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MxAI - Generative AI serving pharmaceutical innovation

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MxAI - Generative AI serving pharmaceutical innovation

An innovative startup transforming the drug market access process through generative AI.

+ 1000

NICE reference documents

95%

accuracy in responses

1

month of development


ABOUT THE CLIENT

An INSEAD award-winning startup

Founded by Virginie Simon (MBA '24J), MAxAI won the Grand Prize at the 48th INSEAD Venture Competition, along with €25,000 in funding. Based at Station F, the startup is revolutionizing the drug market access approach by leveraging generative AI potential

TECHNICAL CHALLENGE

Intelligent analysis of complex documentation

Managing and analyzing over 1000 pharmaceutical documents of 400 pages from NICE (National Institute for Health and Care Excellence) presented a major challenge. The complexity lay in the need to understand and extract relevant information from dense technical documentation while ensuring absolute accuracy, critical in the pharmaceutical field.

The complex medical terminology required a sophisticated approach, combining technical expertise and professional validation. The system needed to not only understand specialized language but also provide quick and accurate responses.

SOLUTION

  1. AI Model

    • Use of Gemini for analysis and generation

    • Specialized processing of medical terminology

  2. Data Management

    • MongoDB Atlas for vector storage

    • Optimized chunking for complex medical documents

    • Database consisting of 300 official NICE documents

    • Clinical guidance and therapeutic evaluation documents

  3. RAG System

    • Retrieval-augmented generation architecture

    • Advanced contextual search

    • Specialized medical document processing

CONCRETE EXAMPLE

Analysis of a Pediatric Neurology Case

To illustrate the power of our system, let's take the case of a new drug in development. It's an innovative treatment for a rare neurological disease affecting children, characterized by epileptic seizures and progressive loss of motor and language abilities. This particularly severe condition affects approximately 200 patients worldwide, with about a hundred new cases diagnosed each year.

The proposed treatment requires intrathecal administration over two days of hospitalization. Ongoing clinical studies (phase I/II) primarily evaluate the reduction in seizure frequency and monitor neurodevelopmental evolution through various standardized scales.

By analyzing its NICE document database, our AI identified three comparable treatments, each with its specificities:

  • Cannabidiol stands out for its simple oral administration and proven effectiveness on seizures, but requires close hepatic monitoring due to potential drug interactions.

  • Fenfluramine shows remarkable results in seizure reduction, with significant experience in its use. However, its profile requires regular cardiac monitoring and imposes certain dietary restrictions on patients.

  • Cerliponase has shown encouraging results in preserving motor and language abilities. However, its intrathecal administration mode and high cost are constraints to consider.

This comprehensive analysis, generated in just 30 seconds, relies on NICE-validated data, allowing medical and regulatory teams to make informed decisions quickly.

METHODOLOGY

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