SlideDrug Response Personalised medicine is the holy grail for successful patient outcomes. Many patients do not respond to approved drug treatments or have serious side effects due to drug interactions particularly in complex diseases like cancer where patients have multiple co-morbidities. BioXplor's network pharmacology system unlocks drug safety and efficacy insights on patient response for drug effectiveness. Machine learning is used to analyse patient data for responder & non-responder signals and a network pharmacology knowledge graph is used to prioritize drug combinations based on safety and efficacy insights matching the disease and patient profiles. This approach is applied for better patient outcomes in complex diseases, treatment response analysis in value-based care analysis, secondary analysis in clinical trials, and for pharmacovigilance post-approval stage drugs.

SlideSafety
Efficacy
Pathways
Genotypes
Blood Measurements
Disease Progression
Disease Duration/Stage
Co-morbidities
Patient Responder Analysis

SlideAccess Case Studies 1 Clinical Responders Study: Cosentyx v Enbrel Cosentyx (Novartis) versus Etanercept (Amgen) Clinical Responder Study on 2000 patients with 200+ data points per patient to determin which drug showed the best efficacy in which patient sub-population. 2 Real World Data Analysis from EMRs Analysis of unstructured Electronic Medical Records from Colorectal Cancer patients to identify drug effects and safety issues. 3 Biomarker Analysis for Pancreatic Cancer Analysis of gene expression datasets from patient biopsies, comparing treated versus untreated and healthy versus disease tissue. 4 PDL-1 Biomarker Panel Analysis for Drug Response Assay Design Analysis of gene expression datasets from patient biopsies, comparing treated versus untreated and healthy versus disease tissue.
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