The bioavailability of poorly water-soluble active pharmaceutical ingredients (APIs) can be improved via the formulation of an amorphous solid dispersion (ASD), where the API is incorporated into a suitable polymeric carrier. Optimal carriers that exhibit good compatibility (i.e., solubility and miscibility) with given APIs are typically identified through experimental means, which are routinely labor- and cost-inefficient. Therefore, the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state, a popular thermodynamic model in pharmaceutical applications, is examined in terms of its performance regarding the computational pure prediction of API-polymer compatibility based on activity coefficients (API fusion properties were taken from experiments) without any binary interaction parameters fitted to API-polymer experimental data (that is, kij = 0 in all cases). This kind of prediction does not need any experimental binary information and has been underreported in the literature so far, as the routine modeling strategy used in the majority of the existing PC-SAFT applications to ASDs comprised the use of nonzero kij values. The predictive performance of PC-SAFT was systematically and thoroughly evaluated against reliable experimental data for almost 40 API-polymer combinations. We also examined the effect of different sets of PC-SAFT parameters for APIs on compatibility predictions. Quantitatively, the total average error calculated over all systems was approximately 50% in the weight fraction solubility of APIs in polymers, regardless of the specific API parametrization. The magnitude of the error for individual systems was found to vary significantly from one system to another. Interestingly, the poorest results were obtained for systems with self-associating polymers such as poly(vinyl alcohol). Such polymers can form intramolecular hydrogen bonds, which are not accounted for in the PC-SAFT variant routinely applied to ASDs (i.e., that used in this work). However, the qualitative ranking of polymers with respect to their compatibility with a given API was reasonably predicted in many cases. It was also predicted correctly that some polymers always have better compatibility with the APIs than others. Finally, possible future routes to improve the cost-performance ratio of PC-SAFT in terms of parametrization are discussed.
Prediction of compatibility of the active pharmaceutical ingredient (API) with the polymeric carrier plays an essential role in designing drug delivery systems and estimating their long-term physical stability. A key element in deducing API-polymer compatibility is knowledge of a complete phase diagram, i.e., the solubility of crystalline API in polymer and mutual miscibility of API and polymer. In this work, the phase behavior of ibuprofen (IBU) with different grades of poly(D,L-lactide-co-glycolide) (PLGA) and polylactide (PLA), varying in composition of PLGA and molecular weight of PLGA and PLA, was investigated experimentally using calorimetry and computationally by the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state (EOS). The phase diagrams constructed based on a PC-SAFT EOS modeling optimized using the solubility data demonstrated low solubility at typical storage temperature (25 °C) and limited miscibility (i.e., presence of the amorphous-amorphous phase separation region) of IBU with all polymers studied. The ability of PC-SAFT EOS to capture the experimentally observed trends in the phase behavior of IBU-PLA/PLGA systems with respect to copolymer composition and molecular weight was thoroughly investigated and evaluated.
- Publikační typ
- časopisecké články MeSH
Commonly applied approaches to enhance the dissolution properties of low water-soluble crystalline active pharmaceutical ingredients (APIs) include their amorphization by incorporation into a polymeric matrix and the formation of amorphous solid dispersions, or blending APIs with low-molecular-weight excipients and the formation of a co-amorphous system. This study focused on the preparation and characterization of binary (consisting of indomethacin (IND) and polymer - copovidone (PVP VA 64), as a carrier, or amino acid - L-arginine (ARG), as a co-former) and ternary (comprising the same API, polymer, and amino acid) formulations. Formulations were produced by ball milling (BM) and/or hot-melt extrusion (HME), and extensive physicochemical characterization was performed. Specifically, the physicochemical and solid-state properties of a model IND-ARG system incorporated into a polymeric matrix of PVP VA 64 by HME and BM as well as by combined BM/HME method together with the impact of the preparation strategy on the dissolution profiles and long-term physical stability were investigated. Ball-milled binary and ternary formulations were found to be amorphous. The residual crystals corresponding to IND-ARG salt were identified in the ternary formulations produced via HME. Despite the presence of a crystalline phase, dissolution tests showed that ternary systems prepared by HME exhibited improved IND solubility when compared to pure crystalline IND and their corresponding physical mixture. None of the binary and ternary formulations that were initially fully amorphous did undergo recrystallization during the entire period of preservation (minimum of 12 months) in dry conditions at 25 °C.
- MeSH
- arginin * MeSH
- indomethacin * MeSH
- polymery MeSH
- rozpustnost MeSH
- vinylové sloučeniny MeSH
- Publikační typ
- časopisecké články MeSH
A pair of popular thermodynamic models for pharmaceutical applications, namely, the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state and the conductor-like screening model for real solvents (COSMO-RS) are thoroughly benchmarked for their performance in predicting the solubility of active pharmaceutical ingredients (APIs) in pure solvents. The ultimate goal is to provide an illustration of what to expect from these progressive frameworks when applied to the thermodynamic solubility of APIs based on activity coefficients in a purely predictive regime without specific experimental solubility data (the fusion properties of pure APIs were taken from experiments). While this kind of prediction represents the typical modus operandi of the first-principles-aided COSMO-RS, PC-SAFT is a relatively highly parametrized model that relies on experimental data, against which its pure-substance and binary interaction parameters (kij) are fitted. Therefore, to make this benchmark as fair as possible, we omitted any binary parameters of PC-SAFT (i.e., kij = 0 in all cases) and preferred pure-substance parameter sets for APIs not trained to experimental solubility data. This computational approach, together with a detailed assessment of the obtained solubility predictions against a large experimental data set, revealed that COSMO-RS convincingly outperformed PC-SAFT both qualitatively (i.e., COSMO-RS was better in solvent ranking) and quantitatively, even though the former is independent of both substance- and mixture-specific experimental data. Regarding quantitative comparison, COSMO-RS outperformed PC-SAFT for 9 of the 10 APIs and for 63% of the API-solvent systems, with root-mean-square deviations of the predicted data from the entire experimental data set being 0.82 and 1.44 log units, respectively. The results were further analyzed to expand the picture of the performance of both models with respect to the individual APIs and solvents. Interestingly, in many cases, both models were found to qualitatively incorrectly predict the direction of deviations from ideality. Furthermore, we examined how the solubility predictions from both models are sensitive to different API parametrizations.
- MeSH
- léčivé přípravky MeSH
- rozpouštědla MeSH
- rozpustnost * MeSH
- termodynamika MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Knowledge of the active pharmaceutical ingredient (API) solubility in a polymer is imperative for successful amorphous solid dispersion design and formulation but acquiring this information at storage temperature is challenging. Various solubility determination methods have been established, which utilize differential scanning calorimetry (DSC). In this work, three commonly used DSC-based protocols [i.e., melting point depression (MPD), recrystallization, and zero-enthalpy extrapolation (Z-EE)] and a method that we have developed called "step-wise dissolution" (S-WD) were analyzed. For temperature-composition phase diagram construction, two glass-transition temperature equations (i.e., those of Gordon-Taylor and Kwei) and three solid-liquid equilibrium curve modeling approaches [i.e., the Flory-Huggins model, an empirical equation, and the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state (EOS)] were considered. Indomethacin (IND) and Kollidon 12 PF (PVP K12) were selected as the API and polymer, respectively. An annealing time investigation revealed that the IND-PVP K12 dissolution process was remarkably faster than demixing, which contradicted previously published statements. Thus, the recrystallization method overestimated the solubility of IND in PVP K12 when a 2-h time of annealing was set as the benchmark. Likewise, the MPD and Z-EE methods overestimated the API solubility because of unreliable IND melting endotherm evaluation at lower API loadings and a relatively slow heating rate, respectively. When the experimental results obtained using the S-WD method (in conjunction with the Kwei equation) were applied to the PC-SAFT EOS, which was regarded as the most reliable combination, the predicted IND solubility in PVP K12 at T = 25 °C was approximately 40 wt %. When applicable, the S-WD method offers the advantage of using a limited number of DSC sample pans and API-polymer physical mixture compositions, which is both cost- and time-effective.
The preparation of an amorphous solid dispersion (ASD) is a promising strategy for improving the poor oral bioavailability of many active pharmaceutical ingredients (APIs). However, poor predictability of ASD long-term physical stability remains a prevalent problem. The purpose of this study was to evaluate and compare the predictive performance of selected models concerning solid-liquid equilibrium (SLE) curve and glass-transition temperature (Tg) line modeling of ibuprofen (IBU) in cellulosic polymers (i.e., hydroxypropyl methylcellulose (HPMC) and hydroxypropyl methylcellulose acetate succinate (HPMCAS)). For SLE curve modeling, an empiricalanalyticalapproach(Kyeremateng et al., 2014)and the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) equation of state (EOS) were chosen. Due to the unavailability of PC-SAFT parameters for both polymers, an approximation procedure for parametrization was applied. The Gordon-Taylor equation and Kwei equation were considered for Tg line determination. The impact of various computational set-ups (e.g., model parametrization or extrapolation length) on IBU solubility prediction at storage conditions was thoroughly investigated, assessed and confronted with the results from an 18-month physical stability study. IBU developed stable 20 wt% API content ASDs with both HPMC and HPMCAS.The extrapolation behavior and subsequent ASD thermodynamic stability prediction at storage conditions deduced from the aforementioned models weresignificantly different. Overall, the PC-SAFT EOS predicted higher IBU solubility in both polymers and, thus, a lower recrystallization tendency when compared to the empirical analytical approach. At higherIBU concentrations, liquid-liquid demixing inIBU-polymer systems was predicted by the PC-SAFT EOS, which was in qualitative disagreement with experimental observation.
