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

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 r...

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Autores principales: Tsanaktsidou, Eleni, Karavasili, Christina, Zacharis, Constantinos K., Fatouros, Dimitrios G., Markopoulou, Catherine K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7144563/
https://www.ncbi.nlm.nih.gov/pubmed/32197506
http://dx.doi.org/10.3390/molecules25061387
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author Tsanaktsidou, Eleni
Karavasili, Christina
Zacharis, Constantinos K.
Fatouros, Dimitrios G.
Markopoulou, Catherine K.
author_facet Tsanaktsidou, Eleni
Karavasili, Christina
Zacharis, Constantinos K.
Fatouros, Dimitrios G.
Markopoulou, Catherine K.
author_sort Tsanaktsidou, Eleni
collection PubMed
description 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 (P(app)) of 33 steroids with their properties (physicochemical and structural). The P(app) 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 (R(2)Y = 0.902, RMSEE = 0.00265379, Q(2)Y = 0.722, RMSEP = 0.0077). Based on the results specific properties (logS, logP, logD, PSA and VD(ss)) were proved to be more important than others in terms of drugs P(app). 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.
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spelling pubmed-71445632020-04-15 Partial Least Square Model (PLS) as a Tool to Predict the Diffusion of Steroids Across Artificial Membranes Tsanaktsidou, Eleni Karavasili, Christina Zacharis, Constantinos K. Fatouros, Dimitrios G. Markopoulou, Catherine K. Molecules Article 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 (P(app)) of 33 steroids with their properties (physicochemical and structural). The P(app) 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 (R(2)Y = 0.902, RMSEE = 0.00265379, Q(2)Y = 0.722, RMSEP = 0.0077). Based on the results specific properties (logS, logP, logD, PSA and VD(ss)) were proved to be more important than others in terms of drugs P(app). 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. MDPI 2020-03-18 /pmc/articles/PMC7144563/ /pubmed/32197506 http://dx.doi.org/10.3390/molecules25061387 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tsanaktsidou, Eleni
Karavasili, Christina
Zacharis, Constantinos K.
Fatouros, Dimitrios G.
Markopoulou, Catherine K.
Partial Least Square Model (PLS) as a Tool to Predict the Diffusion of Steroids Across Artificial Membranes
title Partial Least Square Model (PLS) as a Tool to Predict the Diffusion of Steroids Across Artificial Membranes
title_full Partial Least Square Model (PLS) as a Tool to Predict the Diffusion of Steroids Across Artificial Membranes
title_fullStr Partial Least Square Model (PLS) as a Tool to Predict the Diffusion of Steroids Across Artificial Membranes
title_full_unstemmed Partial Least Square Model (PLS) as a Tool to Predict the Diffusion of Steroids Across Artificial Membranes
title_short Partial Least Square Model (PLS) as a Tool to Predict the Diffusion of Steroids Across Artificial Membranes
title_sort partial least square model (pls) as a tool to predict the diffusion of steroids across artificial membranes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7144563/
https://www.ncbi.nlm.nih.gov/pubmed/32197506
http://dx.doi.org/10.3390/molecules25061387
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