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Physiologically based pharmacokinetic modeling and simulation to predict drug–drug interactions of ivosidenib with CYP3A perpetrators in patients with acute myeloid leukemia

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Abstract

Purpose

Develop a physiologically based pharmacokinetic (PBPK) model of ivosidenib using in vitro and clinical PK data from healthy participants (HPs), refine it with clinical data on ivosidenib co-administered with itraconazole, and develop a model for patients with acute myeloid leukemia (AML) and apply it to predict ivosidenib drug–drug interactions (DDI).

Methods

An HP PBPK model was developed in Simcyp Population-Based Simulator (version 15.1), with the CYP3A4 component refined based on a clinical DDI study. A separate model accounting for the reduced apparent oral clearance in patients with AML was used to assess the DDI potential of ivosidenib as the victim of CYP3A perpetrators.

Results

For a single 250 mg ivosidenib dose, the HP model predicted geometric mean ratios of 2.14 (plasma area under concentration–time curve, to infinity [AUC0-∞]) and 1.04 (maximum plasma concentration [Cmax]) with the strong CYP3A4 inhibitor, itraconazole, within 1.26-fold of the observed values (2.69 and 1.0, respectively). The AML model reasonably predicted the observed ivosidenib concentration–time profiles across all dose levels in patients. Predicted ivosidenib geometric mean steady-state AUC0-∞ and Cmax ratios were 3.23 and 2.26 with ketoconazole, and 1.90 and 1.52 with fluconazole, respectively. Co-administration of the strong CYP3A4 inducer, rifampin, predicted a greater DDI effect on a single dose of ivosidenib than on multiple doses (AUC ratios 0.35 and 0.67, Cmax ratios 0.91 and 0.81, respectively).

Conclusion

Potentially clinically relevant DDI effects with CYP3A4 inducers and moderate and strong inhibitors co-administered with ivosidenib were predicted. Considering the challenges of conducting clinical DDI studies in patients, this PBPK approach is valuable in ivosidenib DDI risk assessment and management.

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References

  1. Popovici-Muller J, Lemieux RM, Artin E, Saunders JO, Salituro FG, Travins J, Cianchetta G, Cai Z, Zhou D, Cui D, Chen P, Straley K, Tobin E, Wang F, David MD, Penard-Lacronique V, Quivoron C, Saada V, de Botton S, Gross S, Dang L, Yang H, Utley L, Chen Y, Kim H, Jin S, Gu Z, Yao G, Luo Z, Lv X, Fang C, Yan L, Olaharski A, Silverman L, Biller S, Su SM, Yen K (2018) Discovery of AG-120 (ivosidenib): a first-in-class mutant IDH1 inhibitor for the treatment of IDH1 mutant cancers. ACS Med Chem Lett 9(4):300–305

    Article  CAS  Google Scholar 

  2. DiNardo CD, Stein EM, de Botton S, Roboz GJ, Altman JK, Mims AS, Swords R, Collins RH, Mannis GN, Pollyea DA, Donnellan W, Fathi AT, Pigneux A, Erba HP, Prince GT, Stein AS, Uy GL, Foran JM, Traer E, Stuart RK, Arellano ML, Slack JL, Sekeres MA, Willekens C, Choe S, Wang H, Zhang V, Yen KE, Kapsalis SM, Yang H, Dai D, Fan B, Goldwasser M, Liu H, Agresta S, Wu B, Attar EC, Tallman MS, Stone RM, Kantarjian HM (2018) Durable remissions with ivosidenib in IDH1-mutated relapsed or refractory AML. N Engl J Med 378(25):2386–2398

    Article  CAS  Google Scholar 

  3. Roboz GJ, DiNardo CD, Stein EM, de Botton S, Mims AS, Prince GT, Altman JK, Arellano ML, Donnellan W, Erba HP, Mannis GN, Pollyea DA, Stein AS, Uy GL, Watts JM, Fathi AT, Kantarjian HM, Tallman MS, Choe S, Dai D, Fan B, Wang H, Zhang V, Yen KE, Kapsalis SM, Hickman D, Liu H, Agresta SV, Wu B, Attar EC, Stone RM (2020) Ivosidenib induces deep durable remissions in patients with newly diagnosed IDH1-mutant acute myeloid leukemia. Blood 135(7):463–471

    Article  CAS  Google Scholar 

  4. Fan B, Mellinghoff IK, Wen PY, Lowery MA, Goyal L, Tap WD, Pandya SS, Manyak E, Jiang L, Liu G, Nimkar T, Gliser C, Prahl Judge M, Agresta S, Yang H, Dai D (2020) Clinical pharmacokinetics and pharmacodynamics of ivosidenib, an oral, targeted inhibitor of mutant IDH1, in patients with advanced solid tumors. Invest New Drugs 38(2):433–444

    Article  CAS  Google Scholar 

  5. Prakash C, Fan B, Altaf S, Agresta S, Liu H, Yang H (2019) Pharmacokinetics, absorption, metabolism, and excretion of [(14)C]ivosidenib (AG-120) in healthy male subjects. Cancer Chemother Pharmacol 83(5):837–848

    Article  CAS  Google Scholar 

  6. Dai D, Yang H, Nabhan S, Liu H, Hickman D, Liu G, Zacher J, Vutikullird A, Prakash C, Agresta S, Bowden C, Fan B (2019) Effect of itraconazole, food, and ethnic origin on the pharmacokinetics of ivosidenib in healthy subjects. Eur J Clin Pharmacol 75(8):1099–1108

    Article  CAS  Google Scholar 

  7. Jiang X, Wada R, Poland B, Kleijn HJ, Fan B, Liu G, Liu H, Kapsalis S, Yang H, Le K (2020) Population pharmacokinetic and exposure-response analyses of ivosidenib (AG-120) in patients with IDH1-mutant advanced hematologic malignancies. Clin Pharmacol Ther (In review)

  8. Almond LM, Mukadam S, Gardner I, Okialda K, Wong S, Hatley O, Tay S, Rowland-Yeo K, Jamei M, Rostami-Hodjegan A, Kenny JR (2016) Prediction of drug-drug interactions arising from CYP3A induction using a physiologically based dynamic model. Drug Metab Dispos 44(6):821–832

    Article  CAS  Google Scholar 

  9. Howgate EM, Rowland Yeo K, Proctor NJ, Tucker GT, Rostami-Hodjegan A (2006) Prediction of in vivo drug clearance from in vitro data. I: impact of inter-individual variability. Xenobiotica 36(6):473–497

    Article  CAS  Google Scholar 

  10. Inoue S, Howgate EM, Rowland-Yeo K, Shimada T, Yamazaki H, Tucker GT, Rostami-Hodjegan A (2006) Prediction of in vivo drug clearance from in vitro data. II: potential inter-ethnic differences. Xenobiotica 36(6):499–513

    Article  CAS  Google Scholar 

  11. Leil TA, Kasichayanula S, Boulton DW, LaCreta F (2014) Evaluation of 4beta-hydroxycholesterol as a clinical biomarker of CYP3A4 drug interactions using a Bayesian mechanism-based pharmacometric model. CPT Pharmacometrics Syst Pharmacol 3(6):e120

    Article  CAS  Google Scholar 

  12. Wagner C, Pan Y, Hsu V, Sinha V, Zhao P (2016) Predicting the effect of CYP3A inducers on the pharmacokinetics of substrate drugs using physiologically based pharmacokinetic (PBPK) modeling: an analysis of PBPK submissions to the US FDA. Clin Pharmacokinet 55(4):475–483

    Article  CAS  Google Scholar 

  13. Ke A, Barter Z, Rowland-Yeo K, Almond L (2016) Towards a best practice approach in PBPK modeling: case example of developing a unified efavirenz model accounting for induction of CYPs 3A4 and 2B6. CPT Pharmacometrics Syst Pharmacol 5(7):367–376

    Article  CAS  Google Scholar 

  14. Jalava KM, Partanen J, Neuvonen PJ (1997) Itraconazole decreases renal clearance of digoxin. Ther Drug Monit 19(6):609–613

    Article  CAS  Google Scholar 

  15. Gorski JC, Vannaprasaht S, Hamman MA, Ambrosius WT, Bruce MA, Haehner-Daniels B, Hall SD (2003) The effect of age, sex, and rifampin administration on intestinal and hepatic cytochrome P450 3A activity. Clin Pharmacol Ther 74(3):275–287

    Article  CAS  Google Scholar 

  16. Jones HM, Chen Y, Gibson C, Heimbach T, Parrott N, Peters SA, Snoeys J, Upreti VV, Zheng M, Hall SD (2015) Physiologically based pharmacokinetic modeling in drug discovery and development: a pharmaceutical industry perspective. Clin Pharmacol Ther 97(3):247–262

    Article  CAS  Google Scholar 

  17. Luzon E, Blake K, Cole S, Nordmark A, Versantvoort C, Berglund EG (2017) Physiologically based pharmacokinetic modeling in regulatory decision-making at the European Medicines Agency. Clin Pharmacol Ther 102(1):98–105

    Article  CAS  Google Scholar 

  18. Sager JE, Yu J, Ragueneau-Majlessi I, Isoherranen N (2015) Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: a systematic review of published models, applications, and model verification. Drug Metab Dispos 43(11):1823–1837

    Article  CAS  Google Scholar 

  19. European Medicines Agency (2012) Guideline on the investigation of drug interactions. https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-investigation-drug-interactions_en.pdf. Accessed 17 April 2020

  20. U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) (2017) Clinical drug interaction studies — study design, data analysis, and clinical implications guidance for industry. https://www.fda.gov/files/drugs/published/Clinical-Drug-Interaction-Studies-%E2%80%94-Study-Design--Data-Analysis--and-Clinical-Implications-Guidance-for-Industry.pdf. Accessed 17 April 2020

  21. Grimstein M, Yang Y, Zhang X, Grillo J, Huang SM, Zineh I, Wang Y (2019) Physiologically based pharmacokinetic modeling in regulatory science: an update from the U.S. Food and Drug Administration's Office of Clinical Pharmacology. J Pharm Sci 108(1):21–25

    Article  CAS  Google Scholar 

  22. Coutant DE, Kulanthaivel P, Turner PK, Bell RL, Baldwin J, Wijayawardana SR, Pitou C, Hall SD (2015) Understanding disease-drug interactions in cancer patients: implications for dosing within the therapeutic window. Clin Pharmacol Ther 98(1):76–86

    Article  CAS  Google Scholar 

  23. Cousin L, Berre ML, Launay-Vacher V, Izzedine H, Deray G (2003) Dosing guidelines for fluconazole in patients with renal failure. Nephrol Dial Transplant 18(11):2227–2231

    Article  Google Scholar 

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Acknowledgements

We would like to dedicate this article to the memory of our colleague, David Dai. We would like to thank the healthy participants and patients taking part in these studies. Assistance with manuscript preparation was provided by Christine Ingleby, PhD, CMPP, Excel Medical Affairs, Horsham, UK, and supported by Agios.

Funding

This study was supported financially by Agios Pharmaceuticals, Inc.

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Authors and Affiliations

  1. Agios Pharmaceuticals, Inc., Cambridge, MA, USA

    Chandra Prakash, Bin Fan, Kha Le & Hua Yang

  2. Certara UK Limited, Sheffield, UK

    Alice Ke

Authors
  1. Chandra Prakash
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  2. Bin Fan
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  4. Kha Le
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  5. Hua Yang
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Contributions

All authors performed data analysis and interpretation, as well as manuscript writing, review, and approval.

Corresponding author

Correspondence to Chandra Prakash.

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Conflict of interest

C.P., B.F., K.L., and H.Y. were employees of and stockholders in Agios Pharmaceuticals, Inc., at the time of the study. A.K. is an employee of a contract research organization, Certara UK Ltd.

Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

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Informed consent was obtained from all individual participants included in the studies.

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Bin Fan, Kha Le, and Hua Yang: affiliation at time of study.

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Prakash, C., Fan, B., Ke, A. et al. Physiologically based pharmacokinetic modeling and simulation to predict drug–drug interactions of ivosidenib with CYP3A perpetrators in patients with acute myeloid leukemia. Cancer Chemother Pharmacol 86, 619–632 (2020). https://doi.org/10.1007/s00280-020-04148-3

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