About

Stefano Menegatti is a Professor and University Faculty Scholar in the Department of Chemical and Biomolecular Engineering at NC State University. His research focuses on data and computational engineering within the broader fields of chemical engineering. As a faculty member, he contributes to advancing knowledge in these areas, supporting the department's research and educational missions.

Research topics

• Computer Science
• Materials science
• Chemistry
• Nanotechnology
• Biochemistry
• Biology
• Composite material
• Medicine
• Chromatography
• Biochemical engineering
• Engineering
• Organic chemistry
• Artificial Intelligence
• Combinatorial chemistry
• Radiology
• Chemical engineering
• Biomedical engineering
• Pathology
• Immunology
• Microbiology
• Biotechnology

Selected publications

• Label-Free Quantification of Virus Titer using Machine Learning-Enhanced Immunosensors

  IEEE Sensors Journal · 2026-01-01

  article

  Process analytical technology (PAT) for gene therapy manufactur-ing requires real-time monitoring of critical quality attributes (CQAs), yet current methods remain labor-intensive and incompatible with in-line deployment. We de-veloped a label-free electrochemical impedance spectroscopy (EIS) immunosen-sor integrated with machine learning (ML) algorithms that directly extract features from raw impedance spectra, eliminating the need for equivalent circuit modeling and enabling real-time classification and quantification. We demonstrated this platform via simultaneous classification of buffer pH conditions and quantification of adeno-associated virus titer, using legacy serotype 2 (AAV2) for proof-of-con-cept. Gold microelectrodes functionalized with anti-AAV2 antibodies were tested across four pH conditions (4, 6, 7.4, 9) and AAV2 titers spanning 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> to 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">12</sup> cap-sids·mL<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup>, capturing highly overlapping and complex electrochemical signatures. Task-specific feature selection identified optimal descriptors for classification and regression. Logistic regression and k-nearest neighbors (KNN) achieved high pH classification accuracies (training: 0.99; testing: 0.94 and 0.95, respectively). For AAV2 quantification, ensemble re-gression models XGBoost, Random Forest, and Gradient Boosting outperformed linear models, yielding training set R² values of 0.90, 0.85, and 0.81 and test set R² values of 0.78, 0.72, and 0.68, respectively. The non-Faradaic impedi-metric sensing platform, coupled with physics-informed ML feature engineering, provides a robust platform for auto-mated, label-free monitoring of viral vector CQAs in biomanufacturing environments.

  PublisherDOI

• Adaptive Machine Learning Framework for Optimizing the Affinity Purification of Adeno‐Associated Viral Vectors

  Biotechnology and Bioengineering · 2026-01-19 · 1 citations

  article

  Adeno-associated viral (AAV) vectors for gene therapy are becoming integral to modern medicine, providing therapeutic options for diseases once deemed incurable. Currently, viral vector purification is a critical bottleneck in the gene therapy industry, impacting product efficacy and safety as well as accessibility and cost to patients. Traditional methods for improving viral vector purity are resource-intensive and often fail to adjust the purification process parameters to maximize the resulting product yield and quality. To address this challenge, we developed a machine learning framework that leverages Bayesian optimization to systematically refine affinity chromatography parameters (sample load, flow rate, and the formulation of chromatographic media) to improve AAV purification. The efficiency of this closed-loop workflow in iteratively optimizing the vector's yield, purity, and transduction efficiency was demonstrated by purifying clinically relevant serotypes AAV2, AAV5, AAV6, and AAV9 from HEK293 cell lysates using the affinity adsorbent AvXcel. We show that in three (or fewer) cycles of Bayesian optimization, we elevated yields from a baseline of 70% to a remarkable 97%-99%, while reducing host cell impurities by 230- to 400-fold across all serotypes. Performing the purification process with optimized parameters consistently produced vectors with high purity and preserved high transduction activity, essential for therapeutic efficacy and safety, demonstrating the applicability of the framework across multiple serotypes-a key challenge in AAV manufacturing. This study represents the first reported application of closed-loop, data-driven Bayesian optimization for enhancing AAV productivity and quality at the affinity capture step, with demonstrated transferability of historical purification data and process knowledge. The proposed adaptive machine learning framework is efficient and applicable across serotypes, enabling rapid process development, reduced costs, and advancing the accessibility and clinical translation of AAV-based gene therapies.

  PublisherDOI

• Engineering Next Generation Cyclized Peptide Ligands for Light‐Controlled Capture and Release of Therapeutic Proteins

  UNC Libraries · 2026-04-08

  articleOpen access

  Photo‐affinity adsorbents (i.e., translucent matrices functionalized with ligands featuring light‐controlled biorecognition) represent a futuristic technology for purifying labile biologics. In this study, a framework for prototyping photo‐affinity adsorbents comprising azobenzene‐cyclized peptides (ACPs) conjugated to translucent porous beads (ChemMatrix) is presented. This approach combines computational and experimental tools for designing ACPs and investigating their light‐controlled isomerization kinetics and protein biorecognition. First, a modular design for tailoring ACP's conformation, facilitating sequencing, and streamlining the in silico modeling of cis/trans isomers and their differential protein binding is introduced. Then, a spectroscopic system for measuring the photo‐isomerization kinetics of ACPs on ChemMatrix beads is reported; using this device, it is demonstrated that the isomerization at different light intensities is correlated to the cyclization geometry, specifically the energy difference of trans versus cis isomers as calculated in silico. Also, a microfluidic device for sorting ACP‐ChemMatrix beads to select and validate photo‐affinity ligands using Vascular Cell Adhesion Molecule 1 (VCAM‐1) as target protein and cyclo AZOB [GVHAKQHRN‐K*]‐G‐ChemMatrix as model photo‐affinity adsorbent is presented. The proposed ACPs exhibit rapid and defined light‐controlled isomerization and biorecognition. Controlling the adsorption and release of VCAM‐1 using light demonstrates the potential of photo‐affinity adsorbents for targets whose biochemical liability poses challenges to its purification.

  PublisherDOI

• Synthetic Platelet Microgels Containing Fibrin Knob B Mimetic Motifs Enhance Clotting Responses

  UNC Libraries · 2026-04-15

  articleOpen access

  Native platelets are crucial players in wound healing. Key to their role is the ability of their surface receptor GPIIb/IIIa to bind fibrin at injury sites, thereby promoting clotting. When platelet activity is impaired as a result of traumatic injury or certain diseases, uncontrolled bleeding can result. To aid clotting and tissue repair in cases of poor platelet activity, our lab has previously developed synthetic platelet-like particles capable of promoting clotting and improving wound healing responses. These are constructed by functionalizing highly deformable hydrogel microparticles (microgels) with fibrin-binding ligands including a fibrin-specific whole antibody or a single-domain variable fragment. To improve the translational potential of these clotting materials, we explored the use of fibrin-binding peptides as cost-effective, robust, high-specificity alternatives to antibodies. Herein, we present the development and characterization of soft microgels decorated with the peptide AHRPYAAK that mimics fibrin knob 'B' and targets fibrin hole 'b'. These "Fibrin-Affine Microgels with Clotting Yield" (FAMCY) were found to significantly increase clot density <em>in vitro</em> and decrease bleeding in a rodent trauma model <em>in vivo</em>. These results indicate that FAMCYs are capable of recapitulating the platelet-mimetic properties of previous designs while utilizing a less costly, more translational design.

  PublisherDOI

• Sensing the future: next-generation sensors enabling the automated manufacturing of advanced biotherapeutics

  IEEE Sensors Magazine · 2026-01-01

  articleSenior author
  PublisherDOI

• Development of Peptide Glucosyltransferase Inhibitors With Comprehensive Coverage Across Clostridioides difficile Toxin B Sub‐Types

  UNC Libraries · 2026-02-06

  articleOpen access

  Clostridioides difficile infection presents an escalating clinical challenge due to the proliferation of hypervirulent and antibiotic-resistant strains. The primary symptoms of disease, namely colitis and diarrhea, are induced by the release of two toxins: TcdA and TcdB. Targeting these toxins with peptide inhibitors provides an attractive therapeutic strategy that can be used alone or synergistically with standard antibiotic treatments to alleviate severe symptoms and reduce the risk of resistance development. In this study, we present the rational discovery and optimization of potent TcdB peptide inhibitors. The lead sequences effectively inhibit TcdB glucosyltransferase activity, the crucial enzymatic process leading to disease symptoms, by directly competing with the toxin's molecular targets, Rho proteins. Detailed enzymatic studies also elucidate distinct Michaelis constants, K_M, for each substrate, UDP-glucose and Rho-proteins, for multiple TcdB GTD subtypes. The selected peptides demonstrated broad efficacy against the three most common TcdB subtypes, which are used in over 90% of clinical isolates. Additionally, the peptides delayed TcdB-induced loss of barrier integrity and decreased apoptosis in a primary human colon epithelial monolayer model. This study highlights a novel therapeutic avenue with significant potential to enhance the treatment and management of C. difficile infections.

  PublisherDOI

• Adaptive Machine Learning Framework for Optimizing the Affinity Purification of Adeno‐Associated Viral Vectors

  Open MIND · 2026-01-01

  article

  Adeno-associated viral (AAV) vectors for gene therapy are becoming integral to modern medicine, providing therapeutic options for diseases once deemed incurable. Currently, viral vector purification is a critical bottleneck in the gene therapy industry, impacting product efficacy and safety as well as accessibility and cost to patients. Traditional methods for improving viral vector purity are resource-intensive and often fail to adjust the purification process parameters to maximize the resulting product yield and quality. To address this challenge, we developed a machine learning framework that leverages Bayesian optimization to systematically refine affinity chromatography parameters (sample load, flow rate, and the formulation of chromatographic media) to improve AAV purification. The efficiency of this closed-loop workflow in iteratively optimizing the vector's yield, purity, and transduction efficiency was demonstrated by purifying clinically relevant serotypes AAV2, AAV5, AAV6, and AAV9 from HEK293 cell lysates using the affinity adsorbent AvXcel. We show that in three (or fewer) cycles of Bayesian optimization, we elevated yields from a baseline of 70% to a remarkable 97%-99%, while reducing host cell impurities by 230- to 400-fold across all serotypes. Performing the purification process with optimized parameters consistently produced vectors with high purity and preserved high transduction activity, essential for therapeutic efficacy and safety, demonstrating the applicability of the framework across multiple serotypes-a key challenge in AAV manufacturing. This study represents the first reported application of closed-loop, data-driven Bayesian optimization for enhancing AAV productivity and quality at the affinity capture step, with demonstrated transferability of historical purification data and process knowledge. The proposed adaptive machine learning framework is efficient and applicable across serotypes, enabling rapid process development, reduced costs, and advancing the accessibility and clinical translation of AAV-based gene therapies.

• Development of Peptide Glucosyltransferase Inhibitors With Comprehensive Coverage Across Clostridioides difficile Toxin B Sub‐Types

  Open MIND · 2026-01-01

  article

  Clostridioides difficile infection presents an escalating clinical challenge due to the proliferation of hypervirulent and antibiotic-resistant strains. The primary symptoms of disease, namely colitis and diarrhea, are induced by the release of two toxins: TcdA and TcdB. Targeting these toxins with peptide inhibitors provides an attractive therapeutic strategy that can be used alone or synergistically with standard antibiotic treatments to alleviate severe symptoms and reduce the risk of resistance development. In this study, we present the rational discovery and optimization of potent TcdB peptide inhibitors. The lead sequences effectively inhibit TcdB glucosyltransferase activity, the crucial enzymatic process leading to disease symptoms, by directly competing with the toxin's molecular targets, Rho proteins. Detailed enzymatic studies also elucidate distinct Michaelis constants, K_M, for each substrate, UDP-glucose and Rho-proteins, for multiple TcdB GTD subtypes. The selected peptides demonstrated broad efficacy against the three most common TcdB subtypes, which are used in over 90% of clinical isolates. Additionally, the peptides delayed TcdB-induced loss of barrier integrity and decreased apoptosis in a primary human colon epithelial monolayer model. This study highlights a novel therapeutic avenue with significant potential to enhance the treatment and management of C. difficile infections.

• Adaptive Machine Learning Framework for Optimizing the Affinity Purification of Adeno‐Associated Viral Vectors

  UNC Libraries · 2026-01-29

  articleOpen access

  Adeno-associated viral (AAV) vectors for gene therapy are becoming integral to modern medicine, providing therapeutic options for diseases once deemed incurable. Currently, viral vector purification is a critical bottleneck in the gene therapy industry, impacting product efficacy and safety as well as accessibility and cost to patients. Traditional methods for improving viral vector purity are resource-intensive and often fail to adjust the purification process parameters to maximize the resulting product yield and quality. To address this challenge, we developed a machine learning framework that leverages Bayesian optimization to systematically refine affinity chromatography parameters (sample load, flow rate, and the formulation of chromatographic media) to improve AAV purification. The efficiency of this closed-loop workflow in iteratively optimizing the vector's yield, purity, and transduction efficiency was demonstrated by purifying clinically relevant serotypes AAV2, AAV5, AAV6, and AAV9 from HEK293 cell lysates using the affinity adsorbent AvXcel. We show that in three (or fewer) cycles of Bayesian optimization, we elevated yields from a baseline of 70% to a remarkable 97%-99%, while reducing host cell impurities by 230- to 400-fold across all serotypes. Performing the purification process with optimized parameters consistently produced vectors with high purity and preserved high transduction activity, essential for therapeutic efficacy and safety, demonstrating the applicability of the framework across multiple serotypes-a key challenge in AAV manufacturing. This study represents the first reported application of closed-loop, data-driven Bayesian optimization for enhancing AAV productivity and quality at the affinity capture step, with demonstrated transferability of historical purification data and process knowledge. The proposed adaptive machine learning framework is efficient and applicable across serotypes, enabling rapid process development, reduced costs, and advancing the accessibility and clinical translation of AAV-based gene therapies.

  PublisherDOI

• Peptonics: Shear protection and foam prevention in mammalian cell cultures producing monoclonal antibodies and viral vectors for gene therapy

  Journal of Biotechnology · 2026-03-11

  articleOpen accessSenior authorCorresponding

  Biologics produced by cell culture are central to modern pharmaceutical production. In this context, the surfactants added to culture media for protecting cells from shear are essential for ensuring product yield and quality. Commercial surfactants exhibit lot-to-lot variability, which has caused failures in production campaigns. Addressing this issue, our team introduced Peptonics, a family of peptidic surfactants that offer comparable tensioactivity to Pluronics while improving cell productivity and removing foaming issues. Peptonics were originally demonstrated on model Chinese Hamster Ovary (CHO) cells cultured in stirred-tank bioreactors and different growth media. Herein, we extend the application of Peptonic ih-T1010 to other CHO and HEK293 cells producing monoclonal antibodies (mAbs) and adeno-associated virus (AAVs) in selected media. In fed-batch bioreactors, ih-T1010 supported peak viable CHO cell densities up to 1.3·10 7 cells·mL -1 , comparable to Poloxamer F68 (1.35·10 7 cells·mL -1 ) and significantly higher than surfactant-free controls. Final mAb titers achieved using ih-T1010 (4.0 - 7.5 g·L -1 ) were on par with those obtained with Poloxamer F68 (4.5 - 7.3 g·L -1 ), with no significant difference in product glycosylation or aggregation profiles. Performance robustness was confirmed across three commercial media and an additional CHO cell line, where ih-T1010 consistently supported cell growth and productivity within 5% of Poloxamer F68. When tested on HEK293 cells expressing AAV9, both surfactant yielded titers of 1.4 - 1.6 · 10 12 vp·mL -1 and maintained cell viability >90% throughout the cultures. Notably, ih-T1010 eliminated foam formation, enabling antifoam-free operation, whereas Poloxamer-containing cultures required antifoam to avoid foam-out risks. Finally, the choice of surfactant did not affect the performance of affinity resins, as the binding capacity, yield, and purity remained equivalent for both mAbs and AAVs. • Peptonics provide consistent shear protection in both CHO and HEK293 cell cultures. • Peptonic ih-T1010 prevents the formation of foam in stirred bioreactors. • ih-T1010 supported viable densities up to 1.3·10 7 CHO cells·mL -1 and 5·10 6 HEK293 cells·mL -1 . • CHO cells cultured in media supplemented with ih-T1010 expressed up to 7.5 g of mAb per L. • HEK293 cells cultured in media supplemented with ih-T1010 expressed up to 1.6·10 12 AAV/mL.

  PublisherDOI

Recent grants

• A Therapeutic Cell Distillery: Light-Controlled Fractionation of Stem Cells for Next-Generation Biomanufacturing Processes

  NSF · $381k · 2018–2023

• CAREER: Light- and temperature- controlled peptide ligands for purifying blood factors and orphan enzyme drugs

  NSF · $522k · 2017–2023

Frequent coauthors

• Michael A. Daniele

  University of North Carolina at Chapel Hill

  87 shared

• Eduardo Barbieri

  North Carolina State University

  47 shared

• Wenning Chu

  North Carolina State University

  37 shared

• Shriarjun Shastry

  North Carolina State University

  35 shared

• Ruben G. Carbonell

  North Carolina State University

  32 shared

• Sobhana A. Sripada

  North Central State College

  24 shared

• Ryan Kilgore

  24 shared

• Brandyn Moore

  North Carolina State University

  22 shared

Education

• Ph.D., Chemical Engineering

  University of California, Berkeley

  2005

• M.S., Chemical Engineering

  University of California, Berkeley

  2001

• B.S., Chemical Engineering

  University of California, Berkeley

  1999

Awards & honors

• ACS BIOT Young Investigator Award (2025)
• Goodnight Early Career Innovator (2021)
• ALCOA Foundation Research Achievement Award (2021)
• University Faculty Scholar (2021)
• AIM-Bio Grant (2019)