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Novel peptide-based therapeutics for reprogramming the tumour stroma extracellular matrix using molecular modelling and computational engineering

Project description

Peptide-based therapy against ovarian cancer

Cancer is the second leading cause of death globally, with solid tumours accounting for 90 % of cases. These tumours develop a dense extracellular matrix (ECM) that fosters cancer growth and resistance to treatment. Current therapies, including monoclonal antibodies, often fail due to the stromal barrier and off-target effects. The EIC-funded ACT-MATRIX project aims to address this challenge by developing small peptides that bind to ECM proteins, disrupting interactions that promote tumour aggressiveness. In 15 preclinical models, including ovarian and pancreatic cancers, the TAX2 peptide has demonstrated potential in slowing tumour progression and improving the efficacy of antibody therapies. The project will primarily target ovarian cancer, which remains a disease with significant unmet medical needs and a high mortality rate.

Objective

Cancer is the second biggest cause of death worldwide. 90% of cancers are solid tumours, where tumour stromal cells secrete a dense mass of proteins called the extracellular matrix (ECM), part of a protective tumour microenvironment that promotes cancer proliferation, invasiveness, and resistance to chemotherapy and biologics. Several therapies now available or being developed rely on monoclonal antibodies aimed at tumour-expressed antigens, or activation of immune cells. Many such promising immunotherapeutic approaches fail because of this stromal barrier, and/or because of toxicity due to off-target effects. Targeting proteins and pathways in the tumour microenvironment is now considered by oncology experts and pharmaceutical concerns to be critical to the success of beating solid tumours, but no therapies have yet emerged to address this €200+B market. Our platform uses novel molecular computational simulation models to generate small peptides that bind to highly specific sites on ECM proteins and block interactions known to promote tumour aggressiveness. The first of these, TAX2, selectively targets a clinically relevant interaction between the ECM signalling protein Thrombospondin-1 and its receptors, to regulate tumour proliferation, vascularisation, and immune cell (T-cell) infiltration of the tumour. TAX2 shows extremely promising results in 15 preclinical models, including ovarian, pancreatic, colorectal, and melanoma cancers, where it inhibits tumour progression and metastasis. TAX2 also substantially improves the efficacy of antibody therapies when combined in these models. Our current focus is on ovarian cancer, a cancer with high unmet need, and very high mortality rate. EIC support will enable large-scale manufacture, regulatory steps, and first-in-human, multi-indication clinical trials (Ph I and Ph IIa) in preparation for outlicensing of TAX2 to a pharmaceutical partner who would take the therapy through more advanced clinical trial stages.

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-EIC-ACC - HORIZON EIC Accelerator

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Call for proposal

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(opens in new window) HORIZON-EIC-2024-ACCELERATOR-02

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Coordinator

APMONIA THERAPEUTICS
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 2 434 790,33
Address
MDA 2 RUE LEON PATOUX
51100 REIMS
France

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SME

The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.

Yes
Region
Grand Est Champagne-Ardenne Marne
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Links
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

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