Computational approaches for drug repurposing in oncology: untapped opportunity for high value innovation

Dalwadi, Shraddha M.; Hunt, Andrew; Bonnen, Mark; Ghebre, Yohannes T.

doi:10.3389/fonc.2023.1198284

Cited by 9 publications

(6 citation statements)

References 24 publications

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“…Future research should improve advanced computational tools, like machine learning and artificial intelligence, to improve therapeutic efficacy and safety prediction ( Prasad and Kumar, 2021 ). Furthermore, computational methods can be used to address current anticancer medications or radiation therapy issues in addition to cancer treatment ( Dalwadi et al, 2023 ). The most commonly used computational approaches are discussed below.…”

Section: Approaches Used For Drug Repurposing In Cancer Therapymentioning

confidence: 99%

Current trends and future prospects of drug repositioning in gastrointestinal oncology

Fatemi,

Karimpour,

Bahrami

et al. 2024

Front. Pharmacol.

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Gastrointestinal (GI) cancers comprise a significant number of cancer cases worldwide and contribute to a high percentage of cancer-related deaths. To improve survival rates of GI cancer patients, it is important to find and implement more effective therapeutic strategies with better prognoses and fewer side effects. The development of new drugs can be a lengthy and expensive process, often involving clinical trials that may fail in the early stages. One strategy to address these challenges is drug repurposing (DR). Drug repurposing is a developmental strategy that involves using existing drugs approved for other diseases and leveraging their safety and pharmacological data to explore their potential use in treating different diseases. In this paper, we outline the existing therapeutic strategies and challenges associated with GI cancers and explore DR as a promising alternative approach. We have presented an extensive review of different DR methodologies, research efforts and examples of repurposed drugs within various GI cancer types, such as colorectal, pancreatic and liver cancers. Our aim is to provide a comprehensive overview of employing the DR approach in GI cancers to inform future research endeavors and clinical trials in this field.

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Section: Approaches Used For Drug Repurposing In Cancer Therapymentioning

confidence: 99%

Current trends and future prospects of drug repositioning in gastrointestinal oncology

Fatemi,

Karimpour,

Bahrami

et al. 2024

Front. Pharmacol.

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“…This means that optimized approaches are needed to identify “old” candidate drugs with such actions. Indeed, the range of computational predictive tools, high-throughput screening methods, machine learning algorithms, bioinformatics analysis, and artificial intelligence that facilitate the drug repurposing process, unraveling molecular signatures, and contribute to novel, affordable, and tailored treatment options, is very impressive [ 22 , 23 , 24 , 25 ].…”

Section: Drug Repurposing In Cancermentioning

confidence: 99%

Repurposing Therapeutic Drugs Complexed to Vanadium in Cancer

De Sousa-Coelho,

Fraqueza,

Aureliano

2023

Pharmaceuticals

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Repurposing drugs by uncovering new indications for approved drugs accelerates the process of establishing new treatments and reduces the high costs of drug discovery and development. Metal complexes with clinically approved drugs allow further opportunities in cancer therapy—many vanadium compounds have previously shown antitumor effects, which makes vanadium a suitable metal to complex with therapeutic drugs, potentially improving their efficacy in cancer treatment. In this review, covering the last 25 years of research in the field, we identified non-oncology-approved drugs suitable as ligands to obtain different vanadium complexes. Metformin-decavanadate, vanadium-bisphosphonates, vanadyl(IV) complexes with non-steroidal anti-inflammatory drugs, and cetirizine and imidazole-based oxidovanadium(IV) complexes, each has a parent drug known to have different medicinal properties and therapeutic indications, and all showed potential as novel anticancer treatments. Nevertheless, the precise mechanisms of action for these vanadium compounds against cancer are still not fully understood.

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“…Computational methods, on the other hand, are efficient and effective, and they play an important role in many areas of bioinformatics. For example, in silico techniques are being used rapidly in research on diseasegene interactions [25,26], protein structure prediction [27], peptide therapeutic function, gene editing experiments [28], meaningful pattern detection [29], and drug repurposing [30,31]. Previously, researchers have been proposed a few computational models for predicting the 2-OM sites based on single machine learning (ML) and deep learning (DL) approaches [32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Meta-2OM: A multi-classifier meta-model for the accurate prediction of RNA 2′-O-methylation sites in human RNA

Harun-Or-Roshid,

Pham,

Manavalan

et al. 2024

PLoS ONE

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2′-O-methylation (2-OM or Nm) is a widespread RNA modification observed in various RNA types like tRNA, mRNA, rRNA, miRNA, piRNA, and snRNA, which plays a crucial role in several biological functional mechanisms and innate immunity. To comprehend its modification mechanisms and potential epigenetic regulation, it is necessary to accurately identify 2-OM sites. However, biological experiments can be tedious, time-consuming, and expensive. Furthermore, currently available computational methods face challenges due to inadequate datasets and limited classification capabilities. To address these challenges, we proposed Meta-2OM, a cutting-edge predictor that can accurately identify 2-OM sites in human RNA. In brief, we applied a meta-learning approach that considered eight conventional machine learning algorithms, including tree-based classifiers and decision boundary-based classifiers, and eighteen different feature encoding algorithms that cover physicochemical, compositional, position-specific and natural language processing information. The predicted probabilities of 2-OM sites from the baseline models are then combined and trained using logistic regression to generate the final prediction. Consequently, Meta-2OM achieved excellent performance in both 5-fold cross-validation training and independent testing, outperforming all existing state-of-the-art methods. Specifically, on the independent test set, Meta-2OM achieved an overall accuracy of 0.870, sensitivity of 0.836, specificity of 0.904, and Matthew’s correlation coefficient of 0.743. To facilitate its use, a user-friendly web server and standalone program have been developed and freely available at http://kurata35.bio.kyutech.ac.jp/Meta-2OM and https://github.com/kuratahiroyuki/Meta-2OM.

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Computational approaches for drug repurposing in oncology: untapped opportunity for high value innovation

Cited by 9 publications

References 24 publications

Current trends and future prospects of drug repositioning in gastrointestinal oncology

Current trends and future prospects of drug repositioning in gastrointestinal oncology

Repurposing Therapeutic Drugs Complexed to Vanadium in Cancer

Meta-2OM: A multi-classifier meta-model for the accurate prediction of RNA 2′-O-methylation sites in human RNA

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