Enhanced clinical responder analysis combining real world evidence with human interactome insights
Clinical Responder Analysis
MOST CANCER CLINICAL TRIALS FAIL TO MEET ENDPOINTS DUE TO THE HIGH VARIABILITY BETWEEN PATIENTS AND TUMOR. BIOXPLOR APPLIES ITS PROPRIETARY PLATFORM TO COMBINE REAL WORLD EVIDENCE, CLINICAL TRIALS DATA AND MEDICAL LITERATURE TO DISCOVER NEW INSIGHTS WHICH CAN BETTER DEFINE PATIENT POPULATIONS FOR IMPROVED SUCCESS IN CLINICAL DEVELOPMENT AND OUTCOMES RESEARCH. THIS APPROACH IS APPLIED TO SECONDARY ANALYSIS OF CLINICAL TRIALS DATA AND TO REAL WORLD EVIDENCE TO IDENTIFY ENHANCED RESPONDER & NON-RESPONDER SIGNALS FROM CLINICL DATA.
Enhanced Clinical Responder Analysis
Data to Knowledge to Insights
In 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