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; Franziska Michor

doi:10.1038/s41467-021-23912-4

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

Nat Commun. 2021 Jun 17;12(1):3697. doi: 10.1038/s41467-021-23912-4.

Authors

Kamrine E Poels^{1

2}, Adam J Schoenfeld³, Alex Makhnin³, Yosef Tobi³, Yuli Wang⁴, Heidie Frisco-Cabanos⁵, Shaon Chakrabarti^{1

2

6}, Manli Shi⁴, Chelsi Napoli⁵, Thomas O McDonald^{1

2

6

7}, Weiwei Tan⁸, Aaron Hata^{5

9

10}, Scott L Weinrich⁴, Helena A Yu¹¹, Franziska Michor^{12

13

14

15

16

17}

Affiliations

¹ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
² Department of Data Science, Dana Farber Cancer Institute, Boston, MA, USA.
³ Division of Solid Tumor Oncology, Department of Medicine, Thoracic Oncology Service, Memorial Sloan-Kettering Cancer Center, Weill Cornell Medical College, New York, NY, USA.
⁴ Oncology Research and Development, Pfizer Inc, La Jolla, CA, USA.
⁵ Massachusetts General Hospital Cancer Center, Boston, MA, USA.
⁶ Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.
⁷ The Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA, USA.
⁸ Clinical Pharmacology Oncology, Global Product Development, Pfizer Inc, San Diego, CA, USA.
⁹ The Ludwig Center at Harvard, Boston, MA, USA.
¹⁰ Department of Medicine, Harvard Medical School, Boston, MA, USA.
¹¹ Division of Solid Tumor Oncology, Department of Medicine, Thoracic Oncology Service, Memorial Sloan-Kettering Cancer Center, Weill Cornell Medical College, New York, NY, USA. yuh@mskcc.org.
¹² Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. michor@jimmy.harvard.edu.
¹³ Department of Data Science, Dana Farber Cancer Institute, Boston, MA, USA. michor@jimmy.harvard.edu.
¹⁴ Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA. michor@jimmy.harvard.edu.
¹⁵ The Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA, USA. michor@jimmy.harvard.edu.
¹⁶ The Ludwig Center at Harvard, Boston, MA, USA. michor@jimmy.harvard.edu.
¹⁷ The Broad Institute of MIT and Harvard, Cambridge, MA, USA. michor@jimmy.harvard.edu.

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.

Publication types

Clinical Trial, Phase I
Clinical Trial, Phase II
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Acrylamides / administration & dosage*
Acrylamides / pharmacokinetics
Acrylamides / pharmacology*
Acrylamides / toxicity
Aniline Compounds / administration & dosage*
Aniline Compounds / pharmacokinetics
Aniline Compounds / pharmacology*
Aniline Compounds / toxicity
Antineoplastic Agents / administration & dosage
Antineoplastic Agents / pharmacokinetics
Antineoplastic Agents / pharmacology
Antineoplastic Agents / toxicity
Antineoplastic Combined Chemotherapy Protocols
Carcinoma, Non-Small-Cell Lung / drug therapy*
Carcinoma, Non-Small-Cell Lung / genetics
Carcinoma, Non-Small-Cell Lung / pathology
Carcinoma, Non-Small-Cell Lung / secondary
Cell Line, Tumor
Cell Survival / drug effects
Cell Survival / genetics
Cohort Studies
Computer Simulation
Drug Resistance, Neoplasm*
ErbB Receptors / antagonists & inhibitors
ErbB Receptors / genetics
ErbB Receptors / metabolism
Humans
Lung Neoplasms / diet therapy*
Lung Neoplasms / genetics
Lung Neoplasms / pathology
Models, Statistical
Models, Theoretical
Mutation
Quinazolinones / administration & dosage*
Quinazolinones / pharmacokinetics
Quinazolinones / pharmacology*
Quinazolinones / toxicity

Substances

Acrylamides
Aniline Compounds
Antineoplastic Agents
Quinazolinones
osimertinib
dacomitinib
EGFR protein, human
ErbB Receptors

Associated data

ClinicalTrials.gov/NCT03810807

Abstract

Publication types

MeSH terms

Substances

Associated data

Grants and funding