Scientist adopts computer models to tackle cancer resistance

A Lab scientist

Wale Akinselure

A  Nigerian scientist, Prosper Chukwuemeka is combining data-driven insights with biological research to evolve a smarter and effective cancer treatment.

Chukwuemeka’s research targets uncovering the mechanisms behind tumour resistance and identifying novel therapeutic strategies.

In a statement, Chukwuemeka, a researcher in Systems Biology and Systems Immunology, explained that one key focus of his work is using computer models to predict how cancer behaves and how drugs might stop it.

He added that the research used cutting-edge computational and experimental methods to explore the complex interactions within the tumour microenvironment.

“My passion lies in understanding the complexity of diseases like cancer and finding smarter ways to treat them. I use systems biology, which combines data analysis, computer modelling, and biology to figure out why treatments sometimes stop working and how we can do better. It’s like solving a giant, life-saving puzzle.”

“One big focus of my work is using computer models to predict how cancer behaves and how drugs might stop it. Recently, we identified a naphthyridine-containing small molecule called CPO, which has the potential to disrupt key interactions that cancer cells use to survive. What’s exciting is that we discovered this entirely through computational tools, making it faster and more precise than traditional methods.

“We’ve used computer models to predict how CPO works, and the results are promising. The next step is for experimental scientists to test it in the lab, which includes studies in cell cultures and animal models. We’re hopeful that our interdisciplinary approach combining computational biology with experimental research will expedite the process,” he said.

He explained that one big focus of his work is using computer models to predict how cancer behaves and how drugs might stop it.

“Recently, we identified a naphthyridine-containing small molecule, entitled CPO, that has the potential to disrupt key interactions that cancer cells use to survive. What’s exciting is that we discovered this entirely through computational tools, which means it’s faster and more precise than traditional methods.

“CPO, a promising cancer drug candidate, works by targeting two proteins, MDM2 and MDMX, which block the action of p53—a crucial protein that keeps cells from growing uncontrollably. Cancer cells often disable p53 to promote unchecked growth. In 2021, Prosper’s team screened over one million compounds and identified CPO as a promising inhibitor of MDM2. However, their latest research, published in Molecular Diversity in 2024, has revealed that CPO could potentially target both MDM2 and MDMX at the same time, a dual approach that could make it more effective than current treatments.”

Chukwuemeka stressed that the use of computational models will speed up the drug discovery process and discover why cancer resists treatment.

“Traditional drug discovery can take years. By using computational models, we can screen thousands of compounds in a fraction of the time. It’s like having a map to guide us straight to the treasure.

“Cancer keeps finding ways to resist treatment. If CPO proves to be as effective as we believe, it could become a powerful new tool in the fight against drug resistance,” he said.

He maintained that his focus was on using systems biology to explore new cancer treatment strategies.

When asked about the future of his work, Chukwuemeka emphasised the importance of collaboration.

He added, “Science is about teamwork and innovation. With the tools we have today like computational modeling, I’m hopeful that we’re closer than ever to breakthroughs that will make a real difference in people’s lives.”

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