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Pires CL, Moreno MJ. Improving the Accuracy of Permeability Data to Gain Predictive Power: Assessing Sources of Variability in Assays Using Cell Monolayers. MEMBRANES 2024; 14:157. [PMID: 39057665 PMCID: PMC11278619 DOI: 10.3390/membranes14070157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 07/05/2024] [Accepted: 07/11/2024] [Indexed: 07/28/2024]
Abstract
The ability to predict the rate of permeation of new compounds across biological membranes is of high importance for their success as drugs, as it determines their efficacy, pharmacokinetics, and safety profile. In vitro permeability assays using Caco-2 monolayers are commonly employed to assess permeability across the intestinal epithelium, with an extensive number of apparent permeability coefficient (Papp) values available in the literature and a significant fraction collected in databases. The compilation of these Papp values for large datasets allows for the application of artificial intelligence tools for establishing quantitative structure-permeability relationships (QSPRs) to predict the permeability of new compounds from their structural properties. One of the main challenges that hinders the development of accurate predictions is the existence of multiple Papp values for the same compound, mostly caused by differences in the experimental protocols employed. This review addresses the magnitude of the variability within and between laboratories to interpret its impact on QSPR modelling, systematically and quantitatively assessing the most common sources of variability. This review emphasizes the importance of compiling consistent Papp data and suggests strategies that may be used to obtain such data, contributing to the establishment of robust QSPRs with enhanced predictive power.
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Affiliation(s)
- Cristiana L. Pires
- Coimbra Chemistry Center—Institute of Molecular Sciences (CQC-IMS), University of Coimbra, 3004-535 Coimbra, Portugal
- Chemistry Department, Faculty of Science and Technology, University of Coimbra, 3004-535 Coimbra, Portugal
| | - Maria João Moreno
- Coimbra Chemistry Center—Institute of Molecular Sciences (CQC-IMS), University of Coimbra, 3004-535 Coimbra, Portugal
- Chemistry Department, Faculty of Science and Technology, University of Coimbra, 3004-535 Coimbra, Portugal
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2
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O' Donovan DH, De Fusco C, Kuhnke L, Reichel A. Trends in Molecular Properties, Bioavailability, and Permeability across the Bayer Compound Collection. J Med Chem 2023; 66:2347-2360. [PMID: 36752336 DOI: 10.1021/acs.jmedchem.2c01577] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
For oral drugs, medicinal chemists aim to design compounds with high oral bioavailability, of which permeability is a key determinant. Taking advantage of >2000 compounds tested in rat bioavailability studies and >20,000 compounds tested in Caco2 assays at Bayer, we have examined the molecular properties governing bioavailability and permeability. In addition to classical parameters such as logD and molecular weight, we also investigated the relationship between calculated pKa and permeability. We find that neutral compounds retain permeability up to a molecular weight limit of 700, while stronger acids and bases are restricted to weights of 400-500. We also investigate trends for common properties such as hydrogen bond donors and acceptors, polar surface area, aromatic ring count, and rotatable bonds, including compounds which exceed Lipinski's rule of five (Ro5). These property-structure relationships are combined to provide design guidelines for bioavailable drugs in both traditional and "beyond rule of 5" (bRo5) chemical space.
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Affiliation(s)
| | | | - Lara Kuhnke
- Drug Discovery Sciences, Bayer AG, 13342 Berlin, Germany
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3
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Gousiadou C, Doganis P, Sarimveis H. Development of artificial neural network models to predict the PAMPA effective permeability of new, orally administered drugs active against the coronavirus SARS-CoV-2. NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2023; 12:16. [PMID: 36778642 PMCID: PMC9901841 DOI: 10.1007/s13721-023-00410-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 01/05/2023] [Accepted: 01/06/2023] [Indexed: 02/08/2023]
Abstract
Responding to the pandemic caused by SARS-CoV-2, the scientific community intensified efforts to provide drugs effective against the virus. To strengthen these efforts, the "COVID Moonshot" project has been accepting public suggestions for computationally triaged, synthesized, and tested molecules. The project aimed to identify molecules of low molecular weight with activity against the virus, for oral treatment. The ability of a drug to cross the intestinal cell membranes and enter circulation decisively influences its bioavailability, and hence the need to optimize permeability in the early stages of drug discovery. In our present work, as a contribution to the ongoing scientific efforts, we employed artificial neural network algorithms to develop QSAR tools for modelling the PAMPA effective permeability (passive diffusion) of orally administered drugs. We identified a set of 61 features most relevant in explaining drug cell permeability and used them to develop a stacked regression ensemble model, subsequently used to predict the permeability of molecules included in datasets made available through the COVID Moonshot project. Our model was shown to be robust and may provide a promising framework for predicting the potential permeability of molecules not yet synthesized, thus guiding the process of drug design. Supplementary Information The online version contains supplementary material available at 10.1007/s13721-023-00410-9.
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Affiliation(s)
- Chrysoula Gousiadou
- School of Chemical Engineering, National Technical University of Athens, Heroon Polytechneiou 9, 15780 Zografou, Athens, Greece
| | - Philip Doganis
- School of Chemical Engineering, National Technical University of Athens, Heroon Polytechneiou 9, 15780 Zografou, Athens, Greece
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, Heroon Polytechneiou 9, 15780 Zografou, Athens, Greece
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4
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Physicochemical Profile of Antiandrogen Drug Bicalutamide: Solubility, Distribution, Permeability. Pharmaceutics 2022; 14:pharmaceutics14030674. [PMID: 35336047 PMCID: PMC8954523 DOI: 10.3390/pharmaceutics14030674] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/14/2022] [Accepted: 03/16/2022] [Indexed: 02/04/2023] Open
Abstract
The pharmacologically relevant physicochemical properties of the antiandrogen drug bicalutamide (BCL) have been determined for the first time. Solubility in aqueous solution, 1-octanol, n-hexane, and ethanol was measured by the shake flask method in the temperature range of 293.15−313.15 K. The compound was shown to be poorly soluble in aqueous medium and n-hexane; at the same time, an essentially higher solubility in the alcohols was revealed. The following order of molar solubility was determined: ethanol > 1-octanol > water ≈ n-hexane. The solubility was correlated with the Van’t Hoff and Apelblat equations. Evaluation of the Hansen solubility parameters and the atomic group contribution approach of Hoftyzer and Van Krevelen demonstrated consistency with the experimental data and good potential adsorption of bicalutamide. The temperature dependences of the distribution coefficients in the 1-octanol/water and n-hexane/water two-phase systems were measured and discussed in the framework of the thermodynamic approach. The ∆logD parameter determined from the distribution experiment clearly demonstrated the preference of the lipophilic delivery pathways for the compound in the biological media. The overall thermodynamic analysis based on the solubility and distribution results of the present study and the sublimation characteristics published previously has been performed. To this end, the thermodynamic parameters of the dissolution, solvation, and transfer processes were calculated and discussed in view of the solute-solvent interactions. The permeation rate of BCL through the PermeaPad barrier was measured and compared with PAMPA permeability.
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5
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Williams J, Siramshetty V, Nguyễn ÐT, Padilha EC, Kabir M, Yu KR, Wang AQ, Zhao T, Itkin M, Shinn P, Mathé EA, Xu X, Shah P. Using in vitro ADME data for lead compound selection: An emphasis on PAMPA pH 5 permeability and oral bioavailability. Bioorg Med Chem 2022; 56:116588. [PMID: 35030421 PMCID: PMC9843724 DOI: 10.1016/j.bmc.2021.116588] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 12/13/2021] [Accepted: 12/19/2021] [Indexed: 01/19/2023]
Abstract
Membrane permeability plays an important role in oral drug absorption. Caco-2 and Madin-Darby Canine Kidney (MDCK) cell culture systems have been widely used for assessing intestinal permeability. Since most drugs are absorbed passively, Parallel Artificial Membrane Permeability Assay (PAMPA) has gained popularity as a low-cost and high-throughput method in early drug discovery when compared to high-cost, labor intensive cell-based assays. At the National Center for Advancing Translational Sciences (NCATS), PAMPA pH 5 is employed as one of the Tier I absorption, distribution, metabolism, and elimination (ADME) assays. In this study, we have developed a quantitative structure activity relationship (QSAR) model using our ∼6500 compound PAMPA pH 5 permeability dataset. Along with ensemble decision tree-based methods such as Random Forest and eXtreme Gradient Boosting, we employed deep neural network and a graph convolutional neural network to model PAMPA pH 5 permeability. The classification models trained on a balanced training set provided accuracies ranging from 71% to 78% on the external set. Of the four classifiers, the graph convolutional neural network that directly operates on molecular graphs offered the best classification performance. Additionally, an ∼85% correlation was obtained between PAMPA pH 5 permeability and in vivo oral bioavailability in mice and rats. These results suggest that data from this assay (experimental or predicted) can be used to rank-order compounds for preclinical in vivo testing with a high degree of confidence, reducing cost and attrition as well as accelerating the drug discovery process. Additionally, experimental data for 486 compounds (PubChem AID: 1645871) and the best models have been made publicly available (https://opendata.ncats.nih.gov/adme/).
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Affiliation(s)
- Jordan Williams
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Vishal Siramshetty
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Ðắc-Trung Nguyễn
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Elias Carvalho Padilha
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Md Kabir
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States,The Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029, United States
| | - Kyeong-Ri Yu
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States,Department of Surgery, Virginia Commonwealth University Health Systems, 1200 E Broad St, Richmond, Virginia 23298, United States
| | - Amy Q. Wang
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Tongan Zhao
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Misha Itkin
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Paul Shinn
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Ewy A. Mathé
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Xin Xu
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Pranav Shah
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States,Corresponding Author: Pranav Shah,
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6
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Siramshetty V, Williams J, Nguyễn ÐT, Neyra J, Southall N, Mathé E, Xu X, Shah P. Validating ADME QSAR Models Using Marketed Drugs. SLAS DISCOVERY 2021; 26:1326-1336. [PMID: 34176369 DOI: 10.1177/24725552211017520] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Problems with drug ADME are responsible for many clinical failures. By understanding the ADME properties of marketed drugs and modeling how chemical structure contributes to these inherent properties, we can help new projects reduce their risk profiles. Kinetic aqueous solubility, the parallel artificial membrane permeability assay (PAMPA), and rat liver microsomal stability constitute the Tier I ADME assays at the National Center for Advancing Translational Sciences (NCATS). Using recent data generated from in-house lead optimization Tier I studies, we update quantitative structure-activity relationship (QSAR) models for these three endpoints and validate in silico performance against a set of marketed drugs (balanced accuracies range between 71% and 85%). Improved models and experimental datasets are of direct relevance to drug discovery projects and, together with the prediction services that have been made publicly available at the ADME@NCATS web portal (https://opendata.ncats.nih.gov/adme/), provide important tools for the drug discovery community. The results are discussed in light of our previously reported ADME models and state-of-the-art models from scientific literature.Graphical Abstract[Figure: see text].
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Affiliation(s)
- Vishal Siramshetty
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), Rockville, MD, USA
| | - Jordan Williams
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), Rockville, MD, USA
| | - Ðắc-Trung Nguyễn
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), Rockville, MD, USA
| | - Jorge Neyra
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), Rockville, MD, USA
| | - Noel Southall
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), Rockville, MD, USA
| | - Ewy Mathé
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), Rockville, MD, USA
| | - Xin Xu
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), Rockville, MD, USA
| | - Pranav Shah
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), Rockville, MD, USA
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7
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Rice M, Wong B, Oja M, Samuels K, Williams AK, Fong J, Sapse AM, Maran U, Korobkova EA. A role of flavonoids in cytochrome c-cardiolipin interactions. Bioorg Med Chem 2021; 33:116043. [PMID: 33530021 DOI: 10.1016/j.bmc.2021.116043] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 01/13/2021] [Accepted: 01/20/2021] [Indexed: 11/26/2022]
Abstract
The processes preceding the detachment of cytochrome c (cyt c) from the inner mitochondrial membrane in intrinsic apoptosis involve peroxidation of cardiolipin (CL) catalyzed by cyt c-CL complex. In the present work, we studied the effect of 17 dietary flavonoids on the peroxidase activity of cyt c bound to liposomes. Specifically, we explored the relationship between peroxidase activity and flavonoids' (1) potential to modulate cyt c unfolding, (2) effect on the oxidation state of heme iron, (3) membrane permeability, (4) membrane binding energy, and (5) structure. The measurements revealed that flavones, flavonols, and flavanols were the strongest, while isoflavones were the weakest inhibitors of the oxidation. Flavonoids' peroxidase inhibition activity correlated positively with their potential to suppress Trp-59 fluorescence in cyt c as well as the number of OH groups. Hydrophilic flavonoids, such as catechin, having the lowest membrane permeability and the strongest binding with phosphocholine (PC) based on the quantum chemical calculations exhibited the strongest inhibition of Amplex Red (AR) peroxidation, suggesting a membrane-protective function of flavonoids at the surface. The results of the present research specify basic principles for the design of molecules that will control the catalytic oxidation of lipids in mitochondrial membranes. These principles take into account the number of hydroxyl groups and hydrophilicity of flavonoids.
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Affiliation(s)
- Malaysha Rice
- Department of Sciences, John Jay College of Criminal Justice, City University of New York, 524 W 59th St., NY 10019, USA
| | - Bokey Wong
- Department of Sciences, John Jay College of Criminal Justice, City University of New York, 524 W 59th St., NY 10019, USA
| | - Mare Oja
- Institute of Chemistry, University of Tartu, Ravila 14A, Tartu 50411, Estonia
| | - Kelley Samuels
- Department of Sciences, John Jay College of Criminal Justice, City University of New York, 524 W 59th St., NY 10019, USA
| | - Alicia K Williams
- Department of Sciences, John Jay College of Criminal Justice, City University of New York, 524 W 59th St., NY 10019, USA
| | - Jenny Fong
- Department of Sciences, John Jay College of Criminal Justice, City University of New York, 524 W 59th St., NY 10019, USA
| | - Anne-Marie Sapse
- Department of Sciences, John Jay College of Criminal Justice, City University of New York, 524 W 59th St., NY 10019, USA; The Graduate Center at the City University of New York, 365 5th Ave, New York, NY 10016, USA
| | - Uko Maran
- Institute of Chemistry, University of Tartu, Ravila 14A, Tartu 50411, Estonia
| | - Ekaterina A Korobkova
- Department of Sciences, John Jay College of Criminal Justice, City University of New York, 524 W 59th St., NY 10019, USA.
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8
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He S, Zhiti A, Barba-Bon A, Hennig A, Nau WM. Real-Time Parallel Artificial Membrane Permeability Assay Based on Supramolecular Fluorescent Artificial Receptors. Front Chem 2020; 8:597927. [PMID: 33330387 PMCID: PMC7673371 DOI: 10.3389/fchem.2020.597927] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 10/06/2020] [Indexed: 01/17/2023] Open
Abstract
Parallel artificial membrane permeability assay (PAMPA) is a screening tool for the evaluation of drug permeability across various biological membrane systems in a microplate format. In PAMPA, a drug candidate is allowed to pass through the lipid layer of a particular well during an incubation period of, typically, 10–16 h. In a second step, the samples of each well are transferred to a UV-Vis–compatible microplate and optically measured (applicable only to analytes with sufficient absorbance) or sampled by mass-spectrometric analysis. The required incubation period, sample transfer, and detection methods jointly limit the scalability of PAMPA to high-throughput screening format. We introduce a modification of the PAMPA method that allows direct fluorescence detection, without sample transfer, in real time (RT-PAMPA). The method employs the use of a fluorescent artificial receptor (FAR), composed of a macrocycle in combination with an encapsulated fluorescent dye, administered in the acceptor chamber of conventional PAMPA microplates. Because the detection principle relies on the molecular recognition of an analyte by the receptor and the associated fluorescence response, concentration changes of any analyte that binds to the receptor can be monitored (molecules with aromatic residues in the present example), regardless of the spectroscopic properties of the analyte itself. Moreover, because the fluorescence of the (upper) acceptor well can be read out directly by fluorescence in a microplate reader, the permeation of the drug through the planar lipid layer can be monitored in real time. Compared with the traditional assay, RT-PAMPA allows not only quantification of the permeability characteristics but also rapid differentiation between fast and slow diffusion events.
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Affiliation(s)
- Suhang He
- Department of Life Sciences and Chemistry, Jacobs University Bremen, Bremen, Germany
| | - Anxhela Zhiti
- Department of Life Sciences and Chemistry, Jacobs University Bremen, Bremen, Germany
| | - Andrea Barba-Bon
- Department of Life Sciences and Chemistry, Jacobs University Bremen, Bremen, Germany
| | - Andreas Hennig
- Department of Life Sciences and Chemistry, Jacobs University Bremen, Bremen, Germany
| | - Werner M Nau
- Department of Life Sciences and Chemistry, Jacobs University Bremen, Bremen, Germany
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9
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Zukić S, Maran U. Modelling of antiproliferative activity measured in HeLa cervical cancer cells in a series of xanthene derivatives. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:905-921. [PMID: 33236957 DOI: 10.1080/1062936x.2020.1839131] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 10/15/2020] [Indexed: 06/11/2023]
Abstract
Cancer remains one of the leading causes of death in humans, and new drug substances are therefore being developed. Thus, the anti-cancer activity of xanthene derivatives has become an important topic in the development of new and potent anti-cancer drug substances. Previously published novel series of xanthen-3-one and xanthen-1,8-dione derivatives have been synthesized in one of our laboratories and showed anti-proliferative activity in HeLa cancer cell lines. This series serves as a good basis to develop quantitative structure-activity relationship (QSAR), to study the relations between anti-proliferative activity and chemical structures. A QSAR model has been derived that relies only on two-dimensional molecular descriptors, providing mechanistic insight into the anti-proliferative activity of xanthene derivatives. The model is validated internally and externally and additionally with the set of inactive compounds of the original data, confirming model applicability for the design and discovery of novel xanthene derivatives. The QSAR model is available at the QsarDB repository (http://dx.doi.10.15152/QDB.237).
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Affiliation(s)
- S Zukić
- Department of Pharmaceutical Chemistry, University of Sarajevo , Sarajevo, Bosnia and Herzegovina
| | - U Maran
- Department of Chemistry, University of Tartu , Tartu, Estonia
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10
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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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11
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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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12
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Brocke SA, Degen A, MacKerell AD, Dutagaci B, Feig M. Prediction of Membrane Permeation of Drug Molecules by Combining an Implicit Membrane Model with Machine Learning. J Chem Inf Model 2019; 59:1147-1162. [PMID: 30540459 PMCID: PMC6433486 DOI: 10.1021/acs.jcim.8b00648] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Lipid membrane permeation of drug molecules was investigated with Heterogeneous Dielectric Generalized Born (HDGB)-based models using solubility-diffusion theory and machine learning. Free energy profiles were obtained for neutral molecules by the standard HDGB and Dynamic HDGB (DHDGB) to account for the membrane deformation upon insertion of drugs. We also obtained hybrid free energy profiles where the neutralization of charged molecules was taken into account upon membrane insertion. The evaluation of the predictions was done against experimental permeability coefficients from Parallel Artificial Membrane Permeability Assays (PAMPA), and effects of partial charge sets, CGenFF, AM1-BCC, and OPLS, on the performance of the predictions were discussed. (D)HDGB-based models improved the predictions over the two-state implicit membrane models, and partial charge sets seemed to have a strong impact on the predictions. Machine learning increased the accuracy of the predictions, although it could not outperform the physics-based approach in terms of correlations.
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Affiliation(s)
- Stephanie A. Brocke
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, USA
| | - Alexandra Degen
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, USA
| | - Alexander D. MacKerell
- Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, Baltimore, Maryland 21201, USA
- University of Maryland Computer-Aided Drug Design Center, Baltimore, Maryland 21201, USA
| | - Bercem Dutagaci
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, USA
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, USA
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13
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Oja M, Sild S, Maran U. Logistic Classification Models for pH–Permeability Profile: Predicting Permeability Classes for the Biopharmaceutical Classification System. J Chem Inf Model 2019; 59:2442-2455. [DOI: 10.1021/acs.jcim.8b00833] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Mare Oja
- Institute of Chemistry, University of Tartu, Ravila 14A, Tartu 50411, Estonia
| | - Sulev Sild
- Institute of Chemistry, University of Tartu, Ravila 14A, Tartu 50411, Estonia
| | - Uko Maran
- Institute of Chemistry, University of Tartu, Ravila 14A, Tartu 50411, Estonia
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14
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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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Ermondi G, Vallaro M, Caron G. Learning how to use IAM chromatography for predicting permeability. Eur J Pharm Sci 2018; 114:385-390. [PMID: 29305983 DOI: 10.1016/j.ejps.2018.01.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Revised: 12/15/2017] [Accepted: 01/01/2018] [Indexed: 10/18/2022]
Abstract
The interest for IAM (Immobilized Artificial Membranes) chromatography in the prediction of drug permeability is increasing. Here we firstly set-up a dataset of 253 molecules including neutral and ionized drugs and few organic compounds for which we either measured or retrieved from the literature IAM.PC.DD2 log KwIAM data. Then we applied block relevance (BR) analysis to extract from PLS models the relative contribution of intermolecular forces governing log KwIAM and Δlog KwIAM (a combined descriptor calculated from log KwIAM). Finally, the relationship between log KwIAM, Δlog KwIAM and passive permeability determined in both PAMPA and MDCK-LE systems was looked for. Models provided the basis for a rational application of IAM chromatography in permeability prediction.
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Affiliation(s)
- Giuseppe Ermondi
- Molecular Biotechnology and Health Sciences Dept., Università degli Studi di Torino, via Quarello 15, 10135 Torino, Italy.
| | - Maura Vallaro
- Molecular Biotechnology and Health Sciences Dept., Università degli Studi di Torino, via Quarello 15, 10135 Torino, Italy.
| | - Giulia Caron
- Molecular Biotechnology and Health Sciences Dept., Università degli Studi di Torino, via Quarello 15, 10135 Torino, Italy.
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16
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Viira B, García-Sosa AT, Maran U. Chemical structure and correlation analysis of HIV-1 NNRT and NRT inhibitors and database-curated, published inhibition constants with chemical structure in diverse datasets. J Mol Graph Model 2017; 76:205-223. [PMID: 28738270 DOI: 10.1016/j.jmgm.2017.06.019] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Revised: 06/18/2017] [Accepted: 06/19/2017] [Indexed: 01/26/2023]
Abstract
Human immunodeficiency virus (HIV-1) reverse transcriptase is a major target for designing anti-HIV drugs. Developed inhibitors are divided into non-nucleoside analog reverse-transcriptase inhibitors (NNRTIs) and nucleoside analog reverse-transcriptase inhibitors (NRTIs) depending on their mechanism. Given that many inhibitors have been studied and for many of them binding affinity constants have been calculated, it is beneficial to analyze the chemical landscape of these families of inhibitors and correlate these inhibition constants with molecular structure descriptors. For this, the HIV-1 RT data was retrieved from the ChEMBL database, carefully curated, and original literature verified, grouped into NRTIs and NNRTIs, analyzed using a hierarchical scaffold classification method and modelled with best multi-linear regression approach. Analysis of the HIV-1 NNRTIs subset results in ten different common structural parent types of oxazepanone, piperazinone, pyrazine, oxazinanone, diazinanone, pyridine, pyrrole, diazepanone, thiazole, and triazine. The same analysis for HIV-1 NRTIs groups structures into four different parent types of uracil, pyrimide, pyrimidione, and imidazole. Each scaffold tree corresponding to the parent types has been carefully analyzed and examined, and changes in chemical structure favorable to potency and stability are highlighted. For both subsets, descriptive and predictive QSAR models are derived, discussed and externally validated, revealing general trends in relationships between molecular structure and binding affinity constants in structurally diverse datasets. Data and QSAR models are available at the QsarDB repository (http://dx.doi.org/10.15152/QDB.202).
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Affiliation(s)
- Birgit Viira
- Institute of Chemistry, University of Tartu, Tartu 50411, Estonia
| | | | - Uko Maran
- Institute of Chemistry, University of Tartu, Tartu 50411, Estonia.
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17
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In vitro prediction of gastrointestinal absorption of novel β-hydroxy-β-arylalkanoic acids using PAMPA technique. Eur J Pharm Sci 2017; 100:36-41. [DOI: 10.1016/j.ejps.2017.01.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 12/19/2016] [Accepted: 01/06/2017] [Indexed: 11/22/2022]
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18
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Oja M, Maran U. Quantitative structure-permeability relationships at various pH values for neutral and amphoteric drugs and drug-like compounds. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:813-832. [PMID: 27748631 DOI: 10.1080/1062936x.2016.1238408] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 09/15/2016] [Indexed: 06/06/2023]
Abstract
Human intestinal absorption is a key property for orally administered drugs and is dependent on pH. This study focuses on neutral and amphoteric compounds and their membrane permeabilities across the range of pH values found in the human intestine. The membrane permeability values for 15 neutral and 60 amphoteric compounds at pH 3, 5, 7.4 and 9 were measured using the parallel artificial membrane permeability assay (PAMPA). For each data series the quantitative structure-permeability relationships were developed and analysed. The results show that the membrane permeability of neutral compounds is attributed to a single structural characteristic, the hydrogen bond donor ability. Amphoteric compounds are more complex because of their chemical constitution, and therefore require three-parameter models to describe and predict membrane permeability. Analysis of the models for amphoteric compounds reveals that membrane permeability depends on multiple structural characteristics: the partition coefficient, hydrogen bond properties and the shape of the molecules. In addition to conventional validation strategies, two external compounds (isradipine and omeprazole) were tested and revealed very good agreement of pH profiles between experimental and predicted membrane permeability for all of the developed models. Selected QSAR models are available at the QsarDB repository (http://dx.doi.org/10.15152/QDB.184).
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Affiliation(s)
- Mare Oja
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Uko Maran
- Institute of Chemistry, University of Tartu, Tartu, Estonia
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