1
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Makowski EK, Chen HT, Wang T, Wu L, Huang J, Mock M, Underhill P, Pelegri-O’Day E, Maglalang E, Winters D, Tessier PM. Reduction of monoclonal antibody viscosity using interpretable machine learning. MAbs 2024; 16:2303781. [PMID: 38475982 PMCID: PMC10939158 DOI: 10.1080/19420862.2024.2303781] [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: 05/26/2023] [Accepted: 01/05/2024] [Indexed: 03/14/2024] Open
Abstract
Early identification of antibody candidates with drug-like properties is essential for simplifying the development of safe and effective antibody therapeutics. For subcutaneous administration, it is important to identify candidates with low self-association to enable their formulation at high concentration while maintaining low viscosity, opalescence, and aggregation. Here, we report an interpretable machine learning model for predicting antibody (IgG1) variants with low viscosity using only the sequences of their variable (Fv) regions. Our model was trained on antibody viscosity data (>100 mg/mL mAb concentration) obtained at a common formulation pH (pH 5.2), and it identifies three key Fv features of antibodies linked to viscosity, namely their isoelectric points, hydrophobic patch sizes, and numbers of negatively charged patches. Of the three features, most predicted antibodies at risk for high viscosity, including antibodies with diverse antibody germlines in our study (79 mAbs) as well as clinical-stage IgG1s (94 mAbs), are those with low Fv isoelectric points (Fv pIs < 6.3). Our model identifies viscous antibodies with relatively high accuracy not only in our training and test sets, but also for previously reported data. Importantly, we show that the interpretable nature of the model enables the design of mutations that significantly reduce antibody viscosity, which we confirmed experimentally. We expect that this approach can be readily integrated into the drug development process to reduce the need for experimental viscosity screening and improve the identification of antibody candidates with drug-like properties.
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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
| | - Hsin-Ting Chen
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, 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
| | - Lina Wu
- 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
| | - Marissa Mock
- Therapeutic Discovery, Research, Amgen Inc, Thousand Oaks, CA, USA
| | - Patrick Underhill
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| | | | - Erick Maglalang
- Drug Product Technologies, Amgen Inc, Thousand Oaks, CA, USA
| | - Dwight Winters
- Therapeutic Discovery, Research, Amgen Inc, Thousand Oaks, CA, 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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Grempler R, Ahlberg J, Germovsek E, Gupta P, Li H, Pilvankar M, Sharma A, Stopfer P, Hansel S. Human Dose and Pharmacokinetic Predictions for Biologics at Boehringer Ingelheim: A Retrospective Analysis. Adv Ther 2024; 41:364-378. [PMID: 37971653 DOI: 10.1007/s12325-023-02710-y] [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] [Received: 09/02/2023] [Accepted: 10/06/2023] [Indexed: 11/19/2023]
Abstract
INTRODUCTION Accurate predictions of pharmacokinetics and efficacious doses for biologics in humans are critical for selecting appropriate first-in-human starting doses and dose ranges and for estimating clinical material needs and cost of goods. This also impacts clinical feasibility, particularly for subcutaneously administered biologics. METHODS We performed a comprehensive comparison between predicted and observed clearances and doses in humans for a set of 22 biologic drugs developed at Boehringer Ingelheim (BI) over the last 2 decades. The analysis included biologics across three therapeutic areas comprising a wide variety of modalities: mono- and bispecific monoclonal antibodies (mAbs) and nanobodies and a Fab fragment. RESULTS Our analysis showed that observed clearances in humans were within twofold of predicted clearances for 17 out of 20 biologics (85%). Six biologics had uncharacteristically high observed human clearances (range 32-280 mL/h) for their respective molecular classes, impacting their clinical developability. For three molecules, molecular characteristics contributed to the high clearance. Clinically selected doses were within twofold of predicted for 58% of projects. With 42% and 25% of projects selecting clinical doses higher than two- or threefold the predicted value, respectively, the importance of better understanding not only the pharmacokinetic (PK) but also the predictivity of pharmacodynamic models is highlighted. CONCLUSIONS We provide a clinical pharmacology perspective on the commonly accepted twofold range of human clearance predictions as well as the implications of higher than predicted targeted efficacious plasma concentration on clinical development. Finally, an analysis of key success factors for biologics at BI was conducted, which may be relevant for the entire pharmaceutical industry. This is one of the largest retrospective analyses for biologics and provides further evidence that successful predictions of human PK and efficacious dose will be further facilitated by gathering key translational data early in research.
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Affiliation(s)
- Rolf Grempler
- Department of Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma Inc, 900 Ridgebury Road, Ridgefield, CT, 06877, USA.
| | - Jennifer Ahlberg
- Department of Biotherapeutics Discovery, Boehringer Ingelheim Pharma Inc, Connecticut, USA
| | - Eva Germovsek
- Department of Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co.KG, Ingelheim am Rhein, Germany
| | - Priyanka Gupta
- Department of Biotherapeutics Discovery, Boehringer Ingelheim Pharma Inc, Connecticut, USA
| | - Hua Li
- Department of Biotherapeutics Discovery, Boehringer Ingelheim Pharma Inc, Connecticut, USA
| | - Minu Pilvankar
- Department of Biotherapeutics Discovery, Boehringer Ingelheim Pharma Inc, Connecticut, USA
| | - Ashish Sharma
- Department of Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma Inc, 900 Ridgebury Road, Ridgefield, CT, 06877, USA
| | - Peter Stopfer
- Department of Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co.KG, Biberach an der Riss, Germany
| | - Steven Hansel
- Department of Biotherapeutics Discovery, Boehringer Ingelheim Pharma Inc, Connecticut, USA
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3
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Dai J, Izadi S, Zarzar J, Wu P, Oh A, Carter PJ. Variable domain mutational analysis to probe the molecular mechanisms of high viscosity of an IgG 1 antibody. MAbs 2024; 16:2304282. [PMID: 38269489 PMCID: PMC10813588 DOI: 10.1080/19420862.2024.2304282] [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] [Received: 10/19/2023] [Accepted: 01/08/2024] [Indexed: 01/26/2024] Open
Abstract
Subcutaneous injection is the preferred route of administration for many antibody therapeutics for reasons that include its speed and convenience. However, the small volume limit (typically ≤ 2 mL) for subcutaneous delivery often necessitates antibody formulations at high concentrations (commonly ≥100 mg/mL), which may lead to physicochemical problems. For example, antibodies with large hydrophobic or charged patches can be prone to self-interaction giving rise to high viscosity. Here, we combined X-ray crystallography with computational modeling to predict regions of an anti-glucagon receptor (GCGR) IgG1 antibody prone to self-interaction. An extensive mutational analysis was undertaken of the complementarity-determining region residues residing in hydrophobic surface patches predicted by spatial aggregation propensity, in conjunction with residue-level solvent accessibility, averaged over conformational ensembles from molecular dynamics simulations. Dynamic light scattering (DLS) was used as a medium throughput screen for self-interaction of ~ 200 anti-GCGR IgG1 variants. A negative correlation was found between the viscosity determined at high concentration (180 mg/mL) and the DLS interaction parameter measured at low concentration (2-10 mg/mL). Additionally, anti-GCGR variants were readily identified with reduced viscosity and antigen-binding affinity within a few fold of the parent antibody, with no identified impact on overall developability. The methods described here may be useful in the optimization of other antibodies to facilitate their therapeutic administration at high concentration.
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Affiliation(s)
- Jing Dai
- Department of Antibody Engineering, Genentech, Inc, South San Francisco, CA, USA
| | - Saeed Izadi
- Department of Pharmaceutical Development, Genentech, Inc, South San Francisco, CA, USA
| | - Jonathan Zarzar
- Department of Pharmaceutical Development, Genentech, Inc, South San Francisco, CA, USA
| | - Patrick Wu
- Department of Bioanalytical Sciences, Genentech, Inc, South San Francisco, CA, USA
| | - Angela Oh
- Department of Structural Biology, Genentech, Inc, South San Francisco, CA, USA
| | - Paul J. Carter
- Department of Antibody Engineering, Genentech, Inc, South San Francisco, CA, USA
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4
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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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5
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Larsen HA, Atkins WM, Nath A. The origins of nonideality exhibited by monoclonal antibodies and Fab fragments in human serum. Protein Sci 2023; 32:e4812. [PMID: 37861473 PMCID: PMC10659951 DOI: 10.1002/pro.4812] [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: 04/30/2023] [Revised: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 10/21/2023]
Abstract
The development of therapeutic antibodies remains challenging, time-consuming, and expensive. A key contributing factor is a lack of understanding of how proteins are affected by complex biological environments such as serum and plasma. Nonideality due to attractive or repulsive interactions with cosolutes can alter the stability, aggregation propensity, and binding interactions of proteins in solution. Fluorescence correlation spectroscopy (FCS) can be used to measure apparent second virial coefficient (B2,app ) values for therapeutic and model monoclonal antibodies (mAbs) that capture the nature and strength of interactions with cosolutes directly in undiluted serum and similar complex biological media. Here, we use FCS-derived B2,app measurements to identify the components of human serum responsible for nonideal interactions with mAbs and Fab fragments. Most mAbs exhibit neutral or slightly attractive interactions with intact serum. Generally, mAbs display repulsive interactions with albumin and mildly attractive interactions with IgGs in the context of whole serum. Crucially, however, these attractive interactions are much stronger with pooled IgGs isolated from other serum components, indicating that the effects of serum nonideality can only be understood by studying the intact medium (rather than isolated components). Moreover, Fab fragments universally exhibited more attractive interactions than their parental mAbs, potentially rendering them more susceptible to nonideality-driven perturbations. FCS-based B2,app measurements have the potential to advance our understanding of how physiological environments impact protein-based therapeutics in general. Furthermore, incorporating such assays into preclinical biologics development may help de-risk molecules and make for a faster and more efficient development process.
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Affiliation(s)
- Hayli A. Larsen
- Department of Medicinal ChemistryUniversity of WashingtonSeattleWashingtonUSA
| | - William M. Atkins
- Department of Medicinal ChemistryUniversity of WashingtonSeattleWashingtonUSA
| | - Abhinav Nath
- Department of Medicinal ChemistryUniversity of WashingtonSeattleWashingtonUSA
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6
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Delevsky YP, Zinchenko OA. The role of acidification of the medium on the erythrocyte agglutinating and adsorbing properties of blood group-specific monoclonal antibodies. KOREAN JOURNAL OF TRANSPLANTATION 2023; 37:189-196. [PMID: 37751966 PMCID: PMC10583970 DOI: 10.4285/kjt.23.0033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/28/2023] [Accepted: 08/11/2023] [Indexed: 10/03/2023] Open
Abstract
Background Studies on the specificity of ABH antigen-antibody interactions at different pH values are rare. Therefore, the aim of this study was to estimate the effect of acidification of the reacting medium on the agglutinating ability of anti-A monoclonal antibodies (mABs) and their inhibition by glycoconjugates of red blood cell membranes. Methods Anti-A mABs were obtained from the Fourth International Workshop on Monoclonal Antibodies Against Human Red Blood Cell and Related Antigens (on July 19-20, 2002, in Paris, France). The glycoconjugates were isolated from erythrocytes' membranes. The inhibition of the lipid and protein isotypes of the blood group A antigen was assessed. Results The acidic medium decreased the agglutinating ability of acid immunoglobulin M (IgM) anti-A mABs, in contrast to alkaline immunoglobulin G (IgG) mABs. Meanwhile, at pH 6.5, IgM antibodies exhibited a high adsorption capacity, while IgG antibodies demonstrated a strong adsorption capacity at an alkaline pH. mABs 2-19, 2-28, 2-22, and 2-8 were inhibited by the acidic lipid and protein glycotopes of erythrocyte membranes. However, mAB 2-8 was inhibited by both acidic and alkaline glycotope variants. Conclusions The agglutinating and adsorbing abilities of mABs, which are revealed at opposing pH values, should be taken into account during studies of antigen-antibody interactions.
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Affiliation(s)
- Yuriy Pavlovich Delevsky
- Sytenko Institute of Spine and Joint Pathology, National Academy of Medical Sciences of Ukraine, Kharkiv, Ukraine
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7
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Arras P, Yoo HB, Pekar L, Clarke T, Friedrich L, Schröter C, Schanz J, Tonillo J, Siegmund V, Doerner A, Krah S, Guarnera E, Zielonka S, Evers A. AI/ML combined with next-generation sequencing of VHH immune repertoires enables the rapid identification of de novo humanized and sequence-optimized single domain antibodies: a prospective case study. Front Mol Biosci 2023; 10:1249247. [PMID: 37842638 PMCID: PMC10575757 DOI: 10.3389/fmolb.2023.1249247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 08/31/2023] [Indexed: 10/17/2023] Open
Abstract
Introduction: In this study, we demonstrate the feasibility of yeast surface display (YSD) and nextgeneration sequencing (NGS) in combination with artificial intelligence and machine learning methods (AI/ML) for the identification of de novo humanized single domain antibodies (sdAbs) with favorable early developability profiles. Methods: The display library was derived from a novel approach, in which VHH-based CDR3 regions obtained from a llama (Lama glama), immunized against NKp46, were grafted onto a humanized VHH backbone library that was diversified in CDR1 and CDR2. Following NGS analysis of sequence pools from two rounds of fluorescence-activated cell sorting we focused on four sequence clusters based on NGS frequency and enrichment analysis as well as in silico developability assessment. For each cluster, long short-term memory (LSTM) based deep generative models were trained and used for the in silico sampling of new sequences. Sequences were subjected to sequence- and structure-based in silico developability assessment to select a set of less than 10 sequences per cluster for production. Results: As demonstrated by binding kinetics and early developability assessment, this procedure represents a general strategy for the rapid and efficient design of potent and automatically humanized sdAb hits from screening selections with favorable early developability profiles.
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Affiliation(s)
- Paul Arras
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
- Institute for Organic Chemistry and Biochemistry, Technical University of Darmstadt, Darmstadt, Germany
| | - Han Byul Yoo
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Lukas Pekar
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Thomas Clarke
- Bioinformatics, EMD Serono, Billerica, MA, United States
| | - Lukas Friedrich
- Computational Chemistry and Biologics, Merck Healthcare KGaA, Darmstadt, Germany
| | | | - Jennifer Schanz
- ADCs & Targeted NBE Therapeutics, Merck KGaA, Darmstadt, Germany
| | - Jason Tonillo
- ADCs & Targeted NBE Therapeutics, Merck KGaA, Darmstadt, Germany
| | - Vanessa Siegmund
- Early Protein Supply and Characterization, Merck Healthcare KGaA, Darmstadt, Germany
| | - Achim Doerner
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Simon Krah
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Enrico Guarnera
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Stefan Zielonka
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
- Institute for Organic Chemistry and Biochemistry, Technical University of Darmstadt, Darmstadt, Germany
| | - Andreas Evers
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
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8
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Velayutham M, Poncelet M, Perini JA, Kupec JT, Dietz MJ, Driesschaert B, Khramtsov VV. EPR Viscometric Measurements Using a 13C-Labeled Triarylmethyl Radical in Protein-based Biotherapeutics and Human Synovial Fluids. APPLIED MAGNETIC RESONANCE 2023; 54:779-791. [PMID: 38707765 PMCID: PMC11068027 DOI: 10.1007/s00723-023-01556-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 06/22/2023] [Accepted: 06/22/2023] [Indexed: 05/07/2024]
Abstract
The viscosity measurements are of clinical significance for evaluation of the potential pathological conditions of biological lubricants such as synovial fluids of joints, and for formulation and characterization of peptide- and protein-based biotherapeutics. Due to inherent potential therapeutic activity, protein drugs have proven to be one of the most efficient therapeutic agents in treatment of several life-threatening disorders, such as diabetes and autoimmune diseases. However, home-use applications for treating chronic inflammatory diseases, such as diabetes and rheumatoid arthritis, necessitate the development of high-concentration insulin and monoclonal antibodies formulations for patient self-administration. High protein concentrations can affect viscosity of the corresponding drug solutions complicating their manufacture and administration. The measurements of the viscosity of new insulin analogs and monoclonal antibodies solutions under development is of practical importance to avoid unwanted highly viscous, and therefore, painful for injection drug formulations. Recently, we have demonstrated capability of the electron paramagnetic resonance (EPR) viscometry using viscosity-sensitive 13C-labeled trityl spin probe (13C1-dFT) to report the viscosity of human blood, and interstitial fluids measured in various organs in mice ex-vivo and in anesthetized mice, in vivo. In the present work, we demonstrate utility of the EPR viscometry using 13C1-dFT to measure microviscosity of commercial insulin samples, antibodies solution, and human synovial fluids using small microliter volume samples (5-50 μL). This viscometry analysis approach provides useful tool to control formulations and administration of new biopharmaceuticals, and for evaluation of the state of synovial fluids of importance for clinical applications.
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Affiliation(s)
- Murugesan Velayutham
- In Vivo Multifunctional Magnetic Resonance center, Robert C. Byrd Health Sciences Center
- Department of Biochemistry and Molecular Medicine
| | - Martin Poncelet
- In Vivo Multifunctional Magnetic Resonance center, Robert C. Byrd Health Sciences Center
- Department of Pharmaceutical Sciences
| | | | | | | | - Benoit Driesschaert
- In Vivo Multifunctional Magnetic Resonance center, Robert C. Byrd Health Sciences Center
- Department of Pharmaceutical Sciences
- C. Eugene Bennett Department of Chemistry, West Virginia University, Morgantown, WV 26506, USA
| | - Valery V. Khramtsov
- In Vivo Multifunctional Magnetic Resonance center, Robert C. Byrd Health Sciences Center
- Department of Biochemistry and Molecular Medicine
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9
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Liu S, Shah DK. Physiologically Based Pharmacokinetic Modeling to Characterize the Effect of Molecular Charge on Whole-Body Disposition of Monoclonal Antibodies. AAPS J 2023; 25:48. [PMID: 37118220 DOI: 10.1208/s12248-023-00812-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 04/11/2023] [Indexed: 04/30/2023] Open
Abstract
Motivated by a series of work demonstrating the effect of molecular charge on antibody pharmacokinetics (PK), physiological-based pharmacokinetic (PBPK) models are emerging that relate in silico calculated charge or in vitro measures of polyspecificity to antibody PK parameters. However, only plasma data has been used for model development in these studies, leading to unvalidated assumptions. Here, we present an extended platform PBPK model for antibodies that incorporate charge-dependent endothelial cell pinocytosis rate and nonspecific off-target binding in the interstitial space and on circulating blood cells, to simultaneously characterize whole-body disposition of three antibody charge variants. Predictive potential of various charge metrics was also explored, and the difference between positive charge patches and negative charge patches (i.e., PPC-PNC) was used as the charge parameter to establish quantitative relationships with nonspecific binding affinities and endothelial cell uptake rate. Whole-body disposition of these charge variants was captured well by the model, with less than 2-fold predictive error in area under the curve of most plasma and tissue PK data. The model also predicted that with greater positive charge, nonspecific binding was more substantial, and pinocytosis rate increased especially in brain, heart, kidney, liver, lung, and spleen, but remained unchanged in adipose, bone, muscle, and skin. The presented PBPK model contributes to our understanding of the mechanisms governing the disposition of charged antibodies and can be used as a platform to guide charge engineering based on desired plasma and tissue exposures.
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Affiliation(s)
- Shufang Liu
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, 455 Pharmacy Building, Buffalo, Ney York, 14214-8033, USA
| | - Dhaval K Shah
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, 455 Pharmacy Building, Buffalo, Ney York, 14214-8033, USA.
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10
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Okuyama H, Kodama Y, Takemura K, Yamashita H, Oshiba Y, Yamaguchi T. Design of a highly sensitive and versatile membrane-based immunosensor using a Cu-free click reaction. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:1494-1499. [PMID: 36892549 DOI: 10.1039/d2ay02110b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
A highly sensitive immunosensor is developed using membrane pores as the recognition interface. In this sensor, a Cu-free click reaction is used to efficiently immobilize antibodies, and the sensor inhibits the adsorption of nonspecific proteins that degrade sensitivity. Furthermore, the sensor demonstrates rapid interleukin-6 detection in the picogram per milliliter range.
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Affiliation(s)
- Hiroto Okuyama
- Laboratory for Chemistry and Life Science, Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, 226-8503, Japan.
| | - Yukari Kodama
- Laboratory for Chemistry and Life Science, Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, 226-8503, Japan.
| | - Kazuya Takemura
- Laboratory for Chemistry and Life Science, Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, 226-8503, Japan.
| | - Hiroki Yamashita
- Laboratory for Chemistry and Life Science, Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, 226-8503, Japan.
| | - Yuhei Oshiba
- Laboratory for Chemistry and Life Science, Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, 226-8503, Japan.
| | - Takeo Yamaguchi
- Laboratory for Chemistry and Life Science, Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, 226-8503, Japan.
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11
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Chowdhury A, Manohar N, Guruprasad G, Chen AT, Lanzaro A, Blanco M, Johnston KP, Truskett TM. Characterizing Experimental Monoclonal Antibody Interactions and Clustering Using a Coarse-Grained Simulation Library and a Viscosity Model. J Phys Chem B 2023; 127:1120-1137. [PMID: 36716270 DOI: 10.1021/acs.jpcb.2c07616] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Attractive protein-protein interactions in concentrated monoclonal antibody (mAb) solutions may lead to the formation of clusters that increase viscosity. Here, we propose an analytical model that relates mAb solution viscosity to clustering by accounting for the contributions of suboptimal mAb packing within a cluster and cluster fractal dimension. The influence of short-range, anisotropic attractions and long-range Coulombic repulsion on cluster properties is investigated by analyzing the cluster-size distributions, cluster fractal dimensions, radial distribution functions, and static structure factors from a library of coarse-grained molecular dynamics simulations. The library spans a vast range of mAb charges and attractive interactions in solutions of varying ionic strength. We present a framework for combining the viscosity model and simulation library to successfully characterize the attraction, repulsion, and clustering of an experimental mAb in three different pH and cosolute conditions by fitting the measured viscosity or structure factor from small-angle X-ray scattering. At low ionic strength, the cluster-size distribution is impacted by strong charges, and both the viscosity and net charge or structure factor and net charge must be considered to deconvolute the effects of short-range attraction and long-range repulsion.
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Affiliation(s)
- Amjad Chowdhury
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas78712, United States
| | - Neha Manohar
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas78712, United States
| | - Geetika Guruprasad
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas78712, United States
| | - Amy T Chen
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas78712, United States
| | - Alfredo Lanzaro
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas78712, United States
| | - Marco Blanco
- Analytical Enabling Capabilities, Analytical R&D, Merck & Co., Inc., Rahway, New Jersey07065, United States
| | - Keith P Johnston
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas78712, United States
| | - Thomas M Truskett
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas78712, United States.,Department of Physics, The University of Texas at Austin, Austin, Texas78712, United States
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12
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Jain T, Boland T, Vásquez M. Identifying developability risks for clinical progression of antibodies using high-throughput in vitro and in silico approaches. MAbs 2023; 15:2200540. [PMID: 37072706 PMCID: PMC10114995 DOI: 10.1080/19420862.2023.2200540] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/03/2023] [Accepted: 04/04/2023] [Indexed: 04/20/2023] Open
Abstract
With the growing significance of antibodies as a therapeutic class, identifying developability risks early during development is of paramount importance. Several high-throughput in vitro assays and in silico approaches have been proposed to de-risk antibodies during early stages of the discovery process. In this review, we have compiled and collectively analyzed published experimental assessments and computational metrics for clinical antibodies. We show that flags assigned based on in vitro measurements of polyspecificity and hydrophobicity are more predictive of clinical progression than their in silico counterparts. Additionally, we assessed the performance of published models for developability predictions on molecules not used during model training. We find that generalization to data outside of those used for training remains a challenge for models. Finally, we highlight the challenges of reproducibility in computed metrics arising from differences in homology modeling, in vitro assessments relying on complex reagents, as well as curation of experimental data often used to assess the utility of high-throughput approaches. We end with a recommendation to enable assay reproducibility by inclusion of controls with disclosed sequences, as well as sharing of structural models to enable the critical assessment and improvement of in silico predictions.
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Affiliation(s)
| | - Todd Boland
- Computational Biology, Adimab LLC, Lebanon, NH, USA
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13
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Evers A, Malhotra S, Bolick WG, Najafian A, Borisovska M, Warszawski S, Fomekong Nanfack Y, Kuhn D, Rippmann F, Crespo A, Sood V. SUMO: In Silico Sequence Assessment Using Multiple Optimization Parameters. Methods Mol Biol 2023; 2681:383-398. [PMID: 37405660 DOI: 10.1007/978-1-0716-3279-6_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/06/2023]
Abstract
To select the most promising screening hits from antibody and VHH display campaigns for subsequent in-depth profiling and optimization, it is highly desirable to assess and select sequences on properties beyond only their binding signals from the sorting process. In addition, developability risk criteria, sequence diversity, and the anticipated complexity for sequence optimization are relevant attributes for hit selection and optimization. Here, we describe an approach for the in silico developability assessment of antibody and VHH sequences. This method not only allows for ranking and filtering multiple sequences with regard to their predicted developability properties and diversity, but also visualizes relevant sequence and structural features of potentially problematic regions and thereby provides rationales and starting points for multi-parameter sequence optimization.
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Affiliation(s)
- Andreas Evers
- Computational Chemistry & Biologics (CCB), Merck Healthcare KGaA, Darmstadt, Germany.
| | - Shipra Malhotra
- Computational Chemistry & Biologics (CCB), EMD Serono, Billerica, MA, USA
| | | | - Ahmad Najafian
- Computational Chemistry & Biologics (CCB), EMD Serono, Billerica, MA, USA
| | - Maria Borisovska
- Computational Chemistry & Biologics (CCB), EMD Serono, Billerica, MA, USA
| | | | | | - Daniel Kuhn
- Computational Chemistry & Biologics (CCB), Merck Healthcare KGaA, Darmstadt, Germany
| | - Friedrich Rippmann
- Computational Chemistry & Biologics (CCB), Merck Healthcare KGaA, Darmstadt, Germany
| | - Alejandro Crespo
- Computational Chemistry & Biologics (CCB), EMD Serono, Billerica, MA, USA
| | - Vanita Sood
- Computational Chemistry & Biologics (CCB), EMD Serono, Billerica, MA, USA
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14
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Licari G, Martin KP, Crames M, Mozdzierz J, Marlow MS, Karow-Zwick AR, Kumar S, Bauer J. Embedding Dynamics in Intrinsic Physicochemical Profiles of Market-Stage Antibody-Based Biotherapeutics. Mol Pharm 2022; 20:1096-1111. [PMID: 36573887 PMCID: PMC9906779 DOI: 10.1021/acs.molpharmaceut.2c00838] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Adequate stability, manufacturability, and safety are crucial to bringing an antibody-based biotherapeutic to the market. Following the concept of holistic in silico developability, we introduce a physicochemical description of 91 market-stage antibody-based biotherapeutics based on orthogonal molecular properties of variable regions (Fvs) embedded in different simulation environments, mimicking conditions experienced by antibodies during manufacturing, formulation, and in vivo. In this work, the evaluation of molecular properties includes conformational flexibility of the Fvs using molecular dynamics (MD) simulations. The comparison between static homology models and simulations shows that MD significantly affects certain molecular descriptors like surface molecular patches. Moreover, the structural stability of a subset of Fv regions is linked to changes in their specific molecular interactions with ions in different experimental conditions. This is supported by the observation of differences in protein melting temperatures upon addition of NaCl. A DEvelopability Navigator In Silico (DENIS) is proposed to compare mAb candidates for their similarity with market-stage biotherapeutics in terms of physicochemical properties and conformational stability. Expanding on our previous developability guidelines (Ahmed et al. Proc. Natl. Acad. Sci. 2021, 118 (37), e2020577118), the hydrodynamic radius and the protein strand ratio are introduced as two additional descriptors that enable a more comprehensive in silico characterization of biotherapeutic drug candidates. Test cases show how this approach can facilitate identification and optimization of intrinsically developable lead candidates. DENIS represents an advanced computational tool to progress biotherapeutic drug candidates from discovery into early development by predicting drug properties in different aqueous environments.
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Affiliation(s)
- Giuseppe Licari
- Early
Stage Pharmaceutical Development, Pharmaceutical Development Biologicals
& In silico Team, Boehringer Ingelheim
International GmbH & Co. KG, Biberach/Riss 88397, Germany
| | - Kyle P. Martin
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States
| | - Maureen Crames
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States
| | - Joseph Mozdzierz
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States
| | - Michael S. Marlow
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States
| | - Anne R. Karow-Zwick
- Early
Stage Pharmaceutical Development, Pharmaceutical Development Biologicals
& In silico Team, Boehringer Ingelheim
International GmbH & Co. KG, Biberach/Riss 88397, Germany
| | - Sandeep Kumar
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States,
| | - Joschka Bauer
- Early
Stage Pharmaceutical Development, Pharmaceutical Development Biologicals
& In silico Team, Boehringer Ingelheim
International GmbH & Co. KG, Biberach/Riss 88397, Germany,
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15
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Makowski EK, Chen H, Lambert M, Bennett EM, Eschmann NS, Zhang Y, Zupancic JM, Desai AA, Smith MD, Lou W, Fernando A, Tully T, Gallo CJ, Lin L, Tessier PM. Reduction of therapeutic antibody self-association using yeast-display selections and machine learning. MAbs 2022; 14:2146629. [PMID: 36433737 PMCID: PMC9704398 DOI: 10.1080/19420862.2022.2146629] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Self-association governs the viscosity and solubility of therapeutic antibodies in high-concentration formulations used for subcutaneous delivery, yet it is difficult to reliably identify candidates with low self-association during antibody discovery and early-stage optimization. Here, we report a high-throughput protein engineering method for rapidly identifying antibody candidates with both low self-association and high affinity. We find that conjugating quantum dots to IgGs that strongly self-associate (pH 7.4, PBS), such as lenzilumab and bococizumab, results in immunoconjugates that are highly sensitive for detecting other high self-association antibodies. Moreover, these conjugates can be used to rapidly enrich yeast-displayed bococizumab sub-libraries for variants with low levels of immunoconjugate binding. Deep sequencing and machine learning analysis of the enriched bococizumab libraries, along with similar library analysis for antibody affinity, enabled identification of extremely rare variants with co-optimized levels of low self-association and high affinity. This analysis revealed that co-optimizing bococizumab is difficult because most high-affinity variants possess positively charged variable domains and most low self-association variants possess negatively charged variable domains. Moreover, negatively charged mutations in the heavy chain CDR2 of bococizumab, adjacent to its paratope, were effective at reducing self-association without reducing affinity. Interestingly, most of the bococizumab variants with reduced self-association also displayed improved folding stability and reduced nonspecific binding, revealing that this approach may be particularly useful for identifying antibody candidates with attractive combinations of drug-like properties.Abbreviations: AC-SINS: affinity-capture self-interaction nanoparticle spectroscopy; CDR: complementarity-determining region; CS-SINS: charge-stabilized self-interaction nanoparticle spectroscopy; FACS: fluorescence-activated cell sorting; Fab: fragment antigen binding; Fv: fragment variable; IgG: immunoglobulin; QD: quantum dot; PBS: phosphate-buffered saline; VH: variable heavy; VL: variable light.
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Affiliation(s)
- Emily K. Makowski
- Departments of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA,Biointerfaces Institute, University of Michigan, Ann Arbor, MI48109, USA
| | - Hongwei Chen
- Departments of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA,Biointerfaces Institute, University of Michigan, Ann Arbor, MI48109, USA,Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | | | | | | | - Yulei Zhang
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI48109, USA,Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jennifer M. Zupancic
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI48109, USA,Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alec A. Desai
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI48109, USA,Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Matthew D. Smith
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI48109, USA,Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Wenjia Lou
- Departments of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA,Biointerfaces Institute, University of Michigan, Ann Arbor, MI48109, USA,Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Timothy Tully
- Bioprocess Research & Development, Pfizer Inc., St. Louis, MO, USA
| | | | - Laura Lin
- BioMedicine Design, Pfizer Inc, Cambridge, MA, USA
| | - Peter M. Tessier
- Departments of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA,Biointerfaces Institute, University of Michigan, Ann Arbor, MI48109, USA,Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA,CONTACT Peter M. Tessier Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA
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