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Translational bioinformatics and its application
This book offers a detailed overview of translational bioinformatics together with real-case applications. Translational bioinformatics integrates the areas of basic bioinformatics, clinical informatics, statistical genetics and informatics in order to further our understanding of the molecular basis of diseases. By analyzing voluminous amounts of molecular and clinical data, it also provides clinical information, which can then be applied. Filling the gap between clinic research and informatics, the book is a valuable resource for human geneticists, clinicians, health educators and policy makers, as well as graduate students majoring in biology, biostatistics, and bioinformatics.
Preface; Contents; Part I: Computer-Aided Drug Discovery; Chapter 1: Drug Discovery; 1.1 Introduction; 1.1.1 Need for Drug Design; 1.2 Target Identification; 1.3 Computer-Aided Drug Design; 1.3.1 Structure-Based Drug Design (SBDD); 1.3.1.1 Molecular Docking; 1.3.1.2 Types of Docking; 1.3.1.3 Scoring Functions; 1.3.1.4 Limitations and Challenges; 1.3.2 Ligand-Based Drug Design (LBDD); 1.3.2.1 Pharmacophore Modelling; Structure-Based Pharmacophore Modelling; Ligand-Based Pharmacophore Modelling; 1.3.2.2 Virtual Screening; Lipinski Rule.
1.3.2.3 Quantitative Structure-Activity Relationship (QSAR)1.3.3 Illustrated Examples Using CADD; 1.4 Clinical Trials; 1.4.1 Preclinical Trials; 1.4.2 Human Clinical Trials; 1.4.3 Types of Clinical Trials; 1.5 Conclusions; Appendices; Appendix I; Docking Algorithms; Appendix II; Virtual Screening Methods: Appendix II; References; Chapter 2: Translational Bioinformatics and Drug Discovery; 2.1 Introduction; 2.1.1 Translational Bioinformatics; 2.2 Supporting Resources; 2.2.1 Online Database; 2.2.2 Small Chemical Structure Database; 2.3 Chemical Data Mining Strategies; 2.4 Genomic Technologies.
2.4.1 Next-Generation Sequencing (NGS)2.4.2 NGS and Personalized Medicine; 2.5 Structure-Based Drug Discovery; 2.5.1 Molecular Docking; 2.6 Ligand-Based Drug Discovery; 2.6.1 Quantitative Structural Activity Relationship (QSAR); 2.6.1.1 Model Development; 2.6.1.2 Data Analysis Method; 2.6.1.3 Regression Method; 2.6.1.4 2D QSAR (Girgis et al. 2015); 2.6.1.5 QSAR Model Validation; 2.7 Pharmacokinetic and Pharmacodynamic (PKPD) Simulation (Nielsen and Friberg 2013); 2.7.1 Pharmacokinetics; 2.7.2 Pharmacodynamics; 2.8 Conclusion; References.
Chapter 3: Translational Research in Drug Discovery and Development3.1 Translational Research; 3.1.1 How Is It Different from Traditional Research?; 3.1.2 Translation Continuum from Benchside to Bedside; 3.1.3 Translational Research Phases; 3.1.4 Translational and Clinical Science; 3.1.5 Reengineering Translational Science; 3.1.6 Opportunities in Translational Research; 3.1.6.1 Opportunities for Researchers; 3.1.6.2 Opportunities for Institutions; 3.1.7 Challenges in Translational Research; 3.1.8 Controversies in Translational Research; 3.2 Translational Drug Discovery.
3.2.1 Translational Drug Development for Diseases3.2.1.1 Nervous System Disorders; 3.2.1.2 Psychiatric Disorders; 3.2.1.3 Cancer; 3.3 Strategies to Accelerate Translational Research in Drug Development; 3.3.1 Prioritizing Area of Research and Objectives; 3.3.2 Meaningful Collaboration; 3.3.3 Technology Upgradation; 3.3.4 Bridging Interventional Development Gaps (BrIDGs) Scheme; 3.3.5 Drug Repurposing; 3.3.5.1 Computational Chemistry; 3.3.5.2 Literature Mining; 3.3.5.3 Genome-Wide Association Study (GWAS); 3.4 Conclusion; References.
Dong-Qing Wei, Yilong Ma, William C.S. Cho, Qin Xu, Fengfeng Zhou, editors.
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Dordrecht, The Netherlands : Springer, 2017