Data ManagementPharmaceutical IndustryResearch
August 13, 2024

AI in Pharmaceuticals: Embracing a Competitive Advantage 

The integration of generative AI in the pharmaceutical industry is revolutionizing drug development and manufacturing processes. This transformation is driven by the rapid advancements in AI technology, offering a competitive advantage to companies that adopt these innovations.  LLMs & AI Agents  Large Language Models (LLMs) such as GPT-4, are a type of AI that excels at understanding and generating natural language text. In pharmaceuticals, LLMs are useful for summarizing large amounts of scientific literature, generating hypotheses based on existing research, and facilitating communication between researchers and clinicians. They can also be used to create patient education materials and interpret complex medical data.  LLMs differ from AI agents in that agents have more autonomy. Agents are designed to automate tasks they are trained for, and don’t require as much human direction and input. Agents are designed for analyzing data, predicting outcomes, and optimizing processes. Some of their complex roles include drug discovery tasks, drug development and manufacturing optimization, and optimizing clinical…
Read More
BusinessData ManagementPharmaceutical Development
June 7, 2024

Big Data Meets Big Pharma

Burgeoning Data The amount of data generated daily has grown exponentially in recent history. According to an article by Fabio Duarte, almost 329 million terabytes of data are generated each day, totaling 120 zettabytes annually (1 zettabyte = 1,000,000,000,000,000,000,000 bytes). Sensors on manufacturing equipment generate data based on current conditions and equipment performance. IoT devices can process this data to make immediate adjustments for optimal performance, quality, and regulatory compliance. Processing this “big data” efficiently can be a major source of competitive advantage for pharma companies. Big Data Analytics Aggregated data generated by equipment-bound sensors can differ from the data found in traditional datasets and processed by analytical methods. Traditional analysis focuses on static data and historical trends but is less effective with high-volume, real-time data. Data veracity is vital to transforming data into usable information; big datasets have a huge pool of data to offset outliers and reduce human error during analysis. Big data frameworks like open-sourced Hadoop and…
Read More