Improving New Therapies in Oncology and Related Fields using mathematical models and biomedical data


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.

We intend to develop validated mathematical models and use them as platforms for finding optimal therapeutic approaches with the potential to improve novel treatments based on (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.

Why Mathematics?

The costs of clinical trials of new drugs are growing to unsustainable levels, and times from concept to implementation are just too long. Mathematical models 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.

The Team

Project is being developed by MOLAB-Ciudad Real researchers together with mathematicians from University of Córdoba (Magdalena Caballero and Miguel A. Alejo). Some international collaborators are Helen Byrne & Philip Maini (University of Oxford), Alison Boyce, Babak Saboury, Luis Fernández de Castro and Vardit Kram (NIH Clinical Center, USA) and Andrés Hidalgo (Yale University, USA).


This project may have a substantial social and economic impact on health. Immunotherapy treatments with CAR T cells cost hundreds of thousands of euros per patient. Radiopharmaceutical drug doses cost tens of thousands of euros per patient, with several doses being required per patient, although responses in patients often last only a few months. There is a clear need for finding the optimal use of those drugs, alone or in combination with other treatments, to optimize health system costs while, at the same time, improving response and survival times.

Mathematics can even help in trying to cure late-stage cancers using combination approaches based on these treatments. The same can be said about fibrous dysplasia, a non-lethal disease but still difficult to manage. Improving the outcome of current clinical trials with denosumab and/or finding the best combination schemes with other treatments such as bisphosphonates may have a direct impact on the health of the patients with this pathology.

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