Identification of drug targets Flashcards
17th October 2024
What are the key properties of an ideal drug target?
- Involvement in a crucial biological disease pathway
- Expressed in a relevant tissue and not uniformly throughout the body (reduces toxicity)
- Druggable
- Functionally and structurally characterised (improves in silico screening, docking, drug design)
- Assayability for high throughput screening
- Homology with other species
- Distinction from previously known targets (market share)
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 prioritising genes as potential targets based on disease relevance.
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.
Target quality
Level of confidence that modulation of a target will translate to disease modifying or symptom alleviating effects in humans based on evidence from genetics, expression levels, KO studies, and pathway understanding amongst others.
Techniques used in target ID and validation
- CRISPR screening
- Organ-on-a-chip
- Animal gene editing
- 3D cell culture models
- High-throughput structural techniques
- Biologics
- Patient-derived models
- Clinical genetics
- Big data analytics
- Target engagement
Target identification strategies - Deconvolution
- Start with a drug that appears efficacious and the target is identified retrospectively
- Phenotypic approach
- Goes beyond individual proteins or nucleic acids and involves the study of entire signalling pathways
- Advantage: Efficacy of a drug in demonstrated in the context of a cellular environment
Target identification strategies - Discovery
- Once a target is identified compound libraries are screened to find a drug
- Target’s role in disease process is known and is used to create relevant system-based assays
- Knowledge of a drug candidate’s molecular mechanism is known from the start
What is the basis of DNA microarray technology?
DNA oligonucleotide probes complementary to sequences of interest are synthesized in situ on a glass slide or silicon chip, enabling high-density hybridization.
What are the steps in a DNA microarray experiment?
- Fluorescent labeling of DNA/RNA samples.
- Hybridization of labeled samples to the probe array.
- Laser scanning to detect fluorescence intensity.
- Data analysis to compare gene expression
How is gene expression profiling performed using DNA microarrays?
mRNAs from two populations are reverse transcribed into cDNA, labeled (e.g., Cy5 for cell A, Cy3 for cell B), and competitively hybridized to a DNA microarray. Fluorescence images are analyzed to identify over- and under-expressed genes.
What are the key applications of DNA microarrays in drug discovery?
- Target Deconvolution: Transcriptional profiling of cell responses to hit compounds, linking profiles with known targets.
- Target Discovery: Identifying differentially expressed genes, splice variants, and pathways in disease vs. normal tissues.
What are the advantages of DNA microarrays?
Genome-wide, high-throughput profiling of thousands of sequences in a parallel and quantitative fashion.
How do DNA microarrays synergize with other approaches?
Integrate with omics, CRISPR screens, and computational models in an integrated target identification pipeline.
