# Smart Data Analytics for Risk Based Regulatory Science and Bioprocessing Decisions

> **NIH FDA U01** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2020 · $999,979

## Abstract

Project Summary/Abstract
This proposal aims to apply data analytic solutions to challenges encountered in biomanufacturing
operations. We will develop and validate two process modeling tools that manufacturers can use
to quantitatively assess the process risks associated with their choice of manufacturing model.
The first tool will assist manufacturers in their selection of the appropriate manufacturing model.
The second tool provides a comprehensive first-principles model of a biopharmaceutical
manufacturing operation which allows the user to test in silico a variety of models and quantitate
the risk incurred in each choice. We will experimentally validate performance of these tools in a
batch and continuous monoclonal antibody manufacturing process in a testbed facility.
Additionally, we will investigate data analytic methods to appropriately incorporate textual data
sources into plant-wide operational models so that manufacturers are able to utilize all of the
relevant information available to them to optimize their ability to supply quality medicines to
patients. Finally, we will investigate the use of data analytic methods to better connect the
manufacturing process to clinical experiential data by incorporation of external data sources
generated after commercial product launch, such as adverse events, outcomes data, published
research, and other textual data. This work will demonstrate how data analytics can leverage
additional data sources to inform manufacturers’ risk-based decision making. In addition, the tools
developed under this proposal will also function as training tools for regulators, who can use them
to increase their own understanding of how analytical tools used in development of the
manufacturing model and control strategy affect product quality and the risk incurred through
selection of an inappropriate model. At its completion this work will improve (a) the regulatory
process by increasing understanding around the process of choosing manufacturing process
models, (b) product quality by ensuring manufacturers have the skills to choose the appropriate
tools for their application, and (c) safety and efficiency through optimization of manufacturing
operations.

## Key facts

- **NIH application ID:** 9976991
- **Project number:** 5U01FD006483-03
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Richard Dean Braatz
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** FDA
- **Fiscal year:** 2020
- **Award amount:** $999,979
- **Award type:** 5
- **Project period:** 2018-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9976991

## Citation

> US National Institutes of Health, RePORTER application 9976991, Smart Data Analytics for Risk Based Regulatory Science and Bioprocessing Decisions (5U01FD006483-03). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/9976991. Licensed CC0.

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