ResearchPad - drug-interactions https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[COMBSecretomics: A pragmatic methodological framework for higher-order drug combination analysis using secretomics]]> https://www.researchpad.co/article/elastic_article_14596 Multi drug treatments are increasingly used in the clinic to combat complex and co-occurring diseases. However, most drug combination discovery efforts today are mainly focused on anticancer therapy and rarely examine the potential of using more than two drugs simultaneously. Moreover, there is currently no reported methodology for performing second- and higher-order drug combination analysis of secretomic patterns, meaning protein concentration profiles released by the cells. Here, we introduce COMBSecretomics (https://github.com/EffieChantzi/COMBSecretomics.git), the first pragmatic methodological framework designed to search exhaustively for second- and higher-order mixtures of candidate treatments that can modify, or even reverse malfunctioning secretomic patterns of human cells. This framework comes with two novel model-free combination analysis methods; a tailor-made generalization of the highest single agent principle and a data mining approach based on top-down hierarchical clustering. Quality control procedures to eliminate outliers and non-parametric statistics to quantify uncertainty in the results obtained are also included. COMBSecretomics is based on a standardized reproducible format and could be employed with any experimental platform that provides the required protein release data. Its practical use and functionality are demonstrated by means of a proof-of-principle pharmacological study related to cartilage degradation. COMBSecretomics is the first methodological framework reported to enable secretome-related second- and higher-order drug combination analysis. It could be used in drug discovery and development projects, clinical practice, as well as basic biological understanding of the largely unexplored changes in cell-cell communication that occurs due to disease and/or associated pharmacological treatment conditions.

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<![CDATA[A compound attributes-based predictive model for drug induced liver injury in humans]]> https://www.researchpad.co/article/Ndeb57c49-a1cc-41d4-9618-08dc56c45dac

Drug induced liver injury (DILI) is one of the key safety concerns in drug development. To assess the likelihood of drug candidates with potential adverse reactions of liver, we propose a compound attributes-based approach to predicting hepatobiliary disorders that are routinely reported to US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Specifically, we developed a support vector machine (SVM) model with recursive feature extraction, based on physicochemical and structural properties of compounds as model input. Cross validation demonstrates that the predictive model has a robust performance with averaged 70% of both sensitivity and specificity over 500 trials. An independent validation was performed on public benchmark drugs and the results suggest potential utility of our model for identifying safety alerts. This in silico approach, upon further validation, would ultimately be implemented, together with other in vitro safety assays, for screening compounds early in drug development.

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<![CDATA[Prediction of ultra-high-order antibiotic combinations based on pairwise interactions]]> https://www.researchpad.co/article/5c5b52b4d5eed0c4842bcea4

Drug combinations are a promising approach to achieve high efficacy at low doses and to overcome resistance. Drug combinations are especially useful when drugs cannot achieve effectiveness at tolerable doses, as occurs in cancer and tuberculosis (TB). However, discovery of effective drug combinations faces the challenge of combinatorial explosion, in which the number of possible combinations increases exponentially with the number of drugs and doses. A recent advance, called the dose model, uses a mathematical formula to overcome combinatorial explosion by reducing the problem to a feasible quadratic one: using data on drug pairs at a few doses, the dose model accurately predicts the effect of combinations of three and four drugs at all doses. The dose model has not yet been tested on higher-order combinations beyond four drugs. To address this, we measured the effect of combinations of up to ten antibiotics on E. coli growth, and of up to five tuberculosis (TB) drugs on the growth of M. tuberculosis. We find that the dose model accurately predicts the effect of these higher-order combinations, including cases of strong synergy and antagonism. This study supports the view that the interactions between drug pairs carries key information that largely determines higher-order interactions. Therefore, systematic study of pairwise drug interactions is a compelling strategy to prioritize drug regimens in high-dimensional spaces.

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<![CDATA[Patterns of multimorbidity and polypharmacy in young and adult population: Systematic associations among chronic diseases and drugs using factor analysis]]> https://www.researchpad.co/article/5c648d0cd5eed0c484c81e2d

Objectives

The objective was to identify the systematic associations among chronic diseases and drugs in the form of patterns and to describe and clinically interpret the constituted patterns with a focus on exploring the existence of potential drug-drug and drug-disease interactions and prescribing cascades.

Methods

This observational, cross-sectional study used the demographic and clinical information from electronic medical databases and the pharmacy billing records of all users of the public health system of the Spanish region of Aragon in 2015. An exploratory factor analysis was conducted based on the tetra-choric correlations among the diagnoses of chronic diseases and the dispensed drugs in 887,572 patients aged ≤65 years. The analysis was stratified by age and sex. To name the constituted patterns, assess their clinical nature, and identify potential interactions among diseases and drugs, the associations found in each pattern were independently reviewed by two pharmacists and two doctors and tested against the literature and the information reported in the technical medicinal forms.

Results

Six multimorbidity-polypharmacy patterns were found in this large-scale population study, named as respiratory, mental health, cardiometabolic, endocrinological, osteometabolic, and mechanical-pain. The nature of the patterns in terms of diseases and drugs differed by sex and age and became more complex as age advanced.

Conclusions

The six clinically sound multimorbidity-polypharmacy patterns described in this non-elderly population confirmed the existence of systematic associations among chronic diseases and medications, and revealed some unexpected associations suggesting the prescribing cascade phenomenon as a potential underlying factor. These findings may help to broaden the focus and orient the early identification of potential interactions when caring for multimorbid patients at high risk of adverse health outcomes due to polypharmacy.

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<![CDATA[Triple oral beta-lactam containing therapy for Buruli ulcer treatment shortening]]> https://www.researchpad.co/article/5c58d647d5eed0c484031a67

The potential use of clinically approved beta-lactams for Buruli ulcer (BU) treatment was investigated with representative classes analyzed in vitro for activity against Mycobacterium ulcerans. Beta-lactams tested were effective alone and displayed a strong synergistic profile in combination with antibiotics currently used to treat BU, i.e. rifampicin and clarithromycin; this activity was further potentiated in the presence of the beta-lactamase inhibitor clavulanate. In addition, quadruple combinations of rifampicin, clarithromycin, clavulanate and beta-lactams resulted in multiplicative reductions in their minimal inhibitory concentration (MIC) values. The MIC of amoxicillin against a panel of clinical isolates decreased more than 200-fold within this quadruple combination. Amoxicillin/clavulanate formulations are readily available with clinical pedigree, low toxicity, and orally and pediatric available; thus, supporting its potential inclusion as a new anti-BU drug in current combination therapies.

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<![CDATA[Screening-based approach to discover effective platinum-based chemotherapies for cancers with poor prognosis]]> https://www.researchpad.co/article/5c59ff02d5eed0c4841358d8

Drug combinations are extensively used to treat cancer and are often selected according to complementary mechanisms. Here, we describe a cell-based high-throughput screening assay for identification of synergistic combinations between broadly applied platinum-based chemotherapeutics and drugs from a library composed of 1280 chemically and pharmacologically diverse (mostly FDA approved) compounds. The assay was performed on chemoresistant cell lines derived from lung (A549) and pancreatic (PANC-1) carcinoma, where platinum-based combination regimens are currently applied though with limited success. The synergistic combinations identified during the screening were validated by synergy quantification using the combination index method and via high content fluorescent microscopy analysis. New promising synergistic combinations discovered using this approach include compounds currently not used as anticancer drugs, such as cisplatin or carboplatin with hycanthone and cisplatin with spironolactone in pancreatic carcinoma, and carboplatin and deferoxamine in non-small cell lung cancer. Strong synergy between cisplatin or carboplatin and topotecan in PANC-1 cells, compared to A549 cells, suggests that this combination, currently used in lung cancer treatment regimens, could be applied to pancreatic carcinoma as well. Several drugs used to treat diseases other than cancer, including pyrvinium pamoate, auranofin, terfenadine and haloprogin, showed strong cytotoxicity on their own and synergistic interactions with platinum drugs. This study demonstrates that non-obvious drug combinations that would not be selected based on complementary mechanisms can be identified via high-throughput screening.

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<![CDATA[Synthetic lethality guiding selection of drug combinations in ovarian cancer]]> https://www.researchpad.co/article/5c57e6d4d5eed0c484ef3f0b

Background

Synthetic lethality describes a relationship between two genes where single loss of either gene does not trigger significant impact on cell viability, but simultaneous loss of both gene functions results in lethality. Targeting synthetic lethal interactions with drug combinations promises increased efficacy in tumor therapy.

Materials and methods

We established a set of synthetic lethal interactions using publicly available data from yeast screens which were mapped to their respective human orthologs using information from orthology databases. This set of experimental synthetic lethal interactions was complemented by a set of predicted synthetic lethal interactions based on a set of protein meta-data like e.g. molecular pathway assignment. Based on the combined set, we evaluated drug combinations used in late stage clinical development (clinical phase III and IV trials) or already in clinical use for ovarian cancer with respect to their effect on synthetic lethal interactions. We furthermore identified a set of drug combinations currently not being tested in late stage ovarian cancer clinical trials that however have impact on synthetic lethal interactions thus being worth of further investigations regarding their therapeutic potential in ovarian cancer.

Results

Twelve of the tested drug combinations addressed a synthetic lethal interaction with the anti-VEGF inhibitor bevacizumab in combination with paclitaxel being the most studied drug combination addressing the synthetic lethal pair between VEGFA and BCL2. The set of 84 predicted drug combinations for example holds the combination of the PARP inhibitor olaparib and paclitaxel, which showed efficacy in phase II clinical studies.

Conclusion

A set of drug combinations currently not tested in late stage ovarian cancer clinical trials was identified having impact on synthetic lethal interactions thus being worth of further investigations regarding their therapeutic potential in ovarian cancer.

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<![CDATA[Potential off-target effects of beta-blockers on gut hormone receptors: In silico study including GUT-DOCK—A web service for small-molecule docking]]> https://www.researchpad.co/article/5c57e6e2d5eed0c484ef412a

The prolonged use of many currently available drugs results in the severe side effect of the disruption of glucose metabolism leading to type 2 diabetes mellitus (T2DM. Gut hormone receptors including glucagon receptor (GCGR) and the incretin hormone receptors: glucagon-like peptide 1 receptor (GLP1R) and gastric inhibitory polypeptide receptor (GIPR) are important drug targets for the treatment of T2DM, as they play roles in the regulation of glucose and insulin levels and of food intake. In this study, we hypothesized that we could compensate for the negative influences of specific drugs on glucose metabolism by the positive incretin effect enhanced by the off-target interactions with incretin GPCR receptors. As a test case, we chose to examine beta-blockers because beta-adrenergic receptors and incretin receptors are expressed in a similar location, making off-target interactions possible. The binding affinity of drugs for incretin receptors was approximated by using two docking scoring functions of Autodock VINA (GUT-DOCK) and Glide (Schrodinger) and juxtaposing these values with the medical information on drug-induced T2DM. We observed that beta-blockers with the highest theoretical binding affinities for gut hormone receptors were reported as the least harmful to glucose homeostasis in clinical trials. Notably, a recently discovered beta-blocker compound 15 ([4-((2S)-3-(((S)-3-(3-bromophenyl)-1-(methylamino)-1-oxopropan-2-yl)amino)-2-(2-cyclohexyl-2-phenylacetamido)-3-oxopropyl)benzamide was among the top-scoring drugs, potentially supporting its use in the treatment of hypertension in diabetic patients. Our recently developed web service GUT-DOCK (gut-dock.miningmembrane.com) allows for the execution of similar studies for any drug-like molecule. Specifically, users can compute the binding affinities for various class B GPCRs, gut hormone receptors, VIPR1 and PAC1R.

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<![CDATA[Using the drug-protein interactome to identify anti-ageing compounds for humans]]> https://www.researchpad.co/article/5c3fa5f7d5eed0c484caa9c2

Advancing age is the dominant risk factor for most of the major killer diseases in developed countries. Hence, ameliorating the effects of ageing may prevent multiple diseases simultaneously. Drugs licensed for human use against specific diseases have proved to be effective in extending lifespan and healthspan in animal models, suggesting that there is scope for drug repurposing in humans. New bioinformatic methods to identify and prioritise potential anti-ageing compounds for humans are therefore of interest. In this study, we first used drug-protein interaction information, to rank 1,147 drugs by their likelihood of targeting ageing-related gene products in humans. Among 19 statistically significant drugs, 6 have already been shown to have pro-longevity properties in animal models (p < 0.001). Using the targets of each drug, we established their association with ageing at multiple levels of biological action including pathways, functions and protein interactions. Finally, combining all the data, we calculated a ranked list of drugs that identified tanespimycin, an inhibitor of HSP-90, as the top-ranked novel anti-ageing candidate. We experimentally validated the pro-longevity effect of tanespimycin through its HSP-90 target in Caenorhabditis elegans.

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<![CDATA[The influence of concomitant antiepileptic drugs on lamotrigine serum concentrations in Northwest Chinese Han population with epilepsy]]> https://www.researchpad.co/article/5c478ca5d5eed0c484bd3a7f

Objective

The aims of this study were to identify the influencing factors such as gender, age, dose and combinations of other antiepileptic drugs (AEDs), especially in triple combinations on the pharmacokinetic of Lamotrigine (LTG) in epilepsy patients of Northwest Chinese Han population.

Methods

Data of the LTG concentration and clinical information were analyzed retrospectively from a therapeutic drug monitoring (TDM) database at the Clinical Pharmacy Laboratory of Xi’an Central Hospital between January 1, 2016 and January 1, 2018. The independent-sample t-test, one-way ANOVA analysis and Bonferroni and Tamhane T3 post-hoc test, the stepwise multivariate regression analysis were adopted by IBM SPSS, version 22.0.

Results

226 serum samples met the inclusion criteria and were evaluated. The mean LTG serum concentration was 5.48±3.83 μg/mL. There were no gender differences (P = 0.64), and there were no significant effects by age on LTG serum concentration after age stratification (3–14 years old, 14-45 years old, 45–59 years old) (P = 0.05). Multiple regression analysis showed that the daily LTG dose and co-administration of other AEDs significantly affected LTG serum concentrations. Combination with enzyme-inducer AEDs, the mean steady-state LTG concentration could be decreased by 30.73% compared with LTG monotherapy. Among enzyme-inducer AEDs, particularly strong inducer Carbamazepine (CBZ) could decrease the mean LTG concentration by 53.65%, but weak inducer AEDs such as Oxcarbazepine (OXC) and Topiramate (TPM) had no effect, Valproic acid (VPA) could increase the mean LTG concentration by 93.95%, and the inducer only partially compensated for the inhibitory effect of VPA in triple combination.

Conclusions

There were no significant gender and age effects, but the LTG daily dose and co-administration of other AEDs significantly affected LTG serum concentration. Combination with enzyme-inducer AEDs, especially CBZ could significantly decrease LTG serum concentrations, VPA could significantly increase LTG serum concentrations, and the inducer only partially compensated for the inhibitory effect of VPA in triple combination. In the clinical setting, these findings can help to estimate LTG concentrations and adjust dosage and evaluate adverse drug reactions.

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<![CDATA[Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment]]> https://www.researchpad.co/article/5c6059c7d5eed0c4847cbe16

Antibiotics need to be effective in diverse environments in vivo. However, the pathogen microenvironment can have a significant impact on antibiotic potency. Further, antibiotics are increasingly used in combinations to combat resistance, yet, the effect of microenvironments on drug-combination efficacy is unknown. To exhaustively explore the impact of diverse microenvironments on drug-combinations, here we develop a computational framework—Metabolism And GENomics-based Tailoring of Antibiotic regimens (MAGENTA). MAGENTA uses chemogenomic profiles of individual drugs and metabolic perturbations to predict synergistic or antagonistic drug-interactions in different microenvironments. We uncovered antibiotic combinations with robust synergy across nine distinct environments against both E. coli and A. baumannii by searching through 2556 drug-combinations of 72 drugs. MAGENTA also accurately predicted the change in efficacy of bacteriostatic and bactericidal drug-combinations during growth in glycerol media, which we confirmed experimentally in both microbes. Our approach identified genes in glycolysis and glyoxylate pathway as top predictors of synergy and antagonism respectively. Our systems approach enables tailoring of antibiotic therapies based on the pathogen microenvironment.

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<![CDATA[Determinants of drug-target interactions at the single cell level]]> https://www.researchpad.co/article/5c25450dd5eed0c48442bda5

The physiochemical determinants of drug-target interactions in the microenvironment of the cell are complex and generally not defined by simple diffusion and intrinsic chemical reactivity. Non-specific interactions of drugs and macromolecules in cells are rarely considered formally in assessing pharmacodynamics. Here, we demonstrate that non-specific interactions lead to very slow incorporation kinetics of DNA binding drugs. We observe a rate of drug incorporation in cell nuclei three orders of magnitude slower than in vitro due to anomalous drug diffusion within cells. This slow diffusion, however, has an advantageous consequence: it leads to virtually irreversible binding of the drug to specific DNA targets in cells. We show that non-specific interactions drive slow drug diffusion manifesting as slow reaction front propagation. We study the effect of non-specific interactions in different cellular compartments by permeabilization of plasma and nuclear membranes in order to pinpoint differential compartment effects on variability in intracellular drug kinetics. These results provide the basis for a comprehensive model of the determinants of intracellular diffusion of small-molecule drugs, their target-seeking trajectories, and the consequences of these processes on the apparent kinetics of drug-target interactions.

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<![CDATA[Predicting protein targets for drug-like compounds using transcriptomics]]> https://www.researchpad.co/article/5c141e77d5eed0c484d26bce

An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation of set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions.

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<![CDATA[PathFX provides mechanistic insights into drug efficacy and safety for regulatory review and therapeutic development]]> https://www.researchpad.co/article/5c141eabd5eed0c484d27adc

Failure to demonstrate efficacy and safety issues are important reasons that drugs do not reach the market. An incomplete understanding of how drugs exert their effects hinders regulatory and pharmaceutical industry projections of a drug’s benefits and risks. Signaling pathways mediate drug response and while many signaling molecules have been characterized for their contribution to disease or their role in drug side effects, our knowledge of these pathways is incomplete. To better understand all signaling molecules involved in drug response and the phenotype associations of these molecules, we created a novel method, PathFX, a non-commercial entity, to identify these pathways and drug-related phenotypes. We benchmarked PathFX by identifying drugs’ marketed disease indications and reported a sensitivity of 41%, a 2.7-fold improvement over similar approaches. We then used PathFX to strengthen signals for drug-adverse event pairs occurring in the FDA Adverse Event Reporting System (FAERS) and also identified opportunities for drug repurposing for new diseases based on interaction paths that associated a marketed drug to that disease. By discovering molecular interaction pathways, PathFX improved our understanding of drug associations to safety and efficacy phenotypes. The algorithm may provide a new means to improve regulatory and therapeutic development decisions.

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<![CDATA[Defining pediatric polypharmacy: A scoping review]]> https://www.researchpad.co/article/5c0993d7d5eed0c4842ada7b

Objectives

Lack of consensus regarding the semantics and definitions of pediatric polypharmacy challenges researchers and clinicians alike. We conducted a scoping review to describe definitions and terminology of pediatric polypharmacy.

Methods

Medline, PubMed, EMBASE, CINAHL, PsycINFO, Cochrane CENTRAL, and the Web of Science Core Collection databases were searched for English language articles with the concepts of “polypharmacy” and “children”. Data were extracted about study characteristics, polypharmacy terms and definitions from qualifying studies, and were synthesized by disease conditions.

Results

Out of 4,398 titles, we included 363 studies: 324 (89%) provided numeric definitions, 131 (36%) specified duration of polypharmacy, and 162 (45%) explicitly defined it. Over 81% (n = 295) of the studies defined polypharmacy as two or more medications or therapeutic classes. The most common comprehensive definitions of pediatric polypharmacy included: two or more concurrent medications for ≥1 day (n = 41), two or more concurrent medications for ≥31 days (n = 15), and two or more sequential medications over one year (n = 12). Commonly used terms included polypharmacy, polytherapy, combination pharmacotherapy, average number, and concomitant medications. The term polypharmacy was more common in psychiatry literature while epilepsy literature favored the term polytherapy.

Conclusions

Two or more concurrent medications, without duration, for ≥1 day, ≥31 days, or sequentially for one year were the most common definitions of pediatric polypharmacy. We recommend that pediatric polypharmacy studies specify the number of medications or therapeutic classes, if they are concurrent or sequential, and the duration of medications. We propose defining pediatric polypharmacy as “the prescription or consumption of two or more distinct medications for at least one day”. The term “polypharmacy” should be included among key words and definitions in manuscripts.

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<![CDATA[Multiple morbidities in pregnancy: Time for research, innovation, and action]]> https://www.researchpad.co/article/5bb3df4440307c54ff8ce8f6

In a Guest Editorial, James Beeson and colleagues discuss the contribution of nonobstetric morbidity to mortality during and around pregnancy and what needs to be done to address this global health challenge.

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<![CDATA[BEST: Next-Generation Biomedical Entity Search Tool for Knowledge Discovery from Biomedical Literature]]> https://www.researchpad.co/article/5989da7dab0ee8fa60b993f4

As the volume of publications rapidly increases, searching for relevant information from the literature becomes more challenging. To complement standard search engines such as PubMed, it is desirable to have an advanced search tool that directly returns relevant biomedical entities such as targets, drugs, and mutations rather than a long list of articles. Some existing tools submit a query to PubMed and process retrieved abstracts to extract information at query time, resulting in a slow response time and limited coverage of only a fraction of the PubMed corpus. Other tools preprocess the PubMed corpus to speed up the response time; however, they are not constantly updated, and thus produce outdated results. Further, most existing tools cannot process sophisticated queries such as searches for mutations that co-occur with query terms in the literature. To address these problems, we introduce BEST, a biomedical entity search tool. BEST returns, as a result, a list of 10 different types of biomedical entities including genes, diseases, drugs, targets, transcription factors, miRNAs, and mutations that are relevant to a user’s query. To the best of our knowledge, BEST is the only system that processes free text queries and returns up-to-date results in real time including mutation information in the results. BEST is freely accessible at http://best.korea.ac.kr.

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<![CDATA[Reverse Chemical Genetics: Comprehensive Fitness Profiling Reveals the Spectrum of Drug Target Interactions]]> https://www.researchpad.co/article/5989d9daab0ee8fa60b67260

The emergence and prevalence of drug resistance demands streamlined strategies to identify drug resistant variants in a fast, systematic and cost-effective way. Methods commonly used to understand and predict drug resistance rely on limited clinical studies from patients who are refractory to drugs or on laborious evolution experiments with poor coverage of the gene variants. Here, we report an integrative functional variomics methodology combining deep sequencing and a Bayesian statistical model to provide a comprehensive list of drug resistance alleles from complex variant populations. Dihydrofolate reductase, the target of methotrexate chemotherapy drug, was used as a model to identify functional mutant alleles correlated with methotrexate resistance. This systematic approach identified previously reported resistance mutations, as well as novel point mutations that were validated in vivo. Use of this systematic strategy as a routine diagnostics tool widens the scope of successful drug research and development.

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<![CDATA[Hydrogen Sulfide Inhibits L-Type Calcium Currents Depending upon the Protein Sulfhydryl State in Rat Cardiomyocytes]]> https://www.researchpad.co/article/5989da3bab0ee8fa60b88156

Hydrogen sulfide (H2S) is a novel gasotransmitter that inhibits L-type calcium currents (I Ca, L). However, the underlying molecular mechanisms are unclear. In particular, the targeting site in the L-type calcium channel where H2S functions remains unknown. The study was designed to investigate if the sulfhydryl group could be the possible targeting site in the L-type calcium channel in rat cardiomyocytes. Cardiac function was measured in isolated perfused rat hearts. The L-type calcium currents were recorded by using a whole cell voltage clamp technique on the isolated cardiomyocytes. The L-type calcium channel containing free sulfhydryl groups in H9C2 cells were measured by using Western blot. The results showed that sodium hydrosulfide (NaHS, an H2S donor) produced a negative inotropic effect on cardiac function, which could be partly inhibited by the oxidant sulfhydryl modifier diamide (DM). H2S donor inhibited the peak amplitude of I Ca, L in a concentration-dependent manner. However, dithiothreitol (DTT), a reducing sulfhydryl modifier markedly reversed the H2S donor-induced inhibition of I Ca, L in cardiomyocytes. In contrast, in the presence of DM, H2S donor could not alter cardiac function and L type calcium currents. After the isolated rat heart or the cardiomyocytes were treated with DTT, NaHS could markedly alter cardiac function and L-type calcium currents in cardiomyocytes. Furthermore, NaHS could decrease the functional free sulfhydryl group in the L-type Ca2+ channel, which could be reversed by thiol reductant, either DTT or reduced glutathione. Therefore, our results suggest that H2S might inhibit L-type calcium currents depending on the sulfhydryl group in rat cardiomyocytes.

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<![CDATA[The Interactions of P-Glycoprotein with Antimalarial Drugs, Including Substrate Affinity, Inhibition and Regulation]]> https://www.researchpad.co/article/5989db10ab0ee8fa60bcbe0f

The combination of passive drug permeability, affinity for uptake and efflux transporters as well as gastrointestinal metabolism defines net drug absorption. Efflux mechanisms are often overlooked when examining the absorption phase of drug bioavailability. Knowing the affinity of antimalarials for efflux transporters such as P-glycoprotein (P-gp) may assist in the determination of drug absorption and pharmacokinetic drug interactions during oral absorption in drug combination therapies. Concurrent administration of P-gp inhibitors and P-gp substrate drugs may also result in alterations in the bioavailability of some antimalarials. In-vitro Caco-2 cell monolayers were used here as a model for potential drug absorption related problems and P-gp mediated transport of drugs. Artemisone had the highest permeability at around 50 x 10−6 cm/sec, followed by amodiaquine around 20 x 10−6 cm/sec; both mefloquine and artesunate were around 10 x 10−6 cm/sec. Methylene blue was between 2 and 6 x 10−6 cm/sec depending on the direction of transport. This 3 fold difference was able to be halved by use of P-gp inhibition. MRP inhibition also assisted the consolidation of the methylene blue transport. Mefloquine was shown to be a P-gp inhibitor affecting our P-gp substrate, Rhodamine 123, although none of the other drugs impacted upon rhodamine123 transport rates. In conclusion, mefloquine is a P-gp inhibitor and methylene blue is a partial substrate; methylene blue may have increased absorption if co-administered with such P-gp inhibitors. An upregulation of P-gp was observed when artemisone and dihydroartemisinin were co-incubated with mefloquine and amodiaquine.

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