Abstract

The presently disclosed subject matter provides for antigen-recognizing receptors that specifically target B7-H3 and cells comprising such B7-H3-targeted antigen-recognizing receptors. The presently disclosed subject matter further provides uses of the B7-H3-targeted antigen-recognizing receptors for treatment.

IPC Classes  ?

• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 40/42 - Cancer antigens
• A61P 35/00 - Antineoplastic agents
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells

Abstract

The present disclosure is directed to leucine zipper-based sorting systems adapted to facilitate the expression and coordination of polypeptide sequences capable of improving the function of CAR T cells. The systems enable the generation of T cells engineered to express multiple combinations of CARs (multi-CAR), safety-switches, switch receptors, and/or cytokines.

IPC Classes  ?

• A61K 35/17 - LymphocytesB-cellsT-cellsNatural killer cellsInterferon-activated or cytokine-activated lymphocytes
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 40/42 - Cancer antigens
• A61P 35/02 - Antineoplastic agents specific for leukemia
• A61P 37/04 - Immunostimulants
• C07K 14/725 - T-cell receptors
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C12N 5/00 - Undifferentiated human, animal or plant cells, e.g. cell linesTissuesCultivation or maintenance thereofCulture media therefor
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells
• C12N 9/64 - Proteinases derived from animal tissue, e.g. rennin

Abstract

The present invention relates to the use of one or more biomarkers to evaluate the likelihood that a CDK4 inhibitor would produce an anti-cancer effect in a subject. Accordingly, in certain non-limiting embodiments, the present invention provides for methods, compositions and kits for a companion diagnostic for CDK4 inhibitors, and in particular, for the use of the colocalization of Enigma and CDH18 biomarkers to foci within the cancer for determining whether the cancer can be successfully treated by CDK4 inhibition.

IPC Classes  ?

• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer

Abstract

Presented herein are systems and methods of generating frames across time from sequence data using machine learning (ML) models. A computing system coupled with memory can retrieve a sequence dataset comprising a plurality of identifiers generated using a gene segment from a subject at a first time. Each identifier of the plurality of identifiers can have an alphanumeric character indicating a base type at a respective position in the gene segment. The computing system can convert the plurality of the identifiers of the sequence dataset to generate a first frame comprising a first plurality of coordinates for the gene segment at the first time. The computing system can provide the first frame as an input to a ML model. The computing system can generate, based on providing the input to the ML model, a second frame comprising a second plurality of coordinates for the gene segment at a second time.

IPC Classes  ?

• G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
• G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
• G06N 20/00 - Machine learning
• G06T 7/00 - Image analysis
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• C12Q 1/6874 - Methods for sequencing involving nucleic acid arrays, e.g. sequencing by hybridisation [SBH]

Abstract

Presented herein are systems and methods relating to imputing metabolite information. A method includes receiving a first and second metabolite dataset, normalizing, the first dataset and the second dataset, transforming, the normalized first and second datasets, and aggregating the normalized first dataset and the normalized second dataset to generate a first metabolite matrix, the first metabolite matrix missing a first relative abundance value. The method includes decomposing the first metabolite matrix into a second metabolite matrix and a third metabolite matrix to factorize the first metabolite matrix and generating a fourth metabolite matrix that is the product of the second metabolite matrix and the third metabolite matrix, wherein the fourth metabolite matrix including an imputed first relative abundance value.

IPC Classes  ?

• G16B 5/00 - ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks

Abstract

Provided are compounds for activating TFEB, the compounds being according to Formula (I) or Formula (II) as well as pharmaceutical compositions including such compounds and methods for using the compounds for the treatment of diseases and disorders.

IPC Classes  ?

• C07D 243/06 - Heterocyclic compounds containing seven-membered rings having two nitrogen atoms as the only ring hetero atoms having the nitrogen atoms in positions 1 and 4
• C07D 243/08 - Heterocyclic compounds containing seven-membered rings having two nitrogen atoms as the only ring hetero atoms having the nitrogen atoms in positions 1 and 4 not condensed with other rings
• A61K 31/496 - Non-condensed piperazines containing further heterocyclic rings, e.g. rifampin, thiothixene or sparfloxacin
• A61K 31/33 - Heterocyclic compounds

Abstract

The present technology relates to methods, computing devices, and systems for predicting the fitness of mutant p53 based on the loss of transcription factor function and immunogenicity of a particular TP53 mutation. The fitness of mutant p53 may be used to determine whether a patient will benefit from a particular anti-cancer therapy such as immune checkpoint inhibitor therapy, adoptive T-cell therapy, or prophylactic cancer vaccine therapy.

IPC Classes  ?

• G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
• C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction
• G16B 20/10 - Ploidy or copy number detection
• G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• G16B 20/50 - Mutagenesis

Abstract

The presently disclosed subject matter provides for methods and compositions for treating cancer (e.g., breast cancer). It relates to mutant PIK3CA-targeted TCRs that specifically target a mutant PIK3CA peptide (e.g., a human mutant PIK3CA peptide), and immunoresponsive cells comprising such TCRs. The presently disclosed mutant PIK3CA peptide-specific TCRs have enhanced immune-activating properties, including anti-tumor activity.

IPC Classes  ?

• C07K 14/725 - T-cell receptors
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/32 - T-cell receptors [TCR]
• A61K 40/42 - Cancer antigens
• A61K 40/50 - Cellular immunotherapy characterised by the use of allogeneic cells

Abstract

SRSF2ZRSR2ZRSR2, wherein the mutations are shared across patients with those mutational genotypes, as well as cognate T cell receptors (TCRs) that specifically recognize those neoantigens.

IPC Classes  ?

• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
• G16B 25/10 - Gene or protein expression profilingExpression-ratio estimation or normalisation
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca

Abstract

Presented herein is a sensor platform which can be used for the detection of arbitrary molecules of interest or for clinical diagnosis using test solutions. The sensor may include an array of environmentally sensitive fluorescent reporter probes. Each probe is placed on a separate pixel or several pixels of the sensor array and has a different coating, such that it responds slightly differently to environmental stimuli than every other pixel upon addition of the test solution.

IPC Classes  ?

• G01N 21/64 - FluorescencePhosphorescence
• G01N 33/53 - ImmunoassayBiospecific binding assayMaterials therefor
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• G01N 33/58 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving labelled substances
• G01N 33/84 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving inorganic compounds or pH
• B82Y 20/00 - Nanooptics, e.g. quantum optics or photonic crystals
• G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• G01N 21/77 - Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator

Abstract

The present disclosure provides methods for accurately predicting the risk of recurrence and metastases in lung cancer (e.g., lung adenocarcinoma) patients using ctDNA and 'spread through air spaces' (STAS) biomarkers.

IPC Classes  ?

• G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
• G06N 20/00 - Machine learning
• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
• G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
• G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
• G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture

Abstract

Presented herein are systems, methods, and non-transient computer readable media for determining predicted response scores of subjects. A computing system may identify a first feature set for a first subject to be administered with immunotherapy to address a condition. The first feature set may include one or more of: (i) a first radiological feature identified in a tomogram of a section associated with the condition in the first subject, (ii) a first immunohistochemistry (IHC) feature derived from an image of a sample associated with the first subject, and (iii) a first genomic feature obtained from gene sequencing of the first subject for genes associated with the condition. The computing system may apply the first feature set to a model. The computing system may determine, from applying the first feature set to the model, a predicted score identifying a response to the immunotherapy to be administered to the first subject.

IPC Classes  ?

• G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
• G06T 7/00 - Image analysis
• G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
• G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Aspects of the disclosure provide compositions and methods for reversible expression of a target gene. In some aspects, the disclosure provides nucleic acids comprising multiple pairs of recombinase sites, each pair having first and second members flanking an inverted expression cassette encoding a target gene.

IPC Classes  ?

• C12N 9/22 - Ribonucleases
• C12N 15/67 - General methods for enhancing the expression

Abstract

The present technology provides compositions and methods for modulating T Cell Receptor (TCR) specificity as well as methods for treating cancer in a subject in need thereof. The present disclosure provides engineered cytotoxic T cells comprising a TCR and/or nucleic acid encoding the TCR and a mutant CD8 alpha polypeptide and/or nucleic acid encoding the mutant CD8 alpha polypeptide.

IPC Classes  ?

• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/32 - T-cell receptors [TCR]
• A61K 40/42 - Cancer antigens
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• C07K 14/725 - T-cell receptors
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells

Abstract

The present disclosure relates to methods of treating a vascular malformation in a subject expressing a gain-of-function mutation in a PIK3CA gene comprising administering, to the subject, an effective amount of an agent that inhibits phosphoinositide 3-kinase (“PI3K”).

IPC Classes  ?

• A61K 31/5377 - 1,4-Oxazines, e.g. morpholine not condensed and containing further heterocyclic rings, e.g. timolol
• A61K 31/427 - Thiazoles not condensed and containing further heterocyclic rings
• A61K 31/4375 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with one nitrogen as the only ring hetero atom ortho- or peri-condensed with heterocyclic ring systems the heterocyclic ring system containing a six-membered ring having nitrogen as a ring hetero atom, e.g. quinolizines, naphthyridines, berberine, vincamine
• A61K 31/4439 - Non-condensed pyridinesHydrogenated derivatives thereof containing further heterocyclic ring systems containing a five-membered ring with nitrogen as a ring hetero atom, e.g. omeprazole
• A61K 31/444 - Non-condensed pyridinesHydrogenated derivatives thereof containing further heterocyclic ring systems containing a six-membered ring with nitrogen as a ring hetero atom, e.g. amrinone
• A61K 31/519 - PyrimidinesHydrogenated pyrimidines, e.g. trimethoprim ortho- or peri-condensed with heterocyclic rings
• A61P 9/00 - Drugs for disorders of the cardiovascular system

Abstract

Described herein are systems and methods for intracellular imaging, assessment, and/or treatment of tissue before, during, and/or after surgical procedures using nanoparticles (e.g., less than 50 nanometers in diameter, e.g., photoswitchable nanoparticles) and/or a super-resolution microscope system. The present disclosure describes nanoparticles (e.g., nanosensors and photoswitchable nanoparticles) that are used to monitor and/or track changes in environmental conditions and/or analytes in the cellular microenvironment before, during, and/or after surgical procedures. The present disclosure also describes systems and methods that provide information related to the distribution and/or delivery of photoswitchable nanoparticles at super resolution (e.g., using super-resolution microscopy).

IPC Classes  ?

• A61K 49/00 - Preparations for testing in vivo
• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value
• A61B 5/1455 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value using optical sensors, e.g. spectral photometrical oximeters
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• A61K 47/69 - Medicinal preparations characterised by the non-active ingredients used, e.g. carriers or inert additivesTargeting or modifying agents chemically bound to the active ingredient the non-active ingredient being chemically bound to the active ingredient, e.g. polymer-drug conjugates the conjugate being characterised by physical or galenical forms, e.g. emulsion, particle, inclusion complex, stent or kit

Abstract

Provided herein is a method comprising: obtaining a sample comprising fixed cells or fixed nuclei, hybridizing oligonucleotide probes to RNA in the fixed cells or fixed nuclei, to produce labeled cells or labeled nuclei, enriching for a sub-population of the fixed cells or fixed nuclei based on their labeling, by flow cytometry, treating the enriched cells or enriched nuclei with a double-stranded DNAse for a sufficient time to degrade the oligonucleotide probes, inactivating the double-stranded DNAse, and performing single cell RNA analysis on the cells or nuclei.

IPC Classes  ?

• C12Q 1/68 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving nucleic acids
• C12Q 1/6813 - Hybridisation assays
• C12Q 1/6806 - Preparing nucleic acids for analysis, e.g. for polymerase chain reaction [PCR] assay
• C12N 15/11 - DNA or RNA fragmentsModified forms thereof
• C12N 15/10 - Processes for the isolation, preparation or purification of DNA or RNA

Abstract

The present disclosure is directed to systems and methods for identifying regions of interest (ROIs) in images. A computing system may identify an image including an annotation defining an ROI. The image may have a plurality of pixels in a first color space. The computing system may convert the plurality of pixels from the first color space to a second color space to differentiate the annotation from the ROI. The computing system may select a first subset of pixels corresponding to the annotation based at least on a color value of the first subset of pixels in the second color space. The computing system may identify a second subset of pixels included in the ROI from the image using the first subset of pixels. The computing system may store an association between the second subset of pixels and the ROI defined by the annotation in the image.

IPC Classes  ?

• G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
• G06T 3/40 - Scaling of whole images or parts thereof, e.g. expanding or contracting
• G06T 5/70 - DenoisingSmoothing
• G06T 7/00 - Image analysis
• G06T 7/194 - SegmentationEdge detection involving foreground-background segmentation
• G06V 30/14 - Image acquisition
• G06V 30/18 - Extraction of features or characteristics of the image
• G06V 30/19 - Recognition using electronic means

Abstract

The present application relates generally to identifying regions of interest in images, including but not limited to whole slide image region of interest identification, prioritization, de-duplication, and normalization via interpretable rules, nuclear region counting, point set registration, and histogram specification color normalization. This disclosure describes systems and methods for analyzing and extracting regions of interest from images, for example biomedical images depicting a tissue sample from biopsy or ectomy. Techniques directed to quality control estimation, granular classification, and coarse classification of regions of biomedical images are described herein. Using the described techniques, patches of images corresponding to regions of interest can be extracted and analyzed individually or in parallel to determine pixels correspond to features of interest and pixels that do not. Patches that do not include features of interest, or include disqualifying features, can be disqualified from further analysis. Relevant patches can analyzed and stored with various feature parameters.

IPC Classes  ?

• G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
• G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
• G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
• G06V 10/46 - Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]Salient regional features
• G06V 10/50 - Extraction of image or video features by performing operations within image blocksExtraction of image or video features by using histograms, e.g. histogram of oriented gradients [HoG]Extraction of image or video features by summing image-intensity valuesProjection analysis
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 30/24 - Character recognition characterised by the processing or recognition method

Abstract

The present invention relates to compositions and methods employing conjugates that include a self-assembly and disassembly (SADA) polypeptide and a binding domain. The present invention encompasses the recognition that conjugates with a SADA polypeptide have certain improved biological properties. SADA-conjugates are described, along with uses thereof (e.g., as therapeutic or diagnostic agents) and methods of manufacture.

IPC Classes  ?

• A61K 47/64 - Drug-peptide, drug-protein or drug-polyamino acid conjugates, i.e. the modifying agent being a peptide, protein or polyamino acid which is covalently bonded or complexed to a therapeutically active agent
• A61K 47/54 - Medicinal preparations characterised by the non-active ingredients used, e.g. carriers or inert additivesTargeting or modifying agents chemically bound to the active ingredient the non-active ingredient being chemically bound to the active ingredient, e.g. polymer-drug conjugates the non-active ingredient being a modifying agent the modifying agent being an organic compound
• A61K 51/04 - Organic compounds
• A61K 51/10 - Antibodies or immunoglobulinsFragments thereof
• C07K 14/47 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from animalsPeptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from humans from vertebrates from mammals
• C07K 14/715 - ReceptorsCell surface antigensCell surface determinants for cytokinesReceptorsCell surface antigensCell surface determinants for lymphokinesReceptorsCell surface antigensCell surface determinants for interferons
• C07K 16/30 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants from tumour cells
• C07K 16/44 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material not provided for elsewhere

Abstract

Provided herein are chimeric innate receptor (OR) fusion proteins that enhance the persistence of NK and NK-like T cells in response to AML antigens, such as CD33. The OR fusion proteins of the present technology are useful in methods for treating cancer, such as AML. The OR fusion proteins of the present technology are useful in methods for treating AML.

IPC Classes  ?

• A61K 40/42 - Cancer antigens
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants

Abstract

The present application is directed to multiplex intein-based methods and compositions for the generation engineered cells expressing modular polypeptides, for example CARs and CCRs.

IPC Classes  ?

• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 40/42 - Cancer antigens

Abstract

The present disclosure relates to compounds according to Formula I: or a pharmaceutically acceptable salt thereof, as well as compositions including such compounds and uses thereof. As evidenced by this application, these compounds and compositions are suitable for, among other things, treating cancer.

IPC Classes  ?

• C07F 5/02 - Boron compounds
• A61K 47/68 - Medicinal preparations characterised by the non-active ingredients used, e.g. carriers or inert additivesTargeting or modifying agents chemically bound to the active ingredient the non-active ingredient being chemically bound to the active ingredient, e.g. polymer-drug conjugates the non-active ingredient being a modifying agent the modifying agent being an antibody, an immunoglobulin or a fragment thereof, e.g. an Fc-fragment
• A61K 47/69 - Medicinal preparations characterised by the non-active ingredients used, e.g. carriers or inert additivesTargeting or modifying agents chemically bound to the active ingredient the non-active ingredient being chemically bound to the active ingredient, e.g. polymer-drug conjugates the conjugate being characterised by physical or galenical forms, e.g. emulsion, particle, inclusion complex, stent or kit
• A61P 35/00 - Antineoplastic agents

Abstract

Presented herein are systems, devices, and methods for shortwave infrared (SWIR) fluorescence imaging. The device may include a housing defining an aperture, an optical component having a first end structured to be optically coupled with the aperture of housing, a second end structured to be distal from the housing, the second end configured to accept at least a portion of light having a SWIR wavelength emitted by a fluorophore in at least a target area of an object, and a body configured to convey the light from the second end to the first end, and a sensor disposed within the housing, the sensor optically coupled with the optical component via the aperture, the sensor configured to convert the light to an electrical signal corresponding to the light.

IPC Classes  ?

• G01N 21/64 - FluorescencePhosphorescence
• G01N 21/359 - Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light

Goods & Services

Providing scientific research and analysis services in the field of immunology, namely, proprietary methods for analyzing T cell receptor sequencing data for scientific and medical research and health treatment purposes; providing software for analyzing T cell receptor sequences and identifying T cell clonal lineages; bioinformatics services for identifying and tracking T cell populations in connection with monitoring immune response dynamics, endogenous and with therapeutic interventions

Abstract

The present disclosure is directed to systems and methods for classifying biomedical images. A feature classifier may generate a plurality of tiles from a biomedical image. Each tile may correspond to a portion of the biomedical image. The feature classifier may select a subset of tiles from the plurality of tiles by applying an inference model. The subset of tiles may have highest scores. Each score may indicate a likelihood that the corresponding tile includes a feature indicative of the presence of the condition. The feature classifier may determine a classification result for the biomedical image by applying an aggregation model. The classification result may indicate whether the biomedical includes the presence or lack of the condition.

IPC Classes  ?

• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
• G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
• G06F 18/2113 - Selection of the most significant subset of features by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
• G06F 18/2415 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
• G06F 18/2431 - Multiple classes
• G06N 20/00 - Machine learning
• G06T 7/00 - Image analysis
• G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06V 10/98 - Detection or correction of errors, e.g. by rescanning the pattern or by human interventionEvaluation of the quality of the acquired patterns
• G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing

Abstract

Presented herein is an imaging technique named nonstop gated cone-beam computer tomograph (CBCT), for guiding free-breathing respiratory gating radiotherapy of thoracic and abdominal cancer patients (e.g., lung, liver, pancreas). The nonstop gated CBCT technique reduces the data acquisition time to 1 minute and decreases the imaging dose by >40% while simultaneously retaining high quality images was developed. This is done by allowing the gantry to rotate continuously and having the kV x-ray beam on when the patient breathing signal is within the respiratory gate and the x-ray beam off when it is out of the respiratory gate. A machine learning model can be used to reconstruct high-quality CBCT images from the acquired data.

IPC Classes  ?

• A61B 6/00 - Apparatus or devices for radiation diagnosisApparatus or devices for radiation diagnosis combined with radiation therapy equipment
• G06T 11/00 - 2D [Two Dimensional] image generation
• G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
• G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
• G06N 20/00 - Machine learning
• A61B 5/00 - Measuring for diagnostic purposes Identification of persons

Abstract

The present disclosure provides chimeric cell-penetrating polypeptides, barcodes, conjugates comprising a cargo moiety and a chimeric cell-penetrating polypeptide described herein, alone or in combination with a barcode of the present technology, and methods of using the conjugates to monitor delivery of the cargo moiety into target tissues or cells.

IPC Classes  ?

• C07K 14/47 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from animalsPeptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from humans from vertebrates from mammals
• A61K 47/64 - Drug-peptide, drug-protein or drug-polyamino acid conjugates, i.e. the modifying agent being a peptide, protein or polyamino acid which is covalently bonded or complexed to a therapeutically active agent

Abstract

Presented herein are systems and methods of determining density values from mammograms. A computing system may identify a first mammogram of a first breast region of a first subject. The first mammogram may have a first region of interest (ROI) corresponding to a first dense area of the first breast region. The computing system may apply the first mammogram to a machine learning (ML) model to generate a first segmentation map identifying the first ROI within the first mammogram. The computing system may determine a density value for the first dense area of the first breast region based on the first segmentation map.

IPC Classes  ?

• G06T 7/00 - Image analysis
• A61B 6/00 - Apparatus or devices for radiation diagnosisApparatus or devices for radiation diagnosis combined with radiation therapy equipment
• A61B 6/50 - Apparatus or devices for radiation diagnosisApparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body partsApparatus or devices for radiation diagnosisApparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific clinical applications
• G06T 7/11 - Region-based segmentation
• G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment

Abstract

Described herein are nanoparticle drug conjugates (NDCs), which, in certain embodiments, comprise a non-toxic, multi-modality, clinically proven silica-based nanoparticle platform with covalently attached drug molecules/moieties. The nanoparticle drug conjugates (NDCs) demonstrate imaging capability and targeting ligands which efficiently clear through the kidneys. Furthermore, the conjugates incorporate therapeutic agents for cancer detection, prevention, and/or treatment.

IPC Classes  ?

• A61K 47/69 - Medicinal preparations characterised by the non-active ingredients used, e.g. carriers or inert additivesTargeting or modifying agents chemically bound to the active ingredient the non-active ingredient being chemically bound to the active ingredient, e.g. polymer-drug conjugates the conjugate being characterised by physical or galenical forms, e.g. emulsion, particle, inclusion complex, stent or kit
• A61K 31/506 - PyrimidinesHydrogenated pyrimidines, e.g. trimethoprim not condensed and containing further heterocyclic rings
• A61K 31/5377 - 1,4-Oxazines, e.g. morpholine not condensed and containing further heterocyclic rings, e.g. timolol
• A61K 47/60 - Medicinal preparations characterised by the non-active ingredients used, e.g. carriers or inert additivesTargeting or modifying agents chemically bound to the active ingredient the non-active ingredient being chemically bound to the active ingredient, e.g. polymer-drug conjugates the non-active ingredient being a modifying agent the modifying agent being an organic macromolecular compound, e.g. an oligomeric, polymeric or dendrimeric molecule obtained otherwise than by reactions only involving carbon-to-carbon unsaturated bonds, e.g. polyureas or polyurethanes the organic macromolecular compound being a polyoxyalkylene oligomer, polymer or dendrimer, e.g. PEG, PPG, PEO or polyglycerol
• A61K 47/65 - Peptidic linkers, binders or spacers, e.g. peptidic enzyme-labile linkers
• A61K 49/00 - Preparations for testing in vivo
• A61K 51/12 - Preparations containing radioactive substances for use in therapy or testing in vivo characterised by a special physical form, e.g. emulsion, microcapsules, liposomes

Abstract

The present invention provides immunoresponsive cells, including T cells, cytotoxic T cells, regulatory T cells, and Natural Killer (NK) cells, expressing an antigen recognizing receptor and an inhibitory chimeric antigen receptor (iCAR). Methods of using the immunoresponsive cell include those for the treatment of neoplasia and other pathologies where an increase in an antigen-specific immune response is desired.

IPC Classes  ?

• C07K 16/40 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against enzymes
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/22 - Immunosuppressive or immunotolerising
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 40/36 - Immune checkpoint inhibitors
• A61K 40/41 - Vertebrate antigens
• A61K 40/42 - Cancer antigens
• A61K 40/50 - Cellular immunotherapy characterised by the use of allogeneic cells
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• C07K 14/725 - T-cell receptors
• C07K 14/73 - CD4
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells

Abstract

The present disclosure relates generally to compositions comprising an antibody-drug conjugate comprising an anti-TROP-2 antibody or antigen binding fragment thereof and methods for using the same to treat diffuse pleural mesothelioma (DPM).

IPC Classes  ?

• A61K 47/68 - Medicinal preparations characterised by the non-active ingredients used, e.g. carriers or inert additivesTargeting or modifying agents chemically bound to the active ingredient the non-active ingredient being chemically bound to the active ingredient, e.g. polymer-drug conjugates the non-active ingredient being a modifying agent the modifying agent being an antibody, an immunoglobulin or a fragment thereof, e.g. an Fc-fragment
• C07K 16/30 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants from tumour cells
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells
• A61P 35/00 - Antineoplastic agents

Abstract

Presented herein are systems and methods for determining scores related to metastatic recurrence of cancers in subjects using multimodal machine learning (ML) architectures. A computing system may obtain, for a subject at risk of recurrence of cancer, a dataset comprising at least one of: (i) a biomedical image of a tissue sample from an organ associated with the cancer or (ii) a text report identifying a plurality of characteristics of a tumor associated with the cancer. The computing system may apply an ML architecture to at least one of the biomedical image or the text report of the dataset. The computing system may determine, based on applying the ML architecture, a score indicating a likelihood of recurrence associated with cancer in the subject. The computing system may generate a classification of the subject for administration of a therapy for the cancer, in accordance with the score.

IPC Classes  ?

• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G06T 7/00 - Image analysis
• C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
• G06V 10/771 - Feature selection, e.g. selecting representative features from a multi-dimensional feature space
• G06V 10/80 - Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
• G06N 3/02 - Neural networks
• G06N 20/00 - Machine learning
• G06F 18/211 - Selection of the most significant subset of features
• G06F 18/25 - Fusion techniques
• G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing

Abstract

The presently disclosed subject matter provides novel T cell receptors (TCRs) that target an EWSR1/WT1 fusion protein. The presently disclosed subject matter further provides cells comprising such TCRs, and methods of using such cells for treating cancers associated with EWSR1/WT1 fusion protein.

IPC Classes  ?

• A61K 35/17 - LymphocytesB-cellsT-cellsNatural killer cellsInterferon-activated or cytokine-activated lymphocytes
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/15 - Natural-killer [NK] cellsNatural-killer T [NKT] cells
• A61K 40/32 - T-cell receptors [TCR]
• A61K 40/42 - Cancer antigens
• A61P 35/00 - Antineoplastic agents
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• C07K 16/32 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against translation products from oncogenes
• C12N 15/86 - Viral vectors
• C12N 15/88 - Introduction of foreign genetic material using processes not otherwise provided for, e.g. co-transformation using microencapsulation, e.g. using liposome vesicle

Abstract

Systems and methods for HDR brachytherapy planning can include obtaining patient specific data of a patient, and determining, using the patient specific data, an optimization problem to solve for a single-fraction HDR monotherapy plan including an objective function to be optimized subject to one or more first constraints on radiation doses within one or more OAR regions and a second constraint on radiation doses within the PTV. The systems and methods can include optimizing the objective function subject to the one or more first constraints and the second constraint, determining whether the first single-fraction HDR monotherapy plan satisfies the first and second constraints, updating a bound value of the second constraint based on whether the first single-fraction HDR monotherapy plan satisfies the first and second constraints, and optimizing the objective function subject to the one or more first constraints and the second constraint with the updated bound value.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy

Abstract

Presented herein are systems, methods, and non-transient computer readable media for determining risk scores using multimodal feature sets. A computing system may identify a first feature set for a first subject at risk of a condition. The first feature set may include (i) a first radiological feature derived from a tomogram of a section associated with the condition within the first subject, (ii) a first histologic feature acquired using a whole slide image of a sample having the condition from the first subject, and (iii) a first genomic feature obtained from gene sequencing of the first subject for genes associated with the condition. The computing system may apply the first feature set to a model. The computing system may determine, from applying the first feature set to the model, a predicted risk score of the condition for the first subject.

IPC Classes  ?

• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G06T 7/00 - Image analysis
• G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection

Goods & Services

Entertainment services, namely, organizing and presenting live comedy shows for charitable fundraising purposes in support of cancer research

Abstract

Presented herein is a pipeline, integrating deep learning and language models to improve efficiency and accuracy in radiologist's workflow, as well as generate robust structured data abstracting patient's MRIs. A universally compatible pipeline can be integrated across major clinical platforms and shared with various institutions, marking a pivotal step towards reducing radiologist human error.

IPC Classes  ?

• G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
• G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
• G06V 10/40 - Extraction of image or video features
• G06N 20/00 - Machine learning
• G06V 10/00 - Arrangements for image or video recognition or understanding

Abstract

Systems, methods, and computer program products are provided for classifying sequence fragments and labelling sequence fragments that represent tumor markers. A plurality of reference sequences are read. A plurality of sequence fragments obtained from a biological sample of a patient are read. A first read and a second read are selected from the plurality of sequence fragments. A regional probability based on a plurality of regional features from the patient is received from a first trained classifier. A tensor is generated comprising a corresponding reference sequence, the first read, the second read, a first position, a second position, and an alt position. A local probability based on the tensor is received from a second trained classifier comprising a convolutional neural network. A label associated with a tumor marker is determined when the regional probability is above a first predetermined threshold and the local probability is above a second predetermined threshold.

IPC Classes  ?

• C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
• C12Q 1/6874 - Methods for sequencing involving nucleic acid arrays, e.g. sequencing by hybridisation [SBH]
• G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
• G16B 40/20 - Supervised data analysis
• G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis

Abstract

The presently disclosed subject matter provides cells, compositions and methods for enhancing immune responses toward tumor antigens. It relates to cells comprising a first antigen-recognizing receptor that targets CD70. These cells have improved activity and/or efficiency.

IPC Classes  ?

• A61K 35/17 - LymphocytesB-cellsT-cellsNatural killer cellsInterferon-activated or cytokine-activated lymphocytes
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 40/42 - Cancer antigens
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• C07K 14/725 - T-cell receptors
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells

Abstract

Presented herein are systems and methods of determining predicted outcomes of subjects with cancer from biomedical images. A computing system may identify a biomedical image of a tissue sample from a subject with cancer. The biomedical image may have (i) a first region of interest (ROI) corresponding to viable tumor and (ii) a second ROI corresponding to necrotic tumor in the tissue sample. The computing system may apply a machine learning model to the biomedical image to determine (i) a first segment identifying the first ROI and (ii) a second segment identifying the second ROI. The computing system may determine a ratio between a first size of the first segment associated with the viable tumor and a second size of the second segment associated with the necrotic tumor. The computing system may generate a value indicative of a predicted outcome of the cancer in the subject using the ratio.

IPC Classes  ?

• G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
• G06T 7/00 - Image analysis
• G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks

Abstract

The presently disclosed subject matter provides cells, compositions and methods for enhancing immune responses toward tumor antigens. It relates to cells comprising a first antigen-recognizing receptor (e.g., a chimeric antigen receptor (CAR)) and a second antigen-recognizing receptor (e.g., a TCR-like fusion molecule). These cells have improved activity and/or efficiency.

IPC Classes  ?

• A61K 40/42 - Cancer antigens
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• C07K 14/725 - T-cell receptors
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants

Abstract

The presently disclosed subject matter provides for chimeric receptors that target ADGRE2 and chimeric receptors that target CLEC12A. The presently disclosed subject matter also provides for cells comprising the ADGRE2-targeted chimeric receptors, cells comprising the CLEC12A-targeted chimeric receptors, and cells comprising the ADGRE2-targeted chimeric receptors and the CLEC12A-targeted chimeric receptors. The presently disclosed subject matter further provides uses of such cells for treating tumors, e.g., AML.

IPC Classes  ?

• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• A61K 38/00 - Medicinal preparations containing peptides
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 40/42 - Cancer antigens
• A61P 35/00 - Antineoplastic agents
• A61P 35/02 - Antineoplastic agents specific for leukemia
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• C07K 14/725 - T-cell receptors

Abstract

The present disclosure encompasses systems, methods, and compositions for for identifying a cellular or molecular target for therapy and/or predicting responsiveness to a therapy based on transcriptional and epigenetic signatures in circulating cells in peripheral blood of a subject. Particular methods relate to detecting transcriptional and epigenetic signatures related to anti-tumor immunity in circulating cells of a subject after administering a therapy, such as a microbial therapy and/or immunotherapy, and treating the subject based on the analysis.

IPC Classes  ?

• C12Q 1/6806 - Preparing nucleic acids for analysis, e.g. for polymerase chain reaction [PCR] assay
• C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
• C12Q 1/6883 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer

Abstract

A neoantigen model discrimination method to determine if the immune system can discriminate it from “self” by estimating if a neoantigen has sufficient antigenic distance from its wild-type peptide to differentially bind the MHC or activate a T cell. In one embodiment, a model can be integrated to estimate the fitness of tumor clones as the aggregate of the cost due to T cells recognizing high quality neoantigens offset by the gain from mutations in canonical oncogenes. In an additional embodiment, a model can be used to rationally identify antigens that either elicit desired, or prevent undesired responses of any immunologically based medicine.

IPC Classes  ?

• G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
• C12Q 1/6881 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for tissue or cell typing, e.g. human leukocyte antigen [HLA] probes
• G16B 20/10 - Ploidy or copy number detection
• G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection

Abstract

Provided are pharmaceutical compositions and methods of treating or preventing edema, using an anti-T cell agent, an anti-TGF-β1 agent, or an anti-angiotensin agent, preferably a combination of at least two such agents. The pharmaceutical compositions can be formulated for systemic or local administration, and are preferably administered topically.

IPC Classes  ?

• A61K 31/4412 - Non-condensed pyridinesHydrogenated derivatives thereof having oxo groups directly attached to the heterocyclic ring
• A61K 9/00 - Medicinal preparations characterised by special physical form
• A61K 9/06 - OintmentsBases therefor
• A61K 31/277 - NitrilesIsonitriles having a ring, e.g. verapamil
• A61K 31/401 - ProlineDerivatives thereof, e.g. captopril
• A61K 31/42 - Oxazoles
• A61K 31/436 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with one nitrogen as the only ring hetero atom ortho- or peri-condensed with heterocyclic ring systems the heterocyclic ring system containing a six-membered ring having oxygen as a ring hetero atom, e.g. rapamycin
• A61K 31/4418 - Non-condensed pyridinesHydrogenated derivatives thereof having a carbocyclic ring directly attached to the heterocyclic ring, e.g. cyproheptadine
• A61K 38/13 - Cyclosporins
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• A61K 47/10 - AlcoholsPhenolsSalts thereof, e.g. glycerolPolyethylene glycols [PEG]PoloxamersPEG/POE alkyl ethers
• A61P 7/10 - Antioedematous agentsDiuretics

Goods & Services

Charitable fundraising services by means of raising money for cancer research, cancer treatment and cancer awareness. Construction of facilities for use in cancer research, cancer treatment, and cancer awareness. Research in the biomedical sciences fields, including molecular biology, cell biology, genetics, and immunology; medical initiative and research and development services pertaining to the development of cancer vaccines that target neoantigens. Medical services; Healthcare services for treating cancer; Hospital services, namely, patient treatment and care; Pharmaceutical prescription formulation being compounding, and dispensing services; Providing medical services in the nature of patient consultations and medical information via telemedicine.

Abstract

Provided herein are compositions, kits, and methods for manufacturing cells for adoptive cell therapy comprising engineered immune cells that overexpress Glucose Transporter 5 (GLUTS).

IPC Classes  ?

• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/42 - Cancer antigens
• A61P 35/00 - Antineoplastic agents
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells

Abstract

The present technology relates generally to compounds, compositions, and methods useful for treating, preventing, and/or ameliorating pathogenic cellular proliferation, angiogenesis, cancer, metastatic disease, and/or a pathogenic vascular proliferative disease in a subject.

IPC Classes  ?

• C07C 217/62 - Compounds containing amino and etherified hydroxy groups bound to the same carbon skeleton having etherified hydroxy groups bound to carbon atoms of at least one six-membered aromatic ring and amino groups bound to acyclic carbon atoms or to carbon atoms of rings other than six-membered aromatic rings of the same carbon skeleton with amino groups linked to the six-membered aromatic ring, or to the condensed ring system containing that ring, by carbon chains not further substituted by singly-bound oxygen atoms linked by carbon chains having at least three carbon atoms between the amino groups and the six-membered aromatic ring or the condensed ring system containing that ring
• A61K 31/137 - Arylalkylamines, e.g. amphetamine, epinephrine, salbutamol, ephedrine
• A61K 31/343 - Heterocyclic compounds having oxygen as the only ring hetero atom, e.g. fungichromin having five-membered rings with one oxygen as the only ring hetero atom, e.g. isosorbide condensed with a carbocyclic ring, e.g. coumaran, bufuralol, befunolol, clobenfurol, amiodarone
• A61K 31/444 - Non-condensed pyridinesHydrogenated derivatives thereof containing further heterocyclic ring systems containing a six-membered ring with nitrogen as a ring hetero atom, e.g. amrinone
• A61P 35/00 - Antineoplastic agents
• A61P 35/04 - Antineoplastic agents specific for metastasis
• C07C 211/52 - Compounds containing amino groups bound to a carbon skeleton having amino groups bound to carbon atoms of six-membered aromatic rings of the carbon skeleton having amino groups bound to only one six-membered aromatic ring the carbon skeleton being further substituted by halogen atoms or by nitro or nitroso groups
• C07D 213/38 - Radicals substituted by singly-bound nitrogen atoms having only hydrogen or hydrocarbon radicals attached to the substituent nitrogen atom
• C07D 317/58 - Radicals substituted by nitrogen atoms

Abstract

Presented herein are systems, methods, and non-transient computer readable media for determining radiation therapy dosages to administer. A computing system may identify a first dataset comprising: (i) a biomedical image derived from a first sample to be administered with radiotherapy and (ii) an identifier corresponding to an organ from which the first sample is obtained. The computing system may apply, to the first dataset, a machine learning (ML) model comprising a plurality of weights trained using a plurality of second datasets in accordance with a moment loss for each of a plurality of organs. The computing system may determine, from applying the first dataset to the ML model, a radiation therapy dose to administer to the sample from which the biomedical image is derived. The computing system may store an association between the first dataset and the radiation therapy dose.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G06N 20/00 - Machine learning
• G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Abstract

The presently disclosed subject matter provides anti-CD40 antibodies or antigen-binding fragments thereof, anti-CD40 Fc fusion proteins (e.g., scFv-Fc fusion proteins binding to CD40), cells comprising the anti-CD antibodies or antigen-binding fragments thereof, cells comprising the anti-CD40 Fc fusion proteins, compositions comprising such cells, and methods of using such cells and compositions for diagnosis and therapies.

IPC Classes  ?

• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61P 35/00 - Antineoplastic agents

Goods & Services

Charitable fundraising services by means of raising money for cancer research, cancer treatment, cancer awareness and the construction of facilities for use in cancer research, cancer treatment and cancer awareness. Educational services, namely, training clinicians and healthcare providers in analysis of clinical data utilizing diagnostic, clinic-genomic, and harmonized oncologic dataset and database models pertaining to cancer outcome predictions and treatment recommendations; Educational services, namely, conducting community lectures on cancer and related health issues, and distribution of related informational materials therewith; Educational services, namely, conducting classes, seminars, training programs, correspondence courses, and scientific conferences via television, radio, and global computer networks, all in the fields of medicine and biomedical sciences. Research in the biomedical sciences fields, including molecular biology, cell biology, genetics, and immunology; medical services; Healthcare services for treating cancer; Hospital services, namely, patient treatment and care; Pharmaceutical prescription formulation being compounding, and dispensing services; Providing medical services in the nature of patient consultations and medical information via telemedicine; Medical initiative and research and development services pertaining to the development of cancer vaccines that target neoantigens; Scientific research services in the biomedical sciences fields, including molecular biology, cell biology, genetics, and immunology; Compiling medical data for research purposes in the field of gene mutations in rare and common cancers for evaluation and assessing tumor biology, prognosis, and treatment and patient drug response; Providing non-downloadable on-line software, software applications, software tools, and databases using natural language processing and machine learning algorithms, for extracting, characterizing, defining, annotating, curating, structuring, and communicating clinical information and health records; compiling medical data for research purposes. Medical services; Healthcare services for treating cancer; Hospital services, namely, patient treatment and care; pharmaceutical prescription formulation being compounding, and dispensing services; providing medical services in the nature of patient health consultations for patients and providing medical information via telemedicine, videoconferencing, and teleconferencing software

Goods & Services

medical services; Healthcare services for treating cancer; Hospital services, namely, patient treatment and care; Providing medical services in the nature of patient consultations and medical information via telemedicine Medical services; Healthcare services for treating cancer; Hospital services, namely, patient treatment and care; pharmaceutical prescription formulation being compounding, and dispensing services; providing medical services in the nature of patient health consultations for patients and providing medical information via telemedicine, videoconferencing, and teleconferencing software

Goods & Services

medical services; Healthcare services for treating cancer; Hospital services, namely, patient treatment and care; Providing medical services in the nature of patient consultations and medical information via telemedicine Medical services; Healthcare services for treating cancer; Hospital services, namely, patient treatment and care; pharmaceutical prescription formulation being compounding, and dispensing services; providing medical services in the nature of patient health consultations for patients and providing medical information via telemedicine, videoconferencing, and teleconferencing software

Abstract

The present invention relates to the field of stem cell biology, in particular the linage specific differentiation of pluripotent or multipotent stem cells, which can include, but is not limited to, human embryonic stem cells (hESC), human induced pluripotent stem cells (hiPSC), somatic stem cells, cancer stem cells, or any other cell capable of lineage specific differentiation. Specifically described are methods to direct the lineage specific differentiation of hESC and/or hiPSC to nociceptors (i.e. nociceptor cells) using novel culture conditions. The nociceptors made using the methods of the present invention are further contemplated for various uses including, but limited to, use in in vitro drug discovery assays, pain research, and as a therapeutic to reverse disease of, or damage to, the peripheral nervous system (PNS). Further, compositions and methods are provided for producing melanocytes from human pluripotent stem cells for use in disease modeling.

IPC Classes  ?

• C12N 5/0793 - Neurons
• C12N 5/071 - Vertebrate cells or tissues, e.g. human cells or tissues
• C12N 5/079 - Neural cells
• G01N 33/50 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing

Abstract

Truncated saponin analogues are disclosed. Also disclosed are pharmaceutical compositions comprising truncated saponin analogues and methods of using the truncated saponin analogues.

IPC Classes  ?

• C07H 15/256 - Polyterpene radicals
• A61K 31/7024 - Esters of saccharides
• A61K 31/704 - Compounds having saccharide radicals attached to non-saccharide compounds by glycosidic linkages attached to a carbocyclic compound, e.g. phloridzin attached to a condensed carbocyclic ring system, e.g. sennosides, thiocolchicosides, escin, daunorubicin, digitoxin
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61K 39/39 - Medicinal preparations containing antigens or antibodies characterised by the immunostimulating additives, e.g. chemical adjuvants
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• C07H 15/18 - Acyclic radicals, substituted by carbocyclic rings
• C07H 15/24 - Condensed ring systems having three or more rings

Abstract

The presently disclosed subject matter provides novel T cell receptors (TCRs) that target a mutated RAS protooncogene. The presently disclosed subject matter further provides cells comprising such TCRs, and methods of using such cells for treating cancers associated with RAS.

IPC Classes  ?

• A61K 35/17 - LymphocytesB-cellsT-cellsNatural killer cellsInterferon-activated or cytokine-activated lymphocytes
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/15 - Natural-killer [NK] cellsNatural-killer T [NKT] cells
• A61K 40/32 - T-cell receptors [TCR]
• A61K 40/42 - Cancer antigens
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• C07K 14/73 - CD4
• C07K 16/40 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against enzymes
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells
• C12N 15/86 - Viral vectors

Abstract

The present invention relates to methods and compositions for inhibiting metastatic spread of cancer and/or inhibiting progression of pre-existing metastatic disease in a subject using L1CAM inhibition.

IPC Classes  ?

• A61K 48/00 - Medicinal preparations containing genetic material which is inserted into cells of the living body to treat genetic diseasesGene therapy
• A61K 31/337 - Heterocyclic compounds having oxygen as the only ring hetero atom, e.g. fungichromin having four-membered rings, e.g. taxol
• A61K 31/519 - PyrimidinesHydrogenated pyrimidines, e.g. trimethoprim ortho- or peri-condensed with heterocyclic rings
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• A61P 35/04 - Antineoplastic agents specific for metastasis
• C12N 15/113 - Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides
• C12Q 1/02 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving viable microorganisms
• G01N 33/50 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing

Abstract

The present disclosure provides compositions comprising IFNE and methods of using the same to treat cancer and/or enhancing responsiveness to immune checkpoint blockade therapy in a patient in need thereof. Also disclosed herein are compositions including tandem bicistronic expression cassettes, and methods of using the same to generate large genomic deletions and/or knock-in gene alterations.

IPC Classes  ?

• A61K 48/00 - Medicinal preparations containing genetic material which is inserted into cells of the living body to treat genetic diseasesGene therapy
• A61K 38/21 - Interferons
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• A61P 35/00 - Antineoplastic agents
• C12N 9/22 - Ribonucleases
• C12N 15/11 - DNA or RNA fragmentsModified forms thereof
• C12N 15/90 - Stable introduction of foreign DNA into chromosome

Abstract

The presently disclosed subject matter provides methods for improving the production of cells comprising an antigen-recognizing receptor (e.g., a chimeric antigen receptor (CAR) or a TCR like fusion molecule). The methods disclosed herein can improve the activity and/or efficiency of the cells.

IPC Classes  ?

• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 40/42 - Cancer antigens
• A61P 35/00 - Antineoplastic agents
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C07K 16/30 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants from tumour cells

Abstract

Described herein are Deep Multi-Magnification Networks (DMMNs). The method identifies, by a computing system, for a first tile of a biomedical image, the first tile comprising a portion of the biomedical image, a first patch associated with the first tile at a first magnification factor and a second patch associated with the first tile at a second magnification factor; applies, by the computing system, the first patch and the second patch to a machine learning (ML) model, the ML model comprising: a first network to generate a first feature map using the first patch, and a second network to generate a second feature map using the second patch; and determine a combination of the first feature map and the second feature map. Additionally, a computing system having one or more processors coupled with memory, configured to execute the method.

IPC Classes  ?

• G06T 7/11 - Region-based segmentation
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06N 3/045 - Combinations of networks
• G06T 7/00 - Image analysis
• G06T 19/00 - Manipulating 3D models or images for computer graphics
• G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]

Abstract

Monoclonal antibodies directed to ceramide that inhibit apoptosis are disclosed. Humanized and scFv versions of the antibodies are also disclosed. Methods for prevention or treatment of apoptosis in a subject by administration of the anti-ceramide antibodies are disclosed.

IPC Classes  ?

• C07K 16/44 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material not provided for elsewhere
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants

Abstract

The present invention provides, among other things, methods and compositions for diagnosing and/or treating cancer by targeting CCR8. In particular, the present invention provides technologies for depleting Treg cells, and particularly tumor-infiltrating Treg cells.

IPC Classes  ?

• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants

Abstract

The present disclosure provides methods for simultaneously identifying dynamic and disease-associated RNA binding protein (RBP)-RNA interaction (PRI) sites at single nucleotide resolution across the entire transcriptome. The methods disclosed herein recapitulates PRI profiles obtained with several distinct conventional PRI methods in a single assay at efficiencies that are improved by orders of magnitude, and permit the identification of the specific RBP bound at a PRI.

IPC Classes  ?

• C12N 15/10 - Processes for the isolation, preparation or purification of DNA or RNA
• C12N 9/12 - Transferases (2.) transferring phosphorus containing groups, e.g. kinases (2.7)
• C12N 9/22 - Ribonucleases
• C12N 9/50 - Proteinases
• C12Q 1/68 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving nucleic acids
• G01N 33/52 - Use of compounds or compositions for colorimetric, spectrophotometric or fluorometric investigation, e.g. use of reagent paper
• B01D 15/38 - Selective adsorption, e.g. chromatography characterised by the separation mechanism involving specific interaction not covered by one or more of groups , e.g. affinity, ligand exchange or chiral chromatography

Abstract

The present invention provides immunoresponsive cells, including T cells, cytotoxic T cells, regulatory T cells, and Natural Killer (NK) cells, expressing at least one of an antigen recognizing receptor and one of a chimeric costimulatory receptor. Methods of using the immunoresponsive cell include those for the treatment of neoplasia and other pathologies where an increase in an antigen-specific immune response is desired.

IPC Classes  ?

• A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61K 39/44 - Antibodies bound to carriers
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/42 - Cancer antigens
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• C07K 14/725 - T-cell receptors
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C07K 16/30 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants from tumour cells
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells
• C12N 9/24 - Hydrolases (3.) acting on glycosyl compounds (3.2)

Abstract

The present disclosure provides methods for determining whether a cancer patient with homologous recombination deficiency will benefit from treatment with PARP inhibitors or platinum agents. These methods are based on screening a cancer patient for the presence of templated insertions (TINS), including direct-repeat templated insertions (drTINS) and inverted templated insertions (iTINS).

IPC Classes  ?

• C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
• A61K 31/166 - Amides, e.g. hydroxamic acids having aromatic rings, e.g. colchicine, atenolol, progabide having the carbon atom of a carboxamide group directly attached to the aromatic ring, e.g. procainamide, procarbazine, metoclopramide, labetalol
• A61K 31/282 - Platinum compounds
• A61K 31/4184 - 1,3-Diazoles condensed with carbocyclic rings, e.g. benzimidazoles
• A61K 31/454 - Non-condensed piperidines, e.g. piperocaine containing further heterocyclic ring systems containing a five-membered ring with nitrogen as a ring hetero atom, e.g. pimozide, domperidone
• A61K 31/502 - PyridazinesHydrogenated pyridazines ortho- or peri-condensed with carbocyclic ring systems, e.g. cinnoline, phthalazine
• A61K 31/5025 - PyridazinesHydrogenated pyridazines ortho- or peri-condensed with heterocyclic ring systems
• A61K 31/506 - PyrimidinesHydrogenated pyrimidines, e.g. trimethoprim not condensed and containing further heterocyclic rings
• A61K 31/55 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having seven-membered rings, e.g. azelastine, pentylenetetrazole
• A61K 33/243 - PlatinumCompounds thereof
• A61P 35/00 - Antineoplastic agents

Abstract

The present disclosure relates to an array-based assay for transposase-accessible chromatin and prognostic molecular markers of treatment-resistant/early recurrent cancer.

IPC Classes  ?

• C12N 15/10 - Processes for the isolation, preparation or purification of DNA or RNA
• C12Q 1/6806 - Preparing nucleic acids for analysis, e.g. for polymerase chain reaction [PCR] assay
• C12Q 1/6874 - Methods for sequencing involving nucleic acid arrays, e.g. sequencing by hybridisation [SBH]
• C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer

Abstract

The present disclosure relates generally to the fields of oncology, virology and immunotherapy. It concerns poxviruses, specifically the highly attenuated modified vaccinia virus Ankara (MVA), and a recombinant modified vaccinia Ankara virus with deletion of vaccinia virulence factor E3 (MVAΔE3L), each further modified to express human Fms-like tyrosine kinase 3 ligand (Flt3L) or GM-CSF. The disclosure relates to use of the foregoing recombinant viruses as cancer immunotherapeutic agents. The foregoing recombinant poxviruses can also be used in combination with immune checkpoint blockade therapy.

IPC Classes  ?

• A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
• A61K 9/00 - Medicinal preparations characterised by special physical form
• A61K 35/768 - Oncolytic viruses not provided for in groups
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61P 35/00 - Antineoplastic agents
• C07K 14/52 - CytokinesLymphokinesInterferons
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C12N 15/86 - Viral vectors

Abstract

Disclosed herein are methods and compositions related to the treatment, prevention, and/or amelioration of cancer in a subject in need thereof. In particular aspects, the present technology relates to the use of genetically engineered or recombinant poxviruses, including a modified vaccinia Ankara (MVA) virus comprising a deletion of E3L (MVAΔE3L) engineered to express OX40L (MVAΔE3L-OX40L), an MVA virus comprising a deletion of C7L (MVAΔC7L) engineered to express OX40L (MVAΔC7L-OX40L), a MVAΔC7L engineered to express OX40L and human Fms-like tyrosine kinase 3 ligand (hFlt3L) (MVAΔC7L-hFlt3L-OX40L), an MVA comprising a deletion of E5R (MVAΔE5R), a vaccinia virus comprising a deletion of C7L (VACVΔC7L) engineered to express OX40L (VACVΔC7L-OX40L), a VACVΔC7L engineered to express both OX40L and hFlt3L (VACVΔC7L-hFlt3L-OX40L), a VACV comprising a deletion of E5R (VACVΔE5R), a myxoma virus (MYXV) comprising a deletion of M31R (MYXVΔM3IR), or combinations thereof, alone or in combination with other agents, as an oncolytic and immunotherapeutic composition.

IPC Classes  ?

• C12N 15/86 - Viral vectors
• A61K 9/00 - Medicinal preparations characterised by special physical form
• A61K 31/137 - Arylalkylamines, e.g. amphetamine, epinephrine, salbutamol, ephedrine
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61K 39/39 - Medicinal preparations containing antigens or antibodies characterised by the immunostimulating additives, e.g. chemical adjuvants
• A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• A61P 35/00 - Antineoplastic agents
• A61P 37/04 - Immunostimulants
• C07K 14/54 - Interleukins [IL]
• C07K 14/55 - IL-2
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• C07K 14/71 - ReceptorsCell surface antigensCell surface determinants for growth factorsReceptorsCell surface antigensCell surface determinants for growth regulators
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C12N 7/00 - Viruses, e.g. bacteriophagesCompositions thereofPreparation or purification thereof

Abstract

Disclosed is an approach to large-scale combination drug screens. Example versions employ an active learning platform as part of an orthogonal approach that includes conducting experiments dynamically in batches. Each batch may be designed to be maximally informative based on the results of previous experiments. Example designs have been demonstrated to discover highly effective and synergistic combinations of therapeutics. The disclosed approach provides for models that can identify a panel of top therapeutics ("drug") combinations for various diseases. Results demonstrate that adaptive experiments enable large-scale unbiased combination drug screens with a relatively small number of experiments, thereby powering a new wave of combination drug discoveries.

IPC Classes  ?

• C12Q 1/68 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving nucleic acids
• A61K 31/33 - Heterocyclic compounds
• G06F 18/22 - Matching criteria, e.g. proximity measures
• G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
• G16H 70/40 - ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
• A61P 35/02 - Antineoplastic agents specific for leukemia
• A61P 43/00 - Drugs for specific purposes, not provided for in groups

Abstract

Presented herein are systems and methods of detecting retinoblastoma using images of eyes. The systems and methods include identifying a first image of at least one first eye of a first subject prior to administration of treatment for retinoblastoma, the at least one eye having a structure of interest corresponding to an abnormality, applying the first image to a machine learning (ML) model established using a training dataset including examples, each of the examples identifying a respective second image of at least one second eye of a second subject and a respective second classification identifying one of presence or absence of retinoblastoma in the at least second eye, determining from applying the ML model, a first classification identifying one of presence or absence of retinoblastoma in the at least one first eye, and storing using one or more data structures, an association between the first subject and the classification.

IPC Classes  ?

• A61B 3/14 - Arrangements specially adapted for eye photography
• A61B 3/12 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
• G06N 3/08 - Learning methods
• G06V 40/19 - Sensors therefor
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks

Abstract

Provided herein are compositions, kits, and methods for manufacturing cells for adoptive cell therapy comprising engineered monocytes or engineered monocyte-derived macrophages that overexpress ID3 for the treatment of cancer.

IPC Classes  ?

• C07K 14/47 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from animalsPeptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from humans from vertebrates from mammals
• C12N 15/85 - Vectors or expression systems specially adapted for eukaryotic hosts for animal cells
• C12N 5/00 - Undifferentiated human, animal or plant cells, e.g. cell linesTissuesCultivation or maintenance thereofCulture media therefor
• A61K 38/17 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from animalsPeptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from humans
• A61K 40/17 - MonocytesMacrophages
• A61P 35/00 - Antineoplastic agents

Abstract

The present technology generally relates to improved processes for the preparation of (2-(4-(4-(4-(4-aminostyryl)-3-methoxystyryl)phenyIsulfonyl)piperazin- 1 -yljethanol (11), as well as novel intermediates employed in the process, which may be useful as a nen e visualization agent for fluorescence image-guided surgery. The present technology also relates to novel radiolabeled analogs of 11 and compositions, which may have a number of uses, including use as a nerve visualization agent. Also, methods for their synthesis are also included. The present technology further relates to methods of imaging nerves in a surgical field by contacting a surgical site of a subject with a pharmaceutical composition comprising novel radiolabeled analogs of 11 and compositions.

IPC Classes  ?

• A61K 49/06 - Nuclear magnetic resonance [NMR] contrast preparationsMagnetic resonance imaging [MRI] contrast preparations
• A61K 49/08 - Nuclear magnetic resonance [NMR] contrast preparationsMagnetic resonance imaging [MRI] contrast preparations characterised by the carrier
• A61K 51/02 - Preparations containing radioactive substances for use in therapy or testing in vivo characterised by the carrier
• A61K 51/04 - Organic compounds

Abstract

The present disclosure provides methods for predicting responsiveness to IL-2 therapy in patients diagnosed with or suffering from melanoma. These methods are based on screening for expression of membranous MHC class I in tumor cells of untreated lesions.

IPC Classes  ?

• A61K 38/20 - Interleukins
• A61P 35/04 - Antineoplastic agents specific for metastasis
• C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids

Abstract

Disclosed herein are compositions including cytotoxic innate lymphoid cells (ILCs), methods for preparing ILCs for adoptive cell therapy, and methods of using ILCs to treat cancer.

IPC Classes  ?

• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• A61P 35/00 - Antineoplastic agents
• C12N 15/85 - Vectors or expression systems specially adapted for eukaryotic hosts for animal cells

Abstract

Described herein are compositions including killer innate-like T cells (ILTCks), methods for preparing IL TCks for adoptive cell therapy, an methods of using IL TCks to treat cancer. Further, wherein an engineered killer innate-like T cell (ILTCk) comprises a non-endogenous expression vector including a mammalian IL-15 nucleic acid sequence or a mammalian STAT5B nucleic acid sequence is disclosed.

IPC Classes  ?

• A61K 35/17 - LymphocytesB-cellsT-cellsNatural killer cellsInterferon-activated or cytokine-activated lymphocytes
• A61K 38/20 - Interleukins
• A61K 40/15 - Natural-killer [NK] cellsNatural-killer T [NKT] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 40/42 - Cancer antigens
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• A61P 35/00 - Antineoplastic agents
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells
• C12N 15/86 - Viral vectors
• C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer

Abstract

The present disclosure relates to compounds according to Formula (I), Formula (I) or a pharmaceutically acceptable salt and/or solvate thereof, compositions including such compounds, and methods useful for treating, preventing, and/or ameliorating a CDK4 and/or CDK6-mediated disorder, disease, or condition (e.g., cancer such as breast cancer) in a subject. The present disclosure relates to compounds according to Formula (I), Formula (I) or a pharmaceutically acceptable salt and/or solvate thereof, compositions including such compounds, and methods useful for treating, preventing, and/or ameliorating a CDK4 and/or CDK6-mediated disorder, disease, or condition (e.g., cancer such as breast cancer) in a subject.

IPC Classes  ?

• A61K 31/519 - PyrimidinesHydrogenated pyrimidines, e.g. trimethoprim ortho- or peri-condensed with heterocyclic rings
• A61P 35/00 - Antineoplastic agents
• C07D 471/04 - Ortho-condensed systems

Abstract

Disclosed is a method for enhancing tumor response to chemotherapy, the method comprising administering a short-acting anti-angiogenic agent (AAA) capable of activating ASMase to a subject afflicted with a solid tumor, and thereby creating a time interval of increased susceptibility of said tumor to one or more chemotherapeutic agents, followed by administration of at least one chemotherapeutic agent within the interval. The interval can be defined in terms of a short-duration activation of ASMase signaling by the AAA. Disclosed are also methods for predicting the tumor response in a patient afflicted with a solid tumor to a chemotherapeutic agent, using as an indicator of the response ASMase level or activity (or ceramide level) in the patient following the administration of the chemotherapeutic agent to the patient, or dynamic IVIM based DW-MRI to measure perfusion alterations following administration of the chemotherapeutic agent.

IPC Classes  ?

• A61K 31/4439 - Non-condensed pyridinesHydrogenated derivatives thereof containing further heterocyclic ring systems containing a five-membered ring with nitrogen as a ring hetero atom, e.g. omeprazole
• A61K 31/337 - Heterocyclic compounds having oxygen as the only ring hetero atom, e.g. fungichromin having four-membered rings, e.g. taxol
• A61K 31/404 - Indoles, e.g. pindolol
• A61K 31/7048 - Compounds having saccharide radicals and heterocyclic rings having oxygen as a ring hetero atom, e.g. leucoglucosan, hesperidin, erythromycin, nystatin
• A61K 31/7068 - Compounds having saccharide radicals and heterocyclic rings having nitrogen as a ring hetero atom, e.g. nucleosides, nucleotides containing six-membered rings with nitrogen as a ring hetero atom containing condensed or non-condensed pyrimidines having oxo groups directly attached to the pyrimidine ring, e.g. cytidine, cytidylic acid
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• A61P 35/00 - Antineoplastic agents
• C12Q 1/48 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving transferase
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer

Abstract

The presently disclosed subject matter provides methods for identifying subjects that are responsive or non-responsive to certain adoptive cell therapies or compositions comprising the same. In addition, methods for identifying subjects that have an increased likelihood or a reduced likelihood to response to certain adoptive cell therapies or compositions comprising the same are also provided. Moreover, the presently disclosed subject matter provides cells and compositions comprising a CD19-targeted chimeric receptor.

IPC Classes  ?

• A61P 35/02 - Antineoplastic agents specific for leukemia
• C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer

Abstract

The present disclosure provides methods for overcoming tazemetostat resistance in patients diagnosed with or suffering from sarcomas. In some embodiments, the methods of the present technology include administering tazemetostat in combination with an ATR inhibitor, a CDK4/6 inhibitor or an AURK inhibitor.

IPC Classes  ?

• C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
• A61K 31/5377 - 1,4-Oxazines, e.g. morpholine not condensed and containing further heterocyclic rings, e.g. timolol
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer

Abstract

The disclosure provides methods of preventing or treating Type 2 diabetes in a mammalian subject, reducing risk factors associated with Type 2 diabetes, and/or reducing the likelihood or severity of Type 2 diabetes. The methods comprise administering to the subject an effective amount of an inhibitor of PTEN expression and/or activity.

IPC Classes  ?

• C12N 15/113 - Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides
• A61P 3/10 - Drugs for disorders of the metabolism for glucose homeostasis for hyperglycaemia, e.g. antidiabetics
• G01N 33/573 - ImmunoassayBiospecific binding assayMaterials therefor for enzymes or isoenzymes

Abstract

The present disclosure provides compositions for inducing intratumoral CD4 T cell::CD8 T cell::APC triads and methods for using the same to enhance the efficacy of immune checkpoint blockade (ICB) therapy, vaccines, and/or adoptive T cell transfer (ACT) to treat cancer in a subject in need thereof.

IPC Classes  ?

• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61K 39/12 - Viral antigens
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• A61K 47/64 - Drug-peptide, drug-protein or drug-polyamino acid conjugates, i.e. the modifying agent being a peptide, protein or polyamino acid which is covalently bonded or complexed to a therapeutically active agent
• A61P 35/00 - Antineoplastic agents
• C12N 7/00 - Viruses, e.g. bacteriophagesCompositions thereofPreparation or purification thereof

Abstract

Disclosed herein are systems and methods based on tumor infiltrating leukocyte (TIL) fractal geometry to predict clinical implications in breast cancer samples. Using machine learning techniques, an algorithmic classifier is constructed and trained on a cohort of tumor images (e.g. radiographic, immunohistochemical (IHC), and H&E images) to discriminate between TILs and cancer cells, and breast cancer samples are classified based on TIL fractal geometry/anatomic distribution in the tumor stroma. Fractal geometry-directed reassessment of classifications may prompt tumor type reclassification resulting in altered cancer therapy.

IPC Classes  ?

• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G06T 7/00 - Image analysis
• G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
• G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture

Abstract

The presently disclosed subject matter provides methods and compositions for enhancing immune responses toward tumor and pathogen antigens. It relates to fusion polypeptide that can be expressed in cells (e.g., immunoresponsive cells comprising an antigen-recognizing receptor) to improve the activity and/or efficiency of the cells.

IPC Classes  ?

• C07K 14/54 - Interleukins [IL]
• A61K 35/17 - LymphocytesB-cellsT-cellsNatural killer cellsInterferon-activated or cytokine-activated lymphocytes
• A61K 38/20 - Interleukins
• A61P 35/00 - Antineoplastic agents
• A61P 35/02 - Antineoplastic agents specific for leukemia

Abstract

The presently disclosed subject matter provides for antigen-recognizing receptors that specifically target DLL3 and cells comprising such DLL3-targeted antigen-recognizing receptors. The presently disclosed subject matter further provides uses of the DLL3-targeted antigen-recognizing receptors for treatment.

IPC Classes  ?

• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61K 9/51 - Nanocapsules
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• C07K 16/30 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants from tumour cells
• C12N 15/86 - Viral vectors
• A61K 38/20 - Interleukins
• A61P 35/00 - Antineoplastic agents
• C07K 14/54 - Interleukins [IL]

Abstract

Presented herein are systems and methods for determining a likelihood of responsiveness of a subject suffering from cancer to an anti-cancer therapy. A computing system may construct, based on ribonucleic acid (RNA) sequence data, a network. In the network, each node may be associated with a RNA expression and each edge may identify a weight metric between RNA expression levels for a pair of nodes. The computing system may perform, for a plurality of iterations, a diffusion operation throughout the network. The computing system may determine a curvature metric for each iteration using the weight metric defined by each edge or node of the network. The computing system may select a critical iteration based on a difference of curvature metrics. The computing system may apply a plurality of features of the network to a model to generate a score indicating the likelihood of responsiveness of the subject to the anti-cancer therapy.

IPC Classes  ?

• C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
• A61P 35/00 - Antineoplastic agents
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• G06N 3/08 - Learning methods
• G06N 20/00 - Machine learning
• G16C 20/70 - Machine learning, data mining or chemometrics
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders

Abstract

Described herein are chimeric Newcastle disease viruses comprising a packaged genome comprising a transgene encoding interleukin-12. The chimeric Newcastle disease viruses and compositions thereof are useful in combination with an antagonist of programmed cell death protein 1 (“PD-1”) or a ligand thereof in the treatment of cancer. In particular, described herein are methods for treating cancer comprising administering the chimeric Newcastle disease viruses in combination with an antagonist of PD-1 or a ligand thereof, wherein the chimeric Newcastle disease virus comprises a packaged genome comprising a transgene encoding interleukin-12.

IPC Classes  ?

• A61K 39/17 - Newcastle disease virus
• A61K 35/768 - Oncolytic viruses not provided for in groups
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
• A61P 35/00 - Antineoplastic agents
• C07K 14/54 - Interleukins [IL]
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C12N 15/86 - Viral vectors

Abstract

The present disclosure provides methods for treating acute myelogenous leukemia (AML) in a subject in need thereof comprising administering to the subject an effective amount of an anti-PD-L1 antibody or antigen binding fragment thereof and cytokine induced memory-like natural killer (CIMN) cells.

IPC Classes  ?

• A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
• A61K 40/15 - Natural-killer [NK] cellsNatural-killer T [NKT] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61P 35/02 - Antineoplastic agents specific for leukemia
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• A61K 35/17 - LymphocytesB-cellsT-cellsNatural killer cellsInterferon-activated or cytokine-activated lymphocytes

Abstract

The presently disclosed subject matter provides methods and compositions for enhancing immune responses toward tumor and pathogen antigens. It relates to chimeric receptors that can be expressed in cells (e.g., NK cells) to improve the activity of the cells.

IPC Classes  ?

• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells
• C12N 5/10 - Cells modified by introduction of foreign genetic material, e.g. virus-transformed cells
• A61K 35/17 - LymphocytesB-cellsT-cellsNatural killer cellsInterferon-activated or cytokine-activated lymphocytes
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
• C07K 19/00 - Hybrid peptides

Abstract

In one aspect, the invention relates to substituted 1-phenyl-3-(piperidin-4-yl)urea analogs, derivatives thereof, and related compounds, which are useful as inhibitors of the DCN1-UBC12 interaction inhibitors of DCN1-mediated cullin-RING ligase activity, methods of making same, pharmaceutical compositions comprising same, methods of treating disorders using the disclosed compounds and compositions, methods of treating disorders associated with a DCN1-UBC12 interaction dysfunction, methods of treating disorders associated with a DCN1-mediated cullin-RING ligase activity dysfunction, methods of male contraception comprising the disclosed compounds and compositions, and kits comprising the disclosed compounds and compositions. This abstract is intended as a scanning tool for purposes of searching in the particular art and is not intended to be limiting of the present invention.

IPC Classes  ?

• A61K 31/4545 - Non-condensed piperidines, e.g. piperocaine containing further heterocyclic ring systems containing a six-membered ring with nitrogen as a ring hetero atom, e.g. pipamperone, anabasine
• A61K 31/17 - Amides, e.g. hydroxamic acids having the group N—C(O)—N or N—C(S)—N, e.g. urea, thiourea, carmustine
• A61K 31/437 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with one nitrogen as the only ring hetero atom ortho- or peri-condensed with heterocyclic ring systems the heterocyclic ring system containing a five-membered ring having nitrogen as a ring hetero atom, e.g. indolizine, beta-carboline
• A61K 31/451 - Non-condensed piperidines, e.g. piperocaine having a carbocyclic ring directly attached to the heterocyclic ring, e.g. glutethimide, meperidine, loperamide, phencyclidine, piminodine
• A61K 31/4525 - Non-condensed piperidines, e.g. piperocaine containing further heterocyclic ring systems containing a five-membered ring with oxygen as a ring hetero atom
• A61K 31/454 - Non-condensed piperidines, e.g. piperocaine containing further heterocyclic ring systems containing a five-membered ring with nitrogen as a ring hetero atom, e.g. pimozide, domperidone
• A61K 31/506 - PyrimidinesHydrogenated pyrimidines, e.g. trimethoprim not condensed and containing further heterocyclic rings
• A61P 15/16 - Masculine contraceptives
• A61P 31/04 - Antibacterial agents
• A61P 31/12 - Antivirals
• C07D 211/58 - Nitrogen atoms attached in position 4
• C07D 401/04 - Heterocyclic compounds containing two or more hetero rings, having nitrogen atoms as the only ring hetero atoms, at least one ring being a six-membered ring with only one nitrogen atom containing two hetero rings directly linked by a ring-member-to-ring- member bond
• C07D 401/06 - Heterocyclic compounds containing two or more hetero rings, having nitrogen atoms as the only ring hetero atoms, at least one ring being a six-membered ring with only one nitrogen atom containing two hetero rings linked by a carbon chain containing only aliphatic carbon atoms
• C07D 401/12 - Heterocyclic compounds containing two or more hetero rings, having nitrogen atoms as the only ring hetero atoms, at least one ring being a six-membered ring with only one nitrogen atom containing two hetero rings linked by a chain containing hetero atoms as chain links
• C07D 405/06 - Heterocyclic compounds containing both one or more hetero rings having oxygen atoms as the only ring hetero atoms, and one or more rings having nitrogen as the only ring hetero atom containing two hetero rings linked by a carbon chain containing only aliphatic carbon atoms
• C07D 405/12 - Heterocyclic compounds containing both one or more hetero rings having oxygen atoms as the only ring hetero atoms, and one or more rings having nitrogen as the only ring hetero atom containing two hetero rings linked by a chain containing hetero atoms as chain links
• C07D 409/14 - Heterocyclic compounds containing two or more hetero rings, at least one ring having sulfur atoms as the only ring hetero atoms containing three or more hetero rings
• C07D 417/04 - Heterocyclic compounds containing two or more hetero rings, at least one ring having nitrogen and sulfur atoms as the only ring hetero atoms, not provided for by group containing two hetero rings directly linked by a ring-member-to-ring- member bond
• C07D 417/06 - Heterocyclic compounds containing two or more hetero rings, at least one ring having nitrogen and sulfur atoms as the only ring hetero atoms, not provided for by group containing two hetero rings linked by a carbon chain containing only aliphatic carbon atoms
• C07D 471/04 - Ortho-condensed systems

Abstract

e.g.e.g.e.g., venetoclax). Kits for use in practicing the methods are also provided.

IPC Classes  ?

• A61K 31/4192 - 1,2,3-Triazoles
• A61K 31/437 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with one nitrogen as the only ring hetero atom ortho- or peri-condensed with heterocyclic ring systems the heterocyclic ring system containing a five-membered ring having nitrogen as a ring hetero atom, e.g. indolizine, beta-carboline
• A61P 35/02 - Antineoplastic agents specific for leukemia
• C12N 15/113 - Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides
• C12Q 1/6827 - Hybridisation assays for detection of mutation or polymorphism
• C12Q 1/6841 - In situ hybridisation
• C12Q 1/686 - Polymerase chain reaction [PCR]
• C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer

Abstract

The present invention, in one form, is a method for deriving respiratory gated PET image reconstruction from raw PET data. In reconstructing the respiratory gated images in accordance with the present invention, respiratory motion information derived from individual voxel signal fluctuations, is used in combination to create usable respiratory phase information. Employing this method allows the respiratory gated PET images to be reconstructed from PET data without the use of external hardware, and in a fully automated manner.

IPC Classes  ?

• A61B 6/03 - Computed tomography [CT]
• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• A61B 6/00 - Apparatus or devices for radiation diagnosisApparatus or devices for radiation diagnosis combined with radiation therapy equipment

Abstract

e.g.e.g., cancer).

IPC Classes  ?

• C07K 14/715 - ReceptorsCell surface antigensCell surface determinants for cytokinesReceptorsCell surface antigensCell surface determinants for lymphokinesReceptorsCell surface antigensCell surface determinants for interferons
• A61K 38/20 - Interleukins
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61P 35/00 - Antineoplastic agents

Abstract

The present disclosure provides (i) methods of improving hematopoietic stem cell transplantation using anti-ceramide antibodies or antigen-binding fragments thereof, for treating various non-malignant and malignant diseases; and (ii) compositions for such treatments.

IPC Classes  ?

• C07K 16/44 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material not provided for elsewhere
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• C12N 5/077 - Mesenchymal cells, e.g. bone cells, cartilage cells, marrow stromal cells, fat cells or muscle cells
• A61K 35/28 - Bone marrowHaematopoietic stem cellsMesenchymal stem cells of any origin, e.g. adipose-derived stem cells

Abstract

Systems, methods, and computer program products are provided for diagnosing, prognosing, or monitoring cancer in a subject, particularly the assessment of minimal residual disease (MRD).

IPC Classes  ?

• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• G16B 25/10 - Gene or protein expression profilingExpression-ratio estimation or normalisation
• G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance

Abstract

The present application relates generally to image tiling, including but not limited to systems and methods of fast whole slide tissue tiling. A computing system may identify a first dimension of a first image from which to select one or more tiles. The computing system may perform a reduction operation on the first image to generate a second dimension of a second image. The computing system may perform a smoothening operation on the second image. The computer system may identify a first set of pixels in the second image corresponding to a presence of a feature and a second set of pixels corresponding to an absence of the feature. The computing system may select, from a plurality of tiles corresponding to the first image, a subset of tiles corresponding to the first set of pixels identified from the second image.

IPC Classes  ?

• G06T 7/00 - Image analysis
• G01N 1/28 - Preparing specimens for investigation
• G06T 3/40 - Scaling of whole images or parts thereof, e.g. expanding or contracting
• G06T 5/50 - Image enhancement or restoration using two or more images, e.g. averaging or subtraction
• G06T 5/70 - DenoisingSmoothing
• G06T 7/11 - Region-based segmentation
• G06T 7/174 - SegmentationEdge detection involving the use of two or more images
• G06V 10/20 - Image preprocessing
• G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts

Abstract

Disclosed herein are methods and compositions related to the treatment, prevention, and/or amelioration of cancer in a subject in need thereof. In particular, the present technology relates to the use of a MVAΔE3LΔE5R-hFlt3L-OX40LΔWR199-IL-12 virus, a MVAΔE5R-hFlt3L-OX40L virus (MQ710), a MVAΔE3LΔE5R-hFlt3L-OX40LΔWR199 virus (MQ832), a MVAΔE3LΔE5R-hFlt3L-OX40LΔWR199-IL-12ΔC12L virus (MQ953), a MVAΔE3LΔE5R-hFlt3L-OX40LΔWR199-IL-12ΔC12L-hG-CSF virus (MQ954), a MVAΔE3LΔE5R-hFlt3L-OX40LΔWR199-IL-12ΔC12L-hG-CSF-ΔNlL-hIL-7 virus (MQ955), or combinations thereof, and optionally G-CSF, IL-7, and/or one or more immune checkpoint blockade inhibitors as an immunotherapeutic composition, in methods for treating a solid tumor wherein the solid tumor is resistant to immune checkpoint blockade inhibitor treatment, methods for treating a tumor in a subject in need thereof wherein the subject has a deficient adaptive immune system response, and methods for altering the tumor immune microenvironment (TIME) in a tumor in a subject in need thereof.

IPC Classes  ?

• A61K 35/768 - Oncolytic viruses not provided for in groups
• A61K 38/18 - Growth factorsGrowth regulators
• A61P 35/00 - Antineoplastic agents
• A61P 37/02 - Immunomodulators
• C07K 14/52 - CytokinesLymphokinesInterferons
• C12N 15/09 - Recombinant DNA-technology

Abstract

The present technology relates generally to compositions that specifically recognize and bind to SIRPα polypeptides. The compositions of the present technology are useful in methods for enhancing the anti -turn or activity of monoclonal antibody therapy in a subject in need thereof.

IPC Classes  ?

• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C07K 16/32 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against translation products from oncogenes
• C12N 15/85 - Vectors or expression systems specially adapted for eukaryotic hosts for animal cells
• A61P 35/00 - Antineoplastic agents

Abstract

Disclosed are methods for determining the immunological status of the adaptive immune system of a subject by identifying and quantifying rearranged DNA (and/or subsequently transcribed RNA) sequences encoding T cell receptor (TCR) and/or immunoglobulin (IG) polypeptides, in a lymphoid DNA-containing sample from the subject. TCR and/or IG sequence diversity and sequence distribution permit immunocompetence and immune repertoire assessment and reflect the degree of T cell or B cell clonality and clonal expansion in the sample. Methods for stratifying patient populations on the basis of immunocompetence including likelihood of responding to immunotherapy are also described.

IPC Classes  ?

• C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
• C12Q 1/6851 - Quantitative amplification
• C12Q 1/6883 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
• G16B 5/00 - ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
• G16B 30/10 - Sequence alignmentHomology search

Abstract

The present disclosure relates generally to immunoglobulin-related compositions (e.g., antibodies or antigen binding fragments thereof) that can bind to and neutralize the activity of GPA33 protein. The antibodies of the present technology are useful in methods for detecting and treating an GPA33 -positive cancer in a subject in need thereof.

IPC Classes  ?

• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C07K 16/32 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against translation products from oncogenes
• C07K 16/46 - Hybrid immunoglobulins
• A61P 35/00 - Antineoplastic agents