This report is structured in five parts: national framework for traditional and complementary medicine (T&CM); product regulation; practices and practitioners; the challenges faced by countries; and finally the country profiles. Apart from the section on practices and practitioners the report is consistent with the format of the report of the first global survey in order to provide a useful comparison. The section on practices and practitioners which covers providers education and health insurance is a new section incorporated to reflect the emerging trends in T&CM and to gather new information regarding these topics at a national level. All new information received has been incorporated into individual country profiles and data graphs. The report captures the three phases of progress made by Member States; that is before and after the first WHO Traditional Medicine Strategy (1999?2005) from the first global survey to the second global survey (2005?2012) and from the second survey to the most recent timeline (2012?2018).

Computational Methods in Drug Discovery and Repurposing for Cancer Therapy provides knowledge about ongoing research as well as computational approaches for drug discovery and repurposing for cancer therapy. The book also provides detailed descriptions about target molecules, pathways, and their inhibitors for easy understanding and applicability. The book discusses tools and techniques such as integrated bioinformatics approaches, systems biology tools, molecular docking, computational chemistry, artificial intelligence, machine learning, structure-based virtual screening, biomarkers, and transcriptome; those are discussed in the context of different cancer types, such as colon, pancreatic, glioblastoma, endometrial, and retinoblastoma, among others. This book is a valuable resource for researchers, students, and members of the biomedical and medical fields who want to learn more about the use of computational modeling to better tailor the treatment for cancer patients. - Discusses in silico remodeling of effective phytochemical compounds for discovering improved anticancer agents for substantial/significant cancer therapy - Covers potential tools of bioinformatics that are applied toward discovering new targets by drug repurposing and strategies to cure different types of cancers - Demonstrates the significance of computational and artificial intelligence approaches in anticancer drug discovery - Explores how these various advances can be integrated into a precision and personalized medicine approach that can eventually enhance patient care

Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.

Richly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. Omitting tedious details, heavy formalisms, and cryptic notations, the text takes a hands-on, example-based approach that teaches students the basics of R and microarray technology as well as how to choose and apply the proper data analysis tool to specific problems. New to the Second EditionCompletely updated and double the size of its predecessor, this timely second edition replaces the commercial software with the open source R and Bioconductor environments. Fourteen new chapters cover such topics as the basic mechanisms of the cell, reliability and reproducibility issues in DNA microarrays, basic statistics and linear models in R, experiment design, multiple comparisons, quality control, data pre-processing and normalization, Gene Ontology analysis, pathway analysis, and machine learning techniques. Methods are illustrated with toy examples and real data and the R code for all routines is available on an accompanying downloadable resource. With all the necessary prerequisites included, this best-selling book guides students from very basic notions to advanced analysis techniques in R and Bioconductor. The first half of the text presents an overview of microarrays and the statistical elements that form the building blocks of any data analysis. The second half introduces the techniques most commonly used in the analysis of microarray data.

Drug repurposing is defined as identifying new pharmacological indications from old, existing, failed, investigational, already marketed, or FDA-approved drugs and prodrugs, and applying these new uses in the treatment of diseases other than the drug’s original intended therapeutic use. The application of computational techniques in discovery research not only helps in the development of drugs from leads or existing drug molecules but can also be useful for the repurposing of existing drug candidates. This new volume presents exciting recent advances in drug repurposing and computational approaches for the discovery and development of drugs against certain difficult-to-treat and life-threatening diseases. With contributions from a global team of experts (academicians, scientists, and researchers), it explores the sophisticated tools and techniques of drug repurposing and computational drug discovery. It delivers valuable information on computational techniques, tools, and databases being utilized for drug repurposing and for identifying the uses of existing drug candidates on different emerging or deadly diseases. Drug repurposing and computational approaches addressed in the book target the discovery and development of drugs for microbial infections (bacterial, fungal, viral, COVID-19), parasitic diseases and neglected tropical diseases (NTDs), malignant diseases (cancer), inflammatory diseases, cardiovascular disorders, diabetes, and aging and neurological (CNS) disorders. In addition, the challenges and regulatory issues encountered in drug repurposing and computational drug discovery programs are looked at, offering perspectives for future directions.

In Silico Drug Design: Repurposing Techniques and Methodologies explores the application of computational tools that can be utilized for this approach. The book covers theoretical background and methodologies of chem-bioinformatic techniques and network modeling and discusses the various applied strategies to systematically retrieve, integrate and analyze datasets from diverse sources. Other topics include in silico drug design methods, computational workflows for drug repurposing, and network-based in silico screening for drug efficacy. With contributions from experts in the field and the inclusion of practical case studies, this book gives scientists, researchers and R&D professionals in the pharmaceutical industry valuable insights into drug design. - Discusses the theoretical background and methodologies of useful techniques of cheminformatics and bioinformatics that can be applied for drug repurposing - Offers case studies relating to the in silico modeling of FDA-approved drugs for the discovery of antifungal, anticancer, antiplatelet agents, and for drug therapies against diseases - Covers tools and databases that can be utilized to facilitate in silico methods for drug repurposing

Natural Products have been important sources of useful drugs from prehistoric times to the present. This book gives an overview about this field and provides important recent contributions to the discovery of new drugs generated by research on natural products. Total synthesis of natural products with interesting biological activities is paving the way for the preparation of new and improved analogs. The methods of combinatorial chemistry permit the selection of the best drug from a large number of candidates. Beyond synthesis and evaluation of organic molecules a number of new bioorganic methods are coming to the fore and will be discucced in this isue of the ERnst schering Research Foundation workshop proceedings.