TY - CONF
T1 - Intelligent Multi-Electrode Array for Real-Time Treatment Monitoring of Antipsychotic Clozapine
AU - Shukla, Rajendra P.
AU - Kelly, Deanna L.
AU - Ben-Yoav, Hadar
N1 - Funding Information:
The authors thank the Brain and Behavior Research Foundation NARSAD Young Investigator and the Jeanne Marie Lee Investigator Grant (Grant number 26038) for funding the project. We also thank the Helmsley Charitable Trust through the Agricultural, Biological and Cognitive Robotics Initiative for the financial support in this research at Ben-Gurion University of the Negev. The authors also thank the Kreitman School for the Mid-way Negev fellowship for their support. The authors also thank Deanna L. Kelly for supplying the patient’s samples and guidance.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Schizophrenia is a challenging mental health disorder [1]. While various antipsychotics have been used to treat schizophrenia, monitoring schizophrenia treatment requires patients to frequently travel to hospitals in order to test and maintain efficacious levels. Yet, current technologies for antipsychotic drug monitoring require benchtop equipment and long sample preparation times, impeding the ability to rapidly measure various antipsychotics levels at the point-of-care. For example, clozapine is the most effective antipsychotic medication for schizophrenia, but it is dramatically underutilized due to a burdensome monitoring scheme. We propose to overcome the analytical challenges by designing an intelligent multi-sensor array that will be modified with micro/nanometer-thick films [2]. The films are based on 2D materials (reduced graphene oxide, MoS2 and WS2) that increase the electrocatalytic activity of the sensors and the underlying variability of the electrochemical signals generated by the antipsychotics. Here, we have shown, (1) the development of microelectrodes modified with 2D materials, (2) the development of an intelligent multi-electrode array framework, and (3) the proof-of-concept extraction of antipsychotic levels from schizophrenia patients by using intelligent chemometric models (Figure 1). By rapidly deciphering the electrochemical signals in whole blood and quantifying the levels of the antipsychotics, better schizophrenia treatment outcome can be enabled.
AB - Schizophrenia is a challenging mental health disorder [1]. While various antipsychotics have been used to treat schizophrenia, monitoring schizophrenia treatment requires patients to frequently travel to hospitals in order to test and maintain efficacious levels. Yet, current technologies for antipsychotic drug monitoring require benchtop equipment and long sample preparation times, impeding the ability to rapidly measure various antipsychotics levels at the point-of-care. For example, clozapine is the most effective antipsychotic medication for schizophrenia, but it is dramatically underutilized due to a burdensome monitoring scheme. We propose to overcome the analytical challenges by designing an intelligent multi-sensor array that will be modified with micro/nanometer-thick films [2]. The films are based on 2D materials (reduced graphene oxide, MoS2 and WS2) that increase the electrocatalytic activity of the sensors and the underlying variability of the electrochemical signals generated by the antipsychotics. Here, we have shown, (1) the development of microelectrodes modified with 2D materials, (2) the development of an intelligent multi-electrode array framework, and (3) the proof-of-concept extraction of antipsychotic levels from schizophrenia patients by using intelligent chemometric models (Figure 1). By rapidly deciphering the electrochemical signals in whole blood and quantifying the levels of the antipsychotics, better schizophrenia treatment outcome can be enabled.
UR - http://www.scopus.com/inward/record.url?scp=85145319643&partnerID=8YFLogxK
U2 - 10.3390/ecsa-7-08211
DO - 10.3390/ecsa-7-08211
M3 - Abstract
AN - SCOPUS:85145319643
SP - 4
T2 - 7th International Electronic Conference on Sensors and Applications
Y2 - 15 November 2020 through 30 November 2020
ER -
