Discipline: Chemistry and Chemical Sciences
Subcategory: Chemistry (not Biochemistry)
Jamira Stephenson - North Carolina A&T State University
Co-Author(s): Lareisha Maple, Hope Kumkali, A’ja V. Duncan, and Sayo O. Fakayode, North Carolina A&T State University, Greensboro, NC
The abuse of controlled substances, illicit drugs, and prescription drugs continue to be problematic with far fetch and negative health impacts on the drug addicts, the public safety, legal and criminal justice system, and socio-economic fabric of the society. Consequently, effective monitoring of the abuse of controlled substances, illicit drugs, or prescription drugs continues to generate considerable interest in public safety, bioanalytical studies, medical diagnostics, forensic science, and law enforcement departments. The overreaching goal of this study is to utilize human scalp hair samples as biomarkers for the screening and mapping of the controlled substances, illicit drugs, and prescription drugs in human subjects. Human scalp hair samples were collected from 197 participants in Greensboro and Durham, North Carolina, USA from salons and barber shops. The hair samples were washed with acetone to remove extraneous particulates. The washed samples were subjected to a methanolic extraction using a water bath at 450C for 36 hrs. The hair sample extracts were filtered using 0.2 μm, Whatman filter cartridge. A known aliquot of the hair sample extract was injected and separated using a GC (Varian 450-GC) instrument equipped with auto-sampling accessory and a massspectrometer detector (Varian 220-MS). The GC separation was performed in a temperature programming mode to ensure better analyte resolution. Each analyte peak in the GC chromatogram of the hair sample was accurately identified using the mass spectrometry detector and the library search of the MS spectra. The GC-MS data and the structural activity relationships (SAR) of the metabolite residues of the hair samples were subjected to multivariate and principal component analysis (PCA) for pattern recognition. The influence of age, gender, race, and smoking habits on the type of metabolite residues detected in the hair samples were also investigated. Over 600 different metabolite residues were detected in hair samples, with an average of 8 metabolite residues detected in each hair sample. Preliminary study also suggests that the age, gender, smoking habit, and work environment plays a role in the detected residues in humans. The results of the GC-MS analysis of the hair samples and principal component analysis of structural activity relationship of the metabolites residues for pattern recognition will be presented.
Funder Acknowledgement(s): HBCU-UP TALENT-21 Program
Faculty Advisor: Sayo Fakayode,