Skip to main content

Advertisement

SpringerLink
Log in

Oncology dose optimization paradigms: knowledge gained and extrapolated from approved oncology therapeutics

  • Review Article
  • Published:
Cancer Chemotherapy and Pharmacology Aims and scope Submit manuscript

Abstract

There has been increasing attention to dose optimization in the development of targeted oncology therapeutics. The current report has analyzed the dose selection approaches for 116 new molecular entities (NMEs) approved for oncology indications by the US FDA from 2010 to August 2021, with the goal to extract learnings about the ways to select the optimal dose. The analysis showed that: (1) the initial label dose was lower than the maximum tolerated dose (MTD) or maximum studied dose (MSD) in Phase 1 for the majority of approved NMEs, and that the MTD approach is no longer the mainstay for dose selection; (2) there was no dose ranging or optimization beyond Phase 1 dose escalation for ~ 80% of the NMEs; (3) integrated dose/exposure–response analyses were commonly used to justify the dose selection; (4) lack of dose optimization led to dose-related PMRs/PMCs in 14% of cases, but 82% of these did not result in change of the initial label dose; and (5) depending on properties of the NME and specific benefit/risk considerations for the target patient population, there could be different dose selection paradigms leading to identification of the appropriate clinical dose. The analysis supports the need to incorporate more robust dose optimization during oncology clinical development, through comparative assessment of benefit/risk of multiple dose levels, over a wide exposure range using therapeutically relevant endpoints and adequate sample size. On the other hand, in certain cases, data from FIP dose escalation may be adequate to support the dose selection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Sacks LV, Shamsuddin HH, Yasinskaya YI, Bouri K, Lanthier ML, Sherman RE (2014) Scientific and regulatory reasons for delay and denial of FDA approval of initial applications for new drugs, 2000–2012. JAMA J Am Med Assoc 311(4):378–384. https://doi.org/10.1001/jama.2013.282542

    Article  CAS  Google Scholar 

  2. Minasian L, Rosen O, Auclair D, Rahman A, Pazdur R, Schilsky RL (2014) Optimizing dosing of oncology drugs. Clin Pharmacol Ther 96(5):572–579. https://doi.org/10.1038/clpt.2014.153

    Article  CAS  PubMed  Google Scholar 

  3. Janne PA, Kim G, Shaw AT, Sridhara R, Pazdur R, McKee AE (2016) Dose finding of small-molecule oncology drugs: optimization throughout the development life cycle. Clin Cancer Res 22(11):2613–2617. https://doi.org/10.1158/1078-0432.CCR-15-2643

    Article  CAS  PubMed  Google Scholar 

  4. Mathijssen RH, Sparreboom A, Verweij J (2014) Determining the optimal dose in the development of anticancer agents. Nat Rev Clin Oncol 11(5):272–281. https://doi.org/10.1038/nrclinonc.2014.40

    Article  CAS  PubMed  Google Scholar 

  5. Corbaux P, El-Madani M, Tod M, Peron J, Maillet D, Lopez J, Freyer G, You B (2019) Clinical efficacy of the optimal biological dose in early-phase trials of anti-cancer targeted therapies. Eur J Cancer 120:40–46. https://doi.org/10.1016/j.ejca.2019.08.002

    Article  CAS  PubMed  Google Scholar 

  6. Shah M, Rahman A, Theoret MR, Pazdur R (2021) The drug-dosing conundrum in oncology - when less is more. New Engl J Med 385(16):1445–1447. https://doi.org/10.1056/NEJMp2109826

    Article  PubMed  Google Scholar 

  7. Sachs JR, Mayawala K, Gadamsetty S, Kang SP, de Alwis DP (2016) Optimal dosing for targeted therapies in oncology: drug development cases leading by example. Clin Cancer Res 22(6):1318–1324. https://doi.org/10.1158/1078-0432.Ccr-15-1295

    Article  CAS  PubMed  Google Scholar 

  8. Lu D, Lu T, Stroh M, Graham RA, Agarwal P, Musib L, Li CC, Lum BL, Joshi A (2016) A survey of new oncology drug approvals in the USA from 2010 to 2015: a focus on optimal dose and related postmarketing activities. Cancer Chemother Pharmacol 77(3):459–476. https://doi.org/10.1007/s00280-015-2931-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Friends of Cancer Research White Paper (2021) Optimizing dosing in oncology drug development. Friends of cancer research annual meeting 2021.

  10. Project Optimus (2022) Reforming the dose optimization and dose selection paradigm in oncology. https://www.fda.gov/about-fda/oncology-center-excellence/project-optimus. Accessed 26 Apr 2022

  11. Goldberg P (2021) RIP MTD: FDA to require sponsors to determine optimal dosage before initiating pivotal trials in cancer. Cancer Lett 47(23):13–14

    Google Scholar 

  12. Ratain MJSG, Tannock IF, Lichter AS (2021) Optimize the dose: an optimal step forward for FDA. Cancer Lett 47(23):15–16

    Google Scholar 

  13. (2022) Drugs@FDA: FDA-Approved Drugs https://www.accessdata.fda.gov/scripts/cder/daf/. Accessed 26 Apr 2022

  14. (2022) Postmarket Requirements and Commitments. https://www.accessdata.fda.gov/scripts/cder/pmc/index.cfm. Accessed 26 Apr 2022

  15. FDA (2012) ICLUSIG® (ponatinib) Approval letter(s). https://www.accessdata.fda.gov/drugsatfda_docs/appletter/2012/203469Orig1s000ltr.pdf. Accessed 26 Apr 2022

  16. Cortes J, Apperley J, Lomaia E, Moiraghi B, Undurraga Sutton M, Pavlovsky C, Chuah C, Sacha T, Lipton JH, Schiffer CA, McCloskey J, Hochhaus A, Rousselot P, Rosti G, de Lavallade H, Turkina A, Rojas C, Arthur CK, Maness L, Talpaz M, Mauro M, Hall T, Lu V, Srivastava S, Deininger M (2021) Ponatinib dose–ranging study in chronic-phase chronic myeloid leukemia: a randomized, open-label phase 2 clinical trial. Blood 138(21):2042–2050. https://doi.org/10.1182/blood.2021012082

    Article  CAS  PubMed  Google Scholar 

  17. ICLUSIG (2020) (ponatinib) [package insert]. ARIAD Pharmaceuticals Inc., Cambridge

    Google Scholar 

  18. FDA (2014) ZYKADIA® (ceritinib) Approval letter(s). https://www.accessdata.fda.gov/drugsatfda_docs/nda/2014/205755Orig1s000Approv.pdf. Accessed 26 Apr 2022

  19. Cho BC, Kim DW, Bearz A, Laurie SA, McKeage M, Borra G, Park K, Kim SW, Ghosn M, Ardizzoni A, Maiello E, Greystoke A, Yu R, Osborne K, Gu W, Scott JW, Passos VQ, Lau YY, Wrona A (2017) ASCEND-8: A randomized phase 1 study of ceritinib, 450 mg or 600 mg, taken with a low-fat meal versus 750 mg in fasted state in patients with anaplastic lymphoma kinase (ALK)-rearranged metastatic non-small cell lung cancer (NSCLC). J Thorac Oncol 12(9):1357–1367. https://doi.org/10.1016/j.jtho.2017.07.005

    Article  PubMed  Google Scholar 

  20. FDA (2014) ZYKADIA® (ceritinib) [package insert]. Novartis Pharmaceutical Corporation, East Hanover

    Google Scholar 

  21. ODOMZO® (2015) (sonidegib) [package insert]. Novartis Pharmaceutical Corporation, East Hanover

  22. FDA (2015) ODOMZO® (sonidegib) clinical pharmacology and biopharmaceutics review. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2015/205266Orig1s000ClinPharmR.pdf. Accessed 26 Apr 2022

  23. FDA (2012) XTANDI® (enzaltutamide) clinical pharmacology and biopharmaceutics review. http://www.accessdata.fda.gov/drugsatfda_docs/nda/2012/203415Orig1s000ClinPharmR.pdf. Accessed 26 Apr 2022

  24. IBRANCE® (2015) (palbociclib) [package insert]. Pfizer Inc., New York

  25. Flaherty KT, Lorusso PM, Demichele A, Abramson VG, Courtney R, Randolph SS, Shaik MN, Wilner KD, O’Dwyer PJ, Schwartz GK (2012) Phase I, dose-escalation trial of the oral cyclin-dependent kinase 4/6 inhibitor PD 0332991, administered using a 21-day schedule in patients with advanced cancer. Clin Cancer Res 18(2):568–576. https://doi.org/10.1158/1078-0432.CCR-11-0509

    Article  CAS  PubMed  Google Scholar 

  26. FDA (2015) IBRANCE® (palbociclib) clinical pharmacology and biopharmaceutics review. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2015/207103Orig1s000ClinPharmR.pdf. Accessed 26 Apr 2022

  27. Pallmann P, Bedding AW, Choodari-Oskooei B, Dimairo M, Flight L, Hampson LV, Holmes J, Mander AP, Odondi L, Sydes MR, Villar SS, Wason JMS, Weir CJ, Wheeler GM, Yap C, Jaki T (2018) Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Med 16(1):29. https://doi.org/10.1186/s12916-018-1017-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. INLYTA® (2012) (axitinib) [package insert]. Pfizer Inc., New York.

  29. JAKAFI® (2011) (ruxolitinib) [package insert]. Incyte Corp., Wilmington

  30. Bosulif® (2012) (bosutinib) [package insert]. Pfizer Inc., New York

Download references

Acknowledgements

The authors would like to acknowledge Drs. Gianluca Nucci, Kourosh Parivar, Laurie Strawn, Sriram Krishnaswami, and Yazdi Pithavala for their valuable input and discussions related to the manuscript.

Funding

Funding was provided by Pfizer.

Author information

Authors and Affiliations

  1. Clinical Pharmacology, Early Clinical Development, Pfizer Inc., 10777 Science Center Drive, San Diego, CA, 92121, USA

    Rajendar K. Mittapalli, Cen Guo, Stefanie K. Drescher & Donghua Yin

Authors
  1. Rajendar K. Mittapalli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cen Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefanie K. Drescher
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Donghua Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rajendar K. Mittapalli.

Ethics declarations

Conflict of interest

RKM, CG, SD, and DY are employees of Pfizer and hold Pfizer stock or stock options.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 374 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mittapalli, R.K., Guo, C., Drescher, S.K. et al. Oncology dose optimization paradigms: knowledge gained and extrapolated from approved oncology therapeutics. Cancer Chemother Pharmacol 90, 207–216 (2022). https://doi.org/10.1007/s00280-022-04444-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00280-022-04444-0

Keywords

Search

Navigation