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Cavalcante BRR, Freitas RD, Siquara da Rocha LO, Santos RSB, Souza BSDF, Ramos PIP, Rocha GV, Gurgel Rocha CA. In silico approaches for drug repurposing in oncology: a scoping review. Front Pharmacol 2024; 15:1400029. [PMID: 38919258 PMCID: PMC11196849 DOI: 10.3389/fphar.2024.1400029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 05/14/2024] [Indexed: 06/27/2024] Open
Abstract
Introduction: Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show in silico DR approaches and the gap between these strategies and their ultimate application in oncology. Methods: The scoping review was conducted according to the Arksey and O'Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021. Results: We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies. Discussion: DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications.
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Affiliation(s)
- Bruno Raphael Ribeiro Cavalcante
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Pathology and Forensic Medicine of the School of Medicine, Federal University of Bahia, Salvador, Brazil
| | - Raíza Dias Freitas
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Social and Pediatric Dentistry of the School of Dentistry, Federal University of Bahia, Salvador, Brazil
| | - Leonardo de Oliveira Siquara da Rocha
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Pathology and Forensic Medicine of the School of Medicine, Federal University of Bahia, Salvador, Brazil
| | | | - Bruno Solano de Freitas Souza
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- D’Or Institute for Research and Education (IDOR), Salvador, Brazil
| | - Pablo Ivan Pereira Ramos
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil
| | - Gisele Vieira Rocha
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- D’Or Institute for Research and Education (IDOR), Salvador, Brazil
| | - Clarissa Araújo Gurgel Rocha
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Pathology and Forensic Medicine of the School of Medicine, Federal University of Bahia, Salvador, Brazil
- D’Or Institute for Research and Education (IDOR), Salvador, Brazil
- Department of Propaedeutics, School of Dentistry of the Federal University of Bahia, Salvador, Brazil
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Guendouzi A, Belkhiri L, Guendouzi A, Derouiche TMT, Djekoun A. A combined in silico approaches of 2D-QSAR, molecular docking, molecular dynamics and ADMET prediction of anti-cancer inhibitor activity for actinonin derivatives. J Biomol Struct Dyn 2024; 42:119-133. [PMID: 36995063 DOI: 10.1080/07391102.2023.2192801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 03/10/2023] [Indexed: 03/31/2023]
Abstract
Inhibition of human mitochondrial peptide deformylase (HsPDF) plays a major role in reducing growth, proliferation, and cellular cancer survival. In this work, a series of 32 actinonin derivatives for HsPDF (PDB: 3G5K) inhibitor's anticancer activity was computationally analyzed for the first time, using an in silico study considering 2D-QSAR modeling, and molecular docking studies, and validated by molecular dynamics and ADMET properties. The results of multilinear regression (MLR) and artificial neural networks (ANN) statistical analysis reveal a good correlation between pIC50 activity and the seven (7) descriptors. The developed models were highly significant with cross-validation, the Y-randomization test and their applicability range. In addition, all considered data sets show that the AC30 compound, exhibits the best binding affinity (docking score = -212.074 kcal/mol and H-bonding energy = -15.879 kcal/mol). Furthermore, molecular dynamics simulations were performed at 500 ns, confirming the stability of the studied complexes under physiological conditions and validating the molecular docking results. Five selected actinonin derivatives (AC1, AC8, AC15, AC18 and AC30), exhibiting best docking score, were rationalized as potential leads for HsPDF inhibition, in well agreement with experimental outcomes. Furthermore, based on the in silico study, new six molecules (AC32, AC33, AC34, AC35, AC36 and AC37) were suggested as HsPDF inhibition candidates, which would be combined with in-vitro and in-vivo studies to perspective validation of their anticancer activity. Indeed, the ADMET predictions indicate that these six new ligands have demonstrated a fairly good drug-likeness profile.
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Affiliation(s)
| | - Lotfi Belkhiri
- Centre de Recherche en Sciences Pharmaceutiques CRSP, Constantine, Algeria
- Laboratoire de Physique Mathématique et Subatomique LPMS, Département de Chimie, Université des Frères Mentouri, Constantine, Algeria
| | - Abdelkrim Guendouzi
- Laboratoire de Chimie, Synthèse, Propriétés et Applications LCSPA, Département de Chimie, Faculté des Sciences, Université Dr Moulay Tahar de Saida, Saïda, Algeria
| | - Tahar Mohamed Taha Derouiche
- Centre de Recherche en Sciences Pharmaceutiques CRSP, Constantine, Algeria
- Laboratoire Innovation Développement des Actifs Pharmaceutiques LIDAP, Faculté de Médecine, Département Pharmacie, Université Salah Boubnider Constantine 3, El Khroub, Algeria
| | - Abdelhamid Djekoun
- Centre de Recherche en Sciences Pharmaceutiques CRSP, Constantine, Algeria
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Sriram N, Mukherjee S, Sah MK. Gene expression profiling and protein-protein interaction analysis reveals the dynamic role of MCM7 in Alzheimer's disorder and breast cancer. 3 Biotech 2022; 12:146. [PMID: 35698583 PMCID: PMC9187790 DOI: 10.1007/s13205-022-03207-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 05/14/2022] [Indexed: 11/01/2022] Open
Abstract
The interrelation of cancer and Alzheimer's disorder (AD)-associated molecular mechanisms, reported last decade, paved the path for drug discoveries. In this direction, while chemotherapy is well established for breast cancer (BC), the detection and targeted therapy for AD is not advanced due to a lack of recognized peripheral biomarkers. The present study aimed to find diagnostic and prognostic molecular signature markers common to both BC and AD for possible drug targeting and repurposing. For these disorders, two corresponding microarray datasets (GSE42568, GSE33000) were used for identifying the differentially expressed genes (DEGs), resulting in recognition of CD209 and MCM7 as the two common players. While the CD209 gene was upregulated in both disorders and has been studied vastly, the MCM7 gene showed a strikingly reverse pattern of expression level, downregulated in the case of BC while upregulated in the case of AD. Thus, the MCM7 gene was further analyzed for expression, predictions, and validations of its structure and protein-protein interaction (PPI) for the possible development of new treatment methods for AD. The study concluded with indicative drug repurposing studies to check the effect of existing clinically approved drugs for BC for rectifying the expression levels of the mutated MCM7 gene in AD. Supplementary Information The online version contains supplementary material available at 10.1007/s13205-022-03207-1.
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Affiliation(s)
- Navneeth Sriram
- Department of Biotechnology, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab 144011 India
| | - Sunny Mukherjee
- Department of Biotechnology, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab 144011 India
| | - Mahesh Kumar Sah
- Department of Biotechnology, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab 144011 India
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Cabrera-Andrade A, López-Cortés A, Jaramillo-Koupermann G, González-Díaz H, Pazos A, Munteanu CR, Pérez-Castillo Y, Tejera E. A Multi-Objective Approach for Anti-Osteosarcoma Cancer Agents Discovery through Drug Repurposing. Pharmaceuticals (Basel) 2020; 13:ph13110409. [PMID: 33266378 PMCID: PMC7700154 DOI: 10.3390/ph13110409] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 11/11/2020] [Accepted: 11/12/2020] [Indexed: 02/08/2023] Open
Abstract
Osteosarcoma is the most common type of primary malignant bone tumor. Although nowadays 5-year survival rates can reach up to 60–70%, acute complications and late effects of osteosarcoma therapy are two of the limiting factors in treatments. We developed a multi-objective algorithm for the repurposing of new anti-osteosarcoma drugs, based on the modeling of molecules with described activity for HOS, MG63, SAOS2, and U2OS cell lines in the ChEMBL database. Several predictive models were obtained for each cell line and those with accuracy greater than 0.8 were integrated into a desirability function for the final multi-objective model. An exhaustive exploration of model combinations was carried out to obtain the best multi-objective model in virtual screening. For the top 1% of the screened list, the final model showed a BEDROC = 0.562, EF = 27.6, and AUC = 0.653. The repositioning was performed on 2218 molecules described in DrugBank. Within the top-ranked drugs, we found: temsirolimus, paclitaxel, sirolimus, everolimus, and cabazitaxel, which are antineoplastic drugs described in clinical trials for cancer in general. Interestingly, we found several broad-spectrum antibiotics and antiretroviral agents. This powerful model predicts several drugs that should be studied in depth to find new chemotherapy regimens and to propose new strategies for osteosarcoma treatment.
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Affiliation(s)
- Alejandro Cabrera-Andrade
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito 170125, Ecuador;
- Carrera de Enfermería, Facultad de Ciencias de la Salud, Universidad de Las Américas, Quito 170125, Ecuador
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, 15071 A Coruña, Spain; (A.L.-C.); (A.P.); (C.R.M.)
- Correspondence: (A.C.-A.); (E.T.)
| | - Andrés López-Cortés
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, 15071 A Coruña, Spain; (A.L.-C.); (A.P.); (C.R.M.)
- Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito 170129, Ecuador
- Latin American Network for Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), 28029 Madrid, Spain
| | - Gabriela Jaramillo-Koupermann
- Laboratorio de Biología Molecular, Subproceso de Anatomía Patológica, Hospital de Especialidades Eugenio Espejo, Quito 170403, Ecuador;
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, and Basque Center for Biophysics CSIC-UPV/EHU, University of the Basque Country UPV/EHU, 48940 Leioa, Spain;
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
| | - Alejandro Pazos
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, 15071 A Coruña, Spain; (A.L.-C.); (A.P.); (C.R.M.)
- Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), 15006 A Coruña, Spain
| | - Cristian R. Munteanu
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, 15071 A Coruña, Spain; (A.L.-C.); (A.P.); (C.R.M.)
- Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), 15006 A Coruña, Spain
| | - Yunierkis Pérez-Castillo
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito 170125, Ecuador;
- Escuela de Ciencias Físicas y Matemáticas, Universidad de Las Américas, Quito 170125, Ecuador
| | - Eduardo Tejera
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito 170125, Ecuador;
- Facultad de Ingeniería y Ciencias Agropecuarias, Universidad de Las Américas, Quito 170125, Ecuador
- Correspondence: (A.C.-A.); (E.T.)
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