Predicting the side effects of drugs using matrix factorization on spontaneous reporting database.
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ABSTRACT:
SUBMITTER: Fukuto K
PROVIDER: S-EPMC8671428 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND: Studies of toxicity and unintended side effects can lead to improved drug safety and efficacy. One promising form of study comes from molecular systems biology in the form of "systems pharmacology". Systems pharmacology combines data from clinical observation and molecular biology. This approach is new, however, and there are few examples of how it can practically predict adverse reactions (ADRs) from an experimental drug with acceptable accuracy. RESULTS: We have developed a new and practical computational framework to accurately predict ADRs of trial drugs. We combine clinical observation data with drug target data, protein-protein interaction (PPI) networks, and gene ontology (GO) annotations. We use cardiotoxicity, one of the major causes for drug withdrawals, as a case study to demonstrate the power of the framework. Our results show that an in silico model built on this framework can achieve a satisfactory cardiotoxicity ADR prediction performance (median AUC = 0.771, Accuracy = 0.675, Sensitivity = 0.632, and Specificity = 0.789). Our results also demonstrate the significance of incorporating prior knowledge, including gene networks and gene annotations, to improve future ADR assessments. CONCLUSIONS: Biomolecular network and gene annotation information can significantly improve the predictive accuracy of ADR of drugs under development. The use of PPI networks can increase prediction specificity and the use of GO annotations can increase prediction sensitivity. Using cardiotoxicity as an example, we are able to further identify cardiotoxicity-related proteins among drug target expanding PPI networks. The systems pharmacology approach that we developed in this study can be generally applicable to all future developmental drug ADR assessments and predictions.
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Project description:MotivationMost gene prioritization methods model each disease or phenotype individually, but this fails to capture patterns common to several diseases or phenotypes. To overcome this limitation, we formulate the gene prioritization task as the factorization of a sparsely filled gene-phenotype matrix, where the objective is to predict the unknown matrix entries. To deliver more accurate gene-phenotype matrix completion, we extend classical Bayesian matrix factorization to work with multiple side information sources. The availability of side information allows us to make non-trivial predictions for genes for which no previous disease association is known.ResultsOur gene prioritization method can innovatively not only integrate data sources describing genes, but also data sources describing Human Phenotype Ontology terms. Experimental results on our benchmarks show that our proposed model can effectively improve accuracy over the well-established gene prioritization method, Endeavour. In particular, our proposed method offers promising results on diseases of the nervous system; diseases of the eye and adnexa; endocrine, nutritional and metabolic diseases; and congenital malformations, deformations and chromosomal abnormalities, when compared to Endeavour.Availability and implementationThe Bayesian data fusion method is implemented as a Python/C++ package: https://github.com/jaak-s/macau. It is also available as a Julia package: https://github.com/jaak-s/BayesianDataFusion.jl. All data and benchmarks generated or analyzed during this study can be downloaded at https://owncloud.esat.kuleuven.be/index.php/s/UGb89WfkZwMYoTn.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Introduction: The panorama of drug-induced ototoxicity has widened in the last decades; moreover, post-marketing data are necessary to gain a better insight on ototoxic adverse drug reactions (ADRs). The aim of this study was to perform an analysis of ADR reports describing drug-induced ototoxicity from the Italian spontaneous reporting system (SRS). Methods: As a measure of disproportionality, we calculated the reporting odds ratios (RORs) and 95% confidence intervals (CIs) with a case/non-case methodology. Cases were all suspected ADR reports regarding drug-induced ototoxicity collected into the Italian SRS from 2001 to 2017. Non-cases included all other ADRs reported in the same period. Results: Of 325,980 reports, 652 included at least one ototoxic ADR, compared with 325,328 non-cases. Statistically significant adjusted RORs were found for drugs for cardiovascular disorders, urologicals, teriparatide, amikacin, prulifloxacin, rifampicin and isoniazid, cisplatin, hormone antagonists, tacrolimus, pomalidomide, tramadol, and antidepressants. Significant adjusted RORs in relation to tinnitus were also observed for doxazosin (ROR 5.55, 95% CI 2.06-14.93), bisoprolol (4.28, 1.59-11.53), nebivolol (8.06, 3.32-19.56), ramipril (3.96, 2.17-7.23), irbesartan (19.60, 9.19-41.80), betamethasone (4.01, 1.28-12.52), moxifloxacin (4.56, 1.71-12.34), ethambutol (12.25, 3.89-38.57), efavirenz (16.82, 5.34-52.96), sofosbuvir/ledipasvir (5.95, 1.90-18.61), etoposide (7.09, 2.63-19.12), abatacept (6.51, 2.42-17.53), indometacin (6.30, 2.02-19.72), etoricoxib (5.00, 2.23-11.23), tapentadol (4.37, 1.09-17.62), and timolol combinations (23.29, 9.53-56.95). Moreover, significant adjusted RORs for hypoacusis regarded clarithromycin (3.95, 1.86-8.40), azithromycin (10.23, 5.03-20.79), vancomycin (6.72, 2.14-21.11), methotrexate (3.13, 1.00-9.81), pemetrexed (4.38, 1.40-13.76), vincristine (5.93, 1.88-18.70), vinorelbine (21.60, 8.83-52.82), paclitaxel (2.34, 1.03-5.30), rituximab (3.20, 1.19-8.63), interferon alfa-2b (17.44, 8.56-35.53), thalidomide (16.92, 6.92-41.38), and deferasirox (41.06, 20.07-84.01). Conclusions: This study is largely consistent with results from literature. Nevertheless, propafenone, antituberculars, hormone antagonists, teriparatide, tramadol, and pomalidomide are unknown for being ototoxic. Hypoacusis after the use of vinorelbine, methotrexate, and pemetrexed is unexpected, such as tinnitus related with etoposide, nebivolol, betamethasone, abatacept, sofosbuvir/ledipasvir, and tapentadol, but these considerations require further investigation to better define the risk due to the paucity of data. Moreover, physicians should be aware of the clinical significance of ototoxicity and be conscious about the importance of their contribution to spontaneous reporting.
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Project description:PurposeThe purpose of this study was to investigate the adverse events (AEs) related to the use of off-label drugs.Materials and methodsA cross-sectional study was carried out using available data pertaining to off-label drug were sourced from U.S. FDA spontaneous adverse drug reaction reporting database (FAERS) and Korea Adverse Event Reporting System database (KIDS-KD) for the years 2014 to 2018. The number and frequencies of AE cases were calculated. Disproportionality was analyzed using the proportional reporting ratio (PRR), the reporting odds ratio (ROR), the information component (IC), and the empirical Bayes geometric mean (EBGM) methods.ResultsThe reported AEs associated with off-label drug use were more common among older patients compared with younger patients. Gastric nonspecific symptoms and therapeutic procedure (4.16-4.57%) and haemorrage term (4.16-5.29%) were the most common AE symptoms and antithrombotic agents and immunosuppressants were the drugs most commonly reported to cause AEs in FAERS. Secondary term events (43.45-48.62%) including inappropriate schedule of drug administration and medication error were the most common AEs, and immunosuppressants and antipsychotics were the most common AE-related drugs from KIDS-KD. The numbers of reported AEs in new drug categories such as other antineoplastic agents trended to increase from 2014 to 2018 in both datasets.ConclusionThe numbers of reported AEs with off-label drug increased annually. AEs associated with off-label drugs may have a significant impact on older patients. Healthcare experts should be concerned about prescriptions of off-label drugs, especially anticoagulants and newly developed drugs such as immunosuppressants and antineoplastic agents.
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Project description:A drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industries to develop computational methods for predicting the side effects of drugs. In this study, a novel computational method was developed to predict the side effects of drug compounds by hybridizing the chemical-chemical and protein-chemical interactions. Compared to most of the previous works, our method can rank the potential side effects for any query drug according to their predicted level of risk. A training dataset and test datasets were constructed from the benchmark dataset that contains 835 drug compounds to evaluate the method. By a jackknife test on the training dataset, the 1st order prediction accuracy was 86.30%, while it was 89.16% on the test dataset. It is expected that the new method may become a useful tool for drug design, and that the findings obtained by hybridizing various interactions in a network system may provide useful insights for conducting in-depth pharmacological research as well, particularly at the level of systems biomedicine.
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