Therapy synthesis for personalized treatment of perinatal depression
|Marco Esposito||Dottorando/Assegnista/Specializzando componente non strutturato del gruppo di ricerca|
The management of mental illness during pregnancy and lactation represents a complex clinical situation
involving a minimum of two concomitant medical conditions (i.e., pregnancy and a psychiatric disorder) and also demanding consideration of the welfare of at least two patients (i.e., mother and child).
In this project, we aim at designing algorithms and software tools to perform automatic synthesis of personalized pharmacological therapies for treating severe mood disorders in pregnant and lactating women. Existing techniques cannot solve our problem, as (i) the number of all possible therapies is extremely large, (ii) models simulation may take months and may be given as black-box (e.g., software executables), (iii) inter-patient variability and measurement errors must be taken into account.
This project aims to use advanced Artificial Intelligence (AI) techniques to model an optimization problem, allowing us to find, in a reasonable time, the best therapy for a given patient in a conveniently modeled space of therapies. We will inspect the use of Black-Box Optimization (BBO) software for efficient exploration of the space of therapies and the synthesis of therapies that grant maximum efficacy while meeting given safety requirements.
Our project is built on the following pillars:
Pillar 1. Design and implementation of a software tool to perform automatic synthesis of personalized therapies for both black- and white-box (e.g., SBML) models.
Pillar 2. Experimental evaluation via in silico clinical trials based on models of the human physiology linked to depression and PKPD models of antipsychotic drugs.
On such pillars, we will build the four main tasks of our project, i.e., two tasks for pillar one (design of mathematical model and implementation of the software) and two tasks for pillar 2 (generating the virtual patients and performing the actual evaluation on such virtual patients).