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Chakraborty C, Bhattacharya M, Lee SS, Wen ZH, Lo YH. The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges. MOLECULAR THERAPY. NUCLEIC ACIDS 2024; 35:102295. [PMID: 39257717 PMCID: PMC11386122 DOI: 10.1016/j.omtn.2024.102295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Due to the transformation of artificial intelligence (AI) tools and technologies, AI-driven drug discovery has come to the forefront. It reduces the time and expenditure. Due to these advantages, pharmaceutical industries are concentrating on AI-driven drug discovery. Several drug molecules have been discovered using AI-based techniques and tools, and several newly AI-discovered drug molecules have already entered clinical trials. In this review, we first present the data and their resources in the pharmaceutical sector for AI-driven drug discovery and illustrated some significant algorithms or techniques used for AI and ML which are used in this field. We gave an overview of the deep neural network (NN) models and compared them with artificial NNs. Then, we illustrate the recent advancement of the landscape of drug discovery using AI to deep learning, such as the identification of drug targets, prediction of their structure, estimation of drug-target interaction, estimation of drug-target binding affinity, design of de novo drug, prediction of drug toxicity, estimation of absorption, distribution, metabolism, excretion, toxicity; and estimation of drug-drug interaction. Moreover, we highlighted the success stories of AI-driven drug discovery and discussed several collaboration and the challenges in this area. The discussions in the article will enrich the pharmaceutical industry.
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Affiliation(s)
- Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal 700126, India
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha 756020, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Gangwon-Do 24252, Republic of Korea
| | - Zhi-Hong Wen
- Department of Marine Biotechnology and Resources, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
| | - Yi-Hao Lo
- Department of Family Medicine, Zuoying Armed Forces General Hospital, Kaohsiung 813204, Taiwan
- Shu-Zen Junior College of Medicine and Management, Kaohsiung 821004, Taiwan
- Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung 804201, Taiwan
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Asfand-E-Yar M, Hashir Q, Shah AA, Malik HAM, Alourani A, Khalil W. Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events. Sci Rep 2024; 14:4076. [PMID: 38374325 PMCID: PMC10876630 DOI: 10.1038/s41598-024-54409-x] [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: 12/05/2023] [Accepted: 02/12/2024] [Indexed: 02/21/2024] Open
Abstract
Drug-to-drug interaction (DDIs) occurs when a patient consumes multiple drugs. Therefore, it is possible that any medication can influence other drugs' effectiveness. The drug-to-drug interactions are detected based on the interactions of chemical substructures, targets, pathways, and enzymes; therefore, machine learning (ML) and deep learning (DL) techniques are used to find the associated DDI events. The DL model, i.e., Convolutional Neural Network (CNN), is used to analyze the DDI. DDI is based on the 65 different drug-associated events, which is present in the drug bank database. Our model uses the inputs, which are chemical structures (i.e., smiles of drugs), enzymes, pathways, and the target of the drug. Therefore, for the multi-model CNN, we use several layers, activation functions, and features of drugs to achieve better accuracy as compared to traditional prediction algorithms. We perform different experiments on various hyperparameters. We have also carried out experiments on various iterations of drug features in different sets. Our Multi-Modal Convolutional Neural Network - Drug to Drug Interaction (MCNN-DDI) model achieved an accuracy of 90.00% and an AUPR of 94.78%. The results showed that a combination of the drug's features (i.e., chemical substructure, target, and enzyme) performs better in DDIs-associated events prediction than other features.
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Affiliation(s)
- Muhammad Asfand-E-Yar
- Department of Computer Science, CoE-AI, Center of Excellence Artificial Intelligence, Bahria University, Islamabad, Pakistan
| | - Qadeer Hashir
- Department of Computer Science, CoE-AI, Center of Excellence Artificial Intelligence, Bahria University, Islamabad, Pakistan
| | - Asghar Ali Shah
- Department of Computer Science, Bahria University, Islamabad , Pakistan
| | | | - Abdullah Alourani
- Department of Management Information Systems and Production Management, College of Business and Economics, Qassim University, Buraydah 51452, Saudi Arabia.
| | - Waqar Khalil
- Department of Computer Science, CoE-AI, Center of Excellence Artificial Intelligence, Bahria University, Islamabad, Pakistan
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MSResG: Using GAE and Residual GCN to Predict Drug-Drug Interactions Based on Multi-source Drug Features. Interdiscip Sci 2023; 15:171-188. [PMID: 36646843 DOI: 10.1007/s12539-023-00550-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 01/05/2023] [Accepted: 01/07/2023] [Indexed: 01/18/2023]
Abstract
Drug-drug interaction refers to taking the two drugs may produce certain reaction which may be a threat to patients' health, or enhance the efficacy helpful for medical work. Therefore, it is necessary to study and predict it. In fact, traditional experimental methods can be used for drug-drug interaction prediction, but they are time-consuming and costly, so we prefer to use more accurate and convenient calculation methods to predict the unknown drug-drug interaction. In this paper, we proposed a deep learning framework called MSResG that considers multi-sources features of drugs and combines them with Graph Auto-Encoder to predicting. Firstly, the model obtains four feature representations of drugs from the database, namely, chemical substructure, target, pathway and enzyme, and then calculates the Jaccard similarity of the drugs. To balance different drug features, we perform similarity integration by finding the mean value. Then we will be comprehensive similarity network combined with drug interaction network, and encodes and decodes it using the graph auto-encoder based on residual graph convolution network. Encoding is to learn the potential feature vectors of drugs, which contain similar information and interaction information. Decoding is to reconstruct the network to predict unknown drug-drug interaction. The experimental results show that our model has advanced performance and is superior to other existing advanced methods. Case study also shows that MSResG has practical significance.
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Ren ZH, Yu CQ, Li LP, You ZH, Pan J, Guan YJ, Guo LX. BioChemDDI: Predicting Drug-Drug Interactions by Fusing Biochemical and Structural Information through a Self-Attention Mechanism. BIOLOGY 2022; 11:biology11050758. [PMID: 35625486 PMCID: PMC9138786 DOI: 10.3390/biology11050758] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/12/2022] [Accepted: 05/13/2022] [Indexed: 01/13/2023]
Abstract
Simple Summary Throughout history, combining drugs has been a common method in the fight against complex diseases. However, potential drug–drug interactions could give rise to unknown toxicity issues, which requires the urgent proposal of efficient methods to identify potential interactions.We use computer technology and machine learning techniques to propose a novel computational framework to calculate scores of drug–drug interaction probability for simplifying the screening process. Additionally, we built an online prescreening tool for biological researchers to further verify possible interactions in the fields of biomedicine and pharmacology. Overall, our study can provide new insights and approaches for rapidly identifying potential drug–drug interactions. Abstract During the development of drug and clinical applications, due to the co-administration of different drugs that have a high risk of interfering with each other’s mechanisms of action, correctly identifying potential drug–drug interactions (DDIs) is important to avoid a reduction in drug therapeutic activities and serious injuries to the organism. Therefore, to explore potential DDIs, we develop a computational method of integrating multi-level information. Firstly, the information of chemical sequence is fully captured by the Natural Language Processing (NLP) algorithm, and multiple biological function similarity information is fused by Similarity Network Fusion (SNF). Secondly, we extract deep network structure information through Hierarchical Representation Learning for Networks (HARP). Then, a highly representative comprehensive feature descriptor is constructed through the self-attention module that efficiently integrates biochemical and network features. Finally, a deep neural network (DNN) is employed to generate the prediction results. Contrasted with the previous supervision model, BioChemDDI innovatively introduced graph collapse for extracting a network structure and utilized the biochemical information during the pre-training process. The prediction results of the benchmark dataset indicate that BioChemDDI outperforms other existing models. Moreover, the case studies related to three cancer diseases, including breast cancer, hepatocellular carcinoma and malignancies, were analyzed using BioChemDDI. As a result, 24, 18 and 20 out of the top 30 predicted cancer-related drugs were confirmed by the databases. These experimental results demonstrate that BioChemDDI is a useful model to predict DDIs and can provide reliable candidates for biological experiments. The web server of BioChemDDI predictor is freely available to conduct further studies.
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Affiliation(s)
- Zhong-Hao Ren
- School of Information Engineering, Xijing University, Xi’an 710123, China; (Z.-H.R.); (Y.-J.G.); (L.-X.G.); (J.P.)
| | - Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi’an 710123, China; (Z.-H.R.); (Y.-J.G.); (L.-X.G.); (J.P.)
- Correspondence: (C.-Q.Y.); (L.-P.L.); Tel.: +86-189-9118-5758 (C.-Q.Y.); +86-173-9276-3836 (L.-P.L.)
| | - Li-Ping Li
- College of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi 830052, China
- Correspondence: (C.-Q.Y.); (L.-P.L.); Tel.: +86-189-9118-5758 (C.-Q.Y.); +86-173-9276-3836 (L.-P.L.)
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China;
| | - Jie Pan
- School of Information Engineering, Xijing University, Xi’an 710123, China; (Z.-H.R.); (Y.-J.G.); (L.-X.G.); (J.P.)
| | - Yong-Jian Guan
- School of Information Engineering, Xijing University, Xi’an 710123, China; (Z.-H.R.); (Y.-J.G.); (L.-X.G.); (J.P.)
| | - Lu-Xiang Guo
- School of Information Engineering, Xijing University, Xi’an 710123, China; (Z.-H.R.); (Y.-J.G.); (L.-X.G.); (J.P.)
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Zhang C, Lu Y, Zang T. CNN-DDI: a learning-based method for predicting drug-drug interactions using convolution neural networks. BMC Bioinformatics 2022; 23:88. [PMID: 35255808 PMCID: PMC8902704 DOI: 10.1186/s12859-022-04612-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 02/14/2022] [Indexed: 01/07/2023] Open
Abstract
Background Drug–drug interactions (DDIs) are the reactions between drugs. They are compartmentalized into three types: synergistic, antagonistic and no reaction. As a rapidly developing technology, predicting DDIs-associated events is getting more and more attention and application in drug development and disease diagnosis fields. In this work, we study not only whether the two drugs interact, but also specific interaction types. And we propose a learning-based method using convolution neural networks to learn feature representations and predict DDIs. Results In this paper, we proposed a novel algorithm using a CNN architecture, named CNN-DDI, to predict drug–drug interactions. First, we extract feature interactions from drug categories, targets, pathways and enzymes as feature vectors and employ the Jaccard similarity as the measurement of drugs similarity. Then, based on the representation of features, we build a new convolution neural network as the DDIs’ predictor. Conclusion The experimental results indicate that drug categories is effective as a new feature type applied to CNN-DDI method. And using multiple features is more informative and more effective than single feature. It can be concluded that CNN-DDI has more superiority than other existing algorithms on task of predicting DDIs.
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Affiliation(s)
- Chengcheng Zhang
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yao Lu
- General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
| | - Tianyi Zang
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
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Yan C, Duan G, Zhang Y, Wu FX, Pan Y, Wang J. Predicting Drug-Drug Interactions Based on Integrated Similarity and Semi-Supervised Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:168-179. [PMID: 32310779 DOI: 10.1109/tcbb.2020.2988018] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
A drug-drug interaction (DDI) is defined as an association between two drugs where the pharmacological effects of a drug are influenced by another drug. Positive DDIs can usually improve the therapeutic effects of patients, but negative DDIs cause the major cause of adverse drug reactions and even result in the drug withdrawal from the market and the patient death. Therefore, identifying DDIs has become a key component of the drug development and disease treatment. In this study, we propose a novel method to predict DDIs based on the integrated similarity and semi-supervised learning (DDI-IS-SL). DDI-IS-SL integrates the drug chemical, biological and phenotype data to calculate the feature similarity of drugs with the cosine similarity method. The Gaussian Interaction Profile kernel similarity of drugs is also calculated based on known DDIs. A semi-supervised learning method (the Regularized Least Squares classifier) is used to calculate the interaction possibility scores of drug-drug pairs. In terms of the 5-fold cross validation, 10-fold cross validation and de novo drug validation, DDI-IS-SL can achieve the better prediction performance than other comparative methods. In addition, the average computation time of DDI-IS-SL is shorter than that of other comparative methods. Finally, case studies further demonstrate the performance of DDI-IS-SL in practical applications.
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ISCMF: Integrated similarity-constrained matrix factorization for drug–drug interaction prediction. ACTA ACUST UNITED AC 2020. [DOI: 10.1007/s13721-019-0215-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity. Sci Rep 2019; 9:13645. [PMID: 31541145 PMCID: PMC6754439 DOI: 10.1038/s41598-019-50121-3] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 09/06/2019] [Indexed: 01/04/2023] Open
Abstract
Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and health. Proposing appropriate computational methods for predicting unknown DDI with high precision is challenging. We proposed "NDD: Neural network-based method for drug-drug interaction prediction" for predicting unknown DDIs using various information about drugs. Multiple drug similarities based on drug substructure, target, side effect, off-label side effect, pathway, transporter, and indication data are calculated. At first, NDD uses a heuristic similarity selection process and then integrates the selected similarities with a nonlinear similarity fusion method to achieve high-level features. Afterward, it uses a neural network for interaction prediction. The similarity selection and similarity integration parts of NDD have been proposed in previous studies of other problems. Our novelty is to combine these parts with new neural network architecture and apply these approaches in the context of DDI prediction. We compared NDD with six machine learning classifiers and six state-of-the-art graph-based methods on three benchmark datasets. NDD achieved superior performance in cross-validation with AUPR ranging from 0.830 to 0.947, AUC from 0.954 to 0.994 and F-measure from 0.772 to 0.902. Moreover, cumulative evidence in case studies on numerous drug pairs, further confirm the ability of NDD to predict unknown DDIs. The evaluations corroborate that NDD is an efficient method for predicting unknown DDIs. The data and implementation of NDD are available at https://github.com/nrohani/NDD.
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Zhang W, Jing K, Huang F, Chen Y, Li B, Li J, Gong J. SFLLN: A sparse feature learning ensemble method with linear neighborhood regularization for predicting drug–drug interactions. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2019.05.017] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Yi L, Zhang H, Zhang JW, You XM, Ning ZQ, Yu J, Qian LF, Miao LY. Study on Drug-Drug Interactions Between Chiglitazar, a Novel PPAR Pan-Agonist, and Metformin Hydrochloride in Healthy Subjects. Clin Pharmacol Drug Dev 2019; 8:934-941. [PMID: 30809967 DOI: 10.1002/cpdd.668] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Accepted: 01/28/2019] [Indexed: 11/10/2022]
Abstract
Chiglitazar (CHI) is a potent and selective peroxisome proliferator-activated receptor potentially for the treatment of patients with type 2 diabetes mellitus (T2DM). An open-label, randomized, 3-period crossover and self-controlled study was conducted to investigate drug-drug interaction potential between CHI and metformin hydrochloride (MET). Eligible subjects received a single oral dose of CHI (48 mg), MET (1000 mg), or a combination in each period, followed by serial blood sampling collected for up to 48 hours postdose, and safety was assessed throughout the trial. The area under the plasma concentration-time curves from time 0 to 48 hours (AUC0-48 h ) of CHI was similar following administration alone or with MET (AUC0-48h , 12 540 ng·h/mL [9811-15 269 ng·h/mL] vs 12 130 ng·h/mL [9304-14 956 ng·h/mL]; 90% confidence interval [CI] of its geometric mean ratio [GMR], 89.7%-103.8%), whereas the maximum concentration (Cmax ) of CHI was reduced during coadministration, as its 90%CI of the GMR was slightly outside the acceptance range for bioequivalence (Cmax , 1620 ng/mL [1418-1822 ng/mL] vs 1420 ng/mL [1049-1791 ng/mL], 90%CI GMR, 77.%-94.1%). However, it was not considered clinically meaningful. The MET exposures remained consistent in the absence or presence of CHI (AUC0-48 h , 12 570 ng·h/mL [10681-14 459 ng·h/mL] vs 13 190 [10973-15 407 ng·h/mL); 90%CI of GMR: 99.1%-110.5%; Cmax , 1790 ng/mL [1448-2132 ng/mL] vs 1820 ng/mL [1510-2130 ng/mL]; 90%CI of GMR, 94.2%-110.9%). No moderate to severe adverse events were reported. Our study indicated no clinically significant pharmacokinetic drug-drug interaction between CHI and MET and demonstrated good tolerance in subjects. These results support future application of CHI in combination with MET for treatment of T2DM.
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Affiliation(s)
- Ling Yi
- Drug Clinical Trials Institution, The First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R.China.,Laboratory of Phase I Clinical Study, The First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R.China
| | - Hua Zhang
- Drug Clinical Trials Institution, The First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R.China.,Laboratory of Phase I Clinical Study, The First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R.China
| | - Jin-Wen Zhang
- Exploratory Research Department, Shenzhen Chipscreen Biosciences Ltd., BIO-Incubator, Shenzhen Hi-Tech Industrial Park, Guangdong, Shenzhen, China
| | - Xiao-Ming You
- Drug Clinical Trials Institution, The First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R.China.,Laboratory of Phase I Clinical Study, The First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R.China
| | - Zhi-Qiang Ning
- Exploratory Research Department, Shenzhen Chipscreen Biosciences Ltd., BIO-Incubator, Shenzhen Hi-Tech Industrial Park, Guangdong, Shenzhen, China
| | - Jia Yu
- Exploratory Research Department, Shenzhen Chipscreen Biosciences Ltd., BIO-Incubator, Shenzhen Hi-Tech Industrial Park, Guangdong, Shenzhen, China
| | - Li-Fang Qian
- Drug Clinical Trials Institution, The First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R.China.,Laboratory of Phase I Clinical Study, The First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R.China
| | - Li-Yan Miao
- Drug Clinical Trials Institution, The First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R.China.,Laboratory of Phase I Clinical Study, The First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R.China
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Manifold regularized matrix factorization for drug-drug interaction prediction. J Biomed Inform 2018; 88:90-97. [DOI: 10.1016/j.jbi.2018.11.005] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 11/03/2018] [Accepted: 11/11/2018] [Indexed: 12/20/2022]
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Shameer K, Perez-Rodriguez MM, Bachar R, Li L, Johnson A, Johnson KW, Glicksberg BS, Smith MR, Readhead B, Scarpa J, Jebakaran J, Kovatch P, Lim S, Goodman W, Reich DL, Kasarskis A, Tatonetti NP, Dudley JT. Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining. BMC Med Inform Decis Mak 2018; 18:79. [PMID: 30255805 PMCID: PMC6156906 DOI: 10.1186/s12911-018-0653-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Worldwide, over 14% of individuals hospitalized for psychiatric reasons have readmissions to hospitals within 30 days after discharge. Predicting patients at risk and leveraging accelerated interventions can reduce the rates of early readmission, a negative clinical outcome (i.e., a treatment failure) that affects the quality of life of patient. To implement individualized interventions, it is necessary to predict those individuals at highest risk for 30-day readmission. In this study, our aim was to conduct a data-driven investigation to find the pharmacological factors influencing 30-day all-cause, intra- and interdepartmental readmissions after an index psychiatric admission, using the compendium of prescription data (prescriptome) from electronic medical records (EMR). METHODS The data scientists in the project received a deidentified database from the Mount Sinai Data Warehouse, which was used to perform all analyses. Data was stored in a secured MySQL database, normalized and indexed using a unique hexadecimal identifier associated with the data for psychiatric illness visits. We used Bayesian logistic regression models to evaluate the association of prescription data with 30-day readmission risk. We constructed individual models and compiled results after adjusting for covariates, including drug exposure, age, and gender. We also performed digital comorbidity survey using EMR data combined with the estimation of shared genetic architecture using genomic annotations to disease phenotypes. RESULTS Using an automated, data-driven approach, we identified prescription medications, side effects (primary side effects), and drug-drug interaction-induced side effects (secondary side effects) associated with readmission risk in a cohort of 1275 patients using prescriptome analytics. In our study, we identified 28 drugs associated with risk for readmission among psychiatric patients. Based on prescription data, Pravastatin had the highest risk of readmission (OR = 13.10; 95% CI (2.82, 60.8)). We also identified enrichment of primary side effects (n = 4006) and secondary side effects (n = 36) induced by prescription drugs in the subset of readmitted patients (n = 89) compared to the non-readmitted subgroup (n = 1186). Digital comorbidity analyses and shared genetic analyses further reveals that cardiovascular disease and psychiatric conditions are comorbid and share functional gene modules (cardiomyopathy and anxiety disorder: shared genes (n = 37; P = 1.06815E-06)). CONCLUSIONS Large scale prescriptome data is now available from EMRs and accessible for analytics that could improve healthcare outcomes. Such analyses could also drive hypothesis and data-driven research. In this study, we explored the utility of prescriptome data to identify factors driving readmission in a psychiatric cohort. Converging digital health data from EMRs and systems biology investigations reveal a subset of patient populations that have significant comorbidities with cardiovascular diseases are more likely to be readmitted. Further, the genetic architecture of psychiatric illness also suggests overlap with cardiovascular diseases. In summary, assessment of medications, side effects, and drug-drug interactions in a clinical setting as well as genomic information using a data mining approach could help to find factors that could help to lower readmission rates in patients with mental illness.
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Affiliation(s)
- Khader Shameer
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, New York, NY, USA
| | | | - Roy Bachar
- Department of Psychiatry, Mount Sinai Health System, New York, NY, USA
- Hackensack Meridian Health Hackensack University Medical Center, Hackensack, NJ, USA
| | - Li Li
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, New York, NY, USA
| | - Amy Johnson
- Department of Psychiatry, Mount Sinai Health System, New York, NY, USA
| | - Kipp W Johnson
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, New York, NY, USA
| | - Benjamin S Glicksberg
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, New York, NY, USA
| | - Milo R Smith
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, New York, NY, USA
| | - Ben Readhead
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, New York, NY, USA
| | - Joseph Scarpa
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, New York, NY, USA
| | | | - Patricia Kovatch
- Mount Sinai Data Warehouse, Mount Sinai Health System, New York, NY, USA
| | - Sabina Lim
- Department of Psychiatry, Mount Sinai Health System, New York, NY, USA
| | - Wayne Goodman
- Department of Psychiatry, Mount Sinai Health System, New York, NY, USA
| | - David L Reich
- Department of Anesthesiology, Mount Sinai Health System, New York, NY, USA
| | - Andrew Kasarskis
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, New York, NY, USA
| | - Nicholas P Tatonetti
- Departments of Biomedical Informatics, Systems Biology and Medicine, Columbia University, New York, NY, USA
| | - Joel T Dudley
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, New York, NY, USA.
- Department of Population Health Science and Policy; Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York, NY, USA.
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Zhang W, Chen Y, Liu F, Luo F, Tian G, Li X. Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data. BMC Bioinformatics 2017; 18:18. [PMID: 28056782 PMCID: PMC5217341 DOI: 10.1186/s12859-016-1415-9] [Citation(s) in RCA: 148] [Impact Index Per Article: 21.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Accepted: 12/09/2016] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Drug-drug interactions (DDIs) are one of the major concerns in drug discovery. Accurate prediction of potential DDIs can help to reduce unexpected interactions in the entire lifecycle of drugs, and are important for the drug safety surveillance. RESULTS Since many DDIs are not detected or observed in clinical trials, this work is aimed to predict unobserved or undetected DDIs. In this paper, we collect a variety of drug data that may influence drug-drug interactions, i.e., drug substructure data, drug target data, drug enzyme data, drug transporter data, drug pathway data, drug indication data, drug side effect data, drug off side effect data and known drug-drug interactions. We adopt three representative methods: the neighbor recommender method, the random walk method and the matrix perturbation method to build prediction models based on different data. Thus, we evaluate the usefulness of different information sources for the DDI prediction. Further, we present flexible frames of integrating different models with suitable ensemble rules, including weighted average ensemble rule and classifier ensemble rule, and develop ensemble models to achieve better performances. CONCLUSIONS The experiments demonstrate that different data sources provide diverse information, and the DDI network based on known DDIs is one of most important information for DDI prediction. The ensemble methods can produce better performances than individual methods, and outperform existing state-of-the-art methods. The datasets and source codes are available at https://github.com/zw9977129/drug-drug-interaction/ .
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Affiliation(s)
- Wen Zhang
- State Key Lab of Software Engineering, Wuhan University, Wuhan, 430072, China. .,School of Computer, Wuhan University, Wuhan, 430072, China.
| | - Yanlin Chen
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China
| | - Feng Liu
- International School of software, Wuhan University, Wuhan, 430072, China
| | - Fei Luo
- State Key Lab of Software Engineering, Wuhan University, Wuhan, 430072, China.,School of Computer, Wuhan University, Wuhan, 430072, China
| | - Gang Tian
- State Key Lab of Software Engineering, Wuhan University, Wuhan, 430072, China.,School of Computer, Wuhan University, Wuhan, 430072, China
| | - Xiaohong Li
- State Key Lab of Software Engineering, Wuhan University, Wuhan, 430072, China.,School of Computer, Wuhan University, Wuhan, 430072, China
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