Off-label drug database

Background: Many drugs are still being prescribed in a "off-label mode" and especially in psychiatry. Off-label prescription situations may vary depending on several factors and such practice is not well identifiable in the literature. Methods: A new public academic drug database has been recently created and is able to contain off-label indications, especially in psychiatry in the context of the PSYHAMM French research project. For each situation, bibliographic references have been collected to make the scientific information available to all. Results: this new off-label drug database contains more than 18,154 lines. It is freely available at https://www.hetop.eu/hetop/medicaments. Several off-label usages have been formally described and the system is extensible to all drugs and all specialties. Conclusion: An off-label drug database can be a valuable tool for health professionals and students.

Patient Drug Database: Construction of Database for Patient Leading Drug Side Effects Exploration Using Patient Generated Health Data

Objectives: This study focuses on building a database for patient-led search on drug side effects using basic drug information, drug analysis results information, patient information, and patient-generated health data (PGHD).Methods: After collecting data from the Health Insurance Review and Assessment Institute, the Korean Pharmaceutical Information Center, the Ministry of Food and Drug Safety, and the Korean Pharmaceutical Association, basic drug information was created. By utilizing the Korea Average Event Reporting System (KAERS) side effect report data provided by the Korea Drug Safety Administration and MetaLAB, a drug side effect detection algorithm applied on the Konyang university hospital’s real data, we designed and built a database using Oracle DB, which contains a table of patient information and PGHD. For drug information, a total of 49,553 drugs were mapped, and drug analysis results used KAERS and MetaLAB.Results: Based on the collected drug information, a total of 15 tables containing basic drug information (7 tables), drug analysis results (2 tables), patient information (1 table), and patient generation information (5 tables) were created using EDI codes, following mapping and normalization. Basic drug information included 49,553 EDI and 2,099 ATC codes. Drug analysis results included 2,046 KAERS ATC codes, 1,701 WHOART-ARRN (PT) that the result of 33 WHOART-SEQ (IT), 15,861 MetaLABEDI codes, and 101ATC codes. TheADR results were constructed using 62 DRUG_IDs and 73 MedDRA_PTI_IDs.Conclusions: The Patient Drug Database (PD2B) in this study was employed to allow patients to voluntarily report on their perception and drug side effects through application tools, which can provide quick measures against drug side effects and assist in the discovery of new ones.

Identification of Genes Related to 68Ga-DOTATATE Organ Distribution in Homo Sapiens

Purpose 68Ga-DOTATATE is a somatostatin analogue that has been used for imaging neuroendocrine tumours. However, there is nonspecific uptake in some organs, and the reasons for that are not clear. The aim of the study is to outline the dynamic distribution pattern of 68Ga-DOTATATE in human body, and identify the genes responsible for 68Ga-DOTATATE uptake by bioinformatics analysis.Methods 68Ga-DOTATATE PET/CT was performed in 32 patients, and dynamic total-body PET scanning was performed with uEXPLORER. The gene expression datasets of human organs were downloaded from the Human Protein Atlas. WGCNA analysis was performed to screen the potential genes related to 68Ga-DOTATATE. BindingDB, SEA and SwissTargetPrediction databases were used to predict the potential binding proteins of DOTATATE based on molecular structure. Results Dynamic total-body PET scanning showed that 68Ga-DOTATATE uptake was not consistent with expression of SSTR2 in human organs. WGCNA analysis revealed 800 genes whose expression level was positively correlated to 68Ga-DOTATATE uptake. According to the molecular structure of DOTATATE, 135 proteins with potential binding ability to DOTATATE were screened by analysis based on drug database. Based on the results of the above two parts, 8 proteins were finally obtained, respectively AVPR1A, EPHA8, EPHB4, OPRL1, SSTR2, SSTR5, ST3GAL1 and NPY1R, which had potential binding possibility with DOTATATE, and their expression was positively correlated with 68Ga-DOTATATE uptake.Conclusion WGCNA analysis was combined with the drug database to obtain new potential binding targets of DOTATATE. The bioinformatics approach developed in this study could potentially be used to discover new potential binding targets for molecular imaging agents.

Computational Estimation of Potential Inhibitors from the Known Drugs against the Main Protease of SARS-CoV-2

The coronavirus disease (COVID-19) pandemic caused by a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread worldwide recently, leading to a global social and economic disruption. Although the emergently approved vaccine programs against SARS-CoV-2 have been rolled out globally, the number of COVID-19 daily cases and deaths has remained significantly high. Here, we attempted to computationally screen for possible medications for COVID-19 via rapidly estimate the highly potential inhibitors from an FDA-approved drug database against the main protease (Mpro) of SARS-CoV-2. The approach combined molecular docking and fast pulling of ligand (FPL) simulations that were demonstrated to be accurate and suitable for quick prediction of SARS-CoV-2 Mpro inhibitors. The results suggested that twentyseven compounds were capable of strongly associating with SARS-CoV-2 Mpro. Among them, the seven top leads are daclatasvir, teniposide, etoposide, levoleucovorin, naldemedine, cabozantinib, and irinotecan. The potential application of these drugs in COVID-19 therapy has thus been discussed.

Computational Estimation of Potential Inhibitors from the Known Drugs against the Main Protease of SARS-CoV-2

The coronavirus disease (COVID-19) pandemic caused by a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread worldwide recently, leading to a global social and economic disruption. Although the emergently approved vaccine programs against SARS-CoV-2 have been rolled out globally, the number of COVID-19 daily cases and deaths has remained significantly high. Here, we attempted to computationally screen for possible medications for COVID-19 via rapidly estimate the highly potential inhibitors from an FDA-approved drug database against the main protease (Mpro) of SARS-CoV-2. The approach combined molecular docking and fast pulling of ligand (FPL) simulations that were demonstrated to be accurate and suitable for quick prediction of SARS-CoV-2 Mpro inhibitors. The results suggested that twentyseven compounds were capable of strongly associating with SARS-CoV-2 Mpro. Among them, the seven top leads are daclatasvir, teniposide, etoposide, levoleucovorin, naldemedine, cabozantinib, and irinotecan. The potential application of these drugs in COVID-19 therapy has thus been discussed.

Using Drug Descriptions and Molecular Structures for Drug-Drug Interaction Extraction from Literature

Motivation Neural methods to extract drug-drug interactions (DDIs) from literature require a large number of annotations. In this study, we propose a novel method to effectively utilize external drug database information as well as information from large-scale plain text for DDI extraction. Specifically, we focus on drug description and molecular structure information as the drug database information. Results We evaluated our approach on the DDIExtraction 2013 shared task data set. We obtained the following results. First, large-scale raw text information can greatly improve the performance of extracting DDIs when combined with the existing model and it shows the state-of-the-art performance. Second, each of drug description and molecular structure information is helpful to further improve the DDI performance for some specific DDI types. Finally, the simultaneous use of the drug description and molecular structure information can significantly improve the performance on all the DDI types. We showed that the plain text, the drug description information, and molecular structure information are complementary and their effective combination are essential for the improvement. Availability https://github.com/tticoin/DESC_MOL-DDIE

DockCoV2: a drug database against SARS-CoV-2

The current state of the COVID-19 pandemic is a global health crisis. To fight the novel coronavirus, one of the best-known ways is to block enzymes essential for virus replication. Currently, we know that the SARS-CoV-2 virus encodes about 29 proteins such as spike protein, 3C-like protease (3CLpro), RNA-dependent RNA polymerase (RdRp), Papain-like protease (PLpro), and nucleocapsid (N) protein. SARS-CoV-2 uses human angiotensin-converting enzyme 2 (ACE2) for viral entry and transmembrane serine protease family member II (TMPRSS2) for spike protein priming. Thus in order to speed up the discovery of potential drugs, we develop DockCoV2, a drug database for SARS-CoV-2. DockCoV2 focuses on predicting the binding affinity of FDA-approved and Taiwan National Health Insurance (NHI) drugs with the seven proteins mentioned above. This database contains a total of 3,109 drugs. DockCoV2 is easy to use and search against, is well cross-linked to external databases, and provides the state-of-the-art prediction results in one site. Users can download their drug-protein docking data of interest and examine additional drug-related information on DockCoV2. Furthermore, DockCoV2 provides experimental information to help users understand which drugs have already been reported to be effective against MERS or SARS-CoV. DockCoV2 is available at https://covirus.cc/drugs/.

The Impact of Information Technology on the Diffusion of New Pharmaceuticals

Do information differences across US physicians contribute to treatment disparities? This paper uses a unique new dataset to evaluate how changes in physician access to a decision-relevant drug database affect prescribing decisions. Our results indicate doctors using the reference have a significantly greater propensity to prescribe generic drugs, are faster to begin prescribing new generics, and prescribe a more diverse set of products. These results are consistent with database users responding primarily to the increased accessibility of non-clinical information such as pricing and insurance formulary data, and suggest improvements to physician information access have important implications for aggregate healthcare costs. (JEL D83, I11, I18, L65)