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1.
J Pharmacol Toxicol Methods ; 111: 107098, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34229067

RESUMO

Secondary pharmacology studies are utilized by the pharmaceutical industry as a cost-efficient tool to identify potential safety liabilities of drugs before entering Phase 1 clinical trials. These studies are recommended by the Food and Drug Administration (FDA) as a part of the Investigational New Drug (IND) application. However, despite the utility of these assays, there is little guidance on which targets should be screened and which format should be used. Here, we evaluated 226 secondary pharmacology profiles obtained from close to 90 unique sponsors. The results indicated that the most tested target in our set was the GABA benzodiazepine receptor (tested 168 times), the most hit target was adenosine 3 (hit 24 times), and the target with the highest hit percentage was the quinone reductase 2 (NQO2) receptor (hit 29% of the time). The overall results were largely consistent with those observed in previous publications. However, this study also identified the need for improvement in the submission process of secondary pharmacology studies by industry, which could enhance their utility for regulatory purpose. FDA-industry collaborative working groups will utilize this data to determine the best methods for regulatory submission of these studies and evaluate the need for a standard target panel.

2.
Clin Transl Sci ; 14(6): 2208-2219, 2021 11.
Artigo em Inglês | MEDLINE | ID: mdl-34080766

RESUMO

Following a decision to require label warnings for concurrent use of opioids and benzodiazepines and increased risk of respiratory depression and death, the US Food and Drug Administratioin (FDA) recognized that other sedative psychotropic drugs may be substituted for benzodiazepines and be used concurrently with opioids. In some cases, data on the ability of these alternatives to depress respiration alone or in conjunction with an opioid are lacking. A nonclinical in vivo model was developed that could detect worsening respiratory depression when a benzodiazepine (diazepam) was used in combination with an opioid (oxycodone) compared to the opioid alone based on an increased arterial partial pressure of carbon dioxide (pCO2 ). The current study used that model to assess the impact on respiration of non-benzodiazepine sedative psychotropic drugs representative of different drug classes (clozapine, quetiapine, risperidone, zolpidem, trazodone, carisoprodol, cyclobenzaprine, mirtazapine, topiramate, paroxetine, duloxetine, ramelteon, and suvorexant) administered alone and with oxycodone. At clinically relevant exposures, paroxetine, trazodone, and quetiapine given with oxycodone significantly increased pCO2 above the oxycodone effect. Analyses indicated that most pCO2 interaction effects were due to pharmacokinetic interactions resulting in increased oxycodone exposure. Increased pCO2 recorded with oxycodone-paroxetine co-administration exceeded expected effects from only drug exposure suggesting another mechanism for the increased pharmacodynamic response. This study identified drug-drug interaction effects depressing respiration in an animal model when quetiapine or paroxetine were co-administered with oxycodone. Clinical pharmacodynamic drug interaction studies are being conducted with these drugs to assess translatability of these findings.

3.
Front Immunol ; 12: 639491, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33777032

RESUMO

Vaccines stimulate various immune factors critical to protective immune responses. However, a comprehensive picture of vaccine-induced immune factors and pathways have not been systematically collected and analyzed. To address this issue, we developed VaximmutorDB, a web-based database system of vaccine immune factors (abbreviated as "vaximmutors") manually curated from peer-reviewed articles. VaximmutorDB currently stores 1,740 vaccine immune factors from 13 host species (e.g., human, mouse, and pig). These vaximmutors were induced by 154 vaccines for 46 pathogens. Top 10 vaximmutors include three antibodies (IgG, IgG2a and IgG1), Th1 immune factors (IFN-γ and IL-2), Th2 immune factors (IL-4 and IL-6), TNF-α, CASP-1, and TLR8. Many enriched host processes (e.g., stimulatory C-type lectin receptor signaling pathway, SRP-dependent cotranslational protein targeting to membrane) and cellular components (e.g., extracellular exosome, nucleoplasm) by all the vaximmutors were identified. Using influenza as a model, live attenuated and killed inactivated influenza vaccines stimulate many shared pathways such as signaling of many interleukins (including IL-1, IL-4, IL-6, IL-13, IL-20, and IL-27), interferon signaling, MARK1 activation, and neutrophil degranulation. However, they also present their unique response patterns. While live attenuated influenza vaccine FluMist induced significant signal transduction responses, killed inactivated influenza vaccine Fluarix induced significant metabolism of protein responses. Two different Yellow Fever vaccine (YF-Vax) studies resulted in overlapping gene lists; however, they shared more portions of pathways than gene lists. Interestingly, live attenuated YF-Vax simulates significant metabolism of protein responses, which was similar to the pattern induced by killed inactivated Fluarix. A user-friendly web interface was generated to access, browse and search the VaximmutorDB database information. As the first web-based database of vaccine immune factors, VaximmutorDB provides systematical collection, standardization, storage, and analysis of experimentally verified vaccine immune factors, supporting better understanding of protective vaccine immunity.


Assuntos
Anticorpos Antivirais/imunologia , Imunidade/imunologia , Fatores Imunológicos/imunologia , Vacinas/imunologia , Animais , Bases de Dados Factuais , Humanos , Internet , Transdução de Sinais/imunologia , Vacinação/métodos
4.
Clin Pharmacol Ther ; 109(5): 1232-1243, 2021 05.
Artigo em Inglês | MEDLINE | ID: mdl-33090463

RESUMO

We improved a previous pharmacological target adverse-event (TAE) profile model to predict adverse events (AEs) on US Food and Drug Administration (FDA) drug labels at the time of approval. The new model uses more drugs and features for learning as well as a new algorithm. Comparator drugs sharing similar target activities to a drug of interest were evaluated by aggregating AEs from the FDA Adverse Event Reporting System (FAERS), FDA drug labels, and medical literature. An ensemble machine learning model was used to evaluate FAERS case count, disproportionality scores, percent of comparator drug labels with a specific AE, and percent of comparator drugs with the reports of the event in the literature. Overall classifier performance was F1 of 0.71, area under the precision-recall curve of 0.78, and area under the receiver operating characteristic curve of 0.87. TAE analysis continues to show promise as a method to predict adverse events at the time of approval.


Assuntos
Sistemas de Notificação de Reações Adversas a Medicamentos , Algoritmos , Farmacovigilância , Mineração de Dados , Rotulagem de Medicamentos , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos , Humanos , Aprendizado de Máquina , Estados Unidos , United States Food and Drug Administration
5.
Eur J Clin Pharmacol ; 76(9): 1291-1299, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32495081

RESUMO

PURPOSE: Drug indications and disease symptoms often confound adverse event reports in real-world datasets, including electronic health records and reports in the FDA Adverse Event Reporting System (FAERS). A thorough, standardized set of indications and symptoms is needed to identify these confounders in such datasets for drug research and safety assessment. The aim of this study is to create a comprehensive list of drug-indication associations and disease-symptom associations using multiple resources, including existing databases and natural language processing. METHODS: Drug indications for drugs approved in the USA were extracted from two databases, RxNorm and Side Effect Resource (SIDER). Symptoms for these indications were extracted from MedlinePlus and using natural language processing from PubMed abstracts. RESULTS: A total of 1361 unique drugs, 1656 unique indications, and 2201 unique symptoms were extracted from a wide variety of MedDRA System Organ Classes. Text-mining precision was maximized at 0.65 by examining Term Frequency Inverse Document Frequency (TF-IDF) scores of the disease-symptom associations. CONCLUSION: The drug-indication associations and disease-symptom associations collected in this study may be useful in identifying confounders in other datasets, such as safety reports. With further refinement and additional drugs, indications, and symptoms, this dataset may become a quality resource for disease symptoms.


Assuntos
Sistemas de Notificação de Reações Adversas a Medicamentos/estatística & dados numéricos , Bases de Dados Factuais/estatística & dados numéricos , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/epidemiologia , Fatores de Confusão Epidemiológicos , Mineração de Dados , Aprovação de Drogas , Humanos , Processamento de Linguagem Natural , Estados Unidos
6.
BMC Bioinformatics ; 21(1): 163, 2020 Apr 29.
Artigo em Inglês | MEDLINE | ID: mdl-32349656

RESUMO

BACKGROUND: While clinical trials are considered the gold standard for detecting adverse events, often these trials are not sufficiently powered to detect difficult to observe adverse events. We developed a preliminary approach to predict 135 adverse events using post-market safety data from marketed drugs. Adverse event information available from FDA product labels and scientific literature for drugs that have the same activity at one or more of the same targets, structural and target similarities, and the duration of post market experience were used as features for a classifier algorithm. The proposed method was studied using 54 drugs and a probabilistic approach of performance evaluation using bootstrapping with 10,000 iterations. RESULTS: Out of 135 adverse events, 53 had high probability of having high positive predictive value. Cross validation showed that 32% of the model-predicted safety label changes occurred within four to nine years of approval (median: six years). CONCLUSIONS: This approach predicts 53 serious adverse events with high positive predictive values where well-characterized target-event relationships exist. Adverse events with well-defined target-event associations were better predicted compared to adverse events that may be idiosyncratic or related to secondary target effects that were poorly captured. Further enhancement of this model with additional features, such as target prediction and drug binding data, may increase accuracy.


Assuntos
Sistemas de Notificação de Reações Adversas a Medicamentos , Biologia Computacional/métodos , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/diagnóstico , Algoritmos , Humanos
7.
PLoS One ; 15(3): e0229646, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32126112

RESUMO

Kratom is a botanical substance that is marketed and promoted in the US for pharmaceutical opioid indications despite having no US Food and Drug Administration approved uses. Kratom contains over forty alkaloids including two partial agonists at the mu opioid receptor, mitragynine and 7-hydroxymitragynine, that have been subjected to the FDA's scientific and medical evaluation. However, pharmacological and toxicological data for the remaining alkaloids are limited. Therefore, we applied the Public Health Assessment via Structural Evaluation (PHASE) protocol to generate in silico binding profiles for 25 kratom alkaloids to facilitate the risk evaluation of kratom. PHASE demonstrates that kratom alkaloids share structural features with controlled opioids, indicates that several alkaloids bind to the opioid, adrenergic, and serotonin receptors, and suggests that mitragynine and 7-hydroxymitragynine are the strongest binders at the mu opioid receptor. Subsequently, the in silico binding profiles of a subset of the alkaloids were experimentally verified at the opioid, adrenergic, and serotonin receptors using radioligand binding assays. The verified binding profiles demonstrate the ability of PHASE to identify potential safety signals and provide a tool for prioritizing experimental evaluation of high-risk compounds.


Assuntos
Mitragyna/química , Plantas Medicinais/química , Alcaloides de Triptamina e Secologanina/química , Animais , Sítios de Ligação , Células HEK293 , Humanos , Técnicas In Vitro , Simulação de Acoplamento Molecular , Ensaio Radioligante , Receptores Adrenérgicos/efeitos dos fármacos , Receptores Adrenérgicos/metabolismo , Receptores Opioides/efeitos dos fármacos , Receptores Opioides/metabolismo , Receptores Opioides mu/efeitos dos fármacos , Receptores Opioides mu/metabolismo , Receptores de Serotonina/efeitos dos fármacos , Receptores de Serotonina/metabolismo , Alcaloides de Triptamina e Secologanina/farmacocinética , Alcaloides de Triptamina e Secologanina/farmacologia , Relação Estrutura-Atividade
8.
Clin Pharmacol Ther ; 106(1): 116-122, 2019 07.
Artigo em Inglês | MEDLINE | ID: mdl-30957872

RESUMO

The US Food and Drug Administration's Center for Drug Evaluation and Research (CDER) developed an investigational Public Health Assessment via Structural Evaluation (PHASE) methodology to provide a structure-based evaluation of a newly identified opioid's risk to public safety. PHASE utilizes molecular structure to predict biological function. First, a similarity metric quantifies the structural similarity of a new drug relative to drugs currently controlled in the Controlled Substances Act (CSA). Next, software predictions provide the primary and secondary biological targets of the new drug. Finally, molecular docking estimates the binding affinity at the identified biological targets. The multicomponent computational approach coupled with expert review provides a rapid, systematic evaluation of a new drug in the absence of in vitro or in vivo data. The information provided by PHASE has the potential to inform law enforcement agencies with vital information regarding newly emerging illicit opioids.


Assuntos
Analgésicos Opioides/química , Substâncias Controladas/química , Controle de Medicamentos e Entorpecentes/organização & administração , Simulação de Acoplamento Molecular/métodos , United States Food and Drug Administration/organização & administração , Simulação por Computador , Desenho de Fármacos , Fentanila/química , Humanos , Saúde Pública , Relação Estrutura-Atividade , Estados Unidos
9.
PLoS Comput Biol ; 14(12): e1006614, 2018 12.
Artigo em Inglês | MEDLINE | ID: mdl-30532240

RESUMO

Failure to demonstrate efficacy and safety issues are important reasons that drugs do not reach the market. An incomplete understanding of how drugs exert their effects hinders regulatory and pharmaceutical industry projections of a drug's benefits and risks. Signaling pathways mediate drug response and while many signaling molecules have been characterized for their contribution to disease or their role in drug side effects, our knowledge of these pathways is incomplete. To better understand all signaling molecules involved in drug response and the phenotype associations of these molecules, we created a novel method, PathFX, a non-commercial entity, to identify these pathways and drug-related phenotypes. We benchmarked PathFX by identifying drugs' marketed disease indications and reported a sensitivity of 41%, a 2.7-fold improvement over similar approaches. We then used PathFX to strengthen signals for drug-adverse event pairs occurring in the FDA Adverse Event Reporting System (FAERS) and also identified opportunities for drug repurposing for new diseases based on interaction paths that associated a marketed drug to that disease. By discovering molecular interaction pathways, PathFX improved our understanding of drug associations to safety and efficacy phenotypes. The algorithm may provide a new means to improve regulatory and therapeutic development decisions.


Assuntos
Algoritmos , Desenvolvimento de Medicamentos/estatística & dados numéricos , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos , Biologia Computacional , Bases de Dados de Produtos Farmacêuticos , Tomada de Decisões , Aprovação de Drogas , Desenvolvimento de Medicamentos/legislação & jurisprudência , Desenvolvimento de Medicamentos/normas , Descoberta de Drogas/legislação & jurisprudência , Descoberta de Drogas/normas , Descoberta de Drogas/estatística & dados numéricos , Interações Medicamentosas , Reposicionamento de Medicamentos , Controle de Medicamentos e Entorpecentes , Humanos , Segurança , Resultado do Tratamento , Estados Unidos , United States Food and Drug Administration
10.
CPT Pharmacometrics Syst Pharmacol ; 7(12): 809-817, 2018 12.
Artigo em Inglês | MEDLINE | ID: mdl-30354029

RESUMO

Clinical trials can fail to detect rare adverse events (AEs). We assessed the ability of pharmacological target adverse-event (TAE) profiles to predict AEs on US Food and Drug Administration (FDA) drug labels at least 4 years after approval. TAE profiles were generated by aggregating AEs from the FDA adverse event reporting system (FAERS) reports and the FDA drug labels for drugs that hit a common target. A genetic algorithm (GA) was used to choose the adverse event (AE) case count (N), disproportionality score in FAERS (proportional reporting ratio (PRR)), and percent of comparator drug labels with an AE to maximize F-measure. With FAERS data alone, precision, recall, and specificity were 0.57, 0.78, and 0.61, respectively. After including FDA drug label data, precision, recall, and specificity improved to 0.67, 0.81, and 0.71, respectively. Eighteen of 23 (78%) postmarket label changes were identified correctly. TAE analysis shows promise as a method to predict AEs at the time of drug approval.


Assuntos
Farmacovigilância , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos , Humanos , Projetos Piloto
11.
Clin Transl Sci ; 11(3): 322-329, 2018 05.
Artigo em Inglês | MEDLINE | ID: mdl-29575568

RESUMO

Case reports suggest an association between second-generation antipsychotics (SGAs) and serotonin syndrome (SS). The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) was analyzed to generate hypotheses about how SGAs may interact with pharmacological targets associated with SS. FAERS was integrated with additional sources to link information about adverse events with drugs and targets. Using Proportional Reporting Ratios, we identified signals that were further investigated with the literature to evaluate mechanistic hypotheses formed from the integrated FAERS data. Analysis revealed common pharmacological targets perturbed in both SGA and SS cases, indicating that SGAs may induce SS. The literature also supported 5-HT2A antagonism and 5-HT1A agonism as common mechanisms that may explain the SGA-SS association. Additionally, integrated FAERS data mining and case studies suggest that interactions between SGAs and other serotonergic agents may increase the risk for SS. Computational analysis can provide additional insights into the mechanisms underlying the relationship between SGAs and SS.


Assuntos
Antipsicóticos/efeitos adversos , Transtornos Mentais/tratamento farmacológico , Agonistas do Receptor 5-HT1 de Serotonina/farmacologia , Antagonistas do Receptor 5-HT2 de Serotonina/farmacologia , Síndrome da Serotonina/induzido quimicamente , Sistemas de Notificação de Reações Adversas a Medicamentos/estatística & dados numéricos , Antipsicóticos/farmacologia , Biologia Computacional , Interações Medicamentosas , Humanos , Estados Unidos , United States Food and Drug Administration/estatística & dados numéricos
12.
AMIA Annu Symp Proc ; 2017: 1793-1801, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-29854250

RESUMO

A critical issue in the usage of cancer drugs is its association with various adverse events (AEs) in some, but not all, patients. The National Cancer Institute (NCI) Common Terminology Criteria for Adverse Events (CTCAE) is a controlled terminology for AE classification and analysis in cancer clinical trials. The Ontology of Adverse Events (OAE) is a community-based ontology in the domain of AEs. In this study, OAE was first updated by including AE severity grading and OAE-CTCAE mapping. An OAE subset containing CTCAE-related terms and their associated OAE terms was generated to facilitate term usage. A use case study based on a published cancer drug clinical trial demonstrates that OAE provides better hierarchical representation, includes semantic relations, and supports automated reasoning. Demonstrated with a single patient analysis, the OAE framework supports precision informatics for representing AEs and related genetic and clinical conditions in individual patients treated with cancer drugs.


Assuntos
Antineoplásicos/efeitos adversos , Ontologias Biológicas , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/classificação , Neoplasias/tratamento farmacológico , Farmacovigilância , Antineoplásicos/uso terapêutico , Humanos , Informática Médica , Semântica , Índice de Gravidade de Doença
13.
J Biomed Semantics ; 7: 29, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-27213033

RESUMO

BACKGROUND: Neuropathy often occurs following drug treatment such as chemotherapy. Severe instances of neuropathy can result in cessation of life-saving chemotherapy treatment. RESULTS: To support data representation and analysis of drug-associated neuropathy adverse events (AEs), we developed the Ontology of Drug Neuropathy Adverse Events (ODNAE). ODNAE extends the Ontology of Adverse Events (OAE). Our combinatorial approach identified 215 US FDA-licensed small molecule drugs that induce signs and symptoms of various types of neuropathy. ODNAE imports related drugs from the Drug Ontology (DrON) with their chemical ingredients defined in ChEBI. ODNAE includes 139 drug mechanisms of action from NDF-RT and 186 biological processes represented in the Gene Ontology (GO). In total ODNAE contains 1579 terms. Our analysis of the ODNAE knowledge base shows neuropathy-inducing drugs classified under specific molecular entity groups, especially carbon, pnictogen, chalcogen, and heterocyclic compounds. The carbon drug group includes 127 organic chemical drugs. Thirty nine receptor agonist and antagonist terms were identified, including 4 pairs (31 drugs) of agonists and antagonists that share targets (e.g., adrenergic receptor, dopamine, serotonin, and sex hormone receptor). Many drugs regulate neurological system processes (e.g., negative regulation of dopamine or serotonin uptake). SPARQL scripts were used to query the ODNAE ontology knowledge base. CONCLUSIONS: ODNAE is an effective platform for building a drug-induced neuropathy knowledge base and for analyzing the underlying mechanisms of drug-induced neuropathy. The ODNAE-based methods used in this study can also be extended to the representation and study of other categories of adverse events.


Assuntos
Ontologias Biológicas , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos , Doenças do Sistema Nervoso/induzido quimicamente
14.
Methods Mol Biol ; 1404: 741-752, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-27076334

RESUMO

A DNA vaccine is a vaccine that uses a mammalian expression vector to express one or more protein antigens and is administered in vivo to induce an adaptive immune response. Since the 1990s, a significant amount of research has been performed on DNA vaccines and the mechanisms behind them. To meet the needs of the DNA vaccine research community, we created DNAVaxDB ( http://www.violinet.org/dnavaxdb ), the first Web-based database and analysis resource of experimentally verified DNA vaccines. All the data in DNAVaxDB, which includes plasmids, antigens, vaccines, and sources, is manually curated and experimentally verified. This chapter goes over the detail of DNAVaxDB system and shows how the DNA vaccine database, combined with the Vaxign vaccine design tool, can be used for rational design of a DNA vaccine against a pathogen, such as Mycobacterium bovis.


Assuntos
Bases de Dados de Ácidos Nucleicos , Internet , Vacinas de DNA , Vacinas Bacterianas/genética , Vacinas Bacterianas/imunologia , Apresentação de Dados , Disseminação de Informação , Mycobacterium avium/imunologia , Plasmídeos/genética , Vacinas de DNA/genética , Vacinas de DNA/imunologia
16.
BMC Bioinformatics ; 15 Suppl 4: S2, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-25104313

RESUMO

Since the first DNA vaccine studies were done in the 1990s, thousands more studies have followed. Here we report the development and analysis of DNAVaxDB (http://www.violinet.org/dnavaxdb), the first publically available web-based DNA vaccine database that curates, stores, and analyzes experimentally verified DNA vaccines, DNA vaccine plasmid vectors, and protective antigens used in DNA vaccines. All data in DNAVaxDB are annotated from reliable resources, particularly peer-reviewed articles. Among over 140 DNA vaccine plasmids, some plasmids were more frequently used in one type of pathogen than others; for example, pCMVi-UB for G- bacterial DNA vaccines, and pCAGGS for viral DNA vaccines. Presently, over 400 DNA vaccines containing over 370 protective antigens from over 90 infectious and non-infectious diseases have been curated in DNAVaxDB. While extracellular and bacterial cell surface proteins and adhesin proteins were frequently used for DNA vaccine development, the majority of protective antigens used in Chlamydophila DNA vaccines are localized to the inner portion of the cell. The DNA vaccine priming, other vaccine boosting vaccination regimen has been widely used to induce protection against infection of different pathogens such as HIV. Parasitic and cancer DNA vaccines were also systematically analyzed. User-friendly web query and visualization interfaces are available in DNAVaxDB for interactive data search. To support data exchange, the information of DNA vaccines, plasmids, and protective antigens is stored in the Vaccine Ontology (VO). DNAVaxDB is targeted to become a timely and vital source of DNA vaccines and related data and facilitate advanced DNA vaccine research and development.


Assuntos
Bases de Dados de Ácidos Nucleicos , Vacinas de DNA/imunologia , Antígenos de Bactérias/imunologia , Bactérias/imunologia , Infecções Bacterianas/imunologia , Infecções Bacterianas/prevenção & controle , Vacinas Bacterianas/imunologia , Vacinas Anticâncer/imunologia , Bases de Dados de Ácidos Nucleicos/estatística & dados numéricos , Humanos , Internet , Neoplasias/imunologia , Neoplasias/prevenção & controle , Proteínas/imunologia , Software , Vacinas Virais/imunologia , Viroses/imunologia , Viroses/prevenção & controle , Vírus/imunologia
17.
Nucleic Acids Res ; 42(Database issue): D1124-32, 2014 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-24259431

RESUMO

The integrative Vaccine Investigation and Online Information Network (VIOLIN) vaccine research database and analysis system (http://www.violinet.org) curates, stores, analyses and integrates various vaccine-associated research data. Since its first publication in NAR in 2008, significant updates have been made. Starting from 211 vaccines annotated at the end of 2007, VIOLIN now includes over 3240 vaccines for 192 infectious diseases and eight noninfectious diseases (e.g. cancers and allergies). Under the umbrella of VIOLIN, >10 relatively independent programs are developed. For example, Protegen stores over 800 protective antigens experimentally proven valid for vaccine development. VirmugenDB annotated over 200 'virmugens', a term coined by us to represent those virulence factor genes that can be mutated to generate successful live attenuated vaccines. Specific patterns were identified from the genes collected in Protegen and VirmugenDB. VIOLIN also includes Vaxign, the first web-based vaccine candidate prediction program based on reverse vaccinology. VIOLIN collects and analyzes different vaccine components including vaccine adjuvants (Vaxjo) and DNA vaccine plasmids (DNAVaxDB). VIOLIN includes licensed human vaccines (Huvax) and veterinary vaccines (Vevax). The Vaccine Ontology is applied to standardize and integrate various data in VIOLIN. VIOLIN also hosts the Ontology of Vaccine Adverse Events (OVAE) that logically represents adverse events associated with licensed human vaccines.


Assuntos
Bases de Dados Genéticas , Vacinas/imunologia , Adjuvantes Imunológicos , Antígenos/química , Antígenos/genética , Mineração de Dados , Genes , Genômica , Humanos , Internet , Plasmídeos/genética , Proteínas/imunologia , Alinhamento de Sequência , Software , Integração de Sistemas , Vacinas/efeitos adversos , Vacinas/química , Vacinas/genética , Vacinas Atenuadas/genética , Vacinas de DNA/genética , Fatores de Virulência/genética , Fatores de Virulência/imunologia
18.
Vaccine ; 31(5): 797-805, 2013 Jan 21.
Artigo em Inglês | MEDLINE | ID: mdl-23219434

RESUMO

Live attenuated vaccines are usually generated by mutation of genes encoding virulence factors. "Virmugen" is coined here to represent a gene that encodes for a virulent factor of a pathogen and has been proven feasible in animal models to make a live attenuated vaccine by knocking out this gene. Not all virulence factors are virmugens. VirmugenDB is a web-based virmugen database (http://www.violinet.org/virmugendb). Currently, VirmugenDB includes 225 virmugens that have been verified to be valuable for vaccine development against 57 bacterial, viral, and protozoan pathogens. Bioinformatics analysis has revealed significant patterns in virmugens. For example, 10 Gram-negative and 1 Gram-positive bacterial aroA genes are virmugens. A sequence analysis has revealed at least 50% of identities in the protein sequences of the 10 Gram-negative bacterial aroA virmugens. As a pathogen case study, Brucella virmugens were analyzed. Out of 15 verified Brucella virmugens, 6 are related to carbohydrate or nucleotide transport and metabolism, and 2 involving cell membrane biogenesis. In addition, 54 virmugens from 24 viruses and 12 virmugens from 4 parasites are also stored in VirmugenDB. Virmugens tend to involve metabolism of nutrients (e.g., amino acids, carbohydrates, and nucleotides) and cell membrane formation. Host genes whose expressions were regulated by virmugen mutation vaccines or wild type virulent pathogens have also been annotated and systematically compared. The bioinformatics annotation and analysis of virmugens helps to elucidate enriched virmugen profiles and the mechanisms of protective immunity, and further supports rational vaccine design.


Assuntos
Vacinas Bacterianas/imunologia , Técnicas de Inativação de Genes , Vacinas Protozoárias/imunologia , Vacinas Virais/imunologia , Fatores de Virulência/genética , Fatores de Virulência/metabolismo , Animais , Vacinas Bacterianas/genética , Biologia Computacional , Bases de Dados Genéticas , Humanos , Vacinas Protozoárias/genética , Vacinas Atenuadas/genética , Vacinas Atenuadas/imunologia , Vacinas Virais/genética
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