Trial reportNature communications2021
Identification of optimal dosing schedules of dacomitinib and osimertinib for a phase I/II trial in advanced EGFR-mutant non-small cell lung cancer.
Trial report in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT03810807 (Phase 1 Study of Combination Dacomitinib and Osimertinib for Patients With Metastatic EGFR Mutant Lung Cancers), which is not on this map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Phase 1 Study of Combination Dacomitinib and Osimertinib for Patients With Metastatic EGFR Mutant Lung Cancers
Who cites it
24 citing papers in PubMed, 33 citations in OpenAlex.
- R-package agentBayes: Likelihood-based statistical methods for agent-based models.PLoS computational biology · 2026Article
- Tumor evolution: signaling pathways, molecular mechanisms and therapeutic targets.Signal transduction and targeted therapy · 2026Review
- Radiation therapy plus osimertinibWorld journal of methodology · 2026Article
- Growth rate-driven modelling suggests that phenotypic adaptation drives drug resistance in BRAFV600E-mutant melanoma.Communications biology · 2026Article
- Can evolutionary therapy be applied in non-small cell lung cancer?Scientific reports · 2026Article
- Mathematical Oncology: How Modeling Is Transforming Clinical Decision-Making.Cancer research · 2025Review
- DeepEGFR a graph neural network for bioactivity classification of EGFR inhibitors.Scientific reports · 2025Article
- Cyclodextrin-Based Formulations as a Promising Strategy to Overcome the Blood-Brain Barrier: Historical Overview and Prospects in Glioblastoma Treatment.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Quantitative measurement of phenotype dynamics during cancer drug resistance evolution using genetic barcoding.Nature communications · 2025Article
- Fitness seascapes are necessary for realistic modeling of the evolutionary response to drug therapy.Science advances · 2025Article
- Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight.PLoS computational biology · 2025Article
- A Bayesian network meta-analysis of EGFR-tyrosine kinase inhibitor treatments in patients withCancer pathogenesis and therapy · 2025Review
- Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development.CPT: pharmacometrics & systems pharmacology · 2024Review
- Designing combination therapies for cancer treatment: application of a mathematical framework combining CAR T-cell immunotherapy and targeted radionuclide therapy.Frontiers in immunology · 2024Article
- Long non-coding RNAJournal of thoracic disease · 2023Article
- In the SARS-CoV-2 Pandora Pandemic: Can the Stance of Premorbid Intestinal Innate Immune System as Measured by Fecal Adnab-9 Binding of p87:Blood Ferritin, Yielding the FERAD Ratio, Predict COVID-19 Susceptibility and Survival in a Prospective Population Database?International journal of molecular sciences · 2023Article
- Brief Report: Combination of Osimertinib and Dacomitinib to Mitigate Primary and Acquired Resistance in EGFR-Mutant Lung Adenocarcinomas.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Article
- Spatial cumulant models enable spatially informed treatment strategies and analysis of local interactions in cancer systems.Journal of mathematical biology · 2023Article
- Embracing Project Optimus: Can we Leverage Evolutionary Theory to Optimize Dosing in Oncology?Pharmaceutical research · 2022Article
- Applications of covalent chemistry in targeted protein degradation.Chemical Society reviews · 2022Review
Corrections and comments
- Erratum issued
Authors and funding
15 authors at 5 institutions in 1 country.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.