Bayesian analysis Archives - Medical Design Sourcing

In an AI hype-filled biopharma industry, one company is taking a back-to-basics yet supercomputer-powered approach — using Bayesian analysis on massive patient datasets to guide drug discovery. The company crunches trillions of data points per patient. “It’s massive, which is why we use a supercomputer,” said Niven R. Narain, Ph.D., BPGbio CEO. The company has an exclusive relationship with Oak Ridge National Labs, using its Frontier supercomputer to perform complex computational tasks, including the analysis of multi-omics data, the development of predictive models, and the simulation of biological systems. Frontier is hailed as the world’s first exascale supercomputer, meaning it can perform more than 1 quintillion calculations per second.

BPGbio’s AI-powered platform, known as NAi Interrogative Biology, illustrates its approach to drug and diagnostic discovery. The platform includes a lmassive biobank of multi-omi…

Simulated drug plasma concentration over time curves following IV infusion and multiple oral doses. Image courtesy of Wikimedia Commons.

Historically, healthcare organizations have followed the lead of published research on the efficacy of pharmacokinetic (PK) models to support point-of-care precision dosing. When a respected journal publishes a robust, peer-validated PK model analysis, clinicians often heed the results and accept that model as their organization’s default for the drug in question. However, new research shows that PK models require frequent reassessment and reevaluation — and that their utility may vary significantly depending on the site. In short, large-scale findings might not apply to an individual hospital’s patient population.

Let’s look at vancomycin, a drug commonly associated with model-informed precision dosing (MIPD). In 2020, a new consensus guideline for the therapeutic monitori…

Bayesian analysis

Supercomputer-based Bayesian approach to AI pays dividends for BPGbio

The role of the democratized research network in optimizing precision dosing