Predicting new molecular targets for known drugs

Although drugs are intended to be selective, at least some bind to several physiological targets, explaining side effects and efficacy. Because many drug–target combinations exist, it would be useful to explore possible interactions computationally. Here we compared 3,665 US Food and Drug Administration (FDA)-approved and investigational drugs against hundreds of targets, defining each target by its ligands. Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations. Thirty were tested experimentally, including the antagonism of the β1 receptor by the transporter inhibitor Prozac, the inhibition of the 5-hydroxytryptamine (5-HT) transporter by the ion channel drug Vadilex, and antagonism of the histamine H4 receptor by the enzyme inhibitor Rescriptor. Overall, 23 new drug–target associations were confirmed, five of which were potent (less than 100 nM). The physiological relevance of one, the drug N,N-dimethyltryptamine (DMT) on serotonergic receptors, was confirmed in a knockout mouse. The chemical similarity approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs

mmpdb: An Open Source Matched Molecular Pair Platform for Large Multi-Property Datasets

We present mmpdb, an open source Matched Molecular Pair (MMP) platform to create, compile, store, retrieve, and use MMP rules. mmpdb is suitable for the large datasets typically found in pharmaceutical and agrochemical companies and provides new algorithms for fragment canonicalization and stereochemistry handling. The platform is written in Python and based on the RDKit toolkit. mmpdb is freely available

mmpdb: An Open Source Matched Molecular Pair Platform for Large Multi-Property Datasets

We present mmpdb, an open source Matched Molecular Pair (MMP) platform to create, compile, store, retrieve, and use MMP rules. mmpdb is suitable for the large datasets typically found in pharmaceutical and agrochemical companies and provides new algorithms for fragment canonicalization and stereochemistry handling. The platform is written in Python and based on the RDKit toolkit. mmpdb is freely available

Perinteisen budjetoinnin kritiikki yrityksen talouden ohjaamisessa

The first large scale analysis of in vitro absorption, distribution, metabolism, excretion, and toxicity (ADMET) data shared across multiple major pharma has been performed. Using advanced matched molecular pair analysis (MMPA), we combined data from three pharmaceutical companies and generated ADMET rules, avoiding the need to disclose the full chemical structures. On top of the very large exchange of knowledge, all companies involved synergistically gained approximately 20% more rules from the shared transformations. There is good quantitative agreement between the rules based on shared data compared to both individual companies’ rules and rules published in the literature. Known correlations between log <i>D</i>, solubility, in vitro clearance, and plasma protein binding also hold in transformation space, but there are also interesting exceptions. Data pools such as this allow focusing on particular functional groups and characterizing their ADMET profile. Finally the role of a corpus of robustly tested medicinal chemistry knowledge in the training of medicinal chemistry is discussed

Health promotion in mental health care: perceptions from patients and mental health nurses

Aims and objectives. To gain insight into the factors influencing the integration of physical activity and healthy eating into the daily care of individuals with mental disorders (MD) living in sheltered housing and to increase the understanding of the relationships between and complexities of these factors. Background. Growing attention is given to the implementation of health promotion activities in mental health care. By improving the understanding of perceptions of patients and mental health nurses, health promotion programmes targeting physical activity and healthy eating can be developed that better meet the patients’ needs. Design. A descriptive qualitative study. Methods. Based on a purposive sampling strategy, three focus groups including 17 mental health nurses and individual interviews with 15 patients were conducted. Results. Although physical and mental health benefits of physical activity and healthy eating were identified, several barriers to integrate healthy lifestyles into the daily life of patients were reported. Important barriers identified by the patients consisted of lack of energy and motivation as a result of the MD, side effects of psychotropic drug use, and hospitalisation. Lack of time and personal views and attitudes towards health promotion were reported by the mental health nurses as important elements influencing the way in which they integrate health promotion in the care provided. Support from the mental health nurse was considered important by the patients in changing their unhealthy lifestyle behaviour. Conclusions. The results of the study provide insight into important factors influencing the integration of health promotion activities targeting physical activity and healthy eating in individuals with MD living in sheltered housing. Relevance to clinical practice. The information derived from this study is useful and relevant in the design and implementation of health promotion interventions targeting physical activity and healthy eating in people with MD living in sheltered housing