Finding hidden insights in clinical responder data using the human interactome and network analysis Clinical Responder Analysis

SlidePRECISION MEDICINE IS THE HOLY GRAIL FOR SUCCESSFUL CLINICAL OUTCOMES. MOST CLINICAL TRIALS FAIL TO MEET ENDPOINTS MOSTLY 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

SlideIn a study with Novartis, BioXplor was provided with patient data from a phase 3 clinical trial to study patient response to an IL17A inhibitor, Secukinumab, versus Etanercept (Amgen), an TNF inhibitor in an immuno-inflammatory therapeutic area. 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 data platform and tool suite together with the clients clinical datasets. Additionally, novel SNPs linking adverse events to genotypes and drug efficacy were identified. Case Study - Secukinumab vs Etanercept