Predicting steady-state endoxifen plasma concentrations in breast cancer patients by CYP2D6 genotyping or phenotyping. Which approach is more reliable?

In previous studies, steady-state Z-endoxifen plasma concentrations (ENDOss) correlated with relapse-free survival in women on tamoxifen (TAM) treatment for breast cancer. ENDOss also correlated significantly with CYP2D6 genotype (activity score) and CYP2D6 phenotype (dextromethorphan test). Our aim was to ascertain which method for assessing CYP2D6 activity is more reliable in predicting ENDOss. The study concerned 203 Caucasian women on tamoxifen-adjuvant therapy (20 mg q.d.). Before starting treatment, CYP2D6 was genotyped (and activity scores computed), and the urinary log(dextromethorphan/dextrorphan) ratio [log(DM/DX)] was calculated after 15 mg of oral dextromethorphan. Plasma concentrations of TAM, N-desmethyl-tamoxifen (ND-TAM), Z-4OH-tamoxifen (4OH-TAM) and ENDO were assayed 1, 4, and 8 months after first administering TAM. Multivariable regression analysis was used to identify the clinical and laboratory variables predicting log-transformed ENDOss (log-ENDOss). Genotype-derived CYP2D6 phenotypes (PM, IM, NM, EM) and log(DM/DX) correlated independently with log-ENDOss. Genotype-phenotype concordance was almost complete only for poor metabolizers, whereas it emerged that 34% of intermediate, normal, and ultrarapid metabolizers were classified differently based on log(DM/DX). Multivariable regression analysis selected log(DM/DX) as the best predictor, with patients' age, weak inhibitor use, and CYP2D6 phenotype decreasingly important: log-ENDOss = 0.162 - log(DM/DX) × 0.170 + age × 0.0063 - weak inhibitor use × 0.250 + IM × 0.105 + (NM + UM) × 0.210; (R2  = 0.51). In conclusion, log(DM/DX) seems superior to genotype-derived CYP2D6 phenotype in predicting ENDOss.

Systematic review of the biological variation data for diabetes related analytes.

BACKGROUND: Objective interpretation of laboratory test results used to diagnose and monitor diabetes mellitus in part requires the application of biological variation data (BVD). The quality of published BVD has been questioned. The aim of this study was to quality assess publications reporting BVD for diabetes-related analytes using the Biological Variation Data Critical Appraisal Checklist (BIVAC); to assess whether published BVD are fit for purpose and whether the study design and population attributes influence BVD estimates and to undertake a meta-analysis of the BVD from BIVAC-assessed publications. METHODS: Publications reporting data for glucose, HbA1c, adiponectin, C-peptide, fructosamine, insulin like growth factor 1 (IGF-1), insulin like growth factor binding protein 3 (IGFBP-3), insulin, lactate and pyruvate were identified using a systematic literature search. These publications were assessed using the BIVAC, receiving grades A, B, C or D, where A is of highest quality. A meta-analysis of the BVD from the assessed studies utilised weightings based upon BIVAC grades and the width of the data confidence intervals to generate global BVD estimates. RESULTS: BIVAC assessment of 47 publications delivered 1 A, 3 B, 39C and 4 D gradings. Publications relating to adiponectin, C-peptide, IGF-1, IGFBP-3, lactate and pyruvate were all assessed as grade C. Meta-analysis enabled global BV estimates for all analytes except pyruvate, lactate and fructosamine. CONCLUSIONS: This study delivers updated and evidence-based BV estimates for diabetes-related analytes. There remains a need for delivery of new high-quality BV studies for several clinically important analytes.