Plasma Partition Coefficients Research Articles

Beta2-adrenergic agonist salbutamol exhibits enantioselective disposition in skeletal muscle of lean young men following oral administration.

Salbutamol is a common short-acting beta2-adrenergic agonist used in treatment of asthma and exercise-induced bronchoconstriction but also possesses anabolic and metabolic actions in skeletal muscle. As a chiral compound, salbutamol is a racemic 1:1 mixture of two enantiomers, (R)-salbutamol and (S)-salbutamol, which exhibit divergent pharmacokinetic and pharmacodynamic actions. Despite salbutamol being available for decades, information on the enantioselective disposition of salbutamol enantiomers in human skeletal muscle is absent. In this study, we determined concentrations of (R)-salbutamol and (S)-salbutamol by UHPLC-MS/MS in arterial plasma and vastus lateralis muscle samples from 12 lean young men 2½ and 7 h following ingestion of 24 mg oral salbutamol. Mean (range) arterial plasma concentrations were 10-fold higher (p < 0.001) for (S)-salbutamol than (R)-salbutamol, being 33(9-62) and 49(30-84) ng·mL-1 for (S)-salbutamol and 4 (1-6) and 4 (2-5) ng·mL-1 for (R)-salbutamol 2½ and 7 h following administration, respectively, reflecting faster elimination of the (R)-enantiomer. Mean (range) muscle concentrations were higher (p < 0.001) for (S)-salbutamol than (R)-salbutamol 2½h (0.17 [0.1-0.26] vs. 0.04 [0.02-0.06]) and 7 h (0.31 [0.21-0.46] vs. 0.06 [0.04-0.12] ng·mgd.w. -1) after administration. However, muscle:plasma partition coefficient was two-fold higher (p < 0.001) for (R)-salbutamol than (S)-salbutamol 7 h following administration. These observations demonstrate that oral salbutamol exhibits enantioselective disposition in systemic circulation and muscle favoring the (S)-enantiomer but with higher relative partitioning of the (R)-enantiomer in skeletal muscle. Furthermore, the concentration-time profiles of salbutamol enantiomers are different in skeletal muscle and systemic circulation following oral ingestion. These findings have implications for the application of chiral switch (R)-salbutamol in doping control.

Issues in assessing tissue: Plasma partition coefficients (KP) in PBPK models

Issues in assessing tissue: Plasma partition coefficients (KP) in PBPK models

Novel LC-MS/MS analysis of the GLP-1 analog semaglutide with its application to pharmacokinetics and brain distribution studies in rats

Semaglutide, one of the most potent glucagon-like peptide (GLP)-1 analogs, has widely been used to treat type II diabetes mellitus and obesity. Recent studies have shown that semaglutide also works on the brain, suggesting its potential utility for various diseases, including Parkinson’s disease and Alzheimer’s disease. This study aimed to develop a novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis of semaglutide in both plasma and brain to characterize the pharmacokinetics and brain distribution in rats. Semaglutide was extracted by simple protein precipitation with methanol from plasma and by solid phase extraction from brain tissue. Liraglutide was used as an internal standard. Gradient elution profiles with mobile phases comprising 0.1 % formic acid in water and acetonitrile were used for chromatographic separation. The lower limit of quantification (LLOQ) of the LC-MS/MS assay was 0.5 ng/mL for both rat plasma and brain. Intra- and inter-day accuracy ranged 89.20–109.50 % in the plasma and 92.00–105.00 % in the brain. Precision was within 8.92 % in the plasma and 7.94 % in the brain. Sprague-Dawley rats were given semaglutide by intravenous (IV, 0.02 mg/kg) and subcutaneous (SC, 0.1 and 0.2 mg/kg) injection. Plasma concentrations of semaglutide showed a multi-exponential decline with an average half-life of 7.22–9.26 hr in rats. The subcutaneous bioavailability of semaglutide was 76.65–82.85 %. The brain tissue to plasma partition coefficient (Kp) value of semaglutide was estimated as <0.01. Among the different regions of the brain, semaglutide concentrations were significantly higher in the hypothalamus. The analytical method and pharmacokinetic information may be helpful toward a better understanding of the effect of semaglutide in the brain and further development of GLP-1 analogs for various brain diseases.

Investigation of simplified physiologically-based pharmacokinetic models in rat and human.

Whole-body physiologically-based pharmacokinetic (PBPK) models have many applications in drug research and development. It is often necessary to inform these models with animal or clinical data, updating model parameters, and making the model more predictive for future applications. This provides an opportunity and a challenge given the large number of parameters of such models. The aim of this work was to propose new mechanistic model structures with reduced complexity allowing for parameter optimization. These models were evaluated for their ability to estimate realistic values for unbound tissue to plasma partition coefficients (Kpu) and simulate observed pharmacokinetic (PK) data. Two approaches are presented: using either established kinetic lumping methods based on tissue time constants (drug-dependent) or a novel clustering analysis to identify tissues sharing common Kpu values or Kpu scalars based on similarities of tissue composition (drug-independent). PBPK models derived from these approaches were assessed using PK data of diazepam in rats and humans. Although the clustering analysis produced minor differences in tissue grouping depending on the method used, two larger groups of tissues emerged. One including the kidneys, liver, spleen, gut, heart, and lungs, and another including bone, brain, muscle, and pancreas whereas adipose and skin were generally considered distinct. Overall, a subdivision into four tissue groups appeared most physiologically relevant in terms of tissue composition. Several models were found to have similar abilities to describe diazepam i.v. data as empirical models. Comparability of estimated Kpus to experimental Kpu values for diazepam was one criterion for selecting the appropriate PK model structure.

A "middle-out approach" for the prediction of human drug disposition from preclinical data using simplified physiologically based pharmacokinetic (PBPK) models.

Simplified physiologically based pharmacokinetic (PBPK) models using estimated tissue-to-unbound plasma partition coefficients (Kpus) were previously investigated by fitting them to in vivo pharmacokinetic (PK) data. After optimization with preclinical data, the performance of these models for extrapolation of distribution kinetics to human were evaluated to determine the best approach for the prediction of human drug disposition and volume of distribution (Vss) using PBPK modeling. Three lipophilic bases were tested (diazepam, midazolam, and basmisanil) for which intravenous PK data were available in rat, monkey, and human. The models with Kpu scalars using k-means clustering were generally the best for fitting data in the preclinical species and gave plausible Kpu values. Extrapolations of plasma concentrations for diazepam and midazolam using these models and parameters obtained were consistent with the observed clinical data. For diazepam and midazolam, the human predictions of Vss after optimization in rats and monkeys were better compared with the Vss estimated from the traditional PBPK modeling approach (varying from 1.1 to 3.1 vs. 3.7-fold error). For basmisanil, the sparse preclinical data available could have affected the model performance for fitting and the subsequent extrapolation to human. Overall, this work provides a rational strategy to predict human drug distribution using preclinical PK data within the PBPK modeling strategy.

Predictive performance of next generation human physiologically based kinetic (PBK) models based on in vitro and in silico input data.

The goal of the present study was to assess the predictive performance of a generic human physiologically based kinetic (PBK) model based on in vitro and in silico input data and the effect of using different input approaches for chemical parameterization on those predictions. For this purpose, a dataset was created of 38,772 Cmax predictions for 44 compounds by applying different combinations of in vitro and in silico approaches for chemical parameterization, and these predicted Cmax values were compared to reported in vivo data. Best results were achieved when the hepatic clearance was parameterized based on in vitro (i.e., hepatocytes or liver S9) measured intrinsic clearance values, the method of Rodgers and Rowland for calculating tissue:plasma partition coefficients, and the method of Lobell and Sivarajah for calculating the fraction unbound in plasma. With these parameters, the median Cmax values of 34 out of the 44 compounds were predicted within 5-fold of the observed Cmax, and the Cmax values of 19 compounds were predicted within 2-fold. The median Cmax values of 10 compounds were more than 5-fold overestimated. Underestimations (> 5-fold) did not occur. A comparison of the current generic PBK model structure with chemical-specific PBK models available in literature was made to identify possible kinetic processes not included in the generic PBK model that might explain the overestimations. Overall, the results provide crucial insights into the predictive performance of PBK models based on in vitro and in silico input and the influence of different input approaches on the model predictions.

Physiologically Based Pharmacokinetic Modeling of Cefadroxil in Mouse, Rat, and Human to Predict Concentration-Time Profile at Infected Tissue.

The aim of this study was to develop physiologically based pharmacokinetic (PBPK) models capable of simulating cefadroxil concentrations in plasma and tissues in mouse, rat, and human. PBPK models in this study consisted of 14 tissues and 2 blood compartments. They were established using measured tissue to plasma partition coefficient (K p) in mouse and rat, absolute expression levels of hPEPT1 along the entire length of the human intestine, and the transporter kinetic parameters. The PBPK models also assumed that all the tissues were well-stirred compartments with perfusion rate limitations, and the ratio of the concentration in tissue to the unbound concentration in plasma is identical across species. These PBPK models were validated strictly by a series of observed plasma concentration–time profile data. The average fold error (AFE) and absolute average fold error (AAFE) values were all less than 2. The models’ rationality and accuracy were further demonstrated by the almost consistent V ss calculated by the PBPK model and noncompartmental method, as well as the good allometric scaling relationship of V ss and CL. The model suggests that hPEPT1 is the major transporter responsible for the oral absorption of cefadroxil in human, and the plasma concentration–time profiles of cefadroxil were not sensitive to dissolution rate faster than T85% = 2 h. The cefadroxil PBPK model in human is reliable and can be used to predict concentration–time profile at infected tissue. It may be useful for dose selection and informative decision-making during clinical trials and dosage form design of cefadroxil and provide a reference for the PBPK model establishment of hPEPT1 substrate.

Reviewing Data Integrated for PBPK Model Development to Predict Metabolic Drug-Drug Interactions: Shifting Perspectives and Emerging Trends.

Physiologically-based pharmacokinetics (PBPK) modeling is a robust tool that supports drug development and the pharmaceutical industry and regulatory authorities. Implementation of predictive systems in the clinics is more than ever a reality, resulting in a surge of interest for PBPK models by clinicians. We aimed to establish a repository of available PBPK models developed to date to predict drug-drug interactions (DDIs) in the different therapeutic areas by integrating intrinsic and extrinsic factors such as genetic polymorphisms of the cytochromes or environmental clues. This work includes peer-reviewed publications and models developed in the literature from October 2017 to January 2021. Information about the software, type of model, size, and population model was extracted for each article. In general, modeling was mainly done for DDI prediction via Simcyp® software and Full PBPK. Overall, the necessary physiological and physio-pathological parameters, such as weight, BMI, liver or kidney function, relative to the drug absorption, distribution, metabolism, and elimination and to the population studied for model construction was publicly available. Of the 46 articles, 32 sensibly predicted DDI potentials, but only 23% integrated the genetic aspect to the developed models. Marked differences in concentration time profiles and maximum plasma concentration could be explained by the significant precision of the input parameters such as Tissue: plasma partition coefficients, protein abundance, or Ki values. In conclusion, the models show a good correlation between the predicted and observed plasma concentration values. These correlations are all the more pronounced as the model is rich in data representative of the population and the molecule in question. PBPK for DDI prediction is a promising approach in clinical, and harmonization of clearance prediction may be helped by a consensus on selecting the best data to use for PBPK model development.

Neuroaxial Infantile Hemangiomas: Imaging Manifestations and Association with Hemangioma Syndromes

Infantile hemangiomas are common lesions in the pediatric population; in rare cases, an infantile hemangioma can be detected along the neural axis. The purposes of our study included determination of the incidence, location, and imaging appearance of neuroaxial infantile hemangiomas and their syndromic association. We also assessed additional features of cerebral and cardiovascular anomalies that may be associated with neuroaxial lesions. A retrospective cohort study was performed, searching the radiology database for patients with segmental infantile hemangiomas referred for assessment of possible hemangioma syndromes. We retrospectively reviewed brain and spine MR imaging studies, with particular attention paid to neuroaxial vascular lesions, as well as the relevant clinical data. Neuroaxial hemangioma imaging findings were described, and comparison of segmental cutaneous infantile hemangioma location with the imaging findings was performed in patients with confirmed hemangioma syndromes and in patients with isolated skin infantile hemangioma. Ninety-five patients with segmental infantile hemangioma were included in the study, 42 of whom had a hemangioma syndrome; of those, 41 had posterior fossa brain malformations, hemangioma, arterial lesions, cardiac abnormalities, and eye abnormalities (PHACE) syndrome and 1 had diffuse neonatal hemangiomatosis. Neuroaxial involvement was detected in 20/42 patients (48%) with hemangioma syndromes and in no subjects with isolated segmental infantile hemangioma (P < .001). The most common intracranial hemangioma location was within the ipsilateral internal auditory canal (83%). Many pediatric patients with segmental infantile hemangioma in the setting of hemangioma syndromes, especially those with PHACE, had neuroaxial hemangiomas. This finding may potentially lead to requiring additional clinical evaluation and management of these patients.

Quantification of the Impact of Partition Coefficient Prediction Methods on Physiologically Based Pharmacokinetic Model Output Using a Standardized Tissue Composition.

Tissue:plasma partition coefficients are key parameters in physiologically based pharmacokinetic (PBPK) models, yet the coefficients are challenging to measure in vivo. Several mechanistic-based equations have been developed to predict partition coefficients using tissue composition information and the compound's physicochemical properties, but it is not clear which, if any, of the methods is most appropriate under given circumstances. Complicating the evaluation, each prediction method was developed, and is typically employed, using a different set of tissue composition information, thereby making a controlled comparison impossible. This study proposed a standardized tissue composition for humans that can be used as a common input for each of the five frequently used prediction methods. These methods were implemented in R and were used to predict partition coefficients for 11 drugs, classified as strong bases, weak bases, acids, neutrals, and zwitterions. PBPK models developed in R (mrgsolve) for each drug and each set of partition coefficient predictions were compared with respective observed plasma concentration data. Percent root mean square error and half-life percent error were used to evaluate the accuracy of the PBPK model predictions using each partition coefficient method as summarized by strong bases, weak bases, acids, neutrals, and zwitterions characterization. The analysis indicated that no partition coefficient method consistently yielded the most accurate PBPK model predictions. As such, PBPK model predictions using all partition coefficient methods should be considered during drug development. SIGNIFICANCE STATEMENT: Several mechanistic-based methods exist to predict tissue:plasma partition coefficients critical to PBPK modeling. Controlled comparisons are confounded by the use of different tissue composition values for each method; a standardized tissue composition was proposed. Resulting assessments indicated that no method was consistently superior; therefore, sensitivity of PBPK predictions to each method may be warranted prior to model optimization.

Plasma and cerebrospinal fluid pharmacokinetics of ondansetron in humans.

Changes in serotonergic sensory modulation associated with overexpression of 5-HT3 receptors in the central nervous system (CNS) have been implicated in the pathophysiology of neuropathic pain after peripheral nerve damage. 5-HT3 receptor antagonists such as ondansetron can potentially alleviate neuropathic pain, but have limited effectiveness, due potentially to limited CNS access. However, there is currently limited information on CNS disposition of systemically-administered 5-HT3 receptor antagonists. This study evaluated the cerebrospinal fluid (CSF) disposition of ondansetron, as a surrogate of CNS penetration. Fifteen patients were given a single 16 mg intravenous 15 minute infusion of ondansetron, followed by serial blood and a single CSF sampling. Population pharmacokinetic (PK) modelling was implemented to describe the average and individual plasma and CSF profiles of ondansetron. A two-compartmental model was used to capture ondansetron plasma PK with a single CSF compartment to describe distribution to the CNS. The individual model-estimated CSF to plasma partition coefficients of ondansetron were between 0.09 and 0.20. These values were mirrored in the calculated CSF penetration ratios, ranging from 0.08 to 0.26. After intravenous administration, CSF concentrations of ondansetron were approximately 7-fold lower than those observed in the plasma. A model could be developed to describe individual CSF concentration-time profiles of ondansetron based on a single CSF data point. The low CSF penetration of ondansetron may explain its limited analgesic effectiveness, and affords an opportunity to explore enhancing its CNS penetration for targeting conditions such as neuropathic pain.

Temperature effect on perfluorooctane sulfonate toxicokinetics in rainbow trout (Oncorhynchus mykiss): Exploration via a physiologically based toxicokinetic model

Salmonids are poikilotherms, which means that their internal temperature varies with that of water. Water temperature thus controls many of their lifecycle processes and physiological functions, which could influence the mechanisms of absorption, distribution, metabolism and excretion (ADME) of many substances, including perfluorinated alkyl acids (PFAAs). However, the processes governing the fate of PFAAs are still poorly understood in fish. Here we developed a physiologically-based toxicokinetic (PBTK) model for rainbow trout (Oncorhynchus mykiss) to study changes in physiological functions and PFAA ADME at different temperatures. The model was calibrated using experimental data from dietary exposure to perfluorooctane sulfonate at 7 °C and 19 °C. Predictions of PFOS concentrations were globally satisfactory at both temperatures, when accounting for the influence of temperature on growth, ventilation rate, cardiac output, clearances, and absorption rates. Accounting for the influence of temperature on tissue–plasma partition coefficients significantly improved predicted in-organ PFOS concentrations.

Risk assessment for humans using physiologically based pharmacokinetic model of diethyl phthalate and its major metabolite, monoethyl phthalate.

Diethyl phthalate (DEP) belongs to phthalates with short alkyl chains. It is a substance frequently used to make various products. Thus, humans are widely exposed to DEP from the surrounding environment such as food, soil, air, and water. As previously reported in many studies, DEP is an endocrine disruptor with reproductive toxicity. Monoethyl phthalate (MEP), a major metabolite of DEP in vivo, is a biomarker for DEP exposure assessment. It is also an endocrine disruptor with reproductive toxicity, similar to DEP. However, toxicokinetic studies on both MEP and DEP have not been reported in detail yet. Therefore, the objective of this study was to evaluate and develop physiologically based pharmacokinetic (PBPK) model for both DEP and MEP in rats and extend this to human risk assessment based on human exposure. This study was conducted in vivo after intravenous or oral administration of DEP into female (2mg/kg dose) and male (0.1-10mg/kg dose) rats. Biological samples consisted of urine, plasma, and 11 different tissues. These samples were analyzed using UPLC-ESI-MS/MS method. For DEP, the tissue to plasma partition coefficient was the highest in the kidney, followed by that in the liver. For MEP, the tissue to plasma partition coefficient was the highest in the liver. It was less than unity in all other tissues. Plasma, urine, and fecal samples were also obtained after IV administration of MEP (10mg/kg dose) to male rats. All results were reflected in a model developed in this study, including in vivo conversion from DEP to MEP. Predicted concentrations of DEP and MEP in rat urine, plasma, and tissue samples using the developed PBPK model fitted well with observed values. We then extrapolated the PBPK model in rats to a human PBPK model of DEP and MEP based on human physiological parameters. Reference dose of 0.63mg/kg/day (or 0.18mg/kg/day) for DEP and external doses of 0.246μg/kg/day (pregnant), 0.193μg/kg/day (fetus), 1.005-1.253μg/kg/day (adults), 0.356-0.376μg/kg/day (adolescents), and 0.595-0.603μg/kg/day (children) for DEP for human risk assessment were estimated using Korean biomonitoring values. Our study provides valuable insight into human health risk assessment regarding DEP exposure.

Global Sensitivity Analysis of the Rodgers and Rowland Model for Prediction of Tissue: Plasma Partitioning Coefficients: Assessment of the Key Physiological and Physicochemical Factors That Determine Small-Molecule Tissue Distribution.

In physiologically based pharmacokinetic (PBPK) modelling, the large number of input parameters, limited amount of available data and the structural model complexity generally hinder simultaneous estimation of uncertain and/or unknown parameters. These parameters are generally subject to estimation. However, the approaches taken for parameter estimation vary widely. Global sensitivity analyses are proposed as a method to systematically determine the most influential parameters that can be subject to estimation. Herein, a global sensitivity analysis was conducted to identify the key drug and physiological parameters influencing drug disposition in PBPK models and to potentially reduce the PBPK model dimensionality. The impact of these parameters was evaluated on the tissue-to-unbound plasma partition coefficients (Kpus) predicted by the Rodgers and Rowland model using Latin hypercube sampling combined to partial rank correlation coefficients (PRCC). For most drug classes, PRCC showed that LogP and fraction unbound in plasma (fup) were generally the most influential parameters for Kpu predictions. For strong bases, blood:plasma partitioning was one of the most influential parameter. Uncertainty in tissue composition parameters had a large impact on Kpu and Vss predictions for all classes. Among tissue composition parameters, changes in Kpu outputs were especially attributed to changes in tissue acidic phospholipid concentrations and extracellular protein tissue:plasma ratio values. In conclusion, this work demonstrates that for parameter estimation involving PBPK models and dimensionality reduction purposes, less influential parameters might be assigned fixed values depending on the parameter space, while influential parameters could be subject to parameters estimation.

Predicting human pharmacokinetics from preclinical data: volume of distribution.

This tutorial introduces background and methods to predict the human volume of distribution (Vd) of drugs using in vitro and animal pharmacokinetic (PK) parameters. The physiologically based PK (PBPK) method is based on the familiar equation: Vd = Vp + ∑T(VT × ktp). In this equation, Vp (plasma volume) and VT (tissue volume) are known physiological values, and ktp (tissue plasma partition coefficient) is experimentally measured. Here, the ktp may be predicted by PBPK models because it is known to be correlated with the physicochemical property of drugs and tissue composition (fraction of lipid and water). Thus, PBPK models' evolution to predict human Vd has been the efforts to find a better function giving a more accurate ktp. When animal PK parameters estimated using i.v. PK data in ≥ 3 species are available, allometric methods can also be used to predict human Vd. Unlike the PBPK method, many different models may be compared to find the best-fitting one in the allometry, a kind of empirical approach. Also, compartmental Vd parameters (e.g., Vc, Vp, and Q) can be predicted in the allometry. Although PBPK and allometric methods have long been used to predict Vd, there is no consensus on method choice. When the discrepancy between PBPK-predicted Vd and allometry-predicted Vd is huge, physiological plausibility of all input and output data (e.g., r2-value of the allometric curve) may be reviewed for careful decision making.

CNS Penetration of Cyclophosphamide and Metabolites in Mice Bearing Group 3 Medulloblastoma and Non-Tumor Bearing Mice.

Cyclophosphamide is widely used to treat children with medulloblastoma; however, little is known about its brain penetration. We performed cerebral microdialysis to characterize the brain penetration of cyclophosphamide (130 mg/kg, IP) and its metabolites [4-hydroxy-cyclophosphamide (4OH-CTX) and carboxyethylphosphoramide mustard (CEPM)] in non-tumor bearing mice and mice bearing orthotopic Group 3 medulloblastoma. A plasma pharmacokinetic study was performed in non-tumor-bearing CD1- nude mice, and four cerebral microdialysis studies were performed in non-tumor-bearing (M1 and M3) and tumor- bearing mice (M2 and M4). Plasma samples were collected up to 6-hours post-dose, and extracellular fluid (ECF) samples were collected over 60-minute intervals for 24-hours post-dose. To stabilize and quantify 4OH-CTX, a derivatizing solution was added in blood after collection, and either directly in the microdialysis perfusate (M1 and M2) or in ECF collection tubes (M3 and M4). Plasma/ECF cyclophosphamide and CEPM, and 4OH-CTX concentrations were separately measured using different LC-MS/MS methods. All plasma/ECF concentrations were described using a population-based pharmacokinetic model. Plasma exposures of cyclophosphamide, 4OH-CTX, and CEPM were similar across studies (mean AUC=112.6, 45.6, and 80.8 µmol∙hr/L). Hemorrhage was observed in brain tissue when the derivatizing solution was in perfusate compared with none when in collection tubes, which suggested potential sample contamination in studies M1 and M2. Model-derived unbound ECF to plasma partition coefficients (Kp,uu) were calculated to reflect CNS penetration of the compounds. Lower cyclophosphamide Kp,uu was obtained in tumor-bearing mice versus non-tumor bearing mice (mean 0.15 versus 0.22, p=0.019). No differences in Kp,uu were observed between these groups for 4OH- CTX and CEPM (overall mean 0.10 and 0.07). Future studies will explore potential mechanisms at the brain-tumor barrier to explain lower cyclophosphamide brain penetration in tumor-bearing mice. These results will be used to further investigate exposure-response relationships in medulloblastoma xenograft models.

CNS penetration and pharmacodynamics of the CHK1 inhibitor prexasertib in a mouse Group 3 medulloblastoma model

Prexasertib (LY2606368) is a potent and selective small molecule inhibitor of cell-cycle checkpoint CHK1 and CHK2 protein kinases and is currently under clinical evaluation for treatment of pediatric malignancies. As a candidate therapy for pediatric Group 3 medulloblastoma (G3MB), prexasertib CNS penetration was evaluated in mice using cerebral microdialysis and pharmacokinetic modeling. A plasma pharmacokinetic study with a population-based design was performed in CD1 nude mice bearing G3MB orthotopically implanted in the brain and receiving a single dose of prexasertib (10 mg/kg, subcutaneously) to characterize prexasertib disposition and to establish a limited plasma sampling model for the microdialysis studies. The microdialysis studies were performed in both non-tumor bearing mice and in mice bearing G3MB receiving 10 mg/kg prexasertib subcutaneously, for up to 24 h post-dose. Plasma and extracellular fluid (ECF) concentrations were quantified using validated LC MS/MS methods, and analyzed using a population pharmacokinetic model. Model-derived prexasertib tumor/ECF to plasma partition coefficient Kp,uu (ratio of tumor/brain ECF to unbound plasma AUC0-24 h) was significantly greater in G3MB tumor-bearing mice (0.17 ± 0.08) compared to non-tumor bearing mice (0.09 ± 0.04, p = 0.04). A pharmacodynamic study was then performed in mice bearing G3MB (20 mg/kg, IV) to evaluate prexasertib-induced target engagement after a single dose. Phosphorylated CHK1 serine 345 (pCHK1 S345), phosphorylated Histone 2A variant (γ-H2AX), and cleaved caspase-3 were quantified in mouse G3MB tumor tissues by immunohistochemistry at different time points up to 24 h post-dose. The induction of pCHK1 S345 and γ-H2AX peaked at 2 h after the dose and was elevated above baseline for at least 6 h, reflecting relevant CHK1 inhibition and DNA damage. Cleaved caspase-3 levels increased at 24 h suggesting initiation of cell apoptosis. Adequate unbound prexasertib exposure reached the brain tumor site relative to target engagement in G3MB tumor bearing mice at a clinically relevant dosage. These results support further preclinical and clinical development of prexasertib to treat children with medulloblastoma.

Prediction of Tissue-Plasma Partition Coefficients Using Microsomal Partitioning: Incorporation into Physiologically based Pharmacokinetic Models and Steady-State Volume of Distribution Predictions.

Drug distribution is a necessary component of models to predict human pharmacokinetics. A new membrane-based tissue-plasma partition coefficient (K p) method (K p,mem) to predict unbound tissue to plasma partition coefficients (K pu) was developed using in vitro membrane partitioning [fraction unbound in microsomes (f um)], plasma protein binding, and log P The resulting K p values were used in a physiologically based pharmacokinetic (PBPK) model to predict the steady-state volume of distribution (V ss) and concentration-time (C-t) profiles for 19 drugs. These results were compared with K p predictions using a standard method [the differential phospholipid K p prediction method (K p,dPL)], which differentiates between acidic and neutral phospholipids. The K p,mem method was parameterized using published rat K pu data and tissue lipid composition. The K pu values were well predicted with R 2 = 0.8. When used in a PBPK model, the V ss predictions were within 2-fold error for 12 of 19 drugs for K p,mem versus 11 of 19 for Kp,dPL With one outlier removed for K p,mem and two for K p,dPL, the V ss predictions for R 2 were 0.80 and 0.79 for the K p,mem and K p,dPL methods, respectively. The C-t profiles were also predicted and compared. Overall, the K p,mem method predicted the V ss and C-t profiles equally or better than the K p,dPL method. An advantage of using f um to parameterize membrane partitioning is that f um data are used for clearance prediction and are, therefore, generated early in the discovery/development process. Also, the method provides a mechanistically sound basis for membrane partitioning and permeability for further improving PBPK models. SIGNIFICANCE STATEMENT: A new method to predict tissue-plasma partition coefficients was developed. The method provides a more mechanistic basis to model membrane partitioning.

Methods to Predict Volume of Distribution.

Prior to human studies, knowledge of drug disposition in the body is useful to inform decisions on drug safety and efficacy, first in human dosing, and dosing regimen design. It is therefore of interest to develop predictive models for primary pharmacokinetic parameters, clearance, and volume of distribution. The volume of distribution of a drug is determined by the physiological properties of the body and physiochemical properties of the drug, and is used to determine secondary parameters, including the half-life. The purpose of this review is to provide an overview of current methods for the prediction of volume of distribution of drugs, discuss a comparison between the methods, and identify deficiencies in current predictive methods for future improvement. Several volumes of distribution prediction methods are discussed, including preclinical extrapolation, physiological methods, tissue composition-based models to predict tissue:plasma partition coefficients, and quantitative structure-activity relationships. Key factors that impact the prediction of volume of distribution, such as permeability, transport, and accuracy of experimental inputs, are discussed. A comparison of current methods indicates that in general, all methods predict drug volume of distribution with an absolute average fold error of 2-fold. Currently, the use of composition-based PBPK models is preferred to models requiring in vivo input. Composition-based models perfusion-limited PBPK models are commonly used at present for prediction of tissue:plasma partition coefficients and volume of distribution, respectively. A better mechanistic understanding of important drug distribution processes will result in improvements in all modeling approaches.

Simultaneous determination of diethyl phthalate and its major metabolite, monoethyl phthalate, in rat plasma, urine, and various tissues collected from a toxicokinetic study by ultrahigh performance liquid chromatography-tandem mass spectrometry

This study describes the development of a novel and sensitive UPLC-MS/MS method for simultaneous determination of diethyl phthalate (DEP) and its major metabolite, monoethyl phthalate (MEP), in rat plasma, urine, and 11 different tissues collected from a toxicokinetic study. Analytes were separated using 0.1% (v/v) aqueous formic acid and acetonitrile containing 0.1% (v/v) formic acid as a mobile phase by gradient elution at a flow rate of 0.25 mL/min with a KINETEX core-shell C18 column (50 × 2.1 mm, 1.7 μm). Quantitation was performed on a triple quadrupole mass spectrometer employing electrospray ionization technique operated in multiple reaction monitoring. The assay achieved lower limit of quantification for 0.04 ng/mL of DEP and 0.1 ng/mL of MEP in plasma, urine, and all tissues. The disposition of DEP was characterized by short half-life (1.30–1.34 h) and a high clearance (11.76 ± 0.08 L/h/kg). It was rapidly metabolized to MEP. Its levels consistently exceeded DEP levels. The tissue distribution of DEP and MEP of liver, kidney, gastro-intestinal tract, spleen, testis, lung, brain, muscle, thymus, heart, and adipose was determined at 24 h after drug administration. For DEP, the tissue to plasma partition coefficient was the highest in the kidney (16.72), followed by that in the liver (10.04), spleen (1.35), and adipose (1.18). For MEP, the tissue to plasma partition coefficient was the highest in the liver (2.18). It was less than unity for all other tissues. The developed analytical method satisfied the criteria of international guidance. It could be successfully applied to toxicokinetic studies of DEP after oral and intravenous administration of DEP to rats. In addition, findings of this study may be useful to evaluate exposure and toxic potential of DEP and its metabolite in risk assessment.