Bayesian Network Based Risk and Sensitivity Analysis for Production Process Stability Control

The biomanufacturing industry is growing rapidly and becoming one of the key drivers of personalized medicine and life science. However, biopharmaceutical production faces critical challenges, including complexity, high variability, long lead time and rapid changes in technologies, processes, and regulatory environment. Driven by these challenges, we explore the biotechnology domain knowledge and propose a rigorous risk and sensitivity analysis framework for biomanufacturing innovation. Built on the causal relationships of raw material quality attributes, production process, and bio-drug properties in safety and efficacy, we develop a Bayesian Network (BN) to model the complex probabilistic interdependence between process parameters and quality attributes of raw materials/in-process materials/drug substance. It integrates various sources of data and leads to an interpretable probabilistic knowledge graph of the end-to-end production process. Then, we introduce a systematic risk analysis to assess the criticality of process parameters and quality attributes. The complex production processes often involve many process parameters and quality attributes impacting on the product quality variability. However, the real-world (batch) data are often limited, especially for customized and personalized bio-drugs. We propose uncertainty quantification and sensitivity analysis to analyze the impact of model risk. Given very limited process data, the empirical results show that we can provide reliable and inter-Corresponding author Email addresses: w.xie@northeastern.edu Thus, the proposed framework can provide the science-and risk-based guidance on the process monitoring, data collection, and process parameters specifications to facilitate the production process learning and stability control. Keywords: Decision analysis, biomanufacturing, Bayesian network, production process risk analysis, sensitivity analysis 2017 MSC: 00-01, 99-00 1. Introduction In the past decades, pharmaceutical companies have invested billions of dollars in the research and development (R&D) of new biomedicines for the treatment of many severe illnesses, including cancer cells and adult blindness. More than 40 percent of the overall pharmaceutical industry R&D and products in the development pipeline are biopharmaceuticals and this percentage is expected to continuously increase. Compared to the classical pharmaceutical manufacturing, biopharmaceutical production faces several challenges, including complexity, high variability, long lead time and rapid changes in technologies, processes, and regulatory environment (Kaminsky & Wang, 2015). Biotechnology products are produced in living organisms, which induces a lot of uncertainty in the production process.