Modernization has changed human lifestyles and increased various health issues, including the rising incidence of cancer.
By understanding the proteins involved in cancer development, researchers can design drugs that more accurately target cancer cells without harming the surrounding healthy cells.
Technological advancements and the Fourth Industrial Revolution have paved the way for breakthroughs in developing more effective and targeted anticancer agents.
Given the many proteins involved in cancer cell regulation and progression, identifying which proteins to use must leverage big data and bioinformatics.
According to Professor Adam Hermawan, the latest technological advances and the growing availability of big data provide opportunities to address challenges in drug discovery.
Big data in drug discovery refers to a collection of biological, chemical, pharmacological, and clinical resources. In the drug discovery process, big data can be applied from target validation to the final stages of clinical trials.
“On this occasion, I will focus solely on the application of big data in identifying anticancer protein targets,” he said at the UGM Senate Hall on Thursday (Nov. 21), delivering his inaugural address as professor of macromolecular engineering at the UGM Faculty of Pharmacy.
In his speech, “Utilizing Integrated Big Data and Bioinformatics in Target Protein Identification for Anticancer Agent Development,” Professor Hermawan discussed how big data analysis using Artificial Intelligence (AI), Machine Learning (ML), and integrated bioinformatics has been employed over the last five years for biomarker selection, target protein identification, drug candidate selection, and drug sensitivity prediction.
AI has even been successfully used to discover drugs and accelerate phase I and phase II clinical trials.
“AI has been utilized for several purposes, including discovering drug target proteins, designing new small molecules, and developing vaccines. More than 50 percent of AI-discovered molecules that have undergone phase I clinical trials are anticancer agents,” he explained.
Professor Hermawan also noted that the success rate of phase I clinical trials for AI-derived molecules reaches 80-90 percent. This is due to well-validated biological targets and pathways in AI-derived molecules, which help reduce the toxicity of the designed drugs.
However, in phase II clinical trials, the success rate for AI-discovered molecules drops to only 40 percent, as phase II is the stage for proving the concepts and biological mechanisms previously suggested by AI regarding disease-relevant targets and signaling pathways.
“With the continuous development of AI, the success rate of clinical trials for AI-discovered molecules is expected to increase, allowing for the development of more effective drugs,” he concluded.
Author: Agung Nugroho
Post-editor: Afifudin Baliya
Photographer: Firsto