In the project we will continue the development of different In Silico Trials results, which were selected to represent the entire solutions space in term of Technology Readiness Levels and target medical products class. 


BoneStrength: this is a solution aimed to assess the efficacy of antiresorptive drugs, a class of drugs aimed to treat osteoporosis and prevent low-energy impact bone fractures.

BoneStrength relies on a patient-specific modelling pipeline that can predict the force required to break a specific bone under specified loading conditions, starting from a CT scan of the region of interest. This biomarker, usually called Biomechanical Computed Tomography (BCT) already underwent extensive validation ex vivo and in vivo.

In the ISW project UNIBO plans to develop the In Silico Trials starting from the BCT biomarker by:

 adding a virtual population

adding a disease progression model and a treatment model

automating some data processing steps

validating it reproducing published interventional trials

using a collection of CT scans of the femur available at the Rizzoli Orthopaedic Institute.

We shall then pursue regulatory qualification with EMA.

The loss of muscle force, also known as dynapenia, has multiple causes. It could be that your muscles became weaker as they lost mass (sarcopenia), or they cannot be activated efficiently (deficit in neuromuscular control) or because the link between brain and muscles, both working fine per se, is broken (innervation problem).

Each of the above would require a different and specific treatment.  

The ForceLoss project aims to enable the identification of the originating cause (differential diagnosis) of dynapenia, leveraging on the use of computer models of the human musculoskeletal system to support clinical decision making.

Such models, built off a person’s own data (e.g., medical imaging data), will be employed to simulate a maximal contraction during knee extension.

Different scenarios (e.g., real vs optimal) will be tested and the simulation results will be compared to real data collected on the same person in the lab and data from a healthy population. 

The ForceLoss solution may be of further use in the design of clinical trials for drugs aiming to prevent sarcopenia, where the enrollment of patients with dynapenia not due to sarcopenia could be detrimental.   

InSole aims to combine subject-specific musculoskeletal models with novel predictive simulations for the personalized prescription of 3D printed corrective shoe insoles.

The integrated framework utilizes experimental measurements from a dedicated movement analysis dataset and plantar pressure to predict the optimal corrective stiffness and geometry of a custom 3D printed insoles to correct dynamic foot deformities during locomotion. The framework has been developed and validated in a healthy population and will be extended to a larger patient group and other dynamic foot pathologies.

The framework consists of the following steps:
• Patient complaint/presentation
• Patient assessment (i.e., anatomy, movement pattern, and plantar pressure)
• Personalised computational model development
• Simulation of movement using measured data
• Insole optimization simulations (stiffness and geometry)
• Prescription of optimized insoles

In the In Silico World project, KU Leuven will develop and validate it as a solution to evaluate and design patient-specific insoles for numerous deformities.

KU Leuven will provide the methodological development and validation, whereas Materialise Motion will be responsible for insole framework deployment as an online service, integration with the industrial manufacturing process and associated legal and regulatory issues.

The 3D in-stent restenosis (ISR3D) application is intended for simulation of smooth muscle cells (SMC) proliferation and restenosis process as a complication after coronary stenting procedure. ISR3D is a fully coupled 3D multiscale model, which includes several single-scale submodels as well as utility modules that facilitate communication between the submodels.

In Silico Trials of new stent designs is a 3D multiscale model of tissue growth and in-stent restenosis (ISR) developed by UvA. It includes a detailed agent-based model of the arterial wall, which is coupled to a model of blood flow in the stented vessel.

Neointima (new tissue) formation plays a large role in the recovery after medical intravascular intervention. The dynamics of this process are largely governed by the interaction between the damaged wall segment and blood flow. An excessive neointima formation leads to restriction of blood flow, while an insufficient neointima may leave thrombogenic surfaces exposed and may lead to late thrombosis. Currently, the efforts are focused on adapting the model for use in diseased human vessels, with the goal of applying it to in silico clinical trials.

The clinical endpoint is to predict if restenosis develops or not, and this will be the final validation point. However, to put the models to stronger test, ISW consortium will also collect data on neointimal area and if possible, histology, and validate the model on that data as well.

ISR3D is a specific example of a more general model for neointima (new tissue) formation in the cardiovascular system, which plays an especially large role in recovery after medical intravascular intervention, e.g. in healing and scar formation. Such a general model is here used to develop NeoIntima3D, a solution that predicts the risk of late thrombosis in vascular stenting.

Late thrombosis is a particularly dangerous condition. The thrombus that forms on a thrombogenic surface, such as an arterial ulcer or exposed metal struts of a stent, may break off and completely cut off the blood flow in the artery. The exact reasons and mechanisms of late thrombosis are still ill-understood. It is a rather infrequent event, e.g. late stent thrombosis happens in less than 1% cases, but if it occurs, it leads to myocardial infarction and death in up to 80% of cases.

What is currently missing are models that are capable to predict such long-term effects as late thrombosis. Validation will then be along two lines, one to predict if and where a thrombosis may form, and one to predict when a thrombus could form. The first line will depend on microfluidics data, the second on clinical data.

OcDefects is a mechanobiology model of the osteochondral cartilage regeneration process, to be used for In Silico trials of an Advanced Therapeutic Medicinal Product (ATMP) for the repair of deep osteochondral defects. Here, the focus will be on knee defects, but the approach is generic and could be used to develop models to test other ATMPs.

Starting from gait data, the mechanical requirements of the ATMP are derived. An atlas of the OA process, built at ULG from single-cell RNA Seq data available in the literature, will be used to identify potential drug targets in a systems biology approach using a regulatory network inferred from the transcriptomics data (prototype available).

In order to capture the behaviour of the ATMP in its clinical setting, the mechanical model will be coupled to the in-house developed cell growth model as well as the intracellular model through the mechanobiology-related pathways. Existing OA collections will be used to an in silico OA population.

The Universal Immune System Simulator – COVID19  (UISS-COVID19) predicts the dynamics of SARS-CoV-2 immune system competition within the host. COVID19 is a severe respiratory infection that affects humans and its outburst recently became a pandemic emergency. 

To promptly and rapidly respond to these dramatic pandemic events, the application of in silico trials can be used for designing and testing medicines against SARS-CoV-2 and speedup the vaccine discovery pipeline, predicting any therapeutic failure and minimizing undesired effects. 

UISS-COVID19 consists in an in silico platform that showed to be in very good agreement with the latest literature outcome in predicting SARS-CoV-2 dynamics and related immune system host response, including cytokines involvement.

Moreover, it has been used to predict the outcome of several approaches to design an effective vaccines and therapies. UISS-COVID19 is ready to be used as an in silico trial platform to predict the outcome of vaccination strategy against SARS-CoV-2.

The In Silico World planned work is to validate this code with a medium-size cohort of clinical cases collected from specific literature clinical studies. 

The Universal Immune System Simulator – Mammary Carcinoma (UISS-MC) is an agent-based model specifically tailored to simulate the effects of tumor-preventive cell vaccines in HER-2/neu transgenic mice prone to the development of mammary carcinoma.

This solution combines a genetic algorithm engine to optimize dosage for vaccination schedules, suggesting effective cancer-preventive vaccination protocols competitive with human-designed protocols.

Università degli Studi di Catania (UNICT) will use anonymized data coming from the Istituto Europeo di Oncologia in order to extend UISS-MC for clinical usage, validating its general predictive accuracy of the patient-specific modelling pipeline.

With the retrospective collections of experimental data, it will be possible to test the prediction accuracy of UISS-MC for immunotherapies against mammary carcinoma in humans.

The Universal Immune System Simulator – Multiple Sclerosis (UISS-MS) predicts the outcome of specific treatments in patients affected by a relapsing-remitting form of multiple sclerosis. 

It provides decision-support to MS specialist about the best treatment based on the disease characteristics and on the immunological profile of the patient. It can be also used to run in silico trials of new treatments. 

For the validation of this solution, UNICT will use data collected at third party Multiple Sclerosis Center, Neurology Unit, Garibaldi Hospital, Catania, Italy. 

Data involves at least 150 adults affected by the two most common form of multiple sclerosis, i.e. primary progressing relapsing-remitting MS and secondary progressing MS. 

All data will be collected with after the written approval of hospital local ethics committee (Comitato Etico dell’Azienda Ospedaliera “Garibaldi”), followed by patients informed consent to participate to this study for research purposes. 

The Universal Immune System Simulator – Tuberculosis (UISS-TB) is a simulator of the immune system dynamics, particularly tailored to predict the response of the human immune system to the exposure to Mycobacterium tuberculosis. 

It can be used as an in silico lab to predict the outcome of new vaccination strategies for active tuberculosis and it has already been validated with available data coming from clinical trials of two vaccines (RUTI and ID93+GLA-SE). 

The consortium plans to complete its validation using anonymized data coming from patients enrolled in the STriTuVaD EC funded project, with a regulatory submission of qualification advice.

VirtualFD will enable of the in silico regulatory evaluation of flow diverters (FDs) for intracranial aneurisms. Right now the quality criteria regarding FDs are not properly defined, this solution will aid the development of criteria to evaluate newly developed FDs.

In the centre of this application there is a code which can simulate virtual FD deployment, taking into account their detailed mechanical and hydrodynamic resistance properties, both supported by measurements. For the clinical validation of this model, Budapesti Muszaki Es Gazdasagtudomanyi Egyetem (BME) built a worldwide unique measurement device to quantify the hydrodynamic resistance of the flow diverters, an essential input information to the predictive model, relative to perianeurismal flow simulations.

The In Silico World planned work is to validate this code with a medium-size cohort of clinical cases collected in a separate project at National Institute of Clinical Neurosciences. In addition to medical data, a mechanical and hydrodynamic resistance database will be built using our in-house measurement systems.


For this Deep Accreditation Track solution, UNIBO will use data collected at its linked third party Istituto Ortopedico Rizzoli, as part of a separately funded running project. This study is recruiting at least 10 adult healthy volunteers and at least 10 elderly patients with severe dynapenia (loss of muscle force).

For each subject it will be collected:

  • an MRI of the lower body
  • an isokinetic dynamometry of leg extension
  • a maximal isometric dynamometry of leg extension,
  • a multi-channel electromyography of the most relevant muscles of the thigh

all with informed consent that explicitly authorizes the sharing of these data in anonymized form for research purposes.

A preliminary study on the optimization of the MRI protocol to be used suggested a typical spatial resolution of 0.4297×0.4297mm2 with a 2 mm slice thickness.

The atlas-based algorithm to be employed for semi-automatic segmentation of individual muscles on MRI data enables the reconstruction of muscle volumes with a high level of accuracy (average percentage difference < 10%, compared to standard manual segmentations), in a considerably reduced amount of time (few hours vs days), and an average reproducibility of muscle segmentation. For the dynamometry, an instrumentation less advanced than the one we plan to use ensures 0.33 Nm of standard error.

In collaboration with the Catharina hospital in Eindhoven the angio-based data that has been gathered in the FAME 1 and FAME 3 clinical trials to validate FFR guided versus angio-based PCI and FFR guided PCI versus CABG respectively. The data collections consist of 1000 and 1500 X-ray angio’s partly mono-plane and partly biplane combined with measured local and pull-back FFR at specific annotated branches of the coronary tree.

Automated segmentation will be developed and carried out to represent these data in terms of centrelines and local diameter. Patient phenotype and all available and relevant patent record data will be collected in a predefined database structure. Data uncertainty will be defined based on known or estimated accuracy measures. The resulting data collection will be suitable for validation of angio-based computed (virtual) FFR procedures that can be used in ISTs for medical devices such as stents, hypothermia procedures, and decision support systems for PCI planning.

The data collection will also be used to develop procedures for data assimilation, generation of virtual patient cohorts, sensitivity analysis and uncertainty quantification, hybrid modelling combining data-driven models with mechanistic models and meta-modelling. The FAME studies are hallmark clinical trials with real impact on guidelines see also e.g. conclusion in Interventional Cardiological Review 2016, 11(2), 116-9 “The FAME trials have lifted the last roadblocks towards the general acceptance by the cardiology community of this novel paradigm in which the diagnostic focus of coronary lesions is shifted from anatomical characteristics to a functional impact on myocardial perfusion.

This unique vision on how to evaluate the severity and prognosis of coronary lesions should force us to rethink our current algorithm for the treatment of patients with chest pain where FFR measurements may supplant non-invasive stress tests, which are only being performed in <50 % of patients prior to elective PCI. However, it will take some time to alter the current course of patient care. 

Through its linked third party Istituto Ortopedico Rizzoli (IOR), UNIBO has access to over 4000 CT scans of the thigh region, originally collected to provide CT-based surgical planning of a total hip replacement procedure, but under an informed consent that included also secondary use for research.

Of these, around 5% are of patients are now deceased, and thus can be publicly shared when irreversibly anonymized, only after the permission of IOR local ethical committee.

To generate a heterogeneous finite element model of the patient’s femur the BoneStrength simulation workflow requires the CT images to be:

densitometrically calibrated


meshed with parabolic tetrahedra

re-projected back onto the calibrated CT data

Thus, in addition to the primary medical imaging data, UNIBO will share collections of derivative data such as polygonal surfaces from segmentations, finite element meshes, mineral density 3D maps, etc.

UNIBO will also plan to explore the generation of synthetic data collections, specifically toward the generation of virtual cohorts for phase III In Silico Trials (with over 1000 virtual patients).

Through its third-party Multiple Sclerosis Center (Neurology Unit of the Garibaldi Hospital located in Catania, Italy) UNICT will have access to data collections that involve at least 150 adults affected by the two most common forms of multiple sclerosis: primary progressing relapsing-remitting MS and secondary progressing MS.

For each patient’s age at MS onset, baseline MRI lesion load, oligoclonal bands status and the administered treatment will be collected.

These features are widely accepted as prognostic parameters of MS. The expected quality of the data will be guaranteed by the procedures followed by the Hospital data center.

In particular, demographic and clinical data are collected by medical personnel experienced in the management of MS patients and with certification of ability to perform the Neurostatus.

MRIs are acquired according to the protocols described in the international guidelines for MS and by neuroradiologists with experience in the field. Examination of the cerebrospinal fluid is conducted by an internal laboratory that annually participates in quality checks by the Italian Association of Neuroimmunology. 

A data collection of 846 and 1500 angio-based coronary artery datasets will be made available as a validation collection for the evaluation of PCI procedures with different stent types.

The outcome of a study in which patients were treated with several drug-eluting stent and in which other 1500 patients were randomized and treated with two drug-eluting stent types, one biodegradable (Optimax) and one coated (Abbott).

These data collections will be used to validate activities in the ISR3D solution. Also, here the outcome of the IST can be compared with real clinical data. Again, tools to derive a synthetic (virtual) certified patient cohort will be tested and results will be published and made available for the community.

The StentValid cohort from the EMC for the validation for the ISR3D will be mainly based on the large database of the biodegradable stent studies (Absorb/Abbot) organized and executed by and within EMC.

The data were published in various papers before and these studies were geared towards comparing the efficacy of different stent types, with a focus on clinical imaging end-points, including neo-intima formation.

To be able to do so, several studies were conducted to validate neo-intima measurements based on imaging data, and these data will therefore be extremely applicable to validate ISR3D models.

As a partner of the H2020 STRITUVAD project, Università degli Studi di Catania (UNICT) has access to data of dose versus immunogenicity response in healthy volunteers and patients with active tuberculosis for two therapeutic vaccines (Archivel RUTI and IDRI ID93). In addition, by the time the In Silico World project starts, UNICT will have access also to early response results for RUTI therapy in a phase II interventional trial.

The expected quality of the data will be guaranteed by the procedures described and applied in the clinical trial dossier. In particular, the data entries will be checked and supervised by the monitors for all patients and for all entered data.

The data collected in a database will be kept for a period of 15 years. Clean File for the final database will be declared when all data have been entered and a quality check on a sample of the data has been performed. The database will be locked after Clean File has been declared and data extracted for statistical analysis.

A total number of 600 CTA’s of patients that received an aortic valve replacement via a TAVI procedure will be made available as a validation collection for TAVI procedures but will also be used to design and execute an IST in which to different valve designs (Medtronic/Edwards) with respect to the need of pacing the heart will be compared.

The outcome of the IST can be compared with real clinical data that show a significant difference between the two brands. Software tools to derive a synthetic (virtual) certified patient cohort will be tested and results will be published and made available for the community.

With the In Silico World project the exact extend of what part of the data will be used and which accuracy is needed is part of the research.

The clinical study of which the data is used has been carried out by the same team as the one that carried out the FAME studies and similar quality regarding accuracy and quality can be expected.

If the quality of the data is representative of other clinical centres needs to be investigated in a separate study.


Submission request for Qualification Advice for BoneStrength methodology. More info soon.

EU framework of technical standards to assess the credibility of in silico methods. More info soon.

Educational Curriculum analysis to introduce In Silico Trials in the higher education of stakeholders. More info soon.

First consensus of the ISW Community of Practice on the Good Simulation Practices. More info soon.

Massive open online course on In Silico Trials. More info soon.

Report on the specific engagement actions emerged from the RRI workshops. More info soon.

White paper on policies to support the wider adoption of In Silico Trials

Detailed SWOT analysis procedure to evaluate the adoption of In Silico Trials methods for executives of medical industries

Submission request for Qualification Advice for UISS-TB methodology. More info soon.


Scalability of Agent-based models. More info soon.

Service for the training of AI-based surrogate models of predictive simulations. More info soon.

Online service for so-called Biomechanical Computed Tomography, prediction of bone strength from CT Data. More info soon.

Collaborative Sharing of Validation Collections: online service to support collaborative sharing. More info soon.

In silico assessment of custom-made insoles. More info soon.

Online service to predict the risk of restenosis for stents. More info soon.

Computational infrastructure for the execution of In Silico Trials on more than 1000 virtual patients. More info soon.

Online community of practice to host all consensus processes, disseminate results and engage with the community. More info soon.

Consulting services based on UISS-MS to estimate the efficacy of new MS therapies. More info soon.

Consulting services based on UISS-TB to estimate the efficacy of new tuberculosis therapies. More info soon.