Method Development and Validation for the Estimation of Decitabine in Pharmaceutical Dosage form by RP-HPLC Method

INTRODUCTION: Pharmaceutical analysis is a branch of chemistry involving a process of identification, determination, quantification, purification and separation of components in a mixture or determination of chemical structure of compounds. There are two main types of analysis – Qualitative and Quantitative analysis. Qualitative analysis is performed to establish composition of a substance. It is done to determine the presence of a compound or substance in a given sample or not. The various qualitative tests are detection of evolved gas, limit tests, color change reactions, determination of melting point and boiling point, mass spectroscopy, determination of nuclear half life etc. Quantitative analysis techniques are mainly used to determine the amount or concentration of analyte in a sample and expressed as a numerical value in appropriate units. These techniques are based on suitable chemical reaction and either measuring the amount of reagent added to complete the reaction or measuring the amount of reaction product obtained the characteristic movement of a substance through a defined medium under controlled conditions, electrical measurement or measurement of spectroscopic properties of the compound. AIM: To develop New Rapid, Precise, Accurate RP HPLC method for the estimation of decitabine in pharmaceutical dosage form. PLAN OF WORK: 1. Solubility determination of Decitabine in various solvents and buffers. 2. Determine the absorption maxima of both the drugs in UV–Visible region in different solvents/buffers and selecting the solvents for HPLC method development. 3. Optimize the mobile phase and flow rates for proper resolution and retention times. 4. Validate the developed method as per ICH guidelines. SUMMARY: Each and every day a number of diseases are being diagnosed. Several cancer diseases are on a rise not only for the rich but also for the poor. So, various pharmaceutical organizations are working to develop new drug molecules and new combinations of anticancer drugs for better treatment. This is the reason for a greater competition in the pharmaceutical sector, and the future scenario is likely to be the same. The scope of developing and validating a method is to ensure a suitable strategy for evaluation of a particular analyte which is more specific, accurate and precise. The main focus is drawn to achieve improvement in the manufacturing and analytical conditions and making proper amendments in the standard operating procedures being followed. The above review indicates that there are fewer methods for the estimation of Decitabine in pharmaceutical formulations. But the buffers used in this method was at acidic pH which may affect the column life and some method were with more a run time. So my aim was to develop a new method with minimum run time and less solvent consumption for the estimation of Decitabine in combination of drugs. Hence the present study aims to develop rapid, precise and accurate methods for the determination of Decitabine by RP-HPLC in pharmaceutical dosage forms.CONCLUSION: A new precise, accurate, rapid method has been developed for the simultaneous estimation of Decitabine in pharmaceutical dosage form by RPHPLC. The optimum wavelength for the determination of Decitabine was selected at 244 nm. Various trials were performed with different mobile phases in different ratios, but Ammonium Acetate buffer pH 4.5: ACN (985:15) was selected as good peak symmetry. The Retention time of decitabine was found to be 3.786 min. The different analytical performance parameters such as linearity, precision, accuracy, and specificity, LOD, LOQ were determined according to International Conference on Harmonization ICH Q2B guidelines. The calibration curves were obtained by plotting peak area versus the concentration over the range of 50-150 μg/mL. From linearity the correlation coefficient R2 value was found to be 0.998. The proposed HPLC method was also validated for system suitability, system precision and method precision. The % RSD in the peak area of drug was found to be less than 2%. The number of theoretical plates was found to be more than 2000, which indicates efficient performance of the column. The LOD for this method was found to be 0.0003μg/mL. The LOQ for this method was found to be 0.0009 μg/mL, indicates the sensitivity of the method. The percentage of recovery of was found to be 99.77 shows that the proposed method is highly accurate. Hence the proposed method is highly sensitive, precise and accurate and it successfully applied for the quantification of API content in the commercial formulations of decitabine in Educational institutions and Quality control laboratories

Positioning and Surveying Requirements for Exploration and Exploitation of Ocean Wealth

Deep sea mining, such as is now being planned to be carried out in the Indian Ocean, requires an accurate positioning system for navigation and for the control of the equipment. Short range systems using electromagnetic principles cover only a limited area while the longer range systems which can be used for offshore, deep ocean work although covering large areas, have limited accuracy. This paper reviews the requirements for position fixing systems for deep ocean mining and the ways to reach the best solution at the most reasonable cost

Deep Learning Approach for Intelligent Intrusion Detection System

Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyberattacks at the network-level and the host-level in a timely and automatic manner. However, many challenges arise since malicious attacks are continually changing and are occurring in very large volumes requiring a scalable solution. There are different malware datasets available publicly for further research by cyber security community. However, no existing study has shown the detailed analysis of the performance of various machine learning algorithms on various publicly available datasets. Due to the dynamic nature of malware with continuously changing attacking methods, the malware datasets available publicly are to be updated systematically and benchmarked. In this paper, a deep neural network (DNN), a type of deep learning model, is explored to develop a flexible and effective IDS to detect and classify unforeseen and unpredictable cyberattacks. The continuous change in network behavior and rapid evolution of attacks makes it necessary to evaluate various datasets which are generated over the years through static and dynamic approaches. This type of study facilitates to identify the best algorithm which can effectively work in detecting future cyberattacks. A comprehensive evaluation of experiments of DNNs and other classical machine learning classifiers are shown on various publicly available benchmark malware datasets. The optimal network parameters and network topologies for DNNs are chosen through the following hyperparameter selection methods with KDDCup 99 dataset. All the experiments of DNNs are run till 1,000 epochs with the learning rate varying in the range [0.01–0.5]. The DNN model which performed well on KDDCup 99 is applied on other datasets, such as NSL-KDD, UNSW-NB15, Kyoto, WSN-DS, and CICIDS 2017, to conduct the benchmark. Our DNN model learns the abstract and high-dimensional feature representation of the IDS data by passing them into many hidden layers. Through a rigorous experimental testing, it is confirmed that DNNs perform well in comparison with the classical machine learning classifiers. Finally, we propose a highly scalable and hybrid DNNs framework called scale-hybrid-IDS-AlertNet which can be used in real-time to effectively monitor the network traffic and host-level events to proactively alert possible cyberattacks

Advanced Technologies for Oral Controlled Release: Cyclodextrins for oral controlled release

Cyclodextrins (CDs) are used in oral pharmaceutical formulations, by means of inclusion complexes formation, with the following advantages for the drugs: (1) solubility, dissolution rate, stability and bioavailability enhancement; (2) to modify the drug release site and/or time profile; and (3) to reduce or prevent gastrointestinal side effects and unpleasant smell or taste, to prevent drug-drug or drug-additive interactions, or even to convert oil and liquid drugs into microcrystalline or amorphous powders. A more recent trend focuses on the use of CDs as nanocarriers, a strategy that aims to design versatile delivery systems that can encapsulate drugs with better physicochemical properties for oral delivery. Thus, the aim of this work was to review the applications of the CDs and their hydrophilic derivatives on the solubility enhancement of poorly water soluble drugs in order to increase their dissolution rate and get immediate release, as well as their ability to control (to prolong or to delay) the release of drugs from solid dosage forms, either as complexes with the hydrophilic (e.g. as osmotic pumps) and/ or hydrophobic CDs. New controlled delivery systems based on nanotechonology carriers (nanoparticles and conjugates) have also been reviewed

Processing and characterization of chitosan microspheres to be used as templates for layer-by-layer assembly

Chitosan (Ch) microspheres have been developed by precipitation method, cross-linked with glutaraldehyde and used as a template for layer-by-layer (LBL) deposition of two natural polyelectrolytes. Using a LBL methodology, Ch microspheres were alternately coated with hyaluronic acid (HA) and Ch under mild conditions. The roughness of the Ch-based crosslinked microspheres was characterized by atomic force microscopy (AFM). Morphological characterization was performed by environmental scanning electron microscopy (ESEM), scanning electron microscopy (SEM) and stereolight microscopy. The swelling behaviour of the microspheres demonstrated that the ones with more bilayers presented the highest water uptake and the uncoated cross-linked Ch microspheres showed the lowest uptake capability. Microspheres presented spherical shape with sizes ranging from 510 to 840 lm. ESEM demonstrated that a rougher surface with voids is formed in multilayered microspheres caused by the irregular stacking of the layers. A short term mechanical stability assay was also performed, showing that the LBL procedure with more than five bilayers of HA/Ch over Ch cross-linked microspheres provide higher mechanical stability

Biomaterials Approaches to Combating Oral Biofilms and Dental Disease

Background: Possibilities for biomaterials to impact the dental caries epidemic are reviewed with emphasis placed on novel delivery biomaterials and new therapeutic targets

Transport of small anionic and neutral solutes through chitosan membranes: Dependence on cross-linking and chelation of divalent cations

Chitosan membranes were prepared by solvent casting and cross-linked with glutaraldehyde at several ratios under homogeneous conditions. The cross-linking degree, varying from 0 to 20%, is defined as the ratio between the total aldehyde groups and the amine groups of chitosan. Permeability experiments were conducted using a side-by-side diffusion cell to determine the flux of small molecules of similar size but with different chemical moieties, either ionized (benzoic acid, salicylic acid, and phthalic acid) or neutral (2-phenylethanol) at physiological pH. The permeability of the different model molecules revealed to be dependent on the affinity of those structurally similar molecules to chitosan. The permeability of the salicylate anion was significantly enhanced by the presence of metal cations commonly present in biological fluids, such as calcium and magnesium, but remained unchanged for the neutral 2-phenylethanol. This effect could be explained by the chelation of metal cations on the amine groups of chitosan, which increased the partition coefficient. The cross-linking degree was also correlated with the permeability and partition coefficient. The change in the permeation properties of chitosan to anionic solutes in the presence of these metallic cations is an important result and should be taken into consideration when trying to make in vitro predictions of the drug release from chitosan-based controlled release systems

Polymeric Micelles in Anticancer Therapy: Targeting, Imaging and Triggered Release

Micelles are colloidal particles with a size around 5–100 nm which are currently under investigation as carriers for hydrophobic drugs in anticancer therapy. Currently, five micellar formulations for anticancer therapy are under clinical evaluation, of which Genexol-PM has been FDA approved for use in patients with breast cancer. Micelle-based drug delivery, however, can be improved in different ways. Targeting ligands can be attached to the micelles which specifically recognize and bind to receptors overexpressed in tumor cells, and chelation or incorporation of imaging moieties enables tracking micelles in vivo for biodistribution studies. Moreover, pH-, thermo-, ultrasound-, or light-sensitive block copolymers allow for controlled micelle dissociation and triggered drug release. The combination of these approaches will further improve specificity and efficacy of micelle-based drug delivery and brings the development of a ‘magic bullet’ a major step forward