Binding of 16 alpha-[18F]fluoro-17 beta-estradiol to alphafetoprotein in Sprague-Dawley female rats affects blood levels

The quest for improving the management of breast cancer by functional imaging: The discovery and development of 16α-[18F]fluoroestradiol (FES), a PET radiotracer for the estrogen receptor, a historical review

INTRODUCTION

16α-[18F]Fluoroestradiol (FES), a PET radiotracer for the estrogen receptor (ER) in breast cancer, was the first receptor-targeted PET radiotracer for oncology and is continuing to prove its value in clinical research, antiestrogen development, and breast cancer care. The story of its conception, design, evaluation and use in clinical studies parallels the evolution of the whole field of receptor-targeted radiotracers, one greatly influenced by the research and intellectual contributions of William C. Eckelman.

METHODS AND RESULTS

The development of methods for efficient production of fluorine-18, for conversion of [18F]fluoride ion into chemically reactive form, and for its rapid and efficient incorporation into suitable estrogen precursor molecules at high molar activity, were all methodological underpinnings required for the preparation of FES. FES binds to ER with very high affinity, and its in vivo uptake by ER-dependent target tissues in animal models was efficient and selective, findings that preceded its use for PET imaging in patients with breast cancer.

ADVANCES IN KNOWLEDGE AND IMPLICATIONS FOR PATIENT CARE

Comparisons between ER levels measured by FES-PET imaging of breast tumors with tissue-specimen ER quantification by IHC and other methods show that imaging provided improved prediction of benefit from endocrine therapies. Serial imaging of ER by FES-PET, before and after dosing patients with antiestrogens, is used to determine the efficacious dose for established antiestrogens and to facilitate clinical development of new ER antagonists. Beyond FES imaging, PET-based hormone challenge tests, which evaluate the functional status of ER by monitoring rapid changes in tumor metabolic or transcriptional activity after a brief estrogen challenge, provide highly sensitive and selective predictions of whether or not there will be a favorable response to endocrine therapies. There is sufficient interest in the clinical applications of FES that FDA approval is being sought for its wider use in breast cancer.

CONCLUSIONS

FES was the first PET probe for a receptor in cancer, and its development and clinical applications in breast cancer parallel the conceptual evolution of the whole field of receptor-binding radiotracers.

A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals

Endocrine disruptors such as polychlorinated biphenyls (PCBs), diethylstilbestrol (DES) and dichlorodiphenyltrichloroethane (DDT) are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest) and the molecular descriptors calculated from two-dimensional structures by Mold² software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69%) and external validations using 22 chemicals (balanced accuracy of 71%). Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence) in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.