Accelerated pharmaceutical repurposing by finding anticorrelations and by text mining

Utilizing a computing device to assist in repurposing of a pharmaceutical. An identification of a pharmaceutical for repurposing study is received by a computing device. A pharmaceutical expression signature is retrieved based upon the identification of the pharmaceutical, the pharmaceutical expression signature indicating differential expressions of a plurality of biomolecules regulated by the pharmaceutical. A plurality of disease expression signatures are retrieved from a disease omics database, each disease expression signature indicating differential expressions of a plurality of biomolecules affected by a disease. A pharmaceutical vector is generated based upon the pharmaceutical expression signature for the pharmaceutical. A plurality of disease vectors are generated based upon the plurality of disease expression signatures for each disease. N hypotheses correlating the pharmaceutical vector and one or more of the plurality of disease vectors are generated, each hypothesis indicating a potential repurposing for the pharmaceutical to treat the disease.

BACKGROUND

The present invention relates generally to the field of pharmaceutical repurposing, and more particularly to accelerated pharmaceutical repurposing utilizing omics data and literature text mining.

BRIEF SUMMARY

Embodiments of the present invention disclose a method, system, and computer program product for utilizing a computing device to assist in repurposing of a pharmaceutical. An identification of a pharmaceutical is received by a computing device for repurposing study. The computing device retrieves a pharmaceutical expression signature for the pharmaceutical based upon the identification of the pharmaceutical, the pharmaceutical expression signature indicating differential expressions of a plurality of biomolecules regulated by the pharmaceutical versus a control. The computing device retrieves a plurality of disease expression signatures from a disease omics database, each disease expression signature indicating differential expressions of a plurality of biomolecules affected by a disease versus the control. A pharmaceutical vector is generated by the computing device based upon the pharmaceutical expression signature for the pharmaceutical. A plurality of disease vectors are generated by the computing device based upon the plurality of disease expression signatures for each disease. N hypotheses are generated correlating the pharmaceutical vector and one or more of the plurality of disease vectors, each of the N hypotheses generated when an anticorrelation exists between the pharmaceutical expression signature and one or more disease expression signatures of the plurality of disease expression signatures. Each hypothesis indicates a potential repurposing for the pharmaceutical to tread the disease.

DETAILED DESCRIPTION

Pharmaceutical repurposing (also known as “repositioning,” “re-profiling,” “indication expansion,” or “therapeutic switching”) is the development of existing pharmaceuticals for new therapeutic indications. Repositioning known pharmaceuticals has many advantages over developing new pharmaceuticals, including greatly decreasing cost associated with development, lessened safety concerns since the pharmaceutical has already undergone extensive clinical testing, and therefore significant acceleration in treatment of novel therapeutic areas. Traditional approaches to pharmaceutical repositioning involve use of assays, which are both time-consuming and costly. Computational approaches offer the opportunity to greatly reduce or eliminate the assay process. Presented is a method, system, and computer program product for accelerated pharmaceutical repurposing utilizing omics data and literature text mining.

Computational approaches for pharmaceutical repurposing as described herein involve literature text mining utilizing the considerable body of biomedical literature which has developed over time regarding known pharmaceuticals as well as known diseases or conditions. This body of biomedical literature, including scientific articles, clinical trials, patents, and otherwise is simply more massive than any human being could process in a lifetime, while modern computational approaches using natural language processing combined with machine learning present an opportunity to process this body of literature in a realistic time frame. Text mining of biomedical literature, such as described herein, is specifically applied to pharmaceutical repurposing, but also has applications in drug discovery (using text mining to show support for gene expression data suggesting disease or conditions), determining suggested mechanisms of action (since gene expression data merely gives a list of genes up-regulated and down-regulated, and text mining can show interplay between the genes and suggest a biological pathway or mechanism of action), and precision medicine (using individual patient omics data (such as genomic) combined with literature text mining to propose pharmaceuticals specifically tailored to the patient's profile).

“Omics data,” as used herein, refers to quantification of levels of genes, proteins, and metabolites corresponding to genomics, proteomics or metabolomics (collectively, “biomolecules”). Most commonly, omics data reflects gene expression. In the present invention, by examining biomolecules that have differential expressions from a control, based upon pharmaceuticals or diseases, it may be possible to obtain a pharmaceutical expression signature or a disease expression signature indicating which biomolecules are up-regulated and which are down-regulated by the pharmaceutical or disease. This “omics data” may be utilized in the present invention by seeking an inverse relationship (also known as an “anticorrelation”) between the pharmaceutical expression signature and the disease expression signature. If a strong anticorrelation is found between the pharmaceutical signature and the disease signature, the repurposed pharmaceutical may potentially be a cure for the disease, since the pharmaceutical may return the biomolecules to their normal levels. The repurpose for the pharmaceutical may be further reviewed via text mining of a text database, as further discussed below.

FIG. 1is a functional block diagram illustrating an environment100for accelerated pharmaceutical repurposing utilizing omics data and literature text mining, in accordance with an embodiment of the present invention. In various embodiments, a user associated with user computer110is interested in repurposing a pharmaceutical in the most efficient manner possible or seeking a treatment for a disease or a condition (referred to herein as a “disease” or collectively “diseases”) using a repurposed pharmaceutical in the most efficient manner. The user computer110transmits an identification of the pharmaceutical (such as, for example, a trade name, a generic name, a chemical name, a chemical formula, or any other way of uniquely identifying the pharmaceutical) or an identification of the disease to repurposing module130. The repurposing module130, based upon data obtained from pharmaceutical database150, disease database160, and literature database170, as further described herein, determines likely repurposing for the pharmaceutical or, alternatively, a pharmaceutical that may be repurposed for treatment of the identified disease, and transmits the results to the user computer110. All of user computer110, repurposing module130, pharmaceutical database150, disease database160, and literature database170are connected via network199.

In various embodiments, network199represents, for example, an internet, a local area network (LAN), a wide area network (WAN) such as the Internet, and includes wired, wireless, or fiber optic connections. In general, network199may be any combination of connections and protocols that will support communications between user computer110, repurposing module130, pharmaceutical database150, disease database160, and literature database170, in accordance with an embodiment of the invention.

In various embodiments, user computer110, repurposing module130, pharmaceutical database150, disease database160, and literature database170may be, for example, a mainframe or a mini computer, a terminal, a laptop, a tablet, a netbook personal computer (PC), a mobile device, a desktop computer, or any other sort of computing device, in accordance with embodiments described herein. User computer110, repurposing module130, pharmaceutical database150, disease database160, and literature database170may include internal and external hardware components as depicted and described further in detail with reference toFIG. 4, below. In other embodiments, each of user computer110, repurposing module130, pharmaceutical database150, disease database160, and literature database170may be implemented in a cloud computing environment, as described in relation toFIGS. 5 and 6, below. In a still further embodiment, some or all of user computer110, repurposing module130, pharmaceutical database150, disease database160, and literature database170are embodied in physically the same computing device, with all communications between various components internally.

User computer110, repurposing module130, pharmaceutical database150, disease database160, and literature database170, in effect, represent any sort of computing device possessing sufficient processing power to execute software for accelerated pharmaceutical repurposing utilizing omics data and literature text mining.

Computing devices associated with user computer110, repurposing module130, pharmaceutical database150, disease database160, and literature database170may utilize a hosted workload96as displayed in connection withFIG. 7below, and/or perform other tasks as further described herein.

In the exemplary embodiment, the user computer110includes user interface112and communication module115.

User interface112represents software and/or hardware for user at user computer110to enter (such as via keyboard922) or select such as via a graphic user interface, in the preferred embodiment, a pharmaceutical for repurposing study. The user interface112will also display results of the repurposing study, as performed by the repurposing module130, as further described herein. In the alternative embodiment, the user interface112allows a user to provide an identification of a disease for repurposing study. The user interface112also allows a user to select a number N of hypotheses that are to be generated by the repurposing module130, each hypothesis indicating a potential repurposing of a pharmaceutical to treat a disease.

Communication module115represents software and/or hardware for user computer110to communicate with repurposing module130. After the user at user computer110, in the preferred embodiment, identifies the pharmaceutical for repurposing study or in the alternative embodiment identifies the disease for repurposing study, the communication module115transmits the identification of the pharmaceutical for repurposing study or the identification of the disease for repurposing study to the repurposing module130. Hardware associated with communication module115may include a network adapter or interface916, such as a TCP/IP adapter card or wireless communication adapter (such as a 4G wireless communication adapter using OFDMA technology) such as described below in connection withFIG. 5.

In the exemplary embodiment, repurposing module130includes communication module131, pharmaceutical vector module133, disease vector module135, hypothesis generator136, hypothesis confirmation module137, and testing module138.

Communication module131represents software and/or hardware to receive from the user computer110, in the preferred embodiment, an identification of a pharmaceutical for repurposing study or, in the alternative embodiment, an identification of a disease for repurposing study. The identification of the pharmaceutical or the disease is further utilized as discussed herein. Hardware associated with communication module115may include a network adapter or interface916, such as a TCP/IP adapter card or wireless communication adapter (such as a 4G wireless communication adapter using OFDMA technology) such as described below in connection withFIG. 5.

Pharmaceutical vector module133represents software for retrieving from the pharmaceutical database150pharmaceutical expression signatures for pharmaceuticals, which are used in the generation of pharmaceutical vectors. The pharmaceutical expression signatures indicate differential expressions of a plurality of biomolecules regulated by the pharmaceutical versus a control. The differential expression of the plurality of biomolecules regulated by the pharmaceutical indicate biomolecules up-regulated by the pharmaceutical and biomolecules down-regulated by the pharmaceutical. A sample pharmaceutical may, for example, up-regulate four genes and down-regulate six genes. The pharmaceutical vector module133utilizes the pharmaceutical expression signatures to generate pharmaceutical vectors. Each pharmaceutical vector is a list in computer-available form (such as a linked-list, spreadsheet, object etc.) of biomolecules affected by the pharmaceutical, for further use as discussed. With regard to the sample pharmaceutical, the pharmaceutical vector may include multiple entries indicating which proteins are up-regulated and which are down-regulated.

In the preferred embodiment, the pharmaceutical vector module133may retrieve a single pharmaceutical expression signature from the pharmaceutical database150for a pharmaceutical identified by the user computer110to generate a pharmaceutical vector to be matched against a plurality of disease vectors in determining possible repurposing. In the alternative embodiment, the pharmaceutical vector module133retrieves a plurality of pharmaceutical expression signatures from the pharmaceutical database150for generating a plurality of pharmaceutical vectors, to be matched against a single disease vector representing a disease identified by the user computer110in determining possible repurposing.

The disease vector module135represents software for retrieving from the disease database160disease expression signatures which are used in the generation of disease expression vectors. The disease expression signatures indicate differential expressions of a plurality of biomolecules affected by the disease versus a control (i.e. for a normal healthy human being or tissue sample). The differential expression of the plurality of biomolecules affected by the disease indicate biomolecules up-regulated by the disease and biomolecules down-regulated by the disease. A sample disease may, for example, down-regulate four proteins and up-regulate six proteins. The disease vector module135utilizes the disease expression signatures to generate disease vectors. Each disease vector may be a list in computer-available form (such as a linked-list, spreadsheet, object etc.) of biomolecules affected by the disease, for further use as discussed. With regard to the sample disease, the disease vector may include multiple entries indicating which proteins are up-regulated and which are down-regulated.

In the preferred embodiment, the disease vector module135retrieves a plurality of disease expression signatures from the disease database160for generating a plurality of disease vectors, to be matched against a single pharmaceutical vector representing a pharmaceutical identified by the user computer110in determining possible repurposing. In the alternative embodiment, the disease vector module135may retrieve a single disease expression signature from the disease database160for a disease identified by the user computer110to generate a disease vector to be matched against a plurality of pharmaceutical vectors in determining possible repurposing.

Hypothesis generator136represents software for correlation of pharmaceutical vectors and disease vectors to generate N hypotheses indicating a pharmaceutical may be an indication (i.e. treatment) for a given disease. As further discussed herein, in the presently disclosed invention, these N hypotheses are further confirmed via text mining, and in practice various levels of lab study are still required, followed by controlled study in humans, but the presently disclosed invention offers minimization of the amount of lab study required. N represents any number of hypotheses requested by user computer110, the N hypotheses being the best match between pharmaceutical vectors and disease vectors correlated. A hypothesis is generated by the hypothesis generator136if, after correlating the available pharmaceutical vectors and disease vectors, an anticorrelation is noted between a pharmaceutical vector and a disease vector, i.e. that the pharmaceutical vector displays up-regulation or down-regulation of the same biomolecules the disease vector down-regulates or up-regulates, respectively.

In the preferred embodiment, N hypotheses are generated by the hypothesis generator136correlating the pharmaceutical vector representing the pharmaceutical identified by the user computer110with one or more disease vectors. This indicates the identified pharmaceutical the pharmaceutical vector represents may be repurposed to treat one or more diseases represented by the disease vectors. In the alternative embodiment, N hypotheses are generated by the hypothesis generator136correlating the disease vector representing the disease identified by the user computer110with one or more pharmaceutical vectors, indicating the disease could be potentially be treated by the pharmaceutical represented by the pharmaceutical vectors.

Hypothesis confirmation module137represents software for determining the strongest one or more hypotheses of the N hypotheses, each hypothesis linking a potential repurposing of the pharmaceutical to treat the diseases. After the hypothesis generator136generates the N hypotheses, the hypothesis confirmation module137accesses the literature database170to obtain biomedical literature (including, by means of non-limiting example, scientific articles, clinical trials, textbooks, and patents) to confirm one or more of the hypotheses. The biomedical literature available in the literature database170may be utilized by the repurposing module130in mining the literature database170to confirm one or more of the N hypotheses, when biomedical literature available via the literature database170has previously noticed a connection between the pharmaceutical and the disease. Text mining of the literature database170such as via utilization of natural language processing combined with machine learning (based upon previous mining of the literature database170) is utilized, in an embodiment of the invention, to confirm one or more of the N hypotheses. In an alternative embodiment of the invention, the N hypotheses are ranked by the hypothesis confirmation module137according to their strength, based upon text mining the literature database170and comparing results of text mining with the N hypotheses.

When text mining the literature database170, the hypothesis confirmation module137may utilize various techniques to better interpret the literature database170. The hypothesis confirmation module137may, when text mining the literature database170, extract direct relationships from two or more references within the literature database170. If one reference, for example, indicates a pharmaceutical affects certain biomolecules, and a second reference indicates a disease affects the same certain biomolecules, the literature database170may utilize this information to confirm one or more of the N hypotheses, indicating a pharmaceutical may be an appropriate remedy for the disease, confirming one or more of the N hypotheses is strongest. The hypothesis confirmation module137may extract indirect relationships from two or more references within the literature database170. If, for example, one reference indicates a pharmaceutical affects certain biomolecules, and a second reference indicates a disease affects other biomolecules which, in turn, affect the same certain biomolecules, the literature database170when text mining references within the literature database170may utilize this information to confirm one of the N hypotheses, indicating a pharmaceutical may be an appropriate remedy for the disease, confirming one or more of the N hypotheses is strongest. The hypothesis confirmation module137may infer semantic (or text) similarity from two or more references within the literature database170. If, for example, one reference indicates a certain biomolecule is affected by a pharmaceutical, whereas a second reference indicates a semantically similar biomolecule is affected by a disease, the literature database170when text mining within the literature database170may utilize this information to confirm one of the N hypotheses. Semantic similarity is determined by the hypothesis confirmation module137when words and phrases mentioned around the named biomolecules in biomedical literature available in the literature database170are very similar.

Testing module138represents software and/or hardware for testing the hypotheses confirmed by the hypothesis confirmation module137for real-world efficacy. Hardware associated with testing module138may represent hardware to perform automated chemical assays or fluorescence assays to test the hypotheses on cells or tissue samples to determine an efficacy. Testing preferably occurs in the ranked order determined by the hypothesis confirmation module137, to minimize expenses in repurposing pharmaceuticals, since if a stronger hypothesis is confirmed, it may become unnecessary to test the less highly ranked hypotheses. Testing module138may be absent, in embodiments of the invention, with testing of hypotheses performed in a traditional lab setting, including via testing of plasma, skin, hair, nails, bone marrow, etc., followed by later-stage clinical testing of hypotheses.

In the exemplary embodiment, pharmaceutical database150includes pharmaceutical omics database155.

Pharmaceutical omics database155represents hardware and/or software for storing pharmaceutical expression signatures indicating which biomolecules are up-regulated and which are down-regulated by each pharmaceutical. Multiple pharmaceutical expression signatures regarding multiple pharmaceuticals are stored by the pharmaceutical omics database155, and are transmitted to the pharmaceutical vector module133upon request. Pharmaceutical expression signatures stored in pharmaceutical omics database155are generated from lab-testing of pharmaceuticals, such as via a microarray, and may be made available from a public repository (such as Cmap), proprietary libraries maintained by a pharmaceutical company, internal experiments utilizing various technologies, etc. The pharmaceutical expression signatures stored in pharmaceutical omics database155may be continuously updated as discoveries continue to be made by medical professionals and are uploaded to pharmaceutical omics database155, published in biomedical literature, or made publicly available in another way. Pharmaceutical expression signatures stored within the pharmaceutical omics database155are further utilized as discussed herein.

In the exemplary embodiment, disease database160includes disease omics database165.

Disease omics database165represents hardware and/software for storing disease expression signature indicating which biomolecules are up-regulated and which are down-regulated by a disease. Multiple disease expression signatures regarding multiple diseases are stored by the disease omics database165, and are transmitted to the disease vector module135upon request. Disease expression signatures stored in disease omics database165are generated from lab-testing of diseases, such as via a microarray, and may be made available from a public library (such as Gene Expression Omnibus for Diseases), proprietary library, internal experiments utilizing various technologies, etc. The disease expression signatures stored in the disease omics database165may be continuously updated as discoveries continue to be made by medical professionals and are uploaded to the disease omics database165, published in biomedical literature, or made publicly available in another way. Disease expression signatures stored within the disease omics database165are further utilized as discussed herein.

In the exemplary embodiment, literature database170includes biomedical literature database175.

Biomedical literature database175represents hardware and/or software for storing biomedical literature of various sorts, including journal articles, scientific articles, news articles, magazine articles, textbooks, patents, symposium proceedings, clinical studies, medical manuals, and any other sort of written text discussing pharmaceuticals and diseases which may be utilized within the presently disclosed invention. All of the biomedical literature available in the biomedical literature database175is continuously updated as new articles are published or made publicly available. All of the biomedical literature available in biomedical literature database175is digital in original form, or has been optical-character recognized for text mining and other uses within the presently disclosed invention. As discussed elsewhere herein, biomedical literature may have data regarding a pharmaceutical or diseases which may be directly, indirectly, or semantically inferred to confirm hypotheses, as further discussed herein.

FIG. 2is a directed graph model200illustrating steps in accelerated pharmaceutical repurposing using omics data and literature text mining by the repurposing module130in conjunction with the user computer110, pharmaceutical database150, disease database160, and literature database170, in accordance with an embodiment of the invention. As displayed withinFIG. 2, after the communication module131receives an identification of a pharmaceutical for repurposing study, the identified pharmaceutical becomes source205for the directed graph model200pictured inFIG. 2. The pharmaceutical vector module133retrieves from the pharmaceutical omics database155of pharmaceutical database150a pharmaceutical expression signature for the pharmaceutical. The differential expressions of biomolecules affected by the pharmaceutical are utilized by the pharmaceutical vector module133to generate pharmaceutical vectors, represented inFIG. 2as nodes215. The disease vector module135retrieves from the disease omics database165of disease database160a plurality of disease expression signatures which are utilized by the disease vector module135to generate disease vectors, represented inFIG. 2as225and229. The disease associated with each disease vector225,229is displayed inFIG. 2as223and227. Diseases223and227, are in effect, sinks in the directed graph model200displayed inFIG. 2. Although only two diseases223,227are displayed inFIG. 2for ease of understanding, the presently disclosed invention contemplates the disease vector module135generating dozens or more of disease vectors for dozens or more of diseases, to generate the strongest one or more hypotheses. The hypothesis generator136then generates N hypotheses correlating one of the pharmaceutical vectors215and the disease vectors225,229. The two hypotheses generated by hypothesis generator136are represented inFIG. 2as group of nodes235and node237. Each node243,245,247in the hypotheses235,237represents a biomolecule up-regulated or down-regulated by drug205, and, correspondingly, down-regulated or up-regulated by diseases223,227. Hypothesis confirmation module137determines which of the two hypotheses235,237is strongest (in the example displayed in

FIG. 2), via utilization of text mining, as further discussed herein. In confirming one of the hypotheses235,237, hypothesis confirmation module137may perform a graph traversal of directed graph model200utilizing a graph label propagation algorithm or another graph traversal algorithm, with each node243,245,247in the hypotheses235,237accorded a weight, and all weights in the graph traversal summed or multiplied, with the highest total for hypotheses235,237determined by the hypothesis confirmation module137to be the strongest hypothesis.

FIGS. 3A, 3B, 3C, and 3Dare process flow diagrams illustrating various operational steps in accelerated pharmaceutical repurposing using omics data and literature text mining by the hypothesis generator136and hypothesis confirmation module137, in accordance with an embodiment of the invention. AtFIG. 3A, displayed is a pharmaceutical vector310and a disease vector315, after both vectors are generated as discussed herein or obtained in an alternative manner. The pharmaceutical vector310indicates as displayed that biomolecule G1is differentially expressed by pharmaceutical 0.43 from normal, biomolecule G2is differentially expressed by pharmaceutical 2.56 from normal, and biomolecule G3is differentially expressed by pharmaceutical 3.67 from normal. Disease vector315indicates as displayed that biomolecule G1is differentially expressed by disease 1.76 from normal, biomolecule G2is differentially expressed by disease 0.45 from normal, and biomolecule G3is differentially expressed by disease −3.52 from normal. Hypothesis generator136may correlate pharmaceutical vector310and disease vector315to generate one of the N hypotheses by confirming an anticorrelation between each of biomolecule G1, G2, and G3within vectors310and315, specifically because pharmaceutical vector310down-regulates

G1and up-regulates G2and G3, whereas disease vector315up-regulates G1and down-regulates G2and G3. Thus, an anticorrelation is noted between pharmaceutical vector310and disease vector315, and therefore the associated pharmaceutical and diseases, and the hypothesis generator136generates one of the N hypotheses, for further use as discussed.

FIG. 3Bdisplays results of text mining by the hypothesis confirmation module137a literature database170to extract direct relationships. Two references are mined by the hypothesis confirmation module137, one regarding a pharmaceutical330and one regarding a disease335. After text mining, noted is that biomolecule G4340and biomolecule G1345are referred to directly by the pharmaceutical reference330and the disease reference335. In these circumstances, the hypothesis confirmation module137may confirm one of the N hypotheses which indicated a potential repurposing for the pharmaceutical to treat the disease.

FIG. 3Cdisplays results of text mining a literature database170to extract indirect relationships by the hypothesis confirmation module137. Again, two references are mined, one regarding a pharmaceutical350and one regarding a disease355. After text mining by the hypothesis confirmation module137, noted is that biomolecule G4357and biomolecule G2359are indirectly related (represented as nodes357,359). Noted also is that biomolecule G1361and biomolecule D2363are indirectly related (represented as nodes361,363). Finally, note that biomolecules G8365, D2367, and G5369are indirectly related (represented as nodes365,367,369). Any or all of these indirect connections may confirm one or more of the N hypotheses which notes an anticorrelation between each of these indirect relationships. In an embodiment of the invention, hypothesis confirmation module137determines which indirect relationship(s) are strongest by determining the smallest number of nodes connecting pharmaceutical350and disease355. With regard toFIG. 3C, path of biomolecule G4357and biomolecule G2359has two nodes between pharmaceutical350and disease355. Path of biomolecule G1361and biomolecule D2363also has two nodes between pharmaceutical350and disease355. Path of biomolecule G8365, biomolecule D2367, and biomolecule G5369has three nodes between pharmaceutical350and disease355. The hypothesis confirmation module137thus determines that the two steps between biomolecule G4357and biomolecule G2359, as well as the two steps between biomolecule G1361and biomolecule D2363are the shortest number of steps between pharmaceutical350and disease355and are thus the most likely to support the strongest one or more hypotheses of the N hypotheses. In alternative embodiments, various weights are placed upon every node357-369, and all weights along a path of nodes summed or multiplied to determine an indirect relationship score for each path of nodes357-369, with the path with the highest summed or multiplied weight used to confirm the strongest one or more hypotheses of the N hypotheses.

FIG. 3Ddisplays results of text mining by the hypothesis confirmation module137of literature database170to infer semantic similarity between references in seeking to confirm one of the N hypotheses. Two references are considered, one pharmaceutical reference regarding multiple pharmaceuticals370and one disease reference regarding multiple diseases380. Within the pharmaceutical reference370, after text analysis by the hypothesis confirmation module137, Pharmaceutical1377is discussed in the pharmaceutical reference370, and the term “stroke” has a relevance of 0.43, the term “lipid” has a relevance of 0.56, and the term “heart” has a relevance of 0.67. Pharmaceutical2379is also discussed in the pharmaceutical reference370, and the term “stroke” has a relevance of 0.34, the term “lipid” has a 0.21 relevance, and the term “heart” has a 0.56 relevance. Within the disease reference380, after text analysis by the hypothesis conformation module137, disease1381is discussed in the disease reference380, and the term “stroke” has a relevance of 0.76, the term “lipid” has a 0.45 relevance, and the term “heart” has a 0.52 relevance. Disease2383is discussed in the disease reference380, and the term “stroke” has a relevance of 0.80, the term “lipid” has a 0.08 relevance, and the term “heart” has a 0.67 connection. The hypothesis confirmation module137, after inferring semantic similarity between the pharmaceutical reference370and the disease reference380, notes a 0.31 correlation between pharmaceutical1and disease1, as displayed392, and a 0.08 correlation between pharmaceutical1and disease2, also as displayed392, indicating that pharmaceutical1is more likely to be repurposed to be a treatment for disease1. As displayed394, the hypothesis confirmation module137notes a 0.60 correlation between pharmaceutical2and disease1, and a 0.33 correlation between pharmaceutical2and disease2, also as displayed394, indicating that pharmaceutical2is more likely to be repurposed a treatment for disease1, confirming one of the N hypotheses. In an embodiment of the invention, the hypothesis confirmation module137may determine a semantic similarity score between terminology utilized in pharmaceutical reference370and disease reference380. If pharmaceutical reference370and disease reference380share a large number of relevant terms, a high semantic similarity score might be calculated, while if pharmaceutical reference370and disease reference380do not share a large number of relevant terms, a low semantic similarity score may be calculated. The hypothesis confirmation module137may multiply each correlation392,394by the semantic similarity score, for better results in confirming one of the N hypotheses.

FIGS. 4A and 4Bare a flowchart depicting operational steps that a hardware component, multiple hardware components, and/or a hardware appliance may execute, in accordance with an embodiment of the invention. As execution begins inFIG. 4A, at step402, the communication module131of repurposing module130receives an identification of a pharmaceutical from the user computer110for repurposing. At step405, the pharmaceutical vector module133of the repurposing module130retrieves from the pharmaceutical database150a pharmaceutical expression signature based upon the identification. The pharmaceutical expression signature indicates differential expressions of a plurality of biomolecules regulated by the pharmaceutical versus a control. At step410, the disease vector module135of the repurposing module130retrieves a plurality of disease expression signatures from a disease database160, each disease expression signature indicating differential expressions of a plurality of biomolecules affected by a disease versus a control. At step415, the pharmaceutical vector module133of repurposing module130generates a pharmaceutical vector based upon the pharmaceutical expression signature for the pharmaceutical.

Continuing inFIG. 4B, at step420, the disease vector module135of repurposing module130generates a plurality of disease vectors based upon the plurality of disease expression signatures for each disease. At step425, the hypothesis generator136of repurposing module130generates N hypotheses correlating the pharmaceutical vector and one or more of the plurality of disease vectors, each of the N hypotheses indicating an anticorrelation between the pharmaceutical expression signature and one or more disease expression signatures of the plurality of disease expression signatures. Each hypothesis indicates a potential repurposing for the pharmaceutical to treat the disease. At step430, the hypothesis confirmation module137of repurposing module130determines a strongest one or more hypotheses by text mining the literature database170and comparing results of the text mining with the N hypotheses. The hypothesis confirmation module137may further rank the N hypotheses according to each of their strengths. At step435, the communication module131of repurposing module130transmits the strongest one or more hypotheses (or the N hypotheses according to strength) to the user computer110.

FIG. 5depicts a block diagram of components of user computer110, repurposing module130, pharmaceutical database150, disease database160, and literature database170in the environment100for accelerated pharmaceutical repurposing utilizing omics data and literature text mining, in accordance with an embodiment of the present invention. It should be appreciated thatFIG. 5provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made.

User computer110, repurposing module130, pharmaceutical database150, disease database160, and literature database170may include one or more processors902, one or more computer-readable RAMs904, one or more computer-readable ROMs906, one or more computer readable storage media908, device drivers912, read/write drive or interface914, network adapter or interface916, all interconnected over a communications fabric918. Communications fabric918may be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system.

One or more operating systems910, and one or more application programs911, for example, environment100for accelerated pharmaceutical repurposing utilizing omics data and literature text mining, are stored on one or more of the computer readable storage media908for execution by one or more of the processors902via one or more of the respective RAMs904(which typically include cache memory). In the illustrated embodiment, each of the computer readable storage media908may be a magnetic disk storage device of an internal hard drive, CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical disk, a semiconductor storage device such as RAM, ROM, EPROM, flash memory or any other computer-readable tangible storage device that can store a computer program and digital information.

User computer110, repurposing module130, pharmaceutical database150, disease database160, and literature database170may also include a R/W drive or interface914to read from and write to one or more portable computer readable storage media926. Application programs911on user computer110, repurposing module130, pharmaceutical database150, disease database160, and literature database170may be stored on one or more of the portable computer readable storage media926, read via the respective R/W drive or interface914and loaded into the respective computer readable storage media908.

User computer110, repurposing module130, pharmaceutical database150, disease database160, and literature database170may also include a network adapter or interface916, such as a TCP/IP adapter card or wireless communication adapter (such as a 4G wireless communication adapter using OFDMA technology). Application programs911on user computer110, repurposing module130, pharmaceutical database150, disease database160, and literature database170may be downloaded to the computing device from an external computer or external storage device via a network (for example, the Internet, a local area network or other wide area network or wireless network) and network adapter or interface916. From the network adapter or interface916, the programs may be loaded onto computer readable storage media908. The network may comprise copper wires, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers.

User computer110, repurposing module130, pharmaceutical database150, disease database160, and literature database170may also include a display screen920, a keyboard or keypad922, and a computer mouse or touchpad924. Device drivers912interface to display screen920for imaging, to keyboard or keypad922, to computer mouse or touchpad924, and/or to display screen920for pressure sensing of alphanumeric character entry and user selections. The device drivers912, R/W drive or interface914and network adapter or interface916may comprise hardware and software (stored on computer readable storage media908and/or ROM906).

The present invention may be a method, computer program product, and/or computer system at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Workloads layer90provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation91; software development and lifecycle management92; virtual classroom education delivery93; data analytics processing94; transaction processing95; and the environment100for accelerated pharmaceutical repurposing utilizing omics data and literature text mining.

Based on the foregoing, a method, system, and computer program product have been disclosed. However, numerous modifications and substitutions can be made without deviating from the scope of the present invention. Therefore, the present invention has been disclosed by way of example and not limitation.