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Target Identification and Validation in Drug Discovery

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Cover of 'Target Identification and Validation in Drug Discovery'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 The Path to Oncology Drug Target Validation: An Industry Perspective
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    Chapter 2 Identification of Aptamers as Specific Binders and Modulators of Cell-Surface Receptor Activity
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    Chapter 3 The Design and Structure–Functional Properties of DNA-Based Immunomodulatory Sequences
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    Chapter 4 siRNA Design Principles and Off-Target Effects
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    Chapter 5 Western Blot Evaluation of siRNA Delivery by pH-Responsive Peptides
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    Chapter 6 High-Throughput RNAi Screening for the Identification of Novel Targets
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    Chapter 7 Integration of RNAi and Small Molecule Screens to Identify Targets for Drug Development
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    Chapter 8 CellProfiler and KNIME: Open Source Tools for High Content Screening.
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    Chapter 9 PARP inhibition as a prototype for synthetic lethal screens.
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    Chapter 10 Structure-Based Target Druggability Assessment
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    Chapter 11 Validating Pharmacological Disruption of Protein–Protein Interactions by Acceptor Photobleaching FRET Imaging
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    Chapter 12 Systematic Analysis of Complex Signal Transduction Pathways Using Protein Fragment Complementation Assays
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    Chapter 13 Reverse Phase Protein Microarrays and Their Utility in Drug Development
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    Chapter 14 A Cell Culture System That Mimics Chronic Lymphocytic Leukemia Cells Microenvironment for Drug Screening and Characterization
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    Chapter 15 Two-Dimensional vs. Three-Dimensional In Vitro Tumor Migration and Invasion Assays
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    Chapter 16 Tumor Spheroid-Based Migration Assays for Evaluation of Therapeutic Agents
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    Chapter 17 The Neurosphere Assay Applied to Neural Stem Cells and Cancer Stem Cells
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    Chapter 18 Genetically engineered animal models for in vivo target identification and validation in oncology.
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    Chapter 19 Target Validation in Mice by Constitutive and Conditional RNAi
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    Chapter 20 In Vivo Target Validation by Inducible RNAi in Human Xenograft Mouse Models
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    Chapter 21 Bright-Field In Situ Hybridization Methods to Discover Gene Amplifications and Rearrangements in Clinical Samples
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    Chapter 22 Combined MicroRNA In Situ Hybridization and Immunohistochemical Detection of Protein Markers
Attention for Chapter 18: Genetically engineered animal models for in vivo target identification and validation in oncology.
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Chapter title
Genetically engineered animal models for in vivo target identification and validation in oncology.
Chapter number 18
Book title
Target Identification and Validation in Drug Discovery
Published in
Methods in molecular biology, February 2013
DOI 10.1007/978-1-62703-311-4_18
Pubmed ID
Book ISBNs
978-1-62703-310-7, 978-1-62703-311-4
Authors

Texidó G, Gemma Texidó

Abstract

In vitro approaches using human cancer cell lines aimed to identify and validate oncology targets, have pinpointed a number of key targets and signalling pathways which control cell growth and cell death. However, tumors are more than insular masses of proliferating cancer cells. Instead they are complex tissues composed of multiple distinct cell types that participate in homotypic and heterotypic interactions and depend upon each other for their growth. Therefore, many targets in oncology need to be validated in the context of the whole animal. This review provides an overview on how animal models can be generated and used for target identification and validation in vivo.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 1 5%
Unknown 18 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 21%
Researcher 3 16%
Student > Master 3 16%
Other 1 5%
Unknown 8 42%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 26%
Agricultural and Biological Sciences 3 16%
Engineering 2 11%
Physics and Astronomy 1 5%
Unknown 8 42%