Trial reportCPT: pharmacometrics & systems pharmacology2020
Modeling Tumor Growth and Treatment Resistance Dynamics Characterizes Different Response to Gefitinib or Chemotherapy in Non-Small Cell Lung Cancer.
Trial report in CPT: pharmacometrics & systems pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00322452 (Open Label, Randomised, Parallel Group, Multicentre, Ph III Study To Assess Efficacy, Safety & Tolerability Of Gefitinib), which is not on this map. Cited by 7 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.
Open Label, Randomised, Parallel Group, Multicentre, Ph III Study To Assess Efficacy, Safety & Tolerability Of Gefitinib (IRESSA™) Versus Carboplatin/Paclitaxel DC As 1st-Line Treatment In Selected Patients With Stage IIIB / IV NSCLC In Asia
Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- A Systematic Comparative Analysis of Tumor Size Models Based on Erlotinib Clinical Data in Advanced NSCLC.CPT: pharmacometrics & systems pharmacology · 2025Article
- Optimizing First-Line Therapeutics in Non-Small Cell Lung Cancer: Insights From Joint Modeling and Large-Scale Data Analysis.CPT: pharmacometrics & systems pharmacology · 2025Article
- Understanding therapeutic tolerance through a mathematical model of drug-induced resistance.NPJ systems biology and applications · 2025Article
- Shenqi Fuzheng injection restores the sensitivity to gefitinib in non-small cell lung cancer by inhibiting the IL-22/STAT3/AKT pathway.Pharmaceutical biology · 2024Article
- Knowledge-based mechanistic modeling accurately predicts disease progression with gefitinib in EGFR-mutant lung adenocarcinoma.NPJ systems biology and applications · 2023Article
- Article
- Tumor Growth Dynamic Modeling in Oncology Drug Development and Regulatory Approval: Past, Present, and Future Opportunities.CPT: pharmacometrics & systems pharmacology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Differences in the effect of gefitinib and chemotherapy on tumor burden in non-small cell lung cancer remain to be fully understood. Using a Bayesian hierarchical model of tumor size dynamics, we estimated the rates of tumor growth and treatment resistance for patients in the Iressa Pan-Asia Study study (NCT00322452). The following relationships characterize greater efficacy of gefitinib in epidermal growth factor receptor (EGFR) positive tumors: Maximum drug effect is, in decreasing order, gefitinib in EGFR-positive, chemotherapy in EGFR-positive, chemotherapy in EGFR-negative, and gefitinib in EGFR-negative tumors; the rate of resistance emergence is, in increasing order: gefitinib in EGFR positive, chemotherapy in EGFR positive, while each is plausibly similar to the rate in EGFR negative tumors, which are estimated with less certainty. The rate of growth is smaller in EGFR-positive than in EGFR-negative fully resistant tumors, regardless of treatment. The model can be used to compare treatment effects and resistance dynamics among different drugs.
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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.