Investigating Various Parametrization Strategies for Pharmaceuticals within the PC-SAFT Equation of State

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F20%3A43921048" target="_blank" >RIV/60461373:22340/20:43921048 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1021/acs.jced.0c00707" target="_blank" >https://doi.org/10.1021/acs.jced.0c00707</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1021/acs.jced.0c00707" target="_blank" >10.1021/acs.jced.0c00707</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Investigating Various Parametrization Strategies for Pharmaceuticals within the PC-SAFT Equation of State

Popis výsledku v původním jazyce

Computational modeling is of great importance in solvent selection for new active pharmaceutical ingredients (APIs), with the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) equation of state being among the most popular tools for modeling the API solubility. The PC-SAFT parameters for APIs are traditionally fitted to experimental solubility data, leaving the PC-SAFT performance for other thermodynamic properties of pure APIs and API-solvent mixtures unknown. Therefore, the intention of this study was to investigate the PC-SAFT performance for the solubility as well as pure component properties (liquid density and vapor pressure) of five model APIs: paracetamol, ibuprofen, naproxen, indomethacin, and dibenzofuran. To this end, five different parametrization strategies were defined, the corresponding new parameter sets were identified (using the simulated annealing technique), and their impact on the PC-SAFT performance was evaluated. These strategies differed mainly in the combination of properties included in the parameter regression. The results showed that the API parameters fitted only to solubility data provided a very poor estimation of the pure API properties, whereas those fitted to the liquid density and vapor pressure provided not only an accurate description of such properties but, in many cases, solubility predictions comparable to those obtained using parameters based merely on the solubility. It was also revealed that the inclusion of the vapor pressure in addition to solubility improved the solubility prediction for API-solvent systems not included in the parameter regression. Moreover, the effect of explicitly accounting for the API dipole moment in the PC-SAFT framework was examined. Copyright © 2020 American Chemical Society.

Název v anglickém jazyce

Investigating Various Parametrization Strategies for Pharmaceuticals within the PC-SAFT Equation of State

Popis výsledku anglicky

Computational modeling is of great importance in solvent selection for new active pharmaceutical ingredients (APIs), with the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) equation of state being among the most popular tools for modeling the API solubility. The PC-SAFT parameters for APIs are traditionally fitted to experimental solubility data, leaving the PC-SAFT performance for other thermodynamic properties of pure APIs and API-solvent mixtures unknown. Therefore, the intention of this study was to investigate the PC-SAFT performance for the solubility as well as pure component properties (liquid density and vapor pressure) of five model APIs: paracetamol, ibuprofen, naproxen, indomethacin, and dibenzofuran. To this end, five different parametrization strategies were defined, the corresponding new parameter sets were identified (using the simulated annealing technique), and their impact on the PC-SAFT performance was evaluated. These strategies differed mainly in the combination of properties included in the parameter regression. The results showed that the API parameters fitted only to solubility data provided a very poor estimation of the pure API properties, whereas those fitted to the liquid density and vapor pressure provided not only an accurate description of such properties but, in many cases, solubility predictions comparable to those obtained using parameters based merely on the solubility. It was also revealed that the inclusion of the vapor pressure in addition to solubility improved the solubility prediction for API-solvent systems not included in the parameter regression. Moreover, the effect of explicitly accounting for the API dipole moment in the PC-SAFT framework was examined. Copyright © 2020 American Chemical Society.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10403 - Physical chemistry

Projekt

<a href="/cs/project/GA19-02889S" target="_blank" >GA19-02889S: Stabilita amorfních pevných disperzí: Predikce pomocí stavových rovnic SAFT a jejich experimentální ověření</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Chemical Engineering Data

ISSN

0021-9568

e-ISSN

—

Svazek periodika

65

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

5753-5767

Kód UT WoS článku

000599530800013

EID výsledku v databázi Scopus

2-s2.0-85094601378