Predictix ™ - a user-friendly procedure that analyses genomic, clinical and demographic data to generate a personalized report on the efficacy of antidepressants and their side effects

Informations projet

Predictix

N° de convention de subvention: 874082

DOI

10.3030/874082

Projet clôturé

Date de signature de la CE 19 Octobre 2020

Date de début 1 Septembre 2020

Date de fin 31 Août 2023

Financé au titre de

• INDUSTRIAL LEADERSHIP - Innovation In SMEs

Coût total

€ 3 545 737,50

Contribution de l’UE

€ 2 482 016,25

Coordonné par

TALIAZ LTD
Israel

Periodic Reporting for period 5 - Predictix (Predictix ™ - a user-friendly procedure that analyses genomic, clinical and demographic data to generate a personalized report on the efficacy of antidepressants and their side effects)

Période du rapport: 2022-09-01 au 2023-08-31

Résumé du contexte et des objectifs généraux du projet

Major depressive disorder (MDD) is a common psychiatric disorder that is associated with a high burden on patients and their families. The World Health Organization estimates that, for every euro spent on digital solutions for mental health, countries can reap a return of four euros in improved health and productivity. Unfortunately, clinical trials indicate that current best-practice methods of determining the optimal treatment for a specific MDD patient lack efficiency. This inefficiency is plausibly caused by the polygenic nature and the phenotypic heterogeneity of MDD. Recent technological advancements lead to the accumulation of data through electronic health records, next-generation sequencing technologies, and sensory devices. This has paved the way to a new era of brain research; now we can start using Machine Learning (ML) as an advanced approach to understanding MDD.
Applying machine learning methods to the accumulating data derived from Next Generation Sequencing (NGS) technologies, Electronic Health Records (EHR), and sensory devices can transform the way psychiatric disorders are treated.
The PREDICTIX Antidepressant tool was developed out of this growing need for personalized treatment selection for patients diagnosed as suffering from depression. We aspired to create a prediction tool that relies on combinatorial data of clinical, demographic, and genetic information of each patient, in accordance with the applicable literature and guidelines. The project has the overall objective of optimizing and further improving the software engineering and clinical validation of the platform

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

The first iteration of the platform has been completed, and the second iteration is currently underway. Taliaz has worked on a number of features within the platform, to improve the patient follow-up capabilities, the AI prediction models and the server architecture to support a scalable and dynamic system. Taliaz has also implemented a testing & automation pipeline to enable the detection of issues within platform and their communication between the different teams.

The multi-center clinical study Protocol has been submitted and approved and the first patients were recruited for the study. The recruitment rate is growing and Taliaz is in the process of meeting the regulatory requirements for 2 additional clinical centres in order to increase the recruitment rate. The company has also furthered its business development and exploitation objectives having updated the Marketing and Communication plan. Moreover, Taliaz has published an additional 2 peer reviewed articles and is in the process of finalising another 2 publications. Taliaz has also successfully published an article in Nature’s Translational Psychiatry, and a medical opinion paper in the Journal Clinical of Medicine to compliment the communication strategy.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

One challenge in designing any prediction algorithm is in selecting the right combination of features that will predict a well-defined clinical outcome. The PREDICTIX algorithm and pipeline perfectly tackles this challenge and act as a decision-supporting tool for clinicians, generating precise, efficient, quick, and cost-effective treatment for depression.

PREDICTIX workflow