Technologies

Advances in antibody engineering have revolutionised cancer therapy, and biological drugs now constitute a cornerstone of frontline treatment across most cancer subtypes. We drive continued innovation and clinical success by continuously enhancing local capabilities in Lund to develop tailored molecules for targeted cancer treatment- specific antigens, immune checkpoints, or as components of cell-based therapies. 

The Antibody unit serves as a unique resource for the L2CancerBridge project while advancing technology development. 

In 2025, CREATE Health and the Drug Discovery and Development (DDD) platform within SciLifeLab signed a strategic collaborative agreement to advance cancer immunotherapy. This partnership marks a significant milestone, enabling principal investigators to access cutting-edge antibody libraries as a critical resource in developing therapeutic antibodies and binders for CAR T-cell therapy. The agreement facilitates streamlined access to state-of-the-art antibody technologies, empowering researchers to accelerate the discovery and engineering of novel therapeutic candidates. These resources are essential for precision targeting cancer and enhancing CAR T-cell therapies' efficacy, which represent a transformative approach in personalised cancer treatment.

A local scientific steering committee, composed of leading experts with decades of field experience, supports this initiative. The committee includes Profs Peter Ellmark, Mats Ohlin, Malin Lindstedt, Mikael Mattsson, and the director, Prof. Sara Ek.

Bioinformatics and artificial intelligence (AI) have revolutionized cancer research, propelling the field toward unprecedented advancements in precision medicine. By harnessing the power of computational analysis and machine learning algorithms, scientists can sift through vast genomic and clinical data with remarkable speed and accuracy. Bioinformatics enables the organization and interpretation of complex biological information, identifying genetic variations and cancer-associated biomarkers.  

AI algorithms, particularly deep learning models, excel at recognizing subtle patterns within these datasets, unveiling hidden correlations and predicting tumor behavior. This synergy between bioinformatics and AI allows researchers to tailor treatments to individual patients based on their genetic makeup and the specific characteristics of their cancer cells. Developed through these technologies, personalized therapies enhance treatment efficacy while minimizing adverse effects.  Furthermore, this approach enables the discovery of novel drug targets and the development of innovative treatments. AI-driven simulations and predictive modelling also facilitate drug screening, significantly expediting the drug discovery. 

Ultimately, integrating bioinformatics and AI in cancer research promises more effective, targeted treatments, offering hope to patients by increasing survival rates and improving their quality of life. As these technologies advance, the cancer treatment landscape is evolving, moving steadily toward a future where each patient receives tailored, precise care.