This paper presents the findings of a comprehensive review on common bee pollen processing methods which can impact extraction efficiency and lead to differences in measured total phenolic content (TPC) and radical scavenging activity based on 2,2-diphenyl-1-picrylhydrazyl (DPPH) and ferric reducing antioxidant power (FRAP) data. This hampers the comparative analysis of bee pollen from different floral sources and geographical locations. Based on the review, an in-depth investigation was carried out to identify the most efficient process to maximise the extraction of components for measurement of TPC, DPPH and FRAP antioxidant activity for two bee pollen samples from western Australia (Jarrah and Marri pollen). Optimisation by Design of Experiment with Multilevel Factorial Analysis (Categorical) modelling was performed. The independent variables included pollen pulverisation, the extraction solvent (70% aqueous ethanol, ethanol, methanol and water) and the extraction process (agitation, maceration, reflux and sonication). The data demonstrate that non-pulverised bee pollen extracted with 70% aqueous ethanol using the agitation extraction method constitute the optimal conditions to maximise the extraction of phenolics and antioxidant principles in these bee pollen samples.

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Tramadol is a bitter atypical opioid analgesic drug and is prescribed to treat postoperative pain in children. However, in many countries there is no licensed paediatric tramadol formulation available. We have formulated a novel chewable chocolate-based drug delivery system for the administration of tramadol to children. This pilot, single-centre, open-label, randomised clinical study assessed the taste tolerability and comparative population pharmacokinetics of the novel tramadol chewable tablet against a compounded tramadol hydrochloride oral liquid, at a dose of 1 mg.kg -1 . A 5-point facial hedonic scale was used by the children, parents and nurses to assess tolerability. One hundred and forty-one children aged 3-16 years were given tramadol 30 min before general anaesthesia. Blood samples were taken following the induction of anaesthesia and for up to 5 h following tramadol administration. Tramadol and its active metabolite O-desmethyltramadol were analysed using reversed-phase high-performance liquid chromatography. A population pharmacokinetic model was built using non-linear mixed effects modelling. The relative bioavailability for the tablet was 1.25 times higher (95%CI 1.16-1.35) than for tramadol hydrochloride oral liquid, while the absorption rate constant for the tablet was significantly lower (1.97 h -1 vs. 3.34 h -1 , p < 0.001). Larger inter-individual variability in absorption rates were observed with the liquid tramadol. The tramadol chewable tablet was more acceptable in taste to children when assessed by the children, parents and nurses (all p < 0.001). We conclude that the novel tramadol chewable tablet has favourable acceptability and more reliable relative bioavailability in children compared with tramadol hydrochloride oral liquid.

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This review paper explores the role of human taste panels and artificial neural networks (ANNs) in taste-masking paediatric drug formulations. Given the ethical, practical, and regulatory challenges of employing children, young adults (18–40) can serve as suitable substitutes due to the similarity in their taste sensitivity. Taste panellists need not be experts in sensory evaluation so long as a reference product is used during evaluation; however, they should be screened for bitterness taste detection thresholds. For a more robust evaluation during the developmental phase, considerations of a scoring system and the calculation of an acceptance value may be beneficial in determining the likelihood of recommending a formulation for further development. On the technological front, artificial neural networks (ANNs) can be exploited in taste-masking optimisation of medicinal formulations as they can model complex relationships between variables and enable predictions not possible previously to optimise product profiles. Machine learning classifiers may therefore tackle the challenge of predicting the bitterness intensity of paediatric formulations. While advancements have been made, further work is needed to identify effective taste-masking techniques for specific drug molecules. Continuous refinement of machine learning algorithms, using human panellist acceptability scores, can aid in enhancing paediatric formulation development and overcoming taste-masking challenges.