Biotechnology firms are increasingly integrating artificial intelligence (AI) into drug development processes, showcasing its potential to revolutionize research methodologies and outcomes. At the Kadans Oncology Summit in Paris, discussions highlighted various applications of AI, particularly in drug target discovery and the development of preclinical cancer models.
One notable innovation is the use of in silico models, which aim to replace traditional cellular and animal testing methods. Paul Dickinson from Seda Pharmaceuticals emphasized that these digital models can be just as effective as their biological counterparts in addressing cancer research questions. However, the efficacy of these models raises concerns, as a 2025 review indicated that a significant number of studies employing in silico models lacked reproducibility and publicly available data.
Despite the enthusiasm for AI, not all companies are moving away from animal testing. Laura Zoia from Charles River Laboratories pointed out that while there is a gradual shift towards virtual models, the demand for animal testing remains steady, particularly in regions like Asia, where biotech growth is surging. This contrasts with the funding challenges faced by the US biomedical sector, which have hindered research advancements.
Beyond animal model replacements, AI applications in biotech are expanding. For instance, Stanford Medicine is developing a CRISPR-GPT engine to streamline drug development, aiming to significantly reduce the time required for research. Additionally, companies like Orakl Oncology and Prima Mente are leveraging AI to enhance predictions of treatment responses and early disease detection, respectively. These advancements highlight AI's transformative potential in the life sciences, paving the way for more efficient and effective therapeutic developments.