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Dobričić V, Marodi M, Marković B, Tomašič T, Durcik M, Zidar N, Mašič LP, Ilaš J, Kikelj D, Čudina O. Estimation of passive gastrointestinal absorption of new dual DNA gyrase and topoisomerase IV inhibitors using PAMPA and biopartitioning micellar chromatography and quantitative structure-retention relationship analysis. J Chromatogr B Analyt Technol Biomed Life Sci 2024; 1240:124158. [PMID: 38776787 DOI: 10.1016/j.jchromb.2024.124158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 04/09/2024] [Accepted: 05/16/2024] [Indexed: 05/25/2024]
Abstract
DNA gyrase and topoisomerase IV play significant role in maintaining the correct structure of DNA during replication and they have been identified as validated targets in antibacterial drug discovery. Inadequate pharmacokinetic properties are responsible for many failures during drug discovery and their estimation in the early phase of this process maximizes the chance of getting useful drug candidates. Passive gastrointestinal absorption of a selected group of thirteen dual DNA gyrase and topoisomerase IV inhibitors was estimated using two in vitro tests - parallel artificial membrane permeability assay (PAMPA) and biopartitioning micellar chromatography (BMC). Due to good correlation between obtained results, passive gastrointestinal absorption of remaining ten compounds was estimated using only BMC. With this experimental setup, it was possible to identify compounds with high values of retention factors (k) and highest expected passive gastrointestinal absorption, and compounds with low values of k for which low passive gastrointestinal absorption is predicted. Quantitative structure-retention relationship (QSRR) modelling was performed by creating multiple linear regression (MLR), partial least squares (PLS) and support vector machines (SVM) models. Descriptors with the highest influence on retention factor were identified and their interpretation can be used for the design of new compounds with improved passive gastrointestinal absorption.
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Affiliation(s)
- Vladimir Dobričić
- Department of Pharmaceutical Chemistry, University of Belgrade - Faculty of Pharmacy, Vojvode Stepe 450, 11000 Belgrade, Serbia.
| | - Marko Marodi
- Department of Pharmaceutical Chemistry, University of Belgrade - Faculty of Pharmacy, Vojvode Stepe 450, 11000 Belgrade, Serbia
| | - Bojan Marković
- Department of Pharmaceutical Chemistry, University of Belgrade - Faculty of Pharmacy, Vojvode Stepe 450, 11000 Belgrade, Serbia
| | - Tihomir Tomašič
- University of Ljubljana, Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Aškerčeva cesta 7, 1000 Ljubljana, Slovenia
| | - Martina Durcik
- University of Ljubljana, Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Aškerčeva cesta 7, 1000 Ljubljana, Slovenia
| | - Nace Zidar
- University of Ljubljana, Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Aškerčeva cesta 7, 1000 Ljubljana, Slovenia
| | - Lucija Peterlin Mašič
- University of Ljubljana, Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Aškerčeva cesta 7, 1000 Ljubljana, Slovenia
| | - Janez Ilaš
- University of Ljubljana, Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Aškerčeva cesta 7, 1000 Ljubljana, Slovenia
| | - Danijel Kikelj
- University of Ljubljana, Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Aškerčeva cesta 7, 1000 Ljubljana, Slovenia
| | - Olivera Čudina
- Department of Pharmaceutical Chemistry, University of Belgrade - Faculty of Pharmacy, Vojvode Stepe 450, 11000 Belgrade, Serbia
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Advanced Analytical Tools for the Estimation of Gut Permeability of Compounds of Pharmaceutical Interest. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031326] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The present study aims at developing a quantitative structure–activity relationship (QSAR) model for the determination of gut permeability of 228 pharmacological drugs at different pH conditions (3, 5, 7.4, 9, intrinsic). As a consequence, five different datasets (according to the diverse permeability shown by the compounds at the different pH values) were handled, with the aim of discriminating compounds as low-permeable or high-permeable. In order to achieve this goal, molecular descriptors for all the investigated compounds were computed and then classification models calculated by means of partial least squares discriminant analysis (PLS-DA). A high predictive capability was achieved for all models, providing correct classification rates in external validation between 80% and 96%. In order to test whether a reduction in the molecular descriptors would improve predictions and provide information about the most relevant variables, a feature selection approach, covariance selection, was used to select the most relevant subsets of predictors. This led to a slight improvement in the predictive accuracies, and it has indicated that the most relevant descriptors for the discrimination of the investigated compounds into low- and high-permeable were associated with the 2D and 3D structures.
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Zhu T, Chen W, Singh RP, Cui Y. Versatile in silico modeling of partition coefficients of organic compounds in polydimethylsiloxane using linear and nonlinear methods. JOURNAL OF HAZARDOUS MATERIALS 2020; 399:123012. [PMID: 32544766 DOI: 10.1016/j.jhazmat.2020.123012] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 05/15/2020] [Accepted: 05/20/2020] [Indexed: 06/11/2023]
Abstract
Environmental fate, behavior and effects of hazardous organic compounds have recently received great attention in diverse environmental phases, including water, atmosphere, soil and sediment. Considering polydimethylsiloxane (PDMS) fibers were validated for the wide application in the determination of partition behavior in passive sampling, in this work, several in silico models were established to predict PDMS-water (KPDMS-w), PDMS-air (KPDMS-a) and PDMS-seawater partition coefficients (KPDMS-sw) of diverse chemicals. This is an attempt to combine conventional linear method and popular nonlinear algorithm for the estimation of partition coefficients between PDMS and different environmental media. All of the developed models showed satisfactory goodness-of-fit with high adjusted correlation coefficient (R2adj) and were validated to be robust, stable and predictable by various internal and external validation techniques, deriving a wide series of statistical checks. Moreover, it was found that hydrophobicity, polarizability, charge distribution and molecular size of compounds contributed significantly to the model development by interpreting the selected descriptors. Based on the broad applicability domains (ADs), the current study provides suitable tools to fill the experimental data gap for other compounds and to help researchers better understand the mechanistic basis of adsorption behavior of PDMS.
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Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China.
| | - Wenxuan Chen
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | | | - Yanran Cui
- Institute for Integrated Catalysis, Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99354, United States
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Tubić B, Dobričić V, Poljarević J, Savić A, Sabo T, Marković B. Estimation of passive gastrointestinal absorption and membrane retention using PAMPA test, quantitative structure-permeability and quantitative structure-retention relationship analyses of ethylenediamine-N,N'-di-2-(3-cyclohexyl)propanoic acid and 1,3-propanediamine-N,N'-di-2-(3-cyclohexyl)propanoic acid derivatives. J Pharm Biomed Anal 2020; 184:113213. [PMID: 32126457 DOI: 10.1016/j.jpba.2020.113213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 02/23/2020] [Accepted: 02/24/2020] [Indexed: 11/30/2022]
Abstract
Passive gastrointestinal absorption and membrane retention of twelve esters of (S,S)-ethylenediamine-N,N'-di-2-(3-cyclohexyl)propanoic acid (EDCP) and (S,S)-1,3-propanediamine-N,N'-di-2-(3-cyclohexyl)propanoic acid (PDCP), as well as of these two non-esterified acids were estimated using PAMPA test. Artificial PAMPA membrane used in this study for the simulation of gastrointestinal barrier was solution of egg lecithin in dodecane (1 % w/v). All tested compounds belong to class III (high membrane retention and low permeation), whereas EDCP, dipentyl ester of PDCP (DPE-PDCP) and diisopentyl ester of PDCP (DIPE-PDCP) belong to class I (negligible membrane retention and low permeation). Finally, quantitative structure - permeability and structure - retention relationships models were created in order to find quantitative relationships between physico-chemical properties of tested compounds and PAMPA membrane permeability/membrane retention parameters. Statistically the most reliable models were analysed and used for the design of new compounds for which favourable membrane permeability and retention can be expected.
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Affiliation(s)
- Biljana Tubić
- Department of Pharmaceutical Chemistry, University of Belgrade-Faculty of Pharmacy, Vojvode Stepe 450, 11000 Belgrade, Serbia
| | - Vladimir Dobričić
- Department of Pharmaceutical Chemistry, University of Belgrade-Faculty of Pharmacy, Vojvode Stepe 450, 11000 Belgrade, Serbia.
| | - Jelena Poljarević
- Department of General and Inorganic Chemistry, University of Belgrade - Faculty of Chemistry, Studentski trg 12-16, 11158 Belgrade, Serbia
| | - Aleksandar Savić
- Department of General and Inorganic Chemistry, University of Belgrade - Faculty of Chemistry, Studentski trg 12-16, 11158 Belgrade, Serbia
| | - Tibor Sabo
- Department of General and Inorganic Chemistry, University of Belgrade - Faculty of Chemistry, Studentski trg 12-16, 11158 Belgrade, Serbia
| | - Bojan Marković
- Department of Pharmaceutical Chemistry, University of Belgrade-Faculty of Pharmacy, Vojvode Stepe 450, 11000 Belgrade, Serbia
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In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach. Int J Mol Sci 2019; 20:ijms20133170. [PMID: 31261723 PMCID: PMC6651837 DOI: 10.3390/ijms20133170] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 06/12/2019] [Accepted: 06/26/2019] [Indexed: 12/15/2022] Open
Abstract
Oral administration is the preferred and predominant route of choice for medication. As such, drug absorption is one of critical drug metabolism and pharmacokinetics (DM/PK) parameters that should be taken into consideration in the process of drug discovery and development. The cell-free in vitro parallel artificial membrane permeability assay (PAMPA) has been adopted as the primary screening to assess the passive diffusion of compounds in the practical applications. A classical quantitative structure–activity relationship (QSAR) model and a machine learning (ML)-based QSAR model were derived using the partial least square (PLS) scheme and hierarchical support vector regression (HSVR) scheme to elucidate the underlying passive diffusion mechanism and to predict the PAMPA effective permeability, respectively, in this study. It was observed that HSVR executed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical assessments. When applied to the mock test, which was designated to mimic real challenges, HSVR also showed better predictive performance. PLS, conversely, cannot cover some mechanistically interpretable relationships between descriptors and permeability. Accordingly, the synergy of predictive HSVR and interpretable PLS models can be greatly useful in facilitating drug discovery and development by predicting passive diffusion.
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Diukendjieva A, Tsakovska I, Alov P, Pencheva T, Pajeva I, Worth AP, Madden JC, Cronin MT. Advances in the prediction of gastrointestinal absorption: Quantitative Structure-Activity Relationship (QSAR) modelling of PAMPA permeability. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.comtox.2018.12.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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pH-permeability profiles for drug substances: Experimental detection, comparison with human intestinal absorption and modelling. Eur J Pharm Sci 2018; 123:429-440. [PMID: 30100533 DOI: 10.1016/j.ejps.2018.07.014] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Revised: 06/19/2018] [Accepted: 07/04/2018] [Indexed: 01/05/2023]
Abstract
The influence of pH on human intestinal absorption is frequently not considered in early drug discovery studies in the modelling and subsequent prediction of intestinal absorption for drug candidates. To bridge this gap, in this study, experimental membrane permeability data were measured for current and former drug substances with a parallel artificial membrane permeability assay (PAMPA) at different pH values (3, 5, 7.4 and 9). The presented data are in good agreement with human intestinal absorption, showing a clear influence of pH on the efficiency of intestinal absorption. For the measured data, simple and general quantitative structure-activity relationships (QSARs) were developed for each pH that makes it possible to predict the pH profiles for passive membrane permeability (i.e., a pH-permeability profile), and these predictions coincide well with the experimental data. QSARs are also proposed for the data series of highest and intrinsic membrane permeability. The molecular descriptors in the models were analysed and mechanistically related to the interaction pattern of permeability in membranes. In addition to the regression models, classification models are also proposed. All models were successfully validated and blind tested with external data. The models are available in the QsarDB repository (http://dx.doi.org/10.15152/QDB.203).
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