The untapped power of multivariate analysis for target identification in cancer drug discovery - a LaBiotech Article

Hamburg, Germany (June 11, 2024) - Feature in LaBiotech

In the hunt for novel targets for the next generation of cancer drugs, single gene analysis is not enough. In LaBiotech our Vice President of Biomathematics and Bioinformatics, Daniel Beck, reveals how we’re using multivariate analysis to uncover new targets.

All over the world, drug developers are hungry for new targets to create the next generation of life-changing therapeutics. But conventional gene-based target discovery approaches are reaching their limit.

AI and ML-based approaches are being widely touted as the solution to this challenge. However, as Daniel explains, even sophisticated AI models may not be able to cope with the complexity of multidimensional data that can now be gathered from biological samples, potentially generating spurious correlations and unreliable leads.

At Indivumed Therapeutics we’re moving beyond genes in the search for new targets. We use cutting edge mathematical and statistical methods to sift through multi-level data derived from our unique resource of thousands of tumor and matched normal tissues, all collected to our exacting clinical standards and rapidly frozen before they can start to degrade.

We bring together genomics, transcriptomics, proteomics, phosphoproteomics, protein-protein interactions and clinical information to build a detailed picture of biology that is as close to the molecular reality within the patient as possible. This allows us to identify and understand the dysregulated pathways in cancer where the real targets lie.

It all adds up to creating high value screening-ready target packages for our pharma partners to take forward together into their drug discovery pipeline.

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