In silico therapy optimization

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