Unveiling potential anticancer drugs through in silico drug repurposing approaches

Current cancer therapy: are we ready to battle cancer?

The current chemotherapeutic approaches are unsuccessful in targeting the stem cells from which cancer cells originate, and they merely focus on a limited number of genetic mutations that may not account for massive genetic variations linked with malignancies. Moreover, these therapies are imprecise as they presume normal somatic cells to possess malignant potential, thereby imposing cytotoxicity. On the other hand, deficient activation of certain enzymes that are responsible for conversion of prodrugs to their active forms, aberrant drug transporters or efflux pumps that undermine the drug concentrations within the cancer cells, irreparable DNA damage after a direct or indirect insult, and evasion of apoptosis are some of the factors that are likely to culminate in a drug gaining resistance. Although downsizing of tumor is evident after successful completion of chemotherapy cycles, there exists a plethora of cases where these agents fail to totally eliminate cancer stem cells with metastatic potential, thereby resulting in recurrence. This instigated the researchers to develop new drug regimens or combinational therapies for a successful treatment [ ].

De novo drug discovery versus drug repurposing

Novel drug discovery is a complex process that consumes an average of 10 to 15 years for translation of a new molecule into an approved drug. The drug approval process is tedious and encounters higher attrition rates due to changing regulatory requirements. These rate-limiting steps in de novo synthesis of a drug necessitated a paradigm shift from conventional drug discovery pathways to contemporary drug repurposing research to expedite unraveling new indications for existing, banned, and investigational drugs ( Fig. 4.1 ).

Drug repositioning bypasses the elaborative processes involved in conventional drug development and dramatically reduces the time required. It demands an investment of 1600 million USD in contrast to the 12,000 million USD required for traditional drug discovery. Furthermore, the failure rates are low as safety profiles of repurposable drugs are already established. The lower cost involved in drug repositioning research is advantageous for economically backward countries to satisfy their unmet medical needs [ ]. Latest update on repurposed drugs in cancer therapy is listed in Table 4.1 .

Fundamental steps for a fruitful drug repurposing expedition

Drug repurposing mandates a thorough insight of polypharmacology, which facilitates the exploration of multitarget actions of a single drug and its involvement in other disease pathways. Latest information pertaining to unexplored pathways involved in disease pathogenesis and progression, along with their associated biomarkers, is required before initiating drug repurposing approaches. Drug repurposing investigations oriented toward genetic disorders demand supportive literature on the influence of environment and drugs on gene expression, transcription, translation, epigenome, and metabolism. The data acquired and accumulated over years are massive and too huge to be handled manually. This situation has enforced knowledge-based, signature-based, target-based, and network-based computational approaches to untangle the hidden relationships across drugs, targets, and diseases [ ].

Inclusion of novel informatics approach, systems biology, and genomic information to reveal unknown targets or mechanisms of approved drugs improves drug repurposing methods by accelerating the timelines. The compounds derived from computational studies can be further validated through experimental testing. Hence, a combination of both computational and experimental assays is desirable to repurpose drugs for new indications [ , ] ( Fig. 4.2 ).

Drugs are repurposed by employing omics data, such as genomics [ ], transcriptomics [ ], proteomics [ ], epigenetics [ ], and metabolomics [ ]. Alongside omics databases, electronic health records and side effect data [ ] also provide valuable hints to predict novel indications of existing drugs [ ]. Further progress in this field has led to the construction and incorporation of mathematical algorithms and Machine Learning (ML) platforms for a rapid and accurate drug repurposing forecast analysis [ , ] ( Fig. 4.3 ).

DeSigN tool: a web-based drug repurposing algorithm based on gene expression patterns

Lee et al. [ ] developed a web-based algorithm or tool named “DeSigN” (Differentially Expressed Gene Signatures) to predict phenotypic characteristics of drugs in cancer cell lines by considering their half maximal Inhibitory Concentration (IC ₅₀ ) values and individual gene expression patterns. This algorithm construction took place in three steps. Firstly, a reference database with sensitivity patterns of cell lines to drugs extracted from Genomics of Drug Sensitivity in Cancer (GDSC) [ ] was created, which contained 140 drugs with their unique rank order–based gene signatures. The second step was generation of query inputs for DeSigN database using DEGs from microarray or RNA-Seq gene expression data of cell lines in tumor and control samples. In the third step, nonparametric modified Kolmogorov–Smirnov (KS) statistics, a rank order–based pattern-matching algorithm was implemented in Connectivity Map (CMap) [ ] to correlate the query signatures with specific drug-associated gene expression profiles. Later, the drug candidates were prioritized by computing connectivity score obtained from modified KS test. Eventually, to demonstrate the validity of the tool in predicting candidate drugs, four datasets (two estrogen receptor positive breast cancer, one non-small cell lung cancer, one pancreatic cancer) from the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) [ ] were selected. For all the included datasets, a DEG list was prepared to use as a query in DeSigN. The designed tool was experimentally validated among Oral Squamous Cell Carcinoma (OSCC) cell lines for identification of growth inhibitors. Thus obtained gene signatures containing 69 upregulated genes and 86 downregulated genes were used as a query in DeSigN that returned nine potential candidates namely, GSK-650394, pyrimethamine, RDEA 119, BIBW2992, CGP-082996, lapatinib, PF-562271, bosutinib, and PD-0325901. Among the abovementioned nine candidates, BIBW2992 and bosutinib were reported recently for their efficacy in head and neck squamous cell carcinoma cell lines. This in silico prediction for bosutinib was experimentally validated in ORL196, ORL-204, and ORL-48 OSCC cell lines, and the drug exhibited significant cytotoxicity at one micromolar concentration [ ].

Anticancer drug repositioning through genome-wide association studies

Zhang et al. [ ] designed an in silico pipeline that mapped 50 SNPs located in rectal mucosal cells obtained from National Human Genome Research Institute GWAS [ ] catalog to 140 genes associated with Colorectal Cancer (CRC) using snp2gene algorithm. The mapped genes were prioritized based on (i) functional annotation using Database for Annotation, Visualization, and Integrated Discovery [ ] bioinformatics resources, (ii) cis-expression quantitative trait loci effects using peripheral blood mononuclear cell data generated from 5311 European subjects, (iii) PubMed text mining (TM) via Gene Relationships among Implicated Loci tool [ ], (iv) Protein–Protein Interaction (PPI) analyzed through Disease Association Protein—Protein Link Evaluator [ ], (v) genetic overlaps with cancer somatic mutations by means of COSMIC database [ ], (vi) genes mapped with knockout mouse phenotypes from Mouse Genome Informatics [ ] database, and (vii) SNPs from linkage disequilibrium (r ² > 0.80) that were annotated as missense variants using NIH Roadmap Epigenomics Mapping Consortium [ ]. Thereafter, Pearson correlation method was used for gene scoring, which ranged between zero and seven, wherein 35 genes that scored ≥ two were considered as biological risk genes. These top priority genes were used as query signature inputs to predict repurposable drugs from Drugbank [ ] and Therapeutic Target Database [ ]. This study revealed anticancer potential of crizotinib, arsenic trioxide, vrinostat, dasatinib, estramustine, and tamibarotene against CRC.