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Modeling exposure-driven adverse events of EGFR TKIs in the treatment of patients with non-small cell lung cancer

Acta Pharmacologica Sinica (2025)Cite this article

Abstract

The adverse events associated with antitumour drugs have recently emerged as an increasingly significant clinical concern. Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs) serve as pivotal therapeutic agents for non-small cell lung cancer (NSCLC). However, considerable interindividual variability exists in drug exposure, along with a high incidence and severity of adverse events. In this study, we quantitatively investigated the impacts of EGFR TKI exposure and other covariates on the severity of the maximum grade of drug-related adverse events (MDRAE) in NSCLC patients treated with EGFR TKIs. Data were collected from 277 patients treated with gefitinib, icotinib, afatinib or osimertinib. Population pharmacokinetic (PopPK) models were constructed for each drug, and individual exposure metrics were derived through model simulations. Normalized individual exposures to different EGFR TKIs based on their IC50 values and MDRAE data were integrated to develop an ordinal logistic regression model for an exposure–safety analysis. A user-friendly nomogram was subsequently designed. The probability of high-grade MDRAE was significantly associated with normalized exposure levels, a history of EGFR TKI treatment, sex and other factors. Model simulations revealed substantial interindividual variability in drug exposure and the probability of different grades of MDRAE for the same treatment regimen. This study quantitatively elucidates the influences of drug exposure and other critical factors on safety, thereby contributing to the formulation of individualized treatment strategies to prevent and promptly address drug safety-related issues.

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Fig. 1: Goodness-of-fit plots of the PopPK models for EGFR TKIs.
Fig. 2: Visual predictive check plots for PopPK models of EGFR TKIs.
Fig. 3: Dependencies of MDRAE on the variables of the final ordinal logistic regression model.
Fig. 4: Simulated curves of the probability of different MDRAE grades over CNORM, stratified by different variables.
Fig. 5: Boxplots of model-predicted normalized exposure.
Fig. 6: Box plots of the model-predicted probabilities of different MDRAE grades for EGFR TKIs.
Fig. 7: Nomogram of the ordinal logistic regression model for MDRAEs.

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References

  1. Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73:17–48.

    Article  PubMed  Google Scholar 

  2. Reck M, Rabe KF. Precision diagnosis and treatment for advanced non-small-cell lung cancer. N Engl J Med. 2017;377:849–61.

    Article  CAS  PubMed  Google Scholar 

  3. Midha A, Dearden S, McCormack R. EGFR mutation incidence in non-small-cell lung cancer of adenocarcinoma histology: a systematic review and global map by ethnicity (mutMapII). Am J Cancer Res. 2015;5:2892–911.

    PubMed  PubMed Central  Google Scholar 

  4. Sequist LV, Yang JC, Yamamoto N, O’Byrne K, Hirsh V, Mok T, et al. Phase III study of afatinib or cisplatin plus pemetrexed in patients with metastatic lung adenocarcinoma with EGFR mutations. J Clin Oncol. 2013;31:3327–34.

    Article  CAS  PubMed  Google Scholar 

  5. Jänne PA, Ou SI, Kim DW, Oxnard GR, Martins R, Kris MG, et al. Dacomitinib as first-line treatment in patients with clinically or molecularly selected advanced non-small-cell lung cancer: a multicentre, open-label, phase 2 trial. Lancet Oncol. 2014;15:1433–41.

    Article  PubMed  Google Scholar 

  6. Jänne PA, Yang JC, Kim DW, Planchard D, Ohe Y, Ramalingam SS, et al. AZD9291 in EGFR inhibitor-resistant non-small-cell lung cancer. N Engl J Med. 2015;372:1689–99.

    Article  PubMed  Google Scholar 

  7. Zhao YY, Li S, Zhang Y, Zhao HY, Liao H, Guo Y, et al. The relationship between drug exposure and clinical outcomes of non-small cell lung cancer patients treated with gefitinib. Med Oncol. 2011;28:697–702.

    Article  CAS  PubMed  Google Scholar 

  8. Tiseo M, Andreoli R, Gelsomino F, Mozzoni P, Azzoni C, Bartolotti M, et al. Correlation between erlotinib pharmacokinetics, cutaneous toxicity and clinical outcomes in patients with advanced non-small cell lung cancer (NSCLC). Lung Cancer. 2014;83:265–71.

    Article  PubMed  Google Scholar 

  9. Rodier T, Puszkiel A, Cardoso E, Balakirouchenane D, Narjoz C, Arrondeau J, et al. Exposure-response analysis of osimertinib in patients with advanced non-small-cell lung cancer. Pharmaceutics. 2022;14.

  10. Swaisland HC, Smith RP, Laight A, Kerr DJ, Ranson M, Wilder-Smith CH, et al. Single-dose clinical pharmacokinetic studies of gefitinib. Clin Pharmacokinet. 2005;44:1165–77.

    Article  CAS  PubMed  Google Scholar 

  11. Hu P, Chen J, Liu D, Zheng X, Zhao Q, Jiang J. Development of population pharmacokinetics model of icotinib with non-linear absorption characters in healthy Chinese volunteers to assess the CYP2C19 polymorphism and food-intake effect. Eur J Clin Pharmacol. 2015;71:843–50.

    Article  CAS  PubMed  Google Scholar 

  12. Brown K, Comisar C, Witjes H, Maringwa J, de Greef R, Vishwanathan K, et al. Population pharmacokinetics and exposure-response of osimertinib in patients with non-small cell lung cancer. Br J Clin Pharmacol. 2017;83:1216–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kiang TK, Sherwin CM, Spigarelli MG, Ensom MH. Fundamentals of population pharmacokinetic modelling: modelling and software. Clin Pharmacokinet. 2012;51:515–25.

    Article  CAS  PubMed  Google Scholar 

  14. Kawata T, Higashimori M, Itoh Y, Tomkinson H, Johnson MG, Tang W, et al. Gefitinib exposure and occurrence of interstitial lung disease in Japanese patients with non-small-cell lung cancer. Cancer Chemother Pharm. 2019;83:849–58.

    Article  CAS  Google Scholar 

  15. Nakao K, Kobuchi S, Marutani S, Iwazaki A, Tamiya A, Isa S, et al. Population pharmacokinetics of afatinib and exposure-safety relationships in Japanese patients with EGFR mutation-positive non-small cell lung cancer. Sci Rep. 2019;9. 18202.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wang QQ, Yu SC, Qi X, Hu YH, Zheng WJ, Shi JX, et al. Overview of logistic regression model analysis and application. Zhonghua Yu Fang Yi Xue Za Zhi. 2019;53:955–60.

    CAS  PubMed  Google Scholar 

  17. Xiong X, Zhang Y, Wang Z, Zhou C, Yang P, Du X, et al. Simultaneous quantitative detection of afatinib, erlotinib, gefitinib, icotinib, osimertinib and their metabolites in plasma samples of patients with non-small cell lung cancer using liquid chromatography-tandem mass spectrometry. Clin Chim Acta. 2022;527:1–10.

    Article  CAS  PubMed  Google Scholar 

  18. Freiwald M, Schmid U, Fleury A, Wind S, Stopfer P, Staab A. Population pharmacokinetics of afatinib, an irreversible ErbB family blocker, in patients with various solid tumors. Cancer Chemother Pharmacol. 2014;73:759–70.

    Article  CAS  PubMed  Google Scholar 

  19. Chan Kwong AHP, Calvier EAM, Fabre D, Gattacceca F, Khier S. Prior information for population pharmacokinetic and pharmacokinetic/pharmacodynamic analysis: overview and guidance with a focus on the NONMEM PRIOR subroutine. J Pharmacokinet Pharmacodyn. 2020;47:431–46.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Al-Huniti N, Feng Y, Yu JJ, Lu Z, Nagase M, Zhou D, et al. Tumor growth dynamic modeling in oncology drug development and regulatory approval: past, present, and future opportunities. CPT Pharmacomet Syst Pharmacol. 2020;9:419–27.

    Article  CAS  Google Scholar 

  21. Shah RR, Shah DR. Safety and tolerability of Epidermal Growth Factor Receptor (EGFR) tyrosine kinase inhibitors in oncology. Drug Saf. 2019;42:181–98.

    Article  CAS  PubMed  Google Scholar 

  22. Gadkar K, Friedrich C, Hurez V, Ruiz ML, Dickmann L, Kumar Jolly M, et al. Quantitative systems pharmacology model-based investigation of adverse gastrointestinal events associated with prolonged treatment with PI3-kinase inhibitors. CPT Pharmacomet Syst Pharmacol. 2022;11:616–27.

    Article  CAS  Google Scholar 

  23. Shulgin B, Kosinsky Y, Omelchenko A, Chu L, Mugundu G, Aksenov S, et al. Dose dependence of treatment-related adverse events for immune checkpoint inhibitor therapies: a model-based meta-analysis. Oncoimmunology. 2020;9:1748982.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Zhang Z. Missing data imputation: focusing on single imputation. Ann Transl Med. 2016;4:9.

    PubMed  PubMed Central  Google Scholar 

  25. Kwak SK, Kim JH. Statistical data preparation: management of missing values and outliers. Korean J Anesthesiol. 2017;70:407–11.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Aggarwal CC. An introduction to outlier analysis. In: Aggarwal CC, editor. Outlier analysis. Cham: Springer International Publishing; 2017, p. 1–34.

  27. Singh V, Dwivedi SN, Deo SVS. Ordinal logistic regression model describing factors associated with extent of nodal involvement in oral cancer patients and its prospective validation. BMC Med Res Methodol. 2020;20:95.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Nakamura T, Obata JE, Hirano M, Kitta Y, Fujioka D, Saito Y, et al. Predictive value of remnant lipoprotein for cardiovascular events in patients with coronary artery disease after achievement of LDL-cholesterol goals. Atherosclerosis. 2011;218:163–7.

    Article  CAS  PubMed  Google Scholar 

  29. Kim JH. Multicollinearity and misleading statistical results. Korean J Anesthesiol. 2019;72:558–69.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Brant R. Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometrics. 1990;46:1171–8.

    Article  CAS  PubMed  Google Scholar 

  31. David W, Hosmer SL. Assessing the fit of the model. In: Walter A. Shewhart SSW, editors. Applied logistic regression. John Wiley & Sons, Inc.; 2000. p 143–202.

  32. Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5:1315–6.

    Article  PubMed  Google Scholar 

  33. Overgaard RV, Ingwersen SH, Tornøe CW. Establishing good practices for exposure-response analysis of clinical endpoints in drug development. CPT Pharmacomet Syst Pharmacol. 2015;4:565–75.

    Article  CAS  Google Scholar 

  34. Joel S, Owen JF-K. Simulation basics. In: Introduction to population pharmacokinetic/pharmacodynamic analysis with nonlinear mixed effects models. John Wiley & Sons, Inc.; 2014. p. 265–84.

  35. Demirel OF, Willemain TR. Generation of simulation input scenarios using bootstrap methods. Journal Oper Res Soc. 2002;53:69–78.

    Article  Google Scholar 

  36. Shah DR, Shah RR, Morganroth J. Tyrosine kinase inhibitors: their on-target toxicities as potential indicators of efficacy. Drug Saf. 2013;36:413–26.

    Article  CAS  PubMed  Google Scholar 

  37. Cella M, Gorter de Vries F, Burger D, Danhof M, Della Pasqua O. A model-based approach to dose selection in early pediatric development. Clin Pharm Ther. 2010;87:294–302.

    Article  CAS  Google Scholar 

  38. Knebel W, Gastonguay MR, Malhotra B, El-Tahtawy A, Jen F, Gandelman K. Population pharmacokinetics of atorvastatin and its active metabolites in children and adolescents with heterozygous familial hypercholesterolemia: selective use of informative prior distributions from adults. J Clin Pharmacol. 2013;53:505–16.

    Article  CAS  PubMed  Google Scholar 

  39. Chen J, Liu D, Zheng X, Zhao Q, Jiang J, Hu P. Relative contributions of the major human CYP450 to the metabolism of icotinib and its implication in prediction of drug-drug interaction between icotinib and CYP3A4 inhibitors/inducers using physiologically based pharmacokinetic modeling. Expert Opin Drug Metab Toxicol. 2015;11:857–68.

    Article  CAS  PubMed  Google Scholar 

  40. Ni J, Liu DY, Hu B, Li C, Jiang J, Wang HP, et al. Relationship between icotinib hydrochloride exposure and clinical outcome in Chinese patients with advanced non-small cell lung cancer. Cancer. 2015;121:3146–56. Suppl 17.

    Article  CAS  PubMed  Google Scholar 

  41. FDA Center for Drug Evaluation and Research. TAGRISSO: pharmacology reviews [updated 2015 Nov 4; cited 16 Aug 2024]. Available from: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2015/208065Orig1s000PharmR.pdf.

  42. FDA Center for Drug Evaluation and Research. Gilotrif: CLINICAL Pharmacology and biopharmaceutics reviews [updated 2012 Nov 14; cited 16 Aug 2024]. Available from: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2013/201292Orig1s000ClinPharmR.pdf.

  43. Chen J, Zheng X, Liu DY, Zhao Q, Wu YW, Tan FL, et al. Therapeutic effects and adverse drug reactions are affected by icotinib exposure and CYP2C19 and EGFR genotypes in Chinese non-small cell lung cancer patients. Asian Pac J Cancer Prev. 2014;15:7195–200.

    Article  PubMed  Google Scholar 

  44. Kobayashi H, Sato K, Niioka T, Miura H, Ito H, Miura M. Relationship among gefitinib exposure, polymorphisms of its metabolizing enzymes and transporters, and side effects in japanese patients with non-small-cell lung cancer. Clin Lung Cancer. 2015;16:274–81.

    Article  CAS  PubMed  Google Scholar 

  45. Westover D, Zugazagoitia J, Cho BC, Lovly CM, Paz-Ares L. Mechanisms of acquired resistance to first- and second-generation EGFR tyrosine kinase inhibitors. Ann Oncol. 2018;29:i10–i9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Liu Q, Yu S, Zhao W, Qin S, Chu Q, Wu K. EGFR-TKIs resistance via EGFR-independent signaling pathways. Mol Cancer. 2018;17:53.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Ă–zdemir BC, Gerard CL, Espinosa da Silva C. Sex and gender differences in anticancer treatment toxicity: a call for revisiting drug dosing in oncology. Endocrinology. 2022;163:bqac058.

  48. Li Z, Zou W, Yuan J, Zhong Y, Fu Z. Gender differences in adverse events related to Osimertinib: a real-world pharmacovigilance analysis of FDA adverse event reporting system. Expert Opin Drug Saf. 2024;23:763–70.

    Article  CAS  PubMed  Google Scholar 

  49. Xu L, Xu F, Kong H, Zhao M, Ye Y, Zhang Y. Effects of reduced platelet count on the prognosis for patients with non-small cell lung cancer treated with EGFR-TKI: a retrospective study. BMC Cancer. 2020;20:1152.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Jiang Y, Su Z, Lin Y, Xiong Y, Li C, Li J, et al. Prognostic and predictive impact of creatine kinase level in non-small cell lung cancer treated with tyrosine kinase inhibitors. Transl Lung Cancer Res. 2021;10:3771–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. He J, Su C, Liang W, Xu S, Wu L, Fu X, et al. Icotinib versus chemotherapy as adjuvant treatment for stage II-IIIA EGFR-mutant non-small-cell lung cancer (EVIDENCE): a randomised, open-label, phase 3 trial. Lancet Respir Med. 2021;9:1021–9.

    Article  CAS  PubMed  Google Scholar 

  52. Zhao Y, Cheng B, Chen Z, Li J, Liang H, Chen Y, et al. Toxicity profile of epidermal growth factor receptor tyrosine kinase inhibitors for patients with lung cancer: a systematic review and network meta-analysis. Crit Rev Oncol Hematol. 2021;160:103305.

    Article  PubMed  Google Scholar 

  53. Shi Y, Hu X, Zhang S, Lv D, Wu L, Yu Q, et al. Efficacy, safety, and genetic analysis of furmonertinib (AST2818) in patients with EGFR T790M mutated non-small-cell lung cancer: a phase 2b, multicentre, single-arm, open-label study. Lancet Respir Med. 2021;9:829–39.

    Article  CAS  PubMed  Google Scholar 

  54. FDA Center for Drug Evaluation and Research. Iressa: Pharmacology reviews: [updated 2014 Sep 17; cited 16 Aug 2024]. Available from: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2015/206995Orig1s000PharmR.pdf.

  55. Lavezzi SM, Borella E, Carrara L, De Nicolao G, Magni P, Poggesi I. Mathematical modeling of efficacy and safety for anticancer drugs clinical development. Expert Opin Drug Discov. 2018;13:5–21.

    Article  CAS  PubMed  Google Scholar 

  56. Ooi QX, Plan E, Bergstrand M. A tutorial on pharmacometric Markov models. CPT Pharmacomet Syst Pharmacol. 2025;14:197–216.

    Article  CAS  Google Scholar 

  57. Lu T, Yang Y, Jin JY, Kågedal M. Analysis of longitudinal-ordered categorical data for muscle spasm adverse event of vismodegib: comparison between different pharmacometric models. CPT Pharmacomet Syst Pharmacol. 2020;9:96–105.

    Article  CAS  Google Scholar 

  58. Xu C, Ravva P, Dang JS, Laurent J, Adessi C, McIntyre C, et al. A continuous-time multistate Markov model to describe the occurrence and severity of diarrhea events in metastatic breast cancer patients treated with lumretuzumab in combination with pertuzumab and paclitaxel. Cancer Chemother Pharmacol. 2018;82:395–406.

    Article  CAS  PubMed  Google Scholar 

  59. Suleiman AA, Frechen S, Scheffler M, Zander T, Nogova L, Kocher M, et al. A modeling and simulation framework for adverse events in erlotinib-treated non-small-cell lung cancer patients. AAPS J. 2015;17:1483–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The study was supported by the National Science Fund of China (No. 82104294), Fund for Fostering Talents of Peking University Third Hospital (No. BYSYFY2021016), and National Key R&D Program of China (No. 2020AAA0105203).

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  1. These authors contributed equally: Ling Yong, Yan’e Liu.

Authors and Affiliations

  1. Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System, Department of Pharmaceutics, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China

    Ling Yong, Wei-zhe Jian, Lei Cai, Tian-yu Bao, Chen Liu, En-ze Gan, Tian-yu Wang, Ping-yao Luo & Tian-yan Zhou

  2. Department of Pharmacy, Peking University Third Hospital, Beijing, 100191, China

    Ling Yong & Wei Liu

  3. Department of Medical Oncology and Radiation Sickness, Peking University Third Hospital, Beijing, 100191, China

    Yan’e Liu & Bao-shan Cao

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Contributions

TYZ, WL and BSC designed the research; LY, YEL, WL, LC, TYB, CL, EZG, TYW and PYL performed the experiments; LY and WL analyzed the data; and LY, WZJ, TYZ and WL wrote and revised the manuscript. All the authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Bao-shan Cao, Wei Liu or Tian-yan Zhou.

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Yong, L., Liu, Y., Jian, Wz. et al. Modeling exposure-driven adverse events of EGFR TKIs in the treatment of patients with non-small cell lung cancer. Acta Pharmacol Sin (2025). https://doi.org/10.1038/s41401-025-01573-z

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