2021
DOI: 10.3390/molecules26185432
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Central Composite Design for Formulation and Optimization of Solid Lipid Nanoparticles to Enhance Oral Bioavailability of Acyclovir

Abstract: Treatment of herpes simplex infection requires high and frequent doses of oral acyclovir to attain its maximum therapeutic effect. The current therapeutic regimen of acyclovir is known to cause unwarranted dose-related adverse effects, including acute kidney injury. For this reason, a suitable delivery system for acyclovir was developed to improve the pharmacokinetic limitations and ultimately administer the drug at a lower dose and/or less frequently. In this study, solid lipid nanoparticles were designed to … Show more

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Cited by 37 publications
(22 citation statements)
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“…The design, however, does not include any of these value groups. CCDs (central composite designs) are fractional factorial or factorial designs with axial (star) points amplified by center points that allow curvature evaluation [ 14 ]. The number of star points in a central composite design is always twice as many as the number of components in the plan.…”
Section: Introductionmentioning
confidence: 99%
“…The design, however, does not include any of these value groups. CCDs (central composite designs) are fractional factorial or factorial designs with axial (star) points amplified by center points that allow curvature evaluation [ 14 ]. The number of star points in a central composite design is always twice as many as the number of components in the plan.…”
Section: Introductionmentioning
confidence: 99%
“…The optimized formulation was prepared using the emulsion-gelation technique, and the actual values of the responses were 98.84% for entrapment efficiency, 11.25 h for in vitro buoyancy, and 67.76% for drug release Q6h. it was found that the actual values were found to be close to the predicted values, which indicated the validity of the central composite design [ 52 ].…”
Section: Resultsmentioning
confidence: 82%
“…The behavior of the response surface was evaluated for the selected response function (dependent variable) using a polynomial equation. The equation below describes the generalized response surface model, wherein y stands for the predicted response; β 0 is constant, and β 1 and β 2 are the linear, quadratic, and interaction coefficients, respectively [ 37 ]. Y i = β 0 + β 1 x 1 + β 2 x 2 + β 11 x 1 2 + β 22 x 2 2 + β 12 x 1 x 2 …”
Section: Methodsmentioning
confidence: 99%