Bioelectronic NAMS for testing safety and efficacy of ATMPS. (BIOEL-NAMs)

The primary goal of BIOEL-NAMS is to develop and validate a sustainable-materials-based bioelectronic cell-on-chip device able to monitor and modulate physiological/pathological bioelectric signals and cellular responses to ATMPs. Gene therapies, somatic cell therapies, tissue engineering, and combined products fall under ATMPs. Though promising for otherwise incurable diseases, ATMPs follow the same regulatory framework as conventional drugs (Reg. EC 1394/2007) yet require specific expertise and testing. A major bottleneck remains the poor translatability from model systems to humans due to species-specific biological processes, delaying preclinical phases. The COVID-19 pandemic stressed the need to shorten ATMPs’ time-to-market without compromising safety. These challenges foster the adoption of New Approach Methodologies (NAMs), advanced tools from biotechnology, microengineering, and IT. NAMs support human-centered research in physiologically relevant conditions, offering valid tool to complement data on animal models. BIOEL-NAMS will leverage sustainable biomaterials, bioelectronics, microfluidics, neurophysiology, tissue engineering, and stem cell biology from ER-Ecosystem partners to realize a device capable of detecting and modulating cell bioelectric states (healthy, pathological, live, dead, stem, differentiated), assessing ATMP effects. The 3-year project will: 1) design, fabricate, and validate sustainable biomaterials-based microfluidic "cells-on-chip" platforms integrating 2D/3D models for electrophysiological monitoring and nanomolar ATMP delivery (MISTER-CNR); 2) include molecular/functional validation using stem and neural-differentiated cells (IRET, CNR); 3) perform GLP toxicology testing (IRET); 4) assess ATMPs (e.g., mRNA, gene editing) via bioelectric signals and transcriptomics (MISTER, IRET); 5) compare the in vitro with in vivo data (TRANSMED);6) upscale cell/material production to achieve reproducible scalable cell in vitro model 6) generate a human-cell-based dataset supporting precision medicine and in vitro–in silico digital twins (MISTER-CNR)

 A recent Deloitte Center for Health Solutions report on measuring the return from innovation illustrates how some pharmaceutical organizations are integrating gen AI and other advanced technologies into their R&D strategies. The traditional R&D process is often slow and stage-gated, typically requiring large trials to establish meaningful impact. Additionally, failure rates for new drug candidates can be as high as 90%. In response, some biopharma companies are leveraging AI and digital twins. Digital twins, which serve as virtual replicas of patients, allow for early testing of new drug candidates. Sanofi, for example, uses digital twins to test novel drug candidates during the early phases of drug development. In vitro BIOEL-NAM that can feed these simulations and can help determine also the potential effectiveness of therapies and speed up pre clinical phase toward a faster clinical development. Such bunch of data generated by BIOEL-NAM could support the training of AI programs to improved predictive modeling to shorten R&D time from weeks to hours. The current TRL of the technology is 6, the expected TRL at the of the project will between 7.

BIOEL-NAMS will generate scalable in vitro models that will be highly reproducible allowing to test ATMPs and to generate a datased for creating a pipeline for generating a digital twin of the model, to test and predict the effect of ATMPs in silico

We will need help in consortium building to find the following profile and competences:

Big pharma in ATMPs interested in providing ATMs to be tested to proof the efficiency of the proposed BIOEL-NAM;

Bioelectronic Partner that would be interest in exploiting or partnering to scale up the device production

A partner for big data collection and management and digital twin generation. A partner for scalable cell production for the in vitro models.

Public Hospitals or Private Hospital Companies for aligning the data achieved in vitro in preclinical phase with those already available in clinics. A Company or institution for data management

A compani for highly reproducible cell production and maintainence