Preclinical translational physiologically based pharmacokinetic modeling for predicting human pharmacokinetics of proteolysis targeting chimeras: Case studies of vepdegestrant (ARV-471) and bavdegalutamide (ARV-110).

The major aim of this study was to develop a strategy for predicting human pharmacokinetics using physiologically based pharmacokinetic (PBPK) modelling. This was compared with allometry (of plasma concentration-time profiles using the Dedrick approach), in order to determine the best approaches and strategies for the prediction of human pharmacokinetics. PBPK and Dedrick predictions were made for 19 F. Hoffmann-La Roche compounds. A strategy for the prediction of human pharmacokinetics using PBPK modelling was proposed in this study. Predicted values (pharmacokinetic parameters, plasma concentrations) were compared with observed values obtained after intravenous and oral administration in order to assess the accuracy of the prediction methods. By following the proposed strategy for PBPK, a prediction would have been made prospectively for approximately 70% of the compounds. The prediction accuracy for these compounds in terms of the percentage of compounds with an average-fold error of <2-fold was 83%, 50%, 75%, 67%, 92% and 100% for apparent oral clearance (CL/F), apparent volume of distribution during terminal phase after oral administration (V(z)/F), terminal elimination half-life (t(1/2)), peak plasma concentration (C(max)), area under the plasma concentration-time curve (AUC) and time to reach C(max) (t(max)), respectively. For the other 30% compounds, unacceptable prediction accuracy was obtained in animals; therefore, a prospective prediction of human pharmacokinetics would not have been made using PBPK. For these compounds, prediction accuracy was also poor using the Dedrick approach. In the majority of cases, PBPK gave more accurate predictions of pharmacokinetic parameters and plasma concentration-time profiles than the Dedrick approach. Based on the dataset evaluated in this study, PBPK gave reasonable predictions of human pharmacokinetics using preclinical data and is the recommended approach in the majority of cases. In addition, PBPK modelling is a useful tool to gain insights into the properties of a compound. Thus, PBPK can guide experimental efforts to obtain the relevant information necessary to understand the compound's properties before entry into human, ultimately resulting in a higher level of prediction accuracy.

Physiologically based pharmacokinetic (PBPK) modeling was used to predict the human pharmacokinetics and drug-drug interaction (DDI) of GDC-2394. PBPK models were developed using in vitro and in vivo data to reflect the oral and intravenous PK profiles of mouse, rat, dog, and monkey. The learnings from preclinical PBPK models were applied to a human PBPK model for prospective human PK predictions. The prospective human PK predictions were within 3-fold of the clinical data from the first-in-human study, which was used to optimize and validate the PBPK model and subsequently used for DDI prediction. Based on the majority of PBPK modeling scenarios using the in vitro CYP3A induction data (mRNA and activity), GDC-2394 was predicted to have no-to-weak induction potential at 900 mg twice daily (BID). Calibration of the induction mRNA and activity data allowed for the convergence of DDI predictions to a narrower range. The plasma concentrations of the 4β-hydroxycholesterol (4β-HC) were measured in the multiple ascending dose study to assess the hepatic CYP3A induction risk. There was no change in plasma 4β-HC concentrations after 7 days of GDC-2394 at 900 mg BID. A dedicated DDI study found that GDC-2394 has no induction effect on midazolam in humans, which was reflected by the totality of predicted DDI scenarios. This work demonstrates the prospective utilization of PBPK for human PK and DDI prediction in early drug development of GDC-2394. PBPK modeling accompanied with CYP3A biomarkers can serve as a strategy to support clinical pharmacology development plans. SIGNIFICANCE STATEMENT: This work presents the application of physiologically based pharmacokinetic modeling for prospective human pharmacokinetic (PK) and drug-drug interaction (DDI) prediction in early drug development. The strategy taken in this report represents a framework to incorporate various approaches including calibration of in vitro induction data and consideration of CYP3A biomarkers to inform on the overall CYP3A-related DDI risk of GDC-2394.

YQA-14 is a novel and selective dopamine D3 receptor antagonist, with potential for the treatment of drug addiction. However, earlier compounds in its structural class tend to have poor oral bioavailability. The objectives of this study were to characterize the preclinical absorption, distribution, metabolism and excretion (ADME) properties and pharmacokinetics (PK) of YQA-14, then to simulate the clinical PK of YQA-14 using a physiologically based pharmacokinetics (PBPK) model to assess the likelihood of developing YQA-14 as a clinical candidate. For human PK prediction, PBPK models were first built in preclinical species, rats and dogs, for validation purposes. The model was then modified by input of human in vitro ADME data obtained from in vitro studies. The study data showed that YQA-14 is a basic lipophilic compound, with rapid absorption (Tmax ~ 1 h) in both rats and dogs. Liver microsomal clearances and in vivo clearances were moderate in rats and dogs consistent with the moderate bioavailability observed in both species. The PBPK models built for rats and dogs simulated the observed PK data well in both species. The PBPK model refined with human data predicted that YQA-14 would have a clearance of 8.0 ml/min/kg, a volume distribution of 1.7 l/kg and a bioavailability of 16.9%. These acceptable PK properties make YQA-14 an improved candidate for further research and development as a potential dopamine D3R antagonism for the treatment of drug addiction in the clinic.

Prediction of pharmacokinetics in humans is essential for translating preclinical data to humans and planning safe and efficient clinical studies. The performance of various methods in extrapolation of preclinical pharmacokinetic data to humans is usually benchmarked by the fraction of predictions falling within a predefined interval that is centred on the value observed clinically. Recently, such an approach was used to compare physiologically based pharmacokinetic (PBPK) modelling and allometry in predicting the pharmacokinetics of a set of compounds in humans. Here, we present an analysis of the same dataset, focusing on predictions falling outside such a relatively narrow and centrally located interval. These are the main risk determinants in extrapolation of preclinical pharmacokinetic data to humans and should therefore be thoroughly understood in a risk mitigation approach to the design of early-phase human studies. Values that had been previously predicted by allometry and by PBPK modelling in terms of the apparent total clearance after oral administration, apparent volume of distribution, area under the plasma concentration-time curve, maximum plasma drug concentration, time to reach the maximum plasma concentration and terminal elimination half-life in humans were used to generate a log-transformed dataset of predicted/observed ratios. The probabilities of mispredicting the values of these pharmacokinetic parameters using PBPK modelling and allometry were estimated by a bootstrap procedure on this set of ratios. Our results, albeit from a limited dataset, indicated that although PBPK modelling yielded higher fractions of satisfactory predictions than allometry, both methodologies were associated with a significant and occasionally high probability of obtaining mispredictions of pharmacokinetic parameters by factors of >2, >3 and >10. In line with recent proposals to extend the goals of early-phase human studies beyond safety and tolerability, and considering the need to mitigate risks in studies dealing with novel and highly potent drug candidates, we discuss these results in a pharmacological context. Concise recommendations are given regarding the use of allometric and PBPK extrapolation methodologies in the translation process. The results presented here should alert clinical investigators to the limitations inherent in all approaches to prediction of human pharmacokinetics from preclinical data. We propose an adaptive approach to the design of early-phase clinical studies, particularly when dealing with compounds that are characterized by novel and only partially understood pharmacological profiles.

The importance of predicting human pharmacokinetics during compound selection has been recognized in the pharmaceutical industry. To this end there are many different approaches that are applied. In this study we compared the accuracy of physiologically based pharmacokinetic (PBPK) methodologies implemented in GastroPlus™ with the one-compartment approach routinely used at Pfizer for human pharmacokinetic plasma concentration-time profile prediction. Twenty-one Pfizer compounds were selected based on the availability of relevant preclinical and clinical data. Intravenous and oral human simulations were performed for each compound. To understand any mispredictions, simulations were also performed using the observed clearance (CL) value as input into the model. The simulation results using PBPK were shown to be superior to those obtained via traditional one-compartment analyses. In many cases, this difference was statistically significant. Specifically, the results showed that the PBPK approach was able to accurately predict passive distribution and absorption processes. Some issues and limitations remain with respect to the prediction of CL and active transport processes and these need to be improved to further increase the utility of PBPK modelling. A particular advantage of the PBPK approach is its ability to accurately predict the multiphasic shape of the pharmacokinetic profiles for many of the compounds tested. The results from this evaluation demonstrate the utility of PBPK methodology for the prediction of human pharmacokinetics. This methodology can be applied at different stages to enhance the understanding of the compounds in a particular chemical series, guide experiments, aid candidate selection and inform clinical trial design.

Introduction:Human pharmacokinetic (PK) prediction can be a significant challenge to drug candidates undergoing transporter-mediated clearance, when only animal data and in vitro human parameters are available in the drug discovery stage. Areas covered:The extended clearance concept (ECC) that incorporates the processes of hepatic uptake, passive diffusion, metabolism and biliary secretion has been adapted to determine the rate-determining process of hepatic clearance and drug-drug interactions (DDIs). However, since the ECC is derived from the well-stirred model and does not consider the liver as a drug distribution organ to reflect the time-dependent variation of drug concentrations between the liver and plasma, it can be misused for compound selection in drug discovery. Expert opinion:The PBPK model consists of a set of differential equations of drug mass balance, and can overcome the shortcomings of the ECC in predicting human PK. The predictability, relevance and reliability of the model and the scaling factors for IVIVE must be validated using either the measured liver concentrations or DDI data with known transporter inhibitors, or both, in monkeys. A human PBPK model that incorporates in vitro human data and SFs obtained from the validated monkey PBPK model can be used for compound selection in the drug discovery phase.

Patupilone (EPO906) is a novel potent microtubule stabilizer, which has been evaluated for cancer treatment. A novel physiologically based pharmacokinetics (PBPK) model was developed based on nonclinical data to predict the disposition of patupilone in cancer patients. After a single intravenous dose (1.2 mg/kg) in male Han-Wistar rats, the tissue distribution of (14)C-patupilone was investigated by quantitative whole-body autoradiography (QWBA). The blood radioactivity and patupilone concentration were determined by LC-MS/MS and liquid scintillation counting. A novel PBPK model was developed based on rat tissue concentration data to predict blood concentration-time profiles of patupilone in cancer patients. PBPK parameters derived from the rat were applied to a human PBPK model. Phase I clinical pharmacokinetic data in Caucasian and Japanese cancer patients at various doses ranging from 0.75 to 10 mg/m(2) were successfully described using the PBPK approach. Patupilone dispositions in lung, heart, muscle, spleen, liver, brain, adipose, and testes of rats were well described using the PBPK model developed assuming a perfusion rate-limited distribution between different compartments. For skin and bone marrow, concentration-time profiles were modeled assuming a permeability-limited distribution between different compartments. The simulated human pharmacokinetic profiles from the PBPK model showed good agreement with observed clinical pharmacokinetic data, where the model predicted AUC, t(1/2), V(ss), and CL values were within approximately twofold of the observed values for all dose groups. The distribution of patupilone in rats was well described by a PBPK model based on measured tissue distribution profiles generated by QWBA combined with metabolism data. The human PBPK model adequately predicted blood pharmacokinetics of patupilone in cancer patients. The PBPK model based upon preclinical tissue distribution data can aid in successful prediction of pharmacokinetics in humans.

Pharmacokinetic (PK) modeling is a useful method for investigating drug absorption, distribution, metabolism, and excretion. The most commonly used mathematical models in PK modeling are the compartment model and physiologically based pharmacokinetic (PBPK) model. Although the theoretical characteristics of each model are well known, there have been few comparative studies of the compatibility of the models. Therefore, we evaluated the compatibility of PBPK and compartment models using the lumping method with 20 model compounds. The PBPK model was theoretically reduced to the lumped model using the principle of grouping tissues and organs that show similar kinetic behaviors. The area under the concentration–time curve (AUC) based on the simulated concentration and PK parameters (drug clearance [CL], central volume of distribution [Vc], peripheral volume of distribution [Vp]) in each model were compared, assuming administration to humans. The AUC and PK parameters in the PBPK model were similar to those in the lumped model within the 2-fold range for 17 of 20 model compounds (85%). In addition, the relationship of the calculated Vd/fu (volume of distribution [Vd], drug-unbound fraction [fu]) and the accuracy of AUC between the lumped model and compartment model confirmed their compatibility. Accordingly, the compatibility between PBPK and compartment models was confirmed by the lumping method. This method can be applied depending on the requirement of compatibility between the two models.

Objective: Preclinical studies have demonstrated that CFAK-C4 has anti-tumor efficacy in a variety of malignancies. To maximize its efficacy, it is necessary to understand the pharmacokinetic (PK) properties of CFAK-C4 in the body. A PK study was conducted to characterize CFAK-C4 disposition in plasma and various tissues, including brain, heart, liver, lung, muscle, spleen, and sternum. Subsequently, a physiologically-based pharmacokinetic (PBPK) model was developed to simultaneously characterize and predict plasma and tissue CFAK-C4 concentrations and thus help guide future dosing strategies alone and in combination. Methods: Female CD-1 mice received a single IP injection of CFAK-C4 with a dose of 50mg/kg, and then plasma and tissue samples were collected at serial time points after injection. Three mice were sacrificed at each time point. CFAK-C4 concentrations were determined by a validated LC-MS/MS method. Noncompartmental PK analysis was performed using WinNonlin (Pharsight, Version 5.3) for PK parameters. CFAK-C4 concentration-time profiles were fitted with a PBPK model composed of compartments for plasma, all the measured tissues, peritoneum, and a remainder compartment which represented all other tissues where CFAK-C4 was not measured, using ADAPT5 (BMSR, USC). The PBPK model was assessed by goodness-of-fit plots together with agreement of estimated parameters with noncompartmental analysis. Results: CFAK-C4 concentrations followed a monoexponential decay in plasma, while there was a longer elimination phase observed in tissues. As a result, CFAK-C4 concentration-time profiles in plasma and tissues were simultaneously fitted into a plasma-flow-rate-limited PBPK model successfully. Partition coefficients (Kp), as a measure of the extent of tissue distribution, and plasma clearance (Cl) were estimated by the PBPK model, while the volumes of distribution and plasma flow rates of tissues were fixed to physiological values. The model predicts that CFAK-C4 can be well distributed to various tissues quickly, and Cmax is achieved within half an hour after IP injection. The estimated Cl, 0.111 (±7.4%) l/h, was similar to the value from non-compartmental analysis (NCA) (0.0925 L/h). The model also predicts CFAK-C4 has the highest penetration to lung, with a Kp of 28.1 (±15.2%), followed by brain, with a Kp of 16.6 (±11.6%), and it has the lowest penetration to muscle, with a Kp of 3.6 (±8.3%). Conclusions: The wide tissue distribution of CFAK-C4 provides a great advantage for maximizing its anti-tumor efficacy. The PBPK model predicts CFAK-C4 plasma and tissue concentrations reasonably well. This PBPK model will be used as a tool to build PK/PD models to characterize the temporal relationship between CFAK-C4 pharmacokinetics and its antitumor efficacy and thus help decide future dosing strategies for the treatment of various malignancies. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3786. doi:1538-7445.AM2012-3786

Predicting human exposure of active drug after oral prodrug administration, using a joined in vitro/in silico–in vivo extrapolation and physiologically-based pharmacokinetic modeling approach

The quantitative prediction of human pharmacokinetics (PK) including the PK profile and key PK parameters are critical for early drug development decisions, successful phase I clinical trials, and the establishment of a range of doses to enable phase II clinical dose selection. Here, we describe an approach employing physiologically based pharmacokinetic (PBPK) modeling (Simcyp) to predict human PK and to validate its performance through retrospective analysis of 18 Genentech compounds for which clinical data are available. In short, physicochemical parameters and in vitro data for preclinical species were integrated using PBPK modeling to predict the in vivo PK observed in mouse, rat, dog, and cynomolgus monkey. Through this process, the in vitro to in vivo extrapolation (IVIVE) was determined and then incorporated into PBPK modeling in order to predict human PK. Overall, the prediction obtained using this PBPK-IVIVE approach captured the observed human PK profiles of the compounds from the dataset well. The predicted Cmax was within 2-fold of the observed Cmax for 94% of the compounds while the predicted area under the curve (AUC) was within 2-fold of the observed AUC for 72% of the compounds. Additionally, important IVIVE trends were revealed through this investigation, including application of scaling factors determined from preclinical IVIVE to human PK prediction for each molecule. Based upon the analysis, this PBPK-based approach now serves as a practical strategy for human PK prediction at the candidate selection stage at Genentech.

Requirements for predicting human pharmacokinetics in drug discovery are increasing. Developing different methods of human pharmacokinetic prediction will facilitate lead optimization, candidate nomination, and dosing regimens before clinical trials at various early drug discovery stages. To develop and validate generic methods of human pharmacokinetic prediction to meet the requirements in early drug discovery. The physiologically based pharmacokinetic (PBPK) model implemented in Gastroplus™ was used for human pharmacokinetic predictions. The absorption, distribution, metabolism, and excretion properties of drugs in humans predicted from molecular structure and extrapolated from tested preclinical data were used as inputs in the PBPK model. The approaches were validated by comparison of the predicted pharmacokinetic parameters with actual pharmacokinetic parameters of 15 marketed small-molecule compounds approved by the US Food and Drug Administration. Based on the validation and reported approaches, we proposed a strategy for human pharmacokinetic prediction at different drug discovery stages. Obvious underestimation of exposure (< 1/3 of actual exposure) was not observed using in silico prediction as inputs, which may reduce the probability of missing the potential compounds with predicted false low exposure. The simulated human pharmacokinetic results using tested data as inputs were superior to those obtained via in silico prediction. Both methods similarly predicted the multiphasic shape of pharmacokinetic profiles. These generic PBPK approaches of full in silico prediction or perdition using a combination of tested in vivo and in vitro data were validated and proved useful for human pharmacokinetic predictions.

Hydroxychloroquine (HCQ) is a lysotropic autophagy inhibitor that is being used in over 45 clinical trials either alone or in combination with another chemotherapeutic. Pharmacokinetic (PK) and pharmacodynamic (PD) studies with HCQ have shown that drug exposure in the blood does not correlate with autophagy inhibition in either peripheral blood mononuclear cells (PBMCs) or tumor tissue (Autophagy 10:1415). HCQ exhibits primarily pH-driven PK and has been shown, by way of heightened levels of autophagy markers, to generate a therapeutic effect longer than PK data suggests. A physiologically-based pharmacokinetic model (PBPK) was developed for HCQ to describe the tissue-specific absorption, distribution, metabolism, and excretion as well as lysosome-specific sequestration. Physiologic parameters were adapted from literature, or obtained from experimental data when necessary, and used to simulate physiologically-based HCQ PK following designated dosing regimen in mice and rats. Experimentally derived PK data from these species were compared to simulation generated data to drive model development and subsequently determine model accuracy, achieving statistically-similar PK predictions of blood and tissues. Through allometric scaling and species-specific parameter modifications this model can be easily adapted for accurate prediction of HCQ PK in dogs and humans, as determined by comparison with respective blood levels. The value of this model lies in its ability to simulate HCQ PK in cancer patients with tumor types deemed autophagy-dependent. Model data simulating HCQ uptake in a neutral tumor compartment (pH = 7.2) shows that peak concentration in the lysosomes, the active site of the drug, is roughly four-fold higher than the peak concentration of lysosomes in an acidic tumor compartment (pH = 6.8), yet there is only a small change between whole-tumor concentrations. This suggests that the ability of HCQ to inhibit autophagy in acidic tumors would be significantly reduced, which is currently being investigated through in vitro and in vivo uptake of HCQ in MDA-MB-231 and MCF-7 tumor cell lines. Additionally, HCQ PK exhibits large interpatient variability, thus model utilization is beneficial in determining if therapeutic levels of HCQ are achieved at the tumor site and the impact of variability in the local tumor environment on HCQ disposition. The flexibility and simulation capabilities of the developed PBPK model also allows for investigation of how HCQ PK and subsequent autophagy inhibition may potentially be modified by other treatment modalities and suggest dosing schedules to optimize therapeutic response. Citation Format: Keagan P. Collins, Kristen M. Jackson, Daniel L. Gustafson. A physiologically-based pharmacokinetic (PBPK) model of hydroxychloroquine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1503. doi:10.1158/1538-7445.AM2017-1503

Allosteric oncogenic mutations occur outside the canonical ATP-binding site of EGFR and HER2, for which there are no approved single agent therapies that target this family. BDTX-189 is a potent, selective, irreversible inhibitor of the family of nearly 50 allosteric EGFR and HER2 mutant variants. The goal of this translational analysis was to predict the clinical pharmacokinetic (PK) profile of BDTX-189 utilizing in vitro data on the absorption, distribution and metabolism of BDTX-189 as well as in vivo PK data in preclinical species. The prospective PK modeling was conducted prior to initiation of a Phase 1/2 study, to provide predictions of clinical exposures and active dose range. A challenge in the design of irreversible inhibitors with optimal PK properties is the lack of reliable methods to predict their disposition and elimination in human. The PK of covalent drugs is often driven by extrahepatic elimination pathways, and therefore conventional approaches to predict human clearance using human hepatocytes or allometric scaling can lead to poor predictive accuracy. We employed a novel physiologically-based PK (PBPK) modeling strategy that accounted for compound-specific determinants of BDTX-189 metabolism and disposition. PK studies following intravenous (IV) and oral (PO) administration were conducted in preclinical species as well as in vitro studies to understand the ADME properties of BDTX-189. These preclinical data formed the basis of a PBPK modeling approach to predict the likely PK profile of BDTX-189 in human. The mechanistic assumptions used in the final models were able to recapitulate the observed animal PK after both IV and PO administration and thus predictions utilizing similar assumptions for human were considered plausible. Taken together with BDTX-189 exposure-response data in mouse models of anti-tumor efficacy, this enabled the prediction of potentially active dose levels in patients. Preclinical PBPK modeling indicated that BDTX-189 would be readily orally absorbed with a short elimination half-life (approximately 2 hours) while maintaining suppression of ErbB pathway biomarkers over the dosing interval, consistent with the irreversible mechanism of action and the desired ‘hit-and-run' PK/pharmacodynamic (PD) profile. Active dose levels in human were projected to be in the 400 - 800 mg QD range, based on the exposure - tumor growth inhibition relationship in multiple mouse PDX models harboring ErbB allosteric mutations. This study demonstrates that a PBPK modeling approach and an understanding of the determinants of clearance can provide an effective framework for preclinical-to-clinical translation. BDTX-189 is currently under clinical evaluation in the ongoing MasterKey-01 trial (NCT04209465), and clinical PK will be reported in due course. Citation Format: Giorgio Ottaviani, Matthew O'Connor, Alexander Flohr, Darlene Romashko, Alexander Mayweg, Elizabeth Buck, Nigel Waters. Prospective preclinical modeling to estimate clinical pharmacokinetics and doses of BDTX-189, an inhibitor of allosteric ErbB mutations in advanced solid malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB127.

Intratumoral pharmacokinetic (PK) and pharmacodynamic (PD) heterogeneity contribute to variability in NB tumor response to chemotherapy and can be responsible for tumor relapse. Herein we propose to develop a whole body PBPK model with an individualized tumor compartment to derive individual tumor specific concentration-time profiles for the NB standard of care drug TPT. This model can then relate intratumoral heterogeneity in tumor blood flow to PD response and antitumor effects. PK studies of TPT (0.6, 1.25, 5, and 20 mg/kg, IV bolus) will be performed in CD1 nude mice (n = 3 mice/time point) bearing orthotopic NB (NB5) xenograft. Blood samples will be collected at predetermined time points using cardiac puncture, and plasma separated and stored until analysis. Animals will be perfused using saline solution to remove residual blood, and tissue samples including tumor, muscle, adipose, bone, liver, gallbladder, kidney, spleen, lungs, brain, heart, duodenum, and large intestine collected. TPT concentrations in plasma and tissue homogenate samples will be quantified using a validated HPLC fluorescence spectrophotometry method. Tumor samples will be divided into two sections each, one for TPT quantification and one for immunohistochemistry of PD markers for DNA damage (γ-H2AX) and apoptosis (CASP3). A cohort of mice will be used to quantify tumor blood flow using contrast-enhanced ultrasound (CEUS) using MicroMarker® microbubbles prior to dosing the mice for the PK study. TPT plasma and tissue concentration-time data will be used to develop the whole-body PBPK model with an individualized tumor compartment using NONMEM. Individual tumor perfusion data obtained using CEUS will be combined with the PBPK model to derive tumor specific concentration-time profiles. A preliminary study conducted in non-tumor bearing mice receiving TPT 5 mg/kg showed that TPT plasma and tissue concentration-time data were reasonably described by our PBPK model. As expected from our previous studies, the brain tissue was found to have the lowest exposure to TPT with a brain to plasma partition coefficient (Kp,brain ∼ 8%). We also observed high permeability of TPT (Kp &amp;gt; 1) into the gallbladder, duodenum, large intestine, spleen, liver and kidney. In future we will study the correlations between individual tumor concentrations based on our comprehensive PBPK model and γ-H2AX and CASP3 activity. Citation Format: Yogesh T. Patel, Megan O. Jacus, Abbas Shirinifard, Abigail D. Davis, Suresh Thiagarajan, Stacy L. Throm, Vinay M. Daryani, Andras Sablauer, Clinton F. Stewart. Development of a whole body physiologically-based pharmacokinetic (PBPK) model with individualized tumor compartment for topotecan (TPT) in mice bearing neuroblastoma (NB). [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4519. doi:10.1158/1538-7445.AM2015-4519