Merck Sharp + Dohme Corp.

Merck + Co., Inc.

Msd Italia S.r.l.

Ansuman Bagchi

Belma Dogdas

Gennaro Ciliberto

Nicola La Monica

Arthur Fridman

Luigi Aurisicchio

Xiang Yu

Mark Cancilla

Robert H Pierce

Jennifer H Yearley

Wendy Bailey

Scott P Turner

Elisa Scarselli

Embodiments of the present invention avoid the processing problems associated with using conventional computer systems for identifying and characterizing all of the substructures (e.g., metabolites) of large complex molecules by using a defined minimum cleavable unit (MCU) and an MCU graph for a chosen molecule, as well as a 'cut vertex' in the MCU graph for the chosen molecule. The system splits the MCU graph of the chosen molecule at the specified cut vertex to produce two separate MCU graph components (i.e., a first MCU subgraph and a second MCU subgraph) of the chosen molecule, and generates and traverses a first line graph component and a second line graph component, respectively, for the two MCU subgraph components with a graph traversing algorithm to generate and store in memory a first database of substructures and molecular weights for the first component, and a second database of substructures and molecular weights for the second line graph component. Subsequently, embodiments of the present invention can perform binary searches on the two databases (or the two subsections of a single database) to identify and produce graphic representations of all of the substructures of the chosen molecule that have molecular weights that match the query molecular weight (or range of query molecular weights), including the substructures of the chosen molecule that straddle (i.e., include) the cut vertex.

Cut vertex method for identifying complex molecule substructures

Embodiments of the present invention provide a computer-implemented system and method for generating and searching a database containing all of the potential substructures (e.g., metabolites) of a chosen complex molecule based on minimum cleavable units (MCUs) of the chosen complex molecule, wherein each record in the generated database suitably defines the molecular weight and physical arrangement of each substructure. Embodiments of the invention also provide a user interface and a search engine for searching the database based on a query molecular weight (or query molecular weight range) to identify all of the substructures having a total molecular weight matching the query molecular weight or range. Embodiments of the invention are also capable of transmitting to a display device operated by an end user a description and/or a graphical representation of every identified substructure of the chosen complex molecule.

Complex molecule substructure identification systems, apparatuses and methods

The present disclosure describes an IHC assay for detecting and quantifying spatially proximal pairs of PD-1-expressing cells (PD-1+ cells) and PD-Ligand-expressing cells (PD-L+ cells) in tumor tissue, and the use of the assay to generate proximity biomarkers that are predictive of which cancer patients are most likely to benefit from treatment with a PD-1 antagonist. The disclosure also provides methods for testing tumor samples for the proximity biomarkers, as well as methods for treating subjects with a PD-1 antagonist based on the test results.

Immunohistochemical proximity assay for PD-1 positive cells and PD-ligand positive cells in tumor tissue

Methodology for the automated selection and/or optimization of T-cell epitopes is disclosed. The invention provides a data processing system which utilizes sequence-based statistical pattern recognition to compute an epitope selection matrix based on the informational content of epitopes known to bind to a particular major histocompatibility class I allele. The resulting Bayes-corrected scoring matrix is used to predict the relative binding affinities of candidate T-cell epitopes derived from immunologically relevant antigens of self or foreign origin. One aspect of the invention describes an analytical method for identification of modifications in known or predicted T-cell epitopes that confer upon the epitopes the ability to elicit stronger cellular immune response due to more efficient processing and/or presentation to T-cells. The disclosed epitope identification algorithm is applicable to the design of vaccines for infectious diseases, cancer and autoimmune diseases as well as for developing methods for the in vitro evaluation of cellular immunity.

System and method for automated selection of T-cell epitopes

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Plainsboro, NJ, United States of America

Ansuman Bagchi