Functionalization of Glycals Leading to 2-Deoxy-O-glycosides, Aminosugars, Nitrosugars and Glycosidase Inhibitors: Our Experience

Glycals have been transformed into a variety of functionalized substrates which have been found to be useful in synthesizing some aminosugars, N-glycopeptides, nitrosugars and some iminosugars which are potential glycosidase inhibitors. An account of work that has been done in our laboratory is briefly discussed here

Oscillation Criteria for Third-Order Nonlinear Functional Difference Equations with Damping

In this paper, we obtain some new criteria for the oscillation of certain third-order difference equations using comparison principles with a suitable couple of first-order difference equations. The presented results improve and extend the earlier ones. Examples are provided to illustrate the main results

Simultaneous RP-HPLC method for the stress degradation studies of atorvastatin calcium and ezetimibe in multicomponent dosage form

Se desarrolló y validó un método estable de cromatografía líquida de alta eficacia de fase reversa (RP-HPLC) para la estimación simultánea de atorvastatina de calcio y ezetimiba en su forma de dosificación multicomponente. El método RP-HPLC propuesto utiliza, a temperatura ambiente, una columna C-18 Phenomenex de 125 mm x 4,6 mm y d.i de 5 μm; la fase móvil óptima consta de acetonitrilo y 0,4% v/v de trietilamina (pH ajustado a 5,5 con ácido ortofosfórico) en una proporción de 55:45, v/v, respectivamente, y una velocidad de flujo de 1,0 ml/min. Las medidas se realizaron a una longitud de onda de 231 nm. La forma de dosificación multicomponente se expuso a estrés oxidativo, hidrolítico, fotolítico y térmico. No se observaron, en la degradación de productos, ni impurezas ni picos de coelución o interferencia por excipientes, y, además, el método resultó ser específico. El método fue linear, en el rango de 5-25 μg/ml para atorvastatina de calcio y ezetimiba. Las recuperaciones medias fueron del 98,82% y 98,72% para atorvastatina de calcio y ezetimiba, respectivamente. El método se validó para linealidad, rango, precisión, exactitud, especificidad, selectividad, precisión intermedia, dureza, robustez, estabilidad de la disolución e idoneidad.A stability-indicating reversed-phase high performance liquid chromatographic (RP-HPLC) method has been developed and validated for simultaneous estimation of atorvastatin calcium and ezetimibe for their multicomponent dosage form. The proposed RP-HPLC method utilizes a 125 mm x 4.6 mm i.d 5 μm Phenomenex C-18 column at ambient temperature; the optimum mobile phase consists of acetonitrile and 0.4% v/v triethylamine (pH adjusted to 5.5 with ortho-phosphoric acid) in the ratio of 55:45, v/v respectively, flow rate of 1.0 ml/min. Measurements were made at a wavelength of 231 nm. Multicomponent dosage form was exposed to thermal, photolytic, hydrolytic and oxidative stress. No co eluting, interfering peaks from excipients, impurities were observed for the degradation products and hence the method was found to be specific. The method was linear in the range of 5-25 μg/ml for atorvastatin calcium and ezetimibe. The mean recoveries were 98.82% and 98.72% for atorvastatin calcium and ezetimibe respectively. The method was validated for linearity, range, precision, accuracy, specificity, selectivity, intermediate precision, ruggedness, robustness, solution stability and suitability

Identifying Orientation-specific Lipid-protein Fingerprints using Deep Learning

Improved understanding of the relation between the behavior of RAS and RAF proteins and the local lipid environment in the cell membrane is critical for getting insights into the mechanisms underlying cancer formation. In this work, we employ deep learning (DL) to learn this relationship by predicting protein orientational states of RAS and RAS-RAF protein complexes with respect to the lipid membrane based on the lipid densities around the protein domains from coarse-grained (CG) molecular dynamics (MD) simulations. Our DL model can predict six protein states with an overall accuracy of over 80%. The findings of this work offer new insights into how the proteins modulate the lipid environment, which in turn may assist designing novel therapies to regulate such interactions in the mechanisms associated with cancer development

Gradijentna HPLC analiza raloksifen hidroklorida i primjena u kontroli kvalitete

A rapid, sensitive and selective method for the determination of raloxifene hydrochloride (RLX) in pure drug and in tablets was developed using gradient high performance liquid chromatography (HPLC). The devised method involved separation of RLX on a reversed phase Hypersil ODS column and determination with UV detection at 284 nm. The standard curve was linear (R = 0.999) over the concentration range of 50-600 μg mL1 with a detection limit of 0.04 μg mL1 and a quantification limit of 0.16 μg mL1. Intra-day and inter-day precision and accuracy of the method were established according to the current ICH guidelines. Intra-day RSD values at three QC levels (250, 450 and 550 μg mL1) were 0.20.5% based on the peak area. The intra-day relative error (er) was between 0.2 and 0.5%. The developed method was successfully applied to the determination of RLX in tablets and the results were statistically compared with those obtained by a literature method. Accuracy, evaluated by means of the spike recovery method, was excellent with percent recovery in the range 97.7103.2 with precision in the range 1.62.2%. No interference was observed from the conformulated substances. The method was economical in terms of the time taken and the amount of solvent used.Koristeći gradijentnu tekućinsku kromatografiju visoke učinkovitosti razvijena je brza, osjetljiva i selektivna metoda za određivanje raloksifen hidroklorida (RLX), čiste supstancije i u tabletama. U radu je primijenjena reverzno-fazna kolona Hypersil ODS te UV detekcija pri 284 nm. Standardna krivulja bila je linearna (R = 0,999) u koncentracijskom području 50600 μg mL1. Granica detekcije bila je 0,04 μg mL1 a granica određivanja 0,16 μg mL1. Repetabilnost, intermedijalna preciznost i ispravnost ispitivane su prema važećim ICH uputama. Mjerenjem površine ispod pika na tri koncentracijske razine (250, 450 i 550 μg mL1) procijenjena je repetabilnost na 0,20,5%. Relativna pogreška procijenjena unutar jednog dana (er) bila je između 0,2 i 0,5%. Razvijena metoda uspješno je primijenjena za određivanje RLX u tabletama. Rezultati su statistički uspoređeni s rezultatima dobivenim prema ranije objavljenoj metodi. Analitički povrat bio je u rasponu 97,7103,2 uz preciznost od 1,6 do 2,2%. Nije primijećena interferencija pomoćnih tvari. Metoda je ekonomična s obzirom na utrošeno vrijeme i količine upotrebljenog otapala

Methylene blue not ferrocene: Optimal reporters for electrochemical detection of protease activity

AbstractElectrochemical peptide-based biosensors are attracting significant attention for the detection and analysis of proteins. Here we report the optimisation and evaluation of an electrochemical biosensor for the detection of protease activity using self-assembled monolayers (SAMs) on gold surfaces, using trypsin as a model protease. The principle of detection was the specific proteolytic cleavage of redox-tagged peptides by trypsin, which causes the release of the redox reporter, resulting in a decrease of the peak current as measured by square wave voltammetry. A systematic enhancement of detection was achieved through optimisation of the properties of the redox-tagged peptide; this included for the first time a side-by-side study of the applicability of two of the most commonly applied redox reporters used for developing electrochemical biosensors, ferrocene and methylene blue, along with the effect of changing both the nature of the spacer and the composition of the SAM. Methylene blue-tagged peptides combined with a polyethylene-glycol (PEG) based spacer were shown to be the best platform for trypsin detection, leading to the highest fidelity signals (characterised by the highest sensitivity (signal gain) and a much more stable background than that registered when using ferrocene as a reporter). A ternary SAM (T-SAM) configuration, which included a PEG-based dithiol, minimised the non-specific adsorption of other proteins and was sensitive towards trypsin in the clinically relevant range, with a Limit of Detection (LoD) of 250pM. Kinetic analysis of the electrochemical response with time showed a good fit to a Michaelis–Menten surface cleavage model, enabling the extraction of values for kcat and KM. Fitting to this model enabled quantitative determination of the solution concentration of trypsin across the entire measurement range. Studies using an enzyme inhibitor and a range of real world possible interferents demonstrated a selective response to trypsin cleavage. This indicates that a PEG-based peptide, employing methylene blue as redox reporter, and deposited on an electrode as a ternary SAM configuration, is a suitable platform to develop clinically-relevant and quantitative electrochemical peptide-based protease biosensing

Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure

Interdependence across time and length scales is common in biology, where atomic interactions can impact larger-scale phenomenon. Such dependence is especially true for a well-known cancer signaling pathway, where the membrane-bound RAS protein binds an effector protein called RAF. To capture the driving forces that bring RAS and RAF (represented as two domains, RBD and CRD) together on the plasma membrane, simulations with the ability to calculate atomic detail while having long time and large length- scales are needed. The Multiscale Machine-Learned Modeling Infrastructure (MuMMI) is able to resolve RAS/RAF protein-membrane interactions that identify specific lipid-protein fingerprints that enhance protein orientations viable for effector binding. MuMMI is a fully automated, ensemble-based multiscale approach connecting three resolution scales: (1) the coarsest scale is a continuum model able to simulate milliseconds of time for a 1 μm2 membrane, (2) the middle scale is a coarse-grained (CG) Martini bead model to explore protein-lipid interactions, and (3) the finest scale is an all-atom (AA) model capturing specific interactions between lipids and proteins. MuMMI dynamically couples adjacent scales in a pairwise manner using machine learning (ML). The dynamic coupling allows for better sampling of the refined scale from the adjacent coarse scale (forward) and on-the-fly feedback to improve the fidelity of the coarser scale from the adjacent refined scale (backward). MuMMI operates efficiently at any scale, from a few compute nodes to the largest supercomputers in the world, and is generalizable to simulate different systems. As computing resources continue to increase and multiscale methods continue to advance, fully automated multiscale simulations (like MuMMI) will be commonly used to address complex science questions

Machine learning–driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins

RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades

Development of 2nd generation aminomethyl spectinomycins that overcome native efflux in Mycobacterium abscessus

Mycobacterium abscessus (Mab), a nontuberculous mycobacterial (NTM) species, is an emerging pathogen with high intrinsic drug resistance. Current standard-of-care therapy results in poor outcomes, demonstrating the urgent need to develop effective antimycobacterial regimens. Through synthetic modification of spectinomycin (SPC), we have identified a distinct structural subclass of N-ethylene linked aminomethyl SPCs (eAmSPCs) that are up to 64-fold more potent against Mab over the parent SPC. Mechanism of action and crystallography studies demonstrate that the eAmSPCs display a mode of ribosomal inhibition consistent with SPC. However, they exert their increased antimicrobial activity through enhanced accumulation, largely by circumventing efflux mechanisms. The N-ethylene linkage within this series plays a critical role in avoiding TetV-mediated efflux, as lead eAmSPC 2593 displays a mere fourfold susceptibility improvement against Mab ΔtetV, in contrast to the 64-fold increase for SPC. Even a minor shortening of the linkage by a single carbon, akin to 1st generation AmSPC 1950, results in a substantial increase in MICs and a 16-fold rise in susceptibility against Mab ΔtetV. These shifts suggest that longer linkages might modify the kinetics of drug expulsion by TetV, ultimately shifting the equilibrium towards heightened intracellular concentrations and enhanced antimicrobial efficacy. Furthermore, lead eAmSPCs were also shown to synergize with various classes of anti-Mab antibiotics and retain activity against clinical isolates and other mycobacterial strains. Encouraging pharmacokinetic profiles coupled with robust efficacy in Mab murine infection models suggest that eAmSPCs hold the potential to be developed into treatments for Mab and other NTM infections

A peptide nucleic acid (PNA)-DNA ferrocenyl intercalator for electrochemical sensing

A ferrocenyl intercalator was investigated to develop an electrochemical DNA biosensor employing a peptide nucleic acid (PNA) sequence as capture probe. After hybridization with single strand DNA sequence, a naphthalene diimide intercalator bearing ferrocene moieties (FND) was introduced to bind with the PNA-DNA duplex and the electrochemical signal of the ferrocene molecules was used to monitor the DNA recognition.Electrochemical impedance spectroscopy was used to characterize the different modification steps. Differential pulse voltammetry was employed to evaluate the electrochemical signal of the FND intercalator related to its interaction with the complementary PNA-DNA hybrid. The ferrocene oxidation peaks were utilised for the target DNA quantification.The developed biosensor demonstrated a good linear dependence of FND oxidation peak on DNA concentration in the range 1 fM to 100 nM of target DNA, with a low detection limit of 11.68 fM. Selectivity tests were also investigated with a non-complementary DNA sequence, indicating that the FND intercalator exhibits a selective response to the target PNA-DNA duplex