Parkinson's disease (PD) is the second most common neurodegenerative disease. One of the limitations of its treatment is the occurrence of unwanted side effects, such as motor fluctuations, dyskinesias, and other motor disturbances. Furthermore, patients do not always respond to treatment in the same way, leading to a wide disparity in responses and significant variability in clinical progression. In medical practice, clinicians often still use a trial-and-error approach when optimizing their patients' treatments (for example, increasing or decreasing doses, deciding whether to change a drug, or combining it with another).

Another limitation of current clinical practice is the infrequent monitoring of patients with Parkinson's disease (PD). Clinical examinations and follow-up are limited to short, infrequent visits. The use of new activity and movement monitoring methodologies, as well as new data computing, storage, and analysis techniques, would allow for continuous monitoring aimed at detecting symptoms of great value for optimizing treatments in this disease. Despite the solid preclinical foundation of GiMo-PD, there is still limited experience in using clinical decision support systems that integrate multiple biomarkers in PD compared to other fields such as cancer.

Furthermore, other limitations currently hinder the implementation of these systems, such as the lack of digitization of certain information and the harmonization of information sources. GiMo-PD is a project that bridges neurology, neuroscience and neurotechnology (including neuroimaging), genetically based pharmacology, and computational science (machine learning and advanced data analytics for decision support). It aims to develop an innovative and disruptive solution to overcome the limitations described in the treatment of Parkinson's disease (PD) and thus improve the quality of life for these patients. The optimization proposed by GiMo-PD is based on the integration of information from multiple sources, such as genetic background, molecular markers, neuroimaging, and clinical information obtained through continuous monitoring in natural settings, in a safe, efficient, and effective manner that guarantees data integrity. This proposal also seeks to improve existing knowledge about the etiology of PD and introduce certain standards into the diagnostic and treatment process from the perspective of personalized medicine.

The overall goal of GiMo-PD is to create a new, innovative, and disruptive platform to help physicians prescribe the best personalized therapy for each patient with Parkinson's disease. To this end, a technological solution is proposed that will allow:

• Acquire, integrate, and analyze genetic, neuroimaging, and clinical data.
• To offer ubiquitous and continuous monitoring mechanisms in PD for the patient's motor disorders, seeking a personalized solution.
• To offer mechanisms for the periodic monitoring of psychocognitive aspects.
• To perform an advanced analysis of all data so that its combined processing, guided by clinical criteria, provides specialists with support for decision-making regarding the most appropriate treatment for each patient. All of this will be achieved through the digitization of clinical practice guidelines.

This is a collaborative project, in which, in addition to SOLTEL, the following partners participate:

• Andalusian Public Foundation for the Management of Health Research
• Madrija Consultoría, S.L.
• Qubiotech Health Intelligence, S.L.
• University of Seville

The project is supported by the Ministry of Science and Innovation and co-financed with FEDER funds, within the Challenges-Collaboration call of the State Program for Research, Development and Innovation Oriented to the Challenges of Society, within the framework of the State Plan for Scientific and Technical Research and Innovation 2017-2020. (File RTC-2019-007150-1).