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Stępnik K, Kukula-Koch W, Boguszewska-Czubara A, Gawel K. Astragaloside IV as a Memory-Enhancing Agent: In Silico Studies with In Vivo Analysis and Post Mortem ADME-Tox Profiling in Mice. Int J Mol Sci 2024; 25:4021. [PMID: 38612831 PMCID: PMC11012721 DOI: 10.3390/ijms25074021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 03/30/2024] [Accepted: 04/02/2024] [Indexed: 04/14/2024] Open
Abstract
Many people around the world suffer from neurodegenerative diseases associated with cognitive impairment. As life expectancy increases, this number is steadily rising. Therefore, it is extremely important to search for new treatment strategies and to discover new substances with potential neuroprotective and/or cognition-enhancing effects. This study focuses on investigating the potential of astragaloside IV (AIV), a triterpenoid saponin with proven acetylcholinesterase (AChE)-inhibiting activity naturally occurring in the root of Astragalus mongholicus, to attenuate memory impairment. Scopolamine (SCOP), an antagonist of muscarinic cholinergic receptors, and lipopolysaccharide (LPS), a trigger of neuroinflammation, were used to impair memory processes in the passive avoidance (PA) test in mice. This memory impairment in SCOP-treated mice was attenuated by prior intraperitoneal (ip) administration of AIV at a dose of 25 mg/kg. The attenuation of memory impairment by LPS was not observed. It can therefore be assumed that AIV does not reverse memory impairment by anti-inflammatory mechanisms, although this needs to be further verified. All doses of AIV tested did not affect baseline locomotor activity in mice. In the post mortem analysis by mass spectrometry of the body tissue of the mice, the highest content of AIV was found in the kidneys, then in the spleen and liver, and the lowest in the brain.
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Affiliation(s)
- Katarzyna Stępnik
- Department of Physical Chemistry, Institute of Chemical Sciences, Faculty of Chemistry, Maria Curie–Skłodowska University in Lublin, Pl. M. Curie-Skłodowskiej 3, 20-031 Lublin, Poland
- Department of Pharmacognosy with Medicinal Plants Garden, Medical University of Lublin, 1 Chodzki St., 20-093 Lublin, Poland;
| | - Wirginia Kukula-Koch
- Department of Pharmacognosy with Medicinal Plants Garden, Medical University of Lublin, 1 Chodzki St., 20-093 Lublin, Poland;
| | - Anna Boguszewska-Czubara
- Department of Medical Chemistry, Medical University of Lublin, 4A Chodźki St., 20-093 Lublin, Poland;
| | - Kinga Gawel
- Department of Experimental and Clinical Pharmacology, Medical University of Lublin, 8B Jaczewskiego St., 20-090 Lublin, Poland;
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Yin Y, Yang Z, Li N, Yu X, Chen ML, Wang M, Ren XL. Least Absolute Shrinkage and Selection Operator-Based Prediction of the Binding Constant of p-Sulfonatocalix[6]/[8]arenes with Alkaloids. J Chem Inf Model 2024; 64:359-377. [PMID: 38164000 DOI: 10.1021/acs.jcim.3c01272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
p-Sulfonatocalix[n]arenes (SCnA) have demonstrated great potential for drug encapsulation through host-guest complexation to improve solubility, stability, and bioavailability. In this study, the solubilization effect of SCnA (n = 4, 6, 8) on 95 active compounds derived from traditional Chinese medicine (TCM) was investigated. Based on the significant solubilization effect on alkaloids, SC6A/SC8A and 76 alkaloids were selected as the host and guest, respectively, to determine the binding constant by competitive fluorescence titration. LASSO regression was adopted to investigate the mechanism of the complex of SCnA with alkaloids. The binding constant of alkaloids-SC6A and alkaloids-SC8A was related to the alkaloid alkalinity. Also, the electronegativity, polarization, first ionization potential, hydrogen bond potential, the molecular size, and shape of alkaloids are critical properties to determine alkaloids-SC6A binding constant as well as electronegativity, polarization, hydrophobicity, and the molecular size and shape of alkaloids play an important role for the alkaloids-SC8A binding constant.
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Affiliation(s)
- Yu Yin
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Zhen Yang
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Na Li
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Xuan Yu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Mei-Ling Chen
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Meng Wang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Xiao-Liang Ren
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
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Vuppala S, Chitumalla RK, Choi S, Kim T, Park H, Jang J. Machine Learning-Assisted Computational Screening of Adhesive Molecules Derived from Dihydroxyphenyl Alanine. ACS Omega 2024; 9:994-1000. [PMID: 38222596 PMCID: PMC10785072 DOI: 10.1021/acsomega.3c07208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/28/2023] [Accepted: 11/30/2023] [Indexed: 01/16/2024]
Abstract
Marine mussels adhere to virtually any surface via 3,4-dihydroxyphenyl-L-alanines (L-DOPA), an amino acid largely contained in their foot proteins. The biofriendly, water-repellent, and strong adhesion of L-DOPA are unparalleled by any synthetic adhesive. Inspired by this, we computationally designed diverse derivatives of DOPA and studied their potential as adhesives or coating materials. We used first-principles calculations to investigate the adsorption of the DOPA derivatives on graphite. The presence of an electron-withdrawing group, such as nitrogen dioxide, strengthens the adsorption by increasing the π-π interaction between DOPA and graphite. To quantify the distribution of electron charge and to gain insights into the charge distribution at interfaces, we performed Bader charge analysis and examined charge density difference plots. We developed a quantitative structure-property relationship (QSPR) model using an artificial neural network (ANN) to predict the adsorption energy. Using the three-dimensional and quantum mechanical electrostatic potential of a molecule as a descriptor, the present quantum NN model shows promising performance as a predictive QSPR model.
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Affiliation(s)
- Srimai Vuppala
- Department
of Nanoenergy Engineering, Pusan National
University, Busan 46241, Republic
of Korea
| | - Ramesh Kumar Chitumalla
- Department
of Nanoenergy Engineering, Pusan National
University, Busan 46241, Republic
of Korea
| | - Seyong Choi
- Department
of Nanoenergy Engineering, Pusan National
University, Busan 46241, Republic
of Korea
| | - Taeho Kim
- Department
of Bioscience and Biotechnology, Sejong
University, Seoul 05006, Republic
of Korea
| | - Hwangseo Park
- Department
of Bioscience and Biotechnology, Sejong
University, Seoul 05006, Republic
of Korea
| | - Joonkyung Jang
- Department
of Nanoenergy Engineering, Pusan National
University, Busan 46241, Republic
of Korea
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Janicka M, Sztanke M, Sztanke K. Modeling the Blood-Brain Barrier Permeability of Potential Heterocyclic Drugs via Biomimetic IAM Chromatography Technique Combined with QSAR Methodology. Molecules 2024; 29:287. [PMID: 38257200 DOI: 10.3390/molecules29020287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 12/21/2023] [Accepted: 12/27/2023] [Indexed: 01/24/2024] Open
Abstract
Penetration through the blood-brain barrier (BBB) is desirable in the case of potential pharmaceuticals acting on the central nervous system (CNS), but is undesirable in the case of drug candidates acting on the peripheral nervous system because it may cause CNS side effects. Therefore, modeling of the permeability across the blood-brain barrier (i.e., the logarithm of the brain to blood concentration ratio, log BB) of potential pharmaceuticals should be performed as early as possible in the preclinical phase of drug development. Biomimetic chromatography with immobilized artificial membrane (IAM) and the quantitative structure-activity relationship (QSAR) methodology were successful in modeling the blood-brain barrier permeability of 126 drug candidates, whose experimentally-derived lipophilicity indices and computationally-derived molecular descriptors (such as molecular weight (MW), number of rotatable bonds (NRB), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA), topological polar surface area (TPSA), and polarizability (α)) varied by class. The QSARs model established by multiple linear regression showed a positive effect of the lipophilicity (log kw, IAM) and molecular weight of the compound, and a negative effect of the number of hydrogen bond donors and acceptors, on the log BB values. The model has been cross-validated, and all statistics indicate that it is very good and has high predictive ability. The simplicity of the developed model, and its usefulness in screening studies of novel drug candidates that are able to cross the BBB by passive diffusion, are emphasized.
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Affiliation(s)
- Małgorzata Janicka
- Department of Physical Chemistry, Faculty of Chemistry, Institute of Chemical Science, Maria Curie-Skłodowska University, 20-031 Lublin, Poland
| | - Małgorzata Sztanke
- Department of Medical Chemistry, Medical University of Lublin, 4A Chodźki Street, 20-093 Lublin, Poland
| | - Krzysztof Sztanke
- Laboratory of Bioorganic Compounds Synthesis and Analysis, Medical University of Lublin, 4A Chodźki Street, 20-093 Lublin, Poland
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Shaker B, Lee J, Lee Y, Yu MS, Lee HM, Lee E, Kang HC, Oh KS, Kim HW, Na D. A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds. Bioinformatics 2023; 39:btad577. [PMID: 37713469 PMCID: PMC10560102 DOI: 10.1093/bioinformatics/btad577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/30/2023] [Accepted: 09/14/2023] [Indexed: 09/17/2023] Open
Abstract
MOTIVATION Efficient assessment of the blood-brain barrier (BBB) penetration ability of a drug compound is one of the major hurdles in central nervous system drug discovery since experimental methods are costly and time-consuming. To advance and elevate the success rate of neurotherapeutic drug discovery, it is essential to develop an accurate computational quantitative model to determine the absolute logBB value (a logarithmic ratio of the concentration of a drug in the brain to its concentration in the blood) of a drug candidate. RESULTS Here, we developed a quantitative model (LogBB_Pred) capable of predicting a logBB value of a query compound. The model achieved an R2 of 0.61 on an independent test dataset and outperformed other publicly available quantitative models. When compared with the available qualitative (classification) models that only classified whether a compound is BBB-permeable or not, our model achieved the same accuracy (0.85) with the best qualitative model and far-outperformed other qualitative models (accuracies between 0.64 and 0.70). For further evaluation, our model, quantitative models, and the qualitative models were evaluated on a real-world central nervous system drug screening library. Our model showed an accuracy of 0.97 while the other models showed an accuracy in the range of 0.29-0.83. Consequently, our model can accurately classify BBB-permeable compounds as well as predict the absolute logBB values of drug candidates. AVAILABILITY AND IMPLEMENTATION Web server is freely available on the web at http://ssbio.cau.ac.kr/software/logbb_pred/. The data used in this study are available to download at http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip.
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Affiliation(s)
- Bilal Shaker
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Jingyu Lee
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Yunhyeok Lee
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Myeong-Sang Yu
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Hyang-Mi Lee
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Eunee Lee
- Division of Pediatric Neurology, Department of Pediatrics, Severance Children’s Hospital, Yonsei University College of Medicine, Epilepsy Research Institute, Seoul 03722, Republic of Korea
| | - Hoon-Chul Kang
- Department of Anatomy College of Medicine, Yonsei University, Seoul 03722, Republic of Korea
| | - Kwang-Seok Oh
- Convergence Drug Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea
| | - Hyung Wook Kim
- Department of Bio-integrated Science and Technology, College of Life Sciences, Sejong University, Seoul 05006, Republic of Korea
| | - Dokyun Na
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
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Stępnik K, Kukula-Koch W, Plazinski W, Rybicka M, Gawel K. Neuroprotective Properties of Oleanolic Acid-Computational-Driven Molecular Research Combined with In Vitro and In Vivo Experiments. Pharmaceuticals (Basel) 2023; 16:1234. [PMID: 37765042 PMCID: PMC10536188 DOI: 10.3390/ph16091234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/24/2023] [Accepted: 08/29/2023] [Indexed: 09/29/2023] Open
Abstract
Oleanolic acid (OA), as a ubiquitous compound in the plant kingdom, is studied for both its neuroprotective and neurotoxic properties. The mechanism of acetylcholinesterase (AChE) inhibitory potential of OA is investigated using molecular dynamic simulations (MD) and docking as well as biomimetic tests. Moreover, the in vitro SH-SY5Y human neuroblastoma cells and the in vivo zebrafish model were used. The inhibitory potential towards the AChE enzyme is examined using the TLC-bioautography assay (the IC50 value is 9.22 μM). The CH-π interactions between the central fragment of the ligand molecule and the aromatic cluster created by the His440, Phe288, Phe290, Phe330, Phe331, Tyr121, Tyr334, Trp84, and Trp279 side chains are observed. The results of the in vitro tests using the SH-SY5Y cells indicate that the viability rate is reduced to 71.5%, 61%, and 43% at the concentrations of 100 µg/mL, 300 µg/mL, and 1000 µg/mL, respectively, after 48 h of incubation, whereas cytotoxicity against the tested cell line with the IC50 value is 714.32 ± 32.40 µg/mL. The in vivo tests on the zebrafish prove that there is no difference between the control and experimental groups regarding the mortality rate and morphology (p > 0.05).
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Affiliation(s)
- Katarzyna Stępnik
- Department of Physical Chemistry, Institute of Chemical Sciences, Faculty of Chemistry, Maria Curie–Sklodowska University in Lublin, Pl. M. Curie-Skłodowskiej 3, 20-031 Lublin, Poland
- Department of Pharmacognosy with Medicinal Plants Garden, Medical University of Lublin, ul. Chodzki 1, 20-093 Lublin, Poland;
| | - Wirginia Kukula-Koch
- Department of Pharmacognosy with Medicinal Plants Garden, Medical University of Lublin, ul. Chodzki 1, 20-093 Lublin, Poland;
| | - Wojciech Plazinski
- Department of Biopharmacy, Medical University of Lublin, ul. Chodzki 4a, 20-093 Lublin, Poland;
- Jerzy Haber Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences, ul. Niezapominajek 8, 30-239 Kraków, Poland
| | - Magda Rybicka
- Department of Photobiology and Molecular Diagnostics, Intercollegiate Faculty of Biotechnology, University of Gdansk and Medical University of Gdansk, ul. Abrahama 58, 80-307 Gdańsk, Poland;
| | - Kinga Gawel
- Department of Experimental and Clinical Pharmacology, Medical University of Lublin, ul. Jaczewskiego Str. 8b, 20-090 Lublin, Poland;
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Abstract
Predicting from first-principles the rate of passive permeation of small molecules across the biological membrane represents a promising strategy for screening lead compounds upstream in the drug-discovery and development pipeline. One popular avenue for the estimation of permeation rates rests on computer simulations in conjunction with the inhomogeneous solubility-diffusion model, which requires the determination of the free-energy change and position-dependent diffusivity of the substrate along the translocation pathway through the lipid bilayer. In this Perspective, we will clarify the physical meaning of the membrane permeability inferred from such computer simulations, and how theoretical predictions actually relate to what is commonly measured experimentally. We will also examine why these calculations remain both technically challenging and overly computationally expensive, which has hitherto precluded their routine use in nonacademic settings. We finally synopsize possible research directions to meet these challenges, increase the predictive power of physics-based rates of passive permeation, and, by ricochet, improve their practical usefulness.
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Affiliation(s)
- Christophe Chipot
- Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana-Champaign, Unité Mixte de Recherche n◦7019, Université de Lorraine, 54500 Vandœuvre-lès-Nancy cedex, France
- Beckman Institute for Advanced Science and Technology, and Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61820, United States
- Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois 60637, United States
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Singh R, Kumar P, Sindhu J, Devi M, Kumar A, Lal S, Singh D. Parsing structural fragments of thiazolidin-4-one based α-amylase inhibitors: A combined approach employing in vitro colorimetric screening and GA-MLR based QSAR modelling supported by molecular docking, molecular dynamics simulation and ADMET studies. Comput Biol Med 2023; 157:106776. [PMID: 36947906 DOI: 10.1016/j.compbiomed.2023.106776] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/20/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023]
Abstract
α-Amylase (EC.3.2.1.1) is a ubiquitous digestive endoamylase. The abrupt rise in blood glucose levels due to the hydrolysis of carbohydrates by α-amylase at a faster rate is one of the main reasons for type 2 diabetes. The inhibitors prevent the action of digestive enzymes, slowing the digestion of carbs and eventually assisting in the management of postprandial hyperglycemia. In the course of developing α-amylase inhibitors, we have screened 2-aryliminothiazolidin-4-one based analogs for their in vitro α-amylase inhibitory potential and employed various in silico approaches for the detailed exploration of the bioactivity. The DNSA bioassay revealed that compounds 5c, 5e, 5h, 5j, 5m, 5o and 5t were more potent than the reference drug (IC60 value = 22.94 ± 0.24 μg mL-1). The derivative 5o with -NO2 group at both the rings was the most potent analog with an IC60 value of 19.67 ± 0.20 μg mL-1 whereas derivative 5a with unsubstituted aromatic rings showed poor inhibitory potential with an IC60 value of 33.40 ± 0.15 μg mL-1. The reliable QSAR models were developed using the QSARINS software. The high value of R2ext = 0.9632 for model IM-9 showed that the built model can be applied to predict the α-amylase inhibitory activity of the untested molecules. A consensus modelling approach was also employed to test the reliability and robustness of the developed QSAR models. Molecular docking and molecular dynamics were employed to validate the bioassay results by studying the conformational changes and interaction mechanisms. A step further, these compounds also exhibited good ADMET characteristics and bioavailability when tested for in silico pharmacokinetics prediction parameters.
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Affiliation(s)
- Rahul Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India.
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, 125004, India
| | - Meena Devi
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, GJUS&T, Hisar, 125001, India
| | - Sohan Lal
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India
| | - Devender Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, 124001, India
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Bourouai MA, Si Larbi K, Bouchoucha A, Terrachet-Bouaziz S, Djebbar S. New Ni(II) and Pd(II) complexes bearing derived sulfa drug ligands: synthesis, characterization, DFT calculations, and in silico and in vitro biological activity studies. Biometals 2023; 36:153-88. [PMID: 36427181 DOI: 10.1007/s10534-022-00469-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 11/16/2022] [Indexed: 11/26/2022]
Abstract
In the present study, the synthesis of six new Ni(II) and Pd(II) complexes with three derived sulfamethoxazole drug ligands is reported. The coordination mode, geometry, and chemical formula of all the synthesized compounds have been determined by elemental analysis, mass spectrometry, emission atomic spectroscopy, conductivity measurements, magnetic susceptibility, FTIR, TGA, 1H-NMR, electronic absorption spectroscopy, SEM-EDX along with DFT calculations. The Schiff Base ligands were found to be bidentate and coordinated to the metal ions through sulfonamidic nitrogen and oxazolic nitrogen atoms leading to a square planar geometry for palladium (II) while a distorted octahedral geometry around Nickel (II) ion was suggested. Biological applications of the new complexes including in vitro antimicrobial, antioxidant and anticancer properties were investigated. The results showed that the new metal (II) compounds exhibit remarkable antibacterial inhibition activity against both Gram-positive and Gram-negative bacteria, in addition to noticeable DPPH free radical scavenging activity. The in vitro cytotoxicity assay of the complexes against cell lines of chronic myelogenous leukaemia (K562) showed promising potential for the application of the coordination compounds in antitumor therapy. Subsequently, to evaluate the pharmaceutical potential of the metal-containing compounds, pharmacokinetics and toxicity were studied by ADMET simulations while interactions between the complexes and bacterial proteins were evaluated by molecular docking.
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Janicka M, Śliwińska A, Sztanke M, Sztanke K. Combined Micellar Liquid Chromatography Technique and QSARs Modeling in Predicting the Blood-Brain Barrier Permeation of Heterocyclic Drug-like Compounds. Int J Mol Sci 2022; 23. [PMID: 36555527 DOI: 10.3390/ijms232415887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/06/2022] [Accepted: 12/08/2022] [Indexed: 12/15/2022] Open
Abstract
The quantitative structure-activity relationship (QSAR) methodology was used to predict the blood-brain permeability (log BB) for 65 synthetic heterocyclic compounds tested as promising drug candidates. The compounds were characterized by different descriptors: lipophilicity, parachor, polarizability, molecular weight, number of hydrogen bond acceptors, number of rotatable bonds, and polar surface area. Lipophilic properties of the compounds were evaluated experimentally by micellar liquid chromatography (MLC). In the experiments, sodium dodecyl sulfate (SDS) as the effluent component and the ODS-2 column were used. Using multiple linear regression and leave-one-out cross-validation, we derived the statistically significant and highly predictive quantitative structure-activity relationship models. Thus, this study provides valuable information on the expected properties of the substances that can be used as a support tool in the design of new therapeutic agents.
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Dechouk LF, Bouchoucha A, Abdi Y, Si Larbi K, Bouzaheur A, Terrachet-bouaziz S. Coordination of new palladium (II) complexes with derived furopyran-3,4‑dione ligands: Synthesis, characterization, redox behaviour, DFT, antimicrobial activity, molecular docking and ADMET studies. J Mol Struct 2022; 1257:132611. [DOI: 10.1016/j.molstruc.2022.132611] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Kim T, You BH, Han S, Shin HC, Chung KC, Park H. Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage. Int J Mol Sci 2021; 22:ijms222010995. [PMID: 34681653 PMCID: PMC8537149 DOI: 10.3390/ijms222010995] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 10/07/2021] [Accepted: 10/10/2021] [Indexed: 01/07/2023] Open
Abstract
A successful passage of the blood–brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood–brain partitioning (LogBB) has served as an effective index of molecular BBB permeability. Using the three-dimensional (3D) distribution of the molecular electrostatic potential (ESP) as the numerical descriptor, a quantitative structure-activity relationship (QSAR) model termed AlphaQ was derived to predict the molecular LogBB values. To obtain the optimal atomic coordinates of the molecules under investigation, the pairwise 3D structural alignments were conducted in such a way to maximize the quantum mechanical cross correlation between the template and a target molecule. This alignment method has the advantage over the conventional atom-by-atom matching protocol in that the structurally diverse molecules can be analyzed as rigorously as the chemical derivatives with the same scaffold. The inaccuracy problem in the 3D structural alignment was alleviated in a large part by categorizing the molecules into the eight subsets according to the molecular weight. By applying the artificial neural network algorithm to associate the fully quantum mechanical ESP descriptors with the extensive experimental LogBB data, a highly predictive 3D-QSAR model was derived for each molecular subset with a squared correlation coefficient larger than 0.8. Due to the simplicity in model building and the high predictability, AlphaQ is anticipated to serve as an effective computational screening tool for molecular BBB permeability.
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Affiliation(s)
- Taeho Kim
- Department of Bioscience and Biotechnology, Sejong University, Kwangjin-gu, Seoul 05006, Korea;
| | - Byoung Hoon You
- Whan In Pharmaceutical Co., Ltd., 11, Songpa-gu, Seoul 05855, Korea; (B.H.Y.); (S.H.); (H.C.S.)
| | - Songhee Han
- Whan In Pharmaceutical Co., Ltd., 11, Songpa-gu, Seoul 05855, Korea; (B.H.Y.); (S.H.); (H.C.S.)
| | - Ho Chul Shin
- Whan In Pharmaceutical Co., Ltd., 11, Songpa-gu, Seoul 05855, Korea; (B.H.Y.); (S.H.); (H.C.S.)
| | - Kee-Choo Chung
- Department of Bioscience and Biotechnology, Sejong University, Kwangjin-gu, Seoul 05006, Korea;
- Correspondence: (K.-C.C.); (H.P.); Tel.: +82-2-2963-1635 (K.-C.C.); +82-2-3408-3766 (H.P.); Fax: +82-2-3408-4334 (K.-C.C. & H.P.)
| | - Hwangseo Park
- Department of Bioscience and Biotechnology, Sejong University, Kwangjin-gu, Seoul 05006, Korea;
- Correspondence: (K.-C.C.); (H.P.); Tel.: +82-2-2963-1635 (K.-C.C.); +82-2-3408-3766 (H.P.); Fax: +82-2-3408-4334 (K.-C.C. & H.P.)
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Belkhir-Talbi D, Ghemmit-Doulache N, Terrachet-Bouaziz S, Makhloufi-Chebli M, Rabahi A, Ismaili L, Silva AM. Transition-metal complexes of N,N′-di(4-bromophenyl)-4-hydroxycoumarin-3-carboximidamide: synthesis, characterization, biological activities, ADMET and drug-likeness analysis. INORG CHEM COMMUN 2021. [DOI: 10.1016/j.inoche.2021.108509] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Moussa N, Hassan A, Gharaghani S. Pharmacophore model, docking, QSAR, and molecular dynamics simulation studies of substituted cyclic imides and herbal medicines as COX-2 inhibitors. Heliyon 2021; 7:e06605. [PMID: 33889764 PMCID: PMC8047494 DOI: 10.1016/j.heliyon.2021.e06605] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 02/22/2021] [Accepted: 03/24/2021] [Indexed: 01/09/2023] Open
Abstract
Cyclooxygenase-2 (COX-2) enzyme inhibitors have not eliminated the necessity for developed drugs not only in the nonsteroidal anti-inflammatory drug (NSAIDs) area, but also in other therapeutic applications including prevention of cancer and Alzheimer's disease. A series of novel substituted cyclic imides have been reported as selective COX-2 inhibitors. To understand the structural features responsible for their activity, a 3D validated pharmacophore and quantitative structure−activity relationship (QSAR) model have been developed. The values of enrichment factor (EF), goodness of hit score (GH), area under the ROC curve (AUC), sensitivity, and specificity refer to the good ability of the pharmacophore model to identify active compounds. Multiple linear regression (MLR) produced statistically significant QSAR model with (R2training = 0.763, R2test = 0.96) and predictability (Q2training = 0.66, Q2test = 0.84). Then, using the pharmacophore and QSAR models, eight authenticated botanicals in two herbal medicines and the ZINC compounds database, were virtually screened for ligands to COX-2. The retrieved hits which also obey lipinski's rule of five (RO5) were docked in the COX-2 3D structure to investigate their binding mode and affinity. Finally, based on the docking results, nine molecules were prioritized as promising hits that could be used as leads to discover novel COX-2 inhibitors. COX-2 inhibition of most of these hits has not been reported previously. Ten-nanosecond molecular dynamics simulation (10-ns MD) was performed on the initial structure COX-2 complex with ZINC000113253375 and ZINC000043170560 resulted from the docking. Our utilization of the 3D pharmacophore model, QSAR, molecular docking, and molecular dynamics simulation trials can be a potent strategy to successfully predict activity, efficiently design drugs, and screen large numbers of new compounds as active drug candidates.
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Affiliation(s)
- Nathalie Moussa
- Department of Pharmaceutical Chemistry and Quality Control of Medicaments, Faculty of Pharmacy, Damascus University, Damascus, Syria
| | - Ahmad Hassan
- Department of Pharmaceutical Chemistry and Quality Control of Medicaments, Faculty of Pharmacy, Damascus University, Damascus, Syria
| | - Sajjad Gharaghani
- Laboratory of Bioinformatics and Drug Design, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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Gonzalez-Jimenez A, Suzuki A, Chen M, Ashby K, Alvarez-Alvarez I, Andrade RJ, Lucena MI. Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach. Arch Toxicol 2021; 95:1793-803. [PMID: 33666709 DOI: 10.1007/s00204-021-03013-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 02/25/2021] [Indexed: 12/19/2022]
Abstract
Drug-induced liver injury (DILI) presentation varies biochemically and histologically. Certain drugs present quite consistent injury patterns, i.e., DILI signatures. In contrast, others are manifested as broader types of liver injury. The variety of DILI presentations by a single drug suggests that both drugs and host factors may contribute to the phenotype. However, factors determining the DILI types have not been yet elucidated. Identifying such factors may help to accurately predict the injury types based on drugs and host information and assist the clinical diagnosis of DILI. Using prospective DILI registry datasets, we sought to explore and validate the associations of biochemical injury types at the time of DILI recognition with comprehensive information on drug properties and host factors. Random forest models identified a set of drug properties and host factors that differentiate hepatocellular from cholestatic damage with reasonable accuracy (69-84%). A simplified logistic regression model developed for practical use, consisting of patient's age, drug's lipoaffinity, and hybridization ratio, achieved a fair prediction (68-74%), but suggested potential clinical usability, computing the likelihood of liver injury type based on two properties of drugs taken by a patient and patient's age. In summary, considering both drug and host factors in evaluating DILI risk and phenotypes open an avenue for future DILI research and aid in the refinement of causality assessment.
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16
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Hadhoum N, Zohra Hadjadj-Aoul F, Hocine S, Bouaziz-Terrachet S, Seklaoui N, Boubrit F, Abderrahim W, Redouane Mekacher L. Design and One-Pot Synthesis of Some New [3,5-Di(4’,5’-diphenyl-2’-substituted)-1H-imidazol-1-yl)]-1H-1,2,4-triazole Derivatives: in silico ADMET and Docking Study, Antibacterial and Antifungal Activities Evaluation. HETEROCYCLES 2021. [DOI: 10.3987/com-21-14503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Cappelli CI, Manganelli S, Toma C, Benfenati E, Mombelli E. Prediction of the Partition Coefficient between Adipose Tissue and Blood for Environmental Chemicals: From Single QSAR Models to an Integrated Approach. Mol Inform 2020; 40:e2000072. [PMID: 33135856 DOI: 10.1002/minf.202000072] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 09/07/2020] [Indexed: 12/15/2022]
Abstract
The adipose tissue:blood partition coefficient is a key-endpoint to predict the pharmacokinetics of chemicals in humans and animals, since other organ:blood affinities can be estimated as a function of this parameter. We performed a search in the literature to select all the available rat in vivo data. This approach resulted into two improvements to existing models: a homogeneous definition of the endpoint and an expanded data collection. The resulting dataset was used to develop QSAR models as a function of linear and non-linear algorithms. Several applicability domain definitions were assessed and the definition corresponding to a good balance between performance and coverage was retained. We assessed the pertinence of combining single models into integrated approaches to increase the accuracy in predictions. The best integrated model outperformed the single models and it was characterized by an external mean absolute error (MAE) equal to 0.26, while preserving an adequate coverage (84 %). This performance is comparable to experimental variability and it highlights the pertinence of the integrated model.
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Affiliation(s)
- Claudia Ileana Cappelli
- Unité Modèles pour l'Ecotoxicologie et la Toxicologie (METO), Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil en Halatte, France.,Currently at S-IN Soluzioni Informatiche S.r.l., Vicenza, Italy
| | - Serena Manganelli
- Unité Modèles pour l'Ecotoxicologie et la Toxicologie (METO), Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil en Halatte, France.,Currently at Chemical Food Safety Group, Nestlé Research, Lausanne, Switzerland
| | - Cosimo Toma
- Laboratory of Environmental Chemistry and Toxicology, Department Environmental Health Sciences, IRCCS - Istituto di Ricerche Farmacologiche Mario, Negri, Milan, Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department Environmental Health Sciences, IRCCS - Istituto di Ricerche Farmacologiche Mario, Negri, Milan, Italy
| | - Enrico Mombelli
- Unité Modèles pour l'Ecotoxicologie et la Toxicologie (METO), Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil en Halatte, France
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Wang S, Hu S, Zhang H. Nonlinear Method to Predict the Distribution of Structurally Diverse Compounds between Blood and Tissue. J Pharm Sci 2020; 109:3697-715. [PMID: 32918917 DOI: 10.1016/j.xphs.2020.08.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 08/23/2020] [Accepted: 08/24/2020] [Indexed: 11/20/2022]
Abstract
In this work, methods for predicting the distribution of compounds between blood and tissue were investigated using nonlinear regression analysis. For the tissue/blood partition coefficient, 282 compounds and 810 activity data for seven tissues were selected. Twenty-four parameters were studied for each state of the compound. A study set with the most compounds and activity data in similar studies was established, and a model with good prediction ability was obtained. A total of 773 data points were randomly divided into two data sets: the training set contained 623 data points (n = 623, r = 0.822, s = 0.438, F = 142.2, and Q = 0.814) and the test set contained 150 data points (n = 150, r = 0.814, and s = 0.334). Furthermore, individual tissue/blood distribution coefficients were also studied in depth to obtain separate models for predicting the distribution coefficient of a drug between blood and a tissue. By applying these models, not only can the tissue/blood distribution coefficient or single tissue/blood distribution coefficient of the seven tissues and organs of the human body be predicted, but the distribution of the drug molecules in different states (neutral, cation, and anion) in the three tissue components can also be predicted.
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Meneses-Sagrero SE, Rascón-Valenzuela LA, Sotelo-Mundo R, Vilegas W, Velazquez C, García-Ramos JC, Robles-Zepeda RE. Antiproliferative activity of cardenolides on cell line A549: structure-activity relationship analysis. Mol Divers 2021; 25:2289-305. [PMID: 32627094 DOI: 10.1007/s11030-020-10119-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 06/19/2020] [Indexed: 01/22/2023]
Abstract
Since the beginning, natural products have represented an important source of bioactive molecules for cancer treatment. Among them, cardenolides attract the attention of different research groups due to their cardiotonic and antitumor activity. The observed biological activity is closely related to their Na+/K+-ATPase inhibition potency. Currently, the discovery of new compounds against cancer is an urgent need in modern pharmaceutical research. Thus, the aim of this work is to determine the physicochemical properties and substituent effects that module the antiproliferative activity of cardenolides on the human lung cancer cell line A549. We build and curate a library with results obtained from literature; molecular descriptors were calculated in PaDEL software, and SAR/QSAR analysis was performed. The SAR results showed that cardenolides were sensitive to modifications in C and D steroidal ring and required substituent groups with the function of hydrogen bond acceptor at the C3 position. QSAR models to doubly linked-type cardenolides indicated that properties as lipoaffinity and atoms with the capacity to be hydrogen bond acceptors are involved in the increment of antiproliferative activity on A549 cell line. In contrast, the presence and position of very electro-negative atoms on the molecule decreased the antiproliferative effect on A549 cells. These results suggest that the antiproliferative capacity of cardenolides on the cell line A549 is strongly related to substituent groups on the C3 position, which must not be carbohydrate. Additionally, the steroidal rings C and D must remain without modifications.
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Santos VHJM, Pontin D, Rambo RS, Seferin M. The Application of Quantitative Structure–Property Relationship Modeling and Exploratory Analysis to Screen Catalysts for the Synthesis of Oleochemical Carbonates from
CO
2
and Bio‐Based Epoxides. J AM OIL CHEM SOC 2020. [DOI: 10.1002/aocs.12361] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Victor Hugo Jacks Mendes Santos
- School of TechnologyPUCRS—Pontifical Catholic University of Rio Grande do Sul 6681 Ipiranga Avenue—Building 12 Porto Alegre 90619‐900 Brazil
- Engineering and Materials Technology Graduate ProgramPontifical Catholic University of Rio Grande do Sul 6681 Ipiranga Avenue—Building 32 Porto Alegre 90619‐900 Brazil
- Institute of Petroleum and Natural ResourcesPontifical Catholic University of Rio Grande do Sul 6681 Ipiranga Avenue—Building 96J Porto Alegre 90619‐900 Brazil
| | - Darlan Pontin
- School of TechnologyPUCRS—Pontifical Catholic University of Rio Grande do Sul 6681 Ipiranga Avenue—Building 12 Porto Alegre 90619‐900 Brazil
| | - Raoní Scheibler Rambo
- Institute of Petroleum and Natural ResourcesPontifical Catholic University of Rio Grande do Sul 6681 Ipiranga Avenue—Building 96J Porto Alegre 90619‐900 Brazil
| | - Marcus Seferin
- School of TechnologyPUCRS—Pontifical Catholic University of Rio Grande do Sul 6681 Ipiranga Avenue—Building 12 Porto Alegre 90619‐900 Brazil
- Engineering and Materials Technology Graduate ProgramPontifical Catholic University of Rio Grande do Sul 6681 Ipiranga Avenue—Building 32 Porto Alegre 90619‐900 Brazil
- Institute of Petroleum and Natural ResourcesPontifical Catholic University of Rio Grande do Sul 6681 Ipiranga Avenue—Building 96J Porto Alegre 90619‐900 Brazil
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Mando H, Hassan A, Gharaghani S. Novel and Predictive QSAR Model for Steroidal and Nonsteroidal 5α- Reductase Type II Inhibitors. Curr Drug Discov Technol 2020; 18:317-332. [PMID: 32208118 DOI: 10.2174/1570163817666200324170457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 01/16/2020] [Accepted: 01/16/2020] [Indexed: 11/22/2022]
Abstract
AIMS AND OBJECTIVE In this study, a novel quantitative structure activity relationship (QSAR) model has been developed for inhibitors of human 5-alpha reductase type II, which are used to treat benign prostate hypertrophy (BPH). METHODS The dataset consisted of 113 compounds-mainly nonsteroidal-with known inhibitory concentration. Then 3D structures of compounds were optimized and molecular structure descriptors were calculated. The stepwise multiple linear regression was used to select descriptors encoding the inhibitory activity of the compounds. Multiple linear regression (MLR) was used to build up the linear QSAR model. RESULTS The results obtained revealed that the descriptors which best describe the activity were atom type electropological state, carbon type, radial distribution function (RDF), barysz matrix and molecular linear free energy relation. The suggested model could achieve satisfied square correlation coefficient of R2 = 0.72, higher than of many previous studies, indicating its superiority. Rigid validation criteria were met using external data with Q2 ˃ 0.5 and R2 = 0.75, reflecting the predictive power of the model. CONCLUSION The QSAR model was applied for screening botanical components of herbal preparations used to treat BPH, and could predict the activity of some, among others, making reasonable attribution to the proposed effect of these preparations. Gamma tocopherol was found to be an active inhibitor, in consistence with many previous studies, anticipating the power of this model in the prediction of new candidate molecules and suggesting further investigations.
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Affiliation(s)
- Huda Mando
- Department of Pharmaceutical Chemistry and Quality Control of Medicaments, Faculty of Pharmacy, Damascus University, Damascus, Syrian Arab Republic
| | - Ahmad Hassan
- Department of Pharmaceutical Chemistry and Quality Control of Medicaments, Faculty of Pharmacy, Damascus University, Damascus, Syrian Arab Republic
| | - Sajjad Gharaghani
- Laboratory of Bioinformatics and Drug Design, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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22
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Abstract
In the effort to define a set of rules useful in tuning the properties for a successful blood-brain barrier (BBB) permeation, we statistically analyzed a set of 328 compounds and correlated their experimental in vivo logBB with a series of computed descriptors. Contingency tables were constructed, observed and expected distributions were calculated, and chi-square (χ2) distributions were evaluated. This allowed to point out a significant dependence of certain physicochemical properties in influencing the BBB permeation. Of over 15 computed descriptors, 9 resulted to be particularly important showing highly significant χ2 distribution: polar surface area (χ2 = 66.79; p = 1.08 × 10-13), nitrogen and oxygen count (χ2 = 51.17; p = 2.06 × 10-10), logP (χ2 = 47.38; p = 1.27 × 10-9), nitrogen count (χ2 = 38.29; p = 9.77 × 10-8), logD (χ2 = 36.80; p = 36.80), oxygen count (χ2 = 35.83; p = 3.13 × 10-7), ionization state (χ2 = 33.02, p = 3.19 × 10-7), hydrogen bond acceptors (χ2 = 30.80; p = 3.36 × 10-6), and hydrogen bond donors (χ2 = 29.29; p = 6.81 × 10-6). Other parameters describing the mass and size of the molecules (molecular weight: 11.18; p = 2.46 × 10-2) resulted in being not significant since the population within the observed and expected distribution was similar. Depending on the combination of the significant descriptors, we set a three cases probabilistic scenario (BBB+, BBB-, BBB+/BBB-) that would prospectively be used to tune properties for BBB permeation.
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Affiliation(s)
- Maria Dichiara
- Department of Drug Sciences, Medicinal Chemistry Section, Università degli Studi di Catania, Viale Andrea Doria 6, 95125 Catania, Italy
| | - Benedetto Amata
- Department of Drug Sciences, Medicinal Chemistry Section, Università degli Studi di Catania, Viale Andrea Doria 6, 95125 Catania, Italy
| | - Rita Turnaturi
- Department of Drug Sciences, Medicinal Chemistry Section, Università degli Studi di Catania, Viale Andrea Doria 6, 95125 Catania, Italy
| | - Agostino Marrazzo
- Department of Drug Sciences, Medicinal Chemistry Section, Università degli Studi di Catania, Viale Andrea Doria 6, 95125 Catania, Italy
| | - Emanuele Amata
- Department of Drug Sciences, Medicinal Chemistry Section, Università degli Studi di Catania, Viale Andrea Doria 6, 95125 Catania, Italy
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Przybyłek M, Studziński W, Gackowska A, Gaca J. The use of fast molecular descriptors and artificial neural networks approach in organochlorine compounds electron ionization mass spectra classification. Environ Sci Pollut Res Int 2019; 26:28188-28201. [PMID: 31363975 PMCID: PMC6791912 DOI: 10.1007/s11356-019-05968-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2018] [Accepted: 07/12/2019] [Indexed: 06/10/2023]
Abstract
Developing of theoretical tools can be very helpful for supporting new pollutant detection. Nowadays, a combination of mass spectrometry and chromatographic techniques are the most basic environmental monitoring methods. In this paper, two organochlorine compound mass spectra classification systems were proposed. The classification models were developed within the framework of artificial neural networks (ANNs) and fast 1D and 2D molecular descriptor calculations. Based on the intensities of two characteristic MS peaks, namely, [M] and [M-35], two classification criterions were proposed. According to criterion I, class 1 comprises [M] signals with the intensity higher than 800 NIST units, while class 2 consists of signals with the intensity lower or equal than 800. According to criterion II, class 1 consists of [M-35] signals with the intensity higher than 100, while signals with the intensity lower or equal than 100 belong to class 2. As a result of ANNs learning stage, five models for both classification criterions were generated. The external model validation showed that all ANNs are characterized by high predicting power; however, criterion I-based ANNs are much more accurate and therefore are more suitable for analytical purposes. In order to obtain another confirmation, selected ANNs were tested against additional dataset comprising popular sunscreen agents disinfection by-products reported in previous works.
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Affiliation(s)
- Maciej Przybyłek
- Chair and Department of Physical Chemistry, Pharmacy Faculty, Collegium Medicum of Bydgoszcz, Nicolaus Copernicus University in Toruń, Kurpińskiego 5, 85-950, Bydgoszcz, Poland.
| | - Waldemar Studziński
- Faculty of Chemical Technology and Engineering, University of Technology and Life Science, Seminaryjna 3, 85-326, Bydgoszcz, Poland
| | - Alicja Gackowska
- Faculty of Chemical Technology and Engineering, University of Technology and Life Science, Seminaryjna 3, 85-326, Bydgoszcz, Poland
| | - Jerzy Gaca
- Faculty of Chemical Technology and Engineering, University of Technology and Life Science, Seminaryjna 3, 85-326, Bydgoszcz, Poland
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Przybyłek M, Recki Ł, Mroczyńska K, Jeliński T, Cysewski P. Experimental and theoretical solubility advantage screening of bi-component solid curcumin formulations. J Drug Deliv Sci Technol 2019. [DOI: 10.1016/j.jddst.2019.01.023] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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25
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Gonzalez-Jimenez A, McEuen K, Chen M, Suzuki A, Robles-Diaz M, Medina-Caliz I, Bessone F, Hernandez N, Arrese M, Parana R, Lucena MI, Stephens C, Andrade RJ. The influence of drug properties and host factors on delayed onset of symptoms in drug-induced liver injury. Liver Int 2019; 39:401-410. [PMID: 30195258 DOI: 10.1111/liv.13952] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 08/22/2018] [Accepted: 08/27/2018] [Indexed: 12/20/2022]
Abstract
BACKGROUND & AIMS Most patients with drug-induced liver injury (DILI) manifest clinical symptoms while on therapy, while some patients manifest days or weeks after drug cessation (delayed onset). This challenges DILI causality assessment and diagnosis. Factors contributing to the delayed onset phenotype are currently unknown. We explored factors contributing to delayed onset of DILI by analysing culprit drug properties, host factors and their interactions in a large patient population from the Spanish DILI Registry. METHODS Clinical information from 388 patients (69 presented delayed onset) and drug properties of 43 causative drugs (45 active ingredients) were analysed. A two-tier regression-based model was used to assess host/drug interactions affecting the probability of delayed onset. RESULTS Antibacterial and anti-inflammatory drugs accounted for the delayed onset cases. Drug property of <50% hepatic metabolism (odds ratio [OR] 11.06, 95% confidence interval [95% CI]: 4.4-32.2, P = 0.0003), daily dose ≥1000 mg (OR: 2.77, 95% CI: 1.3-6.1, P = 0.0063) and the absence of pre-existing conditions in a patient (OR: 2.55, 95% CI: 1.3-4.9, P = 0.0043) were independently associated with delayed onset. The findings were consistent when externally validated using Latin American DILI Network cases (N = 131). Likewise, drug properties of mitochondrial liability and Pauling electronegativity were associated with delayed onset, but dependent on specific host factors such as age, sex and pre-existing cardiac diseases. CONCLUSIONS This study demonstrated that delayed onset, a specific DILI phenotype, is explained by complex interactions among drug properties and host factors and provided mechanistic hypotheses for future studies. These findings can help improve the diagnostic capability and causality assessment.
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Affiliation(s)
- Andres Gonzalez-Jimenez
- Unidad de Gestión Clínica del Aparato Digestivo, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, CIBERehd, Málaga, Spain
| | - Kristin McEuen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA
| | - Minjun Chen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA
| | - Ayako Suzuki
- Gastroenterology, Duke University, Durham, North Carolina, USA.,Gastroenterology, Durham VA Medical Center, Durham, North Carolina, USA
| | - Mercedes Robles-Diaz
- Unidad de Gestión Clínica del Aparato Digestivo, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, CIBERehd, Málaga, Spain
| | - Inmaculada Medina-Caliz
- Unidad de Gestión Clínica del Aparato Digestivo, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, CIBERehd, Málaga, Spain
| | - Fernando Bessone
- Facultad de Medicina, Hospital Provincial del Centenario, Universidad de Rosario, Rosario, Argentina
| | - Nelia Hernandez
- Facultad de Medicina, Hospital de Clínicas, UDELAR, Montevideo, Uruguay
| | - Marco Arrese
- Departamento de Gastroenterología, Facultad de Medicina, Pontificia Universidad Católica de Chile y Centro de Envejecimiento y Regeneración (CARE), Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Raymundo Parana
- Hospital Universitario Prof. Edgard Santos, Universidad Federal da Bahía, Salvador de Bahía, Brazil
| | - M Isabel Lucena
- Unidad de Gestión Clínica del Aparato Digestivo, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, CIBERehd, Málaga, Spain.,UICEC IBIMA, Plataforma SCReN (Spanish Clinical Research Network), Servicio de Farmacología Clínica, Hospital Universitario Virgen de la Victoria, Málaga, Spain
| | - Camilla Stephens
- Unidad de Gestión Clínica del Aparato Digestivo, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, CIBERehd, Málaga, Spain
| | - Raúl J Andrade
- Unidad de Gestión Clínica del Aparato Digestivo, Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Universitario Virgen de la Victoria, Universidad de Málaga, CIBERehd, Málaga, Spain
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Przybyłek M, Jeliński T, Cysewski P. Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Hansen Solubility Parameters Based on 1D and 2D Molecular Descriptors Computed from SMILES String. J CHEM-NY 2019; 2019:1-15. [DOI: 10.1155/2019/9858371] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
A new method of Hansen solubility parameters (HSPs) prediction was developed by combining the multivariate adaptive regression splines (MARSplines) methodology with a simple multivariable regression involving 1D and 2D PaDEL molecular descriptors. In order to adopt the MARSplines approach to QSPR/QSAR problems, several optimization procedures were proposed and tested. The effectiveness of the obtained models was checked via standard QSPR/QSAR internal validation procedures provided by the QSARINS software and by predicting the solubility classification of polymers and drug-like solid solutes in collections of solvents. By utilizing information derived only from SMILES strings, the obtained models allow for computing all of the three Hansen solubility parameters including dispersion, polarization, and hydrogen bonding. Although several descriptors are required for proper parameters estimation, the proposed procedure is simple and straightforward and does not require a molecular geometry optimization. The obtained HSP values are highly correlated with experimental data, and their application for solving solubility problems leads to essentially the same quality as for the original parameters. Based on provided models, it is possible to characterize any solvent and liquid solute for which HSP data are unavailable.
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Tseng CH, Tung CW, Peng SI, Chen YL, Tzeng CC, Cheng CM. Discovery of Pyrazolo[4,3- c]quinolines Derivatives as Potential Anti-Inflammatory Agents through Inhibiting of NO Production. Molecules 2018; 23:molecules23051036. [PMID: 29710774 PMCID: PMC6102577 DOI: 10.3390/molecules23051036] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Revised: 04/25/2018] [Accepted: 04/27/2018] [Indexed: 12/18/2022] Open
Abstract
The synthesis and anti-inflammatory effects of certain pyrazolo[4,3-c]quinoline derivatives 2a–2r are described. The anti-inflammatory activities of these derivatives were evaluated by means of inhibiting nitric oxide (NO) production in lipopolysaccharide (LPS)-induced RAW 264.7 cells. Among them, 3-amino-4-(4-hydroxyphenylamino)-1H-pyrazolo[4,3-c]-quinoline (2i) and 4-(3-amino-1H-pyrazolo[4,3-c]quinolin-4-ylamino)benzoic acid (2m) exhibited significant inhibition of LPS-stimulated NO production with a potency approximately equal to that of the positive control, 1400 W. Important structure features were analyzed by quantitative structure–activity relationship (QSAR) analysis to give better insights into the structure determinants for predicting the inhibitory effects on the accumulation of nitric oxide for RAW 264.7 cells in response to LPS. In addition, our results indicated that their anti-inflammatory effects involve the inhibition of inducible nitric oxide synthase (iNOS) and cyclooxygenase 2 (COX-2) protein expression. Further studies on the structural optimization are ongoing.
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Affiliation(s)
- Chih-Hua Tseng
- School of Pharmacy, College of Pharmacy, Kaohsiung Medical University, Kaohsiung City 807, Taiwan.
- Department of Fragrance and Cosmetic Science, College of Pharmacy, Kaohsiung Medical University, Kaohsiung City 807, Taiwan.
- Center for Infectious Disease and Cancer Research, Kaohsiung Medical University, Kaohsiung City 807, Taiwan.
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung City 807, Taiwan.
| | - Chun-Wei Tung
- School of Pharmacy, College of Pharmacy, Kaohsiung Medical University, Kaohsiung City 807, Taiwan.
- Ph.D. Program in Toxicology, Kaohsiung Medical University, Kaohsiung City 807, Taiwan.
| | - Shin-I Peng
- Department of Medicinal and Applied Chemistry, College of Life Science, Kaohsiung Medical University, Kaohsiung City 807, Taiwan.
| | - Yeh-Long Chen
- Department of Medicinal and Applied Chemistry, College of Life Science, Kaohsiung Medical University, Kaohsiung City 807, Taiwan.
| | - Cherng-Chyi Tzeng
- Department of Medicinal and Applied Chemistry, College of Life Science, Kaohsiung Medical University, Kaohsiung City 807, Taiwan.
| | - Chih-Mei Cheng
- Department of Biomedical Science and Environmental Biology, College of Life Science, Kaohsiung Medical University, Kaohsiung City 807, Taiwan.
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Zhang X, Liu T, Fan X, Ai N. In silico modeling on ADME properties of natural products: Classification models for blood-brain barrier permeability, its application to traditional Chinese medicine and in vitro experimental validation. J Mol Graph Model 2017; 75:347-54. [DOI: 10.1016/j.jmgm.2017.05.021] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Cysewski P, Przybyłek M. Selection of effective cocrystals former for dissolution rate improvement of active pharmaceutical ingredients based on lipoaffinity index. Eur J Pharm Sci 2017; 107:87-96. [PMID: 28687528 DOI: 10.1016/j.ejps.2017.07.004] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 06/06/2017] [Accepted: 07/03/2017] [Indexed: 10/19/2022]
Abstract
New theoretical screening procedure was proposed for appropriate selection of potential cocrystal formers possessing the ability of enhancing dissolution rates of drugs. The procedure relies on the training set comprising 102 positive and 17 negative cases of cocrystals found in the literature. Despite the fact that the only available data were of qualitative character, performed statistical analysis using binary classification allowed to formulate quantitative criterions. Among considered 3679 molecular descriptors the relative value of lipoaffinity index, expressed as the difference between values calculated for active compound and excipient, has been found as the most appropriate measure suited for discrimination of positive and negative cases. Assuming 5% precision, the applied classification criterion led to inclusion of 70% positive cases in the final prediction. Since lipoaffinity index is a molecular descriptor computed using only 2D information about a chemical structure, its estimation is straightforward and computationally inexpensive. The inclusion of an additional criterion quantifying the cocrystallization probability leads to the following conjunction criterions Hmix<-0.18 and ΔLA>3.61, allowing for identification of dissolution rate enhancers. The screening procedure was applied for finding the most promising coformers of such drugs as Iloperidone, Ritonavir, Carbamazepine and Enthenzamide.
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Affiliation(s)
- Piotr Cysewski
- Chair and Department of Physical Chemistry, Pharmacy Faculty, Collegium Medicum of Bydgoszcz, Nicolaus Copernicus University in Toruń, Kurpińskiego 5, 85-950 Bydgoszcz, Poland.
| | - Maciej Przybyłek
- Chair and Department of Physical Chemistry, Pharmacy Faculty, Collegium Medicum of Bydgoszcz, Nicolaus Copernicus University in Toruń, Kurpińskiego 5, 85-950 Bydgoszcz, Poland
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Tseng CH, Tung CW, Wu CH, Tzeng CC, Chen YH, Hwang TL, Chen YL. Discovery of Indeno[1,2-c]quinoline Derivatives as Potent Dual Antituberculosis and Anti-Inflammatory Agents. Molecules 2017; 22:E1001. [PMID: 28621733 DOI: 10.3390/molecules22061001] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
A series of indeno[1,2-c]quinoline derivatives were designed, synthesized and evaluated for their anti-tuberculosis (anti-TB) and anti-inflammatory activities. The minimum inhibitory concentration (MIC) of the newly synthesized compound was tested against Mycobacterium tuberculosis H37RV. Among the tested compounds, (E)-N′-[6-(4-hydroxypiperidin-1-yl)-11H-indeno[1,2-c]quinolin-11-ylidene]isonicotino-hydrazide (12), exhibited significant activities against the growth of M. tuberculosis (MIC values of 0.96 μg/mL) with a potency approximately equal to that of isoniazid (INH), an anti-TB drug. Important structure features were analyzed by quantitative structure–activity relationship (QSAR) analysis to give better insights into the structure determinants for predicting the anti-TB activity. The anti-inflammatory activity was induced by superoxide anion generation and neutrophil elastase (NE) release using the formyl-l-methionyl-l-leucyl-l-phenylalanine (fMLF)-activated human neutrophils method. Results indicated that compound 12 demonstrated a potent dual inhibitory effect on NE release and superoxide anion generation with IC50 values of 1.76 and 1.72 μM, respectively. Our results indicated that compound 12 is a potential lead compound for the discovery of dual anti-TB and anti-inflammatory drug candidates. In addition, 6-[3-(hydroxymethyl)piperidin-1-yl]-9-methoxy-11H-indeno[1,2-c]quinolin-11-one (4g) showed a potent dual inhibitory effect on NE release and superoxide anion generation with IC50 values of 0.46 and 0.68 μM, respectively, and is a potential lead compound for the discovery of anti-inflammatory drug candidates.
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Bennion BJ, Be NA, McNerney MW, Lao V, Carlson EM, Valdez CA, Malfatti MA, Enright HA, Nguyen TH, Lightstone FC, Carpenter TS. Predicting a Drug's Membrane Permeability: A Computational Model Validated With in Vitro Permeability Assay Data. J Phys Chem B 2017; 121:5228-5237. [PMID: 28453293 DOI: 10.1021/acs.jpcb.7b02914] [Citation(s) in RCA: 154] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Membrane permeability is a key property to consider during the drug design process, and particularly vital when dealing with small molecules that have intracellular targets as their efficacy highly depends on their ability to cross the membrane. In this work, we describe the use of umbrella sampling molecular dynamics (MD) computational modeling to comprehensively assess the passive permeability profile of a range of compounds through a lipid bilayer. The model was initially calibrated through in vitro validation studies employing a parallel artificial membrane permeability assay (PAMPA). The model was subsequently evaluated for its quantitative prediction of permeability profiles for a series of custom synthesized and closely related compounds. The results exhibited substantially improved agreement with the PAMPA data, relative to alternative existing methods. Our work introduces a computational model that underwent progressive molding and fine-tuning as a result of its synergistic collaboration with numerous in vitro PAMPA permeability assays. The presented computational model introduces itself as a useful, predictive tool for permeability prediction.
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Affiliation(s)
- Brian J Bennion
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory , Livermore, California 94550, United States
| | - Nicholas A Be
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory , Livermore, California 94550, United States
| | - M Windy McNerney
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory , Livermore, California 94550, United States.,War Related Illness and Injury Study Center, Veterans Affairs , Palo Alto, California 94304, United States
| | - Victoria Lao
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory , Livermore, California 94550, United States
| | - Emma M Carlson
- U.S. Naval Academy , Annapolis, Maryland 21402, United States
| | - Carlos A Valdez
- Nuclear and Chemical Sciences Division, Lawrence Livermore National Laboratory , Livermore, California 94550, United States
| | - Michael A Malfatti
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory , Livermore, California 94550, United States
| | - Heather A Enright
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory , Livermore, California 94550, United States
| | - Tuan H Nguyen
- Global Security Directorate, Lawrence Livermore National Laboratory , Livermore, California 94550, United States
| | - Felice C Lightstone
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory , Livermore, California 94550, United States
| | - Timothy S Carpenter
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory , Livermore, California 94550, United States
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Gobrogge CA, Blanchard HS, Walker RA. Temperature-Dependent Partitioning of Coumarin 152 in Phosphatidylcholine Lipid Bilayers. J Phys Chem B 2017; 121:4061-4070. [DOI: 10.1021/acs.jpcb.6b10893] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Christine A. Gobrogge
- Department of Chemistry and
Biochemistry, Montana State University, Bozeman, Montana 59717, United States
| | - Heather S. Blanchard
- Department of Chemistry and
Biochemistry, Montana State University, Bozeman, Montana 59717, United States
| | - Robert A. Walker
- Department of Chemistry and
Biochemistry, Montana State University, Bozeman, Montana 59717, United States
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Tsugawa H, Ikeda K, Tanaka W, Senoo Y, Arita M, Arita M. Comprehensive identification of sphingolipid species by in silico retention time and tandem mass spectral library. J Cheminform 2017; 9:19. [PMID: 28316657 PMCID: PMC5352698 DOI: 10.1186/s13321-017-0205-3] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Accepted: 03/06/2017] [Indexed: 01/05/2023] Open
Abstract
Background Liquid chromatography coupled with electrospray ionization tandem mass spectrometry (LC–ESI–MS/MS) is used for comprehensive metabolome and lipidome analyses. Compound identification relies on similarity matching of the retention time (RT), precursor m/z, isotopic ratio, and MS/MS spectrum with reference compounds. For sphingolipids, however, little information on the RT and MS/MS references is available. Results Negative-ion ESI–MS/MS is a useful method for the structural characterization of sphingolipids. We created theoretical MS/MS spectra for 21 sphingolipid classes in human and mouse (109,448 molecules), with substructure-level annotation of unique fragment ions by MS-FINDER software. The existence of ceramides with β-hydroxy fatty acids was confirmed in mouse tissues based on cheminformatic- and quantum chemical evidences. The RT of sphingo- and glycerolipid species was also predicted for our LC condition. With this information, MS-DIAL software for untargeted metabolome profiling could identify 415 unique structures including 282 glycerolipids and 133 sphingolipids from human cells (HEK and HeLa) and mouse tissues (ear and liver).
Conclusions MS-DIAL and MS-FINDER software programs can identify 42 lipid classes (21 sphingo- and 21 glycerolipids) with the in silico RT and MS/MS library. The library is freely available as Microsoft Excel files at the software section of our RIKEN PRIMe website (http://prime.psc.riken.jp/). Electronic supplementary material The online version of this article (doi:10.1186/s13321-017-0205-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hiroshi Tsugawa
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045 Japan
| | - Kazutaka Ikeda
- RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045 Japan.,Japan Agency for Medical Research and Development (AMED-PRIME), 1-7-1 Yomiuri Shimbun Building, Otemachi, Chiyoda-ku, Tokyo, 100-0004 Japan.,Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045 Japan
| | - Wataru Tanaka
- Department of Genetics, SOKENDAI (The Graduate University for Advanced Studies), 1111 Yata, Mishima, Shizuoka 411-8540 Japan
| | - Yuya Senoo
- RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045 Japan
| | - Makoto Arita
- RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045 Japan.,Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045 Japan.,Division of Physiological Chemistry and Metabolism, Graduate School of Pharmaceutical Sciences, Keio University, 1-5-30 Shibakoen, Minato-ku, Tokyo, 105-8512 Japan
| | - Masanori Arita
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045 Japan.,Department of Genetics, SOKENDAI (The Graduate University for Advanced Studies), 1111 Yata, Mishima, Shizuoka 411-8540 Japan.,National Institute of Genetics, 1111 Yata, Mishima, Shizuoka 411-8540 Japan
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Setlur AS, Naik SY, Skariyachan S. Herbal Lead as Ideal Bioactive Compounds Against Probable Drug Targets of Ebola Virus in Comparison with Known Chemical Analogue: A Computational Drug Discovery Perspective. Interdiscip Sci 2016; 9:254-277. [DOI: 10.1007/s12539-016-0149-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2015] [Revised: 01/05/2016] [Accepted: 01/25/2016] [Indexed: 12/17/2022]
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35
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Ryu EH, Kim MY, Yoon YJ, Im KH, Jeong HM, Jeon H, Lee H, Kim H. Solubility Prediction of Organic Ionic Compounds with Computational Methods for Photoresist Application. J PHOTOPOLYM SCI TEC 2016. [DOI: 10.2494/photopolymer.29.731] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
| | | | | | | | | | | | - Hankyul Lee
- Graduate School of EEWS, Korea Advanced Institute of Science and Technology (KAIST)
| | - Hyungjun Kim
- Graduate School of EEWS, Korea Advanced Institute of Science and Technology (KAIST)
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Abstract
The blood-brain barrier (BBB) is a microvascular unit which selectively regulates the permeability of drugs to the brain. With the rise in CNS drug targets and diseases, there is a need to be able to accurately predict a priori which compounds in a company database should be pursued for favorable properties. In this review, we will explore the different computational tools available today, as well as underpin these to the experimental methods used to determine BBB permeability. These include in vitro models and the in vivo models that yield the dataset we use to generate predictive models. Understanding of how these models were experimentally derived determines our accurate and predicted use for determining a balance between activity and BBB distribution.
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Gupta S, Basant N, Singh KP. Estimating sensory irritation potency of volatile organic chemicals using QSARs based on decision tree methods for regulatory purpose. Ecotoxicology 2015; 24:873-86. [PMID: 25707485 DOI: 10.1007/s10646-015-1431-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/12/2015] [Indexed: 05/26/2023]
Abstract
Volatile organic compounds (VOCs) are among the priority atmospheric pollutants that have high indoor and outdoor exposure potential. The toxicity assessment of VOCs to living ecosystems has received considerable attention in recent years. Development of computational methods for safety assessment of chemicals has been advocated by various regulatory agencies. The paper proposes robust and reliable quantitative structure-activity relationships (QSARs) for estimating the sensory irritation potency and screening of the VOCs. Here, decision tree (DT) based classification and regression QSARs models, such as single DT, decision tree forest (DTF), and decision tree boost (DTB) were developed using the sensory irritation data on VOCs in mice following the OECD principles. Structural diversity and nonlinearity in the data were evaluated through the Euclidean distance and Brock-Dechert-Scheinkman statistics. The constructed QSAR models were validated with external test data and the predictive performance of these models was established through a set of coefficients recommended in QSAR literature. The performance of all three classification and regression QSAR models was satisfactory, but DTF and DTB performed relatively better. The classification and regression QSAR models (DTF, DTB) rendered classification accuracies of 98.59 and 100 %, and yielded correlations (R(2)) of 0.950 and 0.971, respectively in complete data. The lipoaffinity index and SwHBa were identified as the most influential descriptors in proposed QSARs. The developed QSARs performed better than the previous studies. The developed models exhibited high statistical confidence and identified the structural properties of the VOCs responsible for their sensory irritation, and hence could be useful tools in screening of chemicals for regulatory purpose.
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Affiliation(s)
- Shikha Gupta
- Academy of Scientific and Innovative Research, Anusandhan Bhawan, Rafi Marg, New Delhi, 110 001, India
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38
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Sun L, Zhang C, Chen Y, Li X, Zhuang S, Li W, Liu G, Lee PW, Tang Y. In silico prediction of chemical aquatic toxicity with chemical category approaches and substructural alerts. Toxicol Res (Camb) 2015. [DOI: 10.1039/c4tx00174e] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aquatic toxicity is an important endpoint in the evaluation of chemically adverse effects on ecosystems.
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Affiliation(s)
- Lu Sun
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Chen Zhang
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Yingjie Chen
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Xiao Li
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Shulin Zhuang
- College of Environmental and Resource Sciences
- Zhejiang University
- Hangzhou 310058
- China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Philip W. Lee
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
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Liu H, Wang J, Zhou W, Wang Y, Yang L. Systems approaches and polypharmacology for drug discovery from herbal medicines: an example using licorice. J Ethnopharmacol 2013; 146:773-93. [PMID: 23415946 DOI: 10.1016/j.jep.2013.02.004] [Citation(s) in RCA: 201] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2012] [Revised: 02/03/2013] [Accepted: 02/04/2013] [Indexed: 05/08/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Licorice, one of the oldest and most popular herbal medicines in the world, has been widely used in traditional Chinese medicine as a cough reliever, anti-inflammatory, anti-anabrosis, immunomodulatory, anti-platelet, antiviral (hepatitis) and detoxifying agent. Licorice was used as an example to show drug discovery from herbal drugs using systems approaches and polypharmacology. AIM OF THE STUDY Herbal medicines are becoming more mainstream in clinical practice and show value in treating and preventing diseases. However, due to its extreme complexity both in chemical components and mechanisms of action, deep understanding of botanical drugs is still difficult. Thus, a comprehensive systems approach which could identify active ingredients and their targets in the crude drugs and more importantly, understand the biological basis for the pharmacological properties of herbal medicines is necessary. MATERIALS AND METHODS In this study, a novel systems pharmacology model that integrates oral bioavailability screening, drug-likeness evaluation, blood-brain barrier permeation, target identification and network analysis has been established to investigate the herbal medicines. RESULTS The comprehensive systems approach effectively identified 73 bioactive components from licorice and 91 potential targets for this medicinal herb. These 91 targets are closely associated with a series of diseases of respiratory system, cardiovascular system, and gastrointestinal system, etc. These targets are further mapped to drug-target and drug-target-disease networks to elucidate the mechanism of this herbal medicine. CONCLUSION This work provides a novel in silico strategy for investigation of the botanical drugs containing a huge number of components, which has been demonstrated by the well-studied licorice case. This attempt should be helpful for understanding definite mechanisms of action for herbal medicines and discovery of new drugs from plants.
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Affiliation(s)
- Hui Liu
- Bioinformatics Center, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
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40
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Swift RV, Amaro RE. Back to the future: can physical models of passive membrane permeability help reduce drug candidate attrition and move us beyond QSPR? Chem Biol Drug Des 2013; 81:61-71. [PMID: 23066853 PMCID: PMC3527668 DOI: 10.1111/cbdd.12074] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
It is widely recognized that adsorption, distribution, metabolism, excretion, and toxicology liabilities kill the majority of drug candidates that progress to clinical trials. The development of computational models to predict small molecule membrane permeability is therefore of considerable scientific and public health interest. Empirical qualitative structure permeability relationship models of permeability have been a mainstay in industrial applications, but lack a deep understanding of the underlying biologic physics. Others and we have shown that implicit solvent models to predict passive permeability for small molecules exhibit mediocre predictive performance when validated across experimental test sets. Given the vast increase in computer power, more efficient parallelization schemes, and extension of current atomistic simulation codes to general use graphical processing units, the development and application of physical models based on all-atom simulations may now be feasible. Preliminary results from rigorous free energy calculations using all-atom simulations indicate that performance relative to implicit solvent models may be improved, but many outstanding questions remain. Here, we review the current state-of-the-art physical models for passive membrane permeability prediction and present a prospective look at promising new directions for all-atom approaches.
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Abstract
The biophysical basis of passive membrane permeability is well-understood, but most methods for predicting membrane permeability in the context of drug design are based on statistical relationships that indirectly capture the key physical aspects. Here, we investigate molecular mechanics-based models of passive membrane permeability and evaluate their performance against different types of experimental data, including parallel artificial membrane permeability assays (PAMPA), cell-based assays, in vivo measurements, and other in silico predictions. The experimental data sets we use in these tests are diverse, including peptidomimetics, congeneric series, and diverse FDA approved drugs. The physical models are not specifically trained for any of these data sets; rather, input parameters are based on standard molecular mechanics force fields, such as partial charges, and an implicit solvent model. A systematic approach is taken to analyze the contribution from each component in the physics-based permeability model. A primary factor in determining rates of passive membrane permeation is the conformation-dependent free energy of desolvating the molecule, and this measure alone provides good agreement with experimental permeability measurements in many cases. Other factors that improve agreement with experimental data include deionization and estimates of entropy losses of the ligand and the membrane, which lead to size-dependence of the permeation rate.
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Affiliation(s)
- Siegfried S. F. Leung
- Department of Pharmaceutical Chemistry, University of California, San Francisco, California, 94158
| | - Jona Mijalkovic
- Department of Pharmaceutical Chemistry, University of California, San Francisco, California, 94158
| | - Kenneth Borrelli
- Schrödinger, Inc. 120 West 4 Street, 32 Floor, New York, New York, 10036
| | - Matthew P. Jacobson
- Department of Pharmaceutical Chemistry, University of California, San Francisco, California, 94158
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42
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Wang S, Li Y, Wang J, Chen L, Zhang L, Yu H, Hou T. ADMET evaluation in drug discovery. 12. Development of binary classification models for prediction of hERG potassium channel blockage. Mol Pharm 2012; 9:996-1010. [PMID: 22380484 DOI: 10.1021/mp300023x] [Citation(s) in RCA: 118] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Inhibition of the human ether-a-go-go related gene (hERG) potassium channel may result in QT interval prolongation, which causes severe cardiac side effects and is a major problem in clinical studies of drug candidates. The development of in silico tools to filter out potential hERG potassium channel blockers in early stages of the drug discovery process is of considerable interest. Here, a diverse set of 806 compounds with hERG inhibition data was assembled, and the binary hERG classification models using naive Bayesian classification and recursive partitioning (RP) techniques were established and evaluated. The naive Bayesian classifier based on molecular properties and the ECFP_8 fingerprints yielded 84.8% accuracy for the training set using the leave-one-out (LOO) cross-validation procedure and 85% accuracy for the test set of 120 molecules. For the two additional test sets, the model achieved 89.4% accuracy for the WOMBAT-PK test set, and 86.1% accuracy for the PubChem test set. The naive Bayesian classifiers gave better predictions than the RP classifiers. Moreover, the Bayesian classifier, employing molecular fingerprints, highlights the important structural fragments favorable or unfavorable for hERG potassium channel blockage, which offers extra valuable information for the design of compounds avoiding undesirable hERG activity.
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Affiliation(s)
- Sichao Wang
- Institute of Functional Nano & Soft Materials-FUNSOM and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
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Muehlbacher M, Spitzer GM, Liedl KR, Kornhuber J. Qualitative prediction of blood-brain barrier permeability on a large and refined dataset. J Comput Aided Mol Des 2011; 25:1095-106. [PMID: 22109848 PMCID: PMC3241963 DOI: 10.1007/s10822-011-9478-1] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2011] [Accepted: 10/10/2011] [Indexed: 12/14/2022]
Abstract
The prediction of blood-brain barrier permeation is vitally important for the optimization of drugs targeting the central nervous system as well as for avoiding side effects of peripheral drugs. Following a previously proposed model on blood-brain barrier penetration, we calculated the cross-sectional area perpendicular to the amphiphilic axis. We obtained a high correlation between calculated and experimental cross-sectional area (r = 0.898, n = 32). Based on these results, we examined a correlation of the calculated cross-sectional area with blood-brain barrier penetration given by logBB values. We combined various literature data sets to form a large-scale logBB dataset with 362 experimental logBB values. Quantitative models were calculated using bootstrap validated multiple linear regression. Qualitative models were built by a bootstrapped random forest algorithm. Both methods found similar descriptors such as polar surface area, pKa, logP, charges and number of positive ionisable groups to be predictive for logBB. In contrast to our initial assumption, we were not able to obtain models with the cross-sectional area chosen as relevant parameter for both approaches. Comparing those two different techniques, qualitative random forest models are better suited for blood-brain barrier permeability prediction, especially when reducing the number of descriptors and using a large dataset. A random forest prediction system (n(trees) = 5) based on only four descriptors yields a validated accuracy of 88%.
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Affiliation(s)
- Markus Muehlbacher
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen-Nuremberg, Germany
| | - Gudrun M. Spitzer
- Theoretical Chemistry, Center for Molecular Biosciences, University of Innsbruck, Innsbruck, Austria
| | - Klaus R. Liedl
- Theoretical Chemistry, Center for Molecular Biosciences, University of Innsbruck, Innsbruck, Austria
| | - Johannes Kornhuber
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen-Nuremberg, Germany
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Abstract
The use of Quantitative Structure-Activity Relationship models to address problems in drug discovery has a mixed history, generally resulting from the misapplication of QSAR models that were either poorly constructed or used outside of their domains of applicability. This situation has motivated the development of a variety of model performance metrics (r(2), PRESS r(2), F-tests, etc.) designed to increase user confidence in the validity of QSAR predictions. In a typical workflow scenario, QSAR models are created and validated on training sets of molecules using metrics such as Leave-One-Out or many-fold cross-validation methods that attempt to assess their internal consistency. However, few current validation methods are designed to directly address the stability of QSAR predictions in response to changes in the information content of the training set. Since the main purpose of QSAR is to quickly and accurately estimate a property of interest for an untested set of molecules, it makes sense to have a means at hand to correctly set user expectations of model performance. In fact, the numerical value of a molecular prediction is often less important to the end user than knowing the rank order of that set of molecules according to their predicted end point values. Consequently, a means for characterizing the stability of predicted rank order is an important component of predictive QSAR. Unfortunately, none of the many validation metrics currently available directly measure the stability of rank order prediction, making the development of an additional metric that can quantify model stability a high priority. To address this need, this work examines the stabilities of QSAR rank order models created from representative data sets, descriptor sets, and modeling methods that were then assessed using Kendall Tau as a rank order metric, upon which the Shannon entropy was evaluated as a means of quantifying rank-order stability. Random removal of data from the training set, also known as Data Truncation Analysis (DTA), was used as a means for systematically reducing the information content of each training set while examining both rank order performance and rank order stability in the face of training set data loss. The premise for DTA ROE model evaluation is that the response of a model to incremental loss of training information will be indicative of the quality and sufficiency of its training set, learning method, and descriptor types to cover a particular domain of applicability. This process is termed a "rank order entropy" evaluation or ROE. By analogy with information theory, an unstable rank order model displays a high level of implicit entropy, while a QSAR rank order model which remains nearly unchanged during training set reductions would show low entropy. In this work, the ROE metric was applied to 71 data sets of different sizes and was found to reveal more information about the behavior of the models than traditional metrics alone. Stable, or consistently performing models, did not necessarily predict rank order well. Models that performed well in rank order did not necessarily perform well in traditional metrics. In the end, it was shown that ROE metrics suggested that some QSAR models that are typically used should be discarded. ROE evaluation helps to discern which combinations of data set, descriptor set, and modeling methods lead to usable models in prioritization schemes and provides confidence in the use of a particular model within a specific domain of applicability.
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Kornhuber J, Muehlbacher M, Trapp S, Pechmann S, Friedl A, Reichel M, Mühle C, Terfloth L, Groemer TW, Spitzer GM, Liedl KR, Gulbins E, Tripal P. Identification of novel functional inhibitors of acid sphingomyelinase. PLoS One 2011; 6:e23852. [PMID: 21909365 PMCID: PMC3166082 DOI: 10.1371/journal.pone.0023852] [Citation(s) in RCA: 125] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2011] [Accepted: 07/26/2011] [Indexed: 12/19/2022] Open
Abstract
We describe a hitherto unknown feature for 27 small drug-like molecules, namely functional inhibition of acid sphingomyelinase (ASM). These entities named FIASMAs (Functional Inhibitors of Acid SphingoMyelinAse), therefore, can be potentially used to treat diseases associated with enhanced activity of ASM, such as Alzheimer's disease, major depression, radiation- and chemotherapy-induced apoptosis and endotoxic shock syndrome. Residual activity of ASM measured in the presence of 10 µM drug concentration shows a bimodal distribution; thus the tested drugs can be classified into two groups with lower and higher inhibitory activity. All FIASMAs share distinct physicochemical properties in showing lipophilic and weakly basic properties. Hierarchical clustering of Tanimoto coefficients revealed that FIASMAs occur among drugs of various chemical scaffolds. Moreover, FIASMAs more frequently violate Lipinski's Rule-of-Five than compounds without effect on ASM. Inhibition of ASM appears to be associated with good permeability across the blood-brain barrier. In the present investigation, we developed a novel structure-property-activity relationship by using a random forest-based binary classification learner. Virtual screening revealed that only six out of 768 (0.78%) compounds of natural products functionally inhibit ASM, whereas this inhibitory activity occurs in 135 out of 2028 (6.66%) drugs licensed for medical use in humans.
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Affiliation(s)
- Johannes Kornhuber
- Department of Psychiatry and Psychotherapy, University of Erlangen, Erlangen, Germany.
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Chen L, Li Y, Zhao Q, Peng H, Hou T. ADME Evaluation in Drug Discovery. 10. Predictions of P-Glycoprotein Inhibitors Using Recursive Partitioning and Naive Bayesian Classification Techniques. Mol Pharm 2011; 8:889-900. [DOI: 10.1021/mp100465q] [Citation(s) in RCA: 127] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Lei Chen
- Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
| | - Youyong Li
- Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
| | - Qing Zhao
- Department of Molecular Immunology, Institute of Basic Medical Sciences, Beijing 100850, China
| | - Hui Peng
- Department of Molecular Immunology, Institute of Basic Medical Sciences, Beijing 100850, China
| | - Tingjun Hou
- Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
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Contrera JF. Improved in silico prediction of carcinogenic potency (TD50) and the risk specific dose (RSD) adjusted Threshold of Toxicological Concern (TTC) for genotoxic chemicals and pharmaceutical impurities. Regul Toxicol Pharmacol 2011; 59:133-41. [DOI: 10.1016/j.yrtph.2010.09.016] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2010] [Revised: 09/28/2010] [Accepted: 09/29/2010] [Indexed: 11/28/2022]
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48
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Affiliation(s)
- David Hecht
- Southwestern College, Chula Vista, California
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Zhang YH, Xia ZN, Qin LT, Liu SS. Prediction of blood-brain partitioning: a model based on molecular electronegativity distance vector descriptors. J Mol Graph Model 2010; 29:214-20. [PMID: 20637670 DOI: 10.1016/j.jmgm.2010.06.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2009] [Revised: 06/14/2010] [Accepted: 06/17/2010] [Indexed: 11/25/2022]
Abstract
The objective of this paper is to build a reliable model based on the molecular electronegativity distance vector (MEDV) descriptors for predicting the blood-brain barrier (BBB) permeability and to reveal the effects of the molecular structural segments on the BBB permeability. Using 70 structurally diverse compounds, the partial least squares regression (PLSR) models between the BBB permeability and the MEDV descriptors were developed and validated by the variable selection and modeling based on prediction (VSMP) technique. The estimation ability, stability, and predictive power of a model are evaluated by the estimated correlation coefficient (r), leave-one-out (LOO) cross-validation correlation coefficient (q), and predictive correlation coefficient (R(p)). It has been found that PLSR model has good quality, r=0.9202, q=0.7956, and R(p)=0.6649 for M1 model based on the training set of 57 samples. To search the most important structural factors affecting the BBB permeability of compounds, we performed the values of the variable importance in projection (VIP) analysis for MEDV descriptors. It was found that some structural fragments in compounds, such as -CH(3), -CH(2)-, =CH-, =C, triple bond C-, -CH<, =C<, =N-, -NH-, =O, and -OH, are the most important factors affecting the BBB permeability.
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Affiliation(s)
- Yong-Hong Zhang
- College of Bioengineering, Chongqing University, Chongqing 400030, People's Republic of China
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50
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Fan Y, Unwalla R, Denny RA, Di L, Kerns EH, Diller DJ, Humblet C. Insights for Predicting Blood-Brain Barrier Penetration of CNS Targeted Molecules Using QSPR Approaches. J Chem Inf Model 2010; 50:1123-33. [DOI: 10.1021/ci900384c] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Yi Fan
- Chemical and Screening Sciences, Wyeth Research, Princeton, CN8000, New Jersey 08543-8000, Chemical and Screening Sciences, Wyeth Research, 500 Arcola Road, Collegeville, Pennsylvania 19426, and Chemical and Screening Sciences, Wyeth Research, 35 Cambridge Park Drive, Cambridge, Massachusetts 02140
| | - Rayomand Unwalla
- Chemical and Screening Sciences, Wyeth Research, Princeton, CN8000, New Jersey 08543-8000, Chemical and Screening Sciences, Wyeth Research, 500 Arcola Road, Collegeville, Pennsylvania 19426, and Chemical and Screening Sciences, Wyeth Research, 35 Cambridge Park Drive, Cambridge, Massachusetts 02140
| | - Rajiah A. Denny
- Chemical and Screening Sciences, Wyeth Research, Princeton, CN8000, New Jersey 08543-8000, Chemical and Screening Sciences, Wyeth Research, 500 Arcola Road, Collegeville, Pennsylvania 19426, and Chemical and Screening Sciences, Wyeth Research, 35 Cambridge Park Drive, Cambridge, Massachusetts 02140
| | - Li Di
- Chemical and Screening Sciences, Wyeth Research, Princeton, CN8000, New Jersey 08543-8000, Chemical and Screening Sciences, Wyeth Research, 500 Arcola Road, Collegeville, Pennsylvania 19426, and Chemical and Screening Sciences, Wyeth Research, 35 Cambridge Park Drive, Cambridge, Massachusetts 02140
| | - Edward H. Kerns
- Chemical and Screening Sciences, Wyeth Research, Princeton, CN8000, New Jersey 08543-8000, Chemical and Screening Sciences, Wyeth Research, 500 Arcola Road, Collegeville, Pennsylvania 19426, and Chemical and Screening Sciences, Wyeth Research, 35 Cambridge Park Drive, Cambridge, Massachusetts 02140
| | - David J. Diller
- Chemical and Screening Sciences, Wyeth Research, Princeton, CN8000, New Jersey 08543-8000, Chemical and Screening Sciences, Wyeth Research, 500 Arcola Road, Collegeville, Pennsylvania 19426, and Chemical and Screening Sciences, Wyeth Research, 35 Cambridge Park Drive, Cambridge, Massachusetts 02140
| | - Christine Humblet
- Chemical and Screening Sciences, Wyeth Research, Princeton, CN8000, New Jersey 08543-8000, Chemical and Screening Sciences, Wyeth Research, 500 Arcola Road, Collegeville, Pennsylvania 19426, and Chemical and Screening Sciences, Wyeth Research, 35 Cambridge Park Drive, Cambridge, Massachusetts 02140
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