PhD Position F/ M Statistical methods for meta-model integration in pharmacology

Contexte et atouts du poste

The PhD will be funded by the Governmental Acceleration Funding - PEPR Santé Numérique. The candidate will work with all partners of the PEPR DIGPHAT project.

Mission confiée

Context:

Pharmacology inherently requires modelling mechanisms across various scales: molecular (identifying drug action mechanisms), cellular (e.g., characterizing tissue lesions and biomarkers), and patient (pharmacokinetics/pharmacodynamics population variability). Notably, all these models are fundamentally longitudinal. Integrating these multiple scales is crucial to individualize treatments, including drug selection, optimal dosage, and associated regimens.

Despite the development of mechanistic models (such as those utilizing differential equations) at each scale by different research communities, there is still a lack of interoperability. Combining all relevant meta-models (each representing its own scale) will create a comprehensive digital pharmacology twin.

Traditionally, multiscale modelling has been predominantly developed within the biological sciences. Examples include the integration of data from proteins to organs and the application of multiscale and multimodal approaches for image reconstruction in medical applications. A notable characteristic of these studies is the use of a single model to address each specific outcome.

Objectives:

To propose a general pathway towards pharmacological digital twins by integrating several multiscale and longitudinal meta-models. Digital twins that enable the a priori investigation of a treatment strategy the dynamic assessment of the probability of success based on a patient’s longitudinal features.

Principales activités

Main activities:

Develop models for:

Additional activities:

Compétences

Avantages