1
|
Toni E, Ayatollahi H, Abbaszadeh R, Fotuhi Siahpirani A. Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review. Pharmaceuticals (Basel) 2024; 17:795. [PMID: 38931462 PMCID: PMC11206653 DOI: 10.3390/ph17060795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Drug safety relies on advanced methods for timely and accurate prediction of side effects. To tackle this requirement, this scoping review examines machine-learning approaches for predicting drug-related side effects with a particular focus on chemical, biological, and phenotypical features. METHODS This was a scoping review in which a comprehensive search was conducted in various databases from 1 January 2013 to 31 December 2023. RESULTS The results showed the widespread use of Random Forest, k-nearest neighbor, and support vector machine algorithms. Ensemble methods, particularly random forest, emphasized the significance of integrating chemical and biological features in predicting drug-related side effects. CONCLUSIONS This review article emphasized the significance of considering a variety of features, datasets, and machine learning algorithms for predicting drug-related side effects. Ensemble methods and Random Forest showed the best performance and combining chemical and biological features improved prediction. The results suggested that machine learning techniques have some potential to improve drug development and trials. Future work should focus on specific feature types, selection techniques, and graph-based methods for even better prediction.
Collapse
Affiliation(s)
- Esmaeel Toni
- Medical Informatics, Student Research Committee, Iran University of Medical Sciences, Tehran, Iran 14496-14535;
| | - Haleh Ayatollahi
- Medical Informatics, Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran 1996-713883
| | - Reza Abbaszadeh
- Pediatric Cardiology, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran 19956-14331;
| | - Alireza Fotuhi Siahpirani
- Systems Biology and Bioinformatics, Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran 14176-14411;
| |
Collapse
|
2
|
Kim HY, Kwon HS, Lim JO, Jang HJ, Muthamil S, Shin UC, Lyu JH, Park YJ, Nam HH, Lee NY, Oh HJ, Yun SI, Jin JS, Park JH. Gonadal efficacy of Thymus quinquecostatus Celakovski: Regulation of testosterone levels in aging mouse models. Biomed Pharmacother 2024; 175:116700. [PMID: 38703505 DOI: 10.1016/j.biopha.2024.116700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 04/29/2024] [Accepted: 05/01/2024] [Indexed: 05/06/2024] Open
Abstract
Late-onset hypogonadism (LOH) is an age-related disease in men characterized by decreased testosterone levels with symptoms such as decreased libido, erectile dysfunction, and depression. Thymus quinquecostatus Celakovski (TQC) is a plant used as a volatile oil in traditional medicine, and its bioactive compounds have anti-inflammatory potential. Based on this knowledge, the present study aimed to investigate the effects of TQC extract (TE) on LOH in TM3 Leydig cells and in an in vivo aging mouse model. The aqueous extract of T. quinquecostatus Celakovski (12.5, 25, and 50 µg/mL concentrations) was used to measure parameters such as cell viability, testosterone level, body weight, and gene expression, via in vivo studies. Interestingly, TE increased testosterone levels in TM3 cells in a dose-dependent manner without affecting cell viability. Furthermore, TE significantly increased the expression of genes involved in the cytochrome P450 family (Cyp11a1, Cyp17a1, Cyp19a1, and Srd5a2), which regulate testosterone biosynthesis. In aging mouse models, TE increased testosterone levels without affecting body weight and testicular tissue weight tissue of an aging animal group. In addition, the high-dose TE-treated group (50 mg/kg) showed significantly increased expression of the cytochrome p450 enzymes, similar to the in vitro results. Furthermore, HPLC-MS analysis confirmed the presence of caffeic acid and rosmarinic acid as bioactive compounds in TE. Thus, the results obtained in the present study confirmed that TQC and its bioactive compounds can be used for LOH treatment to enhance testosterone production.
Collapse
Affiliation(s)
- Hyun-Yong Kim
- Herbal Medicine Resources Research Center, Korea Institute of Oriental Medicine, Naju, Jeollanam-do 58245, Republic of Korea
| | - Hyuck Se Kwon
- R&D Team, Food & Supplement Health Claims, Vitech, #602 Giyeon B/D 141 Anjeon-ro, Iseo-myeon, Wanju-gun, Jeollabuk-do 55365, Republic of Korea; Department of Food Science and Technology, College of Agriculture and Life Sciences, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Je-Oh Lim
- Herbal Medicine Resources Research Center, Korea Institute of Oriental Medicine, Naju, Jeollanam-do 58245, Republic of Korea
| | - Hyun-Jun Jang
- Herbal Medicine Resources Research Center, Korea Institute of Oriental Medicine, Naju, Jeollanam-do 58245, Republic of Korea
| | - Subramanian Muthamil
- Herbal Medicine Resources Research Center, Korea Institute of Oriental Medicine, Naju, Jeollanam-do 58245, Republic of Korea
| | - Ung Cheol Shin
- Herbal Medicine Resources Research Center, Korea Institute of Oriental Medicine, Naju, Jeollanam-do 58245, Republic of Korea
| | - Ji-Hyo Lyu
- Herbal Medicine Resources Research Center, Korea Institute of Oriental Medicine, Naju, Jeollanam-do 58245, Republic of Korea
| | - Yeo Jin Park
- Korean Medicine (KM) Application Center, Korea Institute of Oriental Medicine, Daegu 41062, Republic of Korea
| | - Hyeon-Hwa Nam
- Herbal Medicine Resources Research Center, Korea Institute of Oriental Medicine, Naju, Jeollanam-do 58245, Republic of Korea
| | - Na-Young Lee
- R&D Team, Food & Supplement Health Claims, Vitech, #602 Giyeon B/D 141 Anjeon-ro, Iseo-myeon, Wanju-gun, Jeollabuk-do 55365, Republic of Korea
| | - Hyun-Jeong Oh
- R&D Team, Food & Supplement Health Claims, Vitech, #602 Giyeon B/D 141 Anjeon-ro, Iseo-myeon, Wanju-gun, Jeollabuk-do 55365, Republic of Korea
| | - Soon-Il Yun
- Department of Food Science and Technology, College of Agriculture and Life Sciences, Jeonbuk National University, Jeonju 54896, Republic of Korea; Department of Agricultural Convergence Technology, College of Agriculture and Life Sciences, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Jong-Sik Jin
- Department of Oriental Medicine Resources, Jeonbuk National University, 79 Gobong-ro, Iksan, Jeollabuk-do 54596, Republic of Korea
| | - Jun Hong Park
- Herbal Medicine Resources Research Center, Korea Institute of Oriental Medicine, Naju, Jeollanam-do 58245, Republic of Korea; University of Science & Technology (UST), KIOM Campus, Korean Convergence Medicine Major, Daejeon 34054, Republic of Korea.
| |
Collapse
|
3
|
Seal S, Spjuth O, Hosseini-Gerami L, García-Ortegón M, Singh S, Bender A, Carpenter AE. Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank. J Chem Inf Model 2024; 64:1172-1186. [PMID: 38300851 PMCID: PMC10900289 DOI: 10.1021/acs.jcim.3c01834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/11/2024] [Accepted: 01/16/2024] [Indexed: 02/03/2024]
Abstract
Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10-14% of postmarket withdrawals. In this study, we explored the capabilities of chemical and biological data to predict cardiotoxicity, using the recently released DICTrank data set from the United States FDA. We found that such data, including protein targets, especially those related to ion channels (e.g., hERG), physicochemical properties (e.g., electrotopological state), and peak concentration in plasma offer strong predictive ability for DICT. Compounds annotated with mechanisms of action such as cyclooxygenase inhibition could distinguish between most-concern and no-concern DICT. Cell Painting features for ER stress discerned most-concern cardiotoxic from nontoxic compounds. Models based on physicochemical properties provided substantial predictive accuracy (AUCPR = 0.93). With the availability of omics data in the future, using biological data promises enhanced predictability and deeper mechanistic insights, paving the way for safer drug development. All models from this study are available at https://broad.io/DICTrank_Predictor.
Collapse
Affiliation(s)
- Srijit Seal
- Imaging
Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - Ola Spjuth
- Department
of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box
591, SE-75124 Uppsala, Sweden
| | - Layla Hosseini-Gerami
- Ignota
Labs, The Bradfield Centre, Cambridge Science Park, County Hall, Westminster Bridge Road, Cambridge CB4 0GA, U.K.
| | - Miguel García-Ortegón
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - Shantanu Singh
- Imaging
Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Andreas Bender
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - Anne E. Carpenter
- Imaging
Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| |
Collapse
|
4
|
Seal S, Spjuth O, Hosseini-Gerami L, García-Ortegón M, Singh S, Bender A, Carpenter AE. Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA DICTrank. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.15.562398. [PMID: 37905146 PMCID: PMC10614794 DOI: 10.1101/2023.10.15.562398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10-14% of postmarket withdrawals. In this study, we explored the capabilities of various chemical and biological data to predict cardiotoxicity, using the recently released Drug-Induced Cardiotoxicity Rank (DICTrank) dataset from the United States FDA. We analyzed a diverse set of data sources, including physicochemical properties, annotated mechanisms of action (MOA), Cell Painting, Gene Expression, and more, to identify indications of cardiotoxicity. We found that such data, including protein targets, especially those related to ion channels (such as hERG), physicochemical properties (such as electrotopological state) as well as peak concentration in plasma offer strong predictive ability as well as valuable insights into DICT. We also found compounds annotated with particular mechanisms of action, such as cyclooxygenase inhibition, could distinguish between most-concern and no-concern DICT compounds. Cell Painting features related to ER stress discern the most-concern cardiotoxic compounds from non-toxic compounds. While models based on physicochemical properties currently provide substantial predictive accuracy (AUCPR = 0.93), this study also underscores the potential benefits of incorporating more comprehensive biological data in future DICT predictive models. With the availability of - omics data in the future, using biological data promises enhanced predictability and delivers deeper mechanistic insights, paving the way for safer therapeutic drug development. All models and data used in this study are publicly released at https://broad.io/DICTrank_Predictor.
Collapse
Affiliation(s)
- Srijit Seal
- Imaging Platform, Broad Institute of MIT and Harvard, US
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, Sweden
| | | | | | - Shantanu Singh
- Imaging Platform, Broad Institute of MIT and Harvard, US
| | | | | |
Collapse
|
5
|
Mesa NC, Alves IA, Vilela FMP, E Silva DM, Forero LAP, Novoa DMA, de Carvalho da Costa J. Fruits as nutraceuticals: A review of the main fruits included in nutraceutical patents. Food Res Int 2023; 170:113013. [PMID: 37316080 DOI: 10.1016/j.foodres.2023.113013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 04/24/2023] [Accepted: 05/19/2023] [Indexed: 06/16/2023]
Abstract
Fruits have relevant usefulness in the elaboration of nutraceutical compositions and, as it is considered a "natural medicine", its market has been growing exponentially each year. Fruits, in general, contain a large source of phytochemicals, carbohydrates, vitamins, amino acids, peptides and antioxidants that are of interest to be prepared as nutraceuticals. The biological properties of its nutraceuticals can range from antioxidant, antidiabetic, antihypertensive, anti-Alzheimer, antiproliferative, antimicrobial, antibacterial, anti-inflammatory, among others. Furthermore, the need for innovative extraction methods and products reveals the importance of developing new nutraceutical compositions. This review was developed by searching patents of nutraceuticals from January 2015 until January 2022 in Espacenet, the search database of the European Patent Office (EPO). Of 215 patents related to nutraceuticals, 43% (92 patents) were including fruits, mainly berries. A great number of patents were focused on the treatment of metabolic diseases, representing 45% of the total patents. The principal patent applicant was the United States of America (US), with 52%. The patents were applied by researchers, industries, research centers and institutes. It is important to highlight that from 92 fruit nutraceutical patent applications reviewed, 13 already have their products available on the market.
Collapse
Affiliation(s)
- Natalia Casas Mesa
- Faculty of Science, Chemistry Department, National University of Colombia, Bogotá, Colombia; Chemistry Department, Exact Science Institute, Federal University of Juiz de Fora, Juiz de Fora, Minas Gerais, Brazil
| | - Izabel Almeida Alves
- Faculty of Pharmacy, Medicine Department, Federal University of Bahia, Salvador, Bahia, Brazil
| | - Fernanda Maria Pinto Vilela
- Faculty of Pharmacy, Pharmaceutical Sciences Department, Federal University of Juiz de Fora, Juiz de Fora, Minas Gerais, Brazil
| | - Dominique Mesquita E Silva
- Faculty of Pharmacy, Pharmaceutical Sciences Department, Federal University of Juiz de Fora, Juiz de Fora, Minas Gerais, Brazil
| | | | | | - Juliana de Carvalho da Costa
- Faculty of Pharmacy, Pharmaceutical Sciences Department, Federal University of Juiz de Fora, Juiz de Fora, Minas Gerais, Brazil.
| |
Collapse
|
6
|
Park M, Kim D, Kim I, Im SH, Kim S. Drug approval prediction based on the discrepancy in gene perturbation effects between cells and humans. EBioMedicine 2023; 94:104705. [PMID: 37453362 PMCID: PMC10366401 DOI: 10.1016/j.ebiom.2023.104705] [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: 02/19/2023] [Revised: 06/15/2023] [Accepted: 06/27/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND Poor translation between in vitro and clinical studies due to the cells/humans discrepancy in drug target perturbation effects leads to safety failures in clinical trials, thus increasing drug development costs and reducing patients' life quality. Therefore, developing a predictive model for drug approval considering the cells/humans discrepancy is needed to reduce drug attrition rates in clinical trials. METHODS Our machine learning framework predicts drug approval in clinical trials based on the cells/humans discrepancy in drug target perturbation effects. To evaluate the discrepancy to predict drug approval (1404 approved and 1070 unapproved drugs), we analysed CRISPR-Cas9 knockout and loss-of-function mutation rate-based gene perturbation effects on cells and humans, respectively. To validate the risk of drug targets with the cells/humans discrepancy, we examined the targets of failed and withdrawn drugs due to safety problems. FINDINGS Drug approvals in clinical trials were correlated with the cells/humans discrepancy in gene perturbation effects. Genes tolerant to perturbation effects on cells but intolerant to those on humans were associated with failed drug targets. Furthermore, genes with the cells/humans discrepancy were related to drugs withdrawn due to severe side effects. Motivated by previous studies assessing drug safety through chemical properties, we improved drug approval prediction by integrating chemical information with the cells/humans discrepancy. INTERPRETATION The cells/humans discrepancy in gene perturbation effects facilitates drug approval prediction and explains drug safety failures in clinical trials. FUNDING S.K. received grants from the Korean National Research Foundation (2021R1A2B5B01001903 and 2020R1A6A1A03047902) and IITP (2019-0-01906, Artificial Intelligence Graduate School Program, POSTECH).
Collapse
Affiliation(s)
- Minhyuk Park
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, South Korea
| | - Donghyo Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, South Korea
| | - Inhae Kim
- ImmunoBiome Inc., Pohang, South Korea
| | - Sin-Hyeog Im
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, South Korea; ImmunoBiome Inc., Pohang, South Korea
| | - Sanguk Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, South Korea.
| |
Collapse
|
7
|
Sutherland JJ, Yonchev D, Fekete A, Urban L. A preclinical secondary pharmacology resource illuminates target-adverse drug reaction associations of marketed drugs. Nat Commun 2023; 14:4323. [PMID: 37468498 DOI: 10.1038/s41467-023-40064-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 07/11/2023] [Indexed: 07/21/2023] Open
Abstract
In vitro secondary pharmacology assays are an important tool for predicting clinical adverse drug reactions (ADRs) of investigational drugs. We created the Secondary Pharmacology Database (SPD) by testing 1958 drugs using 200 assays to validate target-ADR associations. Compared to public and subscription resources, 95% of all and 36% of active (AC50 < 1 µM) results are unique to SPD, with bias towards higher activity in public resources. Annotating drugs with free maximal plasma concentrations, we find 684 physiologically relevant unpublished off-target activities. Furthermore, 64% of putative ADRs linked to target activity in key literature reviews are not statistically significant in SPD. Systematic analysis of all target-ADR pairs identifies several putative associations supported by publications. Finally, candidate mechanisms for known ADRs are proposed based on SPD off-target activities. Here we present a freely-available resource for benchmarking ADR predictions, explaining phenotypic activity and investigating clinical properties of marketed drugs.
Collapse
Affiliation(s)
| | - Dimitar Yonchev
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | | | - Laszlo Urban
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA.
| |
Collapse
|
8
|
Hillen JB, Stanford T, Ward M, Roughead EE, Kalisch Ellett L, Pratt N. Rituximab and Pyoderma Gangrenosum: An Investigation of Disproportionality Using a Systems Biology-Informed Approach in the FAERS Database. Drugs Real World Outcomes 2022; 9:639-647. [PMID: 35933497 DOI: 10.1007/s40801-022-00322-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/26/2022] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND Studies have found an increased risk of pyoderma gangrenosum associated with rituximab. The structural properties and pharmacological action of rituximab may affect the risk of pyoderma gangrenosum. Additionally, pyoderma gangrenosum is associated with autoimmune disorders for which rituximab is indicated. OBJECTIVE We aimed to determine whether rituximab is disproportionally associated with pyoderma gangrenosum using a systems biology-informed approach. METHODS Adverse event reports were extracted from the US Food and Drug Administration Adverse Event Reporting System (FAERS, 2013-20). The Bayesian Confidence Propagation Neural Network Information Component was used to test for disproportionality. Comparators used to determine potential causal pathways included all other medicines, all medicines with a similar structure (monoclonal antibodies), all medicines with the same pharmacological target (CD20 antagonists) and all medicines used for the same indication(s) as rituximab. RESULTS Thirty-two pyoderma gangrenosum cases were identified, 62.5% were female, with a median age of 48 years. There was an increased association of pyoderma gangrenosum with rituximab compared with all other medicines (exponentiated Information Component 6.75, 95% confidence interval (CI) 4.66-9.23). No association was observed when the comparator was either monoclonal antibodies or CD20 antagonists. Conditions for which an association of pyoderma gangrenosum with rituximab was observed were multiple sclerosis (6.68, 95% CI 1.63-15.15), rheumatoid arthritis (2.67, 95% CI 1.14-4.80) and non-Hodgkin's lymphoma (2.94, 95% CI 1.80-3.73). CONCLUSIONS Pyoderma gangrenosum was reported more frequently with rituximab compared with all other medicines. The varying results when restricting medicines for the same condition suggest the potential for confounding by indication. Post-market surveillance of biologic medicines in FAERS should consider a multi-faceted approach, particularly when the outcome of interest is associated with the underlying immune condition being treated by the medicine of interest.
Collapse
Affiliation(s)
- Jodie Belinda Hillen
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Playford Building Level 6, Frome Rd, Adelaide, SA, 5000, Australia
| | - Ty Stanford
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Playford Building Level 6, Frome Rd, Adelaide, SA, 5000, Australia
| | - Michael Ward
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Playford Building Level 6, Frome Rd, Adelaide, SA, 5000, Australia.,Pharmacy Education, Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - E E Roughead
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Playford Building Level 6, Frome Rd, Adelaide, SA, 5000, Australia
| | - Lisa Kalisch Ellett
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Playford Building Level 6, Frome Rd, Adelaide, SA, 5000, Australia
| | - Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Playford Building Level 6, Frome Rd, Adelaide, SA, 5000, Australia.
| |
Collapse
|
9
|
Identification of New Toxicity Mechanisms in Drug-Induced Liver Injury through Systems Pharmacology. Genes (Basel) 2022; 13:genes13071292. [PMID: 35886075 PMCID: PMC9315637 DOI: 10.3390/genes13071292] [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: 05/20/2022] [Revised: 07/19/2022] [Accepted: 07/19/2022] [Indexed: 02/05/2023] Open
Abstract
Among adverse drug reactions, drug-induced liver injury presents particular challenges because of its complexity, and the underlying mechanisms are still not completely characterized. Our knowledge of the topic is limited and based on the assumption that a drug acts on one molecular target. We have leveraged drug polypharmacology, i.e., the ability of a drug to bind multiple targets and thus perturb several biological processes, to develop a systems pharmacology platform that integrates all drug–target interactions. Our analysis sheds light on the molecular mechanisms of drugs involved in drug-induced liver injury and provides new hypotheses to study this phenomenon.
Collapse
|
10
|
Ji X, Wang L, Hua L, Wang X, Zhang P, Shendre A, Feng W, Li J, Li L. Improved Adverse Drug Event Prediction Through Information Component Guided Pharmacological Network Model (IC-PNM). IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1113-1121. [PMID: 31443040 DOI: 10.1109/tcbb.2019.2928305] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Improving adverse drug event (ADE) prediction is highly critical in pharmacovigilance research. We propose a novel information component guided pharmacological network model (IC-PNM) to predict drug-ADE signals. This new method combines the pharmacological network model and information component, a Bayes statistics method. We use 33,947 drug-ADE pairs from the FDA Adverse Event Reporting System (FAERS) 2010 data as the training data, and the new 21,065 drug-ADE pairs from FAERS 2011-2015 as the validations samples. The IC-PNM data analysis suggests that both large and small sample size drug-ADE pairs are needed in training the predictive model for its prediction performance to reach an area under the receiver operating characteristic curve [Formula: see text]. On the other hand, the IC-PNM prediction performance improved to [Formula: see text] if we removed the small sample size drug-ADE pairs from the prediction model during validation.
Collapse
|
11
|
Liu X, Zhang H, Xue Q, Pan W, Zhang A. In silico health effect prioritization of environmental chemicals through transcriptomics data exploration from a chemo-centric view. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 762:143082. [PMID: 33143927 DOI: 10.1016/j.scitotenv.2020.143082] [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: 08/09/2020] [Revised: 10/11/2020] [Accepted: 10/11/2020] [Indexed: 06/11/2023]
Abstract
With the explosive growth of synthetic compounds, the health effects caused by exogenous chemical exposure have attracted more and more public attention. The prediction of health effect is a never-ending story. Collective resource of transcriptomics data offers an opportunity to understand and identify the multiple health effects of small molecule. Inspired by the fact that environmental chemicals of high health risk frequently share both similar gene expression profile and common structural feature of certain drugs, we here propose a novel computational effect prioritization method for environmental chemicals through transcriptomics data exploration from a chemo-centric view. Specifically, non-negative matrix factorization (NMF) method has been adopted to get the association network linking structural features with transcriptomics characteristics of drugs with specific effects. The model yields 13 pivotal types of effects, so-called components, that represent drug categories with common chemo- and geno- type features. Moreover, the established model effectively prioritizes potential toxic effects for the external chemicals from the endocrine disruptor screening program (EDSP) for their potential estrogenicity and other verified risks. Even if only the highest priority is set for the estrogenic effect, the precision and recall can reach 0.76 and 0.77 respectively for these chemicals. Our effort provides a successful endeavor as to profile potential toxic effects simultaneously for environmental chemicals using both chemical and omics data.
Collapse
Affiliation(s)
- Xian Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China.
| | - Huazhou Zhang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China.
| | - Qiao Xue
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China.
| | - Wenxiao Pan
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China.
| | - Aiqian Zhang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China; Institute of Environment and Health, Jianghan University, Wuhan 430056, PR China.
| |
Collapse
|
12
|
Smit IA, Afzal AM, Allen CHG, Svensson F, Hanser T, Bender A. Systematic Analysis of Protein Targets Associated with Adverse Events of Drugs from Clinical Trials and Postmarketing Reports. Chem Res Toxicol 2020; 34:365-384. [PMID: 33351593 DOI: 10.1021/acs.chemrestox.0c00294] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Adverse drug reactions (ADRs) are undesired effects of medicines that can harm patients and are a significant source of attrition in drug development. ADRs are anticipated by routinely screening drugs against secondary pharmacology protein panels. However, there is still a lack of quantitative information on the links between these off-target proteins and the reporting of ADRs in humans. Here, we present a systematic analysis of associations between measured and predicted in vitro bioactivities of drugs and adverse events (AEs) in humans from two sources of data: the Side Effect Resource, derived from clinical trials, and the Food and Drug Administration Adverse Event Reporting System, derived from postmarketing surveillance. The ratio of a drug's therapeutic unbound plasma concentration over the drug's in vitro potency against a given protein was used to select proteins most likely to be relevant to in vivo effects. In examining individual target bioactivities as predictors of AEs, we found a trade-off between the positive predictive value and the fraction of drugs with AEs that can be detected. However, considering sets of multiple targets for the same AE can help identify a greater fraction of AE-associated drugs. Of the 45 targets with statistically significant associations to AEs, 30 are included on existing safety target panels. The remaining 15 targets include 9 carbonic anhydrases, of which CA5B is significantly associated with cholestatic jaundice. We include the full quantitative data on associations between measured and predicted in vitro bioactivities and AEs in humans in this work, which can be used to make a more informed selection of safety profiling targets.
Collapse
Affiliation(s)
- Ines A Smit
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Avid M Afzal
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Chad H G Allen
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Fredrik Svensson
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Thierry Hanser
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds LS11 5PS, United Kingdom
| | - Andreas Bender
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| |
Collapse
|
13
|
Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker. Nat Biotechnol 2020; 38:1087-1096. [PMID: 32440005 DOI: 10.1038/s41587-020-0502-7] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 03/27/2020] [Indexed: 02/07/2023]
Abstract
Small molecules are usually compared by their chemical structure, but there is no unified analytic framework for representing and comparing their biological activity. We present the Chemical Checker (CC), which provides processed, harmonized and integrated bioactivity data on ~800,000 small molecules. The CC divides data into five levels of increasing complexity, from the chemical properties of compounds to their clinical outcomes. In between, it includes targets, off-targets, networks and cell-level information, such as omics data, growth inhibition and morphology. Bioactivity data are expressed in a vector format, extending the concept of chemical similarity to similarity between bioactivity signatures. We show how CC signatures can aid drug discovery tasks, including target identification and library characterization. We also demonstrate the discovery of compounds that reverse and mimic biological signatures of disease models and genetic perturbations in cases that could not be addressed using chemical information alone. Overall, the CC signatures facilitate the conversion of bioactivity data to a format that is readily amenable to machine learning methods.
Collapse
|
14
|
Auto-Generated Physiological Chain Data for an Ontological Framework for Pharmacology and Mechanism of Action to Determine Suspected Drugs in Cases of Dysuria. Drug Saf 2019; 42:1055-1069. [PMID: 31119651 DOI: 10.1007/s40264-019-00833-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
INTRODUCTION Patients often take several different medications for multiple conditions concurrently. Therefore, when adverse drug events (ADEs) occur, it is necessary to consider the mechanisms responsible. Few approaches consider the mechanisms of ADEs, such as changes in physiological states. We proposed that the ontological framework for pharmacology and mechanism of action (pharmacodynamics) we developed could be used for this approach. However, the existing knowledge base contains little data on physiological chains (PCs). OBJECTIVE We aimed to investigate a method for automatically generating missing PC from the viewpoint of anatomical structures. This study was conducted to determine dysuria-related adverse events more likely to occur during multidrug administration. METHODS We adopted a systematic approach to determine drugs suspected to cause adverse events and incorporated existing data and data generated in our newly developed method into our ontological framework. The performance of automated data generation was evaluated using this newly developed system. Suspected drugs determined by the system were compared with those derived from adverse events databases. RESULTS Of the 242 drugs involving suspected drug-induced urinary retention or dysuria, 26 suspected drugs were determined. Of these, five were drugs with side effects not listed in drug package inserts. The system derived potential mechanisms of action, PCs, and suspected drugs. CONCLUSION Our method is novel in that it generates PC data from anatomical structural properties and could serve as a knowledge base for determining suspected drugs by potential mechanisms of action.
Collapse
|
15
|
Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects. Nat Commun 2019; 10:1579. [PMID: 30952858 PMCID: PMC6450952 DOI: 10.1038/s41467-019-09407-3] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 03/07/2019] [Indexed: 12/19/2022] Open
Abstract
Only a small fraction of early drug programs progress to the market, due to safety and efficacy failures, despite extensive efforts to predict safety. Characterizing the effect of natural variation in the genes encoding drug targets should present a powerful approach to predict side effects arising from drugging particular proteins. In this retrospective analysis, we report a correlation between the organ systems affected by genetic variation in drug targets and the organ systems in which side effects are observed. Across 1819 drugs and 21 phenotype categories analyzed, drug side effects are more likely to occur in organ systems where there is genetic evidence of a link between the drug target and a phenotype involving that organ system, compared to when there is no such genetic evidence (30.0 vs 19.2%; OR = 1.80). This result suggests that human genetic data should be used to predict safety issues associated with drug targets. Safety issues including side effects are one of the major factors causing failure of clinical trials in drug development. Here, the authors leverage information about phenotypes associated with variation in genes encoding drug targets to predict drug-treatment-related side effects.
Collapse
|
16
|
Dey S, Luo H, Fokoue A, Hu J, Zhang P. Predicting adverse drug reactions through interpretable deep learning framework. BMC Bioinformatics 2018; 19:476. [PMID: 30591036 PMCID: PMC6300887 DOI: 10.1186/s12859-018-2544-0] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs. METHODS In this paper, we developed machine learning models including a deep learning framework which can simultaneously predict ADRs and identify the molecular substructures associated with those ADRs without defining the substructures a-priori. RESULTS We evaluated the performance of our model with ten different state-of-the-art fingerprint models and found that neural fingerprints from the deep learning model outperformed all other methods in predicting ADRs. Via feature analysis on drug structures, we identified important molecular substructures that are associated with specific ADRs and assessed their associations via statistical analysis. CONCLUSIONS The deep learning model with feature analysis, substructure identification, and statistical assessment provides a promising solution for identifying risky components within molecular structures and can potentially help to improve drug safety evaluation.
Collapse
Affiliation(s)
- Sanjoy Dey
- Center for Computational Health, IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY USA
| | - Heng Luo
- Center for Computational Health, IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY USA
| | - Achille Fokoue
- Cognitive Computing, IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY USA
| | - Jianying Hu
- Center for Computational Health, IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY USA
| | - Ping Zhang
- Center for Computational Health, IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY USA
| |
Collapse
|
17
|
Mansouri M, Yuan B, Ross CJD, Carleton BC, Ester M. HUME: large-scale detection of causal genetic factors of adverse drug reactions. Bioinformatics 2018; 34:4274-4283. [PMID: 29931042 DOI: 10.1093/bioinformatics/bty475] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 06/14/2018] [Indexed: 11/12/2022] Open
Abstract
Motivation Adverse drug reactions are one of the major factors that affect the wellbeing of patients and financial costs of healthcare systems. Genetic variations of patients have been shown to be a key factor in the occurrence and severity of many ADRs. However, the large number of confounding drugs and genetic biomarkers for each adverse reaction case demands a method that evaluates all potential genetic causes of ADRs simultaneously. Results To address this challenge, we propose HUME, a multi-phase algorithm that recommends genetic factors for ADRs that are causally supported by the patient record data. HUME consists of the construction of a network from co-prevalence between significant genetic biomarkers and ADRs, a link score phase for predicting candidate relations based on the Adamic-Adar measure, and a causal refinement phase based on multiple hypothesis testing of quasi experimental designs for evaluating evidence and counter evidence of candidate relations in the patient records. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Mehrdad Mansouri
- Department of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Bowei Yuan
- Department of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Colin J D Ross
- Child and Family Research Institute, Children's and Women's Health Research Centre of British Columbia, Vancouver, British Columbia, Canada.,Department of Medical Genetics, University of British Columbia, Centre for Molecular Medicine and Therapeutics, Vancouver, British Columbia, Canada
| | - Bruce C Carleton
- Child and Family Research Institute, Children's and Women's Health Research Centre of British Columbia, Vancouver, British Columbia, Canada.,Department of Paediatrics, Faculty of Pharmaceutical Sciences, Pharmaceutical Outcomes Programme, University of British Columbia, Vancouver, British Columbia, Canada
| | - Martin Ester
- Department of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada
| |
Collapse
|
18
|
Bean DM, Wu H, Iqbal E, Dzahini O, Ibrahim ZM, Broadbent M, Stewart R, Dobson RJB. Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records. Sci Rep 2017; 7:16416. [PMID: 29180758 PMCID: PMC5703951 DOI: 10.1038/s41598-017-16674-x] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Accepted: 11/16/2017] [Indexed: 01/31/2023] Open
Abstract
Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph containing four types of node: drugs, protein targets, indications and adverse reactions. Using this graph, we developed a machine learning algorithm based on a simple enrichment test and first demonstrated this method performs extremely well at classifying known causes of adverse reactions (AUC 0.92). A cross validation scheme in which 10% of drug-adverse reaction edges were systematically deleted per fold showed that the method correctly predicts 68% of the deleted edges on average. Next, a subset of adverse reactions that could be reliably detected in anonymised electronic health records from South London and Maudsley NHS Foundation Trust were used to validate predictions from the model that are not currently known in public databases. High-confidence predictions were validated in electronic records significantly more frequently than random models, and outperformed standard methods (logistic regression, decision trees and support vector machines). This approach has the potential to improve patient safety by predicting adverse reactions that were not observed during randomised trials.
Collapse
Affiliation(s)
- Daniel M Bean
- Department of Biostatistics and Health Informatics, Institute of Psychiatry Psychology and Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AF, United Kingdom
| | - Honghan Wu
- Department of Biostatistics and Health Informatics, Institute of Psychiatry Psychology and Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AF, United Kingdom
| | - Ehtesham Iqbal
- Department of Biostatistics and Health Informatics, Institute of Psychiatry Psychology and Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AF, United Kingdom
| | - Olubanke Dzahini
- South London and Maudsley NHS Foundation Trust, Denmark Hill, London, SE5 8AZ, United Kingdom
- Institute of Pharmaceutical Science, King's College, London, 5th Floor, Franklin-Wilkins Building, 150 Stamford Street, London, SE1 9NH, United Kingdom
| | - Zina M Ibrahim
- Department of Biostatistics and Health Informatics, Institute of Psychiatry Psychology and Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AF, United Kingdom
- Farr Institute of Health Informatics Research, UCL Institute of Health Informatics, University College London, London, WC1E 6BT, United Kingdom
| | - Matthew Broadbent
- South London and Maudsley NHS Foundation Trust, Denmark Hill, London, SE5 8AZ, United Kingdom
| | - Robert Stewart
- South London and Maudsley NHS Foundation Trust, Denmark Hill, London, SE5 8AZ, United Kingdom
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AF, United Kingdom
| | - Richard J B Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry Psychology and Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AF, United Kingdom.
- Farr Institute of Health Informatics Research, UCL Institute of Health Informatics, University College London, London, WC1E 6BT, United Kingdom.
| |
Collapse
|
19
|
Moya-García A, Adeyelu T, Kruger FA, Dawson NL, Lees JG, Overington JP, Orengo C, Ranea JAG. Structural and Functional View of Polypharmacology. Sci Rep 2017; 7:10102. [PMID: 28860623 PMCID: PMC5579063 DOI: 10.1038/s41598-017-10012-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Accepted: 08/02/2017] [Indexed: 02/06/2023] Open
Abstract
Protein domains mediate drug-protein interactions and this principle can guide the design of multi-target drugs i.e. polypharmacology. In this study, we associate multi-target drugs with CATH functional families through the overrepresentation of targets of those drugs in CATH functional families. Thus, we identify CATH functional families that are currently enriched in drugs (druggable CATH functional families) and we use the network properties of these druggable protein families to analyse their association with drug side effects. Analysis of selected druggable CATH functional families, enriched in drug targets, show that relatives exhibit highly conserved drug binding sites. Furthermore, relatives within druggable CATH functional families occupy central positions in a human protein functional network, cluster together forming network neighbourhoods and are less likely to be within proteins associated with drug side effects. Our results demonstrate that CATH functional families can be used to identify drug-target interactions, opening a new research direction in target identification.
Collapse
Affiliation(s)
- Aurelio Moya-García
- University College London, Institute of Structural and Molecular Biology, London, UK.
- Department of Molecular Biology and Biochemistry, Universidad de Malaga, 29071, Málaga Spain, CIBER de Enfermedades Raras (CIBERER), 29071, Málaga, Spain.
| | - Tolulope Adeyelu
- University College London, Institute of Structural and Molecular Biology, London, UK
| | - Felix A Kruger
- European Molecular Laboratory - European Bioinformatics Institute, Hinxton, UK
- BenevolentAI, Churchway 40, NW1 1LW, London, UK
| | - Natalie L Dawson
- University College London, Institute of Structural and Molecular Biology, London, UK
| | - Jon G Lees
- University College London, Institute of Structural and Molecular Biology, London, UK
| | - John P Overington
- European Molecular Laboratory - European Bioinformatics Institute, Hinxton, UK
- Medicines Discovery Catapult, Mereside, Alderley Park, Alderley Edge, Cheshire, SK10 4TG, UK
| | - Christine Orengo
- University College London, Institute of Structural and Molecular Biology, London, UK
| | - Juan A G Ranea
- Department of Molecular Biology and Biochemistry, Universidad de Málaga, 29071, Málaga, Spain
- CIBER de Enfermedades Raras (CIBERER), 29071, Málaga, Spain
| |
Collapse
|
20
|
Chen L, Lu J, Huang T, Cai YD. A computational method for the identification of candidate drugs for non-small cell lung cancer. PLoS One 2017; 12:e0183411. [PMID: 28820893 PMCID: PMC5562320 DOI: 10.1371/journal.pone.0183411] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Accepted: 08/03/2017] [Indexed: 11/25/2022] Open
Abstract
Lung cancer causes a large number of deaths per year. Until now, a cure for this disease has not been found or developed. Finding an effective drug through traditional experimental methods invariably costs millions of dollars and takes several years. It is imperative that computational methods be developed to integrate several types of existing information to identify candidate drugs for further study, which could reduce the cost and time of development. In this study, we tried to advance this effort by proposing a computational method to identify candidate drugs for non-small cell lung cancer (NSCLC), a major type of lung cancer. The method used three steps: (1) preliminary screening, (2) screening compounds by an association test and a permutation test, (3) screening compounds using an EM clustering algorithm. In the first step, based on the chemical-chemical interaction information reported in STITCH, a well-known database that reports interactions between chemicals and proteins, and approved NSCLC drugs, compounds that can interact with at least one approved NSCLC drug were picked. In the second step, the association test selected compounds that can interact with at least one NSCLC-related chemical and at least one NSCLC-related gene, and subsequently, the permutation test was used to discard nonspecific compounds from the remaining compounds. In the final step, core compounds were selected using a powerful clustering algorithm, the EM algorithm. Six putative compounds, protoporphyrin IX, hematoporphyrin, canertinib, lapatinib, pelitinib, and dacomitinib, were identified by this method. Previously published data show that all of the selected compounds have been reported to possess anti-NSCLC activity, indicating high probabilities of these compounds being novel candidate drugs for NSCLC.
Collapse
Affiliation(s)
- Lei Chen
- College of Life Science, Shanghai University, Shanghai, People’s Republic of China
- College of Information Engineering, Shanghai Maritime University, Shanghai, People’s Republic of China
| | - Jing Lu
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, People’s Republic of China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Yu-Dong Cai
- College of Life Science, Shanghai University, Shanghai, People’s Republic of China
- * E-mail:
| |
Collapse
|
21
|
Chen X, Shi H, Yang F, Yang L, Lv Y, Wang S, Dai E, Sun D, Jiang W. Large-scale identification of adverse drug reaction-related proteins through a random walk model. Sci Rep 2016; 6:36325. [PMID: 27805066 PMCID: PMC5090865 DOI: 10.1038/srep36325] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 10/13/2016] [Indexed: 12/19/2022] Open
Abstract
Adverse drug reactions (ADRs) are responsible for drug failure in clinical trials and affect life quality of patients. The identification of ADRs during the early phases of drug development is an important task. Therefore, predicting potential protein targets eliciting ADRs is essential for understanding the pathogenesis of ADRs. In this study, we proposed a computational algorithm,Integrated Network for Protein-ADR relations (INPADR), to infer potential protein-ADR relations based on an integrated network. First, the integrated network was constructed by connecting the protein-protein interaction network and the ADR similarity network using known protein-ADR relations. Then, candidate protein-ADR relations were further prioritized by performing a random walk with restart on this integrated network. Leave-one-out cross validation was used to evaluate the ability of the INPADR. An AUC of 0.8486 was obtained, which was a significant improvement compared to previous methods. We also applied the INPADR to two ADRs to evaluate its accuracy. The results suggested that the INPADR is capable of finding novel protein-ADR relations. This study provides new insight to our understanding of ADRs. The predicted ADR-related proteins will provide a reference for preclinical safety pharmacology studies and facilitate the identification of ADRs during the early phases of drug development.
Collapse
Affiliation(s)
- Xiaowen Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Hongbo Shi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Feng Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yingli Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Shuyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Enyu Dai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Dianjun Sun
- Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, 150081, China
| | - Wei Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| |
Collapse
|
22
|
Siragusa L, Luciani R, Borsari C, Ferrari S, Costi MP, Cruciani G, Spyrakis F. Comparing Drug Images and Repurposing Drugs with BioGPS and FLAPdock: The Thymidylate Synthase Case. ChemMedChem 2016; 11:1653-66. [PMID: 27404817 DOI: 10.1002/cmdc.201600121] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 06/08/2016] [Indexed: 12/14/2022]
Abstract
Repurposing and repositioning drugs has become a frequently pursued and successful strategy in the current era, as new chemical entities are increasingly difficult to find and get approved. Herein we report an integrated BioGPS/FLAPdock pipeline for rapid and effective off-target identification and drug repurposing. Our method is based on the structural and chemical properties of protein binding sites, that is, the ligand image, encoded in the GRID molecular interaction fields (MIFs). Protein similarity is disclosed through the BioGPS algorithm by measuring the pockets' overlap according to which pockets are clustered. Co-crystallized and known ligands can be cross-docked among similar targets, selected for subsequent in vitro binding experiments, and possibly improved for inhibitory potency. We used human thymidylate synthase (TS) as a test case and searched the entire RCSB Protein Data Bank (PDB) for similar target pockets. We chose casein kinase IIα as a control and tested a series of its inhibitors against the TS template. Ellagic acid and apigenin were identified as TS inhibitors, and various flavonoids were selected and synthesized in a second-round selection. The compounds were demonstrated to be active in the low-micromolar range.
Collapse
Affiliation(s)
- Lydia Siragusa
- Molecular Discovery Limited, 215 Marsh Road, Pinner Middlesex, London, HA5 5NE, UK
| | - Rosaria Luciani
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy
| | - Chiara Borsari
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy
| | - Stefania Ferrari
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy
| | - Maria Paola Costi
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy
| | - Gabriele Cruciani
- Department of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123, Perugia, Italy
| | - Francesca Spyrakis
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy. .,Department of Food Science, University of Parma, Viale delle Scienze 17A, 43124, Parma, Italy.
| |
Collapse
|
23
|
Shaked I, Oberhardt M, Atias N, Sharan R, Ruppin E. Metabolic Network Prediction of Drug Side Effects. Cell Syst 2016; 2:209-13. [DOI: 10.1016/j.cels.2016.03.001] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Revised: 12/11/2015] [Accepted: 03/01/2016] [Indexed: 11/17/2022]
|
24
|
Wildenhain J, Spitzer M, Dolma S, Jarvik N, White R, Roy M, Griffiths E, Bellows DS, Wright GD, Tyers M. Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning. Cell Syst 2015; 1:383-95. [PMID: 27136353 DOI: 10.1016/j.cels.2015.12.003] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2015] [Revised: 11/03/2015] [Accepted: 12/02/2015] [Indexed: 12/12/2022]
Abstract
The structure of genetic interaction networks predicts that, analogous to synthetic lethal interactions between non-essential genes, combinations of compounds with latent activities may exhibit potent synergism. To test this hypothesis, we generated a chemical-genetic matrix of 195 diverse yeast deletion strains treated with 4,915 compounds. This approach uncovered 1,221 genotype-specific inhibitors, which we termed cryptagens. Synergism between 8,128 structurally disparate cryptagen pairs was assessed experimentally and used to benchmark predictive algorithms. A model based on the chemical-genetic matrix and the genetic interaction network failed to accurately predict synergism. However, a combined random forest and Naive Bayesian learner that associated chemical structural features with genotype-specific growth inhibition had strong predictive power. This approach identified previously unknown compound combinations that exhibited species-selective toxicity toward human fungal pathogens. This work demonstrates that machine learning methods trained on unbiased chemical-genetic interaction data may be widely applicable for the discovery of synergistic combinations in different species.
Collapse
Affiliation(s)
- Jan Wildenhain
- Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK
| | - Michaela Spitzer
- Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK; Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - Sonam Dolma
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada
| | - Nick Jarvik
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada
| | - Rachel White
- Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK
| | - Marcia Roy
- Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK
| | - Emma Griffiths
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - David S Bellows
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada
| | - Gerard D Wright
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - Mike Tyers
- Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK; Institute for Research in Immunology and Cancer, Department of Medicine, Université de Montréal, Montréal, QC H3C 3J7, Canada.
| |
Collapse
|
25
|
Boland MR, Jacunski A, Lorberbaum T, Romano JD, Moskovitch R, Tatonetti NP. Systems biology approaches for identifying adverse drug reactions and elucidating their underlying biological mechanisms. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2015; 8:104-22. [PMID: 26559926 DOI: 10.1002/wsbm.1323] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Revised: 09/30/2015] [Accepted: 10/01/2015] [Indexed: 01/06/2023]
Abstract
Small molecules are indispensable to modern medical therapy. However, their use may lead to unintended, negative medical outcomes commonly referred to as adverse drug reactions (ADRs). These effects vary widely in mechanism, severity, and populations affected, making ADR prediction and identification important public health concerns. Current methods rely on clinical trials and postmarket surveillance programs to find novel ADRs; however, clinical trials are limited by small sample size, whereas postmarket surveillance methods may be biased and inherently leave patients at risk until sufficient clinical evidence has been gathered. Systems pharmacology, an emerging interdisciplinary field combining network and chemical biology, provides important tools to uncover and understand ADRs and may mitigate the drawbacks of traditional methods. In particular, network analysis allows researchers to integrate heterogeneous data sources and quantify the interactions between biological and chemical entities. Recent work in this area has combined chemical, biological, and large-scale observational health data to predict ADRs in both individual patients and global populations. In this review, we explore the rapid expansion of systems pharmacology in the study of ADRs. We enumerate the existing methods and strategies and illustrate progress in the field with a model framework that incorporates crucial data elements, such as diet and comorbidities, known to modulate ADR risk. Using this framework, we highlight avenues of research that may currently be underexplored, representing opportunities for future work.
Collapse
Affiliation(s)
- Mary Regina Boland
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,Department of Systems Biology, Columbia University, New York, NY, USA.,Department of Medicine, Columbia University, New York, NY, USA.,Observational Health Data Science and Informatics (OHDSI), New York, NY, USA
| | - Alexandra Jacunski
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,Department of Systems Biology, Columbia University, New York, NY, USA.,Department of Medicine, Columbia University, New York, NY, USA.,Integrated Program in Cellular, Molecular and Biomedical Studies, Columbia University, New York, NY, USA
| | - Tal Lorberbaum
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,Department of Systems Biology, Columbia University, New York, NY, USA.,Department of Medicine, Columbia University, New York, NY, USA.,Department of Physiology and Cellular Biophysics, Columbia University, New York, NY, USA
| | - Joseph D Romano
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,Department of Systems Biology, Columbia University, New York, NY, USA.,Department of Medicine, Columbia University, New York, NY, USA
| | - Robert Moskovitch
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,Department of Systems Biology, Columbia University, New York, NY, USA.,Department of Medicine, Columbia University, New York, NY, USA
| | - Nicholas P Tatonetti
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,Department of Systems Biology, Columbia University, New York, NY, USA.,Department of Medicine, Columbia University, New York, NY, USA.,Observational Health Data Science and Informatics (OHDSI), New York, NY, USA
| |
Collapse
|
26
|
Kuhn M, Letunic I, Jensen LJ, Bork P. The SIDER database of drugs and side effects. Nucleic Acids Res 2015; 44:D1075-9. [PMID: 26481350 PMCID: PMC4702794 DOI: 10.1093/nar/gkv1075] [Citation(s) in RCA: 622] [Impact Index Per Article: 69.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Accepted: 10/06/2015] [Indexed: 01/10/2023] Open
Abstract
Unwanted side effects of drugs are a burden on patients and a severe impediment in the development of new drugs. At the same time, adverse drug reactions (ADRs) recorded during clinical trials are an important source of human phenotypic data. It is therefore essential to combine data on drugs, targets and side effects into a more complete picture of the therapeutic mechanism of actions of drugs and the ways in which they cause adverse reactions. To this end, we have created the SIDER (‘Side Effect Resource’, http://sideeffects.embl.de) database of drugs and ADRs. The current release, SIDER 4, contains data on 1430 drugs, 5880 ADRs and 140 064 drug–ADR pairs, which is an increase of 40% compared to the previous version. For more fine-grained analyses, we extracted the frequency with which side effects occur from the package inserts. This information is available for 39% of drug–ADR pairs, 19% of which can be compared to the frequency under placebo treatment. SIDER furthermore contains a data set of drug indications, extracted from the package inserts using Natural Language Processing. These drug indications are used to reduce the rate of false positives by identifying medical terms that do not correspond to ADRs.
Collapse
Affiliation(s)
- Michael Kuhn
- Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstr. 108, 01307 Dresden, Germany
| | - Ivica Letunic
- Biobyte solutions GmbH, Bothestr. 142, 69117 Heidelberg, Germany
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Peer Bork
- European Molecular Biology Laboratory, Structural and Computational Biology Unit, Molecular Medicine Partnership Unit, Meyerhofstrasse 1, 69117 Heidelberg, Germany Max-Delbrück-Centre for Molecular Medicine, Robert-Rössle-Strasse 10, 13092 Berlin, Germany
| |
Collapse
|
27
|
Garcia-Serna R, Vidal D, Remez N, Mestres J. Large-Scale Predictive Drug Safety: From Structural Alerts to Biological Mechanisms. Chem Res Toxicol 2015; 28:1875-87. [PMID: 26360911 DOI: 10.1021/acs.chemrestox.5b00260] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The recent explosion of data linking drugs, proteins, and pathways with safety events has promoted the development of integrative systems approaches to large-scale predictive drug safety. The added value of such approaches is that, beyond the traditional identification of potentially labile chemical fragments for selected toxicity end points, they have the potential to provide mechanistic insights for a much larger and diverse set of safety events in a statistically sound nonsupervised manner, based on the similarity to drug classes, the interaction with secondary targets, and the interference with biological pathways. The combined identification of chemical and biological hazards enhances our ability to assess the safety risk of bioactive small molecules with higher confidence than that using structural alerts only. We are still a very long way from reliably predicting drug safety, but advances toward gaining a better understanding of the mechanisms leading to adverse outcomes represent a step forward in this direction.
Collapse
Affiliation(s)
- Ricard Garcia-Serna
- Chemotargets SL , Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain
| | - David Vidal
- Chemotargets SL , Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain
| | - Nikita Remez
- Chemotargets SL , Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain.,Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra , Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain
| | - Jordi Mestres
- Chemotargets SL , Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain.,Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra , Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain
| |
Collapse
|
28
|
Pérez-Nueno VI, Souchet M, Karaboga AS, Ritchie DW. GESSE: Predicting Drug Side Effects from Drug–Target Relationships. J Chem Inf Model 2015; 55:1804-23. [DOI: 10.1021/acs.jcim.5b00120] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Violeta I. Pérez-Nueno
- Harmonic Pharma, Espace Transfert, 615 rue du Jardin Botanique, 54600 Villers-les-Nancy, France
| | - Michel Souchet
- Harmonic Pharma, Espace Transfert, 615 rue du Jardin Botanique, 54600 Villers-les-Nancy, France
| | - Arnaud S. Karaboga
- Harmonic Pharma, Espace Transfert, 615 rue du Jardin Botanique, 54600 Villers-les-Nancy, France
| | - David W. Ritchie
- INRIA Nancy − Grand Est, Equipe Capsid, 615 rue du Jardin Botanique, 54600 Villers-les-Nancy, France
| |
Collapse
|
29
|
Abstract
INTRODUCTION Over the past three decades, the predominant paradigm in drug discovery was designing selective ligands for a specific target to avoid unwanted side effects. However, in the last 5 years, the aim has shifted to take into account the biological network in which they interact. Quantitative and Systems Pharmacology (QSP) is a new paradigm that aims to understand how drugs modulate cellular networks in space and time, in order to predict drug targets and their role in human pathophysiology. AREAS COVERED This review discusses existing computational and experimental QSP approaches such as polypharmacology techniques combined with systems biology information and considers the use of new tools and ideas in a wider 'systems-level' context in order to design new drugs with improved efficacy and fewer unwanted off-target effects. EXPERT OPINION The use of network biology produces valuable information such as new indications for approved drugs, drug-drug interactions, proteins-drug side effects and pathways-gene associations. However, we are still far from the aim of QSP, both because of the huge effort needed to model precisely biological network models and the limited accuracy that we are able to reach with those. Hence, moving from 'one molecule for one target to give one therapeutic effect' to the 'big systems-based picture' seems obvious moving forward although whether our current tools are sufficient for such a step is still under debate.
Collapse
Affiliation(s)
- Violeta I Pérez-Nueno
- a Harmonic Pharma, Espace Transfert , 615 rue du Jardin Botanique, 54600 Villers lès Nancy, France +33 354 958 604 ; +33 383 593 046 ;
| |
Collapse
|
30
|
In silico assessment of adverse drug reactions and associated mechanisms. Drug Discov Today 2015; 21:58-71. [PMID: 26272036 DOI: 10.1016/j.drudis.2015.07.018] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Revised: 07/15/2015] [Accepted: 07/31/2015] [Indexed: 12/31/2022]
Abstract
During recent years, various in silico approaches have been developed to estimate chemical and biological drug features, for example chemical fragments, protein targets, pathways, among others, that correlate with adverse drug reactions (ADRs) and explain the associated mechanisms. These features have also been used for the creation of predictive models that enable estimation of ADRs during the early stages of drug development. In this review, we discuss various in silico approaches to predict these features for a certain drug, estimate correlations with ADRs, establish causal relationships between selected features and ADR mechanisms and create corresponding predictive models.
Collapse
|
31
|
Zu S, Chen T, Li S. Global optimization-based inference of chemogenomic features from drug-target interactions. Bioinformatics 2015; 31:2523-9. [PMID: 25819672 DOI: 10.1093/bioinformatics/btv181] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2014] [Accepted: 03/24/2015] [Indexed: 02/02/2023] Open
Abstract
MOTIVATION Gaining insight into chemogenomic drug-target interactions, such as those involving the substructures of synthetic drugs and protein domains, is important in fragment-based drug discovery and drug repositioning. Previous studies evaluated the interactions locally, thereby ignoring the competitive effects of different substructures or domains, but this could lead to high false-positive estimation, calling for a computational method that presents more predictive power. RESULTS A statistical model, termed Global optimization-based InFerence of chemogenomic features from drug-Target interactions, or GIFT, is proposed herein to evaluate substructure-domain interactions globally such that all substructure-domain contributions to drug-target interaction are analyzed simultaneously. Combinations of different chemical substructures were included since they may function as one unit. When compared to previous methods, GIFT showed better interpretive performance, and performance for the recovery of drug-target interactions was good. Among 53 known drug-domain interactions, 81% were accurately predicted by GIFT. Eighteen of the top 100 predicted combined substructure-domain interactions had corresponding drug-target structures in the Protein Data Bank database, and 15 out of the 18 had been proved. GIFT was then implemented to predict substructure-domain interactions based on drug repositioning. For example, the anticancer activities of tazarotene, adapalene, acitretin and raloxifene were identified. In summary, GIFT is a global chemogenomic inference approach and offers fresh insight into drug-target interactions.
Collapse
Affiliation(s)
- Songpeng Zu
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China and
| | - Ting Chen
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China and Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Shao Li
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China and
| |
Collapse
|
32
|
Lorberbaum T, Nasir M, Keiser MJ, Vilar S, Hripcsak G, Tatonetti NP. Systems pharmacology augments drug safety surveillance. Clin Pharmacol Ther 2014; 97:151-8. [PMID: 25670520 PMCID: PMC4325423 DOI: 10.1002/cpt.2] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2014] [Accepted: 09/12/2014] [Indexed: 12/21/2022]
Abstract
Small molecule drugs are the foundation of modern medical practice yet their use is limited by the onset of unexpected and severe adverse events (AEs). Regulatory agencies rely on post-marketing surveillance to monitor safety once drugs are approved for clinical use. Despite advances in pharmacovigilance methods that address issues of confounding bias, clinical data of AEs are inherently noisy. Systems pharmacology– the integration of systems biology and chemical genomics – can illuminate drug mechanisms of action. We hypothesize that these data can improve drug safety surveillance by highlighting drugs with a mechanistic connection to the target phenotype (enriching true positives) and filtering those that do not (depleting false positives). We present an algorithm, the modular assembly of drug safety subnetworks (MADSS), to combine systems pharmacology and pharmacovigilance data and significantly improve drug safety monitoring for four clinically relevant adverse drug reactions.
Collapse
Affiliation(s)
- T Lorberbaum
- Department of Physiology and Cellular Biophysics, Columbia University, New York, New York, USA; Department of Biomedical Informatics, Columbia University, New York, New York, USA; Departments of Systems Biology and Medicine, Columbia University, New York, New York, USA
| | | | | | | | | | | |
Collapse
|
33
|
Kennedy EJ. EMBO conference series: Chemical Biology 2014. Chembiochem 2014; 15:2783-7. [PMID: 25318996 DOI: 10.1002/cbic.201402527] [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: 09/11/2014] [Indexed: 11/07/2022]
Abstract
Around 300 people from 18 countries took part in the fourth biennial Chemical Biology conference at The European Molecular Biology Laboratory (EMBL) in Heidelberg, from August 20 to 23, 2014. Many advances in the field of chemical biology were presented in talks and poster sessions. Picture: Petra Riedinger (EMBL).
Collapse
Affiliation(s)
- Eileen J Kennedy
- Department of Pharmaceutical and Biomedical Sciences, College of Pharmacy, University of Georgia, 240 W. Green Street, Athens, GA 30602 (USA).
| |
Collapse
|
34
|
Abstract
Efforts to compile the phenotypic effects of drugs and environmental chemicals offer the opportunity to adopt a chemo-centric view of human health that does not require detailed mechanistic information. Here, we consider thousands of chemicals and analyze the relationship of their structures with adverse and therapeutic responses. Our study includes molecules related to the etiology of 934 health threatening conditions and used to treat 835 diseases. We first identify chemical moieties that could be independently associated with each phenotypic effect. Using these fragments, we build accurate predictors for approximately 400 clinical phenotypes, finding many privileged and liable structures. Finally, we connect two diseases if they relate to similar chemical structures. The resulting networks of human conditions are able to predict disease comorbidities, as well as identifying potential drug side effects and opportunities for drug repositioning, and show a remarkable coincidence with clinical observations.
Collapse
|
35
|
Juan-Blanco T, Duran-Frigola M, Aloy P. IntSide: a web server for the chemical and biological examination of drug side effects. Bioinformatics 2014; 31:612-3. [PMID: 25380960 DOI: 10.1093/bioinformatics/btu688] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
SUMMARY Drug side effects are one of the main health threats worldwide, and an important obstacle in drug development. Understanding how adverse reactions occur requires knowledge on drug mechanisms at the molecular level. Despite recent advances, the need for tools and methods that facilitate side effect anticipation still remains. Here, we present IntSide, a web server that integrates chemical and biological information to elucidate the molecular mechanisms underlying drug side effects. IntSide currently catalogs 1175 side effects caused by 996 drugs, associated with drug features divided into eight categories, belonging to either biology or chemistry. On the biological side, IntSide reports drug targets and off-targets, pathways, molecular functions and biological processes. From a chemical viewpoint, it includes molecular fingerprints, scaffolds and chemical entities. Finally, we also integrate additional biological data, such as protein interactions and disease-related genes, to facilitate mechanistic interpretations. AVAILABILITY AND IMPLEMENTATION Our data and web resource are available online (http://intside.irbbarcelona.org/). CONTACT patrick.aloy@irbbarcelona.org SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Teresa Juan-Blanco
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona) and Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Miquel Duran-Frigola
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona) and Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona) and Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona) and Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| |
Collapse
|
36
|
Cami A, Reis BY. Concordance and predictive value of two adverse drug event data sets. BMC Med Inform Decis Mak 2014; 14:74. [PMID: 25149292 PMCID: PMC4150549 DOI: 10.1186/1472-6947-14-74] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Accepted: 08/18/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Accurate prediction of adverse drug events (ADEs) is an important means of controlling and reducing drug-related morbidity and mortality. Since no single "gold standard" ADE data set exists, a range of different drug safety data sets are currently used for developing ADE prediction models. There is a critical need to assess the degree of concordance between these various ADE data sets and to validate ADE prediction models against multiple reference standards. METHODS We systematically evaluated the concordance of two widely used ADE data sets - Lexi-comp from 2010 and SIDER from 2012. The strength of the association between ADE (drug) counts in Lexi-comp and SIDER was assessed using Spearman rank correlation, while the differences between the two data sets were characterized in terms of drug categories, ADE categories and ADE frequencies. We also performed a comparative validation of the Predictive Pharmacosafety Networks (PPN) model using both ADE data sets. The predictive power of PPN using each of the two validation sets was assessed using the area under Receiver Operating Characteristic curve (AUROC). RESULTS The correlations between the counts of ADEs and drugs in the two data sets were 0.84 (95% CI: 0.82-0.86) and 0.92 (95% CI: 0.91-0.93), respectively. Relative to an earlier snapshot of Lexi-comp from 2005, Lexi-comp 2010 and SIDER 2012 introduced a mean of 1,973 and 4,810 new drug-ADE associations per year, respectively. The difference between these two data sets was most pronounced for Nervous System and Anti-infective drugs, Gastrointestinal and Nervous System ADEs, and postmarketing ADEs. A minor difference of 1.1% was found in the AUROC of PPN when SIDER 2012 was used for validation instead of Lexi-comp 2010. CONCLUSIONS In conclusion, the ADE and drug counts in Lexi-comp and SIDER data sets were highly correlated and the choice of validation set did not greatly affect the overall prediction performance of PPN. Our results also suggest that it is important to be aware of the differences that exist among ADE data sets, especially in modeling applications focused on specific drug and ADE categories.
Collapse
Affiliation(s)
- Aurel Cami
- Division of Emergency Medicine, Boston Children's Hospital, 1 Autumn Street, 5th Floor, Boston, MA 02215, USA.
| | | |
Collapse
|
37
|
Davis AP, Wiegers TC, Roberts PM, King BL, Lay JM, Lennon-Hopkins K, Sciaky D, Johnson R, Keating H, Greene N, Hernandez R, McConnell KJ, Enayetallah AE, Mattingly CJ. A CTD-Pfizer collaboration: manual curation of 88,000 scientific articles text mined for drug-disease and drug-phenotype interactions. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2013; 2013:bat080. [PMID: 24288140 PMCID: PMC3842776 DOI: 10.1093/database/bat080] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Improving the prediction of chemical toxicity is a goal common to both environmental health research and pharmaceutical drug development. To improve safety detection assays, it is critical to have a reference set of molecules with well-defined toxicity annotations for training and validation purposes. Here, we describe a collaboration between safety researchers at Pfizer and the research team at the Comparative Toxicogenomics Database (CTD) to text mine and manually review a collection of 88 629 articles relating over 1 200 pharmaceutical drugs to their potential involvement in cardiovascular, neurological, renal and hepatic toxicity. In 1 year, CTD biocurators curated 2 54 173 toxicogenomic interactions (1 52 173 chemical–disease, 58 572 chemical–gene, 5 345 gene–disease and 38 083 phenotype interactions). All chemical–gene–disease interactions are fully integrated with public CTD, and phenotype interactions can be downloaded. We describe Pfizer’s text-mining process to collate the articles, and CTD’s curation strategy, performance metrics, enhanced data content and new module to curate phenotype information. As well, we show how data integration can connect phenotypes to diseases. This curation can be leveraged for information about toxic endpoints important to drug safety and help develop testable hypotheses for drug–disease events. The availability of these detailed, contextualized, high-quality annotations curated from seven decades’ worth of the scientific literature should help facilitate new mechanistic screening assays for pharmaceutical compound survival. This unique partnership demonstrates the importance of resource sharing and collaboration between public and private entities and underscores the complementary needs of the environmental health science and pharmaceutical communities. Database URL: http://ctdbase.org/
Collapse
Affiliation(s)
- Allan Peter Davis
- Department of Biological Sciences, 3510 Thomas Hall, North Carolina State University, Raleigh, NC 27695-7617, USA, Computational Sciences Center of Emphasis, 200 Cambridgepark Drive, Pfizer Inc., Cambridge, MA 02139, USA, Department of Bioinformatics, P.O. Box 35, Old Bar Harbor Road, MDI Biological Laboratory, Salisbury Cove, ME 04672, USA, Compound Safety Prediction, MS 8118-B3, Eastern Point Road, Pfizer Inc., Groton, CT 06340, USA, Computational Sciences Center of Emphasis, Pfizer Inc., Ramsgate Road, Sandwich, Kent CT13 9NJ, UK, Computational Sciences Center of Emphasis, 558 Eastern Point Road, Pfizer Inc., Groton, CT 06340, USA and Drug Safety Research and Development, 558 Eastern Point Road, Pfizer Inc., Groton, CT 06340, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
38
|
Kuhn M, Szklarczyk D, Pletscher-Frankild S, Blicher TH, von Mering C, Jensen LJ, Bork P. STITCH 4: integration of protein-chemical interactions with user data. Nucleic Acids Res 2013; 42:D401-7. [PMID: 24293645 PMCID: PMC3964996 DOI: 10.1093/nar/gkt1207] [Citation(s) in RCA: 297] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
STITCH is a database of protein-chemical interactions that integrates many sources of experimental and manually curated evidence with text-mining information and interaction predictions. Available at http://stitch.embl.de, the resulting interaction network includes 390 000 chemicals and 3.6 million proteins from 1133 organisms. Compared with the previous version, the number of high-confidence protein-chemical interactions in human has increased by 45%, to 367 000. In this version, we added features for users to upload their own data to STITCH in the form of internal identifiers, chemical structures or quantitative data. For example, a user can now upload a spreadsheet with screening hits to easily check which interactions are already known. To increase the coverage of STITCH, we expanded the text mining to include full-text articles and added a prediction method based on chemical structures. We further changed our scheme for transferring interactions between species to rely on orthology rather than protein similarity. This improves the performance within protein families, where scores are now transferred only to orthologous proteins, but not to paralogous proteins. STITCH can be accessed with a web-interface, an API and downloadable files.
Collapse
Affiliation(s)
- Michael Kuhn
- Biotechnology Center, TU Dresden, 01062 Dresden, Germany, Institute of Molecular Life Sciences, University of Zurich and Swiss Institute of Bioinformatics, Winterthurerstrasse 190, 8057 Zurich, Switzerland, Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany and Max-Delbrück-Centre for Molecular Medicine, Robert-Rössle-Strasse 10, 13092 Berlin, Germany
| | | | | | | | | | | | | |
Collapse
|
39
|
Berland-Benhaïm C, Bartoli C, Karsenty G, Piercecchi-Marti MD. Prescrire un médicament en 2013 : aspects légaux. Prog Urol 2013; 23:1201-7. [DOI: 10.1016/j.purol.2013.09.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2013] [Accepted: 09/16/2013] [Indexed: 11/15/2022]
|