International Agency for Research on Cancer, a unit of the World Health Organisation, through its Global Cancer Observatory, reported nearly 20 million new cases of cancer and 9.6 million related deaths in the year 2022 alone. Its 2024 newsletter projects 35 million new cases for 2050, a staggering 77% increase. With the growing number of cancer incidences and mortality, researchers are in the constant pursuit of newer and more effective anti-cancer drugs and therapies to target and combat this dreaded disease. An approach gaining traction in recent years is the use of computational tools to complement traditional drug discovery.
In one such effort, a study led by Dr Athavan Anand, Senior Researcher, Prayoga Institute of Education Research, in collaboration with researchers from MS Ramaiah University of Applied Sciences and Annamalai University, published in ‘Innovative Medicines & Omics’, has employed computational tools and simulations to identify potential inhibitors of a key cancer-linked enzyme.
Cancer, essentially the result of uncontrolled cell division, is driven by many enzymes, including Aurora B kinase. Under normal health conditions, this enzyme is vital for regulating cell division with proper segregation of cellular contents. However, in cancerous conditions, Aurora B kinase becomes dysregulated and is elevated. These abnormally high levels lead to unabated growth and spread of cancer, making Aurora B-Kinase a potential target for anti-cancer drugs. The team identified five potential inhibitors of this enzyme that could serve as early-stage drug candidates.
While traditional methods of biomedical research, including drug design, synthesis and screening through in vitro and in vivo studies conducted in laboratories, continue to form the crux of drug discovery, computational approaches have emerged as powerful tools to complement and accelerate the process. The computer-based methods, also referred to as in silico studies, serve as insightful launchpads for empirical testing, where computational predictions are validated and corroborated through laboratory experiments.
By simulating molecular interactions in silico, researchers can quickly screen and identify potential drug candidates with reasonable precision. In this study, the researchers used three such approaches - pharmacophore modelling, protein-ligand docking and molecular dynamics simulations – to screen large chemical libraries containing anti-cancer-associated compounds for potential Aurora B kinase inhibition.
To this end, the researchers screened over 300,000 anti-cancer molecules from established chemical databases. These were subjected to various in silico tests and filtered using multiple criteria to identify promising inhibitor candidates. One of the tests was pharmacophore modelling, a method used to predict the features a drug should have to bind to the target molecule, Aurora B-Kinase, in this case. Like testing different keys for a lock, the team tried fitting the filtered molecules into a crucial region in the enzyme, called the active site. Known as molecular docking, it simulates how well a drug molecule fits into an enzyme’s active site, enabling the team to shortlist five promising molecules. These five best fits were tested again, this time for the stability of their interactions with the enzyme, using a molecular dynamics simulation, validating their potential as inhibitors of Aurora B kinase.
Dr. Athavan explains, "The molecules were selected based on multiple criteria, including in silico pharmacokinetic properties such as absorption, distribution, metabolism and excretion (ADME), along with compliance to Lipinski’s Rule of Five for drug-like properties. The five molecules are versatile heterocyclic compounds containing various bioactive scaffolds that match the pharmacophore."
The studies enabled them to develop a computationally validated model for identifying potential inhibitors of Aurora B kinase. The results of molecular docking, combined with those from simulation studies, demonstrated that the five molecules interacted stably with critical regions of the enzyme, suggesting that further experimental research is needed to establish their in vitro and in vivo roles. These findings mark a promising step toward the development of new anti-cancer drugs, informed by computational insights.
Dr Athavan shares, “This research highlights how computer-based tools can speed up the search for new cancer drugs. By narrowing down potential candidates, we can direct experimental efforts toward the most promising molecules.”
Recent advances in integrating computational approaches with experimental biology have sparked the mantle of the next generation of science. Computational tools not only accelerate drug discovery but also reduce the costs, effort, and uncertainties associated with laboratory research. Despite the findings being preliminary, in the larger picture that requires extended experimental validation, they offer critical and valuable insights. Research in this frontier is helping shape the future of precision medicine.
This article was written by Deepika S, science communication specialist, Prayoga Institute of Education Research
