1
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Makowski EK, Wang T, Zupancic JM, Huang J, Wu L, Schardt JS, De Groot AS, Elkins SL, Martin WD, Tessier PM. Optimization of therapeutic antibodies for reduced self-association and non-specific binding via interpretable machine learning. Nat Biomed Eng 2024; 8:45-56. [PMID: 37666923 PMCID: PMC10842909 DOI: 10.1038/s41551-023-01074-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 06/29/2023] [Indexed: 09/06/2023]
Abstract
Antibody development, delivery, and efficacy are influenced by antibody-antigen affinity interactions, off-target interactions that reduce antibody bioavailability and pharmacokinetics, and repulsive self-interactions that increase the stability of concentrated antibody formulations and reduce their corresponding viscosity. Yet identifying antibody variants with optimal combinations of these three types of interactions is challenging. Here we show that interpretable machine-learning classifiers, leveraging antibody structural features descriptive of their variable regions and trained on experimental data for a panel of 80 clinical-stage monoclonal antibodies, can identify antibodies with optimal combinations of low off-target binding in a common physiological-solution condition and low self-association in a common antibody-formulation condition. For three clinical-stage antibodies with suboptimal combinations of off-target binding and self-association, the classifiers predicted variable-region mutations that optimized non-affinity interactions while maintaining high-affinity antibody-antigen interactions. Interpretable machine-learning models may facilitate the optimization of antibody candidates for therapeutic applications.
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Affiliation(s)
- Emily K Makowski
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Tiexin Wang
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer M Zupancic
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Jie Huang
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Lina Wu
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - John S Schardt
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | | | | | | | - Peter M Tessier
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, USA.
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA.
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
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2
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Congdon EE, Ji C, Tetlow AM, Jiang Y, Sigurdsson EM. Tau-targeting therapies for Alzheimer disease: current status and future directions. Nat Rev Neurol 2023; 19:715-736. [PMID: 37875627 PMCID: PMC10965012 DOI: 10.1038/s41582-023-00883-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2023] [Indexed: 10/26/2023]
Abstract
Alzheimer disease (AD) is the most common cause of dementia in older individuals. AD is characterized pathologically by amyloid-β (Aβ) plaques and tau neurofibrillary tangles in the brain, with associated loss of synapses and neurons, which eventually results in dementia. Many of the early attempts to develop treatments for AD focused on Aβ, but a lack of efficacy of these treatments in terms of slowing disease progression led to a change of strategy towards targeting of tau pathology. Given that tau shows a stronger correlation with symptom severity than does Aβ, targeting of tau is more likely to be efficacious once cognitive decline begins. Anti-tau therapies initially focused on post-translational modifications, inhibition of tau aggregation and stabilization of microtubules. However, trials of many potential drugs were discontinued because of toxicity and/or lack of efficacy. Currently, the majority of tau-targeting agents in clinical trials are immunotherapies. In this Review, we provide an update on the results from the initial immunotherapy trials and an overview of new therapeutic candidates that are in clinical development, as well as considering future directions for tau-targeting therapies.
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Affiliation(s)
- Erin E Congdon
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY, USA
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA
| | - Changyi Ji
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY, USA
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA
| | - Amber M Tetlow
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY, USA
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA
| | - Yixiang Jiang
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY, USA
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA
| | - Einar M Sigurdsson
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY, USA.
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA.
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA.
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3
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Hess R, Faessler J, Yun D, Saleh D, Grosch JH, Schwab T, Hubbuch J. Antibody sequence-based prediction of pH gradient elution in multimodal chromatography. J Chromatogr A 2023; 1711:464437. [PMID: 37865026 DOI: 10.1016/j.chroma.2023.464437] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 10/03/2023] [Accepted: 10/05/2023] [Indexed: 10/23/2023]
Abstract
Multimodal chromatography has emerged as a promising technique for antibody purification, owing to its capacity to selectively capture and separate target molecules. However, the optimization of chromatography parameters remains a challenge due to the intricate nature of protein-ligand interactions. To tackle this issue, efficient predictive tools are essential for the development and optimization of multimodal chromatography processes. In this study, we introduce a methodology that predicts the elution behavior of antibodies in multimodal chromatography based on their amino acid sequences. We analyzed a total of 64 full-length antibodies, including IgG1, IgG4, and IgG-like multispecific formats, which were eluted using linear pH gradients from pH 9.0 to 4.0 on the anionic mixed-mode resin Capto adhere. Homology models were constructed, and 1312 antibody-specific physicochemical descriptors were calculated for each molecule. Our analysis identified six key structural features of the multimodal antibody interaction, which were correlated with the elution behavior, emphasizing the antibody variable region. The results show that our methodology can predict pH gradient elution for a diverse range of antibodies and antibody formats, with a test set R² of 0.898. The developed model can inform process development by predicting initial conditions for multimodal elution, thereby reducing trial and error during process optimization. Furthermore, the model holds the potential to enable an in silico manufacturability assessment by screening target antibodies that adhere to standardized purification conditions. In conclusion, this study highlights the feasibility of using structure-based prediction to enhance antibody purification in the biopharmaceutical industry. This approach can lead to more efficient and cost-effective process development while increasing process understanding.
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Affiliation(s)
- Rudger Hess
- Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Jan Faessler
- DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Doil Yun
- DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - David Saleh
- DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Jan-Hendrik Grosch
- DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Thomas Schwab
- DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Jürgen Hubbuch
- Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
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4
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Chowdhury AA, Manohar N, Lanzaro A, Kimball WD, Witek MA, Woldeyes MA, Majumdar R, Qian KK, Xu S, Gillilan RE, Huang Q, Truskett TM, Johnston KP. Characterizing Protein-Protein Interactions and Viscosity of a Monoclonal Antibody from Low to High Concentration Using Small-Angle X-ray Scattering and Molecular Dynamics Simulations. Mol Pharm 2023; 20:5563-5578. [PMID: 37782765 DOI: 10.1021/acs.molpharmaceut.3c00484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Understanding protein-protein interactions and formation of reversible oligomers (clusters) in concentrated monoclonal antibody (mAb) solutions is necessary for designing stable, low viscosity (η) concentrated formulations for processing and subcutaneous injection. Here we characterize the strength (K) of short-range anisotropic attractions (SRA) for 75-200 mg/mL mAb2 solutions at different pH and cosolute conditions by analyzing structure factors (Seff(q)) from small-angle X-ray scattering (SAXS) using coarse-grained molecular dynamics simulations. Best fit simulations additionally provide cluster size distributions, fractal dimensions, cluster occluded volume, and mAb coordination numbers. These equilibrium properties are utilized in a model to account for increases in viscosity caused by occluded volume in the clusters (packing effects) and dissipation of stress across lubricated fractal clusters. Seff(q) is highly sensitive to K at 75 mg/mL where mAbs can mutually align to form SRA contacts but becomes less sensitive at 200 mg/mL as steric repulsion due to packing becomes dominant. In contrast, η at 200 mg/mL is highly sensitive to SRA and the average cluster size from SAXS/simulation, which is observed to track the cluster relaxation time from shear thinning. By analyzing the distribution of sub-bead hot spots on the 3D mAb surface, we identify a strongly attractive hydrophobic patch in the complementarity determining region (CDR) at pH 4.5 that contributes to the high K and consequently large cluster sizes and high η. Adding NaCl screens electrostatic interactions and increases the impact of hydrophobic attraction on cluster size and raises η, whereas nonspecific binding of Arg attenuates all SRA, reducing η. The hydrophobic patch is absent at higher pH values, leading to smaller K, smaller clusters, and lower η. This work constitutes a first attempt to use SAXS and CG modeling to link both structural and rheological properties of concentrated mAb solutions to the energetics of specific hydrophobic patches on mAb surfaces. As such, our work opens an avenue for future research, including the possibility of designing coarse-grained models with physically meaningful interacting hot spots.
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Affiliation(s)
- Amjad A Chowdhury
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Neha Manohar
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Alfredo Lanzaro
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - William D Kimball
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Marta A Witek
- Eli Lilly and Company, Indianapolis, Indiana 46225, United States
| | | | - Ranajoy Majumdar
- Eli Lilly and Company, Indianapolis, Indiana 46225, United States
| | - Ken K Qian
- Eli Lilly and Company, Indianapolis, Indiana 46225, United States
| | - Shifeng Xu
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Richard E Gillilan
- Center for High Energy X-ray Sciences at CHESS, Cornell University, Ithaca, New York 14853, United States
| | - Qingqiu Huang
- Center for High Energy X-ray Sciences at CHESS, Cornell University, Ithaca, New York 14853, United States
| | - Thomas M Truskett
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Department of Physics, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Keith P Johnston
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
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5
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Emonts J, Buyel J. An overview of descriptors to capture protein properties - Tools and perspectives in the context of QSAR modeling. Comput Struct Biotechnol J 2023; 21:3234-3247. [PMID: 38213891 PMCID: PMC10781719 DOI: 10.1016/j.csbj.2023.05.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/23/2023] [Accepted: 05/23/2023] [Indexed: 01/13/2024] Open
Abstract
Proteins are important ingredients in food and feed, they are the active components of many pharmaceutical products, and they are necessary, in the form of enzymes, for the success of many technical processes. However, production can be challenging, especially when using heterologous host cells such as bacteria to express and assemble recombinant mammalian proteins. The manufacturability of proteins can be hindered by low solubility, a tendency to aggregate, or inefficient purification. Tools such as in silico protein engineering and models that predict separation criteria can overcome these issues but usually require the complex shape and surface properties of proteins to be represented by a small number of quantitative numeric values known as descriptors, as similarly used to capture the features of small molecules. Here, we review the current status of protein descriptors, especially for application in quantitative structure activity relationship (QSAR) models. First, we describe the complexity of proteins and the properties that descriptors must accommodate. Then we introduce descriptors of shape and surface properties that quantify the global and local features of proteins. Finally, we highlight the current limitations of protein descriptors and propose strategies for the derivation of novel protein descriptors that are more informative.
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Affiliation(s)
- J. Emonts
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Germany
| | - J.F. Buyel
- University of Natural Resources and Life Sciences, Vienna (BOKU), Department of Biotechnology (DBT), Institute of Bioprocess Science and Engineering (IBSE), Muthgasse 18, 1190 Vienna, Austria
- Institute for Molecular Biotechnology, Worringerweg 1, RWTH Aachen University, 52074 Aachen, Germany
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6
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Chowdhury AA, Manohar N, Witek MA, Woldeyes MA, Majumdar R, Qian KK, Kimball WD, Xu S, Lanzaro A, Truskett TM, Johnston KP. Subclass Effects on Self-Association and Viscosity of Monoclonal Antibodies at High Concentrations. Mol Pharm 2023. [PMID: 37191356 DOI: 10.1021/acs.molpharmaceut.3c00023] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
The effects of a subclass of monoclonal antibodies (mAbs) on protein-protein interactions, formation of reversible oligomers (clusters), and viscosity (η) are not well understood at high concentrations. Herein, we quantify a short-range anisotropic attraction between the complementarity-determining region (CDR) and CH3 domains (KCDR-CH3) for vedolizumab IgG1, IgG2, or IgG4 subclasses by fitting small-angle X-ray scattering (SAXS) structure factor Seff(q) data with an extensive library of 12-bead coarse-grained (CG) molecular dynamics simulations. The KCDR-CH3 bead attraction strength was isolated from the strength of long-range electrostatic repulsion for the full mAb, which was determined from the theoretical net charge and a scaling parameter ψ to account for solvent accessibility and ion pairing. At low ionic strength (IS), the strongest short-range attraction (KCDR-CH3) and consequently the largest clusters and highest η were observed with IgG1, the subclass with the most positively charged CH3 domain. Furthermore, the trend in KCDR-CH3 with the subclass followed the electrostatic interaction energy between the CDR and CH3 regions calculated with the BioLuminate software using the 3D mAb structure and molecular interaction potentials. Whereas the equilibrium cluster size distributions and fractal dimensions were determined from fits of SAXS with the MD simulations, the degree of cluster rigidity under flow was estimated from the experimental η with a phenomenological model. For the systems with the largest clusters, especially IgG1, the inefficient packing of mAbs in the clusters played the largest role in increasing η, whereas for other systems, the relative contribution from stress produced by the clusters was more significant. The ability to relate η to short-range attraction from SAXS measurements at high concentrations and to theoretical characterization of electrostatic patches on the 3D surface is not only of fundamental interest but also of practical value for mAb discovery, processing, formulation, and subcutaneous delivery.
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Affiliation(s)
- Amjad A Chowdhury
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Neha Manohar
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Marta A Witek
- Eli Lilly and Company, Indianapolis, Indiana 46225, United States
| | | | - Ranajoy Majumdar
- Eli Lilly and Company, Indianapolis, Indiana 46225, United States
| | - Ken K Qian
- Eli Lilly and Company, Indianapolis, Indiana 46225, United States
| | - William D Kimball
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Shifeng Xu
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Alfredo Lanzaro
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Thomas M Truskett
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Department of Physics, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Keith P Johnston
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
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7
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Tuning charge density of chimeric antigen receptor optimizes tonic signaling and CAR-T cell fitness. Cell Res 2023; 33:341-354. [PMID: 36882513 PMCID: PMC10156745 DOI: 10.1038/s41422-023-00789-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 02/10/2023] [Indexed: 03/09/2023] Open
Abstract
Tonic signaling of chimeric antigen receptor (CAR), i.e., the spontaneous CAR activation in the absence of tumor antigen stimulation, is considered to be a pivotal event controlling CAR-T efficacy. However, the molecular mechanism underlying the spontaneous CAR signals remains elusive. Here, we unveil that positively charged patches (PCPs) on the surface of the CAR antigen-binding domain mediate CAR clustering and result in CAR tonic signaling. For CARs with high tonic signaling (e.g., GD2.CAR and CSPG4.CAR), reducing PCPs on CARs or boosting ionic strength in the culture medium during ex vivo CAR-T cell expansion minimizes spontaneous CAR activation and alleviates CAR-T cell exhaustion. In contrast, introducing PCPs into the CAR with weak tonic signaling, such as CD19.CAR, results in improved in vivo persistence and superior antitumor function. These results demonstrate that CAR tonic signaling is induced and maintained by PCP-mediated CAR clustering. Notably, the mutations we generated to alter the PCPs maintain the antigen-binding affinity and specificity of the CAR. Therefore, our findings suggest that the rational tuning of PCPs to optimize tonic signaling and in vivo fitness of CAR-T cells is a promising design strategy for the next-generation CAR.
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8
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Shmool T, Martin LK, Matthews RP, Hallett JP. Ionic Liquid-Based Strategy for Predicting Protein Aggregation Propensity and Thermodynamic Stability. JACS AU 2022; 2:2068-2080. [PMID: 36186557 PMCID: PMC9516703 DOI: 10.1021/jacsau.2c00356] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 08/17/2022] [Accepted: 08/18/2022] [Indexed: 05/26/2023]
Abstract
Novel drug candidates are continuously being developed to combat the most life-threatening diseases; however, many promising protein therapeutics are dropped from the pipeline. During biological and industrial processes, protein therapeutics are exposed to various stresses such as fluctuations in temperature, solvent pH, and ionic strength. These can lead to enhanced protein aggregation propensity, one of the greatest challenges in drug development. Recently, ionic liquids (ILs), in particular, biocompatible choline chloride ([Cho]Cl)-based ILs, have been used to hinder stress-induced protein conformational changes. Herein, we develop an IL-based strategy to predict protein aggregation propensity and thermodynamic stability. We examine three key variables influencing protein misfolding: pH, ionic strength, and temperature. Using dynamic light scattering, zeta potential, and variable temperature circular dichroism measurements, we systematically evaluate the structural, thermal, and thermodynamic stability of fresh immunoglobin G4 (IgG4) antibody in water and 10, 30, and 50 wt % [Cho]Cl. Additionally, we conduct molecular dynamics simulations to examine IgG4 aggregation propensity in each system and the relative favorability of different [Cho]Cl-IgG4 packing interactions. We re-evaluate each system following 365 days of storage at 4 °C and demonstrate how to predict the thermodynamic properties and protein aggregation propensity over extended storage, even under stress conditions. We find that increasing [Cho]Cl concentration reduced IgG4 aggregation propensity both fresh and following 365 days of storage and demonstrate the potential of using our predictive IL-based strategy and formulations to radically increase protein stability and storage.
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Affiliation(s)
- Talia
A. Shmool
- Department
of Chemical Engineering, Imperial College
London, South Kensington Campus, London SW7 2AZ, U.K.
| | - Laura K. Martin
- Department
of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, U.K.
| | - Richard P. Matthews
- Department
of Chemical Engineering, Imperial College
London, South Kensington Campus, London SW7 2AZ, U.K.
| | - Jason P. Hallett
- Department
of Chemical Engineering, Imperial College
London, South Kensington Campus, London SW7 2AZ, U.K.
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9
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Shahfar H, Du Q, Parupudi A, Shan L, Esfandiary R, Roberts CJ. Electrostatically Driven Protein-Protein Interactions: Quantitative Prediction of Second Osmotic Virial Coefficients to Aid Antibody Design. J Phys Chem Lett 2022; 13:1366-1372. [PMID: 35112863 DOI: 10.1021/acs.jpclett.1c03669] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Electrostatically driven attractions between proteins can result in issues for therapeutic protein formulations such as solubility limits, aggregation, and high solution viscosity. Previous work showed that a model monoclonal antibody displayed large and potentially problematic electrostatically driven attractions at typical pH (5-8) and ionic strength conditions (∼10-100 mM). Molecular simulations of a hybrid coarse-grained model (1bC/D, one bead per charged site and per domain) were used to predict potential point mutations to identify key charge changes (charge-to-neutral or charge-swap) that could greatly reduce the net attractive protein-protein self-interactions. A series of variants were tested experimentally with static and dynamic light scattering to quantify interactions and compared to model predictions at low and intermediate ionic strength. Differential scanning calorimetry and circular dichroism confirmed minimal impact on structural or thermal stability of the variants. The model provided quantitative/semiquantitative predictions of protein self-interactions compared to experimental results as well as showed which amino acid pairings or groups had the most impact.
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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
| | - Qun Du
- Department of Antibody Discovery & Protein Engineering, AstraZeneca, 1 MedImmune Way, Gaithersburg, Maryland 20878, United States
| | - Arun Parupudi
- Department of Antibody Discovery & Protein Engineering, AstraZeneca, 1 MedImmune Way, Gaithersburg, Maryland 20878, United States
| | - Lu Shan
- Department of Antibody Discovery & Protein Engineering, AstraZeneca, 1 MedImmune Way, Gaithersburg, Maryland 20878, United States
| | - Reza Esfandiary
- Department of Dosage Form and Design Development, AstraZeneca, 1 MedImmune Way, Gaithersburg, Maryland 20878, United States
| | - Christopher J Roberts
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
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10
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Han X, Shih J, Lin Y, Chai Q, Cramer SM. Development of QSAR models for in silico screening of antibody solubility. MAbs 2022; 14:2062807. [PMID: 35442164 PMCID: PMC9037471 DOI: 10.1080/19420862.2022.2062807] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Although monoclonal antibodies (mAbs) have been shown to be extremely effective in treating a number of diseases, they often suffer from poor developability attributes, such as high viscosity and low solubility at elevated concentrations. Since experimental candidate screening is often materials and labor intensive, there is substantial interest in developing in silico tools for expediting mAb design. Here, we present a strategy using machine learning-based QSAR models for the a priori estimation of mAb solubility. The extrapolated protein solubilities of a set of 111 antibodies in a histidine buffer were determined using a high throughput PEG precipitation assay. 3D homology models of the antibodies were determined, and a large set of in house and commercially available molecular descriptors were then calculated. The resulting experimental and descriptor data were then used for the development of QSAR models of mAb solubilities. After feature selection and training with different machine learning algorithms, the models were evaluated with external test sets. The resulting regression models were able to estimate the solubility values of external test set data with R2 of 0.81 and 0.85 for the two regression models developed. In addition, three class and binary classification models were developed and shown to be good estimators of mAb solubility behavior, with overall test set accuracies of 0.70 and 0.95, respectively. The analysis of the selected molecular descriptors in these models was also found to be informative and suggested that several charge-based descriptors and isotype may play important roles in mAb solubility. The combination of high throughput relative solubility experimental techniques in concert with efficient machine learning QSAR models offers an opportunity to rapidly screen potential mAb candidates and to design therapeutics with improved solubility characteristics.
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Affiliation(s)
- Xuan Han
- Department of Chemical and Biological Engineering and Center for Biotechnology and interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York, USA
| | - James Shih
- Biotechnology Discovery Research, Eli Lilly Biotechnology Center, San Diego, California, USA
| | - Yuhao Lin
- Research Information & Digital Solutions, Eli Lilly Biotechnology Center, San Diego, California, USA
| | - Qing Chai
- Biotechnology Discovery Research, Eli Lilly Biotechnology Center, San Diego, California, USA
| | - Steven M Cramer
- Department of Chemical and Biological Engineering and Center for Biotechnology and interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York, USA
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11
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Phan S, Walmer A, Shaw EW, Chai Q. High-throughput profiling of antibody self-association in multiple formulation conditions by PEG stabilized self-interaction nanoparticle spectroscopy. MAbs 2022; 14:2094750. [PMID: 35830420 PMCID: PMC9291693 DOI: 10.1080/19420862.2022.2094750] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS) is an assay developed to monitor the propensity of antibody self-association, hence assessing its colloidal stability. It has been widely used by pharmaceutical companies to screen antibodies at the early discovery stages, aiming to flag potential issues with high concentration formulation. However, the original assay format is not suitable for certain formulation conditions, in particular histidine buffer. In addition, the previous data extrapolation method is suboptimal and cumbersome for processing large amounts of data (100s of molecules) in a high-throughput fashion. To address these limitations, we developed an assay workflow with two major improvements: 1) use of a stabilizing reagent to enable screening of a broader range of formulation conditions beyond phosphate-buffered saline, pH 7.4; and 2) inclusion of a novel algorithm and robust data processing schema that empowers streamlined data analysis. The optimized assay format expands the screening applicability to a wider range of formulation conditions critical for downstream development. Such capability is enhanced by a custom data management workflow for optimal data extraction, analysis, and automation. Our protocol and the R/Shiny application for analysis are publicly available and open-source to benefit the broader scientific community.
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Affiliation(s)
- Samantha Phan
- Biotechnology Discovery Research, Lilly Research Laboratories, Lilly Biotechnology Center, San Diego, CA, USA
| | - Auralee Walmer
- Research Information & Digital Solutions, Lilly Biotechnology Center, San Diego, CA, USA
| | - Eudean W Shaw
- Research Information & Digital Solutions, Lilly Biotechnology Center, San Diego, CA, USA
| | - Qing Chai
- Biotechnology Discovery Research, Lilly Research Laboratories, Lilly Biotechnology Center, San Diego, CA, USA
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12
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Heads JT, Kelm S, Tyson K, Lawson ADG. A computational method for predicting the aggregation propensity of IgG1 and IgG4(P) mAbs in common storage buffers. MAbs 2022; 14:2138092. [PMID: 36418193 PMCID: PMC9704409 DOI: 10.1080/19420862.2022.2138092] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
The propensity for some monoclonal antibodies (mAbs) to aggregate at physiological and manufacturing pH values can prevent their use as therapeutic molecules or delay time to market. Consequently, developability assessments are essential to select optimum candidates, or inform on mitigation strategies to avoid potential late-stage failures. These studies are typically performed in a range of buffer solutions because factors such as pH can dramatically alter the aggregation propensity of the test mAbs (up to 100-fold in extreme cases). A computational method capable of robustly predicting the aggregation propensity at the pH values of common storage buffers would have substantial value. Here, we describe a mAb aggregation prediction tool (MAPT) that builds on our previously published isotype-dependent, charge-based model of aggregation. We show that the addition of a homology model-derived hydrophobicity descriptor to our electrostatic aggregation model enabled the generation of a robust mAb developability indicator. To contextualize our aggregation scoring system, we analyzed 97 clinical-stage therapeutic mAbs. To further validate our approach, we focused on six mAbs (infliximab, tocilizumab, rituximab, CNTO607, MEDI1912 and MEDI1912_STT) which have been reported to cover a large range of aggregation propensities. The different aggregation propensities of the case study molecules at neutral and slightly acidic pH were correctly predicted, verifying the utility of our computational method.
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Affiliation(s)
- James T. Heads
- UCB Pharma, 208 Bath Road, SloughSL1 3WE, UK,CONTACT James T. Heads UCB Pharma, 208 Bath Road, SloughSL1 3WE, UK
| | | | - Kerry Tyson
- UCB Pharma, 208 Bath Road, SloughSL1 3WE, UK
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13
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Congdon EE, Jiang Y, Sigurdsson EM. Targeting tau only extracellularly is likely to be less efficacious than targeting it both intra- and extracellularly. Semin Cell Dev Biol 2021; 126:125-137. [PMID: 34896021 DOI: 10.1016/j.semcdb.2021.12.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 12/01/2021] [Accepted: 12/01/2021] [Indexed: 12/11/2022]
Abstract
Aggregation of the tau protein is thought to be responsible for the neurodegeneration and subsequent functional impairments in diseases that are collectively named tauopathies. Alzheimer's disease is the most common tauopathy, but the group consists of over 20 different diseases, many of which have tau pathology as their primary feature. The development of tau therapies has mainly focused on preventing the formation of and/or clearing these aggregates. Of these, immunotherapies that aim to either elicit endogenous tau antibodies or deliver exogenous ones are the most common approach in clinical trials. While their mechanism of action can involve several pathways, both extra- and intracellular, pharmaceutical companies have primarily focused on antibody-mediated clearance of extracellular tau. As we have pointed out over the years, this is rather surprising because it is well known that most of pathological tau protein is found intracellularly. It has been repeatedly shown by several groups over the past decades that antibodies can enter neurons and that their cellular uptake can be enhanced by various means, particularly by altering their charge. Here, we will briefly describe the potential extra- and intracellular mechanisms involved in antibody-mediated clearance of tau pathology, discuss these in the context of recent failures of some of the tau antibody trials, and finally provide a brief overview of how the intracellular efficacy of tau antibodies can potentially be further improved by certain modifications that aim to enhance tau clearance via specific intracellular degradation pathways.
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Affiliation(s)
- Erin E Congdon
- Department of Neuroscience and Physiology, Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, United States.
| | - Yixiang Jiang
- Department of Neuroscience and Physiology, Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, United States
| | - Einar M Sigurdsson
- Department of Neuroscience and Physiology, Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, United States; Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, United States.
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14
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Tang Y, Cain P, Anguiano V, Shih JJ, Chai Q, Feng Y. Impact of IgG subclass on molecular properties of monoclonal antibodies. MAbs 2021; 13:1993768. [PMID: 34763607 PMCID: PMC8726687 DOI: 10.1080/19420862.2021.1993768] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
Immunoglobulin G-based monoclonal antibodies (mAbs) have become a dominant class of biotherapeutics in recent decades. Approved antibodies are mainly of the subclasses IgG1, IgG2, and IgG4, as well as their derivatives. Over the decades, the selection of IgG subclass has frequently been based on the needs of Fc gamma receptor engagement and effector functions for the desired mechanism of action, while the effect on drug product developability has been less thoroughly characterized. One of the major reasons is the lack of systematic understanding of the impact of IgG subclass on the molecular properties. Several efforts have been made recently to compare molecular property differences among these IgG subclasses, but the conclusions from these studies are sometimes obscured by the interference from variable regions. To further establish mechanistic understandings, we conducted a systematic study by grafting three independent variable regions onto human IgG1, an IgG1 variant, IgG2, and an IgG4 variant constant domains and evaluating the impact of subclass and variable regions on their molecular properties. Structural and computational analysis revealed specific molecular features that potentially account for the differential behavior of the IgG subclasses observed experimentally. Our data indicate that IgG subclass plays a significant role on molecular properties, either through direct effects or via the interplay with the variable region, the IgG1 mAbs tend to have higher solubility than either IgG2 or IgG4 mAbs in a common pH 6 buffer matrix, and solution behavior relies heavily on the charge status of the antibody at the desirable pH.
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Affiliation(s)
- Yu Tang
- Pharmaceutical Development, Syndax Pharmaceuticals, Waltham, Massachusetts, USA
| | - Paul Cain
- Biotechnology Discovery Research, Lilly Research Laboratories, Lilly Technology Center North, Indianapolis, Indiana, USA
| | - Victor Anguiano
- Bioproduct Research & Development, Lilly Research Laboratories, Lilly Technology Center North, Indianapolis, Indiana, USA
| | - James J Shih
- Biotechnology Discovery Research, Lilly Research Laboratories, Lilly Biotechnology Center, San Diego, California, USA
| | - Qing Chai
- Biotechnology Discovery Research, Lilly Research Laboratories, Lilly Biotechnology Center, San Diego, California, USA
| | - Yiqing Feng
- Biotechnology Discovery Research, Lilly Research Laboratories, Lilly Technology Center North, Indianapolis, Indiana, USA
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15
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Lai PK, Ghag G, Yu Y, Juan V, Fayadat-Dilman L, Trout BL. Differences in human IgG1 and IgG4 S228P monoclonal antibodies viscosity and self-interactions: Experimental assessment and computational predictions of domain interactions. MAbs 2021; 13:1991256. [PMID: 34747330 PMCID: PMC8583000 DOI: 10.1080/19420862.2021.1991256] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Human/humanized IgG4 antibodies have reduced effector function relative to IgG1 antibodies, which is desirable for certain therapeutic purposes. However, the developability and biophysical properties for IgG4 antibodies are not well understood. This work focuses on the head-to-head comparison of key biophysical properties, such as self-interaction and viscosity, for 14 human/humanized, and chimeric IgG1 and IgG4 S228P monoclonal antibody pairs that contain the identical variable regions. Experimental measurements showed that the IgG4 S228P antibodies have similar or higher self-interaction and viscosity than that of IgG1 antibodies in 20 mM sodium acetate, pH 5.5. We report sequence and structural drivers for the increased viscosity and self-interaction detected in IgG4 S228P antibodies through a combination of experimental data and computational models. Further, we applied and extended a previously established computational model for IgG1 antibodies to predict the self-interaction and viscosity behavior for each antibody pair, providing insight into the structural characteristics and differences of these two isotypes. Interestingly, we observed that the IgG4 S228P swapped variants, where the CH3 domain was swapped for that of an IgG1, showed reduced self-interaction behavior. These domain swapped IgG4 S228P molecules also showed reduced viscosity from experiment and coarse-grained simulations. We also observed that experimental diffusion interaction parameter (kD) values have a high correlation with computational diffusivity prediction for both IgG1 and IgG4 S228P isotypes. Abbreviations: AHc, constant region Hamaker constant; AHv, variable region Hamaker constant; CDRs, Complementarity-determining regions; CG, Coarse-grained model; CH1, Constant heavy chain 1; CH2 Constant heavy chain 2; CH3 Constant heavy chain 3; chgCH3 Effective charge on the CH3 region; CL Constant light chain; cP, Centipoise; DLS, Dynamic light scattering; Fab, Fragment antigen-binding; Fc, Fragment crystallizable; Fv, Variable domaing; (r) Radial distribution function; H1 CDR1 of Heavy Chain; H2 CDR2 of Heavy Chain; H3 CDR3 of Heavy Chain; HVI, High viscosity index; IgG1 human immunoglobulin of IgG1 subclass; IgG4 human immunoglobulin of IgG4 subclass; kD, Diffusion interaction parameter; L1 CDR1 of Light Chain; L2 CDR2 of Light Chain; L3 CDR3 of Light Chain; mAb, Monoclonal antibody; MD, Molecular dynamics; PPI Protein–protein interactions; SCM, Spatial charge map; UP-SEC, Ultra-high-performance size-exclusion chromatography; VH, Variable domain of Heavy Chain; VL, Variable domain of Light Chain
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Affiliation(s)
- Pin-Kuang Lai
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts USA.,Current Address: Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, New Jersey USA
| | - Gaurav Ghag
- Merck & Co, Discovery Biologics, Protein Sciences Department, South San Francisco, CA , USA
| | - Yao Yu
- Merck & Co, Discovery Biologics, Protein Sciences Department, South San Francisco, CA , USA
| | - Veronica Juan
- Merck & Co, Discovery Biologics, Protein Sciences Department, South San Francisco, CA , USA
| | | | - Bernhardt L Trout
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts USA
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16
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Thorsteinson N, Gunn JR, Kelly K, Long W, Labute P. Structure-based charge calculations for predicting isoelectric point, viscosity, clearance, and profiling antibody therapeutics. MAbs 2021; 13:1981805. [PMID: 34632944 PMCID: PMC8510563 DOI: 10.1080/19420862.2021.1981805] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
The effect of hydrophobicity on antibody aggregation is well understood, and it has been shown that charge calculations can be useful for high-concentration viscosity and pharmacokinetic (PK) clearance predictions. In this work, structure-based charge descriptors are evaluated for their predictive performance on recently published antibody pI, viscosity, and clearance data. From this, we devised four rules for therapeutic antibody profiling which address developability issues arising from hydrophobicity and charged-based solution behavior, PK, and the ability to enrich for those that are approved by the U.S. Food and Drug Administration. Differences in strategy for optimizing the solution behavior of human IgG1 antibodies versus the IgG2 and IgG4 isotypes and the impact of pH alterations in formulation are discussed.
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Affiliation(s)
- Nels Thorsteinson
- Research and Development, Chemical Computing Group ULC, Montreal, Quebec, Canada
| | - John R Gunn
- Research and Development, Chemical Computing Group ULC, Montreal, Quebec, Canada
| | - Kenneth Kelly
- Research and Development, Chemical Computing Group ULC, Montreal, Quebec, Canada
| | - Will Long
- Research and Development, Chemical Computing Group ULC, Montreal, Quebec, Canada
| | - Paul Labute
- Research and Development, Chemical Computing Group ULC, Montreal, Quebec, Canada
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17
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Chi B, De Oliveira G, Gallagher T, Mitchell L, Knightley L, Gonzalez CC, Russell S, Germaschewski V, Pearce C, Sellick CA. Pragmatic mAb lead molecule engineering from a developability perspective. Biotechnol Bioeng 2021; 118:3733-3743. [PMID: 33913507 DOI: 10.1002/bit.27802] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 03/26/2021] [Accepted: 04/15/2021] [Indexed: 01/08/2023]
Abstract
As the number of antibody drugs being approved and marketed increases, our knowledge of what makes potential drug candidates a successful product has increased tremendously. One of the critical parameters that have become clear in the field is the importance of mAb "developability." Efforts are being increasingly focused on simultaneously selecting molecules that exhibit both desirable biological potencies and manufacturability attributes. In the current study mutations to improve the developability profile of a problematic antibody that inconsistently precipitates in a batch scale-dependent fashion using a standard platform purification process are described. Initial bioinformatic analysis showed the molecule has no obvious sequence or structural liabilities that might lead it to precipitate. Subsequent analysis of the molecule revealed the presence of two unusual positively charged mutations on the light chain at the interface of VH and VL domains, which were hypothesized to be the primary contributor to molecule precipitation during process development. To investigate this hypothesis, straightforward reversion to the germline of these residues was carried out. The resulting mutants have improved expression titers and recovered stability within a forced precipitation assay, without any change to biological activity. Given the time pressures of drug development in industry, process optimization of the lead molecule was carried out in parallel to the "retrospective" mutagenesis approach. Bespoke process optimization for large-scale manufacturing was successful. However, we propose that such context-dependent sequence liabilities should be included in the arsenal of in silico developability screening early in development; particularly since this specific issue can be efficiently mitigated without the requirement for extensive screening of lead molecule variants.
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Affiliation(s)
| | | | - Tom Gallagher
- Kymab Ltd., Cambridge, UK.,F-star Therapeutics Ltd., Cambridge, UK
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18
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Huang C, Wang Y, Xu X, Mills J, Jin W, Ghose S, Li ZJ. Hydrophobic property of cation-exchange resins affects monoclonal antibody aggregation. J Chromatogr A 2020; 1631:461573. [DOI: 10.1016/j.chroma.2020.461573] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 09/06/2020] [Accepted: 09/19/2020] [Indexed: 12/29/2022]
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19
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Relationship of PEG-induced precipitation with protein-protein interactions and aggregation rates of high concentration mAb formulations at 5 °C. Eur J Pharm Biopharm 2020; 151:53-60. [PMID: 32197816 DOI: 10.1016/j.ejpb.2020.03.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 03/13/2020] [Accepted: 03/14/2020] [Indexed: 12/12/2022]
Abstract
Native protein-protein interactions can play an important role in determining the tendency of monoclonal antibodies (mAbs) to aggregate under storage conditions. In this context, phase separation of mAb solutions induced by the addition of neutral polymers such as poly(ethylene glycol) (PEG) represents a simple method to assess the tendency of proteins to self-associate in the native state. Here, we investigated their relationships between PEG-induced phase separation, protein-protein interactions and long-term aggregation rate of several formulations of four mAbs at 100 mg/mL and 5 °C over 12 weeks of storage. We observed that the location of the phase boundary correlated well with the osmotic second virial coefficient B22 determined in absence of the polymer, indicating that for our solutions PEG primarily leads to depletion forces between protein molecules, which are additive to protein-protein interactions. However, limited correlation between aggregation rate at 5 °C and phase behavior was observed across different mAbs, pH values and ionic strengths, indicating that colloidal stability is not the only determinant of aggregation even at such low temperature and high protein concentration. Our results contribute to the growing realization that aggregation propensity in the context of antibody developability is a complex feature, which depends on a variety of biophysical properties rather than one single parameter.
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