Identification of optimal dosing schedules of dacomitinib and osimertinib for a phase I/II trial in advanced EGFR-mutant non-small cell lung cancer

Kamrine E. Poels, Adam J. Schoenfeld, Alex Makhnin, Yosef Tobi, Yuli Wang, Heidie Frisco-Cabanos, Shaon Chakrabarti, Manli Shi, Chelsi Napoli, Thomas O. McDonald, Weiwei Tan, Aaron Hata, Scott L. Weinrich, Helena A. Yu () and Franziska Michor ()
Additional contact information
Kamrine E. Poels: Harvard T.H. Chan School of Public Health
Adam J. Schoenfeld: Weill Cornell Medical College
Alex Makhnin: Weill Cornell Medical College
Yosef Tobi: Weill Cornell Medical College
Yuli Wang: Pfizer Inc
Heidie Frisco-Cabanos: Massachusetts General Hospital Cancer Center
Shaon Chakrabarti: Harvard T.H. Chan School of Public Health
Manli Shi: Pfizer Inc
Chelsi Napoli: Massachusetts General Hospital Cancer Center
Thomas O. McDonald: Harvard T.H. Chan School of Public Health
Weiwei Tan: Pfizer Inc
Aaron Hata: Massachusetts General Hospital Cancer Center
Scott L. Weinrich: Pfizer Inc
Helena A. Yu: Weill Cornell Medical College
Franziska Michor: Harvard T.H. Chan School of Public Health

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract Despite the clinical success of the third-generation EGFR inhibitor osimertinib as a first-line treatment of EGFR-mutant non-small cell lung cancer (NSCLC), resistance arises due to the acquisition of EGFR second-site mutations and other mechanisms, which necessitates alternative therapies. Dacomitinib, a pan-HER inhibitor, is approved for first-line treatment and results in different acquired EGFR mutations than osimertinib that mediate on-target resistance. A combination of osimertinib and dacomitinib could therefore induce more durable responses by preventing the emergence of resistance. Here we present an integrated computational modeling and experimental approach to identify an optimal dosing schedule for osimertinib and dacomitinib combination therapy. We developed a predictive model that encompasses tumor heterogeneity and inter-subject pharmacokinetic variability to predict tumor evolution under different dosing schedules, parameterized using in vitro dose-response data. This model was validated using cell line data and used to identify an optimal combination dosing schedule. Our schedule was subsequently confirmed tolerable in an ongoing dose-escalation phase I clinical trial (NCT03810807), with some dose modifications, demonstrating that our rational modeling approach can be used to identify appropriate dosing for combination therapy in the clinical setting.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-021-23912-4 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23912-4

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-021-23912-4

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().