(712f) Semi-Rational Design of Steroid Biosensors Using Compositionally Controlled Corona Phase Molecular Recognition: Pathway Towards In Vivo Monitoring

Authors: 
Lee, M. A., Massachusetts Institute of Technology
Wang, S., Zhejiang University
Bakh, N., Massachusetts Institute of Technology
Pham, C., Massachusetts Institute of Technology
Jones, K. K., Massachusetts Institute of Technology
Nguyen, F. T., Massachusetts Institute of Technology
Bisker, G., Massachusetts Institute of Technology
Strano, M., Massachusetts Institute of Technology
Steroid hormones contribute to numerous biochemical pathways controlling various physiological and pathological processes. Measurements of these molecules form the basis of many medical diagnoses; such as cortisol for Cushing’s disease and mental illnesses; progesterone for female fertility; testosterone for androgenic traits; and aldosterone for adrenal cancer and irregular blood pressure. Moreover, these molecules are commonly administered through various routes as therapeutics. As such, real-time, continuous measurements of these steroids may provide new biomedical insights, enable new medical diagnoses, and become an integral part of therapy. In this study, we applied near-infrared, fluorescent single walled carbon nanotubes (SWNT) non-covalently functionalized with custom-synthesized polymers for steroid hormone detection. A novel steroid templating strategy was employed in the semi-rational design of the polymers. Thirty polymers varying in monomer composition and length were synthesized and interfaced with SWNT to create a sensor library. The sensor library was screened against a panel of 9 steroid hormones selected for their biological significance. Design principles were established by tracking sensor response against polymer composition. Two constructs selective for cortisol and progesterone were discovered. The progesterone sensor was encapsulated in a biocompatible hydrogel and shown to function in a biological environment. Preliminary experiments in rodents demonstrate the potential for in vivo monitoring.