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Wang QX, Cai J, Chen ZJ, Liu JC, Wang JJ, Zhou H, Li QQ, Wang ZX, Wang YB, Tong ZJ, Yang J, Wei TH, Zhang MY, Zhou Y, Dai WC, Ding N, Leng XJ, Yin XY, Sun SL, Yu YC, Li NG, Shi ZH. Exploring drug repositioning possibilities of kinase inhibitors via molecular simulation. Mol Inform 2024:e202300336. [PMID: 39031899 DOI: 10.1002/minf.202300336] [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: 12/06/2023] [Revised: 04/09/2024] [Accepted: 04/28/2024] [Indexed: 07/22/2024]
Abstract
Kinases, a class of enzymes controlling various substrates phosphorylation, are pivotal in both physiological and pathological processes. Although their conserved ATP binding pockets pose challenges for achieving selectivity, this feature offers opportunities for drug repositioning of kinase inhibitors (KIs). This study presents a cost-effective in silico prediction of KIs drug repositioning via analyzing cross-docking results. We established the KIs database (278 unique KIs, 1834 bioactivity data points) and kinases database (357 kinase structures categorized by the DFG motif) for carrying out cross-docking. Comparative analysis of the docking scores and reported experimental bioactivity revealed that the Atypical, TK, and TKL superfamilies are suitable for drug repositioning. Among these kinase superfamilies, Olverematinib, Lapatinib, and Abemaciclib displayed enzymatic activity in our focused AKT-PI3K-mTOR pathway with IC50 values of 3.3, 3.2 and 5.8 μM. Further cell assays showed IC50 values of 0.2, 1.2 and 0.6 μM in tumor cells. The consistent result between prediction and validation demonstrated that repositioning KIs via in silico method is feasible.
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Affiliation(s)
- Qing-Xin Wang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Jiao Cai
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Zi-Jun Chen
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Jia-Chuan Liu
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Jing-Jing Wang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Hai Zhou
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Qing-Qing Li
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Zi-Xuan Wang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Yi-Bo Wang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Zhen-Jiang Tong
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Jin Yang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Tian-Hua Wei
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Meng-Yuan Zhang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Yun Zhou
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Wei-Chen Dai
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, 211198, Nanjing, Jiangsu, China
| | - Ning Ding
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Xue-Jiao Leng
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Xiao-Ying Yin
- School of Chemistry and Chemical Engineering, Shanghai University of Engineering Science, 201620, Shanghai, China
| | - Shan-Liang Sun
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Yan-Cheng Yu
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Nian-Guang Li
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, 210023, Nanjing, Jiangsu, China
| | - Zhi-Hao Shi
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, 211198, Nanjing, Jiangsu, China
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Shukla H, John D, Banerjee S, Tiwari AK. Drug repurposing for neurodegenerative diseases. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 207:249-319. [PMID: 38942541 DOI: 10.1016/bs.pmbts.2024.03.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
Neurodegenerative diseases (NDDs) are neuronal problems that include the brain and spinal cord and result in loss of sensory and motor dysfunction. Common NDDs include Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS) etc. The occurrence of these diseases increases with age and is one of the challenging problems among elderly people. Though, several scientific research has demonstrated the key pathologies associated with NDDs still the underlying mechanisms and molecular details are not well understood and need to be explored and this poses a lack of effective treatments for NDDs. Several lines of evidence have shown that NDDs have a high prevalence and affect more than a billion individuals globally but still, researchers need to work forward in identifying the best therapeutic target for NDDs. Thus, several researchers are working in the directions to find potential therapeutic targets to alter the disease pathology and treat the diseases. Several steps have been taken to identify the early detection of the disease and drug repurposing for effective treatment of NDDs. Moreover, it is logical that current medications are being evaluated for their efficacy in treating such disorders; therefore, drug repurposing would be an efficient, safe, and cost-effective way in finding out better medication. In the current manuscript we discussed the utilization of drugs that have been repurposed for the treatment of AD, PD, HD, MS, and ALS.
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Affiliation(s)
- Halak Shukla
- Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India
| | - Diana John
- Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India
| | - Shuvomoy Banerjee
- Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India
| | - Anand Krishna Tiwari
- Genetics and Developmental Biology Laboratory, Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India.
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B S N, P K KN, Akey KS, Sankaran S, Raman RK, Natarajan J, Selvaraj J. Vitamin D analog calcitriol for breast cancer therapy; an integrated drug discovery approach. J Biomol Struct Dyn 2023; 41:11017-11043. [PMID: 37054526 DOI: 10.1080/07391102.2023.2199866] [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: 07/25/2022] [Accepted: 12/11/2022] [Indexed: 04/15/2023]
Abstract
As breast cancer remains leading cause of cancer death globally, it is essential to develop an affordable breast cancer therapy in underdeveloped countries. Drug repurposing offers potential to address gaps in breast cancer treatment. Molecular networking studies were performed for drug repurposing approach by using heterogeneous data. The PPI networks were built to select the target genes from the EGFR overexpression signaling pathway and its associated family members. The selected genes EGFR, ErbB2, ErbB4 and ErbB3 were allowed to interact with 2637 drugs, leads to PDI network construction of 78, 61, 15 and 19 drugs, respectively. As drugs approved for treating non cancer-related diseases or disorders are clinically safe, effective, and affordable, these drugs were given considerable attention. Calcitriol had shown significant binding affinities with all four receptors than standard neratinib. The RMSD, RMSF, and H-bond analysis of protein-ligand complexes from molecular dynamics simulation (100 ns), confirmed the stable binding of calcitriol with ErbB2 and EGFR receptors. In addition, MMGBSA and MMP BSA also affirmed the docking results. These in-silico results were validated with in-vitro cytotoxicity studies in SK-BR-3 and Vero cells. The IC50 value of calcitriol (43.07 mg/ml) was found to be lower than neratinib (61.50 mg/ml) in SK-BR-3 cells. In Vero cells the IC50 value of calcitriol (431.05 mg/ml) was higher than neratinib (404.95 mg/ml). It demonstrates that calcitriol suggestively downregulated the SK-BR-3 cell viability in a dose-dependent manner. These implications revealed calcitriol has shown better cytotoxicity and decreased the proliferation rate of breast cancer cells than neratinib.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Nagaraj B S
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - Krishnan Namboori P K
- Amrita Molecular Modeling and Synthesis (AMMAS) Research lab, Amrita Vishwavidyapeetham, Coimbatore, Tamilnadu, India
| | - Krishna Swaroop Akey
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - Sathianarayanan Sankaran
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Karpagam Academy of Higher Education, Coimbatore, Tamilnadu, India
| | - Rajesh Kumar Raman
- Department of Pharmaceutical Biotechnology, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - Jawahar Natarajan
- Department of Pharmaceutics, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - Jubie Selvaraj
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
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Zhao J, Yang CY, Hu L, Xu L, Dou WT. Cage-based sensors for circular dichroism analysis. Dalton Trans 2023; 52:15303-15312. [PMID: 37547938 DOI: 10.1039/d3dt02054a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Quantitative chiral sensing relying on circular dichroism (CD) is very important for determining the enantiomeric excess or concentration of small molecules without strong chromophores, because they form chiral complexes with sensors, yielding strong CD signals. Three-dimensional cages are promising platforms for chiral CD due to their stereochemical flexibility and their variety of cavity and external binding sites that can be used as chiral CD sensors. In this minireview, we discuss recent advances, future challenges, and opportunities in the quantitative sensing of small molecules in host-guest and peripheral complexes with cage sensors by chiral CD. We aim to provide inspiration for the rational design of cage sensors for quantitative chiral sensing of small molecules based on CD.
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Affiliation(s)
- Jianjian Zhao
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, 3663 N. Zhongshan Road, Shanghai 200062, P. R. China.
| | - Chang-Yin Yang
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, 3663 N. Zhongshan Road, Shanghai 200062, P. R. China.
| | - Lianrui Hu
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, 3663 N. Zhongshan Road, Shanghai 200062, P. R. China.
| | - Lin Xu
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, 3663 N. Zhongshan Road, Shanghai 200062, P. R. China.
| | - Wei-Tao Dou
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, 3663 N. Zhongshan Road, Shanghai 200062, P. R. China.
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5
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Miura S, Sawada R, Yorita A, Kida H, Kamada T, Yamanishi Y. A trial of topiramate for patients with hereditary spinocerebellar ataxia. Clin Case Rep 2023; 11:e6980. [PMID: 36855409 PMCID: PMC9968455 DOI: 10.1002/ccr3.6980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/24/2022] [Accepted: 02/06/2023] [Indexed: 02/27/2023] Open
Abstract
In an open pilot trial, six patients with various hereditary forms of spinocerebellar ataxia (SCA) were assigned to topiramate (50 mg/day) for 24 weeks. Four patients completed the protocol without adverse events. Of these four patients, topiramate was effective for three patients. Some patients with SCA could respond to treatment with topiramate.
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Affiliation(s)
- Shiroh Miura
- Department of Neurology and Geriatric MedicineEhime University Graduate School of MedicineToonEhimeJapan
| | - Ryusuke Sawada
- Department of PharmacologyOkayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesKita‐kuOkayamaJapan
| | - Akiko Yorita
- Division of Respirology, Neurology and Rheumatology, Department of MedicineKurume University School of MedicineKurumeFukuokaJapan
| | - Hiroshi Kida
- Department of AnatomyFukuoka University School of MedicineJonan‐kuFukuokaJapan
| | - Takashi Kamada
- Department of NeurologyFukuoka Sanno HospitalSawara‐kuFukuokaJapan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems EngineeringKyushu Institute of TechnologyIizukaFukuokaJapan
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Liu Y, Liu Y, Fan R, Kehriman N, Zhang X, Zhao B, Huang L. Pharmacovigilance-based drug repurposing: searching for putative drugs with hypohidrosis or anhidrosis adverse events for use against hyperhidrosis. Eur J Med Res 2023; 28:95. [PMID: 36829251 PMCID: PMC9951540 DOI: 10.1186/s40001-023-01048-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 02/07/2023] [Indexed: 02/26/2023] Open
Abstract
BACKGROUND Drug repurposing refers to the application of existing drugs to new therapeutic indications. As phenotypic indicators of human drug response, drug side effects may provide direct signals and unique opportunities for drug repurposing. OBJECTIVES We aimed to identify drugs frequently associated with hypohidrosis or anhidrosis adverse reactions (that is, the opposite condition of hyperhidrosis) from the pharmacovigilance database, which could be potential candidates as anti-hyperhidrosis treatment agents. METHODS In this observational, retrospective, pharmacovigilance study, adverse event reports of hypohidrosis or anhidrosis in the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) were assessed between January 2004 and December 2021 using reporting odds ratio (ROR) estimates and categorized by the World Health Organization Anatomical Therapeutic Chemical (ATC) classification code. The onset time of drug-associated hypohidrosis or anhidrosis was also examined. RESULTS There were 540 reports of 192 drugs with suspected drug-associated hypohidrosis or anhidrosis in the FAERS database, of which 39 drugs were found to have statistically significant signals. Nervous system drugs were most frequently reported (187 cases, 55.82%), followed by alimentary tract and metabolism drugs (35 cases, 10.45%), genitourinary system and sex hormones (28 cases, 8.36%), and dermatologicals (22 cases, 6.57%). The top 3 drug subclasses were antiepileptics, drugs for urinary frequency and incontinence, and antidepressants. Taking disproportionality signals, pharmacological characteristics of drugs and appropriate onset time into consideration, the main putative drugs for hyperhidrosis were glycopyrronium, solifenacin, oxybutynin, and botulinum toxin type A. Other drugs, such as topiramate, zonisamide, agalsidase beta, finasteride, metformin, lamotrigine, citalopram, ciprofloxacin, bupropion, duloxetine, aripiprazole, prednisolone, and risperidone need more investigation. CONCLUSIONS Several candidate agents among hypohidrosis or anhidrosis-related drugs were identified that may be redirected for diminishing sweat production. There are affirmative data for some candidate drugs, and the remaining proposed candidate drugs without already known sweat reduction mechanisms of action should be further explored.
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Affiliation(s)
- Yi Liu
- grid.411634.50000 0004 0632 4559Department of Pharmacy, Peking University People’s Hospital, Beijing, China
| | - Yanguo Liu
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.
| | - Rongrong Fan
- grid.411634.50000 0004 0632 4559Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, China
| | - Nurmuhammat Kehriman
- grid.11135.370000 0001 2256 9319Department of Pharmaceutical Analysis, School of Pharmacy, Peking University, Beijing, China
| | - Xiaohong Zhang
- grid.411634.50000 0004 0632 4559Department of Pharmacy, Peking University People’s Hospital, Beijing, China
| | - Bin Zhao
- Department of Pharmacy, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
| | - Lin Huang
- Department of Pharmacy, Peking University People's Hospital, Beijing, China.
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Lang X, Liu J, Zhang G, Feng X, Dan W. Knowledge Mapping of Drug Repositioning's Theme and Development. Drug Des Devel Ther 2023; 17:1157-1174. [PMID: 37096060 PMCID: PMC10122475 DOI: 10.2147/dddt.s405906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 04/11/2023] [Indexed: 04/26/2023] Open
Abstract
Background In recent years, the emergence of new diseases and resistance to known diseases have led to increasing demand for new drugs. By means of bibliometric analysis, this paper studied the relevant articles on drug repositioning in recent years and analyzed the current research foci and trends. Methodology The Web of Science database was searched to collect all relevant literature on drug repositioning from 2001 to 2022. These data were imported into CiteSpace and bibliometric online analysis platforms for bibliometric analysis. The processed data and visualized images predict the development trends in the research field. Results The quality and quantity of articles published after 2011 have improved significantly, with 45 of them cited more than 100 times. Articles posted by journals from different countries have high citation values. Authors from other institutions have also collaborated to analyze drug rediscovery. Keywords found in the literature include molecular docking (N=223), virtual screening (N=170), drug discovery (N=126), machine learning (N=125), and drug-target interaction (N=68); these words represent the core content of drug repositioning. Conclusion The key focus of drug research and development is related to the discovery of new indications for drugs. Researchers are starting to retarget drugs after analyzing online databases and clinical trials. More and more drugs are being targeted at other diseases to treat more patients, based on saving money and time. It is worth noting that researchers need more financial and technical support to complete drug development.
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Affiliation(s)
- Xiaona Lang
- Pharmacy Department, Tianjin Hospital, Tianjin, People’s Republic of China
| | - Jinlei Liu
- Cardiology Department, Guang ‘anmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, People’s Republic of China
| | - Guangzhong Zhang
- Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China
| | - Xin Feng
- Pharmacy Department, Tianjin Hospital, Tianjin, People’s Republic of China
| | - Wenchao Dan
- Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China
- Correspondence: Wenchao Dan, Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China, Tel +86 13652001152, Email
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Nunes M, Duarte D, Vale N, Ricardo S. The Antineoplastic Effect of Carboplatin Is Potentiated by Combination with Pitavastatin or Metformin in a Chemoresistant High-Grade Serous Carcinoma Cell Line. Int J Mol Sci 2022; 24:ijms24010097. [PMID: 36613537 PMCID: PMC9820586 DOI: 10.3390/ijms24010097] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 12/19/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022] Open
Abstract
The combination of Carboplatin with Paclitaxel is the mainstay treatment for high-grade serous carcinoma; however, many patients with advanced disease undergo relapse due to chemoresistance. Drug repurposing coupled with a combination of two or more compounds with independent mechanisms of action has the potential to increase the success rate of the antineoplastic treatment. The purpose of this study was to explore whether the combination of Carboplatin with repurposed drugs led to a therapeutic benefit. Hence, we assessed the cytotoxic effects of Carboplatin alone and in combination with several repurposed drugs (Pitavastatin, Metformin, Ivermectin, Itraconazole and Alendronate) in two tumoral models, i.e., Carboplatin (OVCAR8) and Carboplatin-Paclitaxel (OVCAR8 PTX R P) chemoresistant cell lines and in a non-tumoral (HOSE6.3) cell line. Cellular viability was measured using the Presto Blue assay, and the synergistic interactions were evaluated using the Chou-Talalay, Bliss Independence and Highest Single Agent reference models. Combining Carboplatin with Pitavastatin or Metformin displayed the highest cytotoxic effect and the strongest synergism among all combinations for OVCAR8 PTX R P cells, resulting in a chemotherapeutic effect superior to Carboplatin as a single agent. Concerning HOSE6.3 cells, combining Carboplatin with almost all the repurposed drugs demonstrated a safe pharmacological profile. Overall, we propose that Pitavastatin or Metformin could act synergistically in combination with Carboplatin for the management of high-grade serous carcinoma patients with a Carboplatin plus Paclitaxel resistance profile.
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Affiliation(s)
- Mariana Nunes
- Differentiation and Cancer Group, Institute for Research and Innovation in Health (i3S) of the University of Porto, 4200-135 Porto, Portugal
- Institute of Biomedical Sciences Abel Salazar (ICBAS), University of Porto, 4050-313 Porto, Portugal
| | - Diana Duarte
- OncoPharma Research Group, Center for Health Technology and Services Research (CINTESIS), 4200-450 Porto, Portugal
- Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
- CINTESIS@RISE, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | - Nuno Vale
- OncoPharma Research Group, Center for Health Technology and Services Research (CINTESIS), 4200-450 Porto, Portugal
- CINTESIS@RISE, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
- Department of Community Medicine, Health Information and Decision (MEDCIDS), Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
| | - Sara Ricardo
- Differentiation and Cancer Group, Institute for Research and Innovation in Health (i3S) of the University of Porto, 4200-135 Porto, Portugal
- Toxicology Research Unit (TOXRUN), University Institute of Health Sciences, Polytechnic and University Cooperative (CESPU), 4585-116 Gandra, Portugal
- Department of Pathology, Faculty of Medicine, University of Porto (FMUP), 4200-319 Porto, Portugal
- Correspondence:
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Chen J, Chen Z, Chen R, Feng D, Li T, Han H, Bi X, Wang Z, Li K, Li Y, Li X, Wang L, Li J. HCDT: an integrated highly confident drug-target resource. Database (Oxford) 2022; 2022:6843794. [PMID: 36420558 PMCID: PMC9684616 DOI: 10.1093/database/baac101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/12/2022] [Accepted: 11/01/2022] [Indexed: 11/25/2022]
Abstract
Drug-target association plays an important role in drug discovery, drug repositioning, drug synergy prediction, etc. Currently, a lot of drug-related databases, such as DrugBank and BindingDB, have emerged. However, these databases are separate, incomplete and non-uniform with different criteria. Here, we integrated eight drug-related databases; collected, filtered and supplemented drugs, target genes and experimentally validated (highly confident) associations and built a highly confident drug-target (HCDT: http://hainmu-biobigdata.com/hcdt) database. HCDT database includes 500 681 HCDT associations between 299 458 drugs and 5618 target genes. Compared to individual databases, HCDT database contains 1.1 to 254.2 times drugs, 1.8-5.5 times target genes and 1.4-27.7 times drug-target associations. It is normative, publicly available and easy for searching, browsing and downloading. Together with multi-omics data, it will be a good resource in analyzing the drug functional mechanism, mining drug-related biological pathways, predicting drug synergy, etc. Database URL: http://hainmu-biobigdata.com/hcdt.
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Affiliation(s)
| | | | - Rufei Chen
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Bioinformatics for Major Diseases Science Innovation Group, College of Biomedical Informatics and Engineering, Hainan Medical University, Haikou 571199, China
| | - Dehua Feng
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Bioinformatics for Major Diseases Science Innovation Group, College of Biomedical Informatics and Engineering, Hainan Medical University, Haikou 571199, China
| | - Tianyi Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Bioinformatics for Major Diseases Science Innovation Group, College of Biomedical Informatics and Engineering, Hainan Medical University, Haikou 571199, China
| | - Huirui Han
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Bioinformatics for Major Diseases Science Innovation Group, College of Biomedical Informatics and Engineering, Hainan Medical University, Haikou 571199, China
| | - Xiaoman Bi
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Bioinformatics for Major Diseases Science Innovation Group, College of Biomedical Informatics and Engineering, Hainan Medical University, Haikou 571199, China
| | - Zhenzhen Wang
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Bioinformatics for Major Diseases Science Innovation Group, College of Biomedical Informatics and Engineering, Hainan Medical University, Haikou 571199, China
| | - Kongning Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Bioinformatics for Major Diseases Science Innovation Group, College of Biomedical Informatics and Engineering, Hainan Medical University, Haikou 571199, China
| | - Yongsheng Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Bioinformatics for Major Diseases Science Innovation Group, College of Biomedical Informatics and Engineering, Hainan Medical University, Haikou 571199, China
| | - Xia Li
- *Corresponding author: Tel: +86-451-86615922; Fax: +86-451-86615922; Correspondence may also be addressed to Limei Wang. Tel: +86-898-66893770; Fax: +86-898-66893770; and Jin Li. Tel: +86-898-66893770; Fax: +86-898-66893770;
| | - Limei Wang
- *Corresponding author: Tel: +86-451-86615922; Fax: +86-451-86615922; Correspondence may also be addressed to Limei Wang. Tel: +86-898-66893770; Fax: +86-898-66893770; and Jin Li. Tel: +86-898-66893770; Fax: +86-898-66893770;
| | - Jin Li
- *Corresponding author: Tel: +86-451-86615922; Fax: +86-451-86615922; Correspondence may also be addressed to Limei Wang. Tel: +86-898-66893770; Fax: +86-898-66893770; and Jin Li. Tel: +86-898-66893770; Fax: +86-898-66893770;
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10
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Pitavastatin and Ivermectin Enhance the Efficacy of Paclitaxel in Chemoresistant High-Grade Serous Carcinoma. Cancers (Basel) 2022; 14:cancers14184357. [PMID: 36139522 PMCID: PMC9496819 DOI: 10.3390/cancers14184357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/01/2022] [Accepted: 09/03/2022] [Indexed: 11/29/2022] Open
Abstract
Simple Summary The main challenge in high-grade serous carcinoma management is to unveil therapeutic approaches to overcome chemoresistance. Drug combinations and repurposing of non-oncological agents are attractive strategies that allow for higher efficacy, decreased toxicity, and the overcoming of chemoresistance. Several non-oncological drugs display an effective anti-cancer activity and have been studied to be repurposed in multi-drug resistant neoplasms. The purpose of our study was to explore whether combining Paclitaxel with repurposed drugs (Pitavastatin, Metformin, Ivermectin, Itraconazole and Alendronate) led to a therapeutic benefit. Our results showed that the combination of Paclitaxel with Pitavastatin or Ivermectin demonstrates the highest cytotoxic effect and the strongest synergism among all combinations for two chemoresistant cell lines. Thus, the combination of these repurposed drugs with Paclitaxel could be a particularly valuable strategy to treat ovarian cancer patients with intrinsic or acquired chemoresistance. Abstract Chemotherapy is a hallmark in high-grade serous carcinoma management; however, chemoresistance and side effects lead to therapeutic interruption. Combining repurposed drugs with chemotherapy has the potential to improve antineoplastic efficacy, since drugs can have independent mechanisms of action and suppress different pathways simultaneously. This study aimed to explore whether the combination of Paclitaxel with repurposed drugs led to a therapeutic benefit. Thus, we evaluated the cytotoxic effects of Paclitaxel alone and in combination with several repurposed drugs (Pitavastatin, Metformin, Ivermectin, Itraconazole and Alendronate) in two tumor chemoresistant (OVCAR8 and OVCAR8 PTX R P) and a non-tumoral (HOSE6.3) cell lines. Cellular viability was assessed using Presto Blue assay, and the synergistic interactions were evaluated using Chou–Talalay, Bliss Independence and Highest Single Agent reference models. The combination of Paclitaxel with Pitavastatin or Ivermectin showed the highest cytotoxic effect and the strongest synergism among all combinations for both chemoresistant cell lines, resulting in a chemotherapeutic effect superior to both drugs alone. Almost all the repurposed drugs in combination with Paclitaxel presented a safe pharmacological profile in non-tumoral cells. Overall, we suggest that Pitavastatin and Ivermectin could act synergistically in combination with Paclitaxel, being promising two-drug combinations for high-grade serous carcinoma management.
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11
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Song Y, Cui H, Zhang T, Yang T, Li X, Xuan P. Prediction of Drug-Related Diseases Through Integrating Pairwise Attributes and Neighbor Topological Structures. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2963-2974. [PMID: 34133286 DOI: 10.1109/tcbb.2021.3089692] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Identifying new disease indications for the approved drugs can help reduce the cost and time of drug development. Most of the recent methods focus on exploiting the various information related to drugs and diseases for predicting the candidate drug-disease associations. However, the previous methods failed to deeply integrate the neighborhood topological structure and the node attributes of an interested drug-disease node pair. We propose a new prediction method, ANPred, to learn and integrate pairwise attribute information and neighbor topology information from the similarities and associations related to drugs and diseases. First, a bi-layer heterogeneous network with intra-layer and inter-layer connections is established to combine the drug similarities, the disease similarities, and the drug-disease associations. Second, the embedding of a pair of drug and disease is constructed based on integrating multiple biological premises about drugs and diseases. The learning framework based on multi-layer convolutional neural networks is designed to learn the attribute representation of the pair of drug and disease nodes from its embedding. The sequences composed of neighbor nodes are formed based on random walk on the heterogeneous network. A framework based on fully-connected autoencoder and skip-gram module is constructed to learn the neighbor topological representations of nodes. The cross-validation results indicate the performance of ANPred is superior to several state-of-the-art methods. The case studies on 5 drugs further confirm the ability of ANPred in discovering the potential drug-disease association candidates.
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12
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Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther 2022; 7:156. [PMID: 35538061 PMCID: PMC9090746 DOI: 10.1038/s41392-022-00994-0] [Citation(s) in RCA: 68] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 03/14/2022] [Accepted: 04/05/2022] [Indexed: 02/08/2023] Open
Abstract
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.
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13
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Fisher JL, Jones EF, Flanary VL, Williams AS, Ramsey EJ, Lasseigne BN. Considerations and challenges for sex-aware drug repurposing. Biol Sex Differ 2022; 13:13. [PMID: 35337371 PMCID: PMC8949654 DOI: 10.1186/s13293-022-00420-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/06/2022] [Indexed: 01/09/2023] Open
Abstract
Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration [33]. The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health's (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER) policies to motivate researchers to consider sex differences [204]. However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses [7, 11, 14, 33]. Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information [1, 7, 155]. They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex [114]. Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods [7]. However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods [151, 159]. Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.
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Affiliation(s)
- Jennifer L. Fisher
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Emma F. Jones
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Victoria L. Flanary
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Avery S. Williams
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Elizabeth J. Ramsey
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Brittany N. Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
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14
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Qin L, Wang J, Wu Z, Li W, Liu G, Tang Y. Drug Repurposing for Newly Emerged Diseases via Network-Based Inference on A Gene-Disease-Drug Network. Mol Inform 2022; 41:e2200001. [PMID: 35338586 DOI: 10.1002/minf.202200001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 03/25/2022] [Indexed: 11/06/2022]
Abstract
Identification of disease-drug associations is an effective strategy for drug repurposing, especially in searching old drugs for newly emerged diseases like COVID-19. In this study, we put forward a network-based method named NEDNBI to predict disease-drug associations based on a gene-disease-drug tripartite network, which could be applied in drug repurposing. The novelty of our method lies in the fact that no negative data are required, and new disease could be added into the disease-drug network with gene as the bridge. The comprehensive evaluation results showed that the proposed method had good performance, with AUC value 0.948 ± 0.009 for 10-fold cross validation. In a case study, 8 of the 20 predicted old drugs have been tested clinically for the treatment of COVID-19, which illustrated the usefulness of our method in drug repurposing. The source code and data of the method are available at https://github.com/Qli97/NEDNBI.
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Affiliation(s)
- Li Qin
- East China University of Science and Technology School of Pharmacy, CHINA
| | - Jiye Wang
- East China University of Science and Technology School of Pharmacy, CHINA
| | - Zengrui Wu
- East China University of Science and Technology, CHINA
| | | | - Guixia Liu
- East China University of Science and Technology, CHINA
| | - Yun Tang
- East China University of Science and Technology, CHINA
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15
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Schuler J, Falls Z, Mangione W, Hudson ML, Bruggemann L, Samudrala R. Evaluating the performance of drug-repurposing technologies. Drug Discov Today 2022; 27:49-64. [PMID: 34400352 PMCID: PMC10014214 DOI: 10.1016/j.drudis.2021.08.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 06/20/2021] [Accepted: 08/08/2021] [Indexed: 01/22/2023]
Abstract
Drug-repurposing technologies are growing in number and maturing. However, comparisons to each other and to reality are hindered because of a lack of consensus with respect to performance evaluation. Such comparability is necessary to determine scientific merit and to ensure that only meaningful predictions from repurposing technologies carry through to further validation and eventual patient use. Here, we review and compare performance evaluation measures for these technologies using version 2 of our shotgun repurposing Computational Analysis of Novel Drug Opportunities (CANDO) platform to illustrate their benefits, drawbacks, and limitations. Understanding and using different performance evaluation metrics ensures robust cross-platform comparability, enabling us to continue to strive toward optimal repurposing by decreasing the time and cost of drug discovery and development.
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Affiliation(s)
- James Schuler
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
| | - Zackary Falls
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - William Mangione
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Matthew L Hudson
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Liana Bruggemann
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
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16
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Duarte D, Vale N. Combining repurposed drugs to treat colorectal cancer. Drug Discov Today 2021; 27:165-184. [PMID: 34592446 DOI: 10.1016/j.drudis.2021.09.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 07/19/2021] [Accepted: 09/22/2021] [Indexed: 02/08/2023]
Abstract
The drug development process, especially of antineoplastic agents, has become increasingly costly and ineffective. Drug repurposing and drug combination are alternatives to de novo drug development, being low cost, rapid, and easy to apply. These strategies allow higher efficacy, decreased toxicity, and overcoming of drug resistance. The combination of antineoplastic agents is already being applied in cancer therapy, but the combination of repurposed drugs is still under-explored in pre- and clinical development. In this review, we provide a set of pharmacological concepts focusing on drug repurposing for treating colorectal cancer (CRC) and that are relevant for the application of new drug combinations against this disease.
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Affiliation(s)
- Diana Duarte
- OncoPharma Research Group, Center for Health Technology and Services Research (CINTESIS), Rua Dr. Plácido da Costa, 4200-450 Porto, Portugal; Faculty of Pharmacy, University of Porto, Rua de Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal
| | - Nuno Vale
- OncoPharma Research Group, Center for Health Technology and Services Research (CINTESIS), Rua Dr. Plácido da Costa, 4200-450 Porto, Portugal; Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Al. Prof. Hernâni Monteiro, 4200-319 Porto, Portugal.
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17
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Cui C, Ding X, Wang D, Chen L, Xiao F, Xu T, Zheng M, Luo X, Jiang H, Chen K. Drug repurposing against breast cancer by integrating drug-exposure expression profiles and drug-drug links based on graph neural network. Bioinformatics 2021; 37:2930-2937. [PMID: 33739367 PMCID: PMC8479657 DOI: 10.1093/bioinformatics/btab191] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 03/16/2021] [Accepted: 03/18/2021] [Indexed: 02/02/2023] Open
Abstract
MOTIVATION Breast cancer is one of the leading causes of cancer deaths among women worldwide. It is necessary to develop new breast cancer drugs because of the shortcomings of existing therapies. The traditional discovery process is time-consuming and expensive. Repositioning of clinically approved drugs has emerged as a novel approach for breast cancer therapy. However, serendipitous or experiential repurposing cannot be used as a routine method. RESULTS In this study, we proposed a graph neural network model GraphRepur based on GraphSAGE for drug repurposing against breast cancer. GraphRepur integrated two major classes of computational methods, drug network-based and drug signature-based. The differentially expressed genes of disease, drug-exposure gene expression data and the drug-drug links information were collected. By extracting the drug signatures and topological structure information contained in the drug relationships, GraphRepur can predict new drugs for breast cancer, outperforming previous state-of-the-art approaches and some classic machine learning methods. The high-ranked drugs have indeed been reported as new uses for breast cancer treatment recently. AVAILABILITYAND IMPLEMENTATION The source code of our model and datasets are available at: https://github.com/cckamy/GraphRepur and https://figshare.com/articles/software/GraphRepur_Breast_Cancer_Drug_Repurposing/14220050. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chen Cui
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoyu Ding
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dingyan Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lifan Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fu Xiao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | | | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- To whom correspondence should be addressed. or
| | - Xiaomin Luo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- To whom correspondence should be addressed. or
| | - Hualiang Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 200031, China
| | - Kaixian Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 200031, China
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18
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Chrétien B, Jourdan JP, Davis A, Fedrizzi S, Bureau R, Sassier M, Rochais C, Alexandre J, Lelong-Boulouard V, Dolladille C, Dallemagne P. Disproportionality analysis in VigiBase as a drug repositioning method for the discovery of potentially useful drugs in Alzheimer's disease. Br J Clin Pharmacol 2020; 87:2830-2837. [PMID: 33274491 DOI: 10.1111/bcp.14690] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 11/09/2020] [Accepted: 11/28/2020] [Indexed: 12/12/2022] Open
Abstract
Drug repositioning aims to propose new indications for marketed drugs. Although several methods exist, the utility of pharmacovigilance databases for this purpose is unclear. We conducted a disproportionality analysis in the World Health Organization pharmacovigilance database VigiBase to identify potential anticholinesterase drug candidates for repositioning in Alzheimer's disease (AD). METHODS Disproportionality analysis is a validated method for detecting significant associations between drugs and adverse events (AEs) in pharmacovigilance databases. We applied this approach in VigiBase to establish the safety profile displayed by the anticholinesterase drugs used in AD and searched the database for drugs with similar safety profiles. The detected drugs with potential activity against acetylcholinesterase and butyrylcholinesterases (BuChEs) were then evaluated to confirm their anticholinesterase potential. RESULTS We identified 22 drugs with safety profiles similar to AD medicines. Among these drugs, 4 (clozapine, aripiprazole, sertraline and S-duloxetine) showed a human BuChE inhibition rate of over 70% at 10-5 M. Their human BuChE half maximal inhibitory concentration values were compatible with clinical anticholinesterase action in humans at their normal doses. The most active human BuChE inhibitor in our study was S-duloxetine, with a half maximal inhibitory concentration of 1.2 μM. Combined with its ability to inhibit serotonin (5-HT) reuptake, the use of this drug could represent a novel multitarget directed ligand therapeutic strategy for AD. CONCLUSION We identified 4 drugs with repositioning potential in AD using drug safety profiles derived from a pharmacovigilance database. This method could be useful for future drug repositioning efforts.
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Affiliation(s)
- Basile Chrétien
- Department of Pharmacology, Caen University Hospital, Caen, F-14000, France.,Pharmacovigilance Regional Center, Caen University Hospital, Caen, F-14000, France
| | - Jean-Pierre Jourdan
- Department of Pharmacy, Caen University Hospital, Caen, F-14000, France.,Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Normandie Univ, UNICAEN, Caen, F-14000, France
| | - Audrey Davis
- Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Normandie Univ, UNICAEN, Caen, F-14000, France
| | - Sophie Fedrizzi
- Department of Pharmacology, Caen University Hospital, Caen, F-14000, France.,Pharmacovigilance Regional Center, Caen University Hospital, Caen, F-14000, France
| | - Ronan Bureau
- Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Normandie Univ, UNICAEN, Caen, F-14000, France
| | - Marion Sassier
- Department of Pharmacology, Caen University Hospital, Caen, F-14000, France.,Pharmacovigilance Regional Center, Caen University Hospital, Caen, F-14000, France
| | - Christophe Rochais
- Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Normandie Univ, UNICAEN, Caen, F-14000, France
| | - Joachim Alexandre
- Department of Pharmacology, Caen University Hospital, Caen, F-14000, France.,Pharmacovigilance Regional Center, Caen University Hospital, Caen, F-14000, France.,EA4650, Signalisation, électrophysiologie et imagerie des lésions d'ischémie-reperfusion myocardique, Normandie Univ, UNICAEN, Caen, 14000, France
| | - Véronique Lelong-Boulouard
- Department of Pharmacology, Caen University Hospital, Caen, F-14000, France.,INSERM UMR 1075, COMETE-MOBILITES "Vieillissement, Pathologie, Santé", Normandie Univ, UNICAEN, Caen, 14000, France
| | - Charles Dolladille
- Department of Pharmacology, Caen University Hospital, Caen, F-14000, France.,EA4650, Signalisation, électrophysiologie et imagerie des lésions d'ischémie-reperfusion myocardique, Normandie Univ, UNICAEN, Caen, 14000, France
| | - Patrick Dallemagne
- Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Normandie Univ, UNICAEN, Caen, F-14000, France
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19
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Mi Z, Guo B, Yang X, Yin Z, Zheng Z. LAMP: disease classification derived from layered assessment on modules and pathways in the human gene network. BMC Bioinformatics 2020; 21:487. [PMID: 33126852 PMCID: PMC7597061 DOI: 10.1186/s12859-020-03800-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 10/05/2020] [Indexed: 11/10/2022] Open
Abstract
Background Classification of diseases based on genetic information is of great significance as the basis for precision medicine, increasing the understanding of disease etiology and revolutionizing personalized medicine. Much effort has been directed at understanding disease associations by constructing disease networks, and classifying patient samples according to gene expression data. Integrating human gene networks overcomes limited coverage of genes. Incorporating pathway information into disease classification procedure addresses the challenge of cellular heterogeneity across patients.
Results In this work, we propose a disease classification model LAMP, which concentrates on the layered assessment on modules and pathways. Directed human gene interactions are the foundation of constructing the human gene network, where the significant roles of disease and pathway genes are recognized. The fast unfolding algorithm identifies 11 modules in the largest connected component. Then layered networks are introduced to distinguish positions of genes in propagating information from sources to targets. After gene screening, hierarchical clustering and refined process, 1726 diseases from KEGG are classified into 18 categories. Also, it is expounded that diseases with overlapping genes may not belong to the same category in LAMP. Within each category, entropy is applied to measure the compositional complexity, and to evaluate the prospects for combination diagnosis and gene-targeted therapy for diseases. Conclusion In this work, by collecting data from BioGRID and KEGG, we develop a disease classification model LAMP, to support people to view diseases from the perspective of commonalities in etiology and pathology. Comprehensive research on existing diseases can help meet the challenges of unknown diseases. The results provide suggestions for combination diagnosis and gene-targeted therapy, which motivates clinicians and researchers to reposition the understanding of diseases and explore diagnosis and therapy strategies.
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Affiliation(s)
- Zhilong Mi
- Beijing Advanced Innovation Center for Big Data and Brain Computing and LMIB, Beihang University, Beijing, China.,Peng Cheng Laboratory, Shenzhen, Guangdong Province, China.,School of Mathematical Sciences and Shenyuan Honors College, Beihang University, Beijing, China
| | - Binghui Guo
- Beijing Advanced Innovation Center for Big Data and Brain Computing and LMIB, Beihang University, Beijing, China. .,Peng Cheng Laboratory, Shenzhen, Guangdong Province, China. .,School of Mathematical Sciences and Shenyuan Honors College, Beihang University, Beijing, China.
| | - Xiaobo Yang
- Beijing Advanced Innovation Center for Big Data and Brain Computing and LMIB, Beihang University, Beijing, China.,Peng Cheng Laboratory, Shenzhen, Guangdong Province, China.,School of Mathematical Sciences and Shenyuan Honors College, Beihang University, Beijing, China
| | - Ziqiao Yin
- Beijing Advanced Innovation Center for Big Data and Brain Computing and LMIB, Beihang University, Beijing, China.,Peng Cheng Laboratory, Shenzhen, Guangdong Province, China.,School of Mathematical Sciences and Shenyuan Honors College, Beihang University, Beijing, China
| | - Zhiming Zheng
- Beijing Advanced Innovation Center for Big Data and Brain Computing and LMIB, Beihang University, Beijing, China.,Peng Cheng Laboratory, Shenzhen, Guangdong Province, China.,School of Mathematical Sciences and Shenyuan Honors College, Beihang University, Beijing, China
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20
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Masoudi-Sobhanzadeh Y, Masoudi-Nejad A. Synthetic repurposing of drugs against hypertension: a datamining method based on association rules and a novel discrete algorithm. BMC Bioinformatics 2020; 21:313. [PMID: 32677879 PMCID: PMC7469914 DOI: 10.1186/s12859-020-03644-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 07/06/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Drug repurposing aims to detect the new therapeutic benefits of the existing drugs and reduce the spent time and cost of the drug development projects. The synthetic repurposing of drugs may prove to be more useful than the single repurposing in terms of reducing toxicity and enhancing efficacy. However, the researchers have not given it serious consideration. To address the issue, a novel datamining method is introduced and applied to repositioning of drugs for hypertension (HT) which is a serious medical condition and needs some improved treatment plans to help treat it. RESULTS A novel two-step data mining method, which is based on the If-Then association rules as well as a novel discrete optimization algorithm, was introduced and applied to the synthetic repurposing of drugs for HT. The required data were also extracted from DrugBank, KEGG, and DrugR+ databases. The findings indicated that based on the different statistical criteria, the proposed method outperformed the other state-of-the-art approaches. In contrast to the previously proposed methods which had failed to discover a list on some datasets, our method could find a combination list for all of them. CONCLUSION Since the proposed synthetic method uses medications in small dosages, it might revive some failed drug development projects and put forward a suitable plan for treating different diseases such as COVID-19 and HT. It is also worth noting that applying efficient computational methods helps to produce better results.
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Affiliation(s)
- Yosef Masoudi-Sobhanzadeh
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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21
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Jourdan JP, Bureau R, Rochais C, Dallemagne P. Drug repositioning: a brief overview. J Pharm Pharmacol 2020; 72:1145-1151. [PMID: 32301512 PMCID: PMC7262062 DOI: 10.1111/jphp.13273] [Citation(s) in RCA: 179] [Impact Index Per Article: 44.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 03/17/2020] [Accepted: 03/21/2020] [Indexed: 12/21/2022]
Abstract
OBJECTIVES Drug repositioning, that is, the use of a drug in an indication other than the one for which it was initially marketed, is a growing trend. Its origins lie mainly in the attrition experienced in recent years in the field of new drug discovery. KEY FINDINGS Despite some regulatory and economic challenges, drug repositioning offers many advantages, and a number of recent successes have confirmed both its public health benefits and its commercial value. The first examples of successful drug repositioning mainly came about through serendipity like acetylsalicylic acid, thalidomide, sildenafil or dimethylfumarate. CONCLUSION The history of great-repositioned drugs has given some solutions to various pathologies. Serendipity is not yet useful to find repositioning drugs. Drug repositioning is of growing interest. Nowadays, a more rational approach to the identification of drug candidates for repositioning is possible, especially using data mining.
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Affiliation(s)
- Jean-Pierre Jourdan
- UNICAEN, Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Normandie Univ., Caen, France.,Pharmacy Department, CHRU de Caen, Caen, France
| | - Ronan Bureau
- UNICAEN, Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Normandie Univ., Caen, France
| | - Christophe Rochais
- UNICAEN, Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Normandie Univ., Caen, France
| | - Patrick Dallemagne
- UNICAEN, Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Normandie Univ., Caen, France
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22
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Oerton E, Roberts I, Lewis PSH, Guilliams T, Bender A. Understanding and predicting disease relationships through similarity fusion. Bioinformatics 2020; 35:1213-1220. [PMID: 30169824 PMCID: PMC6449746 DOI: 10.1093/bioinformatics/bty754] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Revised: 08/09/2018] [Accepted: 08/29/2018] [Indexed: 12/15/2022] Open
Abstract
Motivation Combining disease relationships across multiple biological levels could aid our understanding of common processes taking place in disease, potentially indicating opportunities for drug sharing. Here, we propose a similarity fusion approach which accounts for differences in information content between different data types, allowing combination of each data type in a balanced manner. Results We apply this method to six different types of biological data (ontological, phenotypic, literature co-occurrence, genetic association, gene expression and drug indication data) for 84 diseases to create a ‘disease map’: a network of diseases connected at one or more biological levels. As well as reconstructing known disease relationships, 15% of links in the disease map are novel links spanning traditional ontological classes, such as between psoriasis and inflammatory bowel disease. 62% of links in the disease map represent drug-sharing relationships, illustrating the relevance of the similarity fusion approach to the identification of potential therapeutic relationships. Availability and implementation Freely available under the MIT license at https://github.com/e-oerton/disease-similarity-fusion Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Erin Oerton
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.,Healx Ltd, Park House, Castle Park, Cambridge, UK
| | - Ian Roberts
- Healx Ltd, Park House, Castle Park, Cambridge, UK
| | | | | | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.,Healx Ltd, Park House, Castle Park, Cambridge, UK
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23
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Ayed M, Lim H, Xie L. Biological representation of chemicals using latent target interaction profile. BMC Bioinformatics 2019; 20:674. [PMID: 31861982 PMCID: PMC6924142 DOI: 10.1186/s12859-019-3241-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Background Computational prediction of a phenotypic response upon the chemical perturbation on a biological system plays an important role in drug discovery, and many other applications. Chemical fingerprints are a widely used feature to build machine learning models. However, the fingerprints that are derived from chemical structures ignore the biological context, thus, they suffer from several problems such as the activity cliff and curse of dimensionality. Fundamentally, the chemical modulation of biological activities is a multi-scale process. It is the genome-wide chemical-target interactions that modulate chemical phenotypic responses. Thus, the genome-scale chemical-target interaction profile will more directly correlate with in vitro and in vivo activities than the chemical structure. Nevertheless, the scope of direct application of the chemical-target interaction profile is limited due to the severe incompleteness, biasness, and noisiness of bioassay data. Results To address the aforementioned problems, we developed a novel chemical representation method: Latent Target Interaction Profile (LTIP). LTIP embeds chemicals into a low dimensional continuous latent space that represents genome-scale chemical-target interactions. Subsequently LTIP can be used as a feature to build machine learning models. Using the drug sensitivity of cancer cell lines as a benchmark, we have shown that the LTIP robustly outperforms chemical fingerprints regardless of machine learning algorithms. Moreover, the LTIP is complementary with the chemical fingerprints. It is possible for us to combine LTIP with other fingerprints to further improve the performance of bioactivity prediction. Conclusions Our results demonstrate the potential of LTIP in particular and multi-scale modeling in general in predictive modeling of chemical modulation of biological activities.
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Affiliation(s)
- Mohamed Ayed
- Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY, USA
| | - Hansaim Lim
- Ph.D. Program in Biochemistry, The Graduate Center, The City University of New York, New York, NY, USA
| | - Lei Xie
- Department of Computer Science, Hunter College, & The Graduate Center, The City University of New York, New York, NY, USA.
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24
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Güvenç Paltun B, Mamitsuka H, Kaski S. Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches. Brief Bioinform 2019; 22:346-359. [PMID: 31838491 PMCID: PMC7820853 DOI: 10.1093/bib/bbz153] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 11/01/2019] [Accepted: 11/04/2019] [Indexed: 12/17/2022] Open
Abstract
Predicting the response of cancer cell lines to specific drugs is one of the central problems in personalized medicine, where the cell lines show diverse characteristics. Researchers have developed a variety of computational methods to discover associations between drugs and cell lines, and improved drug sensitivity analyses by integrating heterogeneous biological data. However, choosing informative data sources and methods that can incorporate multiple sources efficiently is the challenging part of successful analysis in personalized medicine. The reason is that finding decisive factors of cancer and developing methods that can overcome the problems of integrating data, such as differences in data structures and data complexities, are difficult. In this review, we summarize recent advances in data integration-based machine learning for drug response prediction, by categorizing methods as matrix factorization-based, kernel-based and network-based methods. We also present a short description of relevant databases used as a benchmark in drug response prediction analyses, followed by providing a brief discussion of challenges faced in integrating and interpreting data from multiple sources. Finally, we address the advantages of combining multiple heterogeneous data sources on drug sensitivity analysis by showing an experimental comparison. Contact: betul.guvenc@aalto.fi
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Affiliation(s)
- Betül Güvenç Paltun
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
| | - Hiroshi Mamitsuka
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan
| | - Samuel Kaski
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan
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25
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Xuan P, Cui H, Shen T, Sheng N, Zhang T. HeteroDualNet: A Dual Convolutional Neural Network With Heterogeneous Layers for Drug-Disease Association Prediction via Chou's Five-Step Rule. Front Pharmacol 2019; 10:1301. [PMID: 31780934 PMCID: PMC6856670 DOI: 10.3389/fphar.2019.01301] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 10/11/2019] [Indexed: 11/14/2022] Open
Abstract
Identifying new treatments for existing drugs can help reduce drug development costs and explore novel indications of drugs. The prediction of associations between drugs and diseases is challenging because their similarities and relations are complicated and non-linear. We propose a HeteroDualNet model to address this issue. Firstly, three types of matrices are extracted to represent intra-drug similarities, intra-disease similarity and drug-disease associations. The intra-drug similarities consider three drug features and a newly introduced drug-related disease correlation. Secondly, an embedding mechanism is proposed to integrate these matrices in a heterogenous drug-disease association layer (hetero-layer). Further, a neighbouring heterogeneous layer (hetero-layer-N) is constructed to incorporate the biological premise that similar drugs can often treat related diseases. Finally, a dual convolutional neural network is built with hetero-layer and hetero-layer-N as two branches to learn from characteristics of drug-disease and the relations of their neighbours simultaneously. HeteroDualNet outperformed the other four methods in comparison over a public dataset of 763 drugs and 681 diseases in terms of Areas Under the Curves of Receiver Operating Characteristics and Precision-Recall, and recall rate at top k. Case study of five drugs further proved the capacity of HeteroDualNet in finding reliable disease candidates of drugs as validated by database records or literature. Our findings show that the embedded heterogenous layers of original and neighbouring drug-disease representations in a dual neural network improved the association prediction performance.
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Affiliation(s)
- Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Bundoora, VIC, Australia
| | - Tonghui Shen
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Nan Sheng
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Tiangang Zhang
- School of Mathematical Science, Heilongjiang University, Harbin, China
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26
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Yang G, Ma A, Qin ZS. An Integrated System Biology Approach Yields Drug Repositioning Candidates for the Treatment of Heart Failure. Front Genet 2019; 10:916. [PMID: 31608126 PMCID: PMC6773955 DOI: 10.3389/fgene.2019.00916] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 08/29/2019] [Indexed: 12/20/2022] Open
Abstract
Identifying effective pharmacological treatments for heart failure (HF) patients remains critically important. Given that the development of drugs de novo is expensive and time consuming, drug repositioning has become an increasingly important branch. In the present study, we propose a two-step drug repositioning pipeline and investigate the novel therapeutic effects of existing drugs approved by the US Food and Drug Administration to discover potential therapeutic drugs for HF. In the first step, we compared the gene expression pattern of HF patients with drug-induced gene expression profiles to obtain preliminary candidates. In the second step, we performed a systems biology approach based on the known protein–protein interaction information and targets of drugs to narrow down preliminary candidates to obtain final candidates. Drug set enrichment analysis and literature search were applied to assess the performance of our repositioning approach. We also constructed a mode of action network for each candidate and performed pathway analysis for each candidate using genes contained in their mode of action network to uncover pathways that potentially reflect the mechanisms of candidates’ therapeutic efficacy to HF. We discovered numerous preliminary candidates, some of which are used in clinical practice and supported by the literature. The final candidates contained nearly all of the preliminary candidates supported by previous studies. Drug set enrichment analysis and literature search support the validity of our repositioning approach. The mode of action network for each candidate not only displayed the underlying mechanisms of drug efficacy but also uncovered potential biomarkers and therapeutic targets for HF. Our two-step drug repositioning approach is efficient to find candidates with potential therapeutic efficiency to HF supported by the literature and might be of particular use in the discovery of novel effective pharmacological therapies for HF.
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Affiliation(s)
- Guodong Yang
- Department of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States
| | - Aiqun Ma
- Department of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Zhaohui S Qin
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States
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27
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Rifaioglu AS, Atas H, Martin MJ, Cetin-Atalay R, Atalay V, Doğan T. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Brief Bioinform 2019; 20:1878-1912. [PMID: 30084866 PMCID: PMC6917215 DOI: 10.1093/bib/bby061] [Citation(s) in RCA: 223] [Impact Index Per Article: 44.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 05/25/2018] [Indexed: 01/16/2023] Open
Abstract
The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as 'virtual screening' (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance. The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.
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Affiliation(s)
- Ahmet Sureyya Rifaioglu
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
- Department of Computer Engineering, İskenderun Technical University, Hatay, Turkey
| | - Heval Atas
- Cancer System Biology Laboratory (CanSyL), Graduate School of Informatics, Middle East Technical University, Ankara, Turkey
| | - Maria Jesus Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Cambridge, Hinxton, UK
| | - Rengul Cetin-Atalay
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Volkan Atalay
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Tunca Doğan
- Cancer System Biology Laboratory (CanSyL), Graduate School of Informatics, Middle East Technical University, Ankara, Turkey and European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Cambridge, Hinxton, UK
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28
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Xuan P, Song Y, Zhang T, Jia L. Prediction of Potential Drug-Disease Associations through Deep Integration of Diversity and Projections of Various Drug Features. Int J Mol Sci 2019; 20:ijms20174102. [PMID: 31443472 PMCID: PMC6747548 DOI: 10.3390/ijms20174102] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 08/19/2019] [Accepted: 08/20/2019] [Indexed: 11/17/2022] Open
Abstract
Identifying new indications for existing drugs may reduce costs and expedites drug development. Drug-related disease predictions typically combined heterogeneous drug-related and disease-related data to derive the associations between drugs and diseases, while recently developed approaches integrate multiple kinds of drug features, but fail to take the diversity implied by these features into account. We developed a method based on non-negative matrix factorization, DivePred, for predicting potential drug–disease associations. DivePred integrated disease similarity, drug–disease associations, and various drug features derived from drug chemical substructures, drug target protein domains, drug target annotations, and drug-related diseases. Diverse drug features reflect the characteristics of drugs from different perspectives, and utilizing the diversity of multiple kinds of features is critical for association prediction. The various drug features had higher dimensions and sparse characteristics, whereas DivePred projected high-dimensional drug features into the low-dimensional feature space to generate dense feature representations of drugs. Furthermore, DivePred’s optimization term enhanced diversity and reduced redundancy of multiple kinds of drug features. The neighbor information was exploited to infer the likelihood of drug–disease associations. Experiments indicated that DivePred was superior to several state-of-the-art methods for prediction drug-disease association. During the validation process, DivePred identified more drug-disease associations in the top part of prediction result than other methods, benefitting further biological validation. Case studies of acetaminophen, ciprofloxacin, doxorubicin, hydrocortisone, and ampicillin demonstrated that DivePred has the ability to discover potential candidate disease indications for drugs.
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Affiliation(s)
- Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Yingying Song
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Tiangang Zhang
- School of Mathematical Science, Heilongjiang University, Harbin 150080, China.
| | - Lan Jia
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
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29
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Mi Z, Guo B, Yin Z, Li J, Zheng Z. Disease classification via gene network integrating modules and pathways. ROYAL SOCIETY OPEN SCIENCE 2019; 6:190214. [PMID: 31417727 PMCID: PMC6689581 DOI: 10.1098/rsos.190214] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Accepted: 06/04/2019] [Indexed: 06/10/2023]
Abstract
Disease classification based on gene information has been of significance as the foundation for achieving precision medicine. Previous works focus on classifying diseases according to the gene expression data of patient samples, and constructing disease network based on the overlap of disease genes, as many genes have been confirmed to be associated with diseases. In this work, the effects of diseases on human biological functions are assessed from the perspective of gene network modules and pathways, and the distances between diseases are defined to carry out the classification models. In total, 1728 diseases are divided into 12 and 14 categories by the intensity and scope of effects on pathways, respectively. Each category is a mix of several types of diseases identified based on congenital and acquired factors as well as diseased tissues and organs. The disease classification models on the basis of gene network are parallel with traditional pathology classification based on anatomic and clinical manifestations, and enable us to look at diseases in the viewpoint of commonalities in etiology and pathology. Our models provide a foundation for exploring combination therapy of diseases, which in turn may inform strategies for future gene-targeted therapy.
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Affiliation(s)
- Zhilong Mi
- Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing 100191, People’s Republic of China
- LMIB and School of Mathematics and Systems Science, Beihang University, Beijing 100191, People’s Republic of China
- Peng Cheng Laboratory, Shenzhen, Guangdong Province 518055, People’s Republic of China
| | - Binghui Guo
- Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing 100191, People’s Republic of China
- LMIB and School of Mathematics and Systems Science, Beihang University, Beijing 100191, People’s Republic of China
- Peng Cheng Laboratory, Shenzhen, Guangdong Province 518055, People’s Republic of China
| | - Ziqiao Yin
- Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing 100191, People’s Republic of China
- LMIB and School of Mathematics and Systems Science, Beihang University, Beijing 100191, People’s Republic of China
- Shenyuan Honors College, Beihang University, Beijing 100191, People’s Republic of China
- Peng Cheng Laboratory, Shenzhen, Guangdong Province 518055, People’s Republic of China
| | - Jiahui Li
- Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing 100191, People’s Republic of China
- LMIB and School of Mathematics and Systems Science, Beihang University, Beijing 100191, People’s Republic of China
- Peng Cheng Laboratory, Shenzhen, Guangdong Province 518055, People’s Republic of China
| | - Zhiming Zheng
- Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing 100191, People’s Republic of China
- LMIB and School of Mathematics and Systems Science, Beihang University, Beijing 100191, People’s Republic of China
- Peng Cheng Laboratory, Shenzhen, Guangdong Province 518055, People’s Republic of China
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30
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Dozmorov MG. Disease classification: from phenotypic similarity to integrative genomics and beyond. Brief Bioinform 2019; 20:1769-1780. [DOI: 10.1093/bib/bby049] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 05/01/2018] [Indexed: 02/06/2023] Open
Abstract
Abstract
A fundamental challenge of modern biomedical research is understanding how diseases that are similar on the phenotypic level are similar on the molecular level. Integration of various genomic data sets with the traditionally used phenotypic disease similarity revealed novel genetic and molecular mechanisms and blurred the distinction between monogenic (Mendelian) and complex diseases. Network-based medicine has emerged as a complementary approach for identifying disease-causing genes, genetic mediators, disruptions in the underlying cellular functions and for drug repositioning. The recent development of machine and deep learning methods allow for leveraging real-life information about diseases to refine genetic and phenotypic disease relationships. This review describes the historical development and recent methodological advancements for studying disease classification (nosology).
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Affiliation(s)
- Mikhail G Dozmorov
- Department of Biostatistics, Virginia Commonwealth University, 830 East Main Street, Richmond, VA, USA
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31
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Abstract
Background Drug repositioning, also known as drug repurposing, defines new indications for existing drugs and can be used as an alternative to drug development. In recent years, the accumulation of large volumes of information related to drugs and diseases has led to the development of various computational approaches for drug repositioning. Although herbal medicines have had a great impact on current drug discovery, there are still a large number of herbal compounds that have no definite indications. Results In the present study, we constructed a computational model to predict the unknown pharmacological effects of herbal compounds using machine learning techniques. Based on the assumption that similar diseases can be treated with similar drugs, we used four categories of drug-drug similarity (e.g., chemical structure, side-effects, gene ontology, and targets) and three categories of disease-disease similarity (e.g., phenotypes, human phenotype ontology, and gene ontology). Then, associations between drug and disease were predicted using the employed similarity features. The prediction models were constructed using classification algorithms, including logistic regression, random forest and support vector machine algorithms. Upon cross-validation, the random forest approach showed the best performance (AUC = 0.948) and also performed well in an external validation assessment using an unseen independent dataset (AUC = 0.828). Finally, the constructed model was applied to predict potential indications for existing drugs and herbal compounds. As a result, new indications for 20 existing drugs and 31 herbal compounds were predicted and validated using clinical trial data. Conclusions The predicted results were validated manually confirming the performance and underlying mechanisms – for example, irinotecan as a treatment for neuroblastoma. From the prediction, herbal compounds were considered to be drug candidates for related diseases which is important to be further developed. The proposed prediction model can contribute to drug discovery by suggesting drug candidates from herbal compounds which have potentials but few were studied. Electronic supplementary material The online version of this article (10.1186/s12859-019-2811-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Eunyoung Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea
| | - A-Sol Choi
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea
| | - Hojung Nam
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea.
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32
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Masoudi-Sobhanzadeh Y, Omidi Y, Amanlou M, Masoudi-Nejad A. DrugR+: A comprehensive relational database for drug repurposing, combination therapy, and replacement therapy. Comput Biol Med 2019; 109:254-262. [PMID: 31096089 DOI: 10.1016/j.compbiomed.2019.05.006] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 04/26/2019] [Accepted: 05/06/2019] [Indexed: 12/14/2022]
Abstract
Drug repurposing or repositioning, which introduces new applications of the existing drugs, is an emerging field in drug discovery scope. To enhance the success rate of the research and development (R&D) process in a cost- and time-effective manner, a number of pharmaceutical companies worldwide have made tremendous investments. Besides, many researchers have proposed various methods and databases for the repurposing of various drugs. However, there is not a proper and well-organized database available. To this end, for the first time, we developed a new database based on DrugBank and KEGG data, which is named "DrugR+". Our developed database provides some advantages relative to the DrugBank, and its interface supplies new capabilities for both single and synthetic repositioning of drugs. Moreover, it includes four new datasets which can be used for predicting drug-target interactions using supervised machine learning methods. As a case study, we introduced novel applications of some drugs and discussed the obtained results. A comparison of several machine learning methods on the generated datasets has also been reported in the Supplementary File. Having included several normalized tables, DrugR + has been organized to provide key information on data structures for the repurposing and combining applications of drugs. It provides the SQL query capability for professional users and an appropriate method with different options for unprofessional users. Additionally, DrugR + consists of repurposing service that accepts a drug and proposes a list of potential drugs for some usages. Taken all, DrugR+ is a free web-based database and accessible using (http://www.drugr.ir), which can be updated through a map-reduce parallel processing method to provide the most relevant information.
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Affiliation(s)
- Yosef Masoudi-Sobhanzadeh
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Yadollah Omidi
- Research Center for Pharmaceutical Nanotechnology and Department of Pharmaceutics, Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Massoud Amanlou
- Drug Design and Development Research Center, The Institute of Pharmaceutical Sciences (TIPS), Tehran University of Medical Sciences, Tehran, 14176-53955, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. http://LBB.ut.ac.ir
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Xuan P, Cao Y, Zhang T, Wang X, Pan S, Shen T. Drug repositioning through integration of prior knowledge and projections of drugs and diseases. Bioinformatics 2019; 35:4108-4119. [DOI: 10.1093/bioinformatics/btz182] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 02/24/2019] [Accepted: 03/12/2019] [Indexed: 12/20/2022] Open
Abstract
Abstract
Motivation
Identifying and developing novel therapeutic effects for existing drugs contributes to reduction of drug development costs. Most of the previous methods focus on integration of the heterogeneous data of drugs and diseases from multiple sources for predicting the candidate drug–disease associations. However, they fail to take the prior knowledge of drugs and diseases and their sparse characteristic into account. It is essential to develop a method that exploits the more useful information to predict the reliable candidate associations.
Results
We present a method based on non-negative matrix factorization, DisDrugPred, to predict the drug-related candidate disease indications. A new type of drug similarity is firstly calculated based on their associated diseases. DisDrugPred completely integrates two types of disease similarities, the associations between drugs and diseases, and the various similarities between drugs from different levels including the chemical structures of drugs, the target proteins of drugs, the diseases associated with drugs and the side effects of drugs. The prior knowledge of drugs and diseases and the sparse characteristic of drug–disease associations provide a deep biological perspective for capturing the relationships between drugs and diseases. Simultaneously, the possibility that a drug is associated with a disease is also dependant on their projections in the low-dimension feature space. Therefore, DisDrugPred deeply integrates the diverse prior knowledge, the sparse characteristic of associations and the projections of drugs and diseases. DisDrugPred achieves superior prediction performance than several state-of-the-art methods for drug–disease association prediction. During the validation process, DisDrugPred also can retrieve more actual drug–disease associations in the top part of prediction result which often attracts more attention from the biologists. Moreover, case studies on five drugs further confirm DisDrugPred’s ability to discover potential candidate disease indications for drugs.
Availability and implementation
The fourth type of drug similarity and the predicted candidates for all the drugs are available at https://github.com/pingxuan-hlju/DisDrugPred.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Yangkun Cao
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Tiangang Zhang
- School of Mathematical Science, Heilongjiang University, Harbin, China
| | - Xiao Wang
- School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
| | - Shuxiang Pan
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Tonghui Shen
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
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34
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Farha MA, Brown ED. Drug repurposing for antimicrobial discovery. Nat Microbiol 2019; 4:565-577. [PMID: 30833727 DOI: 10.1038/s41564-019-0357-1] [Citation(s) in RCA: 181] [Impact Index Per Article: 36.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 01/03/2019] [Indexed: 12/17/2022]
Abstract
Antimicrobial resistance continues to be a public threat on a global scale. The ongoing need to develop new antimicrobial drugs that are effective against multi-drug-resistant pathogens has spurred the research community to invest in various drug discovery strategies, one of which is drug repurposing-the process of finding new uses for existing drugs. While still nascent in the antimicrobial field, the approach is gaining traction in both the public and private sector. While the approach has particular promise in fast-tracking compounds into clinical studies, it nevertheless has substantial obstacles to success. This Review covers the art of repurposing existing drugs for antimicrobial purposes. We discuss enabling screening platforms for antimicrobial discovery and present encouraging findings of novel antimicrobial therapeutic strategies. Also covered are general advantages of repurposing over de novo drug development and challenges of the strategy, including scientific, intellectual property and regulatory issues.
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Affiliation(s)
- Maya A Farha
- Michael G. DeGroote Institute for Infectious Disease Research, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Eric D Brown
- Michael G. DeGroote Institute for Infectious Disease Research, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada.
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35
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Iwata M, Hirose L, Kohara H, Liao J, Sawada R, Akiyoshi S, Tani K, Yamanishi Y. Pathway-Based Drug Repositioning for Cancers: Computational Prediction and Experimental Validation. J Med Chem 2018; 61:9583-9595. [PMID: 30371064 DOI: 10.1021/acs.jmedchem.8b01044] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Developing drugs with anticancer activity and low toxic side-effects at low costs is a challenging issue for cancer chemotherapy. In this work, we propose to use molecular pathways as the therapeutic targets and develop a novel computational approach for drug repositioning for cancer treatment. We analyzed chemically induced gene expression data of 1112 drugs on 66 human cell lines and searched for drugs that inactivate pathways involved in the growth of cancer cells (cell cycle) and activate pathways that contribute to the death of cancer cells (e.g., apoptosis and p53 signaling). Finally, we performed a large-scale prediction of potential anticancer effects for all the drugs and experimentally validated the prediction results via three in vitro cellular assays that evaluate cell viability, cytotoxicity, and apoptosis induction. Using this strategy, we successfully identified several potential anticancer drugs. The proposed pathway-based method has great potential to improve drug repositioning research for cancer treatment.
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Affiliation(s)
- Michio Iwata
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering , Kyushu Institute of Technology , 680-4 Kawazu , Iizuka , Fukuoka 820-8502 , Japan
| | - Lisa Hirose
- Project Division of ALA Advanced Medical Research, The Institute of Medical Science , The University of Tokyo , 4-6-1 Shirokanedai , Minato-ku , Tokyo 108-8639 , Japan
| | - Hiroshi Kohara
- Project Division of ALA Advanced Medical Research, The Institute of Medical Science , The University of Tokyo , 4-6-1 Shirokanedai , Minato-ku , Tokyo 108-8639 , Japan.,Division of Molecular and Clinical Genetics, Department of Molecular Genetics, Medical Institute of Bioregulation , Kyushu University , 3-1-1 Maidashi , Higashi-ku , Fukuoka, Fukuoka 812-8582 , Japan
| | - Jiyuan Liao
- Project Division of ALA Advanced Medical Research, The Institute of Medical Science , The University of Tokyo , 4-6-1 Shirokanedai , Minato-ku , Tokyo 108-8639 , Japan.,Division of Molecular and Clinical Genetics, Department of Molecular Genetics, Medical Institute of Bioregulation , Kyushu University , 3-1-1 Maidashi , Higashi-ku , Fukuoka, Fukuoka 812-8582 , Japan
| | - Ryusuke Sawada
- Medical Institute of Bioregulation , Kyushu University , 3-1-1 Maidashi , Higashi-ku , Fukuoka, Fukuoka 812-8582 , Japan
| | - Sayaka Akiyoshi
- Medical Institute of Bioregulation , Kyushu University , 3-1-1 Maidashi , Higashi-ku , Fukuoka, Fukuoka 812-8582 , Japan
| | - Kenzaburo Tani
- Project Division of ALA Advanced Medical Research, The Institute of Medical Science , The University of Tokyo , 4-6-1 Shirokanedai , Minato-ku , Tokyo 108-8639 , Japan.,Division of Molecular Design, Research Center for Systems Immunology, Medical Institute of Bioregulation , Kyushu University , 3-1-1 Maidashi , Higashi-ku , Fukuoka, Fukuoka 812-8582 , Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering , Kyushu Institute of Technology , 680-4 Kawazu , Iizuka , Fukuoka 820-8502 , Japan.,PRESTO , Japan Science and Technology Agency , Kawaguchi , Saitama 332-0012 , Japan
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36
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Lu L, Yu H. DR2DI: a powerful computational tool for predicting novel drug-disease associations. J Comput Aided Mol Des 2018; 32:633-642. [DOI: 10.1007/s10822-018-0117-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Accepted: 04/01/2018] [Indexed: 01/01/2023]
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Predicting inhibitory and activatory drug targets by chemically and genetically perturbed transcriptome signatures. Sci Rep 2018; 8:156. [PMID: 29317676 PMCID: PMC5760621 DOI: 10.1038/s41598-017-18315-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 12/08/2017] [Indexed: 02/06/2023] Open
Abstract
Genome-wide identification of all target proteins of drug candidate compounds is a challenging issue in drug discovery. Moreover, emerging phenotypic effects, including therapeutic and adverse effects, are heavily dependent on the inhibition or activation of target proteins. Here we propose a novel computational method for predicting inhibitory and activatory targets of drug candidate compounds. Specifically, we integrated chemically-induced and genetically-perturbed gene expression profiles in human cell lines, which avoided dependence on chemical structures of compounds or proteins. Predictive models for individual target proteins were simultaneously constructed by the joint learning algorithm based on transcriptomic changes in global patterns of gene expression profiles following chemical treatments, and following knock-down and over-expression of proteins. This method discriminates between inhibitory and activatory targets and enables accurate identification of therapeutic effects. Herein, we comprehensively predicted drug-target-disease association networks for 1,124 drugs, 829 target proteins, and 365 human diseases, and validated some of these predictions in vitro. The proposed method is expected to facilitate identification of new drug indications and potential adverse effects.
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38
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Chen H, Zhang Z, Peng W. miRDDCR: a miRNA-based method to comprehensively infer drug-disease causal relationships. Sci Rep 2017; 7:15921. [PMID: 29162848 PMCID: PMC5698443 DOI: 10.1038/s41598-017-15716-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Accepted: 10/31/2017] [Indexed: 01/10/2023] Open
Abstract
Revealing the cause-and-effect mechanism behind drug-disease relationships remains a challenging task. Recent studies suggested that drugs can target microRNAs (miRNAs) and alter their expression levels. In the meanwhile, the inappropriate expression of miRNAs will lead to various diseases. Therefore, targeting specific miRNAs by small-molecule drugs to modulate their activities provides a promising approach to human disease treatment. However, few studies attempt to discover drug-disease causal relationships through the molecular level of miRNAs. Here, we developed a miRNA-based inference method miRDDCR to comprehensively predict drug-disease causal relationships. We first constructed a three-layer drug-miRNA-disease heterogeneous network by combining similarity measurements, existing drug-miRNA associations and miRNA-disease associations. Then, we extended the algorithm of Random Walk to the three-layer heterogeneous network and ranked the potential indications for drugs. Leave-one-out cross-validations and case studies demonstrated that our method miRDDCR can achieve excellent prediction power. Compared with related methods, our causality discovery-based algorithm showed superior prediction ability and highlighted the molecular basis miRNAs, which can be used to assist in the experimental design for drug development and disease treatment. Finally, comprehensively inferred drug-disease causal relationships were released for further studies.
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Affiliation(s)
- Hailin Chen
- School of Software, East China Jiaotong University, Nanchang, China.
| | - Zuping Zhang
- School of Information Science and Engineering, Central South University, Changsha, China
| | - Wei Peng
- Computer Center of Kunming University of Science and Technology, Kunming, China
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Murtazalieva KA, Druzhilovskiy DS, Goel RK, Sastry GN, Poroikov VV. How good are publicly available web services that predict bioactivity profiles for drug repurposing? SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2017; 28:843-862. [PMID: 29183230 DOI: 10.1080/1062936x.2017.1399448] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Accepted: 10/29/2017] [Indexed: 06/07/2023]
Abstract
Drug repurposing provides a non-laborious and less expensive way for finding new human medicines. Computational assessment of bioactivity profiles shed light on the hidden pharmacological potential of the launched drugs. Currently, several freely available computational tools are available via the Internet, which predict multitarget profiles of drug-like compounds. They are based on chemical similarity assessment (ChemProt, SuperPred, SEA, SwissTargetPrediction and TargetHunter) or machine learning methods (ChemProt and PASS). To compare their performance, this study has created two evaluation sets, consisting of (1) 50 well-known repositioned drugs and (2) 12 drugs recently patented for new indications. In the first set, sensitivity values varied from 0.64 (TarPred) to 1.00 (PASS Online) for the initial indications and from 0.64 (TarPred) to 0.98 (PASS Online) for the repurposed indications. In the second set, sensitivity values varied from 0.08 (SuperPred) to 1.00 (PASS Online) for the initial indications and from 0.00 (SuperPred) to 1.00 (PASS Online) for the repurposed indications. Thus, this analysis demonstrated that the performance of machine learning methods surpassed those of chemical similarity assessments, particularly in the case of novel repurposed indications.
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Affiliation(s)
- K A Murtazalieva
- a Institute of Biomedical Chemistry , Moscow , Russia
- b Pirogov Russian National Research Medical University , Moscow , Russia
| | | | - R K Goel
- c Punjabi University , Patiala , Punjab , India
| | - G N Sastry
- d CSIR-Indian Institute of Chemical Technology , Hyderabad , India
| | - V V Poroikov
- a Institute of Biomedical Chemistry , Moscow , Russia
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40
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March-Vila E, Pinzi L, Sturm N, Tinivella A, Engkvist O, Chen H, Rastelli G. On the Integration of In Silico Drug Design Methods for Drug Repurposing. Front Pharmacol 2017; 8:298. [PMID: 28588497 PMCID: PMC5440551 DOI: 10.3389/fphar.2017.00298] [Citation(s) in RCA: 113] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Accepted: 05/10/2017] [Indexed: 11/13/2022] Open
Abstract
Drug repurposing has become an important branch of drug discovery. Several computational approaches that help to uncover new repurposing opportunities and aid the discovery process have been put forward, or adapted from previous applications. A number of successful examples are now available. Overall, future developments will greatly benefit from integration of different methods, approaches and disciplines. Steps forward in this direction are expected to help to clarify, and therefore to rationally predict, new drug–target, target–disease, and ultimately drug–disease associations.
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Affiliation(s)
- Eric March-Vila
- Molecular Modelling & Drug Design Lab, Department of Life Sciences, University of Modena and Reggio EmiliaModena, Italy
| | - Luca Pinzi
- Molecular Modelling & Drug Design Lab, Department of Life Sciences, University of Modena and Reggio EmiliaModena, Italy
| | - Noé Sturm
- Molecular Modelling & Drug Design Lab, Department of Life Sciences, University of Modena and Reggio EmiliaModena, Italy
| | - Annachiara Tinivella
- Molecular Modelling & Drug Design Lab, Department of Life Sciences, University of Modena and Reggio EmiliaModena, Italy
| | - Ola Engkvist
- Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&D GothenburgMölndal, Sweden
| | - Hongming Chen
- Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&D GothenburgMölndal, Sweden
| | - Giulio Rastelli
- Molecular Modelling & Drug Design Lab, Department of Life Sciences, University of Modena and Reggio EmiliaModena, Italy
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41
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Brown AS, Patel CJ. MeSHDD: Literature-based drug-drug similarity for drug repositioning. J Am Med Inform Assoc 2017; 24:614-618. [PMID: 27678460 PMCID: PMC5391732 DOI: 10.1093/jamia/ocw142] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2016] [Revised: 08/17/2016] [Accepted: 08/23/2016] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVE Drug repositioning is a promising methodology for reducing the cost and duration of the drug discovery pipeline. We sought to develop a computational repositioning method leveraging annotations in the literature, such as Medical Subject Heading (MeSH) terms. METHODS We developed software to determine significantly co-occurring drug-MeSH term pairs and a method to estimate pair-wise literature-derived distances between drugs. RESULTS We found that literature-based drug-drug similarities predicted the number of shared indications across drug-drug pairs. Clustering drugs based on their similarity revealed both known and novel drug indications. We demonstrate the utility of our approach by generating repositioning hypotheses for the commonly used diabetes drug metformin. CONCLUSION Our study demonstrates that literature-derived similarity is useful for identifying potential repositioning opportunities. We provided open-source code and deployed a free-to-use, interactive application to explore our database of similarity-based drug clusters (available at http://apps.chiragjpgroup.org/MeSHDD/ ).
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Affiliation(s)
- Adam S Brown
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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42
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Prachayasittikul V, Prathipati P, Pratiwi R, Phanus-Umporn C, Malik AA, Schaduangrat N, Seenprachawong K, Wongchitrat P, Supokawej A, Prachayasittikul V, Wikberg JES, Nantasenamat C. Exploring the epigenetic drug discovery landscape. Expert Opin Drug Discov 2017; 12:345-362. [PMID: 28276705 DOI: 10.1080/17460441.2017.1295954] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
INTRODUCTION Epigenetic modification has been implicated in a wide range of diseases and the ability to modulate such systems is a lucrative therapeutic strategy in drug discovery. Areas covered: This article focuses on the concepts and drug discovery aspects of epigenomics. This is achieved by providing a survey of the following concepts: (i) factors influencing epigenetics, (ii) diseases arising from epigenetics, (iii) epigenetic enzymes as druggable targets along with coverage of existing FDA-approved drugs and pharmacological agents, and (iv) drug repurposing/repositioning as a means for rapid discovery of pharmacological agents targeting epigenetics. Expert opinion: Despite significant interests in targeting epigenetic modifiers as a therapeutic route, certain classes of target proteins are heavily studied while some are less characterized. Thus, such orphan target proteins are not yet druggable with limited report of active modulators. Current research points towards a great future with novel drugs directed to the many complex multifactorial diseases of humans, which are still often poorly understood and difficult to treat.
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Affiliation(s)
- Veda Prachayasittikul
- a Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology , Mahidol University , Bangkok , Thailand
| | - Philip Prathipati
- b National Institutes of Biomedical Innovation, Health and Nutrition , Osaka , Japan
| | - Reny Pratiwi
- a Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology , Mahidol University , Bangkok , Thailand
| | - Chuleeporn Phanus-Umporn
- a Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology , Mahidol University , Bangkok , Thailand
| | - Aijaz Ahmad Malik
- a Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology , Mahidol University , Bangkok , Thailand
| | - Nalini Schaduangrat
- a Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology , Mahidol University , Bangkok , Thailand
| | - Kanokwan Seenprachawong
- c Department of Clinical Microscopy, Faculty of Medical Technology , Mahidol University , Bangkok , Thailand
| | - Prapimpun Wongchitrat
- d Center for Research and Innovation, Faculty of Medical Technology , Mahidol University , Bangkok , Thailand
| | - Aungkura Supokawej
- c Department of Clinical Microscopy, Faculty of Medical Technology , Mahidol University , Bangkok , Thailand
| | - Virapong Prachayasittikul
- e Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology , Mahidol University , Bangkok , Thailand
| | - Jarl E S Wikberg
- f Department of Pharmaceutical Biosciences , Uppsala University , Uppsala , Sweden
| | - Chanin Nantasenamat
- a Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology , Mahidol University , Bangkok , Thailand
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Santucci A, Bernardini G, Braconi D, Petricci E, Manetti F. 4-Hydroxyphenylpyruvate Dioxygenase and Its Inhibition in Plants and Animals: Small Molecules as Herbicides and Agents for the Treatment of Human Inherited Diseases. J Med Chem 2017; 60:4101-4125. [PMID: 28128559 DOI: 10.1021/acs.jmedchem.6b01395] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
This review mainly focuses on the physiological function of 4-hydroxyphenylpyruvate dioxygenase (HPPD), as well as on the development and application of HPPD inhibitors of several structural classes. Among them, one illustrative example is represented by compounds belonging to the class of triketone compounds. They were discovered by serendipitous observations on weed growth and were developed as bleaching herbicides. Informed reasoning on nitisinone (NTBC, 14), a triketone that failed to reach the final steps of the herbicidal design and development process, allowed it to become a curative agent for type I tyrosinemia (T1T) and to enter clinical trials for alkaptonuria. These results boosted the research of new compounds able to interfere with HPPD activity to be used for the treatment of the tyrosine metabolism-related diseases.
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Affiliation(s)
- Annalisa Santucci
- Dipartimento di Biotecnologie, Chimica e Farmacia, Università degli Studi di Siena , via A. Moro 2, I-53100 Siena, Italy
| | - Giulia Bernardini
- Dipartimento di Biotecnologie, Chimica e Farmacia, Università degli Studi di Siena , via A. Moro 2, I-53100 Siena, Italy
| | - Daniela Braconi
- Dipartimento di Biotecnologie, Chimica e Farmacia, Università degli Studi di Siena , via A. Moro 2, I-53100 Siena, Italy
| | - Elena Petricci
- Dipartimento di Biotecnologie, Chimica e Farmacia, Università degli Studi di Siena , via A. Moro 2, I-53100 Siena, Italy
| | - Fabrizio Manetti
- Dipartimento di Biotecnologie, Chimica e Farmacia, Università degli Studi di Siena , via A. Moro 2, I-53100 Siena, Italy
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Liang X, Zhang P, Yan L, Fu Y, Peng F, Qu L, Shao M, Chen Y, Chen Z. LRSSL: predict and interpret drug–disease associations based on data integration using sparse subspace learning. Bioinformatics 2017; 33:1187-1196. [DOI: 10.1093/bioinformatics/btw770] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Accepted: 12/02/2016] [Indexed: 02/07/2023] Open
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Wooden B, Goossens N, Hoshida Y, Friedman SL. Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases. Gastroenterology 2017; 152:53-67.e3. [PMID: 27773806 PMCID: PMC5193106 DOI: 10.1053/j.gastro.2016.09.065] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Revised: 09/15/2016] [Accepted: 09/25/2016] [Indexed: 12/13/2022]
Abstract
Technologies such as genome sequencing, gene expression profiling, proteomic and metabolomic analyses, electronic medical records, and patient-reported health information have produced large amounts of data from various populations, cell types, and disorders (big data). However, these data must be integrated and analyzed if they are to produce models or concepts about physiological function or mechanisms of pathogenesis. Many of these data are available to the public, allowing researchers anywhere to search for markers of specific biological processes or therapeutic targets for specific diseases or patient types. We review recent advances in the fields of computational and systems biology and highlight opportunities for researchers to use big data sets in the fields of gastroenterology and hepatology to complement traditional means of diagnostic and therapeutic discovery.
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Affiliation(s)
- Benjamin Wooden
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Nicolas Goossens
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York; Division of Gastroenterology and Hepatology, Department of Medical Specialties, Geneva University Hospital, Geneva, Switzerland
| | - Yujin Hoshida
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
| | - Scott L Friedman
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
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Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction. PLoS Comput Biol 2016; 12:e1005219. [PMID: 27893735 PMCID: PMC5125559 DOI: 10.1371/journal.pcbi.1005219] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Accepted: 10/21/2016] [Indexed: 12/23/2022] Open
Abstract
De novo experimental drug discovery is an expensive and time-consuming task. It requires the identification of drug-target interactions (DTIs) towards targets of biological interest, either to inhibit or enhance a specific molecular function. Dedicated computational models for protein simulation and DTI prediction are crucial for speed and to reduce the costs associated with DTI identification. In this paper we present a computational pipeline that enables the discovery of putative leads for drug repositioning that can be applied to any microbial proteome, as long as the interactome of interest is at least partially known. Network metrics calculated for the interactome of the bacterial organism of interest were used to identify putative drug-targets. Then, a random forest classification model for DTI prediction was constructed using known DTI data from publicly available databases, resulting in an area under the ROC curve of 0.91 for classification of out-of-sampling data. A drug-target network was created by combining 3,081 unique ligands and the expected ten best drug targets. This network was used to predict new DTIs and to calculate the probability of the positive class, allowing the scoring of the predicted instances. Molecular docking experiments were performed on the best scoring DTI pairs and the results were compared with those of the same ligands with their original targets. The results obtained suggest that the proposed pipeline can be used in the identification of new leads for drug repositioning. The proposed classification model is available at http://bioinformatics.ua.pt/software/dtipred/. The emergence of multi-resistant bacterial strains and the existing void in the discovery and development of new classes of antibiotics is a growing concern. Indeed, some bacterial strains are now resistant to last-line antibiotics and considered untreatable. Drug repositioning has been suggested as a strategy to minimize time and cost expenses until the drug reaches the market, compared to traditional drug design. Drug-target interactions (DTIs) are the basis of rational drug design and thus, we proposed a computational approach to predict DTIs solely based on the primary sequence of the protein and the simplified molecular-input line-entry system of the ligand. In addition, network metrics are used to identify vital putative drug-targets in bacteria. Molecular docking experiments were performed to compare the binding affinities between a given ligand and a putative drug-target, as well as with their original targets. According to the docking results, the predicted DTIs have better or similar binding activities than the ligand and their real target, indicating the validity of the proposed model.
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Paik H, Chen B, Sirota M, Hadley D, Butte AJ. Integrating Clinical Phenotype and Gene Expression Data to Prioritize Novel Drug Uses. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2016; 5:599-607. [PMID: 27860440 PMCID: PMC5192994 DOI: 10.1002/psp4.12108] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 08/05/2016] [Indexed: 12/22/2022]
Abstract
Drug repositioning has been based largely on genomic signatures of drugs and diseases. One challenge in these efforts lies in connecting the molecular signatures of drugs into clinical responses, including therapeutic and side effects, to the repurpose of drugs. We addressed this challenge by evaluating drug‐drug relationships using a phenotypic and molecular‐based approach that integrates therapeutic indications, side effects, and gene expression profiles induced by each drug. Using cosine similarity, relationships between 445 drugs were evaluated based on high‐dimensional spaces consisting of phenotypic terms of drugs and genomic signatures, respectively. One hundred fifty‐one of 445 drugs comprising 450 drug pairs displayed significant similarities in both phenotypic and genomic signatures (P value < 0.05). We also found that similar gene expressions of drugs do indeed yield similar clinical phenotypes. We generated similarity matrixes of drugs using the expression profiles they induce in a cell line and phenotypic effects.
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Affiliation(s)
- H Paik
- Institute for Computational Health Sciences, School of Medicine, University of California San Francisco, San Francisco, California, USA.,Department of Pediatrics, School of Medicine, University of California - San Francisco, San Francisco, California, USA
| | - B Chen
- Institute for Computational Health Sciences, School of Medicine, University of California San Francisco, San Francisco, California, USA.,Department of Pediatrics, School of Medicine, University of California - San Francisco, San Francisco, California, USA
| | - M Sirota
- Institute for Computational Health Sciences, School of Medicine, University of California San Francisco, San Francisco, California, USA.,Department of Pediatrics, School of Medicine, University of California - San Francisco, San Francisco, California, USA
| | - D Hadley
- Institute for Computational Health Sciences, School of Medicine, University of California San Francisco, San Francisco, California, USA.,Department of Pediatrics, School of Medicine, University of California - San Francisco, San Francisco, California, USA
| | - A J Butte
- Institute for Computational Health Sciences, School of Medicine, University of California San Francisco, San Francisco, California, USA.,Department of Pediatrics, School of Medicine, University of California - San Francisco, San Francisco, California, USA
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Raevsky OA, Polianczyk DE, Mukhametov A, Grigorev VY. Assessment of the classification abilities of the CNS multi-parametric optimization approach by the method of logistic regression. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:629-635. [PMID: 27477321 DOI: 10.1080/1062936x.2016.1212922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Accepted: 07/11/2016] [Indexed: 06/06/2023]
Abstract
Assessment of "CNS drugs/CNS candidates" classification abilities of the multi-parametric optimization (CNS MPO) approach was performed by logistic regression. It was found that the five out of the six separately used physical-chemical properties (topological polar surface area, number of hydrogen-bonded donor atoms, basicity, lipophilicity of compound in neutral form and at pH = 7.4) provided accuracy of recognition below 60%. Only the descriptor of molecular weight (MW) could correctly classify two-thirds of the studied compounds. Aggregation of all six properties in the MPOscore did not improve the classification, which was worse than the classification using only MW. The results of our study demonstrate the imperfection of the CNS MPO approach; in its current form it is not very useful for computer design of new, effective CNS drugs.
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Affiliation(s)
- O A Raevsky
- a Department of Computer-aided Molecular Design , Institute of Physiologically Active Compounds of the Russian Academy of Science , Chernogolovka , Russian Federation
| | - D E Polianczyk
- a Department of Computer-aided Molecular Design , Institute of Physiologically Active Compounds of the Russian Academy of Science , Chernogolovka , Russian Federation
| | - A Mukhametov
- a Department of Computer-aided Molecular Design , Institute of Physiologically Active Compounds of the Russian Academy of Science , Chernogolovka , Russian Federation
| | - V Y Grigorev
- a Department of Computer-aided Molecular Design , Institute of Physiologically Active Compounds of the Russian Academy of Science , Chernogolovka , Russian Federation
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Moghadam H, Rahgozar M, Gharaghani S. Scoring multiple features to predict drug disease associations using information fusion and aggregation. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:609-628. [PMID: 27455069 DOI: 10.1080/1062936x.2016.1209241] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2016] [Accepted: 06/30/2016] [Indexed: 06/06/2023]
Abstract
Prediction of drug-disease associations is one of the current fields in drug repositioning that has turned into a challenging topic in pharmaceutical science. Several available computational methods use network-based and machine learning approaches to reposition old drugs for new indications. However, they often ignore features of drugs and diseases as well as the priority and importance of each feature, relation, or interactions between features and the degree of uncertainty. When predicting unknown drug-disease interactions there are diverse data sources and multiple features available that can provide more accurate and reliable results. This information can be collectively mined using data fusion methods and aggregation operators. Therefore, we can use the feature fusion method to make high-level features. We have proposed a computational method named scored mean kernel fusion (SMKF), which uses a new method to score the average aggregation operator called scored mean. To predict novel drug indications, this method systematically combines multiple features related to drugs or diseases at two levels: the drug-drug level and the drug-disease level. The purpose of this study was to investigate the effect of drug and disease features as well as data fusion to predict drug-disease interactions. The method was validated against a well-established drug-disease gold-standard dataset. When compared with the available methods, our proposed method outperformed them and competed well in performance with area under cover (AUC) of 0.91, F-measure of 84.9% and Matthews correlation coefficient of 70.31%.
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Affiliation(s)
- H Moghadam
- a DBRG, CIPCE, School of Electrical and Computer Engineering, College of Engineering , University of Tehran , Tehran , Iran
| | - M Rahgozar
- a DBRG, CIPCE, School of Electrical and Computer Engineering, College of Engineering , University of Tehran , Tehran , Iran
| | - S Gharaghani
- b LBD, Institute of Biochemistry and Biophysics , University of Tehran , Tehran , Iran
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50
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Lee H, Kang S, Kim W. Drug Repositioning for Cancer Therapy Based on Large-Scale Drug-Induced Transcriptional Signatures. PLoS One 2016; 11:e0150460. [PMID: 26954019 PMCID: PMC4783079 DOI: 10.1371/journal.pone.0150460] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Accepted: 02/15/2016] [Indexed: 11/18/2022] Open
Abstract
An in silico chemical genomics approach is developed to predict drug repositioning (DR) candidates for three types of cancer: glioblastoma, lung cancer, and breast cancer. It is based on a recent large-scale dataset of ~20,000 drug-induced expression profiles in multiple cancer cell lines, which provides i) a global impact of transcriptional perturbation of both known targets and unknown off-targets, and ii) rich information on drug's mode-of-action. First, the drug-induced expression profile is shown more effective than other information, such as the drug structure or known target, using multiple HTS datasets as unbiased benchmarks. Particularly, the utility of our method was robustly demonstrated in identifying novel DR candidates. Second, we predicted 14 high-scoring DR candidates solely based on expression signatures. Eight of the fourteen drugs showed significant anti-proliferative activity against glioblastoma; i.e., ivermectin, trifluridine, astemizole, amlodipine, maprotiline, apomorphine, mometasone, and nortriptyline. Our DR score strongly correlated with that of cell-based experimental results; the top seven DR candidates were positive, corresponding to an approximately 20-fold enrichment compared with conventional HTS. Despite diverse original indications and known targets, the perturbed pathways of active DR candidates show five distinct patterns that form tight clusters together with one or more known cancer drugs, suggesting common transcriptome-level mechanisms of anti-proliferative activity.
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Affiliation(s)
- Haeseung Lee
- Ewha Research Center for Systems Biology, Division of Molecular & Life Sciences, Ewha Womans University, Seoul, Korea
| | - Seungmin Kang
- Ewha Research Center for Systems Biology, Division of Molecular & Life Sciences, Ewha Womans University, Seoul, Korea
| | - Wankyu Kim
- Ewha Research Center for Systems Biology, Division of Molecular & Life Sciences, Ewha Womans University, Seoul, Korea
- * E-mail:
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