Funding for developing the data integration workflow was obtained from European Community&#8217;s Seventh Framework Programme under grant agreement nu. 279113 (OCTIPS). Adaption of data within this publication was kindly approved by Public Library of Sciences Publications and Impact Journals, LLC.

<ol>
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AH and PP were employees of emergentec biodevelopment GmbH at the time of performing the analyses leading to the results presented in the representative results section. MM and MK have nothing to disclose.

We have outlined a workflow to identify drug combinations impacting synthetic lethal interactions. This workflow makes use of (i) data on synthetic lethal interactions from model organisms, (ii) information of human orthologs, (iii) information on drug-target associations, (iv) drug information on clinical trials in the context of cancer, as well as (v) on information of drug-disease and gene-disease associations extracted from scientific literature. The consolidated information can be used to evaluate the impact of a gi...

Synthetic lethality defines an association of two genes, where loss of one gene does not affect viability, but loss of both genes leads to cell death. It was first described in 1946 by Dobzhansky while analyzing various phenotypes of drosophila by breeding homozygous mutants1. Mutants that did not produce viable offspring, although viable themselves, exhibited lethal phenotypes when crossed with certain other mutants, setting ground for the establishment of the theory of synthetic lethality. Hartwell and colleagues suggested that this concept might be applicable for cancer therapy in humans2. Pharmacologically provoked synthetic lethality could rely on just one mutation, given that the mutated gene&#8217;s synthetic lethal partner is targetable by a pharmacological compound. The first gene pair to enable pharmacological induction of synthetic lethality was BRCA(1/2) and PARP1. PARP1 functions as a sensor for DNA damage, and is tied to sites of double and single DNA strand-breaks, supercoils and crossovers3. BRCA1 and 2 play major roles in repair of DNA double-strand breaks through homologous recombination4. Farmer and colleagues published findings that cells deficient for BRCA1/2 were susceptible to PARP inhibition, while no cytotoxicity was observed in BRCA wild-type cells5. Ultimately, PARP inhibitors were approved for the treatment of BRCA deficient breast and ovarian cancer6,7. Further, synthetic lethality gene pairs leading to clinical approval of pharmacological compounds are much anticipated and a major area of recent cancer research efforts8.
Synthetic lethal gene interactions were modelled in multiple organisms including fruit flies, C. elegans and yeast2. Using various approaches including RNA-interference- and CRISPR/CAS-library knockouts, novel synthetic lethal gene pairs were discovered in recent years9,10,11. A protocol on the experimental procedures of RNAi in combination with CRISPR/CAS was recently published by Housden and colleagues12. Meanwhile, researchers also conducted large screens in haploid human cells to identify synthetic lethal interactions13,14. In silico methods like biological network analysis and machine learning have also shown promise in the discovery of synthetic lethal interactions15,16.
Conceptionally, one approach to make use of synthetic lethal interactions in the context of anti-tumor therapy is to identify mutated or non-functional proteins in tumor cells, making their synthetic lethal interaction partners promising drug targets for therapeutic intervention. Due to the heterogeneity of most tumor types, researchers have started the search for so-called synthetic lethal hub proteins. These synthetic lethal hubs have a number of synthetic lethal interaction partners that are either mutated and therefore non-functional or significantly downregulated in tumor samples. Addressing such synthetic lethal hubs holds promise in increasing drug efficacy or overcoming drug resistance as could be shown for instance in the context of vincristine resistant neuroblastoma17. A second approach to enhance drug treatment making use of the concept of synthetic lethal interactions is to identify drug combinations targeting synthetic lethal interactions. This could lead to new combinations of already approved single anti-tumor therapies and to the repositioning of drugs from other disease areas to the field of oncology.
In this article, we present a step-by-step procedure to yield a list of drug combinations that target synthetic lethal interaction pairs. In this workflow, we (i) use data on synthetic lethal interactions from BioGRID and (ii) information on homologous genes from Ensembl, (iii) retrieve drug-target pairs from DrugBank, (iv) build disease-drug associations from ClinicalTrials.gov, and (v) hence generate a set of drug combinations addressing synthetic lethal interactions. Lastly, we provide drug combinations in the context of ovarian and breast cancer in the representative results section.

<table><tbody><tr><td>BioGRID</td><td>n/a</td><td>n/a</td><td>thebiogrid.org</td></tr><tr><td>ClinicalTrials.gov</td><td>n/a</td><td>n/a</td><td>ClinicalTrials.gov</td></tr><tr><td>DrugBank</td><td>n/a</td><td>n/a</td><td>drugbank.ca</td></tr><tr><td>Ensembl BioMart</td><td>n/a</td><td>n/a</td><td>ensembl.org</td></tr><tr><td>&lt;strong&gt;for alternative computational databases please refer to the manuscript&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>7-AAD</td><td>ebioscience</td><td>00-6993-50</td><td></td></tr><tr><td>AnnexinV-APC</td><td>BD Bioscience</td><td>550474</td><td></td></tr><tr><td>celecoxib</td><td>Sigma-Aldrich</td><td>PZ0008-25MG</td><td></td></tr><tr><td>CellTiter-Blue Viability Assay</td><td>Promega</td><td>G8080</td><td></td></tr><tr><td>FACS Canto II</td><td>BD Bioscience</td><td>n/a</td><td></td></tr><tr><td>fetal bovine serum</td><td>Fisher Scientific/Gibco</td><td>16000044</td><td></td></tr><tr><td>FloJo Software</td><td>FloJo LLC</td><td>V10</td><td></td></tr><tr><td>McCoy&#039;s 5a Medium Modified</td><td>Fisher Scientific/Gibco</td><td>16600082</td><td></td></tr><tr><td>penicillin G/streptomycin sulfate</td><td>Fisher Scientific/Gibco</td><td>15140122</td><td></td></tr><tr><td>SKBR-3 cells</td><td>American Type Culture Collection (ATCC)</td><td>ATCC HTB-30</td><td></td></tr><tr><td>zoledronic acid</td><td>Sigma-Aldrich</td><td>SML0223-50MG</td><td></td></tr><tr><td>&lt;strong&gt;further materials or equipment will be made available upon request&lt;/strong&gt;</td><td></td><td></td><td></td></tr></tbody></table>

a data integration workflow to identify drug combinations targeting synthetic lethal interactions

A synthetic lethal interaction between two genes is given when knock-out of either one of the two genes does not affect cell viability but knock-out of both synthetic lethal interactors leads to loss of cell viability or cell death. The best studied synthetic lethal interaction is between BRCA1/2 and PARP1, with PARP1 inhibitors being used in clinical practice to treat patients with BRCA1/2 mutated tumors. Large genetic screens in model organisms but also in haploid human cell lines have led to the identification of numerous additional synthetic lethal interaction pairs, all being potential targets of interest in the development of novel tumor therapies. One approach is to therapeutically target genes with a synthetic lethal interactor that is mutated or significantly downregulated in the tumor of interest. A second approach is to formulate drug combinations addressing synthetic lethal interactions. In this article, we outline a data integration workflow to evaluate and identify drug combinations targeting synthetic lethal interactions. We make use of available datasets on synthetic lethal interaction pairs, homology mapping resources, drug-target links from dedicated databases, as well as information on drugs being investigated in clinical trials in the disease area of interest. We further highlight key findings of two recent studies of our group on drug combination assessment in the context of ovarian and breast cancer.

Our group has recently published two studies applying the workflow depicted in this manuscript to identify drug combinations targeting synthetic lethal interactions in the context of ovarian and breast cancer24,25. In the first study, we evaluated drug combinations that are currently tested in late stage clinical trials (phase III and IV) or already being used in clinical practice to treat ovarian cancer patients regarding their impact on synthetic lethal interac...

Watch this Scientific Journal Video about A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions at JoVE.com

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Large genetic screens in model organisms have led to the identification of negative genetic interactions. Here, we describe a data integration workflow using data from genetic screens in model organisms to delineate drug combinations targeting synthetic lethal interactions in cancer.

A synthetic lethal interaction between two genes is given when knock-out of either one of the two genes does not affect cell viability but knock-out of ...

a-data-integration-workflow-to-identify-drug-combinations-targeting

Research

JoVE Journal

Cancer Research

4.1K Views.  Medical University of Vienna. Een synthetische letale interactie tussen twee genen treedt op wanneer een enkele knockdown van een van beide genen de levensvatbaarheid van de cel niet beïnvloedt, maar knockdown van beide synthetische dodelijke partners leidt tot verlies van cel levensvatbaarheid of celdood. Het combineren van heterogene datasets kan leiden tot de identificatie van synthetische letale interacties die kunnen worden gericht door medicijncombinaties bij ziekten zoals kanker, met als doel de celproliferatie te...

Video: A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Although antimicrobial drugs have dramatically increased the lifespan and quality of life in the 20th century, antimicrobial resistance threatens our entire society's ability to treat systemic infections. In the United States alone, antibiotic-resistant infections kill approximately 23,000 people a year and cost around 20 billion USD in additional healthcare. One approach to combat antimicrobial resistance is combination therapy, which is particularly useful in the critical early stage of infection, before the infecting organism and its drug resistance profile have been identified. Many antimicrobial treatments use combination therapies. However, most of these combinations are additive, meaning that the combined efficacy is the same as the sum of the individual antibiotic efficacy. Some combination therapies are synergistic: the combined efficacy is much greater than additive. Synergistic combinations are particularly useful because they can inhibit the growth of antimicrobial drug resistant strains. However, these combinations are rare and difficult to identify. This is due to the sheer number of molecules needed to be tested in a pairwise manner: a library of 1,000 molecules has 1 million potential combinations. Thus, efforts have been made to predict molecules for synergy. This article describes our high-throughput method for predicting synergistic small molecule pairs known as the Overlap2 Method (O2M). O2M uses patterns from chemical-genetic datasets to identify mutants that are hypersensitive to each molecule in a synergistic pair but not to other molecules. The Brown lab exploits this growth difference by performing a high-throughput screen for molecules that inhibit the growth of mutant but not wild-type cells. The lab's work previously identified molecules that synergize with the antibiotic trimethoprim and the antifungal drug fluconazole using this strategy. Here, the authors present a method to screen for novel synergistic combinations, which can be altered for multiple microorganisms.

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Synergistic drug combinations are difficult and time-consuming to identify empirically. Here, we describe a method for identifying and validating synergistic small molecules.

Although antimicrobial drugs have dramatically increased the lifespan and quality of life in the 20th century, antimicrobial resistance threatens our ...

Genetics

Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation

Potentiation of hostile monoclonal antibodies (mAb) by chemotherapeutic agents constitutes a valuable strategy for designing effective and safer therapy against cancer. Here we provide a protocol to identify a rational combination at the preclinical step. First, we describe a cell-based assay to assess the synergism between anticancer mAb and cytotoxic drugs, that uses the combination index equation of Chou and Talalay1. This includes the measurement of tumor cell drug- and antibody-sensitivity using an MTT assay, followed by an automated computer analysis to calculate the combination index (CI) values. CI values of &lt;1 indicate synergism between tested mAbs and cytotoxic agents1. To corroborate the in vitro findings in vivo, we further describe a method to assess the combination regimen efficacy in a xenograft tumor model. In this model, the combined regimen significantly delays tumor growth, which results in a significant extended survival in comparison to single-agent controls. Importantly, the in vivo experimentation reveals that the combination regimen is well tolerated. This protocol allows the effective evaluation of anticancer drug combinations in preclinical models and the identification of rational combination to evaluate in clinical trials.

This protocol describes how to assess synergism between an anticancer antibody and antineoplastic drugs in preclinical models by using the combination index equation of Chou and Talalay.

Potentiation of hostile monoclonal antibodies (mAb) by chemotherapeutic agents constitutes a valuable strategy for designing effective and safer therapy ...

A Rapid Screening Workflow to Identify Potential Combination Therapy for GBM using Patient-Derived Glioma Stem Cells

The glioma stem cells (GSCs) are a small fraction of cancer cells which play essential roles in tumor initiation, angiogenesis, and drug resistance in glioblastoma (GBM), the most prevalent and devastating primary brain tumor. The presence of GSCs makes the GBM very refractory to most of individual targeted agents, so high-throughput screening methods are required to identify potential effective combination therapeutics. The protocol describes a simple workflow to enable rapid screening for potential combination therapy with synergistic interaction. The general steps of this workflow consist of establishing luciferase-tagged GSCs, preparing matrigel coated plates, combination drug screening, analyzing, and validating the results.

The glioma stem cells (GSCs) are a small fraction of cancer cells which play essential roles in tumor initiation, angiogenesis, and drug resistance in ...

Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

Understanding clinically relevant driver mechanisms of acquired chemo-resistance is crucial for elucidating ways to circumvent resistance and improve survival in patients with acute myeloid leukemia (AML). A small fraction of leukemic cells that survive chemotherapy have a poised epigenetic state to tolerate chemotherapeutic insult. Further exposure to chemotherapy allows these drug persister cells to attain a fixed epigenetic state, which leads to altered gene expression, resulting in the proliferation of these drug-resistant populations and eventually relapse or refractory disease. Therefore, identifying epigenetic modulations that necessitate the survival of drug-resistant leukemic cells is critical. We detail a protocol to identify epigenetic modulators that mediate resistance to the nucleoside analog cytarabine (AraC) using pooled shRNA library screening in an acquired cytarabine-resistant AML cell line. The library consists of 5,485 shRNA constructs targeting 407 human epigenetic factors, which allows high-throughput epigenetic factor screening.

High-throughput RNA interference (RNAi) screening using a pool of lentiviral shRNAs can be a tool to detect therapeutically relevant synthetic lethal targets in malignancies. We provide a pooled shRNA screening approach to investigate the epigenetic effectors in acute myeloid leukemia (AML).

Understanding clinically relevant driver mechanisms of acquired chemo-resistance is crucial for elucidating ways to circumvent resistance and improve ...

MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data

Systematic screening of gain- or loss-of-function genetic perturbations can be used to characterize the genetic dependencies and mechanisms of regulation for essentially any cellular process of interest. These experiments typically involve profiling from a pool of single gene perturbations and how each genetic perturbation affects the relative cell fitness. When applied in the context of drug efficacy studies, often called chemo-genetic profiling, these methods should be effective at identifying drug mechanisms of action. Unfortunately, fitness-based chemo-genetic profiling studies are ineffective at identifying all components of a drug response. For instance, these studies generally fail to identify which genes regulate drug-induced cell death. Several issues contribute to obscuring death regulation in fitness-based screens, including the confounding effects of proliferation rate variation, variation in the drug-induced coordination between growth and death, and, in some cases, the inability to separate DNA from live and dead cells. MEDUSA is an analytical method for identifying death-regulatory genes in conventional chemo-genetic profiling data. It works by using computational simulations to estimate the growth and death rates that created an observed fitness profile rather than scoring fitness itself. Effective use of the method depends on optimal tittering of experimental conditions, including the drug dose, starting population size, and length of the assay. This manuscript will describe how to set up a chemo-genetic profiling study for MEDUSA-based analysis, and we will demonstrate how to use the method to quantify death rates in chemo-genetic profiling data.

This protocol describes how to use the MEDUSA analytical method to quantify the death regulatory effect of each gene knockout. It includes instructions on determining experimental conditions that optimize sensitivity and a step-by-step tutorial on performing the analysis.

Systematic screening of gain- or loss-of-function genetic perturbations can be used to characterize the genetic dependencies and mechanisms of regulation ...

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

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

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

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

Medicine

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

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

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

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

Bioengineering

Diagonal Method to Measure Synergy Among Any Number of Drugs

A synergistic drug combination has a higher efficacy compared to the effects of individual drugs. Checkerboard assays, where drugs are combined in many doses, allow sensitive measurement of drug interactions. However, these assays are costly and do not scale well for measuring interaction among many drugs. Several recent studies have reported drug interaction measurements using a diagonal sampling of the traditional checkerboard assay. This alternative methodology greatly decreases the cost of drug interaction experiments and allows interaction measurement for combinations with many drugs. Here, we describe a protocol to measure the three pairwise interactions and one three-way interaction among three antibiotics in duplicate, in five days, using only three 96-well microplates and standard laboratory equipment. We present representative results showing that the three-antibiotic combination of Levofloxacin + Nalidixic Acid + Penicillin G is synergistic. Our protocol scales up to measure interactions among many drugs and in other biological contexts, allowing for efficient screens for multi-drug synergies against pathogens and tumors.

In this protocol, we describe how to make Loewe additivity-based drug interaction measurements for pairwise and three-way drug combinations.

A synergistic drug combination has a higher efficacy compared to the effects of individual drugs. Checkerboard assays, where drugs are combined in many ...

Biology

Jiawei Shengjiang San to Address Diabetic Nephropathy Using Network Pharmacology

Antagonistic Effect of Jiawei Shengjiang San on a Rat Model of Diabetic Nephropathy: Related to EGFR/MAPK3/1 Signaling Pathway

We aimed to delve into the mechanisms underpinning Jiawei Shengjiang San's (JWSJS) action in treating diabetic nephropathy and deploying network pharmacology. Employing network pharmacology and molecular docking techniques, we predicted the active components and targets of JWSJS and constructed a meticulous "drug-component-target" network. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses were utilized to discern the therapeutic pathways and targets of JWSJS. Autodock Vina 1.2.0 was deployed for molecular docking verification, and a 100-ns molecular dynamics simulation was conducted to affirm the docking results, followed by in vivo animal verification. The findings revealed that JWSJS shared 227 intersecting targets with diabetic nephropathy, constructing a protein-protein interaction network topology. KEGG enrichment analysis denoted that JWSJS mitigates diabetic nephropathy by modulating lipids and atherosclerosis, the PI3K-Akt signaling pathway, apoptosis, and the HIF-1 signaling pathway, with mitogen-activated protein kinase 1 (MAPK1), MAPK3, epidermal growth factor receptor (EGFR), and serine/threonine-protein kinase 1 (AKT1) identified as collective targets of multiple pathways. Molecular docking asserted that the core components of JWSJS (quercetin, palmitoleic acid, and luteolin) could stabilize conformation with three pivotal targets (MAPK1, MAPK3, and EGFR) through hydrogen bonding. In vivo examinations indicated notable augmentation in body weight and reductions in glycated serum protein (GSP), low-density lipoprotein cholesterol (LDL-C), uridine triphosphate (UTP), and fasting blood glucose (FBG) levels due to JWSJS. Electron microscopy coupled with hematoxylin and eosin (HE) and Periodic acid-Schiff (PAS) staining highlighted the potential of each treatment group in alleviating kidney damage to diverse extents, exhibiting varied declines in p-EGFR, p-MAPK3/1, and BAX, and increments in BCL-2 expression in the kidney tissues of the treated rats. Conclusively, these insights suggest that the protective efficacy of JWSJS on diabetic nephropathy might be associated with suppressing the activation of the EGFR/MAPK3/1 signaling pathway and alleviating renal cell apoptosis.

Author Spotlight: Network Pharmacology and Molecular Docking to Decipher the Action of Jiawei Shengjiang San Against Diabetic Kidney Disease

Here, we present a protocol describing network pharmacology and molecular docking techniques to explore the mechanism of action of Jiawei Shengjiang San (JWSJS) in treating diabetic nephropathy.

We aimed to delve into the mechanisms underpinning Jiawei Shengjiang San's (JWSJS) action in treating diabetic nephropathy and deploying network ...

Quadruple-Checkerboard: A Modification of the Three-Dimensional Checkerboard for Studying Drug Combinations

The concept of drug-combination therapy is becoming very important mainly with the drastic increase in resistance to drugs. The Quadruple&#160;checkerboard, also called the Q-checkerboard, aims at maximizing the number of possible combinations that can be obtained between four drugs in one experiment to minimize the time and work needed to accomplish the same results with other protocols. This protocol is based on the simple micro dilution technique where the drugs are diluted and combined together in several 96-well plates.
In the first set of 96-well plates, Muller-Hinton broth is added followed by the first required drug (e.g., Cefotaxime here) to serially dilute it. After the first step is done, another set of 96-well plates is used to dilute the second drug (e.g., Amikaci), which will be transferred by removing a specific volume of drug 2 and put in the corresponding wells in the first set of 96-well plates that contains drug one. The third step is done by adding the required concentrations of the third drug (e.g., Levofloxacin), to the appropriate plates in the initial set containing combination of drug 1 and 2. The fourth step is done by adding the required concentrations of the fourth drug (e.g., Trimethoprim-sulfamethoxazol) into the appropriate plates in the first set. Then, E. coli ESBL bacterial inoculum will be prepared and added.
This method is important to evaluate all the possible combinations and has a wider range of possibilities to be tested furthermore for in vivo testing. Despite being a tiring technique requiring a lot of focus, the results are remarkable and time saving where a lot of combinations can be tested in a single experiment.

This protocol describes how to study all possible combinations that can be obtained between four drugs in one single experiment. This method is based on the standard 96-well plate micro dilution assay and the calculation of fractional inhibitory concentrations (FICs) to evaluate the results.

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

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