Measurable results, not promises.

Predictive AI-Powered Clinical Trial Optimisation Platform for BioGen Therapeutics

Client

BioGen Therapeutics, a biopharmaceutical company specialised in innovative therapies for rare diseases.

Challenge

Slow patient-recruitment processes and fragmented data across legacy systems were delaying clinical trials by 35%, affecting time-to-market for critical treatments.

Solution

Med4Sups designed a predictive AI platform that analyses public medical histories, electronic records and genomic data to identify ideal trial candidates. The system unifies dispersed databases, uses NLP to extract inclusion/exclusion criteria from protocols, and generates real-time alerts on adverse events.

Impact

The solution reduced recruitment time by 40% and accelerated data-analysis cycles by 30%, enabling BioGen Therapeutics to initiate Phase III of its lead cystic-fibrosis therapy 4 months ahead of schedule. This approach aligns with AI-for-clinical-trials trends described in Nature Medicine (2023: "AI-driven clinical trials: From predictive recruitment to risk mitigation").

AI-Driven Scientific Content Generation Platform for Pharma Innovators

Client

Pharma Innovators, a leading pharmaceutical company.

Challenge

The need to optimise scientific communication processes, especially the generation and management of content for stakeholders such as physicians and regulators, was a bottleneck that delayed the dissemination of critical information.

Solution

Med4Sups developed a generative-AI platform that automates the creation of initial drafts of scientific documents, such as medical information letters, abstracts and summaries, integrating internal and external databases to ensure accuracy and currency.

Impact

Implementation reduced content-generation time by 50% and improved accuracy by 20%, allowing Medical Affairs teams to focus on high-value interactions and strategic planning. This project was inspired by the use of generative AI to accelerate scientific-content creation, as mentioned in McKinsey (Generative AI in the pharmaceutical industry: Moving from hype to reality).

Intelligent Recruitment Platform for Rare-Disease Trials for NeuroGen Solutions

Client

NeuroGen Solutions, a biopharmaceutical company focused on innovative therapies for rare neurological disorders.

Challenge

The scarcity of early-stage amyotrophic lateral sclerosis (ALS) patients was delaying Phase III trials for a neuroprotective treatment, increasing operational costs by 40% and jeopardising regulatory deadlines. According to NEJM AI (2024), 70% of trials for ultra-rare diseases fail due to insufficient recruitment.

Solution

Med4Sups implemented a multimodal AI system that combines:

• Analysis of coded clinical records (HL7/FHIR) and underutilised genomic data.
• Real-time monitoring of activity on patient forums and advocacy networks.
• Predictive models of disease progression to identify pre-symptomatic candidates.
• Intelligent geolocation of medical centres with underutilised diagnostic capacity.

Impact

• 60% acceleration in recruitment (14 vs. 22 projected months).
• 35% more eligible patients identified, including underrepresented minorities.
• Efficacy validation in SOD1 gene carriers, enabling personalised-medicine strategies.
• Cited by the FDA as a "model of diversity in rare-disease trials" (NIH, 2023).

The technical architecture, which integrates FHIR APIs with transformer models to process free clinical narratives, follows the regulatory framework described in Nature Digital Medicine (2023): "AI-driven patient matching in rare diseases: From data silos to dynamic ontologies".