SBIR-STTR Award

Computational Methods for Optimized Biologics Formulation
Award last edited on: 5/21/2023

Sponsored Program
SBIR
Awarding Agency
NIH : NIGMS
Total Award Amount
$1,978,242
Award Phase
2
Solicitation Topic Code
859
Principal Investigator
Sunhwan Jo

Company Information

Silcsbio LLC

801 West Baltimore Street Suite
Baltimore, MD 21201
   (410) 929-5783
   info@silcsbio.com
   www.silcsbio.com
Location: Single
Congr. District: 07
County: Baltimore City

Phase I

Contract Number: 1R43GM130198-01A1
Start Date: 3/5/2019    Completed: 2/29/2020
Phase I year
2019
Phase I Amount
$225,000
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 would otherwise pose an aggregation risk 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 crude computational methods that do not take into account the molecular details of excipient-protein interactions. To understand at the level of atomic interactions how excipient/buffer combinations modulate protein stability and aggregation requires a new approach, which if successfully developed can provide mechanistic insight for rational formulation. The established Site Identification by Ligand Competitive Saturation (SILCS) computational platform technology maps, at atomic resolution, the affinity pattern of the complete 3D surface of a protein for a wide diversity of chemical functional groups. The functional group affinity pattern can be used to determine excipients that can bind to and stabilize the active, folded conformation of a protein and bind to and block those regions of the protein that may participate in PPI, thereby inhibiting aggregation. The broad goal of the proposal is to develop comprehensive and industry-ready workflows for screening excipients that could stabilize the protein structure and prevent aggregation.. New computational tools and a graphical user interface will be developed to manage and apply the extensive information generated by SILCS excipient screening and PPI analysis. The tools will predict excipient/buffer binding locations and potential regions that can participate in PPI across the complete protein surface. This information will then be applied to predict excipients that will stabilize the folded, biologically active state of the protein and block PPI thereby slowing aggregation while simultaneously considering the impact of buffers. The proposed approaches will be thoroughly validated in collaboration with industrial and government partners against established experimental methods on well-characterized systems. 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 formulation of biologics as well as lead to improved formulations thereby improving clinical outcomes. These capabilities will be implemented in the context of industry-ready workflows for direct sale to pharmaceutical companies and for use in contract research for the optimized formulation of biologics.

Public Health Relevance Statement:


Project narrative:
Biopharmaceuticals, including monoclonal antibodies, represent a growing and important area of new therapeutic agent development, but the formulation of biopharmaceuticals remains a bottleneck. Proposed is a rational formulation design technology using computational methods that will allow screening of a large number of excipient/buffer combinations that will result in accelerated and improved biopharmaceutical development thereby facilitating bringing these agents to market as well as improving clinical outcomes. !

Project Terms:
3-Dimensional; Adoption; Affinity; Area; atomic interactions; base; Binding; Binding Proteins; Binding Sites; Biological; Biological Products; Biological Response Modifier Therapy; Buffers; Chemicals; Clinical; Collaborations; computational platform; Computer software; computerized tools; Computing Methodologies; cost; Data; design; Development; Dimensions; Engineering; Excipients; experimental study; Failure; FDA approved; flexibility; Formulation; Fourier Transform; functional group; Goals; Government; graphical user interface; Growth; Human; improved; Industrialization; Industry; injection/infusion; insight; Insulin; Lead; Letters; Ligands; Location; Maps; meetings; Membrane Proteins; Methodology; Methods; Modeling; Molecular; Molecular Conformation; Monoclonal Antibodies; novel strategies; novel therapeutics; Outcome; Patients; Pattern; Pharmaceutical Preparations; Pharmacologic Substance; prevent; Probability; Protein Engineering; protein folding; protein protein interaction; Protein Region; protein structure; Proteins; Recombinants; Research Contracts; Resolution; Risk; Roentgen Rays; Safety; Sales; Sampling; screening; Site; Structural Protein; Structure; Surface; System; Technology; Testing; Therapeutic; therapeutic development; therapeutic protein; three dimensional structure; Time; tool; trend; Viscosity

Phase II

Contract Number: 2R44GM130198-02A1
Start Date: 3/5/2019    Completed: 7/31/2023
Phase II year
2021
(last award dollars: 2022)
Phase II Amount
$1,753,242

Protein-based biologics - therapeutics whose active ingredient is a protein and most commonly a monoclonalantibody (mAb) - make up a $200 billion/year market that is expected to double in size by 2025. A criticalcomponent in the safety and efficacy of biologics is the need to maintain the active protein during long-termstorage 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 unfoldingand to block sites on the folded protein that may pose an aggregation risk and lead to elevated viscosity due toundesirable protein-protein interactions (PPI). Importantly, formulation can be done without altering the sequenceof (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 orcomputational methods that do not take into account the molecular details of excipient-protein interactions. Theestablished Site Identification by Ligand Competitive Saturation (SILCS) computational platform technologymaps 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 inhibitingaggregation and decreasing viscosity. The broad goal of the proposal is the continued development andvalidation of SILCS-Biologics as an industry-ready workflow and a graphical user interface to manage and applythe extensive information generated by SILCS excipient screening and PPI analysis. In the proposed studiesexperimental efforts will be undertaken to generate data for model training and validation across a variety ofproteins and commonly used excipients. That data will be then combined with computed SILCS metrics includingexcipient binding locations and affinities and potential regions that can participate in PPI across the completeprotein surface, including the impact of pH and the unique properties of Arginine as an excipient. This informationwill then be applied in the context of machine learning to develop models that will predict excipients that will blockPPI thereby lowering viscosity and slowing aggregation as well as stabilize the folded, biologically active stateof the protein. The proposed models will be validated at the University of Maryland, Baltimore and with industrialand government partners against established experimental methods on a range of proteins with varioustherapeutic indications. Upon successful completion of the project new offerings will be added to the existingSILCS software suite that will minimize the time and costs requirements for the formulation of biologics as wellas lead to improved formulations thereby improving clinical outcomes. These capabilities will be implemented inthe context of industry-ready workflows for direct sale to pharmaceutical companies and for use in contractresearch for the optimized formulation of biologics.

Public Health Relevance Statement:


Project narrative:
Biopharmaceuticals, including monoclonal antibodies, represent a growing and important area of new therapeutic agent development, but the formulation of biopharmaceuticals remains a bottleneck. Proposed is the continuation of a successful Phase 1 effort to use a rational formulation design technology using computational methods to allow screening of a large number of excipient/buffer combinations that will result in accelerated and improved biopharmaceutical development thereby facilitating bringing these agents to market as well as improving clinical outcomes.

Project Terms: