Cancer is a challenging disease that involves multiple types of biological interactions in
different time and space scales. Often computational modelling has been facing problems that, in the
current technology level, is impracticable to represent in a single space-time continuum. To handle
this sort of problems, complex orchestrations of multiscale models is frequently done. PRIMAGE is
a large EU project that aims to support personalized childhood cancer diagnosis and prognosis. The
goal is to do so predicting the growth of the solid tumour using multiscale in-silico technologies. The
project proposes an open cloud-based platform to support decision making in the clinical management
of paediatric cancers. The orchestration of predictive models is in general complex and would require
a software framework that support and facilitate such task. The present work, proposes the
development of an updated framework, referred herein as the VPH-HFv3, as a part of the PRIMAGE
project. This framework, a complete re-writing with respect to the previous versions, aims to
orchestrate several models, which are in concurrent development, using an architecture as simple as
possible, easy to maintain and with high reusability. This sort of problem generally requires
unfeasible execution times. To overcome this problem was developed a strategy of particularisation,
which maps the upper-scale model results into a smaller number and homogenisation which does the
inverse way and analysed the accuracy of this approach.