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Modeling of Hansen's solubility parameters of aripiprazole, ziprasidone, and their impurities: A nonparametric comparison of models for prediction of drug absorption sites

Authorized Users Only
2018
Authors
Obradović, Darija
Andrić, Filip
Zlatović, Mario
Agbaba, Danica
Article (Published version)
Metadata
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Abstract
Aripiprazole and ziprasidone are atypical antipsychotic drugs with the effect on positive and negative symptoms of schizophrenia, mania, and mixed states of bipolar disorder. Hansen's solubility parameters, (d), (p), and (h), which account for dispersive, polarizable, and hydrogen bonding contributions to the overall cohesive energy of a compound, are often used to assess pharmacokinetic properties of drugs. However, no data exist of solubility parameters for the drugs of interest in this study. Therefore, in the present study, partial least square regression (PLS), artificial neural networks (ANNs), regression trees (RT), boosted trees (BT), and random forests (RF) were applied to estimate Hansen's solubility parameters of ziprasidone, aripiprazole, and their impurities/metabolic derivatives, targeting their biopharmaceutical classes and absorption routes. A training set of 47 structurally diverse and pharmacologically active compounds and 290 molecular descriptors and pharmaceuticall...y important properties were used to build the prediction models. The modeling approaches were compared by the sum of ranking differences, using the consensus values as a reference for the unknowns and the experimentally determined values as a gold standard for the calibration set. In both instances, the PLS models, together with ANNs, demonstrated better performance than RT, BT and especially RF. Based on the best scored models, we were able to pinpoint the most probable absorption sites for each drug and the corresponding metabolite, i.e., the upper parts of the gastrointestinal tract, small intestine, or absorption along entire length of gastrointestinal tract.

Keywords:
artificial neural network (ANN) / drug absorption / Hansen's solubility parameters / partial least square regression (PLS) / sum of ranking difference (SRD)
Source:
Journal of Chemometrics, 2018, 32, 4
Publisher:
  • Wiley, Hoboken
Funding / projects:
  • Synthesis, Quantitative Structure and Activity Relationship, Physico-Chemical Characterisation and Analysis of Pharmacologically Active Substances (RS-172033)

DOI: 10.1002/cem.2996

ISSN: 0886-9383

WoS: 000430668000007

Scopus: 2-s2.0-85040642229
[ Google Scholar ]
4
4
URI
https://farfar.pharmacy.bg.ac.rs/handle/123456789/3036
Collections
  • Radovi istraživača / Researchers’ publications
Institution/Community
Pharmacy
TY  - JOUR
AU  - Obradović, Darija
AU  - Andrić, Filip
AU  - Zlatović, Mario
AU  - Agbaba, Danica
PY  - 2018
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3036
AB  - Aripiprazole and ziprasidone are atypical antipsychotic drugs with the effect on positive and negative symptoms of schizophrenia, mania, and mixed states of bipolar disorder. Hansen's solubility parameters, (d), (p), and (h), which account for dispersive, polarizable, and hydrogen bonding contributions to the overall cohesive energy of a compound, are often used to assess pharmacokinetic properties of drugs. However, no data exist of solubility parameters for the drugs of interest in this study. Therefore, in the present study, partial least square regression (PLS), artificial neural networks (ANNs), regression trees (RT), boosted trees (BT), and random forests (RF) were applied to estimate Hansen's solubility parameters of ziprasidone, aripiprazole, and their impurities/metabolic derivatives, targeting their biopharmaceutical classes and absorption routes. A training set of 47 structurally diverse and pharmacologically active compounds and 290 molecular descriptors and pharmaceutically important properties were used to build the prediction models. The modeling approaches were compared by the sum of ranking differences, using the consensus values as a reference for the unknowns and the experimentally determined values as a gold standard for the calibration set. In both instances, the PLS models, together with ANNs, demonstrated better performance than RT, BT and especially RF. Based on the best scored models, we were able to pinpoint the most probable absorption sites for each drug and the corresponding metabolite, i.e., the upper parts of the gastrointestinal tract, small intestine, or absorption along entire length of gastrointestinal tract.
PB  - Wiley, Hoboken
T2  - Journal of Chemometrics
T1  - Modeling of Hansen's solubility parameters of aripiprazole, ziprasidone, and their impurities: A nonparametric comparison of models for prediction of drug absorption sites
VL  - 32
IS  - 4
DO  - 10.1002/cem.2996
ER  - 
@article{
author = "Obradović, Darija and Andrić, Filip and Zlatović, Mario and Agbaba, Danica",
year = "2018",
abstract = "Aripiprazole and ziprasidone are atypical antipsychotic drugs with the effect on positive and negative symptoms of schizophrenia, mania, and mixed states of bipolar disorder. Hansen's solubility parameters, (d), (p), and (h), which account for dispersive, polarizable, and hydrogen bonding contributions to the overall cohesive energy of a compound, are often used to assess pharmacokinetic properties of drugs. However, no data exist of solubility parameters for the drugs of interest in this study. Therefore, in the present study, partial least square regression (PLS), artificial neural networks (ANNs), regression trees (RT), boosted trees (BT), and random forests (RF) were applied to estimate Hansen's solubility parameters of ziprasidone, aripiprazole, and their impurities/metabolic derivatives, targeting their biopharmaceutical classes and absorption routes. A training set of 47 structurally diverse and pharmacologically active compounds and 290 molecular descriptors and pharmaceutically important properties were used to build the prediction models. The modeling approaches were compared by the sum of ranking differences, using the consensus values as a reference for the unknowns and the experimentally determined values as a gold standard for the calibration set. In both instances, the PLS models, together with ANNs, demonstrated better performance than RT, BT and especially RF. Based on the best scored models, we were able to pinpoint the most probable absorption sites for each drug and the corresponding metabolite, i.e., the upper parts of the gastrointestinal tract, small intestine, or absorption along entire length of gastrointestinal tract.",
publisher = "Wiley, Hoboken",
journal = "Journal of Chemometrics",
title = "Modeling of Hansen's solubility parameters of aripiprazole, ziprasidone, and their impurities: A nonparametric comparison of models for prediction of drug absorption sites",
volume = "32",
number = "4",
doi = "10.1002/cem.2996"
}
Obradović, D., Andrić, F., Zlatović, M.,& Agbaba, D.. (2018). Modeling of Hansen's solubility parameters of aripiprazole, ziprasidone, and their impurities: A nonparametric comparison of models for prediction of drug absorption sites. in Journal of Chemometrics
Wiley, Hoboken., 32(4).
https://doi.org/10.1002/cem.2996
Obradović D, Andrić F, Zlatović M, Agbaba D. Modeling of Hansen's solubility parameters of aripiprazole, ziprasidone, and their impurities: A nonparametric comparison of models for prediction of drug absorption sites. in Journal of Chemometrics. 2018;32(4).
doi:10.1002/cem.2996 .
Obradović, Darija, Andrić, Filip, Zlatović, Mario, Agbaba, Danica, "Modeling of Hansen's solubility parameters of aripiprazole, ziprasidone, and their impurities: A nonparametric comparison of models for prediction of drug absorption sites" in Journal of Chemometrics, 32, no. 4 (2018),
https://doi.org/10.1002/cem.2996 . .

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