Improve Safety in Polymedication by Managing Drug-Drug-Gene Interactions

Informations projet

SafePolyMed

N° de convention de subvention: 101057639

DOI

10.3030/101057639

Date de signature de la CE 23 Mai 2022

Date de début 1 Juin 2022

Date de fin 31 Août 2026

Financé au titre de

• Health

Coût total

€ 5 650 444,56

Contribution de l’UE

€ 5 650 442,81

Coordonné par

UNIVERSITAT DES SAARLANDES
Germany

Periodic Reporting for period 2 - SafePolyMed (Improve Safety in Polymedication by Managing Drug-Drug-Gene Interactions)

Période du rapport: 2023-12-01 au 2025-05-31

Résumé du contexte et des objectifs généraux du projet

Adverse drug reaction (ADRs), a major cause of hospitalization, rank among the leading causes of death in developed countries. SafePolyMed addresses significant public health issues by focusing on drug-drug and drug-gene interactions (DGIs and DDIs) as major causes of ADRs. These can occur, when genetic polymorphisms (DGIs) or drug co-administrations (DDIs), alter a drug’s behavior, and are strongly interconnected. However, studying complex real-world drug-drug gene interactions (DDGIs) scenarios in clinical trials is not feasible due to combinatorial explosion, high costs and ethical concerns, leading to significant knowledge gaps. Furthermore, insufficient patient education on drug intake, polypharmacy and associated risks along with low levels of patient participation in their healthcare management, poses challenges to patient safety and adherence, especially among elderly, and often comorbid individuals under polymedication. SafePolyMed will develop a novel and innovative framework to define, assess, and manage DDGIs resulting in education and empowerment of citizens as well as in reduced healthcare costs by improving patient safety. SafePolyMed aims: (1) to develop an evidence-based risk scoring system using machine learning on real-world datasets to identify patients at risk of developing ADRs; (2) to develop patient-reported outcome measures (PROMs) for patient safety; (3) to empower citizens through a digital Medication Management Center (MMC) allowing patients to manage their therapies, learn about interactions, and collect patient-reported outcomes; (4) to establish individualized model-based dose adaptations of clinically relevant compounds; (5) to validate the developed tools through a proof-of-principle study involving diverse patient cohorts from European clinical sites.

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

To predict the individual risk of a patient to develop ADRs, statistical and AI models are used that were trained on RWD. A first risk prediction model was developed and connected to the MMC. Model refinement is still ongoing.

After two rounds of a DELPHI process, a set of PROMs was selected and implemented in the MMC. During the EmpaSafe study, the safety PROMs will be evaluated. Within the reporting period, two patient trainings were performed. In collaboration with the PEH, the content for a patient training curriculum had been defined that has the overall aim of educating patients on actively managing their polymedication. The trainings addressed D(D)GIs, side effects, regulatory aspects, GDPR and patient-led evidence generation.

The first version of the MMC is availabe for use in the EmpaSafe study. This version conatins the PROMs and is connected to the models from WP1 and WP4. Based on continuous feedback, the MMC will be further developed.

For the precision dosing tool in WP4, PBPK models of atorvastatin, mirtazapine, methotrexate, citalopram as well as amitriptyline and nortriptyline are currently under development and will be implemented in the DSS. In addition, the first version of the DSS is available online and accessibe via an API. A connection to the MMC was estbalished, as well. The DSS contains: metoprolol, atomoxetine, paroxetine, tacrolimus, dasatinib, imatinib and voriconazole.

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)

SafePolyMed employs ML and AI techniques to analyze real-world datasets, including genetic information, demographic data, health conditions, and medication history to extract patterns related to ADRs. This approach goes beyond traditional statistical analysis, aiming to provide a more comprehensive understanding of individual risk factors. Furthermore, the project empowered citizens during patient trainings to improve overall knowledge on polymedication and active patient engagement. Safety-related PROMs were developed to enable individual standardized safety reporting. Their contributions to a more comprehensive understanding of drug safety will be evaluated in the EmpaSafe trial. Additionally, a prototype of an easy-to-use clinical decision support system has been developed based on comprehensive PBPK models to promote the adoption of model-based dose recommendations among physicians, enhancing the precision of patient-tailored drug therapy. The developed MMC integrates the different tools developed within SafePolyMed to increase access to health-relevant information and allow citizens to manage their drug therapy. Overall, SafePolyMed aims to develop a framework to empower citizens, focusing on improving citizen education and their understanding of complex drug therapies, as well as to improve exchange between citizens and healthcare professionals. Of all planned tools, first versions were employed. The MMC containing the safety PROMs is currently used in the EmpaSafe study.

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