Critical Assessment of pH-Dependent Lipophilicity Profiles of Small Molecules: Which One Should We Use and In Which Cases?
Fecha
2023-10-03
Tipo
artículo original
Autores
Bertsch Aguilar, Esteban
Suñer Sánchez, Sebastián
De Souza Pinheiro, Silvana
Zamora Ramírez, William J.
Título de la revista
ISSN de la revista
Título del volumen
Editor
Resumen
Lipophilicity is a physicochemical property with wide relevance in drug design, computational biology, food, environmental and medicinal chemistry. Lipophilicity is commonly expressed as the partition coefficient for neutral molecules, whereas for molecules with ionizable groups, the distribution coefficient (D) at a given pH is used. The logDpH is usually predicted using a pH correction over the logPN using the pKa of ionizable molecules, while often ignoring the apparent ion pair partitioning (Papp). In this work, we studied the impact of Papp on the prediction IP of both the experimental lipophilicity of small molecules and experimental lipophilicity-based applications and metrics such as lipophilic efficiency (LipE), distribution of spiked drugs in milk products, and pH-dependent partition of water contaminants in synthetic passive samples such as
silicones. Our findings show that better predictions are obtained by considering the apparent ion
pair partitioning. In this context, we developed machine learning algorithms to determine the cases
that Papp should be considered. The results indicate that small, rigid, and unsaturated molecules I
with logPN close to zero, which present a significant proportion of ionic species in the aqueous phase, were better modeled using the apparent ion pair partitioning (Papp). Finally, our findings IP can serve as guidance to the scientific community working in early-stage drug design, food, and environmental chemistry.
Descripción
Palabras clave
Partition coefficient, Lipophilicity profiles, Machine learning, Chemoinformatics, Drug Design, Ion pair partitioning