Development of a clinically implementable population pharmacokinetic– pharmacodynamic model and interactive application for therapeutic drug monitoring of amrubicin

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Exposure to its active metabolite, amrubicinol, correlates with neutropenia severity, suggesting its potential role in therapeutic drug monitoring (TDM). This study aimed to develop a clinically implementable pharmacokinetics (PK)–pharmacodynamics (PD) framework and an interactive web-based application to support amrubicin TDM using routinely available clinical data. Methods A previously published population PK–PD model of amrubicin was simplified to enhance its clinical applicability by removing the enterohepatic circulation component of amrubicinol metabolism and the associated genetic covariate. Model performance was evaluated using prediction-corrected visual predictive checks and nonparametric bootstrapping. An interactive web application was developed using R Shiny and RsNLME to simulate individual PK and neutrophil dynamics using empirical Bayes estimates derived from observed data. Results The updated model adequately described the plasma concentration-time profiles of amrubicin and amrubicinol as well as neutrophil dynamics. The prediction-corrected visual predictive checks demonstrated good agreement between the observed and simulated data. The robustness of the parameter estimates was confirmed using nonparametric bootstrapping. The finalized model was integrated into an interactive web application that supports individualized simulations of PK and neutrophil profiles using data observed during the first treatment cycle. Conclusion We developed a simplified and clinically applicable population PK–PD model and integrated it into an interactive web application for amrubicin TDM. This framework provides a practical foundation for prospective studies to evaluate model-informed strategies to mitigate severe neutropenia. Amrubicin Therapeutic drug monitoring Population pharmacokinetic–pharmacodynamic model Neutropenia Small cell lung cancer Figures Figure 1 Figure 2 Figure 3 Introduction Cytotoxic anticancer drugs have a narrow therapeutic window, which poses a major challenge in clinical oncology. During drug development, dosing strategies are typically established based on determining the maximum tolerated dose (MTD) and recommended dose. Hematological toxicities, such as neutropenia and thrombocytopenia, frequently constitute dose-limiting toxicities (DLTs), and MTD is defined as the highest dose that can be administered without causing unacceptable toxicity. However, patient characteristics are considerably more heterogeneous in routine clinical practice than in controlled clinical trials. In particular, impaired performance status (PS), which reflects the overall patient condition, often predisposes patients to DLTs, resulting in treatment interruptions, dose reductions, or delays. Consequently, maintaining the relative dose intensity can be difficult, underscoring the substantial interindividual variability in the tolerability of cytotoxic chemotherapy. Pharmacokinetics (PK)–pharmacodynamics (PD) modeling has been widely applied to better understand and predict the drug-induced toxicities of anticancer agents. For hematological toxicity, Friberg et al. developed a semi-mechanistic myelosuppression model that links systemic drug exposure to the time course of neutrophil counts, enabling the simulation of nadir depth and recovery following chemotherapy initiation. This model and its extensions have been extensively used in the PK–PD analyses of cytotoxic agents [1, 2]. In contrast, PK profiles are highly drug-specific and may involve complex processes, such as active metabolites and multiple metabolic or elimination pathways, necessitating compound-specific PK–PD modeling strategies. Despite increasing evidence linking PD indices of efficacy, including tumor response and survival, to PK exposure for several anticancer drugs, the clinical implementation of therapeutic drug monitoring (TDM) remains limited in oncology. Carboplatin is a notable exception in that dosing based on renal function has been successfully implemented to reduce the risk of severe thrombocytopenia [3]. However, this approach relies primarily on a priori patient information rather than a posteriori exposure data. In contrast, accumulating evidence suggests the potential utility of TDM based on post-hoc PK information for several cytotoxic agents, including 5-fluorouracil [4, 5], busulfan [6–8], methotrexate [9, 10], paclitaxel [11, 12], docetaxel [13–15], and irinotecan [16]. Nevertheless, except for methotrexate and busulfan, TDM has not yet been widely adopted in routine clinical practice in Japan. Amrubicin hydrochloride is an anthracycline derivative that is primarily used to treat lung cancer. We have previously demonstrated a significant PK–PD relationship between systemic exposure to amrubicinol, the active metabolite of amrubicin, and neutropenia severity [17]. Furthermore, our findings have suggested that patients who experience severe neutropenia tend to have shorter survival times following amrubicin treatment, highlighting the clinical relevance of exposure-driven toxicity of this agent. In recent years, the treatment landscape for small cell lung cancer (SCLC) has evolved with the introduction of molecular-targeted therapies and immune checkpoint inhibitors. Nevertheless, amrubicin hydrochloride remains an important therapeutic option, particularly in second-line and later treatment settings. Therefore, optimizing the use of amrubicin to balance its efficacy and toxicity is clinically important. In this context, a practical framework for TDM that can leverage routinely available clinical data and sparse sampling to predict hematological toxicity may facilitate safer and more individualized dosing strategies. Accordingly, the objective of the present study was to develop a simplified and clinically implementable population PK–PD model of amrubicin and integrate this model into an interactive web-based application designed to support TDM in real-world clinical settings. Patients and Methods Patients and data Data from 50 patients were used in this study, comprising 388 plasma concentration measurements of amrubicin and amrubicinol and 357 absolute neutrophil count measurements obtained during the first cycle of amrubicin treatment. These data were collected in our previous research project titled “Study on pharmacokinetics and pharmacodynamics of amrubicin hydrochloride and pharmacogenetics using genetic polymorphisms as indicators" (G2007-006), conducted at the National Cancer Center Hospital between March 2008 and March 2015, for which consent for the secondary use of data was obtained. Population PK–PD model modification A previously published final population PK–PD model of amrubicin [17] was used as the template model. The model was updated using Phoenix® NLME™ 1.3 (Certara LP, Princeton, NJ, USA). To improve clinical applicability, the enterohepatic circulation component describing amrubicinol metabolism and the associated genetic covariate were removed from the PK model. Likelihood ratio testing was applied where appropriate for model comparison. The PD component describing neutrophil dynamics was unchanged from that of the original model. Covariate relationships were reevaluated using a stepwise covariate modeling approach. Model evaluation The model performance was assessed using standard internal validation approaches, including goodness-of-fit diagnostics, prediction-corrected visual predictive checks (VPCs), and nonparametric bootstrap analysis. Prediction-corrected VPCs were generated to account for differences in dosing regimens and covariate distributions among the patients. Bootstrap analysis was performed using 1,000 nonparametric resampling replicates. Model stability was evaluated based on the parameter estimate consistency. Development of the TDM application Based on the updated PK–PD model, a TDM application was developed to estimate the individual time courses of amrubicin and amrubicinol plasma concentrations and absolute neutrophil counts using sparse clinical data. The nonlinear mixed-effects modeling engine RsNLME (Version 1.1; Certara USA, Inc.) was integrated with the R Shiny framework to create a web-based, user-friendly application that enabled empirical Bayesian parameter estimation at the individual patient level. Bayesian estimation and uncertainty visualization Individual parameter estimates were obtained using maximum a posteriori (MAP) Bayesian estimation implemented in RsNLME. Using observational data and the constructed population PK–PD model, the standard errors of the empirical Bayes estimates were calculated following the approach described by Kang et al. [18]. Individual parameter uncertainty was characterized using the variance–covariance matrix of the MAP estimates, assuming a multivariate normal distribution. We performed 1,000 Monte Carlo simulations to propagate the uncertainty and derived 95% prediction intervals for the PK and PD profiles, which were visualized within the application. Ethical considerations This study was conducted in accordance with the Declaration of Helsinki and Ethical Guidelines for Medical Research Involving Human Subjects. The study protocol was approved by the Institutional Review Boards of Saitama Medical University International Medical Center (approval number 20–089) and the National Cancer Center. An opt-out approach was adopted instead of obtaining written informed consent. Results Patient characteristics The demographic and clinical characteristics of the patients included in the analysis are summarized in Supplementary Table 1. The dataset comprised 50 patients with SCLC. The median age was 64 years (range, 39–81 years), 80% of the patients were male, and 65% of the patients had a PS of 0 (the remaining patients had a PS of 1). The mean body surface area (± standard deviation) was 1.688 ± 0.522 m 2 . Model development and structural modification The updated population PK–PD model is shown in Fig. 1 . In the PK component, the enterohepatic circulation process for amrubicinol and the associated delay compartment parameter (kdc), which were included in our previously published [17] final model, were removed to improve clinical applicability. Consequently, the single nucleotide polymorphism of SLC28A3 , previously incorporated as a covariate influencing the metabolic rate constant within the enterohepatic circulation compartment, was excluded. Stepwise covariate analysis identified body surface area as a significant covariate for the metabolic clearance of amrubicin to amrubicinol, central distribution volume, and amrubicinol clearance, which is consistent with the previously published model. The PD structure was unchanged, with PS retained as a covariate that affected the mean maturation time. The updated model converged successfully. Model evaluation The results of the prediction-corrected VPCs are shown in Fig. 2 . The simulated percentiles adequately captured the observed plasma concentration-time profiles of amrubicin and amrubicinol as well as the time course of neutrophil counts. Nonparametric bootstrap analysis (1,000 replicates) demonstrated stable parameter estimates with successful convergence across runs. The population PK–PD parameter estimates of the previously published and updated models, together with the bootstrap results, are summarized in Table 1 . The updated model yielded parameter estimates comparable to those of the previous final model, and bootstrap-derived confidence intervals supported the robustness of the internal model. Goodness-of-fit plots further supported the adequacy of the updated model (Supplementary Fig. 1). Table 1 Comparison of population pharmacokinetic–pharmacodynamic parameter estimates between the previously published final model and the updated model with bootstrap evaluation Fixed effects Final model New modified model Bootstrap Estimate CV% Estimate CV% Estimate Estimation of population PK parameters -2 Log Likelihood -2745.4 -2673.5 tvVp, L 9.8 7.8 8.3 10.4 8.3 tvV2, L 28.5 10.0 31.7 5.6 31.7 tvCL2, L/h 9.2 15.8 10.6 7.6 10.6 tvV3, L 32.3 9.7 28.1 8.2 28.1 tvCL3, L/h 49.5 6.9 44.0 8.1 44.0 tvVm, L 1050.0 4.9 1224.2 4.2 1223.7 tvCLp, L/h 19.5 2.4 19.1 2.6 19.1 tvCLm, L/h 121.3 6.4 140.3 4.0 140.3 tvKdc, 1/h 0.1 7.8 - - - ωVp, % 33.3 34.6 38.4 31.7 38.1 ωV2, % 17.1 41.1 15.4 37.4 15.3 ωV3, % 30.3 22.7 33.3 5.7 33.3 ωCL3, % 33.3 13.4 34.2 22.4 34.1 ωVm, % 27.6 24.0 25.2 21.3 25.2 ωCLp, % 14.3 20.1 14.6 19.1 14.6 ωCLm, % 18.3 46.2 21.9 20.1 21.8 ωCL2, % - - 6.9 12.5 6.9 ωKdc, % - - - - - ωCLp-V3, 0.03 18.7 0.04 14.1 0.04 ωCLp-CL3, 0.04 11.4 0.04 17.1 0.04 ωV3-CL3, 0.10 14.0 0.11 12.1 0.11 ωVm-CLm, - - 0.05 16.6 0.05 Cov BSA (Vm) 1.5 28.2 1.6 14.1 1.6 Cov BSA (CLp) 1.0 16.6 1.0 14.5 1.0 Cov BSA (CLm) 1.8 27.5 1.9 14.3 1.9 Cov SLC28A3 (Kdc) -2.0 -39.1 - - - Cov PS (CLm) -0.3 -26.3 - - - \(\:{CL}_{p}={tvCL}_{p}\bullet\:{\left(\frac{{BSA}_{i}}{{BSA}_{mean}}\right)}^{covBSA}\bullet\:{e}^{\eta\:{CL}_{p}}\) \(\:{V}_{m}={tvV}_{m}\bullet\:{\left(\frac{{BSA}_{i}}{{BSA}_{mean}}\right)}^{covBSA\left({V}_{m}\right)}\bullet\:{e}^{\eta\:{V}_{m}}\) \(\:{CL}_{m}={tvCL}_{m}\bullet\:{\left(\frac{{BSA}_{i}}{{BSA}_{mean}}\right)}^{covBSA\left({CL}_{m}\right)}\bullet\:{e}^{\eta\:{CL}_{m}}\) Estimation of population PD parameters -2 Log Likelihood 746.5 753.8 tvCirc0 3.7 5.9 3.7 5.9 3.7 tvMMT 172.6 4.6 180.5 4.6 180.5 tvGamma, γ 0.4 16.5 0.4 17.9 0.4 tvGamma-m, γm 0.2 27.4 0.2 28.2 0.2 tvSlope 30.6 11.2 45.9 13.1 45.9 ωCirc0, % 14.7 28.4 31.9 34.1 31.9 ωMMT, % 31.4 35.1 14.8 29.7 14.8 ωSlope, % 52.4 22.8 54.5 26.7 54.5 Cov PS (MMT) -0.2 -39.2 -0.2 -42.0 -0.2 \(\:MMT=tvMMT\bullet\:{e}^{covPS\bullet\:\left(PS\right)}\bullet\:{e}^{\eta\:MMT}\) Parameter estimates from the original final model, the updated model developed in this study, and the results of the nonparametric bootstrap analysis (1,000 replicates) are shown. The updated model yielded parameter estimates comparable to those of the previously published model, and the bootstrap results demonstrated good stability and internal validity of the updated model. Abbreviations: CLp, metabolic clearance of amrubicin to amrubicinol; CLm, clearance of amrubicinol; CL2, intercompartmental clearance between the central and peripheral-1 compartments of amrubicin; CL3, intercompartmental clearance between the central and peripheral-2 compartments of amrubicin; Vp, central volume of distribution of amrubicin; V2, volume of distribution of the peripheral-1 compartment of amrubicin; V3, volume of distribution of the peripheral-2 compartment of amrubicin; Vm, central volume of distribution of amrubicinol; kdc, rate constant of the delay compartment representing enterohepatic circulation of amrubicinol (included only in the previously published final model); MMT, mean maturation time of neutrophils; Circ0, baseline absolute neutrophil count; BSA, body surface area; PS, performance status; PK, pharmacokinetics; PD, pharmacodynamics; CV%, coefficient of variation. Development of the TDM application Figure 3 shows the overall architecture of the web-based TDM application. The interface shown in Supplementary Fig. 2 includes modules for patient information, drug information data, blood sampling input, and Bayesian estimation. Using RsNLME, the individualized PK of amrubicin and amrubicinol as well as the predicted neutrophil dynamics during the first treatment cycle can be simulated based on sparse clinical data. The application generates graphical outputs with confidence intervals and provides downloadable reports, including the estimated PK–PD parameters. Discussion In this study, we developed a simplified and clinically applicable population PK–PD model and implemented it in an interactive web application for TDM of amrubicin, providing a practical foundation for prospective clinical studies aimed at evaluating model-informed strategies to mitigate severe neutropenia. During model refinement, we reevaluated the necessity of incorporating the enterohepatic circulation of amrubicinol and the associated kdc, which had been included in our previously published full model, together with the SLC28A3 single nucleotide polymorphism as a source of interindividual variability. Although this mechanism improved the descriptive performance of amrubicinol concentration–time profiles, its contribution to clinically relevant PD outcomes was limited. In our previous analysis, the severity of neutropenia was primarily associated with early systemic exposure to amrubicinol, as represented by the area under the concentration–time curve from 0 to 72 h (AUC 0–72 ), rather than delayed elimination. Consistent with this observation, the removal of the enterohepatic circulation component and kdc did not meaningfully affect the AUC 0–72 of amrubicinol or the ability of the model to describe the time course of neutrophil counts. Therefore, although the full model more closely reflected the detailed PK behavior, the enterohepatic circulation mechanism was unlikely to provide additional predictive value for the risk stratification of neutropenia. Moreover, the inclusion of SLC28A3 genotype information and parameters related to enterohepatic circulation limits the applicability of the model in routine clinical practice, as genetic testing is not feasible in many real-world oncology settings. From the perspective of TDM, which aims to support timely and practical dose optimization, a parsimonious model based solely on clinically obtainable covariates is preferable. Accordingly, we simplified the PK structure while retaining the ability of the model to predict neutropenia during the first treatment cycle. Importantly, despite the structural simplification, the population PK–PD parameter estimates remained consistent with those of the previously published final model. This suggests that the removal of the enterohepatic circulation mechanism and genotype-related variability did not compromise the fundamental exposure–toxicity relationship underlying neutropenia prediction. Nonparametric bootstrap analysis further supported the robustness and stability of the updated model parameters (Table 1 ). Although the present framework was developed for cytotoxic agents characterized by exposure-dependent hematologic toxicity, model-informed precision dosing and PK–PD analyses are increasingly being explored across diverse therapeutic modalities. Immune checkpoint inhibitors have been the subject of exposure–response investigations to characterize their associations with both efficacy and safety outcomes, supporting individualized dosing considerations [19, 20]. In addition, mechanistic PK–PD models have been proposed to describe the cytokine release dynamics associated with T cell-engaging therapies, such as bispecific antibodies [21]. Therefore, the conceptual structure of the current application may serve as a foundation for the broader implementation of model-based individualized dosing strategies beyond conventional cytotoxic chemotherapy. In parallel with the model development, we constructed a web-based TDM tool using R and Shiny to facilitate clinical implementation. The interface allows the entry of patient background information, dosing schedules, observed plasma concentrations, and neutrophil counts, followed by Bayesian estimation using RsNLME and visualization of individualized PK and neutrophil profiles (Supplementary Fig. 2). The graphical presentation was designed to be interpretable for clinicians who are not specialists in pharmacometrics, while preserving methodological rigor. Importantly, the development of the application was directly grounded in the updated population PK–PD model and therefore represents a translational implementation of pharmacometric findings rather than a purely technical software development exercise. We believe that the presentation of prediction intervals enhances the interpretation of simulation results. The median predicted neutrophil trajectory may serve as a primary reference for clinical decision-making, whereas the associated uncertainty provides complementary information that can support physician judgment and promote safer individualized treatment. The model evaluation relied on standard internal validation approaches, including prediction-corrected VPCs and nonparametric bootstrap analyses, which supported the internal consistency and robustness of the updated population PK–PD model. Nevertheless, this study has several limitations. First, model validation was restricted to an internal evaluation using the same dataset of 50 patients employed for model development. External validation in independent cohorts is essential to confirm generalizability and predictive performance in contemporary clinical settings. Second, the dataset was derived from patients treated during an earlier therapeutic era, prior to the widespread incorporation of immune checkpoint inhibitors and other novel agents into standard treatment algorithms for SCLC. Although amrubicin remains clinically relevant, evolving treatment strategies and supportive care practices may affect its toxicity profile and clinical outcomes. Therefore, a prospective evaluation under current treatment contexts is warranted. Finally, the relatively small sample size may have limited the covariate exploration and precision of the parameter estimates. Prospective evaluation of predictive performance metrics such as forecasting bias and precision in independent cohorts is essential to further establish clinical validity. Importantly, the primary objective of this study was not to establish a definitive clinical benefit but to develop a practical and clinically implementable model-informed TDM framework grounded in pharmacometric principles. Therefore, the present model and application should be regarded as a foundation for prospective evaluation rather than as a finalized clinical decision-support system. Ongoing and future clinical studies will determine its utility in optimizing dosing strategies and mitigating severe neutropenia in real-world practice. Conclusion We developed a simplified and clinically applicable population PK–PD model for amrubicin and implemented it in a user-friendly web-based application for TDM. By relying solely on routinely obtainable clinical data and sparse sampling, this framework enables the individualized simulation of PK profiles and neutrophil dynamics during the first treatment cycle. This study provides a practical foundation for prospective clinical studies evaluating model-informed strategies aimed at mitigating severe neutropenia in patients receiving amrubicin. Abbreviations CLp metabolic clearance of amrubicin to amrubicinol CLm clearance of amrubicinol CL2 intercompartmental clearance between the central and peripheral-1 compartments of amrubicin CL3 intercompartmental clearance between the central and peripheral-2 compartments of amrubicin Vp central volume of distribution of amrubicin V2 volume of distribution of the peripheral-1 compartment of amrubicin V3 volume of distribution of the peripheral-2 compartment of amrubicin Vm central volume of distribution of amrubicinol kdc rate constant of the delay compartment representing enterohepatic circulation of amrubicinol (included only in the previously published final model) MMT mean maturation time of neutrophils Circ0 baseline absolute neutrophil count BSA body surface area PS performance status PK pharmacokinetics PD pharmacodynamics CV% coefficient of variation. Declarations Funding information This research was supported by AMED under Grant Number JP20ck0106638. Acknowledgements We would like to thank Editage (www.editage.jp) for English language editing. Conflict of Interest The authors declared no conflict of interest. Author Contributions Y. Makino wrote the manuscript, designed and performed the research, and contributed to creating mockups of the apps to be developed. T. Ogawa developed the TDM application, contributed to the construction and validation of the new modified population PK–PD model, and revised the manuscript. T, Hamaguchi and M. Hirasaki, experts in clinical cancer genomics, provided advice regarding the modification of the amrubicin PK–PD model, specifically on excluding the enterohepatic circulation component (including the variable factor SLC28A3 ) from the model, and reviewed the manuscript. N. Sakiyama and R. 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Supplementary Files MakinoYModelbasedtherapeuticdrugmonitoringofamrubicinSupplementarytableCCP20260322.docx MakinoYModelbasedtherapeuticdrugmonitoringofamrubicinsupplementaryfigures1CCP20260322page1.tif MakinoYModelbasedtherapeuticdrugmonitoringofamrubicinsupplementaryfigures1CCP20260322page2.tif MakinoYModelbasedtherapeuticdrugmonitoringofamrubicinsupplementaryfigures2CCP20260322a.tif MakinoYModelbasedtherapeuticdrugmonitoringofamrubicinsupplementaryfigures2CCP20260322b.tif MakinoYModelbasedtherapeuticdrugmonitoringofamrubicinsupplementaryfigures2CCP20260322c.tif MakinoYModelbasedtherapeuticdrugmonitoringofamrubicinsupplementaryfigures2CCP20260322d.tif MakinoYModelbasedtherapeuticdrugmonitoringofamrubicinsupplementaryfigures2CCP20260322e.tif SupplementaryFigureLegends.docx Cite Share Download PDF Status: Under Revision Version 1 posted Reviewers agreed at journal 01 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers invited by journal 31 Mar, 2026 Editor assigned by journal 23 Mar, 2026 Submission checks completed at journal 23 Mar, 2026 First submitted to journal 22 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Makino","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYHACZoYEBgY5BgbGBpiIAUSYgBZjoJZGmB4itABBYgOSNQZ4XWVw7fBjg4c7bNLntx9uf/Azx45Bt715A8OPGgZ2c1xabqcZJySeScvdcCaxsbF3WzKD2ZljBYw9xxiYLRtwaUkwPpDYdjh3A0NiYwPvNub6bTdyDBh4GxiYDQ7g0pL+GaQlXb7/YWPj3231DGb33xgw/sWrJQfosLbDCQw3EhubebcdZjC7wWPAjM8Wyds5xQaJbWmGG248bJwtu+040C9pBYdljkng9Avf7fTNkj/bbOTl+9MffHy7rZrB7PjhjQ/f1Ngk4woxBay2AwUlknHFjjwO2xkY7PBH6CgYBaNgFIwgAABoiGKSssIDygAAAABJRU5ErkJggg==","orcid":"","institution":"Saitama Medical University International Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Yoshinori","middleName":"","lastName":"Makino","suffix":""},{"id":616040215,"identity":"5e9b0579-9c51-4ad3-980b-3676e00613ff","order_by":1,"name":"Takanori Ogawa","email":"","orcid":"","institution":"Showa Medical University","correspondingAuthor":false,"prefix":"","firstName":"Takanori","middleName":"","lastName":"Ogawa","suffix":""},{"id":616040216,"identity":"0ecff89a-ee26-41b6-b58f-91f848394c7f","order_by":2,"name":"Reiko Makihara–Ando","email":"","orcid":"","institution":"National Cancer Center Hospital","correspondingAuthor":false,"prefix":"","firstName":"Reiko","middleName":"","lastName":"Makihara–Ando","suffix":""},{"id":616040217,"identity":"e9240fcb-5c22-43e2-9325-08a4f472eb30","order_by":3,"name":"Naomi Sakiyama","email":"","orcid":"","institution":"National Cancer Center Hospital","correspondingAuthor":false,"prefix":"","firstName":"Naomi","middleName":"","lastName":"Sakiyama","suffix":""},{"id":616040218,"identity":"f6ecd58b-a097-4bdc-8445-280f3705b328","order_by":4,"name":"Masahito Yamazaki","email":"","orcid":"","institution":"Saitama Medical University International Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Masahito","middleName":"","lastName":"Yamazaki","suffix":""},{"id":616040219,"identity":"464bf5ec-c6c5-4641-a035-8d65e409a3d7","order_by":5,"name":"Chizuru Naito","email":"","orcid":"","institution":"Saitama Medical University International Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Chizuru","middleName":"","lastName":"Naito","suffix":""},{"id":616040220,"identity":"9171c479-e57a-4cbd-9917-2376ebc71edf","order_by":6,"name":"Hiroki Takayama","email":"","orcid":"","institution":"Saitama Medical University International Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Hiroki","middleName":"","lastName":"Takayama","suffix":""},{"id":616040221,"identity":"ac0887d2-6cf0-4fde-9c08-94f43643a44d","order_by":7,"name":"Genji Ueda","email":"","orcid":"","institution":"Saitama Medical University International Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Genji","middleName":"","lastName":"Ueda","suffix":""},{"id":616040222,"identity":"53e43038-3be6-41dd-89ea-64afa4c5446c","order_by":8,"name":"Shunsuke Kohyama","email":"","orcid":"","institution":"Saitama Medical University International Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Shunsuke","middleName":"","lastName":"Kohyama","suffix":""},{"id":616040223,"identity":"e2ce83d1-7381-4618-8922-67bf0448df62","order_by":9,"name":"Maki Todo","email":"","orcid":"","institution":"Saitama Medical University International Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Maki","middleName":"","lastName":"Todo","suffix":""},{"id":616040224,"identity":"22d786f7-5882-4c12-9cdc-c4640e981a98","order_by":10,"name":"Yasuhiro Kuwata","email":"","orcid":"","institution":"Saitama Medical University International Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Yasuhiro","middleName":"","lastName":"Kuwata","suffix":""},{"id":616040225,"identity":"2a1314ad-8d4e-48e0-a4c7-6e6dba8c6b8a","order_by":11,"name":"Masataka Hirasaki","email":"","orcid":"","institution":"Saitama Medical University International Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Masataka","middleName":"","lastName":"Hirasaki","suffix":""},{"id":616040226,"identity":"72440d34-41df-4e68-bd29-8a02c2d01cb0","order_by":12,"name":"Tetsuya Hamaguchi","email":"","orcid":"","institution":"Saitama Medical University International Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Tetsuya","middleName":"","lastName":"Hamaguchi","suffix":""}],"badges":[],"createdAt":"2026-03-22 12:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9191032/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9191032/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106205061,"identity":"db4c4a79-4a4a-4a8d-97aa-4ed45482cfe9","added_by":"auto","created_at":"2026-04-06 05:09:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62337,"visible":true,"origin":"","legend":"\u003cp\u003eUpdated population pharmacokinetic–pharmacodynamic (PK–PD) model of amrubicin\u003c/p\u003e\n\u003cp\u003eSchematic representation of the population PK–PD model describing the plasma concentration–time profiles of amrubicin and amrubicinol, and the time course of absolute neutrophil counts during the first treatment cycle in patients with small cell lung cancer.\u003c/p\u003e\n\u003cp\u003eAMROH, plasma concentration of amrubicinol; Circ, circulating compartment of observed blood cells; CL2, intercompartmental clearance between the central and peripheral-1 compartments of amrubicin; CL3, intercompartmental clearance between the central and peripheral-2 compartments of amrubicin; CLm, clearance of amrubicinol; CLp, metabolic clearance of amrubicin to amrubicinol; Comp, compartment; Kprol, proliferation rate constant determining the rate of cell division; Ktr, transit rate constant; MTT, mean maturation time; V2, peripheral-1 volume of distribution of amrubicin; V3, peripheral-2 volume of distribution of amrubicin; Vm, central volume of distribution of amrubicinol; Vp, central volume of distribution of parent amrubicin.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9191032/v1/e19856a72bb1bb5d34197901.png"},{"id":106205035,"identity":"bf5c1e13-6b8d-4c75-9859-d19e5cc8fba7","added_by":"auto","created_at":"2026-04-06 05:09:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":166237,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction-corrected visual predictive checks (VPCs)\u003c/p\u003e\n\u003cp\u003ePrediction-corrected VPCs for plasma amrubicin (AMR; left) and amrubicinol (AMROH; middle) concentrations and absolute neutrophil counts (right) in patients with small cell lung cancer during the first cycle of amrubicin treatment (days 1–3). Absolute neutrophil counts were Box–Cox transformed with a factor of 0.2. Closed circles represent observed data. Shaded areas indicate 95% prediction intervals of the simulated data. Solid, dashed, and dotted lines represent the 5th, 50th, and 95th percentiles of the observed data, respectively.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9191032/v1/df18d0474aeafae743c34df5.png"},{"id":106205028,"identity":"a68d202c-9414-461b-954d-18707f24c2f2","added_by":"auto","created_at":"2026-04-06 05:09:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42372,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the architecture and workflow of the therapeutic drug monitoring (TDM) application\u003c/p\u003e\n\u003cp\u003ePatient background information, dosing records, sparse PK data, and neutrophil data were integrated into the updated population PK–PD model. Individual parameter estimation was performed using Bayesian methods implemented in RsNLME, followed by the simulation and visualization of individualized PK profiles and neutrophil time courses. PK, pharmacokinetics; PD, pharmacodynamics\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9191032/v1/79a4eb7b32c2763a5a7aa45e.png"},{"id":106403553,"identity":"1f2f3a32-f143-4231-b50c-9615d612556f","added_by":"auto","created_at":"2026-04-08 09:14:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1161976,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9191032/v1/845c3706-21aa-4623-b3ec-a19bf8d026e8.pdf"},{"id":106205089,"identity":"871c7191-16f3-4cb2-970b-d703502051dc","added_by":"auto","created_at":"2026-04-06 05:09:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25048,"visible":true,"origin":"","legend":"","description":"","filename":"MakinoYModelbasedtherapeuticdrugmonitoringofamrubicinSupplementarytableCCP20260322.docx","url":"https://assets-eu.researchsquare.com/files/rs-9191032/v1/3d13b13fe85c11efc3b1613f.docx"},{"id":106205026,"identity":"65762089-9d2f-42fa-a5f9-670c6e704471","added_by":"auto","created_at":"2026-04-06 05:09:33","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8547788,"visible":true,"origin":"","legend":"","description":"","filename":"MakinoYModelbasedtherapeuticdrugmonitoringofamrubicinsupplementaryfigures1CCP20260322page1.tif","url":"https://assets-eu.researchsquare.com/files/rs-9191032/v1/acee26f19d2aaab523019a5d.tif"},{"id":106205133,"identity":"10f18950-6eb1-466c-b4be-f2889661900e","added_by":"auto","created_at":"2026-04-06 05:10:18","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":6541020,"visible":true,"origin":"","legend":"","description":"","filename":"MakinoYModelbasedtherapeuticdrugmonitoringofamrubicinsupplementaryfigures1CCP20260322page2.tif","url":"https://assets-eu.researchsquare.com/files/rs-9191032/v1/222af0581627d02b7ffcee6b.tif"},{"id":106205074,"identity":"1108302f-eaec-45c7-8f7a-d78c0f8da4fc","added_by":"auto","created_at":"2026-04-06 05:09:46","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":7034164,"visible":true,"origin":"","legend":"","description":"","filename":"MakinoYModelbasedtherapeuticdrugmonitoringofamrubicinsupplementaryfigures2CCP20260322a.tif","url":"https://assets-eu.researchsquare.com/files/rs-9191032/v1/2b2d90df16a7cf559ee0bd54.tif"},{"id":106205039,"identity":"b3e597e2-ebb0-4349-b0d2-842b33867d01","added_by":"auto","created_at":"2026-04-06 05:09:35","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":6805518,"visible":true,"origin":"","legend":"","description":"","filename":"MakinoYModelbasedtherapeuticdrugmonitoringofamrubicinsupplementaryfigures2CCP20260322b.tif","url":"https://assets-eu.researchsquare.com/files/rs-9191032/v1/f113d5c0d3996da5dc602838.tif"},{"id":106205067,"identity":"0dccbd84-7d07-413b-bad0-a66f64e930b5","added_by":"auto","created_at":"2026-04-06 05:09:45","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":7531864,"visible":true,"origin":"","legend":"","description":"","filename":"MakinoYModelbasedtherapeuticdrugmonitoringofamrubicinsupplementaryfigures2CCP20260322c.tif","url":"https://assets-eu.researchsquare.com/files/rs-9191032/v1/31ef6c4d576a1b451cf04048.tif"},{"id":106205047,"identity":"5477e451-4f86-42b6-b453-6101f6adeb76","added_by":"auto","created_at":"2026-04-06 05:09:37","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":7229926,"visible":true,"origin":"","legend":"","description":"","filename":"MakinoYModelbasedtherapeuticdrugmonitoringofamrubicinsupplementaryfigures2CCP20260322d.tif","url":"https://assets-eu.researchsquare.com/files/rs-9191032/v1/f03107bbd0582c5d6a5ba27e.tif"},{"id":106205032,"identity":"47198ea8-0fde-4db6-b6a6-34472aecdff0","added_by":"auto","created_at":"2026-04-06 05:09:34","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":9446628,"visible":true,"origin":"","legend":"","description":"","filename":"MakinoYModelbasedtherapeuticdrugmonitoringofamrubicinsupplementaryfigures2CCP20260322e.tif","url":"https://assets-eu.researchsquare.com/files/rs-9191032/v1/20605b6a830ebf760e00fd98.tif"},{"id":106205094,"identity":"99d1ce9e-df81-48ea-aa75-2bf25aa96748","added_by":"auto","created_at":"2026-04-06 05:09:50","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":15952,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureLegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-9191032/v1/a7f6d257c8a7df6551b36d74.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of a clinically implementable population pharmacokinetic– pharmacodynamic model and interactive application for therapeutic drug monitoring of amrubicin","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCytotoxic anticancer drugs have a narrow therapeutic window, which poses a major challenge in clinical oncology. During drug development, dosing strategies are typically established based on determining the maximum tolerated dose (MTD) and recommended dose. Hematological toxicities, such as neutropenia and thrombocytopenia, frequently constitute dose-limiting toxicities (DLTs), and MTD is defined as the highest dose that can be administered without causing unacceptable toxicity. However, patient characteristics are considerably more heterogeneous in routine clinical practice than in controlled clinical trials. In particular, impaired performance status (PS), which reflects the overall patient condition, often predisposes patients to DLTs, resulting in treatment interruptions, dose reductions, or delays. Consequently, maintaining the relative dose intensity can be difficult, underscoring the substantial interindividual variability in the tolerability of cytotoxic chemotherapy.\u003c/p\u003e \u003cp\u003ePharmacokinetics (PK)\u0026ndash;pharmacodynamics (PD) modeling has been widely applied to better understand and predict the drug-induced toxicities of anticancer agents. For hematological toxicity, Friberg et al. developed a semi-mechanistic myelosuppression model that links systemic drug exposure to the time course of neutrophil counts, enabling the simulation of nadir depth and recovery following chemotherapy initiation. This model and its extensions have been extensively used in the PK\u0026ndash;PD analyses of cytotoxic agents [1, 2]. In contrast, PK profiles are highly drug-specific and may involve complex processes, such as active metabolites and multiple metabolic or elimination pathways, necessitating compound-specific PK\u0026ndash;PD modeling strategies.\u003c/p\u003e \u003cp\u003eDespite increasing evidence linking PD indices of efficacy, including tumor response and survival, to PK exposure for several anticancer drugs, the clinical implementation of therapeutic drug monitoring (TDM) remains limited in oncology. Carboplatin is a notable exception in that dosing based on renal function has been successfully implemented to reduce the risk of severe thrombocytopenia [3]. However, this approach relies primarily on a priori patient information rather than a posteriori exposure data. In contrast, accumulating evidence suggests the potential utility of TDM based on post-hoc PK information for several cytotoxic agents, including 5-fluorouracil [4, 5], busulfan [6\u0026ndash;8], methotrexate [9, 10], paclitaxel [11, 12], docetaxel [13\u0026ndash;15], and irinotecan [16]. Nevertheless, except for methotrexate and busulfan, TDM has not yet been widely adopted in routine clinical practice in Japan.\u003c/p\u003e \u003cp\u003eAmrubicin hydrochloride is an anthracycline derivative that is primarily used to treat lung cancer. We have previously demonstrated a significant PK\u0026ndash;PD relationship between systemic exposure to amrubicinol, the active metabolite of amrubicin, and neutropenia severity [17]. Furthermore, our findings have suggested that patients who experience severe neutropenia tend to have shorter survival times following amrubicin treatment, highlighting the clinical relevance of exposure-driven toxicity of this agent.\u003c/p\u003e \u003cp\u003eIn recent years, the treatment landscape for small cell lung cancer (SCLC) has evolved with the introduction of molecular-targeted therapies and immune checkpoint inhibitors. Nevertheless, amrubicin hydrochloride remains an important therapeutic option, particularly in second-line and later treatment settings. Therefore, optimizing the use of amrubicin to balance its efficacy and toxicity is clinically important. In this context, a practical framework for TDM that can leverage routinely available clinical data and sparse sampling to predict hematological toxicity may facilitate safer and more individualized dosing strategies.\u003c/p\u003e \u003cp\u003e Accordingly, the objective of the present study was to develop a simplified and clinically implementable population PK\u0026ndash;PD model of amrubicin and integrate this model into an interactive web-based application designed to support TDM in real-world clinical settings.\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and data\u003c/h2\u003e \u003cp\u003eData from 50 patients were used in this study, comprising 388 plasma concentration measurements of amrubicin and amrubicinol and 357 absolute neutrophil count measurements obtained during the first cycle of amrubicin treatment. These data were collected in our previous research project titled \u0026ldquo;Study on pharmacokinetics and pharmacodynamics of amrubicin hydrochloride and pharmacogenetics using genetic polymorphisms as indicators\" (G2007-006), conducted at the National Cancer Center Hospital between March 2008 and March 2015, for which consent for the secondary use of data was obtained.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePopulation PK–PD model modification\u003c/h3\u003e\n\u003cp\u003eA previously published final population PK\u0026ndash;PD model of amrubicin [17] was used as the template model. The model was updated using Phoenix\u0026reg; NLME\u0026trade; 1.3 (Certara LP, Princeton, NJ, USA).\u003c/p\u003e \u003cp\u003eTo improve clinical applicability, the enterohepatic circulation component describing amrubicinol metabolism and the associated genetic covariate were removed from the PK model. Likelihood ratio testing was applied where appropriate for model comparison. The PD component describing neutrophil dynamics was unchanged from that of the original model. Covariate relationships were reevaluated using a stepwise covariate modeling approach.\u003c/p\u003e\n\u003ch3\u003eModel evaluation\u003c/h3\u003e\n\u003cp\u003eThe model performance was assessed using standard internal validation approaches, including goodness-of-fit diagnostics, prediction-corrected visual predictive checks (VPCs), and nonparametric bootstrap analysis. Prediction-corrected VPCs were generated to account for differences in dosing regimens and covariate distributions among the patients. Bootstrap analysis was performed using 1,000 nonparametric resampling replicates. Model stability was evaluated based on the parameter estimate consistency.\u003c/p\u003e\n\u003ch3\u003eDevelopment of the TDM application\u003c/h3\u003e\n\u003cp\u003eBased on the updated PK\u0026ndash;PD model, a TDM application was developed to estimate the individual time courses of amrubicin and amrubicinol plasma concentrations and absolute neutrophil counts using sparse clinical data.\u003c/p\u003e \u003cp\u003eThe nonlinear mixed-effects modeling engine RsNLME (Version 1.1; Certara USA, Inc.) was integrated with the R Shiny framework to create a web-based, user-friendly application that enabled empirical Bayesian parameter estimation at the individual patient level.\u003c/p\u003e\n\u003ch3\u003eBayesian estimation and uncertainty visualization\u003c/h3\u003e\n\u003cp\u003eIndividual parameter estimates were obtained using maximum a posteriori (MAP) Bayesian estimation implemented in RsNLME. Using observational data and the constructed population PK\u0026ndash;PD model, the standard errors of the empirical Bayes estimates were calculated following the approach described by Kang et al. [18]. Individual parameter uncertainty was characterized using the variance\u0026ndash;covariance matrix of the MAP estimates, assuming a multivariate normal distribution. We performed 1,000 Monte Carlo simulations to propagate the uncertainty and derived 95% prediction intervals for the PK and PD profiles, which were visualized within the application.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki and Ethical Guidelines for Medical Research Involving Human Subjects. The study protocol was approved by the Institutional Review Boards of Saitama Medical University International Medical Center (approval number 20\u0026ndash;089) and the National Cancer Center. An opt-out approach was adopted instead of obtaining written informed consent.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003ePatient characteristics\u003c/h2\u003e\n \u003cp\u003eThe demographic and clinical characteristics of the patients included in the analysis are summarized in Supplementary Table\u0026nbsp;1. The dataset comprised 50 patients with SCLC. The median age was 64 years (range, 39\u0026ndash;81 years), 80% of the patients were male, and 65% of the patients had a PS of 0 (the remaining patients had a PS of 1). The mean body surface area (\u0026plusmn;\u0026thinsp;standard deviation) was 1.688\u0026thinsp;\u0026plusmn;\u0026thinsp;0.522 m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eModel development and structural modification\u003c/h2\u003e\n \u003cp\u003eThe updated population PK\u0026ndash;PD model is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In the PK component, the enterohepatic circulation process for amrubicinol and the associated delay compartment parameter (kdc), which were included in our previously published [17] final model, were removed to improve clinical applicability. Consequently, the single nucleotide polymorphism of \u003cem\u003eSLC28A3\u003c/em\u003e, previously incorporated as a covariate influencing the metabolic rate constant within the enterohepatic circulation compartment, was excluded. Stepwise covariate analysis identified body surface area as a significant covariate for the metabolic clearance of amrubicin to amrubicinol, central distribution volume, and amrubicinol clearance, which is consistent with the previously published model. The PD structure was unchanged, with PS retained as a covariate that affected the mean maturation time. The updated model converged successfully.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eModel evaluation\u003c/h2\u003e\n \u003cp\u003eThe results of the prediction-corrected VPCs are shown in Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The simulated percentiles adequately captured the observed plasma concentration-time profiles of amrubicin and amrubicinol as well as the time course of neutrophil counts.\u003c/p\u003e\n \u003cp\u003eNonparametric bootstrap analysis (1,000 replicates) demonstrated stable parameter estimates with successful convergence across runs. The population PK\u0026ndash;PD parameter estimates of the previously published and updated models, together with the bootstrap results, are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The updated model yielded parameter estimates comparable to those of the previous final model, and bootstrap-derived confidence intervals supported the robustness of the internal model. Goodness-of-fit plots further supported the adequacy of the updated model (Supplementary Fig. 1).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of population pharmacokinetic\u0026ndash;pharmacodynamic parameter estimates between the previously published final model and the updated model with bootstrap evaluation\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eFixed effects\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eFinal model\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\n \u003cp\u003eNew modified model\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eBootstrap\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eCV%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eCV%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\n \u003cp\u003eEstimation of population PK parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e-2 Log Likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-2745.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-2673.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003etvVp, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003etvV2, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003etvCL2, L/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003etvV3, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e32.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003etvCL3, L/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e49.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e44.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e44.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003etvVm, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1050.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1224.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1223.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003etvCLp, L/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003etvCLm, L/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e121.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e140.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e140.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003etvKdc, 1/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026omega;Vp, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e34.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e38.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e38.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026omega;V2, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e41.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e37.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026omega;V3, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e30.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e22.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026omega;CL3, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e34.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e34.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026omega;Vm, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e27.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e24.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e25.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e25.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026omega;CLp, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026omega;CLm, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e46.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e21.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026omega;CL2, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026omega;Kdc, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026omega;CLp-V3,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e18.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026omega;CLp-CL3,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026omega;V3-CL3,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026omega;Vm-CLm,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCov BSA (Vm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e28.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCov BSA (CLp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCov BSA (CLm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e27.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCov SLC28A3 (Kdc)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-39.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCov PS (CLm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-26.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CL}_{p}={tvCL}_{p}\\bullet\\:{\\left(\\frac{{BSA}_{i}}{{BSA}_{mean}}\\right)}^{covBSA}\\bullet\\:{e}^{\\eta\\:{CL}_{p}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{m}={tvV}_{m}\\bullet\\:{\\left(\\frac{{BSA}_{i}}{{BSA}_{mean}}\\right)}^{covBSA\\left({V}_{m}\\right)}\\bullet\\:{e}^{\\eta\\:{V}_{m}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CL}_{m}={tvCL}_{m}\\bullet\\:{\\left(\\frac{{BSA}_{i}}{{BSA}_{mean}}\\right)}^{covBSA\\left({CL}_{m}\\right)}\\bullet\\:{e}^{\\eta\\:{CL}_{m}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\n \u003cp\u003eEstimation of population PD parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e-2 Log Likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e746.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e753.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003etvCirc0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003etvMMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e172.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e180.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e180.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003etvGamma, \u0026gamma;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003etvGamma-m, \u0026gamma;m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e27.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e28.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003etvSlope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e30.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e45.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e45.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026omega;Circ0, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e31.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e34.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e31.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026omega;MMT, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e31.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e35.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e29.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026omega;Slope, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e52.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e22.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e54.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e26.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e54.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCov PS (MMT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-39.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e-42.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:MMT=tvMMT\\bullet\\:{e}^{covPS\\bullet\\:\\left(PS\\right)}\\bullet\\:{e}^{\\eta\\:MMT}\\)\u003c/span\u003e\u003c/span\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eParameter estimates from the original final model, the updated model developed in this study, and the results of the nonparametric bootstrap analysis (1,000 replicates) are shown. The updated model yielded parameter estimates comparable to those of the previously published model, and the bootstrap results demonstrated good stability and internal validity of the updated model.\u003cbr\u003e\n \u003cp\u003eAbbreviations:\u003c/p\u003e\n \u003cp\u003eCLp, metabolic clearance of amrubicin to amrubicinol; CLm, clearance of amrubicinol; CL2, intercompartmental clearance between the central and peripheral-1 compartments of amrubicin; CL3, intercompartmental clearance between the central and peripheral-2 compartments of amrubicin; Vp, central volume of distribution of amrubicin; V2, volume of distribution of the peripheral-1 compartment of amrubicin; V3, volume of distribution of the peripheral-2 compartment of amrubicin; Vm, central volume of distribution of amrubicinol; kdc, rate constant of the delay compartment representing enterohepatic circulation of amrubicinol (included only in the previously published final model); MMT, mean maturation time of neutrophils; Circ0, baseline absolute neutrophil count; BSA, body surface area; PS, performance status; PK, pharmacokinetics; PD, pharmacodynamics; CV%, coefficient of variation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eDevelopment of the TDM application\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the overall architecture of the web-based TDM application. The interface shown in Supplementary Fig. 2 includes modules for patient information, drug information data, blood sampling input, and Bayesian estimation. Using RsNLME, the individualized PK of amrubicin and amrubicinol as well as the predicted neutrophil dynamics during the first treatment cycle can be simulated based on sparse clinical data. The application generates graphical outputs with confidence intervals and provides downloadable reports, including the estimated PK\u0026ndash;PD parameters.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we developed a simplified and clinically applicable population PK\u0026ndash;PD model and implemented it in an interactive web application for TDM of amrubicin, providing a practical foundation for prospective clinical studies aimed at evaluating model-informed strategies to mitigate severe neutropenia.\u003c/p\u003e \u003cp\u003eDuring model refinement, we reevaluated the necessity of incorporating the enterohepatic circulation of amrubicinol and the associated kdc, which had been included in our previously published full model, together with the \u003cem\u003eSLC28A3\u003c/em\u003e single nucleotide polymorphism as a source of interindividual variability. Although this mechanism improved the descriptive performance of amrubicinol concentration\u0026ndash;time profiles, its contribution to clinically relevant PD outcomes was limited.\u003c/p\u003e \u003cp\u003eIn our previous analysis, the severity of neutropenia was primarily associated with early systemic exposure to amrubicinol, as represented by the area under the concentration\u0026ndash;time curve from 0 to 72 h (AUC\u003csub\u003e0\u0026ndash;72\u003c/sub\u003e), rather than delayed elimination. Consistent with this observation, the removal of the enterohepatic circulation component and kdc did not meaningfully affect the AUC\u003csub\u003e0\u0026ndash;72\u003c/sub\u003e of amrubicinol or the ability of the model to describe the time course of neutrophil counts. Therefore, although the full model more closely reflected the detailed PK behavior, the enterohepatic circulation mechanism was unlikely to provide additional predictive value for the risk stratification of neutropenia.\u003c/p\u003e \u003cp\u003eMoreover, the inclusion of \u003cem\u003eSLC28A3\u003c/em\u003e genotype information and parameters related to enterohepatic circulation limits the applicability of the model in routine clinical practice, as genetic testing is not feasible in many real-world oncology settings. From the perspective of TDM, which aims to support timely and practical dose optimization, a parsimonious model based solely on clinically obtainable covariates is preferable. Accordingly, we simplified the PK structure while retaining the ability of the model to predict neutropenia during the first treatment cycle. Importantly, despite the structural simplification, the population PK\u0026ndash;PD parameter estimates remained consistent with those of the previously published final model. This suggests that the removal of the enterohepatic circulation mechanism and genotype-related variability did not compromise the fundamental exposure\u0026ndash;toxicity relationship underlying neutropenia prediction. Nonparametric bootstrap analysis further supported the robustness and stability of the updated model parameters (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the present framework was developed for cytotoxic agents characterized by exposure-dependent hematologic toxicity, model-informed precision dosing and PK\u0026ndash;PD analyses are increasingly being explored across diverse therapeutic modalities. Immune checkpoint inhibitors have been the subject of exposure\u0026ndash;response investigations to characterize their associations with both efficacy and safety outcomes, supporting individualized dosing considerations [19, 20]. In addition, mechanistic PK\u0026ndash;PD models have been proposed to describe the cytokine release dynamics associated with T cell-engaging therapies, such as bispecific antibodies [21]. Therefore, the conceptual structure of the current application may serve as a foundation for the broader implementation of model-based individualized dosing strategies beyond conventional cytotoxic chemotherapy.\u003c/p\u003e \u003cp\u003eIn parallel with the model development, we constructed a web-based TDM tool using R and Shiny to facilitate clinical implementation. The interface allows the entry of patient background information, dosing schedules, observed plasma concentrations, and neutrophil counts, followed by Bayesian estimation using RsNLME and visualization of individualized PK and neutrophil profiles (Supplementary Fig.\u0026nbsp;2). The graphical presentation was designed to be interpretable for clinicians who are not specialists in pharmacometrics, while preserving methodological rigor. Importantly, the development of the application was directly grounded in the updated population PK\u0026ndash;PD model and therefore represents a translational implementation of pharmacometric findings rather than a purely technical software development exercise.\u003c/p\u003e \u003cp\u003eWe believe that the presentation of prediction intervals enhances the interpretation of simulation results. The median predicted neutrophil trajectory may serve as a primary reference for clinical decision-making, whereas the associated uncertainty provides complementary information that can support physician judgment and promote safer individualized treatment.\u003c/p\u003e \u003cp\u003eThe model evaluation relied on standard internal validation approaches, including prediction-corrected VPCs and nonparametric bootstrap analyses, which supported the internal consistency and robustness of the updated population PK\u0026ndash;PD model.\u003c/p\u003e \u003cp\u003eNevertheless, this study has several limitations. First, model validation was restricted to an internal evaluation using the same dataset of 50 patients employed for model development. External validation in independent cohorts is essential to confirm generalizability and predictive performance in contemporary clinical settings. Second, the dataset was derived from patients treated during an earlier therapeutic era, prior to the widespread incorporation of immune checkpoint inhibitors and other novel agents into standard treatment algorithms for SCLC. Although amrubicin remains clinically relevant, evolving treatment strategies and supportive care practices may affect its toxicity profile and clinical outcomes. Therefore, a prospective evaluation under current treatment contexts is warranted. Finally, the relatively small sample size may have limited the covariate exploration and precision of the parameter estimates. Prospective evaluation of predictive performance metrics such as forecasting bias and precision in independent cohorts is essential to further establish clinical validity.\u003c/p\u003e \u003cp\u003eImportantly, the primary objective of this study was not to establish a definitive clinical benefit but to develop a practical and clinically implementable model-informed TDM framework grounded in pharmacometric principles. Therefore, the present model and application should be regarded as a foundation for prospective evaluation rather than as a finalized clinical decision-support system. Ongoing and future clinical studies will determine its utility in optimizing dosing strategies and mitigating severe neutropenia in real-world practice.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e We developed a simplified and clinically applicable population PK\u0026ndash;PD model for amrubicin and implemented it in a user-friendly web-based application for TDM. By relying solely on routinely obtainable clinical data and sparse sampling, this framework enables the individualized simulation of PK profiles and neutrophil dynamics during the first treatment cycle. This study provides a practical foundation for prospective clinical studies evaluating model-informed strategies aimed at mitigating severe neutropenia in patients receiving amrubicin.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCLp\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emetabolic clearance of amrubicin to amrubicinol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCLm\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eclearance of amrubicinol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCL2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintercompartmental clearance between the central and peripheral-1 compartments of amrubicin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCL3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintercompartmental clearance between the central and peripheral-2 compartments of amrubicin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVp\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecentral volume of distribution of amrubicin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eV2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003evolume of distribution of the peripheral-1 compartment of amrubicin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eV3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003evolume of distribution of the peripheral-2 compartment of amrubicin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVm\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecentral volume of distribution of amrubicinol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ekdc\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erate constant of the delay compartment representing enterohepatic circulation of amrubicinol (included only in the previously published final model)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emean maturation time of neutrophils\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCirc0\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebaseline absolute neutrophil count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody surface area\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eperformance status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epharmacokinetics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epharmacodynamics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCV%\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecoefficient of variation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by AMED under Grant Number JP20ck0106638.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Editage (www.editage.jp) for English language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY. Makino wrote the manuscript, designed and performed the research, and contributed to creating mockups of the apps to be developed. T. Ogawa developed the TDM application, contributed to the construction and validation of the new modified population PK\u0026ndash;PD model, and revised the manuscript. T, Hamaguchi and M. Hirasaki, experts in clinical cancer genomics, provided advice regarding the modification of the amrubicin PK\u0026ndash;PD model, specifically on excluding the enterohepatic circulation component (including the variable factor \u003cem\u003eSLC28A3\u003c/em\u003e) from the model, and reviewed the manuscript. N. Sakiyama and R. Makihara revised the manuscript, validated the developed applications, protected the personal information of their previous clinical research subjects, and managed the data used in the development of this application. The other authors revised the manuscript and validated the developed applications.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eFriberg LE, Henningsson A, Maas H, Nguyen L, Karlsson MO (2002) Model of chemotherapy-induced myelosuppression with parameter consistency across drugs. J Clin Oncol 20:4713\u0026ndash;4721. https://doi.org/10.1200/JCO.2002.02.140\u003c/li\u003e\n \u003cli\u003eQuartino AL, Friberg LE, Karlsson MO (2012) A simultaneous analysis of the time-course of leukocytes and neutrophils following docetaxel administration using a semi-mechanistic myelosuppression model. Invest New Drugs 30:833\u0026ndash;845. https://doi.org/10.1007/s10637-010-9603-3\u003c/li\u003e\n \u003cli\u003eJodrell DI, Egorin MJ, Canetta RM, et al. (1992) Relationships between carboplatin exposure and tumor response and toxicity in patients with ovarian cancer. J Clin Oncol 10:520\u0026ndash;528. https://doi.org/10.1200/JCO.1992.10.4.520\u003c/li\u003e\n \u003cli\u003eGamelin E, Delva R, Jacob J, et al. (2008) Individual fluorouracil dose adjustment based on pharmacokinetic follow-up compared with conventional dosage: results of a multicenter randomized trial of patients with metastatic colorectal cancer. J Clin Oncol 26:2099\u0026ndash;2105. https://doi.org/10.1200/JCO.2007.13.3934\u003c/li\u003e\n \u003cli\u003eBeumer JH, Chu E, Allegra C, et al. (2019) Therapeutic drug monitoring in oncology: international association of therapeutic drug monitoring and clinical toxicology recommendations for 5-fluorouracil therapy. Clin Pharmacol Ther 105:598\u0026ndash;613. https://doi.org/10.1002/cpt.1124\u003c/li\u003e\n \u003cli\u003eSlattery JT, Clift RA, Buckner CD, et al. (1997) Marrow transplantation for chronic myeloid leukemia: the influence of plasma busulfan levels on the outcome of transplantation. Blood 89:3055\u0026ndash;3060. https://doi.org/10.1182/blood.V89.8.3055\u003c/li\u003e\n \u003cli\u003eAndersson BS, Thall PF, Madden T, et al. (2002) Busulfan systemic exposure relative to regimen-related toxicity and acute graft-versus-host disease: defining a therapeutic window for i.v. BuCy2 in chronic myelogenous leukemia. Biol Blood Marrow Transplant 8:477\u0026ndash;485. https://doi.org/10.1053/bbmt.2002.v8.pm12374452\u003c/li\u003e\n \u003cli\u003eRezvani AR, McCune JS, Storer BE, et al. (2013) Cyclophosphamide followed by intravenous targeted busulfan for allogeneic hematopoietic cell transplantation: pharmacokinetics and clinical outcomes. Biol Blood Marrow Transplant 19:1033\u0026ndash;1039. https://doi.org/10.1016/j.bbmt.2013.04.005\u003c/li\u003e\n \u003cli\u003eEvans WE, Crom WR, Abromowitch M, et al. (1986) Clinical pharmacodynamics of high-dose methotrexate in acute lymphocytic leukemia. Identification of a relation between concentration and effect. N Engl J Med 314:471\u0026ndash;477. https://doi.org/10.1056/NEJM198602203140803\u003c/li\u003e\n \u003cli\u003eEvans WE, Relling MV, Rodman JH et al. (1998) Conventional compared with individualized chemotherapy for childhood acute lymphoblastic leukemia. N Engl J Med 338:499\u0026ndash;505. https://doi.org/10.1056/NEJM199802193380803\u003c/li\u003e\n \u003cli\u003eJoerger M, von Pawel J, Kraff S, et al. (2016) Open-label, randomized study of individualized, pharmacokinetically (PK)-guided dosing of paclitaxel combined with carboplatin or cisplatin in patients with advanced non-small cell lung cancer (NSCLC). Ann Oncol 27:1895\u0026ndash;1902. https://doi.org/10.1093/annonc/mdw290\u003c/li\u003e\n \u003cli\u003eZhang J, Zhou F, Qi H, et al. (2019) Randomized study of individualized pharmacokinetically-guided dosing of paclitaxel compared with body-surface area dosing in Chinese patients with advanced non-small cell lung cancer. Br J Clin Pharmacol 85:2292\u0026ndash;2301. https://doi.org/10.1111/bcp.13982\u003c/li\u003e\n \u003cli\u003eBruno R, Hille D, Riva A, et al. (1998) Population pharmacokinetics/pharmacodynamics of docetaxel in phase II studies in patients with cancer. J Clin Oncol 16:187\u0026ndash;196. https://doi.org/10.1200/JCO.1998.16.1.187\u003c/li\u003e\n \u003cli\u003eRudek MA, Sparreboom A, Garrett-Mayer ES, et al. (2004) Factors affecting pharmacokinetic variability following doxorubicin and docetaxel-based therapy. Eur J Cancer 40:1170\u0026ndash;1178. https://doi.org/10.1016/j.ejca.2003.12.026\u003c/li\u003e\n \u003cli\u003eEngels FK, Loos WJ, van der Bol JM, et al. (2011) Therapeutic drug monitoring for the individualization of docetaxel dosing: a randomized pharmacokinetic study. Clin Cancer Res 17:353\u0026ndash;362. https://doi.org/10.1158/1078-0432.CCR-10-1636\u003c/li\u003e\n \u003cli\u003eInnocenti F, Schilsky RL, Ram\u0026iacute;rez J, et al. (2014) Dose-finding and pharmacokinetic study to optimize the dosing of irinotecan according to the UGT1A1 genotype of patients with cancer. J Clin Oncol 32:2328\u0026ndash;2334. https://doi.org/10.1200/JCO.2014.55.2307\u003c/li\u003e\n \u003cli\u003eMakino Y, Makihara-Ando R, Ogawa T, et al. (2019) Individual optimal dose of amrubicin to prevent severe neutropenia in Japanese patients with lung cancer. Cancer Sci 110:3573\u0026ndash;3583. https://doi.org/10.1111/cas.14194\u003c/li\u003e\n \u003cli\u003eKang D, Bae KS, Houk BE, Savic RM, Karlsson MO (2012) Standard error of empirical Bayes estimate in NONMEM\u0026reg; VI. Korean J Physiol Pharmacol 16:97\u0026ndash;106. https://doi.org/10.4196/kjpp.2012.16.2.97\u003c/li\u003e\n \u003cli\u003eMonteiro JF, Fernandes A, Tato DG et al. (2025) Optimizing anti-PD1 immunotherapy: an overview of pharmacokinetics, biomarkers, and therapeutic drug monitoring. Cancers (Basel) 17:3262. https://doi.org/10.3390/cancers17193262\u003c/li\u003e\n \u003cli\u003eZhao Y, Tsujimoto A, Ide T et al. (2024) Model-based population pharmacokinetic and exposure response analyses for safety and efficacy of nivolumab as adjuvant treatment in subjects with resected oesophageal or gastroesophageal junction cancer. Br J Clin Pharmacol 90:2920\u0026ndash;2930. https://doi.org/10.1111/bcp.16188\u003c/li\u003e\n \u003cli\u003eLef\u0026egrave;vre A, Parra-Guillen ZP, Troc\u0026oacute;niz IF, Boetsch C, Frances N (2025) Mechanistic PKPD modeling to describe cytokine release associated with CD3 T-cell engager therapies. Front Immunol 15:1463915. https://doi.org/10.3389/fimmu.2024.1463915\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-chemotherapy-and-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccap","sideBox":"Learn more about [Cancer Chemotherapy and Pharmacology](http://link.springer.com/journal/280)","snPcode":"280","submissionUrl":"https://submission.nature.com/new-submission/280/3","title":"Cancer Chemotherapy and Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Amrubicin, Therapeutic drug monitoring, Population pharmacokinetic–pharmacodynamic model, Neutropenia, Small cell lung cancer","lastPublishedDoi":"10.21203/rs.3.rs-9191032/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9191032/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAmrubicin is an important chemotherapeutic agent for small cell lung cancer; however, severe neutropenia is a major dose-limiting toxicity. Exposure to its active metabolite, amrubicinol, correlates with neutropenia severity, suggesting its potential role in therapeutic drug monitoring (TDM). This study aimed to develop a clinically implementable pharmacokinetics (PK)\u0026ndash;pharmacodynamics (PD) framework and an interactive web-based application to support amrubicin TDM using routinely available clinical data.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA previously published population PK\u0026ndash;PD model of amrubicin was simplified to enhance its clinical applicability by removing the enterohepatic circulation component of amrubicinol metabolism and the associated genetic covariate. Model performance was evaluated using prediction-corrected visual predictive checks and nonparametric bootstrapping. An interactive web application was developed using R Shiny and RsNLME to simulate individual PK and neutrophil dynamics using empirical Bayes estimates derived from observed data.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe updated model adequately described the plasma concentration-time profiles of amrubicin and amrubicinol as well as neutrophil dynamics. The prediction-corrected visual predictive checks demonstrated good agreement between the observed and simulated data. The robustness of the parameter estimates was confirmed using nonparametric bootstrapping. The finalized model was integrated into an interactive web application that supports individualized simulations of PK and neutrophil profiles using data observed during the first treatment cycle.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003e We developed a simplified and clinically applicable population PK\u0026ndash;PD model and integrated it into an interactive web application for amrubicin TDM. This framework provides a practical foundation for prospective studies to evaluate model-informed strategies to mitigate severe neutropenia.\u003c/p\u003e","manuscriptTitle":"Development of a clinically implementable population pharmacokinetic– pharmacodynamic model and interactive application for therapeutic drug monitoring of amrubicin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-06 05:08:26","doi":"10.21203/rs.3.rs-9191032/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"335586501933816548130848238334372529446","date":"2026-04-01T14:02:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233238782128904905795391297418862642763","date":"2026-04-01T08:14:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178933762863806831636025554586891520112","date":"2026-03-31T23:41:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-31T22:09:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-23T11:51:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-23T11:51:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Chemotherapy and Pharmacology","date":"2026-03-22T11:59:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-chemotherapy-and-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccap","sideBox":"Learn more about [Cancer Chemotherapy and Pharmacology](http://link.springer.com/journal/280)","snPcode":"280","submissionUrl":"https://submission.nature.com/new-submission/280/3","title":"Cancer Chemotherapy and Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"780b9b03-1064-40c3-a3fb-b5d0381d91ba","owner":[],"postedDate":"April 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-02T21:08:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-06 05:08:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9191032","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9191032","identity":"rs-9191032","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}