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Ray CMP, Yang H, Spangler JB, Mac Gabhann F. Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors. PLoS Comput Biol 2024; 20:e1012157. [PMID: 38848446 PMCID: PMC11189202 DOI: 10.1371/journal.pcbi.1012157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 06/20/2024] [Accepted: 05/10/2024] [Indexed: 06/09/2024] Open
Abstract
The spread of cancer from organ to organ (metastasis) is responsible for the vast majority of cancer deaths; however, most current anti-cancer drugs are designed to arrest or reverse tumor growth without directly addressing disease spread. It was recently discovered that tumor cell-secreted interleukin-6 (IL-6) and interleukin-8 (IL-8) synergize to enhance cancer metastasis in a cell-density dependent manner, and blockade of the IL-6 and IL-8 receptors (IL-6R and IL-8R) with a novel bispecific antibody, BS1, significantly reduced metastatic burden in multiple preclinical mouse models of cancer. Bispecific antibodies (BsAbs), which combine two different antigen-binding sites into one molecule, are a promising modality for drug development due to their enhanced avidity and dual targeting effects. However, while BsAbs have tremendous therapeutic potential, elucidating the mechanisms underlying their binding and inhibition will be critical for maximizing the efficacy of new BsAb treatments. Here, we describe a quantitative, computational model of the BS1 BsAb, exhibiting how modeling multivalent binding provides key insights into antibody affinity and avidity effects and can guide therapeutic design. We present detailed simulations of the monovalent and bivalent binding interactions between different antibody constructs and the IL-6 and IL-8 receptors to establish how antibody properties and system conditions impact the formation of binary (antibody-receptor) and ternary (receptor-antibody-receptor) complexes. Model results demonstrate how the balance of these complex types drives receptor inhibition, providing important and generalizable predictions for effective therapeutic design.
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Affiliation(s)
- Christina M. P. Ray
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Medical-Scientist Training Program, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Huilin Yang
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Jamie B. Spangler
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, Maryland, United States of America
- Bloomberg Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Ophthalmology, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - Feilim Mac Gabhann
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
- Institute for Nano Biotechnology (INBT), Johns Hopkins University, Baltimore, Maryland, United States of America
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2
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Ray CMP, Yang H, Spangler JB, Mac Gabhann F. Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.18.570445. [PMID: 38187701 PMCID: PMC10769311 DOI: 10.1101/2023.12.18.570445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
The spread of cancer from organ to organ (metastasis) is responsible for the vast majority of cancer deaths; however, most current anti-cancer drugs are designed to arrest or reverse tumor growth without directly addressing disease spread. It was recently discovered that tumor cell-secreted interleukin-6 (IL-6) and interleukin-8 (IL-8) synergize to enhance cancer metastasis in a cell-density dependent manner, and blockade of the IL-6 and IL-8 receptors (IL-6R and IL-8R) with a novel bispecific antibody, BS1, significantly reduced metastatic burden in multiple preclinical mouse models of cancer. Bispecific antibodies (BsAbs), which combine two different antigen-binding sites into one molecule, are a promising modality for drug development due to their enhanced avidity and dual targeting effects. However, while BsAbs have tremendous therapeutic potential, elucidating the mechanisms underlying their binding and inhibition will be critical for maximizing the efficacy of new BsAb treatments. Here, we describe a quantitative, computational model of the BS1 BsAb, exhibiting how modeling multivalent binding provides key insights into antibody affinity and avidity effects and can guide therapeutic design. We present detailed simulations of the monovalent and bivalent binding interactions between different antibody constructs and the IL-6 and IL-8 receptors to establish how antibody properties and system conditions impact the formation of binary (antibody-receptor) and ternary (receptor-antibody-receptor) complexes. Model results demonstrate how the balance of these complex types drives receptor inhibition, providing important and generalizable predictions for effective therapeutic design.
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Affiliation(s)
- Christina MP Ray
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Medical-Scientist Training Program, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Huilin Yang
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Jamie B Spangler
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, Maryland, United States of America
- Bloomberg Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Ophthalmology, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - Feilim Mac Gabhann
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
- Institute for Nano Biotechnology (INBT), Johns Hopkins University, Baltimore, Maryland, United States of America
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3
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Niu J, Wang W, Ouellet D. Mechanism-based pharmacokinetic and pharmacodynamic modeling for bispecific antibodies: challenges and opportunities. Expert Rev Clin Pharmacol 2023; 16:977-990. [PMID: 37743720 DOI: 10.1080/17512433.2023.2257136] [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: 06/15/2023] [Accepted: 09/06/2023] [Indexed: 09/26/2023]
Abstract
INTRODUCTION Unlike conventional antibodies, bispecific antibodies (bsAbs) are engineered antibody- or antibody fragment-based molecules that can simultaneously recognize two different epitopes or antigens. Over the past decade, there has been an explosion of bsAbs being developed across therapeutic areas. Development of bsAbs presents unique challenges and mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) modeling has served as a powerful tool to optimize their development and realize their clinical utility. AREAS COVERED In this review, the guiding principles and case examples of how fit-for-purpose, mechanism-based PK/PD models have been applied to answer questions commonly encountered in bsAb development are presented. Such models characterize the key pharmacological elements of bsAbs, and they can be utilized for model-informed drug development. We also include the discussion of challenges, knowledge gaps and future direction for such models. EXPERT OPINION Mechanistic PK/PD modeling is a powerful tool to support the development of bsAbs. These models can be extrapolated to predict treatment outcomes based on mechanisms of action (MoA) and clinical observations to form positive learn-and-confirm cycles during drug development, due to their abilities to differentiate system- and drug-specific parameters. Meanwhile, the models should keep being adapted according to novel drug design and MoA, providing continuous opportunities for model-informed drug development.
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Affiliation(s)
- Jin Niu
- Clinical Pharmacology and Pharmacometrics, Janssen Research & Development, Spring House, PA, USA
| | - Weirong Wang
- Clinical Pharmacology and Pharmacometrics, Janssen Research & Development, Spring House, PA, USA
| | - Daniele Ouellet
- Clinical Pharmacology and Pharmacometrics, Janssen Research & Development, Spring House, PA, USA
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4
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Yadav R, Sukumaran S, Zabka TS, Li J, Oldendorp A, Morrow G, Reyes A, Cheu M, Li J, Wallin JJ, Tsai S, Sun L, Wang P, Ellerman D, Spiess C, Polson A, Stefanich EG, Kamath AV, Ovacik MA. Nonclinical Pharmacokinetics and Pharmacodynamics Characterization of Anti-CD79b/CD3 T Cell-Dependent Bispecific Antibody Using a Surrogate Molecule: A Potential Therapeutic Agent for B Cell Malignancies. Pharmaceutics 2022; 14:pharmaceutics14050970. [PMID: 35631556 PMCID: PMC9147001 DOI: 10.3390/pharmaceutics14050970] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/23/2022] [Accepted: 04/28/2022] [Indexed: 11/16/2022] Open
Abstract
The T cell-dependent bispecific (TDB) antibody, anti-CD79b/CD3, targets CD79b and CD3 cell-surface receptors expressed on B cells and T cells, respectively. Since the anti-CD79b arm of this TDB binds only to human CD79b, a surrogate TDB that binds to cynomolgus monkey CD79b (cyCD79b) was used for preclinical characterization. To evaluate the impact of CD3 binding affinity on the TDB pharmacokinetics (PK), we utilized non-tumor-targeting bispecific anti-gD/CD3 antibodies composed of a low/high CD3 affinity arm along with a monospecific anti-gD arm as controls in monkeys and mice. An integrated PKPD model was developed to characterize PK and pharmacodynamics (PD). This study revealed the impact of CD3 binding affinity on anti-cyCD79b/CD3 PK. The surrogate anti-cyCD79b/CD3 TDB was highly effective in killing CD79b-expressing B cells and exhibited nonlinear PK in monkeys, consistent with target-mediated clearance. A dose-dependent decrease in B cell counts in peripheral blood was observed, as expected. Modeling indicated that anti-cyCD79b/CD3 TDB’s rapid and target-mediated clearance may be attributed to faster internalization of CD79b, in addition to enhanced CD3 binding. The model yielded unbiased and precise curve fits. These findings highlight the complex interaction between TDBs and their targets and may be applicable to the development of other biotherapeutics.
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Affiliation(s)
- Rajbharan Yadav
- Preclinical and Translational Pharmacokinetics and Pharmacodynamics, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA; (S.S.); (A.R.); (E.G.S.); (A.V.K.)
- Correspondence: (R.Y.); (M.A.O.); Tel.: +1-650-467-1723 (R.Y.); +1-650-467-3645 (M.A.O.)
| | - Siddharth Sukumaran
- Preclinical and Translational Pharmacokinetics and Pharmacodynamics, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA; (S.S.); (A.R.); (E.G.S.); (A.V.K.)
| | - Tanja S. Zabka
- Safety Assessment, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA; (T.S.Z.); (J.L.); (A.O.); (G.M.)
| | - Jinze Li
- Safety Assessment, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA; (T.S.Z.); (J.L.); (A.O.); (G.M.)
| | - Amy Oldendorp
- Safety Assessment, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA; (T.S.Z.); (J.L.); (A.O.); (G.M.)
| | - Gary Morrow
- Safety Assessment, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA; (T.S.Z.); (J.L.); (A.O.); (G.M.)
| | - Arthur Reyes
- Preclinical and Translational Pharmacokinetics and Pharmacodynamics, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA; (S.S.); (A.R.); (E.G.S.); (A.V.K.)
| | - Melissa Cheu
- BioAnalytical Sciences, Genentech Inc., South San Francisco, CA 94080, USA;
| | - Jessica Li
- Oncology Biomarker Development (OBD), Genentech Inc., South San Francisco, CA 94080, USA; (J.L.); (J.J.W.)
| | - Jeffrey J. Wallin
- Oncology Biomarker Development (OBD), Genentech Inc., South San Francisco, CA 94080, USA; (J.L.); (J.J.W.)
| | - Siao Tsai
- Biochemical and Cellular Pharmacology, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA;
| | - Laura Sun
- Translational Oncology Department, Genentech Inc., South San Francisco, CA 94080, USA; (L.S.); (P.W.); (A.P.)
| | - Peiyin Wang
- Translational Oncology Department, Genentech Inc., South San Francisco, CA 94080, USA; (L.S.); (P.W.); (A.P.)
| | - Diego Ellerman
- Antibody Engineering, Genentech Inc., South San Francisco, CA 94080, USA; (D.E.); (C.S.)
| | - Christoph Spiess
- Antibody Engineering, Genentech Inc., South San Francisco, CA 94080, USA; (D.E.); (C.S.)
| | - Andy Polson
- Translational Oncology Department, Genentech Inc., South San Francisco, CA 94080, USA; (L.S.); (P.W.); (A.P.)
| | - Eric G. Stefanich
- Preclinical and Translational Pharmacokinetics and Pharmacodynamics, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA; (S.S.); (A.R.); (E.G.S.); (A.V.K.)
| | - Amrita V. Kamath
- Preclinical and Translational Pharmacokinetics and Pharmacodynamics, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA; (S.S.); (A.R.); (E.G.S.); (A.V.K.)
| | - Meric A. Ovacik
- Preclinical and Translational Pharmacokinetics and Pharmacodynamics, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA; (S.S.); (A.R.); (E.G.S.); (A.V.K.)
- Correspondence: (R.Y.); (M.A.O.); Tel.: +1-650-467-1723 (R.Y.); +1-650-467-3645 (M.A.O.)
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5
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Modeling Pharmacokinetics and Pharmacodynamics of Therapeutic Antibodies: Progress, Challenges, and Future Directions. Pharmaceutics 2021; 13:pharmaceutics13030422. [PMID: 33800976 PMCID: PMC8003994 DOI: 10.3390/pharmaceutics13030422] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 03/18/2021] [Accepted: 03/18/2021] [Indexed: 12/29/2022] Open
Abstract
With more than 90 approved drugs by 2020, therapeutic antibodies have played a central role in shifting the treatment landscape of many diseases, including autoimmune disorders and cancers. While showing many therapeutic advantages such as long half-life and highly selective actions, therapeutic antibodies still face many outstanding issues associated with their pharmacokinetics (PK) and pharmacodynamics (PD), including high variabilities, low tissue distributions, poorly-defined PK/PD characteristics for novel antibody formats, and high rates of treatment resistance. We have witnessed many successful cases applying PK/PD modeling to answer critical questions in therapeutic antibodies’ development and regulations. These models have yielded substantial insights into antibody PK/PD properties. This review summarized the progress, challenges, and future directions in modeling antibody PK/PD and highlighted the potential of applying mechanistic models addressing the development questions.
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Gibbs JP, Yuraszeck T, Biesdorf C, Xu Y, Kasichayanula S. Informing Development of Bispecific Antibodies Using Physiologically Based Pharmacokinetic-Pharmacodynamic Models: Current Capabilities and Future Opportunities. J Clin Pharmacol 2020; 60 Suppl 1:S132-S146. [PMID: 33205425 DOI: 10.1002/jcph.1706] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 07/06/2020] [Indexed: 12/17/2022]
Abstract
Antibody therapeutics continue to represent a significant portion of the biotherapeutic pipeline, with growing promise for bispecific antibodies (BsAbs). BsAbs can target 2 different antigens at the same time, such as simultaneously binding tumor-cell receptors and recruiting cytotoxic immune cells. This simultaneous engagement of 2 targets can be potentially advantageous, as it may overcome disadvantages posed by a monotherapy approach, like the development of resistance to treatment. Combination therapy approaches that modulate 2 targets simultaneously offer similar advantages, but BsAbs are more efficient to develop. Unlike combination approaches, BsAbs can facilitate spatial proximity of targets that may be necessary to induce the desired effect. Successful development of BsAbs requires understanding antibody formatting and optimizing activity for both targets prior to clinical trials. To realize maximal efficacy, special attention is required to fully define pharmacokinetic (PK)/pharmacodynamic (PD) relationships enabling selection of dose and regimen. The application of physiologically based pharmacokinetics (PBPK) has been evolving to inform the development of novel treatment modalities such as bispecifics owing to the increase in our understanding of pharmacology, utility of multiscale models, and emerging clinical data. In this review, we discuss components of PBPK models to describe the PK characteristics of BsAbs and expand the discussion to integration of PBPK and PD models to inform development of BsAbs. A framework that can be adopted to build PBPK-PD models to inform the development of BsAbs is also proposed. We conclude with examples that highlight the application of PBPK-PD and share perspectives on future opportunities for this emerging quantitative tool.
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Affiliation(s)
- John P Gibbs
- Quantitative Clinical Pharmacology, Millennium Pharmaceuticals, Inc., Cambridge, Massachusetts, USA
| | - Theresa Yuraszeck
- Clinical Pharmacology, CSL Behring, King of Prussia, Pennsylvania, USA
| | - Carla Biesdorf
- Clinical Pharmacology and Pharmacometrics, AbbVie, North Chicago, Illinois, USA
| | - Yang Xu
- Clinical Pharmacology, Ascentage Pharma Group Inc., Rockville, Maryland, USA
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7
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Betts A, van der Graaf PH. Mechanistic Quantitative Pharmacology Strategies for the Early Clinical Development of Bispecific Antibodies in Oncology. Clin Pharmacol Ther 2020; 108:528-541. [PMID: 32579234 PMCID: PMC7484986 DOI: 10.1002/cpt.1961] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 06/13/2020] [Indexed: 02/06/2023]
Abstract
Bispecific antibodies (bsAbs) have become an integral component of the therapeutic research strategy to treat cancer. In addition to clinically validated immune cell re‐targeting, bsAbs are being designed for tumor targeting and as dual immune modulators. Explorative preclinical and emerging clinical data indicate potential for enhanced efficacy and reduced systemic toxicity. However, bsAbs are a complex modality with challenges to overcome in early clinical trials, including selection of relevant starting doses using a minimal anticipated biological effect level approach, and predicting efficacious dose despite nonintuitive dose response relationships. Multiple factors can contribute to variability in the clinic, including differences in functional affinity due to avidity, receptor expression, effector to target cell ratio, and presence of soluble target. Mechanistic modeling approaches are a powerful integrative tool to understand the complexities and aid in clinical translation, trial design, and prediction of regimens and strategies to reduce dose limiting toxicities of bsAbs. In this tutorial, the use of mechanistic modeling to impact decision making for bsAbs is presented and illustrated using case study examples.
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Affiliation(s)
- Alison Betts
- Applied Biomath, Concord, Massachusetts, USA.,Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Piet H van der Graaf
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden, The Netherlands.,Certara, Canterbury, UK
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8
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Kunstmann S, Engström O, Wehle M, Widmalm G, Santer M, Barbirz S. Increasing the Affinity of an O-Antigen Polysaccharide Binding Site in Shigella flexneri Bacteriophage Sf6 Tailspike Protein. Chemistry 2020; 26:7263-7273. [PMID: 32189378 PMCID: PMC7463171 DOI: 10.1002/chem.202000495] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 03/09/2020] [Indexed: 12/30/2022]
Abstract
Broad and unspecific use of antibiotics accelerates spread of resistances. Sensitive and robust pathogen detection is thus important for a more targeted application. Bacteriophages contain a large repertoire of pathogen-binding proteins. These tailspike proteins (TSP) often bind surface glycans and represent a promising design platform for specific pathogen sensors. We analysed bacteriophage Sf6 TSP that recognizes the O-polysaccharide of dysentery-causing Shigella flexneri to develop variants with increased sensitivity for sensor applications. Ligand polyrhamnose backbone conformations were obtained from 2D 1 H,1 H-trNOESY NMR utilizing methine-methine and methine-methyl correlations. They agreed well with conformations obtained from molecular dynamics (MD), validating the method for further predictions. In a set of mutants, MD predicted ligand flexibilities that were in good correlation with binding strength as confirmed on immobilized S. flexneri O-polysaccharide (PS) with surface plasmon resonance. In silico approaches combined with rapid screening on PS surfaces hence provide valuable strategies for TSP-based pathogen sensor design.
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Affiliation(s)
- Sonja Kunstmann
- Physikalische BiochemieUniversität PotsdamKarl-Liebknecht-Str. 24–2514476PotsdamGermany
- Theory and BiosystemsMax Planck Institute of Colloids and InterfacesAm Mühlenberg 114476PotsdamGermany
- Current address: Department of Biotechnology and BiomedicineTechnical University of DenmarkSøltofts Plads2800 Kgs.LyngbyDenmark
| | - Olof Engström
- Department of Organic ChemistryArrhenius LaboratoryStockholm University10691StockholmSweden
| | - Marko Wehle
- Theory and BiosystemsMax Planck Institute of Colloids and InterfacesAm Mühlenberg 114476PotsdamGermany
| | - Göran Widmalm
- Department of Organic ChemistryArrhenius LaboratoryStockholm University10691StockholmSweden
| | - Mark Santer
- Theory and BiosystemsMax Planck Institute of Colloids and InterfacesAm Mühlenberg 114476PotsdamGermany
| | - Stefanie Barbirz
- Physikalische BiochemieUniversität PotsdamKarl-Liebknecht-Str. 24–2514476PotsdamGermany
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9
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Finlay WJ, Lugovskoy AA. De novo discovery of antibody drugs - great promise demands scrutiny. MAbs 2019; 11:809-811. [PMID: 31122133 PMCID: PMC6601558 DOI: 10.1080/19420862.2019.1622926] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Revised: 05/01/2019] [Accepted: 05/21/2019] [Indexed: 01/10/2023] Open
Abstract
We live in an era of rapidly advancing computing capacity and algorithmic sophistication. "Big data" and "artificial intelligence"find progressively wider use in all spheres of human activity, including healthcare. A diverse array of computational technologies is being applied with increasing frequency to antibody drug research and development (R&D). Their successful applications are met with great interest due to the potential for accelerating and streamlining the antibody R&D process. While this excitement is very likely justified in the long term, it is less likely that the transition from the first use to routine practice will escape challenges that other new technologies had experienced before they began to blossom. This transition typically requires many cycles of iterative learning that rely on the deconstruction of the technology to understand its pitfalls and define vectors for optimization. The study by Vasquez et al. identifies a key obstacle to such learning: the lack of transparency regarding methodology in computational antibody design reports, which has the potential to mislead the community efforts.
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10
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Schropp J, Khot A, Shah DK, Koch G. Target-Mediated Drug Disposition Model for Bispecific Antibodies: Properties, Approximation, and Optimal Dosing Strategy. CPT Pharmacometrics Syst Pharmacol 2019; 8:177-187. [PMID: 30480383 PMCID: PMC6430159 DOI: 10.1002/psp4.12369] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Accepted: 10/17/2018] [Indexed: 12/12/2022] Open
Abstract
Bispecific antibodies (BsAbs) bind to two different targets, and create two binary and one ternary complex (TC). These molecules have shown promise as immuno-oncology drugs, and the TC is considered the pharmacologically active species that drives their pharmacodynamic effect. Here, we have presented a general target-mediated drug disposition (TMDD) model for these BsAbs, which bind to two different targets on different cell membranes. The model includes four different binding events for BsAbs, turnover of the targets, and internalization of the complexes. In addition, a quasi-equilibrium (QE) approximation with decreased number of binding parameters and, if necessary, reduced internalization parameters is presented. The model is further used to investigate the kinetics of BsAb and TC concentrations. Our analysis shows that larger doses of BsAbs may delay the build-up of the TC. Consequently, a method to compute the optimal dosing strategy of BsAbs, which will immediately create and maintain maximal possible TC concentration, is presented.
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Affiliation(s)
- Johannes Schropp
- Department of Mathematics and StatisticsUniversity of KonstanzKonstanzGermany
| | - Antari Khot
- Department of Pharmaceutical SciencesSchool of Pharmacy and Pharmaceutical SciencesState University of New York at BuffaloBuffaloNew YorkUSA
| | - Dhaval K. Shah
- Department of Pharmaceutical SciencesSchool of Pharmacy and Pharmaceutical SciencesState University of New York at BuffaloBuffaloNew YorkUSA
| | - Gilbert Koch
- Department of Pharmaceutical SciencesSchool of Pharmacy and Pharmaceutical SciencesState University of New York at BuffaloBuffaloNew YorkUSA
- Paediatric Pharmacology and Pharmacometrics ResearchUniversity of Basel Children's Hospital (UKBB)BaselSwitzerland
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11
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Trivedi A, Stienen S, Zhu M, Li H, Yuraszeck T, Gibbs J, Heath T, Loberg R, Kasichayanula S. Clinical Pharmacology and Translational Aspects of Bispecific Antibodies. Clin Transl Sci 2017; 10:147-162. [PMID: 28297195 PMCID: PMC5421745 DOI: 10.1111/cts.12459] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Accepted: 01/30/2017] [Indexed: 02/07/2023] Open
Affiliation(s)
- A Trivedi
- Amgen Inc., Thousand Oaks, California, USA
| | - S Stienen
- Amgen Research (Munich), Munich, Germany
| | - M Zhu
- Amgen Inc., Thousand Oaks, California, USA
| | - H Li
- Amgen Inc., Thousand Oaks, California, USA
| | | | - J Gibbs
- Amgen Inc., Thousand Oaks, California, USA.,Current address: AbbVie Inc., North Chicago, Illinois, USA
| | - T Heath
- Amgen Inc., Thousand Oaks, California, USA
| | - R Loberg
- Amgen Inc., Thousand Oaks, California, USA
| | - S Kasichayanula
- Amgen Inc., Thousand Oaks, California, USA.,Current Address: AbbVie Inc., South San Francisco, California, USA
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12
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Sengers BG, McGinty S, Nouri FZ, Argungu M, Hawkins E, Hadji A, Weber A, Taylor A, Sepp A. Modeling bispecific monoclonal antibody interaction with two cell membrane targets indicates the importance of surface diffusion. MAbs 2016; 8:905-15. [PMID: 27097222 PMCID: PMC4968105 DOI: 10.1080/19420862.2016.1178437] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
We have developed a mathematical framework for describing a bispecific monoclonal antibody interaction with two independent membrane-bound targets that are expressed on the same cell surface. The bispecific antibody in solution binds either of the two targets first, and then cross-links with the second one while on the cell surface, subject to rate-limiting lateral diffusion step within the lifetime of the monovalently engaged antibody-antigen complex. At experimental densities, only a small fraction of the free targets is expected to lie within the reach of the antibody binding sites at any time. Using ordinary differential equation and Monte Carlo simulation-based models, we validated this approach against an independently published anti-CD4/CD70 DuetMab experimental data set. As a result of dimensional reduction, the cell surface reaction is expected to be so rapid that, in agreement with the experimental data, no monovalently bound bispecific antibody binary complexes accumulate until cross-linking is complete. The dissociation of the bispecific antibody from the ternary cross-linked complex is expected to be significantly slower than that from either of the monovalently bound variants. We estimate that the effective affinity of the bivalently bound bispecific antibody is enhanced for about 4 orders of magnitude over that of the monovalently bound species. This avidity enhancement allows for the highly specific binding of anti-CD4/CD70 DuetMab to the cells that are positive for both target antigens over those that express only one or the other We suggest that the lateral diffusion of target antigens in the cell membrane also plays a key role in the avidity effect of natural antibodies and other bivalent ligands in their interactions with their respective cell surface receptors.
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Affiliation(s)
- Bram G Sengers
- a Bioengineering Science Research Group, Faculty of Engineering and the Environment, and Institute for Life Sciences , University of Southampton , Southampton , UK
| | - Sean McGinty
- b Division of Biomedical Engineering, Glasgow University , Glasgow , UK
| | - Fatma Z Nouri
- c Laboratoire de Modélisation Mathématiques et Simulation Numérique, Faculté des Sciences, Université Badji-Mokhtar , Annaba , Algeria
| | - Maryam Argungu
- d Department of Bioengineering , Imperial College London , London , UK
| | - Emma Hawkins
- e Department of Mathematics , University of Surrey , Guildford , UK
| | - Aymen Hadji
- f Pharmaceutic Mineral Chemistry Laboratory, Université Badji-Mokhtar , Annaba , Algeria
| | - Andrew Weber
- g Drug Metabolism and Pharmacokinetics, GlaxoSmithKline Plc., King of Prussia , PA , USA
| | - Adam Taylor
- h Respiratory DPU, GlaxoSmithKline Plc. , Stevenage , UK
| | - Armin Sepp
- i Drug Metabolism and Pharmacokinetics, GlaxoSmithKline Plc. , Stevenage , UK
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