logo

High Throughput Modelling and Measurement of Human Epithelial Models using Electrospun Conducting Polymers For Unlocking Data-Driven Drug Discovery

Source: EU Funding & Tenders Portal

Project
Ongoing

Organ on Chip (OoC) technology, which models human tissues in vitro, is poised to refactor the drug discovery pipeline and alleviate the financial burden with the added benefit of reducing/ refining animal experimentation. The advent of non-destructive, biosensing modalities has placed data-driven approaches to identifying new therapeutics within reach, where highly parallelized instances of human

Project Information FAQ

Project Information

5 Q
The project "High Throughput Modelling and Measurement of Human Epithelial Models using Electrospun Conducting Polymers For Unlocking Data-Driven Drug Discovery" is an infrastructure initiative in the Education, Chemical (Industrial), Manufacturing (Industrial), Advanced Electronics sector, located in N/A, United Kingdom (UK). Taiyo aggregates data from EU Funding & Tenders Portal, including information on sponsoring government bodies, EPCs, and contractors.

Want to explore the full details? View the full report

Participants

Sponsoring Agency

Obfuscated Data

Company

Obfuscated Data

Status

Original status

ongoing

Taiyo status

Obfuscated Data

Taiyo last update

00-00-0000

Available timestamps

00-00-0000

Available timestamp type

Obfuscated Data

Contact

Contact name

Obfuscated Data

Phone

0000000000

Email

ObfuscatedData@email.com

Address

Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data

Description

Description

Organ on Chip (OoC) technology, which models human tissues in vitro, is poised to refactor the drug discovery pipeline and alleviate the financial burden with the added benefit of reducing/ refining animal experimentation. The advent of non-destructive, biosensing modalities has placed data-driven approaches to identifying new therapeutics within reach, where highly parallelized instances of human organ models can be mined for AI and ML-augmented discovery. During our ERC CoG grant, we developed a novel technology which combines these two frontiers; by fabricating porous scaffolds from conducting polymer hydrogels, we were able to culture 3D organotypic models of human epithelial tissues, while conducting highly sensitive, non-destructive electrochemical monitoring of the tissues. During our previous IMBIBE PoC grant, we showed that our technology was compatible with a fluidic platform produced by an industry partner. However, in doing so, we identified a major pain point in the OoC ecosystem: the step discontinuity in the level of complexity, both of the tissue model and the typical OoC form factor, is too great to allow for integration into current industry workflows. This barrier to adoption is crippling and needs to be addressed by harmonising platform form factor with industry standards. Here, we propose pivoting our current technology to meet this need – by radically altering our fabrication methodology, opting for hydrogel electrospinning, we can produce simplified OoC platforms, which represent the smallest possible adoption cost to our industry partners, while providing for highly scalable continuous monitoring of the tissues. Further, our proposal will facilitate the process of gradual evolution of tissue model and sensor complexity, without disrupting industrial workflow, to allow convergence between the state of the art in the pharmaceutical ecosystem and the bleeding edge technological advancements being made in the academic sector.

Original sub-sector

Obfuscated

Original Currency

USD

Original budget

000000000000000

Procurement method

Obfuscated Data

Budget

000000000000000

Location

Region

Obfuscated

Country

Obfuscated

State

Obfuscated Data

County

Obfuscated

Location

Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data

Source

Source reliability

High

Data quality score

100%

Source

Obfuscated Data

URL

obfuscated_data,obfuscateddata.com

More Details

Project Type

Obfuscated Data

Article Published Date

Obfuscated Data