Population-based mechanistic prediction of oral drug absorption

A Transcriptomic Approach to Elucidate the Mechanisms of Gefitinib-Induced Toxicity in Healthy Human Intestinal Organoids

Gefitinib is a tyrosine kinase inhibitor (TKI) that selectively inhibits the epidermal growth factor receptor (EGFR), hampering cell growth and proliferation. Due to its action, gefitinib has been used in the treatment of cancers that present abnormally increased expression of EGFR. However, side effects from gefitinib therapy may occur, among which diarrhoea is most common, that can lead to interruption of the planned therapy in the more severe cases. The mechanisms underlying intestinal toxicity induced by gefitinib are not well understood. Therefore, this study aims at providing insight into these mechanisms based on transcriptomic responses induced in vitro. A 3D culture of healthy human colon and small intestine (SI) organoids was exposed to 0.1, 1, 10 and 30 µM of gefitinib, for a maximum of three days. These drug concentrations were selected using physiologically-based pharmacokinetic simulation considering patient dosing regimens. Samples were used for the analysis of viability and caspase 3/7 activation, image-based analysis of structural changes, as well as RNA isolation and sequencing via high-throughput techniques. Differential gene expression analysis showed that gefitinib perturbed signal transduction pathways, apoptosis, cell cycle, FOXO-mediated transcription, p53 signalling pathway, and metabolic pathways. Remarkably, opposite expression patterns of genes associated with metabolism of lipids and cholesterol biosynthesis were observed in colon versus SI organoids in response to gefitinib. These differences in the organoids’ responses could be linked to increased activated protein kinase (AMPK) activity in colon, which can influence the sensitivity of the colon to the drug. Therefore, this study sheds light on how gefitinib induces toxicity in intestinal organoids and provides an avenue towards the development of a potential tool for drug screening and development.

Proof of Concept in Assignment of Within-Subject Variability During Virtual Bioequivalence Studies: Propagation of Intra-Subject Variation in Gastrointestinal Physiology Using Physiologically Based Pharmacokinetic Modeling

While the concept of ‘Virtual Bioequivalence’ (VBE) using a combination of modelling, in vitro tests and integration of pre-existing data on systems and drugs is growing from its infancy, building confidence on VBE outcomes requires demonstration of its ability not only in predicting formulation-dependent systemic exposure but also the expected degree of population variability. The concept of variation influencing the outcome of BE, despite being hidden with the cross-over nature of common BE studies, becomes evident when dealing with the acceptance criteria that consider the 90% confidence interval (CI) around the relative bioavailability. Hence, clinical studies comparing a reference product against itself may fail due to within-subject variations associated with the two occasions that the individual receives the same formulation. In this proof-of-concept study, we offer strategies to capture the most realistic predictions of CI around the pharmacokinetic parameters by propagating physiological variations through physiologically based pharmacokinetic modelling. The exercise indicates feasibility of the approach based on comparisons made between the simulated and observed WSV of pharmacokinetic parameters tested for a clinical bioequivalence case study. However, it also indicates that capturing WSV of a large array of physiological parameters using backward translation modelling from repeated BE studies of reference products would require a diverse set of drugs and formulations. The current case study of delayed-release formulation of posaconazole was able to declare certain combinations of WSV of physiological parameters as ‘not plausible’. The eliminated sets of WSV values would be applicable to PBPK models of other drugs and formulations.

Lilly Absorption Modeling Platform: A Tool for Early Absorption Assessment

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

Application of a Physiologically Based Pharmacokinetic Approach to Predict Theophylline Pharmacokinetics Using Virtual Non-Pregnant, Pregnant, Fetal, Breast-Feeding, and Neonatal Populations

Perinatal pharmacology is influenced by a myriad of physiological variables that are changing dynamically. The influence of these covariates has not been assessed systemically. The objective of this work was to use theophylline as a model drug and to predict its pharmacokinetics before, during (including prediction of the umbilical cord level), and after pregnancy as well as in milk (after single and multiple doses) and in neonates using a physiological-based pharmacokinetic (PBPK) model. Neonatal theophylline exposure from milk consumption was projected in both normal term and preterm subjects. Predicted infant daily doses were calculated using theophylline average and maximum concentration in the milk as well as an estimate of milk consumption. Predicted concentrations and parameters from the PBPK model were compared to the observed data. PBPK predicted theophylline concentrations in non-pregnant and pregnant populations at different gestational weeks were within 2-fold of the observations and the observed concentrations fell within the 5th-95th prediction interval from the PBPK simulations. The PBPK model predicted an average cord-to-maternal plasma ratio of 1.0, which also agrees well with experimental observations. Predicted postpartum theophylline concentration profiles in milk were also in good agreement with observations with a predicted milk-to-plasma ratio of 0.68. For an infant of 2 kg consuming 150 ml of milk per day, the lactation model predicted a relative infant dose (RID) of 12 and 17% using predicted average (C_avg,ss) and maximum (C_max,ss) concentration in milk at steady state. The maximum RID of 17% corresponds to an absolute infant daily dose of 1.4 ± 0.5 mg/kg/day. This dose, when administered as 0.233 mg/kg every 4 h, to resemble breastfeeding frequency, resulted in plasma concentrations as high as 3.9 (1.9-6.8) mg/L and 2.8 (1.3-5.3) (5th-95th percentiles) on day 7 in preterm (32 GW) and full-term neonatal populations.

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.

Intentional Modulation of Ibrutinib Pharmacokinetics through CYP3A Inhibition

Ibrutinib (Imbruvica; PCI-32765) is an orally administered inhibitor of Bruton's tyrosine kinase that has transformed the treatment of B-cell malignancies. However, ibrutinib has very low oral bioavailability that contributes to significant variability in systemic exposure between patients, and this has the potential to affect both efficacy and toxicity. We hypothesized that the oral bioavailability of ibrutinib is limited by CYP3A isoform-mediated metabolism, and that this pathway can be inhibited to improve the pharmacokinetic properties of ibrutinib. Pharmacokinetic studies were performed in wild-type mice and mice genetically engineered to lack all CYP3A isoforms [CYP3A(-/-)] that received ibrutinib alone or in combination with CYP3A inhibitors cobicistat or ketoconazole. Computational modeling was performed to derive doses of ibrutinib that, when given after a CYP3A inhibitor, results in therapeutically-relevant drug levels. Deficiency of CYP3A in mice was associated with a ~10-fold increase in the area under the curve of ibrutinib. This result could be phenocopied by administration of cobicistat before ibrutinib in wild-type mice, but cobicistat did not influence levels of ibrutinib in CYP3A(-/-) mice. Population pharmacokinetic and prospectively validated physiologically-based pharmacokinetic models established preclinical and clinical doses of ibrutinib that could be given safely in combination with cobicistat without negatively affecting anti-leukemic properties. These findings signify a dominant role for CYP3A-mediated metabolism in the elimination of ibrutinib, and suggest a role for pharmacological inhibitors of this pathway to intentionally modulate the plasma levels and improve the therapeutic use of this clinically important agent.

On the usefulness of four in vitro methods in assessing the intraluminal performance of poorly soluble, ionisable compounds in the fasted state

A small-scale two-stage biphasic system, a small-scale two-stage dissolution-permeation system, the Erweka mini-paddle apparatus, and the BioGIT system were evaluated for their usefulness in assessing the intraluminal performance of two low solubility drugs in the fasted state, one with weakly acidic properties (tested in a salt form, diclofenac potassium) and one with weakly alkaline properties [ritonavir, tested as an amorphous solid dispersion (ASD) formulation].

In all in vitro methods, an immediate-release tablet and a powder formulation of diclofenac potassium were both rapidly dissolved in Level II biorelevant media simulating the conditions in the upper small intestine. Physiologically based biopharmaceutics (PBB) modelling for the tablet formulation resulted in a successful simulation of the average plasma profile in adults, whereas for the powder formulation modelling indicated that gastric emptying and transport through the intestinal epithelium limit the absorption rates.

Detailed information on the behaviour of the ritonavir ASD formulation under both simulated gastric and upper small intestinal conditions were crucial for understanding the luminal performance. PBB modelling showed that the dissolution and precipitation parameters, estimated from the Erweka mini-paddle apparatus data and the small-scale two-stage biphasic system data, respectively, were necessary to adequately simulate the average plasma profile after administration of the ritonavir ASD formulation. Simulation of the gastrointestinal transfer process from the stomach to the small intestine was necessary to evaluate the effects of hypochlorhydric conditions on the luminal performance of the ritonavir ASD formulation.

Based on this study, the selection of the appropriate in vitro method for evaluating the intraluminal performance of ionisable lipophilic drugs depends on the characteristics of the drug substance. The results suggest that for (salts of) acidic drugs (e.g., diclofenac potassium) it is only an issue of availability and ease of operation of the apparatus. For weakly alkaline substances (e.g., ritonavir), the results indicate that the dynamic dissolution process needs to be simulated, with the type of requested information (e.g., dissolution parameters, precipitation parameters, luminal concentrations) being key for selecting the most appropriate method. Regardless of the ionisation characteristics, early in the drug development process the use of small-scale systems may be inevitable, due to the limited quantities of drug substance available.

A Bayesian population physiologically based pharmacokinetic absorption modeling approach to support generic drug development: application to bupropion hydrochloride oral dosage forms

We propose a Bayesian population modeling and virtual bioequivalence assessment approach to establishing dissolution specifications for oral dosage forms. A generalizable semi-physiologically based pharmacokinetic absorption model with six gut segments and liver, connected to a two-compartment model of systemic disposition for bupropion hydrochloride oral dosage forms was developed. Prior information on model parameters for gut physiology, bupropion physicochemical properties, and drug product properties were obtained from the literature. The release of bupropion hydrochloride from immediate-, sustained- and extended-release oral dosage forms was described by a Weibull function. In vitro dissolution data were used to assign priors to the in vivo release properties of the three bupropion formulations. We applied global sensitivity analysis to identify the influential parameters for plasma bupropion concentrations and calibrated them. To quantify inter- and intra-individual variability, plasma concentration profiles in healthy volunteers that received the three dosage forms, each at two doses, were used. The calibrated model was in good agreement with both in vitro dissolution and in vivo exposure data. Markov Chain Monte Carlo samples from the joint posterior parameter distribution were used to simulate virtual crossover clinical trials for each formulation with distinct drug dissolution profiles. For each trial, an allowable range of dissolution parameters ("safe space") in which bioequivalence can be anticipated was established. These findings can be used to assure consistent product performance throughout the drug product life-cycle and to support manufacturing changes. Our framework provides a comprehensive approach to support decision-making in drug product development.

Prediction of Drug-Drug Interaction Potential of Tegoprazan Using Physiologically Based Pharmacokinetic Modeling and Simulation

This study aimed to develop a physiologically based pharmacokinetic (PBPK) model of tegoprazan and to predict the drug-drug interaction (DDI) potential between tegoprazan and cytochrome P450 (CYP) 3A4 perpetrators. The PBPK model of tegoprazan was developed using SimCYP Simulator^® and verified by comparing the model-predicted pharmacokinetics (PKs) of tegoprazan with the observed data from phase 1 clinical studies, including DDI studies. DDIs between tegoprazan and three CYP3A4 perpetrators were predicted by simulating the difference in tegoprazan exposure with and without perpetrators, after multiple dosing for a clinically used dose range. The final PBPK model adequately predicted the biphasic distribution profiles of tegoprazan and DDI between tegoprazan and clarithromycin. All ratios of the predicted-to-observed PK parameters were between 0.5 and 2.0. In DDI simulation, systemic exposure to tegoprazan was expected to increase about threefold when co-administered with the maximum recommended dose of clarithromycin or ketoconazole. Meanwhile, tegoprazan exposure was expected to decrease to ~30% when rifampicin was co-administered. Based on the simulation by the PBPK model, it is suggested that the DDI potential be considered when tegoprazan is used with CYP3A4 perpetrator, as the acid suppression effect of tegoprazan is known to be associated with systemic exposure.

A Physiologically Based Pharmacokinetic Model for Predicting Diazepam Pharmacokinetics after Intravenous, Oral, Intranasal, and Rectal Applications

Diazepam is one of the most prescribed anxiolytic and anticonvulsant that is administered through intravenous (IV), oral, intramuscular, intranasal, and rectal routes. To facilitate the clinical use of diazepam, there is a need to develop formulations that are convenient to administer in ambulatory settings. The present study aimed to develop and evaluate a physiologically based pharmacokinetic (PBPK) model for diazepam that is capable of predicting its pharmacokinetics (PK) after IV, oral, intranasal, and rectal applications using a whole-body population-based PBPK simulator, Simcyp^®. The model evaluation was carried out using visual predictive checks, observed/predicted ratios (R_obs/pred), and the average fold error (AFE) of PK parameters. The Diazepam PBPK model successfully predicted diazepam PK in an adult population after doses were administered through IV, oral, intranasal, and rectal routes, as the R_obs/pred of all PK parameters were within a two-fold error range. The developed model can be used for the development and optimization of novel diazepam dosage forms, and it can be extended to simulate drug response in situations where no clinical data are available (healthy and disease).

Physiologically-Based Pharmacokinetic Modelling of Entrectinib Parent and Active Metabolite to Support Regulatory Decision-Making

BACKGROUND AND OBJECTIVE

Entrectinib is a selective inhibitor of ROS1/TRK/ALK kinases, recently approved for oncology indications. Entrectinib is predominantly cleared by cytochrome P450 (CYP) 3A4, and modulation of CYP3A enzyme activity profoundly alters the pharmacokinetics of both entrectinib and its active metabolite M5. We describe development of a combined physiologically based pharmacokinetic (PBPK) model for entrectinib and M5 to support dosing recommendations when entrectinib is co-administered with CYP3A4 inhibitors or inducers.

METHODS

A PBPK model was established in Simcyp^® Simulator. The initial model based on in vitro-in vivo extrapolation was refined using sensitivity analysis and non-linear mixed effects modeling to optimize parameter estimates and to improve model fit to data from a clinical drug-drug interaction study with the strong CYP3A4 inhibitor, itraconazole. The model was subsequently qualified against clinical data, and the final qualified model used to simulate the effects of moderate to strong CYP3A4 inhibitors and inducers on entrectinib and M5 pharmacokinetics.

RESULTS

The final model showed good predictive performance for entrectinib and M5, meeting commonly used predictive performance acceptance criteria in each case. The model predicted that co-administration of various moderate CYP3A4 inhibitors (verapamil, erythromycin, clarithromycin, fluconazole, and diltiazem) would result in an average increase in entrectinib exposure between 2.2- and 3.1-fold, with corresponding average increases for M5 of approximately 2-fold. Co-administration of moderate CYP3A4 inducers (efavirenz, carbamazepine, phenytoin) was predicted to result in an average decrease in entrectinib exposure between 45 and 79%, with corresponding average decreases for M5 of approximately 50%.

CONCLUSIONS

The model simulations were used to derive dosing recommendations for co-administering entrectinib with CYP3A4 inhibitors or inducers. PBPK modeling has been used in lieu of clinical studies to enable regulatory decision-making.

ADMET Predictability at Boehringer Ingelheim: State-of-the-Art, and Do Bigger Datasets or Algorithms Make a Difference?

Computational methods assisting drug discovery and development are routine in the pharmaceutical industry. Digital recording of ADMET assays has provided a rich source of data for development of predictive models. Despite the accumulation of data and the public availability of advanced modeling algorithms, the utility of prediction in ADMET research is not clear. Here, we present a critical evaluation of the relationships between data volume, modeling algorithm, chemical representation and grouping, and temporal aspect (time sequence of assays) using an in-house ADMET database. We find no large difference in prediction algorithms nor any systemic and substantial gain from increasingly large datasets. Temporal-based data enlargement led to performance improvement in only in a limited number of assays, and with fractional improvement at best. Assays that are well-, intermediately-, or poorly-suited for ADMET predictions and reasons for such behavior are systematically identified, generating realistic expectations for areas in which computational models can be used to guide decision making in molecular design and development.

PBPK Modeling as a Tool for Predicting and Understanding Intestinal Metabolism of Uridine 5'-Diphospho-glucuronosyltransferase Substrates

Uridine 5′-diphospho-glucuronosyltransferases (UGTs) are expressed in the small intestines, but prediction of first-pass extraction from the related metabolism is not well studied. This work assesses physiologically based pharmacokinetic (PBPK) modeling as a tool for predicting intestinal metabolism due to UGTs in the human gastrointestinal tract. Available data for intestinal UGT expression levels and in vitro approaches that can be used to predict intestinal metabolism of UGT substrates are reviewed. Human PBPK models for UGT substrates with varying extents of UGT-mediated intestinal metabolism (lorazepam, oxazepam, naloxone, zidovudine, cabotegravir, raltegravir, and dolutegravir) have demonstrated utility for predicting the extent of intestinal metabolism. Drug–drug interactions (DDIs) of UGT1A1 substrates dolutegravir and raltegravir with UGT1A1 inhibitor atazanavir have been simulated, and the role of intestinal metabolism in these clinical DDIs examined. Utility of an in silico tool for predicting substrate specificity for UGTs is discussed. Improved in vitro tools to study metabolism for UGT compounds, such as coculture models for low clearance compounds and better understanding of optimal conditions for in vitro studies, may provide an opportunity for improved in vitro–in vivo extrapolation (IVIVE) and prospective predictions. PBPK modeling shows promise as a useful tool for predicting intestinal metabolism for UGT substrates.

Crosstalk of physiological pH and chemical pKa under the umbrella of physiologically based pharmacokinetic modeling of drug absorption, distribution, metabolism, excretion, and toxicity

Introduction: Physiological pH and chemical pKa are two sides of the same coin in defining the ionization of a drug in the human body. The Henderson-Hasselbalch equation and pH-partition hypothesis form the theoretical base to define the impact of pH-pKa crosstalk on drug ionization and thence its absorption, distribution, metabolism, excretion, and toxicity (ADMET).Areas covered: Human physiological pH is not constant, but a diverse, dynamic state regulated by various biological mechanisms, while the chemical pKa is generally a constant defining the acidic dissociation of the drug at various environmental pH. Works on pH-pKa crosstalk are scattered in the literature, despite its significant contributions to drug pharmacokinetics, pharmacodynamics, safety, and toxicity. In particular, its impacts on drug ADMET have not been effectively linked to the physiologically based pharmacokinetic (PBPK) modeling and simulation, a powerful tool increasingly used in model-informed drug development (MIDD).Expert opinion: Lacking a full consideration of the interactions of physiological pH and chemical pKa in a PBPK model limits scientists' capability in mechanistically describing the drug ADMET. This mini-review compiled literature knowledge on pH-pKa crosstalk and its impacts on drug ADMET, from the viewpoint of PBPK modeling, to pave the way to a systematic incorporation of pH-pKa crosstalk into PBPK modeling and simulation.

Mechanistic PBPK Modelling to Predict the Advantage of the Salt Form of a Drug When Dosed with Acid Reducing Agents

Acid reducing agents (ARAs) reduce the dissolution rate of weakly basic drugs in the stomach potentially leading to lower bioavailability. Formulating the API as a rapidly dissolving salt is one strategy employed to reduce the impact of ARAs on dissolution of such drugs. In the present work, a model drug was selected with an immediate release formulation of the free base dosed in both the absence and presence of the ARA famotidine. In the latter case, bioavailability is restricted and several salt formulations were investigated. To simulate these drug products a mechanistic physiologically based pharmacokinetic (PBPK) model was built using the Simcyp Simulator, which illustrates the advantage of formulating an API as a salt compared to the free base form. The simulations use a mechanistic salt model utilising knowledge of the solubility product which was applied to predict the salt advantage. The developed PBPK model exemplifies that it can be critical to account for the surface pH and solubility when modelling the dissolution of low pKa bases and their salts in the gastric environment. In particular, the mechanistic salt model can be used to aid in screening and salt form selection where the aim is to mitigate effects of ARAs.

Physiologically Based Pharmacokinetic Models Are Effective Support for Pediatric Drug Development

Pediatric drug development faces many difficulties. Traditionally, pediatric drug doses are simply calculated linearly based on the body weight, age, and body surface area of adults. Due to the ontogeny of children, this simple linear scaling may lead to drug overdose in pediatric patients. The physiologically based pharmacokinetic (PBPK) model, as a mathematical model, contributes to the research and development of pediatric drugs. An example of a PBPK model guiding drug dose selection in pediatrics has emerged and has been approved by the relevant regulatory agencies. In this review, we discuss the principle of the PBPK model, emphasize the necessity of establishing a pediatric PBPK model, introduce the absorption, distribution, metabolism, and excretion of the pediatric PBPK model, and understand the various applications and related prospects of the pediatric PBPK model.

Effect of Roux-en-Y gastric bypass on the pharmacokinetic-pharmacodynamic relationships of liquid and controlled-release formulations of oxycodone

The physiological changes following Roux-en-Y gastric bypass (RYGB) surgery may impact drug release from mechanistically different controlled-release tablets, making generic substitution inappropriate. This study aimed to characterise the pharmacokinetic-pharmacodynamic relationships of oxycodone from a lipid-based and water-swellable controlled-release tablet in RYGB patients. Twenty RYGB patients received 10-mg oral solution oxycodone or 20-mg controlled-release (water-swellable or lipid-based) oxycodone in a three-way, randomised, semiblinded and cross-over study. Blood sampling and pupillary recordings were conducted over a 24-h period. A previously established pharmacokinetic-pharmacodynamic model of these three formulations in healthy volunteers was used in the analysis as a reference model. No differences in absorption kinetics were seen between controlled-release formulations in patients. However, the absorption lag time was 11.5 min in patients vs 14 min in healthy volunteers for controlled-release tablets (P < 0.001). Furthermore, oral bioavailability was 14.4% higher in patients compared to healthy volunteers regardless of formulation type (P < 0.001). Oxycodone pharmacodynamics were not significantly affected by formulation or patient status. However, baseline pupil diameter was inversely correlated with age (P < 0.001) and plasma concentrations of oxycodone at half-maximum effect were 31% lower in males compared to females (P < 0.05). Generic substitution of monophasic lipid-based and water-swellable controlled-release oxycodone tablets may be considered safe in RYGB patients.

Development of a Physiologically Based Pharmacokinetic Model for Prediction of Ethanol Concentration-Time Profile in Different Organs

AIMS

A physiologically based pharmacokinetic (PBPK) modeling approach was used to simulate the concentration-time profile of ethanol (EtOH) in stomach, duodenum, plasma and other tissues upon consumption of beer and whiskey under fasted and fed conditions.

METHODS

A full PBPK model was developed for EtOH using the advanced dissolution, absorption and metabolism (ADAM) model fully integrated into the Simcyp Simulator® 15 (Simcyp Ltd., Sheffield, UK). The prediction performance of the developed model was verified and the EtOH concentration-time profile in different organs was predicted.

RESULTS

Simcyp simulation showed ≤ 2-fold difference in values of EtOH area under the concentration-time curve (AUC) in stomach and duodenum as compared to the observed values. Moreover, the simulated EtOH maximum concentration (Cmax), time to reach Cmax (Tmax) and AUC in plasma were comparable to the observed values. We showed that liver is exposed to the highest EtOH concentration, faster than other organs (Cmax = 839.50 mg/L and Tmax = 0.53 h), while brain exposure of EtOH (AUC = 1139.43 mg·h/L) is the highest among all other organs. Sensitivity analyses (SAs) showed direct proportion of EtOH rate and extent of absorption with administered EtOH dose and inverse relationship with gastric emptying time (GE) and steady-state volume of distribution (Vss).

CONCLUSIONS

The current PBPK model approach might help with designing in vitro experiments in the area of alcohol organ damage or alcohol-drug interaction studies.

A Physiologically Based Pharmacokinetic and Drug-Drug Interaction Model for the CB2 Agonist Lenabasum

BACKGROUND AND OBJECTIVES

Lenabasum is a synthetic agonist of the cannabinoid receptor type 2 (CB2) with anti-inflammatory and antifibrotic properties. Utilizing Simcyp, we developed a physiologically based pharmacokinetic (PBPK) model based on physicochemical properties, cell culture data, and cytochrome P450 (CYP) phenotyping, inhibition, and induction data.

METHODS

Clinical data from healthy volunteers treated with 20 mg of lenabasum in a single ascending dose (SAD) study were used for model development. The model was verified using lenabasum SAD (10 and 40 mg) data as well as multiple dose (20 mg three times per day) data. Lenabasum is a CYP substrate, and the model predicted lenabasum clearance of 51% by CYP2C9, 37% by CYP2C8, and 12% by CYP3A4. Lenabasum is also an inhibitor of these isozymes.

RESULTS

The model accurately described the area under the plasma concentration-time curve (AUC) and maximum plasma concentration (C_max) for lenabasum within 1.19-fold and 1.25-fold accuracy, respectively, of the observed clinical values. The simulations of CYP inducers predicted that the strongest interaction would occur with rifampin, with the AUC decreasing to 0.36 of the control value, whereas the simulations of CYP inhibitors predicted that the greatest effect would occur with fluconazole, with a 1.43-fold increase in AUC.

CONCLUSIONS

Our model is a useful tool for predicting the pharmacokinetics of lenabasum and adjustments to its dosing in possible drug-drug interaction scenarios.

Prediction of Fluoxetine and Norfluoxetine Pharmacokinetic Profiles Using Physiologically Based Pharmacokinetic Modeling

Fluoxetine is a selective serotonin reuptake inhibitor that is metabolized to norfluoxetine by cytochrome P450 (CYP) 2D6, CYP2C19, and CYP3A4. A physiologically based pharmacokinetic model for fluoxetine and norfluoxetine metabolism was developed to predict and investigate changes in concentration-time profiles according to fluoxetine dosage in the Korean population. The model was developed based on the Certara repository model and information gleaned from the literature. Digitally extracted clinical study data were used to develop and verify the model. Simulations for plasma concentrations of fluoxetine and norfluoxetine after a single dose of 60 or 80 mg fluoxetine were made based on 1000 virtual healthy Korean individuals using the SimCYP version 19 simulator. The mean ratios (simulated/observed) after a single administration of 80 mg fluoxetine for maximum plasma concentration, area under the plasma concentration-time curve, and apparent clearance were 1.12, 1.08, and 0.93 for fluoxetine; the ratios of maximum plasma concentration and area under the plasma concentration-time curve were 1.08 and 1.08, respectively, for norfluoxetine, indicating that the simulated concentration-time profiles of fluoxetine and norfluoxetine fitted the observed profiles well. The developed model was used to predict plasma fluoxetine and norfluoxetine concentration-time profiles after repeated administrations of fluoxetine in Korean volunteers. This physiologically based pharmacokinetic model could provide basic understanding of the pharmacokinetic profiles of fluoxetine and its metabolite under various situations.

Intestinal Excretion, Intestinal Recirculation, and Renal Tubule Reabsorption Are Underappreciated Mechanisms That Drive the Distribution and Pharmacokinetic Behavior of Small Molecule Drugs

Drug reabsorption following biliary excretion is well-known as enterohepatic recirculation (EHR). Renal tubular reabsorption (RTR) following renal excretion is also common but not easily assessed. Intestinal excretion (IE) and enteroenteric recirculation (EER) have not been recognized as common disposition mechanisms for metabolically stable and permeable drugs. IE and intestinal reabsorption (IR:EHR/EER), as well as RTR, are governed by dug concentration gradients, passive diffusion, active transport, and metabolism, and together they markedly impact disposition and pharmacokinetics (PK) of small molecule drugs. Disruption of IE, IR, or RTR through applications of active charcoal (AC), transporter knockout (KO), and transporter inhibitors can lead to changes in PK parameters. The impacts of intestinal and renal reabsorption on PK are under-appreciated. Although IE and EER/RTR can be an intrinsic drug property, there is no apparent strategy to optimize compounds based on this property. This review seeks to improve understanding and applications of IE, IR, and RTR mechanisms.

A New Intestinal Model for Analysis of Drug Absorption and Interactions Considering Physiological Translocation of Contents

Precise prediction of drug absorption is key to the success of new drug development and efficacious pharmacotherapy. In this study, we developed a new absorption model, the advanced translocation model (ATOM), by extending our previous model, the translocation model. ATOM reproduces the translocation of a substance in the intestinal lumen using a partial differential equation with variable dispersion and convection terms to describe natural flow and micromixing within the intestine under not only fasted but also fed conditions. In comparison with ATOM, it was suggested that a conventional absorption model, advanced compartmental absorption and transit model, tends to underestimate micromixing in the upper intestine, and it is difficult to adequately describe movements under the fasted and fed conditions. ATOM explains the observed nonlinear absorption of midazolam successfully, with a minimal number of scaling factors. Furthermore, ATOM considers the apical and basolateral membrane permeabilities of enterocytes separately and assumes compartmentation of the lamina propria, including blood vessels, to consider intestinal blood flow appropriately. ATOM estimates changes in the intestinal availability caused by drug interaction associated with inhibition of CYP3A and P-glycoprotein in the intestine. Additionally, ATOM can estimate the drug absorption in the fed state considering delayed intestinal drug flow. Therefore, ATOM is a useful tool for the analysis of local pharmacokinetics in the gastrointestinal tract, especially for the estimation of nonlinear drug absorption, which may involve various interactions with intestinal contents or other drugs. SIGNIFICANCE STATEMENT: The newly developed advanced translocation model precisely explains various movements of intestinal contents under fasted and fed conditions, which cannot be adequately described by the current physiological pharmacokinetic models.

Development and evaluation of a physiologically based pharmacokinetic model to predict carvedilol-paroxetine metabolic drug-drug interaction in healthy adults and its extrapolation to virtual chronic heart failure patients for dose optimization

Purpose: The metabolic drug-drug interactions (mDDIs) are one of the most important challenges faced by the pharmaceutical industry during the drug development stage and are frequently associated with labeling restrictions and withdrawal of drugs. The capacity of physiologically based pharmacokinetic (PBPK) models to absorb and upgrade with the newly available information on drug and population-specific parameters, makes them a preferred choice over the conventional pharmacokinetic models for predicting mDDIs.Method: A PBPK model capable of predicting the stereo-selective disposition of carvedilol after administering paroxetine by incorporating mechanism (time) based inhibition of CYP2D6 and CYP3A4 was developed by using the population-based absorption, distribution, metabolism and elimination (ADME) simulator, Simcyp®.Results: The model predictions for both carvedilol enantiomers were in close agreement with the observed PK data, as the ratios for observed/predicted PK parameters were within the 2-fold error range. The developed PBPK model was successful in capturing an increase in exposures of R and S-carvedilol, due to the time-based inhibition of CYP2D6 enzyme caused by paroxetine.Conclusion: The developed model can be used for exploring complex clinical scenarios, where multiple drugs are given concurrently, particularly in diseased populations where no clinical trial data is available.

Physiologically-based pharmacokinetic modeling to predict drug interactions of lemborexant with CYP3A inhibitors

Lemborexant, a recently approved dual orexin receptor antagonist for treatment of adults with insomnia, is eliminated primarily by cytochrome P450 (CYP)3A metabolism. The recommended dose of lemborexant is 5 mg once per night, with a maximum recommended dose of 10 mg once daily. A physiologically-based pharmacokinetic (PBPK) model for lemborexant was developed and applied to integrate data obtained from in vivo drug-drug interaction (DDI) assessments, and to further explore lemborexant interaction with CYP3A inhibitors and inducers. The model predictions were in good agreement with observed pharmacokinetic data and with DDI results from clinical studies with CYP3A inhibitors, itraconazole and fluconazole. The model further predicted that DDI effects of weak CYP3A inhibitors (fluoxetine and ranitidine) are weak, and effects of moderate inhibitors (erythromycin and verapamil) are moderate. Based on the PBPK simulations and clinical efficacy and safety data, the maximum daily recommended lemborexant dose when administered with weak CYP3A inhibitors is 5 mg; co-administration of moderate and strong inhibitors should be avoided except in countries where 2.5 mg has been approved.

Comprehensive PBPK model to predict drug interaction potential of Zanubrutinib as a victim or perpetrator

A physiologically based pharmacokinetic (PBPK) model was developed to evaluate and predict (1) the effect of concomitant cytochrome P450 3A (CYP3A) inhibitors or inducers on the exposures of zanubrutinib, (2) the effect of zanubrutinib on the exposures of CYP3A4, CYP2C8, and CYP2B6 substrates, and (3) the impact of gastric pH changes on the pharmacokinetics of zanubrutinib. The model was developed based on physicochemical and in vitro parameters, as well as clinical data, including pharmacokinetic data in patients with B-cell malignancies and in healthy volunteers from two clinical drug-drug interaction (DDI) studies of zanubrutinib as a victim of CYP modulators (itraconazole, rifampicin) or a perpetrator (midazolam). This PBPK model was successfully validated to describe the observed plasma concentrations and clinical DDIs of zanubrutinib. Model predictions were generally within 1.5-fold of the observed clinical data. The PBPK model was used to predict untested clinical scenarios; these simulations indicated that strong, moderate, and mild CYP3A inhibitors may increase zanubrutinib exposures by approximately four-fold, two- to three-fold, and <1.5-fold, respectively. Strong and moderate CYP3A inducers may decrease zanubrutinib exposures by two- to three-fold or greater. The PBPK simulations showed that clinically relevant concentrations of zanubrutinib, as a DDI perpetrator, would have no or limited impact on the enzyme activity of CYP2B6 and CYP2C8. Simulations indicated that zanubrutinib exposures are not impacted by acid-reducing agents. Development of a PBPK model for zanubrutinib as a DDI victim and perpetrator in parallel can increase confidence in PBPK models supporting zanubrutinib label dose recommendations.

Perampanel and pregnancy

Objective

The objective was to summarize pregnancy and fetal/postnatal outcomes following maternal perampanel exposure using preclinical and clinical data, and to use physiologically based pharmacokinetic (PBPK) modeling to improve understanding of perampanel pharmacokinetics (PK) during pregnancy.

Methods

Preclinical developmental studies with perampanel were conducted in pregnant rats and rabbits. Clinical data were collated from the Eisai global perampanel safety database, comprising reports of perampanel exposure during pregnancy from routine clinical settings, interventional studies, and non‐interventional post‐marketing studies, searched for events coded to Medical Dictionary for Regulatory Activities (MedDRA) high‐level group terms of Pregnancy, Labor, Delivery, and Postpartum Conditions and/or the Standardized MedDRA Query terms of Congenital, Familiar, and Genetic Disorders. A PBPK model was used to predict clinical perampanel PK throughout pregnancy.

Results

Preclinical studies indicated that perampanel may be linked with post‐implantation loss and/or some specific physical development delays but not fertility and early embryonic development. As of August 31, 2018, 96 pregnancies in 90 women receiving perampanel had been reported. No concomitant medications were reported in 26 (28.9%) women taking perampanel. Overall, 43 pregnancies reached full term (all normal live births), 28 did not reach term (induced abortion, n = 18; spontaneous miscarriage, n = 6; incomplete spontaneous miscarriage, n = 2; premature delivery, n = 1; stillbirth [Fallot’s tetralogy], n = 1), 18 were lost to follow‐up, and seven were ongoing at data cut‐off. Adverse events were reported in five full‐term neonates (low Apgar score, n = 2; fatal neonatal aspiration, n = 1; cystic fibrosis and congenital deafness, n = 1; poor sucking reflex and shallow breathing, n = 1). PK simulations predicted perampanel exposure decreases throughout pregnancy and is up to four‐ and three‐fold lower towards the end of pregnancy compared with non‐pregnant women for total and unbound perampanel, respectively.

Significance

These data provide preliminary information on perampanel use during pregnancy and should be interpreted with caution. Further outcome data are required to estimate the prevalence of adverse pregnancy outcomes with perampanel exposure.

Development and Qualification of a Physiologically Based Pharmacokinetic Model of Finasteride and Minoxidil Following Scalp Application

In this study, we aimed to develop and qualify a PBPK model for scalp application using two drugs with marked differences in physicochemical properties and PK profiles. The parameters related to scalp physiology, drug PK, and formulations were incorporated into a Multi-Phase and Multi-Layer (MPML) Mechanistic Dermal Absorption (MechDermA) model within the Simcyp® Simulator V17. The finasteride PBPK model was linked to its effect on dihydrotestosterone (DHT) levels in plasma and scalp using an indirect response model. Predicted PK (and PD for finasteride) profiles and parameters were compared against the clinically reported data and justified by visual predictive checks and two-fold error criteria for model verification. The PBPK/PD model for finasteride reasonably demonstrated an ability to predict its respective PK and PD profiles, and parameters following scalp application under various clinical scenarios. Using the same scalp physiological input parameters, the minoxidil PBPK model was then developed and satisfactorily qualified with independent clinical datasets. Collectively, these results suggested that the established PBPK model may have broader utility for other topical formulations intended for scalp application.

Oral biopharmaceutics tools: recent progress from partnership through the Pharmaceutical Education and Research with Regulatory Links collaboration

OBJECTIVES

To summarise key contributions of the Pharmaceutical Education and Research with Regulatory Links (PEARRL) project (2016-2020) to the optimisation of existing and the development of new biopharmaceutics tools for evaluating the in vivo performance of oral drug products during the development of new drugs and at the regulatory level.

KEY FINDINGS

Optimised biopharmaceutics tools: Based on new clinical data, the composition of biorelevant media for simulating the fed state conditions in the stomach was simplified. Strategies on how to incorporate biorelevant in vitro data of bio-enabling drug products into physiologically based pharmacokinetic (PBPK) modelling were proposed. Novel in vitro biopharmaceutics tools: Small-scale two-stage biphasic dissolution and dissolution-permeation setups were developed to facilitate understanding of the supersaturation effects and precipitation risks of orally administered drugs. A porcine fasted state simulated intestinal fluid was developed to improve predictions and interpretation of preclinical results using in vitro dissolution studies. Based on new clinical data, recommendations on the design of in vitro methodologies for evaluating the GI drug transfer process in the fed state were suggested. The optimized design of in vivo studies for investigating food effects: A food effect study protocol in the pig model was established which successfully predicted the food-dependent bioavailability of two model compounds. The effect of simulated infant fed state conditions in healthy adults on the oral absorption of model drugs was evaluated versus the fasted state and the fed state conditions, as defined by regulatory agencies for adults. Using PBPK modelling, the extrapolated fasted and infant fed conditions data appeared to be more useful to describe early drug exposure in infants, while extrapolation of data collected under fed state conditions, as defined by regulators for adults, failed to capture in vivo infant drug absorption.

SUMMARY

Substantial progress has been made in developing an advanced suite of biopharmaceutics tools for streamlining drug formulation screening and supporting regulatory applications. These advances in biopharmaceutics were achieved through networking opportunities and research collaborations provided under the H2020 funded PEARRL project.

Prediction of Drug Clearance from Enzyme and Transporter Kinetics

Accurate estimation of in vivo clearance in human is pivotal to determine the dose and dosing regimen for drug development. In vitro-in vivo extrapolation (IVIVE) has been performed to predict drug clearance using empirical and physiological scalars. Multiple in vitro systems and mathematical modeling techniques have been employed to estimate in vivo clearance. The models for predicting clearance have significantly improved and have evolved to become more complex by integrating multiple processes such as drug metabolism and transport as well as passive diffusion. This chapter covers the use of conventional as well as recently developed methods to predict metabolic and transporter-mediated clearance along with the advantages and disadvantages of using these methods and the associated experimental considerations. The general approaches to improve IVIVE by use of appropriate scalars, incorporation of extrahepatic metabolism and transport and application of physiologically based pharmacokinetic (PBPK) models with proteomics data are also discussed. The chapter also provides an overview of the advantages of using such dynamic mechanistic models over static models for clearance predictions to improve IVIVE.

Examination of Physiologically-Based Pharmacokinetic Models of Rosuvastatin

Physiologically‐based pharmacokinetic (PBPK) modeling is increasingly used to predict drug disposition and drug–drug interactions (DDIs). However, accurately predicting the pharmacokinetics of transporter substrates and transporter‐mediated DDIs (tDDIs) is still challenging. Rosuvastatin is a commonly used substrate probe in DDI risk assessment for new molecular entities (NMEs) that are potential organic anion transporting polypeptide 1B or breast cancer resistance protein transporter inhibitors, and as such, several rosuvastatin PBPK models have been developed to try to predict the clinical DDI and support NME drug labeling. In this review, we examine five representative PBPK rosuvastatin models, discuss common challenges that the models have come across, and note remaining gaps. These shared learnings will help with the continuing efforts of rosuvastatin model validation, provide more information to understand transporter‐mediated drug disposition, and increase confidence in tDDI prediction.

Measuring dissolution profiles of single controlled-release drug pellets

Many solid-dose oral drug products are engineered to release their active ingredients into the body at a certain rate. Techniques for measuring the dissolution or degradation of a drug product in vitro play a crucial role in predicting how a drug product will perform in vivo. However, existing techniques are often labor-intensive, time-consuming, irreproducible, require specialized analytical equipment, and provide only “snapshots” of drug dissolution every few minutes. These limitations make it difficult for pharmaceutical companies to obtain full dissolution profiles for drug products in a variety of different conditions, as recommended by the US Food and Drug Administration. Additionally, for drug dosage forms containing multiple controlled-release pellets, particles, beads, granules, etc. in a single capsule or tablet, measurements of the dissolution of the entire multi-particle capsule or tablet are incapable of detecting pellet-to-pellet variations in controlled release behavior. In this work, we demonstrate a simple and fully-automated technique for obtaining dissolution profiles from single controlled-release pellets. We accomplished this by inverting the drug dissolution problem: instead of measuring the increase in the concentration of drug compounds in the solution during dissolution (as is commonly done), we monitor the decrease in the buoyant mass of the solid controlled-release pellet as it dissolves. We weigh single controlled-release pellets in fluid using a vibrating tube sensor, a piece of glass tubing bent into a tuning-fork shape and filled with any desired fluid. An electronic circuit keeps the glass tube vibrating at its resonance frequency, which is inversely proportional to the mass of the tube and its contents. When a pellet flows through the tube, the resonance frequency briefly changes by an amount that is inversely proportional to the buoyant mass of the pellet. By passing the pellet back-and-forth through the vibrating tube sensor, we can monitor its mass as it degrades or dissolves, with high temporal resolution (measurements every few seconds) and mass resolution (700 nanogram resolution). As a proof-of-concept, we used this technique to measure the single-pellet dissolution profiles of several commercial controlled-release proton pump inhibitors in simulated stomach and intestinal contents, as well as comparing name-brand and generic formulations of the same drug. In each case, vibrating tube sensor data revealed significantly different dissolution profiles for the different drugs, and in some cases our method also revealed differences between different pellets from the same drug product. By measuring any controlled-release pellets, particles, beads, or granules in any physiologically-relevant environment in a fully-automated fashion, this method can augment and potentially replace current dissolution tests and support product development and quality assurance in the pharmaceutical industry.

Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling to Predict the Impact of CYP2C9 Genetic Polymorphisms, Co-Medication and Formulation on the Pharmacokinetics and Pharmacodynamics of Flurbiprofen

Physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models can serve as a powerful framework for predicting the influence as well as the interaction of formulation, genetic polymorphism and co-medication on the pharmacokinetics and pharmacodynamics of drug substances. In this study, flurbiprofen, a potent non-steroid anti-inflammatory drug, was chosen as a model drug. Flurbiprofen has absolute bioavailability of ~95% and linear pharmacokinetics in the dose range of 50–300 mg. Its absorption is considered variable and complex, often associated with double peak phenomena, and its pharmacokinetics are characterized by high inter-subject variability, mainly due to its metabolism by the polymorphic CYP2C9 (fmCYP2C9 ≥ 0.71). In this study, by leveraging in vitro, in silico and in vivo data, an integrated PBPK/PD model with mechanistic absorption was developed and evaluated against clinical data from PK, PD, drug-drug and gene-drug interaction studies. The PBPK model successfully predicted (within 2-fold) 36 out of 38 observed concentration-time profiles of flurbiprofen as well as the CYP2C9 genetic effects after administration of different intravenous and oral dosage forms over a dose range of 40–300 mg in both Caucasian and Chinese healthy volunteers. All model predictions for C_max, AUC_inf and CL/F were within two-fold of their respective mean or geometric mean values, while 90% of the predictions of C_max, 81% of the predictions of AUC_inf and 74% of the predictions of Cl/F were within 1.25 fold. In addition, the drug-drug and drug-gene interactions were predicted within 1.5-fold of the observed interaction ratios (AUC, C_max ratios). The validated PBPK model was further expanded by linking it to an inhibitory E_max model describing the analgesic efficacy of flurbiprofen and applying it to explore the effect of formulation and genetic polymorphisms on the onset and duration of pain relief. This comprehensive PBPK/PD analysis, along with a detailed translational biopharmaceutic framework including appropriately designed biorelevant in vitro experiments and in vitro-in vivo extrapolation, provided mechanistic insight on the impact of formulation and genetic variations, two major determinants of the population variability, on the PK/PD of flurbiprofen. Clinically relevant specifications and potential dose adjustments were also proposed. Overall, the present work highlights the value of a translational PBPK/PD approach, tailored to target populations and genotypes, as an approach towards achieving personalized medicine.

Mechanistic evaluation of the effect of sodium-dependent glucose transporter 2 inhibitors on delayed glucose absorption in patients with type 2 diabetes mellitus using a quantitative systems pharmacology model of human systemic glucose dynamics

Sodium-dependent glucose transporter (SGLT) 2 is specifically expressed in the kidney, while SGLT1 is present in the kidneys and small intestine. SGLT2 inhibitors are a class of oral antidiabetic drugs that lower elevated plasma glucose levels by promoting the urinary excretion of excess glucose through the inhibition of renal glucose reuptake. The inhibition selectivity for SGLT2 over SGLT1 (SGLT2/1 selectivity) of marketed SGLT2 inhibitors is diverse, while SGLT2/1 selectivity of canagliflozin is relatively low. Although canagliflozin suppresses postprandial glucose levels, the degree of contribution for SGLT1 inhibition to this effect remains unproven. To analyze the effect of SGLT2 inhibitors on postprandial glucose level, we constructed a novel quantitative systems pharmacology (QSP) model, called human systemic glucose dynamics (HSGD) model, integrating intestinal absorption, metabolism, and renal reabsorption of glucose. This HSGD model reproduced the postprandial plasma glucose concentration-time profiles during a meal tolerance test under different clinical trial conditions. Simulations after canagliflozin administration showed a dose-dependent delay of time (T_max,glc ) to reach maximum concentration of glucose (C_max,glc ), and the delay of T_max,glc disappeared when inhibition of SGLT1 was negated. In addition, contribution ratio of intestinal SGLT1 inhibition to the decrease in C_max,glc was estimated to be 23%-28%, when 100 and 300 mg of canagliflozin are administered. This HSGD model enabled us to provide the partial contribution of intestinal SGLT1 inhibition to the improvement of postprandial hyperglycemia as well as to quantitatively describe the plasma glucose dynamics following SGLT2 inhibitors.

Use of a physiologically based pharmacokinetic-pharmacodynamic model for initial dose prediction and escalation during a paediatric clinical trial

AIMS

To build and verify a physiologically based pharmacokinetic (PBPK) model for radiprodil in adults and link this to a pharmacodynamic (PD) receptor occupancy (RO) model derived from in vitro data. Adapt this model to the paediatric population and predict starting and escalating doses in infants based on RO. Use the model to guide individualized dosing in a clinical trial in 2- to 14-month-old children with infantile spasms.

METHODS

A PBPK model for radiprodil was developed to investigate the systemic exposure of the drug after oral administration in fasted and fed adults; this was then linked to RO via a PD model. The model was then expanded to include developmental physiology and ontogeny to predict escalating doses in infants that would result in a specific RO of 20, 40 and 60% based on average unbound concentration following a twice daily (b.i.d.) dosing regimen. Dose progression in the clinical trial was based on observed concentration-time data against PBPK predictions.

RESULTS

For paediatric predictions, the elimination of radiprodil, based on experimental evidence, had no ontogeny. Predicted b.i.d. doses ranged from 0.04 mg/kg for 20% RO, 0.1 mg/kg for 40% RO to 0.21 mg/kg for 60% RO. For all infants recruited in the study, observed concentration-time data following the 0.04 mg/kg and subsequent doses were within the PBPK model predicted 5^th and 95^th percentiles.

CONCLUSION

To our knowledge, this is the first time a PBPK model linked to RO has been used to guide dose selection and escalation in the live phase of a paediatric clinical trial.

A Physiologically Based Pharmacokinetic Model of Vismodegib: Deconvoluting the Impact of Saturable Plasma Protein Binding, pH-Dependent Solubility and Nonsink Permeation

Vismodegib displays unique pharmacokinetic characteristics including saturable plasma protein binding to alpha-1 acid glycoprotein (AAG) and apparent time-dependent bioavailability leading to non-linear PK with dose and time, significantly faster time to steady-state and lower than predicted accumulation. Given these unique characteristics, a PBPK model was developed to explore mechanistic insights into saturable protein binding and complex oral absorption processes and de-convolute the impact of these independent non-linear processes on vismodegib exposure. Simcyp V18 was used for model development; oral absorption was characterized using the multi-layer gut wall (M-ADAM) model and mechanistic permeability model, incorporating transport across an unstirred boundary layer (UBL) between the luminal fluid and enterocyte in each segment of the gastrointestinal tract. PBPK simulations were compared with observed PK data from clinical trials in oncology patients and healthy subjects. Saturation of vismodegib protein binding to AAG led to substantially lower total drug accumulation, time to steady-state, and Css_total. For free exposure, Css_free and accumulation were unchanged, but time to steady-state was substantially reduced. Vismodegib oral absorption declined with both dose and dosing frequency; the concentration gradient driving vismodegib oral absorption declined with multiple doses, leading to a 32% decrease in vismodegib f_a from first dose to steady-state. Fed simulations suggested that increased solubility and dissolution are partially offset by reduced permeability across the UBL due to slower diffusion of micelle-bound drug. This work demonstrates the value of PBPK modeling to simultaneously capture and de-convolute multi-faceted absorption and disposition processes and provide mechanistic insights for compounds with complex pharmacokinetics.

Physiologically based pharmacokinetic modelling of lisinopril in children: A case story of angiotensin converting enzyme inhibitors

AIMS

Lisinopril is an angiotensin converting enzyme inhibitor to treat hypertension. It shows complex pharmacokinetics (PK), and its PK behaviour in paediatric populations is not well characterized. The aim of this study was to develop a physiologically based PK (PBPK) model for lisinopril to describe the drug's PK in children.

METHODS

The PBPK model development was performed in a step-wise manner. An adult model was initially developed to characterize lisinopril's disposition and absorption and verified using literature data. Subsequently, the adult PBPK model was extrapolated to the paediatric population (0.5-18 years old) by accounting for age-dependent physiological and anatomical changes. Model performance was evaluated by comparing the PK profiles and drug exposures of observed vs predicted data.

RESULTS

The disposition of lisinopril was well described by a minimal PBPK model-an effective strategy to capture the biphasic elimination of the drug. The absorption of lisinopril was described by the intestinal peptide transporter-mediated uptake. The adult model adequately described the literature data with predictions within a twofold range of clinical observations. Good model predictivity was also observed in children older than 6 years of age. The model overpredicted the drug exposure in children under 6 years, probably due to not incorporating the actual, unknown ontogeny of the intestinal peptide transporter.

CONCLUSIONS

The PBPK model predicted the PK of lisinopril in adults and children above 6 years of age well. Model refinement in children under 6 years warrants future informative ontogeny data of the intestinal peptide transporter.

Physiologically based pharmacokinetic model outputs depend on dissolution data and their input: Case examples glibenclamide and dipyridamole

A plethora of dissolution tests exists for oral dosage forms, with variations in selection of the dissolution medium, the hydrodynamics and the dissolution equipment. This work aimed at determining the influence of media composition, the type of dissolution test and the method for entering the data into a PBPK model on the ability to simulate the in vivo plasma profile of an immediate release formulation. Using two rDCS IIa substances, glibenclamide and dipyridamole, housed in immediate-release formulations as model dosage forms, dissolution tests were performed in USP apparatus II with the biorelevant media FaSSGF, FaSSIF V1, V2 and V3 using both single-stage and two-stage test designs. The results were then integrated into the PBPK software Simcyp^Ⓡ either as the observed release profile (dissolution rate model, DRM) or using a semi-mechanistic model (diffusion layer model, DLM) and compared with in vivo plasma profiles. The selection of the FaSSIF version did not appear to have any relevant influence on the dissolution of the weakly basic dipyridamole, while the weakly acidic glibenclamide was sensitive to the difference in pH between FaSSIF V1, V2 and FaSSIF V3. Since both compounds have pKa values close to the pH of biorelevant media representing conditions in the small intestine, these results may be specific to compounds with similar ionization behavior. Single-stage and two-stage testing led to equivalent simulations for glibenclamide. Only results from the single-stage test in FaSSGF led to a close simulation of the pharmacokinetic profile of dipyridamole when data were inputted using the DRM, while simulations from two-stage testing were most similar to the observed pharmacokinetic profile when DLM with selection of a dynamic pH profile in the small intestine was selected as the data input method. These results emphasize the importance of data input to the simulation results.

Changes in perampanel levels during de-induction: Simulations following carbamazepine discontinuation

Objective

To evaluate the time course of changes in perampanel levels when co‐administered with carbamazepine, and following carbamazepine discontinuation, using a physiologically based pharmacokinetic (PBPK) model.

Methods

The PBPK model was developed, verified using clinical PK data, and used to simulate the effect of abrupt discontinuation and down‐titration (75 mg twice daily [bid]/wk) of co‐administered carbamazepine 300 mg bid on the PK of perampanel once daily (qd). Perampanel dose tapering (8‐4 mg) and up‐titration (2‐6 mg) were simulated during abrupt carbamazepine 300 mg bid discontinuation to identify a titration schedule that minimizes changes in perampanel plasma concentrations.

Results

The PBPK model accurately reproduced perampanel plasma concentration‐time profiles from clinical studies in single‐ and multiple‐dose regimen simulations, including multiple‐dose carbamazepine co‐administration. The time course of return to pre‐induced perampanel levels occurred more slowly following carbamazepine down‐titration (~48 days after first down‐titration) vs abrupt discontinuation (~25 days). Perampanel dose tapering (8‐4 mg) at abrupt carbamazepine discontinuation produced minimal changes in steady‐state concentrations, which returned to the levels observed during carbamazepine co‐administration in ~15 days from the time of carbamazepine discontinuation. When perampanel was up‐titrated in the presence of carbamazepine, return to steady state occurred more slowly when carbamazepine was down‐titrated weekly (~45 days) vs abrupt discontinuation (~24 days).

Conclusion

This PBPK model simulated and predicted optimal perampanel dose tapering and up‐titration schedules for maintaining perampanel levels during conversion to monotherapy. These results may guide physicians when managing conversion from perampanel polytherapy with concomitant enzyme‐inducing anti‐seizure medications to monotherapy.

Formulation and Bioequivalence Testing of Fixed-Dose Combination Orally Disintegrating Tablets for the Treatment of Tuberculosis in the Paediatric Population

Tuberculosis (TB) is believed to affect around 10 million people worldwide. Treatment for TB includes isoniazid and rifampicin, with fixed-dose combination (FDC) recommended for improved patient compliance. Similarly, orally disintegrating tablets (ODTs) are an increasingly popular dosage form that aid compliance since they do not require swallowing. In this study ODTs of isoniazid and rifampicin, either as discrete or FDC doses, were formulated and bioequivalence between single and combination doses compared using in vitro and in silico approaches. Dissolution profiles were compared using FDA advised difference (f₁) and similarity (f₂) testing in biorelevant media. Rifampicin release from FDCs decreased by approximately 15% in fed-state media (failed f₁ and f₂), which was attributed to enhanced rifampicin degradation in the presence of isoniazid at lower pH. Apparent permeability (P_app) values derived from Caco-2 transport studies were included alongside dissolution results into a physiologically based pharmacokinetic (PBPK) model, to simulate in vivo bioavailability in healthy subjects. Models showed no difference in bioavailability between formulations or dosing (fasted or fed) state, despite the failures in dissolution-based bioequivalence testing, highlighting shortcomings in f₁ and f₂ assessment and the strength of PBPK models.

Dynamic Colon Model (DCM): A Cine-MRI Informed Biorelevant In Vitro Model of the Human Proximal Large Intestine Characterized by Positron Imaging Techniques

This work used in vivo MRI images of human colon wall motion to inform a biorelevant Dynamic Colon Model (DCM) to understand the interplay of wall motion, volume, viscosity, fluid, and particle motion within the colon lumen. Hydrodynamics and particle motion within the DCM were characterized using Positron Emission Tomography (PET) and Positron Emission Particle Tracking (PEPT), respectively. In vitro PET images showed that fluid of higher viscosity follows the wall motion with poor mixing, whereas good mixing was observed for a low viscosity fluid. PEPT data showed particle displacements comparable to the in vivo data. Increasing fluid viscosity favors the net forward propulsion of the tracked particles. The use of a floating particle demonstrated shorter residence times and greater velocities on the liquid surface, suggesting a surface wave that was moving faster than the bulk liquid. The DCM can provide an understanding of flow motion and behavior of particles with different buoyancy, which in turn may improve the design of drug formulations, whereby fragments of the dosage form and/or drug particles are suspended in the proximal colon.

Repository Describing an Aging Population to Inform Physiologically Based Pharmacokinetic Models Considering Anatomical, Physiological, and Biological Age-Dependent Changes

BACKGROUND

Aging is characterized by anatomical, physiological, and biological changes that can impact drug kinetics. The elderly are often excluded from clinical trials and knowledge about drug kinetics and drug-drug interaction magnitudes is sparse. Physiologically based pharmacokinetic modeling can overcome this clinical limitation but detailed descriptions of the population characteristics are essential to adequately inform models.

OBJECTIVE

The objective of this study was to develop and verify a population database for aging Caucasians considering anatomical, physiological, and biological system parameters required to inform a physiologically based pharmacokinetic model that included population variability.

METHODS

A structured literature search was performed to analyze age-dependent changes of system parameters. All collated data were carefully analyzed, and descriptive mathematical equations were derived.

RESULTS

A total of 362 studies were found of which 318 studies were included in the analysis as they reported rich data for anthropometric parameters and specific organs (e.g., liver). Continuous functions could be derived for most system parameters describing a Caucasian population from 20 to 99 years of age with variability. Areas with sparse data were identified such as tissue composition, but knowledge gaps were filled with plausible qualified assumptions. The developed population was implemented in Matlab^® and estimated system parameters from 1000 virtual individuals were in accordance with independent observed data showing the robustness of the developed population.

CONCLUSIONS

The developed repository for aging subjects provides a singular specific source for key system parameters needed for physiologically based pharmacokinetic modeling and can in turn be used to investigate drug kinetics and drug-drug interaction magnitudes in the elderly.

Assessing Potential Drug-Drug Interactions Between Dabigatran Etexilate and a P-Glycoprotein Inhibitor in Renal Impairment Populations Using Physiologically Based Pharmacokinetic Modeling

Plasma concentrations of dabigatran, an active principle of prodrug dabigatran etexilate (DABE), are increased by renal impairment (RI) or coadministration of a P‐glycoprotein inhibitor. Because the combined effects of drug–drug interactions and RI have not been evaluated by means of clinical studies, the decision of DABE dosing for RI patients receiving P‐glycoprotein inhibitors is empirical at its best. We conducted virtual drug–drug interactions studies between DABE and the P‐glycoprotein inhibitor verapamil in RI populations using physiologically based pharmacokinetic modeling. The developed physiologically based pharmacokinetic model for DABE and dabigatran was used to predict trough dabigatran concentrations in the presence and absence of verapamil in virtual RI populations. The population‐based physiologically based pharmacokinetic model provided the most appropriate dosing regimen of DABE for likely clinical scenarios, such as drug–drug interactions in this RI population based on available knowledge of the systems changes and in the absence of actual clinical studies.

Physiologically-Based Pharmacokinetic Predictions of the Effect of Curcumin on Metabolism of Imatinib and Bosutinib: In Vitro and In Vivo Disconnect

PURPOSE

This study aimed to investigate the potential pharmacokinetic interactions between curcumin, imatinib and bosutinib, combining In Vitro and in silico methods.

METHODS

In Vitro metabolism of imatinib and bosutinib were investigated in pooled human liver microsomes and recombinant CYP3A4 enzyme in the presence and absence of curcumin and curcumin glucuronide using an LC-MS/MS assay for N-desmethyl metabolites. A physiologically-based pharmacokinetic (PBPK) model for curcumin formulated as solid lipid nanoparticles (SLN) was constructed using In Vitro glucuronidation kinetics and published clinical pharmacokinetic data. The potential effects of curcumin coadministration on systemic exposures of imatinib and bosutinib were predicted in silico using PBPK simulations.

RESULTS

Curcumin demonstrated potent reversible inhibition of cytochrome P450 (CYP)3A4-mediated N-demethylation of imatinib and bosutinib and CYP2C8-mediated metabolism of imatinib with inhibitory constants (k_i,u) of ≤1.5 μmol. L^-1. A confirmatory In Vitro study with paclitaxel, the 6α-hydroxylation of which is exclusively mediated by CYP2C8, was consistent with a potent inhibition of this enzyme by curcumin. Curcumin glucuronide also inhibited both CYP enzymes In Vitro, albeit to a lesser extent than that of curcumin. PBPK model simulations predicted that at recommended dosing regimens of SLN curcumin, coadministration would result in an increase in systemic exposures of imatinib and bosutinib of up to only 10%.

CONCLUSION

A PBPK model for curcumin in a SLN formulation was successfully developed. Although curcumin possesses a strong In Vitro inhibitory activity towards CYP3A4 and CYP2C8 enzymes, its interactions with imatinib and bosutinib were unlikely to be of clinical importance due to curcumin's poor bioavailability.

Utilization of Physiologically Based Pharmacokinetic Modeling in Clinical Pharmacology and Therapeutics: an Overview

The purpose of this review was to assess the advancement of applications for physiologically based pharmacokinetic (PBPK) modeling in various therapeutic areas. We conducted a PubMed search, and 166 articles published between 2012 and 2018 on FDA-approved drug products were selected for further review. Qualifying publications were summarized according to therapeutic area, medication(s) studied, pharmacokinetic model type utilized, simulator program used, and the applications of that modeling. The results showed a 13-fold increase in the number of papers published from 2012 to 2018, with the largest proportion of articles dedicated to the areas of infectious diseases, oncology, and neurology, and application extensions including prediction of drug-drug interactions due to metabolism and/or transporter-mediated effects and understanding drug kinetics in special populations. In addition, we profiled several high-impact studies whose results were used to guide package insert information and formulate dose recommendations. These results show that while utilization of PBPK modeling has drastically increased over the past several years, regulatory support, lack of easy-to-use systems for clinicians, and challenges with model validation remain major challenges for the widespread adoption of this practice in institutional and ambulatory settings. However, PBPK modeling will continue to be a useful tool in the future to assess therapeutic drug monitoring and the growing field of personalized medicine.

Investigating the Role of Altered Systemic Albumin Concentration on the Disposition of Theophylline in Adult and Pediatric Patients with Asthma by Using the Physiologically Based Pharmacokinetic Approach

Theophylline is commonly used for the treatment of asthma and has a low hepatic clearance. The changes in plasma albumin concentration occurring in asthma may affect the exposure of theophylline. The aim of the presented work was to predict theophylline pharmacokinetics (PK) after incorporating the changes in plasma albumin concentration occurring in patients with asthma into a physiologically based pharmacokinetic (PBPK) model to see whether these changes can affect the systemic theophylline concentrations in asthma. The PBPK model was developed following a systematic model building approach using Simcyp. The predictions were performed initially in healthy adults after intravenous and oral drug administration. Only when the developed adult PBPK model had adequately predicted theophylline PK in healthy adults, the changes in plasma albumin concentrations were incorporated into the model for predicting drug exposure in patients with asthma. After evaluation of the developed model in the adult population, it was scaled to children on physiologic basis. The model evaluation was performed by using visual predictive checks and comparison of ratio of observed and predicted (R_obs/Pre) PK parameters along with their 2-fold error range. The developed PBPK model has effectively described theophylline PK in both healthy and diseased populations, as R_obs/Pre for all the PK parameters were within the 2-fold error limit. The predictions in patients with asthma showed that there were no significant changes in PK parameters after incorporating the changes in serum albumin concentration. The mechanistic nature of the developed asthma-PBPK model can facilitate its extension to other drugs. SIGNIFICANCE STATEMENT: Exposure of a low hepatic clearance drug like theophylline may be susceptible to plasma albumin concentration changes that occur in asthma. These changes in systemic albumin concentrations can be incorporated into a physiologically based pharmacokinetic model to predict theophylline pharmacokinetics in adult and pediatric asthma populations. The presented work is focused on predicting theophylline absorption, distribution, metabolism, and elimination in adult and pediatric asthma populations after incorporating reported changes in serum albumin concentrations to see their impact on the systemic theophylline concentrations.

Insights from pharmacokinetic models of host-microbiome drug metabolism

Increasing evidence suggests a role of the gut microbiota in patients' response to medicinal drugs. In our recent study, we combined genomics of human gut commensals and gnotobiotic animal experiments to quantify microbiota and host contributions to drug metabolism. Informed by experimental data, we built a physiology-based pharmacokinetic model of drug metabolism that includes intestinal compartments with microbiome drug-metabolizing activity. This model successfully predicted serum levels of metabolites of three different drugs, quantified microbial contribution to systemic drug metabolite exposure, and simulated the effect of different parameters on host and microbiota drug metabolism. In this addendum, we expand these simulations to assess the effect of microbiota on the systemic drug and metabolite levels under conditions of altered host physiology, microbiota drug-metabolizing activity or physico-chemical properties of drugs. This work illustrates how and under which circumstances the gut microbiome may influence drug pharmacokinetics, and discusses broader implications of expanded pharmacokinetic models.

Quantitative Assessment of Elagolix Enzyme-Transporter Interplay and Drug-Drug Interactions Using Physiologically Based Pharmacokinetic Modeling

INTRODUCTION

Elagolix is approved for the management of moderate-to-severe pain associated with endometriosis. The aim of this analysis was to develop a physiologically based pharmacokinetic (PBPK) model that describes the enzyme-transporter interplay involved in the disposition of elagolix and to predict the magnitude of drug-drug interaction (DDI) potential of elagolix as an inhibitor of P-glycoprotein (P-gp) and inducer of cytochrome P450 (CYP) 3A4.

METHODS

A PBPK model (SimCYP® version 15.0.86.0) was developed using elagolix data from in vitro, clinical PK and DDI studies. Data from DDI studies were used to quantify contributions of the uptake transporter organic anion transporting polypeptide (OATP) 1B1 and CYP3A4 in the disposition of elagolix, and to quantitatively assess the perpetrator potential of elagolix as a CYP3A4 inducer and P-gp inhibitor.

RESULTS

After accounting for the interplay between elagolix metabolism by CYP3A4 and uptake by OATP1B1, the model-predicted PK parameters of elagolix along with the DDI AUC∞ and Cmax ratios, were within 1.5-fold of the observed data. Based on model simulations, elagolix 200 mg administered twice daily is a moderate inducer of CYP3A4 (approximately 56% reduction in midazolam AUC∞). Simulations of elagolix 150 mg administered once daily with digoxin predicted an increase in digoxin Cmax and AUC∞ by 68% and 19%, respectively.

CONCLUSIONS

A PBPK model of elagolix was developed, verified, and applied to characterize the disposition interplay between CYP3A4 and OATP1B1, and to predict the DDI potential of elagolix as a perpetrator under dosing conditions that were not tested clinically. PBPK model-based predictions were used to support labeling language for DDI recommendations of elagolix.