PRECISION MEDICINE IS THE NEW HOLY GRAIL FOR SUCCESSFUL CLINICAL OUTCOMES ESPECIALLY FOR COMPLEX DISEASES. MOST CLINICAL TRIALS FAIL TO MEET ENDPOINTS DUE TO CLINICAL TRIAL DESIGN AND TREATING THE WRONG PATIENT SUB-TYPE. BIOXPLOR APPLIES ITS PROPRIETARY NETWORK PHARMACOLOGY PLATFORM TO DISCOVER NEW KNOWLEDGE AND IDENTIFY NEW BIOMARKERS TO MEASURE DRUG EFFECTIVENESS IN PATIENT POPULATIONS. THIS IS APPLIED TO SECONDARY ANALYSIS OF CLINICAL TRIALS DATA TO IDENTIFY RESPONDER & NON-RESPONDER SIGNALS, AND C0-DEVELOPMENT OF COMPANION DIAGNOSTICS ASSAYS AND DETECTION OF NEW DRUG TARGETS FOR NON-RESPONDERS.
Clinical Responders Versus Non-Responders
New Layer
End-2-End Translation from DNA & RNA Analysis to Network Pharmacology Interpretation
Clinical Responder Workflow
In a study with Novartis, BioXplor was provided with patient data from a phase 3 clinical trial to study patient response to anti-IL17A inhibitor, Cosentyx, versus Etanercept (Enbrel), anti-TNF inhibitor. A dataset from over 2000 patients was provided with 200 features including patient medical history, drug dosage, investigator scores, and patient genotypes. The goal of the study was to identify clinical responders and non-responders, and to identify which patient's responded better to Cosentyx versus Etanercept, based on disease stage, age, blood tests, genotype and drug dosage. BioXplor applied a custom machine learning model which successfully identified patient responders in each scenario and recommended which drug fits the right patient by combining BioXplor's proprietary knowledge discovery engine and genomics databases together with the clients clinical datasets. Additionally, SNPs linking patient adverse events to genotypes and drug efficacy were identified.
Case Study - Cosentyx vs Etanercept
Patient Responder ID, Personalized Medicine, Adverse Events
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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
The integrated network pharmacology platform has particular advantages for complex immuno/inflammation-based diseases. It works by generating obvious and non-obvious knowledge on the immuno/inflammation interactome, diseases and drug targets from structured and unstructured data. This is achieved using proprietary natural language processing, machine learning and feature engineering algorithms which integrate data from public and private sources. BioXplor leverages it's network pharmacology platform to prioritize optimal multi-therapeutic/multi-target strategies, novel indications, and clinical responders and non-responders, based on safety and efficacy evidence. The platform has been optimized and successfully validated with top tier clients including Novartis, Bayer and Merck, for Biomarker ID and Clinical Responder ID, Drug Combinations, Indication Expansion, Drug Repositioning, Target and Multi-Target ID, Pathway Analysis and DNA/RNA Sequencing Data Interpretation.
BioXplor delivers high-quality and rapid-turnaround contract research services from data-driven discovery to wet lab validation for clinical responder ID, indication expansion and drug combinations. The detailed recommendation report can be purely data-driven using BioXplor's proprietary knowledge discovery platform only, or integrate client data for additional analysis. Via our trusted external partner network, the workflow can be extended to generate and integrate new data from single-cell sequencing analysis, cell-based assays and preclinical animal model studies.
For co-development partnerships we form joint ventures together with select academic and biotech teams to combine domain, target and/or platform synergies to rapidly develop programs from hypotheses to validation at a fraction of the time and cost of traditional approaches. BioXplor is actively engaged in programs in inflammation, autoimmunity and immuno-oncology.
Contract Research & Drug Discovery Partnerships
