# A Novel First-in-class 3D Printing Technology for Advanced Manufacturing of Complex Vaccine Formulations against Influenza and Emerging Infectious Diseases

> **NIH FDA R01** · UNIVERSITY OF TEXAS AT AUSTIN · 2021 · $498,189

## Abstract

PROJECT SUMMARY: Vaccination is known to be the most effective strategy to manage the spread and
deleterious impact of various infectious diseases including the most recent emerging, coronavirus disease 2019,
COVID-19. Recombinant protein subunit vaccines have demonstrated promising results for immunization
against infectious diseases recently. These vaccines are manufactured through recombinant DNA technology in
which the gene fragment that encodes the production of the recombinant protein is introduced to a host cell as
an expression system. The genetically engineered cells can proliferate and produce a high amount of the protein
of the target which can be separated and purified in the succeeding steps. The recent progress in genetic tool
development to manipulate the microorganisms and utilization of mammalian cell lines in biopharmaceutical
manufacturing have projected the global protein markets to reach $228.4 billion by the end of this year. However,
this industry is still overloaded with processes that lack flexibility and process controls or integration needed for
continuous or on demand production capacity. There is no biomanufacturing system that can produce
recombinant proteins through a single-step continuous manufacturing process. So, due to the high demand for
vaccines all over the world, there’s an immense need for highly efficient yet inexpensive technologies. Yeast
expression systems such as Pichia pastoris (P. pastoris) can be used as an expression host cell which offers
numerous advantages over traditional systems including high growth rate, easy genetic manipulation process,
high yield protein expression, performing eukaryotic post-translational modifications, appropriate protein folding
and protein secretion in the external medium and easy purification process.In this project we will utilise a novel
Sprayed Multi Adsorbed-particle Reposing Technology (SMART 3D printing technique to produce biocompatible
Pluronic (F127)-bisurethane methacrylate (F127-BUM) polymers based microcarrier immobilised with P. pastoris
which can be used in large-scale fermentations for production of recombinant proteins. Our SMART technology
meets the requirements for recombinant proteins manufacturing such as ease of scale-up, correct protein folding,
and short post-production processing. It also has the potential to improve agility, flexibility, cost, and robustness
in the manufacturing processes for complex protein-based biologics.Additionally, in contrast to other particulate
fabrication techniques, SMART can incorporate live cells during the single-step microparticle formulation
process. This technology can easily host further ancillary processes such as ultra-low temperature freezing print
bed (-80oC or lower), fibre optic probes for the inline monitoring of critical product quality attributes (CQAs) such
as viscosity, content uniformity and stability, making it accessible to industry in the near term with a robust control
strategy. Our SMART wi...

## Key facts

- **NIH application ID:** 10407414
- **Project number:** 1R01FD007456-01
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** Mohammed Maniruzzaman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** FDA
- **Fiscal year:** 2021
- **Award amount:** $498,189
- **Award type:** 1
- **Project period:** 2021-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10407414

## Citation

> US National Institutes of Health, RePORTER application 10407414, A Novel First-in-class 3D Printing Technology for Advanced Manufacturing of Complex Vaccine Formulations against Influenza and Emerging Infectious Diseases (1R01FD007456-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10407414. Licensed CC0.

---

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