The biggest challenge in drug development is that the process is not an even balance of hit or miss - it is overwhelmingly miss, with around 90% of drugs never making it beyond clinical trials. As a consequence, the cost of developing and bringing a single drug to market is estimated at $2.3 billion. This high attrition rate is a major challenge across the pharmaceutical industry, with strategies to address this inefficiency a key focus for many companies.
Drug development is a multi-step process where drugs can fail for a number of reasons at each step. The first step, target identification, involves identifying genes whose products are good candidates for drug discovery and development. Of the roughly 90% of drugs that fail, a substantial proportion fails because the targets are not the best ones for the purpose of drug development. This is not to say that the drugs fail simply because they have been developed to gene products that are not relevant to the disease. Often, the importance of a particular gene in a pathway can be misinterpreted, because of incomplete information. The consequence of this misstep is that the resulting drug may only work on a much smaller subset of the patient population than expected, reducing the chances of success in clinical trials.
Improving the identification and validation of disease-specific drug targets in a cell-type and patient-specific manner early on will not only reduce the failure rate and cost that is so inherent in current drug development processes but also allow the development of more effective precision medicines, improving patient outcomes.
The complexities of genetic variation in disease
Genome-wide association studies, or GWAS, have identified thousands of genetic variants associated with specific diseases or traits. Around 95% of these variants are found in non-coding regions of the human genome, many of which possess markers of enhancers. However, many of these variants have not been correctly linked to actual gene function or disease. Understanding which genes these enhancers regulate can therefore provide deeper insights into disease mechanisms.
To bridge this gap, there is a growing drive toward integration of more varied datasets obtained using other omics technologies, including analyses of gene expression and chromatin accessibility, which can be used to interpret GWAS variants. But these different approaches do not necessarily produce consistent results. The challenge is not generating data - vast amounts can be produced from different cell types and patients. The real difficulty is making sense of all of the information and piecing it together into a coherent picture.
Deciphering mechanisms of disease through 3D multi-omics
Genomes are often imagined to be linear structures, and a common assumption is that each disease-associated variant simply interacts with the nearest gene(s), influencing their expression. This then becomes the shortlist -- the genes we focus on for further analysis.
While this approach can be effective, it fails to take into account that, although the DNA sequence remains identical across all cells, it is folded into a complex three-dimensional structure. This 3D structure differs from one cell type to another, bringing distant regions of the genome into close physical proximity. Functional interpretations can be made by considering these distal interactions. For example, a variant may influence a gene located a million bases away -- something that cannot be detected by analysing the genome linearly.
One of the most promising emerging techniques to better understand how disease variants change cellular function is 3D genomics. Analysis of 3D genomic data provides deep insight into the changes within non-coding regions of our DNA that regulate cellular function, and therefore have implications for disease. By studying the 3D genome, researchers can map long-range interactions, revealing the genes most likely influenced by a variant. With 3D multi-omics, these long-range folding patterns are used as a foundation to enable integration of other multi-omic data, allowing correct interpretation of the functional effects of disease variants.
3D multi-omics reveals cell-type specific mechanisms of disease
By cataloguing healthy genome folding patterns across different cell types, researchers can determine how disease-associated variants influence gene regulation in a precise biological context. Polygenic risk scores, which calculate the effects of multiple variants on an individual's liability to a trait or disease, often fail to capture cell-specific risk. A more refined approach involves integrating cell-type-specific data, enhancing both signal clarity and clinical relevance, creating the concept of 'polyenhancer scores'. This allows for a better understanding of which variants drive disease in specific tissues, improving target discovery and therapeutic development.
While GWAS has identified numerous disease-associated variants, these do not necessarily act within the same cell type or affect all patients uniformly. Different individuals carry different combinations of variants, and GWAS provides only an aggregate risk score without considering how these variants function together in specific cellular contexts.
By integrating cell-specific information with GWAS metadata, researchers can determine whether individuals with a particular polyenhancer profile will develop a more severe disease form or respond differently to treatment. Once the genetic basis for different response or severity groups is established, predictions can be made for new patients, guiding targeted treatment or drug-development strategies.
By mapping genetic risk at a cell-type-specific level, 3D multi-omics makes it possible to link genetic variation to functional consequences in relevant tissues. This approach improves biomarker identification, enhances drug response predictions, and ultimately supports the development of more effective and personalised treatments.
What 3D multi-omics means for drug development and patient outcomes
By prioritising more specific targets for drug development and identifying biomarkers and genotypes that can be used to stratify patients into sub-groups, pharmaceutical companies can avoid pursuing routes that are likely to fail. The earlier in the pipeline potential issues can be identified, the more time and money will be saved in the long run, which ultimately also improves the efficiency of the drug development process.
For patients, a key benefit will be avoiding suboptimal treatment plans. Typically, patients are prescribed drugs and if they do not work, they move to the next option, and so on. This wastes valuable time, during which disease progression can occur and patients continue to experience symptoms. By improving the ability to match patients with the right drugs from the outset, these delays can be prevented. In some diseases, such as multiple sclerosis (MS), early treatment is critical. If a patient misses the window where the disease is still reversible, it becomes much harder to make a recovery.
3D multi-omics is enhancing researchers' ability to decipher the link between genetic variants and their impact on disease mechanisms in a cell type specific manner. By identifying more biologically relevant targets, 3D multi-omics will accelerate the development of precision medicines, streamlining clinical trials and ultimately delivering more effective treatments for patients.
Photo: Blue Planet Studio, Getty Images
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