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Process Development Considerations for Topical and Transdermal Formulations
Published in Marc B. Brown, Adrian C. Williams, The Art and Science of Dermal Formulation Development, 2019
Marc B. Brown, Adrian C. Williams
Typically, two to three CPPs, based on importance to the product, will be selected for process optimisation. Going above the three factors (CPPs) markedly increases the number of experiments, cost, and time of optimisation. For process optimisation, mostly factorial and response surface design may be used depending on the objective. If more than three factors are to be tested, then the formulator can use a low-resolution fractional factorial design to screen the factors affecting the process significantly. The selected factors can then be evaluated further by using a higher resolution factorial DoE. Based on the DoE studies, the optimal ranges for the CPPs may be defined. The process robustness can be further established by running an additional DoE. The design space can then be used during the scale-up process.
Evaluating eHealth
Published in Lisette van Gemert-Pijnen, Saskia M. Kelders, Hanneke Kip, Robbert Sanderman, eHealth Research, Theory and Development, 2018
Floor Sieverink, Nadine Köhle, Ken Cheung, Anne Roefs, Hester Trompetter, Julia Keizer, Annemarie Braakman-Jansen, Saskia M. Kelders
As stated before, an RCT often treats technologies as singular entities. However, eHealth technologies can be complex interventions, consisting of multiple components, including the intervention content (e.g. the topics in different lessons or a diary), features that promote adherence (e.g. reminders or rewards) and others, such as features aimed at improving intervention fidelity (e.g. delivering the intervention as it was intended by, for instance, enhanced training of counsellors). To create the optimal intervention, you have to know the individual and combined effects of the different components. This way, you can create the most effective intervention (that includes all the components that add to the effectiveness), or you can create the most effective intervention that can be completed within a certain amount of time or with a certain maximum of costs. In sum, a (fractional) factorial design is an experimental research design in which the effects of multiple intervention components are investigated (Collins, Dziak, Kugler, & Trail, 2014).
Modelling process and outcomes in complex interventions
Published in David A. Richards, Ingalill Rahm Hallberg, Complex Interventions in Health, 2015
The screening phase is based on the existing research literature or experience of involved professionals as described in the previous paragraph. The main objective is to explore which intervention components are active and contributing to positive outcomes and should therefore be included in the intervention. The main difference with other approaches is that in MOST trials, decisions are taken based on results (estimated effect size, costs) of randomized experiments and not on the basis of subjective decisions. To do so, they use a factorial design in which many interventions are randomly allocated simultaneously. If only a few components are evaluated simultaneously, then a full factorial design can be used. If many components need to be evaluated (e.g. six, which would lead to 26 or 64 different combinations), a fractional factorial design might be more appropriate. Collins et al. (2005) show how a full six-factor factorial design with 64 combinations can be redesigned in a fractional factorial design with 16 conditions, with the capability to provide main effects estimates for each of the six independent variables and to provide estimates for the selected interactions. The screening phase results in a draft version of the intervention with a selection of the most effective components.
A quality by design approach for the synthesis of palmitoyl-L-carnitine-loaded nanoemulsions as drug delivery systems
Published in Drug Delivery, 2023
E. M. Arroyo-Urea, María Muñoz-Hernando, Marta Leo-Barriga, Fernando Herranz, Ana González-Paredes
Our aim was to use QbD approach to develop an O/W NE with optimal physicochemical and colloidal stability properties as drug carrier for encapsulation of hydrophobic drugs, using palmitoyl-L-carnitine (pC) as model molecule. Different types of experimental designs can be used, and in this work, a fractional factorial design (FFD) is proposed as it is a rapid and reliable tool, allowing the exploration of a maximum number of variables requiring less experimental observations than full factorial without a lack of main effects data (Kuncahyo et al., 2019). pC, the selected model molecule, is an organic compound containing a long-chain acyl fatty acid attached to carnitine through an ester bond. Its low solubility in water (1.2e−05 g/L) makes it an excellent candidate to be used as a model hydrophobic therapeutic drug. As an active compound several biological activities have been described for pC: capability of altering the activity of various enzymes and transporters found in human membrane cells (Bernatoniene et al., 2011), prevention of biofilm formation in Escherichia coli and Pseudomonas aeruginosa (Wenderska et al., 2011) and activation of sphingosine-1-phosphate (S1P) receptors (S1PRs), which are becoming more widely recognized as important regulators of homeostasis and disease for their role in cell survival, activation status and proliferation in all biological systems (Blaho & Hla, 2014).
eHealth Technology in Forensic Mental Healthcare: Recommendations for Achieving Benefits and Overcoming Barriers
Published in International Journal of Forensic Mental Health, 2020
Hanneke Kip, Kira Oberschmidt, Joyce J. P. A. Bierbooms
The recommendations within this main code focus on the use of evaluation studies to investigate to what extent a technology: reaches its goals, adds value to forensic mental healthcare, and further improves the current technology. As can be seen in Table 6, participants indicated that it was not only important to conduct more research to determine if eHealth works, but also to better understand why and for whom the technology works. Suitable and innovative research methods are fractional factorial designs or log data analyses. To illustrate, web-based modules might work better for patients with higher literacy and reflective skills, while VR might be most effective for patients with low educational levels and aggression regulation problems. Furthermore, besides effectiveness, participants also stated that the evaluation should focus on whether a technology is a reliable and a valid method to measure certain behaviors, biases or other phenomena. Finally, participants indicated that research needs to show if the use of eHealth results in decreased costs and more efficient healthcare, especially since this type of information was deemed important for management and healthcare insurance companies.
A discrete choice experiment to assess patients’ preferences for HIV treatment in the rural population in Colombia
Published in Journal of Medical Economics, 2020
Anne J. M. Goossens, Kei Long Cheung, Eric Sijstermans, Rafael Conde, Javier G. R. Gonzalez, Mickael Hiligsmann
Since conducting a full factorial design (i.e. all possible treatment combinations) would not have been feasible, a fractional factorial design was chosen for the present study. This means that the participating HIV patients were presented with a sub-set of treatment profiles. This sub-set was selected by using an efficient experimental design, which uses a-priori information on parameters31. Ngene software (version 1.1.1) was used to design 24 choice sets that were blocked in two versions, 12 for version 1 and 12 for version 214,27. Attribute levels and their associated levels are presented as they are, as detailed in Table 1. Different colour shading was used to distinguish the positive, neutral, and negative levels. An example of a choice set can be found in Figure 1.