Method Devolopment and Validation of Indapamide and Perindopril Erbumine in Bulk and Tablet Dosage Form

INTRODUCTION: CHROMATOGRAPHY: The word chromatography is derived from the Greek letters chromos meaning color and the graph means color writing. The initial use of the terms is attributed to T Swett. It can be defined as “a separation process that is achieved by distribution of substance between two phases that is stationary phase and mobile phase. IMPORTANCE OF CHROMATOGRAPHY: Chromatography is one of the most powerful and versatile analytical techniques available to the modern chemist. Its power arises from its capacity to determine quantitatively many individual components present in a mixture in a single one analytical run. Its versatility comes from its capacity to handle wide variety of samples that may be gaseous, liquid or solid in nature. The sample can range in complexity from a single substance to a multi component mixture containing widely different chemical species. Another aspect of versatility is that the analysis can be carried out on a very costly complex instrument and on the other hand on a simple inexpensive thin layer plate. AIM: The aim of the work is to develop a precise, accurate, simple and reliable, less time consuming validated RP-PLC method for Indapamide and Perindopril erbumine in bulk and tablet dosage form. OBJECTIVE: 1. To develop new, simple, sensitive, accurate and economical analytical method for the simultaneous estimation of Indapamide and Perindopril erbumine. 2. To validate the proposed method in accordance with ICH guidelines for the intended analytical application. 3. To apply the proposed method for analysis of these drugs in their combined dosage form. SUMMARY: System suitability parameters were determined. The number of theoretical plates per column for Indapamide and Perindopril erbumine was found to be 6004 and 2831 respectively. The symmetry factor or tailing factor was found to be 1.3887 and 1.750 for Indapamide and Perindopril erbumine. The resolution of the method was calculated and was found to be 11.020. Specificity of the method was determined. The chromatogram of Indapamide and Perindopril erbumine were analyzed and there is no interference from diluents, excipients and impurities with peaks of Indapamide and Perindopril erbumine. Linearity of the drugs response was found to be in the concentration range of 2-12μg/ml for Indapamide and 5-30 μg/ml for Perindopril erbumine. The correlation coefficient and percentage curve fitting for Indapamide and Perindopril erbumine was found to be 0.999, 0.999 and 99.9%, 99.9% respectively which are well in the acceptance criteria limits. Precision of the system and method was determined. The %RSD values of retention time and Peak area for five injections of Indapamide and Perindopril erbumine were found to be 0.09, 0.48 and 0.0, 0.05 respectively which were well within acceptance criteria limit for system precision. The %RSD values for Retention time and Peak area for five injections of Indapamide and Perindopril erbumine were found to be 0.10, 0.06 and 0.59, 0.24 respectively, which were well within acceptance criteria for method precision. Hence the proposed method was found to provide high degree of precision and reproducibility. Accuracy was determined through recovery studies of Indapamide and Perindopril erbumine. The mean percentage recovery for Indapamide and Perindopril erbumine was found to be between 98.48- 99.90 and 99.89-99.96 respectively, which were well within the acceptance criteria and hence the method was found to be accurate, indicating no interference of the drugs with each other or with the excipients present in the formulation. CONCLUSION: A RP-HPLC method was developed and validated successfully for the estimation of Indapamide and Perindopril erbumine in bulk and tablet dosage formulation. The methods were found to be accurate, precise, linear, specific and reproducible for the simultaneous determination of Indapamide and Perindopril erbumine in bulk and tablet dosage form (tablets). Hence these methods can be used for simultaneous estimation of Indapamide and Perindopril erbumine in routine table

An Operational Approach to Information Leakage via Generalized Gain Functions

We introduce a \emph{gain function} viewpoint of information leakage by proposing \emph{maximal $g$-leakage}, a rich class of operationally meaningful leakage measures that subsumes recently introduced leakage measures -- {maximal leakage} and {maximal $\alpha$-leakage}. In maximal $g$-leakage, the gain of an adversary in guessing an unknown random variable is measured using a {gain function} applied to the probability of correctly guessing. In particular, maximal $g$-leakage captures the multiplicative increase, upon observing $Y$, in the expected gain of an adversary in guessing a randomized function of $X$, maximized over all such randomized functions. We also consider the scenario where an adversary can make multiple attempts to guess the randomized function of interest. We show that maximal leakage is an upper bound on maximal $g$-leakage under multiple guesses, for any non-negative gain function $g$. We obtain a closed-form expression for maximal $g$-leakage under multiple guesses for a class of concave gain functions. We also study maximal $g$-leakage measure for a specific class of gain functions related to the $\alpha$-loss. In particular, we first completely characterize the minimal expected $\alpha$-loss under multiple guesses and analyze how the corresponding leakage measure is affected with the number of guesses. Finally, we study two variants of maximal $g$-leakage depending on the type of adversary and obtain closed-form expressions for them, which do not depend on the particular gain function considered as long as it satisfies some mild regularity conditions. We do this by developing a variational characterization for the R\'{e}nyi divergence of order infinity which naturally generalizes the definition of pointwise maximal leakage to incorporate arbitrary gain functions.Comment: 27 pages, 1 Figure. New results are added. Some results of this paper were presented at ISIT 2021 and ISIT 202

LUNAR: Automated Input Generation and Analysis for Reactive LAMMPS Simulations

Generating simulation-ready molecular models for the LAMMPS molecular dynamics (MD) simulation software package is a difficult task and impedes the more widespread and efficient use of MD in materials design and development. Fixed-bond force fields generally require manual assignment of atom types, bonded interactions, charges, and simulation domain sizes. A new LAMMPS pre- and postprocessing toolkit (LUNAR) is presented that efficiently builds molecular systems for LAMMPS. LUNAR automatically assigns atom types, generates bonded interactions, assigns charges, and provides initial configuration methods to generate large molecular systems. LUNAR can also incorporate chemical reactivity into simulations by facilitating the use of the REACTER protocol. Additionally, LUNAR provides postprocessing for free volume calculations, cure characterization calculations, and property predictions from LAMMPS thermodynamic outputs. LUNAR has been validated via building and simulation of pure epoxy and cyanate ester polymer systems with a comparison of the corresponding predicted structures and properties to benchmark values, including experimental results from the literature. LUNAR provides the tools for the computationally driven development of next-generation composite materials in the Integrated Computational Materials Engineering (ICME) and Materials Genome Initiative (MGI) frameworks. LUNAR is written in Python with the usage of NumPy and can be used via a graphical user interface, a command line interface, or an integrated design environment. LUNAR is freely available via GitHub

An Alphabet of Leakage Measures

We introduce a family of information leakage measures called maximal $\alpha,\beta$-leakage, parameterized by real numbers $\alpha$ and $\beta$. The measure is formalized via an operational definition involving an adversary guessing an unknown function of the data given the released data. We obtain a simple, computable expression for the measure and show that it satisfies several basic properties such as monotonicity in $\beta$ for a fixed $\alpha$, non-negativity, data processing inequalities, and additivity over independent releases. Finally, we highlight the relevance of this family by showing that it bridges several known leakage measures, including maximal $\alpha$-leakage $(\beta=1)$, maximal leakage $(\alpha=\infty,\beta=1)$, local differential privacy $(\alpha=\infty,\beta=\infty)$, and local Renyi differential privacy $(\alpha=\beta)$

Addressing GAN Training Instabilities via Tunable Classification Losses

Generative adversarial networks (GANs), modeled as a zero-sum game between a generator (G) and a discriminator (D), allow generating synthetic data with formal guarantees. Noting that D is a classifier, we begin by reformulating the GAN value function using class probability estimation (CPE) losses. We prove a two-way correspondence between CPE loss GANs and $f$-GANs which minimize $f$-divergences. We also show that all symmetric $f$-divergences are equivalent in convergence. In the finite sample and model capacity setting, we define and obtain bounds on estimation and generalization errors. We specialize these results to $\alpha$-GANs, defined using $\alpha$-loss, a tunable CPE loss family parametrized by $\alpha\in(0,\infty]$. We next introduce a class of dual-objective GANs to address training instabilities of GANs by modeling each player's objective using $\alpha$-loss to obtain $(\alpha_D,\alpha_G)$-GANs. We show that the resulting non-zero sum game simplifies to minimizing an $f$-divergence under appropriate conditions on $(\alpha_D,\alpha_G)$. Generalizing this dual-objective formulation using CPE losses, we define and obtain upper bounds on an appropriately defined estimation error. Finally, we highlight the value of tuning $(\alpha_D,\alpha_G)$ in alleviating training instabilities for the synthetic 2D Gaussian mixture ring as well as the large publicly available Celeb-A and LSUN Classroom image datasets.Comment: arXiv admin note: text overlap with arXiv:2302.1432