Development of Dissolution Test Method for Drotaverine Hydrochloride/Mefenamic Acid Combination Using Derivative Spectrophotometry

Purpose: To develop and validate a dissolution test method for tablets containing 80 mg of drotaverine hydrochloride (DRT) and 250 mg of mefenamic acid (MEF).Methods: Sink conditions, drug stability and specificity in different dissolution media were tested to optimize a dissolution test method using a USP paddle type dissolution test apparatus set at a speed of50 rpm. The dissolution medium consisted of 900 ml of phosphate buffer (pH 6.8) containing 0.25% w/v cetrimide at 37 ± 0.5 oC and 45 min time-point. To determine both drugs simultaneously, a first derivative UV spectrophotometric method was developed and validated. Drug release was analyzed by first derivative UV method at 253.8 nm and 304 nm for DRT and MEF respectively. The dissolution method was validated as per ICH guidelines.Results: The two brands each showed 98% of drug release for both drugs when the developed dissolution method was used. The regression plot was linear in the concentration range 4 - 24 ìg/mL for each of the drugs and regression coefficient (r2) was greater than 0.999 for each drug. Relativestandard deviation (% RSD) for precision and accuracy of proposed method was < 2.Conclusion: The proposed dissolution method is simple, cost-effective, precise, accurate and specific. It can be successfully employed in routine quality control of DRT and MEF combination tablets.Keywords: Drotaverine hydrochloride, Mefenamic acid, First derivative spectrophotometry, Dissolution, Validatio

Implementing the ‘Frozen Potential’ Approach on ADEPT to Analyze Thin Film Solar Cells

Thin film solar cells have higher absorption coefficients than traditional Silicon solar cells. This means that lesser material is required to produce the same power output for a given intensity of solar illumination. As a result, they are less expensive, easier to install and have a wider range of applications. Analyzing the performance of cells requires separating the current into the photocurrent and the injection current based on the ‘Superposition Principle’. For thin film solar cells, this cannot be done using the conventional method. This is because these components are interdependent, and so modeling one’s behavior requires understanding the other. We address this issue by implementing a new modeling approach. This novel ‘Frozen Potential’ Approach separates the photocurrent and injection current from the total current. The currents are then plotted individually. This method is implemented on a rigorous simulation tool called ADEPT 2.0, which is readily available on nanoHUB.org – the premier platform for research and simulation in nanotechnology. Equipped with this new modelling approach, a useful framework is provided for ADEPT 2.0 by tying in a traditional understanding of solar cells to a new class of materials, geometries and illumination profiles relevant for the solar cell community

Metabolic network analysis predicts efficacy of FDA-approved drugs targeting the causative agent of a neglected tropical disease

<p>Abstract</p> <p>Background</p> <p>Systems biology holds promise as a new approach to drug target identification and drug discovery against neglected tropical diseases. Genome-scale metabolic reconstructions, assembled from annotated genomes and a vast array of bioinformatics/biochemical resources, provide a framework for the interrogation of human pathogens and serve as a platform for generation of future experimental hypotheses. In this article, with the application of selection criteria for both <it>Leishmania major </it>targets (e.g. <it>in silico </it>gene lethality) and drugs (e.g. toxicity), a method (MetDP) to rationally focus on a subset of low-toxic Food and Drug Administration (FDA)-approved drugs is introduced.</p> <p>Results</p> <p>This metabolic network-driven approach identified 15 <it>L. major </it>genes as high-priority targets, 8 high-priority synthetic lethal targets, and 254 FDA-approved drugs. Results were compared to previous literature findings and existing high-throughput screens. Halofantrine, an antimalarial agent that was prioritized using MetDP, showed noticeable antileishmanial activity when experimentally evaluated <it>in vitro </it>against <it>L. major </it>promastigotes. Furthermore, synthetic lethality predictions also aided in the prediction of superadditive drug combinations. For proof-of-concept, double-drug combinations were evaluated <it>in vitro </it>against <it>L. major </it>and four combinations involving the drug disulfiram that showed superadditivity are presented.</p> <p>Conclusions</p> <p>A direct metabolic network-driven method that incorporates single gene essentiality and synthetic lethality predictions is proposed that generates a set of high-priority <it>L. major </it>targets, which are in turn associated with a select number of FDA-approved drugs that are candidate antileishmanials. Additionally, selection of high-priority double-drug combinations might provide for an attractive and alternative avenue for drug discovery against leishmaniasis.</p

A novel fault-detection methodology of proposed reduced switch MLI fed induction motor drive using discrete wavelet transforms

Induction motors are typically promoted in industrial applications by adopting energy-efficient power-electronic drive technology. Multilevel inverters (MLI) have been widely recognized in recent days for high-power, medium-voltage-efficient drives. There has been vital interest in forming novel multilevel inverters with reduced switching elements. The newly proposed reduced-switch five-level inverter topology extends with fewer switches, low dv/dt stress, high efficiency, and so on, over the formal multilevel inverter topologies. The multilevel inverter's reputation is greatly affected due to several faults on switching elements and complex switching sequences. In this paper, a novel fault identification process is evaluated in both healthy and faulty conditions using discrete-wavelet transform analysis. The discrete wavelet transform utilizes the multi-resolution analysis with a feature extraction methodology acquired for fault identification over the classical methods. A novel fault identification scheme is implemented on reduced-switch five-level MLI topology using the Matlab/Simulink platform to increase the drive system's reliability. The effectiveness of simulation outcomes is illustrated with proper comparisons. The pro posed topology's hardware model is implemented using a dSPACE DS1103 real-time digital controller and the results of the experiment are presented

Wnt-Dependent Oligodendroglial-Endothelial Interactions Regulate White Matter Vascularization and Attenuate Injury.

Recent studies have indicated oligodendroglial-vascular crosstalk during brain development, but the underlying mechanisms are incompletely understood. We report that oligodendrocyte precursor cells (OPCs) contact sprouting endothelial tip cells in mouse, ferret, and human neonatal white matter. Using transgenic mice, we show that increased or decreased OPC density results in cognate changes in white matter vascular investment. Hypoxia induced increases in OPC numbers, vessel density and endothelial cell expression of the Wnt pathway targets Apcdd1 and Axin2 in white matter, suggesting paracrine OPC-endothelial signaling. Conditional knockout of OPC Wntless resulted in diminished white matter vascular growth in normoxia, whereas loss of Wnt7a/b function blunted the angiogenic response to hypoxia, resulting in severe white matter damage. These findings indicate that OPC-endothelial cell interactions regulate neonatal white matter vascular development in a Wnt-dependent manner and further suggest this mechanism is important in attenuating hypoxic injury.M.C. acknowledges fellowship awards from the American Heart Association and The Children’s Heart Foundation and funding support from a Career Development Grant awarded by Cerebral Palsy Alliance Research Foundation. J.M.G.V is funded by Red deTerapia Celular (TerCel-RD16/0011/0026) and the Valencian Council for Innovation, Universities Science and Digital Society (PROMETEO/2019/075). M.J.U.N was supported by a McDonald Fellowship from the Multiple Sclerosis International Federation. This work was supported by funding from the National Multiple Sclerosis Foundation (to D.H.R.), the Adelson Medical Research Foundation (D.H.R), the European Research Council (D.H.R.) and the National Institutes of Health, NINDS (1K99NS117804 to M.C; P01- NS083513 to D.H.R., E.J.H and P.S.M)

Metabolomics to unveil and understand phenotypic diversity between pathogen populations

Visceral leishmaniasis is caused by a parasite called Leishmania donovani, which every year infects about half a million people and claims several thousand lives. Existing treatments are now becoming less effective due to the emergence of drug resistance. Improving our understanding of the mechanisms used by the parasite to adapt to drugs and achieve resistance is crucial for developing future treatment strategies. Unfortunately, the biological mechanism whereby Leishmania acquires drug resistance is poorly understood. Recent years have brought new technologies with the potential to increase greatly our understanding of drug resistance mechanisms. The latest mass spectrometry techniques allow the metabolome of parasites to be studied rapidly and in great detail. We have applied this approach to determine the metabolome of drug-sensitive and drug-resistant parasites isolated from patients with leishmaniasis. The data show that there are wholesale differences between the isolates and that the membrane composition has been drastically modified in drug-resistant parasites compared with drug-sensitive parasites. Our findings demonstrate that untargeted metabolomics has great potential to identify major metabolic differences between closely related parasite strains and thus should find many applications in distinguishing parasite phenotypes of clinical relevance

Genome-wide association study for type 2 diabetes in Indians identifies a new susceptibility locus at 2q21

Meta-AnalysisThis is the final version of the article. Available from the American Diabetes Association via the DOI in this record.Indians undergoing socioeconomic and lifestyle transitions will be maximally affected by epidemic of type 2 diabetes (T2D). We conducted a two-stage genome-wide association study of T2D in 12,535 Indians, a less explored but high-risk group. We identified a new type 2 diabetes-associated locus at 2q21, with the lead signal being rs6723108 (odds ratio 1.31; P = 3.32 × 10⁻⁹). Imputation analysis refined the signal to rs998451 (odds ratio 1.56; P = 6.3 × 10⁻¹²) within TMEM163 that encodes a probable vesicular transporter in nerve terminals. TMEM163 variants also showed association with decreased fasting plasma insulin and homeostatic model assessment of insulin resistance, indicating a plausible effect through impaired insulin secretion. The 2q21 region also harbors RAB3GAP1 and ACMSD; those are involved in neurologic disorders. Forty-nine of 56 previously reported signals showed consistency in direction with similar effect sizes in Indians and previous studies, and 25 of them were also associated (P < 0.05). Known loci and the newly identified 2q21 locus altogether explained 7.65% variance in the risk of T2D in Indians. Our study suggests that common susceptibility variants for T2D are largely the same across populations, but also reveals a population-specific locus and provides further insights into genetic architecture and etiology of T2D.The major funding for this work comes from Council for Scientific and Industrial Research, Government of India, in the form of the grant “Diabetes mellitus—New drug discovery R&D, molecular mechanisms, and genetic and epidemiological factors” (NWP0032-19). R.T. received a postdoctoral fellowship from the Fogarty International Center and the Eunice Kennedy Shriver National Institute of Child Health and Human Development at the National Institutes of Health (D43-HD-065249)

Partial inhibition and bilevel optimization in flux balance analysis

Motivation: Within Flux Balance Analysis, the investigation of complex subtasks, such as finding the optimal perturbation of the network or finding an optimal combination of drugs, often requires to set up a bilevel optimization problem. In order to keep the linearity and convexity of these nested optimization problems, an ON/OFF description of the effect of the perturbation (i.e. Boolean variable) is normally used. This restriction may not be realistic when one wants, for instance, to describe the partial inhibition of a reaction induced by a drug.Results: In this paper we present a formulation of the bilevel optimization which overcomes the oversimplified ON/OFF modeling while preserving the linear nature of the problem. A case study is considered: the search of the best multi-drug treatment which modulates an objective reaction and has the minimal perturbation on the whole network. The drug inhibition is described and modulated through a convex combination of a fixed number of Boolean variables. The results obtained from the application of the algorithm to the core metabolism of E.coli highlight the possibility of finding a broader spectrum of drug combinations compared to a simple ON/OFF modeling.Conclusions: The method we have presented is capable of treating partial inhibition inside a bilevel optimization, without loosing the linearity property, and with reasonable computational performances also on large metabolic networks. The more fine-graded representation of the perturbation allows to enlarge the repertoire of synergistic combination of drugs for tasks such as selective perturbation of cellular metabolism. This may encourage the use of the approach also for other cases in which a more realistic modeling is required. \ua9 2013 Facchetti and Altafini; licensee BioMed Central Ltd