oral octreotide/OS dosing was shown to be safe in monkeys. OS enhances intestinal absorption of active octreotide, likely by transient alteration of the tight junction protein complex.

Incorporation of physiologically based pharmacokinetic modeling in the evaluation of solubility requirements for the salt selection process: a case study using phenytoin

In the pharmaceutical industry, salt is commonly used to improve the oral bioavailability of poorly soluble compounds. Currently, there is a limited understanding on the solubility requirement for salts that will translate to improvement in oral exposure. Despite the obvious need, there is very little research reported in this area mainly due to the complexity of such a system. To our knowledge, no report has been published to guide this important process and salt solubility requirement still remains unanswered. Physiologically based pharmacokinetic (PBPK) modeling offers a means to dynamically integrate the complex interplay of the processes determining oral absorption. A sensitivity analysis was performed using a PBPK model describing phenytoin to determine a solubility requirement for phenytoin salts needed to achieve optimal oral bioavailability for a given dose. Based on the analysis, it is predicted that phenytoin salts with solubility greater than 0.3 mg/mL would show no further increases in oral bioavailability. A salt screen was performed using a variety of phenytoin salts. The piperazine and sodium salts showed the lowest and highest aqueous solubility and were tested in vivo. Consistent with our analysis, we observed no significant differences in oral bioavailability for these two salts despite an approximate 60 fold difference in solubility. Our study illustrates that higher solubility salts sometimes provide no additional improvements in oral bioavailability and PBPK modeling can be utilized as an important tool to provide guidance to the salt selection and define a salt solubility requirement.

PBPK models for the prediction of in vivo performance of oral dosage forms

Drug absorption from the gastrointestinal (GI) tract is a highly complex process dependent upon numerous factors including the physicochemical properties of the drug, characteristics of the formulation and interplay with the underlying physiological properties of the GI tract. The ability to accurately predict oral drug absorption during drug product development is becoming more relevant given the current challenges facing the pharmaceutical industry. Physiologically-based pharmacokinetic (PBPK) modeling provides an approach that enables the plasma concentration-time profiles to be predicted from preclinical in vitro and in vivo data and can thus provide a valuable resource to support decisions at various stages of the drug development process. Whilst there have been quite a few successes with PBPK models identifying key issues in the development of new drugs in vivo, there are still many aspects that need to be addressed in order to maximize the utility of the PBPK models to predict drug absorption, including improving our understanding of conditions in the lower small intestine and colon, taking the influence of disease on GI physiology into account and further exploring the reasons behind population variability. Importantly, there is also a need to create more appropriate in vitro models for testing dosage form performance and to streamline data input from these into the PBPK models. As part of the Oral Biopharmaceutical Tools (OrBiTo) project, this review provides a summary of the current status of PBPK models available. The current challenges in PBPK set-ups for oral drug absorption including the composition of GI luminal contents, transit and hydrodynamics, permeability and intestinal wall metabolism are discussed in detail. Further, the challenges regarding the appropriate integration of results from in vitro models, such as consideration of appropriate integration/estimation of solubility and the complexity of the in vitro release and precipitation data, are also highlighted as important steps to advancing the application of PBPK models in drug development. It is expected that the "innovative" integration of in vitro data from more appropriate in vitro models and the enhancement of the GI physiology component of PBPK models, arising from the OrBiTo project, will lead to a significant enhancement in the ability of PBPK models to successfully predict oral drug absorption and advance their role in preclinical and clinical development, as well as for regulatory applications.

Utilizing in vitro and PBPK tools to link ADME characteristics to plasma profiles: case example nifedipine immediate release formulation

One of the most prominent food-drug interactions is the inhibition of intestinal cytochrome P450 (CYP) 3A enzymes by grapefruit juice ingredients, and, as many drugs are metabolized via CYP 3A, this interaction can be of clinical importance. Calcium channel-blocking agents of the dihydropyridine type, such as felodipine and nifedipine, are subject to extensive intestinal first pass metabolism via CYP 3A, thus resulting in significantly enhanced in vivo exposure of the drug when administered together with grapefruit juice. Physiologically based pharmacokinetic (PBPK) modeling was used to simulate pharmacokinetics of a nifedipine immediate release formulation following concomitant grapefruit juice ingestion, that is, after inhibition of small intestinal CYP 3A enzymes. For this purpose, detailed data about CYP 3A levels were collected from the literature and implemented into commercial PBPK software. As literature reports show that grapefruit juice (i) leads to a marked delay in gastric emptying, and (ii) rapidly lowers the levels of intestinal CYP 3A enzymes, inhibition of intestinal first pass metabolism following ingestion of grapefruit juice was simulated by altering the intestinal CYP 3A enzyme levels and simultaneously decelerating the gastric emptying rate. To estimate the in vivo dispersion and dissolution behavior of the formulation, dissolution tests in several media simulating both the fasted and fed state stomach and small intestine were conducted, and the results from the in vitro dissolution tests were used as input function to describe the in vivo dissolution of the drug. Plasma concentration-time profiles of the nifedipine immediate release formulation both with and without simultaneous CYP 3A inhibition were simulated, and the results were compared with data gathered from the literature. Using this approach, nifedipine plasma profiles could be simulated well both with and without enzyme inhibition. A reduction in small intestinal CYP 3A levels by 60% was found to yield the best results, with simulated nifedipine concentration-time profiles within 20% of the in vivo observed results. By additionally varying the dissolution input of the PBPK model, a link between the dissolution characteristics of the formulation and its in vivo performance could be established.

Using Bayesian-PBPK modeling for assessment of inter-individual variability and subgroup stratification

Purpose

Inter-individual variability in clinical endpoints and occurrence of potentially severe adverse effects represent an enormous challenge in drug development at all phases of (pre-)clinical research. To ensure patient safety it is important to identify adverse events or critical subgroups within the population as early as possible. Hence, a comprehensive understanding of the processes governing pharmacokinetics and pharmacodynamics is of utmost importance. In this paper we combine Bayesian statistics with detailed mechanistic physiologically-based pharmacokinetic (PBPK) models. On the example of pravastatin we demonstrate that this combination provides a powerful tool to investigate inter-individual variability in groups of patients and to identify clinically relevant homogenous subgroups in an unsupervised approach. Since PBPK models allow the identification of physiological, drug-specific and genotype-specific knowledge separately, our approach supports knowledge-based extrapolation to other drugs or populations.

Methods

PBPK models are based on generic distribution models and extensive collections of physiological parameters and allow a mechanistic investigation of drug distribution and drug action. To systematically account for parameter variability within patient populations, a Bayesian-PBPK approach is developed rigorously quantifying the probability of a parameter given the amount of information contained in the measured data. Since these parameter distributions are high-dimensional, a Markov chain Monte Carlo algorithm is used, where the physiological and drug-specific parameters are considered in separate blocks.

Results

Considering pravastatin pharmacokinetics as an application example, Bayesian-PBPK is used to investigate inter-individual variability in a cohort of 10 patients. Correlation analyses infer structural information about the PBPK model. Moreover, homogeneous subpopulations are identified a posteriori by examining the parameter distributions, which can even be assigned to a polymorphism in the hepatic organ anion transporter OATP1B1.

Conclusions

The presented Bayesian-PBPK approach systematically characterizes inter-individual variability within a population by updating prior knowledge about physiological parameters with new experimental data. Moreover, clinically relevant homogeneous subpopulations can be mechanistically identified. The large scale PBPK model separates physiological and drug-specific knowledge which allows, in combination with Bayesian approaches, the iterative assessment of specific populations by integrating information from several drugs.

Electronic supplementary material

The online version of this article (doi:10.1186/2193-9616-1-6) contains supplementary material, which is available to authorized users.

Keeping a critical eye on the science and the regulation of oral drug absorption: a review

This review starts with an introduction on the theoretical aspects of biopharmaceutics and developments in this field from mid-1950s to late 1970s. It critically addresses issues related to fundamental processes in oral drug absorption such as the complex interplay between drugs and the gastrointestinal system. Special emphasis is placed on drug dissolution and permeability phenomena as well as on the mathematical modeling of oral drug absorption. The review ends with regulatory aspects of oral drug absorption focusing on bioequivalence studies and the US Food and Drug Administration and European Medicines Agency guidelines dealing with Biopharmaceutics Classification System and Biopharmaceutic Drug Disposition Classification System.

A detailed physiologically based model to simulate the pharmacokinetics and hormonal pharmacodynamics of enalapril on the circulating endocrine Renin-Angiotensin-aldosterone system

The renin-angiotensin-aldosterone system (RAAS) plays a key role in the pathogenesis of cardiovascular disorders including hypertension and is one of the most important targets for drugs. A whole body physiologically based pharmacokinetic (wb PBPK) model integrating this hormone circulation system and its inhibition can be used to explore the influence of drugs that interfere with this system, and thus to improve the understanding of interactions between drugs and the target system. In this study, we describe the development of a mechanistic RAAS model and exemplify drug action by a simulation of enalapril administration. Enalapril and its metabolite enalaprilat are potent inhibitors of the angiotensin-converting-enzyme (ACE). To this end, a coupled dynamic parent-metabolite PBPK model was developed and linked with the RAAS model that consists of seven coupled PBPK models for aldosterone, ACE, angiotensin 1, angiotensin 2, angiotensin 2 receptor type 1, renin, and prorenin. The results indicate that the model represents the interactions in the RAAS in response to the pharmacokinetics (PK) and pharmacodynamics (PD) of enalapril and enalaprilat in an accurate manner. The full set of RAAS-hormone profiles and interactions are consistently described at pre- and post-administration steady state as well as during their dynamic transition and show a good agreement with literature data. The model allows a simultaneous representation of the parent-metabolite conversion to the active form as well as the effect of the drug on the hormone levels, offering a detailed mechanistic insight into the hormone cascade and its inhibition. This model constitutes a first major step to establish a PBPK-PD-model including the PK and the mode of action (MoA) of a drug acting on a dynamic RAAS that can be further used to link to clinical endpoints such as blood pressure.

Multiscale mechanistic modeling in pharmaceutical research and development

Discontinuation of drug development projects due to lack of efficacy or adverse events is one of the main cost drivers in pharmaceutical research and development (R&D). Investments have to be written-off and contribute to the total costs of a successful drug candidate receiving marketing authorization and allowing return on invest. A vital risk for pharmaceutical innovator companies is late stage clinical failure since costs for individual clinical trials may exceed the one billion Euro threshold. To guide investment decisions and to safeguard maximum medical benefit and safety for patients recruited in clinical trials, it is therefore essential to understand the clinical consequences of all information and data generated. The complexity of the physiological and pathophysiological processes and the sheer amount of information available overcharge the mental capacity of any human being and prevent a prediction of the success in clinical development. A rigorous integration of knowledge, assumption, and experimental data into computational models promises a significant improvement of the rationalization of decision making in pharmaceutical industry. We here give an overview of the current status of modeling and simulation in pharmaceutical R&D and outline the perspectives of more recent developments in mechanistic modeling. Specific modeling approaches for different biological scales ranging from intracellular processes to whole organism physiology are introduced and an example for integrative multiscale modeling of therapeutic efficiency in clinical oncology trials is showcased.

Integrating cellular metabolism into a multiscale whole-body model

Cellular metabolism continuously processes an enormous range of external compounds into endogenous metabolites and is as such a key element in human physiology. The multifaceted physiological role of the metabolic network fulfilling the catalytic conversions can only be fully understood from a whole-body perspective where the causal interplay of the metabolic states of individual cells, the surrounding tissue and the whole organism are simultaneously considered. We here present an approach relying on dynamic flux balance analysis that allows the integration of metabolic networks at the cellular scale into standardized physiologically-based pharmacokinetic models at the whole-body level. To evaluate our approach we integrated a genome-scale network reconstruction of a human hepatocyte into the liver tissue of a physiologically-based pharmacokinetic model of a human adult. The resulting multiscale model was used to investigate hyperuricemia therapy, ammonia detoxification and paracetamol-induced toxication at a systems level. The specific models simultaneously integrate multiple layers of biological organization and offer mechanistic insights into pathology and medication. The approach presented may in future support a mechanistic understanding in diagnostics and drug development.

Cellular metabolism is a key element in human physiology. Ideally the metabolic network needs to be considered within the context of the surrounding tissue and organism since the various levels of biological organization are mutually influencing each other. To mechanistically describe the interplay between intracellular space and extracellular environment, we here integrate the genome-scale metabolic network model HepatoNet1 at the cellular scale into physiologically-based pharmacokinetic models at the whole-body level. The resulting multiscale model allows the quantitative description of metabolic behavior in the context of time-resolved metabolite concentration profiles in the body and the surrounding liver tissue. The model has been applied to three case studies covering fundamental aspects of medicine and pharmacology: drug administration, biomarker identification and drug-induced toxication. Most notably, our multiscale approach fosters an improved quantitative understanding of drug action and the impact of metabolic disorders at an organism level, based on a genome-scale representation of cellular metabolism. Computational models such as the one presented include various aspects of human physiology and may therefore significantly support rational approaches in medical diagnostics and pharmaceutical drug development in the future.

Physiologically Based Pharmacokinetic Modeling of Tamoxifen and its Metabolites in Women of Different CYP2D6 Phenotypes Provides New Insight into the Tamoxifen Mass Balance

Tamoxifen is a first-line endocrine agent in the mechanism-based treatment of estrogen receptor positive (ER⁺) mammary carcinoma and applied to breast cancer patients all over the world. Endoxifen is a secondary and highly active metabolite of tamoxifen that is formed among others by the polymorphic cytochrome P450 2D6 (CYP2D6). It is widely accepted that CYP2D6 poor metabolizers exert a pronounced decrease in endoxifen steady-state plasma concentrations compared to CYP2D6 extensive metabolizers. Nevertheless, an in-depth understanding of the chain of cause and effect between CYP2D6 genotype, endoxifen steady-state plasma concentration, and subsequent tamoxifen treatment benefit still remains to be evolved. In this study, physiologically based pharmacokinetic (PBPK)-modeling was applied to mechanistically investigate the impact of CYP2D6 phenotype on endoxifen formation in female breast cancer patients undergoing tamoxifen therapy. A PBPK-model of tamoxifen and its pharmacologically important metabolites N-desmethyltamoxifen (NDM-TAM), 4-hydroxytamoxifen (4-OH-TAM), and endoxifen was developed and validated. This model is able to simulate the pharmacokinetics (PK) after single and repeated oral tamoxifen doses in female breast cancer patients in dependence of the CYP2D6 phenotype. A detailed model-based analysis of the mass balance offered support for a recent hypothesis stating a more prominent role for endoxifen formation from 4-OH-TAM. In the future this model provides a good basis to further investigate the linkage of PK, mode of action, and treatment outcome in dependence of factors such as phenotype, ethnicity, or co-treatment with CYP2D6 inhibitors.

Integration of dissolution into physiologically-based pharmacokinetic models III: PK-Sim®

OBJECTIVES

In-silico methods are a cost-effective possibility to support decision making at different stages of the drug development process. Among the various computational methods available, physiologically-based pharmacokinetic (PBPK) modelling represents a well-established tool for mechanistically predicting the pharmacokinetics of drugs and drug candidates. PK-Sim, a component of the Computational Systems Biology Software Suite of Bayer Technology Services GmbH (Leverkusen, Germany) is a commercial PBPK software tool. It is based on a generic model structure for typical animal species from mice to monkey and humans, and allows simultaneous simulation of drug liberation, absorption, distribution, metabolism, and excretion in one model. In this study PK-Sim has been used for the prediction of the in-vivo pharmacokinetics of drugs with a particular focus on the integration of dissolution properties and, due to its leading role in the drug development process, for the performance of different dosage forms administered via the oral route.

METHODS

Three real life case studies have been presented to exemplify the benefits of using PBPK absorption modelling.

KEY FINDINGS

In the first example, the in-vivo dissolution rate was directly predicted from the physical properties of different particle formulations using a mechanistic dissolution model of the Noyes-Whitney type. In the second case study, the PBPK tool was successfully used to predict the food effect in humans based on data obtained in Beagle dogs. In the third example, the utilization of the software for the support of the development of a combined immediate release-controlled release formulation has been described.

CONCLUSIONS

Future perspectives of the use of PBPK modelling have been discussed, with a special focus on the integration of in-vitro dissolution data into PBPK models for oral and non-oral administration of drugs.

Prediction of a potentially effective dose in humans for BAY 60-5521, a potent inhibitor of cholesteryl ester transfer protein (CETP) by allometric species scaling and combined pharmacodynamic and physiologically-based pharmacokinetic modelling

AIMS

The purpose of this work was to support the prediction of a potentially effective dose for the CETP-inhibitor, BAY 60-5521, in humans.

METHODS

A combination of allometric scaling of the pharmacokinetics of the CETP-inhibitor BAY 60-5521 with pharmacodynamic studies in CETP-transgenic mice and in human plasma with physiologically-based pharmacokinetic (PBPK) modelling was used to support the selection of the first-in-man dose.

RESULTS

The PBPK approach predicts a greater extent of distribution for BAY 60-5521 in humans compared with the allometric scaling method as reflected by a larger predicted volume of distribution and longer elimination half-life. The combined approach led to an estimate of a potentially effective dose for BAY 60-5521 of 51 mg in humans.

CONCLUSION

The approach described in this paper supported the prediction of a potentially effective dose for the CETP-inhibitor BAY 60-5521 in humans. Confirmation of the dose estimate was obtained in a first-in-man study.

Mathematical modeling of simultaneous drug release and in vivo absorption

The attention of this review is focussed on the mathematical modeling of the simultaneous processes of drug release and absorption/distribution/metabolism/elimination (ADME processes) following different administration routes. Among all of them, for their clinical importance, the oral, transdermal and local delivery are considered. The bases of the presented mathematical models are shown after the discussion of the most relevant phenomena characterising the particular administration route considered. Then, model performances are compared to experimental evidences in order to evaluate their reliability and soundness. The most important conclusion of this review is that despite the complexity of the problem involved in the description of the fate of the drugs after their administration, the scientific community is close to the solution as witnessed by the various interesting and promising approaches here presented about the oral, transdermal and local administration routes.

Simulation of differential drug pharmacokinetics under heat and exercise stress using a physiologically based pharmacokinetic modeling approach

Under extreme conditions of heat exposure and exercise stress, the human body undergoes major physiological changes. Perturbations in organ blood flows, gastrointestinal properties, and vascular physiology may impact the body's ability to absorb, distribute, and eliminate drugs. Clinical studies on the effect of these stressors on drug pharmacokinetics demonstrate that the likelihood of pharmacokinetic alteration is dependent on drug properties and the intensity of the stressor. The objectives of this study were to use literature data to quantify the correlation between exercise and heat exposure intensity to changing physiological parameters and further, to use this information for the parameterization of a whole-body, physiologically based pharmacokinetic model for the purposes of determining those drug properties most likely to demonstrate altered drug pharmacokinetics under stress. Cardiac output and most organ blood flows were correlated with heart rate using regression analysis. Other altered parameters included hematocrit and intravascular albumin concentration. Pharmacokinetic simulations of intravenous and oral administration of hypothetical drugs with either a low or high value of lipophilicity, unbound fraction in plasma, and unbound intrinsic hepatic clearance demonstrated that the area under the curve of those drugs with a high unbound intrinsic clearance was most affected (up to a 130% increase) following intravenous administration, whereas following oral administration, pharmacokinetic changes were smaller (<40% increase in area under the curve) for all hypothetical compounds. A midazolam physiologically based pharmacokinetic model was also used to demonstrate that simulated changes in pharmacokinetic parameters under exercise and heat stress were generally consistent with those reported in the literature.

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

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

A computational systems biology software platform for multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks

Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multiscale by nature, project work, and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform consisting of PK-Sim^® and MoBi^® capable of building and simulating models that integrate across biological scales. A prototypical multiscale model for the progression of a pancreatic tumor and its response to pharmacotherapy is constructed and virtual patients are treated with a prodrug activated by hepatic metabolization. Tumor growth is driven by signal transduction leading to cell cycle transition and proliferation. Free tumor concentrations of the active metabolite inhibit Raf kinase in the signaling cascade and thereby cell cycle progression. In a virtual clinical study, the individual therapeutic outcome of the chemotherapeutic intervention is simulated for a large population with heterogeneous genomic background. Thereby, the platform allows efficient model building and integration of biological knowledge and prior data from all biological scales. Experimental in vitro model systems can be linked with observations in animal experiments and clinical trials. The interplay between patients, diseases, and drugs and topics with high clinical relevance such as the role of pharmacogenomics, drug–drug, or drug–metabolite interactions can be addressed using this mechanistic, insight driven multiscale modeling approach.

The impact of dosing interval in a novel tandem oral dosing strategy: enhancing the exposure of low solubility drug candidates in a preclinical setting

In drug discovery, time and resource constraints necessitate increasingly early decision making to accelerate or stop preclinical programs. Early discovery drug candidates may be potent inhibitors of new targets, but all too often exhibit poor pharmaceutical or pharmacokinetic properties that limit the in vivo exposure. Low solubility of a drug candidate often leads to poor oral bioavailability and poor dose linearity. This issue is more significant for efficacy and target safety studies where high drug exposures are desired. When solubility issues are confronted, enabling formulations are often required to improve the exposure. However, this approach often requires a substantial and lengthy investment to develop the formulation. Previously, we introduced a gastrointestinal (GI) transit time-based novel oral tandem dosing strategy that enhanced in vivo exposures in rats. In this study, a refined time interval versus dose theory was tested. The resulting in vivo exposures based on altering frequency and doses were compared, and significant impacts were found.

Utility of physiologically based absorption modeling in implementing Quality by Design in drug development

To implement Quality by Design (QbD) in drug development, scientists need tools that link drug products properties to in vivo performance. Physiologically based absorption models are potentially useful tools; yet, their utility of QbD implementation has not been discussed or explored much in the literature. We simulated pharmacokinetics (PK) of carbamazepine (CBZ) after administration of four oral formulations, immediate-release (IR) suspension, IR tablet, extended-release (XR) tablet and capsule, under fasted and fed conditions and presented a general diagram of a modeling and simulation strategy integrated with pharmaceutical development. We obtained PK parameters and absorption scale factors (ASFs) by deconvolution of the PK data for IR suspension under fasted condition. The model was validated for other PK profiles of IR formulations and used to predict PK for XR formulations. We explored three key areas where a modeling and simulation approach impacts QbD. First, the model was used to help identify optimal in vitro dissolution conditions for XR formulations. Second, identification of critical formulations variables was illustrated by a parameter sensitivity analysis of mean particle radius for the IR tablet that showed a PK shift with decreased particle radius, C (max) was increased and T (max) was decreased. Finally, virtual trial simulations allowed incorporation of inter-subject variability in the model. Virtual bioequivalence studies performed for two test formulations suggested that an in vitro dissolution test may be a more sensitive discriminative method than in vivo PK studies. In summary, a well-validated predictive model is a potentially useful tool for QbD implementation in drug development.

Influence of molecular properties on oral bioavailability of lipophilic drugs - mapping of bulkiness and different measures of polarity

The biopharmaceutical assessment of new drug candidates based on their chemical structure is important in drug discovery and development. The scope of this study is to focus on lipophilic drugs and to clarify the role of their chemical predictors on oral bioavailability in humans. First their chemical properties were calculated from molecular modeling and the bioavailability data was obtained from the literature. The data was then analyzed by a partial least square method including non-linear terms. Significant coefficients were identified from a group of polarity- and solubility-related properties. Contour plots were constructed mapping molecular weight together with different polarity factors. Depending on the molecular weight a maximal bioavailability was found at solubility parameters of about 31-35 (J/cm(3))(1/2) and HLB values of roughly 4-12. The mapping of lipophilic drugs also revealed that a solubility parameter of less than 20 (J/cm(3))(1/2) or a HLB of smaller than unity is critical for the drug-likeness of new compounds.