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Granulation Process Modeling
Published in Dilip M. Parikh, Handbook of Pharmaceutical Granulation Technology, 2021
Design of experiments. Here, use is made of the Design of Experiment (DoE) method to identify the most important factors early in the experimentation phase when complete knowledge about the system is usually unavailable. It is recognized that Planckett–Burman and fractional factorial designs [5] are efficient screening methods to identify the active factors, using as few experimental runs as possible.
Historical Review
Published in Gary M. Matoren, The Clinical Research Process in the Pharmaceutical Industry, 2020
Donald D. Vogt, Michael Montagne
The real breakthrough came at the turn of this century when a number of scientists began employing statistical measures in experiments and other research studies. They saw the process of interpreting research results as a statistical exercise, with the primary purpose of attempting to determine or explain the amount of error present in their measurement techniques. The development of the theory and practice of experimental designs, led by R. A. Fisher's Statistical Methods for Research Workers (1925) and The Design of Experiments (1935), signaled a shift to planning and performing experiments with the intent of controlling for errors or chance events. The basic work on experimental designs was undertaken in agricultural research, where a variety of alternative treatments were applied to plots of land, sometimes arranged in blocks, on which a particular crop was grown. Specific measurements were made at various points in time and analyzed comparatively to arrive at a set of results concerning the impact of the treatment given. Consequently, the contemporary experimental method is described as a comparative study of a specific intervention or treatment, i.e., a drug entity, with alternative treatments or no treatment, i.e., the control group(s), involving the randomized selection and placement of cases, i.e., patients, into each of the various treatment groups.
Therapeutic Gases for Neurological Disorders
Published in Sahab Uddin, Rashid Mamunur, Advances in Neuropharmacology, 2020
R. Rachana, Tanya Gupta, Saumya Yadav, Manisha Singh
It has been discussed above that so far these gases have been used in an unorganized manner and there are no sets of fixed protocols which are accepted worldwide. So an efficient planning is required for the future trials/uses of these gases before establishing their utility for such treatments. We need to design the experiments in a scientific manner and take appropriate sample sizes of the patients along with the manpower needed. Also, the inclusion criteria and outcome measures are to be analyzed carefully. We would also need high-end instruments like MRI and others to validate quality of life and diseases during and after the therapy.
Novel linezolid loaded bio-composite films as dressings for effective wound healing: experimental design, development, optimization, and antimicrobial activity
Published in Drug Delivery, 2022
Dina Saeed Ghataty, Reham Ibrahim Amer, Reham Wasfi, Rehab Nabil Shamma
The design of experiments using response surface methodology (RSM) was employed to study the effect of independent variables on a range of responses (dependent variables). Polymer concentration (X1), plasticizer concentration (X2), polymer type (X3), and plasticizer type (X4) were chosen as the independent variables. Whereas, the film thickness (Y1), moisture content (Y2), tensile strength (Y3), elongation at break (Y4), swelling index (Y5), and the percentage drug release at 30 and 180 minutes (Y6 and Y7, respectively) were chosen as the dependent variables. The experimental range and levels of the independent variables are presented in Table 2. I-optimal design using Design-Expert® version 12.0.3.0 software (Stat-Ease Inc., USA) was used for experimental planning and statistical analysis. The design proposed a total number of 19 sets of experimental runs, with 4 center points as replicates for the estimation of the experimental error. Randomized order of the experimental runs was used to reduce the effect of uncontrolled factors. Table 3 displays the experimental runs of the design matrix used to assess the impact of independent variables and the experimental values of the measured responses for different LNZ-loaded bio-composite films. The influence of independent variables was examined by I-optimal design under RSM using the following equation:
Accurate estimation for extra-Poisson variability assuming random effect models
Published in Journal of Applied Statistics, 2021
Ricardo Puziol de Oliveira, Jorge Alberto Achcar
In the literature, a random effect model, also called a variance components model, is a form of hierarchical linear model where its assumed that the data to be analyzed are obtained from a hierarchy of different populations whose differences relate to that hierarchy [27,38,39]. For example, under a biostatistics approach, a researcher may be interested in subject-specific effects that are characterized by random effects. In other way, under a design of experiments approach, the presence of random effects are often assumed when investigators employ blocking, which is the experimental analog to stratification in survey research. Also, it is important to point out that the random effects are not directly estimated. Instead, they are treated as random variables with zero mean and unknown variance
Effect of foam densification and impact velocity on the performance of a football helmet using computational modeling
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2021
Samuel T. Mills, Trevor S. Young, Lillian S. Chatham, Sourav Poddar, R. Dana Carpenter, Christopher M. Yakacki
Current helmet testing standards are set by the National Operating Committee on Standards for Athletic Equipment (NOCSAE), which uses, among other tests, a linear impactor and instrumented head form to test how a helmet will perform during an impact (NOCSAE 2013). This testing method went into effect for NOCSAE on November 1, 2019 (NOCSAE 2016). The acceleration of the head is measured using accelerometers in the headform to quantify the severity of an impact, where a lower linear acceleration on the headform correlates to better helmet performance (Hodgson 1975). There have been many studies using this technique including studies such as the one performed by Coutnoyer et al. looking at the effect of repeated high energy impacts, or the effects of chin strap placement on blunt impacts done at the University of Southern Mississippi. To the best of our knowledge, thus far no one has investigated the effects of the pad material in an FEA helmet model on the acceleration of the head. Generally, experimental testing can be expensive and time consuming to run a large design of experiments.