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Home COUNTRY FRANCE

Paris-based En Carta secures €1.5 million pre-seed to advance platform for early Lyme disease detection

EU Startupsby EU Startups
May 22, 2024
Reading Time: 3 mins read
in FRANCE, VENTURE CAPITAL
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En Carta Diagnostics, a deeptech startup developing rapid Point-of-Care (POC) molecular diagnostics kits, announced €1.5 million in pre-seed funding. This investment will enable En Carta to make a substantial leap forward in achieving preclinical data on a prototype diagnostic kit for its flagship indication, the debilitating Lyme disease, while ensuring its ability to scale industrially. 

CentraleSupélec Venture led the round with participation from a handful of high-profile business angels. Additional funding may be forthcoming to finance other diagnostic platform derivatives.

En Carta’s technology is a rapid Point-of-Care diagnostic platform, in a frugal cassette format, exhibiting high precision through a technology underpinned by more than ten years of intensive research. It uses aptamers, capable of binding with high affinity and specificity to a predetermined target. This ‘signature’ can be programmed to detect any genetic, pathogenic or veterinary indication, opening up an extensive range of applications. 

The company is addressing Lyme disease as its first application, due to this disorder’s escalation as the most prevalent vector-borne disease in the Northern Hemisphere – with 1.2 million cases recorded annually. The rate is increasing alarmingly at 22.5% per year from 2000 to 2018 according to the CDC (Centers for Disease Control and Prevention).

“En Carta is absolutely thrilled to have secured financial backing from CentraleSupélec Venture and business angels, who have deep expertise in healthcare and a solid understanding of Point-of-Care market needs,” said Guillaume Horreard, CEO at En Carta. “Today, we have a very robust platform technology leveraged for accuracy, speed and cost, which are key in the Point-of-Care space. As a first step, we are focusing on Lyme disease, where the sizeable patient population, accelerated growth of the problem and need for rapid diagnosis following a tick bite make our product indispensable.”  

En Carta will use the proceeds to develop a diagnostic kit, with the purpose of autonomously detecting Lyme disease within minutes. The funding will also enable it to build a robust ecosystem and implement its go-to-market strategy. 

“CentraleSupélec Venture is delighted to support this first phase of development in the Point-of-Care diagnostics sector, which meets the expectations of both patients and healthcare systems,” added Cédric Curtil, executive director of CS Venture.

The platform enables the long-awaited early diagnosis of Lyme disease, a critical capability that has been lacking in the standard testing for the disease. Current Lyme disease tests are designed to detect antibodies made by the body in response to infection. As antibodies can take several weeks to develop, tests conducted on recently infected patients may produce a negative result. En Carta’s molecular diagnostic platform is the first to overcome this shortcoming, paving the way for more accurate and rapid diagnosis. The company expects to present preclinical data in early 2025.

“En Carta Diagnostics’ technology, product and team are very promising. Their go-to-market approach for POC diagnostics could fulfill an unmet need in the US retail environment, as practitioners struggle with early diagnosis of Lyme Disease and co-infections,” commented Jean-Pierre Millon, board member of CVS Health and advisor to En Carta.

Strong market growth in the Point-of-Care (POC) molecular diagnostics, and telemedicine sectors, attracted investors to the company, alongside the demonstrable robustness of En Carta’s technology, originally developed by researchers from Harvard Wyss Institute, the Pasteur Institute and the engineering school Centrale Paris.  

Read the orginal article: https://www.eu-startups.com/2024/05/paris-based-en-carta-secures-e1-5-million-pre-seed-to-advance-platform-for-early-lyme-disease-detection/

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