Abstract: System (100) and method (200) for aiding drug development by determining a curable action value of a target protein are disclosed. The system (100) comprises a protein data extraction module (110), a protein data filtration module (120), and a final expression level calculator (150). The protein data extraction module (110) is configured for parsing and identifying information related to target proteins from a public database. The public database comprises data related to proteins. The protein data filtration module (120) is configured for filtering out irrelevant information from the identified information related to the target proteins. The final expression level calculator (150) is configured for calculating the final expression level of the identified relevant information. The curable action of the target protein is based upon the final expression level. The invention aids in drug development by determining the curable action of the protein in the disease based on biomedical data corpus.

Abstract: A method for identifying molecular pathways perturbed under influence of a drug or a disease includes extracting a relationship dataset related to genes and molecular pathways associated with the genes, from a pre-curated database. The method further includes mapping the relationship dataset onto a research graph. The method further includes identifying, sub-networks within the research graph, and assigning a gene score to each gene in the identified sub-networks, based on whether a gene is neutral, dysregulated, or associated with a disease-specific organ. The method further includes determining a perturbed molecular pathway for genes within the identified sub-networks based on the gene score and a molecular pathway interconnectivity within the research graph. The perturbed molecular pathway for genes has a highest association with a pathophysiology of the disease or the drug response as compared to other molecular pathways associated with the genes in the research graph.

Abstract: A system for identifying one or more changes in a Biological Network, comprising a processor configured to construct a Heterogeneous Biological Network that comprises a plurality of nodes and a plurality of edges. The processor is configured to derive one or more sub-networks from the constructed HBN, and determine an embedding vector for each node of each sub-network. The processor is configured to identify one or more changes in each sub-network by comparing the embedding vector of each node of in a respective sub-network before and after an input action associated with a change in at least one sub-network and determine a plurality of scores for each node of each sub-network based on a pre-defined set of parameters. The processor is configured to identify the one or more changes in the BN based on the determined plurality of scores for each node in each sub-network.

Abstract: A system for electronic identification of one or more biomarkers associated with a pathology. The system includes a processor configured to receive an expression data and utilize a predefined degenerative model to generate a Transcription Factor (TF)-target interaction information based on the received expression data. The processor is configured to execute the predefined degenerative model to generate a Gene Regulatory Network (GRN) based on the TF-target interaction information and construct a hierarchical network defining hierarchical relationships among two or more nodes and one or more edges of the GRN. The processor is configured to prioritize a set of TFs and a set of targets using a Protein-Protein Interaction (PPI) network and construct a modified GRN. The system efficiently and reliably identifies the one or more biomarkers associated with the pathology by use of a combination of the GRN, the hierarchical network and the PPI network.

Abstract: A system for an electronic identification of responders and non-responders for one or more drugs, includes a processor to obtain a pre-labeled training dataset from a first database, which includes a first type of input of labeled pretreatment data and a second type of input of labeled post-treatment response data of the plurality of subjects. The processor is configured to pre-process the pre-labeled training data to generate a modified labeled training dataset and train an ensemble machine learning (ML) model from the modified labeled training dataset. The processor is further configured to train a prediction model based on set of features extracted via the ensemble ML model to obtain a trained prediction model to detect whether an unknown subject is a responder or a non-responder for a given drug from the one or more drugs, such as only responders can be given a drug to spare non-responders from unwarranted effects.

Abstract: A system for predicting biological activities or properties of one or more chemical compounds in a new drug. The system includes a processor configured to receive a plurality of input datasets including a plurality of simplified molecular-input line-entry system (SMILES) notations and execute a pre-trained Natural Language Processing (NLP) model to transform the plurality of SMILES notations into a plurality of non-sparse matrices. The processor is configured to train a deep learning model using the plurality of non-sparse matrices to obtain a trained deep learning model, where the trained deep learning model is used to predict one or more biological activities of an untested chemical compound in the new drug when the chemical compound in the drug is subjected to at least one chemical modification to form the new drug. The system efficiently and reliably predicts the biological activities or properties of the untested chemical compound in the new drug.

Abstract: A method and system for retrieval of contextual information related to unmet medical need of an indication. The identification of unmet medical need of an indication becomes a critical information in the drug discovery process. The system for retrieval of contextual information related to unmet medical need of an indication enables providing assistance to scientists through digital pharma. The method comprises scanning plurality of medical literature documents to extract and tokenize the documents into plurality of sentences. The scanned plurality of sentences is modelled, by one or more processors, to identify contextually labelled one or more sentences comprising indications, one or more unmet medical need categories, one or more unmet medical need attributes. The plurality of sentences is modelled using one or more of natural language processing techniques and supervised ML classifier.

Abstract: A method and system for generating a graph neural network comprising association of one or more organs with a disease. The method enables target identification of organs to expediate the drug purposing or discovery process. The method and system is an implementation of digital phama to accelerate target identification for drug design. The method comprises identifying one or more organs associated with a disease from one or more categories, wherein the one or more categories comprises of an organ ontology, one or more information indicative of the disease, plurality of abstract of publications. Weights are assigned to each of the one or more categories and the identified one or more organs, wherein the weights are assigned to the identified one or more organs based on one or more of—frequency of the keywords appearing the one or more categories, and one or more location of the keywords appearing the plurality of abstracts of publications.

Abstract: A system and method for identifying potential targets for a pharmaceutical compound. The system includes a database arrangement operable to store existing data sources and information related to the pharmaceutical compound; and a processing module communicably coupled to database arrangement. The processing module is operable to receive information related to the pharmaceutical compound, wherein the information includes at least one known target associated with the pharmaceutical compound; extract plurality of targets from existing data sources, wherein the existing data sources comprise plurality of data records and wherein each of the plurality of targets is associated with the pharmaceutical compound in at least one data record; analyse druggability of each of plurality of targets to determine a preliminary set of targets; determine net score for each of the targets in preliminary set of targets; and designate targets with a net score above a predefined threshold, as potential targets.

Abstract: An approach is disclosed for mapping biomedical entities, wherein each of the biomedical entities belong to one of a predefined class: target, disease, pathway, and drug. The system comprises a database arrangement operable to store existing data sources; a processing module communicably coupled to the database arrangement. The processing module operable to receive a user-input of a biomedical entity belonging to one of the predefined classes, wherein the predefined class of the biomedical entity defines an input class. Extract a plurality of biomedical entities related to the biomedical entity of the user-input from existing data sources, wherein the plurality of biomedical entities belong to predefined classes except the input class. Identify at least one pair of biomedical entities, from the plurality of extracted biomedical entities, having an association there between and map the plurality of biomedical entities to the biomedical entity of the user-input.

Abstract: Disclosed is a system for gaining mechanistic insights into action of a drug using in-silico techniques. The system is communicably coupled to a phenotype ontological databank; wherein the system comprises a processor communicably coupled to a memory. The processor is configured to receive a first input of the drug, receive a second input relating to at least one phenotype associated with the drug, identify targets of the drug to obtain a drug target list, determine phenotypic targets of the drug, compare the drug target list with the phenotypic targets of the drug to identify a plurality of overlapping targets therebetween, generate a Drug-Target-Phenotype (DTP) network using the plurality of overlapping targets, compute relevant pathways by performing Signaling Pathway Impact Analysis (SPIA) for the plurality of overlapping targets, generate a Pathway-Target-Phenotype (PTP) network, and compute mechanistic insights into the action of the drug from the analysis of PTP network.

Abstract: A computer program product and corresponding computer-implemented method cause the performance of various operations to upgrade a network storage device having first and second storage controllers operating in an active-passive mode and disk media shared by the storage controllers. The first storage controller operating as a passive storage controller is caused to enter a new IQN for each virtual disk into a first iSCSI target configuration file and maintain a corresponding old IQN. The first storage controller is then caused to begin operating as the active storage controller so that the second storage controller, while operating as the passive storage controller, is caused to enter the new IQN for each virtual disk into a second iSCSI target configuration file and maintain the corresponding old IQN. Accordingly, the first and second iSCSI target configuration files map both the old and new IQNs to the virtual disks.

Abstract: A computer program product and a data storage device including first and second storage controllers operating in active-passive mode with a shared disk. Each storage controller includes a storage device storing program instructions and a processor to process the program instructions and perform various operations. The operations include receiving a task to be performed by the storage device containing the first and second storage controllers, wherein the first storage controller is currently operating as an active storage controller and the second storage controller is currently operating as a passive storage controller. The operations further include determining whether the received task has a high priority or a low priority, performing the received task in response to determining that the received task has a high priority, and delegating the received task to the second storage controller for performance in response to determining that the received task has a low priority.

Abstract: A system for enabling in-silico phenotypic screening of drugs, the system is communicably coupled to a phenotype ontological databank. The system include a processor communicably coupled to a memory.

Abstract: A system for screening phenotypic targets associated with a disease using in-silico techniques. The system communicably coupled to a phenotype ontological databank including a plurality of phenotypes and phenotypic targets associated with each of the plurality of phenotypes; wherein the system includes a processor communicably coupled to a memory.

Abstract: A pharmaceutical composition comprising an effective amount of Dacomitinib as epidermal growth factor receptor (EGFR) inhibitor and a prostaglandin-Endoperoxide Synthase 2 (PTGS2) inhibitor, and one or more pharmaceutically acceptable excipients. A pharmaceutical composition comprising an effective amount of Dacomitinib as epidermal growth factor receptor (EGFR) inhibitor and an Adrenoceptor Beta 2 (ADRB2) inhibitor, and one or more pharmaceutically acceptable excipients. A method of treating pancreatic cancer, the method comprising the step of administering a therapeutically effective amount of the pharmaceutical composition to an individual in need thereof, the pharmaceutical composition comprising an effective amount of Dacomitinib as epidermal growth factor receptor (EGFR) inhibitor with a prostaglandin-Endoperoxide Synthase 2 (PTGS2) inhibitor or an Adrenoceptor Beta 2 (ADRB2) inhibitor, and one or more pharmaceutically acceptable excipients.

Abstract: A system for generating drug compositions for a disease target, the system comprises a database arrangement and a processor, wherein the processor is configured to receive information comprising one or more drugs associated with the disease target, identify a plurality of parameters associated with the disease target, using the database arrangement, construct a matrix to identify at least one of direct and indirect synergies of each of the drug with the plurality of parameters and assign weights thereby to each of the parameters with respect to each of the drug, based on the identified at least one of direct and indirect synergies, calculate a total score of each of the drug and rank the plurality of drugs based on the calculated total score and sort thereby the plurality of drugs. The processor then determines the one or more potential drug compositions on the basis of the sorted plurality of drugs.

Abstract: A phosphoinositide 3-kinase inhibitor for use in the treatment of pancreatic cancer and a method for treatment of pancreatic cancer including administration of an phosphoinositide 3-kinase inhibitor an Src inhibitor to a human subject in need thereof. There is also provided a pharmaceutical composition comprising a phosphoinositide 3-kinase inhibitor and an Src inhibitor.

Abstract: A method for evaluating potential drug compositions for a target disease. The method includes providing a data input to a discovery engine, using the discovery engine to identify a first set of potential drug compositions for the target disease. The discovery engine is configured to analyze failed clinical assets of drugs for the target disease, wherein the discovery engine filters clinical trials which have failed due to non-drug safety related issues, perform differential gene expression analysis on normalized target-disease-related data, evaluate effect of known drugs used for diseases similar to the target disease, and perform a network-based analysis to identify repurposable drugs. Furthermore, asset prioritization is used to filter the first set of potential drug compositions to determine at least one potential drug composition for the target disease and validating the at least one potential drug composition for the target disease based on biological evidence and differential expression analysis.

Abstract: A method for determining combination drug and use in pancreatic cancer treatment, includes retrieving Pancreatic cancer datasets from a plurality of data sources based on selected types of expression profiling. A set of feature genes is determined based on differential gene expression analysis of disease samples and control samples in normalized pancreatic cancer datasets. Pancreatic cancer targets are selected for combination analysis based on druggability and determined set of feature genes. Based on node embedded clustering of the selected pancreatic cancer targets, synergistic target pairs is determined. Candidate pairs of drug combinations are selected from a plurality of pairs of drug combinations based on cumulative ranking score of each pair of drug combination and the synergistic target pairs.