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Molecular diagnostic assay modeling and quality control optimization for robust personalised cancer risk assessment
BioLizard supported Mdxhealth in developing a robust prostate cancer risk scoring model and optimizing triplex assay QC. By refining analytics, SOPs, and QC workflows, we enhanced predictive accuracy, reproducibility, and operational efficiency for personalized diagnostics.
Mdxhealth is a front runner in epigenetic research with a proven track record of identifying, developing, validating and delivering molecular diagnostic assays. They provide functional genomic data that can be used for personalised cancer risk assessment, with specific focus on prostate cancer.
BioLizard assisted with development and optimisation of a robust model to calculate patient risk score for prostate cancer based on clinical biomarkers and other clinical features.
The goal was to optimize the client’s current qualification of reagents and equipment. Instead of a head-to-head approach, we developed a QC panel that can be used in each run to see if they fall in between the expected QC ranges.
“We were very pleased with the work BioLizard did. The BioLizard team were very flexible and took the necessary time to fully understand our needs. Their tailored service allowed us to draw meaningful conclusions from our data.” – Mdxhealth
Our approach
Molecular diagnostic assay model
Model building to optimize clinical assay and patient risk score predictions based on a clinical biomarker panel
Leverage advanced analytics to develop robust and personalized patient risk scoring algorithms
BioLizard is proud to have received the Best Poster Award at the European Society for Spatial Biology (ESSB) conference, recognising our work on benchmarking cell segmentation methods for single-cell spatial transcriptomics.
Paul Vauterin has transitioned from Chief Technology Officer (CTO) to Chief Operating Officer (COO) at BioLizard, reflecting a shift in focus towards helping organisations integrate AI into their operations and unlock greater value from their data.
For over a century, the life sciences industry has relied on animal models as the standard for predicting drug safety and effect. But today, the industry faces a harsh reality: over 90% of new drugs that appear safe and effective in animal tests ultimately fail in human clinical trials (see for example Ineichen et al. 2024 [1] and Marshall et al. 2023[2])