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Forder JK, Palakollu V, Adhikari S, Blanco MA, Derebe MG, Ferguson HM, Luthra SA, Munsell EV, Roberts CJ. Electrostatically Mediated Attractive Self-Interactions and Reversible Self-Association of Fc-Fusion Proteins. Mol Pharm 2024; 21:1321-1333. [PMID: 38334418 DOI: 10.1021/acs.molpharmaceut.3c01009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Attractive self-interactions and reversible self-association are implicated in many problematic solution behaviors for therapeutic proteins, such as irreversible aggregation, elevated viscosity, phase separation, and opalescence. Protein self-interactions and reversible oligomerization of two Fc-fusion proteins (monovalent and bivalent) and the corresponding fusion partner protein were characterized experimentally with static and dynamic light scattering as a function of pH (5 and 6.5) and ionic strength (10 mM to at least 300 mM). The fusion partner protein and monovalent Fc-fusion each displayed net attractive electrostatic self-interactions at pH 6.5 and net repulsive electrostatic self-interactions at pH 5. Solutions of the bivalent Fc-fusion contained higher molecular weight species that prevented quantification of typical interaction parameters (B22 and kD). All three of the proteins displayed reversible self-association at pH 6.5, where oligomers dissociated with increased ionic strength. Coarse-grained molecular simulations were used to model the self-interactions measured experimentally, assess net self-interactions for the bivalent Fc-fusion, and probe the specific electrostatic interactions between charged amino acids that were involved in attractive electrostatic self-interactions. Mayer-weighted pairwise electrostatic energies from the simulations suggested that attractive electrostatic self-interactions at pH 6.5 for the two Fc-fusion proteins were due to cross-domain interactions between the fusion partner domain(s) and the Fc domain.
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Affiliation(s)
- James K Forder
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19713, United States
| | - Veerabhadraiah Palakollu
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19713, United States
| | - Sudeep Adhikari
- Analytical R&D, Digital & NMR Sciences, Merck & Co., Inc., Rahway, New Jersey 07065, United States
| | - Marco A Blanco
- Discovery Pharmaceutical Sciences, Merck & Co., Inc., West Point, Pennsylvania 19486, United States
| | - Mehabaw Getahun Derebe
- Discovery Biologics, Protein Sciences, Merck & Co., Inc., South San Francisco, California 94080, United States
| | - Heidi M Ferguson
- Discovery Pharmaceutical Sciences, Merck & Co., Inc., West Point, Pennsylvania 19486, United States
| | - Suman A Luthra
- Discovery Pharmaceutical Sciences, Merck & Co., Inc., Boston, Massachusetts 02115, United States
| | - Erik V Munsell
- Discovery Pharmaceutical Sciences, Merck & Co., Inc., Boston, Massachusetts 02115, United States
| | - Christopher J Roberts
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19713, United States
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Panda C, Kumar S, Gupta S, Pandey LM. Structural, kinetic, and thermodynamic aspects of insulin aggregation. Phys Chem Chem Phys 2023; 25:24195-24213. [PMID: 37674360 DOI: 10.1039/d3cp03103a] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Given the significance of protein aggregation in proteinopathies and the development of therapeutic protein pharmaceuticals, revamped interest in assessing and modelling the aggregation kinetics has been observed. Quantitative analysis of aggregation includes data of gradual monomeric depletion followed by the formation of subvisible particles. Kinetic and thermodynamic studies are essential to gain key insights into the aggregation process. Despite being the medical marvel in the world of diabetes, insulin suffers from the challenge of aggregation. Physicochemical stresses are experienced by insulin during industrial formulation, storage, delivery, and transport, considerably impacting product quality, efficacy, and effectiveness. The present review briefly describes the pathways, mathematical kinetic models, and thermodynamics of protein misfolding and aggregation. With a specific focus on insulin, further discussions include the structural heterogeneity and modifications of the intermediates incurred during insulin fibrillation. Finally, different model equations to fit the kinetic data of insulin fibrillation are discussed. We believe that this review will shed light on the conditions that induce structural changes in insulin during the lag phase of fibrillation and will motivate scientists to devise strategies to block the initialization of the aggregation cascade. Subsequent abrogation of insulin fibrillation during bioprocessing will ensure stable and globally accessible insulin for efficient management of diabetes.
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Affiliation(s)
- Chinmaya Panda
- Bio-interface & Environmental Engineering Lab Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Assam, 781039, India.
| | - Sachin Kumar
- Viral Immunology Lab Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Assam, 781039, India
| | - Sharad Gupta
- Neurodegeneration and Peptide Engineering Research Lab Biological Engineering Discipline, Indian Institute of Technology Gandhinagar, Gujarat, 382355, India
| | - Lalit M Pandey
- Bio-interface & Environmental Engineering Lab Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Assam, 781039, India.
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Pusara S, Yamin P, Wenzel W, Krstić M, Kozlowska M. A coarse-grained xDLVO model for colloidal protein-protein interactions. Phys Chem Chem Phys 2021; 23:12780-12794. [PMID: 34048523 DOI: 10.1039/d1cp01573g] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Colloidal protein-protein interactions (PPIs) of attractive and repulsive nature modulate the solubility of proteins, their aggregation, precipitation and crystallization. Such interactions are very important for many biotechnological processes, but are complex and hard to control, therefore, difficult to be understood in terms of measurements alone. In diluted protein solutions, PPIs can be estimated from the osmotic second virial coefficient, B22, which has been calculated using different methods and levels of theory. The most popular approach is based on the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory and its extended versions, i.e. xDLVO. Despite much efforts, these models are not fully quantitative and must be fitted to experiments, which limits their predictive value. Here, we report an extended xDLVO-CG model, which extends existing models by a coarse-grained representation of proteins and the inclusion of an additional ion-protein dispersion interaction term. We demonstrate for four proteins, i.e. lysozyme (LYZ), subtilisin (Subs), bovine serum albumin (BSA) and immunoglobulin (IgG1), that semi-quantitative agreement with experimental values without the need to fit to experimental B22 values. While most likely not the final step in the nearly hundred years of research in PPIs, xDLVO-CG is a step towards predictive PPIs calculations that are transferable to different proteins.
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Affiliation(s)
- Srdjan Pusara
- Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany.
| | - Peyman Yamin
- Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany.
| | - Wolfgang Wenzel
- Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany.
| | - Marjan Krstić
- Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany. and Institute of Theoretical Solid State Physics, Karlsruhe Institute of Technology (KIT), Wolfgang-Gaede-Str. 1, 76131 Karlsruhe, Germany
| | - Mariana Kozlowska
- Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany.
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Shahfar H, Forder JK, Roberts CJ. Toward a Suite of Coarse-Grained Models for Molecular Simulation of Monoclonal Antibodies and Therapeutic Proteins. J Phys Chem B 2021; 125:3574-3588. [PMID: 33821645 DOI: 10.1021/acs.jpcb.1c01903] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A series of coarse-grained models for molecular simulation of proteins are considered, with emphasis on the application of predicting protein-protein self-interactions for monoclonal antibodies (MAbs). As an illustrative example and for quantitative comparison, the models are used to predict osmotic virial coefficients over a broad range of attractive and repulsive self-interactions and solution conditions for a series of MAbs where the second osmotic virial coefficient has been experimentally determined in prior work. The models are compared based on how well they can predict experimental behavior, their computational burdens, and scalability. An intermediate-resolution model is also introduced that can capture specific electrostatic interactions with improved efficiency and similar or improved accuracy when compared to the previously published models. Guidance is included for the selection of coarse-grained models more generally for capturing a balance of electrostatic, steric, and short-ranged nonelectrostatic interactions for proteins from low to high concentrations.
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Affiliation(s)
- Hassan Shahfar
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States.,Department of Physics and Astronomy, University of Delaware, Newark, Delaware 19716, United States
| | - James K Forder
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
| | - Christopher J Roberts
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
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Lai PK, Fernando A, Cloutier TK, Kingsbury JS, Gokarn Y, Halloran KT, Calero-Rubio C, Trout BL. Machine Learning Feature Selection for Predicting High Concentration Therapeutic Antibody Aggregation. J Pharm Sci 2020; 110:1583-1591. [PMID: 33346034 DOI: 10.1016/j.xphs.2020.12.014] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 11/25/2020] [Accepted: 12/11/2020] [Indexed: 02/03/2023]
Abstract
Protein aggregation can hinder the development, safety and efficacy of therapeutic antibody-based drugs. Developing a predictive model that evaluates aggregation behaviors during early stage development is therefore desirable. Machine learning is a widely used tool to train models that predict data with different attributes. However, most machine learning techniques require more data than is typically available in antibody development. In this work, we describe a rational feature selection framework to develop accurate models with a small number of features. We applied this framework to predict aggregation behaviors of 21 approved monospecific monoclonal antibodies at high concentration (150 mg/mL), yielding a correlation coefficient of 0.71 on validation tests with only two features using a linear model. The nearest neighbors and support vector regression models further improved the performance, which have correlation coefficients of 0.86 and 0.80, respectively. This framework can be extended to train other models that predict different physical properties.
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Affiliation(s)
- Pin-Kuang Lai
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Amendra Fernando
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Theresa K Cloutier
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Yatin Gokarn
- Biologics Development, Sanofi, Framingham, MA, USA
| | | | | | - Bernhardt L Trout
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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Ferreira GM, Shahfar H, Sathish HA, Remmele RL, Roberts CJ. Identifying Key Residues That Drive Strong Electrostatic Attractions between Therapeutic Antibodies. J Phys Chem B 2019; 123:10642-10653. [PMID: 31739660 DOI: 10.1021/acs.jpcb.9b08355] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Attractive electrostatic protein-protein interactions (PPI) necessarily involve identifying oppositely charged regions of the protein surface that interact favorably. This cannot be done reliably if one only considers a single protein in isolation unless there are obvious charge "patches" that result in extreme molecular dipoles. Prior work [ J. Pharm. Sci. 2019 , 108 , 120 - 132 ] identified three monoclonal antibodies (MAbs) that displayed experimental behavior ranging from net repulsive to strongly attractive electrostatic interactions. The present work provides a systematic computational approach for identifying the origin of diverse PPI, in terms of which sets of amino acids or individual amino acids are most influential, and determining if there are different patterns of pairwise amino acid interaction "maps" that result in different behaviors. The charge was eliminated computationally, one by one, for each charged residue in the wild-type sequences, which resulted in predicted changes in the second osmotic virial coefficient. The results highlight interaction "maps" that correspond to cases with qualitatively different net electrostatic PPI for the different MAbs and solution conditions, as well as key sets of residues that contribute to strongly attractive PPI. A more computationally efficient method is also proposed to identify key amino acids based on Mayer-weighted interaction energies.
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Affiliation(s)
- Glenn M Ferreira
- Department of Chemical and Biomolecular Engineering , University of Delaware , Newark , Delaware 19716 , United States
| | - Hassan Shahfar
- Department of Chemical and Biomolecular Engineering , University of Delaware , Newark , Delaware 19716 , United States.,Department of Physics and Astronomy , University of Delaware , Newark , Delaware 19716 , United States
| | | | | | - Christopher J Roberts
- Department of Chemical and Biomolecular Engineering , University of Delaware , Newark , Delaware 19716 , United States
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Schleinitz M, Sadowski G, Brandenbusch C. Protein-protein interactions and water activity coefficients can be used to aid a first excipient choice in protein formulations. Int J Pharm 2019; 569:118608. [PMID: 31415881 DOI: 10.1016/j.ijpharm.2019.118608] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 08/05/2019] [Accepted: 08/09/2019] [Indexed: 10/26/2022]
Abstract
With respect to all biopharmaceuticals marketed to date, monoclonal antibodies represent the largest fraction with more than 48% market share (2012). However, the development of biopharmaceutical formulations is a challenging task, and time-consuming and cost-intensive high-throughput screenings are still state-of-the-art in formulation design. These screening techniques are almost exclusively based on heuristic decisions thus the benefit in terms of mechanistic understanding is often unclear. It requires novel, physical-sound methods to enhance/optimize future formulation development, ideally by understanding molecular interactions in these complex solutions. A suitable and evaluated measure-of-choice to characterize protein-protein interactions in aqueous protein solutions is the second osmotic virial coefficient B22 which can be measured using static light scattering techniques. Furthermore B22 can be modeled/predicted via the extended mxDLVO model for protein-protein interactions in the presence of single excipients and excipient-mixtures. Building up on this approach, giving an additional insight into water-water and water-excipient interactions, the thermodynamic equation-of-state ePC-SAFT is used to calculate water activity coefficients in the presence of excipient-mixtures. Immunoglobulin G (IgG) was chosen as a model protein to predict B22-values for IgG in the presence of model excipient-mixtures (trehalose-NaCl, l-histidine-trehalose, l-histidine-NaCl). The combination of water activity coefficients and B22 allows to quickly identify a first guess on suitable formulation conditions that then can be further evaluated with existing methods/knowledge.
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Affiliation(s)
- Miko Schleinitz
- Laboratory of Thermodynamics, Department of Biochemical and Chemical Engineering, Emil-Figge-Str. 70, 44227 Dortmund, Germany
| | - Gabriele Sadowski
- Laboratory of Thermodynamics, Department of Biochemical and Chemical Engineering, Emil-Figge-Str. 70, 44227 Dortmund, Germany
| | - Christoph Brandenbusch
- Laboratory of Thermodynamics, Department of Biochemical and Chemical Engineering, Emil-Figge-Str. 70, 44227 Dortmund, Germany.
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Calero-Rubio C, Saluja A, Sahin E, Roberts CJ. Predicting High-Concentration Interactions of Monoclonal Antibody Solutions: Comparison of Theoretical Approaches for Strongly Attractive Versus Repulsive Conditions. J Phys Chem B 2019; 123:5709-5720. [PMID: 31241333 DOI: 10.1021/acs.jpcb.9b03779] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Nonspecific protein-protein interactions of a monoclonal antibody were quantified experimentally using light scattering from low to high protein concentrations (c2) and compared with prior work for a different antibody that yielded qualitatively different behavior. The c2 dependence of the excess Rayleigh ratio (Rex) provided the osmotic second virial coefficient (B22) at low c2 and the static structure factor (Sq=0) at high c2, as a function of solution pH, total ionic strength (TIS), and sucrose concentration. Net repulsive interactions were observed at pH 5, with weaker repulsions at higher TIS. Conversely, attractive electrostatic interactions were observed at pH 6.5, with weaker attractions at higher TIS. Refined coarse-grained models were used to fit model parameters using experimental B22 versus TIS data. The parameters were used to predict high-c2 Rex values via Monte Carlo simulations and separately with Mayer-sampling calculations of higher-order virial coefficients. For both methods, predictions for repulsive to mildly attractive conditions were quantitatively accurate. However, only qualitatively accurate predictions were practical for strongly attractive conditions. An alternative, higher resolution model was used to show semiquantitatively and quantitatively accurate predictions of strong electrostatic attractions at low c2 and low ionic strength.
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Affiliation(s)
- Cesar Calero-Rubio
- Department of Chemical and Biomolecular Engineering , University of Delaware , Newark , Delaware 19716 , United States
| | - Atul Saluja
- Drug Product Science and Technology , Bristol-Myers Squibb , New Brunswick , New Jersey 08901 , United States
| | - Erinc Sahin
- Drug Product Science and Technology , Bristol-Myers Squibb , New Brunswick , New Jersey 08901 , United States
| | - Christopher J Roberts
- Department of Chemical and Biomolecular Engineering , University of Delaware , Newark , Delaware 19716 , United States
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Ferreira GM, Calero-Rubio C, Sathish HA, Remmele RL, Roberts CJ. Electrostatically Mediated Protein-Protein Interactions for Monoclonal Antibodies: A Combined Experimental and Coarse-Grained Molecular Modeling Approach. J Pharm Sci 2019; 108:120-132. [DOI: 10.1016/j.xphs.2018.11.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2018] [Revised: 10/27/2018] [Accepted: 11/01/2018] [Indexed: 01/05/2023]
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