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.     

Comparison of Deconvolution-Based and Absorption Modeling IVIVC for Extended Release Formulations of a BCS III Drug Development Candidate

In vitro–in vivo correlations (IVIVC) are predictive mathematical models describing the relationship between dissolution and plasma concentration for a given drug compound. The traditional deconvolution/convolution-based approach is the most common methodology to establish a level A IVIVC that provides point to point relationship between the in vitro dissolution and the in vivo input rate. The increasing application of absorption physiologically based pharmacokinetic model (PBPK) has provided an alternative IVIVC approach. The current work established and compared two IVIVC models, via the traditional deconvolution/convolution method and via absorption PBPK modeling, for two types of modified release (MR) formulations (matrix and multi-particulate tablets) of MK-0941, a BCS III drug development candidate. Three batches with distinct release rates were studied for each formulation technology. A two-stage linear regression model was used for the deconvolution/convolution approach while optimization of the absorption scaling factors (a model parameter that relates permeability and input rate) in Gastroplus^TM Advanced Compartmental Absorption and Transit model was used for the absorption PBPK approach. For both types of IVIVC models established, and for either the matrix or the multiparticulate formulations, the average absolute prediction errors for AUC and C_max were below 10% and 15%, respectively. Both the traditional deconvolution/convolution-based and the absorption/PBPK-based level A IVIVC model adequately described the compound pharmacokinetics to guide future formulation development. This case study highlights the potential utility of absorption PBPK model to complement the traditional IVIVC approaches for MR products.KEY WORDS: absorption modeling, deconvolution, in vitro–in vivo correlation (IVIVC), modified release, physiologically based pharmacokinetic (PBPK) modeling     

The influence of lipophilicity in drug discovery and design

Introduction: The role of lipophilicity in drug discovery and design is a critical one. Lipophilicity is a key physicochemical property that plays a crucial role in determining ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties and the overall suitability of drug candidates. There is increasing evidence to suggest that control of physicochemical properties such as lipophilicity, within a defined optimal range, can improve compound quality and the likelihood of therapeutic success.

Areas covered: This review focuses on understanding lipophilicity, techniques used to measure lipophilicity, and summarizes the importance of lipophilicity in drug discovery and development, including a discussion of its impact on individual ADMET parameters as well as its overall influence on the drug discovery and design process, specifically within the past 15 years.

Expert opinion: A current review of the literature reveals a continued reliance on the synthesis of novel structures with increased potency, rather than a focus on maintaining optimal physicochemical properties associated with ADMET throughout drug optimization. Particular attention to the optimum region of lipophilicity, as well as monitoring of lipophilic efficiency indices, may contribute significantly to the overall quality of candidate drugs at different stages of discovery.     

Selecting oral bioavailability enhancing formulations during drug discovery and development

Introduction: The role of chemical structure, lipophilicity, physico-chemical, absorption, distribution, metabolism, excretion, toxicity (ADMET) and biopharmaceutical properties of compounds including bioavailability are critical in drug discovery and drug dosage forms design.

Areas covered: The authors discuss a number of parameters including computational approaches used for selected chemical structures with biological activity for lead optimization and chemogenomics and preclinical studies for ADMET process development of ligand properties. The authors also look at a number of other parameters including: early drug product formulations with method selection based on the biopharmaceutical classification system (BCS); in vitro–in vivo correlation (IVIVC) and different formulation strategies to enhance solubility; dissolution rate and permeability; bioavailability evaluation and quality by design as an opportunity to develop ‘safe space' regions, where bioavailability is unaffected by pharmaceutical variations.

Expert opinion: The biopharmaceutical requirements for absorption are solubility and permeability. Both are influenced by lipophilicity, but in the opposite way. The genomic methodology, coupled with combinatorial chemistry, high-throughput screening, structure-based design and in silico ADMET would yield parameters as a starting point for the biopharmaceutical properties determination in further preclinical and clinical studies. Consecutive stages in drug discovery and development are irreplaceable, but pharmacokinetics is the critical step. Selection of drug formulations based on the BCS, IVIVC are the principal aspects to enhance the solubility and dissolution rate, while a rationale management of pharmaceutical and technological factors will enhance the bioavailability.     

A Physiologically Based Pharmacokinetic Model of the Minipig: Data Compilation and Model Implementation

In today??s pharmaceutical research and development, physiologically-based pharmacokinetic (PBPK) modeling plays an important role in the design, evaluation and interpretation of pharmacokinetic, toxicokinetic and formulation studies. PBPK models incorporate in vitro physicochemical and biochemical data in a physiologically based model framework to simulate in vivo exposure. The comparison of simulated concentrations to those measured in in vivo studies can be used to gain insights into compound behavior and to inform PBPK based human pharmacokinetic predictions. The G?ttingen minipig is gaining importance as a large animal model in pharmaceutical research due to its physiological and anatomical similarities to human and is increasingly replacing dog and non-human primate in preclinical studies. However, no PBPK model for minipig has yet been published. This review discusses the information available to establish the physiological database for this species and highlights the gaps in current knowledge. A preliminary PBPK model is created from this database and simulations for two drugs dosed both intravenously and orally are compared to measured plasma concentrations. Results support the validity of the model with simulated plasma concentrations within the range of the observations. In conclusion, the model will need to be refined as additional physiological data become available, but it can already provide useful simulations to assist pharmaceutical research and development in the minipig.     

Methodologies to Assess Drug Permeation Through the Blood–Brain Barrier for Pharmaceutical Research

The drug discovery process for drugs that target the central nervous system suffers from a very high rate of failure due to the presence of the blood–brain barrier, which limits the entry of xenobiotics into the brain. To minimise drug failure at different stages of the drug development process, new methodologies have been developed to understand the absorption, distribution, metabolism, excretion and toxicity (ADMET) profile of drug candidates at early stages of drug development. Additionally, understanding the permeation of drug candidates is also important, particularly for drugs that target the central nervous system. During the first stages of the drug discovery process, in vitro methods that allow for the determination of permeability using high-throughput screening methods are advantageous. For example, performing the parallel artificial membrane permeability assay followed by cell-based models with interesting hits is a useful technique for identifying potential drugs. In silico models also provide interesting information but must be confirmed by in vitro models. Finally, in vivo models, such as in situ brain perfusion, should be studied to reduce a large number of drug candidates to a few lead compounds. This article reviews the different methodologies used in the drug discovery and drug development processes to determine the permeation of drug candidates through the blood–brain barrier.     

The use of in vitro testing to refine cumulative assessment groups of pesticides: The example of teratogenic conazoles

The most relevant issues in cumulative risk assessment (CRA) are the identification of cumulative assessment groups and the hypothesis of dose-additivity, at relevant human exposures. In vitro methods can provide meaningful data to help solving those issues. Integration of in vitro studies, selected in vivo studies, and PBPK modeling for teratogenic conazoles confirmed that in vitro studies may give results in a cheaper and faster fashion. In particular, in vitro studies with explanted rat embryos provided support for dose-additivity for conazoles causing cranio-facial malformations. Although this could not be immediately quantitatively transferred to the in vivo situation, they provided indication on how to conduct targeted in vivo studies. In addition, by means of PBPK modeling, it was possible to estimate the dose in humans associated with a defined teratogenic risk and also to conclude that for cumulative risk assessment only exposures occurring within a short period of time (a day or less) need to be cumulated. Although PBPK modeling cannot be widely applied, at least in the short term, it should be considered if available. It is recommended to incorporate in vitro testing and PBPK modeling, whenever available and feasible in the process of risk assessment, particularly of CRA.     

Physiologically Based Pharmacokinetic Modelling of Drug Penetration Across the Blood–Brain Barrier—Towards a Mechanistic IVIVE-Based Approach

Predicting the penetration of drugs across the human blood–brain barrier (BBB) is a significant challenge during their development. A variety of in vitro systems representing the BBB have been described, but the optimal use of these data in terms of extrapolation to human unbound brain concentration profiles remains to be fully exploited. Physiologically based pharmacokinetic (PBPK) modelling of drug disposition in the central nervous system (CNS) currently consists of fitting preclinical in vivo data to compartmental models in order to estimate the permeability and efflux of drugs across the BBB. The increasingly popular approach of using in vitro–in vivo extrapolation (IVIVE) to generate PBPK model input parameters could provide a more mechanistic basis for the interspecies translation of preclinical models of the CNS. However, a major hurdle exists in verifying these predictions with observed data, since human brain concentrations can’t be directly measured. Therefore a combination of IVIVE-based and empirical modelling approaches based on preclinical data are currently required. In this review, we summarise the existing PBPK models of the CNS in the literature, and we evaluate the current opportunities and limitations of potential IVIVE strategies for PBPK modelling of BBB penetration.     

Integrating in vitro testing and physiologically-based pharmacokinetic (PBPK) modelling for chemical liver toxicity assessment—A case study of troglitazone

In vitro to in vivo extrapolation (IVIVE) for next-generation risk assessment (NGRA) of chemicals requires computational modeling and faces unique challenges. Using mitochondria-related toxicity data of troglitazone (TGZ), a prototype drug known for liver toxicity, from HepaRG, HepG2, HC-04, and primary human hepatocytes, we explored inherent uncertainties in IVIVE, including cell models, cellular response endpoints, and dose metrics. A human population physiologically-based pharmacokinetic (PBPK) model for TGZ was developed to predict in vivo doses from in vitro point-of-departure (POD) concentrations. Compared to the 200–800 mg/d dose range of TGZ where liver injury was observed clinically, the predicted POD doses for the mean and top one percentile of the PBPK population were 28–372 and 15–178 mg/d respectively based on C_max dosimetry, and 185–2552 and 83–1010 mg/d respectively based on AUC. In conclusion, although with many uncertainties, integrating in vitro assays and PBPK modeling is promising in informing liver toxicity-inducing TGZ doses.     

Lipophilicity in drug discovery

Importance of the field: The role of lipophilicity in determining the overall quality of candidate drug molecules is of paramount importance. Recent developments suggest that, as well as determining pre-clinical ADMET (absorption, distribution, metabolism, elimination and toxicology) properties, compounds of optimal lipophilicity might have increased chances of success in development.

Areas covered in this review: The review covers aspects of methods of prediction of lipophilicity in frequent use and describes the most relevant literature analyses linking individual ADMET parameters and more composite measures of overall compound ‘quality’ with lipophilicity.

What the reader will gain: The aim is to provide an overview of the relevant literature in an attempt to summarise where the optimum region of lipophilicity lies and to highlight which particular issues and risks might be expected when operating outside this region.

Take home message: The review of the data shows that this optimal space is defined by a narrow range of logD between ～ 1 and 3. Some of the implications of this for medicinal chemistry optimisation are discussed.     

PHRMA CPCDC initiative on predictive models of human pharmacokinetics,part 5: Prediction of plasma concentration–time profiles in human by using the physiologically‐based pharmacokinetic modeling approach

The objective of this study is to assess the effectiveness of physiologically based pharmacokinetic (PBPK) models for simulating human plasma concentration–time profiles for the unique drug dataset of blinded data that has been assembled as part of a Pharmaceutical Research and Manufacturers of America initiative. Combinations of absorption, distribution, and clearance models were tested with a PBPK approach that has been developed from published equations. An assessment of the quality of the model predictions was made on the basis of the shape of the plasma time courses and related parameters. Up to 69% of the simulations of plasma time courses made in human demonstrated a medium to high degree of accuracy for intravenous pharmacokinetics, whereas this number decreased to 23% after oral administration based on the selected criteria. The simulations resulted in a general underestimation of drug exposure (C_max and AUC_0‐t). The explanations for this underestimation are diverse. Therefore, in general it may be due to underprediction of absorption parameters and/or overprediction of distribution or oral first‐pass. The implications of compound properties are demonstrated. The PBPK approach based on in vitro‐input data was as accurate as the approach based on in vivo data. Overall, the scientific benefit of this modeling study was to obtain more extensive characterization of predictions of human PK from PBPK methods. © 2011 Wiley‐Liss, Inc. and the American Pharmacists Association J Pharm Sci 100:4127–4157, 2011     

Prediction of dose-hepatotoxic response in humans based on toxicokinetic/toxicodynamic modeling with or without in vivo data: A case study with acetaminophen

In the present legislations, the use of methods alternative to animal testing is explicitly encouraged, to use animal testing only ‘as a last resort’ or to ban it. The use of alternative methods to replace kinetics or repeated dose in vivo tests is a challenging issue. We propose here a strategy based on in vitro tests and QSAR (Quantitative Structure Activity Relationship) models to calibrate a dose–response model predicting hepatotoxicity. The dose response consists in calibrating and coupling a PBPK (physiologically-based pharmacokinetic) model with a toxicodynamic model for cell viability. We applied our strategy to acetaminophen and compared three different ways to calibrate the PBPK model: only with in vitro and in silico methods, using rat data or using all available data including data on humans. Some estimates of kinetic parameters differed substantially among the three calibration processes, but, at the end, the three models were quite comparable in terms of liver toxicity predictions and close to the usual range of human overdose. For the model based on alternative methods, the good adequation with the two other models resulted from an overestimated renal elimination rate which compensated for the underestimation of the metabolism rate. Our study points out that toxicokinetics/toxicodynamics approaches, based on alternative methods and modelling only, can predict in vivo liver toxicity with accuracy comparable to in vivo methods.     

Comparison of in vivo derived and scaled in vitro metabolic rate constants for several volatile organic compounds (VOCs)

Metabolic rate parameters estimation using in vitro data is necessary due to numbers of chemicals for which data are needed, trend towards minimizing laboratory animal use, and limited opportunity to collect data in human subjects. We evaluated how well metabolic rate parameters derived from in vitro data predict overall in vivo metabolism for a set of environmental chemicals for which well validated and established methods exist. We compared values of VmaxC derived from in vivo vapor uptake studies with estimates of VmaxC scaled up from in vitro hepatic microsomal metabolism studies for VOCs for which data were available in male F344 rats. For 6 of 7 VOCs, differences between the in vivo and scaled up in vitro VmaxC estimates were less than 2.6-fold. For bromodichloromethane (BDCM), the in vivo derived VmaxC was approximately 4.4-fold higher than the in vitro derived and scaled up VmaxC. The more rapid rate of BDCM metabolism estimated based in vivo studies suggests other factors such as extrahepatic metabolism, binding or other non-specific losses making a significant contribution to overall clearance. Systematic and reliable utilization of scaled up in vitro biotransformation rate parameters in PBPK models will require development of methods to predict cases in which extrahepatic metabolism and binding as well as other factors are likely to be significant contributors.     

Predicting human exposure of active drug after oral prodrug administration,using a joined in vitro/in silico–in vivo extrapolation and physiologically-based pharmacokinetic modeling approach

Introduction: Predicting the pharmacokinetics (PK) of prodrugs and their corresponding active drugs is challenging, as there are many variables to consider. Prodrug conversion characteristics in different tissues are generally measured, but integrating these variables to a PK profile is not a common practice. In this paper, a joined in vitro/in silico–in vivo extrapolation (IVIVE) and physiologically-based pharmacokinetic (PBPK) modeling approach is presented to predict active drug exposure in human after oral prodrug administration. Methods: Physico-chemical and in vitro assays as well as in silico predictions were proposed to characterize key pharmacokinetic properties (e.g. clearance, volume of distribution, conversion rates) of three marketed prodrugs. These data were used to parameterize a PBPK model for simulating human PK profiles of the active drugs after prodrug administration, which were compared to literature data by evaluating the accuracy and uncertainty of the predictions. Results: For mycophenate mofetil and midodrine the PK of their active moieties could be adequately predicted. The assumptions of the PBPK–IVIVE approach were valid, i.e. being hepatically cleared, converted in the gut lumen, blood and liver and not metabolized in the gut wall. However, the observed profiles after oral bambuterol administration clearly fell outside the prediction interval as the PBPK model failed to predict the observed bioavailability. Discussion: Adding quantitative information about prodrug conversion in the gut, liver and blood to a PBPK model for the absorption, distribution, metabolism and excretion (ADME) properties of prodrugs and their active moieties resulted, retrospectively, in reasonable predictions of the human PK when the ADME properties are well understood. Also in a prospective compound selection process, this integrative approach can improve decision making on prodrug candidates by putting relative differences in prodrug conversion of a large number of candidates into the perspective of their human PK profile, before conducting any in vivo experiments.     

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.     

Blood concentrations of acrylonitrile in humans after oral administration extrapolated from in vivo rat pharmacokinetics, in vitro human metabolism,and physiologically based pharmacokinetic modeling

The present study defined a simplified physiologically based pharmacokinetic (PBPK) model for acrylonitrile in humans based on in vitro metabolic parameters determined using relevant liver microsomes, coefficients derived in silico, physiological parameters derived from the literature, and a prior previously developed PBPK model in rats. The model basically consists of a chemical absorption compartment, a metabolizing compartment, and a central compartment for acrylonitrile. Evaluation of a previous rat model was performed by comparisons with experimental pharmacokinetic values from blood and urine obtained from rats in vivo after oral treatment with acrylonitrile (30 mg/kg, a no-observed-adverse-effect level) for 14 days. Elimination rates of acrylonitrile in vitro were established using data from rat liver microsomes and from pooled human liver microsomes. Acrylonitrile was expected to be absorbed and cleared rapidly from the body in silico, as was the case for rats confirmed experimentally in vivo with repeated low-dose treatments. These results indicate that the simplified PBPK model for acrylonitrile is useful for a forward dosimetry approach in humans. This model may also be useful for simulating blood concentrations of other related compounds resulting from exposure to low chemical doses.     

Synthesis and pharmacology of a series of new organic nitrate esters

New organic nitrate esters, derived from structurally different (cyclo)aliphatic templates, were synthesized and pharmacologically investigated. Theirin vitro vascular smooth muscle relaxing activities and, occasionally,in vivo haemodynamic profiles were studied and compared to those of the clinically important nitrates, glyceryl trinitrate, isosorbide dinitrate and isosorbide-5-mononitrate. A number of compounds appeared to be even more potent than glyceryl trinitrate. Qualitative structure-activity relationships within the series of new compounds are discussed. In flexiblen-alkylene dinitrates, lipophilicity as well as chain length appears to affectin vitro activity. In semi-rigid cyclohexylene dinitrates, the number of atoms between and the configuration of the nitrate groups may play an important role. Finally, in cycloalkylene mononitrates neither the number of ring carbon atoms nor the lipophilicity clearly affects thein vitro activity. It is suggested that, apart from a limited involvement of compound lipophilicity, other factors such as differences in enzymatic conversion to a common putative bioactive species, nitric oxide, are responsible for the observed differences in activity.     

Quantitative in vitro to in vivo extrapolation of cell-based toxicity assay results

The field of toxicology is currently undergoing a global paradigm shift to use of in vitro approaches for assessing the risks of chemicals and drugs in a more mechanistic and high throughput manner than current approaches relying primarily on in vivo testing. However, reliance on in vitro data entails a number of new challenges associated with translating the in vitro results to corresponding in vivo exposures. Physiologically based pharmacokinetic (PBPK) modeling provides an effective framework for conducting quantitative in vitro to in vivo extrapolation (QIVIVE). Their physiological structure facilitates the incorporation of in silico- and in vitro-derived chemical-specific parameters in order to predict in vivo absorption, distribution, metabolism and excretion. In particular, the combination of in silico- and in vitro parameter estimation with PBPK modeling can be used to predict the in vivo exposure conditions that would produce chemical concentrations in the target tissue equivalent to the concentrations at which effects were observed with in vitro assays of tissue/organ toxicity. This review describes the various elements of QIVIVE and highlights key aspects of the process, with an emphasis on extrapolation of in vitro metabolism data to predict in vivo clearance as the key element. Other important elements include characterization of free concentration in the toxicity assay and potential complications associated with intestinal absorption and renal clearance. Examples of successful QIVIVE approaches are described ranging from a simple steady-state approach that is suitable for a high throughput environment to more complicated approaches requiring full PBPK models.     

Precipitation in the small intestine may play a more important role in the in vivo performance of poorly soluble weak bases in the fasted state: Case example nelfinavir

The aim of this study was to evaluate the utility of biorelevant dissolution tests coupled with in silico simulation technology to forecast in vivo bioperformance of poorly water-soluble bases, using nelfinavir mesylate as a model compound.An in silico physiologically based pharmacokinetic (PBPK) model for poorly water-soluble, weakly basic drugs was used to generate plasma profiles of nelfinavir by coupling dissolution results and estimates of precipitation with standard gastrointestinal (GI) parameters and the disposition pharmacokinetics of nelfinavir. In vitro dissolution of nelfinavir mesylate film-coated tablets was measured in biorelevant and compendial media. Drug precipitation in the small intestine was estimated from crystal growth theory. GI parameters (gastric emptying rate and fluid volume) appropriate to the dosing conditions (fasting and fed states) were used in the PBPK model. The disposition parameters of nelfinavir were estimated by fitting compartmental models to the in vivo oral PK data. The in vivo performance in each prandial state was simulated with the PBPK model, and predicted values for AUC and C_max were compared to observed values.Dissolution results in FaSSIF-V2 and FeSSIF-V2, simulating the fasting and fed small intestinal conditions, respectively, correctly predicted that there would be a positive food effect for nelfinavir mesylate, but overestimated the food effect observed in healthy human volunteers. In order to better predict the food effect, an in silico PBPK simulation model using STELLA® software was evolved. Results with the model indicated that invoking drug precipitation in the small intestine is necessary to describe the in vivo performance of nelfinavir mesylate in the fasted state, whereas a good prediction under fed state conditions is obtained without assuming any precipitation. In vitro–in silico–in vivo relationships (IVISIV-R) may thus be a helpful tool in understanding the critical parameters that affect the oral absorption of poorly soluble weak bases.