Dose individualisation in oncology using chemotherapy-induced neutropenia: Example of docetaxel in non-small cell lung cancer patients

Exploring the discrepancies between clinical trials and real-world data: A small-cell lung cancer study

The potential of real-world data to inform clinical trial design and supplement control arms has gained much interest in recent years. The most common approach relies on reproducing control arm outcomes by matching real-world patient cohorts to clinical trial baseline populations. However, recent studies pointed out that there is a lack of replicability, generalisability, and consensus. In this article, we propose a novel approach that aims to explore and examine these discrepancies by concomitantly investigating the impact of selection criteria and operations on the measurements of outcomes from the patient data. We tested the approach on a dataset consisting of small-cell lung cancer patients receiving platinum-based chemotherapy regimens from a real-world data cohort (n = 223) and six clinical trial control arms (n = 1224). The results showed that the discrepancy between real-world and clinical trial data potentially depends on differences in both patient populations and operational conditions (e.g., frequency of assessments, and censoring), for which further investigation is required. Discovering and accounting for confounders, including hidden effects of differences in operations related to the treatment process and clinical trial study protocol, would potentially allow for improved translation between clinical trials and real-world data. Continued development of the method presented here to systematically explore and account for these differences could pave the way for transferring learning across clinical studies and developing mutual translation between the real-world and clinical trials to inform clinical study design.

A sub-pharmacological test dose does not predict individual docetaxel exposure in prostate cancer patients

PURPOSE

Docetaxel is a cytotoxic drug used for first-line treatment of various malignancies. It has a narrow therapeutic index and shows wide interpatient variability in clearance and toxicity. Tools for individual dose optimization are needed to maximize efficacy and avoid toxicity.

METHODS

We performed a proof-of-concept study (EudraCT 2016-003785-77) to evaluate whether pharmacokinetics after a sub-pharmacological test dose of 1000 µg docetaxel (millidose) could be used to predict therapeutic dose exposure. Thirty prostate cancer patients eligible for treatment with docetaxel as part of routine clinical care were included. An intravenous docetaxel millidose was administered 1-7 days prior to therapeutic docetaxel. After both doses plasma docetaxel concentrations were measured by ultra- high performance liquid chromatography-tandem mass spectrometry. The docetaxel clearance was estimated with non-linear mixed effects modeling.

RESULTS

Geometric mean docetaxel clearance was 57.9 L/h (GCV 78.6%) after admission of a millidose and 40.3 L/h (GCV 60.7%) after admission of a therapeutic dose. The millidose and therapeutic dose in a single patient were not significantly correlated (Spearman's rho R = 0.02, P = 0.92).

CONCLUSION

Docetaxel pharmacokinetics at milli- and therapeutic dose level showed insufficient correlation for individual dose optimization. However, the clearance of a docetaxel millidose and full dose are within the same order of magnitude. Therefore, docetaxel millidose pharmacokinetics could potentially facilitate prediction of docetaxel pharmacokinetics at a population level in situations where therapeutic dose levels are impractical, such as pharmacokinetic drug-drug interaction studies or pediatric studies.

Optimized Dosing: The Next Step in Precision Medicine in Non-Small-Cell Lung Cancer

In oncology, and especially in the treatment of non-small-cell lung cancer (NSCLC), dose optimization is often a neglected part of precision medicine. Many drugs are still being administered in "one dose fits all" regimens or based on parameters that are often only minor determinants for systemic exposure. These dosing approaches often introduce additional pharmacokinetic variability and do not add to treatment outcomes. Fortunately, pharmacological knowledge is increasing, providing valuable information regarding the potential of, for example, therapeutic drug monitoring. This article focuses on the evidence for the most promising and easily implemented optimized dosing approaches for the small-molecule inhibitors, chemotherapeutic agents, and monoclonal antibodies as treatment options currently approved for NSCLC. Despite limitations such as investigations having been conducted in oncological diseases other than NSCLC or the retrospective origin of many analyses, an alternative dosing regimen could be beneficial for treatment outcomes, prescriber convenience, or financial burden on healthcare systems. This review of the literature provides recommendations on the implementation of dose optimization and advice regarding promising strategies that deserve further research in NSCLC.

Development and Internal Validation of a Nomogram Used to Predict Chemotherapy-Induced Neutropenia in Non-Small Cell Lung Cancer Patients: A Retrospective Cohort Study

Purpose

This study was designed to develop a nomogram for predicting neutropenia caused by chemotherapy in non-small cell lung cancer (NSCLC) patients.

Patients and Methods

Information was collected from 376 patients between November 2017 and November 2020. The endpoint was chemotherapy-induced neutropenia (absolute neutrophil count <2×10⁹/L). Logistic regression was performed to appraise the prognostic value of each potential predictor. Risk predictors from the final predictive model were used to generate a nomogram. C-index and calibration curve as well as decision curve analysis (DCA) was applied to evaluate model performance.

Results

The multivariate regression model ultimately included three predictors: previous radiotherapy, the current cycle of chemotherapy and neutrophil counts before current chemotherapy. A nomogram was developed and displayed better discrimination (with C-index of 0.875 in the development group and 0.907 in the validation group). Favorable consistency was shown between predicted probability and observed probability in the calibration curves. DCA illustrated that when the threshold probability was 8%-90%, the predictive model provided a net benefit relative to the intervention-all or the intervention-none strategy, indicating that the nomogram had favorable potential clinical utility.

Conclusion

This nomogram will be an available tool to quantify the risk of neutropenia after chemotherapy in patients who suffer from NSCLC and deserves further external validation.