Log BB Prediction Models Using TLC and HPLC Retention Values as Protein Affinity Data
Background: The penetration of drugs through the blood–brain barrier is one of the key pharmacokinetic aspects of centrally acting active substances and other drugs in terms of the occurrence of side effects on the central nervous system. In our research, several regression models were constructed i...
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2024-11-01
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| author | Karolina Wanat Klaudia Michalak Elżbieta Brzezińska |
| author_facet | Karolina Wanat Klaudia Michalak Elżbieta Brzezińska |
| author_sort | Karolina Wanat |
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| description | Background: The penetration of drugs through the blood–brain barrier is one of the key pharmacokinetic aspects of centrally acting active substances and other drugs in terms of the occurrence of side effects on the central nervous system. In our research, several regression models were constructed in order to observe the connections between the active pharmaceutical ingredients’ properties and their bioavailability in the CNS, presented in the form of the log BB parameter, which refers to the drug concentration on both sides of the blood–brain barrier. Methods: Predictive models were created using the physicochemical properties of drugs, and multiple linear regression and a data mining method, i.e., MARSplines, were used to build them. Retention values from protein-affinity chromatography (TLC and HPLC) were introduced into the analyses. In both experiments, the stationary phases were modified with serum albumin, which enriched the obtained chromatographic data, and were then introduced into the models with good results. Results: The conducted analyses confirm that the variables that influence the log BB include high degree of lipophilicity, ionisation capacity and low capability of forming hydrogen bonds. However, the addition of chromatographic data improved the obtained regression results and increased the robustness of the models against an increased number of cases. The linear regression model with chromatographic parameters explains 85% of the log bb variability, whereas the MARSplines model explains 91%. <b>Conclusions:</b> Based on the obtained results, it can be concluded that the use of chromatographic data can increase the robustness of predictive regression models related to penetration through biological barriers. |
| format | Article |
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| institution | DOAJ |
| issn | 1999-4923 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Pharmaceutics |
| spelling | doaj-art-e9fd335ab739464384c456d57b63a01d2025-08-20T02:51:07ZengMDPI AGPharmaceutics1999-49232024-11-011612153410.3390/pharmaceutics16121534Log BB Prediction Models Using TLC and HPLC Retention Values as Protein Affinity DataKarolina Wanat0Klaudia Michalak1Elżbieta Brzezińska2Department of Analytical Chemistry, Faculty of Pharmacy, Medical University of Lodz, 90-419 Lodz, PolandDepartment of Analytical Chemistry, Faculty of Pharmacy, Medical University of Lodz, 90-419 Lodz, PolandDepartment of Analytical Chemistry, Faculty of Pharmacy, Medical University of Lodz, 90-419 Lodz, PolandBackground: The penetration of drugs through the blood–brain barrier is one of the key pharmacokinetic aspects of centrally acting active substances and other drugs in terms of the occurrence of side effects on the central nervous system. In our research, several regression models were constructed in order to observe the connections between the active pharmaceutical ingredients’ properties and their bioavailability in the CNS, presented in the form of the log BB parameter, which refers to the drug concentration on both sides of the blood–brain barrier. Methods: Predictive models were created using the physicochemical properties of drugs, and multiple linear regression and a data mining method, i.e., MARSplines, were used to build them. Retention values from protein-affinity chromatography (TLC and HPLC) were introduced into the analyses. In both experiments, the stationary phases were modified with serum albumin, which enriched the obtained chromatographic data, and were then introduced into the models with good results. Results: The conducted analyses confirm that the variables that influence the log BB include high degree of lipophilicity, ionisation capacity and low capability of forming hydrogen bonds. However, the addition of chromatographic data improved the obtained regression results and increased the robustness of the models against an increased number of cases. The linear regression model with chromatographic parameters explains 85% of the log bb variability, whereas the MARSplines model explains 91%. <b>Conclusions:</b> Based on the obtained results, it can be concluded that the use of chromatographic data can increase the robustness of predictive regression models related to penetration through biological barriers.https://www.mdpi.com/1999-4923/16/12/1534log BBCNS penetrationregression modelschromatographic dataaffinity chromatographyserum albumin |
| spellingShingle | Karolina Wanat Klaudia Michalak Elżbieta Brzezińska Log BB Prediction Models Using TLC and HPLC Retention Values as Protein Affinity Data Pharmaceutics log BB CNS penetration regression models chromatographic data affinity chromatography serum albumin |
| title | Log BB Prediction Models Using TLC and HPLC Retention Values as Protein Affinity Data |
| title_full | Log BB Prediction Models Using TLC and HPLC Retention Values as Protein Affinity Data |
| title_fullStr | Log BB Prediction Models Using TLC and HPLC Retention Values as Protein Affinity Data |
| title_full_unstemmed | Log BB Prediction Models Using TLC and HPLC Retention Values as Protein Affinity Data |
| title_short | Log BB Prediction Models Using TLC and HPLC Retention Values as Protein Affinity Data |
| title_sort | log bb prediction models using tlc and hplc retention values as protein affinity data |
| topic | log BB CNS penetration regression models chromatographic data affinity chromatography serum albumin |
| url | https://www.mdpi.com/1999-4923/16/12/1534 |
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