Dataset Information

SePreSA: a server for the prediction of populations susceptible to serious adverse drug reactions implementing the methodology of a chemical-protein interactome.

ABSTRACT: Serious adverse drug reactions (SADRs) are caused by unexpected drug-human protein interactions, and some polymorphisms within binding pockets make the population carrying these polymorphisms susceptible to SADR. Predicting which populations are likely to be susceptible to SADR will not only strengthen drug safety, but will also assist enterprises to adjust R&D and marketing strategies. Making such predictions has recently been facilitated by the introduction of a web server named SePreSA. The server has a comprehensive collection of the structural models of nearly all the well known SADR targets. Once a drug molecule is submitted, the scale of its potential interaction with multi-SADR targets is calculated using the DOCK program. The server utilizes a 2-directional Z-transformation scoring algorithm, which computes the relative drug-protein interaction strength based on the docking-score matrix of a chemical-protein interactome, thus achieve greater accuracy in prioritizing SADR targets than simply using dock scoring functions. The server also suggests the binding pattern of the lowest docking score through 3D visualization, by highlighting and visualizing amino acid residues involved in the binding on the customer's browser. Polymorphism information for different populations for each of the interactive residues will be displayed, helping users to deduce the population-specific susceptibility of their drug molecule. The server is freely available at http://SePreSA.Bio-X.cn/.

SUBMITTER: Yang L

PROVIDER: S-EPMC2703957 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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SePreSA: a server for the prediction of populations susceptible to serious adverse drug reactions implementing the methodology of a chemical-protein interactome.

Yang Lun L Luo Heng H Chen Jian J Xing Qinghe Q He Lin L

Nucleic acids research 20090505 Web Server issue

Serious adverse drug reactions (SADRs) are caused by unexpected drug-human protein interactions, and some polymorphisms within binding pockets make the population carrying these polymorphisms susceptible to SADR. Predicting which populations are likely to be susceptible to SADR will not only strengthen drug safety, but will also assist enterprises to adjust R&D and marketing strategies. Making such predictions has recently been facilitated by the introduction of a web server named SePreSA. The s ...[more]

PMID: 19417066

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Project description:BackgroundAdverse Drug Reactions (ADRs) are of great public health concern. FDA-approved drug labeling summarizes ADRs of a drug product mainly in three sections, i.e., Boxed Warning (BW), Warnings and Precautions (WP), and Adverse Reactions (AR), where the severity of ADRs are intended to decrease in the order of BW > WP > AR. Several reported studies have extracted ADRs from labeling documents, but most, if not all, did not discriminate the severity of the ADRs by the different labeling sections. Such a practice could overstate or underestimate the impact of certain ADRs to the public health. In this study, we applied the Medical Dictionary for Regulatory Activities (MedDRA) to drug labeling and systematically analyzed and compared the ADRs from the three labeling sections with a specific emphasis on analyzing serious ADRs presented in BW, which is of most drug safety concern.ResultsThis study investigated New Drug Application (NDA) labeling documents for 1164 single-ingredient drugs using Oracle Text search to extract MedDRA terms. We found that only a small portion of MedDRA Preferred Terms (PTs), 3819 out of 21,920 or 17.42%, were observed in a whole set of documents. In detail, 466/3819 (12.0%) PTs were in BW, 2023/3819 (53.0%) were in WP, and 2961/3819 (77.5%) were in AR sections. We also found a higher overlap of top 20 occurring BW PTs with WP sections compared to AR sections. Within the MedDRA System Organ Class levels, serious ADRs (sADRs) from BW were prevalent in Nervous System disorders and Vascular disorders. A Hierarchical Cluster Analysis (HCA) revealed that drugs within the same therapeutic category shared the same ADR patterns in BW (e.g., nervous system drug class is highly associated with drug abuse terms such as dependence, substance abuse, and respiratory depression).ConclusionsThis study demonstrated that combining MedDRA standard terminologies with data mining techniques facilitated computer-aided ADR analysis of drug labeling. We also highlighted the importance of labeling sections that differ in seriousness and application in drug safety. Using sADRs primarily related to BW sections, we illustrated a prototype approach for computer-aided ADR monitoring and studies which can be applied to other public health documents.

Project description:Oncology-associated adverse drug/device reactions can be fatal. Some clinicians who treat single patients with severe oncology-associated toxicities have researched case series and published this information. We investigated motivations and experiences of select individuals leading such efforts. Clinicians treating individual patients who developed oncology-associated serious adverse drug events were asked to participate. Inclusion criteria included having index patient information, reporting case series, and being collaborative with investigators from two National Institutes of Health funded pharmacovigilance networks. Thirty-minute interviews addressed investigational motivation, feedback from pharmaceutical manufacturers, FDA personnel, and academic leadership, and recommendations for improving pharmacovigilance. Responses were analyzed using constant comparative methods of qualitative analysis. Overall, 18 clinicians met inclusion criteria and 14 interviewees are included. Primary motivations were scientific curiosity, expressed by six clinicians. A less common theme was public health related (three clinicians). Six clinicians received feedback characterized as supportive from academic leaders, while four clinicians received feedback characterized as negative. Three clinicians reported that following the case series publication they were invited to speak at academic institutions worldwide. Responses from pharmaceutical manufacturers were characterized as negative by 12 clinicians. One clinician's wife called the post-reporting time the "Maalox month," while another clinician reported that the manufacturer collaboratively offered to identify additional cases of the toxicity. Responses from FDA employees were characterized as collaborative for two clinicians, neutral for five clinicians, unresponsive for negative by six clinicians. Three clinicians endorsed developing improved reporting mechanisms for individual physicians, while 11 clinicians endorsed safety activities that should be undertaken by persons other than a motivated clinician who personally treats a patient with a severe adverse drug/device reaction. Our study provides some of the first reports of clinician motivations and experiences with reporting serious or potentially fatal oncology-associated adverse drug or device reactions. Overall, it appears that negative feedback from pharmaceutical manufacturers and mixed feedback from the academic community and/or the FDA were reported. Big data, registries, Data Safety Monitoring Boards, and pharmacogenetic studies may facilitate improved pharmacovigilance efforts for oncology-associated adverse drug reactions. These initiatives overcome concerns related to complacency, indifference, ignorance, and system-level problems as barriers to documenting and reporting adverse drug events- barriers that have been previously reported for clinician reporting of serious adverse drug reactions.

Project description:The article presents a Web-based platform for collecting and storing toxicological structural alerts from literature and for virtual screening of chemical libraries to flag potentially toxic chemicals and compounds that can cause adverse side effects. An alert is uniquely identified by a SMARTS template, a toxicological endpoint, and a publication where the alert was described. Additionally, the system allows storing complementary information such as name, comments, and mechanism of action, as well as other data. Most importantly, the platform can be easily used for fast virtual screening of large chemical datasets, focused libraries, or newly designed compounds against the toxicological alerts, providing a detailed profile of the chemicals grouped by structural alerts and endpoints. Such a facility can be used for decision making regarding whether a compound should be tested experimentally, validated with available QSAR models, or eliminated from consideration altogether. The alert-based screening can also be helpful for an easier interpretation of more complex QSAR models. The system is publicly accessible and tightly integrated with the Online Chemical Modeling Environment (OCHEM, http://ochem.eu). The system is open and expandable: any registered OCHEM user can introduce new alerts, browse, edit alerts introduced by other users, and virtually screen his/her data sets against all or selected alerts. The user sets being passed through the structural alerts can be used at OCHEM for other typical tasks: exporting in a wide variety of formats, development of QSAR models, additional filtering by other criteria, etc. The database already contains almost 600 structural alerts for such endpoints as mutagenicity, carcinogenicity, skin sensitization, compounds that undergo metabolic activation, and compounds that form reactive metabolites and, thus, can cause adverse reactions. The ToxAlerts platform is accessible on the Web at http://ochem.eu/alerts, and it is constantly growing.

Project description:Background:The Protecting Canadians from Unsafe Drugs Act will eventually require institutions to report all serious adverse drug reactions (ADRs), although the proposed regulations do not yet define what will need to be reported and by whom. Knowledge about the occurrence of serious ADRs in the hospital setting is needed to optimize the effectiveness of reporting and to determine the potential implications of mandatory reporting. Objectives:To quantify and characterize suspected serious ADRs in patients admitted to a general medicine service, to assess the likelihood of causality, and to determine inter-rater agreement for identification of ADRs and assessment of their likelihood. Methods:This prospective observational study involved 60 consecutive patients admitted to a general medicine service at a tertiary care teaching centre starting on March 28, 2016. The primary outcome was the number of serious ADRs, defined by Health Canada as ADRs that result in hospital admission, congenital malformation, persistent or significant disability or incapacity, or death; that are life-threatening; or that require significant intervention to prevent one of these outcomes. Medical records were reviewed independently by pairs of pharmacists for serious ADRs, and the likelihood of causality was assessed using the World Health Organization-Uppsala Monitoring Centre system. Inter-rater agreement was calculated using the kappa score, and disagreements were resolved by discussion and consensus. Results:Twenty-three serious ADRs occurred in the sample of 60 patients. The proportion of patients experiencing a serious ADR that contributed to the original hospital admission was 19/60 (32%, 95% confidence interval [CI] 20%-43%), and 4 patients (7%, 95% CI 0%-13%) experienced a serious ADR during their hospital stay. Inter-rater agreement for occurrence of serious ADRs was moderate (kappa 0.58, 95% CI 0.35-0.76). Conclusion:Reportable serious ADRs were common among patients admitted to a general medicine service. Canadian hospitals would face difficulties reporting all serious ADRs because of the frequency of their occurrence and the subjectivity of their identification.

Dataset Information

SePreSA: a server for the prediction of populations susceptible to serious adverse drug reactions implementing the methodology of a chemical-protein interactome.

Publications

SePreSA: a server for the prediction of populations susceptible to serious adverse drug reactions implementing the methodology of a chemical-protein interactome.

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