Metabolism and Transport of Tamoxifen
Metabolism and Transport of Tamoxifen
We began our review on CYP2D6 inhibition and the effectiveness of tamoxifen by searching the terms 'tamoxifen' and 'CYP2D6' in PubMed. We did not impose language restrictions. We retrieved all manuscripts published up to 1 March 2013, on drug- or gene-induced inhibition of CYP2D6 activity and tamoxifen effectiveness as measured by breast cancer outcomes. We also used citation lists within each of the included scientific papers to ensure we included all scientific work, including conference abstracts, on CYP2D6 inhibition and tamoxifen effectiveness.
We generated four meta-analytic models to investigate population-based studies focused on concurrent use of medicines that are weak (1.25–2-fold increase in area under the curve [AUC] of enzyme substrate), moderate (>2–5-fold increase in AUC of enzyme substrate) or strong (>5-fold increase in AUC of enzyme substrate) CYP2D6 inhibitors (especially selective serotonin reuptake inhibitors [SSRIs]) and breast cancer outcomes (breast cancer recurrence or breast cancer mortality in breast cancer patients treated with tamoxifen). We ran two separate models, first, population-based studies of the association of any nonfunctional variant (i.e., homozygote and heterozygote carriers) of CYP2D6 and breast cancer recurrence or mortality, and second, two nonfunctional variants (i.e., homozygote carriers) of CYP2D6 and breast cancer recurrence or mortality in patients treated with tamoxifen. Where studies presented separate effect estimates showing the association of heterozygote and homozygote variant alleles, we estimated an inverse variance weighted average of these two associations. We then used the inverse variance weighted average of these two associations in the first of the gene-induced inhibition of CYP2D6 meta-analytic models.
We used random effects meta-analytic models to generate summary effect estimates. In all cases, estimates from fixed effect models were similar. We used funnel plots to assess for evidence of publication bias in the meta-analyses. All analyses were performed using STATA software, version 11.0 (StataCorp LP, TX, USA). All statistical tests were two-sided.
Methods
Search Strategy & Selection Criteria
We began our review on CYP2D6 inhibition and the effectiveness of tamoxifen by searching the terms 'tamoxifen' and 'CYP2D6' in PubMed. We did not impose language restrictions. We retrieved all manuscripts published up to 1 March 2013, on drug- or gene-induced inhibition of CYP2D6 activity and tamoxifen effectiveness as measured by breast cancer outcomes. We also used citation lists within each of the included scientific papers to ensure we included all scientific work, including conference abstracts, on CYP2D6 inhibition and tamoxifen effectiveness.
Meta-analyses
We generated four meta-analytic models to investigate population-based studies focused on concurrent use of medicines that are weak (1.25–2-fold increase in area under the curve [AUC] of enzyme substrate), moderate (>2–5-fold increase in AUC of enzyme substrate) or strong (>5-fold increase in AUC of enzyme substrate) CYP2D6 inhibitors (especially selective serotonin reuptake inhibitors [SSRIs]) and breast cancer outcomes (breast cancer recurrence or breast cancer mortality in breast cancer patients treated with tamoxifen). We ran two separate models, first, population-based studies of the association of any nonfunctional variant (i.e., homozygote and heterozygote carriers) of CYP2D6 and breast cancer recurrence or mortality, and second, two nonfunctional variants (i.e., homozygote carriers) of CYP2D6 and breast cancer recurrence or mortality in patients treated with tamoxifen. Where studies presented separate effect estimates showing the association of heterozygote and homozygote variant alleles, we estimated an inverse variance weighted average of these two associations. We then used the inverse variance weighted average of these two associations in the first of the gene-induced inhibition of CYP2D6 meta-analytic models.
Statistical Analysis
We used random effects meta-analytic models to generate summary effect estimates. In all cases, estimates from fixed effect models were similar. We used funnel plots to assess for evidence of publication bias in the meta-analyses. All analyses were performed using STATA software, version 11.0 (StataCorp LP, TX, USA). All statistical tests were two-sided.