REALMENT aims to characterise the interplay between genetics, pharmacological treatment and clinical trajectories in severe and chronic mental disorders schizophrenia (SCZ), bipolar disorder (BIP) and major depression disorder (MDD), with particular focus on treatment outcome and multimorbidities. Through the identification of risk profiles, we are developing tools for disease management, including early detection and prevention of relapse and adverse effects.

Main aim

To unleash the potential of RCT data in combination with large psychopharmacological RWD (eHRs, health registries, genotyped biobanks) applying novel AI and ML technology to enable a personalised approach (‘precision psychiatry’) for clinical application in a Clinical Management Platform (4MENT).

We exploit our unique access to large-scale available real-world data (RWD) samples derived from cohorts, medical records and the unique Nordic biobanks and registries, where lifespan diagnostic and prescription information is available from each individual. We apply cutting-edge big data analytical approaches to RWD from Nordic (SE, DK, NO, FI, IS), Baltic (Estonian), British, Dutch and Italian cohorts to identify and validate genetic markers of treatment outcome and multimorbidities, develop tools suitable for prediction and stratification of treatment, and identify sub-populations that would benefit from preventive strategies.

The resulting management platform will be made possible through the following objectives:

1. To capture and harmonize RWD pertaining to treatment outcome and multimorbidities in severe mental disorders and develop a data infrastructure.

  1. To capture complementary psychopharmacological treatment outcome data (response, adverse effects) between cross-border eHRs, questionnaires, health registries and genotyped biobanks, applying novel digital tools including machine learning (ML) and artificial intelligence (AI).
  2. To harmonise pharmacological treatment outcome and multimorbidities data within (eHRs, registry data) and between different data sources (RWD, RCT).
  3. To ensure sustainability of the data platform (“REAL-WD”) and the availability of results to others according to Open Science principles.

2. To discover more genetic variation associated with treatment outcome and multimorbidities.

  1. To discover common and rare genetic variants associated with treatment outcome and multimorbidities.
  2. To identify pharma treatment trajectories and discover associated genetic variants.

3. To develop prediction and stratification algorithms for treatment outcome and multimorbidities in severe mental disorders combining multi-source data, ML, AI and modelling.

  1. To develop pipelines to integrate training and test data with ML, AI and modelling approaches adding new samples through the project period.
  2. To develop multimodal stratification and prediction algorithms to target psychopharmacological outcome (response, adverse effects, trajectories) and multimorbidities.

4. To validate the genetic discoveries in independent samples and to validate the prediction and stratification algorithms by running an intervention.

  1. To validate the identified genetic associations with treatment outcomes in independent samples.
  2. To validate the prediction/stratification algorithms in independent cross-border samples, including available RCT samples and yearly updated registry records.
  3. To test the validity of the prediction/stratification algorithms in independent clinical samples through reverse-phenotyping (recall study) and to survey the response of recalled participants.

5. To develop a management platform for improved outcomes and quality of life of individuals with mental disorders combining multi-source data and algorithms.

  1. To develop an efficient and user-friendly software solution for integration of RWD and AI/ML algorithms in collaboration with stakeholders (user groups, clinicians, health care providers).
  2. To combine the software solution with clinical practice developing a management platform for monitoring of individuals with mental disorders in accordance with the wishes of the patients

Main concept

To use RWD from health care systems combined with biobanks and research data. With this approach we can solve the need for large-scale data necessary for genetic discoveries related to mental disorders. Thereby, we can develop prediction and stratification algorithms that can be tested and refined in large RCT data, validated in a clinical intervention, and form the basis for developing the 4MENT Management Platform – which includes monitoring and prevention tools and personalised intervention strategies to improve clinical outcomes and QoL for patients.

Published Nov. 30, 2021 9:54 AM - Last modified Dec. 20, 2021 11:13 AM