A Model-Based Approach To Assessing the Importance of Intracellular

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A model-based approach to assessing the importance of intracellular binding sites in doxorubicin disposition Ilse R. Dubbelboer, Elsa Lilienberg, Erik Sjögren, and Hans Lennernas Mol. Pharmaceutics, Just Accepted Manuscript • DOI: 10.1021/acs.molpharmaceut.6b00974 • Publication Date (Web): 09 Feb 2017 Downloaded from http://pubs.acs.org on February 11, 2017

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Molecular Pharmaceutics

A model-based approach to assessing the importance of intracellular binding sites in doxorubicin disposition Ilse R. Dubbelboer; Elsa Lilienberg, Erik Sjögren, Hans Lennernäs* Affiliation: Department of Pharmacy, Uppsala University, Box 580, 751 23 Uppsala, Sweden *Address correspondence to: Hans Lennernäs, PhD Professor in Biopharmaceutics Department of Pharmacy Uppsala University Box 580 SE-751 23 Uppsala, Sweden Email: [email protected] Phone: +46 – 18 471 4317 Fax: +46 – 18 471 4223

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Abstract Doxorubicin is an anticancer agent, which binds reversibly to topoisomerase I and II, intercalates to DNA base-pairs, and generates free radicals. Doxorubicin has a high tissue:plasma partition coefficient and high intracellular binding to the nucleus and other subcellular compartments. The metabolite doxorubicinol has an extensive tissue distribution. This porcine study investigated whether the traditional implementation of tissue binding, described by the tissue:plasma partition coefficient (Kp,t), could be used to appropriately analyze and/or simulate tissue doxorubicin and doxorubicinol concentrations in healthy pigs, when applying a physiologically based pharmacokinetic (PBPK) model approach or whether intracellular binding is required in the semi-PBPK model. Two semi-PBPK models were developed and evaluated using doxorubicin and doxorubicinol concentrations in healthy pig blood, bile, and urine, and kidney and liver tissues. In the generic semiPBPK model, tissue binding was described using the conventional Kp,t approach. In the bindingspecific semi-PBPK model, tissue binding was described using intracellular binding sites. The best semi-PBPK model was validated against a second data set of healthy pig blood and bile concentrations. Both models could be used for analysis and simulations of biliary and urinary excretion of doxorubicin and doxorubicinol and plasma doxorubicinol concentrations in pigs, but the binding-specific model was better at describing plasma doxorubicin concentrations. Porcine tissue concentrations were 400- to 1250-fold better captured by the binding-specific model. This model adequately predicted plasma doxorubicin concentration-time and biliary doxorubicin excretion profiles against the validation data set. The semi-PBPK models applied were similarly effective for analysis of plasma concentrations and biliary and urinary excretion of doxorubicin and doxorubicinol in healthy pigs. Inclusion of intracellular binding in the doxorubicin semi-PBPK models was important to accurately describe tissue concentrations during in vivo conditions.

Keywords Doxorubicin, physiologically based pharmacokinetic modeling, PBPK, pig


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Abbreviations (A)AFE: (absolute) average fold error; AIC: Akaike Information Criterion; B:P: blood:plasma ratio; BW: average bodyweight of study population; Clexcr: excretion clearance to bile or urine; Clmem: cell membrane clearance in liver and kidney; Cloff: dissociation clearance from intracellular binding site; Clon: association clearance to intracellular binding site; Cmax: maximum plasma concentration; CO: cardiac output; CV%: coefficient of variation; DOX: doxorubicin; DOXol: doxorubicinol; Fu(,p/t): fraction unbound (in plasma/tissue); GFR: glomerular filtration rate; GI: gastro-intestinal tract; HA: hepatic artery; Jmax: total metabolic capacity; Km: Michaelis-Menten constant of DOX-to-DOXol metabolism; Kp,t: tissue:plasma partition coefficient; NC1: non-clinical in vivo study 1;NC2: nonclinical in vivo study 2; PBPK: physiologically based pharmacokinetic; Pdiff: capillary wall diffusion clearance in liver and kidney; SFmet: metabolic scaling factor in liver and kidney; VF: femoral vein; VH: hepatic vein; Vmax: maximum metabolic rate of DOX-to-DOXol metabolism; VP: portal vein



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Introduction Doxorubicin (DOX) is an anthracycline antibiotic antitumor substance that is used for the treatment of a wide range of cancers such as hematological malignancies, solid tumors and soft tissue sarcomas.1 DOX has poor intestinal absorption (bioavailability 1000 times the maximum plasma DOX concentration (Cmax)]. The initial analyses also showed that the model was incapable of discerning binding to the GI/spleen compartment. To reduce interference in the parameter estimations, the GI/spleen binding capacity was consequently set at 1 µM, representing negligible intracellular binding to this compartment. Similarly was GI/spleen compartment Kp,t set to one in the analysis using the generic sim-PBPK model. The intracellular binding capacity of DOXol was at all times set at 1% of the binding capacity of DOX so as to reduce the number of estimated parameters in the regression analysis. Drug disposition in eliminating and metabolizing tissue compartments (kidney and liver) The transport of drug between the three subcompartments of the liver and kidney (vascular, extracellular and cellular) and from the cellular to the excretion compartments (bile or urine) was identically described for the generic and binding-specific model (Fig 1c and d). The mass transport of DOX and DOXol across the arterial capillary wall was described by the capillary wall diffusion clearance (Pdiff), and transport across cellular membranes between the extracellular and cellular spaces was described by the cell membrane clearance (Clmem). Pdiff was assumed to be high (15 L/min), as the capillary wall should offer no resistance to DOX or DOXol.53 Both DOX and DOXol influx over the cell membrane could potentially be affected by carrier mediated processes (mediated by OCT6 and OATP1A2).47-49 However, since no quantification of carrier mediated uptake rates have been reported for these transporters the relative contribution of each process to the uptake could not be differentiated in this model. Consequently, Clmem described the sum of both carrier mediated transport and passive diffusion. In analogy, to avoid over-parameterization of the model, Pdiff and Clmem were assumed to be similar in liver and kidney.


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Several carrier proteins have been identified as mediating cellular efflux of DOX: Pgp (Pglycoprotein, MDR1, ABCB1), BCRP (ABCG2), MRP1 (ABCC1), MRP2 (ABCC2) and RALBP1.49, 54, 55

Of these transporters, porcine Pgp and MRP2 have high hepatic RNA expression and porcine MRP2

has high renal expression.51 In humans, BCRP, Pgp and MRP2 are located in the hepatic canalicular membrane and Pgp and MRP2 are located in the renal proximal tubule membranes facing the urine.56 To our knowledge, the hepatic and renal efflux rates for DOX and DOXol are unknown, and no differentiation between the different types of transport to bile or urine could therefore be made. Consequently, hepatic and renal excretion (Clexcr) of DOX and DOXol to bile and urine was described by unidirectional linear kinetics.57 Biliary excretion was modelled to take place from the hepatocellular subcompartment, while urinary excretion was specified from the vascular space via glomerular filtration (glomerular filtration rate, GFR: 2.4 ml/min/kg31) and from the kidney cellular space via renal excretion. Distribution of DOX and DOXol within the hepatic and renal cellular sub-compartments was modeled differently in the two semi-PBPK models. While no further intracellular distribution was modeled in the generic model, the cellular sub-compartments of kidney and liver in the binding-specific model contained an intracellular binding site. The distribution processes to and from the intracellular binding sites were described in the same way as in the non-eliminating, non-metabolizing tissue compartments. The biotransformation of DOX to DOXol was described identically in both semi-PBPK models. This process is reported to occur mainly in the liver and kidney, and is mediated by cytosolic aldoketoreductases (AKR1A1, AKR1C3) and carbonyl-reductases (CBR1, CBR3).54, 58, 59 The biotransformation step was modeled using non-linear kinetics in the cellular sub-compartment of the liver and the kidney, as total enzyme activity for both liver and kidney was available from human data.59 The maximum velocity of the reaction (Vmax) and the Michaelis-Menten constant (Km) were obtained from human tissue homogenate data: 337 pmol/(mg protein×min) and 163 µM in the liver, and 127 pmol/(mg protein×min) and 134 µM in the kidney.59 The total metabolic capacity (Jmax) of the DOX to DOXol biotransformation was scaled from protein to organ level using the total protein content of hepatocytes in the rat (1.06 mg protein/106 cells), hepatocellularity in humans (120×106



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cells per g of liver) and porcine kidney or liver weights.32 Finally, a metabolic scaling factor (SFmet) was introduced to enable estimation of the final metabolic capacity and DOX-to-DOXol metabolic rate to correct for interspecies and organ differences. Other metabolic routes for DOX were not included as they appear to be minor compared with the metabolism of DOX to DOXol12, 44, initial modeling determined it to be insignificant to the model and in order to avoid over-parameterization of the model. PBPK model selection The two described model strategies for tissue distribution were evaluated by the semi-PBPK models’ ability to describe observations. The semi-PBPK models (generic and binding-specific) were fitted to the observed DOX and DOXol concentrations in plasma, bile, urine, and kidney and the DOX concentrations in liver.10, 24 Parameter estimations were performed to all available observed data simultaneously (not ID marked) using nonlinear least-squares regression. A weighting scheme of 1/( ∗ ) was adopted for blood concentrations while a uniform weighting scheme was used for tissue concentrations, and biliary and urinary excretion profiles. In the analysis using the generic model, 16 parameters were estimated: Kp,t for DOX and DOXol for lung, GI/spleen, and slow and rapid perfused tissues, Clmem for DOX and DOXol, the SFmet for metabolic clearance of DOX to DOXol in the liver and kidney, and biliary and renal clearance (Clexcr) for DOX and DOXol. There were 948 degrees of freedom in the analysis, calculated as: degrees of freedom = number of observations – number of parameters estimated. In the analysis using the binding-specific model, 12 parameters were estimated: Clmem for DOX and DOXol, the SFmet for metabolic clearance of DOX to DOXol in the liver and kidney, biliary and renal clearance (Clexcr) for DOX and DOXol, and Clon and Cloff for DOX and DOXol. There were 951 degrees of freedom in the analysis. Assessment of goodness of fit of the two semi-PBPK models was performed by evaluation of the Akaike Information Criterion (AIC), visual inspection of the curve and review of the precision of the parameter estimates [using the coefficient of variation (CV%)]. AIC and CV% were provided by the modeling software. The overall model performance in capturing observations with multiple


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measurements over time, e.g., plasma concentrations and biliary excretion, was assessed in terms of absolute average fold error (AAFE), which is the geometric mean of the ratio of absolute predicted and observed mean data. The AAFE was calculated using equation 1: = 10

∑

eq. 1

where simulated is the value given by the model at a specific time point, observed is the average observed value at that time point, and N is the total number of observations. An AAFE value of one indicates a perfect fit while a value of two indicates an average 2-fold difference from the mean observed data points. The performance was categorized into three levels: good performance (AAFE ≤ 1.25), adequate performance (AAFE 1.25 – 2) and poor performance (AAFE > 2). The same approach was applied for one-time-point observations, that is, amount excreted in urine and tissue concentrations, giving a value for the AAFE between simulation and observation. The model related to the lowest AIC and AAFE was determined to be the best model for this data and was chosen for continuation of the study. Model performance evaluation The model selected as most appropriate in the model selection stage was then further evaluated using data from the NC1 study.23 Firstly, simulations were performed using the estimated parameters in the model selection stage (based on NC2 data) but with the dosing regimen from the NC1 study. Secondly, the selected model was fitted to the NC1 data set by parameter estimation (as described in the paragraph: semi-PBPK model selection). The NC2 parameters for kidney excretion clearance and tissue distribution were used as there were no urine or tissue measurements from the NC1 study. The six parameters estimated from the NC1 data were: Clmem for DOX and DOXol, the SFmet for DOX to DOXol metabolism in liver and kidney, biliary clearance (Clexcr) for DOX and DOXol. There were 482 degrees of freedom in the analysis. Finally, the parameters estimated from the NC1 data were applied for simulations with the NC2 dosing regimen. Model performance was evaluated by comparing the AAFE between the outcomes and the observed data (as described in the paragraph: semi-PBPK model selection).



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Sensitivity analysis A primary sensitivity analysis of the selected semi-PBPK model was conducted by varying each of the parameter estimates between 50% and 200% of their original value. The NC2 study setup was used to evaluate the sensitivity of the model output to changes in selected parameters. Simulated plasma and tissue concentrations, and biliary and urinary excretion were evaluated for the sensitivity analysis. Changes in concentration (plasma, tissue) and amount (bile, urine) at the last time point (360 min) were estimated by calculating the average fold error (AFE) according to equation 2: =10

∑

eq. 2

The NC2 study also included a study arm in which pigs received a concomitant treatment with DOX and an inhibitor of biliary and renal carrier mediated excretion (cyclosporine A).24 The observed data from this study arm enabled specific evaluation of the semi-PBPK models' capacity to capture the effect(s) of inhibition of DOX and DOXol excretion. A secondary sensitivity analysis was therefore performed where the four excretion parameters (that is excretion of DOX and DOXol to bile and urine) were reduced to 10% of the original parameter estimates (that is a 90% inhibition of excretion) from 200 minutes and onwards. The simulated curve was evaluated by the AAFE calculated on the observed and predicted values of plasma and tissue concentration and biliary and urinary excretion.


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Results PBPK model selection The two semi-PBPK models developed in this study, which were based on the same model structure, are shown in Figure 1a-d. The generic model described tissue binding using Kp,t and the bindingspecific model described tissue binding using an intracellular binding site in each tissue compartment (Figure 1a-d). The fit of the curves for both semi-PBPK models to the observed NC2 data set is shown in Figure 2, and the accuracy is shown in Table 2. The precision of the estimated parameters is shown in Table 3. The DOX plasma concentration-time profiles were poorly captured by the generic model (AAFE > 3, Fig 2a), but were adequately captured by the binding-specific model (AAFE 1.3-1.4, Fig 2c). Both semi-PBPK models captured the DOXol plasma concentration-time profiles adequately (AAFE ≤ 1.5, Fig 2a and c). The fitted biliary amount-time profiles for DOX and DOXol in both semi-PBPK models described the corresponding observed in vivo data adequately (Fig 2b and d), and the DOX profiles were slightly better captured than the DOXol profiles by both semi-PBPK models (Table 2). Urinary excretion of DOX and DOXol was well captured by the generic model (AAFE 1.1) and adequately captured by the binding-specific model (AAFE 1.4; Table 2), and was within the range of the observed values (Fig 2b and d). The generic model was not able to describe DOX and DOXol tissue concentrations in liver and kidney adequately (Fig 2b). The fitted tissue concentrations were at least 475-fold lower than observed data (Table 2). To reflect significant tissue-specific DOX and DOXol binding in liver and kidney, fu,t of 0.1 and 0.01 were added to these compartments (data not shown).The AAFE of the tissue concentrations were improved significantly (~11 at fu,t 0.1) by this exercise and an improvement in model fit (AAFE decreased to 1.2) was also acquired to bile profiles. However, the results for all plasma and urine became significantly worse: AAFE increased to >16 for the DOX plasma curve, >4 for the DOXol plasma curve and >2.1 for urine excretion (data not shown). When the binding-specific model was used to simulate the observed data, concentrations of DOX in liver and kidney tissues were well estimated (AAFE 70%, and 13 of 16 over 100% (Table 3). With the binding-specific model, the precision was acceptable or better than that of the generic model for the 12 estimated parameters, as the CV% was 2.7, Table 2, Fig 3e); the DOX biliary excretion rate was overpredicted (AAFE 1.8) when using the new parameter estimates; the DOXol biliary excretion rate predictions did not change much (Fig 3f and Table 2); the underprediction of urinary excretion of DOX was not affected, but underprediction of DOXol excretion was enhanced (Fig 3f and Table 2); and liver DOX concentration predictions became adequate, kidney DOX concentration predictions remained good, and kidney DOXol concentration predictions remained poor (Fig 3f and Table 2). Sensitivity analysis The primary sensitivity analysis, that is, simulations using parameters that were 50% and 200% of initial NC2 parameter estimates, indicated that the plasma concentration-time profile of DOX was only sensitive (change >10 % in last sample time point) to tissue distribution parameters (Clon and Cloff, Table 4). Clon and Cloff for DOX were the two most important parameters, with large effects on all outputs (Table 4). DOXol plasma concentration-time profiles were sensitive to changes in tissue distribution parameters, DOXol renal metabolism rate and DOXol renal excretion clearance (Table 4). Liver tissue DOX concentrations were not affected by any changes in parameter estimates. The secondary sensitivity analysis aimed to simulate the effects of concomitant administration of DOX and cyclosporine A on biliary and renal DOX and DOXol excretion (Figure 4). Reducing the parameter estimates of hepatic and renal excretion rates for both DOX and DOXol to 10% resulted in adequate simulations of biliary and urinary excretion of DOX and tissue DOX concentrations (Table 2). Plasma DOX concentrations were adequately predicted, while the DOXol plasma time-


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concentration curves were overpredicted after 200 minutes. DOX tissue concentrations were adequately predicted, and kidney DOXol concentrations were 2.2-fold underpredicted.



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Discussion Two semi-PBPK models for predicting systemic and local hepatic and renal concentrations of DOX were developed and described during this investigation and the first steps were taken to obtain a strategic theoretical method for improving intra-arterial hepatic drug delivery and understanding of drug and metabolite distribution. The results showed that the generic semi-PBPK model could describe DOX and DOXol plasma concentration-time profiles and biliary and urinary excretion rates adequately. However, liver and kidney concentrations were underestimated with this model. The binding-specific semi-PBPK model showed overall better performance as it, in addition to DOX and DOXol plasma concentrations and biliary and urinary excretion rates, also was able to capture DOX tissue concentrations. Therefore, the intracellular binding strategy (described by association and dissociation rates) was found more suitable than the traditional tissue distribution approach (adoption of Kp,t). Sensitivity analysis suggested that the binding-specific semi-PBPK model was especially sensitive to changes in the DOX association and dissociation rates (Clon and Cloff), which represent the significant intracellular binding of the drug. The data from both studies, NC1 and NC2, were adequately described by the binding-specific model. PBPK models are designed to describe the disposition of drugs and metabolites in the body, and generally seek to predict not only plasma concentration-time curves but also the effects of changes in physiology on drug disposition.53, 60 This study suggests that a generic semi-PBPK model is not favorable for simulating the disposition of DOX and DOXol, and that a binding-specific semi-PBPK model should be adopted. Tissue concentrations of the DOX molecule are elevated for prolonged periods in several species.16, 61, 62 These high tissue concentrations are reflected in the high tissue:plasma partition coefficients for the distribution of DOX into tissue (20 - 1800) that have been reported in a couple of in vivo studies.10, 11 Prolonged high tissue concentrations can be explained by DOX binding to both DNA and the acidic phospholipid for DOX-to-DOXol metabolism; the binding affinity for DNA is about 4-fold higher than that for cardiolipin.40-43 DNA is found mainly in cellular nuclei and mitochondria, while cardiolipin is located in the mitochondrial inner membrane.63 Intranuclear concentrations of DOX have been previously found to be elevated (50-fold) over


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cytoplasmic concentrations.44 The metabolite DOXol also binds to DNA, although with a 1.3-fold lower affinity and 1.7-fold lower capacity.64 The elevated tissue concentrations over a prolonged time span could not be replicated by the conventional methods such as Kp,t or the fraction of unbound drug in the liver and kidney (as used in the generic model). Although tissue concentrations could be better described with the generic model when including fraction of unbound drug in tissues, the corresponding plasma concentrations were significantly overpredicted by this approach. A previous study that reviewed the published data found that the in vivo Kp,t for DOX is not constant over time, that is tissue concentrations do not follow the decline in plasma concentrations.10 In our generic model, Kp,t was a constant, and thus did not vary over time. This could explain the inability of the generic model to capture the plasma concentrations when the fraction of unbound drug was added to the liver and kidney compartments. Kp,t can also be predicted by methods other than curve fitting; for example, calculations based on binding to neutral and acidic lipids in tissues can be used.65 However, for at least one of these alternative prediction methods, the Kp,t values for DOX were all significantly underpredicted.66 One published PBPK model, using saturable binding of doxorubicin to macromolecules in tissues, showed good prediction to human, canine and mice.16 This supports our finding that the use of Kp,t for modeling of DOX is not the best approach. Prolonged target (tissue) binding can be established when the dissociation rate (Cloff) from the target is slower than the effective elimination rate from the compartment.46 When the dissociation rate is slower than the association rate, there will be prolonged drug-target residence time.67 In this work, a prolonged binding time, and thus residence time, of DOX in body tissues was achieved by adding an intracellular binding site to each of the tissue compartments in the bindingspecific model. This added process has in vivo mechanistic relevance as it reflects the binding of DOX to DNA and cardiolipin. The dissociation clearance of DOX from the intracellular binding sites was slower than the total elimination clearance from each of the tissue compartments, resulting in high affinity and a prolonged residence time at the intracellular binding site. In vitro, the first-order decay of DNA-DOX adducts is biphasic and slow (half-lives: 3–39 h).68 The affinity constant for DOX binding to DNA ranges between 3.6 and 29.6 µM-1.41-43 This suggests that the dissociation rate is slower than the association rate for binding to DNA, and suggests that DOX would have a long drug-



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target residence time. This is in good agreement with well-known drug action of DOX, namely the intercalation of DOX to DNA.44 The rate-limiting step for appearance of DOXol in blood was the clearance between extracellular and cellular spaces in the liver and kidney, because of DOXol's low binding affinity to the intracellular binding site. In vitro, the affinity constant for DOXol binding to DNA is 1.3-fold lower, and the capacity is 1.7-fold lower, than that for DOX.64 However, this in vitro binding was assessed when only one of the substances was available for DNA binding. When DOX is administered to a healthy pig, it is distributed to tissue and DNA, which reduces the DNA binding spaces available when DOXol is formed. Accordingly, the binding capacity of DOXol to the intracellular binding sites was assumed to be 100-fold lower than that of DOX. The in vitro potency of DOXol is 75-fold lower than that of DOX, that is the IC50 is higher for DOXol than for DOX in hepatoblastoma cells.13 This could indicate a lower affinity of DOXol for the targets. This was in agreement with our estimations, where the binding affinity for DOXol was predicted to be 21 times lower than that for DOX. These values should be interpreted with care, however, as DOXol kidney concentrations were poorly fitted (>2-fold lower than observed), and it is possible that there were too few data to achieve optimal estimation of Clon and Cloff for DOXol. These models and substance specific binding processes will be used in the struggle to modify tissue affinity by using nanoparticles and subsequently reach a successful tissue targeting.5 During simulation of the NC1 data, using the parameter estimates from the NC2 study, the plasma DOXol concentrations were overpredicted, while the DOX biliary excretion rate was underpredicted. There was high observed inter-individual variation in the PK profiles of DOX (up to 7-fold) and DOXol (up to 10-fold) for data from both NC1 and NC2 (shown as ranges in Figs. 2 and 3). In the NC1 data set, the variability was clearly caused by one animal. This animal had higher plasma DOXol concentrations, slightly higher terminal plasma DOX concentrations, and a greater fraction of DOX excreted to bile compared to the other animals. The NC1 data set thus had one animal with significant different elimination of both DOX and DOXol than the other three animals. The initial NC1 simulations using NC2 parameter estimations predicted this animal’s PK behavior better than that of the other three animals, suggesting that this animal's PK behavior was more like the PK behavior of the six animals in the NC2 study. The variability in the NC2 data set could not be explained by just


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one animal. In humans, different polymorphisms of the ABCB1 (Pgp) transporter and the CBR1 and CBR3 metabolizing enzymes have been shown to influence exposure to DOX and DOXol.69-71 The variations between the individuals in the NC1 study might therefore be the result of variations in elimination or metabolism. When the binding specific model was fitted to the NC1 data set using estimation of hepatic excretion, SFmet and cell membrane clearance, the DOXol plasma concentration and DOX biliary excretion rate profiles improved. The total intrinsic metabolic clearance was not affected by the parameter estimation using NC1, but the DOX hepatic excretion rate increased 2.2fold. The variability between NC1 and NC2 in the hepatic excretion parameter estimations for both DOX and DOXol seems to suggest that the variability in excretion transporters is more important than the variability in metabolizing enzymes. Conclusion In conclusion, the semi-PBPK model taking intracellular binding into consideration, was superior for describing the prolonged, extensive tissue binding of DOX in comparison to the generic semi-PBPK model applying Kp,t for description of tissue distribution. The fitted concentration-time profiles for DOX and DOXol in liver and kidney, in particular, were better described. The improved fit of tissue concentration, favor the development of a binding-specific semi-PBPK model. Such a model can be used to predict the in vivo performance of novel intrahepatic drug delivery systems in humans with both liver cirrhosis and tumor tissue.3 This theoretical model will be useful in the further optimization of nanoparticle targeting in vivo to certain organ/tissues.

Supporting information The differential equations describing the mass transfer of DOX and DOXol in the generic and bindingspecific PBPK models are presented in the supporting information.



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Tables Table 1 Table 1. Physiological parameters for domestic pigs (weighing ~25 kg) and substance specific parameters (from non-porcine species) used in the semi-pharmacologically based pharmacokinetic (PBPK) models. Weight (fraction Blood flow Phyisological Parameters of BW) (Fraction of CO) Blood 0.0553 25 portal vein 0.21 30 hepatic artery 0.05 26-29 Lung 0.0109 25 1 25 Kidney 0.0055 0.114 31 a 35 Vascular space 0.05 a 32 Extracellular space 0.159 Cellular space 0.791a,b GI/spleen 0.0861c 25 0.21d Liver 0.0316 25 0.26e a 35 Vascular space 0.055 a 32 Extracellular space 0.159 Cellular space 0.786a,b Slow perfused 0.7154f 25 0.09 33, 34 b 0.0952 Rapid perfused 0.537b GFR Hepatocellularity (rat) Total protein content (rat)

2.4 ml/min/kg 31 120×106 cells per g of liver 32 1.06 mg protein/106 cells 32

Substance Specific Parameters 1.3 g,h 11, 37-39 B:P (non-porcine) 0.29g 36 ; 0.24h 36 Fu,p (human) 337 pmol/(mg protein×min) 59 Vmax liver (human) 163 µM 59 Km liver (human) 127 pmol/(mg protein×min) 59 Vmax kidney (human) 134 µM 59 Km kindey (human) a Fraction of tissue weight; b 1 minus the rest; c Summation of stomach, small intestine, large intestine, spleen; d As portal vein; e Summation of portal vein and hepatic artery; f Summation of adipose, bone, muscle and skin; g: Value for doxorubicin; h: Value for doxorubicinol. B:P: blood:plasma ratio; BW: average bodyweight of study population; CO: cardiac output (147 ml/min/kg31); Fu: fraction unbound in plasma; GFR: glomerular filtration rate; GI: gastro-intestinal tract; Km: MichaelisMenten constant; Vmax: maximum metabolic rate.


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 2 Table 2. The absolute average fold error (AAFE) between observed data and curve fitted or simulated data. Curve fitted data (CF) and simulated data (Sim) were generated using either the generic or the binding-specific semi-PBPK model. Parameter estimates used were obtained from curve fitting the models to observed data from either the NC123 or NC210, 24 studies. Binding- Binding- Binding- Binding- BindingGeneric specific specific specific specific specific PBPK model Curve fitting or CF CF Sim Sim CF Sim Simulation Parameter NC1 NC2 NC1 NC2a estimates from Compared with observed data NC2 NC2 NC2 NC1 NC1 NC2a from Plasma sampling site VH DOX 3.07 ↑ 1.33 ↑ 1.28 ↓ 1.88 ↑ 1.69 ↑ 1.3 ↓ VP DOX 3.08 ↑ 1.37 ↑ 1.34 ↑ 1.48 ↑ 1.38 ↑ 1.28 ↑ VF DOX 3.17 ↑ 1.33 ↑ 1.39 ↑ 1.64 ↑ 1.49 ↑ 1.31 ↑ VH DOXol 1.37 ↑ 1.4 ↑ 2.88 ↓ 6.78 ↑ 2.51 ↑ 1.56 ↑ 1.34 ↑ 3.05 ↓ 4.52 ↑ 1.58 ↑ 1.53 ↑ VP DOXol 1.27 ↑ VF DOXol 1.39 ↑ 1.4 ↑ 2.79 ↓ 4.97 ↑ 1.71 ↑ 1.57 ↑ Excretion sampling site Bile DOX 1.23 ↑ 1.24 ↑ 1.84 ↑ 1.85 ↓ 1.45 ↑ 1.18 ↑ 1.87 ↑ Bile DOXol 1.75 ↑ 1.65 ↑ 1.73 ↑ 2.14 ↑ 1.93 ↑ Urine DOX 1.07 ↓ 1.38 ↓ 1.42 ↓ 1.37 ↑ Urine DOXol 1.09 ↓ 1.44 ↓ 2.37 ↓ 1.77 ↓ Tissue sampling site Liver DOX 475 ↓ 1.18 ↓ 1.46 ↓ 1.55 ↓ Kidney DOX 1310 ↓ 1.04 ↓ 1.06 ↓ 1.57 ↓ Kidney DOXol 2550 ↓ 2.06 ↓ 2.11 ↓ 2.19 ↓ ↑The observed data were over-predicted by the semi-PBPK model used, ↓ the observed data were under-predicted by the semi-PBPK model used. White areas represent good performance (AAFE < 1.25), light grey areas represent adequate performance (AAFE 1.25-2), dark grey areas represent poor performance (AAFE > 2). a used Clexcr parameter estimates were set to 10% of estimated value after 200 min and values compared to the part of the NC2 data set where the animals received cyclosporine. DOX: doxorubicin; DOXol: doxorubicinol; VF: femoral vein; VH: hepatic vein; VP: portal vein.



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Table 3 Table 3. Assumed and estimated parameter values used in physiologically based pharmacokinetic (PBPK) modeling. Parameters were estimated using either the generic or the binding-specific semi-PBPK model and by fitting the curve to the observed data from either the NC123 or NC210, 24 studies. Estimated parameters are shown as means (CV%), assumed parameters are shown as specific values. Model settings PBPK model

Generic model

Binding-specific model

Binding-specific model

Study providing data set

NC2

NC2

NC1

Body weight (kg)

26.6

26.6

26.7

Dose 1 (µmol, start t=0 min) Dose 2 (µmol, start t=200 min) Infusion duration (min)

58.5

58.5

85.7

57.5

57.5

0

5.05

5.05

50

Estimated and assumed parameters Flux between compartments Kp,lung (-) Kp,rapid perfusion (-)

DOX

DOXol

DOX

DOXol

DOX

DOXol

2.37 (940) 1.68 (360)

2.76 (31000) 6.86 (1300)

1 1

1 1

1 1

1 1

Kp,slow perfusion (-) Kp,GI/spleen (-)

116 (71) 1.6 (380)

3.04 (550) 5.69 (820)

1 1

1 1

1 1

1 1

Pdiff (L/min)

Clmem (L/min)

15 7.9 (91)

15 0.104 (850)

15 5.49 (37)

15 0.00984 (40)

15 6.12 (64)

15 0.00357 (52)

Intracellular binding site

DOX

DOXol

DOX

DOXol

DOX

DOXol

Volumea (% of tissue volume)

-

-

0.15

-

0.15

-

Clon ( L/min)

-

-

Cloff (L/min)

-

-

Bining affinityb

0.966 (7.3) 0.000115 (11) 8400

0.109 (38) 0.000275 (64)

0.966

f

0.109f

0.000115f

0.000275f

497

Binding capacity (µM) GI-tract and spleen Lung, kidney, liver, slow and rapid perfused Excretion CLexcr,li (L/min) CLexcr,ki (L/min)

-

-

1

0.01

1f

0.01f

-

-

10000

100

10000ff

100f

DOX

DOXol

DOX

DOXol

DOX

DOXol

0.162 (75) 0.0528 (150)

0.619 (890) 0.213 (450)

0.462 (9.2) 0.215 (24)

0.178 (65) 0.0154 (42)

0.991 (12) 0.215f

0.115 (45) 0.0154f

Metabolism (DOX to Liver Kidney Liver Kidney Liver Kidney DOXol) Jmax (µmol/min) 38.7 2.2 38.7 2.2 38.7 2.2 c SFmet (-) 0.195 (120) 3.39 (240) 0.721 (27) 14.7 (17) 1.21 (22) 10.4 (71) Corrected metabolic capacity 7.5 32.6 7.6 27.9 46.8 23.2 (µmol/min)d Intrinsic metabolic clearance 0.056 0.243 0.046 0.171 0.287 0.173 (L/min)e a b c d equal for DOX and DOXol; calculated as Clon/Cloff. scaling factor for Michaelis-Menten equation; calculated as Jmax*SFmet; e calculated as Jmax*SFmet/Km; f assumed parameter values from curve fitting to NC2 data with bindingspecific semi-PBPK model;


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Clmem: cell membrane clearance; Clexcr: excretion clearance to bile or urine; Clon: association clearance to intracellular binding site; Cloff: disassociation clearance to intracellular binding site; CV%: coefficient of variation; DOX: doxorubicin; DOXol: doxorubicinol; Kp,t: tissue:plasma partition coefficient; Pdiff: capillary wall diffusion clearance.

Table 4 Table 4. Results of the sensitivity analysis, where parameter estimates were changed to 50% or 200% of the original parameter estimate. The fold difference in plasma, bile, urine and tissue concentrations at 360 min when using the changed compared to the original parameter estimate value is shown. Association (Clon) Dissociation (Cloff) Tissue distribution DOX DOXol DOX DOXol Comparison site

50%

200%

50%

200%

50%

200%

50%

200%

DOX

VH Plasma VP VF

1.47 1.38

0.64 0.70

1.00 1.00

1.00 1.00

0.59 0.60

1.78 1.64

1.00 1.00

1.00 1.00

1.43

0.66

1.00

1.00

0.59

1.67

1.00

1.00

Bile

1.22

0.78

1.00

1.00

0.91

0.83

1.00

1.00

Urine

1.64

0.56

1.00

1.00

0.86

1.23

1.00

1.00

Liver Kidney

0.92 0.81 1.83 1.83

0.98 1.15 0.53 0.53

1.00 1.00 1.74 1.74

1.00 1.00 0.59 0.59

1.00 1.30 0.82 0.82

1.06 0.66 1.46 1.46

1.00 1.00 0.91 0.91

1.00 1.00 1.38 1.38

1.83

0.53

1.74

0.59

0.82

1.46

0.91

1.38

Bile

1.81

0.49

1.16

0.79

0.90

1.26

0.94

1.10

Urine

2.14

0.47

1.26

0.95

0.81

1.40

0.98

1.19

Kidney

1.01

0.65 0.73 1.01 Liver (Clexcr,li) DOX DOXol

0.90

DOXol

VH Plasma VP VF

Excretion

1.01 1.01 0.76 Kidney (CLexcr,ki) DOX DOXol

Comparison site

50%

200%

50%

200%

50%

200%

50%

200%

DOX

VH Plasma VP VF

1.02 1.01

0.97 0.98

1.00 1.00

1.00 1.00

1.04 1.04

0.94 0.93

1.00 1.00

1.00 1.00

1.02

0.98

1.00

1.00

1.04

0.93

1.00

1.00

Bile

0.85

1.23

1.00

1.00

1.01

0.98

1.00

1.00

Urine

1.01

0.98

1.00

1.00

0.69

1.52

1.00

1.00

Liver Kidney

1.05 1.01 1.02 1.02

0.92 0.98 0.97 0.97

1.00 1.00 1.05 1.05

1.00 1.00 0.96 0.96

1.01 1.07 1.08 1.08

0.98 0.89 0.88 0.88

1.00 1.00 1.38 1.38

1.00 1.00 0.74 0.74

1.02

0.97

1.05

0.96

1.08

0.88

1.38

0.74

Bile

1.05

0.92

0.76

1.18

1.01

0.98

1.00

1.00

Urine

1.02

0.96

1.01

0.99

1.10

0.84

0.79

1.30

Kidney

1.00

1.00

1.00

1.00

1.01

0.97

1.01

0.86

DOXol

VH Plasma VP VF

Metabolism Comparison site DOX VH Plasma VP

Liver (SFmet,li) 50% 200% 1.03 0.95 1.02

0.97

Kidney (SFmet,ki) 50% 200% 1.04 0.93 1.05

0.92

DOXol compariso n site

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Liver (SFmet,li) 50% 200% 0.97 1.06 0.97


1.06

Kidney (SFmet,ki) 50% 200% 0.56 1.94 0.56

1.95


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

VF

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1.02

0.96

1.05

0.92

0.97

1.06

0.56

1.94

Bile

1.03

0.94

1.01

0.98

0.52

1.87

1.01

0.99

Urine

1.01

0.98

1.07

0.89

1.01

0.99

0.48

2.28

Liver

1.03

0.94

1.01

0.98

NA NA NA NA Kidney 1.01 0.98 1.08 0.87 1.00 1.00 0.62 1.02 The colors in the table denote the fold change of the measured variable (i.e. concentration at different sampling sites at 360 min). White areas mark a change between 0.9- and 1.1-fold, light grey areas mark a change between 0.667–0.9-fold and 1.1–1.5-fold, middle grey areas mark a change between 0.5–0.67-fold and 1.5–2-fold, and dark grey areas mark a change below 0.5-fold or above 2-fold. Clexcr: excretion clearance to bile or urine; Clon: association clearance to intracellular binding site; Cloff: dissociation clearance from intracellular binding site; DOX: doxorubicin; DOXol: doxorubicinol; SFmet: scaling factor for metabolism in liver or kidney; VF: femoral vein; VH: hepatic vein; VP: portal vein.


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Figure legends Figure 1. The structure of the semi-hysiologically based pharmacokinetic (PBPK) models for doxorubicin (DOX) and its active metabolite doxorubicinol (DOXol) is depicted in part (a) of the figure. The models consisted of 10 compartments (boxes), of which six described tissue, two described blood and two described excretion compartments. DOX solution (burgundy syringe) was administered to the venous blood compartment. The distribution of the drug into the tissue compartments is depicted for non-eliminating, non-metabolizing tissue (pink, b), kidney (orange, c) and liver (green, d). In each of the tissue compartments, the intracellular binding site (grey box) is only available in the bindingspecific semi-PBPK model. All tissues have blood flow (Q), and a tissue volume (V). Both kidney (c) and liver (d) comprise three subcompartments, i.e. vascular, extracellular and cellular, where the flux of the drug between subcompartments is described by passive diffusion (Pdiff) and cellular membrane clearance (Clmem). In both kidney (c) and liver (d), DOX and DOXol are excreted (Cl,excr) to urine or bile and DOX is metabolized to DOXol (J,met). In the binding-specific model, the volume of the intracellular binding site is 15% of the (cellular) tissue compartment volume, and both DOX and DOXol can be distributed to and from this site. Clexcr: excretion clearance to bile or urine; Clmem: cell membrane clearance in liver and kidney; Cloff: dissociation clearance from intracellular binding site; Clon: association clearance to intracellular binding site; Jmet: corrected total metabolic capacity; DOX: doxorubicin; DOXol: doxorubicinol; Fu,p: fraction unbound plasma; GI: Gastro-intestinal tract; HA: hepatic artery; ki(,b/u): bound/unbound concentration of substance in kidney; li(,b/u): bound/unbound concentration of substance in liver; Pdiff: Passive diffusion between vascular and extracellular space in liver and kidney; Clmem: Transport between extracellular and intracellular space in liver and kidney; t(,b/u): bound/unbound concentration of substance in tissue; V: volume; VH: hepatic vein; VP: portal vein.

Figure 2 Generic (a and b) and binding-specific (c and d) semi-PBPK model curve fits to the observed data from the NC2 study.10, 24 Panels a) and c) show the observed (means as symbols; ranges as grey areas) and curve-fitted (lines) plasma concentration-time profiles from blood sampled from the femoral vein (VF, green), portal vein (VP, blue) and hepatic vein (VH, pink) for DOX (circles, solid



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

lines) and DOXol (squares, dashed lines). Panels b) and d) show the observed (means as symbols; ranges as grey areas) and curve-fitted (lines) amounts excreted in bile over time for DOX (circles, solid lines) and DOXol (squares, dashed lines) and the observed (means and ranges, open symbols) and model-fitted (filled symbols) amounts (urine) and concentrations (liver and kidney) for DOX (circles, solid lines) and DOXol (squares, dashed lines) all collected at 360 min.

Figure 3 Simulation of observed NC1 data23 using the binding-specific semi-PBPK model and the parameter estimates from NC2 (a and b); curve fitting of the observed NC1.23 data using the bindingspecific semi-PBPK model (c and d); and simulation of the observed NC2 data.10, 24 using the bindingspecific semi-PBPK model and the parameter estimates from NC1 (e and f). Panels a), c) and e) show the observed (means as symbols; ranges as grey areas) and curve-fitted (lines) plasma concentrationtime profiles from blood sampled from femoral vein (VF, green), portal vein (VP, blue) and hepatic vein (VH, pink) for DOX (circles, solid lines) and DOXol (squares, dashed lines). Panels b), d) and f) show the observed (means as symbols; ranges as grey areas) and curve-fitted (lines) amounts excreted in bile over time for DOX (circles, solid lines) and DOXol (squares, dashed lines) and the observed (means and ranges, open symbols) and model-fitted (filled symbols) amounts (urine) and concentrations (liver and kidney) for DOX (circles, solid lines) and DOXol (squares, dashed lines) all collected at 360 min.

Figure 4 Simulation of observed NC2 data10, 24, where the animals received a concomitant DOX and transporter inhibitor (Cyclosporine A), using specific semi-PBPK model and parameter estimates from the NC2 study. Parameter estimates for the excretion of DOX and DOXol to bile and urine were reduced to 10% of their original value from 200 minutes during simulation of the observed data. Panel a) shows the observed (means as symbols; ranges as grey areas) and curve-fitted (lines) plasma concentration-time profiles from blood sampled from femoral vein (VF, green), portal vein (VP, blue) and hepatic vein (VH, pink) for DOX (circles, solid lines) and DOXol (squares, dashed lines). Panel b) shows the observed (means as symbols; ranges as grey areas) and curve-fitted (lines) amounts excreted in bile over time for DOX (circles, solid lines) and DOXol (squares, dashed lines) and the observed


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(means and ranges, open symbols) and model-fitted (filled symbols) amounts (urine) and concentrations (liver and kidney) for DOX (circles, solid lines) and DOXol (squares, dashed lines) all collected at 360 min.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19


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Figure 2 Generic (a and b) and binding-specific (c and d) semi-PBPK model curve fits to the observed data from the NC2 study.10, 24 Panels a) and c) show the observed (means as symbols; ranges as grey areas) and curve-fitted (lines) plasma concentration-time profiles from blood sampled from the vena femoralisfemoral vein (VF, green), vena portaportal vein (VP, blue) and vena hepaticahepatic vein (VH, pink) for DOX (circles, solid lines) and DOXol (squares, dashed lines). Panels b) and d) show the observed (means as symbols; ranges as grey areas) and curve-fitted (lines) amounts excreted in bile over time for DOX (circles, solid lines) and DOXol (squares, dashed lines) and the observed (means and ranges, open symbols) and model-fitted (filled symbols) amounts (urine) and concentrations (liver and kidney) for DOX (circles, solid lines) and DOXol (squares, dashed lines) all collected at 360 min. 145x125mm (300 x 300 DPI)



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Figure 3 Simulation of observed NC1 data.23 using the binding-specific semi-PBPK model and the parameter estimates from NC2 (a and b); curve fitting of the observed NC1.23 data using the binding-specific semiPBPK model (c and d); and simulation of the observed NC2 data.10, 24 using the binding-specific semi-PBPK model and the parameter estimates from NC1 (e and f). Panels a), c) and e) show the observed (means as symbols; ranges as grey areas) and curve-fitted (lines) plasma concentration-time profiles from blood sampled from vena femoralisfemoral vein (VF, green), vena portaportal vein (VP, blue) and vena hepaticahepatic vein (VH, pink) for DOX (circles, solid lines) and DOXol (squares, dashed lines). Panels b), d) and f) show the observed (means as symbols; ranges as grey areas) and curve-fitted (lines) amounts excreted in bile over time for DOX (circles, solid lines) and DOXol (squares, dashed lines) and the observed (means and ranges, open symbols) and model-fitted (filled symbols) amounts (urine) and concentrations (liver and kidney) for DOX (circles, solid lines) and DOXol (squares, dashed lines) all collected at 360 min. 191x214mm (300 x 300 DPI)


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Figure 4 Simulation of observed NC2 data.10, 24, where the animals received a concomitant DOPX and transporter inhibitor (Cyclosporine A), using specific semi-PBPK model and parameter estimates from the NC2 study. Parameter estimates for the excretion of DOX and DOXol to bile and urine were reduced to 10% of their original estimate value from 200 minutes during simulation of the observed data. Panel a) shows the observed (means as symbols; ranges as grey areas) and curve-fitted (lines) plasma concentration-time profiles from blood sampled from vena femoralisfemoral vein (VF, green), vena portaportal vein (VP, blue) and vena hepaticahepatic vein (VH, pink) for DOX (circles, solid lines) and DOXol (squares, dashed lines). Panel b) shows the observed (means as symbols; ranges as grey areas) and curvefitted (lines) amounts excreted in bile over time for DOX (circles, solid lines) and DOXol (squares, dashed lines) and the observed (means and ranges, open symbols) and model-fitted (filled symbols) amounts (urine) and concentrations (liver and kidney) for DOX (circles, solid lines) and DOXol (squares, dashed lines) all collected at 360 min.. 76x34mm (300 x 300 DPI)


Molecular Pharmaceutics Page 38 of 38

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A Model-Based Approach To Assessing the Importance of Intracellular

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