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Oral liquid prednisolone medications have poor acceptance among paediatric patients due to ineffective masking of the bitterness taste of prednisolone. This study aimed to develop a child-friendly prednisolone tablet using a patented chewable chocolate-based delivery system (CDS) previously applied successfully to mask the bitterness tastes of midazolam and tramadol. Prednisolone sodium phosphate (PSP) and prednisolone base (PB) CDS tablets were prepared, and the manufacturing process was optimised using a design of experiments (DoE) approach. Stability was assessed by quantifying residual drug content via a validated HPLC assay. A pilot randomised crossover taste study involving 25 young adult volunteers evaluated taste-masking effectiveness against Redipred™, a commercial oral PSP liquid medicine. The results showed that the PSP CDS tablet was chemically stable following storage for three months at ambient temperature, while the PB CDS tablet was unstable. The optimised PSP CDS tablet, manufactured at 50 °C with a stirring time of 26 h, was found to release over 80% of its drug load within 20 min in 0.1 M HCl and had a significantly better mean taste score compared to Redipred™ (7.08 ± 2.40 vs. 5.60 ± 2.33, p = 0.03). Fifty six percent of the participants preferred the PSP CDS tablet. In conclusion, compared to Redipred™, the CDS technology provided a more effective taste masking of PSP, potentially offering a child-friendly prednisolone formulation with improved compliance, dosing accuracy, and storage stability.

Edaravone is one of two main drugs for treating motor neurone disease (MND). This review proposes a specific quality target product profile (QTPP) for edaravone following an appraisal of the issues accounting for the poor clinical uptake of the approved IV and oral liquid edaravone formulations. This is followed by a review of the alternative oral formulations of edaravone described in the published patent and journal literature against the QTPP. A total of 14 texts published by six research groups on 18 novel oral formulations of edaravone for the treatment of MND have been reviewed. The alternative oral formulations included liquid and solid formulations developed with cyclodextrins, lipids, surfactants, co-surfactants, alkalising agents, tablet excipients, and co-solvents. Most were intended to deliver edaravone for drug absorption in the lower gastrointestinal tract (GIT); however, there were also four formulations targeting the oral mucosal absorption of edaravone to avoid first-pass metabolism. All the novel formulations improved the aqueous solubility, stability, and oral bioavailability (BA) of edaravone compared to an aqueous suspension of edaravone. A common limitation of the published formulations is the lack of MND-patient-centred data. Except for TW001, no other formulations have been trialled in MND patients. To meet the QTPP of an oral edaravone formulation for MND patients, it is recommended that a tablet of appropriate size and with acceptable taste and stability be designed for the effective sublingual or buccal absorption of edaravone. This tablet should be designed with input from the MND community.

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.