Molecular Mechanisms Of Drug Action Research Articles

Machine learning-based integration of network features and chemical structure of compounds for SARS-CoV-2 drug effect analysis.

High drug development costs and the limited number of new annual drug approvals increase the need for innovative approaches for drug effect prediction. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), led to a global pandemic with high morbidity and mortality. Although effective preventive measures exist, there are few effective treatments for hospitalized patients with SARS-CoV-2 infection. Drug repurposing and drug effect prediction arepromising strategiesthat could shorten development time and reduce costs compared with de novo drug discovery. In this work, we present a machine learning framework to integrate a variety of target network features and physicochemical properties of compounds, and analyze their influence on the therapeutic effects for SARS-CoV-2 infection and on host cell cytotoxic effects. Random forest models trained on compounds with known experimental effects on SARS-CoV-2 infection and subsequent feature importance analysis based on Shapley values provided insights into the determinants of drug efficacy and cytotoxicity, which can be incorporated into novel drug discovery approaches. Given the complexity of molecular mechanisms of drug action and limited sample sizes, our models achieve a reasonable mean area under the receiver operating characteristic curve (ROC-AUC) of 0.73 on an unseen validation set. To our knowledge, this is the first work to incorporate a combination of network and physicochemical features of compounds into a machine learning model to predict drug effects on SARS-CoV-2 infection. Our systems pharmacology-based machine learning framework can be used to classify other existing drugs for SARS-CoV-2 infection and can easily be adapted to drug effect prediction for future viral outbreaks.

Ayurgenomics-based frameworks in precision and integrative medicine: Translational opportunities.

In today's globalized and flat world, a patient can access and seek multiple health and disease management options. A digitally enabled participatory framework that allows an evidence-based informed choice is likely to assume an immense importance in the future. In India, traditional knowledge systems, like Ayurveda, coexist with modern medicine. However, due to limited crosstalk between the clinicians of both disciplines, a patient attempts integrative medicine by seeking both options independently with limited understanding and evidence. There is a need for an integrative medicine platform with a formalized approach, which allows practitioners from the two diverse systems to crosstalk, coexist, and coevolve for an informed cross-referral that benefits the patients. To be successful, this needs frameworks that enable the bridging of disciplines through a common interface with shared ontologies. Ayurgenomics is an emerging discipline that explores the principles and practices of Ayurveda combined with genomics approaches for mainstream integration. The present review highlights how in conjunction with different disciplines and technologies this has provided frameworks for (1) the discovery of molecular correlates to build ontological links between the two systems, (2) the discovery of biomarkers and targets for early actionable interventions, (3) understanding molecular mechanisms of drug action from its usage perspective in Ayurveda with applications in repurposing, (4) understanding the network and P4 medicine perspective of Ayurveda through a common organizing principle, (5) non-invasive stratification of healthy and diseased individuals using a compendium of system-level phenotypes, and (6) developing evidence-based solutions for practice in integrative medicine settings. The concordance between the two contrasting streams has been built through extensive explorations and iterations of the concepts of Ayurveda and genomic observations using state-of-the-art technologies, computational approaches, and model system studies. These highlight the enormous potential of a trans-disciplinary approach in evolving solutions for personalized interventions in integrative medicine settings.

GC-MS Analysis and In-vitro Apoptosis Induction and Anticancer Activity of Methanol Extract of Aspergillus terreusagainst Lung Cancer

The present study was focusing on qualitative and quantification of bioactive compounds present in Aspergillus terreus and evaluating its anticancer activity and apoptosis detection against lung cancer. Methods: A. terreus was sequentially extracted using the Soxhlet extraction technique with hexane, ethyl acetate, methanol and distilled water. Detection of bioactive compounds was done using Standard biochemical tests and GC-MS analysis was performed with NIST database to identify the bioactive compounds. The toxicity and anticancer activity of crude extract was investigated using MTT assay on L929 cells and lung cancer A549 cells whereas apoptosis study was conducted through Flowcytometry-based surface marker study on the A549 cancer cell line. Results: secondary metabolites analysis showed the presence of phenols and terpenoids as major constituents in the methanol extract whereas other solvent extracts have shown the absence of major bioactive compounds. Quantification studies showed that methanol extract has shown the phenolic content 179 µg/g of Gallic acid equivalent. The GC-MS analysis showed the presence of 1-Flurodecane, Methyl palmitate, Ethyl palmitate, 9, 12-Octadecanopic acid, 10-Octadecanoic acid, Methyl stearate, Octadecadeoinoate, Ethyl 9-hexadecanoate and 1-Monoarachidin as major bioactive compounds. Further, MTT based toxicity study on the L929 cell line revealed that methanol extract at lower concentrations like 50µg, 100µg and150µg shown more than 50% of cell viability and at higher concentration between 200µg-250 µg it was showing toxic nature with 47.89±0.01% viability. In case of anticancer activity against lung cancer A549 cell line the methanol extract have shown the dose dependent activity i.e the percentage of cell viability was decreased with increase in the concentration of methanol extract at 250µg the cell viability was found to be 35.12±0.005%. Flow cytometry based apoptosis study revealed that methanol extract has shown the inducing apoptosis in treated lung cancer A549 cells with percentage of 10.84. Conclusion: overall the present study shown that A. terreus possess different class of bioactive compounds and it has higher phenolic content. Toxicity study showed that methanol extract exhibited toxic nature at higher concentration on tested cell line and Anticancer and Apoptosis study revealed that methanol extract has shown the prominent with inhibiting the growth of lung cancer A549 cells through inducing apoptosis. Further, A. terreus would be a promising natural microorganism that has to be further researched in order to discover and isolate potent drug to treat cancer. Future studies will be on study of in-vivo animal studies and study of molecular mechanism of drug action on particular with anticancer study.

Low-dose cisplatin exposure and SNAIL, Vimentin, E-cadherin expression in HEPG2 cell line

Cisplatin, the first platinum compound approved for cancer treatment, is widely used in the treatment of various cancers including hepatocellular carcinoma (HCC). HCC incidence rates rise globally. Epithelial mesenchymal transition (EMT) is implicated in cancer invasion and metastasis, which are associated with increased mortality. Cisplatin dose might influence cancer invasion and metastatic behavior of the cells. The aim of the study was to investigate the effect of low-dose cisplatin treatment on EMT- related changes in HepG2 cells. Following treatment with 4 µM cisplatin, HepG2 cells were evaluated morphologically. Gene expression of E-cadherin, Vimentin, Snail1 was assessed by quantitative PCR. Immunofluorescence analyses of NA-K ATPase were performed. Although the low-dose cisplatin treated cells exhibited a more stretched morphology, no statistical difference was detected in gene expression of E-cadherin, Vimentin, Snail1 and immunofluorescence of NA-K ATPase. Findings on low-dose cisplatin effects in HepG2 might contribute to the knowledge of antineoplastic inefficacy by further understanding the molecular mechanisms of drug action.

Structural insights into ligand recognition, activation, and signaling of the α2A adrenergic receptor.

The α2A adrenergic receptor (α2AAR) is a G protein (heterotrimeric guanine nucleotide–binding protein)–coupled receptor that mediates important physiological functions in response to the endogenous neurotransmitters norepinephrine and epinephrine, as well as numerous chemically distinct drugs. However, the molecular mechanisms of drug actions remain poorly understood. Here, we report the cryo–electron microscopy structures of the human α2AAR-GoA complex bound to norepinephrine and three imidazoline derivatives (brimonidine, dexmedetomidine, and oxymetazoline). Together with mutagenesis and functional data, these structures provide important insights into the molecular basis of ligand recognition, activation, and signaling at the α2AAR. Further structural analyses uncover different molecular determinants between α2AAR and βARs for recognition of norepinephrine and key regions that determine the G protein coupling selectivity. Overall, our studies provide a framework for understanding the signal transduction of the adrenergic system at the atomic level, which will facilitate rational structure-based discovery of safer and more effective medications for α2AAR.

Unveiling the relevance of the redox character of nitroaromatic and nitroheteroaromatic compounds as potential medicines

This review discusses the state of the art, challenges, and perspectives in recent applications of nitroaromatics and nitroheteroaromatics, which are redox-bio-activated drugs or leads, in Medicinal Chemistry. It deals mainly with the electrochemical approach toward the electron transfer-based molecular mechanisms of drug action, drug design, estimation and measurement of redox potentials, correlation of physicochemical and pharmacological data, and electrochemical studies of the main representatives of nitro-containing prodrugs, along with approaches to combat their toxicity issues, aiming at a better therapeutic profile. Electrochemical investigation plays essential roles, being strategic in the design and discovery of potential medicines. • Nitroaromatics are redox-activated prodrugs. • Electrochemical-guided work on nitroaromatics led to the discovery of new medicinal prototypes. • Hiphenated electrochemical-structural characterization methods allow mimicking in vivo metabolism. • Modulation of toxicity in favor of therapeutic safety and efficacy is challenging. • Electrochemistry can be a useful guide to the understanding of the phenomena of electron transfer and further applications.

Proteome activity landscapes of tumor cell lines determine drug responses

Integrated analysis of genomes, transcriptomes, proteomes and drug responses of cancer cell lines (CCLs) is an emerging approach to uncover molecular mechanisms of drug action. We extend this paradigm to measuring proteome activity landscapes by acquiring and integrating quantitative data for 10,000 proteins and 55,000 phosphorylation sites (p-sites) from 125 CCLs. These data are used to contextualize proteins and p-sites and predict drug sensitivity. For example, we find that Progesterone Receptor (PGR) phosphorylation is associated with sensitivity to drugs modulating estrogen signaling such as Raloxifene. We also demonstrate that Adenylate kinase isoenzyme 1 (AK1) inactivates antimetabolites like Cytarabine. Consequently, high AK1 levels correlate with poor survival of Cytarabine-treated acute myeloid leukemia patients, qualifying AK1 as a patient stratification marker and possibly as a drug target. We provide an interactive web application termed ATLANTiC (http://atlantic.proteomics.wzw.tum.de), which enables the community to explore the thousands of novel functional associations generated by this work.

Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data

BackgroundIndividualized drug response prediction is vital for achieving personalized treatment of cancer and moving precision medicine forward. Large-scale multi-omics profiles provide unprecedented opportunities for precision cancer therapy.MethodsIn this study, we propose a pipeline to identify subpathway signatures for anticancer drug response of individuals by integrating the comprehensive contributions of multiple genetic and epigenetic (gene expression, copy number variation and DNA methylation) alterations.ResultsTotally, 46 subpathway signatures associated with individual responses to different anticancer drugs were identified based on five cancer-drug response datasets. We have validated the reliability of subpathway signatures in two independent datasets. Furthermore, we also demonstrated these multi-omics subpathway signatures could significantly improve the performance of anticancer drug response prediction. In-depth analysis of these 46 subpathway signatures uncovered the essential roles of three omics types and the functional associations underlying different anticancer drug responses. Patient stratification based on subpathway signatures involved in anticancer drug response identified subtypes with different clinical outcomes, implying their potential roles as prognostic biomarkers. In addition, a landscape of subpathways associated with cellular responses to 191 anticancer drugs from CellMiner was provided and the mechanism similarity of drug action was accurately unclosed based on these subpathways. Finally, we constructed a user-friendly web interface-CancerDAP (http://bio-bigdata.hrbmu.edu.cn/CancerDAP/) available to explore 2751 subpathways relevant with 191 anticancer drugs response.ConclusionsTaken together, our study identified and systematically characterized subpathway signatures for individualized anticancer drug response prediction, which may promote the precise treatment of cancer and the study for molecular mechanisms of drug actions.

Decoding the transcriptional programs activated by psychotropic drugs in the brain.

Analysis of drug-induced gene expression in the brain has long held the promise of revealing the molecular mechanisms of drug actions as well as predicting their long-term clinical efficacy. However, despite some successes, this promise has yet to be fulfilled. Here, we present an overview of the current state of understanding of drug-induced gene expression in the brain and consider the obstacles to achieving a robust prediction of the properties of psychoactive compounds based on gene expression profiles. We begin with a comprehensive overview of the mechanisms controlling drug-inducible transcription and the complexity resulting from expression of noncoding RNAs and alternative gene isoforms. Particular interest is placed on studies that examine the associations within drug classes with regard to the effects on gene transcription, alterations in cell signaling and neuropharmacological drug properties. While the ability of gene expression signatures to distinguish specific clinical classes of psychotropic and addictive drugs remains unclear, some reports show that under specific constraints, drug properties can be predicted based on gene expression. Such signatures offer a simple and effective way to classify psychotropic drugs and screen novel psychoactive compounds. Finally, we note that the amount of data regarding molecular programs activated in the brain by drug treatment has grown exponentially in recent years and that future advances may therefore come in large part from integrating the currently available high-throughput data sets.

HOGMMNC: a higher order graph matching with multiple network constraints model for gene-drug regulatory modules identification.

The emergence of large amounts of genomic, chemical, and pharmacological data provides new opportunities and challenges. Identifying gene-drug associations is not only crucial in providing a comprehensive understanding of the molecular mechanisms of drug action, but is also important in the development of effective treatments for patients. However, accurately determining the complex associations among pharmacogenomic data remains challenging. We propose a higher order graph matching with multiple network constraints (HOGMMNC) model to accurately identify gene-drug modules. The HOGMMNC model aims to capture the inherent structural relations within data drawn from multiple sources by hypergraph matching. The proposed technique seamlessly integrates prior constraints to enhance the accuracy and reliability of the identified relations. An effective numerical solution is combined with a novel sampling strategy to solve the problem efficiently. The superiority and effectiveness of our proposed method are demonstrated through a comparison with four state-of-the-art techniques using synthetic and empirical data. The experiments on synthetic data show that the proposed method clearly outperforms other methods, especially in the presence of noise and irrelevant samples. The HOGMMNC model identifies eighteen gene-drug modules in the empirical data. The modules are validated to have significant associations via pathway analysis. Significance: The modules identified by HOGMMNC provide new insights into the molecular mechanisms of drug action and provide patients with more effective treatments. Our proposed method can be applied to the study of other biological correlated module identification problems (e.g. miRNA-gene, gene-methylation, and gene-disease). A matlab package of HOGMMNC is available at https://github.com/scutbioinformatics/HOGMMNC/. Supplementary data are available at Bioinformatics online.

Integration of Antibody Array Technology into Drug Discovery and Development.

Antibody arrays represent a high-throughput technique that enables the parallel detection of multiple proteins with minimal sample volume requirements. In recent years, antibody arrays have been widely used to identify new biomarkers for disease diagnosis or prognosis. Moreover, many academic research laboratories and commercial biotechnology companies are starting to apply antibody arrays in the field of drug discovery. In this review, some technical aspects of antibody array development and the various platforms currently available will be addressed; however, the main focus will be on the discussion of antibody array technologies and their applications in drug discovery. Aspects of the drug discovery process, including target identification, mechanisms of drug resistance, molecular mechanisms of drug action, drug side effects, and the application in clinical trials and in managing patient care, which have been investigated using antibody arrays in recent literature will be examined and the relevance of this technology in progressing this process will be discussed. Protein profiling with antibody array technology, in addition to other applications, has emerged as a successful, novel approach for drug discovery because of the well-known importance of proteins in cell events and disease development.

Chemical Proteomic Approaches Targeting Cancer Stem Cells: A Review of Current Literature.

Cancer stem cells (CSCs) have been proposed as central drivers of tumor initiation, progression, recurrence, and therapeutic resistance. Therefore, identifying stem-like cells within cancers and understanding their properties is crucial for the development of effective anticancer therapies. Recently, chemical proteomics has become a powerful tool to efficiently determine protein networks responsible for CSC pathophysiology and comprehensively elucidate molecular mechanisms of drug action against CSCs. This review provides an overview of major methodologies utilized in chemical proteomic approaches. In addition, recent successful chemical proteomic applications targeting CSCs are highlighted. Future direction of potential CSC research by integrating chemical genomic and proteomic data obtained from a single biological sample of CSCs are also suggested in this review.

En route to precision medicine through the integration of biological sex into pharmacogenomics.

Frequently, pharmacomechanisms are not fully elucidated. Therefore, drug use is linked to an elevated interindividual diversity of effects, whether therapeutic or adverse, and the role of biological sex has as yet unrecognized and underestimated consequences. A pharmacogenomic approach could contribute towards the development of an adapted therapy for each male and female patient, considering also other fundamental features, such as age and ethnicity. This would represent a crucial step towards precision medicine and could be translated into clinical routine. In the present review, we consider recent results from pharmacogenomics and the role of sex in studies that are relevant to cardiovascular therapy. We focus on genome-wide analyses, because they have obvious advantages compared with targeted single-candidate gene studies. For instance, genome-wide approaches do not necessarily depend on prior knowledge of precise molecular mechanisms of drug action. Such studies can lead to findings that can be classified into three categories: first, effects occurring in the pharmacokinetic properties of the drug, e.g. through metabolic and transporter differences; second, a pharmacodynamic or drug target-related effect; and last diverse adverse effects. We conclude that the interaction of sex with genetic determinants of drug response has barely been tested in large, unbiased, pharmacogenomic studies. We put forward the theory that, to contribute towards the realization of precision medicine, it will be necessary to incorporate sex into pharmacogenomics.

Abstract 771: Multi-parametric 3D tumor microtissue-based phenotypic compound classification

Abstract Here, we report on the development of homo- and heterotypic ovarian and pancreatic microtissue tumor models for a screening of up to 40 compounds selected from the NCATS Oncology focused library of small molecules. The goal of this study was to develop a high throughput compatible drug screening platform that could leverage the best of high throughput and high content screening assays to generate a pharmacological profile of activities that we hope will help better predict activity in vivo .This platform utilizes 3D multicellular spheroids from ovarian (HEY and SKOV) and pancreatic (Panc-1) cancer and enables the discrimination of tumor-specific efficacy and nonspecific cytotoxicity of a drug candidate with subsequent identification and validation of the molecular mechanism of action (MMOA). The biological responses measured over a 10-day drug exposure period included (i) growth kinetic (microtissue size), (ii) potency (IC50ATP_10_days) and efficacy (max. response ATP and size). The biological response of the compounds was compared between the different cell culture formats tested, 2D, 3D homotypic and 3D heterotypic. The comparison of IC50 values among the different ovarian cancer cell cultures showed that 21 out of the 38 compounds tested were more potent in 3D than in 2D. Within the pancreatic models, 13 out of 20 compounds tested were more potent in 3D than in 2D. Interestingly, most of the compounds which showed stronger potency in 3D were targeted small molecule agents. Comparing drug responses of homo vs heterotypic ovarian tumor model systems, 3 compounds were effective only in the heterotypic model including the WNT inhibitor PNU-74654 and GABA uptake inhibitor (Gat-1) SK&F-89976A. We also evaluated nonspecific cytotoxic effects on the incorporated NIH3T3 fibroblasts by quantifying a secreted reporter over time. The assay system allowed us to discriminate between acute cytotoxic effects (3-day exposure) and sub-chronic toxicity (7-day exposure). Whereas only a very limited number of compounds had acute cytotoxic effects, there were considerable more compounds with sub-chronic cytotoxicity. Based on the single endpoint classifications, we chose an mTOR inhibitor, Torin-2, to further assess molecular mechanism of drug action for all three tumor microtissue models used in this study, applying reverse phase protein array (RPPA) and transcriptome analysis for signaling pathway profiling. Preliminary results of RPPA analysis revealed a clear down-regulation in the cell cycle and PI3K/Akt/mTOR signaling pathway, especially for S6 ribosomal protein phosphorylated at Ser 235, 236, 240 and 244, indicating a strong inhibitory effect on translation and cell growth control. In summary, we present a 3D tumor microtissue-based phenotypic drug discovery testing scheme which allows a multi-parametric endpoint analysis paired with subsequent molecular pathway identification. Citation Format: Madhu A. Lal, Zoe Weydert, Jens Kelm, Marc Ferrer. Multi-parametric 3D tumor microtissue-based phenotypic compound classification. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 771.

Abstract 2003: Investigating the cellular interactions of BIRB796 analogues using a novel chloroalkane capture tag

Abstract Identifying the targets of a bioactive compound is often the rate limiting step toward understanding the molecular mechanism of drug action. Current approaches rely on linking the bioactive compound to a surface or an affinity handle, permitting selective capture of interacting proteins for identification by mass spectrometry. A major consideration with these methods is insuring that the chemical derivatization of the bioactive compound does not disrupt the binding interactions with the cellular targets. We have developed a method based on a novel chloroalkane capture tag that minimally affects compound potency and cell permeability. This allows verification of the pharmacological activity of the modified compound, thus increasing the confidence in the biological relevance of captured proteins. In addition, by allowing the chloroalkane-modified compound to bind the targets within living cells, the cellular architecture during the binding step is preserved and better represents the conditions that the unaltered compound would normally engage these targets. Following binding with the tagged compound in live cells, the cells are lysed and the chloroalkylated compound and its associated targets are rapidly captured onto immobilized HaloTag protein and then released by competitive elution. The identified targets are then validated for direct binding relationship with the bioactive compound by bioluminescence energy transfer. We tested this target capture/target validation work flow using the interaction of MAPK kinases with two allosteric kinase inhibitors (BIRB796 and a BIRB analog exhibiting 100-fold lower potency). RESULTS: Using the two BIRB-chloroalkane derivatives to selectively enrich for targets from HEPG2 cells, we identified and validated multiple relevant MAPK kinases as well as additional off-targets. Interestingly, all the discovered off-targets bind purines. Kinase inhibitors such as BIRB796 which acts by binding to the kinase ATP binding site can interact in a similar manner with other purine binding proteins. Using bioluminescence energy transfer we interrogated the affinity and residence time of the two BIRB compounds to multiple MAPK kinases inside living cells. Our results indicates that the BIRB796 analog exhibits 30-1000 fold reduced affinity to multiple MAPK kinases as well as significant shorter residence time compared to BIRB796. Taken together these results indicate that our workflow can reveal the direct binding relationships between bioactive compounds and their cellular targets and contribute to further understanding of these interactions. Citation Format: Rachel Friedman Ohana, Robin Hurst, Thomas Kirkland, Carolyn Woodroofe, Sergiy Levin, Paul Otto, Tetsuo Uyeda, Michael Ford, Richard Jones, Danette Daniels, Marjeta Urh, Keith Wood. Investigating the cellular interactions of BIRB796 analogues using a novel chloroalkane capture tag. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2003. doi:10.1158/1538-7445.AM2015-2003

Binding Properties of Biosynthesized Gold Nanoparticles with Calf-Thymus DNA in vitro

The investigation of drug-DNA interaction is important for understanding the molecular mechanism of drug action. Interaction of ct-DNA with Alternanthera sessilis, Acorus calamus, Rhaphidophora aurea, Portulaca oleracea and Sterculia foetida mediated AuNPs was monitored by recording the changes in absorption band before and after interaction of ct-DNA and AuNPs by UV-Visible spectroscopy. The interaction of the complex with calf thymus DNA (ct-DNA) showed blue shift and hyperchromism in the UV-Vis spectra indicating groove binding for SF, PAK, MP and AC-AuNPs. The morphology of the DNA-nanoparticle complexes seems to be dependent on the nanoparticles concentration and is significantly different for each plant-mediated nanoparticles.

Ambient mass spectrometry imaging metabolomics method provides novel insights into the action mechanism of drug candidates.

Elucidation of the mechanism of action for drug candidates is fundamental to drug development, and it is strongly facilitated by metabolomics. Herein, we developed an imaging metabolomics method based on air-flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) under ambient conditions. This method was subsequently applied to simultaneously profile a novel anti-insomnia drug candidate, N(6)-(4-hydroxybenzyl)-adenosine (NHBA), and various endogenous metabolites in rat whole-body tissue sections after the administration of NHBA. The principal component analysis (PCA) represented by an intuitive color-coding scheme based on hyperspectral imaging revealed in situ molecular profiling alterations in response to stimulation of NHBA, which are in a very low intensity and hidden in massive interferential peaks. We found that the abundance of six endogenous metabolites changed after drug administration. The spatiotemporal distribution indicated that five altered molecules—including neurotransmitter γ-aminobutyric acid, neurotransmitter precursors choline and glycerophosphocholine, energy metabolism-related molecules adenosine (an endogenous sleep factor), and creatine—are closely associated with insomnia or other neurological disorders. These findings not only provide insights into a deep understanding on the mechanism of action of NHBA, but also demonstrate that the AFADESI-MSI-based imaging metabolomics is a powerful technique to investigate the molecular mechanism of drug action, especially for drug candidates with multitarget or undefined target in the preclinical study stage.

Targeting the gyrase of Plasmodium falciparum with topoisomerase poisons

Drug-resistant malaria poses a major public health problem throughout the world and the need for new antimalarial drugs is growing. The apicoplast, a chloroplast-like organelle essential for malaria parasite survival and with no counterpart in humans, offers an attractive target for selectively toxic new therapies. The apicoplast genome (plDNA) is a 35kb circular DNA that is served by gyrase, a prokaryotic type II topoisomerase. Gyrase is poisoned by fluoroquinolone antibacterials that stabilize a catalytically inert ternary complex of enzyme, its plDNA substrate, and inhibitor. We used fluoroquinolones to study the gyrase and plDNA of Plasmodium falciparum. New methods for isolating and separating plDNA reveal four topologically different forms and permit a quantitative exam of perturbations that result from gyrase poisoning. In keeping with its role in DNA replication, gyrase is most abundant in late stages of the parasite lifecycle, but several lines of evidence indicate that even in these cells the enzyme is present in relatively low abundance: about 1 enzyme for every two plDNAs or a ratio of 1 gyrase: 70kb DNA. For a spectrum of quinolones, correlation was generally good between antimalarial activity and gyrase poisoning, the putative molecular mechanism of drug action. However, in P. falciparum there is evidence for off-target toxicity, particularly for ciprofloxacin. These studies highlight the utility of the new methods and of fluoroquinolones as a tool for studying the in situ workings of gyrase and its plDNA substrate.

Let's 'play' with molecular pharmacology.

Understanding concepts of molecular mechanisms of drug action involves sequential visualization of physiological processes and drug effects, a task that can be difficult at an undergraduate level. Role-play is a teaching-learning methodology whereby active participation of students as well as clear visualization of the phenomenon is used to convey complex physiological concepts. However, its use in teaching drug action, a process that demands understanding of a second level of complexity over the physiological process, has not been investigated. We hypothesized that role-play can be an effective and well accepted method for teaching molecular pharmacology. In an observational study, students were guided to perform a role-play on a selected topic involving drug activity. Students' gain in knowledge was assessed comparing validated pre- and post-test questionnaires as well as class average normalized gain. The acceptance of role-play among undergraduate medical students was evaluated by Likert scale analysis and thematic analysis of their open-ended written responses. Significant improvement in knowledge (P < 0.001) was noted in the pre- to post-test knowledge scores, while a high gain in class average normalized score was evident. In Likert scale analysis, most students (93%) expressed that role-play was an acceptable way of teaching. In a thematic analysis, themes of both strengths and weaknesses of the session emerged. Role-play can be effectively utilized while teaching selected topics of molecular pharmacology in undergraduate medical curricula.

Utilizing Yeast Surface Human Proteome Display Libraries to Identify Small Molecule-Protein Interactions.

The identification of proteins that interact with small bioactive molecules is a critical but often difficult and time-consuming step in understanding cellular signaling pathways or molecular mechanisms of drug action. Numerous methods for identifying small molecule-interacting proteins have been developed and utilized, including affinity-based purification followed by mass spectrometry analysis, protein microarrays, phage display, and three-hybrid approaches. Although all these methods have been used successfully, there remains a need for additional techniques for analyzing small molecule-protein interactions. A promising method for identifying small molecule-protein interactions is affinity-based selection of yeast surface-displayed human proteome libraries. Large and diverse libraries displaying human protein fragments on the surface of yeast cells have been constructed and subjected to FACS-based enrichment followed by comprehensive exon microarray-based output analysis to identify protein fragments with affinity for small molecule ligands. In a recent example, a proteome-wide search has been successfully carried out to identify cellular proteins binding to the signaling lipids PtdIns(4,5)P2 and PtdIns(3,4,5)P3. Known phosphatidylinositide-binding proteins such as pleckstrin homology domains were identified, as well as many novel interactions. Intriguingly, many novel nuclear phosphatidylinositide-binding proteins were discovered. Although the existence of an independent pool of nuclear phosphatidylinositides has been known about for some time, their functions and mechanism of action remain obscure. Thus, the identification and subsequent study of nuclear phosphatidylinositide-binding proteins is expected to bring new insights to this important biological question. Based on the success with phosphatidylinositides, it is expected that the screening of yeast surface-displayed human proteome libraries will be of general use for the discovery of novel small molecule-protein interactions, thus facilitating the study of cellular signaling pathways and mechanisms of drug action or toxicity.