In a groundbreaking scientific achievement, Prof Sergio Crovella, research professor at the Laboratory Animal Research Centre (LARC) at Qatar University (QU), is leading a specialised research team to revolutionise colorectal cancer (CRC) research.
The project focused on integrating artificial intelligence (AI) to design and discover novel therapeutic molecules, develop accurate rodent models and create cutting-edge diagnostic tools, all with the ultimate goal of improving both animal well-being and human health, according to an official QU statement.
The research team employed AI techniques to analyse extensive libraries of natural products and synthetic compounds to design novel therapeutic molecules, with a focus on targeting key biological pathways involved in CRC progression. These pathways include Wnt/β-catenin signalling, EGFR signalling and angiogenesis, among others. By using advanced technologies such as molecular docking and dynamic simulations, the team optimises the design of drug molecules, fine-tuning their properties to meet therapeutic needs while minimising potential side effects. This innovative approach allows researchers to predict the efficacy of drug candidates and identify the best chemical and molecular characteristics to ensure desired outcomes.
One of the project’s major achievements is the development of accurate rodent models. Using AI, the team selects appropriate animal strains that closely mimic human CRC, enhancing the precision of preclinical studies. These models are meticulously designed to study the effects of new drugs, including their interactions with tumours and surrounding tissues. Such models are essential for evaluating drug efficacy and safety before progressing to clinical trials, thereby reducing the time and resources needed to achieve advancements in the field.
In addition to drug design, the research team dedicates its efforts to developing AI-based diagnostic tools for analysing patient blood samples and identifying biomarkers associated with CRC. These biomarkers, which include circulating tumour DNA , proteins, and metabolites enable the early detection of cancer. Through machine learning, the team identifies subtle patterns distinguishing healthy individuals from CRC patients.
The advanced infrastructure at QU’s LARC provides an ideal environment for conducting AI-driven experiments and evaluations. Researchers leverage these capabilities to optimize drug design in terms of toxicity, efficacy, and immune safety, ensuring the resulting drugs are safer, more effective, and sustainable. AI models predict drug half-life in the bloodstream and potential immune responses, facilitating improved dosing strategies and ensuring optimal therapeutic results.
