An Introduction to Drug Testing: The Expanding Role of Mass Spectrometry

2018 ◽  
pp. 1-10 ◽  
Author(s):  
Christine L. H. Snozek ◽  
Loralie J. Langman ◽  
Steven W. Cotten
Keyword(s):  
Mass Spectrometry ◽  
Drug Testing

scholarly journals Evaluating Targeted Therapies in Ovarian Cancer Metabolism: Novel Role for PCSK9 and Second Generation mTOR Inhibitors

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3727
Author(s):  
Dafne Jacome Sanz ◽  
Juuli Raivola ◽  
Hanna Karvonen ◽  
Mariliina Arjama ◽  
Harlan Barker ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Lipid Metabolism ◽  
Cell Survival ◽  
Drug Testing ◽  
Cancer Metabolism ◽  
Ex Vivo ◽  
Specific Inhibitor ◽  
Low Grade ◽  
Serous Ovarian Cancer

Background: Dysregulated lipid metabolism is emerging as a hallmark in several malignancies, including ovarian cancer (OC). Specifically, metastatic OC is highly dependent on lipid-rich omentum. We aimed to investigate the therapeutic value of targeting lipid metabolism in OC. For this purpose, we studied the role of PCSK9, a cholesterol-regulating enzyme, in OC cell survival and its downstream signaling. We also investigated the cytotoxic efficacy of a small library of metabolic (n = 11) and mTOR (n = 10) inhibitors using OC cell lines (n = 8) and ex vivo patient-derived cell cultures (PDCs, n = 5) to identify clinically suitable drug vulnerabilities. Targeting PCSK9 expression with siRNA or PCSK9 specific inhibitor (PF-06446846) impaired OC cell survival. In addition, overexpression of PCSK9 induced robust AKT phosphorylation along with increased expression of ERK1/2 and MEK1/2, suggesting a pro-survival role of PCSK9 in OC cells. Moreover, our drug testing revealed marked differences in cytotoxic responses to drugs targeting metabolic pathways of high-grade serous ovarian cancer (HGSOC) and low-grade serous ovarian cancer (LGSOC) PDCs. Our results show that targeting PCSK9 expression could impair OC cell survival, which warrants further investigation to address the dependency of this cancer on lipogenesis and omental metastasis. Moreover, the differences in metabolic gene expression and drug responses of OC PDCs indicate the existence of a metabolic heterogeneity within OC subtypes, which should be further explored for therapeutic improvements.

Interpretable machine learning model to detect chemically adulterated urine samples analyzed by high resolution mass spectrometry

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Gabriel L. Streun ◽  
Andrea E. Steuer ◽  
Lars C. Ebert ◽  
Akos Dobay ◽  
Thomas Kraemer
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
High Resolution ◽  
Retention Time ◽  
Drug Testing ◽  
High Resolution Mass Spectrometry ◽  
Urine Samples ◽  
Machine Learning Model ◽  
Resolution Mass

Abstract Objectives Urine sample manipulation including substitution, dilution, and chemical adulteration is a continuing challenge for workplace drug testing, abstinence control, and doping control laboratories. The simultaneous detection of sample manipulation and prohibited drugs within one single analytical measurement would be highly advantageous. Machine learning algorithms are able to learn from existing datasets and predict outcomes of new data, which are unknown to the model. Methods Authentic human urine samples were treated with pyridinium chlorochromate, potassium nitrite, hydrogen peroxide, iodine, sodium hypochlorite, and water as control. In total, 702 samples, measured with liquid chromatography coupled to quadrupole time-of-flight mass spectrometry, were used. After retention time alignment within Progenesis QI, an artificial neural network was trained with 500 samples, each featuring 33,448 values. The feature importance was analyzed with the local interpretable model-agnostic explanations approach. Results Following 10-fold cross-validation, the mean sensitivity, specificity, positive predictive value, and negative predictive value was 88.9, 92.0, 91.9, and 89.2%, respectively. A diverse test set (n=202) containing treated and untreated urine samples could be correctly classified with an accuracy of 95.4%. In addition, 14 important features and four potential biomarkers were extracted. Conclusions With interpretable retention time aligned liquid chromatography high-resolution mass spectrometry data, a reliable machine learning model could be established that rapidly uncovers chemical urine manipulation. The incorporation of our model into routine clinical or forensic analysis allows simultaneous LC-MS analysis and sample integrity testing in one run, thus revolutionizing this field of drug testing.

scholarly journals Placental 13C-DHA metabolism and relationship with maternal BMI, glycemia and birthweight

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Oliver C. Watkins ◽  
Preben Selvam ◽  
Reshma Appukuttan Pillai ◽  
Victoria K. B. Cracknell-Hazra ◽  
Hannah E. J. Yong ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
Lipid Metabolism ◽  
Metabolic Pathways ◽  
Liquid Chromatography Mass Spectrometry ◽  
Fasting Glycemia ◽  
Glucose Treatment ◽  
Maternal Bmi

Abstract Background Fetal docosahexaenoic acid (DHA) supply relies on preferential transplacental transfer, which is regulated by placental DHA lipid metabolism. Maternal hyperglycemia and obesity associate with higher birthweight and fetal DHA insufficiency but the role of placental DHA metabolism is unclear. Methods Explants from 17 term placenta were incubated with 13C-labeled DHA for 48 h, at 5 or 10 mmol/L glucose treatment, and the production of 17 individual newly synthesized 13C-DHA labeled lipids quantified by liquid chromatography mass spectrometry. Results Maternal BMI positively associated with 13C-DHA-labeled diacylglycerols, triacylglycerols, lysophospholipids, phosphatidylcholine and phosphatidylethanolamine plasmalogens, while maternal fasting glycemia positively associated with five 13C-DHA triacylglycerols. In turn, 13C-DHA-labeled phospholipids and triacylglycerols positively associated with birthweight centile. In-vitro glucose treatment increased most 13C-DHA-lipids, but decreased 13C-DHA phosphatidylethanolamine plasmalogens. However, with increasing maternal BMI, the magnitude of the glucose treatment induced increase in 13C-DHA phosphatidylcholine and 13C-DHA lysophospholipids was curtailed, with further decline in 13C-DHA phosphatidylethanolamine plasmalogens. Conversely, with increasing birthweight centile glucose treatment induced increases in 13C-DHA triacylglycerols were exaggerated, while glucose treatment induced decreases in 13C-DHA phosphatidylethanolamine plasmalogens were diminished. Conclusions Maternal BMI and glycemia increased the production of different placental DHA lipids implying impact on different metabolic pathways. Glucose-induced elevation in placental DHA metabolism is moderated with higher maternal BMI. In turn, findings of associations between many DHA lipids with birthweight suggest that BMI and glycemia promote fetal growth partly through changes in placental DHA metabolism.

scholarly journals Paper spray mass spectrometry – A potential complementary technique for the detection of polar compounds in sports drug testing

2020 ◽  
Vol 12 (11-12) ◽  
pp. 1658-1665 ◽  
Author(s):  
Christian Görgens ◽  
Katherine Walker ◽  
Cornelia Boeser ◽  
Neloni Wijeratne ◽  
Claudia Martins ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Drug Testing ◽  
Polar Compounds ◽  
Paper Spray ◽  
Complementary Technique ◽  
Sports Drug Testing
Sign in / Sign up

Export Citation Format

Share Document