General physiological based pharmacokinetic models-

Physiologically based pharmacokinetic PBPK modeling is a mathematical modeling technique for predicting the absorption, distribution, metabolism and excretion ADME of synthetic or natural chemical substances in humans and other animal species. PBPK modeling is used in pharmaceutical research and drug development, and in health risk assessment for cosmetics or general chemicals. PBPK models strive to be mechanistic by mathematically transcribing anatomical, physiological, physical, and chemical descriptions of the phenomena involved in the complex ADME processes. A large degree of residual simplification and empiricism is still present in those models, but they have an extended domain of applicability compared to that of classical, empirical function based, pharmacokinetic models. PBPK models may have purely predictive uses, but other uses, such as statistical inference, have been made possible by the development of Bayesian statistical tools able to deal with complex models.

General physiological based pharmacokinetic models

General physiological based pharmacokinetic models

General physiological based pharmacokinetic models

A verified method for the prediction physioloical tissue-specific permeability-surface area product PStc model parameters has not yet been established. Investigation of size, surface charge, PEGylation degree and concentration on the cellular uptake of polymer nanoparticles. PubMed Google Scholar. Jones HM, et al. Sheila Annie Peters and Hugues Dolgos have no conflicts of interest to declare. Sato M, et al. Resolving parameter non-identifiability through hypothesis generation, testing and verification can ensure a high quality of prospective predictions for absorption-related applications. Thus, human V ss values from 0. Note: Human pharmacokinetics were measured in plasma and GastroPlus predicts plasma pharmacokinetics; pharmacokinwtic for preclinical species where observed pharmacokinetics were measured for blood was it necessary to convert blood Physiologifal General physiological based pharmacokinetic models to plasma V ss using species-specific BPR for comparison to GastroPlus outputs.

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As described above, meals have a bi-directional effect on blood levels: decreasing the plasma concentration and AUC when propranolol is given IV, and increasing the levels and AUC when given orally. Note that, as required, the predicted total absorption equals 80 mg, the experimental oral dose. In this connection, intravenous biodistribution studies with associated PBPK analyses would provide General physiological based pharmacokinetic models most bxsed. The following test of this calculation was carried out. This hypothesis should be tested in experiments involving different doses of one type of NP. Elsevier; Nanoparticle-mediated cellular response is size-dependent. Tissue distribution and pharmacokinetics of stable polyacrylamide nanoparticles following intravenous injection in the rat. Physiologucal Physiol. Connections between compartment follow physiology e.

Clinical Pharmacokinetics.

  • A "physiologically based pharmacokinetic" PBPK approach uses a realistic model of the animal to describe the pharmacokinetics.
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Clinical Pharmacokinetics. When scientifically well-founded, the mechanistic basis of physiologically based pharmacokinetic PBPK models can help reduce the uncertainty and increase confidence in extrapolations outside the studied scenarios or studied populations. However, it is not always possible to establish mechanistically credible PBPK models. Requirements to establishing confidence in PBPK models, and challenges to meeting these requirements, are presented in this article.

Parameter non-identifiability is the most challenging among the barriers to establishing confidence in PBPK models. When the mechanistic basis of a PBPK model cannot be established, we propose the use of simpler models or evidence-based approaches. To leverage the mechanistic strengths of PBPK models, it is essential to establish confidence in the mechanisms that are relevant to an application. Establishing confidence in PBPK models is challenged by poor in vitro-in vivo correlations, knowledge gaps in system parameters and in mechanisms impacting an application, as well as parameter non-identifiability.

Uncertainty analysis and hypothesis testing can be used to overcome some of these challenges. Physiologically based pharmacokinetic PBPK models provide a mechanistic framework in which to integrate compound and system data for prospective predictions of drug exposure in humans [ 1 , 2 ]. When scientifically well-founded, the mechanistic basis of PBPK models can help reduce the uncertainty and increase confidence in extrapolations outside the studied scenarios or studied populations.

PBPK models are therefore increasingly applied during preclinical and clinical development [ 1 , 3 , 4 , 5 , 6 , 7 ]. During preclinical drug development, PBPK can support candidate drug selection and decision making by aiding an understanding of the mechanisms driving drug exposure [ 8 ]. During clinical drug development, PBPK modelling can drive internal decisions and support regulatory submissions [ 9 , 10 , 11 ]. Despite the strengths of PBPK modelling approaches, most of the high-impact regulatory applications that resulted in labelling recommendations or study waivers have tended to be drug—drug interaction DDI -related [ 14 ].

Establishing confidence in PBPK models for non-DDI applications such as pediatric starting dose selection, organ impairment and absorption-related applications is challenged by the difficulty in developing mechanistically credible PBPK models or to verify and validate their prediction performance, either because drug elimination pathways cannot be well-characterized, or, when characterized, there is poor in vitro—in vivo correlation IVIVC.

This is especially true for transporter-dependent or non-cytochrome P CYP -mediated elimination pathways. The lack of a sufficient number of clinical datasets to resolve parameter non-identifiability has further limited model verification and validation.

This work presents a systematic assessment of the current challenges to establishing confidence in PBPK models with respect to parameter estimation and model verification in each of the three major areas of PBPK application—absorption prediction, exposure prediction in a target population, and DDI risk assessment during drug development.

These three areas cover most of the regulatory submissions. This paper also focuses on overcoming parameter non-identifiability issues through hypothesis testing, using case examples related to absorption. In a workshop on modelling and simulation hosted by the EMA and the European Federation of Pharmaceutical Industries and Associations EFPIA , representatives from industry, academia, and regulatory agencies proposed a framework where the degree of regulatory scrutiny, level of documentation, and the need for early dialogue is proportional to the impact of the modelling activity on regulatory decision making [ 15 , 16 ].

Thus, regulatory submissions may be classified as high, medium or low impact depending on the ability of the work to replace, justify or describe an evidence base. In general, these tend to be DDI-related. Pediatric starting dose selection or the study design may be considered an example of an application with moderate impact.

The EMA guidelines require qualification of these platforms [ 12 ]. PBPK platform qualification is defined as a version-specific evaluation to demonstrate its reliability for one or several intended purposes. It involves ensuring proper implementation of computational functionalities, accurate mathematical representation of the physiological processes, reliable system parameters for the library of populations, model verification for the library compounds, transparency regarding the source of system and compound data as well as assumptions in the system, version controlling, quality-controlled software installation, and evaluation of the predictive performance for high-impact applications for the intended purpose using a large, independent, diverse dataset.

Key questions for moderate impact non-DDI regulatory submissions. Model development Building a PBPK model for a new chemical entity NCE by integrating its physicochemical properties, measured in vitro data that are relevant to the key question to be addressed, and estimated sensitive or critical parameters from clinical pharmacokinetic PK data when they become available.

Model verification An iterative process of comparing model-simulated exposure with independent clinical data datasets that were not used in model development steps to establish confidence in the model-simulated exposure.

If model simulations do not match the clinically observed exposure within a predefined acceptance criterion, the model parameters are refined to fit the observations and then verified again. An acceptance criterion that is flexible, clinically relevant and based on sample size, parameter variance, therapeutic index and exposure—response relationship has been proposed [ 17 ].

For example, a PBPK model of a CYP inhibitor, verified against observed PK profiles in a first-in-human FiH trial may be validated for the purpose of predicting drug interaction with one sensitive substrate before it is applied to prospectively predict interactions with other untested CYP substrates.

Validation with one tested scenario would be enough to provide the confidence needed for the prospective predictions of multiple untested scenarios. If the model is developed in a PBPK platform that is already qualified for an intended purpose using an independent, large, diverse dataset, this validation step may be skipped.

Sensitivity analysis identifies sensitive model parameters among the in vitro-generated input parameters for which an uncertainty analysis needs to be performed.

A description of PBPK model qualification and verification is presented elsewhere [ 18 ]. However, in this current work, we distinguish between model verification and validation. While verification is a necessary step in a modelling exercise, in which model-simulated exposure is compared with independent clinical data datasets that were not used in the model development steps , validation refers to the evaluation of the predictive performance of the model and may be part of either platform qualification or a regulatory submission.

Requirements that will allow a high level of confidence in PBPK predictions for the three broad categories of applications. The placement of these three categories of applications along the value chain is also depicted.

If this range is close to the entire range of plausible values, the exercise of parameter estimation is rendered less valuable. These definitions are intended to appreciate the distinction between gut bioavailability and fraction escaping gut metabolism, often used interchangeably in the literature.

At doses where intestinal efflux can be considered saturated, gut metabolism is assumed to be the sole contributor to intestinal loss. Confidence in absorption-related predictions is expected to be high when quantitative assessment of f abs is reliable. In addition, knowledge of mechanisms contributing to gut bioavailability other than absorption, e.

NCE is not a substrate of efflux transporters or enzymes expressed in the gut or, when relevant, should be quantitatively assessed. For example, if gut metabolism is known to be relevant for the NCE, then quantifying the metabolic contribution of the gut requires metabolite measured in intravenous and oral routes. For CYP3A substrates, reasonable quantification is possible even with in vitro data [ 19 ].

For non-CYP drivers of gut metabolism, the availability of PK data following intravenous administration is indispensable in the quantitative mechanistic understanding of gut bioavailability. In the absence of intravenous data, for poorly soluble compounds, it is difficult to characterize the mechanisms relevant for absorption-related applications.

If an NCE is a transporter substrate, the in vivo contribution of the transporter to its elimination should be additionally well-understood. A good recovery of the in vivo clearance in the base population from in vitro intrinsic clearance CL int is then necessary to adjust for differences in protein levels in the target population and will ensure that unique mechanisms relevant to the target population can be accounted for.

If only a single major CYP is involved in the metabolism in the base and target populations, CL int may be derived from observed clearance in an intravenous PK study in the base population. This will allow for appropriate corrections in parameters by accounting for differences between the base population and target populations. To establish confidence in the utility of PBPK modelling for assessing an NCE as a victim drug, the metabolic and elimination pathways, as well as the site contributing to each of the metabolic pathways, should be well-characterized, as described for extrapolation to the target population.

If the NCE is a transporter substrate, the contribution of transporter to elimination of the NCE should be additionally well-understood. Thus, the requirements shown in Fig. In general, for all three major applications of PBPK, the fewer the mechanisms impacting the drug exposure drug dissolution, and metabolic and elimination pathways , the fewer the associated parameters, and therefore overall uncertainty, and the greater the confidence in model predictions. Applications include absorption [ 20 , 21 ], PPI effect [ 22 ], food effect prediction [ 23 , 24 ], bioequivalence assessment through IVIVC for getting a biowaiver for formulation bridging, and DDI assessment [ 25 , 26 , 27 , 28 , 29 , 30 ], to name a few.

A comprehensive list of applications is covered by Shebley et al. Barriers to establishing confidence in the key mechanisms impacting an application. CYP cytochrome P The possibility that mechanisms relevant for the in vivo disposition of a drug can go unidentified in in vitro systems cannot be dismissed.

However, if the intended purpose of the model is to assess the risk for an NCE to be a victim of CYP inhibition, a quantitative knowledge of all elimination and metabolic pathways is needed. Top-down approaches can be helpful, if it is the major metabolic pathway. Application-specific model parameters needed for PBPK model development using a middle-out approach. Effects of food and proton pump inhibitors on absorption, bioequivalence or relative bioavailability. Exposure prediction in a target population: extrapolation from a base population usually a healthy adult Caucasian to other populations.

Other populations: pediatric, geriatric, obese, smoker, organ-impaired, pregnant, PGX, ethnicity. Knowledge of differences in contributing pathways from the base population fm,CYP in the base and target populations. In vitro data related to the metabolic pathways that are unique to the target population. Plasma protein binding f u. Induction EC 50 , E max. Fraction absorbed, f abs and gut bioavailability F g , if the affected isoform of the enzyme metabolizes the inhibitor and is expressed in the gut.

In vivo relevance of transporter in addition to those needed for DDI involving enzymes. In vitro data for reversible transporter inhibition K i in addition to those needed for DDI involving enzymes. The implication of IVIV disconnect is that model parameters may be associated with uncertainty and may not be quantitative enough for a prospective prediction via a bottom-up approach. This requirement is further complicated when multiple interaction sites liver, intestine, kidney, etc.

For a drug with multiple elimination pathways in multiple sites, mass balance studies in humans using radiolabelled compounds can identify and provide quantitative information on the routes of excretion [ 36 ], and, with additional analyses, metabolic pathways [ 37 ]. These studies aid a complete understanding of clearance and potential contributors to intersubject variability and DDIs, all of which are crucial for evaluating an NCE as a victim of drug interaction. However, it should be noted that mass balance cannot distinguish between enzyme isoforms that lead to the same metabolite.

To overcome the uncertainty associated with clearance derived from in vitro systems, a middle-out approach to model building is adopted [ 18 , 38 ] in which clearance is obtained through parameter estimation from clinical data.

This works best for a drug that is not a transporter substrate when its elimination is dominated by a single pathway. When clinical data are associated with high interindividual variability, it is reflected in the wide range of the estimated parameter. Cubitt et al. These authors calculated a confidence interval from geometric mean and geometric standard deviation, making it possible to limit the range, by eliminating any bias from extreme individuals.

In cancer patients, high variability in PK profiles usually from a small cohort renders estimated parameters less reliable as the true mean cannot be captured. Gut bioavailability of an orally administered drug is determined by solubility, permeability, gut metabolism and efflux. The parameters related to these mechanisms cannot be distinguished using the observed plasma exposure data following oral administration of the drug as it allows only the estimation of a composite parameter comprising all parameters related to the contributing mechanisms.

Thus, the mechanisms contributing to gut bioavailability absorption, gut metabolism and efflux are said to be non-identifiable since several sets of parameter values can result in equally good fit to the observed plasma exposure data.

In the absence of any gut metabolism and efflux, such a study, if available, can help identify solubility-limited absorption. If gut metabolism and efflux cannot be excluded e. Model able to recover an observed interaction of NCE with a sensitive substrate. To cover for uncertainty in measured parameters, a sensitivity analysis on model parameters is first performed to identify the most sensitive parameters on which to conduct an uncertainty analysis.

The impact of uncertainty in sensitive model parameters that cannot be precisely measured on endpoints of interest is assessed by varying the sensitive parameters over a range of plausible values for compound-related parameters, and over the 5th to 95th percentile of distributions for system parameters [ 40 ], based on what is known about the mechanism, rather than being arbitrary. Parameter non-identifiability presents the greatest challenge for a proper characterization of underlying mechanisms.

One way to overcome non-identifiability is to measure one or more of the non-identifiable parameters that can be reliably measured and combine with the composite parameter estimated from clinical data, to obtain the other non-identifiable parameter.

The characteristics of these different NPs are summarized in Table 1. Direct calculation of the first pass metabolism or peripheral availability for the case of PO input The procedure described above in section II for determining the rate of PO absorption can be directly used to determine the first pass metabolism. For example, the peak in the blood ethanol after an oral dose is delayed by about 20 minutes by a meal because of the delayed gastric emptying [ 3 , 52 ]. The experimental data are from Wang et al. In equation 4, c B is the free concentration entering the organ.

General physiological based pharmacokinetic models

General physiological based pharmacokinetic models

General physiological based pharmacokinetic models

General physiological based pharmacokinetic models. Background

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Clinical Pharmacokinetics. Physiologically based pharmacokinetic modelling is well established in the pharmaceutical industry and is accepted by regulatory agencies for the prediction of drug—drug interactions.

However, physiologically based pharmacokinetic modelling is valuable to address a much wider range of pharmaceutical applications, and new regulatory impact is expected as its full power is leveraged. As one example, physiologically based pharmacokinetic modelling is already routinely used during drug discovery for in-vitro to in-vivo translation and pharmacokinetic modelling in preclinical species, and this leads to the application of verified models for first-in-human pharmacokinetic predictions.

A consistent cross-industry strategy in this application area would increase confidence in the approach and facilitate further learning. With this in mind, this article aims to enhance a previously published first-in-human physiologically based pharmacokinetic model-building strategy. We have reviewed many relevant scientific publications to identify new findings and highlight gaps that need to be addressed. Finally, four industry case studies for more challenging compounds illustrate and highlight key components of the strategy.

Linking of in-silico quantitative structure—property relationship models with physiologically based pharmacokinetic PBPK modelling is a powerful emerging technique, which is already being employed during early drug discovery. Combined with parameter sensitivity analyses, this can identify the compound properties most influencing systemic exposure and thus guide lead optimisation.

The quality of first-in-human PBPK predictions is greatly improved when measured inputs are available for the most critical parameters. PBPK model verification in preclinical species, which has not always been included in assessments of first-in-human pharmacokinetic predictions, is critical to build confidence and improve accuracy.

Uncertainty analysis is a key consideration to obtain maximal value from first-in-human PBPK predictions. Physiologically based pharmacokinetic PBPK models represent the body as compartments parameterised based on physiology of tissues and organs including composition, volumes and blood flows [ 1 ].

Physiologically based pharmacokinetic models integrate this physiological description with compound-specific data to predict the pharmacokinetics of drugs, allowing simulation of the time course of drug concentrations in plasma and tissues.

Here, we consider a PBPK model as a whole-body model describing systemic disposition linked to a mechanistic absorption model such as the advanced compartmental absorption and transit ACAT model [ 2 ].

The origin of PBPK modelling can be traced back to Teorell in [ 3 , 4 ], but application of PBPK modelling in drug discovery, development and regulation came of age around [ 5 ]. The majority of regulatory submissions including PBPK modelling have focused on drug—drug interactions and paediatric modelling [ 8 , 10 , 11 ]. However, a recent industry perspective on PBPK applications [ 12 ] highlighted diverse uses spanning pharmaceutical discovery and development from preclinical predictions to simulations of variability in different clinical populations, indicating the potential for expansion of regulatory applications [ 8 ].

However, PBPK modelling is increasingly used for regulatory purposes, and has been identified by the European Medicines Agency as a useful tool for assessing an appropriate starting dose for healthy volunteers [ 14 ]. Physiologically based pharmacokinetic models are a systems pharmacology approach that can act as a growing repository of knowledge on the pharmacokinetics of a new chemical entity or drug candidate [ 15 ], evolving to include new input data and mechanisms as scientific knowledge increases.

This is of particular utility in preclinical development when PBPK modelling can be applied to predict clinical pharmacokinetics prior to FIH studies. A seminal paper from Jones et al. Since then, several publications from industry groups have confirmed the superiority of PBPK modelling for this application [ 17 , 18 , 19 ] and many medium and large pharmaceutical companies are now routinely applying the approach as is clear from a recent cross-industry perspective [ 12 ].

Here, we have updated the strategy of Jones et al. Although some information specific to GastroPlus is included and all the examples were conducted using GastroPlus, much of the information presented is generally applicable to PBPK modelling. The strategy has been updated based on a comprehensive review of subsequent publications and on the combined knowledge and experience of the authors who are all PBPK specialists and members of the GastroPlus User Group Steering Committee.

Novel insights in the revised strategy include the use of quantitative structure—property relationship QSPR predictions as inputs for PBPK modelling prior to experimentation, integrating new Absorption, Distribution, Metabolism and Excretion ADME knowledge within the proposed decision trees and stressing the importance of considering uncertainty in predictions.

We believe that a consistent PBPK strategy for FIH predictions, based on best practices and experience across companies, should increase the confidence of regulatory agencies in this application. The complexity of PBPK models, which include many adjustable parameters, mandates the definition of a consistent model building strategy and best practice guidance.

Physiologically based pharmacokinetic models are used within numerous disciplines and by scientists with diverse backgrounds and thus a common approach covering various scenarios will facilitate regulatory evaluation.

Along with a consistent strategy, use of consistent physiological parameters and scaling factors allows a fair comparison between compounds, and some companies undergo internal harmonisation to ensure consistency. As an example of the possible range for a key parameter, liver blood flow has reported values in rats that include The default PBPK models available within GastroPlus, include species-specific values for physiological parameters and scaling factors and thus encourage consistency.

Although PBPK models require a large number of compound-specific inputs, many may be generated using QSPR models, enabling the use of PBPK models in early drug discovery before experimentation, potentially for virtual compounds. Although accurate prediction of pharmacokinetics using many properties predicted from a structure may be possible [ 23 ], it is not guaranteed [ 24 ] and verification with compounds from each chemical class has been recommended [ 25 ].

Physiologically based pharmacokinetic modelling can be successfully applied in discovery with minimal data [ 26 ]. As compounds progress, models should be updated with more experimental data. Later, for FIH predictions, a comprehensive set of measured input data is generally required.

Prediction accuracy is optimised by considering all available preclinical data [ 19 ], filling identified data gaps and verifying the preclinical PBPK model [ 18 ].

Compound assessment from structure using quantitative structure—property relationship QSPR plus physiologically based pharmacokinetic PBPK modelling [ 27 , 30 , 31 , 32 ].

If hepatic metabolism by cytochrome P CYP 3A4 is predicted as the major elimination route, then the impact of intestinal metabolism on oral bioavailability should be considered and reaction phenotyping studies may be performed earlier. Physiologically based pharmacokinetic modelling strategy for elimination [ 42 , 43 , 44 ]. The Extended Clearance Classification System can be a useful guide, and if the major elimination route is predicted as hepatic metabolism, then confidence in human predictions is higher if an in-vitro in-vivo extrapolation IVIVE can be established in preclinical species.

The likelihood of a successful IVIVE is higher for compounds predominantly metabolised by CYP enzymes while non-CYP metabolism, although often captured qualitatively in hepatocyte models, remains more challenging [ 33 ]. The involvement of active uptake in hepatic clearance can be flagged by the Extended Clearance Classification System, and in such cases measurements in hepatocyte models may be useful [ 34 ] and human clearance predictions may be improved with cross-species empirical scaling factors [ 35 ].

More advanced hepatocyte models are also being explored with respect to improved IVIVE for more complex cases [ 36 , 37 , 38 ]. Renal [ 39 , 40 ] and biliary [ 41 ] elimination involving active transport are also challenging to model mechanistically from in-vitro data, and, in general, prediction of human pharmacokinetics for transported molecules is difficult because absorption, distribution and elimination can all be affected [ 18 ].

However, there are encouraging developments. For renal secretion, measurements in organic anion transporter-transfected human embryonic kidney cells successfully predicted renal clearance of 31 diverse drugs [ 42 ] while a mechanistic model for passive tubular reabsorption was verified with a large dataset of drugs [ 43 ].

For hepatobiliary clearance, it was recently demonstrated that data from sandwich-cultured hepatocytes and a consistent IVIVE approach could predict in humans for 17 diverse drugs [ 44 ].

Furthermore, mechanistic IVIVE from the sandwich-cultured model, utilising transporter expression data in-vitro and in-vivo improved prediction for rosuvastatin in the rat [ 45 ]. It has also emerged that monkeys are a valuable model for the verification of hepatic disposition for transported molecules [ 44 ] particularly for substrates of the organic anion transporting polypeptide transporter [ 35 ].

Examples of complex PBPK models incorporating enzyme and transporter kinetics from in-vitro studies already exist and should become more common in the future as models evolve [ 46 ]. Irrespective of the mechanism, if preclinical verification of the clearance prediction can be demonstrated, this builds confidence in FIH pharmacokinetic predictions.

When IVIVE is not successful in preclinical species it may be necessary to use empirical scaling factors for the human prediction as for Compound 1 in Sect. Physiologically based pharmacokinetic modelling strategy for distribution [ 17 , 50 , 53 , 55 ]. For molecules where passive processes dominate, distribution is often predictable using a standard perfusion-limited tissue model.

See Compound 2 Sect. However, for large compounds with slow passive diffusion through tissue membranes as for Compound 4 in Sect. In other cases, measured input data for BPR have been empirically adjusted to match predictions to observed distribution [ 53 ]. The adjustment of BPR to account for phospholipid binding as well as lysosomal trapping of basic lipophilic compounds is currently an empirical fit and exploits the observation that acidic phospholipid content is highest in tissues with high lysosomal volumes [ 53 ].

Equations are emerging to describe the incorporation of lysosomal sequestration into K p predictions [ 54 ] and may be included in future versions of GastroPlus. When tissue partition equations are not predictive, quantitative whole-body autoradiography data, if reflective of the parent compound, may be used to estimate K p values and adequately predict human pharmacokinetics from rat data [ 55 ].

Alternatively, animal K p values can be scaled to human values, assuming unbound K p values are identical and accounting for species differences in plasma protein binding [ 17 ]. Physiologically based pharmacokinetic modelling strategy for oral absorption [ 57 , 58 ].

The current GastroPlus model for bile salt solubilisation estimates the increase in in-vivo solubility relative to aqueous buffer solubility based only on the concentration of bile salts in the fasted and fed state media. In such cases, in-vivo verification in preclinical species should be used to assess the relevance of the BSSR estimates.

Generally, physiological parameters should not be fitted and default GastroPlus ACAT models should be used, although minor adjustments may occasionally be supported. For example, there is significant uncertainty associated with some model parameters such as the amount of fluid in the gastrointestinal tract [ 57 ]. For Compound 1 BCS Class II , the inclusion of known variability in gastrointestinal tract fluid volumes and realistic formulation-specific particle size data was important for predicting absorption.

Other examples of appropriate ACAT model adjustments include stomach transit time which exhibits large inter-occasion variability , stomach pH for patients taking proton pump inhibitors or modification of effective permeability using built-in interspecies correlations. Ideally, preclinical oral pharmacokinetic data should cover human-relevant doses and formulations [ 19 ], so that confidence can be derived by the verification of formulation-specific models at relevant doses. The effect of food can be investigated pre-clinically and predicted for humans [ 59 ].

Physiologically based pharmacokinetic modelling strategy for assessing gut wall metabolism [ 65 ]. CL int hepatic intrinsic clearance, Fg fraction of drug escaping gut wall metabolism, K m concentration of substrate at half V max , V max maximum velocity or rate of enzyme catalyzed reaction.

Note: Gut wall metabolism is often saturable, and thus if V max and K m parameters are available, evaluate saturation relative to dose. While single simulations for the FIH dose prediction are often made, this approach limits the value achieved through PBPK modelling as it ignores uncertainty of inputs and variability in the simulated population.

Considering both uncertainty and variability is important, particularly from a regulatory perspective [ 7 ]. A PSA can be used to determine the implications of key uncertainties [ 10 ], whether owing to limited data, lack of mechanistic understanding, inability to predict saturation of metabolism or absorption mechanisms, or a disconnect between in-vitro and in-vivo data.

Uncertainties can be translated into PBPK model results and determine limits on what might be expected for clinical pharmacokinetics. Sometimes, focusing on a single critical model parameter is valuable.

For example, clearance is often a sensitive parameter not known with great confidence, e. One could give a range of predictions around the uncertain model parameter, or present a perceived most likely estimate and a worst-case scenario. It is important to clearly communicate what the range represents so that the information can be used appropriately.

Sometimes it may be important to convey uncertainty for multiple parameters, e. In this case, a three-dimensional PSA, the third dimension being the predicted pharmacokinetic parameter, presents the simulation results in a helpful manner to enable key decisions in the light of uncertainty.

For predicting variability, the population simulator tool in GastroPlus can be used to simulate a clinical trial by varying multiple often physiological model parameters within ranges defined by their estimated variability distributions. However, simple approaches to understanding variability, e. Physiologically based pharmacokinetic modelling strategy for a potentially useful parameter sensitivity analysis PSA to be driven by the molecule properties and uncertainty evaluation.

Opportunities in physiologically based pharmacokinetic modelling methodology work. Ability to predict clearance when transporters are involved e. Improved methods for predicting V ss when protein binding is too high to be measured. Validation of predicted tissue concentrations including when transporters play a role. Verification of predictions of intestinal metabolism for enzymes expressed in the gut besides CYP3A4 e.

Note: Properties are measured unless stated. Lukacova [ 50 ] adjusted see text. Compounds 1—3 are industry predictions and Compound 4 was based on literature data. However, medicinal chemistry had failed to identify active molecules with lower logP and preclinical in-vitro and in-vivo pharmacology was promising.

General physiological based pharmacokinetic models

General physiological based pharmacokinetic models

General physiological based pharmacokinetic models