ArticleClinical pharmacokinetics2009
Population pharmacokinetics and dosing recommendations for cisplatin during intraperitoneal peroperative administration: development of a limited sampling strategy for toxicity risk assessment.
Article in Clinical pharmacokinetics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- PKPD Modeling and Dosing Considerations in Advanced Ovarian Cancer Patients Treated with Cisplatin-Based Intraoperative Intraperitoneal Chemotherapy.The AAPS journal · 2020Trial
- Intraperitoneal clearance as a potential biomarker of cisplatin after intraperitoneal perioperative chemotherapy: a population pharmacokinetic study.British journal of cancer · 2012Article
- Population pharmacokinetics of the humanised monoclonal antibody, HuHMFG1 (AS1402), derived from a phase I study on breast cancer.British journal of cancer · 2010Article
- Population pharmacokinetics and dosing recommendations for cisplatin during intraperitoneal peroperative administration.Clinical pharmacokinetics · 2009Article
Corrections and comments
- Commented on by
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOvarian cancer is the leading cause of gynaecological cancer-related death in Western countries. Intraperitoneal (IP) peroperative chemotherapy is an interesting therapeutic option. However, very few data are available regarding pharmacokinetics and especially population pharmacokinetics. PATIENTS AND
methodsThirty-one patients with advanced epithelial cancer classified as International Federation of Gynecology and Obstetrics stage IIIC were included in the study. Blood and IP samples were taken over a 24-hour period during and after IP treatment. Both total and ultrafiltered (Uf) platinum (Pt) concentrations were analysed using a population approach with nonlinear mixed-effects modelling (NONMEM) software. Improvement of the model with covariates was performed as well as assessment of the model using bootstrap and posterior visual predictive methods.
resultsBoth IP fluid and serum pharmacokinetics were satisfactorily described with a three-compartment model for both Uf Pt and total Pt concentrations. The covariates were bodyweight for the IP volume of distribution in the Uf Pt model, and both IP and serum protein concentrations for the clearance from the central compartment in the total Pt model. A nomogram, based on the results of the Monte Carlo simulations, displays a dose recommendation regarding both the risk of renal toxicity and the potent efficacy of the treatment. A limited sampling strategy (LSS) allowing the estimation of potential risk of renal toxicity is also described.
conclusionThe pharmacokinetics of cisplatin during peroperative IP chemotherapy could be successfully fitted with the present model, which allowed a dosing strategy accompanied by an LSS to facilitate the follow-up of patients.
Indexed as
Identifiers
19385710What 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.