title: Assessment of bitter taste of pharmaceuticals with multisensor system employing 3 way PLS regression.
abstract: The application of the potentiometric multisensor system (electronic tongue, ET) for quantification of the bitter taste of structurally diverse active pharmaceutical ingredients (API) is reported. The measurements were performed using a set of bitter substances that had been assessed by a professional human sensory panel and the in vivo rat brief access taste aversion (BATA) model to produce bitterness intensity scores for each substance at different concentrations. The set consisted of eight substances, both inorganic and organic - azelastine, caffeine, chlorhexidine, potassium nitrate, naratriptan, paracetamol, quinine, and sumatriptan. With the aim of enhancing the response of the sensors to the studied APIs, measurements were carried out at different pH levels ranging from 2 to 10, thus promoting ionization of the compounds. This experiment yielded a 3 way data array (samples×sensors×pH levels) from which 3wayPLS regression models were constructed with both human panel and rat model reference data. These models revealed that artificial assessment of bitter taste with ET in the chosen set of API's is possible with average relative errors of 16% in terms of human panel bitterness score and 25% in terms of inhibition values from in vivo rat model data. Furthermore, these 3wayPLS models were applied for prediction of the bitterness in blind test samples of a further set of API's. The results of the prediction were compared with the inhibition values obtained from the in vivo rat model.
authors.affiliation: CESAM and Chemistry Department, University of Aveiro, Aveiro, Portugal.
authors.forename: Alisa
authors.initials: A
authors.lastname: Rudnitskaya
authors.forename: Dmitry
authors.initials: D
authors.lastname: Kirsanov
authors.forename: Yulia
authors.initials: Y
authors.lastname: Blinova
authors.forename: Evgeny
authors.initials: E
authors.lastname: Legin
authors.forename: Boris
authors.initials: B
authors.lastname: Seleznev
authors.forename: David
authors.initials: D
authors.lastname: Clapham
authors.forename: Robert S
authors.initials: RS
authors.lastname: Ives
authors.forename: Kenneth A
authors.initials: KA
authors.lastname: Saunders
authors.forename: Andrey
authors.initials: A
authors.lastname: Legin
mesh_terms: D000328:Adult; D000818:Animals; D015374:Biosensing Techniques / Q000379:methods* / Q000639:trends; D002110:Caffeine / Q000737:chemistry / Q000494:pharmacology; D004581:Electronics; D005260:Female; D006801:Humans; D006863:Hydrogen-Ion Concentration; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000737:chemistry; D010793:Phthalazines / Q000737:chemistry / Q000494:pharmacology; D011199:Potentiometry; D051381:Rats; D013649:Taste
chemical_list: D004364:Pharmaceutical Preparations; D010793:Phthalazines; D002110:Caffeine; C020976:azelastine