Virtual Affinity Fingerprints for Target Fishing: A New Application of Drug Profile Matching

We recently introduced Drug Profile Matching (DPM), a novel virtual affinity fingerprinting bioactivity prediction method. DPM is based on the docking profiles of ca. 1200 FDA-approved small-molecule drugs against a set of nontarget proteins and creates bioactivity predictions based on this pattern. The effectiveness of this approach was previously demonstrated for therapeutic effect prediction of drug molecules. In the current work, we investigated the applicability of DPM for target fishing, i.e. for the prediction of biological targets for compounds. Predictions were made for 77 targets, and their accuracy was measured by Receiver Operating Characteristic (ROC) analysis. Robustness was tested by a rigorous 10-fold cross-validation procedure. This procedure identified targets (N = 45) with high reliability based on DPM performance. These 45 categories were used in a subsequent study which aimed at predicting the off-target profiles of currently approved FDA drugs. In this data set, 79% of the known drug-target interactions were correctly predicted by DPM, and additionally 1074 new drug-target interactions were suggested. We focused our further investigation on the suggested interactions of antipsychotic molecules and confirmed several interactions by a review of the literature

Effectiveness of a web platform on university students’ motivation to quit smoking

Objetivo: conhecer a dependência da nicotina e a motivação para parar de fumar em estudantes de Enfermagem e Fisioterapia de uma universidade no sul da Espanha e avaliar o impacto de uma intervenção baseada no uso de tecnologias da informação na motivação para parar de fumar. Método: estudo piloto em duas fases: a primeira transversal e a segunda de intervenção antes-depois. A motivação para parar de fumar foi avaliada usando o questionário Richmond, a dependência da nicotina através do questionário de Fagerström e uma intervenção baseada no uso de uma plataforma online foi realizada para aumentar a motivação para parar de fumar. Estatística descritiva e inferencial foram aplicadas. Resultados: a prevalência de consumo de tabaco foi de 4,33% (n=29). 3,45% dos participantes tinham alta dependência e 6,90%, alta motivação. O nível de motivação não foi alterado após a intervenção (p=0,10). Conclusão: a maioria dos estudantes tem baixo nível de motivação para parar de fumar e dependência física à nicotina. O nível de motivação para parar de fumar não é diferente após a realização da intervenção.Objetivo: conocer la dependencia a la nicotina y la motivación para el cese tabáquico en estudiantes de Enfermería y Fisioterapia de una universidad del sur de España y evaluar el efecto de una intervención basada en el uso de tecnologías de la información en la motivación para el cese tabáquico. Método: estudio piloto de dos fases: la primera transversal y la segunda de intervención antes-después. Se valoró la motivación para dejar de fumar mediante el cuestionario Richmond, la dependencia a la nicotina a través del cuestionario Fagerström, y se llevó a cabo una intervención basada en el uso de una plataforma web para incrementar la motivación del cese tabáquico. Se aplicó estadística descriptiva e inferencial. Resultados: la prevalencia de consumo de tabaco fue del 4.33% (n=29). El 3.45% de los participantes presentó alta dependencia, y el 6.90%, alta motivación. El nivel de motivación no se vio alterado tras la intervención (p=0.10). Conclusión: la mayor parte de los estudiantes tiene un nivel bajo de motivación para dejar de fumar y de dependencia física a la nicotina. El nivel de motivación para el cese tabáquico no es diferente tras realizar la intervención.Objective: to know the dependence on nicotine and the motivation to quit smoking in Nursing and Physiotherapy students of a university in the South of Spain, and to evaluate the impact of an intervention based on the use of information technologies on the motivation to quit smoking. Method: a pilot study in two phases: the first being cross-sectional and the second, a before-and-after intervention. The motivation to quit smoking was assessed by means of the Richmond questionnaire, and the dependence on nicotine through the Fagerström questionnaire; additionally, an intervention was performed based on the use of a web platform to increase motivation to quit smoking. Descriptive and inferential statistics were applied. Results: the prevalence in the use of tobacco was 4.33% (n=29). 3.45% of the participants had a high level of dependence; and 6.90%, a high level of motivation. The level of motivation did not change after the intervention (p=0.10). Conclusion: most of the students have low levels of motivation to quit smoking and of physical dependence to nicotine. The level of motivation to quit smoking does not change after performing the intervention

Drug Effect Prediction by Polypharmacology-Based Interaction Profiling.

Most drugs exert their effects via multitarget interactions, as hypothesized by polypharmacology. While these multitarget interactions are responsible for the clinical effect profiles of drugs, current methods have failed to uncover the complex relationships between them. Here, we introduce an approach which is able to relate complex drug-protein interaction profiles with effect profiles. Structural data and registered effect profiles of all small-molecule drugs were collected, and interactions to a series of nontarget protein binding sites of each drug were calculated. Statistical analyses confirmed a close relationship between the studied 177 major effect categories and interaction profiles of ca. 1200 FDA-approved small-molecule drugs. On the basis of this relationship, the effect profiles of drugs were revealed in their entirety, and hitherto uncovered effects could be predicted in a systematic manner. Our results show that the prediction power is independent of the composition of the protein set used for interaction profile generation