Biopharmaceutical Understanding of Excipient Variability on Drug Apparent Solubility Based on Drug Physicochemical Properties. Case Study: Superdisintegrants

Abstract

The presence of different excipient types/brands in solid oral dosage forms may affect product performance and drug bioavailability. Understanding the biopharmaceutical implications of superdisintegrant variability (changes in material properties), variation (changes in excipient amount) and interchangeability (use of different excipient types with the same intended functionality) in oral drug performance would be beneficial for the development of robust final dosage forms. The current study investigated the impact of superdisintegrants (sodium starch glycolate, croscarmellose sodium, crospovidone) on the apparent solubility of drugs with different physicochemical properties (drug ionisation, drug lipophilicity, drug aqueous solubility). Compendial and biorelevant media were used to assess the impact of gastrointestinal conditions on the effects of excipient on drug apparent solubility. For the majority of compounds, changes in drug apparent solubility were not observed in superdisintegrant presence, apart from the cases of highly ionised compounds (significant decrease in drug solubility) and/or compounds that aggregate/precipitate in solution (significant increase in drug solubility). Excipient variability did not greatly affect the impact of excipients on drug apparent solubility. The use of multivariate data analysis identified the biopharmaceutical factors affecting excipient performance. The construction of roadmaps revealed that superdisintegrants may be of low risk for the impact of excipients on oral drug performance based on drug solubility alone; superdisintegrants activity could still be a risk for oral bioavailability due to their effects on tablet disintegration.

Development of a Novel Vaginal Drug Delivery System to Control Time of Farrowing and Allow Supervision of Piglet Delivery

“…The concentration of HPMC in this formulation (10%) was lower than the 'percolation threshold' reported in other studies [31,35] and may have contributed to the difference in swelling percentage observed in Figure 9B. Although HPMC concentrations as low as 10% (w/v) have been reported [18,[36][37][38], low levels of HPMC and high lactose content can be associated with sudden changes in matrix integrity and inconsistent results [38,39]. Substances with higher particle size require higher concentrations to percolate the tablet [39,40], so combining with a lower viscosity grade may improve tablet robustness.…”

“…In addition to having an unstable release, CR1 also had the weakest detachment force. CR6 and CR3 had higher HPMC concentrations (>24%) and the higher concentrations have also been found to increase mucoadhesive strength in previous studies [37,42]. Increasing the concentration of HPMC in tablet formulations increases the likelihood of chain entanglement between the hydroxyl groups and amide groups of the mucin layer [38].…”

“…In Figure 8, formulations CR1 and CR2 showed signs of erosion after the eighth hour yet CR3 retained a strong gel matrix with no signs of core erosion. At the surface of the gel tablet layer, formulations containing greater concentrations of HPMC are less susceptible to erosion as there are more polymetric chains to withstand forces surrounding the gel [37]. Concentrations of HPMC below the critical polymer concentration cannot withstand the shear forces surrounding the gel for as long, and erosion occurs at a faster rate [41].…”

Evaluating pediatric and adult simulated fluids solubility: Abraham solvation parameters and multivariate analysis

“…The VIP is a weighted summary of the influence of each individual variable on the PLS model. The VIP is calculated as a weighted sum of the squares of the PLS loadings for each variable, which are representations of the variables impact and its correlations in the model, taking into account the amount of Y-variance explained ( 9 – 11 ) .…”

“…In the PLS-R analyses, the outliers were identified by comparison of the distance of each observation to the model in the Y-plane (DModY) with their calculated critical value (DCrit(Y)); an outlier is present if the standardized DCritY for an observation is greater than DCritY ( 9 – 11 ). Outliers were only exluded if (i) the PLS model improved significantly after exclusion and (ii) there was a clear scientific rational on why the data should be excluded ( i.e.…”