Using DrugBank for In Silico Drug Exploration and Discovery

Recent Advances and Techniques for Identifying Novel Antibacterial Targets

BACKGROUND

With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly.

METHODS

In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification.

RESULTS

Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well.

CONCLUSION

The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.

Network Pharmacology-Based Combined with Experimental Validation Study to Explore the Underlying Mechanism of Agrimonia pilosa Ledeb. Extract in Treating Acute Myocardial Infarction

Purpose

The network pharmacology approach and validation experiment were performed to investigate the potential mechanisms of Agrimonia pilosa Ledeb. (APL) extract against acute myocardial infarction (AMI).

Methods

The primary compounds of APL extract were identified by High-Performance Liquid Chromatography (HPLC) analysis. The intersecting targets of active compounds and AMI were determined via network pharmacology analysis. A mouse model of AMI was established by subcutaneous injection of isoproterenol (Iso). Mice were treated with APL extract by intragastric administration. We assessed the effects of APL extract on the electrocardiography (ECG), cardiac representative markers, representative indicators of oxidative stress, pathological changes, and phosphatidylinositol-3-kinase (PI3K)/protein kinase B (Akt) signaling pathway, as well as apoptosis-related indicators in the mice.

Results

Five candidate compounds were identified in APL extract. Enrichment analyses indicated that APL extract could exert myocardial protective effects via the PI3K/Akt pathway. ST segment elevation and increased heart rate were obviously reversed in APL extract groups compared to Iso group. We also detected significant decreases in lactate dehydrogenase (LDH), creatine kinase (CK), creatine kinase MB (CK-MB), malondialdehyde (MDA), and reactive oxygen species (ROS), as well as a significant increase in superoxide dismutase activities (SOD) after APL extract treatment. In addition, APL extract markedly decreased the number of apoptotic cardiomyocytes after AMI. In the APL extract groups of AMI mice, there were increased expression levels of p-PI3K, p-Akt, and B-cell lymphoma-2 (Bcl-2) protein, and there were decreases in Bcl-2-associated X (Bax), cysteinyl aspartate-specific proteases-3 (caspase-3), and cleaved-caspase-3 protein expression levels, as well as the Bax/Bcl-2 ratio.

Conclusion

APL extract had a protective effect against Iso-induced AMI. APL extract could ameliorate AMI through antioxidant and anti-apoptosis actions which may be associated with the activation of the PI3K/Akt signaling pathway.

Icariin: A Potential Lipid Metabolism Regulator in Osteoarthritis

Background: Icariin is a small molecule drug capable of treating osteoarthritis. Additionally, icariin is known to have an excellent ability to regulate lipid metabolism. A growing number of studies have demonstrated that lipid metabolism is related to the pathogenesis of osteoarthritis. Therefore, by regulating lipid metabolism, icariin may have a significant role in osteoarthritis. However, the molecular mechanism by which icariin regulates lipid metabolism in osteoarthritis is currently unknown. Understanding the molecular mechanism would be helpful in the treatment of osteoarthritis. Objective: This study aimed to explore the mechanism of icariin that regulated lipid metabolism in the treatment of osteoarthritis through a combination of molecular docking and network pharmacology. Methods: Firstly, potential targets for icariin were collected from the TCMSP database, Pharm Mapper, and Swiss Target Prediction Server. Targets for osteoarthritis and lipid metabolism were obtained in OMIM, DrugBank, and GeneCards databases. Common targets of icariin, osteoarthritis, and lipid metabolism were acquired by clusterProfiler R package software. We then constructed the drug-target-signaling pathway-disease network after performing GO and KEGG enrichment analyses of common targets. Finally, we performed molecular docking validation. To support our findings, a search of PubMed was performed to find relevant literature published within the last 5 years. Results: We obtained 12 targets that may be important in the regulation of lipid metabolism in osteoarthritis by icariin. Through PPI network analysis, it was determined that 5 core targets, including TNF, PTGS2, CCND1, MMP2, and ESR1, participated in this process. Molecular docking results showed that the icariin had a high affinity to the core target proteins. Relevant studies in the literature suggest that TNF, PTGS2, MMP2, and ESR1 are the core targets. Conclusion: Icariin is a potential modulator of lipid metabolism in osteoarthritis, and the molecular mechanism may be related to core targets such as TNF, PTGS2, MMP2, and ESR1.

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

Characterization and impact of peptide physicochemical properties on oral and subcutaneous delivery

Peptides, an emerging modality within the biopharmaceutical industry, are often delivered subcutaneously with evolving prospects on oral delivery. Barrier biology within the subcutis or gastrointestinal tract is a significant challenge in limiting absorption or otherwise disrupting peptide disposition. Aspects of peptide pharmacokinetic performance and ADME can be mitigated with careful molecular design that tailors for properties such as effective size, hydrophobicity, net charge, proteolytic stability, and albumin binding. In this review, we endeavor to highlight effective techniques in qualifying physicochemical properties of peptides and discuss advancements of in vitro models of subcutaneous and oral delivery. Additionally, we will delineate empirical findings around the relationship of these physicochemical properties and in vivo (animal or human) impact. We conclude that robust peptide characterization methods and in vitro techniques with demonstrated correlations to in vivo data are key routines to incorporate in the drug discovery and development to improve the probability of technical and commercial success of peptide therapeutics.

Worldwide Protein Data Bank (wwPDB): A virtual treasure for research in biotechnology

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RSCB PDB) provides a wide range of digital data regarding biology and biomedicine. This huge internet resource involves a wide range of important biological data, obtained from experiments around the globe by different scientists. The Worldwide Protein Data Bank (wwPDB) represents a brilliant collection of 3D structure data associated with important and vital biomolecules including nucleic acids (RNAs and DNAs) and proteins. Moreover, this database accumulates knowledge regarding function and evolution of biomacromolecules which supports different disciplines such as biotechnology. 3D structure, functional characteristics and phylogenetic properties of biomacromolecules give a deep understanding of the biomolecules' characteristics. An important advantage of the wwPDB database is the data updating time, which is done every week. This updating process helps users to have the newest data and information for their projects. The data and information in wwPDB can be a great support to have an accurate imagination and illustrations of the biomacromolecules in biotechnology. As demonstrated by the SARS-CoV-2 pandemic, rapidly reliable and accessible biological data for microbiology, immunology, vaccinology, and drug development are critical to address many healthcare-related challenges that are facing humanity. The aim of this paper is to introduce the readers to wwPDB, and to highlight the importance of this database in biotechnology, with the expectation that the number of scientists interested in the utilization of Protein Data Bank's resources will increase substantially in the coming years.

Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises

There is an urgent need to identify new therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. This pandemic has thus spurred intensive research in most scientific areas and in a short period of time, several vaccines have been developed. But, while the race to find vaccines for COVID-19 has dominated the headlines, other types of therapeutic agents are being developed. In this mini-review, we report several databases and online tools that could assist the discovery of anti-SARS-CoV-2 small chemical compounds and peptides. We then give examples of studies that combined in silico and in vitro screening, either for drug repositioning purposes or to search for novel bioactive compounds. Finally, we question the overall lack of discussion and plan observed in academic research in many countries during this crisis and suggest that there is room for improvement.

Study on the Potential Mechanism of Fructus Tribuli in the Treatment of Hypertensive Vascular Remodeling Based on Network Pharmacology and Molecular Docking

Background

Hypertensive vascular remodeling (HVR) is the pathophysiological basis of hypertension, which is also an important cause of vascular disease and target organ damage. Treatment with Fructus Tribuli (FT), a traditional Chinese medicine, has a positive effect on HVR. However, the pharmacological mechanisms of FT are still unclear. Therefore, this study aimed to reveal the potential mechanisms involved in the effects of FT on HVR based on network pharmacology and molecular docking.

Materials and Methods

We selected the active compounds and targets of FT according to the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and the Swiss Target Prediction database, and the targets of HVR were collected from the Online Mendelian Inheritance in Man (OMIM), GeneCards, and DrugBank databases. The protein-protein interaction network (PPI) was established using the STRING database. Moreover, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and network analysis were performed to further explore the potential mechanisms. Finally, molecular docking methods were used to evaluate the affinity between the active compounds and the main target.

Results

Seventeen active compounds of FT  and 164 potential targets for the treatment of HVR were identified. Component-target and PPI networks were constructed, and 12 main active components and 33 main targets were identified by analyzing the topological parameters. Additionally, GO analysis indicated that the potential targets were enriched in 483 biological processes, 52 cellular components, and 110 molecular functions. KEGG analysis revealed that the potential targets were correlated with 122 pathways, such as the HIF-1 signaling pathway, ErbB signaling pathway, and VEGF signaling pathway. Finally, molecular docking showed that the 12 main active components had a good affinity for the top five main targets.

Conclusion

This study demonstrated the multiple compounds, targets, and pathway characteristics of FT in the treatment of HVR. The network pharmacology method provided a novel research approach to analyze potential mechanisms.

Carnosine to Combat Novel Coronavirus (nCoV): Molecular Docking and Modeling to Cocrystallized Host Angiotensin-Converting Enzyme 2 (ACE2) and Viral Spike Protein

Aims: Angiotensin-converting enzyme 2 (ACE2) plays an important role in the entry of coronaviruses into host cells. The current paper described how carnosine, a naturally occurring supplement, can be an effective drug candidate for coronavirus disease (COVID-19) on the basis of molecular docking and modeling to host ACE2 cocrystallized with nCoV spike protein. Methods: First, the starting point was ACE2 inhibitors and their structure–activity relationship (SAR). Next, chemical similarity (or diversity) and PubMed searches made it possible to repurpose and assess approved or experimental drugs for COVID-19. Parallel, at all stages, the authors performed bioactivity scoring to assess potential repurposed inhibitors at ACE2. Finally, investigators performed molecular docking and modeling of the identified drug candidate to host ACE2 with nCoV spike protein. Results: Carnosine emerged as the best-known drug candidate to match ACE2 inhibitor structure. Preliminary docking was more optimal to ACE2 than the known typical angiotensin-converting enzyme 1 (ACE1) inhibitor (enalapril) and quite comparable to known or presumed ACE2 inhibitors. Viral spike protein elements binding to ACE2 were retained in the best carnosine pose in SwissDock at 1.75 Angstroms. Out of the three main areas of attachment expected to the protein–protein structure, carnosine bound with higher affinity to two compared to the known ACE2 active site. LibDock score was 92.40 for site 3, 90.88 for site 1, and inside the active site 85.49. Conclusion: Carnosine has promising inhibitory interactions with host ACE2 and nCoV spike protein and hence could offer a potential mitigating effect against the current COVID-19 pandemic.

Targeting abnormal metabolism in Alzheimer's disease: The Drug Repurposing for Effective Alzheimer's Medicines (DREAM) study

Drug discovery for disease-modifying therapies for Alzheimer's disease and related dementias (ADRD) based on the traditional paradigm of experimental animal models has been disappointing. We describe the rationale and design of the Drug Repurposing for Effective Alzheimer's Medicines (DREAM) study, an innovative multidisciplinary alternative to traditional drug discovery. First, we use a systems biology perspective in the "hypothesis generation" phase to identify metabolic abnormalities that may either precede or interact with the accumulation of ADRD neuropathology, accelerating the expression of clinical symptoms of the disease. Second, in the "hypothesis refinement" phase we propose use of large patient cohorts to test whether drugs approved for other indications that also target metabolic drivers of ADRD pathogenesis might alter the trajectory of the disease. We emphasize key challenges in population-based pharmacoepidemiologic studies aimed at quantifying the association between medication use and ADRD onset and outline robust causal inference principles to safeguard against common pitfalls. Candidate ADRD treatments emerging from this approach will hold promise as plausible disease-modifying therapies for evaluation in randomized controlled trials.

Mining Drug-Target Associations in Cancer: Analysis of Gene Expression and Drug Activity Correlations

Cancer is a complex disease affecting millions of people worldwide, with over a hundred clinically approved drugs available. In order to improve therapy, treatment, and response, it is essential to draw better maps of the targets of cancer drugs and possible side interactors. This study presents a large-scale screening method to find associations of cancer drugs with human genes. The analysis is focused on the current collection of Food and Drug Administration (FDA)-approved drugs (which includes about one hundred chemicals). The approach integrates global gene-expression transcriptomic profiles with drug-activity profiles of a set of 60 human cell lines obtained for a collection of chemical compounds (small bioactive molecules). Using a standardized expression for each gene versus standardized activity for each drug, Pearson and Spearman correlations were calculated for all possible pairwise gene-drug combinations. These correlations were used to build a global bipartite network that includes 1007 gene-drug significant associations. The data are integrated into an open web-tool called GEDA (Gene Expression and Drug Activity) which includes a relational view of cancer drugs and genes, disclosing the putative indirect interactions found for FDA-approved drugs as well as the known targets of these drugs. The results also provide insight into the complex action of pharmaceuticals, presenting an alternative view to address predicted pleiotropic effects of the drugs.

Network Pharmacology Identifies the Mechanisms of Action of TaohongSiwu Decoction Against Essential Hypertension

BACKGROUND TaohongSiwu decoction (THSWT), a traditional herbal formula, has been used to treat cardiovascular and cerebrovascular diseases such as essential hypertension (EH) in China. However, the pharmacological mechanism is not clear. To investigate the mechanisms of THSWT in the treatment of EH, we performed compounds, targets prediction and network analysis using a network pharmacology method. MATERIAL AND METHODS We selected chemical constituents and targets of THSWT according to TCMSP and UniProtKB databases and collected therapeutic targets on EH from Online Mendelian Inheritance in Man (OMIM), Drugbank and DisGeNET databases. The protein-protein interaction (PPI) was analyzed by using String database. Then network was constructed by using Cytoscape_v3.7.1, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment was performed by using Database for Annotation, Visualization and Integrated Discovery (DAVID) software. RESULTS The results of our network pharmacology research showed that the THSWT, composed of 6 Chinese herbs, contained 15 compounds, and 23 genes regulated the main signaling pathways related to EH. Moreover, the PPI network based on targets of THSWT on EH revealed the interaction relationship between targets. These core compounds were 6 of the 15 disease-related compounds in the network, kaempferol, quercetin, luteolin, Myricanone, beta-sitosterol, baicalein, and the core genes contained ADRB2, CALM1, HMOX1, JUN, PPARG, and VEGFA, which were regulated by more than 3 compounds and significantly associated with Calcium signaling pathway, cGMP-PKG signaling pathway, cAMP signaling pathway, PI3K-Akt signaling pathway, Rap1 signaling pathway, and Ras signaling pathway. CONCLUSIONS This network pharmacological study can reveal potential mechanisms of multi-target and multi-component THSWT in the treatment of EH, provide a scientific basis for studying the mechanism.

Integrated use of bioinformatic resources reveals that co-targeting of histone deacetylases, IKBK and SRC inhibits epithelial-mesenchymal transition in cancer

With the advent of high-throughput technologies leading to big data generation, increasing number of gene signatures are being published to predict various features of diseases such as prognosis and patient survival. However, to use these signatures for identifying therapeutic targets, use of additional bioinformatic tools is indispensible part of research. Here, we have generated a pipeline comprised of nearly 15 bioinformatic tools and enrichment statistical methods to propose and validate a drug combination strategy from already approved drugs and present our approach using published pan-cancer epithelial-mesenchymal transition (EMT) signatures as a case study. We observed that histone deacetylases were critical targets to tune expression of multiple epithelial versus mesenchymal genes. Moreover, SRC and IKBK were the principal intracellular kinases regulating multiple signaling pathways. To confirm the anti-EMT efficacy of the proposed target combination in silico, we validated expression of targets in mesenchymal versus epithelial subtypes of ovarian cancer. Additionally, we inhibited the pinpointed proteins in vitro using an invasive lung cancer cell line. We found that whereas low-dose mono-therapy failed to limit cell dispersion from collagen spheroids in a microfluidic device as a metric of EMT, the combination fully inhibited dissociation and invasion of cancer cells toward cocultured endothelial cells. Given the approval status and safety profiles of the suggested drugs, the proposed combination set can be considered in clinical trials.

DrugBank 5.0: a major update to the DrugBank database for 2018

DrugBank (www.drugbank.ca) is a web-enabled database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets. First described in 2006, DrugBank has continued to evolve over the past 12 years in response to marked improvements to web standards and changing needs for drug research and development. This year's update, DrugBank 5.0, represents the most significant upgrade to the database in more than 10 years. In many cases, existing data content has grown by 100% or more over the last update. For instance, the total number of investigational drugs in the database has grown by almost 300%, the number of drug-drug interactions has grown by nearly 600% and the number of SNP-associated drug effects has grown more than 3000%. Significant improvements have been made to the quantity, quality and consistency of drug indications, drug binding data as well as drug-drug and drug-food interactions. A great deal of brand new data have also been added to DrugBank 5.0. This includes information on the influence of hundreds of drugs on metabolite levels (pharmacometabolomics), gene expression levels (pharmacotranscriptomics) and protein expression levels (pharmacoprotoemics). New data have also been added on the status of hundreds of new drug clinical trials and existing drug repurposing trials. Many other important improvements in the content, interface and performance of the DrugBank website have been made and these should greatly enhance its ease of use, utility and potential applications in many areas of pharmacological research, pharmaceutical science and drug education.

Overexpression of miR-101 May Target DUSP1 to Promote the Cartilage Degradation in Rheumatoid Arthritis

This study aimed to explore crucial genes that contribute to the development of rheumatoid arthritis (RA). Three GSE77298, GSE55457, and GSE55235 data sets were used to analyze the differentially expressed genes (DEGs) between RA synovial membrane tissue samples and normal synovial membrane tissue samples. Then, the functional enrichment analysis and protein-protein interactions (PPIs) construction were performed for DEGs. Subsequently, submodule analysis and regulatory network that contained transcription factors (TFs), microRNAs, and their targets were conducted. Finally, small-molecule drugs related to the DEGs were predicted. A total of 173 upregulated and 54 downregulated DEGs identified in at least 2 of 3 data sets. TYROBP, CTSS, MMP9, CXCR4, and CXCL10 were both highlighted in the PPI and submodule networks. In addition, miR-101, IRF1 TF, DUSP1, and CXCR4 had high degree in the regulatory network, and regulation pairs of miR-101-DUSP1 and IRF1 TF-CXCR4 were obtained. Drugs such as alemtuzumab and marimastat were negatively related to expression of the DEGs and might be useful drugs for RA treatment. In addition, most DEGs were involved in innate immune response (e.g., TYROBP, CCL5, CXCL10, FCGR1A, and FCGR3B) and phagosome pathway (e.g., CTSS). We suggested that miR-101 that regulated DUSP1, IRF1 TF that regulated CXCR4, as well as DEGs as TYROBP and CTSS might contribute to the RA pathogenesis. In addition, anti-inflammatory agent alemtuzumab and matrix metalloproteinase inhibitor marimastat might be useful drugs for RA treatment through functioning on their target genes.

Clinical value screening, prognostic significance and key pathway identification of miR-204-5p in endometrial carcinoma: A study based on the Cancer Genome Atlas (TCGA), and bioinformatics analysis

BACKGROUND

Endometrial carcinoma is one of the common carcinomas in the female reproductive system. It is reported that miR-204-5p is down-regulated in endometrial carcinoma. However, the mechanism and key pathways of miR-204-5p in endometrial carcinoma have not been clarified.

MATERIAL/METHODS

We evaluated the expression profiles and prognostic value of miR-204-5p expression in endometrial carcinoma by using bioinformatics analysis of a public dataset from TCGA. Drug of endometrial carcinoma from DrugBank, GO analysis, KEGG analysis, PPI network, mutation, as well as assessment of the prognostic significance were performed to the overlapping target genes of miR-204-5p in endometrial carcinoma. The relative expression levels of miR-204-5p target genes in endometrial carcinoma, including SF3B1, FBXW7, SPOP, and BRD4, were assessed by real-time quantitative polymerase chain reaction (RT-qPCR).

RESULTS

First, through DrugBank website, we obtained target drugs for endometrial carcinoma. MiR-204-5p expression was found to be lower in the endometrial carcinoma tissues than in adjacent normal tissues from TCGA. Next, we identified 143 genes as potential targets of miR-204-5p. Then, through GO enrichment analysis, KEGG signaling pathway and PPI analysis, we revealed the key networks in endometrial carcinoma. Next, mutation and assessment of the prognostic significance of endometrial carcinoma were obtained. At last, in endometrial carcinoma, the relative expression of SF3B1 and BRD4 increased, and the relative expression of FBXW7 decreased.

CONCLUSIONS

MiR-204-5p is down-regulated in endometrial carcinoma and affects the prognostic significance of endometrial carcinoma, which might play an important role in the tumorigenesis of endometrial carcinoma.

Aptamers and Clinical Applications

This chapter provides a brief introduction to followed by discussion of recent preclinical studies on potential aptamer drugs grouped into two broad categories, namely, “aptamer structures” and “non-ocular diseases.” Examples of aptamer-based targeting of drugs are then described. Next is an overview of the status of nearly 30 clinical trials of aptamer drugs currently listed in ClinicalTrials.gov, which is a registry and results database of publicly and privately supported clinical studies of human participants conducted around the world, and is a service of the US National Institutes of Health. This overview includes brief descriptions of each study sponsor, aptamer drug, disease(s), and type of study, as well as separate tables for completed studies, withdrawn or terminated studies, and active studies. The final section discusses Conclusions and Prospects.

Cellular Uptake of the Atypical Antipsychotic Clozapine Is a Carrier-Mediated Process

The weak base antipsychotic clozapine is the most effective medication for treating refractory schizophrenia. The brain-to-plasma concentration of unbound clozapine is greater than unity, indicating transporter-mediated uptake, which has been insufficiently studied. This is important, because it could have a significant impact on clozapine's efficacy, drug-drug interaction, and safety profile. A major limitation of clozapine's use is the risk of clozapine-induced agranulocytosis/granulocytopenia (CIAG), which is a rare but severe hematological adverse drug reaction. We first studied the uptake of clozapine into human brain endothelial cells (hCMEC/D3). Clozapine uptake into cells was consistent with a carrier-mediated process, which was time-dependent and saturable ( V_max = 3299 pmol/million cells/min, K_m = 35.9 μM). The chemical inhibitors lamotrigine, quetiapine, olanzapine, prazosin, verapamil, indatraline, and chlorpromazine reduced the uptake of clozapine by up to 95%. This could in part explain the in vivo interactions observed in rodents or humans for these compounds. An extensive set of studies utilizing transporter-overexpressing cell lines and siRNA-mediated transporter knockdown in hCMEC/D3 cells showed that clozapine was not a substrate of OCT1 (SLC22A1), OCT3 (SLC22A3), OCTN1 (SLC22A4), OCTN2 (SLC22A5), ENT1 (SLC29A1), ENT2 (SLC29A2), and ENT4/PMAT (SLC29A4). In a recent genome-wide analysis, the hepatic uptake transporters SLCO1B1 (OATP1B1) and SLCO1B3 (OATP1B3) were identified as additional candidate transporters. We therefore also investigated clozapine transport into OATP1B-transfected cells and found that clozapine was neither a substrate nor an inhibitor of OATP1B1 and OATP1B3. In summary, we have identified a carrier-mediated process for clozapine uptake into brain, which may be partly responsible for clozapine's high unbound accumulation in the brain and its drug-drug interaction profile. Cellular clozapine uptake is independent from currently known drug transporters, and thus, molecular identification of the clozapine transporter will help to understand clozapine's efficacy and safety profile.

Identification of novel drug targets in bovine respiratory disease: an essential step in applying biotechnologic techniques to develop more effective therapeutic treatments

BACKGROUND

Bovine Respiratory Disease (BRD) is a major problem in cattle production which causes substantial economic loss. BRD has multifactorial aetiologies, is multi-microbial, and several of the causative pathogens are unknown. Consequently, primary management practices such as metaphylactic antimicrobial injections for BRD prevention are used to reduce the incidence of BRD in feedlot cattle. However, this poses a serious threat in the form of development of antimicrobial resistance and demands an urgent need to find novel interventions that could reduce the effects of BRD drastically and also delay/prevent bacterial resistance.

MATERIALS AND METHODS

We have employed a subtractive genomics approach that helps delineate essential, host-specific, and druggable targets in pathogens responsible for BRD. We also proposed antimicrobials from FDA green and orange book that could be repositioned for BRD.

RESULTS

We have identified 107 putative targets that are essential, selective and druggable. We have also confirmed the susceptibility of two BRD pathogens to one of the proposed antimicrobials - oxytetracycline.

CONCLUSION

This approach allows for repositioning drugs known for other infections to BRD, predicting novel druggable targets for BRD infection, and providing a new direction in developing more effective therapeutic treatments for BRD.