Analytics in Pharma and Life Sciences

Data science is playing a pivotal role in redefining the Life Sciences businesses starting from discovery phases to commercial activities. Diverse data sources, humongous volume, and varied data types such as text, audio, images, and video, demand the adoption of numerous available algorithms and models for data processing and analysis to aid decision making. 

A few of the use cases that help our Pharma and Life Sciences customers achieve their business objectives through the use of Data Science and Advanced Analytics are highlighted here. 

  • Adverse Event Prediction 

Predicting and preventing ADRs before clinical trials in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs. Analysis of EHR, clinical, social media feeds and literature data along with targets or pathways or side effects profiles or structural details from various resources could create a data science-related comprehensive analysis and an effective pipeline for AE prediction. 

  • Drug Repurposing 

Our solution offers automated data ingestion and integration using drug identifiers from pharma, partners, CRO, third-party, and authenticated open data sources. Phenotypic, therapeutic, structural, and genomic details were used to construct numerical features using statistical measures. Our machine learning pipeline offers easy integration with customer's proprietary and external data with a dynamic selector for the best algorithms. It generates a comprehensive drug-drug interaction network overlaid to the drug-disease network to compute the confidence scores for the mapped diseases. Readily available dashboards provide a quick exploration of drug descriptors, most similar drug and top recommended diseases network. 

  • Product Safety using Sentiment Analysis 

Product Safety acts as an early warning signal for pharmaceutical companies about product safety issues and public sentiments about the product or company. ML models can be constructed to analyze historical as well as live streaming social media feeds and web scraping data for sentiment analysis. 

  • Pharmacovigilance in Phase IV or Post – Marketing Survey 

Surveillance of spontaneously reported adverse events continues till a product is marketed. However, if the reported AE or claims are valid or not remains questionable. Machine Learning-based classification models can offer a solution that includes data acquisition, integration, and unstructured data extraction from AER tools, emails, telephonic conversation related text data.

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