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Bryniarski MA, Tuhin MTH, Acker TM, Wakefield DL, Sethaputra PG, Cook KD, Soto M, Ponce M, Primack R, Jagarapu A, LaGory EL, Conner KP. Cellular Neonatal Fc Receptor Recycling Efficiencies can Differentiate Target-Independent Clearance Mechanisms of Monoclonal Antibodies. J Pharm Sci 2024:S0022-3549(24)00235-1. [PMID: 38906252 DOI: 10.1016/j.xphs.2024.06.013] [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: 02/08/2024] [Revised: 06/15/2024] [Accepted: 06/17/2024] [Indexed: 06/23/2024]
Abstract
In vivo clearance mechanisms of therapeutic monoclonal antibodies (mAbs) encompass both target-mediated and target-independent processes. Two distinct determinants of overall mAb clearance largely separate of target-mediated influences are non-specific cellular endocytosis and subsequent pH-dependent mAb recycling mediated by the neonatal Fc receptor (FcRn), where inter-mAb variability in the efficiency of both processes is observed. Here, we implemented a functional cell-based FcRn recycling assay via Madin-Darby canine kidney type II cells stably co-transfected with human FcRn and its light chain β2-microglobulin. Next, a series of pH-dependent internalization studies using a model antibody demonstrated proper function of the human FcRn complex. We then applied our cellular assays to assess the contribution of both FcRn and non-specific interactions in the cellular turnover for a panel of 8 clinically relevant mAbs exhibiting variable human pharmacokinetic behavior. Our results demonstrate that the interplay of non-specific endocytosis rates, pH-dependent non-specific interactions, and engagement with FcRn all contribute to the overall recycling efficiency of therapeutic monoclonal antibodies. The predictive capacity of our assay approach was highlighted by successful identification of all mAbs within our panel possessing clearance in humans greater than 5 mL/day/kg. These results demonstrate that a combination of cell-based in vitro assays can properly resolve individual mechanisms underlying the overall in vivo recycling efficiency and non-target mediated clearance of therapeutic mAbs.
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Affiliation(s)
- Mark A Bryniarski
- Pharmacokinetics and Drug Metabolism, Amgen Research, 750 Gateway Blvd, Suite 100, South San Francisco, CA 94080, USA.
| | - Md Tariqul Haque Tuhin
- Pharmacokinetics and Drug Metabolism, Amgen Research, 750 Gateway Blvd, Suite 100, South San Francisco, CA 94080, USA
| | - Timothy M Acker
- Pharmacokinetics and Drug Metabolism, Amgen Research, 750 Gateway Blvd, Suite 100, South San Francisco, CA 94080, USA
| | - Devin L Wakefield
- Research Biomics, Amgen Research, 750 Gateway Blvd, Suite 100, South San Francisco, CA 94080, USA
| | - Panijaya Gemy Sethaputra
- Pharmacokinetics and Drug Metabolism, Amgen Research, 750 Gateway Blvd, Suite 100, South San Francisco, CA 94080, USA
| | - Kevin D Cook
- Pharmacokinetics and Drug Metabolism, Amgen Research, 750 Gateway Blvd, Suite 100, South San Francisco, CA 94080, USA
| | - Marcus Soto
- Pharmacokinetics & Drug Metabolism, Amgen Research, One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Manuel Ponce
- Pharmacokinetics & Drug Metabolism, Amgen Research, One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Ronya Primack
- Pharmacokinetics & Drug Metabolism, Amgen Research, One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Aditya Jagarapu
- Pharmacokinetics and Drug Metabolism, Amgen Research, 750 Gateway Blvd, Suite 100, South San Francisco, CA 94080, USA
| | - Edward L LaGory
- Pharmacokinetics and Drug Metabolism, Amgen Research, 750 Gateway Blvd, Suite 100, South San Francisco, CA 94080, USA
| | - Kip P Conner
- Pharmacokinetics and Drug Metabolism, Amgen Research, 750 Gateway Blvd, Suite 100, South San Francisco, CA 94080, USA.
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2
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Dhaouadi S, Bouhaouala-Zahar B, Orend G. Tenascin-C targeting strategies in cancer. Matrix Biol 2024; 130:1-19. [PMID: 38642843 DOI: 10.1016/j.matbio.2024.04.002] [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: 12/20/2023] [Revised: 04/13/2024] [Accepted: 04/14/2024] [Indexed: 04/22/2024]
Abstract
Tenascin-C (TNC) is a matricellular and multimodular glycoprotein highly expressed under pathological conditions, especially in cancer and chronic inflammatory diseases. Since a long time TNC is considered as a promising target for diagnostic and therapeutic approaches in anti-cancer treatments and was already extensively targeted in clinical trials on cancer patients. This review provides an overview of the current most advanced strategies used for TNC detection and anti-TNC theranostic approaches including some advanced clinical strategies. We also discuss novel treatment protocols, where targeting immune modulating functions of TNC could be center stage.
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Affiliation(s)
- Sayda Dhaouadi
- Laboratoire des Venins et Biomolécules Thérapeutiques, Institut Pasteur de Tunis, Université Tunis El Manar, Tunis, Tunisia
| | - Balkiss Bouhaouala-Zahar
- Laboratoire des Venins et Biomolécules Thérapeutiques, Institut Pasteur de Tunis, Université Tunis El Manar, Tunis, Tunisia; Faculté de Médecine de Tunis, Université Tunis el Manar, Tunis, Tunisia
| | - Gertraud Orend
- INSERM U1109, The Tumor Microenvironment laboratory, Université Strasbourg, Hôpital Civil, Institut d'Hématologie et d'Immunologie, Fédération de Médecine Translationnelle de Strasbourg (FMTS), Strasbourg, France.
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3
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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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4
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Obrezanova O. Artificial intelligence for compound pharmacokinetics prediction. Curr Opin Struct Biol 2023; 79:102546. [PMID: 36804676 DOI: 10.1016/j.sbi.2023.102546] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 01/04/2023] [Accepted: 01/13/2023] [Indexed: 02/17/2023]
Abstract
Optimisation of compound pharmacokinetics (PK) is an integral part of drug discovery and development. Animal in vivo PK data as well as human and animal in vitro systems are routinely utilised to evaluate PK in humans. In recent years machine learning and artificial intelligence (AI) emerged as a major tool for modelling of in vivo animal and human PK, enabling prediction from chemical structure early in drug discovery, and therefore offering opportunities to guide the design and prioritisation of molecules based on relevant in vivo properties and, ultimately, predicting human PK at the point of design. This review presents recent advances in machine learning and AI models for in vivo animal and human PK for small-molecule compounds as well as some examples for antibody therapeutics.
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Affiliation(s)
- Olga Obrezanova
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge, CB4 0WJ, UK.
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5
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Müller T, Tasser C, Tesar M, Fucek I, Schniegler-Mattox U, Koch J, Ellwanger K. Selection of bispecific antibodies with optimal developability using FcRn‑Ph‑HPLC as an optimized FcRn affinity chromatography method. MAbs 2023; 15:2245519. [PMID: 37599441 PMCID: PMC10443974 DOI: 10.1080/19420862.2023.2245519] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 07/27/2023] [Accepted: 08/03/2023] [Indexed: 08/22/2023] Open
Abstract
A challenge when developing therapeutic antibodies is the identification of candidates with favorable pharmacokinetics (PK) early in development. A key determinant of immunoglobulin (IgG) serum half‑life in vivo is the efficiency of pH-dependent binding to the neonatal Fc receptor (FcRn). Numerous studies have proposed techniques to assess FcRn binding of IgG-based therapeutics in vitro, enabling prediction of serum half-life prior to clinical assessment. FcRn high-performance liquid chromatography (HPLC) assays FcRn binding of therapeutic IgGs across a pH gradient, allowing the correlation of IgG column retention time to the half‑life of a therapeutic IgG in vivo. However, as FcRn retention time cannot be directly compared to an in vivo parameter, modifications to FcRn-HPLC are required to enable interpretation of the data within a physiological context, to provide more accurate estimations of serum half-life. This study presents an important modification to this method, FcRn-pH-HPLC, which reproducibly measures FcRn dissociation pH, allowing correlation with previously established half-lives of therapeutic antibodies. Furthermore, the influence of incorporating various antibody modifications, binding modules, and their orientations within IgGs and bispecifics on FcRn dissociation pH was evaluated using antibodies from the redirected optimized cell killing (ROCK®) platform. Target and effector antigen-binding domain sequences, their presentation format and orientation within a bispecific antibody alter FcRn retention; tested Fc domain modifications and incorporating stabilizing disulfide bonds had minimal effect. This study may inform the generation of mono-, bi- and multi-specific antibodies with tailored half-lives based on FcRn binding properties in vitro, to differentiate antibody-based therapeutic candidates with optimal developability.
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Affiliation(s)
- Thomas Müller
- Discovery Research and Translational Immunology, Affimed GmbH, Heidelberg, Germany
| | - Carolin Tasser
- Discovery Research and Translational Immunology, Affimed GmbH, Heidelberg, Germany
| | - Michael Tesar
- Discovery Research and Translational Immunology, Affimed GmbH, Heidelberg, Germany
| | - Ivica Fucek
- Discovery Research and Translational Immunology, Affimed GmbH, Heidelberg, Germany
| | | | - Joachim Koch
- Discovery Research and Translational Immunology, Affimed GmbH, Heidelberg, Germany
| | - Kristina Ellwanger
- Discovery Research and Translational Immunology, Affimed GmbH, Heidelberg, Germany
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Liu S, Humphreys SC, Cook KD, Conner KP, Correia AR, Jacobitz AW, Yang M, Primack R, Soto M, Padaki R, Lubomirski M, Smith R, Mock M, Thomas VA. Utility of physiologically based pharmacokinetic modeling to predict inter-antibody variability in monoclonal antibody pharmacokinetics in mice. MAbs 2023; 15:2263926. [PMID: 37824334 PMCID: PMC10572049 DOI: 10.1080/19420862.2023.2263926] [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: 05/31/2023] [Accepted: 09/24/2023] [Indexed: 10/14/2023] Open
Abstract
In this investigation, we tested the hypothesis that a physiologically based pharmacokinetic (PBPK) model incorporating measured in vitro metrics of off-target binding can largely explain the inter-antibody variability in monoclonal antibody (mAb) pharmacokinetics (PK). A diverse panel of 83 mAbs was evaluated for PK in wild-type mice and subjected to 10 in vitro assays to measure major physiochemical attributes. After excluding for target-mediated elimination and immunogenicity, 56 of the remaining mAbs with an eight-fold variability in the area under the curve (A U C 0 - 672 h : 1.74 × 106 -1.38 × 107 ng∙h/mL) and 10-fold difference in clearance (2.55-26.4 mL/day/kg) formed the training set for this investigation. Using a PBPK framework, mAb-dependent coefficients F1 and F2 modulating pinocytosis rate and convective transport, respectively, were estimated for each mAb with mostly good precision (coefficient of variation (CV%) <30%). F1 was estimated to be the mean and standard deviation of 0.961 ± 0.593, and F2 was estimated to be 2.13 ± 2.62. Using principal component analysis to correlate the regressed values of F1/F2 versus the multidimensional dataset composed of our panel of in vitro assays, we found that heparin chromatography retention time emerged as the predictive covariate to the mAb-specific F1, whereas F2 variability cannot be well explained by these assays. A sigmoidal relationship between F1 and the identified covariate was incorporated within the PBPK framework. A sensitivity analysis suggested plasma concentrations to be most sensitive to F1 when F1 > 1. The predictive utility of the developed PBPK model was evaluated against a separate panel of 14 mAbs biased toward high clearance, among which area under the curve of PK data of 12 mAbs was predicted within 2.5-fold error, and the positive and negative predictive values for clearance prediction were 85% and 100%, respectively. MAb heparin chromatography assay output allowed a priori identification of mAb candidates with unfavorable PK.
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Affiliation(s)
- Shufang Liu
- Department of Pharmacokinetics and Drug Metabolism, Amgen Inc, South San Francisco, CA, USA
| | - Sara C. Humphreys
- Department of Pharmacokinetics and Drug Metabolism, Amgen Inc, South San Francisco, CA, USA
| | - Kevin D. Cook
- Department of Pharmacokinetics and Drug Metabolism, Amgen Inc, South San Francisco, CA, USA
| | - Kip P. Conner
- Department of Pharmacokinetics and Drug Metabolism, Amgen Inc, South San Francisco, CA, USA
| | | | | | - Melissa Yang
- Therapeutic Discovery, Amgen, Thousand Oaks, CA, USA
| | - Ronya Primack
- Department of Pharmacokinetics and Drug Metabolism, Amgen Inc, Thousand Oaks, CA, USA
| | - Marcus Soto
- Department of Pharmacokinetics and Drug Metabolism, Amgen Inc, Thousand Oaks, CA, USA
| | - Rupa Padaki
- Process Development, Amgen Inc, Thousand Oaks, CA, USA
| | | | - Richard Smith
- Department of Pharmacokinetics and Drug Metabolism, Amgen Inc, South San Francisco, CA, USA
| | - Marissa Mock
- Therapeutic Discovery, Amgen, Thousand Oaks, CA, USA
| | - Veena A. Thomas
- Department of Pharmacokinetics and Drug Metabolism, Amgen Inc, South San Francisco, CA, USA
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7
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Hu S, Datta-Mannan A, D’Argenio DZ. Physiologically Based Modeling to Predict Monoclonal Antibody Pharmacokinetics in Humans from in vitro Physiochemical Properties. MAbs 2022; 14:2056944. [PMID: 35491902 PMCID: PMC9067474 DOI: 10.1080/19420862.2022.2056944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/28/2022] [Accepted: 03/20/2022] [Indexed: 11/01/2022] Open
Abstract
A model-based framework is presented to predict monoclonal antibody (mAb) pharmacokinetics (PK) in humans based on in vitro measures of antibody physiochemical properties. A physiologically based pharmacokinetic (PBPK) model is used to explore the predictive potential of 14 in vitro assays designed to measure various antibody physiochemical properties, including nonspecific cell-surface interactions, FcRn binding, thermal stability, hydrophobicity, and self-association. Based on the mean plasma PK time course data of 22 mAbs from humans reported in the literature, we found a significant positive correlation (R = 0.64, p = .0013) between the model parameter representing antibody-specific vascular to endothelial clearance and heparin relative retention time, an in vitro measure of nonspecific binding. We also found that antibody-specific differences in paracellular transport due to convection and diffusion could be partially explained by antibody heparin relative retention time (R = 0.52, p = .012). Other physiochemical properties, including antibody thermal stability, hydrophobicity, cross-interaction and self-association, in and of themselves were not predictive of model-based transport parameters. In contrast to other studies that have reported empirically derived expressions relating in vitro measures of antibody physiochemical properties directly to antibody clearance, the proposed PBPK model-based approach for predicting mAb PK incorporates fundamental mechanisms governing antibody transport and processing, informed by in vitro measures of antibody physiochemical properties, and can be expanded to include more descriptive representations of each of the antibody processing subsystems, as well as other antibody-specific information.
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Affiliation(s)
- Shihao Hu
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Amita Datta-Mannan
- Department of Exploratory Medicine and Pharmacology, Lilly Research Laboratories, Lilly Corporate Center, Indianapolis, IN, USA
| | - David Z. D’Argenio
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
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8
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Akbar R, Bashour H, Rawat P, Robert PA, Smorodina E, Cotet TS, Flem-Karlsen K, Frank R, Mehta BB, Vu MH, Zengin T, Gutierrez-Marcos J, Lund-Johansen F, Andersen JT, Greiff V. Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. MAbs 2022; 14:2008790. [PMID: 35293269 PMCID: PMC8928824 DOI: 10.1080/19420862.2021.2008790] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 11/04/2021] [Accepted: 11/17/2021] [Indexed: 12/15/2022] Open
Abstract
Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.
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Affiliation(s)
- Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Habib Bashour
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Philippe A. Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Eva Smorodina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russia
| | | | - Karine Flem-Karlsen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Robert Frank
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Brij Bhushan Mehta
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Mai Ha Vu
- Department of Linguistics and Scandinavian Studies, University of Oslo, Norway
| | - Talip Zengin
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Bioinformatics, Mugla Sitki Kocman University, Turkey
| | | | | | - Jan Terje Andersen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
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9
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Qi T, Cao Y. In Translation: FcRn across the Therapeutic Spectrum. Int J Mol Sci 2021; 22:3048. [PMID: 33802650 PMCID: PMC8002405 DOI: 10.3390/ijms22063048] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/11/2021] [Accepted: 03/15/2021] [Indexed: 12/14/2022] Open
Abstract
As an essential modulator of IgG disposition, the neonatal Fc receptor (FcRn) governs the pharmacokinetics and functions many therapeutic modalities. In this review, we thoroughly reexamine the hitherto elucidated biological and thermodynamic properties of FcRn to provide context for our assessment of more recent advances, which covers antigen-binding fragment (Fab) determinants of FcRn affinity, transgenic preclinical models, and FcRn targeting as an immune-complex (IC)-clearing strategy. We further comment on therapeutic antibodies authorized for treating SARS-CoV-2 (bamlanivimab, casirivimab, and imdevimab) and evaluate their potential to saturate FcRn-mediated recycling. Finally, we discuss modeling and simulation studies that probe the quantitative relationship between in vivo IgG persistence and in vitro FcRn binding, emphasizing the importance of endosomal transit parameters.
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Affiliation(s)
| | - Yanguang Cao
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, Chapel Hill, NC 27599, USA;
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10
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Synthesis and preliminary in vitro evaluation of DOTA-Tenatumomab conjugates for theranostic applications in tenascin expressing tumors. Bioorg Med Chem 2019; 27:3248-3253. [DOI: 10.1016/j.bmc.2019.05.047] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 05/23/2019] [Accepted: 05/29/2019] [Indexed: 12/25/2022]
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