2018 Young Chemist Award Winner
Congratulations to our $10,000 winner: Chi Lin, Arizona State University
Metrohm USA is pleased to announce the winner of the 2018 Young Chemist Award, Chi Lin.
The optimal frequency of a biomarker is an underlying mechanism that can be applied to many biomarkers, allowing the customization of the multimarker biosensor for multiple diseases. Chi’s work can revolutionize the field and provide the needed tools for personalized medicine and care, increasing our medical system’s efficiency and reducing the cost.
Diagnosing and managing complex diseases and their comorbidities have been a pressing need in today’s medical field. However, current state of the art methods not only have limited sensitivity and specificity, but also are time consuming to perform. To address these challenges, a sensitive multimarker detection platform that are rapid and convenient to use can provide the much-needed solution. Due to the nature of a complex disease (and its comorbidity), monitoring a single biomarker is insufficient in grasping a comprehensive picture of the intricate pathophysiological changes. A comprehensive panel of biomarkers should be monitored simultaneously to achieve an efficient and accurate personalized medicine. To do so, a rapid and sensitive assay resembling the design of a blood glucose test strip should be followed. The blood glucose test strips are relatively robust and easy to use comparing to lab-grade assays, and are able to obtain results rapidly. They also have matured manufacturing infrastructures, and can be benchmarked to establish substantial equivalence for FDA safety regulations. Developing a panel of biosensors that mimic the design of a blood glucose sensors is thus paramount for the success. Using electrochemical impedance spectroscopy (EIS), a label-free technique with femto-molar sensitivity, a panel of sensitive and rapid biosensors can be developed.
After determining the design framework and detection mechanism, a panel of biomarkers that are FDA approved with established reimbursement codes was selected. Using diabetes mellitus as the example complex disease, cardiovascular and dry eye disease as example comorbidities, glucose, insulin, low-density lipoprotein, high-density lipoprotein, lactoferrin, and immunoglobulin E (2 biomarkers each, respectively) were selected as the target biomarkers. By characterizing the optimal frequency of each biomarker, a rapid and sensitive biosensor for each biomarker was established. The optimal frequency of a biomarker is the frequency at which its signal best represents the interaction between a target and its molecular recognition element (MRE), allowing accurate measurement of target analytes. By monitoring the signal from the optimal frequency, not only is there an additional means of specificity, the assay time is also reduced significantly. After developing the single-marker EIS biosensors, each biomarker’s MRE was co-immobilized onto a single working electrode, and multimarker detection was achieved by monitoring the response at each biomarker’s optimal frequency. A signal deconvolution algorithm was developed and patented to assist the performance of the sensor when signal overlap occurred. Conjugations of various nanoparticles on the MREs were also explored to “tune” the signal from a biomarker’s optimal frequency if signal deconvolution algorithm alone was insufficient. Through a preliminary design-of-experiment, the molecular weight, zeta potential, and conductivity of a biomarker were found to be vital in shaping its optimal frequency. My work thus far has resulted in 8 publications and 4 patent applications, followed by 5 submitted manuscripts.
Under the mentorship of Dr. Jeffrey T. La Belle, many of my research are currently under the development stage where companies have licensed and sponsored my work. The optimal frequency of a biomarker is an underlaying mechanism that can be applied to many biomarkers, allowing the customization of the multimarker biosensor for multiple diseases. The work can revolutionize the field and provide the needed tools for personalized medicine and care, increasing our medical system’s efficiency and reducing the cost.