Optimizing therapies in silico

The development of cancer treatments and therapeutic interventions is subject to a great deal of uncertainty and involves a fair amount of trial and error. This reduces the potential clinical benefits and also causes costs to shoot up. The outcome from currently available treatments can be improved, for instance by optimizing the schedules, dosage, and combinations with other therapies. Clinical trials on their different phases can also be predesigned virtually to produce better results with lower investment. We describe treatment effects with mathematical models that we then use to conduct in silico clinical trials and explore modifications. This allows us to identify the best results in terms of resistance, survival, and toxicity.


In silico clinical trials for temozolomide administration and radiotherapy application in low- and high-grade gliomas.

CAR T cells treatment of B-cell acute lymphoblastic leukemias.

Improved treatment of neuroblastoma with mesenchymal stem cells loaded with oncolytic virus.

Optimized protocol of denosumab administration in fibrous dysplasia.


There are no publications on this topic yet, but they are coming



In vitro and in vivo experiments are performed by Pilar Sánchez-Gómez (NeuroOncology Unit, Instituto de Salud Carlos III) and Milica Pesic (Institute for Biological Research Siniša Stanković, University of Belgrade), who lead the collaborator groups that provide us biological data and experimental support. Regarding the clinical aspects, our collaborators include Juan M. Sepúlveda (MD Anderson Cancer Center and Hospital 12 de Octubre) and Philippe Schucht (Inselspital – University Hospital of Bern). Manuel Ramírez Orellana (Hospital Infantil Universitario Niño Jesús, Madrid, Spain); Francisco Monroy Muñoz (Hospital Universitario 12 de Octubre, Madrid, Spain); Carlos Torroja Fungairiño (CNIC-Centro Nacional de Investigaciones Cardiovasculares Carlos III, Madrid, Spain); Diego Herráez Aguilar (Universidad Francisco de Vitoria, Madrid, Spain).