Technology
Our Platform
We have pioneered the world’s first human extracellular matrix-based discovery platform to drive the identification and validation of targets and biomarkers as well as drug profiling. The flexible platform which is based on extensive research and corroboration, integrates our bespoke human tissue biobank and Engitomix proprietary bioinformatics platform. It is applicable to multiple organs, disease indications and therapeutic modalities.
We have reliable access to human tissues* with associated clinical information including our own biobank, as well as strong network links to hospitals, biobanks and KOLs worldwide.
Using proven and patented protocols, we can obtain, preserve and utilise human extracellular matrix (ECM) from healthy and diseased tissues to generate cellular models with bioactive scaffolds.
(*All tissues would otherwise be discarded or organs rejected for transplant.)
We have reliable access to human tissues* with associated clinical information including our own biobank, as well as strong network links to hospitals, biobanks and KOLs worldwide.
Using proven and patented protocols, we can obtain, preserve and utilise human extracellular matrix (ECM) from healthy and diseased tissues to generate cellular models with bioactive scaffolds.
(*All tissues would otherwise be discarded or organs rejected for transplant.)
We use our ECM platform to perform wet lab experiments in order to understand both the composition of the healthy and diseased acellular environment as well as the biology of the microenvironment in driving disease progression.
The disease-specific acellular composition of the ECM is carried out by proteomic analysis of decellularized tissues (‘matrisome’) while we are able to deconvolute the role of ECM in modulating cellular phenotype by reseeding into those ECMs different cell types relevant for the disease of interest and then through RNAseq, scRNAseq and other readouts we extrapolate ECM-driven targets.
We use our ECM platform to perform wet lab experiments in order to understand both the composition of the healthy and diseased acellular environment as well as the biology of the microenvironment in driving disease progression.
The disease-specific acellular composition of the ECM is carried out by proteomic analysis of decellularized tissues (‘matrisome’) while we are able to deconvolute the role of ECM in modulating cellular phenotype by reseeding into those ECMs different cell types relevant for the disease of interest and then through RNAseq, scRNAseq and other readouts we extrapolate ECM-driven targets.
Wet lab experiments generate tissue specific and disease “OMICS” data, while our Engitomix platform searches ~10x public databases with billions of data points. These combined datasets are then analysed using our proprietary Engitomix platform and algorithms.
Unbiased automatic target prioritisation is enabled by AI, machine learning and text mining to generate an automatic target report with target scoring.
This rapid target prioritisation enables 10-50 candidates to be selected from a 1,000 putative targets in 1-4 weeks
Wet lab experiments generate tissue specific and disease “OMICS” data, while our Engitomix platform searches ~10x public databases with billions of data points. These combined datasets are then analysed using our proprietary Engitomix platform and algorithms.
Unbiased automatic target prioritisation is enabled by AI, machine learning and text mining to generate an automatic target report with target scoring.
This rapid target prioritisation enables 10-50 candidates to be selected from a 1,000 putative targets in 1-4 weeks
For drug discovery, we incorporate human ECM into our in vitro models, thereby recreating the natural cell microenvironment so that drug candidates can be tested in the context they will ultimately be used in. With the potential to predict the efficacy of candidates more accurately at an earlier stage, the platform can reduce late-stage clinical failures and accelerate discovery.
For drug discovery, we incorporate human ECM into our in vitro models, thereby recreating the natural cell microenvironment so that drug candidates can be tested in the context they will ultimately be used in. With the potential to predict the efficacy of candidates more accurately at an earlier stage, the platform can reduce late-stage clinical failures and accelerate discovery.
Exscalate is a powerful supercomputing platform, developed by our partner Dompé, that operates at a speed of 32 PetaFLOPS to support advanced in silico methods. It facilitates the binding of one billion molecules on a target protein in one hour thereby reducing the processing time from target identification to candidate selection.
Employing Exscalate enables us to access the world’s largest digital ligand library and a virtual compound library composed of over 500 billion molecules. We can then identify chemical structures with the best complementary pattern of interactions with our biological targets, along with other phys-chem characteristics as well as novelty and synthesis feasibility.
Exscalate is a powerful supercomputing platform, developed by our partner Dompé, that operates at a speed of 32 PetaFLOPS to support advanced in silico methods. It facilitates the binding of one billion molecules on a target protein in one hour thereby reducing the processing time from target identification to candidate selection.
Employing Exscalate enables us to access the world’s largest digital ligand library and a virtual compound library composed of over 500 billion molecules. We can then identify chemical structures with the best complementary pattern of interactions with our biological targets, along with other phys-chem characteristics as well as novelty and synthesis feasibility.