Prostate cancer is the most frequently diagnosed cancer in men in Croatia. According to the Cancer Registry, 2,299 new cases were reported in 2020. In terms of mortality, prostate cancer ranked as the ninth leading cause of death among men in 2021 and is the third most common cause of cancer-related deaths in men, following lung and colorectal cancers.

In its early stages, prostate cancer is often effectively managed with hormone therapy. However, over time, cancer cells can develop resistance to standard treatments, leading to the progression of the disease into castration-resistant prostate cancer (CRPC). CRPC represents a more dangerous and difficult-to-treat stage of the disease. As a result, researchers and clinicians are constantly striving to develop new therapeutic strategies to prevent this progression.

One of the primary challenges in prostate cancer treatment lies in identifying optimal drug combinations that can target multiple pathways in cancer cells simultaneously, thereby increasing the likelihood of halting disease progression. This challenge is precisely where Vini, an innovative computational tool, plays a pivotal role.

What is Vini, and How Does It Work?

Vini is a cutting-edge computer tool developed to predict the most effective drug combinations for combating prostate cancer. This breakthrough is the result of close collaboration between scientists at the Ruđer Bošković Institute (IRB) and clinicians from KBC Sestre Milosrdnica.

Using vast datasets from renowned scientific repositories like KEGG, DrugBank, PubChem, and the Protein Data Bank, Vini performs in-depth analyses of biological processes within cancer cells. It then identifies and recommends drug combinations with the potential to be most effective.

“We aimed to create a tool capable of comprehensively analyzing drug interactions at the cellular level and predicting their effectiveness in combination therapies,” explains Dr. Draško Tomić, head of research at the Center for Informatics and Computing at IRB. “By integrating knowledge of target genes, proteins, and molecular interactions, we’ve been able to propose new drug combinations that could help treat prostate cancer before it progresses to an incurable stage.”

Vini’s analysis has pinpointed several critical targets within cancer cells, including ALK, BCL-2, mTOR, DNA, and the androgen axis. By simultaneously addressing these pathways, the tool has proposed drug combinations that may prevent the disease from advancing to the castration-resistant phase. While these findings are currently based on theoretical models, the research team believes they hold great promise for informing more effective treatment strategies.

The Role of Supercomputing in Vini’s Success

Conducting such complex analyses required significant computational power, provided by the EuroHPC Joint Initiative and the Vega supercomputer at the Slovenian Institute of Information Sciences in Maribor (IZUM). This collaboration, supported by the Slovenian national supercomputer network (SLING), ensured the researchers had access to the resources necessary for executing sophisticated simulations.

“Without the computational capabilities of Vega, we wouldn’t have been able to perform these analyses,” Dr. Tomić notes, highlighting the indispensable role of advanced computing in modern cancer research.

Collaboration and the Road Ahead

This innovative research reflects the synergy between scientific and clinical expertise. The team behind Vini includes Dr. Draško Tomić, Dr. Karolja Skala, Dr. Branka Medved Rogin, Branimir Kolarek, and Dr. Viktor Bojović from the Centre for informatics and computing, Ruđer Bošković Institute, alongside clinical collaborators Dr. Jure Murgić, Dr. Ana Fröbe, and Dr. Antonela Vrljičak from the Department of Oncology and Nuclear Medicine at KBC Sestre Milosrdnica.

The next step for Vini involves testing its predictions in clinical trials. If the proposed drug combinations prove effective, this tool could revolutionize prostate cancer treatment, marking a new era in precision oncology. This research underscores the transformative potential of computational models in addressing complex medical challenges and opening new frontiers in cancer therapy.