Neurofilament light chain as a biomarker of axonal damage in sensory neurons and paclitaxel-induced peripheral neuropathy in patients with ovarian cancer.

ABSTRACT: Paclitaxel-induced peripheral neuropathy (PIPN) is a barrier to effective cancer treatment and impacts quality of life among patients with cancer. We used a translational approach to assess the utility of neurofilament light chain (NFL) as a biomarker of PIPN in a human cell model and in patients with ovarian cancer. We measured NFL in medium from human induced pluripotent stem cell-derived sensory neurons (iPSC-SNs) exposed to paclitaxel. Serum NFL (sNFL) levels were quantified in 190 patients with ovarian cancer receiving paclitaxel/carboplatin chemotherapy at baseline and after each of the following 2 or 6 cycles. Adverse outcomes related to PIPN were retrospectively obtained, and Cox regression model was performed with different sNFL cut-offs after first cycle. The apparent elimination half-life of sNFL was estimated in patients who discontinued paclitaxel. Paclitaxel neurotoxicity in iPSC-SNs was accompanied by NFL release in a concentration-dependent manner ( P < 0.001, analysis of variance). Serum NFL levels increased substantially in patients during paclitaxel/carboplatin chemotherapy with considerable interindividual variability. Patients with sNFL >150 pg/mL after first cycle had increased risk to discontinue paclitaxel early (unadjusted HR: 2.47 [95% CI 1.16-5.22], adjusted HR: 2.25 [95% CI: 0.88-5.79]). Similar trends were shown for risk of severe PIPN and paclitaxel dose reduction because of PIPN. The median elimination half-life of sNFL was 43 days (IQR 27-82 days). Neurofilament light chain constitutes an objective biomarker of neurotoxicity in iPSC-SNs and in ovarian cancer patients with high sNFL predicting PIPN-related adverse outcomes. If prospectively validated, NFL can be used to study PIPN and may guide clinical decision making and personalize treatment with paclitaxel.

Tutorial: Statistical analysis and reporting of clinical pharmacokinetic studies.

Pharmacokinetics is the cornerstone of understanding drug absorption, distribution, metabolism, and elimination. It is also the key to describing variability in drug response caused by drug-drug interactions (DDIs), pharmacogenetics, impaired kidney and liver function, etc. This tutorial aims to provide a guideline and step-by-step tutorial on essential considerations when designing clinical pharmacokinetic studies and reporting results. This includes a comprehensive guide on how to conduct the statistical analysis and a complete code for the statistical software R. As an example, we created a mock dataset simulating a clinical pharmacokinetic DDI study with 12 subjects who were administered 2 mg oral midazolam with and without an inducer of cytochrome P450 3A. We provide a step-by-step guide to the statistical analysis of this clinical pharmacokinetic study, including sample size/power calculation, descriptive statistics, noncompartmental analyses, and hypothesis testing. The different analyses and parameters are described in detail, and we provide a complete R code ready to use in supplementary files. Finally, we discuss important considerations when designing and reporting clinical pharmacokinetic studies. The scope of this tutorial is not limited to DDI studies, and with minor adjustments, it applies to all types of clinical pharmacokinetic studies. This work was done by early career researchers for early career researchers. We hope this tutorial may help early career researchers when getting started on their own pharmacokinetic studies. We encourage you to use this as an inspiration and starting point and continuously evolve your statistical skills.