06/03/2025/
Executive Summary For decades, the pharmaceutical industry has faced the same recurring problems with clinical development: the struggle to fully...
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, i.a., to optimise protocol design.
Use advance machine learnng 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.
Success stories with clinical development
Our technology has already made a significant difference in the lives of many patients.
Here are a few examples of how Volv Global and inTrigue have enabled earlier detection and transformed patient care:
Shaping the future today
Executive Summary For decades, the pharmaceutical industry has faced the same recurring problems with clinical development: the struggle to fully...
Alpha-1 antitrypsin deficiency (AATD), a rare genetic condition, can cause lung disease in adults with symptoms similar to chronic obstructive...
Shaping the future today
Ready to shape the future of healthcare? Let’s explore how our machine learning solutions can accelerate diagnoses, refine patient journeys, and drive meaningful impact for those who need it most. We value your ideas and expertise, reach out now to unlock new possibilities together.