The analysis and detection of new psychoactive substances in biological matrices

New psychoactive substances (NPSs) have appeared on the recreational drug market at an unprecedented rate in recent years. Many are not new drugs but failed products of the pharmaceutical industry. The speed and variety of drugs entering the market poses a new complex challenge for the forensic toxicology community. The detection of these substances in biological matrices can be difficult as the exact compounds of interest may not be known. Many NPS are sold under the same brand name and therefore users themselves may not know what substances they have ingested. The majority of analytical methods for the detection of NPSs tend to focus on a specific class of compounds rather than a wide variety. In response to this, a robust and sensitive method was developed for the analysis of various NPS by solid phase extraction (SPE) with gas chromatography mass spectrometry (GCMS). Sample preparation and derivatisation were optimised testing a range of SPE cartridges and derivatising agents, as well as derivatisation incubation time and temperature. The final gas chromatography mass spectrometry method was validated in accordance with SWGTOX 2013 guidelines over a wide concentration range for both blood and urine for 23 and 25 analytes respectively. This included the validation of 8 NBOMe compounds in blood and 10 NBOMe compounds in urine. This GC-MS method was then applied to 8 authentic samples with concentrations compared to those originally identified by NMS laboratories. The rapid influx of NPSs has resulted in the re-analysis of samples and thus, the stability of these substances is crucial information. The stability of mephedrone was investigated, examining the effect that storage temperatures and preservatives had on analyte stability daily for 1 week and then weekly for 10 weeks. Several laboratories identified NPSs use through the cross-reactivity of these substances with existing screening protocols such as ELISA. The application of Immunalysis ketamine, methamphetamine and amphetamine ELISA kits for the detection of NPS was evaluated. The aim of this work was to determine if any cross-reactivity from NPS substances was observed, and to determine whether these existing kits would identify NPS use within biological samples. The cross- reactivity of methoxetamine, 3-MeO-PCE and 3-MeO-PCP for different commercially point of care test (POCT) was also assessed for urine. One of the newest groups of compounds to appear on the NPS market is the NBOMe series. These drugs pose a serious threat to public health due to their high potency, with fatalities already reported in the literature. These compounds are falsely marketed as LSD which increases the chance of adverse effects due to the potency differences between these 2 substances. A liquid chromatography tandem mass spectrometry (LC-MS/MS) method was validated in accordance with SWGTOX 2013 guidelines for the detection for 25B, 25C and 25I-NBOMe in urine and hair. Long-Evans rats were administered 25B-, 25C- and 25I-NBOMe at doses ranging from 30-300 µg/kg over a period of 10 days. Tail flick tests were then carried out on the rats in order to determine whether any analgesic effects were observed as a result of dosing. Rats were also shaved prior to their first dose and reshaved after the 10-day period. Hair was separated by colour (black and white) and analysed using the validated LC-MS/MS method, assessing the impact hair colour has on the incorporation of these drugs. Urine was collected from the rats, analysed using the validated LC-MS/MS method and screened for potential metabolites using both LC-MS/MS and quadrupole time of flight (QToF) instrumentation.

Estimation of whole body muscle, adipose/fat mass, validation in health and during weight loss. Development of prediction equations using MRI as reference method

Background: Body composition is affected by diseases, and affects responses to medical treatments, dosage of medicines, etc., while an abnormal body composition contributes to the causation of many chronic diseases. While we have reliable biochemical tests for certain nutritional parameters of body composition, such as iron or iodine status, and we have harnessed nuclear physics to estimate the body’s content of trace elements, the very basic quantification of body fat content and muscle mass remains highly problematic. Both body fat and muscle mass are vitally important, as they have opposing influences on chronic disease, but they have seldom been estimated as part of population health surveillance. Instead, most national surveys have merely reported BMI and waist, or sometimes the waist/hip ratio; these indices are convenient but do not have any specific biological meaning. Anthropometry offers a practical and inexpensive method for muscle and fat estimation in clinical and epidemiological settings; however, its use is imperfect due to many limitations, such as a shortage of reference data, misuse of terminology, unclear assumptions, and the absence of properly validated anthropometric equations. To date, anthropometric methods are not sensitive enough to detect muscle and fat loss. Aims: The aim of this thesis is to estimate Adipose/fat and muscle mass in health disease and during weight loss through; 1. evaluating and critiquing the literature, to identify the best-published prediction equations for adipose/fat and muscle mass estimation; 2. to derive and validate adipose tissue and muscle mass prediction equations; and 3.to evaluate the prediction equations along with anthropometric indices and the best equations retrieved from the literature in health, metabolic illness and during weight loss. Methods: a Systematic review using Cochrane Review method was used for reviewing muscle mass estimation papers that used MRI as the reference method. Fat mass estimation papers were critically reviewed. Mixed ethnic, age and body mass data that underwent whole body magnetic resonance imaging to quantify adipose tissue and muscle mass (dependent variable) and anthropometry (independent variable) were used in the derivation/validation analysis. Multiple regression and Bland-Altman plot were applied to evaluate the prediction equations. To determine how well the equations identify metabolic illness, English and Scottish health surveys were studied. Statistical analysis using multiple regression and binary logistic regression were applied to assess model fit and associations. Also, populations were divided into quintiles and relative risk was analysed. Finally, the prediction equations were evaluated by applying them to a pilot study of 10 subjects who underwent whole-body MRI, anthropometric measurements and muscle strength before and after weight loss to determine how well the equations identify adipose/fat mass and muscle mass change. Results: The estimation of fat mass has serious problems. Despite advances in technology and science, prediction equations for the estimation of fat mass depend on limited historical reference data and remain dependent upon assumptions that have not yet been properly validated for different population groups. Muscle mass does not have the same conceptual problems; however, its measurement is still problematic and reference data are scarce. The derivation and validation analysis in this thesis was satisfactory, compared to prediction equations in the literature they were similar or even better. Applying the prediction equations in metabolic illness and during weight loss presented an understanding on how well the equations identify metabolic illness showing significant associations with diabetes, hypertension, HbA1c and blood pressure. And moderate to high correlations with MRI-measured adipose tissue and muscle mass before and after weight loss. Conclusion: Adipose tissue mass and to an extent muscle mass can now be estimated for many purposes as population or groups means. However, these equations must not be used for assessing fatness and categorising individuals. Further exploration in different populations and health surveys would be valuable.