Abstract: In a method for predicting a property of potential protein formulations, a set of formulation descriptors is classified as belonging to a specific one of a plurality of predetermined groups that each correspond to a different value range for a protein formulation property. Classifying the set of descriptors includes applying at least a first portion of the set of descriptors as inputs to a first machine learning model. The method also includes selecting, based on the classification, a second machine learning model from among multiple models corresponding to different groups. The method also includes predicting a value of the protein formulation property that corresponds to the set of descriptors, by applying at least a second portion of the set of formulation descriptors as inputs to the selected model. The method further includes causing the value of the protein formulation property to be displayed to a user and/or stored in a memory.

Abstract: Described herein is a method comprising (a) reconstituting a lyophilized formulation comprising a polypeptide, a saccharide, a surfactant, and a buffer, with a diluent comprising an aromatic alcohol, a phenolic compound, or an amino acid with aromatic side chain, and (b) administering the reconstituted formulation to a subject in need thereof within 12 hours of reconstitution, wherein the polypeptide is a bispecific antibody construct, such as a bispecific antibody construct that binds human DLL3 and human CD3 and optionally comprises the amino acid sequence of SEQ ID NO: 75 and SEQ ID NO: 9.

Abstract: In a method for predicting a property of potential protein formulations, a set of formulation descriptors is classified as belonging to a specific one of a plurality of predetermined groups that each correspond to a different value range for a protein formulation property. Classifying the set of descriptors includes applying at least a first portion of the set of descriptors as inputs to a first machine learning model. The method also includes selecting, based on the classification, a second machine learning model from among multiple models corresponding to different groups. The method also includes predicting a value of the protein formulation property that corresponds to the set of descriptors, by applying at least a second portion of the set of formulation descriptors as inputs to the selected model. The method further includes causing the value of the protein formulation property to be displayed to a user and/or stored in a memory.

Abstract: A method for quantifying collagen fiber alignment in periprosthetic tissue in a mammal.

Abstract: A method for quantifying collagen fiber alignment in periprosthetic tissue in a mammal.

Abstract: The invention is directed to, inter alia, injectable materials, which comprise a a zero-length cross-linking agent encapsulated in a thermo-responsive liposome dispersed in a polymer solution where the polymer and cross-linking agent are segregated at a first temperature, and non-segregated at a second temperature, and the material is injectable at the first temperature. The invention is directed to, inter alia, processes for producing the material and use of the material for tissue repair and regeneration and controlled delivery of an agent are also described.