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. Initiation of Queries from Gene, Drug or Disease Terms
<ol>
	<li>Access the homepage for &#34;RE:fine Drugs&#34; at the following link: http://drug-repurposing.nationwidechildrens.org. Begin by entering a query term in the search bar from any of the following three categories: drug (generic drug name), disease (new disease indication) or gene (official HGNC gene symbol).</li>
	<li>Filter the search bar function to include only &#34;Drug Name&#34;, &#34;New Disease Indication&#34;, &#34;Gene Symbol&#34; or &#34;All&#34; categories. The search bar includes an auto-fill function for query entries.</li>
	<li>Put in a keyword, and click on the &#34;Search&#34; button. Sort the table of results by any of the following columns: &#34;Drug&#34;, &#34;Registered Indication&#34;, &#34;P-Value&#34;, &#34;P-Value Adjusted&#34;, &#34;Odds Ratio&#34;, &#34;Study&#34;, &#34;New Indication&#34;, &#34;Drug Bank Indication&#34;, &#34;# of Medline Abstracts&#34;, &#34;# of Clinical Trial Registry&#34;, &#34;Potential&#34;, &#34;SNP&#34;, &#34;Gene&#34; or &#34;action&#34;.</li>
	<li>Navigate to the advanced search option in order to enable the drug information feature. Click on the icon in the &#34;Info&#34; column for a particular drug. Observe a page that lists the all the corresponding information including p-Value for the association, Disease name, Drug name, Gene details (NCBI Gene link) and Drug details (DrugBank link).</li>
</ol>
2. Exploration of Advanced Options
<ol>
	<li>Click on the &#34;advanced&#34; button located on the right side of the page, and several options to further refine the results are provided. The advanced search options include modifications to the following: drug, association, disease, potential, gene and action.</li>
	<li>Export results tables by clicking on the &#34;Export&#34; button on the right side of the page. Click on the &#34;Simple&#34; button in order to fold down the advanced search window.</li>
	<li>Under the advanced option &#34;drug&#34; tab, specify a particular drug indication or an additional drug name to filter results.</li>
	<li>Under the &#34;association&#34; tab, filter results by the significance level P-Value, Adjusted P-Value with FDR, effect size (Odds Ratio), and/or Study type (GWAS, PheWAS or Both).</li>
	<li>Under the &#34;disease&#34; tab, specify a certain disease description for the predicted new use.</li>
	<li>Under the &#34;potential&#34; tab, filter results according to any of the following criteria: (i) whether the drug indication is contained in the DrugBank database, (ii) number of Medline abstracts with co-occurrence of the drug and the disease, (iii) number of ClinicalTrials.gov database entries with co-occurrence of the drug and the disease and (iv) Repurposing Potential. 
	NOTE: The Repurposing Potential option describes the novelty of the discovery: (i) Known: relationship already exists in the DrugBank database, (ii) Strongly supported: some support in both clinical trial registry and the Medline abstracts, (iii) Likely: some support in either clinical trial registry or the Medline abstracts and (iv) Novel: no evidence in clinical trial registry nor in Medline abstracts.</li>
	<li>Under the &#34;gene&#34; tab, enter a SNP identifier or Gene Symbol to filter results by specific drug target genes.</li>
	<li>Under the &#34;action&#34; tab, specify the drug action type against the drug target(s), including agonist, antagonist, other, unknown or all (source: DrugBank database).</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.

The costly and inefficient processes associated with traditional drug discovery approaches, including high-throughput drug and lead compound screening, are contributing to delays in translating research discoveries into therapies for patients 1,2. An average of 1 billion U.S. dollars and 15-20 years is required to bring a new drug from the bench to the bedside 3. Further, 52% of drugs fail during development in phase 1 clinical trials, and only 25% of compounds that enter phase 2 proceed into full phase 3 clinical studies 4. The goal of drug repurposing or drug repositioning is to renew failed drugs and/or find novel indications for approved drugs in order to deliver new therapies to patients faster and with a higher success rate. Drug repurposing may decrease the timeline to make drugs available for use in patients to 3-12 years 5. Important medical applications for drug repurposing include: diseases with poor prognosis and low survival rates, drug-resistant diseases, underfunded disease research areas and impoverished and underserved patient populations.
Computational drug repurposing is defined as the process of designing and validating automated workflows that can generate hypotheses for new indications for a drug candidate 6. Existing computational drug repurposing methods have been categorized target-based, knowledge-based, signature-based, network-based, and targeted-mechanism-based, and can be oriented from gene, disease or drug perspectives. Furthermore, computational approaches may further accelerate proof-of-concept validation experiments and small-scale clinical studies for repurposed drug candidates 7. We have previously reported on &#34;RE:fine Drugs&#34;, a freely available, interactive web-based tool for drug repurposing hypothesis generation based on the transitive theory of Drug-Gene-Disease relationships 8. The overall goal of this method is to systematically integrate diverse types of drug, genetic and clinical data to enable drug repurposing for users from diverse communities, including clinical, industry and regulatory communities. The foundational methods for this system have been previously reported for the use of genome-wide association study (GWAS) and phenome-wide association study (PheWAS) data in drug repurposing research 9,10. The novel combination of these types of data distinguishes our webtool from other target-based methods 6,11.
The RE:fine Drugs system currently contains 60,911 drug repurposing hypotheses covering 916 drugs, 567 genes and 1,770 diseases. The webtool provides a user-friendly interface for researchers to interactively search drug repurposing hypotheses and prioritize them using diverse criteria. For instance, users can filter drug repurposing hypotheses with support in the biomedical literature and clinical trials database, significant p-values, association odds ratios or by specific indications. The only requirement for this system is Internet access.

<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.

In this example, the gene &#34;IL2RB&#34; was entered as a gene-based query, and was automatically recognized as such by the auto-fill function (Figure 1). The twelve drug repurposing hypotheses for the IL2RB gene are returned, as shown in Figure 2. Detailed information page for a particular drug repurposing hypothesis, &#34;daclizumab&#34; in this case, is provided from the &#34;Info&#34; column (Figure 3). The results were filtered on the drug tab was by all results corresponding to the &#34;daclizumab&#34; drug, as shown in Figure 4. Figure 5 shows only those drugs with a known indication for the &#34;transplant&#34; disease term (source: DrugBank database). The association tab allows the user to filter SNP-Phenotype relationships by statistical significance (P-value) and genetic effect size (odds ratio), defined as the ratio of the odds of presence of disease in individuals with a specific genotype (SNP allele) over the odds of presence of disease in individuals without the SNP allele. Figure 6 shows the results filtered under the association tab within the P-Value range of 0.000001 to 0.05. Figure 7 shows drug repurposing hypotheses specific for &#34;asthma&#34; based on the new indications we found in the study. Figure 8 shows results under the potential tab to filter by a minimum number of 5 Medline abstracts containing a co-occurrence of drug and disease terms. In this example, all drugs results under the gene tab are targeted for the &#34;IL2RB&#34; gene, corresponding to the original query term (Figure 9). Finally, Figure 10 shows results filtered under the &#34;action&#34; tab to return all drugs that act as agonists on the IL2RB gene.
<img alt="Figure 1" src="/files/ftp_upload/54948/54948fig1.jpg" /> 
Figure 1: The RE:fine Drugs interactive dashboard homepage. Users may begin a query by entering a drug name, new disease indication or gene symbol. Links are also provided for the GWAS and PheWAS reference papers describing methodologies for generating drug repurposing hypotheses. <a href="http://cloudflare.jove.com/files/ftp_upload/54948/54948fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/54948/54948fig2.jpg" /> 
Figure 2: Auto-fill function for query entries. As an example, the gene query term &#34;IL2RB&#34; was automatically recognized as a gene term. <a href="http://cloudflare.jove.com/files/ftp_upload/54948/54948fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/54948/54948fig3.jpg" /> 
Figure 3: Drug repurposing results table produced from a gene-based query (e.g., IL2RB). Twelve drug repurposing hypotheses for the IL2RB gene are produced. <a href="http://cloudflare.jove.com/files/ftp_upload/54948/54948fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/54948/54948fig4.jpg" /> 
Figure 4: Information page for individual drugs from results page. Clicking on the icon from the &#34;Info&#34; column shows detailed information for the drug daclizumab. <a href="http://cloudflare.jove.com/files/ftp_upload/54948/54948fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/54948/54948fig5.jpg" /> 
Figure 5: Advanced search option under drug tab to filter by a specific drug. In this example, three results are shown for the drug daclizumab. <a href="http://cloudflare.jove.com/files/ftp_upload/54948/54948fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" src="/files/ftp_upload/54948/54948fig6.jpg" /> 
Figure 6: Advanced search option under drug tab to filter by a specific disease indication from the DrugBank database. All drugs with a known indication for the disease term &#34;transplant&#34; are shown. <a href="http://cloudflare.jove.com/files/ftp_upload/54948/54948fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" src="/files/ftp_upload/54948/54948fig7.jpg" /> 
Figure 7: Advanced search option under association tab to filter by significance level. In this case, eight results are provided whose association significance level falls within the P-Value range of 0.000001 to 0.05. <a href="http://cloudflare.jove.com/files/ftp_upload/54948/54948fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" src="/files/ftp_upload/54948/54948fig8.jpg" /> 
Figure 8: Advanced search option under disease tab to filter by a specific disease indication we extracted in this study. In this example, four results are shown for asthma as a new use disease indication. <a href="http://cloudflare.jove.com/files/ftp_upload/54948/54948fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" src="/files/ftp_upload/54948/54948fig9.jpg" /> 
Figure 9: Advanced search option under potential tab to filter by a co-occurrence of drug and disease terms in Medline abstracts. In this example, four results are shown that are supported by a minimum number of 5 Medline abstracts containing a co-occurrence of drug and disease terms. <a href="http://cloudflare.jove.com/files/ftp_upload/54948/54948fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 10" src="/files/ftp_upload/54948/54948fig10.jpg" /> 
Figure 10: Advanced search option under gene tab to filter by a specific gene symbol, where all results correspond to the IL2RB gene used for the original query. In this example, all drugs results under the gene tab are targeted for the &#34;IL2RB&#34; gene, corresponding to the original query term. Advanced search option under action tab to filter by agonist drugs only. In this example, six results are returned for all drugs that act as agonists on the IL2RB gene. <a href="http://cloudflare.jove.com/files/ftp_upload/54948/54948fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</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.

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 ...

drug-repurposing-hypothesis-generation-using-the-refine-drugs-system

Universidad de Cartagena UDEC

Research

JoVE Journal

Medicine

9.2K Views.  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. 

Video: 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.

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

Biochemistry

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.

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

Immunology and Infection

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.

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

Developmental Biology

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.

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.

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.

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

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.

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

Bioengineering

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.

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

Biology

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.

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

Neuroscience

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.

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

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

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

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

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

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

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

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

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

Odkrywanie leków: Przegląd