VALIDATED RP-HPLC METHOD FOR SIMULTANEOUS ESTIMATION OF CEFIXIME AND MOXIFLOXACIN IN COMBINED PHARMACEUTICAL DOSAGE FORM

Objective: To develop a simple, selective and rapid reversed phase high performance liquid chromatographic (HPLC) method for the analysis of cefixime and moxifloxacin in combined pharmaceutical dosage form as per ICH guidelines.Methods: The separation was achieved from C18 column at 350C with a mobile phase consisting of methanol: 0.05M heptane sulfonic acid sodium salt,0.5 ml THF and 0.5 ml TEA [75: 25 v/v]. pH-3.8 was adjusted with ortho phosphoric acid at a flow rate of 0.4 ml/min and the retention time was about 6.08 minutes for cefixime and 6.94 minutes for moxifloxacin. The method was selective to cefixime and moxifloxacin able to resolve the drug peak from formulation excipients.Results: The calibration curve was linear over the concentration range of 20-120 μg/ml (r2 = 0.999) for both drugs. The proposed method was found to be accurate and precise and linear within the desired range. The limit of detection (LOD) and limit of quantitation (LOQ)were calculated statically. Recoveries do not differ significantly from 100% which show there was no interference from the common excipient used in tablet formulation indicating accuracy and reliability of the method. The method was validated as per ICH guidelines and found to be accurate, precise and rugged. The method was validated in terms of linearity, accuracy, precision, specificity, LOD and LOQ.Conclusion: A novel, simple, selective and rapid reversed phase high performance liquid chromatographic (HPLC) method was developed for the analysis of cefixime and moxifloxacin in tablets. Hence,the method can be used for the routine analysis in various pharmaceutical industries.Â

Genetic programming assisted stochastic optimization strategies for optimization of glucose to gluconic acid fermentation

This article presents two hybrid strategies for the modeling and optimization of the glucose to gluconic acid batch bioprocess. In the hybrid approaches, first a novel artificial intelligence formalism, namely, genetic programming (GP), is used to develop a process model solely from the historic process input-output data. In the next step, the input space of the GP-based model, representing process operating conditions, is optimized using two stochastic optimization (SO) formalisms, viz., genetic algorithms (GAs) and simultaneous perturbation stochastic approximation (SPSA). These SO formalisms possess certain unique advantages over the commonly used gradient-based optimization techniques. The principal advantage of the GP-GA and GP-SPSA hybrid techniques is that process modeling and optimization can be performed exclusively from the process input-output data without invoking the detailed knowledge of the process phenomenology. The GP-GA and GP-SPSA techniques have been employed for modeling and optimization of the glucose to gluconic acid bioprocess, and the optimized process operating conditions obtained thereby have been compared with those obtained using two other hybrid modeling-optimization paradigms integrating artificial neural networks (ANNs) and GA/SPSA formalisms. Finally, the overall optimized operating conditions given by the GP-GA method, when verified experimentally resulted in a significant improvement in the gluconic acid yield. The hybrid strategies presented here are generic in nature and can be employed for modeling and optimization of a wide variety of batch and continuous bioprocesses

Ann modeling of dna sequences: new strategies using dna shape code

Two new encoding strategies, namely, wedge and twist codes, which are based on the DNA helical parameters, are introduced to represent DNA sequences in artificial neural network (ANN)-based modeling of biological systems. The performance of the new coding strategies has been evaluated by conducting three case studies involving mapping (modeling) and classification applications of ANNs. The proposed coding schemes have been compared rigorously and shown to outperform the existing coding strategies especially in situations wherein limited data are available for building the ANN models

Nonlinear pH control

A simple new method for designing nonlinear IMC controlles for SISO systems has been developed. The method has been applied to the neutralization of a simulated strong acid-strong base system. The objective of the control effort in this instance is to maintain the effluent pH at 7.00 in the presence of disturbances. An examination of the results shows that the controller provides perfect set point compensation and excellent disturbance rejection. The results also show that to implement this type of controller for pH control a fast CPU with extended precision capabilities and a fast analog-to-digital converter would be required

Optimum DNA Curvature Using a Hybrid Approach Involving an Artificial Neural Network and Genetic Algorithm

In the present paper, a hybrid technique involving artificial neural network (ANN) and genetic algorithm (GA) has been proposed for performing modeling and optimization of complex biological systems. In this approach, first an ANN approximates (models) the non-linear relationship(s) existing between its input and output example data sets. Next, the GA, which is a stochastic optimization technique, searches the input space of the ANN with a view to optimize the ANN output. The efficacy of this formalism has been tested by conducting a case study involving optimization of DNA curvature characterized in terms of the RL value. Using the ANN-GA methodology, a number of sequences possessing high RL values have been obtained and analyzed to verify the existence of features known to be responsible for the occurrence of curvature. A couple of sequences have also been tested experimentally. The experimental results validate qualitatively and also near-quantitatively, the solutions obtained using the hybrid formalism. The ANN-GA technique is a useful tool to obtain, ahead of experimentation, sequences that yield high RL values. The methodology is a general one and can be suitably employed for optimizing any other biological feature

On control of nonlinear system dynamics at unstable steady state

Shifting an oscillatory or a chaotic trajectory to the unstable steady state of a nonlinear system in the presence of stochastic or deterministic load disturbances continues to be a nontrivial task. In the present work, two effective strategies for such control needs are presented. The control laws employed do not contain the process model parameters explicitly. The suggested strategies are demonstrated on two simulated nonlinear reaction systems exhibiting multi-stationarity, limit cycle oscillations, and chaos