In Silico Strategies for Enhancing Antibody Expression, Stability, and Aggregation Resistance in Biopharmaceutical Production

Authors

  • David Aphkhazava PhD, Professor, University of Georgia, Tbilisi, Georgia. Orcid: https://orcid.org/0000- 0001- 6216-6477
  • Ilia Atanelishvili Medical University of South Carolina, Charleston, SC, USA
  • Levan Gulua PhD, Professor, Head of bachelor program of Biomedicine at University of Georgia, Tbilisi, Georgia
  • Cezar Goletiani Professor at Free University of Tbilisi, Tbilisi, Georgia, Head scientist at Agricultural University of Georgia, Tbilisi, Georgia
  • Nino Nebieridze Associate Professor at Free University of Tbilisi, Tbilisi, Georgia
  • Nikita Singh University of Georgia, Tbilisi, Georgia.
  • Reuben Quadras University of Georgia, Tbilisi, Georgia.
  • Anushka PM Rane Alte University, Tbilisi, Georgia.
  • Mohammed Irfan University of Georgia, Tbilisi, Georgia.
  • Lolita Shengelia PhD, Invited lecturer of Georgian National University, Tbilisi, Georgia; Invited lecturer of Georgian American University, Tbilisi, Georgia
  • Ketevan Chakhnashvili Clinical Director at Pineo Medical Ecosystem. Vice Dean of School of Medicine at Grigol Robakidze University. Tbilisi, Georgia
  • Nodar Sulashvili MD, PhD, Doctor of Pharmaceutical and Pharmacological Sciences In Medicine, Invited Lecturer (Professor) of Scientific Research-Skills Center at Tbilisi State Medical University; Professor of Medical and Clinical Pharmacology of International School of Medicine at Alte University; Professor of Pharmacology of Faculty of Medicine at Georgian National University SEU, Associate Affiliated Professor of Medical Pharmacology of Faculty of Medicine at Sulkhan-Saba Orbeliani University; Associate Professor of Medical Pharmacology at School of Medicine at David Aghmashenebeli University of Georgia; Associate Professor of Biochemistry and Pharmacology Direction of School of Health Sciences at the University of Georgia. Associate Professor of Pharmacology of Faculty of Dentistry and Pharmacy at Tbilisi Humanitarian Teaching University; Tbilisi, Georgia; Orcid: https://orcid.org/0000-0002-9005-8577.
  • Mzia Tsiklauri PhD, Affiliated Professor of the Medical Programs of Gr.Robakidze University, Microbiology, Immunology, Virology, Infection Control.Invited Professor of the Medical Programs of Alte University, Tbilisi, Georgia. Invited Professor of the Medical Programs of Caucasus International University, Laboratory Medicine, Tbilisi, Georgia. Member of the Georgian Immunologists Association, Member of the Accreditation Council of the Quality Development, Center of the Ministry of Education of Georgia
  • Manana Makharadze Prof. David Agmashenebeli University of Georgia, Tbilisi, Georgia.
  • George Maglakelidze PhD, Professor, University of Georgia, Tbilisi, Georgia

Keywords:

Antibody developability, therapeutic antibodies, bioinformatics, in silico analysis, recombinant expression, protein stability, aggregation propensity, manufacturability, sequence optimization, structural modeling, physicochemical profiling, biopharmaceutical production, developability assessment, antibody engineering

Abstract

The rapid expansion of therapeutic antibody development has increased the need for robust computational strategies that can identify production liabilities early in the discovery pipeline. In biopharmaceutical manufacturing, poor recombinant expression, limited conformational stability, surface hydrophobicity, non-optimal charge distribution, and sequence-driven aggregation hotspots frequently compromise candidate selection, process efficiency, formulation, and long-term product quality. This article reviews current in silico approaches used to improve antibody developability, with particular emphasis on bioinformatic and computational methods for predicting expression performance, structural stability, and aggregation propensity. Key strategies include sequence-based liability screening, germline comparison, codon and framework optimization, physicochemical profiling, structural modeling of variable domains, prediction of post-translational modification hotspots, and machine learning-assisted developability assessment. Special attention is given to the identification of complementarity-determining region (CDR) features and surface-exposed patches that negatively affect folding, solubility, viscosity, and manufacturability. The article further discusses how integrated computational workflows can support rational antibody engineering by prioritizing candidates with improved production fitness before labor-intensive experimental validation. Overall, in silico developability analysis represents a powerful approach for reducing attrition, accelerating lead optimization, and improving the selection of antibody molecules with superior expression, stability, and resistance to aggregation in industrial bioprocess settings. 

Published

2026-04-06

How to Cite

David Aphkhazava, Ilia Atanelishvili, Levan Gulua, Cezar Goletiani, Nino Nebieridze, Nikita Singh, Reuben Quadras, Anushka PM Rane, Mohammed Irfan, Lolita Shengelia, Ketevan Chakhnashvili, Nodar Sulashvili, Mzia Tsiklauri, Manana Makharadze, & George Maglakelidze. (2026). In Silico Strategies for Enhancing Antibody Expression, Stability, and Aggregation Resistance in Biopharmaceutical Production. Research Retrieval and Academic Letters, (12). Retrieved from https://ojs.scipub.de/index.php/RRAL/article/view/8222

Issue

No. 12 (2026): Research Retrieval and Academic Letters

Section

Biological Sciences

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.