Physiologically based pharmacokinetic study on a cyclosporin derivative, SDZ IMM 125

Determining tissue distribution of the oral antileishmanial agent miltefosine: a physiologically-based pharmacokinetic modeling approach

Miltefosine (MTS) is the only approved oral drug for treating leishmaniasis caused by intracellular Leishmania parasites that localize in macrophages of the liver, spleen, skin, bone marrow, and lymph nodes. MTS is extensively distributed in tissues and has prolonged elimination half-lives due to its high plasma protein binding, slow metabolic clearance, and minimal urinary excretion. Thus, understanding and predicting the tissue distribution of MTS help assess therapeutic and toxicologic outcomes of MTS, especially in special populations, e.g., pediatrics. In this study, a whole-body physiologically-based pharmacokinetic (PBPK) model of MTS was built on mice and extrapolated to rats and humans. MTS plasma and tissue concentration data obtained by intravenous and oral administration to mice were fitted simultaneously to estimate model parameters. The resulting high tissue-to-plasma partition coefficient values corroborate extensive distribution in all major organs except the bone marrow. Sensitivity analysis suggests that plasma exposure is most susceptible to changes in fraction unbound in plasma. The murine oral-PBPK model was further validated by assessing overlay of simulations with plasma and tissue profiles obtained from an independent study. Subsequently, the murine PBPK model was extrapolated to rats and humans based on species-specific physiological and drug-related parameters, as well as allometrically scaled parameters. Fold errors for pharmacokinetic parameters were within acceptable range in both extrapolated models, except for a slight underprediction in the human plasma exposure. These animal and human PBPK models are expected to provide reliable estimates of MTS tissue distribution and assist dose regimen optimization in special populations.

A Combination of Machine Learning and PBPK Modeling Approach for Pharmacokinetics Prediction of Small Molecules in Humans

PURPOSE

Recently, there has been rapid development in model-informed drug development, which has the potential to reduce animal experiments and accelerate drug discovery. Physiologically based pharmacokinetic (PBPK) and machine learning (ML) models are commonly used in early drug discovery to predict drug properties. However, basic PBPK models require a large number of molecule-specific inputs from in vitro experiments, which hinders the efficiency and accuracy of these models. To address this issue, this paper introduces a new computational platform that combines ML and PBPK models. The platform predicts molecule PK profiles with high accuracy and without the need for experimental data.

METHODS

This study developed a whole-body PBPK model and ML models of plasma protein fraction unbound ( f up ), Caco-2 cell permeability, and total plasma clearance to predict the PK of small molecules after intravenous administration. Pharmacokinetic profiles were simulated using a "bottom-up" PBPK modeling approach with ML inputs. Additionally, 40 compounds were used to evaluate the platform's accuracy.

RESULTS

Results showed that the ML-PBPK model predicted the area under the concentration-time curve (AUC) with 65.0 % accuracy within a 2-fold range, which was higher than using in vitro inputs with 47.5 % accuracy.

CONCLUSION

The ML-PBPK model platform provides high accuracy in prediction and reduces the number of experiments and time required compared to traditional PBPK approaches. The platform successfully predicts human PK parameters without in vitro and in vivo experiments and can potentially guide early drug discovery and development.

Physiologically Based Pharmacokinetic Modeling to Unravel the Drug-gene Interactions of Venlafaxine: Based on Activity Score-dependent Metabolism by CYP2D6 and CYP2C19 Polymorphisms

BACKGROUND

Venlafaxine (VEN) is a commonly utilized medication for alleviating depression and anxiety disorders. The presence of genetic polymorphisms gives rise to considerable variations in plasma concentrations across different phenotypes. This divergence in phenotypic responses leads to notable differences in both the efficacy and tolerance of the drug.

PURPOSE

A physiologically based pharmacokinetic (PBPK) model for VEN and its metabolite O-desmethylvenlafaxine (ODV) to predict the impact of CYP2D6 and CYP2C19 gene polymorphisms on VEN pharmacokinetics (PK).

METHODS

The parent-metabolite PBPK models for VEN and ODV were developed using PK-Sim® and MoBi®. Leveraging prior research, derived and implemented CYP2D6 and CYP2C19 activity score (AS)-dependent metabolism to simulate exposure in the drug-gene interactions (DGIs) scenarios. The model's performance was evaluated by comparing predicted and observed values of plasma concentration-time (PCT) curves and PK parameters values.

RESULTS

In the base models, 91.1%, 94.8%, and 94.6% of the predicted plasma concentrations for VEN, ODV, and VEN + ODV, respectively, fell within a twofold error range of the corresponding observed concentrations. For DGI scenarios, these values were 81.4% and 85% for VEN and ODV, respectively. Comparing CYP2D6 AS = 2 (normal metabolizers, NM) populations to AS = 0 (poor metabolizers, PM), 0.25, 0.5, 0.75, 1.0 (intermediate metabolizers, IM), 1.25, 1.5 (NM), and 3.0 (ultrarapid metabolizers, UM) populations in CYP2C19 AS = 2.0 group, the predicted DGI AUC0-96 h ratios for VEN were 3.65, 3.09, 2.60, 2.18, 1.84, 1.56, 1.34, 0.61, and for ODV, they were 0.17, 0.35, 0.51, 0.64, 0.75, 0.83, 0.90, 1.11, and the results were similar in other CYP2C19 groups. It should be noted that PK differences in CYP2C19 phenotypes were not similar across different CYP2D6 groups.

CONCLUSIONS

In clinical practice, the impact of genotyping on the in vivo disposition process of VEN should be considered to ensure the safety and efficacy of treatment.

Physiologically based pharmacokinetic modeling of apixaban to predict exposure in populations with hepatic and renal impairment and elderly populations

BACKGROUND

Apixaban is a factor Xa inhibitor with a limited therapeutic index that belongs to the family of oral direct anticoagulants. The pharmacokinetic (PK) behavior of apixaban may be altered in elderly populations and populations with renal or hepatic impairment, necessitating dosage adjustments.

METHODS

This study was conducted to examine how the physiologically based pharmacokinetic (PBPK) model describes the PKs of apixaban in adult and elderly populations and to determine the PKs of apixaban in elderly populations with renal and hepatic impairment. After PBPK models were constructed using the reported physicochemical properties of apixaban and clinical data, they were validated using data from clinical studies involving various dose ranges. Comparing predicted and observed blood concentration data and PK parameters was utilized to evaluate the model's fit performance.

RESULTS

Doses should be reduced to approximately 70% of the healthy adult population for the healthy elderly population to achieve the same PK exposure; approximately 88%, 71%, and 89% of that for the elderly populations with mild, moderate, and severe renal impairment, respectively; and approximately 96%, 81%, and 58% of that for the Child Pugh-A, Child Pugh-B, and Child Pugh-C hepatic impairment elderly populations, respectively to achieve the same PK exposure.

CONCLUSION

The findings indicate that the renal and hepatic function might be considered for apixaban therapy in Chinese elderly patients and the PBPK model can be used to optimize dosage regimens for specific populations.

A Complete Extension of Classical Hepatic Clearance Models Using Fractional Distribution Parameter fd in Physiologically Based Pharmacokinetics

The classical organ clearance models have been proposed to relate the plasma clearance CLp to probable mechanism(s) of hepatic clearance. However, the classical models assume the intrinsic capability of drug elimination (CLu,int) that is physically segregated from the vascular blood but directly acts upon the unbound drug concentration in the blood (fubCavg), and do not handle the transit-time delay between the inlet/outlet concentrations in their closed-form clearance equations. Therefore, we propose unified model structures that can address the internal blood concentration patterns of clearance organs in a more mechanistic/physiological manner, based on the fractional distribution parameter fd operative in PBPK. The basic partial/ordinary differential equations for four classical models are revisited/modified to yield a more complete set of extended clearance models, i.e., the Rattle, Sieve, Tube, and Jar models, which are the counterparts of the dispersion, series-compartment, parallel-tube, and well-stirred models. We demonstrate the feasibility of applying the resulting extended models to isolated perfused rat liver data for 11 compounds and an example dataset for in vitro-in vivo extrapolation of the intrinsic to the systemic clearances. Based on their feasibilities to handle such real data, these models may serve as an improved basis for applying clearance models in the future.

The Interplay of Permeability, Metabolism, Transporters, and Dosing in Determining the Dynamics of the Tissue/Plasma Partition Coefficient and Volume of Distribution-A Theoretical Investigation Using Permeability-Limited, Physiologically Based Pharmacokinetic Modeling

A permeability-limited physiologically based pharmacokinetic (PBPK) model featuring four subcompartments (corresponding to the intracellular and extracellular water of the tissue, the residual plasma, and blood cells) for each tissue has been developed in MATLAB/SimBiology and applied to various what-if scenario simulations. This model allowed us to explore the complex interplay of passive permeability, metabolism in tissue or residual blood, active uptake or efflux transporters, and different dosing routes (intravenous (IV) or oral (PO)) in determining the dynamics of the tissue/plasma partition coefficient (Kp) and volume of distribution (Vd) within a realistic pseudo-steady state. Based on the modeling exercise, the permeability, metabolism, and transporters demonstrated significant effects on the dynamics of the Kp and Vd for IV bolus administration and PO fast absorption, but these effects were not as pronounced for IV infusion or PO slow absorption. Especially for low-permeability compounds, uptake transporters were found to increase both the Kp and Vd at the pseudo-steady state (Vdss), while efflux transporters had the opposite effect of decreasing the Kp and Vdss. For IV bolus administration and PO fast absorption, increasing tissue metabolism was predicted to elevate the Kp and Vdss, which contrasted with the traditional derivation from the steady-state perfusion-limited PBPK model. Moreover, metabolism in the residual blood had more impact on the Kp and Vdss compared to metabolism in tissue. Due to its ability to offer a more realistic description of tissue dynamics, the permeability-limited PBPK model is expected to gain broader acceptance in describing clinical PK and observed Kp and Vdss, even for certain small molecules like cyclosporine, which are currently treated as perfusion-limited in commercial PBPK platforms.

Expansion of platform physiologically-based pharmacokinetic model for monoclonal antibodies towards different preclinical species: cats, sheep, and dogs

Monoclonal antibodies (mAbs) are becoming an important therapeutic option in veterinary medicine, and understanding the pharmacokinetic (PK) of mAbs in higher-order animal species is also important for human drug development. To better understand the PK of mAbs in these animals, here we have expanded a platform physiological-based pharmacokinetic (PBPK) model to characterize the disposition of mAbs in three different preclinical species: cats, sheep, and dogs. We obtained PK data for mAbs and physiological parameters for the three different species from the literature. We were able to describe the PK of mAbs following intravenous (IV) or subcutaneous administration in cats, IV administration in sheep, and IV administration dogs reasonably well by fixing the physiological parameters and just estimating the parameters related to the binding of mAbs to the neonatal Fc receptor. The platform PBPK model presented here provides a quantitative tool to predict the plasma PK of mAbs in dogs, cats, and sheep. The model can also predict mAb PK in different tissues where the site of action might be located. As such, the mAb PBPK model presented here can facilitate the discovery, development, and preclinical-to-clinical translation of mAbs for veterinary and human medicine. The model can also be modified in the future to account for more detailed compartments for certain organs, different pathophysiology in the animals, and target-mediated drug disposition.

Physiologically based pharmacokinetic modeling of levetiracetam to predict the exposure in hepatic and renal impairment and elderly populations

Levetiracetam (LEV) is an anti-epileptic drug approved for use in various populations. The pharmacokinetic (PK) behavior of LEV may be altered in the elderly and patients with renal and hepatic impairment. Thus, dosage adjustment is required. This study was conducted to investigate how the physiologically-based PK (PBPK) model describes the PKs of LEV in adult and elderly populations, as well as to predict the PKs of LEV in patients with renal and hepatic impairment in both populations. The whole-body PBPK models were developed using the reported physicochemical properties of LEV and clinical data. The models were validated using data from clinical studies with different dose ranges and different routes and intervals of administration. The fit performance of the models was assessed by comparing predicted and observed blood concentration data and PK parameters. It is recommended that the doses be reduced to ~70%, 60%, and 45% of the adult dose for the mild, moderate, and severe renal impairment populations and ~95%, 80%, and 57% of the adult dose for the Child Pugh-A (CP-A), Child Pugh-B (CP-B), and Child Pugh-C (CP-C) hepatic impairment populations, respectively. No dose adjustment is required for the healthy elderly population, but dose reduction is required for the elderly with organ dysfunction accordingly, on a scale similar to that of adults. A PBPK model of LEV was successfully developed to optimize dosing regimens for special populations.

Development of a 2D-QSAR Model for Tissue-to-Plasma Partition Coefficient Value with High Accuracy Using Machine Learning Method, Minimum Required Experimental Values, and Physicochemical Descriptors

BACKGROUND

The demand for physiologically based pharmacokinetic (PBPK) model is increasing currently. New drug application (NDA) of many compounds is submitted with PBPK models for efficient drug development. Tissue-to-plasma partition coefficient (K_p) is a key parameter for the PBPK model to describe differential equations. However, it is difficult to obtain the K_p value experimentally because the measurement of drug concentration in the tissue is much harder than that in plasma.

OBJECTIVE

Instead of experiments, many researchers have sought in silico methods. Today, most of the models for K_p prediction are using in vitro and in vivo parameters as explanatory variables. We thought of physicochemical descriptors that could improve the predictability. Therefore, we aimed to develop the two-dimensional quantitative structure-activity relationship (2D-QSAR) model for K_p using physicochemical descriptors instead of in vivo experimental data as explanatory variables.

METHODS

We compared our model with the conventional models using 20-fold cross-validation according to the published method (Yun et al. J Pharmacokinet Pharmacodyn 41:1-14, 2014). We used random forest algorithm, which is known to be one of the best predictors for the 2D-QSAR model. Finally, we combined minimum in vitro experimental values and physiochemical descriptors. Thus, the prediction method for K_p value using a few in vitro parameters and physicochemical descriptors was developed; this is a multimodal model.

RESULTS

Its accuracy was found to be superior to that of the conventional models. Results of this research suggest that multimodality is useful for the 2D-QSAR model [RMSE and % of two-fold error: 0.66 and 42.2% (Berezohkovsky), 0.52 and 52.2% (Rodgers), 0.65 and 34.6% (Schmitt), 0.44 and 61.1% (published model), 0.41 and 62.1% (traditional model), 0.39 and 64.5% (multimodal model)].

CONCLUSION

We could develop a 2D-QSAR model for K_p value with the highest accuracy using a few in vitro experimental data and physicochemical descriptors.

A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug-Drug Interaction Perpetrators

The antifungal ketoconazole, which is mainly used for dermal infections and treatment of Cushing's syndrome, is prone to drug-food interactions (DFIs) and is well known for its strong drug-drug interaction (DDI) potential. Some of ketoconazole's potent inhibitory activity can be attributed to its metabolites that predominantly accumulate in the liver. This work aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model of ketoconazole and its metabolites for fasted and fed states and to investigate the impact of ketoconazole's metabolites on its DDI potential. The parent-metabolites model was developed with PK-Sim^® and MoBi^® using 53 plasma concentration-time profiles. With 7 out of 7 (7/7) DFI AUC_last and DFI C_max ratios within two-fold of observed ratios, the developed model demonstrated good predictive performance under fasted and fed conditions. DDI scenarios that included either the parent alone or with its metabolites were simulated and evaluated for the victim drugs alfentanil, alprazolam, midazolam, triazolam, and digoxin. DDI scenarios that included all metabolites as reversible inhibitors of CYP3A4 and P-gp performed best: 26/27 of DDI AUC_last and 21/21 DDI C_max ratios were within two-fold of observed ratios, while DDI models that simulated only ketoconazole as the perpetrator underperformed: 12/27 DDI AUC_last and 18/21 DDI C_max ratios were within the success limits.

Development of physiologically based toxicokinetic models for 3-monochloropropane-1,2-diol and glycidol

3-Monochloropropane-1,2-diol (3-MCPD), glycidol, together with their fatty acid esters are commonly presented in various food and have shown carcinogenicity in various laboratory animals. Public health risk assessment of 3-MPCD and glycidol exposure relies on quantitative tools that represent their in vivo toxicokinetics. In order to better understand the absorption, distribution, metabolism, and excretion profiles of 3-MCPD and glycidol in male rats, a physiologically based pharmacokinetic (PBTK) model was developed. The model's predictive power was evaluated by comparing in silico simulations to in vivo time course data obtained from experimental studies. Results indicate that our PBTK model successfully captured the toxicokinetics of both free chemicals in key organs, and their metabolites in accessible biological fluids. With the validated PBTK model, we then gave an animal-free example on how to extrapolate the toxicological knowledge acquired from a single gavage to a realistic dietary intake scenario. Three biomarkers, free compound in serum, urinary metabolite DHPMA, and glycidol-hemoglobin adduct (diHOPrVal) were selected for in silico simulation following constant dietary intakes, and their internal levels were correlated with proposed external daily exposure via reverse dosimetry approaches. Taken together, our model provides a computational approach for extrapolating animal toxicokinetic experiments to biomonitoring measurement and risk assessment.

Physiologically-based toxicokinetic model for the prediction of perchlorate distribution and its application

Perchlorate is a stable and readily transportable thyroid hormone disruptor, and prevalent exposure to perchlorate through food and drinking water has raised public concern about its health effects. The physiologically based toxicokinetic (PBTK) model as a dose prediction method is effective to predict the toxicant exposure dose of an organism and helps quantitatively assess the dose-dependent relationship with toxic effects. The current study aimed to establish a multi-compartment PBTK model based on updated time-course datasets of single oral exposure to perchlorate in rats. With adjustment of the kinetic parameters, the model fitted well the toxicokinetic characteristics of perchlorate in urine, blood, and thyroid from our experiments and the literature, and the coefficient of determination (R²) between the fitting values and the experimental data in regression analysis was greater than 0.91, indicating the robustness of the current model. The results of sensitivity analysis and daily repeated exposure simulations together confirmed its effective renal clearance. According to the distribution characteristic of perchlorate, a correlation study of internal and external exposure was conducted using urinary perchlorate as a bioassay indicator. The developed multi-compartment model for perchlorate updates important toxicokinetic data and kinetic parameters, providing analytical and modeling tools for deriving total exposure levels in the short term.

Prediction of Drug-Drug-Gene Interaction Scenarios of (E)-Clomiphene and Its Metabolites Using Physiologically Based Pharmacokinetic Modeling

Clomiphene, a selective estrogen receptor modulator (SERM), has been used for the treatment of anovulation for more than 50 years. However, since (E)-clomiphene ((E)-Clom) and its metabolites are eliminated primarily via Cytochrome P450 (CYP) 2D6 and CYP3A4, exposure can be affected by CYP2D6 polymorphisms and concomitant use with CYP inhibitors. Thus, clomiphene therapy may be susceptible to drug-gene interactions (DGIs), drug-drug interactions (DDIs) and drug-drug-gene interactions (DDGIs). Physiologically based pharmacokinetic (PBPK) modeling is a tool to quantify such DGI and DD(G)I scenarios. This study aimed to develop a whole-body PBPK model of (E)-Clom including three important metabolites to describe and predict DGI and DD(G)I effects. Model performance was evaluated both graphically and by calculating quantitative measures. Here, 90% of predicted C_max and 80% of AUC_last values were within two-fold of the corresponding observed value for DGIs and DD(G)Is with clarithromycin and paroxetine. The model also revealed quantitative contributions of different CYP enzymes to the involved metabolic pathways of (E)-Clom and its metabolites. The developed PBPK model can be employed to assess the exposure of (E)-Clom and its active metabolites in as-yet unexplored DD(G)I scenarios in future studies.

Predictive performance and verification of physiologically based pharmacokinetic model of propylthiouracil

Background: Propylthiouracil (PTU) treats hyperthyroidism and thyroid crisis in all age groups. A variety of serious adverse effects can occur during clinical use and require attention to its pharmacokinetic and pharmacodynamic characteristics in various populations.

Objective: To provide information for individualized dosing and clinical evaluation of PTU in the clinical setting by developing a physiologically based pharmacokinetic (PBPK) model, predicting ADME characteristics, and extrapolating to elderly and pediatric populations.

Methods: Relevant databases and literature were retrieved to collect PTU’s pharmacochemical properties and ADME parameters, etc. A PBPK model for adults was developed using PK-Sim® software to predict tissue distribution and extrapolated to elderly and pediatric populations. The mean fold error (MFE) method was used to compare the differences between predicted and observed values to assess the accuracy of the PBPK model. The model was validated using PTU pharmacokinetic data in healthy adult populations.

Result: The MFE ratios of predicted to observed values of AUC0-t, Cmax, and Tmax were mainly within 0.5 and 2. PTU concentrations in various tissues are lower than venous plasma concentrations. Compared to healthy adults, the pediatric population requires quantitative adjustment to the appropriate dose to achieve the same plasma exposure levels, while the elderly do not require dose adjustments.

Conclusion: The PBPK model of PTU was successfully developed, externally validated, and applied to tissue distribution prediction and special population extrapolation, which provides a reference for clinical individualized drug administration and evaluation.

Physiologically Based Pharmacokinetic Modeling to Describe the CYP2D6 Activity Score-Dependent Metabolism of Paroxetine, Atomoxetine and Risperidone

The cytochrome P450 2D6 (CYP2D6) genotype is the single most important determinant of CYP2D6 activity as well as interindividual and interpopulation variability in CYP2D6 activity. Here, the CYP2D6 activity score provides an established tool to categorize the large number of CYP2D6 alleles by activity and facilitates the process of genotype-to-phenotype translation. Compared to the broad traditional phenotype categories, the CYP2D6 activity score additionally serves as a superior scale of CYP2D6 activity due to its finer graduation. Physiologically based pharmacokinetic (PBPK) models have been successfully used to describe and predict the activity score-dependent metabolism of CYP2D6 substrates. This study aimed to describe CYP2D6 drug–gene interactions (DGIs) of important CYP2D6 substrates paroxetine, atomoxetine and risperidone by developing a substrate-independent approach to model their activity score-dependent metabolism. The models were developed in PK-Sim^®, using a total of 57 plasma concentration–time profiles, and showed good performance, especially in DGI scenarios where 10/12, 5/5 and 7/7 of DGI AUC_last ratios and 9/12, 5/5 and 7/7 of DGI C_max ratios were within the prediction success limits. Finally, the models were used to predict their compound’s exposure for different CYP2D6 activity scores during steady state. Here, predicted DGI AUC_ss ratios were 3.4, 13.6 and 2.0 (poor metabolizers; activity score = 0) and 0.2, 0.5 and 0.95 (ultrarapid metabolizers; activity score = 3) for paroxetine, atomoxetine and risperidone active moiety (risperidone + 9-hydroxyrisperidone), respectively.

Physiologically Based Pharmacokinetic (PBPK) Modeling of Clopidogrel and Its Four Relevant Metabolites for CYP2B6, CYP2C8, CYP2C19, and CYP3A4 Drug–Drug–Gene Interaction Predictions

The antiplatelet agent clopidogrel is listed by the FDA as a strong clinical index inhibitor of cytochrome P450 (CYP) 2C8 and weak clinical inhibitor of CYP2B6. Moreover, clopidogrel is a substrate of—among others—CYP2C19 and CYP3A4. This work presents the development of a whole-body physiologically based pharmacokinetic (PBPK) model of clopidogrel including the relevant metabolites, clopidogrel carboxylic acid, clopidogrel acyl glucuronide, 2-oxo-clopidogrel, and the active thiol metabolite, with subsequent application for drug–gene interaction (DGI) and drug–drug interaction (DDI) predictions. Model building was performed in PK-Sim^® using 66 plasma concentration-time profiles of clopidogrel and its metabolites. The comprehensive parent-metabolite model covers biotransformation via carboxylesterase (CES) 1, CES2, CYP2C19, CYP3A4, and uridine 5′-diphospho-glucuronosyltransferase 2B7. Moreover, CYP2C19 was incorporated for normal, intermediate, and poor metabolizer phenotypes. Good predictive performance of the model was demonstrated for the DGI involving CYP2C19, with 17/19 predicted DGI AUC_last and 19/19 predicted DGI C_max ratios within 2-fold of their observed values. Furthermore, DDIs involving bupropion, omeprazole, montelukast, pioglitazone, repaglinide, and rifampicin showed 13/13 predicted DDI AUC_last and 13/13 predicted DDI C_max ratios within 2-fold of their observed ratios. After publication, the model will be made publicly accessible in the Open Systems Pharmacology repository.

Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues

Metformin is the primary drug for type 2 diabetes treatment and a promising candidate for other disease treatment. It has significant deviations between individuals in therapy efficiency and pharmacokinetics, leading to the administration of an unnecessary overdose or an insufficient dose. There is a lack of data regarding the concentration-time profiles in various human tissues that limits the understanding of pharmacokinetics and hinders the development of precision therapies for individual patients. The physiologically based pharmacokinetic (PBPK) model developed in this study is based on humans’ known physiological parameters (blood flow, tissue volume, and others). The missing tissue-specific pharmacokinetics parameters are estimated by developing a PBPK model of metformin in mice where the concentration time series in various tissues have been measured. Some parameters are adapted from human intestine cell culture experiments. The resulting PBPK model for metformin in humans includes 21 tissues and body fluids compartments and can simulate metformin concentration in the stomach, small intestine, liver, kidney, heart, skeletal muscle adipose, and brain depending on the body weight, dose, and administration regimen. Simulations for humans with a bodyweight of 70kg have been analyzed for doses in the range of 500-1500mg. Most tissues have a half-life (T_1/2) similar to plasma (3.7h) except for the liver and intestine with shorter T_1/2 and muscle, kidney, and red blood cells that have longer T_1/2. The highest maximal concentrations (C_max) turned out to be in the intestine (absorption process) and kidney (excretion process), followed by the liver. The developed metformin PBPK model for mice does not have a compartment for red blood cells and consists of 20 compartments. The developed human model can be personalized by adapting measurable values (tissue volumes, blood flow) and measuring metformin concentration time-course in blood and urine after a single dose of metformin. The personalized model can be used as a decision support tool for precision therapy development for individuals.

Physiologically Based Pharmacokinetic Modeling of Bupropion and Its Metabolites in a CYP2B6 Drug-Drug-Gene Interaction Network

The noradrenaline and dopamine reuptake inhibitor bupropion is metabolized by CYP2B6 and recommended by the FDA as the only sensitive substrate for clinical CYP2B6 drug-drug interaction (DDI) studies. The aim of this study was to build a whole-body physiologically based pharmacokinetic (PBPK) model of bupropion including its DDI-relevant metabolites, and to qualify the model using clinical drug-gene interaction (DGI) and DDI data. The model was built in PK-Sim® applying clinical data of 67 studies. It incorporates CYP2B6-mediated hydroxylation of bupropion, metabolism via CYP2C19 and 11β-HSD, as well as binding to pharmacological targets. The impact of CYP2B6 polymorphisms is described for normal, poor, intermediate, and rapid metabolizers, with various allele combinations of the genetic variants CYP2B6*1, *4, *5 and *6. DDI model performance was evaluated by prediction of clinical studies with rifampicin (CYP2B6 and CYP2C19 inducer), fluvoxamine (CYP2C19 inhibitor) and voriconazole (CYP2B6 and CYP2C19 inhibitor). Model performance quantification showed 20/20 DGI ratios of hydroxybupropion to bupropion AUC ratios (DGI AUCHBup/Bup ratios), 12/13 DDI AUCHBup/Bup ratios, and 7/7 DDGI AUCHBup/Bup ratios within 2-fold of observed values. The developed model is freely available in the Open Systems Pharmacology model repository.

Pharmacokinetics of the CYP3A4 and CYP2B6 Inducer Carbamazepine and Its Drug-Drug Interaction Potential: A Physiologically Based Pharmacokinetic Modeling Approach

The anticonvulsant carbamazepine is frequently used in the long-term therapy of epilepsy and is a known substrate and inducer of cytochrome P450 (CYP) 3A4 and CYP2B6. Carbamazepine induces the metabolism of various drugs (including its own); on the other hand, its metabolism can be affected by various CYP inhibitors and inducers. The aim of this work was to develop a physiologically based pharmacokinetic (PBPK) parent-metabolite model of carbamazepine and its metabolite carbamazepine-10,11-epoxide, including carbamazepine autoinduction, to be applied for drug-drug interaction (DDI) prediction. The model was developed in PK-Sim, using a total of 92 plasma concentration-time profiles (dosing range 50-800 mg), as well as fractions excreted unchanged in urine measurements. The carbamazepine model applies metabolism by CYP3A4 and CYP2C8 to produce carbamazepine-10,11-epoxide, metabolism by CYP2B6 and UDP-glucuronosyltransferase (UGT) 2B7 and glomerular filtration. The carbamazepine-10,11-epoxide model applies metabolism by epoxide hydroxylase 1 (EPHX1) and glomerular filtration. Good DDI performance was demonstrated by the prediction of carbamazepine DDIs with alprazolam, bupropion, erythromycin, efavirenz and simvastatin, where 14/15 DDI AUClast ratios and 11/15 DDI Cmax ratios were within the prediction success limits proposed by Guest et al. The thoroughly evaluated model will be freely available in the Open Systems Pharmacology model repository.

Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment

Human biomonitoring (HBM) data can provide insight into co-exposure patterns resulting from exposure to multiple chemicals from various sources and over time. Therefore, such data are particularly valuable for assessing potential risks from combined exposure to multiple chemicals. One way to interpret HBM data is establishing safe levels in blood or urine, called Biomonitoring Equivalents (BE) or HBM health based guidance values (HBM-HBGV). These can be derived by converting established external reference values, such as tolerable daily intake (TDI) values. HBM-HBGV or BE values are so far agreed only for a very limited number of chemicals. These values can be established using physiologically based kinetic (PBK) modelling, usually requiring substance specific models and the collection of many input parameters which are often not available or difficult to find in the literature. The aim of this study was to investigate the suitability and limitations of generic PBK models in deriving BE values for several compounds with a view to facilitating the use of HBM data in the assessment of chemical mixtures at a screening level. The focus was on testing the methodology with two generic models, the IndusChemFate tool and High-Throughput Toxicokinetics package, for two different classes of compounds, phenols and phthalates. HBM data on Danish children and on Norwegian mothers and children were used to evaluate the quality of the predictions and to illustrate, by means of a case study, the overall approach of applying PBK models to chemical classes with HBM data in the context of chemical mixture risk assessment. Application of PBK models provides a better understanding and interpretation of HBM data. However, the study shows that establishing safety threshold levels in urine is a difficult and complex task. The approach might be more straightforward for more persistent chemicals that are analysed as parent compounds in blood but high uncertainties have to be considered around simulated metabolite concentrations in urine. Refining the models may reduce these uncertainties and improve predictions. Based on the experience gained with this study, the performance of the models for other chemicals could be investigated, to improve the accuracy of the simulations.

Physiologically based pharmacokinetic-pharmacodynamic modeling for prediction of vonoprazan pharmacokinetics and its inhibition on gastric acid secretion following intravenous/oral administration to rats, dogs and humans

Vonoprazan is characterized as having a long-lasting antisecretory effect on gastric acid. In this study we developed a physiologically based pharmacokinetic (PBPK)-pharmacodynamic (PD) model linking to stomach to simultaneously predict vonoprazan pharmacokinetics and its antisecretory effects following administration to rats, dogs, and humans based on in vitro parameters. The vonoprazan disposition in the stomach was illustrated using a limited-membrane model. In vitro metabolic and transport parameters were derived from hepatic microsomes and Caco-2 cells, respectively. We found the most predicted plasma concentrations and pharmacokinetic parameters of vonoprazan in rats, dogs and humans were within twofold errors of the observed data. Free vonoprazan concentrations (f_u × C₂) in the stomach were simulated and linked to the antisecretory effects of the drug (I) (increases in pH or acid output) using the fomula dI/dt = k × f_u × C₂ × (I_max − I) − k_d × I. The vonoprazan dissociation rate constant k_d (0.00246 min⁻¹) and inhibition index K_I (35 nM) for H⁺/K⁺-ATPase were obtained from literatures. The vonoprazan-H⁺/K⁺-ATPase binding rate constant k was 0.07028 min⁻¹· μM⁻¹ using ratio of k_d to K_I. The predicted antisecretory effects were consistent with the observations following intravenous administration to rats (0.7 and 1.0 mg/kg), oral administration to dogs (0.3 and 1.0 mg/kg) and oral single dose or multidose to humans (20, 30, and 40 mg). Simulations showed that vonoprazan concentrations in stomach were 1000-fold higher than those in the plasma at 24 h following administration to human. Vonoprazan pharmacokinetics and its antisecretory effects may be predicted from in vitro data using the PBPK-PD model of the stomach. These findings may highlight 24-h antisecretory effects of vonoprazan in humans following single-dose or the sustained inhibition throughout each 24-h dosing interval during multidose administration.

Efflux transporter breast cancer resistance protein dominantly expresses on the membrane of red blood cells, hinders partitioning of its substrates into the cells, and alters drug-drug interaction profiles

1. Red blood cell (RBC) partitioning is important in determining pharmacokinetic and pharmacodynamic properties of a compound; however, active transport across RBC membranes is not well understood, particularly without transporter-related cell membrane proteomics data. 2. In this study, we quantified breast cancer resistance protein (BCRP/Bcrp) and MDR1/P-glycoprotein (P-gp) protein expression in RBCs from humans, monkeys, dogs, rats and mice using nanoLC/MS/MS, and evaluated their effect on RBC partitioning and plasma exposure of their substrates. BCRP-specific substrate Cpd-1 and MDR1-specific substrate Cpd-2 were characterized using Caco-2 Transwell® system and then administered to Bcrp or P-gp knockout mice. 3. The quantification revealed BCRP/Bcrp but not MDR1/P-gp to be highly expressed on RBC membranes. The knockout mouse study indicated BCRP/Bcrp pumps the substrate out of RBCs, lowering its partitioning and thus preventing binding to intracellular targets. This result was supported by a Cpd-1 and Bcrp inhibitor ML753286 drug-drug interaction (DDI) study in mice. Because of enhanced partitioning of Cpd-1 into RBCs after BCRP/Bcrp inhibition, Cpd-1 plasma concentration changed much less extent with genetic or chemical knockout of Bcrp albeit marked blood concentration increase, suggesting less DDI effect. 4. This finding is fundamentally meaningful to RBC partitioning, pharmacokinetics and DDI studies of BCRP-specific substrates.

Physiologically based pharmacokinetic modelling of atomoxetine with regard to CYP2D6 genotypes

Atomoxetine is a norepinephrine reuptake inhibitor indicated in the treatment of attention-deficit/hyperactivity disorder. It is primarily metabolized by CYP2D6 to its equipotent metabolite, 4-hydroxyatomoxetine, which promptly undergoes further glucuronidation to an inactive 4-HAT-O-glucuronide. Clinical trials have shown that decreased CYP2D6 activity leads to substantially elevated atomoxetine exposure and increase in adverse reactions. The aim of this study was to to develop a pharmacologically based pharmacokinetic (PBPK) model of atomoxetine in different CYP2D6 genotypes. A single 20 mg dose of atomoxetine was given to 19 healthy Korean individuals with CYP2D6*wt/*wt (*wt = *1 or *2) or CYP2D6*10/*10 genotype. Based on the results of this pharmacokinetic study, a PBPK model for CYP2D6*wt/*wt individuals was developed. This model was scaled to those with CYP2D6*10/*10 genotype, as well as CYP2D6 poor metabolisers. We validated this model by comparing the predicted pharmacokinetic parameters with diverse results from the literature. The presented PBPK model describes the pharmacokinetics after single and repeated oral atomoxetine doses with regard to CYP2D6 genotype and phenotype. This model could be utilized for identification of appropriate dosages of atomoxetine in patients with reduced CYP2D6 activity to minimize the adverse events, and to enable personalised medicine.

Physiologically-based modeling and interspecies prediction of paclitaxel pharmacokinetics

The objective was to develop a physiologically-based pharmacokinetic (PBPK) model to characterize the whole-body disposition of paclitaxel (formulated in Cremophor EL and ethanol-Taxol^®) in mice and to evaluate the utility of this model for predicting pharmacokinetics in other species. Published studies that reported paclitaxel plasma and tissue concentration-time data following single intravenous bolus administration of Taxol^® to mice were used; and the PBPK model included plasma, liver, lungs, kidneys, spleen, heart, gastrointestinal tract, and remainder compartments. The final model resulted in a good description of the experimental plasma and tissues data in mice, where all tissues were represented by a single compartment, except the remainder that included two sub-compartments. The predictive performance of the PBPK model was assessed by evaluating its utility in predicting pharmacokinetics of paclitaxel in rats and humans. The relationship between species body weights (mice, rats, rabbits, and humans) and plasma clearance was determined by power-based regression, and resulting allometric exponent was 0.86. The model demonstrated reasonable predictions of plasma and tissue paclitaxel concentration-time profiles in rats and plasma profiles in humans. The proposed PBPK model represents an important basis that can be further utilized for characterization of novel formulations of paclitaxel.

Whole body physiologically based modelling of β-blockers in the rat: events in tissues and plasma following an i.v. bolus dose

BACKGROUND AND PURPOSE

Whole body physiologically based pharmacokinetic (PBPK) models have been increasingly applied in drug development to describe kinetic events of therapeutic agents in animals and humans. The advantage of such modelling is the ability to incorporate vast amounts of physiological information, such as organ blood flow and volume, to ensure that the model is as close to reality as possible.

EXPERIMENTAL APPROACH

Previous PBPK model development of enantiomers of a series of seven racemic β-blockers, namely, acebutolol, betaxolol, bisoprolol, metoprolol, oxprenolol, pindolol and propranolol, together with S-timolol in rat was based on tissue and blood concentration data at steady state. Compounds were administered in several cassettes with the composition mix and blood and tissue sampling times determined using a D-optimal design.

KEY RESULTS

Closed-loop PBPK models were developed initially based on the application of open loop forcing function models to individual tissues and compounds. For the majority of compounds and tissues, distribution kinetics was adequately characterized by perfusion rate-limited models. For some compounds in the testes and gut, a permeability rate-limited distribution model was required to best fit the data. Parameter estimates of the tissue-to-blood partition coefficient through fitting of individual enantiomers and of racemic pair were generally in agreement and also concur with those from previous steady-state experiments.

CONCLUSIONS AND IMPLICATIONS

PBPK modelling is a very powerful tool to aid drug discovery and development of therapeutic agents in animals and humans. However, careful consideration of the assumptions made during the modelling exercise is essential.

Modeling of Body Weight Metrics for Effective and Cost-Efficient Conventional Factor VIII Dosing in Hemophilia A Prophylaxis

The total body weight-based dosing strategy currently used in the prophylactic treatment of hemophilia A may not be appropriate for all populations. The assumptions that guide weight-based dosing are not valid in overweight and obese populations, resulting in overdosing and ineffective resource utilization. We explored different weight metrics including lean body weight, ideal body weight, and adjusted body weight to determine an alternative dosing strategy that is both safe and resource-efficient in normal and overweight/obese adult patients. Using a validated population pharmacokinetic model, we simulated a variety of dosing regimens using different doses, weight metrics, and frequencies; we also investigated the implications of assuming various levels of endogenous factor production. Ideal body weight performed the best across all of the regimens explored, maintaining safety while moderating resource consumption for overweight and obese patients.

A physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model of the histone deacetylase (HDAC) inhibitor vorinostat for pediatric and adult patients and its application for dose specification

PURPOSE

This study aimed at recommending pediatric dosages of the histone deacetylase (HDAC) inhibitor vorinostat and potentially more effective adult dosing regimens than the approved standard dosing regimen of 400 mg/day, using a comprehensive physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling approach.

METHODS

A PBPK/PD model for vorinostat was developed for predictions in adults and children. It includes the maturation of relevant metabolizing enzymes. The PBPK model was expanded by (1) effect compartments to describe vorinostat concentration-time profiles in peripheral blood mononuclear cells (PBMCs), (2) an indirect response model to predict the HDAC inhibition, and (3) a thrombocyte model to predict the dose-limiting thrombocytopenia. Parameterization of drug and system-specific processes was based on published and unpublished in silico, in vivo, and in vitro data. The PBPK modeling software used was PK-Sim and MoBi.

RESULTS

The PBPK/PD model suggests dosages of 80 and 230 mg/m² for children of 0-1 and 1-17 years of age, respectively. In comparison with the approved standard treatment, in silico trials reveal 11 dosing regimens (9 oral, and 2 intravenous infusion rates) increasing the HDAC inhibition by an average of 31%, prolonging the HDAC inhibition by 181%, while only decreasing the circulating thrombocytes to a tolerable 53%. The most promising dosing regimen prolongs the HDAC inhibition by 509%.

CONCLUSIONS

Thoroughly developed PBPK models enable dosage recommendations in pediatric patients and integrated PBPK/PD models, considering PD biomarkers (e.g., HDAC activity and platelet count), are well suited to guide future efficacy trials by identifying dosing regimens potentially superior to standard dosing regimens.

Development of Physiologically Based Organ Models to Evaluate the Pharmacokinetics of Drugs in the Testes and the Thyroid Gland

We extended a generic whole-body physiologically based pharmacokinetic (PBPK) model for rats and humans for organs of the reproductive and endocrine systems (i.e., the testes and the thyroid gland). An extensive literature search was performed, first, to determine the most generic organ model structures for testes and thyroid across species, and, second, to identify the corresponding anatomic and physiological parameters in rats and humans. The testes and thyroid organ models were implemented in the PBPK modeling software PK-Sim and MoBi. The capability of the PBPK approach to simulate the testes and thyroid tissue concentration data was demonstrated using a series of test compounds. The presented organ model structures and parameterization yielded a close agreement between observed and simulated tissue concentrations over time. The organ models are ready to be used to predict the pharmacokinetics of passively entering drugs in the testes and thyroid tissue in a generic PBPK modeling framework.

Diffusion-limited PBPK model for predicting pulmonary pharmacokinetics of florfenicol in pig

For most bacterial lung infections, the concentration of unbound antimicrobial agent in lung interstitial fluid has been thought to be responsible for antimicrobial efficacy. In this study, a diffusion-limited physiologically based pharmacokinetic (PBPK) model was developed to predict the pulmonary pharmacokinetics of florfenicol (FF) in pigs. The model included separate compartments corresponding to blood, diffusion-limited lung, flow-limited muscle, liver, and kidney and an extra compartment representing the remaining carcass. The absorption rate constant and renal and hepatic clearance of FF were determined in vivo. Other parameters were taken from the literature or optimized based on existing pharmacokinetic data. All mathematical operations during the development of the model were performed using acslXtreme version 3.0.2.1 (Aegis Technologies Group, Inc., Huntsville, AL, USA). The model accurately predicted the concentration-time courses of FF in lung interstitial fluid, serum, and plasma following different dosing schedules, except at the dose of 15 mg/kg. When compared with the tissue residue data, the model generally underestimated the FF concentration at the injection site, whereas it gave good predictions of FF concentrations in lung, liver, and kidney at early time points. The model predictions provide a scientific basis for the dosage regimen design of FF.

Drug-disease modeling in the pharmaceutical industry - where mechanistic systems pharmacology and statistical pharmacometrics meet

Modeling & simulation (M&S) methodologies are established quantitative tools, which have proven to be useful in supporting the research, development (R&D), regulatory approval, and marketing of novel therapeutics. Applications of M&S help design efficient studies and interpret their results in context of all available data and knowledge to enable effective decision-making during the R&D process. In this mini-review, we focus on two sets of modeling approaches: population-based models, which are well-established within the pharmaceutical industry today, and fall under the discipline of clinical pharmacometrics (PMX); and systems dynamics models, which encompass a range of models of (patho-)physiology amenable to pharmacological intervention, of signaling pathways in biology, and of substance distribution in the body (today known as physiologically-based pharmacokinetic models) - which today may be collectively referred to as quantitative systems pharmacology models (QSP). We next describe the convergence - or rather selected integration - of PMX and QSP approaches into 'middle-out' drug-disease models, which retain selected mechanistic aspects, while remaining parsimonious, fit-for-purpose, and able to address variability and the testing of covariates. We further propose development opportunities for drug-disease systems models, to increase their utility and applicability throughout the preclinical and clinical spectrum of pharmaceutical R&D.

The Constraints, Construction, and Verification of a Strain-Specific Physiologically Based Pharmacokinetic Rat Model

The use of in vitro-in vivo extrapolation (IVIVE) techniques, mechanistically incorporated within physiologically based pharmacokinetic (PBPK) models, can harness in vitro drug data and enhance understanding of in vivo pharmacokinetics. This study's objective was to develop a user-friendly rat (250 g, male Sprague-Dawley) IVIVE-linked PBPK model. A 13-compartment PBPK model including mechanistic absorption models was developed, with required system data (anatomical, physiological, and relevant IVIVE scaling factors) collated from literature and analyzed. Overall, 178 system parameter values for the model are provided. This study also highlights gaps in available system data required for strain-specific rat PBPK model development. The model's functionality and performance were assessed using previous literature-sourced in vitro properties for diazepam, metoprolol, and midazolam. The results of simulations were compared against observed pharmacokinetic rat data. Predicted and observed concentration profiles in 10 tissues for diazepam after a single intravenous (i.v.) dose making use of either observed i.v. clearance (CL_iv) or in vitro hepatocyte intrinsic clearance (CL_int) for simulations generally led to good predictions in various tissue compartments. Overall, all i.v. plasma concentration profiles were successfully predicted. However, there were challenges in predicting oral plasma concentration profiles for metoprolol and midazolam, and the potential reasons and according solutions are discussed.

Impact of altered endogenous IgG on unspecific mAb clearance

Immunodeficient mice are crucial models to evaluate the efficacy of monoclonal antibodies (mAbs). When studying mAb pharmacokinetics (PK), protection from elimination by binding to the neonatal Fc receptor (FcRn) is known to be a major process influencing the unspecific clearance of endogenous and therapeutic IgG. The concentration of endogenous IgG in immunodeficient mice, however is reduced, and this effect on the FcRn protection mechanism and subsequently on unspecific mAb clearance is unknown, yet of great importance for the interpretation of mAb PK data. We used a PBPK modelling approach to elucidate the influence of altered endogenous IgG concentrations on unspecific mAb clearance. To this end, we used PK data in immunodeficient mice, i.e. nude and severe combined immunodeficiency mice. To avoid impact of target-mediated clearance processes, we focussed on mAbs without affinity to a target antigen in these mice. In addition, intravenous immunoglobulin (IVIG) data of immunocompetent mice was used to study the impact of increased total IgG concentrations on unspecific therapeutic antibody clearance. The unspecific clearance is linear, whenever therapeutic IgG concentrations, i.e. mAb and IVIG concentrations are lower than FcRn; it can be non-linear if therapeutic IgG concentrations are larger than FcRn and endogenous IgG concentrations (e.g., under IVIG therapy). Unspecific mAb clearance of immunodeficient mice is effectively linear (under mAb doses as typically used in human). Studying the impact of reduced endogenous IgG concentrations on unspecific mAb clearance is of great relevance for the extrapolation to clinical species, e.g., when predicting mAb PK in immunosuppressed cancer patients.

Prediction of Deoxypodophyllotoxin Disposition in Mouse, Rat, Monkey, and Dog by Physiologically Based Pharmacokinetic Model and the Extrapolation to Human

Deoxypodophyllotoxin (DPT) is a potential anti-tumor candidate prior to its clinical phase. The aim of the study was to develop a physiologically based pharmacokinetic (PBPK) model consisting of 13 tissue compartments to predict DPT disposition in mouse, rat, monkey, and dog based on in vitro and in silico inputs. Since large interspecies difference was found in unbound fraction of DPT in plasma, we assumed that K_t:pl,u (unbound tissue-to-plasma concentration ratio) was identical across species. The predictions of our model were then validated by in vivo data of corresponding preclinical species, along with visual predictive checks. Reasonable matches were found between observed and predicted plasma concentrations and pharmacokinetic parameters in all four animal species. The prediction in the related seven tissues of mouse was also desirable. We also attempted to predict human pharmacokinetic profile by both the developed PBPK model and interspecies allometric scaling across mouse, rat and monkey, while dog was excluded from the scaling. The two approaches reached similar results. We hope the study will help in the efficacy and safety assessment of DPT in future clinical studies and provide a reference to the preclinical screening of similar compounds by PBPK model.

Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model

The aim of this tutorial is to introduce the fundamental concepts of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling with a special focus on their practical implementation in a typical PBPK model building workflow. To illustrate basic steps in PBPK model building, a PBPK model for ciprofloxacin will be constructed and coupled to a pharmacodynamic model to simulate the antibacterial activity of ciprofloxacin treatment.

The use of pharmacokinetics in dose individualization of factor VIII in the treatment of hemophilia A

INTRODUCTION

Hemophilia A is a bleeding disorder resulting from a lack of clotting factor VIII (FVIII), and treatment typically consists of prophylactic replacement of the deficient factor. However, high between subject variability precludes the development of a 'one size fits all' dosing strategy and necessitates an individualized approach. We sought to summarize the data on the pharmacokinetics of FVIII available as a basis for the development of population pharmacokinetic models to be used in dose tailoring. Areas covered: We reviewed the pharmacokinetics of FVIII as used for the treatment of hemophilia A, with a focus on the variability observed between patients and the application of pharmacokinetic methods to dose individualization. We also explored the covariates affecting pharmacokinetic parameters, the differences between plasma-derived and recombinant FVIII and the development of extended half-life products. Expert opinion: The pharmacokinetics of factor VIII in patients with hemophilia shows a high interpatient variability, and is affected by age, weight, level of von Willebrand factor, and blood group. A population approach to estimating individual pharmacokinetics is likely to provide the most successful strategy to tailor factor concentrate dosing to the individual needs and to ensure optimal patient outcomes, while also improving the cost-effectiveness of prophylactic replacement therapy.

Physiology-based pharmacokinetic modeling: ready to be used

Physiology-based pharmacokinetic (PBPK) modeling is well recognized as a technology for mechanistically simulating and predicting the fate of substances in a mammalian body. Today, the demand for this methodology is higher than ever. The pharma industry and regulatory agencies are looking for new methods, which help to speed up and increase the efficiency of the development process for new drugs. Implementing PBPK modeling in the drug research and development workflow contributes significantly to reach this goal.:

Monoclonal antibody disposition: a simplified PBPK model and its implications for the derivation and interpretation of classical compartment models

The structure, interpretation and parameterization of classical compartment models as well as physiologically-based pharmacokinetic (PBPK) models for monoclonal antibody (mAb) disposition are very diverse, with no apparent consensus. In addition, there is a remarkable discrepancy between the simplicity of experimental plasma and tissue profiles and the complexity of published PBPK models. We present a simplified PBPK model based on an extravasation rate-limited tissue model with elimination potentially occurring from various tissues and plasma. Based on model reduction (lumping), we derive several classical compartment model structures that are consistent with the simplified PBPK model and experimental data. We show that a common interpretation of classical two-compartment models for mAb disposition-identifying the central compartment with the total plasma volume and the peripheral compartment with the interstitial space (or part of it)-is not consistent with current knowledge. Results are illustrated for the monoclonal antibodies 7E3 and T84.66 in mice.

Prediction of methotrexate CNS distribution in different species - influence of disease conditions

Children and adults with malignant diseases have a high risk of prevalence of the tumor in the central nervous system (CNS). As prophylaxis treatment methotrexate is often given. In order to monitor methotrexate exposure in the CNS, cerebrospinal fluid (CSF) concentrations are often measured. However, the question is in how far we can rely on CSF concentrations of methotrexate as appropriate surrogate for brain target site concentrations, especially under disease conditions. In this study, we have investigated the spatial distribution of unbound methotrexate in healthy rat brain by parallel microdialysis, with or without inhibition of Mrp/Oat/Oatp-mediated active transport processes by a co-administration of probenecid. Specifically, we have focused on the relationship between brain extracellular fluid (brainECF) and CSF concentrations. The data were used to develop a systems-based pharmacokinetic (SBPK) brain distribution model for methotrexate. This model was subsequently applied on literature data on methotrexate brain distribution in other healthy and diseased rats (brainECF), healthy dogs (CSF) and diseased children (CSF) and adults (brainECF and CSF). Important differences between brainECF and CSF kinetics were found, but we have found that inhibition of Mrp/Oat/Oatp-mediated active transport processes does not significantly influence the relationship between brainECF and CSF fluid methotrexate concentrations. It is concluded that in parallel obtained data on unbound brainECF, CSF and plasma concentrations, under dynamic conditions, combined with advanced mathematical modeling is a most valid approach to develop SBPK models that allow for revealing the mechanisms underlying the relationship between brainECF and CSF concentrations in health and disease.

Cyclosporine inhibition of hepatic and intestinal CYP3A4, uptake and efflux transporters: application of PBPK modeling in the assessment of drug-drug interaction potential

PURPOSE

To apply physiologically-based pharmacokinetic (PBPK) modeling to investigate the consequences of reduction in activity of hepatic and intestinal uptake and efflux transporters by cyclosporine and its metabolite AM1.

METHODS

Inhibitory potencies of cyclosporine and AM1 against OATP1B1, OATP1B3 and OATP2B1 were investigated in HEK293 cells +/- pre-incubation. Cyclosporine PBPK model implemented in Matlab was used to assess interaction potential (+/- metabolite) against different processes (uptake, efflux and metabolism) in liver and intestine and to predict quantitatively drug-drug interaction with repaglinide.

RESULTS

Cyclosporine and AM1 were potent inhibitors of OATP1B1 and OATP1B3, IC(50) ranging from 0.019-0.093 μM following pre-incubation. Cyclosporine PBPK model predicted the highest interaction potential against liver uptake transporters, with a maximal reduction of >70% in OATP1B1 activity; the effect on hepatic efflux and metabolism was minimal. In contrast, 80-97% of intestinal P-gp and CYP3A4 activity was reduced due to the 50-fold higher cyclosporine enterocytic concentrations relative to unbound hepatic inlet. The inclusion of AM1 resulted in a minor increase in the predicted maximal reduction of OATP1B1/1B3 activity. Good predictability of cyclosporine-repaglinide DDI and the impact of dose staggering are illustrated.

CONCLUSIONS

This study highlights the application of PBPK modeling for quantitative prediction of transporter-mediated DDIs with concomitant consideration of P450 inhibition.

A mechanistic, model-based approach to safety assessment in clinical development

Assessing the safety of pharmacotherapies is a primary goal of clinical trials in drug development. The low frequency of relevant side effects, however, often poses a significant challenge for risk assessment. Methodologies allowing robust extrapolation of safety statistics based on preclinical data and information from clinical trials with limited numbers of patients are hence needed to further improve safety and efficacy in the drug development process. Here, we present a generic systems pharmacology approach integrating prior physiological and pharmacological knowledge, preclinical data, and clinical trial results, which allows predicting adverse event rates related to drug exposure. Possible fields of application involve high-risk populations, novel drug candidates, and different dosing scenarios. As an example, the approach is applied to simvastatin and pravastatin and the prediction of myopathy rates in a population with a genotype leading to a significantly increased myopathy risk.CPT: Pharmacometrics & Systems Pharmacology (2012) 1, e13; doi:10.1038/psp.2012.14; advance online publication 7 November 2012.

Physiological-based pharmacokinetic modeling of endotoxin in the rat

We have previously measured the distribution and pharmacokinetics of biosynthetically radiolabeled endotoxin of Salmonella typhimurium following intraperitoneal (IP) dosing (200 μg/kg) in Sprague-Dawley rats. In our experiments, the fatty acid residues were labeled with (3)H and the glucosamine residues were labeled with (14)C. To predict the dynamics of endotoxin exposure, we developed a physiological-based pharmacokinetic model using our measured distribution results. The model was validated with published low-dose (30 μg/kg) IP exposure results in rats. Endotoxin pharmacokinetics depended on dose and route. At high IP doses, absorption was followed by biphasic decay over 48 h in plasma. There were tissue accumulations of the fatty acid and glucosamine residues in various target organs, including the brain. We also found that the glucosamine and fatty acid components separated in vivo about 3 h after IP injection. At the lower IP dose, a smaller fraction of the dose was distributed to the tissues, with most of the dose remaining in the blood. Each component had its own dynamic behavior and target tissue distribution in the rat. The fatty acid components tended to remain in the brain stem, caudate nucleus, cerebellum, frontal cortex, hippocampus, and hypothalamus. Other organs (spleen, kidney, meninges, and choroid plexus) had similar biphasic distribution. The liver had the unique accumulation of both glucosamine and fatty acid residues.

Comparison of in-vivo and in-silico methods used for prediction of tissue: plasma partition coefficients in rat

OBJECTIVES

To use methods from the literature to predict rat tissue:plasma partition coefficients (Kps) and volume of distribution values. Determine which model provides the most accurate predictions to increase confidence in the use of predicted pharmacokinetic parameters in physiologically based pharmacokinetic modelling.

METHODS

Six models were used to predict Kps and four to predict V(ss) for a dataset of 81 compounds in 11 rat tissues, and the predictions were compared with experimentally derived values.

KEY FINDINGS

Kp predictions made by the Rodgers et al. model were the most accurate, with 77% within threefold of experimental values. The Poulin & Theil model was the most accurate for the prediction of V(ss) , with 87% of predictions within threefold.

CONCLUSIONS

This study has shown that in-silico models available in the literature can be used to accurately predict Kp and V(ss) in rat. The Rodgers et al. model has been shown to provide the most accurate Kp predictions, with consistent accuracy across all drug classes and tissues. It was also the most accurate V(ss) predictor when no in-vivo data were used as input. However, transporter systems and other mechanisms that are not yet fully understood need to be incorporated into these types of models in the future to further increase their applicability.