Chromatographic analysis and survey studies to evaluate the emerging drugs of synthetic cannabinoids in Scotland and Saudi Arabia

Synthetic cannabinoid receptor agonists or more commonly known as synthetic cannabinoids (SCs) were originally created to obtain the medicinal value of THC but they are an emerging social problem. SCs are mostly produced coated on herbal materials or in powder form and marketed under a variety of brand names, e.g. “Spice”, “K2”. Despite many SCs becoming controlled under drug legislation, many of them remain legal in some countries around the world. In Scotland, SCs are controlled under the Misuse of Drugs Act 1971 and Psychoactive Substances Act 2016 that only cover a few early SCs. In Saudi Arabia, even fewer are controlled. The picture of the SCs-problem in Scotland is vague due to insufficient prevalence data, particularly that using biological samples. Whilst there is evidence of increasing use of SCs throughout the world, in Saudi Arabia, there is currently no data regarding the use of products containing SCs among Saudi people. Several studies indicate that SCs may cause serious toxicity and impairment to health therefore it is important to understand the scale of use within society. A simple and sensitive method was developed for the simultaneous analysis of 10 parent SCs (JWH-018, JWH-073, JWH-250, JWH-200, AM-1248, UR-144, A-796260, AB-FUBINACA, 5F-AKB-48 and 5F-PB-22) in whole blood and 8 corresponding metabolites (JWH-018 4-OH pentyl, JWH-073 3-OH butyl, JWH-250 4-OH pentyl, AM-2201 4-OH pentyl, JWH-122 5-OH pentyl, JWH-210 5-OH pentyl, 5F-AKB-48 (N-4 OH pentyl), 5F-PB-22 3-carboxyindole)in urine using LLE and LC-MS/MS. The method was validated according to the standard practices for method validation in forensic toxicology (SWGTOX, May 2013). All analytes gave acceptable precision, linearity and recovery for analysing blood and urine samples. The method was applied to 1,496 biological samples, a mixture of whole blood and urine. Blood and/or urine samples were analysed from 114 patients presenting at Accident and Emergency in Glasgow Royal Infirmary, in spring 2014 and JuneDecember 2015. 5F-AKB-48, 5F-PB-22 and MDMB-CHMICA were detected in 9, 7 and 9 cases respectively. 904 urine samples from individuals admitted to/liberated from Scottish prisons over November 2013 were tested for the presence of SCs. 5F-AKB-48 (N-4 OH pentyl) was detected in 10 cases and 5F-PB-22 3-carboxyindole in 3 cases. Blood and urine samples from two post-mortem cases in Scotland with suspected ingestion of SCs were analysed. Both cases were confirmed positive for 5F-AKB-48. A total of 463 urine samples were collected from personnel who presented to the Security Forces Hospital in Ryiadh for workplace drug testing as a requirement for their job during July 2014. The results of the analysis found 2 samples to be positive for 5F-PB-22 3carboxyindole. A further study in Saudi Arabia using a questionnaire was carried out among 3 subpopulations: medical professionals, members of the public in and around smoking cafes and known drug users. With regards to general awareness of Spice products, 16%, 11% and 22% of those participants of medical professionals, members of the public in and around smoking cafes and known drug users, respectively, were aware of the existence of SCs or Spice products. The respondents had an overall average of 4.5% who had a friend who used these Spice products. It is clear from the results obtained in both blood and urine testing and surveys that SCs are being used in both Scotland and Saudi Arabia. The extent of their use is not clear and the data presented here is an initial look into their prevalence. Blood and urine findings suggest changing trends in SC use, moving away from JWH and AM SCs to the newer 5F-AKB-48, 5-F-PB-22 and MDMBCHMICA compounds worldwide. In both countries 5F-PB-22 was detected. These findings clarify how the SCs phenomenon is a worldwide problem and how the information of every country regarding what SCs are seized can help and is not specific for that country. The analytes included in the method were selected due to their apparent availability in both countries, however it is possible that some newer analytes have been used and these would not have been detected. For this reason it is important that methods for testing SCs are updated regularly and evolve with the ever-changing availability of these drugs worldwide. In addition, there is little published literature regarding the concentrations of these drugs found in blood and urine samples and this work goes some way towards understanding these.

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.