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Review
. 2021 Mar 13;11(3):201.
doi: 10.3390/jpm11030201.

Generating a Precision Endoxifen Prediction Algorithm to Advance Personalized Tamoxifen Treatment in Patients with Breast Cancer

Thomas Helland  1   2   3 , Sarah Alsomairy  1 , Chenchia Lin  1 , Håvard Søiland  3 , Gunnar Mellgren  2   3 , Daniel Louis Hertz  1
Affiliations
Review

Generating a Precision Endoxifen Prediction Algorithm to Advance Personalized Tamoxifen Treatment in Patients with Breast Cancer

Thomas Helland et al. J Pers Med. .

Abstract

Tamoxifen is an endocrine treatment for hormone receptor positive breast cancer. The effectiveness of tamoxifen may be compromised in patients with metabolic resistance, who have insufficient metabolic generation of the active metabolites endoxifen and 4-hydroxy-tamoxifen. This has been challenging to validate due to the lack of measured metabolite concentrations in tamoxifen clinical trials. CYP2D6 activity is the primary determinant of endoxifen concentration. Inconclusive results from studies investigating whether CYP2D6 genotype is associated with tamoxifen efficacy may be due to the imprecision in using CYP2D6 genotype as a surrogate of endoxifen concentration without incorporating the influence of other genetic and clinical variables. This review summarizes the evidence that active metabolite concentrations determine tamoxifen efficacy. We then introduce a novel approach to validate this relationship by generating a precision endoxifen prediction algorithm and comprehensively review the factors that must be incorporated into the algorithm, including genetics of CYP2D6 and other pharmacogenes. A precision endoxifen algorithm could be used to validate metabolic resistance in existing tamoxifen clinical trial cohorts and could then be used to select personalized tamoxifen doses to ensure all patients achieve adequate endoxifen concentrations and maximum benefit from tamoxifen treatment.

Keywords: 4OHtam; CYP2C; CYP2D6; CYP3A; SULT; UGT; endoxifen; personalized treatment; pharmacogenetics; tamoxifen metabolism.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 2
Figure 2
Overview of tamoxifen metabolites formed during first phase hepatic metabolism. Only isomers of 4OHtam and endoxifen are shown, other metabolites may also be subject to isomerism. Blue, red, orange, and green lines represent hydroxylation, demethylation, N-oxidation and non-enzymatic isomerization, respectively. Numbered arrows represent the following CYP450 enzymes involved in generation of active metabolites and their less active isomers: (1) CYP3A4/5, CYP1A1/2, CYP2C9/19, CYP2D6 (2) Unknown (3) CYP2D6 (4) CYP3A4/5, CYP2C19, CYP2D6. (5) CYP2C9/19, CYP2D6, CYP3A4, CYP2B6 (6) CYP2B6, CYP2D6. Modified from [28].
Figure 1
Figure 1
Approaches for investigating metabolic resistance in breast cancer patients. The CYP2D6-endoxifen relationship is well established. The endoxifen-efficacy relationship has been reported but not sufficiently validated due to a lack of available data. The CYP2D6-efficacy relationship has not been conclusively demonstrated due to the imprecision in using CYP2D6 as a surrogate of endoxifen concentration. Box plot and survival curves are for illustration purposes only. EM = Extensive Metabolizer, IM = Intermediate Metabolizer, PM = Poor Metabolizer.
Figure 3
Figure 3
Approach for generating a precision endoxifen prediction algorithm to enable personalized tamoxifen dosing. A precision endoxifen prediction algorithm that integrates clinical and genetic variables could be generated from existing datasets. The algorithm would then be used to validate the association of endoxifen concentration with tamoxifen efficacy and identify the target endoxifen concentration. This information could then be used to inform personalized dosing that maximizes tamoxifen treatment efficacy.
See this image and copyright information in PMC

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