Volv Global accelerates and de-risks clinical development strategies much earlier in the pharma development lifecycle, improving planning robustness and efficiency through better understanding of patients cohorts and diseases.
Identify a cohort more precisely to establish better clinical development strategies much earlier in the development lifecycle.
Understand disease and disease progression to find the right patients at the right point in the patient journey. Identify patient clusters to support trial site selection.
Elucidate disease heterogeneity and identify novel endpoints to signpost new biomarkers to optimise protocol design.
Use advance machine learning techniques to overcome issues of unstructured data, data sparsity and bias in data sources.
Clinical development in the face of complexity
Variability in phenotypic expression, progression and comorbidities complicates patient stratification and endpoint selection.
Rare diseases or subgroups have small patient populations and may lack sufficient data for robust insights.
Records may over-represent certain populations or healthcare systems, limiting generalisability.
Lack of longitudinal and complete datasets that capture the full trajectory of disease evolution makes it difficult to model disease progression.
Many critical insights are buried in free-text notes, requiring advanced natural language processing (NLP) to extract.
Limited understanding of disease biology hinders biomarker identification and endpoint selection for clinical trials.
Supporting Clinical Development
We find undiagnosed and misdiagnosed patients, expanding the pool of patients for clinical trial recruitment.
We find patients before the development of traditional symptoms, before they are given conventional therapies.
We enable triage of patients according to prognosis of outcomes, e.g., fast progressors, or progressors to more acute symptoms.
We differentiate and cluster heterogeneous patient cohorts for more accurate definition of trial inclusion and exclusion criteria.
Leading AI/ML methodology to support Clinical Development
inTrigue is a robust resource to optimise trial design and patient recruitment by leveraging its AI/ML-driven analysis of extensive electronic health records, claims data, and other real-world evidence. By detecting subtle biomarkers, phenotypes and other clinical features which characterise patients, sponsors can streamline enrollment and target the most relevant participants. Additionally, inTrigue’s predictive modelling helps refine protocols and endpoints based on anticipated disease progression, ensuring sponsors gather higher-quality data and reduce trial timelines.
Shaping the future today
This research on endpoints in collaboration with Sanofi and presented at ISPOR 2026 was the largest machine learning ...
New research demonstrates that machine learning can map clinician-defined endpoints to real-world claims data in Pomp...
Explore Volv Global’s ISPOR 2026 research with Sanofi on Pompe disease, based on use of machine learning-driven endpo...
AI-driven insights & RWD in Rare Disease: Accelerate AATD Diagnosis and identify undiagnosed AATD patients earlie...
Volv Global presented 2 posters at ATS 2025 showing work to assess how machine learning models can identify patients ...
A prediction model to identify symptomatic patients of different races and ethnicities with risk of Alpha-1 Antitryps...
Discover how Volv Global’s inTrigue AI helps identify rare disease patients earlier, improves diagnosis accuracy, and...
Case Study: Detecting signs of Fabry and Pompe disease in UK clinical data
Volv Global applied machine learning to a 2.5M-patient EHR database to surface undiagnosed acute hepatic porphyria ca...
