Adverse drug reactions (ADRs) are common and should be reported to the CSM, particularly for newly marketed drugs. There is under-reporting of ADRs by doctors. Involving the patient in self-reporting, particularly when initiated by pharmacists is feasible and could help to improve reporting rates. This study investigated a comprehensive checklist questionnaire listed symptoms in all body systems to facilitate patient self-reporting using both established and new 'black triangle' centrally-acting drugs. Symptoms reported were compared to their documentation in medical notes and for new drugs to reports from other sources. A novel classification system for ADRs was developed to take account of the minimal data available and used to evaluate the potential accuracy of symptom attribution by patients. An external comparison of a sample of symptom classifications by an ADR expert was also obtained. The questionnaire was sent to 464 patients prescribed carbamazepine, sodium valproate, trazodone, doxepin and co-proxamol from three participating medical practices in a pilot study. Subsequently, it was sent to all patients (n=2307) prescribed tramadol, fentanyl patch, venlafaxine, nefazodone, citalopram, moclobemide, gabapentin, lamotrigine and topiramate from 79 participating medical practices in Grampian during January-March 1997. The overall response rates were 44.6% (n=207) for the pilot study and 36.3% (n=837) for the main study. The most frequently reported symptoms were: drowsiness for carbamazepine, unusual tiredness for sodium valproate, constipation for co-proxamol, dry mouth for trazodone, doxepin, tramadol, venlafaxine, nefazodone, moclobemide and citalopram, weight gain for gabapentin, loss of memory for lamotrigine, weight loss for topiramate and constipation for fentanyl patch. Overall only 22.4% (522/2330) of symptoms reported by patients were recorded by GPs in the 310 medical notes accessed. In general, common symptoms were reported more frequently by patients than in CSM reports and PEM data. Patients tended to report minor and known ADRs which bothered them, while CSM and PEM reports received were of more severe ADRs. Respondents were more likely to report symptoms (6040/8630,70%) potentially caused by the study drugs than those not to be caused by the study drugs. Moderate agreement (Kappa = 0.4-0.5) was found between expert and researcher classifications of symptom causality. It is suggested that interpretation by pharmacists of patient self-reporting using the checklist questionnaire could result in much higher ADR reporting rates, in particular for new drugs.

Drugs exert their (therapeutic) effects via molecular-level interactions with proteins and other biomolecules. Computational prediction of drug-protein interactions plays a significant role in the effort to improve our current and limited knowledge of these interactions. The use of the putative drug-protein interactions could facilitate the discovery of novel applications of drugs, assist in cataloging their targets, and help to explain the details of medicinal efficacy and side-effects of drugs. We investigate current studies related to the computational prediction of drug-protein interactions and categorize them into protein structure-based and similarity-based methods. We evaluate three representative structure-based predictors and develop a Protein-Drug Interaction Database (PDID) that includes the putative drug targets generated by these three methods for the entire structural human proteome. To address the fact that only a limited set of proteins has known structures, we study the similarity-based methods that do not require this information. We review a comprehensive set of 35 high-impact similarity-based predictors and develop a novel, high-quality benchmark database. We group these predictors based on three types of similarities and their combinations that they use. We discuss and compare key architectural aspects of these methods including their source databases, internal databases and predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually or in all possible combinations. We assess predictive quality at the database-wide drug-protein interaction level and we are the first to also include evaluation across individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures AUC of 0.93. We offer a first-of-its-kind analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets.

Tardive dyskinesia is an insidious and debilitating extrapyramidal side effect of neuroleptic drug treatment. Recent research has suggested that lecithin has been effective in treating tardive dyskinesia. Lecithin's effects were evaluated under double-blind placebo controlled conditions. Treatment conditions included a placebo control group, a lecithin treatment group, and a no-treatment control group. Subjects in the lecithin group received 60 gms/day of lecithin (33 gms of phosphatidylcholine) . Subjects in the placebo group received a similar mixture which contained no lecithin. Subjects received mixtures for 9-11 days. Treatment effectiveness was determined by subjective, objective, and global evaluations. All subjects were evaluated 3 to 4 days prior to treatment and following 9 to 11 days of treatment.

Bakgrund: Läkemedelsbehandling är den vanligaste medicinska behandlingsformen för äldre personer och det är viktigt att sjuksköterskor har kunskap om den äldres känslighet för läkemedel för att kunna identifiera läkemedelsbiverkningar. Multisjuka sköra äldre och polyfarmaci är en utmaning för den kommunala hälso- och sjukvårdens sjuksköterskor som ska bedöma om dess symtom beror på sjukdom eller biverkningar. Syfte: Att beskriva sjuksköterskor erfarenheter av att identifiera läkemedelsbiverkan hos multisjuka sköra äldre inom kommunal hälso- och sjukvård. Metod: En kvalitativ induktiv ansats användes. Datainsamlingen gjordes genom semistrukturerad intervju och en manifest analys utfördes. Resultat: Ett övergripande tema, kompetens och säkerhet framkom med tre underkategorier, sjuksköterskans kunskap samverkan med andra professioner samt att arbeta säkert med tydliga rutiner. Sjuksköterskans erfarenheter, engagemang och kunskap var viktiga faktorer för att kunna identifiera läkemedelsbiverkningar. Samverkan i team med andra professioner ansågs som viktigt för att kunna arbeta säkert med läkemedel, under förutsättning att samarbetet var välfungerande. Tydliga läkemedelsrutiner saknades, men man hade goda kunskaper om den årliga läkemedelsgenomgången. Slutsats: För att kunna identifiera läkemedelsbiverkningar krävs goda kunskaper och erfarenheter hos sjuksköterskor, men det behövs även ett bra samarbete med andra professioner och läkemedelsrutiner behöver synliggöras för en säkrare läkemedelshantering inom den kommunal hälso- och sjukvården. / Background: Drug treatment is the most common form of medical treatment for older adults and it is important that nurses have knowledge of the older adults’ sensitivity to drugs in order to identify drug side effects. Multimorbid frail older patients and polypharmacy are a challenge for the municipal health care nurses who must assess whether the symptoms are due to illness or side effects. Purpose: To describe nurses’ experiences of identifying drug sides effects in frail multimorbid older adults in municipal health. Method: A qualitative inductive approach was used. Data collection was done through semi-structured interviews and a manifest analysis was performed. Results: An overarching theme, competence and safety emerged with three subcategories, the nurse´s knowledge and collaboration with other professions and working with clear routines. The nurse´s experience, commitment and knowledge were impotent factors in being able to identify drug side effects. Cooperation in teams with other professions was considered impotent to be able to work safely with medicines, but this was something that did not always work. Clear medication routines were missing, but there was good knowledge of the annual medication review. Conclusion: In order to be able to identify drug side effects, good knowledge and experience is required on the part of nurses, but a good collaboration with other professions is also needed and drugs routines need to be made visible for safer drug handling within the municipal healthcare system.

La compréhension des pathologies humaines et du mode d'action des médicaments passe par la prise en compte des réseaux d'interactions entre biomolécules. Les recherches récentes sur les systèmes biologiques produisent de plus en plus de données sur ces réseaux qui gouvernent les processus cellulaires. L'hétérogénéité et la multiplicité de ces données rendent difficile leur intégration dans les raisonnements des utilisateurs. Je propose ici des approches intégratives mettant en oeuvre des techniques de gestion de données, de visualisation de graphes et de fouille de données, pour tenter de répondre au problème de l'exploitation insuffisante des données sur les réseaux dans la compréhension des phénotypes associés aux maladies génétiques ou des effets secondaires des médicaments. La gestion des données sur les protéines et leurs propriétés est assurée par un système d'entrepôt de données générique, NetworkDB, personnalisable et actualisable de façon semi-automatique. Des techniques de visualisation de graphes ont été couplées à NetworkDB pour utiliser les données sur les réseaux biologiques dans l'étude de l'étiologie des maladies génétiques entrainant une déficience intellectuelle. Des sous-réseaux de gènes impliqués ont ainsi pu être identifiés et caractérisés. Des profils combinant des effets secondaires partagés par les mêmes médicaments ont été extraits de NetworkDB puis caractérisés en appliquant une méthode de fouille de données relationnelles couplée à Network DB. Les résultats permettent de décrire quelles propriétés des médicaments et de leurs cibles (incluant l'appartenance à des réseaux biologiques) sont associées à tel ou tel profil d'effets secondaires / The understanding of human diseases and drug mechanisms requires today to take into account molecular interaction networks. Recent studies on biological systems are producing increasing amounts of data. However, complexity and heterogeneity of these datasets make it difficult to exploit them for understanding atypical phenotypes or drug side-effects. This thesis presents two knowledge-based integrative approaches that combine data management, graph visualization and data mining techniques in order to improve our understanding of phenotypes associated with genetic diseases or drug side-effects. Data management relies on a generic data warehouse, NetworkDB, that integrates data on proteins and their properties. Customization of the NetworkDB model and regular updates are semi-automatic. Graph visualization techniques have been coupled with NetworkDB. This approach has facilitated access to biological network data in order to study genetic disease etiology, including X-linked intellectual disability (XLID). Meaningful sub-networks of genes have thus been identified and characterized. Drug side-effect profiles have been extracted from NetworkDB and subsequently characterized by a relational learning procedure coupled with NetworkDB. The resulting rules indicate which properties of drugs and their targets (including networks) preferentially associate with a particular side-effect profile

The use of methylenedioxymethamphetamine (MDMA) or Ecstasy is a phenomenon that has established itself in the widespread Rave culture. Ecstasy use causes not only physical, social and psychological problems in the development of the adolescent but may also influence his concentration and learning abilities. To prevent these problems educators should be well informed regarding current drug use trends and also be capable of assisting adolescents. Research regarding the nature of Ecstasy use and the characteristics of its users is lacking nationally. The increase in use amongst school going adolescents and young adults and the fact that there are side effects and unknown long term effects has made it imperative that educators learn as much as possible about this drug. The purpose of this research is therefore to furnish the educator with accurate information that will enable him to obtain a reference point from which assistance can be offered to the young Ecstasy user. / Psychology of Education / M.Ed.(Guidance and Counselling)

The use of methylenedioxymethamphetamine (MDMA) or Ecstasy is a phenomenon that has established itself in the widespread Rave culture. Ecstasy use causes not only physical, social and psychological problems in the development of the adolescent but may also influence his concentration and learning abilities. To prevent these problems educators should be well informed regarding current drug use trends and also be capable of assisting adolescents. Research regarding the nature of Ecstasy use and the characteristics of its users is lacking nationally. The increase in use amongst school going adolescents and young adults and the fact that there are side effects and unknown long term effects has made it imperative that educators learn as much as possible about this drug. The purpose of this research is therefore to furnish the educator with accurate information that will enable him to obtain a reference point from which assistance can be offered to the young Ecstasy user. / Psychology of Education / M.Ed.(Guidance and Counselling)