Nonlinear Mixed-Effects Modeling to Characterize the Pharmacokinetics of a Novel Mithramycin Analogue for Ewing Sarcoma in Mice

Nonlinear Mixed-Effects Modeling to Characterize the Pharmacokinetics of a Novel Mithramycin Analogue for Ewing Sarcoma in Mice | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Nonlinear Mixed-Effects Modeling to Characterize the Pharmacokinetics of a Novel Mithramycin Analogue for Ewing Sarcoma in Mice Kumar Kulldeep Niloy, Jamie Horn, Nazmul Hasan Bhuiyan, Khaled A Shaaban, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9035594/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose To develop a pharmacokinetic model for a novel mithramycin analogue, MTMSA-Trp, in mice and characterize dose-dependent disposition to support future pharmacokinetic-pharmacodynamic (PK/PD) and exposure-efficacy analyses. Methods Non-linear mixed-effects modeling was used to develop a population pharmacokinetic (popPK) model in MonolixSuite 2024R1 using 121 plasma concentrations from 70 female athymic nude mice after single IV bolus doses of 0.3, 1, 3, 5, and 10 mg/kg. Model selection was guided by the objective function value (OFV), parameter precision, and diagnostic plots. The final model was evaluated using bootstrap resampling (1000 replicates) and visual predictive checks (VPC; 1000 simulated datasets). Results A one-compartment model with first-order elimination and an empirical power relationship between dose and clearance best described the data. Including dose as a covariate in the clearance model significantly improved model fit relative to the linear base model (ΔOFV = − 26.19). Typical clearance and volume of distribution were 39.18 mL/h/kg (at 3 mg/kg) and 53.06 mL/kg, respectively, and the dose-clearance exponent was β = −0.30, indicating decreasing clearance with increasing dose. Fixed-effect parameters were estimated with high precision (RSE ≤ 11%). Shrinkage was high for clearance (81%) and moderate for volume of distribution (39.1%). Bootstrap and VPC results supported model robustness and predictive performance. Conclusion A robust popPK model describing dose-dependent MTMSA-Trp disposition in mice was developed and is suitable for simulation to support subsequent PK/PD and exposure-efficacy analyses. Population pharmacokinetics Preclinical pharmacokinetics Ewing sarcoma mithramycin analogues Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction MTMSA-Trp is a newly synthesized analogue of mithramycin (MTM) with potent inhibition of EWS-FLI1 activity and strong antitumor effects in Ewing sarcoma mouse xenograft models [ 1 – 4 ]. In addition to improved target engagement and in vivo efficacy, MTMSA-Trp has demonstrated improved pharmacokinetics compared with MTM across multiple species [ 5 ]. A key step in the preclinical development of any new compound is characterizing its PK in relevant species. MTMSA-Trp pharmacokinetics have been evaluated in female athymic nude mice, a commonly used xenograft host, following single IV bolus doses of 0.3, 1, 3, 5, and 10 mg/kg. These studies used mixed sampling approaches (serial and terminal/destructive sampling), resulting in sparse data. Noncompartmental analysis (NCA) provided clearance estimates ranging from 33 to 69 mL/h/kg and suggested non-proportional exposure across the dose range[ 5 ]. While NCA is useful for summarizing pharmacokinetics, population PK modeling provides a flexible framework for integrating pooled data across studies, quantifying inter-individual variability, and supporting simulation, especially when sampling is sparse [ 6 , 7 ]. A validated popPK model enables prediction of exposures under alternative dosing regimens and provides the quantitative foundation for PK/PD and exposure-response modeling. Accordingly, the objective of this study was to develop a popPK model for MTMSA-Trp in female athymic nude mice using pooled plasma concentration-time data from multiple studies and to evaluate dose dependence in pharmacokinetics across the dose range of 0.3 to 10 mg/kg [ 5 ]. The resulting model is intended to provide robust population parameter estimates as inputs in simulations to support the preclinical development of MTMSA-Trp for Ewing sarcoma. Materials and Methods Mice All mice were handled in accordance with the Guide for the Care and Use of Laboratory Animals (National Research Council, 2011) standards. Studies were performed at St. Jude Children’s Research Hospital under an institutionally approved protocol (St. Jude Institutional Animal Care and Use Committee protocol 3164, approved 29 March 2022). Additionally, mouse experiments were performed at the University of Kentucky under institutionally approved protocols (University of Kentucky Institutional Animal Care and Use Committee protocols 2016–2345, approved 14 February 2019, and 2018–2849, approved 15 June 2021). Athymic nu/nu mice were purchased from Envigo RMS (Indianapolis, IN, USA) and housed in AAALAC-certified, temperature- and humidity-controlled (64◦F to 84◦F and 30% to 70%, respectively) facilities with a 12 h light-dark cycle. Mice were provided standard commercial diets (rodent 5R53 Test Diet, Richmond, IN, USA) and chlorinated, reverse-osmosis-treated water ad libitum. Animals were allowed to acclimate for at least a week prior to experimentation. Pharmacokinetic data Plasma concentration-time data were obtained from pharmacokinetic studies conducted in female athymic nude mice. MTMSA-Trp was administered as a single intravenous (IV) bolus dose of 0.3, 1, 3, 5, or 10 mg/kg [ 5 ]. Sampling followed both serial and terminal/destructive designs, resulting in a sparse dataset in which some mice contributed a single observation. MTMSA-Trp plasma concentrations were quantified using a validated LC-MS/MS bioanalytical method [ 8 ]. Software Population pharmacokinetic analyses were conducted using MonolixSuite 2024R1 (Simulations Plus) with the stochastic approximation expectation–maximization (SAEM) algorithm for parameter estimation. Model-based simulations were performed in Simulx 2024R1 (Simulations Plus). Diagnostic and presentation plots were generated using GraphPad Prism 10. Model development One- and two-compartment structural models were evaluated to describe MTMSA-Trp plasma concentration-time data across the studied dose range. Models with first-order elimination were evaluated first. A Michaelis-Menten elimination model was also explored but did not yield stable parameter estimates and was not retained. Between-subject (inter-individual) variability (IIV) in pharmacokinetic parameters was modeled using an exponential (log-normal) model: \({P}_{i}={P}_{pop}.{e}^{{\eta}_{i}}\) [Equation 1] where \({P}_{i}\) is the individual value of parameter 𝑃 for animal 𝑖, and \({P}_{pop}\) is the typical population value for parameter 𝑃. Depending on the structural model, 𝑃 included clearance (CL) and volume of distribution (V), and for two-compartment models also included intercompartmental clearance (Q) and peripheral volume ( \({V}_{p}\) ). The random effect \({\eta}_{i}\) describes the deviation of animal 𝑖 from the population typical value and was assumed to be normally distributed with mean 0 and variance 𝜔 2 (that is, \({\eta}_{i}\) ∼𝑁(0,𝜔 2 )). Additive, proportional, and combined residual error models were evaluated to describe residual unexplained variability. Following selection of the base structural model, dose was tested as a covariate on apparent clearance using a power function to capture non-proportional exposure across dose levels: \({CL}_{i}={CL}_{pop}.{\left(\frac{{Dose}_{i}}{{Dose}_{median}}\right)}^{\beta}.{e}^{{\eta}_{CL,i}}\) [Equation 2] where \({CL}_{pop}\) is the typical population clearance, \({Dose}_{i}\) is the dose administered to animal \(i\) , \({Dose}_{median}\) was set to 3 mg/kg, and \(\beta\) is the exponent estimating the strength of the dose effect. Model selection and covariate retention were guided by objective function value (OFV; −2 loglikelihood) and parameter precision. For nested models, a decrease in OFV of ≥ 3.84 (p < 0.05; 1 degree of freedom) was considered statistically significant. Precision was assessed using relative standard error (RSE), with RSE < 30% for -fixed effect parameters and < 50% for -random effect- parameters were considered acceptable [ 9 ]. Shrinkage was also assessed to gauge the reliability of empirical Bayes estimates (EBEs), recognizing that sparse sampling can increase shrinkage and reduce the interpretability of EBE -based diagnostics [ 10 , 9 , 11 ]. Model evaluation Model adequacy was evaluated using standard graphical and simulation-based diagnostics. Observed concentrations were compared with population predictions (PPRED) and individual predictions (IPRED). Population-weighted residuals (PWRES), individual-weighted residuals (IWRES), and normalized prediction distribution errors (NPDE) were examined as functions of time and predictions to assess bias and model misspecification. Parameter stability and precision were assessed using non-parametric bootstrap resampling (1000 replicates); bootstrap medians and 95% confidence intervals were compared with final parameter estimates. Predictive performance was evaluated using visual predictive checks (VPCs) based on 1000 simulated datasets generated under the original study design (dose levels, group sizes, and sampling times), with observations overlaid on the simulated 5th, 50th, and 95th percentiles. To evaluate the dose effect on clearance, dose-stratified VPCs were generated and compared between the base (linear) and final (dose-dependent) models. As an additional verification of the dose-exposure relationship, concentration-time profiles of 1000 virtual mice were simulated for each dose group (0.3, 1, 3, 5, and 10 mg/kg) using the final model. Noncompartmental analysis was performed on the simulated data to calculate AUC 0àinf and dose-normalized AUC 0àinf , which were plotted versus dose. Results Pharmacokinetic data The popPK dataset comprised 121 plasma concentrations from 70 mice collected following single IV bolus administration of MTMSA-Trp at doses ranging from 0.3 to 10 mg/kg [ 5 ]. Data were pooled across studies that used both serial and terminal/destructive sampling, resulting in a sparse design in which terminal animals contributed a single concentration. A summary of the analysis dataset is provided in Table S1 , and observed concentration-time profiles across dose groups are shown in Fig. 1 . Model development One and two-compartment models with first-order elimination were evaluated, along with additive, proportional, and combined residual error models. A Michaelis-Menten elimination model was explored but produced unstable parameter estimates and was not retained. A one-compartment model with first-order elimination, lognormal IIV on CL and V, and a proportional residual error model adequately described the data and was selected as the base model. The two-compartment model did not improve fit despite added complexity ( Table S2 ). Dose was then evaluated as a covariate on CL using a power model. Inclusion of the dose effect significantly improved model fit relative to the base model (ΔOFV = − 26.19; Table S2 ) and was retained in the final model. Final parameter estimates are summarized in Table 1 . Typical population CL and V were 39.18 mL/h/kg and 53.06 mL/kg, respectively, and the dose-CL exponent was \(\beta\) =−0.30, indicating decreasing apparent clearance with increasing dose. Fixed effect- parameters were estimated with good precision (RSE ≤ 11%). Incorporation of the dose effect reduced unexplained IIV on CL (15% to 5.8%) and decreased IIV on V (58% to 25%; Table S3 ). Shrinkage was high for CL (81%) and moderate for V (39.1%), consistent with the sparse/destructive sampling design. Table 1 Population PK model parameters. Parameter Estimate (%RSE) Bootstrap (n = 1000) Shrinkage (%) Median 95% CI Fixed effect CL (mL/h/kg) 39.18 (4.97) 37.88 31.78–45.76 - V (mL/kg) 53.06 (7.55) 51.48 42.06–64.84 - β -0.30 (10.9) -0.31 -0.40 – -0.18 - Interindividual variability IIV on CL (CV%) 5.8 (42) 0.061 0.028–0.14 81 IIV on V (CV%) 25 (31) 0.21 0.11–0.40 39.1 Residual variability b (%) 0.51 (8.13) 0.5 0.41–0.58 - Model evaluation Goodness-of-fit diagnostics demonstrated acceptable agreement between observed concentrations and model predictions (Fig. 2 ). Observations were symmetrically distributed around the line of identity in plots of observed versus PPRED and IPRED, and PWRES/IWRES/NPDE were centered around zero without systematic trends versus time or predictions (Fig. 2 ; Supplementary Fig. S1 ). Given the sparse sampling design and associated shrinkage for clearance, EBE-based diagnostics were interpreted cautiously, and emphasis was placed on simulation-based evaluation. Overall, VPC results indicated that the majority of observed concentrations fell within the simulated 5th-95th prediction interval and were well described by the simulated median (Fig. 3 ), supporting predictive performance for simulation. Comparison of overall and dose-stratified VPCs showed that the base linear model did not adequately capture the profile at 0.3 mg/kg and did not fully account for variability at later time points, whereas the final model with dose-dependent clearance improved agreement across doses (Fig. 4 ; Supplementary Fig. S2 ). Simulation-based evaluation of exposure supported the estimated dose effect on clearance. Simulated AUC 0àinf increased more than proportionally with dose, and simulated dose-normalized AUC 0àinf increased with dose (Fig. 5 ), consistent with β < 0 and decreasing clearance with increasing dose over the 0.3 to 10 mg/kg range. Bootstrap resampling (1000 replicates) confirmed model stability. Final parameter estimates were consistent with bootstrap medians and lay within the bootstrap 95% confidence intervals (Table 1 ), supporting the robustness and precision of the final model. Discussion Using sparse plasma concentration-time data pooled across studies in female athymic nude mice, we developed a popPK model to characterize MTMSA-Trp disposition over a wide dose range (0.3 to 10 mg/kg). The primary goal was to obtain robust population parameter estimates suitable for simulation efforts to support future PK/PD and exposure-response analyses. A one-compartment model with first-order elimination provided a parsimonious description of the data and was preferred over a two-compartment model, which did not improve the fit. However, diagnostics from the linear base model suggested dose-dependent behavior, most notably at the lowest dose and in the extent of variability at later sampling times. Incorporating dose as a covariate on clearance using an empirical power model substantially improved fit (ΔOFV = − 26.19) and improved predictive performance in both overall and dose-stratified VPCs. Although a mechanistic Michaelis-Menten elimination model was explored, it did not yield stable parameter estimates; therefore, the retained power model should be interpreted as an empirical description of non-proportional exposure within the studied dose range rather than a definitive mechanism. The final model captured the central tendency and variability across doses, as shown by the dose-stratified VPCs, and provides an empirical description of dose-dependent clearance within the therapeutically relevant range [ 1 , 2 , 12 ]. The final model estimated β = −0.30, indicating decreasing clearance with increasing dose and a greater-than-proportional increase in exposure (AUC 0àinf ) across 0.3 to 10 mg/kg. Typical clearance at the reference dose (3 mg/kg) was consistent with prior NCA estimates, supporting concordance with earlier analyses. The estimated volume of distribution (~ 53 mL/kg) is physiologically plausible and close to mouse plasma volume (~ 50 mL/kg) [ 13 ] suggesting distribution largely confined to the vascular space, consistent with prior observations of high plasma protein binding. Incorporating dose as a covariate on CL reduced unexplained IIV in clearance (15% in the base model to 5.8% in the final model), indicating that a substantial portion of the apparent variability in clearance across animals was explained by dose within the studied range. An important limitation, however, was the high shrinkage estimated for CL (81%). High shrinkage is expected when individual information content is limited (e.g., sparse or destructive sampling) and indicates that EBEs for CL are pulled toward the population mean. Under these conditions, IPRED may closely resemble PPRED, and EBE-based diagnostics should be interpreted cautiously. Accordingly, model evaluation emphasized simulation-based diagnostics (VPC and NPDE) and -population level parameter precision (bootstrap). The final model yielded precise population estimates (RSE ≤ 11%), bootstrap medians consistent with final estimates, and simulation-based diagnostics without systematic bias. Given the primary -objective to characterize the population PK of MTMSA-Trp from sparse data to support subsequent exposure-response analyses, the final model was considered suitable for its intended purpose. Importantly, the dose-CL relationship should be interpreted as an empirical description of non-proportional exposure over 0.3 to 10 mg/kg, rather than a definitive mechanistic identification of the source of nonlinearity. In conclusion, a one-compartment popPK model incorporating dose-dependent clearance provided a stable and predictive description of MTMSA-Trp pharmacokinetics in mice across 0.3 to 10 mg/kg. The model yields robust population parameter estimates suitable for simulation and provides a quantitative framework for future PK/PD, exposure-efficacy, and translational modeling efforts. Declarations Competing Interests M.L., J.S.T, K.A.S., and J.R. are inventors on patents and/or patent applications related to mithramycin derivatives with anticancer activity. These intellectual property rights are assigned to the University of Kentucky. The other authors declare no competing interests. Funding: This research was funded by the National Cancer Institute (R01 CA243529), the Kentucky Medical Services Foundation Chair in Pharmacy, the National Institutes of Health (R37 AI052218), the Center of Biomedical Research Excellence (COBRE) for Translational Chemical Biology (CTCB, NIH P20 GM130456), the National Institute of Food and Agriculture (USDA-NIFA-CBGP, Grant No. 2023-38821-39584), the University of Kentucky College of Pharmacy, the University of Kentucky Markey Cancer Center, the National Center for Advancing Translational Sciences (UL1TR000117 and UL1TR001998), and the American Lebanese Syrian Associated Charities. Author Contribution K.K.N. and M.L. conceptualized the study. N.H.B., K.A.S., and S.S.B. synthesized and purified MTMSA-Trp. K.K.N. and J.H. performed animal experiments and sample analysis. M.L., T.E.P., J.S.T., and J.R. provided resources and funding for the experiment. K.K.N. performed data analysis and modeling. K.K.N. and M.L. prepared the manuscript. M.L., T.E.P., J.S.T., and J.R. reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript. Acknowledgements: This research included select experiments conducted by the Animal Resources Center at St. Jude Children’s Research Hospital, which is supported by the American Lebanese Syrian Associated Charities. We acknowledge Joseph Eckenrode, Karen Jackson, Scott Kinison, and Rupam Sarma for technical assistance with pilot experiments. Data Availability Inquiries regarding the datasets should be directed to the corresponding author. References Leggas M, Niloy KK, Yetijaram R, Horn J, Kazuto Y, Prisinzano T, Thorson JS, Tsoikov O, Rohr J (2023) Abstract B150: Targeting EWS-FLI1 with mithramycin analogues for Ewing sarcoma treatment. Mol Cancer Ther 22(12Supplement):B150–B150. 10.1158/1535-7163.Targ-23-b150 Yetirajam R, Acharya S, Niloy KK, Kazuto Y, Horn J, Prisinzano T, Thorson JS, Tsodikov O, Rohr J, Leggas M (2025) Abstract 4360: Development of a novel mithramycin analogue with improved pharmacokinetics and therapeutic window for targeted inhibition of EWS-FLI1 in Ewing sarcoma. Cancer Res 85(8Supplement1):4360–4360. 10.1158/1538-7445.Am2025-4360 Mitra P, Eckenrode JM, Mandal A, Jha AK, Salem SM, Leggas M, Rohr J (2018) Development of Mithramycin Analogues with Increased Selectivity toward ETS Transcription Factor Expressing Cancers. J Med Chem 61(17):8001–8016. 10.1021/acs.jmedchem.8b01107 Rohr J, Tsodikov O, LEGGAS M, Hou C, Eckenrode J, Mitra P, Mandal A (2023) Mithramycin derivatives having increased selectivity and anti-cancer activity. Google Patents Niloy KK, Horn J, Bhuiyan NH, Shaaban KA, Bhosale SS, Prisinzano TE, Thorson JS, Rohr J, Leggas M (2025) Preclinical Pharmacokinetic Evaluation of Mithramycin and Mithramycin SA Tryptophan-Conjugated Analog. Pharmaceutics 17(6). 10.3390/pharmaceutics17060765 Population pharmacokinetics guidance for industry. US Food and Drug Administration (2022) Kotila OA, Ajayi DT, Masimirembwa C, Thelingwani R, Odetunde A, Falusi AG, Babalola CP (2023) Non-compartmental and population pharmacokinetic analysis of dapsone in healthy NIGERIANS: A pilot study. Br J Clin Pharmacol 89(11):3454–3459. 10.1111/bcp.15862 Eckenrode JM, Mitra P, Rohr J, Leggas M (2019) Bioanalytical method for quantitative determination of mithramycin analogs in mouse plasma by HPLC-QTOF. Biomed Chromatogr 33(8):e4544. 10.1002/bmc.4544 He J, Jackson C, Deva S, Hung T, Clarke K, Segelov E, Chao TY, Dai MS, Yeh HT, Ma WW, Kramer D, Chan WK, Kwan R, Cutler D, Zhi J (2022) Population pharmacokinetics for oral paclitaxel in patients with advanced/metastatic solid tumors. CPT Pharmacometrics Syst Pharmacol 11(7):867–879. 10.1002/psp4.12799 Xu XS, Yuan M, Karlsson MO, Dunne A, Nandy P, Vermeulen A (2012) Shrinkage in Nonlinear Mixed-Effects Population Models: Quantification, Influencing Factors, and Impact. AAPS J 14(4):927–936. 10.1208/s12248-012-9407-9 Tang F, Langenhorst J, Dang S, Kassir N, Owen R, Purdon B, Magnusson MO, Deng R (2023) Population Pharmacokinetics of Tenecteplase in Patients With Acute Myocardial Infarction and Application to Patients With Acute Ischemic Stroke. J Clin Pharmacol 63(2):197–209. 10.1002/jcph.2164 Niloy KK, Leggas M (2024) Preclinical tumor growth inhibition modeling and simulation to support dosing regimen selection of a novel EWS-FLI1 inhibitor for Ewing sarcoma treatment. American Conference of Pharmacometrics Davies B, Morris T (1993) Physiological parameters in laboratory animals and humans. Pharm Res 10(7):1093–1095. 10.1023/a:1018943613122 Additional Declarations Competing interest reported. M.L., J.S.T, K.A.S., and J.R. are inventors on patents and/or patent applications related to mithramycin derivatives with anticancer activity. These intellectual property rights are assigned to the University of Kentucky. The other authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9035594","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614807949,"identity":"dd8bd375-7e09-45ad-ac40-7ad77614478f","order_by":0,"name":"Kumar Kulldeep Niloy","email":"","orcid":"","institution":"St. Jude Children’s Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kumar","middleName":"Kulldeep","lastName":"Niloy","suffix":""},{"id":614807950,"identity":"6af8e698-dbf9-408c-9863-cd293f9c8bbb","order_by":1,"name":"Jamie Horn","email":"","orcid":"","institution":"St. Jude Children’s Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jamie","middleName":"","lastName":"Horn","suffix":""},{"id":614807951,"identity":"17dc42d8-7d24-430f-91f1-bfa016b8bd00","order_by":2,"name":"Nazmul Hasan Bhuiyan","email":"","orcid":"","institution":"University of Kentucky","correspondingAuthor":false,"prefix":"","firstName":"Nazmul","middleName":"Hasan","lastName":"Bhuiyan","suffix":""},{"id":614807952,"identity":"8e77a427-5183-4923-b266-e316180da6c8","order_by":3,"name":"Khaled A Shaaban","email":"","orcid":"","institution":"University of Kentucky","correspondingAuthor":false,"prefix":"","firstName":"Khaled","middleName":"A","lastName":"Shaaban","suffix":""},{"id":614807953,"identity":"28b21adb-3f06-4512-a844-88c4e335f343","order_by":4,"name":"Suhas S Bhosale","email":"","orcid":"","institution":"University of Kentucky","correspondingAuthor":false,"prefix":"","firstName":"Suhas","middleName":"S","lastName":"Bhosale","suffix":""},{"id":614807954,"identity":"2db11c4b-6f1a-4ba3-9585-0242de8c4cbe","order_by":5,"name":"Thomas Prisinzano","email":"","orcid":"","institution":"University of Kentucky","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Prisinzano","suffix":""},{"id":614807955,"identity":"7d501b23-c3d1-43f8-8323-ded8d33ec66b","order_by":6,"name":"Jon S Thorson","email":"","orcid":"","institution":"University of Kentucky","correspondingAuthor":false,"prefix":"","firstName":"Jon","middleName":"S","lastName":"Thorson","suffix":""},{"id":614807956,"identity":"5be37374-11db-4262-8af8-f1c9b650d88a","order_by":7,"name":"Jurgen Rohr","email":"","orcid":"","institution":"University of Kentucky","correspondingAuthor":false,"prefix":"","firstName":"Jurgen","middleName":"","lastName":"Rohr","suffix":""},{"id":614807957,"identity":"91a41edb-5baa-4ea1-bf1c-3b9389d71933","order_by":8,"name":"Markos Leggas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYHACZgaGAjCD8QEDAxsDwwGitBhAGAYMCSRqYZNgSGAgrEW3/exjAwYDG7v57sefVf78wZfYd7yB8XHFL9xazM6kGycwGKQlbzyTY3abJ4EtceaZA8yGZ/vwaDmQxnyAweBwsmFDDtttoF8SN9xIYJNs7MGj5fwzkJb/yYb9z58V/gBpuf+AgJYbacxAhx2wk5dIMGPgAdvCwCbZ8AOflmfMBgkGyQkGEm+MpXnS2IxnnklsNmxswOewNGaJDxV29vL96Q8//rA5Jtt3/PDBhw1/cGsBgwQGhsQNB8DMY0DM2MDA2EZACxDYy0OcUgPlE7JlFIyCUTAKRhIAADmoVyN9eKvnAAAAAElFTkSuQmCC","orcid":"","institution":"St. Jude Children’s Research Hospital","correspondingAuthor":true,"prefix":"","firstName":"Markos","middleName":"","lastName":"Leggas","suffix":""}],"badges":[],"createdAt":"2026-03-05 04:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9035594/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9035594/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105979823,"identity":"cde22322-acfe-47d4-9542-5f6dd42df264","added_by":"auto","created_at":"2026-04-02 06:31:24","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":310388,"visible":true,"origin":"","legend":"\u003cp\u003eObserved MTMSA-Trp plasma concentrations (symbols) over time across dose range used for model development. Solid lines represent mean PK profiles.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9035594/v1/7ecda4768ce1ad20cc9232c1.jpg"},{"id":105979824,"identity":"9f697aec-6865-4e26-ab10-29781b42c1e3","added_by":"auto","created_at":"2026-04-02 06:31:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":762846,"visible":true,"origin":"","legend":"\u003cp\u003eGoodness-of-fit plots of final popPK model. Panel a and b show the observations versus population (PPRED) and individual predictions (IPRED), respectively. Panel c and d show population weighted residuals (PWRES) versus population predictions (PPRED) and time, respectively. Black and red lines represent the line of unity and spline, respectively.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9035594/v1/5247bc398c81896ea60340d0.jpg"},{"id":105979826,"identity":"0ee6a642-f75d-417f-bc6b-3c5038341350","added_by":"auto","created_at":"2026-04-02 06:31:24","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":204305,"visible":true,"origin":"","legend":"\u003cp\u003eVisual predictive check of final popPK model following 1000 simulations. Solid line represents median and dashed lines represent 5th and 95th percentiles. Blue circles represent observations.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9035594/v1/b460a0358140e7f90896809a.jpg"},{"id":106093685,"identity":"d92aad6d-741b-4092-bfb4-4baad156e0e6","added_by":"auto","created_at":"2026-04-03 11:38:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":697085,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of VPC plots between base and final popPK models. Top panel (a-e) and bottom panel (f-j) show VPCs for base and final models, respectively, for 0.3 mg/kg (a \u0026amp; f), 1 mg/kg (b \u0026amp; g), 3 mg/kg (c \u0026amp; h), 5 mg/kg (d \u0026amp; i) and 10 mg/kg (e \u0026amp; j).\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9035594/v1/138a5c324c675d7639486143.jpg"},{"id":105979827,"identity":"644efa0f-95fe-4d4d-b35e-71a21157f392","added_by":"auto","created_at":"2026-04-02 06:31:24","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":152601,"visible":true,"origin":"","legend":"\u003cp\u003eDose dependent nonlinearity plots. Panel a shows exposure versus dose and panel b shows dose-normalized exposure versus dose. The black dots represent median simulated exposures and error bars represent upper/lower limits.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9035594/v1/f7f4363932a71e7bbe6ede0e.jpg"},{"id":108016045,"identity":"f8a08596-c1c7-4016-a52b-8a54797a74e9","added_by":"auto","created_at":"2026-04-28 13:41:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2358507,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9035594/v1/1befb826-574d-43f6-9e75-8e4dea36de2f.pdf"},{"id":105979822,"identity":"2a9509e2-8c2c-4c94-8884-ae8992a9bad7","added_by":"auto","created_at":"2026-04-02 06:31:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":497496,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9035594/v1/2ec24801ae2dc7931195d110.docx"}],"financialInterests":"Competing interest reported. M.L., J.S.T, K.A.S., and J.R. are inventors on patents and/or patent applications related to mithramycin derivatives with anticancer activity. These intellectual property rights are assigned to the University of Kentucky. The other authors declare no competing interests.","formattedTitle":"Nonlinear Mixed-Effects Modeling to Characterize the Pharmacokinetics of a Novel Mithramycin Analogue for Ewing Sarcoma in Mice","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMTMSA-Trp is a newly synthesized analogue of mithramycin (MTM) with potent inhibition of EWS-FLI1 activity and strong antitumor effects in Ewing sarcoma mouse xenograft models [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In addition to improved target engagement and in vivo efficacy, MTMSA-Trp has demonstrated improved pharmacokinetics compared with MTM across multiple species [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA key step in the preclinical development of any new compound is characterizing its PK in relevant species. MTMSA-Trp pharmacokinetics have been evaluated in female athymic nude mice, a commonly used xenograft host, following single IV bolus doses of 0.3, 1, 3, 5, and 10 mg/kg. These studies used mixed sampling approaches (serial and terminal/destructive sampling), resulting in sparse data. Noncompartmental analysis (NCA) provided clearance estimates ranging from 33 to 69 mL/h/kg and suggested non-proportional exposure across the dose range[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile NCA is useful for summarizing pharmacokinetics, population PK modeling provides a flexible framework for integrating pooled data across studies, quantifying inter-individual variability, and supporting simulation, especially when sampling is sparse [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A validated popPK model enables prediction of exposures under alternative dosing regimens and provides the quantitative foundation for PK/PD and exposure-response modeling.\u003c/p\u003e \u003cp\u003eAccordingly, the objective of this study was to develop a popPK model for MTMSA-Trp in female athymic nude mice using pooled plasma concentration-time data from multiple studies and to evaluate dose dependence in pharmacokinetics across the dose range of 0.3 to 10 mg/kg [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The resulting model is intended to provide robust population parameter estimates as inputs in simulations to support the preclinical development of MTMSA-Trp for Ewing sarcoma.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMice\u003c/h2\u003e \u003cp\u003e All mice were handled in accordance with the Guide for the Care and Use of Laboratory Animals (National Research Council, 2011) standards. Studies were performed at St. Jude Children\u0026rsquo;s Research Hospital under an institutionally approved protocol (St. Jude Institutional Animal Care and Use Committee protocol 3164, approved 29 March 2022). Additionally, mouse experiments were performed at the University of Kentucky under institutionally approved protocols (University of Kentucky Institutional Animal Care and Use Committee protocols 2016\u0026ndash;2345, approved 14 February 2019, and 2018\u0026ndash;2849, approved 15 June 2021). Athymic nu/nu mice were purchased from Envigo RMS (Indianapolis, IN, USA) and housed in AAALAC-certified, temperature- and humidity-controlled (64◦F to 84◦F and 30% to 70%, respectively) facilities with a 12 h light-dark cycle. Mice were provided standard commercial diets (rodent 5R53 Test Diet, Richmond, IN, USA) and chlorinated, reverse-osmosis-treated water ad libitum. Animals were allowed to acclimate for at least a week prior to experimentation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePharmacokinetic data\u003c/h3\u003e\n\u003cp\u003ePlasma concentration-time data were obtained from pharmacokinetic studies conducted in female athymic nude mice. MTMSA-Trp was administered as a single intravenous (IV) bolus dose of 0.3, 1, 3, 5, or 10 mg/kg [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Sampling followed both serial and terminal/destructive designs, resulting in a sparse dataset in which some mice contributed a single observation. MTMSA-Trp plasma concentrations were quantified using a validated LC-MS/MS bioanalytical method [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eSoftware\u003c/h3\u003e\n\u003cp\u003ePopulation pharmacokinetic analyses were conducted using MonolixSuite 2024R1 (Simulations Plus) with the stochastic approximation expectation\u0026ndash;maximization (SAEM) algorithm for parameter estimation. Model-based simulations were performed in Simulx 2024R1 (Simulations Plus). Diagnostic and presentation plots were generated using GraphPad Prism 10.\u003c/p\u003e\n\u003ch3\u003eModel development\u003c/h3\u003e\n\u003cp\u003eOne- and two-compartment structural models were evaluated to describe MTMSA-Trp plasma concentration-time data across the studied dose range. Models with first-order elimination were evaluated first. A Michaelis-Menten elimination model was also explored but did not yield stable parameter estimates and was not retained.\u003c/p\u003e \u003cp\u003eBetween-subject (inter-individual) variability (IIV) in pharmacokinetic parameters was modeled using an exponential (log-normal) model:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({P}_{i}={P}_{pop}.{e}^{{\\eta}_{i}}\\)\u003c/span\u003e \u003c/span\u003e [Equation 1]\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the individual value of parameter \u0026#119875; for animal \u0026#119894;, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{pop}\\)\u003c/span\u003e\u003c/span\u003e is the typical population value for parameter \u0026#119875;. Depending on the structural model, \u0026#119875; included clearance (CL) and volume of distribution (V), and for two-compartment models also included intercompartmental clearance (Q) and peripheral volume (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({V}_{p}\\)\u003c/span\u003e\u003c/span\u003e). The random effect \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\eta}_{i}\\)\u003c/span\u003e\u003c/span\u003e describes the deviation of animal \u0026#119894; from the population typical value and was assumed to be normally distributed with mean 0 and variance \u0026#120596;\u003csup\u003e2\u003c/sup\u003e (that is, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\eta}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u0026sim;\u0026#119873;(0,\u0026#120596;\u003csup\u003e2\u003c/sup\u003e)). Additive, proportional, and combined residual error models were evaluated to describe residual unexplained variability.\u003c/p\u003e \u003cp\u003eFollowing selection of the base structural model, dose was tested as a covariate on apparent clearance using a power function to capture non-proportional exposure across dose levels:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({CL}_{i}={CL}_{pop}.{\\left(\\frac{{Dose}_{i}}{{Dose}_{median}}\\right)}^{\\beta}.{e}^{{\\eta}_{CL,i}}\\)\u003c/span\u003e \u003c/span\u003e [Equation 2]\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CL}_{pop}\\)\u003c/span\u003e\u003c/span\u003eis the typical population clearance, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Dose}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the dose administered to animal \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Dose}_{median}\\)\u003c/span\u003e\u003c/span\u003e was set to 3 mg/kg, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e is the exponent estimating the strength of the dose effect.\u003c/p\u003e \u003cp\u003eModel selection and covariate retention were guided by objective function value (OFV; \u0026minus;2 loglikelihood) and parameter precision. For nested models, a decrease in OFV of \u0026ge;\u0026thinsp;3.84 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; 1 degree of freedom) was considered statistically significant. Precision was assessed using relative standard error (RSE), with RSE\u0026thinsp;\u0026lt;\u0026thinsp;30% for -fixed effect parameters and \u0026lt;\u0026thinsp;50% for -random effect- parameters were considered acceptable [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Shrinkage was also assessed to gauge the reliability of empirical Bayes estimates (EBEs), recognizing that sparse sampling can increase shrinkage and reduce the interpretability of EBE -based diagnostics [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eModel evaluation\u003c/h3\u003e\n\u003cp\u003eModel adequacy was evaluated using standard graphical and simulation-based diagnostics. Observed concentrations were compared with population predictions (PPRED) and individual predictions (IPRED). Population-weighted residuals (PWRES), individual-weighted residuals (IWRES), and normalized prediction distribution errors (NPDE) were examined as functions of time and predictions to assess bias and model misspecification.\u003c/p\u003e \u003cp\u003eParameter stability and precision were assessed using non-parametric bootstrap resampling (1000 replicates); bootstrap medians and 95% confidence intervals were compared with final parameter estimates. Predictive performance was evaluated using visual predictive checks (VPCs) based on 1000 simulated datasets generated under the original study design (dose levels, group sizes, and sampling times), with observations overlaid on the simulated 5th, 50th, and 95th percentiles.\u003c/p\u003e \u003cp\u003eTo evaluate the dose effect on clearance, dose-stratified VPCs were generated and compared between the base (linear) and final (dose-dependent) models. As an additional verification of the dose-exposure relationship, concentration-time profiles of 1000 virtual mice were simulated for each dose group (0.3, 1, 3, 5, and 10 mg/kg) using the final model. Noncompartmental analysis was performed on the simulated data to calculate AUC\u003csub\u003e0\u0026agrave;inf\u003c/sub\u003e and dose-normalized AUC\u003csub\u003e0\u0026agrave;inf\u003c/sub\u003e, which were plotted versus dose.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePharmacokinetic data\u003c/h2\u003e \u003cp\u003eThe popPK dataset comprised 121 plasma concentrations from 70 mice collected following single IV bolus administration of MTMSA-Trp at doses ranging from 0.3 to 10 mg/kg [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Data were pooled across studies that used both serial and terminal/destructive sampling, resulting in a sparse design in which terminal animals contributed a single concentration. A summary of the analysis dataset is provided in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e, and observed concentration-time profiles across dose groups are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eModel development\u003c/h3\u003e\n\u003cp\u003eOne and two-compartment models with first-order elimination were evaluated, along with additive, proportional, and combined residual error models. A Michaelis-Menten elimination model was explored but produced unstable parameter estimates and was not retained. A one-compartment model with first-order elimination, lognormal IIV on CL and V, and a proportional residual error model adequately described the data and was selected as the base model. The two-compartment model did not improve fit despite added complexity (\u003cb\u003eTable S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eDose was then evaluated as a covariate on CL using a power model. Inclusion of the dose effect significantly improved model fit relative to the base model (ΔOFV\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;26.19; \u003cb\u003eTable S2\u003c/b\u003e) and was retained in the final model.\u003c/p\u003e \u003cp\u003eFinal parameter estimates are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Typical population CL and V were 39.18 mL/h/kg and 53.06 mL/kg, respectively, and the dose-CL exponent was \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e=\u0026minus;0.30, indicating decreasing apparent clearance with increasing dose. Fixed effect- parameters were estimated with good precision (RSE\u0026thinsp;\u0026le;\u0026thinsp;11%). Incorporation of the dose effect reduced unexplained IIV on CL (15% to 5.8%) and decreased IIV on V (58% to 25%; \u003cb\u003eTable S3\u003c/b\u003e). Shrinkage was high for CL (81%) and moderate for V (39.1%), consistent with the sparse/destructive sampling design.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePopulation PK model parameters.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEstimate (%RSE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eBootstrap (n\u0026thinsp;=\u0026thinsp;1000)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eShrinkage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFixed effect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL (mL/h/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.18 (4.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.78\u0026ndash;45.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV (mL/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.06 (7.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.06\u0026ndash;64.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.30 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.40 \u0026ndash; -0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eInterindividual variability\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIIV on CL (CV%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8 (42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.028\u0026ndash;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIIV on V (CV%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u0026ndash;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eResidual variability\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eb (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51 (8.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u0026ndash;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eModel evaluation\u003c/h2\u003e \u003cp\u003eGoodness-of-fit diagnostics demonstrated acceptable agreement between observed concentrations and model predictions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Observations were symmetrically distributed around the line of identity in plots of observed versus PPRED and IPRED, and PWRES/IWRES/NPDE were centered around zero without systematic trends versus time or predictions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; \u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Given the sparse sampling design and associated shrinkage for clearance, EBE-based diagnostics were interpreted cautiously, and emphasis was placed on simulation-based evaluation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, VPC results indicated that the majority of observed concentrations fell within the simulated 5th-95th prediction interval and were well described by the simulated median (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), supporting predictive performance for simulation. Comparison of overall and dose-stratified VPCs showed that the base linear model did not adequately capture the profile at 0.3 mg/kg and did not fully account for variability at later time points, whereas the final model with dose-dependent clearance improved agreement across doses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; \u003cb\u003eSupplementary Fig. S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimulation-based evaluation of exposure supported the estimated dose effect on clearance. Simulated AUC\u003csub\u003e0\u0026agrave;inf\u003c/sub\u003e increased more than proportionally with dose, and simulated dose-normalized AUC\u003csub\u003e0\u0026agrave;inf\u003c/sub\u003e increased with dose (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), consistent with β\u0026thinsp;\u0026lt;\u0026thinsp;0 and decreasing clearance with increasing dose over the 0.3 to 10 mg/kg range.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBootstrap resampling (1000 replicates) confirmed model stability. Final parameter estimates were consistent with bootstrap medians and lay within the bootstrap 95% confidence intervals (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), supporting the robustness and precision of the final model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing sparse plasma concentration-time data pooled across studies in female athymic nude mice, we developed a popPK model to characterize MTMSA-Trp disposition over a wide dose range (0.3 to 10 mg/kg). The primary goal was to obtain robust population parameter estimates suitable for simulation efforts to support future PK/PD and exposure-response analyses.\u003c/p\u003e \u003cp\u003eA one-compartment model with first-order elimination provided a parsimonious description of the data and was preferred over a two-compartment model, which did not improve the fit. However, diagnostics from the linear base model suggested dose-dependent behavior, most notably at the lowest dose and in the extent of variability at later sampling times. Incorporating dose as a covariate on clearance using an empirical power model substantially improved fit (ΔOFV\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;26.19) and improved predictive performance in both overall and dose-stratified VPCs. Although a mechanistic Michaelis-Menten elimination model was explored, it did not yield stable parameter estimates; therefore, the retained power model should be interpreted as an empirical description of non-proportional exposure within the studied dose range rather than a definitive mechanism.\u003c/p\u003e \u003cp\u003eThe final model captured the central tendency and variability across doses, as shown by the dose-stratified VPCs, and provides an empirical description of dose-dependent clearance within the therapeutically relevant range [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The final model estimated β = \u0026minus;0.30, indicating decreasing clearance with increasing dose and a greater-than-proportional increase in exposure (AUC\u003csub\u003e0\u0026agrave;inf\u003c/sub\u003e) across 0.3 to 10 mg/kg. Typical clearance at the reference dose (3 mg/kg) was consistent with prior NCA estimates, supporting concordance with earlier analyses. The estimated volume of distribution (~\u0026thinsp;53 mL/kg) is physiologically plausible and close to mouse plasma volume (~\u0026thinsp;50 mL/kg) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] suggesting distribution largely confined to the vascular space, consistent with prior observations of high plasma protein binding.\u003c/p\u003e \u003cp\u003e Incorporating dose as a covariate on CL reduced unexplained IIV in clearance (15% in the base model to 5.8% in the final model), indicating that a substantial portion of the apparent variability in clearance across animals was explained by dose within the studied range. An important limitation, however, was the high shrinkage estimated for CL (81%). High shrinkage is expected when individual information content is limited (e.g., sparse or destructive sampling) and indicates that EBEs for CL are pulled toward the population mean. Under these conditions, IPRED may closely resemble PPRED, and EBE-based diagnostics should be interpreted cautiously.\u003c/p\u003e \u003cp\u003eAccordingly, model evaluation emphasized simulation-based diagnostics (VPC and NPDE) and -population level parameter precision (bootstrap). The final model yielded precise population estimates (RSE\u0026thinsp;\u0026le;\u0026thinsp;11%), bootstrap medians consistent with final estimates, and simulation-based diagnostics without systematic bias. Given the primary -objective to characterize the population PK of MTMSA-Trp from sparse data to support subsequent exposure-response analyses, the final model was considered suitable for its intended purpose. Importantly, the dose-CL relationship should be interpreted as an empirical description of non-proportional exposure over 0.3 to 10 mg/kg, rather than a definitive mechanistic identification of the source of nonlinearity.\u003c/p\u003e \u003cp\u003eIn conclusion, a one-compartment popPK model incorporating dose-dependent clearance provided a stable and predictive description of MTMSA-Trp pharmacokinetics in mice across 0.3 to 10 mg/kg. The model yields robust population parameter estimates suitable for simulation and provides a quantitative framework for future PK/PD, exposure-efficacy, and translational modeling efforts.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003eM.L., J.S.T, K.A.S., and J.R. are inventors on patents and/or patent applications related to mithramycin derivatives with anticancer activity. These intellectual property rights are assigned to the University of Kentucky. The other authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was funded by the National Cancer Institute (R01 CA243529), the Kentucky Medical Services Foundation Chair in Pharmacy, the National Institutes of Health (R37 AI052218), the Center of Biomedical Research Excellence (COBRE) for Translational Chemical Biology (CTCB, NIH P20 GM130456), the National Institute of Food and Agriculture (USDA-NIFA-CBGP, Grant No. 2023-38821-39584), the University of Kentucky College of Pharmacy, the University of Kentucky Markey Cancer Center, the National Center for Advancing Translational Sciences (UL1TR000117 and UL1TR001998), and the American Lebanese Syrian Associated Charities.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eK.K.N. and M.L. conceptualized the study. N.H.B., K.A.S., and S.S.B. synthesized and purified MTMSA-Trp. K.K.N. and J.H. performed animal experiments and sample analysis. M.L., T.E.P., J.S.T., and J.R. provided resources and funding for the experiment. K.K.N. performed data analysis and modeling. K.K.N. and M.L. prepared the manuscript. M.L., T.E.P., J.S.T., and J.R. reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eThis research included select experiments conducted by the Animal Resources Center at St. Jude Children\u0026rsquo;s Research Hospital, which is supported by the American Lebanese Syrian Associated Charities. We acknowledge Joseph Eckenrode, Karen Jackson, Scott Kinison, and Rupam Sarma for technical assistance with pilot experiments.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eInquiries regarding the datasets should be directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLeggas M, Niloy KK, Yetijaram R, Horn J, Kazuto Y, Prisinzano T, Thorson JS, Tsoikov O, Rohr J (2023) Abstract B150: Targeting EWS-FLI1 with mithramycin analogues for Ewing sarcoma treatment. Mol Cancer Ther 22(12Supplement):B150\u0026ndash;B150. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1535-7163.Targ-23-b150\u003c/span\u003e\u003cspan address=\"10.1158/1535-7163.Targ-23-b150\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYetirajam R, Acharya S, Niloy KK, Kazuto Y, Horn J, Prisinzano T, Thorson JS, Tsodikov O, Rohr J, Leggas M (2025) Abstract 4360: Development of a novel mithramycin analogue with improved pharmacokinetics and therapeutic window for targeted inhibition of EWS-FLI1 in Ewing sarcoma. Cancer Res 85(8Supplement1):4360\u0026ndash;4360. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1538-7445.Am2025-4360\u003c/span\u003e\u003cspan address=\"10.1158/1538-7445.Am2025-4360\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMitra P, Eckenrode JM, Mandal A, Jha AK, Salem SM, Leggas M, Rohr J (2018) Development of Mithramycin Analogues with Increased Selectivity toward ETS Transcription Factor Expressing Cancers. J Med Chem 61(17):8001\u0026ndash;8016. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acs.jmedchem.8b01107\u003c/span\u003e\u003cspan address=\"10.1021/acs.jmedchem.8b01107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRohr J, Tsodikov O, LEGGAS M, Hou C, Eckenrode J, Mitra P, Mandal A (2023) Mithramycin derivatives having increased selectivity and anti-cancer activity. Google Patents\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiloy KK, Horn J, Bhuiyan NH, Shaaban KA, Bhosale SS, Prisinzano TE, Thorson JS, Rohr J, Leggas M (2025) Preclinical Pharmacokinetic Evaluation of Mithramycin and Mithramycin SA Tryptophan-Conjugated Analog. Pharmaceutics 17(6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/pharmaceutics17060765\u003c/span\u003e\u003cspan address=\"10.3390/pharmaceutics17060765\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePopulation pharmacokinetics guidance for industry. US Food and Drug Administration (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKotila OA, Ajayi DT, Masimirembwa C, Thelingwani R, Odetunde A, Falusi AG, Babalola CP (2023) Non-compartmental and population pharmacokinetic analysis of dapsone in healthy NIGERIANS: A pilot study. Br J Clin Pharmacol 89(11):3454\u0026ndash;3459. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/bcp.15862\u003c/span\u003e\u003cspan address=\"10.1111/bcp.15862\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEckenrode JM, Mitra P, Rohr J, Leggas M (2019) Bioanalytical method for quantitative determination of mithramycin analogs in mouse plasma by HPLC-QTOF. Biomed Chromatogr 33(8):e4544. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/bmc.4544\u003c/span\u003e\u003cspan address=\"10.1002/bmc.4544\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe J, Jackson C, Deva S, Hung T, Clarke K, Segelov E, Chao TY, Dai MS, Yeh HT, Ma WW, Kramer D, Chan WK, Kwan R, Cutler D, Zhi J (2022) Population pharmacokinetics for oral paclitaxel in patients with advanced/metastatic solid tumors. CPT Pharmacometrics Syst Pharmacol 11(7):867\u0026ndash;879. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/psp4.12799\u003c/span\u003e\u003cspan address=\"10.1002/psp4.12799\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu XS, Yuan M, Karlsson MO, Dunne A, Nandy P, Vermeulen A (2012) Shrinkage in Nonlinear Mixed-Effects Population Models: Quantification, Influencing Factors, and Impact. AAPS J 14(4):927\u0026ndash;936. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1208/s12248-012-9407-9\u003c/span\u003e\u003cspan address=\"10.1208/s12248-012-9407-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang F, Langenhorst J, Dang S, Kassir N, Owen R, Purdon B, Magnusson MO, Deng R (2023) Population Pharmacokinetics of Tenecteplase in Patients With Acute Myocardial Infarction and Application to Patients With Acute Ischemic Stroke. J Clin Pharmacol 63(2):197\u0026ndash;209. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jcph.2164\u003c/span\u003e\u003cspan address=\"10.1002/jcph.2164\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiloy KK, Leggas M (2024) Preclinical tumor growth inhibition modeling and simulation to support dosing regimen selection of a novel EWS-FLI1 inhibitor for Ewing sarcoma treatment. \u003cem\u003eAmerican Conference of Pharmacometrics\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavies B, Morris T (1993) Physiological parameters in laboratory animals and humans. Pharm Res 10(7):1093\u0026ndash;1095. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1023/a:1018943613122\u003c/span\u003e\u003cspan address=\"10.1023/a:1018943613122\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Population pharmacokinetics, Preclinical pharmacokinetics, Ewing sarcoma, mithramycin analogues","lastPublishedDoi":"10.21203/rs.3.rs-9035594/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9035594/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo develop a pharmacokinetic model for a novel mithramycin analogue, MTMSA-Trp, in mice and characterize dose-dependent disposition to support future pharmacokinetic-pharmacodynamic (PK/PD) and exposure-efficacy analyses.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eNon-linear mixed-effects modeling was used to develop a population pharmacokinetic (popPK) model in MonolixSuite 2024R1 using 121 plasma concentrations from 70 female athymic nude mice after single IV bolus doses of 0.3, 1, 3, 5, and 10 mg/kg. Model selection was guided by the objective function value (OFV), parameter precision, and diagnostic plots. The final model was evaluated using bootstrap resampling (1000 replicates) and visual predictive checks (VPC; 1000 simulated datasets).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA one-compartment model with first-order elimination and an empirical power relationship between dose and clearance best described the data. Including dose as a covariate in the clearance model significantly improved model fit relative to the linear base model (ΔOFV\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;26.19). Typical clearance and volume of distribution were 39.18 mL/h/kg (at 3 mg/kg) and 53.06 mL/kg, respectively, and the dose-clearance exponent was β = \u0026minus;0.30, indicating decreasing clearance with increasing dose. Fixed-effect parameters were estimated with high precision (RSE\u0026thinsp;\u0026le;\u0026thinsp;11%). Shrinkage was high for clearance (81%) and moderate for volume of distribution (39.1%). Bootstrap and VPC results supported model robustness and predictive performance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA robust popPK model describing dose-dependent MTMSA-Trp disposition in mice was developed and is suitable for simulation to support subsequent PK/PD and exposure-efficacy analyses.\u003c/p\u003e","manuscriptTitle":"Nonlinear Mixed-Effects Modeling to Characterize the Pharmacokinetics of a Novel Mithramycin Analogue for Ewing Sarcoma in Mice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 06:31:14","doi":"10.21203/rs.3.rs-9035594/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3f48c177-4ac7-4488-b947-28c68edabdee","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T13:40:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 06:31:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9035594","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9035594","identity":"rs-9035594","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}