Drug development in Biomedicine is a long and expensive process based on empirical approaches. Preclinical analyses in vitro are followed by studies in animal models and finally in humans, what involves trials in increasingly larger patient cohorts to test product safety and efficacy. This long process pushes drug development expenses and times to unsustainable levels, thus stifling innovation and leading to a constantly growing cost of healthcare provision.

In the project "Improving New Therapies in Oncology and Related Fields using mathematical models and biomedical data" (PID2022-142341OB-I009 funded by Spanish Ministerio de Ciencia e Innovación, MOLAB-Ciuda Real researchers together with mathematicians from University of Córdoba (Magdalena Caballero and Miguel A. Alejo). and some international collaborators such as Helen Byrne & Philip Maini (University of Oxford), Babak Saboury, Luis Fernández de Castro & Allison Boyce (NIH Clinical Center, USA) and Andrés Hidalgo (Yale University, USA) will try to use mathematical models to solve those problems.

The project will to develop and validate mathematical models and use them as platforms for finding optimal therapeutic for (i) cellular immunotherapies, (ii) radiopharmaceutical therapies in oncology, and (iii) combination treatments with antibodies in fibrous dysplasia. Models will integrate state-of-the-art biomedical knowledge and data to improve the understanding of the diseases.

Mathematics can help in reducing costs and times from the idea to the clinics and allow for the exploration of a broader range of therapeutic schedules/combinations.