ARTIFICIAL NEURAL NETWORKS: FUNCTIONINGANDAPPLICATIONS IN PHARMACEUTICAL INDUSTRY

Artificial Neural Network (ANN) technology is a group of computer designed algorithms for simulating neurological processing to process information and produce outcomes like the thinking process of humans in learning, decision making and solving problems. The uniqueness of ANN is its ability to deliver desirable results even with the help of incomplete or historical data results without a need for structured experimental design by modeling and pattern recognition. It imbibes data through repetition with suitable learning models, similarly to humans, without actual programming. It leverages its ability by processing elements connected with the user given inputs which transfers as a function and provides as output. Moreover, the present output by ANN is a combinational effect of data collected from previous inputs and the current responsiveness of the system. Technically, ANN is associated with highly monitored network along with a back propagation learning standard. Due to its exceptional predictability, the current uses of ANN can be applied to many more disciplines in the area of science which requires multivariate data analysis. In the pharmaceutical process, this flexible tool is used to simulate various non-linear relationships. It also finds its application in the enhancement of pre-formulation parameters for predicting physicochemical properties of drug substances. It also finds its applications in pharmaceutical research, medicinal chemistry, QSAR study, pharmaceutical instrumental engineering. Its multi-objective concurrent optimization is adopted in the drug discovery process, protein structure, rational data analysis also

SCREENING AND OPTIMIZATION OF VALACYCLOVIR NIOSOMES BY DESIGN OF EXPERIMENTS

Objective: The objective of the study was to perform a screening, optimization of valacyclovir niosomal formulation to achieve a sustained release of drug using the design of experiments by 32 full factorial design.Methods: Valacyclovir loaded niosomes were prepared using thin film hydration method by varying the ratio of Span 60 and Cholesterol. The prepared niosomes were evaluated for vesicle size, entrapment efficiency, cumulative drug release, fourier transformed infrared spectroscopy (FTIR), zeta potential and surface morphology by field emission scanning electron microscopy (FESEM).Results: The valacyclovir was successfully encapsulated and its entrapment efficiency ranged from 36.70 % to 50.62 %. The average vesicle size of the niosomes was found to be 431 to 623 nm. At 8th hour the drug release varied from 77.50% to 96.31 %. The optimized niosomes were multilamellar with a surface charge potential of about-43.2 mV. The studies revealed that the interaction of cholesterol and surfactant had a substantial effect on vesicle size, entrapment efficiency and drug release from the niosomes. The release kinetics of the optimized niosomes followed zero order kinetics with fickian diffusion controlled mechanism. The stability studies were performed for the optimized formulation and found that the formulation is stable at 4°C ± 2°C.Conclusion: Model equations were developed for the responses. No significant difference was observed between the predicted and observed value, showing that the developed model is reliable

23 Full Factorial Model for Particle Size Optimization of Methotrexate Loaded Chitosan Nanocarriers: A Design of Experiments (DoE) Approach

Purpose. To build and inquire a statistically significant mathematical model for manufacturing methotrexate loaded chitosan nanoparticles (CsNP) of desired particle size. The study was also performed to evaluate the effect of formulation variables in the explored design space. Method. Ionotropic gelation technique was followed for chitosan nanocarriers by changing formulation variables suggested as per Design Expert software. Altering the levels of Chitosan, tripolyphosphate, methotrexate by 23 factorial design served the purpose. The CsNP were characterized for nanocarrier formation, particle size, and statistical analysis. Then mathematical model was statistically analyzed for fabricating desired formulation having particle size less than 200nm. Results. FT-IR, XRD reports confirmed the structural change in chitosan which lead to the formation of CsNP. For particle size, linear model was found to be best fit to explain effect of variables. Besides, high R2 (0.9958) defends the constancy of constructed model. Chitosan exhibited higher t-value in Pareto chart and a p-value <0.0001. Based on maximum desirability, optimization was performed and amount of variables for preparing CsNP of 180nm was predicted. The experiment was carried out with software suggested combination and particle size was found to be 176±4nm. Conclusion. Low p-value endorsed the greater dominance of chitosan on particle size. Good model adequacy and small percentage error between predicted and experimented value established the reliability of constructed model for robust preparation of CsNP

Uncertainty of Abrupt Motion Tracking Using Hidden Markov Model

Abstract Ever increasing the robust tracking of abrupt motion is a challenging task in computer vision due to its large motion uncertainty. visual tracking in dynamic scenarios refers to establishing the correspondences of the object of interest between the successive frames. It is a fundamental research topic in video analysis and has a variety of potential applications like visual surveillance and video analysis. Tracking approach is divided into two categories deterministic and sampling. We have presented a new approach for robust motion tracking in various scenarios. In this paper, we introduceda hidden markov model to solve the local-trap problem and occlusion. Occlusion means when one object is hidden by another object that passes between it and the observer. To estimation of the parameter image object using density grid based normal distribution method is applied. Also, Bayesian filter technique is applied on image object for the purpose of smoothing. In this regard, to reduce the computational cost, less memory, better performance and efficiency. Keyword Abrupt Motion, Bayesian filter, Hidden Markov Model, Point Estimation etc. I. Introduction The robust tracking of abrupt motion is a challenging task in computer vision due to its large motion uncertainty. Visual tracking in dynamic scenarios refers to establishing the correspondences of the object of interest between the successive frames. It is a fundamental research topic in video analysis and has a variety of potential applications, including teleconferencing, gesture recognition, visual surveillance, and motility analysis. Tracking approaches divided into two categories Deterministic and Sampling. At first Deterministic, fast and relatively lower computational cost. The major drawbacks of deterministic for getting trapped in local modes in case of background clutter, distractions, or rapid moving object. The other one, sampling-basedisable to deal with the large motion uncertainty induced by abrupt motions.Earlier works on these lines were proposed by authors isXhuaiuzng Zhou et al hassuggestedto an abrupt motion tracking via intensively adaptive markov-chain Monte Carlo sampling. In this regard, we have astochastic approximation monte Carlo (SAMC) for handling the local-trap problem. In addition, new MCMC sampler for improving sampling efficiency which combines with the SAMC sampling named as Intensively Adaptive -Markov -Chain Monte Carlo (IA-MCMC) sampling. However, SAMC method may cause more computational cost. Reduce the computational cost by introducing a density-grid-based predictive model, Vol: 21, Issue: 2, IEEE Transactions, 2012.This system suffers the major problems are measurement for comparing the tracking accuracy for the target objects with differentsizes.Lower value indicates less local trap problem.Position error includes both mean relativeerror and the standard deviation error. Small value indicates the accurate and stable even usinga small number of samples. To solve this problem we have proposed a Hidden Markov Model on abrupt motio

FORMULATION AND EVALUATION OF NAPROXEN-EUDRAGIT® RS 100 NANOSUSPENSION USING 32 FACTORIAL DESIGN

Objective: The objective of the present investigation was to develop drug loaded Eudragit® RS100 nanosuspension as a sustained release carrier. Methods: All the nanosuspensions of Naproxen loaded Eudragit® RS100 were prepared using the quasi emulsion solvent diffusion technique at different drug: polymer ratios. The formulation was optimized using design of experiments by employing a 2-factor, 3-level factorial design. The drug: polymer ratio (X1) and speed of homogenization(X2), were the independent variables; particle size (Y1), zeta potential (Y2) and entrapment efficiency (Y3) as dependent variables. The nanosuspensions were studied for particle size analysis, X-ray diffraction analysis and surface morphology by scanning electron microscopy. The in vitro release study of Naproxen from nanosuspension was carried out using dialysis bag with molecular weight cut-off value of 12,000 to 14,000 Daltons. Results: Average particle size of nanosuspension was between 159 to 435nm and zeta potential ranges from 20.7 to 53.5 mV. The statistical analysis of data revealed that drug: polymer ratio(X1) has a significant positive influence on particle size (p=0.0077) whereas a negative influence on zeta potential (p=0.0045) and Entrapment efficiency (p=0.0003). The developed model was validated using two check point formulations and found no significant difference between the predicted and observed values. An optimized formulation was also identified during the study. Conclusion: This investigation demonstrated the potential of the experimental design in understanding the effect of formulation variables on the development of Nanosuspensions. The results assures, nanosuspension are promising sustained release system to the naproxen and many other drugs

Impact of nanocarrier aggregation on EPR-mediated tumor targeting

The aim of this study was to investigate the influence of excipients on retaining the particle size of methotrexate (MTX) loaded chitosan nanocarriers (CsNP) during lyophilization, which relates to the ability to enlarge the particle size and target specific areas. The nanocarriers were prepared using the ionic gelation technique with tripolyphosphate as a crosslinker. Three lyophilized formulations were used: nanosuspension without Lyoprotectant (NF), with mannitol (NFM), and with sucrose (NFS). The lyophilized powder intended for injection (PI) was examined to assess changes in particle size, product integrity, and comparative biodistribution studies to evaluate targeting ability. After lyophilization, NFS was excluded from in-vivo studies due to the product melt-back phenomenon. The particle size of the NF lyophile significantly increased from 176 nm to 261 nm. In contrast, NFM restricted the nanocarrier size to 194 nm and exhibited excellent cake properties. FTIR, XRD, and SEM analysis revealed the transformation of mannitol into a stable β, δ polymorphic form. Biodistribution studies showed that the nanocarriers significantly increased MTX accumulation in tumor tissue (NF = 2.04 ± 0.27; NFM = 2.73 ± 0.19) compared to the marketed PI (1.45 ± 0.25 μg), but this effect was highly dependent on the particle size. Incorporating mannitol yielded positive results in restricting particle size and favoring successful tumor targeting. This study demonstrates the potential of chitosan nanocarriers as promising candidates for targeted tumor drug delivery and cancer treatment.</p