Cancer Discovery Software Suite
BioXplor leverages it's CompBio platform to accelerate and reduce the cost of early stage drug discovery programs in partnership with academic oncology research institutes.
BioXplor's integrated drug discovery platform works by generating obvious and non-obvious knowledge on a topic from structured and unstructured data using proprietary natural language processing and machine learning algorithms. Next, predictions are validated in vitro and in vivo in our partner labs.
We have successfully applied the platform for Indication Expansion, Drug Repositioning, Pathway Analysis, Target ID, Biomarker and Clinical Trial Responder ID, with top tier clients including Novartis, Bayer and Merck. We are interested in establishing more strategic partnerships with academic oncology institutes.
Cancer Drug Discovery Partnerships
In a study with Merck Life Sciences, BioXplor set out to apply natural language processing for the design of a multiplex PDL1 biomarker ELISA assay, comparing our designs to designs made by in house scientists manually reading papers, and those made by a in house bioinformatics team. BioXplor employed a literature discovery approach to establish a pathway model to prioritize PDL1 related proteins to measure in an ELISA assay for PDL1 activity in drug screening. Then, BioXplor's team extracted the optimal antibody reagents, prioritizing based on similar concentration, tissue-specificity, secondary antibody combinations, and target specificity supported by the highest ranking literature citations. BioXplor successfully identified key biomarkers missed by the human scientist and bioinformatics team, and produced superior results for antibody selection further improved upon the existing commercial assay. Importantly, BioXplor's designs were performed 20x faster than the human scientist outperformed the bioinformatics team in biomarker identification. This approach is highly scaleable, and similar results can be obtained in your workflow via integrated of BioXplor's API or our software tools.
Case Study - PDL1 ELISA Assay Design
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Biomarker Discovery, Pathway Analysis, Antibody Reagent Mining
In a collaboration co-funded by UC Berkeley, BioXplor set out to generate novel biomarker hypotheses for development of a diagnostic assay for Pancreatic Ductal Adenocarcinoma using biomedical literature-based pathway analysis and gene set enrichment on RNAseq data from pancreatic cancer patient tissue. In this study, BioXplor identified a number of novel potential biomarkers from RNAseq data, with evidence in literature, and some others with no supporting evidence. Pathway analysis was performed to further understand these potentially novel biomarkers in pancreatic cancer. Common cancer pathway biomarkers were identified with relationships to the novel biomarkers. A protein-protein interaction database was built to identify network proximity, and RNAseq datasets from heathy versus disease patients were combined to identify 16 potential genes which could distinguish between PDAC and healthy tissue.
Case Study - Pancreatic Cancer (PDAC)
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Biomarker Discovery, Gene Set Enrichment, Pathway Analysis
As part of an ongoing collabroation with a leading cancer research team studying a rare and particularly aggressive form of Leukemia (ETP-ALL), BioXplor set out to analyse the ZEB2 pathway to identify an optimal therapeutic strategy using systematic drug repositioning. BioXplor's proprietary ABCD feature engineering model was employed for pathway analysis to further elucidate obvious and non-obvious knowledge on the topic. The team identified some novel druggable targets for ETP-ALL, and then used literature analysis to identify supporting evidence for the novel hypotheses from in vitro and in Vivo studies. A novel drug was identified with supporting evidence to be effective in treating ETP-ALL. This drug will be screened in vitro and in vivo to validate the mechanisms, safety and efficacy profile using patient tissue and transgenic models. The validation work is ongoing.
Case Study - Leukemia Drug Repositioning
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Pathway Analysis, Mechanisms of Action, Efficacy