BackgroundDrug-drug interactions (DDIs) in subjects enrolling in clinical trials can impact not only safety of the patient but also study drug outcomes and data validity. This makes it critical to adequately screen and manage DDIs. The study objective was to determine the prevalence of DDIs involving study medications in subjects enrolling in National Clinical Trials Network (NCTN) clinical trials at a single institution. DDIs were evaluated based on study protocol recommendations for concomitant medication use (i.e. exclude, avoid or use caution), screening via DDI tool, and pharmacist review.MethodsSubjects enrolled in NCTN trials of commercially available agents between January 2013 and August 2017 were included if a complete medication list was available. Complete medication lists were collected from the date of enrollment or the next available date then screened utilizing protocol guidance and the DDI screening tool, Lexicomp® Drug Interactions (Wolters Kluwer, Hudson, OH). Interactions were reviewed for clinical relevance: defined as a DDI that would require a medication change to ensure study agent safety and efficacy at enrollment.ResultsOne hundred and twenty-eight subjects enrolled in 35 clinical trials were included. Protocol guidance detected 15 unique DDI pairs that should be avoided or used with caution in 10.2% (13/128) of subjects. The majority of these subjects did not have a clinically relevant DDI (69.2%, 9/13) based on pharmacist review. Lexicomp® detected moderate to major DDIs in 24.2% (31/128) of subjects, with 9.4% (12/128) having a clinically relevant DDI.ConclusionsThis study confirms a high prevalence of DDIs present in subjects enrolling in oncology clinical trials. Further efforts should be made to improve methods to detect and manage DDIs in patients enrolling on clinical trials to ensure patient safety and trial data validity.Electronic supplementary materialThe online version of this article (10.1186/s12885-018-5076-0) contains supplementary material, which is available to authorized users.

Due to the low performance of some tools for sensitivity, specificity, and NPV, individual performance should be examined and prioritized on the basis of the intended use when selecting a DDI tool. If a strong-performing subscription-based tool is unavailable, a strong-performing free option, like Drugs.com, is available.

Objectives Tamoxifen bioactivation to endoxifen is primarily mediated by CYP2D6; however, substantial variability remains unexplained. Our objective was to conduct a comprehensive assessment of the effect of genetic variation in tamoxifen-relevant enzymes and transporters on steady-state endoxifen concentrations. Methods Comprehensive genotyping of CYP enzymes and transporters was conducted using the iPLEX® ADME PGx Pro Panel in 302 tamoxifen treated breast cancer patients. Predicted activity phenotype for 19 enzymes and transporters were analyzed for univariate association with endoxifen concentration, and then adjusted for CYP2D6 and clinical covariates. Results In univariate analysis, higher activity of CYP2C8 (regression β=0.22, p=0.020) and CYP2C9 (β=0.20, p=0.04), lower body weight (β=−0.014, p<0.0001), and endoxifen measurement during winter (each β<−0.39, p=0.002), were associated with higher endoxifen concentration. After adjustment for CYP2D6 diplotype, weight and season, CYP2C9 remained significantly associated with higher concentration (p=0.02), but only increased the overall model R-squared by 1.3%. Conclusions Our results further support a minor contribution of CYP2C9 genetic variability on steady state endoxifen concentration. Integration of clinician and genetic variables into individualized tamoxifen dosing algorithms would marginally improve their accuracy, and potentially enhance tamoxifen treatment outcomes.

Aims: Chemotherapy-induced peripheral neuropathy (PN) is a treatment limiting toxicity of paclitaxel. We evaluated if EPHA genetic variation (EPHA4, EPHA5, EPHA6, and EPHA8) is associated with PN sensitivity by accounting for variability in systemic paclitaxel exposure (time above threshold). Methods:Germline DNA from 60 patients with breast cancer was sequenced. PN was measured using the 8-item sensory subscale (CIPN8) of the patient-reported CIPN20. Associations for 3 genetic models were tested by incorporating genetics into previously published PN prediction models integrating measured paclitaxel exposure and cumulative treatment. Significant associations were then tested for association with PN-related treatment disruption.Results: EPHA5 rs7349683 (minor allele frequency = 0.32) was associated with increased PN sensitivity (β-coefficient = 0.39, 95% confidence interval 0.11-0.67, p = 0.007). Setting a maximum tolerable threshold of CIPN8 = 30, optimal paclitaxel exposure target is shorter for rs7349683 homozygous (11.6 h) than heterozygous (12.6 h) or wild-type (13.6 h) patients. Total number of missense variants (median = 0, range 0-2) was associated with decreased PN sensitivity (β-coefficient: −0.42, 95% confidence interval −0.72 to −0.12, P = .006). No association with treatment disruption was detected for the total number of missense variants or rs7349683. Conclusion:Isolating toxicity sensitivity by accounting for exposure is a novel approach, and rs7349683 represents a promising marker for PN sensitivity that may be used to individualize paclitaxel treatment. K E Y W O R D Sbreast cancer, genetic polymorphism, pharmacodynamics, pharmacogenomics, pharmacokinetics Principal statement: The authors confirm that the PI for this paper is D.L. Hertz and that the coinvestigator N. Lynn Henry had direct clinical responsibility for patients.

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