Patents by Inventor Randal Robert Ketchem

Randal Robert Ketchem has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20240404634
    Abstract: Amino acid sequences of base proteins can be analyzed by a neural network component. Loss values can be determined by the neural network component for individual positions of the base proteins. Variant protein sequences can be generated based on the base protein sequences and the loss values.
    Type: Application
    Filed: August 31, 2022
    Publication date: December 5, 2024
    Inventors: Joshua Smith, Jeremy Martin Shaver, Tileli Amimeur, Randal Robert Ketchem, John Alex Taylor
  • Publication number: 20240327486
    Abstract: Provided herein are IL-2 muteins, IL-2 mutein Fc-fusion molecules, anti-IL-2 antibodies, and complexes comprising an anti IL-2 antibody bound to an IL-2 cytokine that preferentially expand and activate T regulatory cells and are amenable to large scale production. Also provided herein are variant human IgG1 Fc molecules lacking or with highly reduced effector function and high stability despite lacking glycosylation at N297. Also provided herein are linker peptides that are glycosylated when expressed in mammalian cells. Also provided herein are methods of making and using the compositions of the present invention.
    Type: Application
    Filed: April 5, 2024
    Publication date: October 3, 2024
    Applicant: Amgen Inc.
    Inventors: Eric Alan BUTZ, Christy Ann THOMSON, Marc Alain GAVIN, Ian Nevin FOLTZ, Dong XIA, Dina N. ALCORN, Randal Robert KETCHEM, Ai Ching LIM, Kathy MANCHULENKO, Laura SEKIROV, Kelly Ann BERRY, Cyr Clovis Chua DE IMUS, Neeraj Jagdish AGRAWAL, Gunasekaran KANNAN, Li LI
  • Patent number: 12080380
    Abstract: Amino acid sequences of proteins can be produced using one or more generative machine learning architectures. The amino acid sequences produced by the one or more generative machine learning architectures can be used to train a classification model architecture. The classification model architecture can classify amino acid sequences according to a number of classifications. Individual classifications of the number of classifications can correspond to at least one of a structural feature of proteins, a range of values of a structural feature of proteins, a biophysical property of proteins, or a range of values of a biophysical property of proteins.
    Type: Grant
    Filed: August 27, 2021
    Date of Patent: September 3, 2024
    Assignee: Just-Evotec Biologics, Inc.
    Inventors: Jeremy Martin Shaver, Tileli Amimeur, Randal Robert Ketchem, Joshua Smith
  • Patent number: 11976103
    Abstract: Provided herein are IL-2 muteins, IL-2 mutein Fc-fusion molecules, anti-IL-2 antibodies, and complexes comprising an anti IL-2 antibody bound to an IL-2 cytokine that preferentially expand and activate T regulatory cells and are amenable to large scale production. Also provided herein are variant human IgG1 Fc molecules lacking or with highly reduced effector function and high stability despite lacking glycosylation at N297. Also provided herein are linker peptides that are glycosylated when expressed in mammalian cells. Also provided herein are methods of making and using the compositions of the present invention.
    Type: Grant
    Filed: October 7, 2020
    Date of Patent: May 7, 2024
    Assignee: Amgen Inc.
    Inventors: Eric Alan Butz, Christy Ann Thomson, Marc Alain Gavin, Ian Nevin Foltz, Dong Xia, Dina N. Alcorn, Randal Robert Ketchem, Ai Ching Lim, Kathy Manchulenko, Laura Sekirov, Kelly Ann Berry, Cyr Clovis Chua De Imus, Neeraj Jagdish Agrawal, Gunasekaran Kannan, Li Li
  • Patent number: 11948664
    Abstract: Amino acid sequences of proteins can be produced using an autoencoder. For example, amino acid sequences of variant proteins can be produced by an autoencoder that is fed an amino acid sequence of a base protein as input. A decoding component of the autoencoder can include at least one or more components of a generative adversarial network.
    Type: Grant
    Filed: September 21, 2021
    Date of Patent: April 2, 2024
    Assignee: Just-Evotec Biologics, Inc.
    Inventors: Jeremy Martin Shaver, Tileli Amimeur, Randal Robert Ketchem
  • Patent number: 11804283
    Abstract: A system for generating a model for predicting a molecular property of a variant of a molecule is provided. For each of a plurality of variants of the molecule, the system for each structural feature, aggregates the values for the structural features of the residues of the molecule that were modified to form the variant to form a feature vector for the variant. The system assigns the value for the molecular property of the variant to the feature vector wherein the feature vector and the assigned value form training data. The system then generates the model for predicting a value for the molecular property using the training data for the plurality of variants.
    Type: Grant
    Filed: June 8, 2018
    Date of Patent: October 31, 2023
    Assignee: Just-Evotec Biologics, Inc.
    Inventors: Jeremy Martin Shaver, Randal Robert Ketchem
  • Publication number: 20230253067
    Abstract: Amino acid sequences of proteins can be produced using one or more generative machine learning architectures. The amino acid sequences produced by the one or more generative machine learning architectures can be used to train a classification model architecture. The classification model architecture can classify amino acid sequences according to a number of classifications. Individual classifications of the number of classifications can correspond to at least one of a structural feature of proteins, a range of values of a structural feature of proteins, a biophysical property of proteins, or a range of values of a biophysical property of proteins.
    Type: Application
    Filed: August 27, 2021
    Publication date: August 10, 2023
    Inventors: Jeremy Martin Shaver, Tileli Amimeur, Randal Robert Ketchem, Joshua Smith
  • Publication number: 20230178186
    Abstract: Amino acid sequences of antibodies can be generated using a generative adversarial network that includes a first generating component that generates amino acid sequences of antibody light chains and a second generating component that generates amino acid sequences of antibody heavy chains. Amino acid sequences of antibodies call be produced by combining the respective amino acid sequences produced by the first generating component and the second generating component. The training of the first generating component and the second generating component can proceed at different rates. Additionally, the antibody amino acids produced by combining amino acid sequences front the first generating component and the second generating component may be evaluated according to complentarity-determining regions of the antibody amino acid sequences.
    Type: Application
    Filed: January 13, 2023
    Publication date: June 8, 2023
    Inventors: Tileli Amimeur, Randal Robert Ketchem, Jeremy Martin Shaver, Rutilio H. Clark, John Alex Taylor
  • Patent number: 11587645
    Abstract: Amino acid sequences of antibodies can be generated using a generative adversarial network that includes a first generating component that generates amino acid sequences of antibody light chains and a second generating component generates amino acid sequences of antibody heavy chains. Amino acid sequences of antibodies can be produced by combining the respective amino acid sequences produced by the first generating component and the second generating component. The training of the first generating component and the second generating component can proceed at different rates. Additionally, the antibody amino acids produced by combining amino acid sequences from the first generating component and the second generating component may be evaluated according to complentarity-determining regions of the antibody amino acid sequences.
    Type: Grant
    Filed: May 19, 2020
    Date of Patent: February 21, 2023
    Assignee: Just-Evotec Biologics, Inc.
    Inventors: Tileli Amimeur, Randal Robert Ketchem, Jeremy Martin Shaver, Rutilio H. Clark, John Alex Taylor
  • Publication number: 20230005567
    Abstract: Systems and techniques are described to generate amino acid sequences of target proteins based on amino acid sequences of template proteins using machine learning techniques. The amino acid sequences of the target proteins can be generated based on data that constrains the modifications that can be made to the amino acid sequences of the template proteins. In illustrative examples, the template proteins can include antibodies produced by a non-human mammal that bind to an antigen and the target proteins can correspond to human antibodies with a region having at least a threshold amount of identity with the binding region of the template antibody. Generative adversarial networks can be used to produce the amino acid sequences of the target proteins.
    Type: Application
    Filed: December 11, 2020
    Publication date: January 5, 2023
    Inventors: Jeremy Martin Shaver et al., Tileli Amimeur, Randal Robert Ketchem, Alex Taylor
  • Publication number: 20220251498
    Abstract: The concepts described herein are directed to implementations of production facilities that can produce molecules used to treat biological conditions, such as biotherapeutics. The biotherapeutics can include various molecules, such as proteins, enzymes, and antibodies. The production facilities can include a number of separate modular cleanrooms that comprise particular pieces of equipment to perform one or more aspects of the processes used to manufacture biotherapeutics. The modular cleanrooms are arranged such that material that is produced by the equipment of one modular cleanroom can be transferred to another modular cleanroom for additional processing. Additionally, systems and processes are described to generate models using machine learning techniques, where the models can be used to predict productivity and/or efficiency metrics for production lines of biotherapeutics. Further, models can be generated to control the operation of pieces of equipment included in the production lines.
    Type: Application
    Filed: February 14, 2020
    Publication date: August 11, 2022
    Inventors: Jeremy Martin Shaver, Tileli Amimeur, Randal Robert Ketchem, Michael W. Vandiver, Brian W. Horman, Fernando Garcia
  • Publication number: 20220230710
    Abstract: Amino acid sequences of antibodies can be generated using a generative adversarial network that includes a first generating component that generates amino acid sequences of antibody light chains and a second generating component generates amino acid sequences of antibody heavy chains. Amino acid sequences of antibodies can be produced by combining the respective amino acid sequences produced by the first generating component and the second generating component. The training of the first generating component and the second generating component can proceed at different rates. Additionally, the antibody amino acids produced by combining amino acid sequences from the first generating component and the second generating component may be evaluated according to complentarity-determining regions of the antibody amino acid sequences.
    Type: Application
    Filed: May 19, 2020
    Publication date: July 21, 2022
    Inventors: Tileli Amimeur, Randal Robert Ketchem, Jeremy Martin Shaver, Rutilio H. Clark, John Alex Taylor
  • Patent number: 11384140
    Abstract: This invention relates to the general field of recombinant expression of polypeptides in animal cell culture. More specifically, the invention concerns improved selection of cells transfected with recombinantly engineered vectors designed to express polypeptides, in particular heteromultimeric polypeptides.
    Type: Grant
    Filed: May 11, 2017
    Date of Patent: July 12, 2022
    Assignee: Amgen Inc.
    Inventors: Randal Robert Ketchem, Jeffrey T. McGrew, Dina A. Fomina Yadlin
  • Publication number: 20210139554
    Abstract: Provided herein are IL-2 muteins, IL-2 mutein Fc-fusion molecules, anti-IL-2 antibodies, and complexes comprising an anti IL-2 antibody bound to an IL-2 cytokine that preferentially expand and activate T regulatory cells and are amenable to large scale production. Also provided herein are variant human IgG1 Fc molecules lacking or with highly reduced effector function and high stability despite lacking glycosylation at N297. Also provided herein are linker peptides that are glycosylated when expressed in mammalian cells. Also provided herein are methods of making and using the compositions of the present invention.
    Type: Application
    Filed: October 7, 2020
    Publication date: May 13, 2021
    Applicant: Amgen Inc.
    Inventors: Eric Alan BUTZ, Christy Ann THOMSON, Marc Alain GAVIN, Ian Nevin FOLTZ, Dong XIA, Dina N. ALCORN, Randal Robert KETCHEM, Ai Ching LIM, Kathy MANCHULENKO, Laura SEKIROV, Kelly Ann BERRY, Cyr Clovis Chua DE IMUS, Neeraj Jagdish AGRAWAL, Gunasekaran KANNAN, Li LI
  • Publication number: 20210043272
    Abstract: Technologies are described related to determining protein structure and properties based on sequences of proteins. In various implementations, a first model can be generated to determine structural features of proteins based on amino acid sequences of the proteins. Additionally, a second model can be generated to determine biophysical properties of proteins based on structural features of the proteins. In particular implementations, an amino acid sequence of a particular protein can be utilized by the first model to determine one or more structural features of the protein. The one or more structural features of the protein generated by the first model can be utilized by the second model to determine at least one biophysical property of the protein.
    Type: Application
    Filed: February 26, 2019
    Publication date: February 11, 2021
    Inventors: Tileli Amimeur, Jeremy Martin Shaver, Randal Robert Ketchem
  • Publication number: 20200408728
    Abstract: Technologies are described related to determining conditions for the purification of proteins. In some implementations, models can be generated that predict yield and purity for various chromatographic techniques at a number of pH levels and salt concentrations. Training data used to produce the models can be obtained by running chromatography columns using stationary phase materials of different chromatographic techniques at various combinations of pH levels and salt concentrations. In additional implementations, the training data can be obtained by analyzing solutions included in a subset of wells of a multi-well plate, where the subset of wells are associated with particular salt concentrations and pH values for a particular stationary phase material. Further, the models can be used to determine optimized conditions for the purification of various proteins based on maximizing yield and purity, while minimizing cost.
    Type: Application
    Filed: February 21, 2019
    Publication date: December 31, 2020
    Inventors: Jeremy Martin Shaver, Tileli Amimeur, Ron Gillespie, Randal Robert Ketchem, Fernando Garcia
  • Publication number: 20200143904
    Abstract: A system for generating a model for predicting a molecular property of a variant of a molecule is provided. For each of a plurality of variants of the molecule, the system for each structural feature, aggregates the values for the structural features of the residues of the molecule that were modified to form the variant to form a feature vector for the variant. The system assigns the value for the molecular property of the variant to the feature vector wherein the feature vector and the assigned value form training data. The system then generates the model for predicting a value for the molecular property using the training data for the plurality of variants.
    Type: Application
    Filed: June 8, 2018
    Publication date: May 7, 2020
    Inventors: Jeremy Martin Shaver, Randal Robert Ketchem
  • Publication number: 20190127452
    Abstract: This invention relates to the general field of recombinant expression of polypeptides in animal cell culture. More specifically, the invention concerns improved selection of cells transfected with recombinantly engineered vectors designed to express polypeptides, in particular heteromultimeric polypeptides.
    Type: Application
    Filed: May 11, 2017
    Publication date: May 2, 2019
    Applicant: AMGEN INC.
    Inventors: Randal Robert KETCHEM, Jeffrey T. MCGREW, Dina A. FOMINA YADLIN, Trent P. MUNRO, Neeraj Jagdish AGRAWAL, Kristine M. DARIS
  • Patent number: 9920134
    Abstract: Antigen binding proteins that interact with Proprotein Convertase Subtilisin Kexin Type 9 (PCSK9) are described. Methods of treating hypercholesterolemia and other disorders by administering a pharmaceutically effective amount of an antigen binding protein to PCSK9 are described. Methods of detecting the amount of PCSK9 in a sample using an antigen binding protein to PCSK9 are described.
    Type: Grant
    Filed: June 30, 2011
    Date of Patent: March 20, 2018
    Assignee: AMGEN INC.
    Inventors: Simon Mark Jackson, Nigel Pelham Clinton Walker, Derek Evan Piper, Bei Shan, Wenyan Shen, Joyce Chi Yee Chan, Chadwick Terrence King, Randal Robert Ketchem, Christopher Mehlin, Teresa Arazas Carabeo, Qiong Cao
  • Patent number: 9493576
    Abstract: Antigen binding proteins that interact with Proprotein Convertase Subtilisin Kexin Type 9 (PCSK9) are described. Methods of treating hypercholesterolemia and other disorders by administering a pharmaceutically effective amount of an antigen binding protein to PCSK9 are described. Methods of detecting the amount of PCSK9 in a sample using an antigen binding protein to PCSK9 are described.
    Type: Grant
    Filed: August 14, 2014
    Date of Patent: November 15, 2016
    Assignee: AMGEN INC.
    Inventors: Simon Mark Jackson, Nigel Pelham Clinton Walker, Derek Evan Piper, Bei Shan, Wenyan Shen, Chadwick Terence King, Randal Robert Ketchem, Christopher Mehlin, Teresa Arazas Carabeo