The startup is developing a proprietary artificial intelligence platform for creating patient-level models of autoimmune diseases, aimed at enhancing the drug discovery process in immunology and inflammation. This technology enables more precise targeting of treatments, improving patient outcomes in the management of autoimmune conditions.
Funding
$4.4M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Current clinical trials often fail to capture individual variability in patients with immune-mediated inflammatory diseases, leading to difficulties in predicting real-life treatment effectiveness. This variability raises questions about which treatments are most likely to succeed for specific patients and why some patients respond differently to the same drug.
Solution
Scienta Lab offers an explainable machine learning platform that models immune-mediated inflammatory diseases to facilitate personalized patient care. By incorporating the platform with global medical networks, Scienta Lab pioneers a data-driven approach to drug development and clinical strategies. The platform combines multimodal immunology datasets, machine learning algorithms, and medical expertise into an integrated map of immuno-inflammation physiopathology, linking datasets of various modalities together to support drug development and clinical strategies. The company develops diagnostic, prognosis, and response to treatment models to support patient diagnosis and personalize treatment plans.
Target Audience
Scienta Lab's primary customers are academic centers, researchers, biotechnology companies, and pharmaceutical laboratories involved in drug development, approval, and clinical use.
Features
- Proprietary foundation model (EVA) dedicated to immunology and its biological mechanisms
- Multimodal data integration, combining clinical, biological, pathology, imaging, and omics data
- Explainable AI toolkit to translate algorithmic signals into clinically actionable information
- Diagnostic models to confirm or refute the presence of a disease
- Prognosis models to predict the course of diseases
- Response to treatment models to predict the effect of a therapy at the patient level