Particle size prediction of copolymer-drug conjugate using partial least squares regression

Abstract

Particle size of the copolymers and the associated polydispersity are among the most important factors affecting biopharmaceutical behavior in a wide variety of therapeutic applications. Particle size provides valuable properties of particles or molecules in liquid medium. This characteristic directly affects bioavailability, dissolution and immunotoxicity. Predicting particle size will often skip many preliminary studies that are necessary to optimize formulations. In this work, the particle size of copolymer-drug conjugates was tried to be predicted using partial least squares regression (PLSR). The aim of this article is to construct a mathematical model for predicting the particle size of the copolymer-drug conjugate produced by a preferred pharmaceutical polymer. PLSR is a method that involves a combination of principal component analysis and multiple regression analysis for building predictive models when the factors are many and highly collinear. In the present study, to calculate the particle size of the copolymer-drug conjugate, we used the zeta potential and the particle size of the copolymer and drug, and different pH values as inputs. © 2018 Bulgarian Academy of Sciences, Union of Chemists in Bulgaria.