Nguyen Khoa Viet Truong

A pharma-robust design method to investigate the effect of PEG and PEO on matrix tablets

Even though polyethyleneoxide (PEO)-polyethyleneglycol (PEG) blends have been used widely for sustained release matrix tablets, evaluations of the effects of PEG or PEO on the matrix properties have been limited. In order to evaluate gelling behavior and drug release profiles of PEG, various contents of the polymers were investigated through a robust experimental design method. When exposed to an aqueous environment, the PEO-PEG matrix hydrated slowly and swelled, causing a thick gel layer to form on the surface, the thickness of which increased significantly depending on the PEG contents. Since polyacrylate plates were used for the study, the matrix was not completely hydrated and gelled even after 5h. However, the results could be applied to the time-oriented responses RD (robust design) models to obtain optimal settings and responses for the observed times. The optimal settings of PEO and PEG were 94.26 and 140.04 mg, respectively (PEG rate of 148.57%). Moreover, as the amount of PEG increased, the release rate also increased. When the formulation contained more than 150% of PEG, most of the drug loaded in the tablet was released in about 12 h. When the amount of PEG was less than 100%, the drug release rate was sustained significantly. Based on the RD optimization model for drug release, the optimal settings were PEG and PEO of 124.3 and 110 mg, respectively (PEG rate of 88.50%). Therefore, PEG rate of about 90-150% is suggested for matrix tablet formulations, and the exact ratio could be formulated according to the resulting tablets properties.

A robust experimental design method to optimize formulations of retinol solid lipid nanoparticles

A robust experimental design method was developed using a response surface methodology and models to facilitate the development process of retinol solid lipid nanoparticles (SLNs). The SLNs were evaluated to determine how different parameters including lipid and surfactant affect size and encapsulation efficiency. This was conducted using factorial analysis and a robust design (RD) method was used to achieve optimal formulations. Two models were developed based on the RD principle and both mean and variance of the response characteristics were estimated functionally using the least squares method. They proved useful in formulation studies aiming to develop optimum by allowing a systematic and reliable design method. A model for maximizing the overall desirability represented by the geometric mean of all objectives was found to provide a better solution. The newly designed method provides useful information to characterize significant factors and obtain optimum formulations, thereby allowing a systematic and reliable design method.

Time-oriented experimental design method to optimize hydrophilic matrix formulations with gelation kinetics and drug release profiles.

A new experimental design methodology was developed by integrating the response surface methodology and the time series modeling. The major purposes were to identify significant factors in determining swelling and release rate from matrix tablets and their relative factor levels for optimizing the experimental responses. Properties of tablet swelling and drug release were assessed with ten factors and two default factors, a hydrophilic model drug (terazosin) and magnesium stearate, and compared with target values. The selected input control factors were arranged in a mixture simplex lattice design with 21 experimental runs. The obtained optimal settings for gelation were PEO, LH-11, Syloid, and Pharmacoat with weight ratios of 215.33 (88.50%), 5.68 (2.33%), 19.27 (7.92%), and 3.04 (1.25%), respectively. The optimal settings for drug release were PEO and citric acid with weight ratios of 191.99 (78.91%) and 51.32 (21.09%), respectively. Based on the results of matrix swelling and drug release, the optimal solutions, target values, and validation experiment results over time were similar and showed consistent patterns with very small biases. The experimental design methodology could be a very promising experimental design method to obtain maximum information with limited time and resources. It could also be very useful in formulation studies by providing a systematic and reliable screening method to characterize significant factors in the sustained release matrix tablet.

Development of a new robust design methodology based on Bayesian perspectives

Robust design (RD), implemented in statistical and mathematical procedures to simultaneously minimise the process bias and variability, is widely used in many areas of engineering and technology to represent complex real-world industrial settings. For RD modelling and optimisation, response surface methodology (RSM) is often utilised as an estimation method to represent the functional relationship between input factors and their associated output responses. Although conventional RSM-based RD methods may offer significant advantages regarding process design, there is room for improvement. In this context, a new RD methodology is developed in this paper by integrating Bayesian principles into the RD procedure. Numerical examples and comparative studies are conducted by using two conventional RSM-based RD models and the proposed model. The results of two numerical examples demonstrate that the proposed RD method provides significantly better RD solutions in terms of the expected quality loss (EQL) than conventional methods.

A new experimental design method to optimize formulations focusing on a lubricant for hydrophilic matrix tablets

A robust experimental design method was developed with the well-established response surface methodology and time series modeling to facilitate the formulation development process with magnesium stearate incorporated into hydrophilic matrix tablets. Two directional analyses and a time-oriented model were utilized to optimize the experimental responses. Evaluations of tablet gelation and drug release were conducted with two factors x1 and x2: one was a formulation factor (the amount of magnesium stearate) and the other was a processing factor (mixing time), respectively. Moreover, different batch sizes (100 and 500 tablet batches) were also evaluated to investigate an effect of batch size. The selected input control factors were arranged in a mixture simplex lattice design with 13 experimental runs. The obtained optimal settings of magnesium stearate for gelation were 0.46 g, 2.76 min (mixing time) for a 100 tablet batch and 1.54 g, 6.51 min for a 500 tablet batch. The optimal settings for drug release were 0.33 g, 7.99 min for a 100 tablet batch and 1.54 g, 6.51 min for a 500 tablet batch. The exact ratio and mixing time of magnesium stearate could be formulated according to the resulting hydrophilic matrix tablet properties. The newly designed experimental method provided very useful information for characterizing significant factors and hence to obtain optimum formulations allowing for a systematic and reliable experimental design method.

Development of a sequential robust-tolerance design model for a destructive quality characteristic

Robust design (RD) and tolerance design (TD) have received much attention from researchers and practitioners for more than two decades, and a number of methodologies for modeling and optimizing the RD and TD processes have been studied. However, there is ample room for improvement. Because most existing research considers RD and TD as separate research fields, the primary objective of this paper is to develop a sequential robust-tolerance design method to jointly determine the best factor settings and the closed-form solutions for the optimal specification limits. We then apply the proposed method to a destructive quality characteristic. Finally, a case study and sensitivity analyses are performed for verification purposes, and further studies are discussed.

Optimal Tolerance Design and Optimization for a Pharmaceutical Quality Characteristic

A number of pharmaceutical quality characteristics are destructive or too costly to inspect. However, most quality improvement tools developed in the pharmaceutical research community typically assume that quality characteristics are nondestructive. This paper proposes a new design system for quality improvement by incorporating the concept of surrogate variables with the concepts of robust design (RD) and tolerance design (TD). The proposed robust-tolerance design paradigm determines the optimal factor setting and specification limits simultaneously, thereby improving quality of pharmaceutical products. In addition, the proposed methodology can provide the optimal tolerance as a mathematical closed-form solution. Finally, a numerical example and its associated sensitivity analysis for a pharmaceutical case are conducted for verification purposes. Based on the numerical example results, the proposed approach could provide robust factor settings with significant tradeoffs between quality and cost.

Robust design modelling and optimisation using response surface methodology and inverse problem

This paper focuses on solving the robust design (RD) problem that occurs in pharmaceutical studies in which output responses are measured over time. In order to handle such a situation, firstly, a customised experimental format is proposed for pharmaceutical experimental design. Using both response surface methodology (RSM) and a new inverse problem (IP) approach, a customised method of estimation also is developed to handle dynamic time-series as well as multiple responses for pharmaceutical experiments. Next, a new inverse problem-based robust design (IPRD) model is proposed based on the mean squared error (MSE) concept. For verification and comparison, a case study of a pharmaceutical experiment is conducted.

Robust Design with Time-Oriented Responses for Regenerative Medicine Industry

In regenerative medicine industry, one of the key problems is to reduce variation of output characteristics so that a generic drug can pass the statistical criteria for bioequivalence tests. Quality by design (QbD) is a set of offline tools in which robust design (RD) plays significant role in controlling variance in the pharmaceutical process recently. The conventional RD approach basically deals with static data while the bioequivalence tests require dynamic (time-oriented) data. The primary objective of this paper is to develop a new RD approach with time-oriented responses to bioequivalence tests for generic drug. Because the responses are a function of control factors and time, it is reasonable that the tentative relationship can be analyzed according to both vertical and horizontal directions in which the response surface methodology (RSM) is utilized. For seeking the optimal control factors setting, a mean squared error (MSE) robust design model is chosen and optimized by utilizing Matlab package. A experimental study is conducted to demonstrate how to apply proposed RD approach in practice and how can it be used to improve the quality of generic drug for meeting statistical criteria of bioequivalence tests.

A new robust design method from an inverse-problem perspective

This paper develops a new approach to robust design (RD). Current RD techniques, including the Taguchi approach, and enhanced approaches using response surface methodology (RSM), can process limited amounts of statistical and engineering information. The primary objective of this paper is to view RD from Bayesian perspectives by incorporating inverse problem (IP) concepts in order to relax the basic assumptions of the least-squares method. The practical benefits of applying IP to RD is the ability of estimation, by treating each model parameter as a random variable, and the flexibility of forwarding and inversing the estimated model. A numerical example is shown, and a comparative study, using conventional response surface robust design (RSRD) models and the proposed IP-based RD (IPRD) models, is present for verification purposes. The numerical example demonstrates that the proposed IPRD models provide significantly better RD solutions than the conventional RSRD models reported in the literature.