Tissue-plasma Partition Coefficients Research Articles

An Evaluation of Sex-Specific Pharmacokinetics and Bioavailability of Kokusaginine: An In Vitro and In Vivo Investigation.

Kokusaginine is a bioactive ingredient extracted from Ruta graveolens L., which has a range of biological activities. Its pharmacokinetic (PK) properties are particularly important for clinical applications; however, they have not been fully elucidated. In addition, the effect of sex differences on drug metabolism is increasingly being recognized, but most studies have ignored this important factor. This study aims to fill this knowledge gap by taking an in-depth look at the PK properties of kokusaginine and how gender affects its metabolism and distribution in the body. It also lays the foundation for clinical drug development. In this study, a sensitive ultra-high-performance liquid chromatography (UPLC) method was developed and validated for quantifying kokusaginine in Sprague Dawley (SD) rat plasma and tissue homogenates. Metabolic stability was evaluated in vitro using gender-specific liver microsomes. Innovatively, we incorporated sex as a variable into both in vitro and in vivo PK studies in SD rats, analyzing key parameters with Phoenix 8.3.5 software. The developed UPLC method demonstrated high sensitivity and precision, essential for PK analysis. Notably, in vitro studies revealed a pronounced sex-dependent metabolic variability (p < 0.05). In vivo, gender significantly affected the Area Under the Moment Curve (AUMC)(0-∞) of the plasma PK parameter (p < 0.05) and the AUMC(0-t) of brain tissue (p < 0.0001), underscoring the necessity of sex-specific PK assessments. The calculated absolute bioavailability of 71.13 ± 12.75% confirmed the favorable oral absorption of kokusaginine. Additionally, our innovative tissue-plasma partition coefficient (Kp) analysis highlighted a rapid and uniform tissue distribution pattern. This study presents a sex-inclusive PK evaluation of kokusaginine, offering novel insights into its metabolic profile and distribution. These findings are instrumental for informing clinical medication practices, dosage optimization, and a nuanced understanding of drug efficacy and safety in a sex-specific context.

Evaluation of the risk of human exposure to thiamethoxam by extrapolation from a toxicokinetic experiment in rats and literature data

Previous studies suggest that exposure to thiamethoxam (TMX) may cause adverse effects to human. However, the distribution of TMX in various organs of human body and the associated risk are little-known. This study aimed to explore the distribution of TMX in human organs by extrapolation from a toxicokinetic experiment in rats and to assess the associated risk based on literature data. The rat exposure experiment was performed using 6-week female SD rats. Five groups of rats were oral-exposed to 1 mg/kg TMX (water as solvent) and executed at 1 h, 2 h, 4 h, 8 h and 24 h after treatment, respectively. The concentrations of TMX and its metabolites in rat liver, kidney, blood, brain, muscle, uterus and urine were measured in different time points using LC-MS. Data on concentrations of TMX in food, human urine and blood as well as human cell-based in vitro toxicity of TMX were collected from the literature. After oral exposure, TMX and its metabolite clothianidin (CLO) were detected in all organs of the rats. The steady-state tissue-plasma partition coefficients of TMX for liver, kidney, brain, uterus and muscle were 0.96, 1.53, 0.47, 0.60 and 1.10, respectively. Based on literature analysis, the concentration of TMX in human urine and blood for general population were 0.06–0.5 ng/mL and 0.04–0.6 ng/mL, respectively. For some people, the concentration of TMX in human urine reached 222 ng/mL. By extraplation from rat experiment, the estimated concentrations of TMX in human liver, kidney, brain, uterus and muscle for general population were 0.038–0.58, 0.061–0.92, 0.019–0.28, 0.024–0.36 and 0.044–0.66 ng/g, respectively, well below the relevant concentrations for cytotoxic endpoints (HQs ≤ 0.012); however, for some people they could be up to 253.44, 403.92, 124.08, 158.40 and 290.40 ng/g, respectively, with very high developmental toxicity (HQ = 5.4). Therefore, the risk for highly exposed people should not be neglected.

Effects of 1α,25-dihydroxyvitamin D3 on the pharmacokinetics and biodistribution of ergothioneine, an endogenous organic cation/carnitine transporter 1 substrate, in rats.

PurposeThis study aimed to investigate the effects of 1α,25-dihydroxyvitamin D3 (1,25(OH)2D3) on the expression levels of organic cation/carnitine transporter 1 (OCTN1) as well as the pharmacokinetics and biodistribution of ergothioneine, an OCTN1 substrate, in rats.MethodsRats pretreated with 1,25(OH)2D3 (2.56 nmol/kg/day) for four days were administered ergothioneine (2 mg/kg) intravenously. The expression levels of rat OCTN1 (rOCTN1) in organs were determined using real-time quantitative polymerase chain reaction. Ergothioneine levels in plasma, urine, and organs (with and without intravenous injection of exogenous ergothioneine) were determined using liquid chromatography-tandem mass spectrometry.Results1,25(OH)2D3 pretreatment resulted in a significant decrease in rOCTN1 mRNA expression levels in the kidney and brain, a significant increase in basal plasma levels of ergothioneine (from 48 h), and a significant decrease in the tissue-plasma partition coefficient (Kp) in all tissues (except the heart and lungs) and the basal urine levels of ergothioneine. After intravenous administration, the pharmacokinetic profiles of ergothioneine were consistent with the basal levels of endogenous ergothioneine, with an increase in AUC∞ by 85%, a significant decrease in total clearance by 49%, and a decrease in Vss by 32% in 1,25(OH)2D3-treated rats. The Kp value and urinary recovery of ergothioneine also decreased in the 1,25(OH)2D3-treated group.ConclusionThis study showed the effects of 1,25(OH)2D3 on the expression and function of rOCTN1 by investigating the interaction between 1,25(OH)2D3 and ergothioneine. Dose adjustment and possible changes in bioavailability should be considered before the co-administration of vitamin D or its active forms and OCTN1 substrates.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40005-022-00563-1.

On the Origin of the Apparent Volume of Distribution and Its Significance in Pharmacokinetics

The apparent volume of distribution was defined for the first time as the phase volume that can hold the total amount of a substance at the measured phase substance concentration, in a system composed of two immiscible media that are in contact under conditions of constant phase volumes, at equilibrium. Its value is not affected by the total system solute mass and it only depends on the total system volume, the phase volumes and the affinity of the solute for the two phases in the system. Using this new concept of the apparent volume of distribution, we were able to demonstrate that under certain conditions compartment volumes in multi-compartment and multi-phasic pharmacokinetic models represent the actual physiological volumes of body fluids accessible by drugs. The classical pharmacokinetic models are now fully explained and can be used to provide accurate estimation of the pharmacokinetic parameters for hydrophilic drugs. In contrast, in the absence of tissue-plasma partition coefficients, lipophilic drugs that do not follow a one-compartment model are unlikely to be adequately described with classical multi-compartment pharmacokinetic models.

A hybrid modeling approach for assessing mechanistic models of small molecule partitioning in vivo using a machine learning-integrated modeling platform

Prediction of the first-in-human dosing regimens is a critical step in drug development and requires accurate quantitation of drug distribution. Traditional in vivo studies used to characterize clinical candidate’s volume of distribution are error-prone, time- and cost-intensive and lack reproducibility in clinical settings. The paper demonstrates how a computational platform integrating machine learning optimization with mechanistic modeling can be used to simulate compound plasma concentration profile and predict tissue-plasma partition coefficients with high accuracy by varying the lipophilicity descriptor logP. The approach applied to chemically diverse small molecules resulted in comparable geometric mean fold-errors of 1.50 and 1.63 in pharmacokinetic outputs for direct tissue:plasma partition and hybrid logP optimization, with the latter enabling prediction of tissue permeation that can be used to guide toxicity and efficacy dosing in human subjects. The optimization simulations required to achieve these results were parallelized on the AWS cloud and generated outputs in under 5 h. Accuracy, speed, and scalability of the framework indicate that it can be used to assess the relevance of other mechanistic relationships implicated in pharmacokinetic-pharmacodynamic phenomena with a lower risk of overfitting datasets and generate large database of physiologically-relevant drug disposition for further integration with machine learning models.

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.

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.

Exploring sex differences in human health risk assessment for PFNA and PFDA using a PBPK model.

Perfluorononanoic acid (PFNA) and perfluorodecanoic acid (PFDA), which are classified as perfluoroalkyl and polyfluoroalkyl substances (PFASs), have been widely used in industrial applications as a surface protectant. PFASs have been detected in wildlife and in humans around the globe. The purposes of this study are to develop and validate a physiologically based pharmacokinetic (PBPK) model for detecting PFNA and PFDA in male and female rats, and to apply the model to a human health risk assessment regarding the sex difference. A PBPK model of PFNA and PFDA was established based on an in vivo study in male and female rats. Analytes in biological samples (plasma, nine tissues, urine, and feces) were determined by ultra-liquid chromatography coupled tandem mass spectrometry (UPLC-MS/MS) method. PFNA and PFDA showed a gender differences in the elimination half-life and volume of distribution. The tissue-plasma partition coefficients were the highest in the liver in both male and female rats. The predicted rat plasma and urine concentrations simulated and fitted were in good agreement with the observed values. The PBPK models of PFNA and PFDA in male and female rats were then extrapolated to a human PBPK model based on human physiological parameters. The external doses were calculated at 3.35ng/kg/day (male) and 17.0ng/kg/day (female) for PFNA and 0.530ng/kg/day (male) and 0.661ng/kg/day (female) for PFDA. Human risk assessment was estimated using Korean biomonitoring values considering the gender differences. This study provides valuable insight into human health risk assessment regarding PFNA and PFDA exposure.

Constraint-based perturbation analysis with cluster Newton method: a case study of personalized parameter estimations with irinotecan whole-body physiologically based pharmacokinetic model

BackgroundDrug development considering individual varieties among patients becomes crucial to improve clinical development success rates and save healthcare costs. As a useful tool to predict individual phenomena and correlations among drug characteristics and individual varieties, recently, whole-body physiologically based pharmacokinetic (WB- PBPK) models are getting more attention. WB-PBPK models generally have a lot of drug-related parameters that need to be estimated, and the estimations are difficult because the observed data are limited. Furthermore, parameter estimation in WB-PBPK models may cause overfitting when applying to individual clinical data such as urine/feces drug excretion for each patient in which Cluster Newton Method (CNM) is applicable for parameter estimation. In order to solve this issue, we came up with the idea of constraint-based perturbation analysis of the CNM. The effectiveness of our approach is demonstrated in the case of irinotecan WB-PBPK model using common organ-specific tissue-plasma partition coefficients (Kp) among the patients as constraints in WB-PBPK parameter estimation.ResultsWe find strong correlations between age, renal clearance and liver functions in irinotecan WB-PBPK model with personalized physiological parameters by observing the distributions of optimized values of strong convergence drug-related parameters using constraint-based perturbation analysis on CNM. The constraint-based perturbation analysis consists of the following three steps: (1) Estimation of all drug-related parameters for each patient; the parameters include organ-specific Kp. (2) Fixing suitable values of Kp for each organ among all patients identically. (3) Re-estimation of all drug-related parameters other than Kp by using the fixed values of Kp as constraints of CNM.ConclusionsConstraint-based perturbation analysis could yield new findings when using CNM with appropriate constraints. This method is a new technique to find suitable values and important insights that are masked by CNM without constraints.

Sex-specific risk assessment of PFHxS using a physiologically based pharmacokinetic model.

Perfluorohexanesulfonate (PFHxS), which belongs to the group of perfluoroalkyl and polyfluoroalkyl substances (PFASs), has been extensively used in industry and subsequently detected in the environment. Its use may be problematic, as PFHxS is known to induce neuronal cell death, and has been associated with early onset menopause in women and with attention deficit/hyperactivity disorder. Due to these impending issues, the aim of this study is to develop and evaluate a physiologically based pharmacokinetic (PBPK) model for PFHxS in male and female rats, and apply this to a human health risk assessment. We conducted this study in vivo after the oral or intravenous administration of PFHxS in male (dose of 10mg/kg) and female rats (dose of 0.5-10mg/kg). The biological samples consisted of plasma, nine tissues, urine, and feces. We analyzed the sample using ultra-liquid chromatography coupled tandem mass spectrometry (UPLC-MS/MS). Our findings showed the tissue-plasma partition coefficients for PFHxS were highest in the liver. The predicted rat plasma and tissue concentrations using a simulation fitted well with the observed values. We extrapolated the PBPK model in male and female rats to a human PBPK model of PFHxS based on human physiological parameters. The reference doses of 0.711µg/kg/day (male) and 0.159µg/kg/day (female) and external doses of 0.007µg/kg/day (male) and 0.006µg/kg/day (female) for human risk assessment were estimated using Korean biomonitoring values. This study provides valuable insight into human health risk assessment regarding PFHxS exposure.

Estimation of residue depletion of cyadox and its marker residue in edible tissues of pigs using physiologically based pharmacokinetic modelling

Physiologically based pharmacokinetic (PBPK) models are powerful tools to predict tissue distribution and depletion of veterinary drugs in food animals. However, most models only simulate the pharmacokinetics of the parent drug without considering their metabolites. In this study, a PBPK model was developed to simultaneously describe the depletion in pigs of the food animal antimicrobial agent cyadox (CYA), and its marker residue 1,4-bisdesoxycyadox (BDCYA). The CYA and BDCYA sub-models included blood, liver, kidney, gastrointestinal tract, muscle, fat and other organ compartments. Extent of plasma-protein binding, renal clearance and tissue-plasma partition coefficients of BDCYA were measured experimentally. The model was calibrated with the reported pharmacokinetic and residue depletion data from pigs dosed by oral gavage with CYA for five consecutive days, and then extrapolated to exposure in feed for two months. The model was validated with 14 consecutive day feed administration data. This PBPK model accurately simulated CYA and BDCYA in four edible tissues at 24–120 h after both oral exposure and 2-month feed administration. There was only slight overestimation of CYA in muscle and BDCYA in kidney at earlier time points (6–12 h) when dosed in feed. Monte Carlo analysis revealed excellent agreement between the estimated concentration distributions and observed data. The present model could be used for tissue residue monitoring of CYA and BDCYA in food animals, and provides a foundation for developing PBPK models to predict residue depletion of both parent drugs and their metabolites in food animals.

Prediction of Drug Distribution in Subcutaneous Xenografts of Human Tumor Cell Lines and Healthy Tissues in Mouse: Application of the Tissue Composition-Based Model to Antineoplastic Drugs

Advanced tissue composition-based models can predict the tissue-plasma partition coefficient (Kp ) values of drugs under in vivo conditions on the basis of in vitro and physiological input data. These models, however, focus on healthy tissues and do not incorporate data from tumors. The objective of this study was to apply a tissue composition-based model to six marketed antineoplastic drugs (docetaxel, DOC; doxorubicin, DOX; gemcitabine, GEM; methotrexate, MTX; topotecan, TOP; and fluorouracil, 5-FU) to predict their Kp values in three human tumor xenografts (HCT-116, H2122, and PC3) as well as in healthy tissues (brain, muscle, lung, and liver) under steady-state in vivo conditions in female NCR nude mice. The mechanisms considered in the tissue/tumor composition-based model are the binding to lipids and to plasma proteins, but the transporter effect was also investigated. The method consisted of analyzing tissue composition, performing the pharmacokinetics studies in mice, and calculating the corresponding in vivo Kp values. Analyses of tumor composition indicated that the tumor xenografts contained no or low amounts of common transporters by contrast to lipids. The predicted Kp values were within twofold and threefold of the measured values in 77% and 93% of cases, respectively. However, predictions for brain for each drug, for liver for MTX, and for each tumor xenograft for GEM were disparate from the observed values, and, therefore, not well served by the model. Overall, this study is the first step toward the mechanism-based prediction of Kp values of small molecules in healthy and tumor tissues in mouse when no transporter and permeation limitation effect is evident. This approach will be useful in selecting compounds based on their abilities to penetrate human cancer xenografts with a physiologically based pharmacokinetic (PBPK) model, thereby increasing therapeutic index for chemotherapy in oncology study.

Correlation of Tissue-Plasma Partition Coefficients Between Normal Tissues and Subcutaneous Xenografts of Human Tumor Cell Lines in Mouse as a Prediction Tool of Drug Penetration in Tumors

Understanding drug distribution and accumulation in tumors would be informative in the assessment of efficacy in targeted therapy; however, existing methods for predicting tissue drug distribution focus on normal tissues and do not incorporate tumors. The main objective of this study was to describe the relationships between tissue-plasma concentration ratios (Kp ) of normal tissues and those of subcutaneous xenograft tumors under nonsteady-state conditions, and establish regression equations that could potentially be used for the prediction of drug levels in several human tumor xenografts in mouse, based solely on a Kp value determined in a normal tissue (e.g., muscle). A dataset of 17 compounds was collected from the literature and from Genentech. Tissue and plasma concentration data in mouse were obtained following oral gavage or intraperitoneal administration. Linear regression analyses were performed between Kp values in several normal tissues (muscle, lung, liver, or brain) and those in human tumor xenografts (CL6, EBC-1, HT-29, PC3, U-87, MCF-7-neo-Her2, or BT474M1.1). The tissue-plasma ratios in normal tissues reasonably correlated with the tumor-plasma ratios in CL6, EBC-1, HT-29, U-87, BT474M1.1, and MCF-7-neo-Her2 xenografts (r(2) in the range 0.62-1) but not with the PC3 xenograft. In general, muscle and lung exhibited the strongest correlation with tumor xenografts, followed by liver. Regression coefficients from brain were low, except between brain and the glioblastoma U-87 xenograft (r(2) in the range 0.62-0.94). Furthermore, reasonably strong correlations were observed between muscle and lung and between muscle and liver (r(2) in the range 0.67-0.96). The slopes of the regressions differed depending on the class of drug (strong vs. weak base) and type of tissue (brain vs. other tissues and tumors). Overall, this study will contribute to our understanding of tissue-plasma partition coefficients for tumors and facilitate the use of physiologically based pharmacokinetics (PBPK) modeling for chemotherapy in oncology studies. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 102:1355-1369, 2013.

On the Accuracy of Estimation of Basic Pharmacokinetic Parameters by the Traditional Noncompartmental Equations and the Prediction of the Steady-State Volume of Distribution in Obese Patients Based Upon Data Derived from Normal Subjects

The steady-state and terminal volumes of distribution, as well as the mean residence time of drug in the body (V(ss), V(β), and MRT) are the common pharmacokinetic parameters calculated using the drug plasma concentration-time profile C(p) (t) following intravenous (i.v. bolus or constant rate infusion) drug administration. These calculations are valid for the linear pharmacokinetic system with central elimination (i.e., elimination rate being proportional to drug concentration in plasma). Formally, the assumption of central elimination is not normally met because the rate of drug elimination is proportional to the unbound drug concentration at elimination site, although equilibration between systemic circulation and the site of clearance for majority of small molecule drugs is fast. Thus, the assumption of central elimination is practically quite adequate. It appears reasonable to estimate the extent of possible errors in determination of these pharmacokinetic parameters due to the absence of central elimination. The comparison of V(ss), V(β), and MRT calculated by exact equations and the commonly used ones was made considering a simplified physiologically based pharmacokinetic model. It was found that if the drug plasma concentration profile is detected accurately, determination of drug distribution volumes and MRT using the traditional noncompartmental calculations of these parameters from C(p) (t) yields the values very close to that obtained from exact equations. Though in practice, the accurate measurement of C(p) (t), especially its terminal phase, may not always be possible. This is particularly applicable for obtaining the distribution volumes of lipophilic compounds in obese subjects, when the possibility of late terminal phase at low drug concentration is quite likely, specifically for compounds with high clearance. An accurate determination of V(ss) is much needed in clinical practice because it is critical for the proper selection of drug treatment regimen. For that reason, we developed a convenient method for calculation of V(ss) in obese (or underweight) subjects. It is based on using the V(ss) values obtained from pharmacokinetic studies in normal subjects and the physicochemical properties of drug molecule. A simple criterion that determines either the increase or decrease of V(ss) (per unit body weight) due to obesity is obtained. The accurate determination of adipose tissue-plasma partition coefficient is crucial for the practical application of suggested method.

Utility of a single adjusting compartment: a novel methodology for whole body physiologically-based pharmacokinetic modelling

BackgroundThere are various methods for predicting human pharmacokinetics. Among these, a whole body physiologically-based pharmacokinetic (WBPBPK) model is useful because it gives a mechanistic description. However, WBPBPK models cannot predict human pharmacokinetics with enough precision. This study was conducted to elucidate the primary reason for poor predictions by WBPBPK models, and to enable better predictions to be made without reliance on complex concepts.MethodsThe primary reasons for poor predictions of human pharmacokinetics were investigated using a generic WBPBPK model that incorporated a single adjusting compartment (SAC), a virtual organ compartment with physiological parameters that can be adjusted arbitrarily. The blood flow rate, organ volume, and the steady state tissue-plasma partition coefficient of a SAC were calculated to fit simulated to observed pharmacokinetics in the rat. The adjusted SAC parameters were fixed and scaled up to the human using a newly developed equation. Using the scaled-up SAC parameters, human pharmacokinetics were simulated and each pharmacokinetic parameter was calculated. These simulated parameters were compared to the observed data. Simulations were performed to confirm the relationship between the precision of prediction and the number of tissue compartments, including a SAC.ResultsIncreasing the number of tissue compartments led to an improvement of the average-fold error (AFE) of total body clearances (CLtot) and half-lives (T1/2) calculated from the simulated human blood concentrations of 14 drugs. The presence of a SAC also improved the AFE values of a ten-organ model from 6.74 to 1.56 in CLtot, and from 4.74 to 1.48 in T1/2. Moreover, the within-2-fold errors were improved in all models; incorporating a SAC gave results from 0 to 79% in CLtot, and from 14 to 93% in T1/2 of the ten-organ model.ConclusionBy using a SAC in this study, we were able to show that poor prediction resulted mainly from such physiological factors as organ blood flow rate and organ volume, which were not satisfactorily accounted for in previous WBPBPK models. The SAC also improved precision in the prediction of human pharmacokinetics. This finding showed that the methodology of our study may be useful for functionally reinforcing a WBPBPK model.

Physiologically based pharmacokinetic modelling: a sub-compartmentalized model of tissue distribution

We present a sub-compartmentalized model of drug distribution in tissue that extends existing approaches based on the well-stirred tissue model. It is specified in terms of differential equations that explicitly account for the drug concentration in erythrocytes, plasma, interstitial and cellular space. Assuming, in addition, steady state drug distribution and by lumping the different sub-compartments, established models to predict tissue-plasma partition coefficients can be derived in an intriguingly simple way. This direct link is exploited to explicitly construct and parameterize the sub-compartmentalized model for moderate to strong bases, acids, neutrals and zwitterions. The derivation highlights the contributions of the different tissue constituents and provides a simple and transparent framework for the construction of novel tissue distribution models.

A Comment on the “Correction” of Tissue–Plasma Partition Coefficients for Drug Extraction in Eliminating Organs by Sven Björkman

A Comment on the “Correction” of Tissue–Plasma Partition Coefficients for Drug Extraction in Eliminating Organs by Sven Björkman

A Comment on the Application of Drug Tissue-Plasma Partition Coefficients Kp in Eliminating Organs to Calculation of Volume of Distribution at Steady State

A comment on the application of drug tissue-plasma partition coefficients Kp in eliminating organs to calculation of volume of distribution at steady state

Prediction of the volume of distribution of a drug: which tissue-plasma partition coefficients are needed?

The aim of this study was to identify the tissue-plasma partition coefficients (Kp) needed for an initial prediction of the volume of distribution at steady state (Vd(ss)) of a drug in humans. Values of Kp were collected from the literature. Only Kp values plausibly representing true steady state distribution were accepted, and data had to be available for muscle, fat, skin and at least five other organs. The apparent volume of distribution of a drug in an organ/tissue (Vapp) was calculated as Kp multiplied by the volume of the organ/tissue, and the Vd(ss) as the sum of all available Vapp values. The percentage contribution of each Vapp to the Vd(ss) was estimated. In addition, linear regressions were calculated between Kp values of all drugs in a specific organ/tissue and Kp in muscle or fat. Finally, the Vd(ss) was re-calculated using (for basic drugs) the Kp in fat to calculate Vapp in fat and lungs and the Kp in muscle for the Vapp of all other organs/tissues. The two sets of estimates of Vd(ss) were compared by linear regression. The same calculations were performed for acidic drugs, except that muscle Kp was used also forthe lungs. Distribution to fat and muscle accounted for 84% (61-91%) (median and range) of the total estimated Vd(ss) of the basic drugs (n = 17). The regressions between Kp in organs/tissues and muscle Kp were statistically significant except in the case of liver. For acidic drugs (n = 18), distribution to fat and muscle accounted for 65% (42-92%) of Vd(ss), and the regressions of Kp were significant for all organs/tissues except kidney and bone. For both types of drugs, correlations between organ/tissue Kp values and Kp in fat were generally worse. There were excellent linear correlations between Vd(ss) calculated by means of only two Kp values and the originally calculated Vd(ss) (r2> or = 0.99 for both basic and acidic drugs; slopes were notsignificantly different from unity). Thus, initial estimation of the Vd(ss) of a new drug can normally be based on only two Kp values, those of muscle and fat. The muscle Kp can be used to represent all lean tissues, including the residual "carcass", with the exception that fat Kp can be used for distribution of basic drugs to lungs.

Prediction of the disposition of midazolam in surgical patients by a physiologically based pharmacokinetic model

The aim of this study was to predict the disposition of midazolam in individual surgical patients by physiologically based pharmacokinetic (PBPK) modeling and explore the causes of interindividual variability. Tissue–plasma partition coefficients (kp) were scaled from rat to human values by a physiologically realistic four‐compartment model for each tissue, incorporating the measured unbound fraction (fu) of midazolam in the plasma of each patient. Body composition (lean body mass versus adipose tissue) was then estimated in each patient, and the volume of distribution at steady state (Vdss) of midazolam was calculated. Total clearance (CL) was calculated from unbound intrinsic CL, fu, and estimated hepatic blood flow. Curves of midazolam plasma concentration versus time were finally predicted by means of a perfusion‐limited PBPK model and compared with measured data. In a first study on 14 young patients undergoing surgery with modest blood loss, Vdss was predicted with an only 3.4% mean error (range −24–+39%) and a correlation between predicted and measured values of 0.818 (p < 0.001). Scaling of kp values by the four‐compartment model gave better predictions of Vdss than scaling using unbound kp. In the PBPK modeling, the mean ± standard deviation (SD) prediction error for all data was 9.7 ± 33%. In a second study with 10 elderly patients undergoing orthopedic surgery, hemodilution and blood loss led to a higher fu of midazolam. The PBPK modeling correctly predicted a marked increase in Vdss, a smaller increase in CL, and a prolonged terminal half‐life of midazolam, as compared with findings in the first study. Interindividual variation in the disposition of midazolam could thus in part be related to the physiological characteristics of the patients and the fu of the drug in their plasma. © 2001 Wiley‐Liss, Inc. and the American Pharmaceutical Association J Pharm Sci 90:1226–1241, 2001