Predicting the outcome of specific treatments in patients affected by Multiple Sclerosis – UISS-MS

Worldwide, over 2.8 million people have multiple sclerosis (National Multiple Sclerosis Society, 2020). Multiple Sclerosis (MS) is a debilitating disease that affects the central nervous system. It’s progressive, which means it gets worse over time and can cause problems with vision, balance, and muscle coordination. It can also lead to numbness and tingling in the limbs, as well as fatigue.

Multiple Sclerosis (MS) is caused by an autoimmune reaction against the central nervous system’s protective myelin sheaths – this means that the individual’s immune system attacks the brain and spinal cord.

This loss of myelin sheaths – which allows electrical impulses to transmit quickly and efficiently along with the nerve cells – causes neurological symptoms, which vary between patients, but often present in a characteristic relapsing-remitting pattern (Relapsing-Remitting Multiple Sclerosis, RRMS), while patients slowly accumulate disability.

In the last years, more than 20 treatments that can reduce the activity and progression of multiple sclerosis have been successfully brought to the market. These drugs have completely transformed the RRMS therapeutic landscape, providing patients with a much brighter outlook and leading to new challenges for the development of MS therapies.

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 specialists regarding 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.

UISS-MS enables the simulation of realistic disease courses in RRMS, based on a mechanistic model of the immune system and its dysregulation in MS. The model incorporates the adaptive and innate immune system, the autoimmune response and four distinct disease-modifying therapies.

Due to the implementation of immune system heterogeneity and variable disease severity based on basal patients’ characteristics, the virtual patients’ disease courses and response to treatment display real-world variability. The relapse rates predicted by the model have been validated with individual clinical data, as well as clinical trial data.

Potentially UISS-MS can be used to perform simulations with a validated state-of-the-art mechanistic model of RRMS and commonly prescribed treatment options. Moreover, it can be used to assess not only relapse rates but also immune system-based secondary outcome measures.

For the validation of this solution, our partner Università di Catania (UNICT) will use data collected at the third-party Multiple Sclerosis Center, Neurology Unit of the Garibaldi Hospital in Catania, Italy. Data involves at least 150 adults affected by the two most common forms of multiple sclerosis, i.e. primary progressing relapsing-remitting MS and secondary progressing MS.