Elucidating Sensorial and Functional Characteristics of Topical Formulations

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Abstract In addition to the defined therapeutic effect caused by an active drug in a product, there is also a placebo and, potentially, a nocebo effect associated with that product. In topical products, these latter effects may account for 30% to 50% of the overall response for some products. They may also explain why some topical products with apparently identical bioavailability are associated with different patient outcomes. This application seeks to address the question of when do subtle excipient and manufacturing changes in a topical product cause a sensorial perception by subjects such that the “feel” of a product has changed either before and/or after it is applied to human skin. A second question is whether the “feel” of a product both before and after application can be quantified by instrumental rheology, tribology and texture analysis methods and whether these, in turn, can be related to the reported sensorial behaviour. We will manufacture topical formulations that systematically vary in Q1, Q2, and/or Q3 attributes and have large and borderline perceptive differences. We will then characterize these products using a range of rheology, tribology and texture analysis methods along with characterization of rate of drying, particle and globule size. In parallel, these products will be evaluated by perceptive testing focus groups, with controls, for their sensory properties or the ‘feel’ of the products. We will then relate these sensorial findings with the variations in formulation nature, composition and manufacture, and their resulting instrumental test results. Our goals are, firstly, to understand the relationships between product nature, instrumental findings and sensorial analyses and, secondly, to derive criteria for instrument tests that indicate what product composition subjects suggest do not differ, uncertain if they differ and do differ in their sensorial behaviour. It is anticipated that we can define the simplest, robust test that accurately and robustly aligns with sensory perceptions. A range of statistical methods, including (potentially) sophisticated, machine learning and deep learning tools will then be used to model the most appropriate instrumental analysis that can, with reasonable confidence predict perceptive attributes. A key outcome is a potential regulatory guideline advocating that generic products should exhibit similar sensorial behaviour as a reference listed drug product, giving boundaries in rheology, tribology and texture analysis as defined by Q1, Q2 and Q3 differences when sensorial behaviour between topical products is likely to be different.