Utilization of a Modified Special‐Cubic Design and an Electronic Tongue for Bitterness Masking Formulation Optimization

Development and evaluation of novel taste-masking tilmicosin microcapsules containing octenylsuccinic anhydride modified starch and maltodextrin as wall materials

Tilmicosin (TMS) is an important antibiotic in veterinary medicine, but its extreme bitter taste limits its use. In this study, TMS was encapsulated in octenyl succinic anhydride modified starch/maltodextrin (HI-CAP/MD) composite capsules with a spray drying method. The TMS microcapsules (TMS-MC) exhibited good drug loading performance with drug loading (DL) and encapsulation efficiency (EE) of 9.90 ± 0.23 % and 98.03 ± 1.56 %, respectively. There was no significant change in particle diameter and zeta potential for the emulsion and redissolved TMS-MC. These results combined with FT-IR, TGA and DSC showed the crystalline shape and chemical structure of TMS did not change during the microencapsulation. In vitro release characterization in an acidic medium (pH 1.2) and an alkaline medium (phosphate buffered solution, pH 6.8) showed that TMS-MC can be rapidly released in vitro. The bitterness evaluation implied the bitterness of TMS was masked after microencapsulation. In vitro bacterial inhibition test showed the bacterial inhibitory activity of TMS was not reduced by the microencapsulation, but was much better than that of the commercially available tylosin (TLS). Therefore, HI-CAP/MD can effectively encapsulate TMS, mask the bitter taste and maintain a good bacterial inhibitory effect, making a new drug formulation with good development prospects.

Taste Sensor Assessment of Bitterness in Medicines: Overview and Recent Topics

In recent decades, taste sensors have been increasingly utilized to assess the taste of oral medicines, particularly focusing on bitterness, a major obstacle to patient acceptance and adherence. This objective and safe method holds promise for enhancing the development of patient-friendly medicines in pharmaceutical companies. This review article introduces its application in measuring the intensity of bitterness in medicine, confirming the achievement of taste masking, distinguishing taste differences between branded and generic medicines, and identifying substances to suppress bitterness in target medicines. Another application of the sensor is to predict a significant increase in bitterness when medicine is taken with certain foods/beverages or concomitant medication. Additionally, to verify the sensor's predictability, a significant correlation has been demonstrated between the output of a bitter-sensitive sensor designed for drug bitterness (BT0) and the bitterness responses of the human taste receptor hT2R14 from BitterDB (huji.ac.il). As a recent advancement, a novel taste sensor equipped with lipid/polymer membranes modified by 3-Br-2,6-dihydroxybenzoic acid (2,6-DHBA), based on the concept of allostery, is introduced. This sensor successfully predicts the bitterness of non-charged pharmaceuticals with xanthine skeletons, such as caffeine or related compounds. Finally, the future prospects of taste sensors are discussed.

Assessment of Bitterness in Non-Charged Pharmaceuticals with a Taste Sensor: A Study on Substances with Xanthine Scaffold and Allopurinol

Taste sensors with an allostery approach have been studied to detect non-charged bitter substances, such as xanthine derivatives, used in foods (e.g., caffeine) or pharmaceuticals (e.g., etofylline). In this study, the authors modified a taste sensor with 3-bromo-2,6-dihydroxybenzoic acid and used it in conjunction with sensory tests to assess the bitterness of non-charged pharmaceuticals with xanthine scaffolds (i.e., acefylline and doxofylline), as well as allopurinol, an analogue of hypoxanthine. The results show that the sensor was able to differentiate between different levels of sample bitterness. For instance, when assessing a 30 mM sample solution, the sensor response to acefylline was 34.24 mV, which corresponded to the highest level of bitterness (τ = 3.50), while the response to allopurinol was lowest at 2.72 mV, corresponding to relatively weaker bitterness (τ = 0.50). Additionally, this study extended the application of the sensor to detect pentoxifylline, an active pharmaceutical ingredient in pediatric medicines. These results underscore the taste sensor's value as an additional tool for early-stage assessment and prediction of bitterness in non-charged pharmaceuticals.

Recent Advances in Bitterness-Sensing Systems

Bitterness is one of the basic tastes, and sensing bitterness plays a significant role in mammals recognizing toxic substances. The bitter taste of food and oral medicines may decrease consumer compliance. As a result, many efforts have been made to mask or decrease the bitterness in food and oral pharmaceutical products. The detection of bitterness is critical to evaluate how successful the taste-masking technology is, and many novel taste-sensing systems have been developed on the basis of various interaction mechanisms. In this review, we summarize the progress of bitterness response mechanisms and the development of novel sensors in detecting bitterness ranging from commercial electronic devices based on modified electrodes to micro-type sensors functionalized with taste cells, polymeric membranes, and other materials in the last two decades. The challenges and potential solutions to improve the taste sensor quality are also discussed.

Study on Masking the Bitterness of Chinese Medicine Decoction-Mate

Background

Traditional Chinese medicine decoction (TCMD) is an oral liquid made by decocting crude medicinal compounds with water. It has complex compositions and diverse odor and taste, most of which have an unacceptable level of bitterness which seriously affects patients' medication compliance. To solve this problem, a variety of taste-masking pathways and different types of taste-masking excipients were combined, using the application of coffee-mate to mask the bitterness of coffee as an existing example. Three composite taste-masking adjuvants were developed to improve the taste of TCMD, referred to as the Chinese Medicine Decoction-Mate (CMD-M). However, whether CMD-M has a good taste-masking effect and whether it affects the chemical compositions and pharmacological effects of the medicine remain unclear.

Method

The commonly used pediatric medicine Qingre Huazhi Decoction (QRHZD) and the personalized decoctions used in clinical practices were used as the masking research carriers. The taste-masking effect of CMD-M on QRHZD was evaluated by both healthy volunteers and an electronic tongue, and the personalized decoctions were evaluated by clinical subjects. The changes of chemical components of QRHZD before and after taste-masking were evaluated by HPLC. The changes in anti-inflammatory effects were evaluated by establishing mice as an acute inflammatory model.

Results

The taste-masking effect evaluation results showed that the bitterness of QRHZD was significantly reduced after adding CMD-M. There was no significant difference in the relative peak areas change rate and total peak areas ratio of common peaks of QRHZD before and after taste-masking (P > 0.05), shown by HPLC analysis. The inhibitory rates of QRHZD on ear swelling in mice before and after taste-masking also showed no significant difference (P > 0.05).

Conclusions

CMD-M can effectively mask the bitterness of decoctions while bringing no significant difference overall in chemical compositions and pharmacological effects before and after QRHZD masking.

The use of electronic tongue and sensory panel on taste evaluation of pediatric medicines: a systematic review

The palatability of medications is an essential factor for children's adherence to drug treatment. Several methods for drug taste assessment have been developed. The aim of this review is to explore the literature reports of the main methods for the evaluation of medicines taste, named electronic tongue (e-tongue, in vitro) and human sensory panel. A systematic search was performed up to March 2020 and a total of 88 articles were selected. The e-tongue (57.5%) has been more frequently described than the sensory panel (10.3%), while some articles (32.2%) used both techniques. 74.7% of the articles mentioned 'pediatric', 'paediatric' or 'children' in the text, but only 19.5% developed formulations targeting pediatric audience and sensory testing in children is rarely seen. The e-tongue has predominance of use in the taste evaluation of pediatric medicines probably since it is fast, easy to perform and risk free, besides presenting less imprecise data and no fatigue. The human panel is more realistic, despite its intrinsic variability. In this sense, it is proposed the use of e-tongue as a fast way to select the most promising sample(s) and, after that, the sensory panel should be applied in order to confirm the taste masking.

Distinguishing paracetamol formulations: Comparison of potentiometric "Electronic Tongue" with established analytical techniques

Fast and inexpensive analytical tools for identification of the origin of pharmaceutical formulations are important to ensure consumers safety. This study explores the potential of potentiometric multisensor systems ("electronic tongues") in this type of application. 72 paracetamol samples purchased in different countries and produced by various companies were studied via infrared spectroscopy (IR), near infrared spectroscopy (NIR), nuclear magnetic resonance spectroscopy (NMR) and multisensor system (ET). A variety of chemometric tools was applied to explore and compare the information yielded by these methods. It was found that ET is capable of distinguishing paracetamol formulations from different producers. The chemical information derived from potentiometric sensor responses has something in common with that derived from NIR and IR; however, it is orthogonal to that from NMR. ET can be a valuable tool in express quality assessment of drugs.

Evaluation of the bitter components of bamboo shoots using a metabolomics approach

The metabolomics methodology can be more convenient to evaluate the function of food.

Optimization and validation of the protocol used to analyze the taste of traditional Chinese medicines using an electronic tongue

Tools to define the active ingredients and flavors of Traditional Chinese Medicines (TCMs) are limited by long analysis times, complex sample preparation and a lack of multiplexed analysis. The aim of the present study was to optimize and validate an electronic tongue (E-tongue) methodology to analyze the bitterness of TCMs. To test the protocol, 35 different TCM concoctions were measured using an E-tongue, and seven replicate measurements of each sample were taken to evaluate reproducibility and precision. E-tongue sensor information was identified and classified using analysis approaches including least squares support vector machine (LS-SVM), support vector machine (SVM), discriminant analysis (DA) and partial least squares (PLS). A benefit of this analytical protocol was that the analysis of a single sample took <15 min for all seven sensors. The results identified that the LS-SVM approach provided the best bitterness classification accuracy (binary classification accuracy, 100%; ternary classification accuracy, 89.66%). The E-tongue protocol developed showed good reproducibility and high precision within a 6 h measurement cycle. To the best of our knowledge, this is the first study of an E-tongue being applied to assay the bitterness of TCMs. This approach could be applied in the classification of the taste of TCMs, and serve important roles in other fields, including foods and beverages.

Evaluation of the Bitterness of Traditional Chinese Medicines using an E-Tongue Coupled with a Robust Partial Least Squares Regression Method

To accurately, safely, and efficiently evaluate the bitterness of Traditional Chinese Medicines (TCMs), a robust predictor was developed using robust partial least squares (RPLS) regression method based on data obtained from an electronic tongue (e-tongue) system. The data quality was verified by the Grubb’s test. Moreover, potential outliers were detected based on both the standardized residual and score distance calculated for each sample. The performance of RPLS on the dataset before and after outlier detection was compared to other state-of-the-art methods including multivariate linear regression, least squares support vector machine, and the plain partial least squares regression. Both R² and root-mean-squares error (RMSE) of cross-validation (CV) were recorded for each model. With four latent variables, a robust RMSECV value of 0.3916 with bitterness values ranging from 0.63 to 4.78 were obtained for the RPLS model that was constructed based on the dataset including outliers. Meanwhile, the RMSECV, which was calculated using the models constructed by other methods, was larger than that of the RPLS model. After six outliers were excluded, the performance of all benchmark methods markedly improved, but the difference between the RPLS model constructed before and after outlier exclusion was negligible. In conclusion, the bitterness of TCM decoctions can be accurately evaluated with the RPLS model constructed using e-tongue data.

Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA-BP neural network

The aim of this study was to predict the bitterness intensity of a drug using an electronic tongue (e-tongue). The model drug of berberine hydrochloride was used to establish a bitterness prediction model (BPM), based on the taste evaluation of bitterness intensity by a taste panel, the data provided by the e-tongue and a genetic algorithm-back-propagation neural network (GA-BP) modeling method. The modeling characteristics of the GA-BP were compared with those of multiple linear regression, partial least square regression and BP methods. The determination coefficient of the BPM was 0.99965±0.00004, the root mean square error of cross-validation was 0.1398±0.0488 and the correlation coefficient of the cross-validation between the true and predicted values was 0.9959±0.0027. The model is superior to the other three models based on these indicators. In conclusion, the model established in this study has a high fitting degree and may be used for the bitterness prediction modeling of berberine hydrochloride of different concentrations. The model also provides a reference for the generation of BPMs of other drugs. Additionally, the algorithm of the study is able to conduct a rapid and accurate quantitative analysis of the data provided by the e-tongue.

Taste sensing systems (electronic tongues) for pharmaceutical applications

Electronic tongues are sensor array systems able to detect single substances as well as complex mixtures by means of particular sensor membranes and electrochemical techniques. Two systems are already commercially available, the Insent taste sensing system and the αAstree electronic tongue. In addition, various laboratory prototype versions exist. Besides the successful use in food industry, the implementation for pharmaceutical purposes has strongly grown within the recent years. A reason for this is the increased interest of developing palatable formulations, especially for children. As taste assessment of drugs comes along with challenges due to possible toxicity and subjectivity of the taste assessors, electronic tongues could offer a safe and objective alternative. In order to provide guidance on the use of these systems, possible fields of interest are presented in this review, as for example, system qualification, quality control, formulation development, comparison between marketed drug products, and the validation of the methods used. Further, different approaches for solid and liquid dosage forms are summarized. But, also the difficulty to obtain absolute statements regarding taste was identified and the need of more validated data was discussed to offer guidance for the next years of research and application of electronic tongues for pharmaceutical applications.

Electronic tongue for the detection of taste-masking microencapsulation of active pharmaceutical substances

Electronic tongues can produce chemical images of samples, whose changes can be correlated with general properties, e.g. taste sensations. In this work, a sensor array equipped with eight types of ion-selective electrodes was coupled with Principal Components Analysis in order to detect microencapsulation effect of two Active Pharmaceutical Ingredients (APIs), which influences their taste properties. The character of change of sensor array responses in samples modified by microencapsulation was the same in two investigated APIs (Ibuprofen and Rixithromycin), proving, that the "sensed taste" becomes similar in both formulations after Eudragit modification. The obtained results show, that the presented electronic tongue can be used for analysis of masking effects in drugs and detection of microencapsulation effect.

An electronic tongue: evaluation of the masking efficacy of sweetening and/or flavoring agents on the bitter taste of epinephrine

An epinephrine (E) tablet is under development for sublingual (SL) administration for the first-aid treatment of anaphylaxis; however, the inherent bitterness of E may hinder acceptability by patients, especially children. To assess the degree of E bitterness and to predict the masking effects of sweetening and/or flavoring non-medicinal ingredients (NMIs), the potential usefulness of an electronic tongue (e-Tongue) was evaluated. The e-Tongue sensors were conditioned, calibrated, and tested for taste discrimination. Six standard active pharmaceutical ingredients were used to build and validate a bitterness model which was then used to assess E bitartrate (EB) solutions from 0.3-9 mM. Taste-masking efficiency of aspartame (ASP), acesulfame potassium (ASK), and citric acid (CA) each at 0.5 mM was evaluated. Using EB 9 mM, the bitterness score was 20 on a scale of 20 (unacceptable) down to 1 (not detected). When NMIs 0.5 mM were added, neither ASK (17.2, unacceptable) nor was ASP (14.0, limit acceptable) effective in masking the bitter taste. When the combination of ASK and ASP was used, the bitterness score was reduced to 9.2 (acceptable). However, the addition of CA alone resulted in the best reduction of the bitterness score to 3.3 (not detected). Using the e-Tongue, the incorporation of a variety of sweetening and/or flavoring NMIs into a SL tablet of E could be shown to mask its bitter taste by up to 80%. These results should be confirmed by in vivo studies.

Chemometric brains for artificial tongues

The last years showed a significant trend toward the exploitation of rapid and economic analytical devices able to provide multiple information about samples. Among these, the so-called artificial tongues represent effective tools which allow a global sample characterization comparable to a fingerprint. Born as taste sensors for food evaluation, such devices proved to be useful for a wider number of purposes. In this review, a critical overview of artificial tongue applications over the last decade is outlined. In particular, the focus is centered on the chemometric techniques, which allow the extraction of valuable information from nonspecific data. The basic steps of signal processing and pattern recognition are discussed and the principal chemometric techniques are described in detail, highlighting benefits and drawbacks of each one. Furthermore, some novel methods recently introduced and particularly suitable for artificial tongue data are presented.

Evaluation of a taste sensor instrument (electronic tongue) for use in formulation development

A taste sensor instrument (electronic tongue) was evaluated to determine its utility in developing a taste-enhanced liquid formulation. To train the electronic tongue, human sensory panel data were collected for two prototype formulations, a solution of the drug in water and several marketed products. Studies using the electronic tongue were conducted to determine taste-masking effectiveness of formulations compared to a matching placebo, to establish correlation with human sensory data, and to evaluate unknown formulations and predict their bitterness scores. In the first experiment, the effectiveness of a proposed taste-masking strategy was determined by comparing formulation prototypes containing a bitter active pharmaceutical ingredient (API) against corresponding placebos (i.e. formulations without an active ingredient) using electronic tongue data. The analysis of the electronic tongue data was based on the assumption that the drug was well taste masked if the placebo matched the formulation with API. In a second set of experiments, electronic tongue data were compared to existing data from a human taste panel for several marketed products and prototype formulations. A good correlation (r(2)=0.99) was achieved from this comparison, and the relative taste of prototype formulations not tasted by humans was predicted.