Application of the MechPeff model to predict passive effective intestinal permeability in the different regions of the rodent small intestine and colon

How PBPK Can Help to Understand Old Drugs and Inform their Dosing in Elderly: Amantadine Case Study

Amantadine, despite being on the market for 55 years, has several unknown aspects of its pharmacokinetics especially related to the influence of covariates such as age, disease, or interactions linked to amantadine's renal elimination. As amantadine is used in Parkinson's disease and is considered a potential candidate in COVID treatment and other diseases, there is an unmet need for thorough understanding of its pharmacokinetic in special populations, such as the elderly. We aimed to mechanistically describe amantadine pharmacokinetics in healthy subjects and shed some light on the differences in drug behavior between healthy volunteers (18-65 years) and an elderly/geriatric population (65-98 years) using PBPK modeling and simulation. The middle-out PBPK model includes mechanistic description of drug renal elimination, specifically an organic cation transporter (OCT)2-mediated electrogenic bidirectional transport (basolateral) and multidrug and toxic compound extrusion (MATE)1-mediated efflux (apical). The model performance was verified against plasma and urine data reported after single and multiple dose administration in healthy volunteers and elderly patients from 18 independent studies. The ratios of predicted vs. observed maximal plasma concentration and area under the concentration-time curve values were within 1.25-fold. The model illustrates that renal transporter activity is expected to decrease in healthy elderly compared to healthy volunteers, which is in line with literature proteomic data for OCT2. The model was applied to assess the potential of reaching toxicity-related plasma concentrations in different age groups of geriatric subjects.

Implications of Incorporating Plasma Lipoprotein Binding into a Physiologically-Based Pharmacokinetic Model: A Simulation Study with Amiodarone

Although various lipophilic drugs are bound to lipoproteins, lipoprotein binding in plasma is not usually considered in current physiologically-based pharmacokinetic (PBPK) models. Amiodarone is extensively bound to serum triglyceride-rich lipoproteins. Total plasma amiodarone concentration, which is the sum of both unbound and bound concentrations, increases with increasing serum triglyceride levels. We investigated the impact of lipoprotein binding on amiodarone pharmacokinetics using PBPK modeling and simulations. An amiodarone PBPK model that incorporates plasma lipoprotein binding (LPP model) was developed based on the correlation between serum triglyceride levels and lipoprotein-bound amiodarone. The predicted unbound fraction of amiodarone in plasma and systemic clearance in the LPP and base models (with albumin binding only) were similar, but the coefficients of variation for the LPP model were greater than those for the base model and were closer to the observed data. The total plasma amiodarone concentration predicted using the LPP model increased with higher levels of plasma lipoprotein binding and serum albumin. In contrast, changes in plasma lipoprotein binding and serum albumin levels did not influence the predicted unbound plasma amiodarone concentration at steady-state. This study demonstrates that incorporating plasma lipoprotein binding into a PBPK model improves the accuracy of predicting interindividual variabilities in amiodarone clearance by more reliably predicting the interindividual variability in the plasma unbound fraction of amiodarone. Plasma lipoprotein binding should be considered in PBPK modeling and simulations for lipoprotein-associated drugs if there is available information on the relationship between plasma lipoprotein binding and hyperlipidemia.

Development of a physiologically based pharmacokinetic model of fostemsavir and its pivotal application to support dosing in pregnancy

It is critical to understand the impact of significant physiological changes during pregnancy on the extent of maternal and fetal drug exposure. Fostemsavir (FTR) is a prodrug of temsavir (TMR) and is approved in combination with other antiretrovirals for multi-drug-resistant human immunodeficiency virus (HIV) infections. This physiologically based pharmacokinetic model (PBPK) study was used to estimate TMR PK in pregnant populations during each trimester of pregnancy to inform FTR dosing. A PBPK model was developed and validated for TMR using PK data collected following intravenous TMR and oral FTR dosing (immediate-release and extended-release tablets) in healthy volunteers. Predicted TMR concentration-time profiles accurately predicted the reported clinical data and variability in healthy (dense data) and pregnant (sparse data) populations. Predicted versus observed TMR geometric mean (CV%) clearance following intravenous administration was 18.01 (29) versus 17 (21) (L/h). Predicted versus observed TMR AUC0-inf (ng.h/mL) in healthy volunteers following FTR administration of the extended-release tablet were 9542 (66) versus 7339 (33). The validated TMR PBPK model was then applied to predict TMR PK in a population of pregnant individuals during each trimester. Simulations showed TMR AUC in pregnant individuals receiving FTR 600 mg twice daily was decreased by 25% and 38% in the second and third trimesters, respectively. However, TMR exposure remained within the range observed in nonpregnant adults with no need for dose adjustment. The current PBPK model can also be applied for the prediction of local tissue concentrations and drug-drug interactions in pregnancy.

A novel physiologically based pharmacokinetic model of rectal absorption, evaluated and verified using clinical data on 10 rectally administered drugs

We present a physiologically based pharmacokinetic (PBPK) model simulating systemic drug concentrations following administration to the human rectum. Rectum physiology is parameterized based on literature data. The model utilizes in vitro release (IVRT) profiles from which drug mass transfer through the rectal fluid and tissue and into the systemic circulation are predicted. Due to a lack of data, rectal fluid and tissue absorption parameters are predicted either from colon absorption, with modifications relevant to rectal physiology, or optimized. The PBPK model is evaluated by simulating 29 clinical studies for 10 drugs. For 8 drugs (diazepam, diclofenac, indomethacin, naproxen, paracetamol, pentobarbital, phenobarbital and theophylline) the bias (average fold error, AFE) and precision (absolute average fold error, AAFE) of Cmax, AUC0-t and AUC0-inf simulations range from 0.87 to 2.22, indicating good agreement with observed values. For prochlorperazine and promethazine, the AFEs and AAFEs of Cmax predictions are 1.30 and 2.52, respectively. TheAUC0-t and AUC0-inf are overpredicted for both compounds(AFEs and AAFEs from 2.66 to 4.90). This results from a lack of reliable elimination data for prochlorperazine and the relevance of the IVRT profiles used in the promethazine model. The model paves the way for more mechanistic rectal drug absorption studies and virtual bioequivalence methods for rectal drug products.

Drug Permeability - Best Practices for Biopharmaceutics Classification System (BCS)-Based Biowaivers: A workshop Summary Report

The workshop "Drug Permeability - Best Practices for Biopharmaceutics Classification System (BCS) Based Biowaivers" was held virtually on December 6, 2021, organized by the University of Maryland Center of Excellence in Regulatory Science and Innovation (M-CERSI), and the Food and Drug Administration (FDA). The workshop focused on the industrial, academic, and regulatory experiences in generating and evaluating permeability data, with the aim to further facilitate implementation of the BCS and efficient development of high-quality drug products globally. As the first international permeability workshop since the BCS based biowaivers was finalized as the ICH M9 guideline, the workshop included lectures, panel discussions, and breakout sessions. Lecture and panel discussion topics covered case studies at IND, NDA, and ANDA stages, typical deficiencies relating to permeability assessment supporting BCS biowaiver, types of evidence that are available to demonstrate high permeability, method suitability of a permeability assay, impact of excipients, importance of global acceptance of permeability methods, opportunities to expand the use of biowaivers (e.g. non-Caco-2 cell lines, totality-of-evidence approach to demonstrate high permeability) and future of permeability testing. Breakout sessions focused on 1) in vitro and in silico intestinal permeability methods; 2) potential excipient effects on permeability and; 3) use of label and literature data to designate permeability class.

Physiologically based pharmacokinetic modeling for development and applications of a virtual celiac disease population using felodipine as a model drug

In celiac disease (CeD), gastrointestinal CYP3A4 abundance and morphology is affected by the severity of disease. Therefore, exposure to CYP3A4 substrates and extent of drug interactions is altered. A physiologically-based pharmacokinetic (PBPK) population for different severities of CeD was developed. Gastrointestinal physiology parameters, such as luminal pH, transit times, morphology, P-gp, and CYP3A4 expression were included in development of the CeD population. Data on physiological difference between healthy and CeD subjects were incorporated into the model as the ratio of celiac to healthy. A PBPK model was developed and verified for felodipine extended-release tablet in healthy volunteers (HVs) and then utilized to verify the CeD populations. Plasma concentration-time profile and PK parameters were predicted and compared against those observed in both groups. Sensitivity analysis was carried out on key system parameters in CeD to understand their impact on drug exposure. For felodipine, the predicted mean concentration-time profiles and 5th and 95th percentile intervals captured the observed profile and variability in the HV and CeD populations. Predicted and observed clearance was 56.9 versus 56.1 (L/h) in HVs. Predicted versus observed mean ± SD area under the curve for extended release felodipine in different severities of CeD were values of 14.5 ± 9.6 versus 14.4 ± 2.1, 14.6 ± 9.0 versus 17.2 ± 2.8, and 28.1 ± 13.5 versus 25.7 ± 5.0 (ng.h/mL), respectively. Accounting for physiology differences in a CeD population accurately predicted the PK of felodipine. The developed CeD population can be applied for determining the drug concentration of CYP3A substrates in the gut as well as for systemic levels, and for application in drug-drug interaction studies.

Canine Intestinal Organoids as a Novel In Vitro Model of Intestinal Drug Permeability: A Proof-of-Concept Study

A key component of efforts to identify the biological and drug-specific aspects contributing to therapeutic failure or unexpected exposure-associated toxicity is the study of drug-intestinal barrier interactions. While methods supporting such assessments are widely described for human therapeutics, relatively little information is available for similar evaluations in support of veterinary pharmaceuticals. There is, therefore, a critical need to develop novel approaches for evaluating drug-gut interactions in veterinary medicine. Three-dimensional (3D) organoids can address these difficulties in a reasonably affordable system that circumvents the need for more invasive in vivo assays in live animals. However, a first step in developing such systems is understanding organoid interactions in a 2D monolayer. Given the importance of orally administered medications for meeting the therapeutic need of companion animals, we demonstrate growth conditions under which canine-colonoid-derived intestinal epithelial cells survive, mature, and differentiate into confluent cell systems with high monolayer integrity. We further examine the applicability of this canine-colonoid-derived 2D model to assess the permeability of three structurally diverse, passively absorbed β-blockers (e.g., propranolol, metoprolol, and atenolol). Both the absorptive and secretive apparent permeability (P_app) of these drugs at two different pH conditions were evaluated in canine-colonoid-derived monolayers and compared with that of Caco-2 cells. This proof-of-concept study provides promising preliminary results with regard to the utility of canine-derived organoid monolayers for species-specific assessments of therapeutic drug passive permeability.

Virtual Bioequivalence Assessment of Elagolix Formulations Using Physiologically Based Pharmacokinetic Modeling

In lieu of large bioequivalence studies and exposing healthy postmenopausal women to additional drug exposure for elagolix coadministered with hormonal add-back therapy, physiologically based pharmacokinetic (PBPK) modeling was used with in vitro dissolution data to test for virtual bioequivalence. For endometriosis, elagolix is approved at doses of 150 mg once daily and 200 mg twice daily as a tablet. As a combination therapy, two individual tablets, consisting of an elagolix tablet and an estradiol/norethindrone acetate 1/0.5 mg (E2/NETA) tablet, were utilized in Phase 3 endometriosis trials. However, the commercial combination drug products consist of a morning capsule (containing an elagolix tablet and E2/NETA tablet as a fixed-dose combination capsule, AM capsule) and an evening capsule (consisting of an elagolix tablet, PM capsule). In vitro dissolution profiles were dissimilar for the tablet and capsule formulations; thus, in vivo bioequivalence studies or a bioequivalence waiver would have been required. To simulate virtual cross-over, bioequivalence trials, in vitro dissolution data was incorporated into a previously verified PBPK model. The updated PBPK model was externally validated using relevant bioequivalence study data. Based on results of the virtual bioequivalence simulations, the commercial drug product capsules met the bioequivalence criteria of 0.80-1.25 when compared to the reference tablets. This was a novel example where PBPK modeling was utilized along with in vitro dissolution data to demonstrate virtual bioequivalence in support of a regulatory bioequivalence waiver.

Data on ADME parameters of bisphenol A and its metabolites for use in physiologically based pharmacokinetic modelling

The paper presents the collection of physicochemical parameters of bisphenol A (BPA) and its sulfate (BPAS) and glucuronide (BPAG) conjugates, accompanied by data characterizing their absorption, distribution, metabolism and excretion (ADME) behavior following oral administration of BPA. The data were collected from open literature sources and publicly available databases. Additionally, data calculated by using the MarvinSketch 18.30.0 software or predicted by relevant QSAR models built in Simcyp® Simulator were also used. All data were analysed and are fit for purpose if necessary to ensure a reliable prediction of pharmacokinetics of BPA and its conjugates. The data selection process and reasoning for fitting is provided to allow critical assessment and to ensure data transparency. Finally, the sensitivity analysis was performed to assess the influence of the selected parameters on the PBPK model predictions.

Integrating In Vitro Biopharmaceutics into Physiologically Based Biopharmaceutic Model (PBBM) to Predict Food Effect of BCS IV Zwitterionic Drug (GSK3640254)

A strategy followed to integrate in vitro solubility and permeability data into a PBBM model to predict the food effect of a BCS IV zwitterionic drug (GSK3640254) observed in clinical studies is described. The PBBM model was developed, qualified and verified using clinical data of an immediate release (IR)-tablet (10-320 mg) obtained in healthy volunteers under fasted and fed conditions. The solubility of GSK3640254 was a function of its ionization state, the media composition and pH, whereas its permeability determined using MDCK cell lines was enhanced by the presence of mixed micelles. In vitro data alongside PBBM modelling suggested that the positive food effect observed in the clinical studies was attributed to micelle-mediated enhanced solubility and permeability. The biorelevant media containing oleic acid and cholesterol in fasted and fed levels enabled the model to appropriately capture the magnitude of the food effect. Thus, by using Simcyp^® v20 software, the PBBM model accurately predicted the results of the food effect and predicted data were within a two-fold error with 70% being within 1.25-fold. The developed model strategy can be effectively adopted to increase the confidence of using PBBM models to predict the food effect of BCS class IV drugs.

Physiologically-Based Pharmacokinetic Models of CYP2D6 Substrate and Inhibitors Nebivolol, Cinacalcet and Mirabegron to Simulate Drug-Drug Interactions

BACKGROUND AND OBJECTIVES

Index substrates and inhibitors to investigate the role of the polymorphic enzyme, cytochrome P450 (CYP) 2D6, in the metabolism of new compounds have been proposed by regulatory agencies. This work describes the development and verification of physiologically-based pharmacokinetic (PBPK) models for the CYP2D6-sensitive substrate, nebivolol and the index CYP2D6 inhibitors, mirabegron and cinacalcet.

METHODS

PBPK models for nebivolol, mirabegron and cinacalcet were developed using in vitro and clinical data. The performance of the PBPK models was verified by comparing the simulated results against reported human systemic exposure and clinical drug-drug interactions (DDIs) studies.

RESULTS

The exposure of nebivolol, cinacalcet and mirabegron predicted by the PBPK models was verified against pharmacokinetic data from 13, 3 and 9 clinical studies, respectively. For nebivolol, the predicted mean maximum plasma concentration (C_max) and area under the plasma concentration-time (AUC) values in CYP2D6 extensive metaboliser subjects were within 0.9- to 1.49-fold of the observed values. In poor metaboliser CYP2D6 subjects, the predicted C_max and AUC values were within 0.41- to 0.81-fold of observed values. For cinacalcet, the predicted C_max and AUC values were within 0.97- to 1.32-fold of the observed data. For mirabegron, the predicted AUC values across all the studies investigated were within 0.71- to 1.88-fold of observed values. The PBPK model-predicted DDIs were in good agreement (within 2-fold) with observed DDIs in all verification studies (n = 8) assessed. The overall precision was 1.26 and 1.21 for C_max and the AUC ratio, respectively.

CONCLUSIONS

The developed PBPK models can be used to assess the DDI potential liability of new chemical entities that are substrates or inhibitors of CYP2D6.

Guide to development of compound files for PBPK modeling in the Simcyp population-based simulator

The Simcyp Simulator is a software platform for population physiologically‐based pharmacokinetic (PBPK) modeling and simulation. It links in vitro data to in vivo absorption, distribution, metabolism, excretion and pharmacokinetic/pharmacodynamic outcomes to explore clinical scenarios and support drug development decisions, including regulatory submissions and drug labels. This tutorial describes the different input parameters required, as well as the considerations needed when developing a PBPK model within the Simulator, for a small molecule intended for oral administration. A case study showing the development and application of a PBPK model for ondansetron is herein used to aid the understanding of different PBPK model development concepts.

Understanding Inter-individual Variability in the Drug Interaction of a Highly Extracted CYP1A2 Substrate Tizanidine: Application of a Permeability-limited Multi-compartment Liver Model in a Population Based PBPK Framework

Tizanidine, a centrally acting skeletal muscle relaxant, is predominantly metabolised by CYP1A2 and undergoes extensive hepatic first-pass metabolism following oral administration. As a highly extracted drug, the systemic exposure to tizanidine exhibits considerable inter-individual variability and is altered substantially when co-administered with CYP1A2 inhibitors or inducers. The aim of the current study was to compare the performance of a permeability-limited multi-compartment liver (PerMCL) model, which operates as an approximation of the dispersion model (DM), and the well-stirred model (WSM) for predicting tizanidine DDIs. Physiologically-based pharmacokinetic (PBPK) models were developed for tizanidine, incorporating the PerMCL model and the WSM, respectively, to simulate the interaction of tizanidine with a range of CYP1A2 inhibitors and inducers. While the WSM showed a tendency to under-predict the fold change of tizanidine AUC (AUC ratio) in the presence of perpetrators, the use of PerMCL model increased precision (absolute average-fold error: 1.32 - 1.42 versus 1.58) and decreased bias (average-fold error: 0.97 - 1.25 versus 0.63) for the predictions of mean AUC ratios as compared to the WSM. The PerMCL model captured the observed range of individual AUC ratios of tizanidine as well as the correlation between individual AUC ratios and CYP1A2 activities without interactions, whereas the WSM was not able to capture these. The results demonstrate the advantage of using the PerMCL model over the WSM in predicting the magnitude and inter-individual variability of DDIs for a highly extracted sensitive substrate tizanidine. Significance Statement This study demonstrates the advantages of the permeability-limited multi-compartment liver (PerMCL) model, which operates as an approximation of the dispersion model (DM), in mitigating the tendency of the well-stirred model (WSM) to under-predict the magnitude and variability of DDIs of a highly extracted CYP1A2 substrate tizanidine when it is administered with CYP1A2 inhibitors or inducers. The PBPK modelling approach described herein is valuable to the understanding of drug interactions of highly extracted substrates and the source of its inter-individual variability.

A Mechanistic Absorption and Disposition Model of Ritonavir to Predict Exposure and Drug-Drug Interaction Potential of CYP3A4/5 and CYP2D6 Substrates

BACKGROUND AND OBJECTIVES

Due to health authority warnings and the recommended limited use of ketoconazole as a model inhibitor of cytochrome P450 (CYP) 3A4 in clinical drug-drug interaction (DDI) studies, there is a need to search for alternatives. Ritonavir is a strong inhibitor for CYP3A4/5-mediated DDIs and has been proposed as a suitable alternative to ketoconazole. It can also be used as a weak inhibitor for CYP2D6-mediated DDIs. Most of the currently available physiologically based pharmacokinetic (PBPK) inhibitor models developed for predicting DDIs use first-order absorption models, which do not mechanistically capture the effect of formulations on the systemic exposure of the inhibitor. Thus, the main purpose of the current study was to verify the predictive performance of a mechanistic absorption and disposition model of ritonavir when it was applied to the inhibition of CYP2D6 and CYP3A4/5 by ritonavir.

METHODS

A PBPK model that incorporates formulation characteristics and enzyme kinetic parameters for post-absorptive pharmacokinetic processes of ritonavir was constructed. Key absorption-related parameters in the model were determined using mechanistic modelling of in vitro biopharmaceutics experiments. The model was verified for systemic exposure and DDI risk assessment using clinical observations from 13 and 18 studies, respectively.

RESULTS

Maximal inhibition of hepatic (3.53% of the activity remaining) and gut (5.16% of the activity remaining) CYP3A4 activity was observed when ritonavir was orally administered in doses of 100 mg or higher. The PBPK model accurately described the concentrations of ritonavir in the different simulated studies. The prediction accuracy for maximum concentration (C_max) and area under the plasma concentration versus time curve (AUC) were assessed. The bias (average fold error, AFE) for the prediction of C_max and AUC was 0.92 and 1.06, respectively, and the precision (absolute average fold error, AAFE) was 1.29 and 1.23, respectively. The PBPK model predictions for all C_max and AUC ratios when ritonavir was used as an inhibitor of CYP metabolism fell within twofold of the clinical observations. The prediction accuracy for C_max and AUC ratios had a bias (AFE) of 0.85 and 0.99, respectively, and a precision (AAFE) of 1.21 and 1.33, respectively.

CONCLUSIONS

The current model, which incorporates formulation characteristics and mechanistic disposition parameters, can be used to assess the DDI potential of CYP3A4/5 and CYP2D6 substrates administered with a twice-daily dose of 100 mg of ritonavir for 14 days.

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.

New insights into the mechanisms underlying 5-fluorouracil-induced intestinal toxicity based on transcriptomic and metabolomic responses in human intestinal organoids

5-Fluorouracil (5-FU) is a widely used chemotherapeutical that induces acute toxicity in the small and large intestine of patients. Symptoms can be severe and lead to the interruption of cancer treatments. However, there is limited understanding of the molecular mechanisms underlying 5-FU-induced intestinal toxicity. In this study, well-established 3D organoid models of human colon and small intestine (SI) were used to characterize 5-FU transcriptomic and metabolomic responses. Clinically relevant 5-FU concentrations for in vitro testing in organoids were established using physiologically based pharmacokinetic simulation of dosing regimens recommended for cancer patients, resulting in exposures to 10, 100 and 1000 µM. After treatment, different measurements were performed: cell viability and apoptosis; image analysis of cell morphological changes; RNA sequencing; and metabolome analysis of supernatant from organoids cultures. Based on analysis of the differentially expressed genes, the most prominent molecular pathways affected by 5-FU included cell cycle, p53 signalling, mitochondrial ATP synthesis and apoptosis. Short time-series expression miner demonstrated tissue-specific mechanisms affected by 5-FU, namely biosynthesis and transport of small molecules, and mRNA translation for colon; cell signalling mediated by Rho GTPases and fork-head box transcription factors for SI. Metabolomic analysis showed that in addition to the effects on TCA cycle and oxidative stress in both organoids, tissue-specific metabolic alterations were also induced by 5-FU. Multi-omics integration identified transcription factor E2F1, a regulator of cell cycle and apoptosis, as the best key node across all samples. These results provide new insights into 5-FU toxicity mechanisms and underline the relevance of human organoid models in the safety assessment in drug development.

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.

Food constituent- and herb-drug interactions in oncology: Influence of quantitative modelling on Drug labelling

AIMS

Herbal products, spices and/or fruits are perceived as inherently healthy; for instance, St. John's wort (SJW) is marketed as a natural antidepressant and patients often self-administer it concomitantly with oncology medications. However, food constituents/herbs can interfere with drug pharmacokinetics, with risk of altering pharmacodynamics and efficacy. The objective of this work was to develop a strategy to prioritize herb- or food constituent-drug interactions (FC-DIs) to better assess oncology drug clinical risk.

METHODS

Physiologically based pharmacokinetic (PBPK) models were developed by integrating in vitro parameters with the clinical pharmacokinetics of food constituents in grapefruit juice (bergamottin), turmeric (curcumin) or SJW (hyperforin). Perpetrator files were linked to verified victim PBPK models through appropriate interaction mechanisms (cytochrome P450 3A, breast cancer resistance protein, P-glycoprotein) and applied in prospective PBPK simulations to inform the likelihood and magnitude of changes in exposure to osimertinib, olaparib or acalabrutinib.

RESULTS

Reported FC-DIs with oncology drugs were well recovered, with absolute average fold error values of 1.10 (bergamottin), 1.05 (curcumin) and 1.01 (hyperforin). Prospective simulations with grapefruit juice and turmeric showed clinically minor to insignificant changes in exposure (<1.50-fold) to acalabrutinib, osimertinib and olaparib, but predicted 1.57-fold FC-DI risk between acalabrutinib and curcumin. Moderate DDI risk was expected when acalabrutinib, osimertinib or olaparib were dosed with SJW.

CONCLUSIONS

A model-informed decision tree based on mechanistic understanding of transporter and/or enzyme-mediated FC-DI is proposed based on bergamottin, curcumin and hyperforin FC-DI clinical data. Adopting this quantitative modelling approach should streamline herbal product safety assessments, assist in FC-DI management, and ultimately promote safe clinical use of oncology drugs.

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.

Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling to Support Waivers of In Vivo Clinical Studies: Current Status, Challenges, and Opportunities

Physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling has been extensively applied to quantitatively translate in vitro data, predict the in vivo performance, and ultimately support waivers of in vivo clinical studies. In the area of biopharmaceutics and within the context of model-informed drug discovery and development (MID3), there is a rapidly growing interest in applying verified and validated mechanistic PBPK models to waive in vivo clinical studies. However, the regulatory acceptance of PBPK analyses for biopharmaceutics and oral drug absorption applications, which is also referred to variously as "PBPK absorption modeling" [Zhang et al. CPT: Pharmacometrics Syst. Pharmacol. 2017, 6, 492], "physiologically based absorption modeling", or "physiologically based biopharmaceutics modeling" (PBBM), remains rather low [Kesisoglou et al. J. Pharm. Sci. 2016, 105, 2723] [Heimbach et al. AAPS J. 2019, 21, 29]. Despite considerable progress in the understanding of gastrointestinal (GI) physiology, in vitro biopharmaceutic and in silico tools, PBPK models for oral absorption often suffer from an incomplete understanding of the physiology, overparameterization, and insufficient model validation and/or platform verification, all of which can represent limitations to their translatability and predictive performance. The complex interactions of drug substances and (bioenabling) formulations with the highly dynamic and heterogeneous environment of the GI tract in different age, ethnic, and genetic groups as well as disease states have not been yet fully elucidated, and they deserve further research. Along with advancements in the understanding of GI physiology and refinement of current or development of fully mechanistic in silico tools, we strongly believe that harmonization, interdisciplinary interaction, and enhancement of the translational link between in vitro, in silico, and in vivo will determine the future of PBBM. This Perspective provides an overview of the current status of PBBM, reflects on challenges and knowledge gaps, and discusses future opportunities around PBPK/PD models for oral absorption of small and large molecules to waive in vivo clinical studies.

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.

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.

Prediction Characteristics of Oral Absorption Simulation Software Evaluated Using Structurally Diverse Low-Solubility Drugs

The purpose of the present study was to characterize current biopharmaceutics modeling and simulation software regarding the prediction of the fraction of a dose absorbed (Fa) in humans. As commercial software products, GastroPlus™ and Simcyp® were used. In addition, the gastrointestinal unified theoretical framework, a simple and publicly accessible model, was used as a benchmark. The Fa prediction characteristics for a total of 96 clinical Fa data of 27 model drugs were systematically evaluated using the default settings of each software product. The molecular weight, dissociation constant, octanol-water partition coefficient, solubility in biorelevant media, dose, and particle size of model drugs were used as input data. Although the same input parameters were used, GastroPlus™, Simcyp®, and the gastrointestinal unified theoretical framework showed different Fa prediction characteristics depending on the rate-limiting steps of oral drug absorption. The results of the present study would be of great help for the overall progression of physiologically based absorption models.

Preincubation With Everolimus and Sirolimus Reduces Organic Anion-Transporting Polypeptide (OATP)1B1- and 1B3-Mediated Transport Independently of mTOR Kinase Inhibition: Implication in Assessing OATP1B1- and OATP1B3-Mediated Drug-Drug Interactions

Organic anion transporting polypeptides (OATP)1B1 and OATP1B3 mediate hepatic uptake of many drugs including lipid-lowering statins. Current studies determined the OATP1B1/1B3-mediated drug-drug interaction (DDI) potential of mammalian target of rapamycin (mTOR) inhibitors, everolimus and sirolimus, using R-value and physiologically based pharmacokinetic models. Preincubation with everolimus and sirolimus significantly decreased OATP1B1/1B3-mediated transport even after washing and decreased inhibition constant values up to 8.3- and 2.9-fold for OATP1B1 and both 2.7-fold for OATP1B3, respectively. R-values of everolimus, but not sirolimus, were greater than the FDA-recommended cutoff value of 1.1. Physiologically based pharmacokinetic models predict that everolimus and sirolimus have low OATP1B1/1B3-mediated DDI potential against pravastatin. OATP1B1/1B3-mediated transport was not affected by preincubation with INK-128 (10 μM, 1 h), which does however abolish mTOR kinase activity. The preincubation effects of everolimus and sirolimus on OATP1B1/1B3-mediated transport were similar in cells before preincubation with vehicle control or INK-128, suggesting that inhibition of mTOR activity is not a prerequisite for the preincubation effects observed for everolimus and sirolimus. Nine potential phosphorylation sites of OATP1B1 were identified by phosphoproteomics; none of these are the predicted mTOR phosphorylation sites. We report the everolimus/sirolimus-preincubation-induced inhibitory effects on OATP1B1/1B3 and relatively low OATP1B1/1B3-mediated DDI potential of everolimus and sirolimus.

Danazol oral absorption modelling in the fasted dog: An example of mechanistic understanding of formulation effects on drug pharmacokinetics

Oral bioavailability of poorly water soluble (BCS II) drugs like danazol can be minimal without the necessary formulation strategies. Availability of in vitro physicochemical and in vivo pharmacokinetic studies can be valuable when designing these strategies but cannot reveal the drug-formulation-gastrointestinal physiology interplay that impact the successful optimization of intestinal solubilization and resulting oral drug absorption. In silico mechanistic oral drug absorption models can serve as a tool for providing this important perspective and for integrating information generated across various in vivo and in vitro studies. In this work, we detail the development and application of the Simcyp canine ADAM model to nine danazol oral formulations and compare the model predictions to caninein vivo pharmacokinetic data from published literature. The application of this mechanistic approach revealed insights suggesting: (1) complete danazol solubilization in vitro may lead to an over-estimation of oral bioavailability when predictions are not corrected for the in vivo conditions promoting gut luminal precipitation; (2) some solubilizing excipients can influence intestinal physiology in a manner that may reduce danazol absorption; (3) danazol-formulation-luminal bile salts interplay can result in the formation of mixed micelles that negatively impact danazol intestinal permeability; and (4) the magnitude of danazol bioavailability enhancement associated with the use of solubilizing agents can be affected by the presence of saturable gut metabolism that can lead to concentration-dependent differences in its influence in vivo formulation behaviour at high versus low doses.

Physiologically Based Pharmacokinetic Modeling of Fimasartan, Amlodipine, and Hydrochlorothiazide for the Investigation of Drug-Drug Interaction Potentials

PURPOSE

To build a physiologically based pharmacokinetic (PBPK) model for fimasartan, amlodipine, and hydrochlorothiazide, and to investigate the drug-drug interaction (DDI) potentials.

METHODS

The PBPK model of each drug was developed using Simcyp software (Version 15.0), based on the information obtained from literature sources and in vitro studies. The predictive performance of the model was assessed by comparing the predicted PK profiles and parameters with the observed data collected from healthy subjects after multiple oral doses of fimasartan, amlodipine, and hydrochlorothiazide. The DDI potentials after co-administration of three drugs were simulated using the final model.

RESULTS

The predicted-to-observed ratios of all the pharmacokinetic parameters met the acceptance criterion. The PBPK model predicted no significant DDI when fimasartan was co-administered with amlodipine or hydrochlorothiazide, which is consistent with the observed clinical data. In the simulation of DDI at steady-state after co-administration of three drugs, the model predicted that fimasartan exposure would be increased by ~24.5%, while no changes were expected for the exposures of amlodipine and hydrochlorothiazide.

CONCLUSIONS

The developed PBPK model adequately predicted the pharmacokinetics of fimasartan, amlodipine, and hydrochlorothiazide, suggesting that the model can be used to further investigate the DDI potential of each drug.

Segmental-Dependent Intestinal Drug Permeability: Development and Model Validation of In Silico Predictions Guided by In Vivo Permeability Values

The goal of this work was to develop an in silico model that allows predicting segmental-dependent permeability throughout the small intestine (SI). In vivo permeability of 11 model drugs in 3 SI segments (jejunum, mid-SI, ileum) was studied in rats, creating a data set that reflects the conditions throughout the SI. Then, a predictive model was developed, combining physicochemical drug properties influencing the underlying mechanism of passive permeability: Log p, polar surface area, M_W, H-bond count, and Log f_u, with microenvironmental SI conditions. Excellent correlation was evident between the predicted and experimental data (R² = 0.914), with similar predictability in each SI segment. Log p and Log f_u were identified as the major determinants of permeability, with similar contribution. Total H-bond count was also a significant determinant, followed by polar surface area and M_W. Leaving out any of the model parameters decreased its predictability. The model was validated against 5 external drugs, with excellent predictability. Notably, the model was able to predict the segmental-dependent permeability of all drugs showing this trend experimentally. Model predictability was better in the high-permeability versus low-permeability range. Overall, our approach of constructing a straightforward in silico model allowed reliable predictions of segmental-dependent intestinal permeability, providing new insights into relative effects of drug-related factors and gastrointestinal environment on permeability.

Population variability in animal health: Influence on dose-exposure-response relationships: Part II: Modelling and simulation

During the 2017 Biennial meeting, the American Academy of Veterinary Pharmacology and Therapeutics hosted a 1-day session on the influence of population variability on dose-exposure-response relationships. In Part I, we highlighted some of the sources of population variability. Part II provides a summary of discussions on modelling and simulation tools that utilize existing pharmacokinetic data, can integrate drug physicochemical characteristics with species physiological characteristics and dosing information or that combine observed with predicted and in vitro information to explore and describe sources of variability that may influence the safe and effective use of veterinary pharmaceuticals.

Prediction of the Pharmacokinetics of Pravastatin as an OATP Substrate Using Plateable Human Hepatocytes With Human Plasma Data and PBPK Modeling

Plateable human hepatocytes with human plasma were utilized to generate the uptake transporter kinetic data for pravastatin, an organic anion-transporting polypeptide (OATP) transporter substrate. The active hepatic uptake of pravastatin was determined with a J_max value of 134.4 pmol/min/million cells and K_m of 76.77 µM in plateable human hepatocytes with human plasma. The physiologically-based pharmacokinetic (PBPK) model with incorporation of these in vitro kinetic data successfully simulated the i.v. pharmacokinetic profile of pravastatin without applying scaling factor (the mean predicted area under the curve (AUC) is within 1.5-fold of the observed). Furthermore, the PBPK model also adequately described the oral plasma concentration-time profiles of pravastatin at different dose levels. The current investigation demonstrates an approach allowing us to build upon the translation of in vitro OATP uptake transporter data to in vivo, with a hope of utilizing the in vitro data for the prospective human pharmacokinetic (PK) prediction.

Mechanistic Physiologically Based Pharmacokinetic Modeling of the Dissolution and Food Effect of a Biopharmaceutics Classification System IV Compound-The Venetoclax Story

Venetoclax, a selective B-cell lymphoma-2 inhibitor, is a biopharmaceutics classification system class IV compound. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model to mechanistically describe absorption and disposition of an amorphous solid dispersion formulation of venetoclax in humans. A mechanistic PBPK model was developed incorporating measured amorphous solubility, dissolution, metabolism, and plasma protein binding. A middle-out approach was used to define permeability. Model predictions of oral venetoclax pharmacokinetics were verified against clinical studies of fed and fasted healthy volunteers, and clinical drug interaction studies with strong CYP3A inhibitor (ketoconazole) and inducer (rifampicin). Model verification demonstrated accurate prediction of the observed food effect following a low-fat diet. Ratios of predicted versus observed C_max and area under the curve of venetoclax were within 0.8- to 1.25-fold of observed ratios for strong CYP3A inhibitor and inducer interactions, indicating that the venetoclax elimination pathway was correctly specified. The verified venetoclax PBPK model is one of the first examples mechanistically capturing absorption, food effect, and exposure of an amorphous solid dispersion formulated compound. This model allows evaluation of untested drug-drug interactions, especially those primarily occurring in the intestine, and paves the way for future modeling of biopharmaceutics classification system IV compounds.