Computational oncology is a semi-new phrase that is gaining prominence in medicine, particularly oncology. As ongoing research on cancer is conducted to determine how cancer develops so that it can be eliminated from the body, more information is obtained with better chances of lasting solutions. While earlier oncology research was conducted using techniques from the biological sciences, computational oncology uses physical sciences to further advance the research.1 This is done by applying physics and mathematics to oncological problems to come up with new insights into the pathogenesis and treatment of malignancies. It also involves software development practices and cloud computing. Moreover, computer models are used for population screening, individual cancer cell modelling and developing tumor marker analytics useful in the area of precision medicine.

A case in point of how computational oncology will work is in the area of next-generation sequencing (NGS) where data about the human genome in healthy and diseased cells is gathered. The work of computational oncology is to take that data and categorise it into a database in such a way that researchers can utilise this data for their projects seamlessly.2

How precision medicine could reduce cancer burden?

Considering the growing number of drug combinations, one cannot be too sure of getting the optimal toxicity-efficacy balance. Hence anti-cancer drugs are prescribed and administered according to standard schedules.

This is where standard empirical approaches for optimising drug dosing and scheduling in patients fall short. And that’s where computational oncology comes to the rescue. For it shifts the therapeutic paradigm towards personalised care. As a result, preclinical and clinical examples focus on current achievements and limitations with regard to computational modelling of drug regimens as well as discuss the potential future implementation of this strategy to achieve precision medicine in oncology.3

New research trends in computational oncology

One of the latest researches in computational oncology is computationally designed antibody entering a clinical trial in patients for the first time ever.4 Designed by Biolojic Design, a unique technology company pioneering computational design of human antibodies. In the trial, AU-007, computationally designed highly differentiated monoclonal antibody is expected to harness the power of the body’s own interleukin-2(IL-2) to eliminate solid cancer tumors.

So while computational oncology’s aim is to acquire and analyse data using improved computing hardware and software, large databases of cellular pathways can be analyzed to understand the interrelationship between complex biological processes. And that’s not all, even routine imaging data such as mammography and chest imaging have improved accuracy and detection rate.

However, there is a major hiccup in the association between oncology community and physical scientists and that is communications. Once this hurdle is overcome, future progress can be achieved in computational oncology by close collaboration between clinicians and investigators.




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