Trial reportJournal of clinical pharmacology2019
Updated Population Pharmacokinetic Model of Cabozantinib Integrating Various Cancer Types Including Hepatocellular Carcinoma.
Trial report in Journal of clinical pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Pooled it
- Cabozantinib exposure-response analyses of efficacy and safety in patients with advanced hepatocellular carcinoma.Journal of pharmacokinetics and pharmacodynamics · 2019Trial
- Population Pharmacokinetics of Cabozantinib: Updated Analyses Including Data in Combination With Atezolizumab in Patients With Castration-Resistant Prostate Cancer and Other Tumor Types.Clinical and translational science · 2026Article
- A Systematic Evaluation of the Dosing Regimens for Approved Targeted Therapies and Immune Checkpoint Inhibitors in Metastatic Renal Cell Carcinoma From a Project OPTIMUS Perspective.Journal of clinical pharmacology · 2025Review
- Using Fisher Information Matrix to predict uncertainty in covariate effects and power to detect their relevance in Non-Linear Mixed Effect Models in pharmacometrics.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- Integration of Clinical Trial Spatial Multiomics Analysis and Virtual Clinical Trials Enables Immunotherapy Response Prediction and Biomarker Discovery.Cancer research · 2024Article
- Population Pharmacokinetics of Cabozantinib in Metastatic Renal Cell Carcinoma Patients: Towards Drug Expenses Saving Regimens.Clinical pharmacokinetics · 2024Article
- Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial.bioRxiv : the preprint server for biology · 2023Article
- Population Pharmacokinetics and Exposure-Response Analysis for the Phase 3 COSMIC-311 Trial of Cabozantinib for Radioiodine-Refractory Differentiated Thyroid Cancer.Clinical pharmacokinetics · 2023Article
- Impact of Cabozantinib Exposure on Proteinuria and Muscle Toxicity in Patients with Unresectable Hepatocellular Carcinoma.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Quantitation of Cabozantinib in Human Plasma by LC-MS/MS.Journal of chromatographic science · 2022Article
- Exposure-toxicity relationship of cabozantinib in patients with renal cell cancer and salivary gland cancer.International journal of cancer · 2022Article
- Article
- Easy and reliable maximum a posteriori Bayesian estimation of pharmacokinetic parameters with the open-source R package mapbayr.CPT: pharmacometrics & systems pharmacology · 2021Article
- Article
- Therapeutic drug monitoring of oral targeted antineoplastic drugs.European journal of clinical pharmacology · 2021Review
- Management of Adverse Events Associated with Cabozantinib Treatment in Patients with Advanced Hepatocellular Carcinoma.Targeted oncology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An integrated population pharmacokinetic (PPK) model was used to evaluate the effects of liver dysfunction on the pharmacokinetics (PK) of cabozantinib in patients with hepatocellular carcinoma and to determine whether clinical dosage adjustment may be necessary in this population. An integrated PPK model previously developed in healthy volunteers and patients with various cancer types was updated with cabozantinib concentration data from hepatocellular carcinoma patients in phase 2 and 3 studies (total 2023; hepatocellular carcinoma 489 patients). Covariate effects of cancer type including hepatocellular carcinoma population and liver dysfunction per the National Cancer Institute Organ Dysfunction Working Group criteria were evaluated (normal 1425; mild liver dysfunction 558; moderate/severe liver dysfunction 15/1 patients). With hepatocellular carcinoma patients, PK parameter estimates and covariate effects were similar to the previous PPK model (2 compartments with first- and zero-order absorption and first-order elimination). Only medullary thyroid cancer had appreciable PK differences from healthy volunteers. PK parameter estimates were similar with and without addition of liver dysfunction covariates. Patients with mild liver dysfunction were predicted to have minimal differences in apparent clearance of cabozantinib relative to patients with normal liver function. Therefore, no initial cabozantinib dosage adjustment is recommended for cancer patients with mild liver dysfunction. The small sample size for patients with moderate and severe liver dysfunction limited dosing recommendations in these subpopulations. The results from this PPK analysis were different from those of the single-dose hepatic impairment study in healthy volunteers and more reflective of exposure in cancer patients following daily cabozantinib dosing.
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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.