Efficient Development of Green Emulsifier/Emollient-Based Emulsion Vehicles: From RSM Optimal Experimental Design to Abridged In Vivo Assessment

Abstract

Since natural-origin, sustainable ingredients are preferred by modern consumers, novel emulsifiers and emollients keep entering the market. This study hypothesizes that a combination of in silico, instrumental tools and simplified sensory studies could be used to efficiently characterize emulsions in a shorter timeframe. A total of 22 rather simple o/w emulsions were prepared by a time/energy-saving emulsification process. A natural mixed emulsifier (Lauryl Glucoside/Myristyl Glucoside/Polyglyceryl-6 Laurate) and two emollients (both with INCI name C15–19 Alkane) were used. The performed D-optimal experimental design within the response surface method (RSM) significantly narrowed down the number of samples about to enter the stage of texture, friction and sensory studies to the samples comprising 30% of a respective Emogreen emollient and 2% or 3% of the emulsifier. The sample comprising 2% emulsifier/30% Emogreen® L15 showed significantly higher firmness (42.12 mN) when compared to the one with 2% emulsifier/30% Emogreen® L19 (33.62 mN), which was somewhat unexpected considering the emollients’ inherent viscosity values (4.5 mPa·s for L15 and 9 mPa·s for L19). The sample with 2% emulsifier/30% Emogreen® L19 managed to maintain the lowest friction, while the one with 3% emulsifier/30% Emogreen® L19 released its full lubricating potential in the second part of the measurement (30–60 s). The obtained results revealed the strengths and weaknesses of each formulation, narrowing down their possible applications in the early development stage.

Development of a “Green” Emulsion with a Milk Protein Hydrolysate: An Evaluation of Rheology, Texture, In Vitro Bioactivity, and Safety

“…The reasons for the widespread use of low-viscosity O/W emulsions (lotions) in skin care products are their high water content (which means lower cost), their light, non-greasy feel, and their good spreadability on the skin. Emulsions must meet numerous requirements and expectations in terms of stability, safety, efficacy (both actual and perceived), and appropriate aesthetic and sensory properties [34]. Bioactive peptides have attracted considerable attention in the cosmetic industry due to their potential to improve skin health and appearance.…”

“…The most cohesive sample was the emulsion containing EG L19 and the 3% NM hydrolysate, which also correlates with the cylindrical probe results. The conical probe provided a smoother appearance of the negative portion of the curve, which is often considered a desirable characteristic in topical formulations [34].…”

“…Two probes were used to investigate the importance of geometric effects, i.e., different forces generated during the measurement. Both probes were made of the same material to assume similar surface energies and force magnitudes [34]. A typical response of a semisolid sample, shown in Figure 4a-d, consists of a positive and a negative curve with the following standard parameters: firmness (maximum on the positive curve; the higher the value, the firmer the sample), consistency (area under the positive curve; the higher the value, the ticker the sample), cohesiveness, a parameter that indicates the strength of internal bonds (maximum on the negative curve), and work of the cohesion/index of viscosity (area under the negative curve) [35,36].…”

“…The lower intrinsic viscosity (4.5 mPa s) and contact angle value (3.3) of this emollient make it more sensitive to processing parameters, such as mixing time and rotor speed, which affects the total interfacial area. As a result, the breakup of the oil into a droplet population during this turbulent flow can sometimes occur simultaneously and sometimes in sequence, resulting in different rheological parameters [34]. The most cohesive sample (with the best substantivity) is the emulsion with EG L19 and the 3% NM hydrolysate.…”

Rheology of Complex Topical Formulations: An Analytical Quality by Design Approach to Method Optimization and Validation

“…Risk quantification is to be performed by considering the severity (S), probability of occurrence (O), and detectability (D) of each parameter using a numerical scale 1–5, with 1 being the lowest severity, probability, and undetectability, and 5 the highest. For each factor, the rank and prioritization of the risk were conducted according to the risk priority number (RPN) given by RPN = S × O × D. The factors presenting higher RPN values were subjected to a further optimization analytical process [ 39 , 40 , 41 ].…”