About

Theodore W. Randolph is a Gillespie Professor in the Department of Chemical and Biological Engineering at the University of Colorado Boulder, where he also serves as Co-Director of the Center for Pharmaceutical Biotechnology. His educational background includes a Bachelor of Science from the University of Colorado (1983) and a PhD from the University of California, Berkeley (1987). Randolph's research focuses on the stabilization and formulation of therapeutic proteins, addressing the challenges associated with converting molecules into drugs, particularly protein-based pharmaceuticals. His work involves understanding the mechanisms of protein degradation and instability, utilizing various spectroscopic and physical techniques to explore how solution and process variables influence protein stability during manufacturing, storage, and delivery. Additionally, Randolph investigates the immunogenicity of protein therapeutics, studying how aggregates and contaminants affect immune responses, with particular attention to vaccine stability and efficacy. His contributions aim to improve the safety, efficacy, and shelf life of therapeutic proteins, tackling complex engineering problems in biopharmaceutical development.

Research topics

• Materials science
• Computer Science
• Chemistry
• Nanotechnology
• Biology
• Mathematics
• Biochemistry
• Artificial Intelligence
• Medicine
• Immunology
• Composite material
• Mechanical engineering
• Statistics
• Biological system
• Chemical engineering
• Biochemical engineering
• Chromatography
• Organic chemistry
• Food science
• Environmental science
• Engineering
• Physics
• Biophysics
• Optics

Selected publications

• Single administration of mosaic-8b RBD-nanoparticle vaccine prepared with atomic layer deposition technology elicits broadly neutralizing anti-sarbecovirus responses

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-06-18

  preprintOpen accessCorresponding

  Atomic layer deposition (ALD), a new vaccine technology, permits multiple dosing with a single administration by pulsatile release of one or more immunogens. We evaluated ALD delivery of mosaic-8b [60-mer nanoparticles presenting 8 different SARS-like betacoronavirus (sarbecovirus) receptor-binding domains (RBDs)] that elicits broadly cross-reactive antibodies and protects against mismatched sarbecoviruses not represented by RBDs on mosaic-8b. Compared with conventional prime-boost immunizations, ALD-delivered mosaic-8b RBD-nanoparticles elicited antibodies in both naïve and pre-vaccinated mice with improved mismatched binding and neutralization. Results of RBD epitope mapping of serum antibodies from ALD-delivered mosaic-8b were consistent with broader coverage of RBD epitopes compared to conventional immunizations, and systems serology revealed distinct IgG subclass and FcγR-binding IgG distributions. These results suggest that ALD is a promising technology for use with mosaic-8b RBD-nanoparticle vaccines to protect against future sarbecovirus spillovers and support applications for ALD vaccine delivery to elicit cross-reactive antibodies against rapidly mutating or diverse pathogens.

  PublisherOA PDFDOI

• Assessing subvisible particle risks in monoclonal antibodies: insights from quartz crystal microbalance with dissipation, machine learning, and in silico analysis

  mAbs · 2025-05-11 · 2 citations

  articleOpen access

  models in evaluating mAb developability and their tendency to form interface-mediated SVPs, providing a strategy to mitigate risks associated with SVP formation in biotherapeutic development.

  PublisherDOI

• Superior immune responses from thermostable, single-administration rabies vaccines prepared using atomic layer deposition

  Journal of Pharmaceutical Sciences · 2025-08-05 · 4 citations

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Representative training data sets are critical for accurate machine-learning classification of microscopy images of particles formed by lipase-catalyzed polysorbate hydrolysis

  Journal of Pharmaceutical Sciences · 2025-01-16 · 1 citations

  articleSenior author
  PublisherDOI

• Broad anti-sarbecovirus responses elicited by a single administration of mosaic-8 RBD-nanoparticle vaccine prepared using atomic layer deposition

  iScience · 2025-09-25 · 3 citations

  articleOpen access

  Atomic layer deposition (ALD), a new vaccine technology, permits multiple dosing with a single administration by pulsatile release of one or more immunogens. We evaluated ALD delivery of mosaic-8b [60-mer nanoparticles presenting 8 different SARS-like betacoronavirus (sarbecovirus) receptor-binding domains (RBDs)] that elicit broadly cross-reactive antibodies and protect against mismatched sarbecoviruses not represented by RBDs on mosaic-8b. Compared with conventional prime-boost immunizations, ALD-delivered mosaic-8b RBD-nanoparticles elicited antibodies in both naive and pre-vaccinated mice with improved mismatched binding and neutralization. Results of the RBD epitope mapping of serum antibodies from ALD-delivered mosaic-8b were consistent with broader coverage of RBD epitopes compared to conventional immunizations, and systems serology revealed distinct IgG subclass and FcγR-binding IgG distributions. These results suggest that ALD is a promising technology for use with mosaic-8b RBD-nanoparticle vaccines to protect against future sarbecovirus spillovers and support applications for ALD vaccine delivery to elicit cross-reactive antibodies against rapidly mutating or diverse pathogens.

  PublisherDOI

• Unsupervised Machine Learning‐Based Process Analytical Tools for Near Real‐Time Cell Morphology Analysis During CAR‐T Cell Manufacturing

  Biotechnology and Bioengineering · 2025-06-16 · 5 citations

  articleSenior authorCorresponding

  Cell therapies like Chimeric Antigen Receptor (CAR)-T cell therapy deliver living cells to patients as active pharmaceutical ingredients. Manufacturing of these cells is complex, often yielding, heterogeneous products and high failure rates. Quality control (QC) assays used in CAR-T cell production primarily provide end-point product testing. Real-time process monitoring would be ideal to reduce failure rates and ensure final product quality. However, current analytical tools often fall short due to the heterogeneity of CAR-T cell products and their sensitivity to process changes. In this study, we showcase unsupervised image-based machine learning as a process analytical tool (PAT) for near real-time process monitoring during the production of CAR-T cells. Flow imaging microscopy (FIM) images of T cells collected from nine healthy donors were recorded during the activation, lentiviral-based transduction (expressing CD19 CAR protein), and expansion stages of CAR-T cell production. These images were used to train a Variational Autoencoder (VAE), allowing quantitative tracking of changes in cell morphologies during the various stages of production of CAR-T cells from each donor. Findings include observation of a new, transient population in T cells transduced to express CAR protein. This population was absent in T cells that were not transduced. The density of the new population was proportional to the transduction efficiency determined by traditional stain-based flow cytometry assays. Together, this study demonstrates the utility of using VAEs as a PAT tool for monitoring patient-to-patient variability and early detection of process deviations/upsets.

  PublisherDOI

• Lipid-free, thermostable mRNA vaccines prepared using atomic layer deposition

  Journal of Pharmaceutical Sciences · 2025-11-17 · 2 citations

  articleSenior author
  PublisherDOI

• Stabilization of an Infectious Enveloped Virus by Spray-Drying and Lyophilization

  Journal of Pharmaceutical Sciences · 2024-04-21 · 6 citations

  articleOpen accessSenior authorCorresponding
  PublisherOA PDFDOI

• Formulation of three tailed bacteriophages by spray-drying and atomic layer deposition for thermal stability and controlled release

  Journal of Pharmaceutical Sciences · 2024-08-22 · 7 citations

  articleSenior author
  PublisherDOI

• Supervised and unsupervised machine learning approaches for monitoring subvisible particles within an aluminum‐salt adjuvanted vaccine formulation

  Biotechnology and Bioengineering · 2024-02-19 · 6 citations

  articleSenior authorCorresponding

  Suspensions of protein antigens adsorbed to aluminum-salt adjuvants are used in many vaccines and require mixing during vial filling operations to prevent sedimentation. However, the mixing of vaccine formulations may generate undesirable particles that are difficult to detect against the background of suspended adjuvant particles. We simulated the mixing of a suspension containing a protein antigen adsorbed to an aluminum-salt adjuvant using a recirculating peristaltic pump and used flow imaging microscopy to record images of particles within the pumped suspensions. Supervised convolutional neural networks (CNNs) were used to analyze the images and create "fingerprints" of particle morphology distributions, allowing detection of new particles generated during pumping. These results were compared to those obtained from an unsupervised machine learning algorithm relying on variational autoencoders (VAEs) that were also used to detect new particles generated during pumping. Analyses of images conducted by applying both supervised CNNs and VAEs found that rates of generation of new particles were higher in aluminum-salt adjuvant suspensions containing protein antigen than placebo suspensions containing only adjuvant. Finally, front-face fluorescence measurements of the vaccine suspensions indicated changes in solvent exposure of tryptophan residues in the protein that occurred concomitantly with new particle generation during pumping.

  PublisherDOI

Recent grants

• Mechanisms of Damage to Pharmaceutical Proteins at Oil-Water Interfaces

  NSF · $339k · 2011–2015

• Quantitative Biotherapeutic Particle Characterization via Deep Neural Networks

  NIH · $222k · 2018–2021

• NIH Grant T32GM008732

  NIH · $2.7M · 2016

• Effect of Container Surface Structure on Cavitation-Induced Protein Particulate Formation

  NIH · $524k · 2018–2020

• Biotechnology Research Partnerships Renewal: Aggregation of Therapeutic Proteins

  NIH · $7.2M · 2006–2018

Frequent coauthors

• John F. Carpenter

  212 shared

• Mark C. Manning

  Office of Legacy Management

  52 shared

• Sampathkumar Krishnan

  34 shared

• Brent S. Kendrick

  29 shared

• Corinne Lengsfeld

  University of Denver

  26 shared

• Eva Y.

  23 shared

• Byeong S. Chang

  21 shared

• Jonathan N. Webb

  Eli Lilly (United States)

  17 shared

Education

• B.S.

  University of Colorado

  1983

• Ph.D.

  University of California, Berkeley

  1987

Awards & honors

• National Academy of Inventors 2021
• College of Engineering Dean's Faculty Fellowship 2016
• College of Engineering Dean’s Faculty Fellowship 2011-12
• AAPS Dale E. Wurster Research Award in Pharmaceutics, 2010
• Triennial John M. Prausnitz Award in Applied Chemical Thermo…