Limited sampling models for reliable estimation of etoposide area under the curve.
ABSTRACT Limited sampling models are able to estimate the area under the concentration-time curve (AUC) from plasma concentrations measured at only a few time points. The purpose of this study was to establish a model estimating etoposide AUC independently of specific chemotherapy protocols, underlying malignancies, concomitant diseases and age. Pharmacokinetic parameters were measured in 30 patients treated with polychemotherapy including etoposide (80-150 mg/m2). Etoposide analysis was performed by thin layer chromatography and consecutive quantitative sample detection by 252Cf-plasma desorption mass spectrometry. Data from the first 15 patients formed the training set. Based on the training data, five different models were generated, with the multiple regression coefficient r ranging from 0.91 to 0.96. The following model was selected as "most accurate": AUC = 343 (min)C4h(micrograms/ml) + 650(min)C8h(micrograms/ml) + 1252 (min micrograms/mol), where C4h is the plasma concentration of etoposide at 4 h after the end of infusion and C8h at 8 h. This model was validated on the test set, comprising the data of the remaining 15 patients. The mean predictive error (MPE) was -0.2% and the root mean square predictive error (RMSE) was 4.7%. When used for a large number of patients, this practicable and simple model is an instrument for use in prospective studies, to measure a correlation between drug dosage and efficacy or toxicity of the drug.
- SourceAvailable from: PubMed Central[Show abstract] [Hide abstract]
ABSTRACT: BACKGROUND: Etoposide is a chemotherapeutic agent, widely used for the treatment of various malignancies, including small cell lung cancer (SCLC), an aggressive disease with poor prognosis. Oral etoposide administration exhibits advantages for the quality of life of the patient as well as economic benefits. However, widespread use of oral etoposide is limited by incomplete and variable bioavailability. Variability in bioavailability was observed both within and between patients. This suggests that some patients may experience suboptimal tumor cytotoxicity, whereas other patients may be at risk for excess toxicity. CONCLUSIONS: The article highlights dilemmas as well as solutions regarding oral treatment with etoposide by presenting and analyzing relevant literature data. Numerous studies have shown that bioavailability of etoposide is influenced by genetic, physiological and environmental factors. Several strategies were explored to improve bioavailability and to reduce pharmacokinetic variability of oral etoposide, including desired and undesired drug interactions ( with ketoconazole), development of suitable drug delivery systems, use of more water-soluble prodrug of etoposide, and influence on gastric emptying. In addition to genotype-based dose administration, etoposide is suitable for pharmacokinetically guided dosing, which enables dose adjustments in individual patient. Further, it is established that oral and intravenous schedules of etoposide in SCLC patients do not result in significant differences in treatment outcome, while results of toxicity are inconclusive. To conclude, the main message of the article is that better prediction of the pharmacokinetics of oral etoposide may encourage its wider use in routine clinical practice.Radiology and Oncology 03/2013; 47(1):1-13. · 1.60 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Model-based drug development (MBDD) is an approach that is used to organize the vast and complex data streams that feed the drug development pipelines of small molecule and biotechnology sponsors. Such data streams are ultimately reviewed by the global regulatory community as evidence of a drug's potential to treat and/or harm patients. Some of this information is captured in the scientific literature and prescribing compendiums forming the basis of how new and existing agents will ultimately be administered and further evaluated in the broader patient community. As this data stream evolves, the details of data qualification, the assumptions and/or critical decisions based on these data are lost under conventional drug development paradigms. MBDD relies on the construction of quantitative relationships to connect data from discrete experiments conducted along the drug development pathway. These relationships are then used to ask questions relevant at critical development stages, hopefully, with the understanding that the various scenarios explored represent a path to optimal decision making. Oncology, as a therapeutic area, presents a unique set of challenges and perhaps a different development paradigm as opposed to other disease targets. The poor attrition of development compounds in the recent past attests to these difficulties and provides an incentive for a different approach. In addition, given the reliance on multimodal therapy, oncological disease targets are often treated with both new and older agents spanning several drug classes. As MBDD becomes more integrated into the pharmaceutical research community, a more rational explanation for decisions regarding the development of new oncology agents as well as the proposed treatment regimens that incorporate both new and existing agents can be expected. Hopefully, the end result is a more focussed clinical development programme, which ultimately provides a means to optimize individual patient care.Expert Opinion on Drug Discovery 02/2007; 2(2):185-209. · 2.30 Impact Factor