Expertise note Vincent Bouget - Co-founder and Scientific Director of Scienta Lab

In January, Future4care launched the January4data campaign to highlight the essential role played by healthcare data in the value chain. To this end, we solicited the expertise of members of our ecosystem, which has given rise to these notes. The genesis of healthcare data, technological developments and uses, ethics and applications in the hospital sector are just some of the topics covered in this series of articles.  

Properly harnessed healthcare data, combined with rigorous and ethical artificial intelligence, have the capacity to implement precision medicine; a paradigm where care is tailored to each individual characteristics.

With Scienta Lab, we aim to bring about the same revolution in the way we develop drugs, particularly for autoimmune diseases. These diseases, which affect around 10% of the world's population, are still poorly understood and difficult to treat.

A patient-centered approach

Autoimmune diseases such as lupus, rheumatoid arthritis and Crohn's disease are unique in their presentation in each patient.

These chronic, incurable diseases have a major impact on the lives of their sufferers, with major, irreversible destruction of the affected organs, leading to disabling symptoms, increased risk of cancer and higher mortality.

Unfortunately, current treatments are only marginally effective on a significant proportion of patients.

Why? Because drugs are developed using a standardized approach that ignores the biological diversity of individuals.

At Scienta Lab, we have chosen to tackle this challenge and enable the development of precision drugs for autoimmune diseases. Using data as a guide. We use health data from cells, animal models or patients to train our EVA foundation model, dedicated to immunology and inflammation.

In this way, EVA acquires a representation of the immune system and its disorders, which is leveraged for personalized drug development. This precision medicine approach is at the heart of my vision of modern, equitable healthcare.

Harnessing data to speed up decision-making

In the field of artificial intelligence, the main difficulty is data accessibility. This is even more important in the healthcare sector. At Scienta Lab, we have developed an AI model specific to these constraints, and capable of extracting relevant information from small healthcare datasets.

By studying targeted datasets and building adapted algorithms, we can answer complex questions such as:

• Will a drug work in humans, and why does it work in some patients but not in others?

• Which drug candidate has the highest probability of passing clinical trials?

• How can clinical trials be optimized to maximize efficacy and minimize costs?

Partnerships to guarantee data quality

An essential element of our success lies in our strategic partnerships with international hospitals and research centers. These collaborations give us access to high-quality healthcare data and ensure that our models reflect the diversity of patients with autoimmune diseases.

We also invest in regular audits and validations to ensure the robustness of our algorithms and guarantee the security of personal data.

Data ethics as a priority

Europe, with initiatives such as the RGPD or the AI Act, enforces high standards on data protection and its exploitation in algorithms. These regulations impose significant constraints on innovation, but I believe they provide a necessary ethical framework to ensure that healthcare data is used for the benefit of all. I also believe that these regulations create opportunities and competitiveness for European start-ups by strengthening the confidence of patients and partners, while positioning Europe as a model of ethical innovation.