Name: drug repurposing hypothesis generation using the "re:fine drugs" system
Uploaded: 2016-12-11T15:00:00.000+00:00
Duration: 5 min 10 s
Description: Watch this Scientific Journal Video about Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System at JoVE.com

This work was partially supported by the National Institutes of Health (NIH) Clinical and Translational Science Awards (CTSA) Grant (UL1TR001070) to the Ohio State University's Center for Clinical and Translational Science (CCTS) and the National Library Of Medicine under Award Number T15LM011270.

1. Termos início de Consultas de Gene, drogas ou doença <ol><li> Acesse a página inicial para &quot;RE: Drogas finos&quot; no seguinte link: http://drug-repurposing.nationwidechildrens.org. Comece digitando um termo de consulta na barra de pesquisa de qualquer uma das seguintes três categorias: drogas (nome genérico), doença (nova indicação doença) ou gene (símbolo oficial do gene HGNC). </li><li> Filtrar a função de pesquisa bar para incluir apenas &quot;nome do medicamento&quot;, &quot;Doença de New Indicação&quot;, &quot;símbolo do gene&quot; ou &quot;all&quot; categorias. A barra de pesquisa inclui uma função de auto-suficiência para as entradas de consulta. </li><li> Coloque em uma palavra-chave e clique no botão &quot;Pesquisar&quot;. Classificar a tabela de resultados por qualquer uma das seguintes colunas: &quot;Drogas&quot;, &quot;Indicação Registered&quot;, &quot;P-Value&quot;, &quot;P-valor ajustado&quot;, &quot;Odds Ratio&quot;, &quot;Estudo&quot;, &quot;New Indicação&quot;, &quot;Drogas Banco Indicação &quot;,&quot; # de Medline Abstracts &quot;,&quot; # do CLITrial Registry téc- &quot;,&quot; potencial &quot;,&quot; SNP &quot;,&quot; Gene &quot;ou&quot; ação &quot;. </li><li> Navegue até a opção de busca avançada, a fim de habilitar o recurso de informação sobre a droga. Clique no ícone na coluna &quot;Info&quot; para um determinado medicamento. Observar uma página que lista a toda a informação correspondente incluindo p-Value para a associação, nome da doença, nome do medicamento, detalhes Gene (NCBI ligação Gene) e detalhes de Drogas (link drugbank). </li></ol> 2. Exploração de Opções Avançadas <ol><li> Clique no botão &quot;Avançado&quot; localizado no lado direito da página, e são fornecidos várias opções para refinar ainda mais os resultados. As opções avançadas de pesquisa incluem modificações para o seguinte: droga, associação, doença, potencial, gene e ação. </li><li> Exportação tabelas de resultados, clicando no botão &quot;Exportar&quot; no lado direito da página. Clique no botão &quot;Simples&quot;, a fim de dobrar para baixo a janela de pesquisa avançada. &lt;/ Li&gt; <li> Sob a guia Avançado opção &quot;droga&quot;, especifique uma indicação determinado medicamento ou um nome de medicamento adicional para filtrar os resultados. </li><li> Sob a guia &quot;associação&quot;, resultados do filtro por o nível de significância P-Value, P-Valor Ajustado com FDR, tamanho do efeito (Odds Ratio), e / ou estudo do tipo (GWAS, PheWAS ou ambos). </li><li> Sob a guia &quot;doença&quot;, especifique uma certa descrição da doença para a nova utilização prevista. </li><li> No separador &quot;potencial&quot;, resultados de filtro de acordo com qualquer um dos seguintes critérios: (i) se a indicação da droga está contida na base de dados drugbank, (ii) número de resumos Medline com co-ocorrência da droga e da doença, ( iii) número de entradas de banco de dados ClinicalTrials.gov com co-ocorrência de droga e a doença e (iv) Repurposing Potencial. NOTA: A opção Potencial Repurposing descreve a novidade da descoberta: (i) Conhecido: relacionamento já existe no banco de dados drugbank, (ii) Fortemente apoiada: Algum apoio, tanto do registro de ensaios clínicos e os resumos Medline, (iii) Provável: algum apoio em qualquer registro de ensaio clínico ou os resumos Medline e (iv) Novel: nenhuma evidência no registro de ensaios clínicos nem em resumos Medline. </li><li> Sob a guia &quot;gene&quot;, digite um identificador SNP ou símbolo do gene para filtrar os resultados por genes alvo de drogas específicas. </li><li> Sob a guia &quot;ação&quot;, especifique o tipo de ação de drogas contra o alvo (s) de drogas, incluindo agonista, antagonista, outro, desconhecido ou todos (fonte: Banco de dados drugbank). </li></ol>

<ol>
	<li>Borisy, A. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+discovery+of+multicomponent+therapeutics.">Systematic discovery of multicomponent therapeutics.</a> Proc Natl Acad Sci U S A. 100 (13), 7977-7982 (2003).</li><li>Zhang, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+synergy+screening+identifies+microbial+metabolites+as+combination+agents+for+the+treatment+of+fungal+infections.">High-throughput synergy screening identifies microbial metabolites as combination agents for the treatment of fungal infections.</a> Proc Natl Acad Sci U S A. 104 (11), 4606-4611 (2007).</li><li>Adams, C. P., Brantner, V. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estimating+the+cost+of+new+drug+development:+is+it+really+802+million+dollars?.">Estimating the cost of new drug development: is it really 802 million dollars?.</a> Health Aff (Millwood). 25 (2), 420-428 (2006).</li><li>Bunnage, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Getting+pharmaceutical+R&amp;D+back+on+target.">Getting pharmaceutical R&amp;D back on target.</a> Nat Chem Biol. 7 (6), 335-339 (2011).</li><li>Ashburn, T. T., Thor, K. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Drug+repositioning:+identifying+and+developing+new+uses+for+existing+drugs.">Drug repositioning: identifying and developing new uses for existing drugs.</a> Nat Rev Drug Discov. 3 (8), 673-683 (2004).</li><li>Hurle, M. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+drug+repositioning:+from+data+to+therapeutics.">Computational drug repositioning: from data to therapeutics.</a> Clin Pharmacol Ther. 93 (4), 335-341 (2013).</li><li>Jin, G., Wong, S. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Toward+better+drug+repositioning:+prioritizing+and+integrating+existing+methods+into+efficient+pipelines.">Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines.</a> Drug Discov Today. 19 (5), 637-644 (2014).</li><li>Moosavinasab, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=34;RE:fine+Drugs&amp;#34;:+An+Interactive+Dashboard+to+Access+Drug+Repurposing+Opportunities.">34;RE:fine Drugs&amp;#34;: An Interactive Dashboard to Access Drug Repurposing Opportunities.</a> Database (Oxford). , (2016).</li><li>Zhang, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Use+of+genome-wide+association+studies+for+cancer+research+and+drug+repositioning.">Use of genome-wide association studies for cancer research and drug repositioning.</a> PLoS One. 10 (3), e0116477 (2015).</li><li>Rastegar-Mojarad, M., Ye, Z., Kolesar, J. M., Hebbring, S. J., Lin, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Opportunities+for+drug+repositioning+from+phenome-wide+association+studies.">Opportunities for drug repositioning from phenome-wide association studies.</a> Nat Biotechnol. 33 (4), 342-345 (2015).</li><li>Li, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+survey+of+current+trends+in+computational+drug+repositioning.">A survey of current trends in computational drug repositioning.</a> Brief Bioinform. 17 (1), 2-12 (2016).</li><li>Denny, J. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+comparison+of+phenome-wide+association+study+of+electronic+medical+record+data+and+genome-wide+association+study+data.">Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data.</a> Nat Biotechnol. 31 (12), 1102-1110 (2013).</li><li>Wang, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mining+drug-disease+relationships+as+a+complement+to+medical+genetics-based+drug+repositioning:+Where+a+recommendation+system+meets+genome-wide+association+studies.">Mining drug-disease relationships as a complement to medical genetics-based drug repositioning: Where a recommendation system meets genome-wide association studies.</a> Clin Pharmacol Ther. 97 (5), 451-454 (2015).</li><li>Sanseau, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Use+of+genome-wide+association+studies+for+drug+repositioning.">Use of genome-wide association studies for drug repositioning.</a> Nat Biotechnol. 30 (4), 317-320 (2012).</li><li>Xu, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Validating+drug+repurposing+signals+using+electronic+health+records:+a+case+study+of+metformin+associated+with+reduced+cancer+mortality.">Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality.</a> J Am Med Inform Assoc. 22 (1), 179-191 (2015).</li></ol>

The authors declare that they have no competing financial interests.

The protocol described here for the RE:fine Drugs interactive dashboard can be modified in different ways according to the user's preferences. This method uniquely integrates GWAS and PheWAS data as a novel paradigm underlying drug repurposing hypothesis generation. Specifically, this system provides access to both 52,966 PheWAS associations and 7,945 GWAS associations with advanced options to filter the results by the study type, effect size and/or significance level. Another advantage of this method over existing computational drug repurposing tools is that queries may be made from drug, gene or disease perspectives. 
There are several limitations to this method. Currently, the PheWAS data is limited to primarily adult patient population from five institutions contained in the Electronic Medical Records and Genomics (eMERGE) network with a mean age of 69.5 years 12. Additionally, the "repurposing potential" feature uses co-occurrence of search terms in Medline abstracts as one of its criteria. It is well known that text mining methods using co-occurrence have limitations with respect to syntactical structure and literature bias. Thus, we recommend this feature be used as a starting point to explore the potential novelty and/or evidence supporting specific drug repurposing hypotheses and recommend additional investigation into the biomedical literature and clinical trial databases. 
Future directions for this work not described here would be to extend this database to additional sources of GWAS and PheWAS data as they become available. Similar efforts to systematically translate results from large-scale GWAS studies into drug repurposing hypotheses have been previously published 9,13-14. It may be useful to compare these different workflows to predict drug candidates from GWAS data in future studies. Additionally, several other methods exist to computationally generate drug repurposing hypotheses from different data sources, including: genomics, transcriptomics, chemical structures, drug side effect profiles, as previously summarized 6,11. Future methodological advancements could also include automating drug combination predictions and providing information on drug toxicity to guide follow up studies for drug candidates. 
Furthermore, the hypotheses generated from RE:fine Drugs may be further validated using electronic health records, before initiating clinical trials 15. Finally, future studies will be needed to compare this system to other target-based drug repurposing methods.

Os processos caros e ineficientes associados com abordagens de descoberta de drogas tradicionais, incluindo drogas de alto rendimento e de triagem composto de chumbo, estão contribuindo para os atrasos na tradução de descobertas da investigação em terapias para pacientes 1,2. Uma média de 1 bilhão de dólares e 15-20 anos é necessário para trazer uma nova droga da bancada à cabeceira 3. Além disso, 52% dos medicamentos falham durante o desenvolvimento na fase 1 de ensaios clínicos, e apenas 25% de compostos que entram fase 2 prosseguir em plena fase 3 estudos clínicos 4. O objetivo do reaproveitamento de drogas ou reposicionamento de drogas é renovar drogas falharam e / ou encontrar novas indicações para medicamentos aprovados, a fim de oferecer novas terapias para pacientes mais rápido e com uma maior taxa de sucesso. Reaproveitamento de drogas pode diminuir o cronograma para tornar os medicamentos disponíveis para uso em pacientes de 3-12 anos 5. aplicações médicas importantes para reaproveitamento de drogas incluem: doenças com Prognos pobresé e baixas taxas de sobrevivência, doenças resistentes aos medicamentos, áreas de investigação das doenças subfinanciados e populações pobres e carentes do paciente. Reaproveitamento de drogas computacional é definida como o processo de criação e validação de fluxos de trabalho automatizados que podem gerar hipóteses para novas indicações para um candidato de drogas 6. Existentes métodos de reaproveitamento de drogas computacionais foram categorizados com base em alvo, baseada no conhecimento, com base em assinatura, baseado em rede, e com base segmentada-mecanismo, e pode ser orientada a partir de genes, doenças ou drogas perspectivas. Além disso, abordagens computacionais pode acelerar mais uma prova de conceito experimentos de validação e estudos clínicos de pequena escala para a droga candidatos reaproveitado 7. Temos relatado anteriormente em &quot;RE: Drogas finos&quot;, uma ferramenta disponível gratuitamente, interativa baseada na web para reaproveitamento de drogas geração de hipóteses baseado na teoria transitiva de relações Drogas Gene-doença 8. O g globaloal deste método é integrar sistematicamente diversos tipos de drogas, genéticas e clínicas de dados para permitir a droga redefinição de objetivos para os usuários de diversas comunidades, incluindo clínicas, indústria e as comunidades reguladoras. Os métodos fundamentais para este sistema foram anteriormente relatados para o uso de estudo genômico de associação (GWAS) e dados de estudo de associação do phenome (PheWAS) em drogas Repurposing pesquisas 9,10. A nova combinação destes tipos de dados distingue a WebTool de outros métodos baseados em 6,11 alvo. O RE: sistema de Drogas fina atualmente contém 60,911 hipóteses reaproveitamento de drogas cobrindo 916 drogas, 567 genes e 1.770 doenças. O WebTool fornece uma interface amigável para os investigadores a procurar interativamente hipóteses Repurposing drogas e priorizá-los usando diversos critérios. Por exemplo, os usuários podem filtrar hipóteses Repurposing droga com suporte na literatura biomédica e de ensaios clínicos Database, p-valores significativos, odds ratio de associação ou por indicações específicas. O único requisito para este sistema é o acesso à Internet.

<table><tbody><tr><td>Access the homepage for &amp;ldquo;RE:fine Drugs&amp;rdquo; at the following link: http://drug-repurposing.nationwidechildrens.org.&amp;nbsp;</td><td>n/a</td><td>n/a</td><td>The only requirement for this system is Internet access</td></tr></tbody></table>

drug repurposing hypothesis generation using the "re:fine drugs" system

The promise of drug repurposing is that existing drugs may be used for new disease indications in order to curb the high costs and time for approval. The goal of computational methods for drug repurposing is to enable solutions for safer, cheaper and faster drug discovery. Towards this end, we developed a novel method that integrates genetic and clinical phenotype data from large-scale GWAS and PheWAS studies with detailed drug information on the concept of transitive Drug-Gene-Disease triads. We created "RE:fine Drugs," a freely available, interactive dashboard that automates gene, disease and drug-based searches to identify drug repurposing candidates. This web-based tool supports a user-friendly interface that includes an array of advanced search and export options. Results can be prioritized in a variety of ways, including but not limited to, biomedical literature support, strength and statistical significance of GWAS and/or PheWAS associations, disease indications and molecular drug targets. Here we provide a protocol that illustrates the functionalities available in the "RE:fine Drugs" system and explores the different advanced options through a case study.

Neste exemplo, o gene &quot;IL2RB&quot; foi introduzido como uma consulta à base de gene, e foi automaticamente reconhecido como tal pela função auto-fill (Figura 1). As hipóteses Repurposing doze drogas para o gene IL2RB são retornados, como mostrado na Figura 2. Página de informações detalhadas para uma determinada hipótese de reaproveitamento de drogas &quot;, daclizumab&quot;, neste caso, é fornecida a partir da coluna &quot;Info&quot; (Figura 3). Os resultados foram filtrados no separador de droga foi de todos os resultados correspondentes à droga &quot;daclizumab&quot;, como mostrado na Figura 4. A Figura 5 mostra apenas os medicamentos com indicação conhecida para o &quot;transplante&quot; termo doença (fonte: Banco de dados drugbank). A guia associação permite ao usuário filtrar SNP-fenótipo relações de significância estatística (P-valor) e tamanho do efeito genético (odds ratio), definido como a relação entre as chances de presence da doença em indivíduos com um genótipo específico (SNP alelo) sobre as probabilidades de presença da doença em indivíduos sem o alelo SNP. A Figura 6 mostra os resultados filtrados sob o separador associação dentro do intervalo P-Valor de 0,000001-0,05. A Figura 7 mostra hipóteses Repurposing drogas específicas para &quot;asma&quot; com base nas novas indicações que encontramos no estudo. A figura 8 mostra resultados sob a guia potencial para filtrar por um número mínimo de 5 resumos Medline contendo um co-ocorrência de termos de drogas e doenças. Neste exemplo, todas as drogas resultados sob a guia de genes são dirigidos para o gene &quot;IL2RB&quot;, correspondente à duração da consulta original (Figura 9). Finalmente, a Figura 10 mostra os resultados filtrados sob a guia &quot;ação&quot; para retornar todos os fármacos que actuam como agonistas no gene IL2RB. <img alt="figura 1"src = &quot;/ files / ftp_upload / 54948 / 54948fig1.jpg&quot; /&gt; Figura 1: A RE: Drogas finas painel interativo homepage. Os usuários podem iniciar uma consulta digitando um nome do medicamento, nova indicação doença ou símbolo do gene. Os links também são fornecidos para os documentos de referência GWAS e PheWAS que descrevem metodologias para gerar hipóteses Repurposing drogas. <a href="http://ecsource.jove.com/files/ftp_upload/54948/54948fig1large.jpg" target="_blank">Por favor clique aqui para ver uma versão maior desta figura.</a> <img alt="Figura 2" src="/files/ftp_upload/54948/54948fig2.jpg" /> Figura 2: A função Auto-suficiência para as entradas de consulta. Como um exemplo, o termo gene de consulta &quot;IL2RB&quot; foi reconhecido automaticamente como um termo gene. <a href="http://ecsource.jove.com/files/ftp_upload/54948/54948fig2large.jpg" target="_blank">Por favor clique aqui para ver uma versão maior desta figura.</a> <pclass = &quot;jove_content&quot; fo: manter-together.within-page = &quot;1&quot;&gt; <img alt="Figura 3" src="/files/ftp_upload/54948/54948fig3.jpg" /> Figura 3: Drogas repurposing tabela de resultados produzido a partir de uma consulta à base de gene (por exemplo, IL2RB). Doze hipóteses reaproveitamento de drogas para o gene IL2RB são produzidos. <a href="http://ecsource.jove.com/files/ftp_upload/54948/54948fig3large.jpg" target="_blank">Por favor clique aqui para ver uma versão maior desta figura.</a> <img alt="Figura 4" src="/files/ftp_upload/54948/54948fig4.jpg" /> Figura 4: Página de Informações para drogas individuais de página de resultados. Ao clicar no ícone da coluna &quot;Info&quot; mostra informações detalhadas para o daclizumab drogas. <a href="http://ecsource.jove.com/files/ftp_upload/54948/54948fig4large.jpg" target="_blank">Por favor clique aqui para ver uma versão maior desta figura.</a> Figura 5: opção de pesquisa avançada na guia de drogas para filtrar por uma droga específica. Neste exemplo, três resultados são apresentados para o daclizumab droga. <a href="http://ecsource.jove.com/files/ftp_upload/54948/54948fig5large.jpg" target="_blank">Por favor clique aqui para ver uma versão maior desta figura.</a> <img alt="Figura 6" src="/files/ftp_upload/54948/54948fig6.jpg" /> Figura 6: opção de pesquisa avançada na guia de drogas para filtrar por uma indicação de doença específica do banco de dados drugbank. Todos os medicamentos com uma indicação conhecida para o termo &quot;transplante&quot; doença são mostrados. <a href="http://ecsource.jove.com/files/ftp_upload/54948/54948fig6large.jpg" target="_blank">Por favor clique aqui para ver uma versão maior desta figura.</a> Figura 7: opção de pesquisa avançada no separador associação para filtrar por nível de significância. Neste caso, oito resultados são fornecidos cujo nível de significância associação cai dentro do intervalo P-Valor de 0.000001 a 0,05. <a href="http://ecsource.jove.com/files/ftp_upload/54948/54948fig7large.jpg" target="_blank">Por favor clique aqui para ver uma versão maior desta figura.</a> <img alt="Figura 8" src="/files/ftp_upload/54948/54948fig8.jpg" /> Figura 8: opção de pesquisa avançada na guia doença para filtrar por uma indicação de doença específica extraímos neste estudo. Neste exemplo, quatro resultados são mostrados para a asma como uma nova indicação de doença utilização. <a href="http://ecsource.jove.com/files/ftp_upload/54948/54948fig8large.jpg" target="_blank">Por favor clique aqui para ver uma versi maiores</a>em dessa figura. <img alt="Figura 9" src="/files/ftp_upload/54948/54948fig9.jpg" /> Figura 9: opção de pesquisa avançada na guia potencial para filtrar por um co-ocorrência de termos de drogas e doenças em resumos Medline. Neste exemplo, quatro resultados são mostrados que são apoiados por um número mínimo de 5 resumos Medline contendo um co-ocorrência de termos de drogas e doenças. <a href="http://ecsource.jove.com/files/ftp_upload/54948/54948fig9large.jpg" target="_blank">Por favor clique aqui para ver uma versão maior desta figura.</a> <img alt="Figura 10" src="/files/ftp_upload/54948/54948fig10.jpg" /> Figura 10: opções de pesquisa avançada na guia gene para filtrar por um símbolo gene específico, onde todos os resultados correspondem ao gene IL2RB utilizado para a consulta original. Neste exemplo, os resultados de todas as drogassob a guia gene são direcionados para o gene &quot;IL2RB&quot;, correspondente à duração da consulta original. Opção de pesquisa avançada na guia ação para filtrar por apenas drogas agonistas. Neste exemplo, seis resultados são devolvidos para todos os fármacos que actuam como agonistas no gene IL2RB. <a href="http://ecsource.jove.com/files/ftp_upload/54948/54948fig10large.jpg" target="_blank">Por favor clique aqui para ver uma versão maior desta figura.</a>

Watch this Scientific Journal Video about Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System at JoVE.com

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Here we describe a protocol using the web-based drug repurposing hypothesis generation tool: "RE:fine Drugs." This protocol can be modified to a user's preferences at the level of the query type (gene, drug or disease) and/or the range of available advanced options.

Drogas Repurposing Hipótese Geração Usando o &quot;RE: Drogas finos&quot; System

The promise of drug repurposing is that existing drugs may be used for new disease indications in order to curb the high costs and time for approval. The ...

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9.2K visualizações.  The Ohio State University. Here we describe a protocol using the web-based drug repurposing hypothesis generation tool: "RE:fine Drugs." This protocol can be modified to a user's preferences at the level of the query type (gene, drug or disease) and/or the range of available advanced options. 

Vídeo: Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Method for Efficient Refolding and Purification of Chemoreceptor Ligand Binding Domain

Identification of natural ligands of chemoreceptors and structural studies aimed at elucidation of the molecular basis of the ligand specificity can be greatly facilitated by the production of milligram amounts of pure, folded ligand binding domains. Attempts to heterologously express periplasmic ligand binding domains of bacterial chemoreceptors in Escherichia coli (E. coli) often result in their targeting into inclusion bodies. Here, a method is presented for protein recovery from inclusion bodies, its refolding and purification, using the periplasmic dCACHE ligand binding domain of Campylobacter jejuni (C. jejuni) chemoreceptor Tlp3 as an example. The approach involves expression of the protein of interest with a cleavable His6-tag, isolation and urea-mediated solubilisation of inclusion bodies, protein refolding by urea depletion, and purification by means of affinity chromatography, followed by tag removal and size-exclusion chromatography. The circular dichroism spectroscopy is used to confirm the folded state of the pure protein. It has been demonstrated that this protocol is generally useful for production of milligram amounts of dCACHE periplasmic ligand binding domains of other bacterial chemoreceptors in a soluble and crystallisable form.

A procedure is presented for the refolding of the dCACHE periplasmic ligand binding domain of Campylobacter jejuni chemoreceptor Tlp3 from inclusion bodies and the purification to yield milligram quantities of protein.

Método para dobrar eficiente e purificação do Chemoreceptor Ligand Binding Domain

Identification of natural ligands of chemoreceptors and structural studies aimed at elucidation of the molecular basis of the ligand specificity can be ...

Bioquímica

Facilitating Drug Discovery: An Automated High-content Inflammation Assay in Zebrafish

Zebrafish larvae are particularly amenable to whole animal small molecule screens1,2 due to their small size and relative ease of manipulation and observation, as well as the fact that compounds can simply be added to the bathing water and are readily absorbed when administered in a &lt;1% DMSO solution. Due to the optical clarity of zebrafish larvae and the availability of transgenic lines expressing fluorescent proteins in leukocytes, zebrafish offer the unique advantage of monitoring an acute inflammatory response in vivo. Consequently, utilizing the zebrafish for high-content small molecule screens aiming at the identification of immune-modulatory compounds with high throughput has been proposed3-6, suggesting inflammation induction scenarios e.g. localized nicks in fin tissue, laser damage directed to the yolk surface of embryos7 or tailfin amputation3,5,6. The major drawback of these methods however was the requirement of manual larva manipulation to induce wounding, thus preventing high-throughput screening. Introduction of the chemically induced inflammation (ChIn) assay8 eliminated these obstacles. Since wounding is inflicted chemically the number of embryos that can be treated simultaneously is virtually unlimited. Temporary treatment of zebrafish larvae with copper sulfate selectively induces cell death in hair cells of the lateral line system and results in rapid granulocyte recruitment to injured neuromasts. The inflammatory response can be followed in real-time by using compound transgenic cldnB::GFP/lysC::DsRED26,9 zebrafish larvae that express a green fluorescent protein in neuromast cells, as well as a red fluorescent protein labeling granulocytes.
In order to devise a screening strategy that would allow both high-content and high-throughput analyses we introduced robotic liquid handling and combined automated microscopy with a custom developed software script. This script enables automated quantification of the inflammatory response by scoring the percent area occupied by red fluorescent leukocytes within an empirically defined area surrounding injured green fluorescent neuromasts. Furthermore, we automated data processing, handling, visualization, and storage all based on custom developed MATLAB and Python scripts.
In brief, we introduce an automated HC/HT screen that allows testing of chemical compounds for their effect on initiation, progression or resolution of a granulocytic inflammatory response. This protocol serves a good starting point for more in-depth analyses of drug mechanisms and pathways involved in the orchestration of an innate immune response. In the future, it may help identifying intolerable toxic or off-target effects at earlier phases of drug discovery and thereby reduce procedural risks and costs for drug development.

Here we describe a novel high-content chemically induced inflammation assay aiming at the identification of immune-modulatory bioactives. We have successfully combined automated microscopy with custom developed software scripts enabling automated quantification of the inflammatory response as well as further data processing, analysis, mining, and storage.

Facilitar Drug Discovery: um ensaio automatizado de alta Inflamação conteúdo em Zebrafish

Zebrafish larvae are particularly amenable to whole animal small molecule screens1,2 due to their small size and relative ease of manipulation and ...

Imunologia e Infecção

Using Human Induced Pluripotent Stem Cell-derived Hepatocyte-like Cells for Drug Discovery

The ability to differentiate human induced pluripotent stem cells (iPSCs) into hepatocyte-like cells (HLCs) provides new opportunities to study inborn errors in hepatic metabolism. However, to provide a platform that supports the identification of small molecules that can potentially be used to treat liver disease, the procedure requires a culture format that is compatible with screening thousands of compounds. Here, we describe a protocol using completely defined culture conditions, which allow the reproducible differentiation of human iPSCs to hepatocyte-like cells in 96-well tissue culture plates. We also provide an example of using the platform to screen compounds for their ability to lower Apolipoprotein B (APOB) produced from iPSC-derived hepatocytes generated from a familial hypercholesterolemia patient. The availability of a platform that is compatible with drug discovery should allow researchers to identify novel therapeutics for diseases that affect the liver.

The protocol presented here describes a platform for identifying small molecules for the treatment of liver disease. A step-by-step description is presented detailing how to differentiate iPSCs into cells with hepatocyte characteristics in 96-well plates, and to use the cells to screen for small molecules with potential therapeutic activity.

Usar humanos induzida pluripotentes células-tronco derivadas hepatócitos células para descoberta de drogas

The ability to differentiate human induced pluripotent stem cells (iPSCs) into hepatocyte-like cells (HLCs) provides new opportunities to study inborn ...

Biologia do Desenvolvimento

NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode

Fragment-based screening (FBS) is a well-validated and accepted concept within the drug discovery process both in academia and industry. The greatest advantage of NMR-based fragment screening is its ability not only to detect binders over 7-8 orders of magnitude of affinity but also to monitor purity and chemical quality of the fragments and thus to produce high quality hits and minimal false positives or false negatives. A prerequisite within the FBS is to perform initial and periodic quality control of the fragment library, determining solubility and chemical integrity of the fragments in relevant buffers, and establishing multiple libraries to cover diverse scaffolds to accommodate various macromolecule target classes (proteins/RNA/DNA). Further, an extensive NMR-based screening protocol optimization with respect to sample quantities, speed of acquisition and analysis at the level of biological construct/fragment-space, in condition-space (buffer, additives, ions, pH, and temperature) and in ligand-space (ligand analogues, ligand concentration) is required. At least in academia, these screening efforts have so far been undertaken manually in a very limited fashion, leading to limited availability of screening infrastructure not only in the drug development process but also in the context of chemical probe development. In order to meet the requirements economically, advanced workflows are presented. They take advantage of the latest state-of-the-art advanced hardware, with which the liquid sample collection can be filled in a temperature-controlled fashion into the NMR-tubes in an automated manner. 1H/19F NMR ligand-based spectra are then collected at a given temperature. High-throughput sample changer (HT sample changer) can handle more than 500 samples in temperature-controlled blocks. This together with advanced software tools speeds up data acquisition and analysis. Further, application of screening routines on protein and RNA samples are described to make aware of the established protocols for a broad user base in biomacromolecular research.

Fragment-based screening by NMR is a robust method to rapidly identify small molecule binders to biomacromolecules (DNA, RNA, or proteins). Protocols describing automation-based sample preparation, NMR experiments &#38; acquisition conditions, and analysis workflows are presented. The technique allows for optimal exploitation of both 1H and 19F NMR-active nuclei for detection.

Triagem de fragmentos baseada em RMN em uma amostra mínima, mas no modo de automação máxima

Fragment-based screening (FBS) is a well-validated and accepted concept within the drug discovery process both in academia and industry. The greatest ...

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Thermal shift assays (TSAs) examine how the melting temperature (Tm) of a target protein changes in response to changes in its environment (e.g., buffer composition). The utility of TSA, and specifically of nano-Differential Scanning Fluorimetry (nano-DSF), has been established over the years, both for finding conditions that help stabilize a specific protein and for looking at ligand binding by monitoring changes in the apparent Tm. This paper presents an efficient screening of the Diamond-SGC-iNEXT Poised (DSi-Poised) fragment library (768 compounds) by the use of nano-DSF, monitoring Tm to identify potential fragment binding. The prerequisites regarding protein quality and concentration for performing nano-DSF experiments are briefly outlined followed by a step-by-step protocol that uses a nano-liter robotic dispenser commonly used in structural biology laboratories for preparing the required samples in 96-well plates. The protocol describes how the reagent mixtures are transferred to the capillaries needed for nano-DSF measurements. In addition, this paper provides protocols to measure thermal denaturation (monitoring intrinsic tryptophan fluorescence) and aggregation (monitoring light back-scattering) and the subsequent steps for data transfer and analysis. Finally, screening experiments with three different protein targets are discussed to illustrate the use of this procedure in the context of lead discovery campaigns. The overall principle of the method described can be easily transferred to other fragment libraries or adapted to other instruments.

Monitoring changes in the melting temperature of a target protein (i.e., thermal shift assay, TSA) is an efficient method for screening fragment libraries of a few hundred compounds. We present a TSA protocol implementing robotics-assisted nano-Differential Scanning Fluorimetry (nano-DSF) for monitoring intrinsic tryptophan fluorescence and light back-scattering for fragment screening.

Fluormetria de varredura nano-diferencial para triagem em descoberta de chumbo baseada em fragmentos

Thermal shift assays (TSAs) examine how the melting temperature (Tm) of a target protein changes in response to changes in its environment (e.g., buffer ...

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Although targeted therapies are initially effective, resistance inevitably emerges. Several methods, such as genetic analysis of resistant clinical specimens, have been applied to uncover these resistance mechanisms to facilitate follow-up care. Although these approaches have led to clinically relevant discoveries, difficulties in attaining the relevant patient material or in deconvoluting the genomic data collected from these specimens have severely hampered the path towards a cure. To this end, we here describe a tool for expeditious discovery that may guide improvement in first-line therapies and alternative clinical management strategies. By coupling preclinical in vitro or in vivo drug selection with next-generation sequencing, it is possible to identify genomic structural variations and/or gene expression alterations that may serve as functional drivers of resistance. This approach facilitates the spontaneous emergence of alterations, enhancing the probability that these mechanisms may be observed in the patients. In this protocol we provide guidelines to maximize the potential for uncovering single nucleotide variants that drive resistance using adherent lines.

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Drug resistance to targeted therapeutics is widespread and the need to identify mechanisms of resistance--prior to or following clinical onset--is critical for guiding alternative clinical management strategies. Here, we present a protocol to couple derivation of drug-resistant lines in vitro with sequencing to expedite discovery of these mechanisms.

Implementação de<em&gt; In Vitro</em&gt; Droga de Resistência Ensaios: maximizar o potencial para Descobrindo clinicamente relevantes mecanismos de resistência

Although targeted therapies are initially effective, resistance inevitably emerges. Several methods, such as genetic analysis of resistant clinical ...

Medicina

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Receptors and enzyme proteins are important biomolecules that act as binding targets for bioactive small molecules. Thus, the rapid and global validation of the drug-protein interactions is highly desirable for not only understanding the molecular mechanisms underlying therapeutic efficacy but also for assessing drug characteristics, such as adsorption, distribution, metabolism, excretion, and toxicity (ADMET) for clinical use. Here, we present a biosensor-based high throughput strategy for the biopanning of T7 phage-displayed short peptides that can be easily displayed on the phage capsid. Subsequent analysis of the amino acid sequences of peptides containing short segments, as &#34;broken relics&#34;, of the drug-binding sites using bioinformatics programs in receptor ligand contact (RELIC) suite, is also shown. By applying this method to two clinically approved drugs, an anti-tumor irinotecan, and an anti-flu oseltamivir, the detailed process for collecting the drug-recognizing peptide sequences and highlighting the drug-binding sites of the target proteins are explained in this paper. The strategy described herein can be applied for any small molecules of interest.

This study aimed to present a strategy for identifying drug-peptide interactions. The strategy involves the biopanning of drug-recognizing short peptides based on a quartz-crystal microbalance (QCM) biosensor, followed by bioinformatics analysis for quantitatively assessing the information obtained for the drug recognition and annotation of the drug-binding sites on proteins.

Estratégia de análise de biopanning e bioinformática baseada em biosensor para a validação global de interações medicamentosas-proteínas

Receptors and enzyme proteins are important biomolecules that act as binding targets for bioactive small molecules. Thus, the rapid and global validation ...

Bioengenharia

Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

REPLACE is a unique strategy developed to more effectively target protein-protein interactions (PPIs). It aims to expand available drug target space by providing improved methodology for the identification of inhibitors for such binding sites and which represent the majority of potential drug targets. The main goal of this paper is to provide a methodological overview of the use and application of the REPLACE strategy which involves computational and synthetic chemistry approaches. REPLACE is exemplified through its application to the development of non-ATP competitive cyclin dependent kinases (CDK) inhibitors as anti-tumor therapeutics. CDKs are frequently deregulated in cancer and hence are considered as important targets for drug development. Inhibition of CDK2/cyclin A in S phase has been reported to promote selective apoptosis of cancer cells in a p53 independent manner through the E2F1 pathway. Targeting the protein-protein interaction at the cyclin binding groove (CBG) is an approach which will allow the specific inhibition of cell cycle over transcriptional CDKs. The CBG is recognized by a consensus sequence derived from CDK substrates and tumor suppressor proteins termed the cyclin binding motif (CBM). The CBM has previously been optimized to an octapeptide from p21Waf (HAKRRIF) and then further truncated to a pentapeptide retaining sufficient activity (RRLIF). Peptides in general are not cell permeable, are metabolically unstable and therefore the REPLACE (REplacement with Partial Ligand Alternatives through Computational Enrichment) strategy has been applied in order to generate more drug-like inhibitors. The strategy begins with the design of Fragment ligated inhibitory peptides (FLIPs) that selectively inhibit cell cycle CDK/cyclin complexes. FLIPs were generated by iteratively replacing residues of HAKRRLIF/RRLIF with fragment like small molecules (capping groups), starting from the N-terminus (Ncaps), followed by replacement on the C-terminus. These compounds are starting points for the generation of non-ATP competitive CDK inhibitors as anti-tumor therapeutics.

We describe implementation of the REPLACE strategy for targeting protein-protein interactions. REPLACE is an iterative strategy involving synthetic and computational approaches for the conversion of optimized peptidic inhibitors into drug like molecules.

Desenvolvimento de inibidores de interacções proteína-proteína através REPLACE: Aplicação para a concepção e desenvolvimento não-ATP inibidores de CDK Competitiva

REPLACE is a unique strategy developed to more effectively target protein-protein interactions (PPIs). It aims to expand available drug target space by ...

Biologia

Chemogenetic Regulation in Reprogrammed Stem Cell-derived Precursor Cells in Treating Neurodegenerative Diseases

The integration of designer receptors exclusively activated by designer drugs (DREADD) with stem cell-based therapies presents an advanced strategy for precise neuronal modulation. Here, we utilized CRISPR-engineered human reprogrammed stem cells expressing excitatory (hM3Dq) or inhibitory (hM4Di) DREADD receptors to evaluate the functional integration and modulation of transplanted dopaminergic precursors in a murine model of Parkinson&#39;s disease (PD). Key steps included generating non-fusion DREADD-expressing stem cell lines, differentiating them into midbrain dopaminergic precursors, and transplanting these cells into the striatum of 6-hydroxydopamine (6-OHDA)-lesioned mice. We conducted behavioral assessments and electrophysiological recordings to analyze the effects of the transplanted cells. Behavioral tests, such as the cylinder test, demonstrated significant modulation of motor function following clozapine-N-oxide (CNO) administration. Specifically, activation of hM4Di reduced contralateral forelimb movement, whereas activation of hM3Dq was associated with enhanced motor behavior. Electrophysiological recordings revealed distinct synaptic responses. hM4Di activation increased interevent intervals and decreased peak amplitudes of spontaneous excitatory postsynaptic currents (sEPSCs), whereas hM3Dq activation decreased interevent intervals and increased peak amplitudes, reflecting enhanced excitatory signaling. In summary, the integration of behavioral and electrophysiological assessments validates the precise functional incorporation of engineered chemically reprogrammed stem cells into host neural circuits.

Here, we describe a protocol for engineering chemically reprogrammed stem cells to achieve precise neuronal modulation by differentiating these cells into dopaminergic precursor cells, transplanting them into mouse models of Parkinson's disease, and evaluating behavioral and electrophysiological outcomes to confirm the successful integration and functional effectiveness of the transplanted cells.

Regulação quimiogenética em células-tronco precursoras derivadas de células-tronco reprogramadas no tratamento de doenças neurodegenerativas

The integration of designer receptors exclusively activated by designer drugs (DREADD) with stem cell-based therapies presents an advanced strategy for ...

Neurociência

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and the pharmaceutical industry.
The process begins with drug screening, which involves various assays to evaluate the pharmacological profile and activity of the drug at different levels, such as molecular, cellular, organ system, and whole animal levels. The candidate molecule undergoes extensive testing to determine its mechanism of action, selectivity, and potential toxic effects. Animal models of human diseases are used to select compounds for further development, especially in cases where preclinical models are well-established.
The candidate compound is screened for its activity on the target protein at the molecular level using techniques like receptor binding assays. Cell function studies determine whether the drug acts as an agonist, partial agonist, inverse agonist, or antagonist at relevant receptors. Whole animal studies assess the drug&#39;s effects on organ systems and disease models.
Lead compounds with the desired pharmacological activity and selectivity are identified during the screening and lead generation processes. These lead compounds serve as the basis for further chemical modification and optimization in the lead optimization phase. Lead optimization involves a range of tests and assays to increase the potency of the compound, optimize selectivity, and evaluate other characteristics, such as pharmacokinetic properties. Animal studies assess the compound&#39;s activity in vivo, potential adverse effects, genotoxicity, and oral availability.
Only a small percentage of drug discovery projects succeed in generating a drug candidate, and the lead optimization phase can be time-consuming and challenging. Many factors can hinder progress, including the inability to optimize lead compounds or the failure to produce desired effects in animal models. Successful drug candidates proceed to the next phase, preclinical development, before entering human clinical trials.

Descoberta de Medicamentos: Visão Geral

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for ...

Drogas Repurposing Hipótese Geração Usando o "RE: Drogas finos" System

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