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

Adam Pacławski, Jakub Szlęk, Aleksander Mendyk

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.

Keywords


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

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References


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

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