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Parameterization of Physiologically Based Biopharmaceutics Models: Workshop Summary Report

This Article shares the proceedings from the August 29th, 2023 (day 1) workshop "Physiologically Based Biopharmaceutics Modeling (PBBM) Best Practices for Drug Product Quality: Regulatory and Industry Perspectives". The focus of the day was on model parametrization; regulatory authorities from Canada, the USA, Sweden, Belgium, and Norway presented their views on PBBM case studies submitted by industry members of the IQ consortium. The presentations shared key questions raised by regulators during the mock exercise, regarding the PBBM input parameters and their justification. These presentations also shed light on the regulatory assessment processes, content, and format requirements for future PBBM regulatory submissions. In addition, the day 1 breakout presentations and discussions gave the opportunity to share best practices around key questions faced by scientists when parametrizing PBBMs. Key questions included measurement and integration of drug substance solubility for crystalline vs amorphous drugs; impact of excipients on apparent drug solubility/supersaturation; modeling of acid-base reactions at the surface of the dissolving drug; choice of dissolution methods according to the formulation and drug properties with a view to predict the in vivo performance; mechanistic modeling of in vitro product dissolution data to predict in vivo dissolution for various patient populations/species; best practices for characterization of drug precipitation from simple or complex formulations and integration of the data in PBBM; incorporation of drug permeability into PBBM for various routes of uptake and prediction of permeability along the GI tract.

Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration

The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure-permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques.

3D printing combined with biopredictive dissolution and PBPK/PD modeling optimization and personalization of pharmacotherapy: Are we there yet?

Precision medicine requires selecting the appropriate dosage regimen for a patient using the right drug, at the right time. Model-Informed Precision Dosing (MIPD) is a concept suggesting utilization of model-based prediction methods for optimizing the treatment benefit-harm balance, based on individual characteristics of the patient, disease, treatment method, and other factors. Here, we discuss a theoretical workflow comprising several elements, beginning from the physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models, through 3D printed tablets with the model proposed dose, information range and flow, and the patient themselves. We also describe each of these elements, and the connection between them, highlighting challenges and potential obstacles.

Food and bile micelle binding of quaternary ammonium compounds

Background and Purpose

Physiologically-based biopharmaceutics modeling (PBBM) has been widely used to predict the oral absorption of drugs. However, the prediction of food effects on oral drug absorption is still challenging, especially for negative food effects. Marked negative food effects have been reported in most cases of quaternary ammonium compounds (QAC). However, the mechanism has remained unclear. The purpose of the present study was to investigate the bile micelle and food binding of QACs as a mechanism of the negative food effect.

Experimental Approach

Trospium (TRS), propantheline (PPT), and ambenonium (AMB) were selected as model QAC drugs. The oral absorption of these QACs has been reported to be reduced by 77% (TRS), > 66% (PPT), and 79% (AMB), when taken with food. The fasted and fed state simulated intestinal fluids (FaSSIF and FeSSIF, containing 3 and 15 mM taurocholic acid, respectively) with or without FDA breakfast homogenate (BFH) were used as the simulated intestinal fluid. The unbound fraction (fu) of the QACs in these media was measured by dynamic dialysis.

Key Results

The fu ratios (FeSSIF/ FaSSIF) were 0.67 (TRS), 0.47 (PPT), and 0.76 (AMB). When BFH was added to FeSSIF, it was reduced to 0.39 (TRS), 0.28 (PPT), and 0.59 (AMB).

Conclusion

These results suggested that bile micelle and food binding play an important role in the negative food effect on the oral absorption of QACs.

Modelling the Tumour Microenvironment, but What Exactly Do We Mean by "Model"?

The Oxford English Dictionary includes 17 definitions for the word "model" as a noun and another 11 as a verb. Therefore, context is necessary to understand the meaning of the word model. For instance, "model railways" refer to replicas of railways and trains at a smaller scale and a "model student" refers to an exemplary individual. In some cases, a specific context, like cancer research, may not be sufficient to provide one specific meaning for model. Even if the context is narrowed, specifically, to research related to the tumour microenvironment, "model" can be understood in a wide variety of ways, from an animal model to a mathematical expression. This paper presents a review of different "models" of the tumour microenvironment, as grouped by different definitions of the word into four categories: model organisms, in vitro models, mathematical models and computational models. Then, the frequencies of different meanings of the word "model" related to the tumour microenvironment are measured from numbers of entries in the MEDLINE database of the United States National Library of Medicine at the National Institutes of Health. The frequencies of the main components of the microenvironment and the organ-related cancers modelled are also assessed quantitatively with specific keywords. Whilst animal models, particularly xenografts and mouse models, are the most commonly used "models", the number of these entries has been slowly decreasing. Mathematical models, as well as prognostic and risk models, follow in frequency, and these have been growing in use.

Exploring the Use of a Kinetic pH Calculation to Correct the ACAT Model with a Single Stomach Compartment Setting: Impact of Stomach Setting on Food Effect Prediction for Basic Compounds

Advanced compartmental absorption and transit (ACAT) based computational models have become increasingly popular in the industry for predicting oral drug product performance. However, due to its complexity, some compromises have been made in practice, and the stomach is often assigned as a single compartment. Although this assignment worked generally, it may not be sufficient to reflect the complexity of the gastric environment under certain conditions. For example, this setting was found to be less accurate in estimating stomach pH and solubilization of certain drugs under food intake, which leads to a misprediction of the food effect. To overcome the above, we explored the use of a kinetic pH calculation (KpH) for the single-compartment stomach setting. Several drugs have been tested with the KpH approach and compared with the default setting of Gastroplus. In general, the Gastroplus prediction of food effect is greatly improved, suggesting this approach is effective in improving the estimation of physicochemical properties related to food effect for several basic drugs by Gastroplus.

Modelling Based Approaches to Support Generic Drug Regulatory Submissions-Practical Considerations and Case Studies

Model informed drug development (MiDD) is useful to predict in vivo exposure of drugs during various stages of the drug development process. This approach employs a variety of quantitative tools to assess the risks during the drug development process. One important tool in the MiDD tool kit is the Physiologically Based Pharmacokinetic Modelling (PBPK). This tool is extensively used to reduce the development cost and to accelerate the access of medicines to the patients. In this work, we provide an overview of PBPK modelling approaches in the generic drug development process, with a special emphasis on the bio-waiver applications. We describe herein approaches and common pitfalls while submitting model based justifications as a response to the regulatory deficiencies during the generic drug development process. With some in-house case studies, we have attempted to provide a clear path for PBPK model based justifications for bio-waivers. With this review, the gap between theoretical knowledge and practical application of modelling and simulation tools for generic drug product development could be potentially reduced.

Integrated Use of In Vitro and In Vivo Information for Comprehensive Prediction of Drug Interactions Due to Inhibition of Multiple CYP Isoenzymes

BACKGROUND

Mechanistic static pharmacokinetic (MSPK) models are simple, have fewer data requirements, and have broader applicability; however, they cannot use in vitro information and cannot distinguish the contributions of multiple cytochrome P450 (CYP) isoenzymes and the hepatic and intestinal first-pass effects appropriately. We aimed to establish a new MSPK analysis framework for the comprehensive prediction of drug interactions (DIs) to overcome these disadvantages.

METHODS

Drug interactions that occurred by inhibiting CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A in the liver and CYP3A in the intestine were simultaneously analyzed for 59 substrates and 35 inhibitors. As in vivo information, the observed changes in the area under the concentration-time curve (AUC) and elimination half-life (t1/2), hepatic availability, and urinary excretion ratio were used. As in vitro information, the fraction metabolized (fm) and the inhibition constant (Ki) were used. The contribution ratio (CR) and inhibition ratio (IR) for multiple clearance pathways and hypothetical volume (VHyp) were inferred using the Markov Chain Monte Carlo (MCMC) method.

RESULT

Using in vivo information from 239 combinations and in vitro 172 fm and 344 Ki values, changes in AUC, and t1/2 were estimated for all 2065 combinations, wherein the AUC was estimated to be more than doubled for 602 combinations. Intake-dependent selective intestinal CYP3A inhibition by grapefruit juice has been suggested. By separating the intestinal contributions, DIs after intravenous dosing were also appropriately inferred.

CONCLUSION

This framework would be a powerful tool for the reasonable management of various DIs based on all available in vitro and in vivo information.

Application of Population Balance Model to Simulate Precipitation of Weak Base and Zwitterionic Drugs in Gastrointestinal pH Environment

The purpose of the present study was to evaluate whether the population balance model (PBM) could be a suitable model for the precipitation of weak base and zwitterionic drugs in the gastrointestinal pH environment. Five poorly soluble drugs were used as model drugs (dipyridamole, haloperidol, papaverine, phenazopyridine, and tosufloxacin). PBM consists of the equations for primary nucleation, secondary nucleation, and particle growth. Each equation has two empirical parameters. The pH shift (pH-dumping) precipitation test (pH 3.0 to 6.5) was used to determine the model parameters for each drug. It was difficult to determine all six parameters by simultaneously fitting them to the precipitation profiles. Therefore, the number of model parameters was reduced from six to three by neglecting the secondary nucleation process and applying a common exponent number for the particle growth equation. Despite reducing the parameter number, PBM appropriately described the precipitation profiles in the pH shift tests. The constructed PBM model was then used to predict the precipitation profiles in an artificial stomach-intestine transfer (ASIT) test. PBM appropriately predicted the precipitation profiles in the ASIT test. These results suggested that PBM can be a suitable model to represent the precipitation of weak base and zwitterionic drugs in the gastrointestinal pH environment for biopharmaceutics modeling and simulation.

Bioequivalence Dissolution Test Criteria for Formulation Development of High Solubility-Low Permeability Drugs

The purpose of the present study was to provide the experimental and theoretical basis of bioequivalence (BE) dissolution test criteria for formulation development of high solubility-low permeability drugs. According to the biowaiver scheme based on the biopharmaceutics classification system (BCS), for BCS class III drugs, a test formulation and a reference formulation are predicted to be BE when 85% of the drug dissolves within 15 min (T_85% < 15 min) in the compendial dissolution test. However, previous theoretical simulation studies have suggested that this criterion may possibly be relaxed for use in practical formulation development. In the present study, the dissolution profiles of 14 famotidine formulations for which BE has been clinically confirmed were evaluated by the compendial dissolution test at pH 1.2 and 6.8. The plasma concentration-time profiles of famotidine formulations were simulated using the dissolution data. In addition, virtual simulations were performed to estimate the range of dissolution rates to be bioequivalent. The fastest and slowest dissolution rates among the famotidine formulations were T_85% = 10 min and T_85% = 60 min at pH 6.8, respectively. The virtual simulation BE study suggested that famotidine formulations can be bioequivalent when T_85% < 99 min. In the case of BCS III drugs, the rate-limiting step of oral drug absorption is the membrane permeation process rather than the dissolution process. Therefore, a difference in the dissolution process has less effect on BE. These results contribute to a better understanding of the biowaiver approach and would be of great help in the formulation development of BCS class III drugs.

Can Mechanistic Static Models for Drug-Drug Interactions Support Regulatory Filing for Study Waivers and Label Recommendations?

BACKGROUND AND OBJECTIVE

Mechanistic static and dynamic physiologically based pharmacokinetic models are used in clinical drug development to assess the risk of drug-drug interactions (DDIs). Currently, the use of mechanistic static models is restricted to screening DDI risk for an investigational drug, while dynamic physiologically based pharmacokinetic models are used for quantitative predictions of DDIs to support regulatory filing. As physiologically based pharmacokinetic model development by sponsors as well as a review of models by regulators require considerable resources, we explored the possibility of using mechanistic static models to support regulatory filing, using representative cases of successful physiologically based pharmacokinetic submissions to the US Food and Drug Administration under different classes of applications.

METHODS

Drug-drug interaction predictions with mechanistic static models were done for representative cases in the different classes of applications using the same data and modelling workflow as described in the Food and Drug Administration clinical pharmacology reviews. We investigated the hypothesis that the use of unbound average steady-state concentrations of modulators as driver concentrations in the mechanistic static models should lead to the same conclusions as those from physiologically based pharmacokinetic modelling for non-dynamic measures of DDI risk assessment such as the area under the plasma concentration-time curve ratio, provided the same input data are employed for the interacting drugs.

RESULTS

Drug-drug interaction predictions of area under the plasma concentration-time curve ratios using mechanistic static models were mostly comparable to those reported in the Food and Drug Administration reviews using physiologically based pharmacokinetic models for all representative cases in the different classes of applications.

CONCLUSIONS

The results reported in this study should encourage the use of models that best fit an intended purpose, limiting the use of physiologically based pharmacokinetic models to those applications that leverage its unique strengths, such as what-if scenario testing to understand the effect of dose staggering, evaluating the role of uptake and efflux transporters, extrapolating DDI effects from studied to unstudied populations, or assessing the impact of DDIs on the exposure of a victim drug with concurrent mechanisms. With this first step, we hope to trigger a scientific discussion on the value of a routine comparison of the two methods for regulatory submissions to potentially create a best practice that could help identify examples where the use of dynamic changes in modulator concentrations could make a difference to DDI risk assessment.

The Finite Absorption Time (FAT) concept en route to PBPK modeling and pharmacometrics

The concept of Finite Absorption Time (FAT) for oral drug administration is set to affect pharmacokinetic analyses, Physiologically-based Pharmacokinetics simulations, and Pharmacometrics.

Physiologically based Pharmacokinetic Models under the Prism of the Finite Absorption Time Concept

To date, mechanistic modeling of oral drug absorption has been achieved via the use of physiologically based pharmacokinetic (PBPK) modeling, and more specifically, physiologically based biopharmaceutics model (PBBM). The concept of finite absorption time (FAT) has been developed recently and the application of the relevant physiologically based finite time pharmacokinetic (PBFTPK) models to experimental data provides explicit evidence that drug absorption terminates at a specific time point. In this manuscript, we explored how PBBM and PBFTPK models compare when applied to the same dataset. A set of six compounds with clinical data from immediate-release formulation were selected. Both models resulted in absorption time estimates within the small intestinal transit time, with PBFTPK models generally providing shorter time estimates. A clear relationship between the absorption rate and the product of permeability and luminal concentration was observed, in concurrence with the fundamental assumptions of PBFTPK models. We propose that future research on the synergy between the two modeling approaches can lead to both improvements in the initial parameterization of PBPK/PBBM models but to also expand mechanistic oral absorption concepts to more traditional pharmacometrics applications.

Prediction of Oral Drug Absorption in Rats from In Vitro Data

PURPOSE

In drug discovery, rats are widely used for pharmacological and toxicological studies. We previously reported that a mechanism-based oral absorption model, the gastrointestinal unified theoretical framework (GUT framework), can appropriately predict the fraction of a dose absorbed (Fa) in humans and dogs. However, there are large species differences between humans and rats. The purpose of the present study was to evaluate the predictability of the GUT framework for rat Fa.

METHOD

The Fa values of 20 model drugs (a total of 39 Fa data) were predicted in a bottom-up manner. Based on the literature survey, the bile acid concentration (C_bile) and the intestinal fluid volume were set to 15 mM and 4 mL/kg, respectively, five and two times higher than in humans. LogP, pK_a, molecular weight, intrinsic solubility, bile micelle partition coefficients, and Caco-2 permeability were used as input data.

RESULTS

The Fa values were appropriately predicted for highly soluble drugs (absolute average fold error (AAFE) = 1.65, 18 Fa data) and poorly soluble drugs (AAFE = 1.57, 21 Fa data). When the species difference in C_bile was ignored, Fa was over- and under-predicted for permeability and solubility limited cases, respectively. High C_bile in rats reduces the free fraction of drug molecules available for epithelial membrane permeation while increasing the solubility of poorly soluble drugs.

CONCLUSION

The Fa values in rats were appropriately predicted by the GUT framework. This result would be of great help for a better understanding of species differences and model-informed preclinical formulation development.

Columbus' egg: Oral drugs are absorbed in finite time

The infinite time of oral drug absorption was conceived from the first day of the birth of pharmacokinetics when H. Dost introduced the term pharmacokinetics in his book published in 1953. He adopted the function developed by H. Bateman back in 1908 for the decay of the nuclei isotopes to describe oral drug absorption as a first-order process. We unveiled this false hypothesis relying on common wisdom i.e. drugs are absorbed in finite time. This false assumption had dramatic effects on the evolution of oral pharmacokinetics but most importantly on the bioavailability and bioequivalence concepts and metrics. This work focuses on the finite absorption time (FAT) concept, the relevant Physiologically Based Finite Time (PBFTPK) models developed and their applications in oral pharmacokinetics, bioavailability and bioequivalence. The crux of the matter is that drug absorption from the gastrointestinal tract takes place under sink conditions because of the high blood flow rate in the vena cava. The termination of oral, pulmonary and intranasal drug absorption at a specific time point, calls for regulatory changes in bioavailability and bioequivalence studies in terms of the study design and metrics used for the bioequivalence assessment.

Modeling Blood–Brain Barrier Permeability to Solutes and Drugs In Vivo

Our understanding of the pharmacokinetic principles governing the uptake of endogenous substances, xenobiotics, and biologicals across the blood–brain barrier (BBB) has advanced significantly over the past few decades. There is now a spectrum of experimental techniques available in experimental animals and humans which, together with pharmacokinetic models of low to high complexity, can be applied to describe the transport processes at the BBB of low molecular weight agents and macromolecules. This review provides an overview of the models in current use, from initial rate uptake studies over compartmental models to physiologically based models and points out the advantages and shortcomings associated with the different methods. A comprehensive pharmacokinetic profile of a compound with respect to brain exposure requires the knowledge of BBB uptake clearance, intra-brain distribution, and extent of equilibration across the BBB. The application of proper pharmacokinetic analysis and suitable models is a requirement not only in the drug development process, but in all of the studies where the brain uptake of drugs or markers is used to make statements about the function or integrity of the BBB.

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

PURPOSE

To develop physiologically based finite time pharmacokinetic (PBFTPK) models for the analysis of oral pharmacokinetic data.

METHODS

The models are based on the passive drug diffusion mechanism under the sink conditions principle. Up to three drug successive input functions of constant rate operating for a total time τ are considered. Differential equations were written for all these models assuming linear one- or two-compartment-model disposition. The differential equations were solved and functions describing the concentration of drug as a function of time for the central and the peripheral compartment were derived. The equations were used to generate simulated data and they were also fitted to a variety of experimental literature oral pharmacokinetic data.

RESULTS

The simulated curves resemble real life data. The end of the absorption processes τ is either equal to t_max or longer than t_max at the descending portion of the concentration time curve. Literature oral pharmacokinetic data of paracetamol, ibuprofen, almotriptan, cyclosporine (a total of four sets of data), and niraparib were analyzed using the PBFTPK models. Estimates for τ corresponding to a single or two or three different in magnitude input rates were derived along with the other model parameters for all data analyzed.

CONCLUSIONS

The PBFTPK models are a powerful tool for the analysis of oral pharmacokinetic data since they rely on the physiologically sound concept of finite absorption time.

Best practices in current models mimicking drug permeability in the gastrointestinal tract - an UNGAP review

The absorption of orally administered drug products is a complex, dynamic process, dependent on a range of biopharmaceutical properties; notably the aqueous solubility of a molecule, stability within the gastrointestinal tract (GIT) and permeability. From a regulatory perspective, the concept of high intestinal permeability is intrinsically linked to the fraction of the oral dose absorbed. The relationship between permeability and the extent of absorption means that experimental models of permeability have regularly been used as a surrogate measure to estimate the fraction absorbed. Accurate assessment of a molecule's intestinal permeability is of critical importance during the pharmaceutical development process of oral drug products, and the current review provides a critique of in vivo, in vitro and ex vivo approaches. The usefulness of in silico models to predict drug permeability is also discussed and an overview of solvent systems used in permeability assessments is provided. Studies of drug absorption in humans are an indirect indicator of intestinal permeability, but in vitro and ex vivo tools provide initial screening approaches are important tools for direct assessment of permeability in drug development. Continued refinement of the accuracy of in silico approaches and their validation with human in vivo data will facilitate more efficient characterisation of permeability earlier in the drug development process and will provide useful inputs for integrated, end-to-end absorption modelling.

Revising Pharmacokinetics of Oral Drug Absorption: II Bioavailability-Bioequivalence Considerations

PURPOSE

To explore the application of the parameters of the physiologically based finite time pharmacokinetic (PBFTPK) models subdivided in first-order (PBFTPK)₁ and zero-order (PBFTPK)₀ models to bioavailability and bioequivalence. To develop a methodology for the estimation of absolute bioavailability, F, from oral data exclusively.

METHODS

Simulated concentration time data were generated from the Bateman equation and compared with data generated from the (PBFTPK)₁ and (PBFTPK)₀ models. The blood concentration C_b(τ) at the end of the absorption process τ, was compared to C_max; the utility of [Formula: see text] and [Formula: see text] in bioequivalence assessment was also explored. Equations for the calculation of F from oral data were derived for the (PBFTPK)₁ and (PBFTPK)₀ models. An estimate for F was also derived from an areas proportionality using oral data exclusively.

RESULTS

The simulated data of the (PBFTPK)₀ models exhibit rich dynamics encountered in complex drug absorption phenomena. Both (PBFTPK)₁ and (PBFTPK)₀ models result either in C_max = C_b(τ) or C_max > C_b(τ) for rapidly- and not rapidly-absorbed drugs, respectively; in the latter case, C_b(τ) and τ are meaningful parameters for drug's rate of exposure. For both (PBFTPK)₁ and (PBFTPK)₀ models, [Formula: see text] or portions of it cannot be used as early exposure rate indicators. [Formula: see text] is a useful parameter for the assessment of extent of absorption for very rapidly absorbed drugs. An estimate for F for theophylline formulations was found close to unity.

CONCLUSION

The (PBFTPK)₁ and (PBFTPK)₀ models are more akin to in vivo conditions. Estimates for F can be derived from oral data exclusively.