# Computational methods for optimized biologics formulation

> **NIH NIH R44** · SILCSBIO, LLC · 2021 · $885,774

## Abstract

Project Summary:
Protein-based biologics – therapeutics whose active ingredient is a protein and most commonly a monoclonal
antibody (mAb) – make up a $200 billion/year market that is expected to double in size by 2025. A critical
component in the safety and efficacy of biologics is the need to maintain the active protein during long-term
storage and subsequent injection/infusion. The selection of excipients and buffers toward this end is termed
“formulation.” Proper formulation of a protein-based drug is essential to stabilize the active protein from unfolding
and to block sites on the folded protein that may pose an aggregation risk and lead to elevated viscosity due to
undesirable protein-protein interactions (PPI). Importantly, formulation can be done without altering the sequence
of (i.e. re-engineering) the protein and is thus an independent tool for bringing a biologic therapeutic to market.
Current approaches to choosing an optimized formulation are either low-throughput experiments or
computational methods that do not take into account the molecular details of excipient-protein interactions. The
established Site Identification by Ligand Competitive Saturation (SILCS) computational platform technology
maps the affinity pattern of the complete 3D surface of a protein for a wide diversity of chemical functional groups.
The functional group affinity pattern is then used to determine excipients that can bind to and stabilize the active,
folded conformation of a protein and bind to regions of the protein that may participate in PPI, thereby inhibiting
aggregation and decreasing viscosity. The broad goal of the proposal is the continued development and
validation of SILCS-Biologics as an industry-ready workflow and a graphical user interface to manage and apply
the extensive information generated by SILCS excipient screening and PPI analysis. In the proposed studies
experimental efforts will be undertaken to generate data for model training and validation across a variety of
proteins and commonly used excipients. That data will be then combined with computed SILCS metrics including
excipient binding locations and affinities and potential regions that can participate in PPI across the complete
protein surface, including the impact of pH and the unique properties of Arginine as an excipient. This information
will then be applied in the context of machine learning to develop models that will predict excipients that will block
PPI thereby lowering viscosity and slowing aggregation as well as stabilize the folded, biologically active state
of the protein. The proposed models will be validated at the University of Maryland, Baltimore and with industrial
and government partners against established experimental methods on a range of proteins with various
therapeutic indications. Upon successful completion of the project new offerings will be added to the existing
SILCS software suite that will minimize the time and costs requirements for the formulatio...

## Key facts

- **NIH application ID:** 10257518
- **Project number:** 2R44GM130198-02A1
- **Recipient organization:** SILCSBIO, LLC
- **Principal Investigator:** Sunhwan Jo
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $885,774
- **Award type:** 2
- **Project period:** 2019-03-05 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10257518

## Citation

> US National Institutes of Health, RePORTER application 10257518, Computational methods for optimized biologics formulation (2R44GM130198-02A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10257518. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*