Explore the vast range of titles available.

Covering the latest advances in CNS drug development, this book will guide all those involved in pre-clinical to early clinical trials.The authors describe how recent innovations can accelerate the development of novel CNS compounds, improve early detection of efficacy and toxicity signals, and increase the safety of later-stage clinical trials.

Pharmaceutical research draws on increasingly complex techniques to solve the challenges of drug design. Bringing together a number of the latest informatics techniques, this book looks at modeling and bioinformatic strategies; structural genomics and X-ray crystallography; virtual screening; lead optimization; ADME profiling and vaccine design. A number of relevant case studies, focusing on techniques that have demonstrated their use, will concentrate on G-protein coupled receptors as potential disease targets.

This handbook provides a comprehensive and unified presentation of the principles and latest statistical methodologies used when modifying trial procedures based on accrued data of ongoing clinical trials. The book also gives a well-balanced summary of current regulatory perspectives and presents real-world examples of a range of adaptive designs. Leading clinical researchers in the pharmaceutical industry, academia, and regulatory agencies examine issues commonly encountered when applying adaptive design methods in clinical trials. They discuss the importance of sample size estimation/allocation, justification, and adjustment when implementing a complicated adaptive design.

This chapter contains sections titled: Introduction Psychopharmacology: drugs, behaviour, physiology and the brain Measuring drug effects Human drug self‐administration Drug withdrawal and craving Summary References Recommended readings

This chapter contains sections titled: Introduction Strategies for Metabolite Identification In Silico Tools Conclusions and Future Trends Acknowledgment References

Background The treatment of complex diseases by taking multiple drugs becomes increasingly popular. However, drug-drug interactions (DDIs) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. DDI detection in the wet lab is expensive and time-consuming. Thus, it is highly desired to develop the computational methods for predicting DDIs. Generally, most of the existing computational methods predict DDIs by extracting the chemical and biological features of drugs from diverse drug-related properties, however some drug properties are costly to obtain and not available in many cases. Results In this work, we presented a novel method (namely DPDDI) to predict DDIs by extracting the network structure features of drugs from DDI network with graph convolution network (GCN), and the deep neural network (DNN) model as a predictor. GCN learns the low-dimensional feature representations of drugs by capturing the topological relationship of drugs in DDI network. DNN predictor concatenates the latent feature vectors of any two drugs as the feature vector of the corresponding drug pairs to train a DNN for predicting the potential drug-drug interactions. Experiment results show that, the newly proposed DPDDI method outperforms four other state-of-the-art methods; the GCN-derived latent features include more DDI information than other features derived from chemical, biological or anatomical properties of drugs; and the concatenation feature aggregation operator is better than two other feature aggregation operators (i.e., inner product and summation). The results in case studies confirm that DPDDI achieves reasonable performance in predicting new DDIs. Conclusion We proposed an effective and robust method DPDDI to predict the potential DDIs by utilizing the DDI network information without considering the drug properties (i.e., drug chemical and biological properties). The method should also be useful in other DDI-related scenarios, such as the detection of unexpected side effects, and the guidance of drug combination.

Background Computational prediction of drug-target interactions (DTI) is vital for drug discovery. The experimental identification of interactions between drugs and target proteins is very onerous. Modern technologies have mitigated the problem, leveraging the development of new drugs. However, drug development remains extremely expensive and time consuming. Therefore, in silico DTI predictions based on machine learning can alleviate the burdensome task of drug development. Many machine learning approaches have been proposed over the years for DTI prediction. Nevertheless, prediction accuracy and efficiency are persisting problems that still need to be tackled. Here, we propose a new learning method which addresses DTI prediction as a multi-output prediction task by learning ensembles of multi-output bi-clustering trees (eBICT) on reconstructed networks. In our setting, the nodes of a DTI network (drugs and proteins) are represented by features (background information). The interactions between the nodes of a DTI network are modeled as an interaction matrix and compose the output space in our problem. The proposed approach integrates background information from both drug and target protein spaces into the same global network framework. Results We performed an empirical evaluation, comparing the proposed approach to state of the art DTI prediction methods and demonstrated the effectiveness of the proposed approach in different prediction settings. For evaluation purposes, we used several benchmark datasets that represent drug-protein networks. We show that output space reconstruction can boost the predictive performance of tree-ensemble learning methods, yielding more accurate DTI predictions. Conclusions We proposed a new DTI prediction method where bi-clustering trees are built on reconstructed networks. Building tree-ensemble learning models with output space reconstruction leads to superior prediction results, while preserving the advantages of tree-ensembles, such as scalability, interpretability and inductive setting.