The process of drug discovery is fraught with inefficiencies, with nearly 90% of drug candidates failing to make it beyond clinical trials. This high failure rate not only inflates costs, reaching an estimated €2.12 billion ($2.3 billion) per successful drug, but also delays the availability of effective treatments. A primary cause of this inefficiency lies in the early-stage identification of disease-specific drug targets. A more precise approach, tailored to individual cell types and patients, has the potential to revolutionise drug discovery, reducing attrition rates and improving patient outcomes. One of the most promising advancements in this regard is the integration of 3D multi-omics, which provides a deeper understanding of genetic variation and its role in disease.
Understanding Genetic Variation in Disease
Genome-wide association studies (GWAS) have identified thousands of genetic variants linked to various diseases. However, a significant limitation of these studies is that around 95% of these variants reside in non-coding regions of the genome, many of which function as enhancers that regulate gene expression. Identifying the genes these enhancers influence is crucial to understanding disease mechanisms.
While additional omics technologies, such as gene expression and chromatin accessibility analysis, can help interpret GWAS data, they often produce inconsistent results. The challenge lies not in data generation but in making sense of vast amounts of information and assembling it into a coherent framework for drug discovery. Misinterpretation of genetic data can lead to drugs that are less effective for broader patient populations, contributing to high failure rates in clinical trials.
Recommended Read: Transforming Pharma: The Cultural Shift for AI Integration
The Role of 3D Multi-Omics in Drug Discovery
Traditional genomic analysis often assumes a linear structure, where disease-associated variants influence the nearest genes. However, this overlooks the three-dimensional organisation of the genome, which varies between cell types and brings distant genomic regions into proximity. This 3D folding structure is fundamental in gene regulation, meaning that a variant may affect a gene located far from its linear position in the genome. By leveraging 3D genomics, researchers can map these long-range interactions, offering a more precise understanding of how genetic variants influence cellular function.
3D multi-omics further integrates additional layers of genomic data, enabling a comprehensive interpretation of disease variants and their biological impact. This approach enhances the identification of disease-relevant targets, making drug development more targeted and efficient. Understanding how these long-range interactions shape disease progression will significantly improve the drug discovery pipeline by reducing reliance on incomplete genetic models.
Enhancing Target Discovery and Treatment Strategies
A major advantage of 3D multi-omics is its ability to map genome folding patterns in different cell types, helping researchers understand how disease-associated variants affect gene regulation. This is crucial, as current tools like polygenic risk scores often overlook cell-specific influences. The development of ‘polyenhancer scores’ enhances disease prediction and drug target identification by integrating GWAS data with cell-type-specific insights, aiding in precision medicine.
This approach allows for better stratification of patients, identifying those more susceptible to severe diseases or varying treatment responses. Consequently, it supports the discovery of biologically relevant drug targets, lowering the chances of clinical trial failures. By focusing on specific targets and employing biomarkers for patient classification, pharmaceutical companies can streamline the drug development process, saving time and resources.
For patients, the primary benefit is more effective treatments from the start, minimising the trial-and-error cycles often associated with finding the right medication. In conditions like multiple sclerosis, where timely intervention is critical, 3D multi-omics enhances treatment outcomes and expedites access to effective therapies.
3D multi-omics is transforming the landscape of drug discovery by providing a deeper understanding of genetic variation and its functional consequences. By identifying disease-relevant targets with greater precision, this approach reduces failure rates in drug development, enhances the efficiency of clinical trials and facilitates the creation of more effective precision medicines. As pharmaceutical companies continue to integrate these technologies, the future of drug discovery will be increasingly data-driven, leading to faster, more targeted and ultimately more successful treatments for patients worldwide.
Source: MedCity News
Image Credit: iStock
