Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning

Abstract Accurate specification of the drugs’ solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO2, has found great interest as a promising solution to dominate t...
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Autor*in:	Walid Kamal Abdelbasset [verfasserIn] Safaa M. Elkholi [verfasserIn] Khadiga Ahmed Ismail [verfasserIn] Sameer Alshehri [verfasserIn] Ahmed Alobaida [verfasserIn] Bader Huwaimel [verfasserIn] Ahmed D. Alatawi [verfasserIn] Amal M. Alsubaiyel [verfasserIn] Kumar Venkatesan [verfasserIn] Mohammed A. S. Abourehab [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Übergeordnetes Werk:	In: Scientific Reports - Nature Portfolio, 2011, 12(2022), 1, Seite 9
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:1 ; pages:9

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1038/s41598-022-17440-4

Katalog-ID:	DOAJ031768725

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allfields	10.1038/s41598-022-17440-4 doi (DE-627)DOAJ031768725 (DE-599)DOAJ44506293c0374859a389fbdbfb96f479 DE-627 ger DE-627 rakwb eng Walid Kamal Abdelbasset verfasserin aut Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate specification of the drugs’ solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO2, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO2SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241). Medicine R Science Q Safaa M. Elkholi verfasserin aut Khadiga Ahmed Ismail verfasserin aut Sameer Alshehri verfasserin aut Ahmed Alobaida verfasserin aut Bader Huwaimel verfasserin aut Ahmed D. Alatawi verfasserin aut Amal M. Alsubaiyel verfasserin aut Kumar Venkatesan verfasserin aut Mohammed A. S. Abourehab verfasserin aut In Scientific Reports Nature Portfolio, 2011 12(2022), 1, Seite 9 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:12 year:2022 number:1 pages:9 https://doi.org/10.1038/s41598-022-17440-4 kostenfrei https://doaj.org/article/44506293c0374859a389fbdbfb96f479 kostenfrei https://doi.org/10.1038/s41598-022-17440-4 kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 1 9
spelling	10.1038/s41598-022-17440-4 doi (DE-627)DOAJ031768725 (DE-599)DOAJ44506293c0374859a389fbdbfb96f479 DE-627 ger DE-627 rakwb eng Walid Kamal Abdelbasset verfasserin aut Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate specification of the drugs’ solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO2, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO2SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241). Medicine R Science Q Safaa M. Elkholi verfasserin aut Khadiga Ahmed Ismail verfasserin aut Sameer Alshehri verfasserin aut Ahmed Alobaida verfasserin aut Bader Huwaimel verfasserin aut Ahmed D. Alatawi verfasserin aut Amal M. Alsubaiyel verfasserin aut Kumar Venkatesan verfasserin aut Mohammed A. S. Abourehab verfasserin aut In Scientific Reports Nature Portfolio, 2011 12(2022), 1, Seite 9 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:12 year:2022 number:1 pages:9 https://doi.org/10.1038/s41598-022-17440-4 kostenfrei https://doaj.org/article/44506293c0374859a389fbdbfb96f479 kostenfrei https://doi.org/10.1038/s41598-022-17440-4 kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 1 9
allfields_unstemmed	10.1038/s41598-022-17440-4 doi (DE-627)DOAJ031768725 (DE-599)DOAJ44506293c0374859a389fbdbfb96f479 DE-627 ger DE-627 rakwb eng Walid Kamal Abdelbasset verfasserin aut Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate specification of the drugs’ solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO2, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO2SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241). Medicine R Science Q Safaa M. Elkholi verfasserin aut Khadiga Ahmed Ismail verfasserin aut Sameer Alshehri verfasserin aut Ahmed Alobaida verfasserin aut Bader Huwaimel verfasserin aut Ahmed D. Alatawi verfasserin aut Amal M. Alsubaiyel verfasserin aut Kumar Venkatesan verfasserin aut Mohammed A. S. Abourehab verfasserin aut In Scientific Reports Nature Portfolio, 2011 12(2022), 1, Seite 9 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:12 year:2022 number:1 pages:9 https://doi.org/10.1038/s41598-022-17440-4 kostenfrei https://doaj.org/article/44506293c0374859a389fbdbfb96f479 kostenfrei https://doi.org/10.1038/s41598-022-17440-4 kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 1 9
allfieldsGer	10.1038/s41598-022-17440-4 doi (DE-627)DOAJ031768725 (DE-599)DOAJ44506293c0374859a389fbdbfb96f479 DE-627 ger DE-627 rakwb eng Walid Kamal Abdelbasset verfasserin aut Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate specification of the drugs’ solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO2, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO2SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241). Medicine R Science Q Safaa M. Elkholi verfasserin aut Khadiga Ahmed Ismail verfasserin aut Sameer Alshehri verfasserin aut Ahmed Alobaida verfasserin aut Bader Huwaimel verfasserin aut Ahmed D. Alatawi verfasserin aut Amal M. Alsubaiyel verfasserin aut Kumar Venkatesan verfasserin aut Mohammed A. S. Abourehab verfasserin aut In Scientific Reports Nature Portfolio, 2011 12(2022), 1, Seite 9 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:12 year:2022 number:1 pages:9 https://doi.org/10.1038/s41598-022-17440-4 kostenfrei https://doaj.org/article/44506293c0374859a389fbdbfb96f479 kostenfrei https://doi.org/10.1038/s41598-022-17440-4 kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 1 9
allfieldsSound	10.1038/s41598-022-17440-4 doi (DE-627)DOAJ031768725 (DE-599)DOAJ44506293c0374859a389fbdbfb96f479 DE-627 ger DE-627 rakwb eng Walid Kamal Abdelbasset verfasserin aut Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate specification of the drugs’ solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO2, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO2SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241). Medicine R Science Q Safaa M. Elkholi verfasserin aut Khadiga Ahmed Ismail verfasserin aut Sameer Alshehri verfasserin aut Ahmed Alobaida verfasserin aut Bader Huwaimel verfasserin aut Ahmed D. Alatawi verfasserin aut Amal M. Alsubaiyel verfasserin aut Kumar Venkatesan verfasserin aut Mohammed A. S. Abourehab verfasserin aut In Scientific Reports Nature Portfolio, 2011 12(2022), 1, Seite 9 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:12 year:2022 number:1 pages:9 https://doi.org/10.1038/s41598-022-17440-4 kostenfrei https://doaj.org/article/44506293c0374859a389fbdbfb96f479 kostenfrei https://doi.org/10.1038/s41598-022-17440-4 kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 1 9
language	English
source	In Scientific Reports 12(2022), 1, Seite 9 volume:12 year:2022 number:1 pages:9
sourceStr	In Scientific Reports 12(2022), 1, Seite 9 volume:12 year:2022 number:1 pages:9
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authorswithroles_txt_mv	Walid Kamal Abdelbasset @@aut@@ Safaa M. Elkholi @@aut@@ Khadiga Ahmed Ismail @@aut@@ Sameer Alshehri @@aut@@ Ahmed Alobaida @@aut@@ Bader Huwaimel @@aut@@ Ahmed D. Alatawi @@aut@@ Amal M. Alsubaiyel @@aut@@ Kumar Venkatesan @@aut@@ Mohammed A. S. Abourehab @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
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author	Walid Kamal Abdelbasset
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topic_title	Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning
topic	misc Medicine misc R misc Science misc Q
topic_unstemmed	misc Medicine misc R misc Science misc Q
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title	Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning
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title_full	Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning
author_sort	Walid Kamal Abdelbasset
journal	Scientific Reports
journalStr	Scientific Reports
lang_code	eng
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author_browse	Walid Kamal Abdelbasset Safaa M. Elkholi Khadiga Ahmed Ismail Sameer Alshehri Ahmed Alobaida Bader Huwaimel Ahmed D. Alatawi Amal M. Alsubaiyel Kumar Venkatesan Mohammed A. S. Abourehab
container_volume	12
format_se	Elektronische Aufsätze
author-letter	Walid Kamal Abdelbasset
doi_str_mv	10.1038/s41598-022-17440-4
author2-role	verfasserin
title_sort	development a novel robust method to enhance the solubility of oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning
title_auth	Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning
abstract	Abstract Accurate specification of the drugs’ solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO2, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO2SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241).
abstractGer	Abstract Accurate specification of the drugs’ solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO2, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO2SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241).
abstract_unstemmed	Abstract Accurate specification of the drugs’ solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO2, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO2SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241).
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container_issue	1
title_short	Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning
url	https://doi.org/10.1038/s41598-022-17440-4 https://doaj.org/article/44506293c0374859a389fbdbfb96f479 https://doaj.org/toc/2045-2322
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author2	Safaa M. Elkholi Khadiga Ahmed Ismail Sameer Alshehri Ahmed Alobaida Bader Huwaimel Ahmed D. Alatawi Amal M. Alsubaiyel Kumar Venkatesan Mohammed A. S. Abourehab
author2Str	Safaa M. Elkholi Khadiga Ahmed Ismail Sameer Alshehri Ahmed Alobaida Bader Huwaimel Ahmed D. Alatawi Amal M. Alsubaiyel Kumar Venkatesan Mohammed A. S. Abourehab
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doi_str	10.1038/s41598-022-17440-4
up_date	2024-07-03T22:21:45.560Z
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