DDCM: A Computational Strategy for Drug Repositioning Based on Support-Vector Regression Algorithm.

Computational drug-repositioning technology is an effective tool for speeding up drug development. As biological data resources continue to grow, it becomes more important to find effective methods to identify potential therapeutic drugs for diseases. The effective use of valuable data has become a more rational and efficient approach to drug repositioning. The disease-drug correlation method (DDCM) proposed in this study is a novel approach that integrates data from multiple sources and different levels to predict potential treatments for diseases, utilizing support-vector regression (SVR). The DDCM approach resulted in potential therapeutic drugs for neoplasms and cardiovascular diseases by constructing a correlation hybrid matrix containing the respective similarities of drugs and diseases, implementing the SVR algorithm to predict the correlation scores, and undergoing a randomized perturbation and stepwise screening pipeline. Some potential therapeutic drugs were predicted by this approach. The potential therapeutic ability of these drugs has been well-validated in terms of the literature, function, drug target, and survival-essential genes. The method's feasibility was confirmed by comparing the predicted results with the classical method and conducting a co-drug analysis of the sub-branch. Our method challenges the conventional approach to studying disease-drug correlations and presents a fresh perspective for understanding the pathogenesis of diseases.

Comprehensive review of the repositioning of non-oncologic drugs for cancer immunotherapy.

Drug repositioning or repurposing has gained worldwide attention as a plausible way to search for novel molecules for the treatment of particular diseases or disorders. Drug repurposing essentially refers to uncovering approved or failed compounds for use in various diseases. Cancer is a deadly disease and leading cause of mortality. The search for approved non-oncologic drugs for cancer treatment involved in silico modeling, databases, and literature searches. In this review, we provide a concise account of the existing non-oncologic drug molecules and their therapeutic potential in chemotherapy. The mechanisms and modes of action of the repurposed drugs using computational techniques are also highlighted. Furthermore, we discuss potential targets, critical pathways, and highlight in detail the different challenges pertaining to drug repositioning for cancer immunotherapy.

Discovery of common molecular signatures and drug repurposing for COVID-19/Asthma comorbidity: ACE2 and multi-partite networks.

Angiotensin-converting enzyme 2 (ACE2) is identified as the functional receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the ongoing global coronavirus disease-2019 (COVID-19) pandemic. This study aimed to elucidate potential therapeutic avenues by scrutinizing approved drugs through the identification of the genetic signature associated with SARS-CoV-2 infection in individuals with asthma. This exploration was conducted through an integrated analysis, encompassing interaction networks between the ACE2 receptor and common host (co-host) factors implicated in COVID-19/asthma comorbidity. The comprehensive analysis involved the identification of common differentially expressed genes (cDEGs) and hub-cDEGs, functional annotations, interaction networks, gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and module construction. Interaction networks were used to identify overlapping disease modules and potential drug targets. Computational biology and molecular docking analyzes were utilized to discern functional drug modules. Subsequently, the impact of the identified drugs on the expression of hub-cDEGs was experimentally validated using a mouse model. A total of 153 cDEGs or co-host factors associated with ACE2 were identified in the COVID-19 and asthma comorbidity. Among these, seven significant cDEGs and proteins - namely, HRAS, IFNG, JUN, CDH1, TLR4, ICAM1, and SCD-were recognized as pivotal host factors linked to ACE2. Regulatory network analysis of hub-cDEGs revealed eight top-ranked transcription factors (TFs) proteins and nine microRNAs as key regulatory factors operating at the transcriptional and post-transcriptional levels, respectively. Molecular docking simulations led to the proposal of 10 top-ranked repurposable drug molecules (Rapamycin, Ivermectin, Everolimus, Quercetin, Estradiol, Entrectinib, Nilotinib, Conivaptan, Radotinib, and Venetoclax) as potential treatment options for COVID-19 in individuals with comorbid asthma. Validation analysis demonstrated that Rapamycin effectively inhibited ICAM1 expression in the HDM-stimulated mice group (p < 0.01). This study unveils the common pathogenesis and genetic signature underlying asthma and SARS-CoV-2 infection, delineated by the interaction networks of ACE2-related host factors. These findings provide valuable insights for the design and discovery of drugs aimed at more effective therapeutics within the context of lung disease comorbidities.

Drug Repositioning: A Monetary Stratagem to Discover a New Application of Drugs.

Drug repurposing, also referred to as drug repositioning or drug reprofiling, is a scientific approach to the detection of any new application for an already approved or investigational drug. It is a useful policy for the invention and development of new pharmacological or therapeutic applications of different drugs. The strategy has been known to offer numerous advantages over developing a completely novel drug for certain problems. Drug repurposing has numerous methodologies that can be categorized as target-oriented, drug-oriented, and problem-oriented. The choice of the methodology of drug repurposing relies on the accessible information about the drug molecule and like pharmacokinetic, pharmacological, physicochemical, and toxicological profile of the drug. In addition, molecular docking studies and other computer-aided methods have been known to show application in drug repurposing. The variation in dosage for original target diseases and novel diseases presents a challenge for researchers of drug repurposing in present times. The present review critically discusses the drugs repurposed for cancer, covid-19, Alzheimer's, and other diseases, strategies, and challenges of drug repurposing. Moreover, regulatory perspectives related to different countries like the United States (US), Europe, and India have been delineated in the present review.

Drug repurposing for cancer therapy.

Cancer, a complex and multifactorial disease, presents a significant challenge to global health. Despite significant advances in surgical, radiotherapeutic and immunological approaches, which have improved cancer treatment outcomes, drug therapy continues to serve as a key therapeutic strategy. However, the clinical efficacy of drug therapy is often constrained by drug resistance and severe toxic side effects, and thus there remains a critical need to develop novel cancer therapeutics. One promising strategy that has received widespread attention in recent years is drug repurposing: the identification of new applications for existing, clinically approved drugs. Drug repurposing possesses several inherent advantages in the context of cancer treatment since repurposed drugs are typically cost-effective, proven to be safe, and can significantly expedite the drug development process due to their already established safety profiles. In light of this, the present review offers a comprehensive overview of the various methods employed in drug repurposing, specifically focusing on the repurposing of drugs to treat cancer. We describe the antitumor properties of candidate drugs, and discuss in detail how they target both the hallmarks of cancer in tumor cells and the surrounding tumor microenvironment. In addition, we examine the innovative strategy of integrating drug repurposing with nanotechnology to enhance topical drug delivery. We also emphasize the critical role that repurposed drugs can play when used as part of a combination therapy regimen. To conclude, we outline the challenges associated with repurposing drugs and consider the future prospects of these repurposed drugs transitioning into clinical application.

Repurposing of Antifungal Drug Flucytosine/Flucytosine Cocrystals for Anticancer Activity against Prostate Cancer Targeting Apoptosis and Inflammatory Signaling Pathways.

This study aimed to repurpose the antifungal drug flucytosine (FCN) for anticancer activity together with cocrystals of nutraceutical coformers sinapic acid (SNP) and syringic acid (SYA). The cocrystal screening experiments with SNP resulted in three cocrystal hydrate forms in which two are polymorphs, namely, FCN-SNP F-I and FCN-SNP F-II, and the third one with different stoichiometry in the asymmetric unit (1:2:1 ratio of FCN:SNP:H2O, FCN-SNP F-III). Cocrystallization with SYA resulted in two hydrated cocrystal polymorphs, namely, FCN-SYA F-I and FCN-SYA F-II. All the cocrystal polymorphs were obtained concomitantly during the slow evaporation method, and one of the polymorphs of each system was produced in bulk by the slurry method. The interaction energy and lattice energies of all cocrystal polymorphs were established using solid-state DFT calculations, and the outcomes correlated with the experimental results. Further, the in vitro cytotoxic activity of the cocrystals was determined against DU145 prostate cancer and the results showed that the FCN-based cocrystals (FCN-SNP F-III and FCN-SYA F-I) have excellent growth inhibitory activity at lower concentrations compared with parent FCN molecules. The prepared cocrystals induce apoptosis by generating oxidative stress and causing nuclear damage in prostate cancer cells. The Western blot analysis also depicted that the cocrystals downregulate the inflammatory markers such as NLRP3 and caspase-1 and upregulate the intrinsic apoptosis signaling pathway marker proteins, such as Bax, p53, and caspase-3. These findings suggest that the antifungal drug FCN can be repurposed for anticancer activity.

Exploring the potential of drug repurposing for liver diseases: A comprehensive study.

Drug repurposing involves the investigation of existing drugs for new indications. It offers a great opportunity to quickly identify a new drug candidate at a lower cost than novel discovery and development. Despite the importance and potential role of drug repurposing, there is no specific definition that healthcare providers and the World Health Organization credit. Unfortunately, many similar and interchangeable concepts are being used in the literature, making it difficult to collect and analyze uniform data on repurposed drugs. This research was conducted based on understanding general criteria for drug repurposing, concentrating on liver diseases. Many drugs have been investigated for their effect on liver diseases even though they were originally approved (or on their way to being approved) for other diseases. Some of the hypotheses for drug repurposing were first captured from the literature and then processed further to test the hypothesis. Recently, with the revolution in bioinformatics techniques, scientists have started to use drug libraries and computer systems that can analyze hundreds of drugs to give a short list of candidates to be analyzed pharmacologically. However, this study revealed that drug repurposing is a potential aid that may help deal with liver diseases. It provides available or under-investigated drugs that could help treat hepatitis, liver cirrhosis, Wilson disease, liver cancer, and fatty liver. However, many further studies are needed to ensure the efficacy of these drugs on a large scale.

Dual Drug Repurposing: The Example of Saracatinib.

Saracatinib (AZD0530) is a dual Src/Abl inhibitor initially developed by AstraZeneca for cancer treatment; however, data from 2006 to 2024 reveal that this drug has been tested not only for cancer treatment, but also for the treatment of other diseases. Despite the promising pre-clinical results and the tolerability shown in phase I trials, where a maximum tolerated dose of 175 mg was defined, phase II clinical data demonstrated a low therapeutic action against several cancers and an elevated rate of adverse effects. Recently, pre-clinical research aimed at reducing the toxic effects and enhancing the therapeutic performance of saracatinib using nanoparticles and different pharmacological combinations has shown promising results. Concomitantly, saracatinib was repurposed to treat Alzheimer's disease, targeting Fyn. It showed great clinical results and required a lower daily dose than that defined for cancer treatment, 125 mg and 175 mg, respectively. In addition to Alzheimer's disease, this Src inhibitor has also been studied in relation to other health conditions such as pulmonary and liver fibrosis and even for analgesic and anti-allergic functions. Although saracatinib is still not approved by the Food and Drug Administration (FDA), the large number of alternative uses for saracatinib and the elevated number of pre-clinical and clinical trials performed suggest the huge potential of this drug for the treatment of different kinds of diseases.

Single-cell transcriptional signature-based drug repurposing and in vitro evaluation in colorectal cancer.

BACKGROUND: The need for intelligent and effective treatment of diseases and the increase in drug design costs have raised drug repurposing as one of the effective strategies in biomedicine. There are various computational methods for drug repurposing, one of which is using transcription signatures, especially single-cell RNA sequencing (scRNA-seq) data, which show us a clear and comprehensive view of the inside of the cell to compare the state of disease and health. METHODS: In this study, we used 91,103 scRNA-seq samples from 29 patients with colorectal cancer (GSE144735 and GSE132465). First, differential gene expression (DGE) analysis was done using the ASAP website. Then we reached a list of drugs that can reverse the gene signature pattern from cancer to normal using the iLINCS website. Further, by searching various databases and articles, we found 12 drugs that have FDA approval, and so far, no one has reported them as a drug in the treatment of any cancer. Then, to evaluate the cytotoxicity and performance of these drugs, the MTT assay and real-time PCR were performed on two colorectal cancer cell lines (HT29 and HCT116). RESULTS: According to our approach, 12 drugs were suggested for the treatment of colorectal cancer. Four drugs were selected for biological evaluation. The results of the cytotoxicity analysis of these drugs are as follows: tezacaftor (IC10 = 19 µM for HCT-116 and IC10 = 2 µM for HT-29), fenticonazole (IC10 = 17 µM for HCT-116 and IC10 = 7 µM for HT-29), bempedoic acid (IC10 = 78 µM for HCT-116 and IC10 = 65 µM for HT-29), and famciclovir (IC10 = 422 µM for HCT-116 and IC10 = 959 µM for HT-29). CONCLUSIONS: Cost, time, and effectiveness are the main challenges in finding new drugs for diseases. Computational approaches such as transcriptional signature-based drug repurposing methods open new horizons to solve these challenges. In this study, tezacaftor, fenticonazole, and bempedoic acid can be introduced as promising drug candidates for the treatment of colorectal cancer. These drugs were evaluated in silico and in vitro, but it is necessary to evaluate them in vivo.

Utilizing machine learning to identify nifuroxazide as an inhibitor of ubiquitin-specific protease 21 in a drug repositioning strategy.

Ubiquitin-specific protease (USP), an enzyme catalyzing protein deubiquitination, is involved in biological processes related to metabolic disorders and cancer proliferation. We focused on constructing predictive models tailored to unveil compounds boasting USP21 inhibitory attributes. Six models, Extra Trees Classifier, Random Forest Classifier, LightGBM Classifier, XGBoost Classifier, Bagging Classifier, and a convolutional neural network harnessed from empirical data were selected for the screening process. These models guided our selection of 26 compounds from the FDA-approved drug library for further evaluation. Notably, nifuroxazide emerged as the most potent inhibitor, with a half-maximal inhibitory concentration of 14.9 ± 1.63 µM. The stability of protein-ligand complexes was confirmed using molecular modeling. Furthermore, nifuroxazide treatment of HepG2 cells not only inhibited USP21 and its established substrate ACLY but also elevated p-AMPKα, a downstream functional target of USP21. Intriguingly, we unveiled the previously unknown capacity of nifuroxazide to increase the levels of miR-4458, which was identified as downregulating USP21. This discovery was substantiated by manipulating miR-4458 levels in HepG2 cells, resulting in corresponding changes in USP21 protein levels in line with its predicted interaction with ACLY. Lastly, we confirmed the in vivo efficacy of nifuroxazide in inhibiting USP21 in mice livers, observing concurrent alterations in ACLY and p-AMPKα levels. Collectively, our study establishes nifuroxazide as a promising USP21 inhibitor with potential implications for addressing metabolic disorders and cancer proliferation. This multidimensional investigation sheds light on the intricate regulatory mechanisms involving USP21 and its downstream effects, paving the way for further exploration and therapeutic development.

Signature reversion of three disease-associated gene signatures prioritizes cancer drug repurposing candidates.

Drug repurposing is promising because approving a drug for a new indication requires fewer resources than approving a new drug. Signature reversion detects drug perturbations most inversely related to the disease-associated gene signature to identify drugs that may reverse that signature. We assessed the performance and biological relevance of three approaches for constructing disease-associated gene signatures (i.e., limma, DESeq2, and MultiPLIER) and prioritized the resulting drug repurposing candidates for four low-survival human cancers. Our results were enriched for candidates that had been used in clinical trials or performed well in the PRISM drug screen. Additionally, we found that pamidronate and nimodipine, drugs predicted to be efficacious against the brain tumor glioblastoma (GBM), inhibited the growth of a GBM cell line and cells isolated from a patient-derived xenograft (PDX). Our results demonstrate that by applying multiple disease-associated gene signature methods, we prioritized several drug repurposing candidates for low-survival cancers.

The Discovery of inhibitors of the SARS-CoV-2 S protein through computational drug repurposing.

SARS-CoV-2 must bind its principal receptor, ACE2, on the target cell to initiate infection. This interaction is largely driven by the receptor binding domain (RBD) of the viral Spike (S) protein. Accordingly, antiviral compounds that can block RBD/ACE2 interactions can constitute promising antiviral agents. To identify such molecules, we performed a virtual screening of the Selleck FDA approved drugs and the Selleck database of Natural Products using a multistep computational procedure. An initial set of candidates was identified from an ensemble docking process using representative structures determined from the analysis of four 3 µ s molecular dynamics trajectories of the RBD/ACE2 complex. Two procedures were used to construct an initial set of candidates including a standard and a pharmacophore guided docking procedure. The initial set was subsequently subjected to a multistep sieving process to reduce the number of candidates to be tested experimentally, using increasingly demanding computational procedures, including the calculation of the binding free energy computed using the MMPBSA and MMGBSA methods. After the sieving process, a final list of 10 candidates was proposed, compounds which were subsequently purchased and tested ex-vivo. The results identified estradiol cypionate and telmisartan as inhibitors of SARS-CoV-2 entry into cells. Our findings demonstrate that the methodology presented here enables the discovery of inhibitors targeting viruses for which high-resolution structures are available.

Multitarget Potential Drug Candidates for High-Grade Gliomas Identified by Multiple Reaction Monitoring Coupled with In Silico Drug Repurposing.

High-grade gliomas (HGGs) are extremely aggressive primary brain tumors with high mortality rates. Despite notable progress achieved by clinical research and biomarkers emerging from proteomics studies, efficacious drugs and therapeutic targets are limited. This study used targeted proteomics, in silico molecular docking, and simulation-based drug repurposing to identify potential drug candidates for HGGs. Importantly, we performed multiple reaction monitoring (MRM) on differentially expressed proteins with putative roles in the development and progression of HGGs based on our previous work and the published literature. Furthermore, in silico molecular docking-based drug repurposing was performed with a customized library of FDA-approved drugs to identify multitarget-directed ligands. The top drug candidates such as Pazopanib, Icotinib, Entrectinib, Regorafenib, and Cabozantinib were explored for their drug-likeness properties using the SwissADME. Pazopanib exhibited binding affinities with a maximum number of proteins and was considered for molecular dynamic simulations and cell toxicity assays. HGG cell lines showed enhanced cytotoxicity and cell proliferation inhibition with Pazopanib and Temozolomide combinatorial treatment compared to Temozolomide alone. To the best of our knowledge, this is the first study combining MRM with molecular docking and simulation-based drug repurposing to identify potential drug candidates for HGG. While the present study identified five multitarget-directed potential drug candidates, future clinical studies in larger cohorts are crucial to evaluate the efficacy of these molecular candidates. The research strategy and methodology used in the present study offer new avenues for innovation in drug discovery and development which may prove useful, particularly for cancers with low cure rates.

Drug repurposing a compelling cancer strategy with bottomless opportunities: Recent advancements in computational methods and molecular mechanisms.

Drug discovery has customarily focused on a de novo design approach, which is extremely expensive and takes several years to evolve before reaching the market. Discovering novel therapeutic benefits for the current drugs could contribute to new treatment alternatives for individuals with complex medical demands that are safe, inexpensive, and timely. In this consequence, when pharmaceutically yield and oncology drug efficacy appear to have hit a stalemate, drug repurposing is a fascinating method for improving cancer treatment. This review gathered about how in silico drug repurposing offers the opportunity to quickly increase the anticancer drug arsenal and, more importantly, overcome some of the limits of existing cancer therapies against both old and new therapeutic targets in oncology. The ancient nononcology compounds' innovative potential targets and important signaling pathways in cancer therapy are also discussed. This review also includes many plant-derived chemical compounds that have shown potential anticancer properties in recent years. Here, we have also tried to bring the spotlight on the new mechanisms to support clinical research, which may become increasingly essential in the future; at the same time, the unsolved or failed clinical trial study should be reinvestigated further based on the techniques and information provided. These encouraging findings, combined together, will through new insight on repurposing more non-oncology drugs for the treatment of cancer.

Synergistic Effects of Tranylcypromine and NRF2 Inhibitor: A Repurposing Strategy for Effective Cancer Therapy.

Drug repurposing has emerged as an attractive strategy for accelerating drug discovery for cancer treatment. In this study, we investigated combining Tranylcypromine (TCP) with a number of well-characterized drugs. Among these combinations, NRF2 inhibitor (ML385) exhibited synergistic effects in combination with TCP. Specifically, our results showed that the combination of TCP and ML385 resulted in a significant reduction in tumor proliferation while neither drug affected cancer cell growth meaningfully on its own. While further studies are needed to understand fully the extent of the synergistic efficacy, the underlying respective mechanisms and the potential side effects of this approach, our study has yielded a promising start for the development of an effective combination cancer therapy.

Drug Repurposing Analysis for Colorectal Cancer through Network Medicine Framework: Novel Candidate Drugs and Small Molecules.

This study aimed to reveal the drug-repurposing candidates for colorectal cancer (CRC) via drug-repurposing methods and network biology approaches. A novel, differentially co-expressed, highly interconnected, and co-regulated prognostic gene module was identified for CRC. Based on the gene module, polyethylene glycol (PEG), gallic acid, pyrazole, cordycepin, phenothiazine, pantoprazole, cysteamine, indisulam, valinomycin, trametinib, BRD-K81473043, AZD8055, dovitinib, BRD-A17065207, and tyrphostin AG1478 presented as drugs and small molecule candidates previously studied in the CRC. Lornoxicam, suxamethonium, oprelvekin, sirukumab, levetiracetam, sulpiride, NVP-TAE684, AS605240, 480743.cdx, HDAC6 inhibitor ISOX, BRD-K03829970, and L-6307 are proposed as novel drugs and small molecule candidates for CRC.

Informatics on Drug Repurposing for Breast Cancer.

Moving a new drug from bench to bedside is a long and arduous process. The tactic of drug repurposing, which solves "new" diseases with "old" existing drugs, is more efficient and economical than conventional ab-initio way for drug development. Information technology has dramatically changed the paradigm of biomedical research in the new century, and drug repurposing studies have been significantly accelerated by implementing informatics techniques related to genomics, systems biology and biophysics during the past few years. A series of remarkable achievements in this field comes with the practical applications of in silico approaches including transcriptomic signature matching, gene-connection-based scanning, and simulated structure docking in repositioning drug therapies against breast cancer. In this review, we systematically curated these impressive accomplishments with summarization of the main findings on potentially repurposable drugs, and provide our insights into the current issues as well as future directions of the field. With the prospective improvement in reliability, the computer-assisted repurposing strategy will play a more critical role in drug research and development.

Network neighborhood operates as a drug repositioning method for cancer treatment.

Computational drug repositioning approaches are important, as they cost less compared to the traditional drug development processes. This study proposes a novel network-based drug repositioning approach, which computes similarities between disease-causing genes and drug-affected genes in a network topology to suggest candidate drugs with highest similarity scores. This new method aims to identify better treatment options by integrating systems biology approaches. It uses a protein-protein interaction network that is the main topology to compute a similarity score between candidate drugs and disease-causing genes. The disease-causing genes were mapped on this network structure. Transcriptome profiles of drug candidates were taken from the LINCS project and mapped individually on the network structure. The similarity of these two networks was calculated by different network neighborhood metrics, including Adamic-Adar, PageRank and neighborhood scoring. The proposed approach identifies the best candidates by choosing the drugs with significant similarity scores. The method was experimented on melanoma, colorectal, and prostate cancers. Several candidate drugs were predicted by applying AUC values of 0.6 or higher. Some of the predictions were approved by clinical phase trials or other in-vivo studies found in literature. The proposed drug repositioning approach would suggest better treatment options with integration of functional information between genes and transcriptome level effects of drug perturbations and diseases.

DHFR Inhibitors Display a Pleiotropic Anti-Viral Activity against SARS-CoV-2: Insights into the Mechanisms of Action.

During the COVID-19 pandemic, drug repurposing represented an effective strategy to obtain quick answers to medical emergencies. Based on previous data on methotrexate (MTX), we evaluated the anti-viral activity of several DHFR inhibitors in two cell lines. We observed that this class of compounds showed a significant influence on the virus-induced cytopathic effect (CPE) partly attributed to the intrinsic anti-metabolic activity of these drugs, but also to a specific anti-viral function. To elucidate the molecular mechanisms, we took advantage of our EXSCALATE platform for in-silico molecular modelling and further validated the influence of these inhibitors on nsp13 and viral entry. Interestingly, pralatrexate and trimetrexate showed superior effects in counteracting the viral infection compared to other DHFR inhibitors. Our results indicate that their higher activity is due to their polypharmacological and pleiotropic profile. These compounds can thus potentially give a clinical advantage in the management of SARS-CoV-2 infection in patients already treated with this class of drugs.

Drug repurposing: Known knowns to unknown unknowns - Network analysis of the repurposome.

DrugRepurposing Online is a database of well-curated literature examples of drug repurposing, structured by reference to compounds and indications, via a generalisation layer (within specific datasets) of mechanism. References are categorised by level of relevance to human application to assist users in prioritising repurposing hypotheses. Users can search freely between any two of the three categories in either direction; results can then be extended to the third category. The concatenation of two (or more) direct relationships to create an indirect, hypothetical new repurposing relationship is intended to offer novel and non-obvious opportunities that can be both patented and efficiently developed. A natural language processing (NLP) powered search capability extends the opportunities from the hand-curated foundation to identify further opportunities.