PK-Sim®: a physiologically based pharmacokinetic ‘whole-body’ model

Developmental neurotoxicity evaluation of acrylamide based on in vitro to in vivo extrapolation by pregnancy PBTK modelling

Acrylamide (ACR) is a known neurotoxicant that can pass the placenta and has been detected in breast milk. Some in vivo and in vitro studies indicate that ACR exposure might lead to developmental neurotoxicity (DNT). Here, we have developed a physiologically-based toxicokinetic model for a pregnant human population using PK-Sim. We performed an in vitro to in vivo extrapolation (IVIVE) of data collected from human neuroblastoma SH-SY5Y cells exposed during differentiation to ACR. The developed PBTK model was successfully evaluated and predicted fetal plasma concentrations in the low nM range after exposing the model to an estimated average daily intake for pregnant women. The IVIVE showed that low concentrations of ACR (fM-nM) that induced attenuated differentiation of the SH-SY5Y neuronal cell model, were relevant for human exposure to ACR from oral intake. However, doses estimated in the IVIVE from concentrations in the µM range, were found to be unrealistic by exposure through food intake for an average daily intake. However, in case of exposure due to environmental pollution or occupational exposure, these concentrations may be reached in fetal plasma. The findings in this study raise the concern regarding ACR exposure during pregnancy as well as the relevance of testing concentrations in vitro that are several orders of magnitude higher than the predicted fetal plasma concentrations.

Physiologically based pharmacokinetic model of brivaracetam to predict the exposure and dose exploration in hepatic impairment and elderly populations

Brivaracetam (BRV) is a new third-generation antiseizure medication for the treatment of focal epileptic seizures. Its use has been increasing among epileptic populations in recent years, but pharmacokinetic (PK) behavior may change in hepatic impairment and the elderly populations. Due to ethical constraints, clinical trials are difficult to conduct and data are limited. This study used PK-Sim® to develop a physiologically based pharmacokinetic (PBPK) model for adults and extrapolate it to hepatic impairment and the elderly populations. The model was evaluated with clinical PK data, and dosage explorations were conducted. For the adult population with mild hepatic impairment, the dose is recommended to be adjusted to 70 % of the recommended dose, and to 60 % for moderate and severe hepatic impairment. For the elderly population with mild hepatic impairment under 80 years old, it is recommended that the dose be adjusted to 60 % of the recommended dose and to 50 % for moderate and severe conditions. The elderly population with hepatic impairment over 80 years old is adjusted to 50 % of the recommended dose for all stages. Healthy elderly do not need to adjust. The BRV PBPK model was successfully developed, studying exposure in hepatic impairment and elderly populations and optimizing dosing regimens.

3D printed benznidazole tablets based on an interpolyelectrolyte complex by melting solidification printing process (MESO-PP): An innovative strategy for personalized treatment of Chagas disease

3D printing technology is revolutionizing pharmaceuticals, offering tailored solutions for solid dosage forms. This innovation is particularly significant for conditions like Chagas disease, which require weight-dependent treatments. In this work, a formulation of benznidazole (BNZ), the primary treatment for this infection, was developed to be utilized with the Melting Solidification Printing Process (MESO-PP) 3D printing technique. Considering the limited aqueous solubility of BNZ, an interpolyelectrolyte complex (IPEC), composed of chitosan and pectin, was integrated to improve its dissolution profile. The formulations, also called inks in this context, with and without IPEC were integrally characterized and compared. The printing process was studied, the release of BNZ from 3D-prints (3DP) was exhaustively analyzed and a physiologically based pharmacokinetic model (PKPB) was developed to forecast their pharmacokinetic performance. 3DP were successfully achieved loading 25, 50 and 100 mg of BNZ. The presence of the IPEC in the ink caused a decrease in the crystalline domain of BNZ and facilitated the printing process, reaching a print success rate of 83.3 %. Interestingly, 3DP-IPEC showed accelerated release dissolution profiles, releasing over 85 % of BNZ in 90 min, while 3DP took up to 48 h for doses above 25 mg. The PBPK model demonstrated that 3DP-IPEC tablets would present high bioavailability (0.92), higher than 3DP (0.36) and similar to the commercial product. This breakthrough holds immense potential for improving treatment outcomes for neglected diseases.

Modeling Metformin and Dapagliflozin Pharmacokinetics in Chronic Kidney Disease

Chronic kidney disease (CKD) is a complication of diabetes that affects circulating drug concentrations and elimination of drugs from the body. Multiple drugs may be prescribed for treatment of diabetes and co-morbidities, and CKD complicates the pharmacotherapy selection and dosing regimen. Characterizing variations in renal drug clearance using models requires large clinical datasets that are costly and time-consuming to collect. We propose a flexible approach to incorporate impaired renal clearance in pharmacokinetic (PK) models using descriptive statistics and secondary data with mechanistic models and PK first principles. Probability density functions were generated for various drug clearance mechanisms based on the degree of renal impairment and used to estimate the total clearance starting from glomerular filtration for metformin (MET) and dapagliflozin (DAPA). These estimates were integrated with PK models of MET and DAPA for simulations. MET renal clearance decreased proportionally with a reduction in estimated glomerular filtration rate (eGFR) and estimated net tubular transport rates. DAPA total clearance varied little with renal impairment and decreased proportionally to reported non-renal clearance rates. Net tubular transport rates were negative to partially account for low renal clearance compared with eGFR. The estimated clearance values and trends were consistent with MET and DAPA PK characteristics in the literature. Dose adjustment based on reduced clearance levels estimated correspondingly lower doses for MET and DAPA while maintaining desired dose exposure. Estimation of drug clearance rates using descriptive statistics and secondary data with mechanistic models and PK first principles improves modeling of CKD in diabetes and can guide treatment selection.

Physiologically Based Pharmacokinetic Modeling of Vancomycin in Critically Ill Neonates: Assessing the Impact of Pathophysiological Changes

Dosing vancomycin for critically ill neonates is challenging owing to substantial alterations in pharmacokinetics (PKs) caused by variability in physiology, disease, and clinical interventions. Therefore, an adequate PK model is needed to characterize these pathophysiological changes. The intent of this study was to develop a physiologically based pharmacokinetic (PBPK) model that reflects vancomycin PK and pathophysiological changes in neonates under intensive care. PK-sim software was used for PBPK modeling. An adult model (model 0) was established and verified using PK profiles from previous studies. A neonatal model (model 1) was then extrapolated from model 0 by scaling age-dependent parameters. Another neonatal model (model 2) was developed based not only on scaled age-dependent parameters but also on quantitative information on pathophysiological changes obtained via a comprehensive literature search. The predictive performances of models 1 and 2 were evaluated using a retrospectively collected dataset from neonates under intensive care (chictr.org.cn, ChiCTR1900027919), comprising 65 neonates and 92 vancomycin serum concentrations. Integrating literature-based parameter changes related to hypoalbuminemia, small-for-gestational-age, and co-medication, model 2 offered more optimized precision than model 1, as shown by a decrease in the overall mean absolute percentage error (50.6% for model 1; 37.8% for model 2). In conclusion, incorporating literature-based pathophysiological changes effectively improved PBPK modeling for critically ill neonates. Furthermore, this model allows for dosing optimization before serum concentration measurements can be obtained in clinical practice.

Physiologically-based pharmacokinetic models versus allometric scaling for prediction of tyrosine-kinase inhibitor exposure from adults to children

PURPOSE

Model-based methods can predict pediatric exposure and support initial dose selection. The aim of this study was to evaluate the performance of allometric scaling of population pharmacokinetic (popPK) versus physiologically based pharmacokinetic (PBPK) models in predicting the exposure of tyrosine kinase inhibitors (TKIs) for pediatric patients (≥ 2 years), based on adult data. The drugs imatinib, sunitinib and pazopanib were selected as case studies due to their complex PK profiles including high inter-patient variability, active metabolites, time-varying clearances and non-linear absorption.

METHODS

Pediatric concentration measurements and adult popPK models were derived from the literature. Adult PBPK models were generated in PK-Sim® using available physicochemical properties, calibrated to adult data when needed. PBPK and popPK models for the pediatric populations were translated from the models for adults and were used to simulate concentration-time profiles that were compared to the observed values.

RESULTS

Ten pediatric datasets were collected from the literature. While both types of models captured the concentration-time profiles of imatinib, its active metabolite, sunitinib and pazopanib, the PBPK models underestimated sunitinib metabolite concentrations. In contrast, allometrically scaled popPK simulations accurately predicted all concentration-time profiles. Trough concentration (Ctrough) predictions from the popPK model fell within a 2-fold range for all compounds, while 3 out of 5 PBPK predictions exceeded this range for the imatinib and sunitinib metabolite concentrations.

CONCLUSION

Based on the identified case studies it appears that allometric scaling of popPK models is better suited to predict exposure of TKIs in pediatric patients ≥ 2 years. This advantage may be attributed to the stable enzyme expression patterns from 2 years old onwards, which can be easily related to adult levels through allometric scaling. In some instances, both methods performed comparably. Understanding where discrepancies between the model methods arise, can further inform model development and ultimately support pediatric dose selection.

Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans

Physiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing with in vitro or in silico methods. However, traditional PBK modelling depends on animal and human data, which limits its usefulness for non-animal methods. To address this limitation, high-throughput PBK modelling aims to rely exclusively on in vitro and in silico data for model generation. Here, we evaluate a variety of in silico tools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000 + publicly available human in vivo concentration-time profiles of 200 + compounds (IV and oral administration), as well as in silico, in vitro and in vivo determined compound-specific parameters required for the PBK modelling of these compounds. Then, we systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy against the collected in vivo concentration-time profiles. Our results show that even simple, generic high-throughput PBK modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC within tenfold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisation strategies, as well as between different compounds. Finally, we outline a strategy for high-throughput PBK modelling that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of high-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation Risk Assessment.

A physiologically-based pharmacokinetic modeling approach for dosing amiodarone in children on ECMO

Extracorporeal membrane oxygenation (ECMO) is a cardiopulmonary bypass device commonly used to treat cardiac arrest in children. The American Heart Association guidelines for cardiopulmonary resuscitation (CPR) and emergency cardiovascular care recommend using amiodarone as a first-line agent to treat ventricular arrhythmias in children with cardiac arrest. However, there are no dosing recommendations for amiodarone to treat ventricular arrhythmias in pediatric patients on ECMO. Amiodarone has a high propensity for adsorption to the ECMO components due to its physicochemical properties leading to altered pharmacokinetics (PK) in ECMO patients. The change in amiodarone PK due to interaction with ECMO components may result in a difference in optimal dosing in patients on ECMO when compared with non-ECMO patients. To address this clinical knowledge gap, a physiologically-based pharmacokinetic model of amiodarone was developed in adults and scaled to children, followed by the addition of an ECMO compartment. The pediatric model included ontogeny functions of cytochrome P450 (CYP450) enzyme maturation across various age groups. The ECMO compartment was parameterized using the adsorption data of amiodarone obtained from ex vivo studies. Model predictions captured observed concentrations of amiodarone in pediatric patients with ECMO well with an average fold error between 0.5 and 2. Model simulations support an amiodarone intravenous (i.v) bolus dose of 22 mg/kg (neonates), 13 mg/kg (infants), 8 mg/kg (children), and 6 mg/kg (adolescents). This PBPK modeling approach can be applied to explore the dosing of other drugs used in children on ECMO.

Investigating clinical pharmacokinetics of brivaracetam by using a pharmacokinetic modeling approach

The development of technology and the processing speed of computing machines have facilitated the evaluation of advanced pharmacokinetic (PK) models, making modeling processes simple and faster. The present model aims to analyze the PK of brivaracetam (BRV) in healthy and diseased populations. A comprehensive literature review was conducted to incorporate the BRV plasma concentration data and its input parameters into PK-Sim software, leading to the creation of intravenous (IV) and oral models for both populations. The developed physiologically based pharmacokinetic (PBPK) model of BRV was then assessed using the visual predictive checks, mean observed/predicted ratios (Robs/pre), and average fold error for PK parameters including the maximum systemic concentration (Cmax), the area under the curve at time 0 to t (AUC0-∞), and drug clearance (CL). The PBPK model of BRV demonstrated that mean Robs/pre ratios of the PK parameters remained within the acceptable limits when assessed against a twofold error margin. Furthermore, model predictions were carried out to assess how AUC0-∞ is affected following the administration of BRV in individuals with varying degrees of liver cirrhosis, ranging from different child-pugh (CP) scores like A, B, and C. Moreover, dose adjustments were recommended by considering the variations in Cmax and CL in various kidney disease stages (mild to severe).

A physiologically-based pharmacokinetic/pharmacodynamic modeling approach for drug-drug-gene interaction evaluation of S-warfarin with fluconazole

Warfarin is a widely used anticoagulant, and its S-enantiomer has higher potency compared to the R-enantiomer. S-warfarin is mainly metabolized by cytochrome P450 (CYP) 2C9, and its pharmacological target is vitamin K epoxide reductase complex subunit 1 (VKORC1). Both CYP2C9 and VKORC1 have genetic polymorphisms, leading to large variations in the pharmacokinetics (PKs) and pharmacodynamics (PDs) of warfarin in the population. This makes dosage management of warfarin difficult, especially in the case of drug-drug interactions (DDIs). This study provides a whole-body physiologically-based pharmacokinetic/PD (PBPK/PD) model of S-warfarin for predicting the effects of drug-drug-gene interactions on S-warfarin PKs and PDs. The PBPK/PD model of S-warfarin was developed in PK-Sim and MoBi. Drug-dependent parameters were obtained from the literature or optimized. Of the 34 S-warfarin plasma concentration-time profiles used, 96% predicted plasma concentrations within twofold range compared to observed data. For S-warfarin plasma concentration-time profiles with CYP2C9 genotype, 364 of 386 predicted plasma concentration values (~94%) fell within the twofold of the observed values. This model was tested in DDI predictions with fluconazole as CYP2C9 perpetrators, with all predicted DDI area under the plasma concentration-time curve to the last measurable timepoint (AUClast) ratio within twofold of the observed values. The anticoagulant effect of S-warfarin was described using an indirect response model, with all predicted international normalized ratio (INR) within twofold of the observed values. This model also incorporates a dose-adjustment method that can be used for dose adjustment and predict INR when warfarin is used in combination with CYP2C9 perpetrators.

Physiologically Based Pharmacokinetic (PBPK) Modeling to Predict CYP3A-Mediated Drug Interaction between Saxagliptin and Nicardipine: Bridging Rat-to-Human Extrapolation

The aim of this study was to predict the cytochrome P450 3A (CYP3A)-mediated drug-drug interactions (DDIs) between saxagliptin and nicardipine using a physiologically based pharmacokinetic (PBPK) model. Initially, in silico and in vitro parameters were gathered from experiments or the literature to construct PBPK models for each drug in rats. These models were integrated to predict the DDIs between saxagliptin, metabolized via CYP3A2, and nicardipine, exhibiting CYP3A inhibitory activity. The rat DDI PBPK model was completed by optimizing parameters using experimental rat plasma concentrations after co-administration of both drugs. Following co-administration in Sprague-Dawley rats, saxagliptin plasma concentration significantly increased, resulting in a 2.60-fold rise in AUC, accurately predicted by the rat PBPK model. Subsequently, the workflow of the rat PBPK model was applied to humans, creating a model capable of predicting DDIs between the two drugs in humans. Simulation from the human PBPK model indicated that nicardipine co-administration in humans resulted in a nearly unchanged AUC of saxagliptin, with an approximate 1.05-fold change, indicating no clinically significant changes and revealing a lack of direct translation of animal interaction results to humans. The animal-to-human PBPK model extrapolation used in this study could enhance the reliability of predicting drug interactions in clinical settings where DDI studies are challenging.

A physiologically-based pharmacokinetic model for predicting doxorubicin disposition in multiple tissue levels and quantitative toxicity assessment

Doxorubicin is a widely-used chemotherapeutic drug, however its high toxicity poses a significant challenge for its clinical use. To address this issue, a physiologically-based pharmacokinetic (PBPK) model was implemented to quantitatively assess doxorubicin toxicity at cellular scale. Due to its unique pharmacokinetic behavior (e.g. high volume of distribution and affinity to extra-plasma tissue compartments), we proposed a modified PBPK model structure and developed the model with multispecies extrapolation to compensate for the limitation of obtaining clinical tissue data. Our model predicted the disposition of doxorubicin in multiple tissues including clinical tissue data with an overall absolute average fold error (AAFE) of 2.12. The model's performance was further validated with 8 clinical datasets in combined with intracellular doxorubicin concentration with an average AAFE of 1.98. To assess the potential cellular toxicity, toxicity levels and area under curve (AUC) were defined for different dosing regimens in toxic and non-toxic scenarios. The cellular concentrations of doxorubicin in multiple organ sites associated with commonly observed adverse effects (AEs) were simulated and calculated the AUC for quantitative assessments. Our findings supported the clinical dosing regimen of 75 mg/m2 with a 21-day interval and suggest that slow infusion and separated single high doses may lower the risk of developing AEs from a cellular level, providing valuable insights for the risk assessment of doxorubicin chemotherapy. In conclusion, our work highlights the potential of PBPK modelling to provide quantitative assessments of cellular toxicity and supports the use of clinical dosing regimens to mitigate the risk of adverse effects.

Physiologically Based Pharmacokinetic Model To Predict Metoprolol Disposition in Healthy and Disease Populations

The evolution in the development of drugs has increased the popularity of physiologically based pharmacokinetic (PBPK) models. This study seeks to assess the PK of metoprolol in populations with healthy, chronic kidney disease (CKD), and acute myocardial infarction (AMI) conditions by developing and evaluating PBPK models. An extensive literature review for identifying and selecting plasma concentration vs time profile data and other drug-related parameters was undergone for their integration into the PK-Sim program followed by the development of intravenous, oral, and diseased models. The developed PBPK model of metoprolol was then evaluated using the visual predictive checks, mean observed/predicted ratios (Robs/pre), and average fold error for all PK parameters, i.e., the area under the curve (AUC), maximal plasma concentration, and clearance. The model evaluation depicted that none of the PK parameters were out of the allowed range (2-fold error) in the case of the mean Robs/pre ratios. The model anticipations were executed to determine the influence of diseases on unbound and total AUC after the application of metoprolol in healthy, moderate, and severe CKD. The dosage reductions were also suggested based on differences in unbound and total AUC in different stages of CKD. The developed PBPK models have successfully elaborated the PK changes of metoprolol occurring in healthy individuals and those with renal and heart diseases (CKD & AMI), which may be fruitful for dose optimization among diseased patients.

Considering developmental neurotoxicity in vitro data for human health risk assessment using physiologically-based kinetic modeling: deltamethrin case study

Developmental neurotoxicity (DNT) is a potential hazard of chemicals. Recently, an in vitro testing battery (DNT IVB) was established to complement existing rodent in vivo approaches. Deltamethrin (DLT), a pyrethroid with a well-characterized neurotoxic mode of action, has been selected as a reference chemical to evaluate the performance of the DNT IVB. The present study provides context for evaluating the relevance of these DNT IVB results for the human health risk assessment of DLT by estimating potential human fetal brain concentrations after maternal exposure to DLT. We developed a physiologically based kinetic (PBK) model for rats which was then translated to humans considering realistic in vivo exposure conditions (acceptable daily intake [ADI] for DLT). To address existing uncertainties, we designed case studies considering the most relevant drivers of DLT uptake and distribution. Calculated human fetal brain concentrations were then compared with the lowest benchmark concentration achieved in the DNT IVB. The developed rat PBK model was validated on in vivo rat toxicokinetic data of DLT over a broad range of doses. The uncertainty based case study evaluation confirmed that repeated exposure to DLT at an ADI level would likely result in human fetal brain concentrations far below the in vitro benchmark. The presented results indicate that DLT concentrations in the human fetal brain are highly unlikely to reach concentrations associated with in vitro findings under realistic exposure conditions. Therefore, the new in vitro DNT results are considered to have no impact on the current risk assessment approach.

Mathematical modeling of regulatory networks of intracellular processes - Aims and selected methods

Regulatory networks structure and signaling pathways dynamics are uncovered in time- and resource consuming experimental work. However, it is increasingly supported by modeling, analytical and computational techniques as well as discrete mathematics and artificial intelligence applied to to extract knowledge from existing databases. This review is focused on mathematical modeling used to analyze dynamics and robustness of these networks. This paper presents a review of selected modeling methods that facilitate advances in molecular biology.

A multi-model approach to predict efficacious clinical dose for an anti-TGF-β antibody (GC2008) in the treatment of osteogenesis imperfecta

Osteogenesis imperfecta (OI) is a heterogeneous group of inherited bone dysplasias characterized by reduced skeletal mass and bone fragility. Although the primary manifestation of the disease involves the skeleton, OI is a generalized connective tissue disorder that requires a multidisciplinary treatment approach. Recent studies indicate that application of a transforming growth factor beta (TGF-β) neutralizing antibody increased bone volume fraction (BVF) and strength in an OI mouse model and improved bone mineral density (BMD) in a small cohort of patients with OI. In this work, we have developed a multitiered quantitative pharmacology approach to predict human efficacious dose of a new anti-TGF-β antibody drug candidate (GC2008). This method aims to translate GC2008 pharmacokinetic/pharmacodynamic (PK/PD) relationship in patients, using a number of appropriate mathematical models and available preclinical and clinical data. Compartmental PK linked with an indirect PD effect model was used to characterize both pre-clinical and clinical PK/PD data and predict a GC2008 dose that would significantly increase BMD or BVF in patients with OI. Furthermore, a physiologically-based pharmacokinetic model incorporating GC2008 and the body's physiological properties was developed and used to predict a GC2008 dose that would decrease the TGF-β level in bone to that of healthy individuals. By using multiple models, we aim to reveal information for different aspects of OI disease that will ultimately lead to a more informed dose projection of GC2008 in humans. The different modeling efforts predicted a similar range of pharmacologically relevant doses in patients with OI providing an informed approach for an early clinical dose setting.

Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools

Cancer treatment and pharmaceutical development require targeted treatment and less toxic therapeutic intervention to achieve real progress against this disease. In this scenario, nanomedicine emerged as a reliable tool to improve drug pharmacokinetics and to translate to the clinical biologics based on large molecules. However, the ability of our body to recognize foreign objects together with carrier transport heterogeneity derived from the combination of particle physical and chemical properties, payload and surface modification, make the designing of effective carriers very difficult. In this scenario, physiologically based pharmacokinetic modeling can help to design the particles and eventually predict their ability to reach the target and treat the tumor. This effort is performed by scientists with specific expertise and skills and familiarity with artificial intelligence tools such as advanced software that are not usually in the "cords" of traditional medical or material researchers. The goal of this review was to highlight the advantages that computational modeling could provide to nanomedicine and bring together scientists with different background by portraying in the most simple way the work of computational developers through the description of the tools that they use to predict nanoparticle transport and tumor targeting in our body.

Does the choice of applied physiologically‐based pharmacokinetics platform matter? A case study on simvastatin disposition and drug–drug interaction

Physiologically‐based pharmacokinetic (PBPK) models have an important role in drug discovery/development and decision making in regulatory submissions. This is facilitated by predefined PBPK platforms with user‐friendly graphical interface, such as Simcyp and PK‐Sim. However, evaluations of platform differences and the potential implications for disposition‐related applications are still lacking. The aim of this study was to assess how PBPK model development, input parameters, and model output are affected by the selection of PBPK platform. This is exemplified via the establishment of simvastatin PBPK models (workflow, final models, and output) in PK‐Sim and Simcyp as representatives of established whole‐body PBPK platforms. The major finding was that the choice of PBPK platform influenced the model development strategy and the final model input parameters, however, the predictive performance of the simvastatin models was still comparable between the platforms. The main differences between the structure and implementation of Simcyp and PK‐Sim were found in the absorption and distribution models. Both platforms predicted equally well the observed simvastatin (lactone and acid) pharmacokinetics (20–80 mg), BCRP and OATP1B1 drug–gene interactions (DGIs), and drug–drug interactions (DDIs) when co‐administered with CYP3A4 and OATP1B1 inhibitors/inducers. This study illustrates that in‐depth knowledge of established PBPK platforms is needed to enable an assessment of the consequences of PBPK platform selection. Specifically, this work provides insights on software differences and potential implications when bridging PBPK knowledge between Simcyp and PK‐Sim users. Finally, it provides a simvastatin model implemented in both platforms for risk assessment of metabolism‐ and transporter‐mediated DGIs and DDIs.

Development and Evaluation of a Physiologically Based Pharmacokinetic Model for Predicting Haloperidol Exposure in Healthy and Disease Populations

The physiologically based pharmacokinetic (PBPK) approach can be used to develop mathematical models for predicting the absorption, distribution, metabolism, and elimination (ADME) of administered drugs in virtual human populations. Haloperidol is a typical antipsychotic drug with a narrow therapeutic index and is commonly used in the management of several medical conditions, including psychotic disorders. Due to the large interindividual variability among patients taking haloperidol, it is very likely for them to experience either toxic or subtherapeutic effects. We intend to develop a haloperidol PBPK model for identifying the potential sources of pharmacokinetic (PK) variability after intravenous and oral administration by using the population-based simulator, PK-Sim. The model was initially developed and evaluated to predict the PK of haloperidol and its reduced metabolite in adult healthy population after intravenous and oral administration. After evaluating the developed PBPK model in healthy adults, it was used to predict haloperidol–rifampicin drug–drug interaction and was extended to tuberculosis patients. The model evaluation was performed using visual assessments, prediction error, and mean fold error of the ratio of the observed-to-predicted values of the PK parameters. The predicted PK values were in good agreement with the corresponding reported values. The effects of the pathophysiological changes and enzyme induction associated with tuberculosis and its treatment, respectively, on haloperidol PK, have been predicted precisely. For all clinical scenarios that were evaluated, the predicted values were within the acceptable two-fold error range.

Use of Real-World Data and Physiologically-Based Pharmacokinetic Modeling to Characterize Enoxaparin Disposition in Children With Obesity

Dosing guidance for children with obesity is often unknown despite the fact that nearly 20% of US children are classified as obese. Enoxaparin, a commonly prescribed low-molecular-weight heparin, is dosed based on body weight irrespective of obesity status to achieve maximum concentration within a narrow therapeutic or prophylactic target range. However, whether children with and without obesity experience equivalent enoxaparin exposure remains unclear. To address this clinical question, 2,825 anti-activated factor X (anti-Xa) surrogate concentrations were collected from the electronic health records of 596 children, including those with obesity. Using linear mixed-effects regression models, we observed that 4-hour anti-Xa concentrations were statistically significantly different in children with and without obesity, even for children with the same absolute dose (P = 0.004). To further mechanistically explore obesity-associated differences in anti-Xa concentration, a pediatric physiologically-based pharmacokinetic (PBPK) model was developed in adults, and then scaled to children with and without obesity. This PBPK model incorporated binding of enoxaparin to antithrombin to form anti-Xa and elimination via heparinase-mediated metabolism and glomerular filtration. Following scaling, the PBPK model predicted real-world pediatric concentrations well, with an average fold error (standard deviation of the fold error) of 0.82 (0.23) and 0.87 (0.26) in children with and without obesity, respectively. PBPK model simulations revealed that children with obesity have at most 20% higher 4-hour anti-Xa concentrations under recommended, total body weight-based dosing compared to children without obesity owing to reduced weight-normalized clearance. Enoxaparin exposure was better matched across age groups and obesity status using fat-free mass weight-based dosing.

Application of Physiologically Based Pharmacokinetic Modeling of Lamotrigine Using PK-Sim in Predicting the Impact of Drug Interactions and Dosage Adjustment

Background

Physiologically based pharmacokinetic (PBPK) models are helpful as mechanistic representations of pharmacokinetic parameters. There were no reports of lamotrigine (LTG) PBPK models developed in open source platforms like PK-Sim.

Objectives

The present work was aimed to build a LTG PBPK model and compare it to the clinical data from South Asian Indian patients and use this model to understand the drug interactions of LTG and explore the optimal doses.

Methods and Material

The PBPK model was developed using the PK-Sim software platform and qualified with LTG plasma concentration data from an Indian study. The European population database was chosen as the patient setting in the software. Physicochemical data of LTG and enzyme kinetic data were incorporated from the literature. Dosing protocols were as per the previous study. Interaction models for drug interactions with carbamazepine and valproate were also simulated.

Results

Most of the model predicted concentration-time profiles of LTG at steady-state were well within the observed concentrations. The developed models were suitably qualified. The drug interaction model was used to assess the impact of induction and inhibition of the pharmacokinetic profile of LTG.

Conclusions

The predicted plasma concentrations of the developed PBPK models using the European population database were very similar to the data from Indian patients. The developed LTG PBPK models are applicable in predicting the impact of drug interactions and can yield appropriate LTG doses to be administered.

Theophylline: Old Drug in a New Light, Application in COVID-19 through Computational Studies

Theophylline (3-methyxanthine) is a historically prominent drug used to treat respiratory diseases, alone or in combination with other drugs. The rapid onset of the COVID-19 pandemic urged the development of effective pharmacological treatments to directly attack the development of new variants of the SARS-CoV-2 virus and possess a therapeutical battery of compounds that could improve the current management of the disease worldwide. In this context, theophylline, through bronchodilatory, immunomodulatory, and potentially antiviral mechanisms, is an interesting proposal as an adjuvant in the treatment of COVID-19 patients. Nevertheless, it is essential to understand how this compound could behave against such a disease, not only at a pharmacodynamic but also at a pharmacokinetic level. In this sense, the quickest approach in drug discovery is through different computational methods, either from network pharmacology or from quantitative systems pharmacology approaches. In the present review, we explore the possibility of using theophylline in the treatment of COVID-19 patients since it seems to be a relevant candidate by aiming at several immunological targets involved in the pathophysiology of the disease. Theophylline down-regulates the inflammatory processes activated by SARS-CoV-2 through various mechanisms, and herein, they are discussed by reviewing computational simulation studies and their different applications and effects.

Physiologically Based Pharmacokinetic Modeling in Pregnant Women Suggests Minor Decrease in Maternal Exposure to Olanzapine

Pregnancy is accompanied by significant physiological changes that might affect the in vivo drug disposition. Olanzapine is prescribed to pregnant women with schizophrenia, while its pharmacokinetics during pregnancy remains unclear. This study aimed to develop a physiologically based pharmacokinetic (PBPK) model of olanzapine in the pregnant population. With the contributions of each clearance pathway determined beforehand, a full PBPK model was developed and validated in the non-pregnant population. This model was then extrapolated to predict steady-state pharmacokinetics in the three trimesters of pregnancy by introducing gestation-related alterations. The model adequately simulated the reported time-concentration curves. The geometric mean fold error of Cmax and AUC was 1.14 and 1.09, respectively. The model predicted that under 10 mg daily dose, the systematic exposure of olanzapine had minor changes (less than 28%) throughout pregnancy. We proposed that the reduction in cytochrome P4501A2 activity is counteracted by the induction of other enzymes, especially glucuronyltransferase1A4. In conclusion, the PBPK model simulations suggest that, at least at the tested stages of pregnancy, dose adjustment of olanzapine can hardly be recommended for pregnant women if effective treatment was achieved before the onset of pregnancy and if fetal toxicity can be ruled out.

Population Pharmacokinetic Modeling and Simulation of TQ-B3101 to Inform Dosing in Pediatric Patients With Solid Tumors

Background: TQ-B3101 is a novel kinase inhibitor currently in development for the treatment of advanced malignant solid tumor and relapsed or refractory ALK-positive anaplastic large cell lymphoma.

Methods: A population pharmacokinetic model was developed using data collected from a Phase 1 study and a Phase 2 study to characterize the pharmacokinetic of TQ-B3101 and its active metabolite (TQ-B3101M). The final model was used to optimize dosing of TQ-B3101 for pediatric patients (6-<18 years) with anaplastic large cell lymphoma.

Results: The pharmacokinetic of TQ-B3101 and TQ-B3101M was adequately described by a 1-compartment model with first-order absorption and elimination for parent drug coupled with a 2-compartment model with time-dependent clearance for the metabolite. The clearance of TQ-B3101M decreased over time with a maximum fractional reduction of 0.41. The estimated apparent clearance and apparent volume of distribution of TQ-B3101 were 2850 L/h and 4200 L, respectively. The elimination half-life of TQ-B3101 was 1.0 h. The distribution and elimination half-lives of TQ-B3101M at steady state were 4.9 and 39.4 h, respectively. The projected exposure of TQ-B3101M in virtual pediatric population following the body surface area tiered dosing regimen was similar to that in children pediatric patients after the recommended pediatric dose of crizotinib (280 mg/m2 twice daily), an analog of TQ-B3101M.

Conclusion: A population pharmacokinetic model was developed to provide optimal dose of regimen for further development of TQ-B3101 in pediatric patients with anaplastic large cell lymphoma.

Use of Physiologically-Based Kinetics Modelling to Reliably Predict Internal Concentrations of the UV Filter, Homosalate, After Repeated Oral and Topical Application

Ethical and legal considerations have led to increased use of non-animal methods to evaluate the safety of chemicals for human use. We describe the development and qualification of a physiologically-based kinetics (PBK) model for the cosmetic UV filter ingredient, homosalate, to support its safety without the need of generating further animal data. The intravenous (IV) rat PBK model, using PK-Sim®, was developed and validated using legacy in vivo data generated prior to the 2013 EU animal-testing ban. Input data included literature or predicted physicochemical and pharmacokinetic properties. The refined IV rat PBK model was subject to sensitivity analysis to identify homosalate-specific sensitive parameters impacting the prediction of Cmax (more sensitive than AUC(0-∞)). These were then considered, together with population modeling, to calculate the confidence interval (CI) 95% Cmax and AUC(0-∞). Final model parameters were established by visual inspection of the simulations and biological plausibility. The IV rat model was extrapolated to oral administration, and used to estimate internal exposures to doses tested in an oral repeated dose toxicity study. Next, a human PBK dermal model was developed using measured human in vitro ADME data and a module to represent the dermal route. Model performance was confirmed by comparing predicted and measured values from a US-FDA clinical trial (Identifier: NCT03582215, https://clinicaltrials.gov/). Final exposure estimations were obtained in a virtual population and considering the in vitro and input parameter uncertainty. This model was then used to estimate the Cmax and AUC(0-24 h) of homosalate according to consumer use in a sunscreen. The developed rat and human PBK models had a good biological basis and reproduced in vivo legacy rat and human clinical kinetics data. They also complied with the most recent WHO and OECD recommendations for assessing the confidence level. In conclusion, we have developed a PBK model which predicted reasonably well the internal exposure of homosalate according to different exposure scenarios with a medium to high level of confidence. In the absence of in vivo data, such human PBK models will be the heart of future completely non-animal risk assessments; therefore, valid approaches will be key in gaining their regulatory acceptance. Clinical Trial Registration: https://clinicaltrials.gov/, identifier, NCT03582215.

Reduced physiologically-based pharmacokinetic model of dabigatran etexilate-dabigatran and its application for prediction of intestinal P-gp-mediated drug-drug interactions

BACKGROUND

Dabigatran etexilate (DABE) has been suggested as a clinical probe for intestinal P-glycoprotein (P-gp)-mediated drug-drug interaction (DDI) studies and, as an alternative to digoxin. Clinical DDI data with various P-gp inhibitors demonstrated a dose-dependent inhibition of P-gp with DABE. The aims of this study were to develop a joint DABE (prodrug)-dabigatran reduced physiologically-based-pharmacokinetic (PBPK) model and to evaluate its ability to predict differences in P-gp DDI magnitude between a microdose and a therapeutic dose of DABE.

METHODS

A joint DABE-dabigatran PBPK model was developed with a mechanistic intestinal model accounting for the regional P-gp distribution in the gastrointestinal tract. Model input parameters were estimated using DABE and dabigatran pharmacokinetic (PK) clinical data obtained after administration of DABE alone or with a strong P-gp inhibitor, itraconazole, and over a wide range of DABE doses (from 375 µg to 400 mg). Subsequently, the model was used to predict extent of DDI with additional P-gp inhibitors and with different DABE doses.

RESULTS

The reduced DABE-dabigatran PBPK model successfully described plasma concentrations of both prodrug and metabolite following administration of DABE at different dose levels and when co-administered with itraconazole. The model was able to capture the dose dependency in P-gp mediated DDI. Predicted magnitude of itraconazole P-gp DDI was higher at the microdose (predicted vs. observed median fold-increase in AUC_+inh/AUC_control (min-max) = 5.88 (4.29-7.93) vs. 6.92 (4.96-9.66) ) compared to the therapeutic dose (predicted median fold-increase in AUC_+inh/AUC_control = 3.48 (2.37-4.84) ). In addition, the reduced DABE-dabigatran PBPK model predicted successfully the extent of DDI with verapamil and clarithromycin as P-gp inhibitors. Model-based simulations of dose staggering predicted the maximum inhibition of P-gp when DABE microdose was concomitantly administered with itraconazole solution; simulations also highlighted dosing intervals required to minimise the DDI risk depending on the DABE dose administered (microdose vs. therapeutic).

CONCLUSIONS

This study provides a modelling framework for the evaluation of P-gp inhibitory potential of new molecular entities using DABE as a clinical probe. Simulations of dose staggering and regional differences in the extent of intestinal P-gp inhibition for DABE microdose and therapeutic dose provide model-based guidance for design of prospective clinical P-gp DDI studies.

Influence of CYP2D6 Phenotypes on the Pharmacokinetics of Aripiprazole and Dehydro-Aripiprazole Using a Physiologically Based Pharmacokinetic Approach

BACKGROUND AND OBJECTIVES

Aripiprazole is an atypical antipsychotic drug that is metabolized by cytochrome P450 (CYP) 2D6 and CYP3A4, which mainly form its active metabolite dehydro-aripiprazole. Because of the genetic polymorphism of CYP2D6, plasma concentrations are highly variable between different phenotypes. In this study, phenotype-related physiologically based pharmacokinetic models were developed and evaluated to suggest phenotype-guided dose adjustments.

METHODS

Physiologically based pharmacokinetic models for single dose (oral and orodispersible formulation), multiple dose, and steady-state condition were built using trial data from genotyped healthy volunteers. Based on evaluated models, dose adjustments were simulated to compensate for genetically caused differences.

RESULTS

Physiologically based pharmacokinetic models were able to accurately predict the pharmacokinetics of aripiprazole and dehydro-aripiprazole according to CYP2D6 phenotypes, illustrated by a minimal bias and a good precision. For single-dose administration, 92.5% (oral formulation) and 79.3% (orodispersible formulation) of the plasma concentrations of aripiprazole were within the 1.25-fold error range. In addition, physiologically based pharmacokinetic steady-state simulations demonstrate that the daily dose for poor metabolizer should be adjusted, resulting in a maximum recommended dose of 10 mg, but no adjustment is necessary for intermediate and ultra-rapid metabolizers.

CONCLUSIONS

In clinical practice, CYP2D6 genotyping in combination with therapeutic drug monitoring should be considered to personalize aripiprazole dosing, especially in CYP2D6 poor metabolizers, to ensure therapy effectiveness and safety.

Physiologically Based Pharmacokinetic Modelling of Cabozantinib to Simulate Enterohepatic Recirculation, Drug-Drug Interaction with Rifampin and Liver Impairment

Cabozantinib (CAB) is a receptor tyrosine kinase inhibitor approved for the treatment of several cancer types. Enterohepatic recirculation (EHC) of the substance is assumed but has not been further investigated yet. CAB is mainly metabolized via CYP3A4 and is susceptible for drug-drug interactions (DDI). The goal of this work was to develop a physiologically based pharmacokinetic (PBPK) model to investigate EHC, to simulate DDI with Rifampin and to simulate subjects with hepatic impairment. The model was established using PK-Sim^® and six human clinical studies. The inclusion of an EHC process into the model led to the most accurate description of the pharmacokinetic behavior of CAB. The model was able to predict plasma concentrations with low bias and good precision. Ninety-seven percent of all simulated plasma concentrations fell within 2-fold of the corresponding concentration observed. Maximum plasma concentration (C_max) and area under the curve (AUC) were predicted correctly (predicted/observed ratio of 0.9-1.2 for AUC and 0.8-1.1 for C_max). DDI with Rifampin led to a reduction in predicted AUC by 77%. Several physiological parameters were adapted to simulate hepatic impairment correctly. This is the first CAB model used to simulate DDI with Rifampin and hepatic impairment including EHC, which can serve as a starting point for further simulations with regard to special populations.

Comparing Predictions of a PBPK Model for Cyclosporine With Drug Levels From Therapeutic Drug Monitoring

This study compared simulations of a physiologically based pharmacokinetic (PBPK) model implemented for cyclosporine with drug levels from therapeutic drug monitoring to evaluate the predictive performance of a PBPK model in a clinical population. Based on a literature search model parameters were determined. After calibrating the model using the pharmacokinetic profiles of healthy volunteers, 356 cyclosporine trough levels of 32 renal transplant outpatients were predicted based on their biometric parameters. Model performance was assessed by calculating absolute and relative deviations of predicted and observed trough levels. The median absolute deviation was 6 ng/ml (interquartile range: 30 to 31 ng/ml, minimum = -379 ng/ml, maximum = 139 ng/ml). 86% of predicted cyclosporine trough levels deviated less than twofold from observed values. The high intra-individual variability of observed cyclosporine levels was not fully covered by the PBPK model. Perspectively, consideration of clinical and additional patient-related factors may improve the model's performance. In summary, the current study has shown that PBPK modeling may offer valuable contributions for pharmacokinetic research in clinical drug therapy.

Physiologically Based Pharmacokinetic Modelling to Describe the Pharmacokinetics of Risperidone and 9-Hydroxyrisperidone According to Cytochrome P450 2D6 Phenotypes

BACKGROUND AND OBJECTIVES

The genetic polymorphism of cytochrome P450 (CYP) 2D6 is characterized by an excessive impact on positive and adverse drug reactions to antipsychotics, such as risperidone. Consequently, the pharmacokinetics of the drug and metabolite can be substantially altered and exhibit a high variability between the different phenotypes. The goal of this study was to develop a physiologically based pharmacokinetic (PBPK) model considering the CYP2D6 genetic polymorphism for risperidone and 9-hydroxyrisperidone (9-OH-RIS) taking CYP3A4 into account. Additionally, risperidone dose adjustments, which would compensate for genetically caused differences in the plasma concentrations of the active moiety (sum of risperidone and 9-OH-RIS) were calculated.

METHODS

Based on available knowledge about risperidone, 9-OH-RIS, and relevant physiological changes according to different CYP2D6 phenotypes, several PBPK models were built. In addition, an initial model was further evaluated based on the plasma concentrations of risperidone and 9-OH-RIS from a single-dose study including 71 genotyped healthy volunteers treated with 1 mg of oral risperidone.

RESULTS

PBPK models were able to accurately describe risperidone exposure after single-dose administration, especially in the concentration range ≥ 1 µg/L, illustrated by a minimal bias and a good precision. About 90.3% of all weighted residuals versus observed plasma concentrations ≥ 1 µg/L were in the ± 30% range. The risperidone/9-OH-RIS ratio increased progressively according to reduced CYP2D6 activity, resulting in a mean ratio of 4.96 for poor metabolizers. Simulations demonstrate that dose adjustment of the drug by - 25% for poor metabolizers and by - 10% for intermediate metabolizers results in a similar exposure to that of extensive metabolizers. Conversely, the risperidone/9-OH-RIS ratio can be used to determine the phenotype of individuals.

CONCLUSION

PBPK modelling can provide a valuable tool to predict the pharmacokinetics of risperidone and 9-OH-RIS in healthy volunteers, according to the different CYP2D6 phenotypes taking CYP3A4 into account. These models are able to ultimately support decision-making regarding dose-optimization strategies, especially for subjects showing lower CYP2D6 activity.

Physiological-based pharmacokinetic modeling trends in pharmaceutical drug development over the last 20-years; in-depth analysis of applications, organizations, and platforms

We assess the advancement of physiologically based pharmacokinetic (PBPK) modeling and simulation (M&S) over the last 20 years (start of 2000 to end of 2019) focusing on the trends in each decade with the relative contributions from different organizations, areas of applications, and software tools used. Unlike many of the previous publications which focused on regulatory applications, our analysis is based on PBPK publications in peer-reviewed journals based on a large sample (>700 original articles). We estimated a rate of growth for PBPK (>40 fold/20 years) that was much steeper than the general pharmacokinetic modeling (<3 fold/20 years) or overall scientific publications (∼3 fold/20 years). The analyses demonstrated that contrary to commonly held belief, commercial specialized PBPK platforms with graphical-user interface were a much more popular choice than open-source alternatives even within academic organizations. These platforms constituted 81% of the whole set of the sample we assessed. The major PBPK applications (top 3) were associated with the study design, predicting formulation effects, and metabolic drug-drug interactions, while studying the fate of drugs in special populations, predicting kinetics in early drug development, and investigating transporter drug interactions have increased proportionally over the last decade. The proportions of application areas based on published research were distinctively different from those shown previously for the regulatory submissions and impact on labels. This may demonstrate the lag time between the research applications versus verified usage within the regulatory framework. The report showed the trend of overall PBPK publications in pharmacology drug development from the past 2 decades stratified by the organizations involved, software used, and area of applications. The analysis showed a more rapid increase in PBPK than that of the pharmacokinetic space itself with an equal contribution from academia and industry. By establishing and recording the journey of PBPK modeling in the past and looking at its current status, the analysis can be used for devising plans based on the anticipated trajectory of future regulatory applications.

Applications of Physiologically Based Pharmacokinetic Modeling of Rivaroxaban-Renal and Hepatic Impairment and Drug-Drug Interaction Potential

The non–vitamin K antagonist oral anticoagulant rivaroxaban is used in several thromboembolic disorders. Rivaroxaban is eliminated via both metabolic degradation and renal elimination as unchanged drug. Therefore, renal and hepatic impairment may reduce rivaroxaban clearance, and medications inhibiting these clearance pathways could lead to drug‐drug interactions. This physiologically based pharmacokinetic (PBPK) study investigated the pharmacokinetic behavior of rivaroxaban in clinical situations where drug clearance is impaired. A PBPK model was developed using mass balance and bioavailability data from adults and qualified using clinically observed data. Renal and hepatic impairment were simulated by adjusting disease‐specific parameters, and concomitant drug use was simulated by varying enzyme activity in virtual populations (n = 1000) and compared with pharmacokinetic predictions in virtual healthy populations and clinical observations. Rivaroxaban doses of 10 mg or 20 mg were used. Mild to moderate renal impairment had a minor effect on area under the concentration‐time curve and maximum plasma concentration of rivaroxaban, whereas severe renal impairment caused a more pronounced increase in these parameters vs normal renal function. Area under the concentration‐time curve and maximum plasma concentration increased with severity of hepatic impairment. These effects were smaller in the simulations compared with clinical observations. AUC and C_max increased with the strength of cytochrome P450 3A4 and P‐glycoprotein inhibitors in simulations and clinical observations. This PBPK model can be useful for estimating the effects of impaired drug clearance on rivaroxaban pharmacokinetics. Identifying other factors that affect the pharmacokinetics of rivaroxaban could facilitate the development of models that approximate real‐world pharmacokinetics more accurately.

Modeling and Simulation of Process Technology for Nanoparticulate Drug Formulations-A Particle Technology Perspective

Crystalline organic nanoparticles and their amorphous equivalents (ONP) have the potential to become a next-generation formulation technology for dissolution-rate limited biopharmaceutical classification system (BCS) class IIa molecules if the following requisites are met: (i) a quantitative understanding of the bioavailability enhancement benefit versus established formulation technologies and a reliable track record of successful case studies are available; (ii) efficient experimentation workflows with a minimum amount of active ingredient and a high degree of digitalization via, e.g., automation and computer-based experimentation planning are implemented; (iii) the scalability of the nanoparticle-based oral delivery formulation technology from the lab to manufacturing is ensured. Modeling and simulation approaches informed by the pharmaceutical material science paradigm can help to meet these requisites, especially if the entire value chain from formulation to oral delivery is covered. Any comprehensive digitalization of drug formulation requires combining pharmaceutical materials science with the adequate formulation and process technologies on the one hand and quantitative pharmacokinetics and drug administration dynamics in the human body on the other hand. Models for the technical realization of the drug production and the distribution of the pharmaceutical compound in the human body are coupled via the central objective, namely bioavailability. The underlying challenges can only be addressed by hierarchical approaches for property and process design. The tools for multiscale modeling of the here-considered particle processes (e.g., by coupled computational fluid dynamics, population balance models, Noyes–Whitney dissolution kinetics) and physiologically based absorption modeling are available. Significant advances are being made in enhancing the bioavailability of hydrophobic compounds by applying innovative solutions. As examples, the predictive modeling of anti-solvent precipitation is presented, and options for the model development of comminution processes are discussed.

A workflow to build PBTK models for novel species

Physiology-based pharmacokinetic and toxicokinetic (PBPK/TK) models allow us to simulate the concentration of xenobiotica in the plasma and different tissues of an organism. PBPK/TK models are therefore routinely used in many fields of life sciences to simulate the physiological concentration of exogenous compounds in plasma and tissues. The application of PBTK models in ecotoxicology, however, is currently hampered by the limited availability of models for focal species. Here, we present a best practice workflow that describes how to build PBTK models for novel species. To this end, we extrapolated eight previously established rabbit models for several drugs to six additional mammalian species (human, beagle, rat, monkey, mouse, and minipig). We used established PBTK models for these species to account for the species-specific physiology. The parameter sensitivity in the resulting 56 PBTK models was systematically assessed to rank the relevance of the parameters on overall model performance. Interestingly, more than 80% of the 609 considered model parameters showed a negligible sensitivity throughout all models. Only approximately 5% of all parameters had a high sensitivity in at least one of the PBTK models. This approach allowed us to rank the relevance of the various parameters on overall model performance. We used this information to formulate a best practice guideline for the efficient development of PBTK models for novel animal species. We believe that the workflow proposed in this study will significantly support the development of PBTK models for new animal species in the future.

Evaluation of Voriconazole CYP2C19 Phenotype-Guided Dose Adjustments by Physiologically Based Pharmacokinetic Modeling

BACKGROUND AND OBJECTIVES

Controversy exists regarding dose adjustment in patients treated with voriconazole due to the severity of the infections for which it is prescribed. The Dutch Pharmacogenetics Working Group (DPWG) recommends a 50% dose increase or decrease for cytochrome P450 (CYP) 2C19 ultrarapid (UM) or poor (PM) metabolizers, respectively. In contrast, for the previous phenotypes, the Clinical Pharmacogenetics Implementation Consortium (CPIC) voriconazole guideline only recommends a change of treatment. Based on observed data from single-dose bioequivalence studies and steady-state observed concentrations, we aimed to investigate voriconazole dose adjustments by means of physiologically based pharmacokinetic (PBPK) modeling.

METHODS

PBPK modeling was used to optimize voriconazole single-dose models for each CYP2C19 phenotype, which were extrapolated to steady state and evaluated for concordance with the therapeutic range of voriconazole. Based on optimized models, dose adjustments were evaluated for better adjustment to the therapeutic range.

RESULTS

Our models suggest that the standard dose may only be appropriate for normal metabolizers (NM), although they would benefit from a 50-100% loading dose increase. Intermediate metabolizers (IMs) and PMs required a daily dose reduction of 50 and 75%, respectively. Rapid metabolizers (RMs) and UMs required a daily dose increase of 100% and 300%, respectively.

CONCLUSION

The prescription of voriconazole in clinical practice should be personalized according to the CYP2C19 phenotype, followed by therapeutic drug monitoring of plasma concentrations to guide dose adjustment.

Physiologically Based Pharmacokinetic Modeling for Trimethoprim and Sulfamethoxazole in Children

OBJECTIVE

The aims of this study were to (1) determine whether opportunistically collected data can be used to develop physiologically based pharmacokinetic (PBPK) models in pediatric patients; and (2) characterize age-related maturational changes in drug disposition for the renally eliminated and hepatically metabolized antibiotic trimethoprim (TMP)-sulfamethoxazole (SMX).

METHODS

We developed separate population PBPK models for TMP and SMX in children after oral administration of the combined TMP-SMX product and used sparse and opportunistically collected plasma concentration samples to validate our pediatric model. We evaluated predictability of the pediatric PBPK model based on the number of observed pediatric data out of the 90% prediction interval. We performed dosing simulations to target organ and tissue (skin) concentrations greater than the methicillin-resistant Staphylococcus aureus (MRSA) minimum inhibitory concentration (TMP 2 mg/L; SMX 9.5 mg/L) for at least 50% of the dosing interval.

RESULTS

We found 67-87% and 71-91% of the observed data for TMP and SMX, respectively, were captured within the 90% prediction interval across five age groups, suggesting adequate fit of our model. Our model-rederived optimal dosing of TMP at the target tissue was in the range of recommended dosing for TMP-SMX in children in all age groups by current guidelines for the treatment of MRSA.

CONCLUSION

We successfully developed a pediatric PBPK model of the combination antibiotic TMP-SMX using sparse and opportunistic pediatric pharmacokinetic samples. This novel and efficient approach has the potential to expand the use of PBPK modeling in pediatric drug development.

Modelling Age-Related Changes in the Pharmacokinetics of Risperidone and 9-Hydroxyrisperidone in Different CYP2D6 Phenotypes Using a Physiologically Based Pharmacokinetic Approach

PURPOSE

Dose-optimization strategies for risperidone are gaining in importance, especially in the elderly. Based on the genetic polymorphism of cytochrome P 450 (CYP) 2D6 genetically and age-related changes cause differences in the pharmacokinetics of risperidone and 9-hydroxyrisperidone. The goal of the study was to develop physiologically based pharmacokinetic (PBPK) models for the elderly aged 65+ years. Additionally, CYP2D6 phenotyping using metabolic ratio were applied and different pharmacokinetic parameter for different age classes predicted.

METHODS

Plasma concentrations of risperidone and 9-hydroxyrisperidone were used to phenotype 17 geriatric inpatients treated under naturalistic conditions. For this purpose, PBPK models were developed to examine age-related changes in the pharmacokinetics between CYP2D6 extensive metabolizer, intermediate metabolizer, poor metabolizer, (PM) and ultra-rapid metabolizer.

RESULTS

PBPK-based metabolic ratio was able to predict different CYP2D6 phenotypes during steady-state. One inpatient was identified as a potential PM, showing a metabolic ratio of 3.39. About 88.2% of all predicted plasma concentrations of the inpatients were within the 2-fold error range. Overall, age-related changes of the pharmacokinetics in the elderly were mainly observed in Cmax and AUC. Comparing a population of young adults with the oldest-old, Cmax of risperidone increased with 24-44% and for 9-hydroxyrisperidone with 35-37%.

CONCLUSIONS

Metabolic ratio combined with PBPK modelling can provide a powerful tool to identify potential CYP2D6 PM during therapeutic drug monitoring. Based on genetic, anatomical and physiological changes during aging, PBPK models ultimately support decision-making regarding dose-optimization strategies to ensure the best therapy for each patient over the age of 65 years.

Physiologically based pharmacokinetic evaluation of cefuroxime in perioperative antibiotic prophylaxis

Aims

Adequate plasma concentrations of antibiotics during surgery are essential for the prevention of surgical site infections. We examined the pharmacokinetics of 1.5 g cefuroxime administered during induction of anaesthesia with follow‐up doses every 2.5 hours until the end of surgery. We built a physiologically based pharmacokinetic model with the aim to ensure adequate antibiotic plasma concentrations in a heterogeneous population.

Methods

A physiologically based pharmacokinetic model (PK‐Sim^®/MoBi^®) was developed to investigate unbound plasma concentrations of cefuroxime. Blood samples from 25 thoracic surgical patients were analysed with high‐performance liquid chromatography. To evaluate optimized dosing regimens, physiologically based pharmacokinetic model simulations were conducted.

Results

Dosing simulations revealed that a standard dosing regimen of 1.5 g every 2.5 hours reached the pharmacokinetic/pharmacodynamic target for Staphylococcus aureus. However, for Escherichia coli, >50% of the study participants did not reach predefined targets. Effectiveness of cefuroxime against E. coli can be improved by administering a 1.5 g bolus immediately followed by a continuous infusion of 3 g cefuroxime over 3 hours.

Conclusion

The use of cefuroxime for perioperative antibiotic prophylaxis to prevent staphylococcal surgical site infections appears to be effective with standard dosing of 1.5 g preoperatively and follow‐up doses every 2.5 hours. In contrast, if E. coli is relevant in surgeries, this dosing regimen appears insufficient. With our derived dose recommendations, we provide a solution for this issue.

A Physiologically-Based Pharmacokinetic Model to Describe Ciprofloxacin Pharmacokinetics Over the Entire Span of Life

Background

Physiologically-based pharmacokinetic (PBPK) modeling has received growing interest as a useful tool for the assessment of drug pharmacokinetics by continuous knowledge integration.

Objective

The objective of this study was to build a ciprofloxacin PBPK model for intravenous and oral dosing based on a comprehensive literature review, and evaluate the predictive performance towards pediatric and geriatric patients.

Methods

The aim of this report was to establish confidence in simulations of the ciprofloxacin PBPK model along the development process to facilitate reliable predictions outside of the tested adult age range towards the extremes of ages. Therefore, mean data of 69 published clinical trials were identified and integrated into the model building, simulation and verification process. The predictive performance on both ends of the age scale was assessed using individual data of 258 subjects observed in own clinical trials.

Results

Ciprofloxacin model verification demonstrated no concentration-related bias and accurate simulations for the adult age range, with only 4.8% of the mean observed data points for intravenous administration and 12.1% for oral administration being outside the simulated twofold range. Predictions towards the extremes of ages for the area under the plasma concentration–time curve (AUC) and the maximum plasma concentration (C_max) over the entire span of life revealed a reliable estimation, with only two pediatric AUC observations outside the 90% prediction interval.

Conclusion

Overall, this ciprofloxacin PBPK modeling approach demonstrated the predictive power of a thoroughly informed middle-out approach towards age groups of interest to potentially support the decision-making process.

Electronic supplementary material

The online version of this article (10.1007/s40262-018-0661-6) contains supplementary material, which is available to authorized users.

Physiologically Based Pharmacokinetic Models for Adults and Children Reveal a Role of Intracellular Tubulin Binding in Vincristine Disposition

Vincristine is a cytotoxic chemotherapeutic agent used as first-line therapy for pediatric acute lymphocytic leukemia. It is cleared by hepatic oxidative metabolism by CYP3A4 and CYP3A5 and via hepatic (biliary) efflux mediated by P-glycoprotein (P-gp) transporter. Bottom-up physiologically based pharmacokinetic (PBPK) models were developed to predict vincristine disposition in pediatric and adult populations. The models incorporated physicochemical properties, metabolism by CYP3A4/5, efflux by P-gp, and intracellular binding to β-tubulin. The adult and pediatric PBPK models predicted pharmacokinetics (PK) within twofold of the observed PK parameters (area under the curve, terminal half-life, volume of distribution, and clearance). Simulating a higher hypothetical (4.9-fold) pediatric expression of β-tubulin relative to adult improved predictions of vincristine PKs. To our knowledge, this is the first time that intracellular binding has been incorporated into a pediatric PBPK model. Utilizing this PBPK modeling approach, safe and effective doses of vincristine could be predicted.

A Physiologically Based Pharmacokinetic Model for Pregnant Women to Predict the Pharmacokinetics of Drugs Metabolized Via Several Enzymatic Pathways

BACKGROUND

Physiologically based pharmacokinetic modeling is considered a valuable tool for predicting pharmacokinetic changes in pregnancy to subsequently guide in-vivo pharmacokinetic trials in pregnant women. The objective of this study was to extend and verify a previously developed physiologically based pharmacokinetic model for pregnant women for the prediction of pharmacokinetics of drugs metabolized via several cytochrome P450 enzymes.

METHODS

Quantitative information on gestation-specific changes in enzyme activity available in the literature was incorporated in a pregnancy physiologically based pharmacokinetic model and the pharmacokinetics of eight drugs metabolized via one or multiple cytochrome P450 enzymes was predicted. The tested drugs were caffeine, midazolam, nifedipine, metoprolol, ondansetron, granisetron, diazepam, and metronidazole. Pharmacokinetic predictions were evaluated by comparison with in-vivo pharmacokinetic data obtained from the literature.

RESULTS

The pregnancy physiologically based pharmacokinetic model successfully predicted the pharmacokinetics of all tested drugs. The observed pregnancy-induced pharmacokinetic changes were qualitatively and quantitatively reasonably well predicted for all drugs. Ninety-seven percent of the mean plasma concentrations predicted in pregnant women fell within a twofold error range and 63% within a 1.25-fold error range. For all drugs, the predicted area under the concentration-time curve was within a 1.25-fold error range.

CONCLUSION

The presented pregnancy physiologically based pharmacokinetic model can quantitatively predict the pharmacokinetics of drugs that are metabolized via one or multiple cytochrome P450 enzymes by integrating prior knowledge of the pregnancy-related effect on these enzymes. This pregnancy physiologically based pharmacokinetic model may thus be used to identify potential exposure changes in pregnant women a priori and to eventually support informed decision making when clinical trials are designed in this special population.

A Comprehensive Framework for Physiologically-Based Pharmacokinetic Modeling in Matlab

Physiologically-based pharmacokinetic (PBPK) models are useful tools to predict clinical scenarios for special populations for whom there are high hurdles to conduct clinical trials such as children or the elderly. However, the coding of a PBPK model in a mathematical programming language can be challenging. This tutorial illustrates how to build a whole-body PBPK model in Matlab to answer specific pharmacological questions involving drug disposition and magnitudes of drug-drug interactions in different patient populations.

PBPK and its Virtual Populations: the Impact of Physiology on Pediatric Pharmacokinetic Predictions of Tramadol

In pediatric PBPK models, age-related changes in the body are known to occur. Given the sparsity of and the variability associated with relevant physiological parameters, different PBPK software providers may vary in their system's data. In this work, three commercially available PBPK software packages (PK-Sim®, Simcyp®, and Gastroplus®) were investigated regarding their differences in system-related information, possibly affecting clearance prediction. Three retrograde PBPK clearance models were set up to enable prediction of pediatric tramadol clearance. These models were qualified in terms of total, CYP2D6, and renal clearance in adults. Tramadol pediatric clearance predictions from PBPK were compared with a pooled popPK model covering clearance ranging from neonates to adults. Fold prediction errors were used to evaluate the results. Marked differences in liver clearance prediction between PBPK models were observed. In general, the prediction bias of total clearance was greatest at the youngest population and decreased with age. Regarding CYP2D6 and renal clearance, important differences exist between PBPK software tools. Interestingly, the PBPK model with the shortest CYP2D6 maturation half-life (PK-Sim) agreed best with the in vivo CYP2D6 maturation model. Marked differences in physiological data explain the observed differences in hepatic clearance prediction in early life between the various PBPK software providers tested. Consensus on the most suited pediatric data to use should harmonize and optimize pediatric clearance predictions. Moreover, the combination of bottom-up and top-down approaches, using a convenient probe substrate, has the potential to update system-related parameters in order to better represent pediatric physiology.

Using quantitative systems pharmacology to evaluate the drug efficacy of COX-2 and 5-LOX inhibitors in therapeutic situations

A quantitative analysis of dose-response relationships is essential in preclinical and clinical drug development in order to optimize drug efficacy and safety, respectively. However, there is a lack of quantitative understanding about the dynamics of pharmacological drug-target interactions in biological systems. In this study, a quantitative systems pharmacology (QSP) approach is applied to quantify the drug efficacy of cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) inhibitors by coupling physiologically based pharmacokinetic models, at the whole-body level, with affected biological networks, at the cellular scale. Both COX-2 and 5-LOX are key enzymes in the production of inflammatory mediators and are known targets in the design of anti-inflammatory drugs. Drug efficacy is here evaluated for single and appropriate co-treatment of diclofenac, celecoxib, zileuton, and licofelone by quantitatively studying the reduction of prostaglandins and leukotrienes. The impact of rifampicin pre-treatment on prostaglandin formation is also investigated by considering pharmacokinetic drug interactions with diclofenac and celecoxib, finally suggesting optimized dose levels to compensate for the reduced drug action. Furthermore, a strong correlation was found between pain relief observed in patients as well as celecoxib- and diclofenac-induced decrease in prostaglandins after 6 h. The findings presented reveal insights about drug-induced modulation of cellular networks in a whole-body context, thereby describing complex pharmacokinetic/pharmacodynamic behavior of COX-2 and 5-LOX inhibitors in therapeutic situations. The results demonstrate the clinical benefit of using QSP to predict drug efficacy and, hence, encourage its use in future drug discovery and development programs.

Physiologically Based Pharmacokinetic Modeling of Renally Cleared Drugs in Pregnant Women

BACKGROUND

Since pregnant women are considerably underrepresented in clinical trials, information on optimal dosing in pregnancy is widely lacking. Physiologically based pharmacokinetic (PBPK) modeling may provide a method for predicting pharmacokinetic changes in pregnancy to guide subsequent in vivo pharmacokinetic trials in pregnant women, minimizing associated risks.

OBJECTIVES

The goal of this study was to build and verify a population PBPK model that predicts the maternal pharmacokinetics of three predominantly renally cleared drugs (namely cefazolin, cefuroxime, and cefradine) at different stages of pregnancy. It was further evaluated whether the fraction unbound (f _u) could be estimated in pregnant women using a proposed scaling approach.

METHODS

Based on a recent literature review on anatomical and physiological changes during pregnancy, a pregnancy population PBPK model was built using the software PK-Sim^®/MoBi^®. This model comprised 27 compartments, including nine pregnancy-specific compartments. The PBPK model was verified by comparing the predicted maternal pharmacokinetics of cefazolin, cefuroxime, and cefradine with observed in vivo data taken from the literature. The proposed scaling approach for estimating the f _u in pregnancy was evaluated by comparing the predicted f _u with experimentally observed f _u values of 32 drugs taken from the literature.

RESULTS

The pregnancy population PBPK model successfully predicted the pharmacokinetics of cefazolin, cefuroxime, and cefradine at all tested stages of pregnancy. All predicted plasma concentrations fell within a 2-fold error range and 85% of the predicted concentrations within a 1.25-fold error range. The f _u in pregnancy could be adequately predicted using the proposed scaling approach, although a slight underestimation was evident in case of drugs bound to α₁-acidic glycoprotein.

CONCLUSION

Pregnancy population PBPK models can provide a valuable tool to predict a priori the pharmacokinetics of predominantly renally cleared drugs in pregnant women. These models can ultimately support informed decision making regarding optimal dosing regimens in this vulnerable special population.

Development and validation of a physiology-based model for the prediction of pharmacokinetics/toxicokinetics in rabbits

The environmental fates of pharmaceuticals and the effects of crop protection products on non-target species are subjects that are undergoing intense review. Since measuring the concentrations and effects of xenobiotics on all affected species under all conceivable scenarios is not feasible, standard laboratory animals such as rabbits are tested, and the observed adverse effects are translated to focal species for environmental risk assessments. In that respect, mathematical modelling is becoming increasingly important for evaluating the consequences of pesticides in untested scenarios. In particular, physiologically based pharmacokinetic/toxicokinetic (PBPK/TK) modelling is a well-established methodology used to predict tissue concentrations based on the absorption, distribution, metabolism and excretion of drugs and toxicants. In the present work, a rabbit PBPK/TK model is developed and evaluated with data available from the literature. The model predictions include scenarios of both intravenous (i.v.) and oral (p.o.) administration of small and large compounds. The presented rabbit PBPK/TK model predicts the pharmacokinetics (Cmax, AUC) of the tested compounds with an average 1.7-fold error. This result indicates a good predictive capacity of the model, which enables its use for risk assessment modelling and simulations.

Development of a Physiologically Based Pharmacokinetic Modelling Approach to Predict the Pharmacokinetics of Vancomycin in Critically Ill Septic Patients

BACKGROUND AND OBJECTIVES

Sepsis is characterised by an excessive release of inflammatory mediators substantially affecting body composition and physiology, which can be further affected by intensive care management. Consequently, drug pharmacokinetics can be substantially altered. This study aimed to extend a whole-body physiologically based pharmacokinetic (PBPK) model for healthy adults based on disease-related physiological changes of critically ill septic patients and to evaluate the accuracy of this PBPK model using vancomycin as a clinically relevant drug.

METHODS

The literature was searched for relevant information on physiological changes in critically ill patients with sepsis, severe sepsis and septic shock. Consolidated information was incorporated into a validated PBPK vancomycin model for healthy adults. In addition, the model was further individualised based on patient data from a study including ten septic patients treated with intravenous vancomycin. Models were evaluated comparing predicted concentrations with observed patient concentration-time data.

RESULTS

The literature-based PBPK model correctly predicted pharmacokinetic changes and observed plasma concentrations especially for the distribution phase as a result of a consideration of interstitial water accumulation. Incorporation of disease-related changes improved the model prediction from 55 to 88% within a threshold of 30% variability of predicted vs. observed concentrations. In particular, the consideration of individualised creatinine clearance data, which were highly variable in this patient population, had an influence on model performance.

CONCLUSION

PBPK modelling incorporating literature data and individual patient data is able to correctly predict vancomycin pharmacokinetics in septic patients. This study therefore provides essential key parameters for further development of PBPK models and dose optimisation strategies in critically ill patients with sepsis.

A generic whole body physiologically based pharmacokinetic model for therapeutic proteins in PK-Sim

Proteins are an increasingly important class of drugs used as therapeutic as well as diagnostic agents. A generic physiologically based pharmacokinetic (PBPK) model was developed in order to represent at whole body level the fundamental mechanisms driving the distribution and clearance of large molecules like therapeutic proteins. The model was built as an extension of the PK-Sim model for small molecules incorporating (i) the two-pore formalism for drug extravasation from blood plasma to interstitial space, (ii) lymph flow, (iii) endosomal clearance and (iv) protection from endosomal clearance by neonatal Fc receptor (FcRn) mediated recycling as especially relevant for antibodies. For model development and evaluation, PK data was used for compounds with a wide range of solute radii. The model supports the integration of knowledge gained during all development phases of therapeutic proteins, enables translation from pre-clinical species to human and allows predictions of tissue concentration profiles which are of relevance for the analysis of on-target pharmacodynamic effects as well as off-target toxicity. The current implementation of the model replaces the generic protein PBPK model available in PK-Sim since version 4.2 and becomes part of the Open Systems Pharmacology Suite.