Integration of Omics Based Technologies in Drug Target Discovery

Omics and drug discovery

• Advancing a single drug from concept to market is extremely expensive, exceeding 1 billion US dollars, while being costly, time consuming, and very risky

• Omics-based technologies can accelerate drug discovery and provide novel, clinically valid targets

Omics

• Adding the suffix "-omics" to a molecular term indicates a comprehensive or global assessment of that set of molecules.

• These fields provide the collective characterization and quantification of pools of biological molecules.

• These approaches enable a deep investigation into the structure, function, and dynamics of a cell, tissue, or organism

Genomics

• Genetic mapping and DNA sequencing of sets of genes or complete genomes of selected organisms

• Can provide evidence of causation

• Identification of disease-associated mutations: assess risk for diseases and discover effective drug targets

• Identify variants: SNPs, INDELs, SVs

Pharmacogenomics

• Patient stratification: study the impact of genetic variations on drug response and drug metabolism

• Patient’s response to treatment could be linked to SNP genomic profile

• Can assess the efficacy and drug-related toxicity

• Helps to determine which available drugs are suitable for a particular patient

• Safer and more powerful medicines

Transcriptomics

• Study of the complete set of RNA transcripts at a given time

• Gene expression profiling, used to evaluate potential adverse effects at an early stage in drug development

• Support alternative methods for chemical risk assessment

• Expanding with NGS technologies

Epigenomics

• Study of epigenetic modifications to DNA and histone proteins that regulate gene expression without altering the DNA sequence

• ChIP-Seq, Bisulfite sequencing, Methylation arrays, ATAC-Seq

• ATAC-Seq (Assay for Transposase-Accessible Chromatin with Sequencing): provide insights into chromatin structure and accessibility

Proteomics

• Analysis of the entire protein content of a cell, tissue, or organism under a specific condition

• Protein expression profiling, functional proteomics, and phospho-proteomics

• Identify and validate druggable proteins, study drug efficacy and toxicology, and disease stratification

• Mass spectrometry is the main technique for proteome analysis

Metabolomics

• Study of all the metabolites present in a cell, tissue or organism under different conditions

• Overview of the metabolic status and global biochemical events associated with a cellular or biological system

• Includes: lipidomics, glycomic, pharmacometabolomics

• Help to identify biomarkers

Multi-omics

• Multi-omics integrates datasets across different omics studies (genome, epigenome, transcriptome, proteome, metabolome, microbiome)

• Approaches are categorized by their initial focus: "genome first", "phenotype first", or "environment first"

• Data integration usually relies on study designs involving correlation or co-mapping

Bio-informatics

• Application of tools of computation to analyze biological data

• Because omics platforms produce large, complex, "data-rich" outputs, concurrent development of bioinformatics methodology is required for data integration and interpretation

• Limitations: data quality, standardization, reproducibility, interpretation, and validation

Omics in drug repurposing

• Multi-omics screening is highly useful for drug reuse (repurposing existing drugs for new treatments)

• Computer-based approaches integrate disease phenotypes and targets using drug-oriented, target-oriented, and disease-oriented classifications

Conclusion

• Omics-based technologies promise the discovery of new therapeutic targets by vastly improving the understanding of molecular disease mechanisms.

• New methodologies are causing a paradigm shift from traditional "hypothesis-driven" research to "discovery-based" research, and from "traditional" medicine to "personalized" medicine.

• There are still many obstacles facing the integration of omics, including experimental, technical, analytical, and financial hurdles.

• The future of drug discovery will rely heavily on integrating these omics technologies through robust bioinformatics platforms, advanced data analysis, and AI-driven insights.