Epigenetic drugs have the potential to revolutionize cancer therapy as they can manipulate and modulate epigenetic genes, in turn affecting the characteristics of cancer.1
This class of drugs, which includes DNA methylation agents and chromatin remodelers, has many different anticancer mechanisms and clinical phases.
Emerging computational tools offer powerful benefits for the field of epigenetic drug design and the treatment of diseases such as cancer, making the drug discovery and design process eminently more efficient and precise.
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Computational Tools: Foundations and Impact
The rapid development of high-performance computational resources and advanced computational methodologies in recent years has positively impacted the field of life sciences.
This has led to the emergence of rational and highly efficient drug discovery and design, which has the potential to revolutionize the treatment of several diseases, such as cancer.
Various molecular modeling techniques exist currently, with structure-based and ligand-based drug discovery being two of the main categories. Techniques such as molecular dynamic simulation and molecular docking are used in structure-based drug discovery.
In contrast, ligand-based methods incorporate techniques such as AI, quantitative structure-activity relationship (QSARs) and pharmacophore modeling.2
These techniques improve drug discovery in multiple ways. QSAR techniques such as 3D-QASR and other ligand-based methods such as scaffold hopping, for example, use information on known inhibitors for activity prediction and hit enrichment. In Silico, druggability assessments allow researchers to prioritize screening endeavors and identify targets rapidly and efficiently.3
Structural-based virtual screens (SBVS) can efficiently identify bioactive targets in the early stage of drug discovery. They are based on advancements in homology modeling and crystallography.
Pharmacokinetic-based computational methods can help to identify factors such as toxicity, metabolism, and absorption in drug candidates.
Overall, computational techniques offer cost-effective strategies to improve the efficiency of drug discovery and design, providing new horizons in cancer treatment.
Applications of Computational Tools in Epigenetic Drug Design
In recent years, computational tools have proven successful in the discovery of epigenetic markers and targets and the rational design of epigenetic drugs.
For instance, computational tools have been applied to the identification of epigenetic writers. These enzymes are involved in the transfer of acetyl or methyl groups to DNA, histone and non-histone substrates, and acetyl coenzyme-A. Epigenetic writers include DNA methyltransferases and histone acetyltransferases.
Homology modeling-driven studies using computational tools have proven to be successful in developing drugs based on epigenetic writes in recent years. Siedlecki et al., for instance, have used performance-based virtual screening of 1,900 compounds, which could be promising drug targets. This led to the discovery of RG108, which proved promising in biochemical assays.3
Another study in 2017 used high-throughput virtual screening to develop a novel non-nucleotide DNTM1 inhibitor. This inhibitor displayed good selectivity against other methyltransferases.
Other studies have reported positive results using computational tools such as quantum mechanical calculation and molecular dynamics simulation.
Advanced computational tools have also been used to understand epigenetic protein-protein interactions.
Techniques such as scaffold hopping, structure-based virtual screening, and pharmacophore profiling have helped researchers in the discovery and design of small molecule inhibitors, providing routes toward novel cancer therapies based upon them.3
Epigenetics: the next revolution in cancer treatments
Techniques and Technologies
Computational drug design employs a number of technologies, such as cheminformatics, molecular dynamics simulations, and docking studies.
Cheminformatics is a technological field which combines physical chemistry and computer/information science techniques to solve prescriptive and descriptive problems in chemistry. This field is applied to areas such as biology, forming a key element of techniques such as computer-driven drug discovery and design.
Docking studies use computational approaches to aid the discovery of ligands which geometrically and energetically fit a protein’s binding site.
Molecular dynamics simulations analyze the physical movements of molecules and atoms, providing key information on a system's dynamic evolution.
Additionally, artificial intelligence and machine learning are increasingly employed in the field of epigenetic drug discovery, offering powerful benefits for studies. These include vastly improved efficiency and predictive power, cost savings, reduced human error, and the ability to process huge amounts of data in a fraction of the time conventional methods, even computational ones, can achieve.
Challenges in Computational Epigenetic Drug Design
Despite significant progress in the field of computational epigenetic drug design, there are still some formidable challenges.
Firstly, extensive computational resources are required. Current computational methodologies applied to areas such as protein flexibility are incredibly time-consuming and can be cost-prohibitive, especially for research organizations with stretched budgets. Significant computational power may be required in studies, which can use up resources such as energy and money.
Conventional docking algorithms also face challenges. For instance, they face difficulties when applied to studies with complicated factors such as the dynamic inclusion of water, protein flexibility, and entropy.
This makes it difficult to predict absolute binding energy for ligand-protein interactions. Current methodologies face severe limitations when it comes to complex problems such as this.
A third key issue is the lack of suitable chemical probes for epigenetic enzymes, which impacts their use as potential drug targets, especially for targets like HATs.
This requires further exploration using advanced computational tools, with some conventional techniques falling short at the moment. Finally, overinterpretation of results can limit epigenetic drug discovery.3
Learn more about drug discovery, manufacturing and development
Future Directions in Computational Epigenetic Drug Design
Clearly, the major challenges associated with epigenetic drug discovery and the current limitations of computational tools are key sticking points in the development of novel cancer therapeutics.
However, some promising future directions could help to overcome these issues and vastly improve the clinical outcome for cancer patients.
Combined structure- and ligand-based virtual screening tools would counterbalance the limitations of both computational methods. The synergistic application of computational and experimental approaches will aid studies on novel targets with fewer reported inhibitors.
Machine learning is also a powerful future direction which could potentially provide researchers with the tools required to accelerate the field of computational epigenetic drug design rapidly. This will require an increasingly multidisciplinary approach. AI can provide powerful opportunities in this field.3
Finally, the rapid development of computational power in the coming years is expected to revolutionize the field, providing many more epigenetic probes and drugs than are currently on the market.3
Summing Up
In summary, the field of epigenetic drug discovery and design has benefited greatly from the application of computational tools over the past few years.
However, persistent challenges associated with these tools remain a key bottleneck in computational epigenetic drug design.
Some solutions are emerging, however, and the increasing incorporation of AI presents a huge opportunity for researchers.
References
- Ghasemi, S (2020) Cancer’s epigenetic drugs: where are they in the cancer medicines? The Pharmacogenomics Journal 20 pp. 367-369 [online] nature.com. Available at: https://www.nature.com/articles/s41397-019-0138-5 (Accessed on 14 August 2024)
- Gurung, A.B et al. (2021) An Updated Review of Computer-Aided Design and Its Application to COVID-19 Biomed Res Int 2021 [online] National Library of Medicine. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8241505/ (Accessed on 14 August 2024)
- Lu, W et al. (2018) Computer-Aided Drug Design in Epigenetics Front Chem. 6: 57. [online] PubMed Central. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5857607 (Accessed on 14 August 2024)
Further Reading
Artificial Intelligence Drives Breakthroughs in Drug Discovery, Reducing Costs and Timelines