Identification of drug targets Flashcards
What are the key properties of an ideal drug target?
An ideal drug target is disease-modifying, part of a critical pathway, expressed specifically in disease-related tissue, minimizes off-target effects, and is structurally characterized. It should also be “druggable” (able to bind with small molecules) and assayable for high-throughput screening.
What is druggability, and how is it assessed?
Druggability is the likelihood of a protein being modulated by a small molecule. It’s assessed by determining if a target belongs to a “druggable” gene family (e.g., GPCRs, kinases) and if its structure allows interaction with small molecules.
Describe the difference between phenotypic and target-based drug discovery.
In phenotypic discovery, compounds are screened based on their effect on cells or organisms without prior knowledge of targets. Target-based discovery starts with a known target and uses assays to find compounds that specifically interact with it. Phenotypic screening may reveal unexpected targets, while target-based is more directed.
Explain genotype vs. phenotype in the context of target identification.
Genotype refers to the genetic makeup (alleles), while phenotype is the observable traits influenced by genotype and environmental factors. In drug discovery, understanding both can reveal how genetic variations affect disease and identify targets associated with disease phenotypes.
What is CRISPR screening, and how is it used in target ID?
CRISPR screening is a gene-editing tool that systematically inactivates genes to identify those involved in disease phenotypes. It is highly specific and helps in discovering essential genes for diseases, validating them as potential drug targets.
What role do genome-wide association studies (GWAS) play in drug target identification?
GWAS statistically links genetic variants with disease risk by analyzing large populations. This helps identify risk loci and prioritize genes for functional studies, establishing genetic links to disease pathways that can serve as drug targets.
How is RNA sequencing (RNA-Seq) used in target identification?
RNA-Seq measures global gene expression, comparing disease vs. normal tissues to identify differentially expressed genes, splice variants, and noncoding RNAs. It is crucial for prioritizing genes as potential targets based on disease relevance.
Describe the function and use of DNA microarrays in drug target discovery.
DNA microarrays analyze gene expression profiles by hybridizing fluorescently labeled cDNA to probes on an array. They identify differentially expressed genes across conditions, aiding in identifying potential drug targets.
What is the Therapeutic Target Database (TTD), and what information does it provide?
TTD contains curated data on over 2,500 targets linked to approved, trial, and exploratory drugs. It includes target validation info, bioactivity, disease associations, pathways, binding sites, and links to approved drugs for drug repurposing.
What is Open Targets, and how does it support drug discovery?
Open Targets integrates genetic, proteomic, pharmacologic data to associate targets with diseases. It provides tractability scores, repurposing opportunities, and interactive visualizations, aiding in prioritizing targets for drug discovery.
What are chemical probe-based methods in target deconvolution?
These methods use modified bioactive ligands with tags (e.g., biotin, azide) to bind and isolate target proteins via affinity purification and mass spectrometry, confirming ligand-target engagement and identifying novel targets.
Describe affinity-based chromatography for target identification.
In affinity chromatography, bioactive probes are immobilized on a support, then exposed to cell lysates to capture target proteins. Bound proteins are isolated and identified by MS, providing insights into target interactions. Tools like Kinobeads enhance this technique.
What is the significance of pathway analysis in drug discovery?
Pathway analysis overlays omics data (e.g., genomics, proteomics) onto biological pathways to identify dysregulated pathways in disease. It prioritizes key pathway components as drug targets, providing context beyond single-gene approaches.
How do bioinformatics and machine learning aid in drug target identification?
Bioinformatics processes large datasets (e.g., genomic, proteomic), while machine learning finds patterns, relationships, and multivariate insights. Together, they extract new drug target hypotheses by integrating diverse biomedical data.
What are the challenges in drug target identification?
Challenges include low success rates (1% from idea to approved drug), complex biology, undruggable targets, and a lack of relevant disease models. Rigorous validation improves the chance of selecting targets that lead to clinical candidates.
