Life Sciences organisations are inexhaustible mines of data, the sheer amount of data generated each minute can be staggering, but the real question is – are you using this pool of data intelligently? Life Sciences organisations need to embrace new age technologies like AI, ML, NLP and self-learning for true intelligence leading to actionable insights. This digital transformation has been accelerated with the onset of Covid, which has disrupted existing trials to give way for new ones; not only has this jumbled the existing analytics but also created a demand for better digital engagement with patients, patient identification, patient outreach and remote monitoring for the new trials.
Content
We live in a data driven economy. Data Strategy, Analytics and Data Sciences are transforming businesses through insights for quick decision making. From pre clinical drug discovery phase to commercialization phase, AI/ML is playing a pivotal role in redefining the life science businesses. Diverse data sources, humongous volume, and varied data types such as text, audio, images, and genomics, demand the adoption of numerous available algorithms and models within AI/ML world.
Find below an illustration of AI/ML applications across the pharma value chain.
We will touch upon some of these use cases –
Drug Repurposing
Drug repositioning of failed or existing drugs near patent expiration can provide other tremendous benefits like >90% reduction in development cost, 50% reduction in time, and 1000 times better success rate. Most drugs fail in clinical studies due to a lack of efficacy or unexpected toxicities. Reasons attributed to an inadequate understanding of drug action include the complexity of human disease biology. Identifying new diseases for existing failed or expired drugs needs a system biology approach.
Data driven drug repositing exercise requires building up of a data pipeline consisting of chemistry, biology, pharmcology, clinical trials, genomics and RWD data sources. Machine learning algorithms, Insilco models, network can help with both drug based as well as target based repositioning.
Pharmacovigilance
Surveillance of spontaneously reported adverse events continues as long as a product is marketed but whether the reported AE or claims are valid/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, and telephonic conversation-related text data.
DRUG Combinatorics
Mining and analysis data from sources like EMR, Drugbank, Twosides, IQVIA can help to identify DDI (Drug Drug Interaction) as well as DCI (Drug condition interaction). Synergistic affects of drug combinations can also be analysis using ML based algorithms.
Product Safety / Sentiment Analysis
It can act 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.
Adverse Events Prediction
Predicting and preventing ADRs before clinical trials in the early stage of the drug development pipeline can help enhance drug safety and reduce financial costs. Analysis of EHR, clinical, social media feeds, and literature data along with targets/pathways/side effects profiles/structural details from various resources could create a data science-related comprehensive analysis and an effective pipeline for AE prediction.
Personalized Marketing
Personalized messages, campaigns can be crafted to KOL’s / HCP’s based on their profiling leveraging AI/ML models and Omnichannel outreach. KOL Identification based on data available from CRM’s, HCP Networks, Trials and Published Papers can be facilitated using AI/ML techniques.
Conclusion
In the rapidly evolving landscape of life sciences, leveraging AI, ML, and NLP is crucial for transforming vast data into actionable insights. From drug repurposing to personalized marketing, these technologies enable smarter, faster decision-making across the pharma value chain. Coforge’s expertise in data and analytics helps organizations modernize and monetize their data, ensuring alignment with business goals. Embrace this digital transformation to stay ahead in Pharma 4.0 and beyond.