#PPI Targeted Drug Discovery

[mysticalpeacenut]

Advancements in PPI Targeted Drug Discovery

In the ever-evolving international of drug discovery, concentrated on protein-protein interactions (PPIs) has emerged as a groundbreaking method with massive healing capacity. While traditional drug discovery has largely focused on focused on single proteins or enzymes, the point of interest is shifting toward the intricate networks fashioned by using protein interactions. These networks are critical to expertise disorder mechanisms and growing innovative treatments. This blog delves into the modern day advancements in PPI targeted drug discovery, highlighting the transformative impact of Protein-Protein Interactions Magna™ technology and different modern tactics.

Understanding Protein-Protein Interactions

Protein-protein interactions are essential to almost each biological manner. Proteins not often function in isolation; as a substitute, they interact with different proteins to carry out their functions. These interactions can manipulate cell signaling pathways, adjust gene expression, and influence cell responses to environmental changes. Disruptions or malfunctions in those interactions are frequently implicated in diseases which include cancer, neurodegenerative problems, and infectious diseases.

PPI Targeted Drug Discovery makes a speciality of designing drugs which could especially modulate these protein interactions. Unlike conventional capsules that focus on a single protein, PPI-centered pills purpose to persuade the interactions among multiple proteins, thereby imparting a greater nuanced approach to therapeutic intervention.

Key Advancements in PPI Targeted Drug Discovery

Protein-Protein Interactions Magna™ Technology

One of the maximum giant advancements in PPI-focused drug discovery is the development of Protein-Protein Interactions Magna™ technology via Depixus. This modern day era gives a complete platform for analyzing and manipulating protein interactions with remarkable precision.

Magna™ generation uses advanced techniques to seize and examine how proteins engage within a cellular surroundings. By offering special insights into these interactions, Magna™ permits researchers to become aware of ability drug goals and design molecules that may in particular disrupt or decorate those interactions. This method is vital for growing healing procedures that focus on complicated sicknesses wherein conventional drug discovery methods may additionally fall brief.

High-Throughput Screening

High-throughput screening (HTS) has revolutionized PPI-centered drug discovery via permitting the rapid evaluation of lots of compounds for their capability to modulate protein interactions. HTS structures use automated structures to test big libraries of molecules against particular PPIs, identifying promising applicants for further development.

Recent improvements in HTS technology have expanded the speed and accuracy of screening processes. Innovations consisting of miniaturized assays, stepped forward detection techniques, and sophisticated statistics analysis gear have substantially better the efficiency of PPI-targeted drug discovery.

Structural Biology Techniques

Structural biology strategies, together with X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and cryo-electron microscopy, have supplied vital insights into the three-dimensional structures of protein complexes. Understanding the ideal structure of protein-protein interactions is essential for designing drugs which could in particular goal those interactions.

Recent traits in structural biology have enabled researchers to visualize protein interactions at atomic resolution, facilitating the design of small molecules and biologics which could efficiently disrupt or stabilize those interactions. This structural records is invaluable for growing centered treatment options with high specificity and decreased off-goal outcomes.

Computational Approaches

Computational methods, which includes molecular docking and molecular dynamics simulations, have end up necessary gear in PPI-targeted drug discovery. These techniques use pc algorithms to predict how small molecules or peptides will interact with protein complexes, guiding the layout of recent drugs.

Advancements in computational techniques have improved the accuracy of predictions and the efficiency of digital screening techniques. By simulating protein interactions and drug binding, researchers can pick out ability drug applicants and optimize their homes earlier than conducting experimental research.

Biologics and Peptide-Based Therapies

Biologics, along with monoclonal antibodies and peptide-based totally remedies, constitute a growing vicinity of hobby in PPI-focused drug discovery. These cures are designed to specially bind to protein interactions and modulate their activity.

Recent advancements in biologics and peptide design have caused the development of novel drugs that concentrate on PPIs with excessive specificity. For instance, bispecific antibodies which could simultaneously bind to two exclusive proteins are being explored as potential cures for numerous sicknesses.

The Future of PPI Targeted Drug Discovery

The subject of PPI focused drug discovery is swiftly advancing, with ongoing research aimed toward overcoming current challenges and increasing the range of druggable objectives. The integration of new technology, which includes Protein-Protein Interactions Magna™, structural biology, excessive-throughput screening, and computational strategies, is riding innovation and opening up new possibilities for therapeutic intervention.

As researchers hold to explore the complexities of protein interactions, the capability for growing novel treatments that concentrate on previously undruggable sicknesses grows. The capability to especially modulate protein-protein interactions offers a brand new stage of precision in drug development, paving the way for extra effective and focused remedies.

Conclusion

PPI focused drug discovery represents a paradigm shift in how we technique drug development, shifting beyond traditional single-protein objectives to focus on the tricky networks of protein interactions. With advancements which include Protein-Protein Interactions Magna™ era and other present day tactics, the sector is poised for great breakthroughs so one can remodel the landscape of medicine. At Depixus, we're at the leading edge of this thrilling field, providing innovative solutions and technologies that aid researchers of their quest to broaden the next generation of centered healing procedures. To study extra approximately how our Protein-Protein Interactions Magna™ generation can decorate your research and pressure innovation contact us at Depixus.

Reposted Blog Post URL: https://petrickzagblogger.wordpress.com/2024/09/04/advancements-in-ppi-targeted-drug-discovery/

[tumimmtxpapers]

Discovery of the Anti-Tumor Mechanism of Calycosin Against Colorectal Cancer by Using System Pharmacology Approach.

Related Articles Discovery of the Anti-Tumor Mechanism of Calycosin Against Colorectal Cancer by Using System Pharmacology Approach. Med Sci Monit. 2019 Jul 28;25:5589-5593 Authors: Huang C, Li R, Shi W, Huang Z Abstract BACKGROUND The aim of our study was to elucidate the biological targets and pharmacological mechanisms for calycosin (CC) against colorectal cancer (CRC) through an approach of system pharmacology. MATERIAL AND METHODS Using a web-based platform, all CRC-causing genes were identified using a database of gene-disease associations (DisGeNET), and all well-known genes of CC identified using the databases of prediction of protein targets of small molecules (Swiss Target Prediction), drug classification, and target prediction (SuperPred). The carefully selected genes of CRC and CC were concurrently constructed by using a database of functional protein association networks (STRING), and use of software for visualizing complex networks (Cytoscape), characterized with production of protein-protein interaction (PPI) network of CC against CRC. The important biological targets of CC against CRC were identified through topological analysis, then the biological processes and molecular pathways of CC against CRC were further revealed for testing these important biotargets by enrichment assays. RESULTS We found that the key predictive targets of CC against CRC were estrogen receptor 2 (ESR2), ATP-binding cassette sub-family G member 2 (ABCG2), breast cancer type 1 susceptibility protein (BRCA1), estrogen receptor 1 (ESR1), cytochrome p450 19A1 (CYP19A1), and epidermal growth factor receptor (EGFR). Visual analysis revealed that the biological processes of CC against CRC were positively linked to hormonal metabolism, regulation of genes, transport, cell communication, and signal transduction. Further, the interrelated molecular pathways were chiefly related to endogenous nuclear estrogen receptor alpha network, forkhead box protein A1 (FOXA1) transcription factor network, activating transcription factor 2 (ATF2) transcription factor network, regulation of telomerase, plasma membrane estrogen receptor signaling, estrogen biosynthesis, androgen receptor, FOXA transcription factor networks, estrogen biosynthesis, and phosphorylation of repair proteins. CONCLUSIONS Use of system pharmacology revealed the biotargets, biological processes, and pharmacological pathways of CC against CRC. Intriguingly, the identifiable predictive biomolecules are likely potential targets for effectively treating CRC. PMID: 31352466 [PubMed - in process] http://dlvr.it/R9HFnD

[cancersfakianakis1]

Autophagy And Apoptosis Specifc Knowledgebases-Guided Systems Pharmacology Drug Research.

Related Articles

Autophagy And Apoptosis Specifc Knowledgebases-Guided Systems Pharmacology Drug Research.

Curr Cancer Drug Targets. 2019 Feb 06;:

Authors: Fan P, Wang N, Wang L, Xie XQ

Abstract BACKGROUNDS: Autophagy and apoptosis are the basic physiological processes in cells that clean up aged and mutant cellular components or even the entire cells. Both autophagy and apoptosis are disrupted in most major diseases such as cancer and neurological disorders. Recently, increasing attention is paid to understand the crosstalk between autophagy and apoptosis due to their tightly synergetic or opposite functions in several pathological processes. OBJECTIVE: This study aims to assist autophagy and apoptosis-related drug research, clarify the intense and complicated connections between two processes, and provide a guide for novel drug development. METHOD: We established two chemical-genomic databases which are specifically designed for autophagy and apoptosis, including autophagy- and apoptosis-related proteins, pathways and compounds. We then performed network analysis on the apoptosis- and autophagy-related proteins and investigated the full protein-protein interaction (PPI) network of these two closely connected processes for the first time. RESULTS: The overlapping targets we discovered show a more intense connection with each other than other targets in the full network, indicating a better efficacy potential for drug modulation. We also found that Death-associated protein kinase 1 (DAPK1) is a critical point linking autophagy- and apoptosis-related pathways beyond the overlapping part, and this finding may reveal some delicate signaling mechanism of the process. Finally, we demonstrated how to utilize our integrated computational chemogenomics tools on in silico target identification for small molecules capable of modulating autophagy- and apoptosis-related pathways. CONCLUSION: The knowledgebases for apoptosis and autophagy and the integrated tools will accelerate our work in autophagy and apoptosis-related research and can be useful sources for information searching, target prediction, and new chemical discovery.

PMID: 30727895 [PubMed - as supplied by publisher]

http://bit.ly/2GgAjJh

[lindamcsherry]

Better Heartburn Drug Prescribing Could Reduce Incidents Of C. Difficile Infections: Study

Reducing the unnecessary use of popular heartburn drugs like Nexium, Prilosec, Prevacid and other proton pump inhibitors (PPI) may lower the number of Clostridium difficile infections (CDIs), according to a new study. 

Researchers from Washington State University and Duke University found that reductions in the prescribing of the widely used acid reflux medications significantly cuts down the number of CDI cases, according to a study published last week in the medical journal Open Forum Infectious Diseases.

The study notes that antibiotic stewardship programs (ASPs) have succeeded in reducing CDI rates. by lowering the unnecessary use of antibiotics, particularly when fluoroquinolones, like Levaquin, Cipro and Avelox, were reduced. They used a mathematical model of C. difficile transmission in an intensive care unit, simulated out to five years and factored in both PPI and antibiotic stewardship effects.

According to the modeling, reducing the use of Nexium, Prilosec and similar heartburn drugs could reduce CDI cases by 9.1 percent. Researchers also predict that patients using fewer proton pump inhibitors will result in a decreased length of stay in the ICU.

“PPI stewardship might prove a valuable adjunct to existing antibiotic stewardship programs,” the researchers concluded. “The reductions in C. difficile transmission were more modest for PPI stewardship as compared with programs targeting fluoroquinolones. PPI stewardship, however, may reach different patient populations, and may represent an additional area for substantial improvement even in facilities that have made substantial gains in reducing fluoroquinolone use.”

PPIs Linked To Increased C. Diff Risks

The findings come after a number of other studies have linked the use of drugs like Nexium and Prilosec with an increased risk of C. diff infections.

Proton pump inhibitors (PPI) includes the most widely used drugs on the market in the United States, working by reducing the amount of stomach acid produced, helping prevent symptoms of heartburn and acid reflux. While the drugs are widely believed to be safe, and often used for years by consumers, a number of studies have raised concerns about the potential risks associated with heartburn drugs in recent years, including both a risk of infections, as well as kidney problems.

A study published in January 2017 indicated that there is nearly three times the risk of a C. diff infection or a Campylobacter infection among heartburn drug users.

In November 2014, a report published in the medical journal Microbiome indicated that long-term use of Nexium and similar drugs could reduce the microbial diversity in the body, lowering its ability to fight off c. diff infections.

C. Diff infections can lead to Clostridium difficile-associated diarrhea (CDAD), causing persistent diarrhea, water stool, abdominal pain and fever. It can eventually lead to more severe intestinal problems if not treated in a timely manner.

The FDA first warned of the link between drugs like Nexium and Prilosec and C. diff infections in a drug safety communication issued in February 2012, advising doctors to be aware of the potential connection when presented with patients taking heartburn medications whose diarrhea does not improve.

While most individuals assume that the drugs are safe, concerns about a number of serious side effects have emerged in recent years, leading to Nexium lawsuits, Prilosec lawsuits, Prevacid lawsuits and similar claims against the drug makers for failing to provide adequate warnings.

More than 4,500 claims involving individuals who developed kidney problems from the heartburn drugs are currently pending in the federal court system, where they are centralized before  U.S. District Judge Claire C. Cecchi in New Jersey, for coordinating the discovery and pretrial proceedings.

As part of the coordinated proceedings, Judge Cecchi has established a “bellwether” programwhere a group of representative cases filed over failure to warn about the potential kidney side effects will be prepared for early trial dates, which are expected to begin in late 2020.

The post Better Heartburn Drug Prescribing Could Reduce Incidents Of C. Difficile Infections: Study appeared first on AboutLawsuits.com.

[naivelocus]

The relative resistance of children to sepsis mortality: from pathways to drug candidates

Introduction

Sepsis is a major cause of global morbidity and mortality for which there remains no targeted therapy (Opal, 2014; Seymour & Rosengart, 2015; Weiss et al, 2015). Central to sepsis pathophysiology is a dysregulated host inflammatory response (Aziz et al, 2013; Wiersinga et al, 2014; Singer et al, 2016), suggesting that host‐directed immunomodulators could be of therapeutic benefit (Delano & Ward, 2016). There is little agreement or certainty about which particular cells or molecules are critical to defining sepsis outcomes (Marshall, 2014). As a result, transcriptome analyses and systems biology approaches have been eagerly embraced as better ways to identify drug targets for sepsis (Maslove & Wong, 2014; Sweeney et al, 2015; Wong et al, 2015; Davenport et al, 2016). Systematic computational analysis represents an exciting class of approaches for prediction and discovery of novel targets and therapeutic indications (Dubus et al, 2009; Dudley et al, 2011; Hurle et al, 2013) reflecting their ability to provide virtual access to large numbers of compounds and data relating to the target disease (Kim, 2015).

However, the hope that “omics‐based approaches” might guide the selection of promising therapeutics to target sepsis has not yet been realized. This is despite the fact that tools like the Connectivity Map (CMap) and Library of Network‐Based Cellular Signatures (LINCS; Lamb et al, 2006; Prathipati & Mizuguchi, 2015), which use gene expression signatures to identify drug candidates, have been available for over a decade. Obstacles to progress in developing interventions for sepsis include discordant results across human studies focused on gene‐level changes (Sweeney & Khatri, 2016), as well as the strongly debated limitations of animal models of sepsis for these types of analyses (Seok et al, 2013; Osuchowski et al, 2014). Here, we address these problems by using available data on human transcriptomes together with a powerful new approach that combines pathway‐level analysis of human transcriptome samples with subsequent in vivo verification of findings in an animal model. We postulate that this “human‐data‐first” approach can improve results compared to prior efforts that began with findings in animal models. Our pathway‐level analysis exploits a natural phenomenon in humans to directly compare two groups with widely disparate rates of survival from sepsis—children and adults. Using novel pathway‐centered bioinformatic tools to optimize data analysis across multiple platforms, we were able to identify key differences in the responses of both age groups to sepsis as well as identify potential therapeutics.

The comparison of data from septic children and adults arose from a striking finding, which at first glance seems unrelated to the problem of sepsis. Despite similar rates of infection during the 1918 influenza pandemic, children aged 5–14 experienced a remarkably lower rate of mortality compared to adults, dubbed the “honeymoon period” (Ahmed et al, 2007). Puberty (~ age 14 in the early 1900s) marked the age range in which mortality increased, suggesting that sex hormones could influence changes in fatality rates. Importantly, the “honeymoon period” is not limited to 1918 influenza‐related resistance to mortality. Historical mortality rates are much lower in children after various high‐fatality challenges, spanning from bubonic plague to measles. Contemporary data for trauma, the recent Ebola outbreaks, and other severe infections (Table 1) confirm the resistance. In particular, these data include lower case fatality rates for children with sepsis, both when linked to specific pathogens (e.g., candidemia, Group A streptococcal sepsis, staphylococcal sepsis), and when analyzed as a broad diagnostic category (Table 1). We postulated that the better outcomes in children reflect age‐based differences in immune and inflammatory responses, possibly magnified by effects of more frequent co‐morbidities in adults.

Table 1. Epidemiological examples of childhood resistance to infectious and non‐infectious injury

To better understand the basis for this childhood resistance, we began by identifying public datasets of transcriptome profiling performed on blood leukocyte samples in the high vs. low survival groups (children and adults, respectively). The analysis used Pathprint (Altschuler et al, 2013; Davis & Ragan, 2013; https://bioconductor.org/packages/pathprint), a tool that is robust to batch effects and allows for comparison of gene expression at the pathway activity level across multiple array platforms. After identifying differences in pathway activity, we applied a novel method that is built upon the correlation of the expression of > 16,000 disease signatures from the Comparative Toxicogenomics Database (CTD), the Pharmacogenomics Knowledgebase (PharmGKB), pathway signatures from Wikipathways, KEGG, Netpath and Reactome, and drug signatures from CTD, PharmGKB, and CMap, across > 50,000 individual microarrays—the pathway drug network (PDN). The network neighborhood of the sepsis pathway signatures was used to identify the drugs that were most positively or negatively linked to high‐survival (child) or high‐mortality (adult) signatures. We assessed the validity of the top drug leads by analyzing prior data collected in preclinical animal models of sepsis and also by direct testing for improved survival in a mouse model of fatal endotoxemic shock.

Results

Key pathways differentiate the adult and child responses to sepsis

A total of 12 datasets reporting transcriptome profiling of whole blood samples from sepsis patients were identified for analysis from The Gene Expression Omnibus (GEO) and ArrayExpress databases (Barrett et al, 2013; Kolesnikov et al, 2015). The ultimate study population included 167 adults and 95 children, composed of 55 and 64% males, and mean ages of 59 and 8, respectively (Table 2). The Pathprint analysis tool was used to compare activity of pathways in adults and children with sepsis. Substantial differences in active or depressed pathways were identified, as illustrated in Fig 1. After applying thresholds based on the greatest age‐associated differences, the four pathway clusters (A–D), detailed in Table 3, were used for further analysis. Tables EV1–EV3 provide additional details of Pathprint scoring and the results for all significantly different pathways.

Table 2. Demographic information on datasets used for data‐mining

Figure 1. Sample heatmap generated from adult vs. child comparison using Pathprint

Pathprint analysis was used to analyze adult and child transcriptomes at the pathway level. To minimize intra‐group variation and maximize inter‐group variation, two filtering criteria were set in the generation of these data: (i) to maximize homogeneity within an age group based on minimizing the standard deviation, a cutoff of SD < 0.475 in the Pathprint score was used; (ii) to maximize differences between group comparisons using t‐tests, Pathprint scores between groups were only included if P < 10−10. The heatmap above was generated using the pheatmap package.Source data are available online for this figure.

Table 3. Pathprint clusters chosen for drug candidate analysis using PDN

PDN base network: construction and benchmarking

The PDN methodology is a novel, pathway‐centric drug discovery approach that tests whether an experimental gene signature is positively or negatively correlated to a gene signature associated with drug treatment. It relies on a base network constructed using the expression correlations between each of 16,150 drug, disease, and pathway gene signatures (collected from eight different databases), averaged across 58,475 publicly available human microarrays. By measuring the correlation between pathway, drug, and disease gene signatures over more than fifty thousand experiments, one can hypothesize whether the action that regulates, or is regulated by two signatures (e.g., a drug and a survival associated phenotype), may be linked and/or have similar actions (or opposing actions in the case of negative correlation). Since no comprehensive gold standard exists for evaluating the relationships between drug and disease signatures, to test the efficacy of the new PDN approach it was necessary to construct our own benchmark. Our benchmarking protocol involved the comparison of curated, known drug–disease relationships from the National Drug File Reference Terminology (NDFRT) and Structured Product Labels (SPL) databases (1,055 in total), with the drug–disease relationships produced by the PDN methodology. Beyond our goal of replicating the NDFRT and SPL drug–disease relationships using the PDN, we also compared our methodology with an alternative approach, Network Enrichment Analysis (NEA), a method based on gene‐level curated protein–protein interactions (PPI; Alexeyenko et al, 2012). While both the PDN and PPI network approaches performed better than randomly assigning drug–disease relationships, the PDN decisively outperformed the PPI network at low false discovery rates (Fig EV1). Based on this benchmarking exercise, true‐positive rates (TPRs) and false‐positive rates (FPRs) were measured for the PDN and used to create a series of network cutoffs (the probability at which an edge is defined as true). From these analyses, a PDN cutoff parameter was chosen for the final base network that yielded as high as possible TPR (40%) while still keeping the FPR low (6%).

Figure EV1. Benchmarking: PDN and PPI Sensitivity vs. Specificity

To provide a benchmark for new PDN methodology, we compared drug–disease relationships produced using PDNs with curated, known drug–disease relationships from the NDFRT and SPL databases. The true‐positive (TP) and false‐positive (FP) rates (Sensitivity and 1‐Specificity) of the PDN predictions were compared to those generated using an alternative approach based on gene‐level curated protein–protein interactions (PPI). The arrow points to the network cutoff parameters used in the study: TP rate, FP rate, pEdge (probability that there is an edge between any pair of nodes), and qval (q‐value or FDR).

PDN methodology results in high rates of positively validated drugs

Once the base network was constructed and subjected to benchmarking analysis, the next step was to challenge the network with a set of query pathways taken from our pre‐defined Pathprint clusters A–D. Sub‐networks of the PDN were constructed that contained these cluster pathways, together with their neighborhood of connected nodes. After several pruning steps (described in the Materials and Methods), the resulting network focuses on the gene signatures that relate most strongly to our cluster pathways. Through this method, four network modules incorporating each of the Pathprint clusters A–D were created, containing 45 drug leads in total (Table 4).

Table 4. Curation of drug lists by literature search through PubMed

This approach and other drug discovery methodologies generate enormous quantities of possible drug leads that necessitate efficient validation methods. Considering the large number of previous studies that have evaluated compounds for possible benefit in sepsis using animal models, we reasoned that one metric for evaluating the results from the PDN would be how often the identified drug leads corresponded to agents already shown to have positive (or negative) effects experimentally. Hence, we conducted extensive literature curation for each of the 45 compounds or closely related agents (e.g., ibuprofen for NSAIDs) and scored the presence of prior publications showing benefit or harm for survival in animal models of sepsis.

The validation efficacy of the drug list derived from Pathprint‐to‐PDN analysis was compared to three other gene‐level drug discovery approaches as well as to a control approach (drugs selected at random from the entire list of CMap compounds). The first, a gene‐level approach, also used PDN, but analyzed differentially expressed genes (DEGs) generated from a standard Limma analysis of children vs. adult transcriptomes, rather than pathway clusters (Appendix Fig S1). We found a substantially higher rate of positives in the list produced by a pathway‐level PDN approach: 54%, compared to 27% for the gene‐level approach, and 16% for randomly selected drugs. We also obtained up‐ and down‐regulated DEGs from the BarCode method (McCall et al, 2010; Table EV4), an approach that categorizes gene expression as on or off, and used these genes, as well as the standard DEG list to query the LINCS database (Wang et al, 2016), a greatly expanded version of CMap (Lamb et al, 2006). The lists of compounds expected to have a positive effect on sepsis mortality (i.e., up‐ and down‐regulated in adults compared to children) were also curated to assess the frequency of prior positive results in the literature. The percentage of positive drug leads achieved by the Pathprint‐to‐PDN methodology was significantly higher than with each of the four other methods (P < 0.02 by Fisher's exact test). The percent positives for each of the five categories of drug leads are summarized in Fig 2, and details of the lists and references identified are provided in Appendix Tables S1–S5.

Figure 2. Comparison of several methods of drug candidate identification

Five methods of transcriptome analysis/drug candidate identification were compared in their ability to successfully produce drug targets in at least one prior study showing a survival benefit from sepsis. (i) Pathprint‐PDN: Comparison of pathways by Pathprint and drug candidate analysis by pathway drug network (PDN); (ii) DEGs‐PDN: Comparison of differentially expressed genes (DEGs) by standard methods and drug candidate analysis by PDN; (iii) Random: Drugs chosen at random from the CMap database; (iv) DEGs‐LINCS: Comparison of DEGs generated by standard methods and drug candidate analysis using LINCS database; and (v) BarCode‐LINCS: Comparison of DEGs generated by BarCode method and drug candidate analysis using LINCS database. The three gene‐level methods were found to be no better at generating positive drugs than picking drugs at random. All methods produced significantly lower percent positive rates than the Pathprint‐PDN method (P < 0.02). Prism software (GraphPad) was used to compare the frequency of prior studies showing benefit for drug leads Fisher's exact tests.

PDN‐derived therapeutic leads improve survival in murine endotoxemic shock

To directly investigate the utility of the PDN approach, we tested 10 of the top ranked compounds generated by the Pathprint‐to‐PDN method (Table EV5) for their effects on survival in an endotoxin shock model. Our goal was to use these drugs to directly modulate adult pathway signatures to match pathway signatures in children and potentially improve survival. Mice were pre‐treated with the compounds as described in the Materials and Methods, followed by intraperitoneal administration of endotoxin. Five of the 10 compounds improved survival in this model (Fig 3). In all, eight of the 10 compounds had not been previously reported in sepsis survival studies; three of these eight showed benefits in our endotoxin shock model. The remaining two compounds were likely to be effective based on prior publications (topotecan, a water‐soluble analog of camptothecin, and chlorpromazine, similar to piperacetazine) and they decreased mortality as expected.

Figure 3. Validation of select PDN drug candidates in an in vivo endotoxemia model

Therapeutic leads generated using PDN were directly tested for survival benefit using a murine model of endotoxemia. Select compounds were injected 24 h before and on the day of LPS administration, using routes and doses specified in the methods. C57bl/6 female mice were injected with a high‐lethality dose of Escherichia coli LPS (38–40 μg/g) followed by a subcutaneous injection of sterile saline. Significant differences in concentration between drug and vehicle‐treated pre‐ and post‐pubertal mice are labeled with ****P < 0.0001, ***P < 0.001, **P < 0.01, or *P < 0.05. Percent survival was compared using a log‐rank Mantel–Cox test.

Discussion

In this study, we sought to address the dearth of effective drug treatments for sepsis by combining two novel approaches, summarized in Fig 4. Firstly, we focused on a remarkable natural experiment—the relative resistance to mortality in children vs. adults with sepsis. By data‐mining publicly available whole blood transcriptomes, we were able to identify key differences in pathway regulation between the two age groups. Continuing with a pathway‐centric approach, we used pathway‐based correlation to build a novel in silico drug discovery system to find drugs that might promote beneficial pathways (i.e., activated in children) or inhibit harmful ones (i.e., activated in adults). Evaluation of the resulting drug list by both curation and direct experimentation showed substantial enrichment for promising candidates.

Figure 4. Summary workflow

We began by identifying publicly available datasets from transcriptome profiling experiments that analyzed blood leukocyte samples from adult and child sepsis patients. After data processing, we used Pathprint to translate these gene expression patterns to the pathway activity level. By comparing samples at the pathway level, the Pathprint method is robust to batch effect and allows for comparison across multiple array platforms. After identifying age‐associated differences in pathway activity, we used them to facilitate drug discovery by constructing targeted pathway drug networks (PDNs). This novel method works by incorporating our target pathways into a base network built upon the correlation in the expression of > 16,000 disease, pathway, and drug gene signatures across > 50,000 individual microarrays. The resultant network neighborhood was used to identify drugs with positive or negative association with high‐survival (child) or high‐mortality (adult) pathways, respectively. We validated top drug leads by curating and analyzing prior data collected in preclinical models of sepsis and also by directly testing their ability to improve survival in a mouse model of fatal endotoxemia.

The profiles of the five drugs found to be effective in vivo are diverse. Topotecan has broad anti‐inflammatory effects attributed to inhibition of topoisomerase‐dependent transcriptional activity of pathogen‐induced genes (Rialdi et al, 2016). Chlorpromazine and amitriptyline share tricyclic structure and myriad potential mechanisms of action, e.g., interactions with neural receptors, but also inhibition of acid sphingomyelinase, which has been linked to decreased inflammation (Sakata et al, 2007). Vinpocetine is a synthetic derivative of the vinca alkaloid vincamine, with known anti‐inflammatory properties (Jeon et al, 2010). Khellin is a folk medicine derived from the plant Ammi Visnaga and has a furanochrome structure, but benefits and mechanism(s) of action are poorly characterized. Although four of the five agents have anti‐inflammatory properties, at least three of the five drugs that had no effect also have reported anti‐inflammatory action [topiramate (Dudley et al, 2011), noscapine (Zughaier et al, 2010), and ethacrynic acid (Han et al, 2005)]. Additional investigation of these drugs (and other leads in addition to the top 10) may help identify meaningful commonalities more precisely.

By starting with human transcriptomic data in our comparison of children vs. adults, we substantially increased the potential value of subsequent analyses. Effectively, our approach uses human samples and then verifies the methodology in an animal model. The limitations of using animal models (especially mice) in preclinical sepsis studies are well recognized. Mice typically lack many of the common features of human sepsis patients (e.g., age, comorbidities, drug treatments, supportive care; Osuchowski et al, 2014; Efron et al, 2015) and exhibit highly species‐specific transcriptomes after injury or sepsis (Seok et al, 2013). In addition, no model of sepsis in mice [e.g., endotoxemia, bacterial pneumonia, cecal ligation and puncture (CLP)] can completely replicate the physiological responses seen in human sepsis (Dejager et al, 2011). The strategy used here avoids reliance on an animal model of sepsis as the initial source of genetic information for the generation of a drug candidate list. Moreover, even the “gold standard” sepsis model, cecal ligation and puncture (CLP), is recognized as being technically difficult and variable; different responses are elicited from lab‐to‐lab or even from person‐to‐person within a given laboratory.

The (admittedly also imperfect) LPS model used in these studies, does fulfill an important criterion: It mirrors the pre‐ vs. post‐pubertal human epidemiology that interests us, as detailed in our recent publication (Joachim et al, 2017). Thus, we believe that the endotoxemia model is a sufficient tool to begin our investigation of the underlying mechanisms driving pre‐pubertal resistance. Ultimately, we would like to expand the pre‐pubertal resistance model to a species that is more similar to humans in sensitivity to endotoxin and sepsis—the rabbit. We especially note that a similar resistance to mortality from LPS in pre‐pubertal vs. pubertal rabbits has been reported (Watson & Kim, 1963) although this finding was not the focus of the cited study.

Unfortunately, due to the absence of effective drugs for human sepsis, it is not possible to validate our method using human data. Therefore, we instead relied on outcomes in mice for both the in vivo testing (Fig 3) and the curation results (Table 4), which compiled drug treatment effects in studies mostly performed in murine models of sepsis. While imperfect, the “reverse‐translational” methodology (Efron et al, 2015) used in this work attempts to exploit the many remaining similarities in the murine and human responses to injury (Takao & Miyakawa, 2015). By limiting our study to pathways identified as important in humans, we diminish the risks of identifying murine‐specific biology. Further assessment of the efficacy of the identified drug “hits” will need to be conducted in larger animal models and ultimately human patients. Despite the limitations, our approach offers a substantial improvement in isolating drugs that merit further evaluation in preclinical assays. The child vs. adult difference in the resistance to mortality may also prove useful as a starting point for drug discovery in other severe infections and disorders (Table 1). The change in resistance is linked to the puberty transition, suggesting a role for sex hormones. Indeed, other experimental studies from our laboratory support this idea (Joachim et al, 2017; Suber & Kobzik, 2017) and indicate this topic merits further investigation in human studies.

In addition to the drug discovery goal of this work, the differences in pathway activation between adults and children also provide clues to the mechanisms driving childhood resistance to mortality. The initial pathway clusters generated through Pathprint were selected using relatively stringent criteria to maximize differences (see Materials and Methods). Using this approach, the pathways that were down‐regulated in children in comparison to adults (Clusters A & C, see Table 3) involved response to infections (e.g., Shigellosis, Pathogenic Escherichia coli infection, viral myocarditis), canonical inflammatory and oxidative stress signaling pathways (e.g., IL‐2 down‐regulated targets, B cell Receptor down‐regulated targets, p38 MAPK Signaling Pathway, Keap1‐Nrf2 Pathway, TGF beta receptor up‐regulated targets), pathways involved in growth and cell proliferation (e.g., Signaling by NGF, EGFR1 Signaling Pathway, Kit Receptor up‐regulated targets), and pathways involved in chromatin modification. These pathways suggest a chronic up‐regulation of the inflammatory response in adults in comparison with children. In general, there were fewer pathways that met our criteria for significant up‐regulation in children in comparison to adults (Clusters B & D), and these were found to lack direct associations with inflammatory/immune responses. These pathways include lipid biosynthesis and regulation (e.g., Steroid hormone biosynthesis, Steroid Biosynthesis, Statin pathway, cytochrome P450 activity), as well as proto‐oncogenic genes and cancer (e.g., {CTNNB1, 130} [Static Module], {FLI1, 10} [Static Module], Melanoma [KEGG]). Using somewhat less stringent criteria, we identified the top 50 pathways (out of 633, ~ top 8%) that were up‐regulated in adults but down‐regulated in children or vice versa (ranked by the sum of their respective percentile ranks; Appendix Tables S1 and S2). The inflammatory (adult) vs. metabolic (child) difference is also evident in this comparison. The change in resistance is linked to the pubertal transition, suggesting a key role for sex hormones. Indeed, other experimental studies from our laboratory support this idea (Joachim et al, 2017; Suber & Kobzik, 2017) and indicate that this topic merits further investigation in human studies.

We were able to carry out the comparisons reported here due to the large number of datasets available that report whole blood transcriptomes in sepsis. This reflects the systemic nature of the condition, the accepted scientific importance of leukocytes in sepsis pathogenesis, and the relative ease of obtaining blood samples. However, whole blood transcriptomes have limitations. The expression profiles of whole blood essentially represent a weighted sum of the patterns of gene expression for each blood cell type and patients with sepsis exhibit heterogeneity in the leukocyte composition of the blood. No white blood cell count data were available in the data annotations for these studies, making us unable to directly control for these differences between individuals. However, the overall leukocyte differential in septic children in the 5–11 age range is very similar to that seen in adults (Stone et al, 1985; Park et al, 2014; Wong et al, 2015). Finally, the analysis of whole blood does not address potentially important contributions from endothelial, epithelial, tissue‐resident immune, and parenchymal cell types (Cavaillon & Annane, 2006).

The novel drug development strategy applied here has more general applicability beyond sepsis. Classical approaches to understanding drug–disease relationships rely on experimental assays to relate cell states and perturbations to the etiology of different diseases, but cannot sample all possible interactions. Fully connected approaches such as the Molecular Signature Map (Ge, 2011) quantify interactions based on overlapping gene membership. This method successfully integrates our knowledge of gene lists but fails to address the issue of how drug, pathway, and disease signatures influence each other. We have used microarray data from the most highly represented platforms in GEO to determine the correlation of the expression of over 16,000 drugs, diseases, and pathway gene signatures in humans. In constructing the PDN, we used partial correlations in order to quantify the relationship between network nodes while still accounting for the influence of the other gene signatures. The resulting network enables us to interpret the cell as a whole based on the relationships and flow of information among the myriad processes occurring within it.

Prior to the development of the PDN methodology, the steps used in the CMap pipeline have been the main transcriptome‐based drug discovery paradigm. The standard CMap pipeline tests whether an experimentally derived up‐ and down‐regulated gene signature is also up‐ or down‐regulated in a set of drug perturbation expression data. Broadly, this is equivalent to querying whether the transcriptional impact of the experiment is similar, or opposite to the transcriptional impact of a drug in CMap. Our alternative approach tests whether an experimental gene signature is correlated or anti‐correlated to the gene signature associated with drug treatment. Importantly, the correlation is measured not just in the setting of the transcriptome data from a single experiment, but across many experiments (over 50,000 arrays from over 2,000 experiments). The rationale for the PDN methodology is to quantify the relationship between two signatures across many experiments rather than assessing their similarity in a single test. If correlation is detected, we can hypothesize that the action that regulates, or is regulated by, those two signatures [i.e., from the drug and from the experimental phenotype (e.g., better survival)] may be linked and/or have a similar action (or opposing action in the case of negative correlation).

Our approach is not meant to directly replace CMap, but to greatly expand its power by exploiting experimental linkage. CMap can take any signature as an input, but the pre‐defined array sets upon which is tested are fixed, and also limited in scope to experimentally testable perturbations. The core focus and strength of the PDN methodology is the ability to link any pair of gene signatures in terms of their transcriptional regulation, irrespective of their source. We have included a range of additional gene signatures in the analysis so that the links are not restricted to drug interactions. In addition, the output of the method is a network, rather than a list of pair‐wise relationships, meaning that clusters of drugs can be detected along with any closely associated pathways. The long‐term goal is to make use of the relationships between the drug, pathway, and diseases signatures in the network to suggest mechanisms of action for drug leads. The eventual aim is to link pathways, drugs, mutations, and diseases all based on the same background dataset.

Limitations of this approach include some of the well‐recognized problems in meta‐analysis of microarray data in general (Tseng et al, 2012) and in sepsis specifically (Fiusa et al, 2014; Sweeney & Khatri, 2016). The Pathprint approach overcomes some of the problems in merging data from different platforms. However, because it cannot integrate all platforms, some sepsis studies could not be included. The study relied on very useful, but imperfect databases. For example, the extensive reliance of CMap (and LINCS) on cancer cell lines may skew results. The background data used for the calculation of the PDN correlations will be subject to a similar investigation bias in the samples uploaded to the GEO database. It is also important to note that the quantity of available transcriptomic data (microarrays & RNA‐Seq datasets) has grown (and continues to expand daily) since the PDN base network was first constructed. A rich collection of other approaches to data‐mining exists in the literature (Pathan et al, 2015; Henriques et al, 2017; Li et al, 2017). Integration of other analytic strategies might offer additional insights, and this exploration merits future consideration. Similarly, an expanded PDN based on a current version of the LINCS database (now accessed at clue.io) might provide additional power.

Indeed, the overall success rate of drugs identified by “reversal of signature” methods is unknown, but supported by individual successes (Iorio et al, 2013; Musa et al, 2017). A further limitation of existing, pair‐wise approaches to determine drug–disease relationships, including that approach presented here, is that no mechanistic data can be inferred. The integration of pathway and experimental gene signatures in the network allows for the identification of tightly connected pathway sub‐networks around each drug–disease connection. Furthermore, the network allows for both negative and positive connections to be identified, significantly distinguishing this approach from existing overlap‐based in silico methods. These features improve the identification of drugs with synergistic effects or sets of drugs with independent mechanisms of action on a disease, factors that are vitally important in overcoming polygenic drug resistance.

The ultimate aim of this work was to discover novel drug candidates for the treatment of sepsis by data‐mining and comparing whole blood transcriptomes from two populations with naturally high (adults) or low (children) susceptibility to death from sepsis. Pathways with age‐specific activation were identified through Pathprint and successfully used to interrogate the pathway drug network (PDN), which allowed for the identification of medications that could promote beneficial pathways during sepsis (i.e., activated in children) or inhibit harmful ones (i.e., activated in adults). Validation by literature curation and direct experimentation in endotoxemic mice indicated that the resulting drug list contained many promising therapeutic candidates. These findings suggest that our unique, pathway‐centric approach to drug discovery may prove a powerful new tool in identifying novel therapeutics for sepsis and other complex medical conditions.

Materials and Methods

Study design

The objective of this study was to collect publicly available whole blood transcriptomes from septic adults and children, and then employ pathway‐based bioinformatics tools to identify differentially regulated pathways and discover novel drug candidates for the treatment of sepsis. The GEO and ArrayExpress databases (Barrett et al, 2013; Kolesnikov et al, 2015) were used to identify publicly available microarray transcriptome datasets from whole blood samples of septic patients. The criteria for inclusion of microarrays were (i) the availability of annotation data for the age of subjects, (ii) the use of microarray platforms supported by the Pathprint tool, and (iii) satisfactory evaluation by quality control analysis.

Pathways with age‐specific activation were identified through Pathprint and used to interrogate the base PDN. The reliability of the PDN in its ability to identify accurate drug‐disease relationships was benchmarked against known, curated relationships from the NDFRT and SPL databases. Further validation of the drugs identified through the PDN methodology was performed through literature curation as well as direct experimentation in endotoxemic mice.

All validation experiments using mice were conducted in strict adherence with the NIH Guide for the Care and Use of Laboratory Animals. The number of mice was chosen based on past success in evaluating interventions to improve survival in infectious disease models. Endpoints in these studies were survival (for over 72 h) or mortality. Analysis of mortality included counting deceased mice as well as humane euthanasia of mice with severe, pre‐terminal morbidity.

Transcriptome data processing

GEO and ArrayExpress databases were queried to identify microarray transcriptome datasets from sepsis whole blood samples. Samples from patients aged 5–11 comprised the children's group while samples from patients 18 years of age or older comprised the adult group (details of demographics and datasets used are provided in Table 2). The age range for the children's group was chosen because it is similar to the 5–14 age group that showed greater survival in the 1918 influenza pandemic (Ahmed et al, 2007), but adjusted to reflect the earlier onset of puberty in modern times (Ong et al, 2006; Toppari & Juul, 2010).

The main workflow began by curating datasets and metadata of interest. This curation process involved both automated steps (e.g., database searches for keywords) as well as manual work to compile and identify whether the required metadata were available (e.g., age of subject providing sample in a given dataset). In some cases, the authors of the individual studies were contacted to obtain such information. The retrieved metadata were filtered and standardized and the relevant annotations of interest extracted (i.e., age, sepsis status, gender, data locations). The curated metadata from each study were then combined to create a covariate table that was used to download each sample's expression data using the GEOquery package in R (Davis & Meltzer, 2007). To analyze data from multiple array platforms, differential activation of pathways was assessed using the R package Pathprint. To identify pathways with minimal intra‐group variation and maximal inter‐group variation, two filtering criteria were set: (i) to maximize homogeneity within an age group based on minimizing the standard deviation (a cutoff of SD < 0.475 in the Pathprint score was used); (ii) to maximize differences between group comparisons using t‐tests (Pathprint scores between groups were only included if P < 10−10). Heatmaps to visualize differences in pathway activation were generated using the pheatmap package (cran.r-project.org/web/packages/pheatmap/index.html).

A gene‐level analysis was also performed on the subset of datasets that used the same array (Affymetrix HG‐U133 Plus 2.0, GEO accession GPL570) as follows. First, quality control and normalization were performed using the arrayQualityMetrics (Kauffmann et al, 2009) and RMA packages (Irizarry et al, 2003). DEGs were identified using Limma (Ritchie et al, 2015). The problem of batch effects in gene expression analysis is well known (Leek et al, 2010; Lazar et al, 2012). Pathprint addresses this issue by aggregating expression at the level of a pre‐defined pathway. In contrast, an earlier methodology called the Gene Expression Barcode (McCall et al, 2011) operates at the level of the gene. To allow for comparison of results, the Barcode method was applied using the fRMA package (McCall et al, 2010). Filtering by binary entropy measures (< 0.295 for intra‐group binary entropy and > 0.3 for inter‐group binary entropy) was used to identify genes with maximal expression differences between age groups. The top up‐ and down‐regulated genes in the adult vs. child comparisons were used to query the LINCS database (Duan et al, 2014). Using the percentile rank (“mean_rankpt_2”), the top 45 compounds anti‐correlated to the adult profile were selected and subsequently evaluated for published evidence of efficacy in sepsis models as described below.

Selection of pathways for analysis by PDN

Four different clusters of pathways, generated through Pathprint analysis, were identified based on similar patterns of relative expression: cluster A) expression up in adults, down in children; cluster B) expression down in adults, up in children; cluster C) expression unregulated (not significantly changed) in adults, down in children; cluster D) expression unregulated in adults, up in children. Pathways from each cluster that showed the greatest difference between the two comparison groups were selected for further PDN analysis. This selection was based on the percentage (N) of samples that satisfied the criteria (N = 80% for clusters A–C; N = 70% for cluster D). For example, the pathways selected from cluster A were up‐regulated in at least 80% of samples in adults AND down‐regulated in at least 80% of samples in children. For cluster D, use of the 80% criterion produced only one pathway for evaluation. Hence, the threshold was lowered to allow inclusion of the four pathways that were up‐regulated in at least 70% of the samples from children and unregulated in adults.

Curation of gene‐sets for PDN base network creation

A set of drug, disease, and pathway gene‐sets were curated from the following resources:

Comparative Toxicogenomics Database (CTD) (2,452 chemical/drug and 609 disease gene‐sets):

The CTD (Davis et al, 2017) includes curated data describing cross‐species interactions between chemicals and genes/proteins as well associations between chemicals, genes, and diseases. The data were retrieved from the CTD, MDI Biological Laboratory, Salisbury Cove, Maine, and NC State University, Raleigh, North Carolina (http://ctdbase.org/) [5 November 2012 retrieval].

The Pharmacogenomics Knowledgebase (PharmGKB) (178 chemical/drug gene‐sets, 78 disease gene‐sets):

PharmGKB (Whirl‐Carrillo et al, 2012) is a pharmacogenomics knowledge resource that encompasses clinical information including dosing guidelines and drug labels, potentially clinically actionable gene–drug associations and genotype–phenotype relationships. Data (updated 11/6/12) were downloaded from the PharmGKB website (www.pharmgkb.org).

Connectivity Map (CMap) (12,200 chemical/drug gene‐sets):

CMap (Lamb et al, 2006) is a collection of genome‐wide transcriptional expression data from cultured human cells treated with bioactive small molecules. CMap contains 6,100 expression profiles representing 1,309 compounds. The data can be retrieved from http://www.broadinstitute.org/cmap. The rank matrix available on the website (contains 22,283 gene probes and 6,100 samples) was used to build unique gene signatures for each perturbation (drug treatment). Probe sets were ranked in descending order of the ratio of the treatment‐to‐control values. The probe that was most up‐regulated relative to the control was designated as top rank (#1), while the probe that was most down‐regulated relative to the control was designated as bottom rank (#22,283). Separate up‐ and down‐regulated gene signatures in response to each drug were compiled using the top and bottom 1% of ranked genes, respectively. These gene signatures served as a proxy for the transcriptional impact of a drug and allowed for the addition of CMap nodes to the PDN.

Pathprint (633 gene‐sets):

Gene‐sets from the pathways used by the Pathway Fingerprint (Pathprint; Altschuler et al, 2013) were taken from the R package Pathprint (compbio.sph.harvard.edu/hidelab/pathprint/Pathprint.html) implemented in Bioconductor (bioconductor.org/packages/pathprint/). The pathway list contains gene‐sets derived from a range of pathway databases (Reactome, KEGG, Wikipathways, Netpath; see Pathway Fingerprint for description and references), and modules derived from a functional gene interaction network known as “static modules” (Wu et al, 2010).

The gene‐sets derived from each of the resources described above were combined to create a library of 16,150 unique gene signatures.

PDN base network construction

A base network was constructed using the correlation between the expression levels of each of the 16,150 signatures, across 58,475 publicly available human microarrays (Affymetrix HGU133 Plus2) obtained from GEO. The array set contained 2,120 experiments, the same set of microarrays that make up the GPL570 expression background in the Pathprint package (see Bioconductor package for full list). For each microarray, the genes were ranked by expression level, from #1 (low expression) to T (high expression), where T is the total number of genes in the array. The expression score, En(G), for a gene signature, G, of size k, represented in an array by genes g1, g2…gk, is defined by the mean squared rank of the member genes,

, where Ri is the rank of gene gi in a pathway containing n genes. The network edges are represented by the partial correlation between each gene signature expression score, which is the correlation coefficient between two gene signature expression scores after accounting for the influence of the other gene signatures. The partial correlation was calculated using the R package GeneNet, which makes use of shrinkage estimators of partial correlation for fast and statistically efficient processing of the data (cran.r-project.org/web/packages/GeneNet/index.html).

The significance of each of the connecting edges was assessed by fitting a mixture model to the partial correlations, where the null model is estimated from the data. The calculation used the R package, fdrtool (http://cran.r-project.org/web/packages/fdrtool/index.html) to generate two‐sided P‐values for the test of non‐zero correlation for each edge, corresponding posterior probabilities for edges, and q‐values (Schafer & Strimmer, 2005). The PDN method creates a network that is dynamic and can be extended to cover any number of additional signatures. The network was benchmarked using curated case–control interactions.

Network characterization

PDN network topology.

The PDN degree distribution and degree cumulative probability are shown in Fig EV2. At first glance, the degree distribution of the PDN plotted on a log‐log scale may be considered roughly linear—indicative of a scale‐free network following a power law distribution with gamma of approximately 0.61. However, the cumulative probability plot, which would also be linear on a log‐log scale under scale‐free conditions, clearly demonstrates significant divergence from a power law distribution. An exponential distribution or power law with exponential cutoff provides more reasonable but not exact fits. Scale‐free networks were thought to be a common characteristic of biological networks (Albert, 2005), generally rationalized by the hypothesis that such networks are robust to random breaks and facilitate rapid inter‐node communication by short average path lengths and high clustering coefficients. However, as higher resolution experimental data have become available, the general scale‐free nature of biological networks has been increasingly questioned (Lima‐Mendez & van Helden, 2009). The degree distribution of the PDN is evidence of a denser structure than would be expected from a power‐law distributed network and provides an additional example of departure from scale‐free topology. It should be noted that the presence of the drug perturbation and disease‐state signatures in the PDN would also be expected to disrupt the structural characteristics of a network based on canonical pathways alone.

Figure EV2. PDN degree distribution

The degree distribution of the PDN plotted as a probability density function (PDF) and cumulative density function (CDF). The data are shown in black, together with fits to power law (red), exponential (green), and power law with exponential cutoff (blue) distributions.

Biological interpretation of PDN pathway relationships.

We wished to establish whether pathways are correlated within the PDN in biologically meaningful ways. We created a sub‐network consisting of all pathways from the network. Markov Clustering (van Dongen, 2000) of pathway–pathway correlation using the Graphia Pro environment (Kajeka.com) generated 38 biologically consistent clusters containing between 6 and 34 pathways as nodes (Table EV6). Pathway nodes vary in degree from 244 (Pathway.{PRKACA,33} (StaticModule)) to 4 [Pathway.TNF‐alpha/NF‐kB Signaling Pathway (Wikipathways)]. Interpretation of cluster membership is complicated by the fact that only a partial understanding of known functional relationships between pathways exists. We have begun to address this challenge in a separate study of global pathway relationships (Pita‐Juarez et al, 2018).

Clusters show pathways related by function. As cluster size decreases, functions become more specific. One example of this can be seen in our largest cluster, Cluster 1, which contains pathways sharing functionality across cellular responses to stress, infections and cancers, B and T Cell receptor signaling pathways, as well as Tuberculosis, Leishmaniasis, and Toxoplasmosis pathways. Other clusters are enriched for pathways in lipid metabolism (Cluster 3); cell cycle and DNA replication (Cluster 6); immune signaling (Cluster 8); DNA repair and replication and RAS family genes (Cluster 10); extracellular matrix function (Cluster 12); and electron transport, respiratory chain function, oxidative phosphorylation, and Parkinson's disease (Cluster 14). Functional relationships between pathways structured in this clustering approach may be insightful in terms of providing data driven context to known relationships. For instance, shared functionality in immune‐mediated mechanisms of stress surveillance in cancer is an existing observation (Seelige et al, 2018).

Benchmarking the PDN

In an effort to benchmark the PDN, we compiled two sets of curated drug–disease relationships: 149 documented relationships from the NDFRT database (46 diseases and 92 drugs) and 906 documented relationships from the SPL database (58 diseases and 122 drugs). The drug and disease terms from both of these databases have been previously mapped to the PharmGKB identifiers (Zhu et al, 2013) used in construction of the PDN. This allowed for direct comparison of the two methods. CMap datasets have not been mapped to the terms in the NDFRT and SPL databases and were thus not used for benchmarking. True‐ and false‐positive rates (TPRs, FPRs) were measured for the PDN and used to create a series of network cutoffs, and an ROC curve was plotted (Fig EV1). Beyond the goal of replicating these drug–disease relationships using the PDN, we also compared our methodology with an alternative approach, NEA.

Determining CMap drugs associated with query cluster pathways

Once the base network was constructed, we interrogated it with a set of query pathways from pre‐defined Pathprint clusters A–D. A sub‐network was constructed for each cluster (e.g., Cluster A) that contained the nodes representing each of the member pathways of that cluster, together with all base network nodes with connecting edges to the cluster members. To assure that the new network was specific to correlations associated with a given cluster of pathways, we further pruned the sub‐networks by removing base network nodes if they did not connect to at least three or more of the pathways in the cluster. Next, we ranked the significance of the remaining base nodes using the P‐values of the edges connecting them to each of the cluster nodes, aggregated by Fisher's method. For all non‐CMap nodes, P‐values were simply aggregated across the entire pathway cluster into a single P‐value. For CMap nodes, P‐values were first aggregated across the separate CMap up‐ and down‐regulated gene signatures for each drug and secondly across the entire pathway cluster. An overall positive correlation between a cluster pathway and a pair of CMap nodes was determined by combining the P‐values calculated for positive correlation with the up‐regulated CMap drug signature and negative correlation with the corresponding down‐regulated CMap drug signature. Overall, negative correlation between a cluster pathway and a pair of CMap nodes was established in a similar way. The P‐values for positive and negative correlation were then ranked and combined into a simple combined association score: Score = rank(negativeRank – positiveRank)/(nDrugs/2) – 1, where negativeRank is the rank of the negative P‐value, positiveRank is the rank of the positive P‐value, and nDrugs is the number of drugs tested. Any CMap drug with a P‐value of > 0.1 for both positive and negative association was given a score of 0. Thus, a negative score means that the drug opposes the activity of the cluster pathways, a positive score means that drug enhances the activity of the cluster pathways. A score of 0 means no significant interaction. The 10 highest negatively scoring drugs each for clusters A and C, the 10 highest positively scoring drugs each for clusters B and D, as well as the top 5 within the overlap of clusters A and C (a total of 45 drugs) were prioritized for validation as described in the following section. While the PharmGKB database also contained drug signatures, they were non‐directional (up and down‐regulated genes are not distinguished). Because it would have been impossible to distinguish a correlated vs. anti‐correlated signature using the PharmGKB signature, we chose to perform all final analyses using the CMap signatures.

Determining CMap drugs associated with an individual gene signature

To compare PDN functionality based on a network analysis of a target cluster of pathways with standard single gene list‐based analysis, we queried the PDN directly with a differential signature derived from adult and child transcriptomes (see above). The top 500 up‐ and down‐ regulated probes from a comparison of children vs. adults using datasets limited to a single array platform (GPL570) were matched to 427 and 405 up‐ and down‐regulated genes in CMap. These gene signatures were incorporated into the PDN and ranked for positive or negative association with CMap drug signatures by a similar approach as the Pathprint cluster pathways described above. The differentially expressed genes were split into up‐regulated and down‐regulated gene‐sets. These gene‐sets were then introduced into the PDN as two new nodes and evaluated separately. The P‐values of the edges connecting each of the new nodes to the separate CMap up‐ and down‐regulated gene signatures were aggregated for each drug. This calculation helped to quantify whether the up‐ and down‐regulated components of the adult vs. child differentially expressed signatures were positively or negatively associated with each CMap drug. Then, an overall positive or negative correlation between a differential gene signature and a CMap drug was determined by combining the P‐values calculated for the up‐regulated and down‐regulated parts of the signature. The top 45 negatively associated drugs were selected for further validation.

Validation of drug leads

To validate the top 45 therapeutic leads generated by each drug discovery methodology, a literature search using PubMed was performed. We used terms (i.e., keywords: survival, mortality, sepsis, endotoxin) to identify studies that tested a particular drug, or a closely related compound, for in vivo benefit in animal models of sepsis. Compounds were scored as follows: positive (prior studies showing survival benefit were identified); both (prior studies showing both benefit and harm to survival were identified); negative (prior studies showing only harm to survival); no score was assigned when no relevant studies were identified. The efficacy of the Pathprint‐to‐PDN methodology was compared to several other transcriptome‐to‐drug discovery approaches (described in results and Fig 2) as well as to drugs randomly chosen from the CMap database.

Ten drugs were selected from the pool of drugs identified using the Pathprint‐to‐PDN methodology. These were chosen to sample from all clusters and to include agents both with and without prior evidence of potential benefit. Substitutions with highly similar compounds were made for some of the predicted drugs. For three of the four substitutions made (topotecan, chlorpromazine, amitriptyline), the rationale was driven by the existence of publications showing survival benefit in animal models using the substituted drug ((Brand et al, 2008; Rialdi et al, 2016; Villa et al, 1995), respectively). These data did not exist for the original predicted drugs. For the final pair, vincamine/vinpocetine, there are no published data demonstrating a survival benefit, but there are data showing that vinpocetine has some anti‐inflammatory activity (Jeon et al, 2010). No similar data were found for vincamine, thus motivating our choice of vinpocetine.

The compounds were directly tested for effects on survival using a murine model of endotoxemia. These experiments were conducted in strict adherence to the NIH Guide for the Care and Use of Laboratory Animals, and under a protocol approved by the Harvard Medical Area Institutional Animal Care and Use Committee (IACUC). C57bl/6 female mice (5 weeks old, Charles River, Wilmington, DE) were injected with a high‐lethality dose (e.g., 38–40 μg/g) of E. coli LPS (L3755; Lot: 123M4096V; Sigma‐Aldrich, St. Louis, MO, USA) between hours 5 and 7 of the light period in the animal facility (12–2 PM). In order to mitigate fluid loss and dehydration, each mouse was also given a subcutaneous injection of sterile saline (equal to 2.5% of body weight). To test the effects of drug leads, compounds were injected 24 h before and on the day of LPS administration, using routes and doses specified in Table EV7. Analysis of mortality included counting deceased mice as well as humane euthanasia of mice with severe, pre‐terminal morbidity (scored by evaluation of appearance, movement, and response to touch).

Statistical analysis

The statistical methods used in transcriptome comparisons as well as the creation and application of the PDN methodology are detailed in the sections above. Fisher's exact test was used to compare the frequency of prior studies showing benefit for drug leads across the multiple transcriptome‐to‐drug methodologies. A log‐rank (Mantel–Cox) test was used to analyze murine endotoxemia survival data. Both of these statistical analyses were performed using Prism software (GraphPad, San Diego, CA).

— Molecular Systems Biology current issue

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Discovery of small molecule inhibitors of protein interactions

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A receptor-based drug discovery process approach can be applied when an accurate threedimensional (3D) structure of a specific PPI complex is available. A novel, complementary and transformative approach for the rational design of small molecule inhibitors based on the crystal structure of the p53-Mdm2 complex was developed. This method is based on a tight interplay of structural biology information, the “anchor” concept, efficient chemical synthesis via multicomponent reactions (MCRs), as well as virtual and real screening processes. Applying the method we efficiently discovered several new scaffolds of inhibitors of the p53/Mdm2 interaction with lower micromolar affinity binding to Mdm2, which can serve as starting point for medicinal chemistry optimization. Advantages of our approach include high hit rates and less attrition based on the parallel discovery of multiple scaffolds, built-in optimization pathways using efficient MCRs, and fast generation of potential lead compounds. Potential anticancer drug candidates were identified by biochemical assays, co-crystallization, cell based assays, as well as further preclinical evaluations (solubility, metabolism, pharmacokinetics, and xenograft studies).

A ligand-based drug discovery approach was explored since PPIs are critically dependent on “anchor” residues, which can serve as the pharmacophore model for small molecules. Multicomponent reactions were employed for design of novel scaffolds and DOS of drug-like compounds, since hit identification of PPI inhibitors via traditional approaches such as high throughput screening (HTS) is fundamentally limited by chemotypes present in the library collections. Novel and diverse scaffolds based on the privileged structures (1,4-benzodiazepines, 1,4-thienodiazepines) and “anchor” residues, which can be accessible from multicomponent reactions, were designed and synthesized. Compared with conventional methods, these approaches are advantageous to generate small molecules targeting PPIs in terms of efficiency, diversity, and economy.

In summary, the approaches described in this dissertation constitute important contributions to the fields of medicinal chemistry and structure-based drug discovery process, which combine structural insights and ligand design to expedite the discovery of novel small molecule inhibitors of PPIs.

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Comparing RNA vs. PPI Drug Discovery Methods

In the world of modern drug discovery, two cutting-edge approaches stand out: RNA-targeted drug discovery and PPI-targeted drug discovery. Both methods have the potential to revolutionize therapeutic development, offering novel ways to tackle diseases that were previously thought to be untreatable. Understanding the distinctions between these approaches, along with how MAGNA™ Technology plays a role in advancing them, sheds light on their respective strengths and applications in drug development.

Understanding RNA-Targeted Drug Discovery

RNA-targeted drug discovery is an innovative approach that focuses on interfering with RNA molecules to modulate gene expression and subsequently address disease mechanisms. RNA plays a crucial role in the transcription and translation processes, converting genetic information from DNA into proteins. By targeting RNA, scientists can intervene in this process before harmful proteins are produced, effectively tackling diseases at a more fundamental level.

This approach has gained considerable attention in recent years, particularly in the context of diseases like cancer, viral infections, and genetic disorders. RNA-targeted therapies offer the ability to modulate gene activity, suppress disease-causing genes, and enhance the body's ability to repair itself at a molecular level.

Key benefits of RNA-targeted drug discovery include:

The ability to influence diseases at their genetic roots.

The potential to treat a broad spectrum of conditions, including those involving previously "undruggable" targets.

Flexibility in targeting various RNA types, such as mRNA, siRNA, and lncRNA.

The development of drugs that target RNA has already seen successes in treatments for genetic diseases like spinal muscular atrophy (SMA) and amyotrophic lateral sclerosis (ALS). This method holds promise in expanding the range of treatable conditions, especially as our understanding of RNA biology grows.

Exploring PPI-Targeted Drug Discovery

On the other hand, PPI-targeted drug discovery focuses on disrupting protein-protein interactions (PPIs). Proteins frequently interact with one another to carry out biological processes, and these interactions are critical for the function of healthy cells. However, in the case of many diseases-particularly cancer and neurodegenerative disorders-these interactions become abnormal, leading to harmful cellular activities.

The objective of PPI-targeted drug discovery is to develop small molecules or biologics that can selectively disrupt or modulate these protein interactions. By doing so, it is possible to halt the disease-causing processes at their source.

PPIs were once considered difficult to target, mainly due to the large and often featureless interaction surfaces of proteins. However, advances in biomolecular insights and drug development technologies, such as MAGNA™ Technology, have made it more feasible to target these previously elusive interactions.

Benefits of PPI-targeted drug discovery include:

The ability to target diseases involving protein misfolding, aggregation, or abnormal protein networks.

Access to therapeutic targets that were once deemed undruggable.

Potential applications in treating complex diseases such as cancer, Alzheimer's, and autoimmune disorders.

RNA vs. PPI Drug Discovery: A Comparative Perspective

While both RNA-targeted and PPI-targeted drug discovery methods have the potential to transform modern medicine, they approach disease treatment from different angles. Here’s a comparison of the two:

1. Mechanism of Action:

RNA-targeted drug discovery aims to modulate gene expression by targeting RNA molecules before they are translated into proteins. This method can effectively prevent the synthesis of harmful proteins.

PPI-targeted drug discovery, on the other hand, focuses on disrupting harmful interactions between proteins, stopping disease-causing proteins from working together.

2. Disease Targets:

RNA-based therapies have shown great promise in treating genetic diseases, rare disorders, and viral infections, as well as certain cancers.

PPI-targeted therapies are particularly relevant in diseases where protein interactions go awry, such as cancers, neurodegenerative diseases, and immune system disorders.

3. Technological Innovations:

RNA-targeted therapies have benefited greatly from advancements in RNA delivery systems, such as lipid nanoparticles, which have improved the efficacy and safety of RNA-based drugs.

For PPI-targeted therapies, advancements in structural biology and MAGNA™ Technology have been instrumental in identifying and targeting key protein interactions that were previously considered undruggable.

4. Challenges:

RNA-targeted drug discovery faces challenges related to RNA instability and ensuring targeted delivery to specific tissues.

PPI-targeted therapies are still overcoming the complexities of identifying suitable binding sites on protein surfaces and ensuring specificity.

Both methods hold incredible potential, but the choice between them depends on the specific disease, target, and therapeutic goals. Researchers and pharmaceutical companies often explore both avenues to determine which approach offers the most effective solution for a particular condition.

MAGNA™ Technology: A Common Ground

MAGNA™ Technology, a platform developed by Depixus, plays a crucial role in both RNA and PPI drug discovery. This advanced technology allows researchers to study biomolecular interactions at an unprecedented level of detail, providing critical insights into how molecules such as RNA and proteins interact within cells. MAGNA™ enhances the ability to identify key targets and develop drugs that can modulate these interactions effectively.

In RNA-targeted drug discovery, MAGNA™ Technology helps scientists understand how RNA molecules interact with other cellular components, enabling the design of more precise and potent therapies. For PPI-targeted drug discovery, MAGNA™ provides valuable data on the structural and functional aspects of protein interactions, helping researchers develop drugs that can disrupt these interactions more effectively.

By facilitating deeper insights into molecular interactions, MAGNA™ Technology is driving innovation in both RNA and PPI drug discovery, bringing us closer to developing treatments for diseases that have long been resistant to traditional therapies.

Conclusion

In the dynamic field of drug discovery, both RNA-targeted and PPI-targeted drug discovery represent powerful approaches to addressing some of the most challenging diseases. With advancements in MAGNA™ Technology and our growing understanding of biomolecular interactions, the future of both methods looks incredibly promising. Whether by targeting RNA or disrupting protein interactions, these technologies hold the potential to revolutionize treatment options for patients worldwide.

For more information on how Depixus is leading the way in RNA and PPI drug discovery, feel free to contact us today!

Reposted Blog Post URL:  https://zagpetrick.livepositively.com/comparing-rna-vs-ppi-drug-discovery-methods/ 

[mysticalpeacenut]

How Biomolecular Interactions Drive Innovation

Biomolecular interactions lie on the coronary heart of almost every biological method that occurs inside living organisms. From cell verbal exchange and protein folding to gene expression and immune responses, these interactions govern the complex dance of lifestyles on a molecular level. Understanding and leveraging these interactions is driving a brand new wave of innovation in biotechnology, drug discovery, and therapeutic improvement.

This blog explores how biomolecular interactions, which includes the superior Magna™ Biomolecular Interactions generation, are fueling scientific breakthroughs and transforming the destiny of medication.

The Fundamentals of Biomolecular Interactions

At their center, biomolecular interactions talk to the methods wherein different molecules-along with proteins, nucleic acids, lipids, and small molecules-have interaction with every different to perform organic functions. These interactions are incredibly specific and involve non-covalent forces inclusive of hydrogen bonding, ionic interactions, hydrophobic effects, and van der Waals forces.

Key kinds of biomolecular interactions include:

Protein-Protein Interactions (PPIs): These are vital for in reality each factor of mobile function, along with cell signaling, metabolism, and immune responses.

Protein-DNA/RNA Interactions: Central to processes like gene expression and regulation, these interactions manage how genetic information is transcribed and translated.

Enzyme-Substrate Interactions: These power catalytic reactions, important for metabolic pathways, and other biochemical strategies.

Understanding those molecular interactions offers scientists insights into how illnesses increase and development, leading to modern healing strategies that can intervene at the molecular degree.

Unlocking Potential Through Molecular Interactions

As our expertise of molecular interactions deepens, the potential for brand new programs in remedy and biotechnology grows exponentially. Researchers at the moment are capable of layout capsules and remedies that particularly goal key biomolecules, allowing greater precise remedy techniques with fewer side results.

One of the most promising regions of innovation lies within the manipulation of molecular interactions to expand focused healing procedures. For instance, researchers have recognized specific protein-protein interactions that make contributions to cancer development, which has brought about the improvement of medicine that disrupt those interactions, halting tumor growth.

In addition to drug discovery, the look at of biomolecular interactions is transforming fields like synthetic biology, personalized remedy, and diagnostics. By engineering molecules that engage in unique approaches, scientists are growing new biomolecular equipment for gene modifying, ailment modeling, and greater.

Magna™ Biomolecular Interactions: A Technological Leap

Among the maximum exciting advancements on this area is Magna™ Biomolecular Interactions, a modern-day era advanced by means of Depixus. This step forward technology permits researchers to precisely have a look at the interactions between biomolecules, supplying unheard of detail and accuracy.

Magna™ generation makes use of superior methods to seize and analyze how unique biomolecules engage in actual time. This facts can be used to are expecting how proteins will behave in one-of-a-kind environments, how small molecules can also bind to target proteins, or how genetic fabric may additionally engage with regulatory proteins. The capability to reveal these interactions at this kind of granular stage opens up new opportunities for drug improvement and customized remedies.

For instance, in RNA-focused drug discovery, information the right interactions between RNA molecules and therapeutic compounds is essential. Magna™ Biomolecular Interactions allows scientists to explore these interactions in great detail, helping to increase RNA-centered capsules with extra efficacy and specificity.

Biomolecular Interactions in Drug Discovery

The role of biomolecular interactions in drug discovery cannot be overstated. Modern drug layout frequently hinges on the identity of important molecular interactions that make a contribution to disease. By concentrated on these interactions, scientists can expand treatment plans that interfere on the earliest levels of disorder progression.

For example, in illnesses like Alzheimer's, most cancers, and autoimmune disorders, sure biomolecular interactions can pressure the disease technique. By inhibiting or enhancing those interactions, researchers can create remedies that particularly address the underlying reasons of the sickness, instead of simply treating the signs and symptoms.

This focused technique to drug discovery is mainly relevant inside the technology of personalized medication, in which treatments may be tailor-made to an man or woman's specific molecular profile. The ability to examine molecular interactions at a extraordinarily unique degree, made possible with the aid of innovations like Magna™ technology, is paving the way for extra effective and customized healing options.

The Future of Innovation

The destiny of innovation in biotechnology and medicinal drug is being shaped by our increasing potential to apprehend and manipulate biomolecular interactions. As equipment like Magna™ Biomolecular Interactions hold to adapt, we can expect even extra advancements in the observe of molecular processes, leading to new discoveries so as to trade the manner we approach disorder remedy and prevention.

This generation holds promise no longer handiest for drug discovery however additionally for regions along with artificial biology, where engineered biomolecular interactions can be used to create new biological systems with custom designed capabilities. The capability programs are significant and sundry, from growing novel gene healing procedures to developing biosensors that stumble on sickness markers at an early level.

Conclusion

The observe of biomolecular interactions is driving a revolution in how we understand, diagnose, and treat diseases. With technology like Magna™ Biomolecular Interactions, researchers are gaining unprecedented insights into the complicated molecular networks that govern existence itself. These innovations are not only remodeling drug discovery however also paving the way for brand spanking new packages in biotechnology, personalised medicine, and past.

As we hold to free up the secrets of molecular interactions, the destiny of drugs looks brighter than ever. To explore how Magna™ Biomolecular Interactions era can guide your studies and drive innovation on your discipline, visit Depixus. Discover how our superior solutions are shaping the future of biomolecular studies and therapeutic improvement.

Reposted Blog Post URL: https://petrickzagblogger.wordpress.com/2024/08/20/biomolecular-interactions-drive-innovation/

[mysticalpeacenut]

The Secrets of Protein-Protein Interactions: MAGNA™ Technology Revolutionizes Drug Discovery

Protein-protein interactions (PPIs) are the intricate dance that underlies nearly every cellular process. These interactions dictate how proteins function, and malfunctions in these dances are linked to a vast array of diseases, from cancer to neurodegenerative disorders. Consequently, PPIs are a highly attractive target for drug discovery.

However, traditional techniques for analyzing PPIs often fall short. They may struggle to detect the subtle, dynamic forces at play, or lack the throughput needed for large-scale screening. This is where MAGNA™, Depixus's pioneering technology, steps in.

MAGNA™: A Powerful Tool for Deciphering the PPI Landscape

MAGNA™ leverages the power of magnetic force spectroscopy to provide researchers with an unprecedented view of PPIs. It offers a unique set of advantages:

Unveiling Low-Force Interactions: Unlike some techniques, MAGNA™ has the sensitivity to detect the weak yet crucial forces that govern many PPIs. This allows for a more accurate understanding of the interaction dynamics.

Scalability for High-Throughput Screening: Drug discovery hinges on efficient screening of numerous potential drug candidates. MAGNA™'s design facilitates the analysis of multiple PPIs simultaneously, significantly accelerating the process.

Kinetic Details in Focus: MAGNA™ goes beyond static snapshots. It measures the binding kinetics of PPIs, revealing the rates of association and dissociation between interacting proteins. This kinetic information is essential for optimizing drug design.

Modular Design for Broad Applicability: MAGNA™'s modular design allows for the use of various binders, such as antibodies, aptamers, and nucleic acids. This versatility empowers researchers to study a wide range of PPIs.

Revolutionizing Drug Discovery with MAGNA™

MAGNA™'s capabilities hold immense promise for the future of drug discovery. Here's how it can transform the field:

Decoding Disease Mechanisms: By precisely measuring PPIs, MAGNA™ can shed light on how PPI malfunctions contribute to diseases. This deeper understanding can guide the development of targeted therapies.

Identifying Novel Drug Targets: The vast PPI network offers a treasure trove of potential drug targets. MAGNA™'s ability to analyze PPIs at scale can accelerate the discovery of these novel targets.

Optimizing Lead Molecules: MAGNA™'s real-time binding measurements enable researchers to assess how potential drugs interact with PPIs. This information can be used to rapidly optimize lead molecules for better efficacy.

MAGNA™: Ushering in a New Era of PPI-Targeted Therapies

The world of drug discovery is on the cusp of a paradigm shift. With MAGNA™ as a powerful tool, researchers can delve deeper into the intricate world of PPIs, unlocking a new era of targeted therapies for a wide range of diseases.Stay tuned for future blog posts where we'll delve deeper into the specifics of MAGNA™ technology and its applications in PPI-targeted drug discovery.

[mysticalpeacenut]

MAGNA™: A Revolutionary New Technology for Studying Protein-Protein Interactions

[mysticalpeacenut]

MAGNA™: Revolutionizing Targeting RNA & Protein Interactions

MAGNA™ is a groundbreaking technology that enables real-time, single-molecule studies of RNA and protein-protein interactions. Discover how it's transforming biomolecular research.

[mysticalpeacenut]

protein-protein interactions Targeted Drug Discovery Solutions | Depixus

Explore cutting-edge protein-protein interactions -targeted drug discovery at Depixus. Our innovative approach harnesses precision medicine, unlocking new possibilities for therapeutic breakthroughs. Learn more about our advanced solutions and join the future of personalized healthcare.

[lindamcsherry]

Information on Heartburn Drugs and Kidney Injuries to be Presented During “Science Day”

The U.S. District Judge presiding over all federal kidney injury lawsuits involving a popular class of heartburn drugs, known as proton pump inhibitors (PPI), will hold a “science day” this week to hear presentations on medical and scientific issues that will come up during the litigation.

There are currently more than 2,300 Nexium lawsuits, Prilosec lawsuits, Prevacid lawsuits, Protonix lawsuits and other PPI claims pending before U.S. District Judge Claire C. Cecchi in the District of New Jersey, who is presiding over coordinated discovery and pretrial proceedings as part of a federal multidistrict litigation (MDL).

Each of the complaints raise similar allegations that the drug manufacturers failed to warn about the risk that users developed an acute kidney injury, chronic kidney disease, renal failure or other complications following use of the heartburn and acid reflux drugs.

In a case management order (PDF) issued on May 4, Judge Checchi outlined the structure for the “science day”, which will be held on May 16, 2018.

Given the early stage of the litigation and discovery in the cases, the parties agreed to several “ground rules” to avoid duplication in presentations, which are designed to educate the Court on the basic issues in a non-adversarial manner.

The presentations will be limited to background information on diseases treated by the drugs; background on the targeted heartburn drugs and other acid suppressors; injuries alleged by plaintiffs; and medical literature on the link between the heartburn drugs and kidney injuries.

Up to two experts will be presented per side, but they will not be questioned by the opposing counsel or other experts. The experts will give lecture-style presentations, and the conference will be closed to the public. None of the presentations will be admissible, discoverable or used for any purpose other than to educate the Court.

In complex pharmaceutical litigation, where large numbers of claims are being presented involving similar injuries associated with the same types of drugs, it is common for the Court to schedule such scientific presentations prior to ruling on discovery disputes, admissibility of evidence and other issues.

Proton Pump Inhibitor Kidney Risks

The proton pump inhibitor (PPI) litigation rapidly emerged following the publication of several studies in recent years, which suggest that users may face certain kidney risks that are not disclosed on the warning labels for the popular heartburn drugs, which are used by millions of Americans on a daily basis.

In December 2014, the FDA required new warnings for the first time about a form of kidney damage associated with proton pump inhibitors, known as acute interstitial nephritis (AIN), which involves a sudden inflammation of the kidneys, which can lead to more severe problems.

More recent studies have highlighted the potential link between Nexium and kidney problems, suggesting that the popular drugs make also cause users to experience acute kidney injury, chronic kidney disease and end-stage kidney failure, often resulting in the need for dialysis treatment or a kidney transplant.

Last year, a study published in the medical journal JAMA Internal Medicine also found an increased risk of chronic kidney disease with the heartburn medications, indicating that users of Nexium, Prilosec and other PPI may be 50% more likely when compared to non-users.

These findings were supported by another study published in April 2016, in which researchers with the Department of Veterans Affairs found that users of Nexium, Prilosec or other PPIs may be 96% more likely to develop kidney failure and 28% more likely to develop chronic kidney disease after five years of use.

As part of the MDL proceedings, it is expected that Judge Cecchi will eventually establish a “bellwether” program, where small groups of cases against each drug maker will be prepared for early trial dates. While the outcomes of such trials are not binding on other plaintiffs, they are designed to help gauge how juries may respond to certain evidence and testimony that is likely to be repeated throughout the litigation.

The post Information on Heartburn Drugs and Kidney Injuries to be Presented During “Science Day” appeared first on AboutLawsuits.com.

[cancersfakianakis1]

Repositioning of proton pump inhibitors in cancer therapy

Abstract

Drug repositioning, as a smart way to exploit new molecular targets of a known drug, has been gaining increasing attention in the discovery of anti-cancer drugs. Proton pump inhibitors (PPIs) as benzimidazole derivatives, which are essentially H+–K+-ATPases inhibitors, are commonly used in the treatment of acid-related diseases such as gastric ulcer. In recent years, exploring the new application of PPIs in anti-cancer field has become a hot research topic. Interestingly, cancer cells display an alkaline intracellular pH and an acidic extracellular pH. The extracellular acidity of tumors can be corrected by PPIs that are selectively activated in an acid milieu. It is generally believed that PPIs might provoke disruption of pH homeostasis by targeting V-ATPase on cancer cells, which is the theoretical basis for PPIs to play an anti-cancer role. Numerous studies have shown specialized effects of the PPIs on tumor cell growth, metastasis, chemoresistance, and autophagy. PPIs may really represent new anti-cancer drugs due to better safety and tolerance, the potential selectivity in targeting tumor acidity, and the ability to inhibit mechanism pivotal for cancer homeostasis. In this review, we focus on the new therapeutic applications of PPIs in multiple cancers, explaining the rationale behind this approach and providing practical evidence.

http://ift.tt/2zkOHfn

[cancersfakianakis1]

Repositioning of proton pump inhibitors in cancer therapy

Abstract

Drug repositioning, as a smart way to exploit new molecular targets of a known drug, has been gaining increasing attention in the discovery of anti-cancer drugs. Proton pump inhibitors (PPIs) as benzimidazole derivatives, which are essentially H+–K+-ATPases inhibitors, are commonly used in the treatment of acid-related diseases such as gastric ulcer. In recent years, exploring the new application of PPIs in anti-cancer field has become a hot research topic. Interestingly, cancer cells display an alkaline intracellular pH and an acidic extracellular pH. The extracellular acidity of tumors can be corrected by PPIs that are selectively activated in an acid milieu. It is generally believed that PPIs might provoke disruption of pH homeostasis by targeting V-ATPase on cancer cells, which is the theoretical basis for PPIs to play an anti-cancer role. Numerous studies have shown specialized effects of the PPIs on tumor cell growth, metastasis, chemoresistance, and autophagy. PPIs may really represent new anti-cancer drugs due to better safety and tolerance, the potential selectivity in targeting tumor acidity, and the ability to inhibit mechanism pivotal for cancer homeostasis. In this review, we focus on the new therapeutic applications of PPIs in multiple cancers, explaining the rationale behind this approach and providing practical evidence.

http://ift.tt/2zkOHfn

[medicilon-blog]

Discovery of small molecule inhibitors of protein-protein interactions

Medicilon offers fully integrated pharmaceutical services for the global scientific community. We focus on providing an exceptional client-centered experience and advancing the drug discovery process.

Email: Marketing@medicilon.com.cn                    Website: www.medicilon.com

Protein-protein interactions (PPIs) constitute an emerging class of targets for the next generation of therapeutic intervention. Despite their fundamental role in many biological processes and diseases such as cancer, PPIs are still largely underrepresented in drug discovery. Although small molecule PPI inhibitors are highly valuable due to a number of advantages relative to biological agents in terms of production, delivery, titratability and cost, the robust discovery of lead compounds remains a great challenge. Two structure-based drug discovery strategies are described in this work to generate small molecules to target PPIs.

A receptor-based drug discovery approach can be applied when an accurate threedimensional (3D) structure of a specific PPI complex is available. A novel, complementary and transformative approach for the rational design of small molecule inhibitors based on the crystal structure of the p53-Mdm2 complex was developed. This method is based on a tight interplay of structural biology information, the “anchor” concept, efficient chemical synthesis via multicomponent reactions (MCRs), as well as virtual and real screening processes. Applying the method we efficiently discovered several new scaffolds of inhibitors of the p53/Mdm2 interaction with lower micromolar affinity binding to Mdm2, which can serve as starting point for medicinal chemistry optimization. Advantages of our approach include high hit rates and less attrition based on the parallel discovery of multiple scaffolds, built-in optimization pathways using efficient MCRs, and fast generation of potential lead compounds. Potential anticancer drug candidates were identified by biochemical assays, co-crystallization, cell based assays, as well as further preclinical evaluations (solubility, metabolism, pharmacokinetics, and xenograft studies).

A ligand-based drug discovery approach was explored since PPIs are critically dependent on “anchor” residues, which can serve as the pharmacophore model for small molecules. Multicomponent reactions were employed for design of novel scaffolds and DOS of drug-like compounds, since hit identification of PPI inhibitors via traditional approaches such as high throughput screening (HTS) is fundamentally limited by chemotypes present in the library collections. Novel and diverse scaffolds based on the privileged structures (1,4-benzodiazepines, 1,4-thienodiazepines) and “anchor” residues, which can be accessible from multicomponent reactions, were designed and synthesized. Compared with conventional methods, these approaches are advantageous to generate small molecules targeting PPIs in terms of efficiency, diversity, and economy.

In summary, the approaches described in this dissertation constitute important contributions to the fields of medicinal chemistry and structure-based drug discovery, which combine structural insights and ligand design to expedite the discovery of novel small molecule inhibitors of PPIs.