Computational intelligence for prediction of biological effects of drugs absorption in lungs

Adam Pacławski; Jakub Szlęk; Aleksander Mendyk

doi:10.7494/csci.2019.20.1.2992

Authors

Adam Pacławski Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Medyczna 9 St, 30-688 Cracow, Poland http://orcid.org/0000-0003-2801-6197
Jakub Szlęk Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Medyczna 9 St, 30-688 Cracow, Poland
Aleksander Mendyk Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Medyczna 9 St, 30-688 Cracow, Poland

DOI:

https://doi.org/10.7494/csci.2019.20.1.2992

Keywords:

empirical model, absorption enhancers, pulmonary drugs, genetic programming, symbolic regression, computational intelligence

Abstract

Lungs are recently extensively examined as a route for delivering drugs (active pharmaceutical ingredients, API) into the bloodstream mainly due to the possibility of noninvasive administration of macromolecules, such as proteins and peptides. Absorption mechanisms of chemical compounds in lungs are still not fully understood, what makes pulmonary formulation composition development challenging. The manuscript presents development of an empirical model capable of predicting the excipients’ influence on the absorption of the drug in lungs. Due to complexity of the problem and not fully understood mechanisms of absorption, computational intelligence tools were applied. As a result, a mathematical formula was established and analyzed. The normalized root-mean-squared error (NRMSE) and R2 of the model were 4.57%, and 0.83, respectively. The presented approach is beneficial both practically by developing \textit{in sillico} predictive model and theoretically by gaining knowledge about the influence of API and excipient structure on absorption in lungs.

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