flecainide has been researched along with buspirone in 9 studies
Timeframe | Studies, this research(%) | All Research% |
---|---|---|
pre-1990 | 0 (0.00) | 18.7374 |
1990's | 0 (0.00) | 18.2507 |
2000's | 2 (22.22) | 29.6817 |
2010's | 7 (77.78) | 24.3611 |
2020's | 0 (0.00) | 2.80 |
Authors | Studies |
---|---|
Benz, RD; Contrera, JF; Kruhlak, NL; Matthews, EJ; Weaver, JL | 1 |
Barnes, JC; Bradley, P; Day, NC; Fourches, D; Reed, JZ; Tropsha, A | 1 |
Choi, SS; Contrera, JF; Hastings, KL; Kruhlak, NL; Sancilio, LF; Weaver, JL; Willard, JM | 1 |
Glen, RC; Lowe, R; Mitchell, JB | 1 |
Sen, S; Sinha, N | 1 |
Cantin, LD; Chen, H; Kenna, JG; Noeske, T; Stahl, S; Walker, CL; Warner, DJ | 1 |
Dalvie, D; Loi, CM; Smith, DA | 1 |
Bellman, K; Knegtel, RM; Settimo, L | 1 |
Chen, M; Hu, C; Suzuki, A; Thakkar, S; Tong, W; Yu, K | 1 |
1 review(s) available for flecainide and buspirone
Article | Year |
---|---|
DILIrank: the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans.
Topics: Chemical and Drug Induced Liver Injury; Databases, Factual; Drug Labeling; Humans; Pharmaceutical Preparations; Risk | 2016 |
8 other study(ies) available for flecainide and buspirone
Article | Year |
---|---|
Assessment of the health effects of chemicals in humans: II. Construction of an adverse effects database for QSAR modeling.
Topics: Adverse Drug Reaction Reporting Systems; Artificial Intelligence; Computers; Databases, Factual; Drug Prescriptions; Drug-Related Side Effects and Adverse Reactions; Endpoint Determination; Models, Molecular; Quantitative Structure-Activity Relationship; Software; United States; United States Food and Drug Administration | 2004 |
Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species.
Topics: Animals; Chemical and Drug Induced Liver Injury; Cluster Analysis; Databases, Factual; Humans; MEDLINE; Mice; Models, Chemical; Molecular Conformation; Quantitative Structure-Activity Relationship | 2010 |
Development of a phospholipidosis database and predictive quantitative structure-activity relationship (QSAR) models.
Topics: | 2008 |
Predicting phospholipidosis using machine learning.
Topics: Animals; Artificial Intelligence; Databases, Factual; Drug Discovery; Humans; Lipidoses; Models, Biological; Phospholipids; Support Vector Machine | 2010 |
Predicting hERG activities of compounds from their 3D structures: development and evaluation of a global descriptors based QSAR model.
Topics: Computer Simulation; Ether-A-Go-Go Potassium Channels; Humans; Molecular Structure; Organic Chemicals; Quantitative Structure-Activity Relationship | 2011 |
Mitigating the inhibition of human bile salt export pump by drugs: opportunities provided by physicochemical property modulation, in silico modeling, and structural modification.
Topics: Animals; ATP Binding Cassette Transporter, Subfamily B, Member 11; ATP-Binding Cassette Transporters; Bile Acids and Salts; Cell Line; Chemical and Drug Induced Liver Injury; Humans; Quantitative Structure-Activity Relationship | 2012 |
Which metabolites circulate?
Topics: Humans; Metabolic Clearance Rate; Pharmaceutical Preparations | 2013 |
Comparison of the accuracy of experimental and predicted pKa values of basic and acidic compounds.
Topics: Chemistry, Pharmaceutical; Forecasting; Hydrogen-Ion Concentration; Pharmaceutical Preparations; Random Allocation | 2014 |