Partial Least Square Model (PLS) as a Tool to Predict the Diffusion of Steroids Across Artificial Membranes

Abstract

One of the most challenging goals in modern pharmaceutical research is to develop models that can predict drugs’ behavior, particularly permeability in human tissues. Since the permeability is closely related to the molecular properties, numerous characteristics are necessary in order to develop a reliable predictive tool. The present study attempts to decode the permeability by correlating the apparent permeability coefficient (Papp) of 33 steroids with their properties (physicochemical and structural). The Papp of the molecules was determined by in vitro experiments and the results were plotted as Y variable on a Partial Least Squares (PLS) model, while 37 pharmacokinetic and structural properties were used as X descriptors. The developed model was subjected to internal validation and it tends to be robust with good predictive potential (R2Y = 0.902, RMSEE = 0.00265379, Q2Y = 0.722, RMSEP = 0.0077). Based on the results specific properties (logS, logP, logD, PSA and VDss) were proved to be more important than others in terms of drugs Papp. The models can be utilized to predict the permeability of a new candidate drug avoiding needless animal experiments, as well as time and material consuming experiments.

Quantitative Structure–Activity Relationship Modeling of Pea Protein-Derived Acetylcholinesterase and Butyrylcholinesterase Inhibitory Peptides

“…Analysis of the Data Set. PLS regression was used to analyze the linear correlation of numerous observations and multiple X variables with Y variables, 27 and this was carried out using SIMCA software, version 17.0.2 (Sartorius Lab Instruments, Goettingen, Germany). Individual amino acids were characterized using the 5z scores, namely, z 1 , z 2 , z 3 , z 4 , and z 5 .…”

Machine Learning: A Suitable Method for Biocatalysis

“…PCA: bacterial cell behaviour in the presence of organic solvents [104], bioreactor monitoring [105,106], protein sequence clusters [107], enzyme screening [108], mode of action of antibiotics and discovery of new bioactive compounds [109] and analysis of cereals [110] MLR: prediction of secondary protein structure [111], screening of protease inhibitors [112]; LR: effect of active metabolites in a population [113], effect of linear transformation on the input features, as achieved via placing an amino acid at each position or the presence or absence of a mutation [93]; effects of blocks of sequence in a library of chimeric proteins made through recombination [94]. PLS: monitoring [114] and control of bioreactors [115]; development of a biosensor device for analysis of binary mixtures of phenols [116]; and prediction of steroid diffusion across artificial membranes [117].…”

The Effect of Logistical-Crossfunctional Drivers on the Competitive Strategy of the Supply Chain of SMEs: A Case Study

“…In the major hypothesis, the model of the influence of logistical-crossfunctional drivers on the competitive strategy of SMEs' supply chain in East Java is good and relevant. The criteria for a good prediction model use the coefficient of determination (R 2 ), and the criteria for the relevant prediction model use the Q 2 value [29] [30] [31].…”