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Progress in medicinal chemistry. Volume fifty seven /

Progress in Medicinal Chemistry, Volume 57, provides a review of eclectic developments in medicinal chemistry, with this volume including chapters on the CaSR field, CFTR modulators in cystic fibrosis, macrocycles, VMAT2 inhibitors, and Big Data in Drug Discovery.- Provides extended, timely reviews...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Witty, D. R. (David R.) (Editor), Cox, Brian (Editor)
Format: eBook
Language:English
Published: Amsterdam, Netherlands ; Oxford, United Kingdom ; Cambridge, MA : Elsevier, [2018]
Subjects:
Pharmaceutical chemistry.
Chemistry, Pharmaceutical
Chimie pharmaceutique.
Pharmaceutical chemistry
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Progress in Medicinal Chemistry
  • Copyright
  • Contents
  • Contributors
  • Preface
  • Chapter One: Discovery and Development of Calcimimetic and Calcilytic Compounds
  • 1. Introduction
  • 2. The Extracellular Calcium Receptor
  • 2.1. Discovery of the Parathyroid Calcium Receptor
  • 2.1.1. Regulation of Systemic Ca2+ Homeostasis
  • 2.1.2. Physiological and Biochemical Evidence
  • 2.1.3. Pharmacological Evidence
  • 2.1.4. The Calcium Channel Hypothesis and the Discovery of Allosteric Modulators of the CaR
  • 3. Screening for Calcimimetics and Calcilytics
  • 3.1. Primary Cultures of Bovine Parathyroid Cells
  • 3.2. High-Throughput Screening
  • 3.3. Calcimimetic and Calcilytic Defined
  • 4. Calcimimetics
  • 4.1. Phenyl- and Naphthylalkylamines
  • 4.1.1. NPS R-568 (Tecalcet)
  • 4.1.2. NPS 1493 (Cinacalcet)
  • 4.1.3. Modifications to the Cinacalcet Chemotype
  • 4.2. Other Arylalkylamines
  • 4.3. Etelcalcetide
  • 4.4. Urea-Based Compounds
  • 4.5. 1-(Benzothiazol-2-yl)phenylethanols (AC-265347)
  • 4.6. Naturally Occurring l-Amino Acids and Peptides
  • 5. Calcilytics
  • 5.1. PTH as a Therapy for Osteoporosis
  • 5.2. Amino Alcohols
  • 5.3. Quinazolinones
  • 5.4. Pyrimidinones
  • 5.5. Pyrimidines and Pyridines
  • 5.6. Benzimidazoles
  • 5.7. Diaminocyclohexanes
  • 5.8. Clinical Studies of Calcilytics in Postmenopausal Women With Osteoporosis
  • 5.9. Repurposing Calcilytics for New Indications
  • 5.9.1. Hypoparathyroidism
  • 5.9.2. Asthma
  • 5.9.3. Pulmonary Arterial Hypertension
  • 6. Molecular Mechanism of Action of Calcimimetics and Calcilytics
  • 6.1. Orthosteric-Binding Sites
  • 6.2. Allosteric-Bindings Sites
  • 7. Conclusion
  • References
  • Chapter Two: VMAT2 Inhibitors and the Path to Ingrezza (Valbenazine)
  • 1. Introduction
  • 2. VMAT2 Inhibitors
  • 3. Tetrabenazine and Metabolites
  • 4. TBZ Metabolite Pharmacologic Characterization.
  • 2.1. When Does Data Become Big Data?
  • 2.2. What Is Big Data and Where Do the Challenges Lie?
  • 2.3. What Are the Characteristics of Big Data?
  • 2.4. Big Data in Biosciences
  • 2.5. Data Integration
  • 2.6. Applications of Big Data in Biosciences
  • 2.7. Data Challenges
  • 3. Using Big Data
  • 3.1. ML and AI
  • 3.2. What Is Machine Learning?
  • 3.3. Machine Learning vs Statistics
  • 3.4. Predictive vs Explanatory Models
  • 3.5. Representation Learning vs Feature Engineering
  • 3.6. Discriminative vs Generative Models
  • 3.7. Supervised Learning vs Unsupervised Learning vs Reinforcement Learning
  • 3.8. Big vs Small Data
  • 3.9. ML Applications in Drug Discovery
  • 3.10. Information Extraction
  • 3.11. Cheminformatics
  • 3.12. Bioinformatics
  • 4. Big Data in Biology
  • 4.1. Big Data in Drug Discovery Target Identification and Validation
  • 4.2. Genomics and Genetics
  • 4.3. New Technologies for Understanding Disease Biology
  • 4.4. Challenges for Big Data in Target Validation
  • 4.5. Successful Target Validation Requires Decision-Making Experiments
  • 4.6. Discussion
  • 5. Big Data in Chemistry
  • 5.1. Introduction
  • 5.2. Data Types in Chemistry
  • 5.2.1. Chemical Structures
  • 5.2.2. Chemical Properties
  • 5.2.3. Biological Data
  • 5.2.4. ADMET Data
  • 5.3. Data Availability
  • 5.3.1. Community: PubChem
  • 5.3.2. Chemical Probes: Chemical Probes Portal
  • 5.3.3. Literature: ChEMBL
  • 5.3.4. Patents: SureChEMBL
  • 5.3.5. Vendor Sources: ZINC
  • 5.3.6. Bioisosteric Pairs: SwissBioisostere
  • 5.3.7. Small-Molecule Crystal Structures: CSD, COD
  • 5.3.8. Protein Structures: PDB
  • 5.3.9. Drugs: DrugBank
  • 5.3.10. GDB-17
  • 5.3.11. Derived Data
  • 5.4. Data Challenges
  • 5.4.1. Errors, Reproducibility, Standardisation and Formatting of Data
  • 5.4.2. Information Held in Silos
  • 5.4.3. Loss of Contextual Information
  • 5.4.4. Limited and Biased Data.
  • 5.4.5. Augmenting Chemistry Space
  • 5.5. Big Data Applications
  • 5.5.1. Predictions
  • 5.5.1.1. Molecular Properties: Physicochemical Properties
  • 5.5.1.2. Molecular Similarities: Similar Property Principle
  • 5.6. Molecular Design
  • 5.6.1. Exploration and Exploitation
  • 5.6.2. De Novo Molecular Design
  • 5.6.3. Multiparameter Optimisation
  • 5.6.4. Estimating Synthetic Accessibility
  • 5.6.5. Synthesis Planning
  • 5.6.6. Bioisosteres and Scaffold Hopping
  • 5.6.7. Polypharmacology
  • 6. Summary and Outlook
  • 7. Big Data in Clinical Trials and Health Care
  • 7.1. Big Data in Clinical Trials
  • 7.2. Clinical Trial Registries
  • 7.2.1. Mining Registry Data
  • 7.2.2. Data Quality and Transparency
  • 7.3. Big Data in Health
  • 7.4. Electronic Health Records
  • 7.5. Genetic Data and Biobanks
  • 7.6. Other Patient-Level Data Sources
  • 7.7. Applications of Health Data
  • 7.7.1. Association Mining
  • 7.7.2. Phenome-Wide Association Studies
  • 7.7.3. Pharmacogenomics
  • 7.7.4. Pharmacovigilance
  • 7.7.5. Predictive Modelling
  • 7.7.6. Cohort Querying
  • 7.7.7. Patient Stratification and Personalised Medicine
  • 7.8. Challenges
  • 7.8.1. Data Quality
  • 7.8.2. Interoperability and Linked Data
  • 7.8.3. Privacy, Consent, and Deidentification
  • 8. Conclusions
  • References
  • Back Cover.