Wednesday, November 21, 2018 | 2:00 pm - 3:00 pm | CETLS, S510DAdd to Calendar: Google Outlook Apple
Presenter: Marta Concheiro-Guisan, Professor of Forensic Toxicology at John Jay College of Criminal Justice
Significant changes in drug use patterns in recent years, such as the current opioid epidemic in the USA, have led to a growing interest in investigating new, fast and efficient tools that can monitor drug use and trends in a community. Wastewater-based epidemiology satisfies this need of quick and objective testing of drug consumption within a particular geographical area by analyzing human excretion products (biomarkers) in wastewater. In this talk, we will describe the analytical methods developed and validated for the determination of drugs in wastewater and their application in the analysis of authentic wastewater samples. In one method, we determined tobacco (cotinine), cocaine (benzoylecognine, cocaethylene, and cocaine), amphetamines (methamphetamine, MDMA, MDA and amphetamine), opioids (6-monoacetilmorphine, morphine, codeine, oxymorphone, oxycodone, hydromorphone, hydrocodone, fentanyl, norfentanyl, methadone, EDDP) and cannabis (delta-9-tetrahydrocannabinol and 11-nor-9-carboxy-tetrahydrocannabinol) biomarkers in 50 mL of wastewater, achieving a limit of quantification (LOQ) of 5-10 ng/L; and we also developed an analytical method for the determination of creatinine, a breakdown product of muscle metabolism commonly used as normalization factor in urine, in 0.2 mL of wastewater. Creatinine was analyzed using a simple dilution and vial filtration by LC-MS/MS. The LOQ was 0.01mg/L. These methods were applied to 48 wastewater samples collected from 6 treatment plants in New York City (The Bronx, Brooklyn, Queens, and Manhattan) throughout the course of one year. The most frequently detected drug group was cocaine, followed by tobacco, opiates, amphetamines and cannabis. This study provided a rapid and solid mean to track drug trends in different boroughs within New York City.
This event is organized by the STEM FIG. Please contact Jun Liang or Daniel Torres Rangel (Science) for more information, or to RSVP.