1
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Voget R, Breidenbach J, Claff T, Hingst A, Sylvester K, Steinebach C, Vu LP, Weiße RH, Bartz U, Sträter N, Müller CE, Gütschow M. Development of an active-site titrant for SARS-CoV-2 main protease as an indispensable tool for evaluating enzyme kinetics. Acta Pharm Sin B 2024; 14:2349-2357. [PMID: 38799620 PMCID: PMC11121168 DOI: 10.1016/j.apsb.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 01/25/2024] [Accepted: 02/27/2024] [Indexed: 05/29/2024] Open
Abstract
A titrant for the SARS-CoV-2 main protease (Mpro) was developed that enables, for the first time, the exact determination of the concentration of the enzymatically active Mpro by active-site titration. The covalent binding mode of the tetrapeptidic titrant was elucidated by the determination of the crystal structure of the enzyme-titrant complex. Four fluorogenic substrates of Mpro, including a prototypical, internally quenched Dabcyl-EDANS peptide, were compared in terms of solubility under typical assay conditions. By exploiting the new titrant, key kinetic parameters for the Mpro-catalyzed cleavage of these substrates were determined.
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Affiliation(s)
- Rabea Voget
- Pharmaceutical Institute, Pharmaceutical & Medicinal Chemistry, University of Bonn, Bonn 53121, Germany
| | - Julian Breidenbach
- Pharmaceutical Institute, Pharmaceutical & Medicinal Chemistry, University of Bonn, Bonn 53121, Germany
| | - Tobias Claff
- Pharmaceutical Institute, Pharmaceutical & Medicinal Chemistry, University of Bonn, Bonn 53121, Germany
| | - Alexandra Hingst
- Pharmaceutical Institute, Pharmaceutical & Medicinal Chemistry, University of Bonn, Bonn 53121, Germany
| | - Katharina Sylvester
- Pharmaceutical Institute, Pharmaceutical & Medicinal Chemistry, University of Bonn, Bonn 53121, Germany
| | - Christian Steinebach
- Pharmaceutical Institute, Pharmaceutical & Medicinal Chemistry, University of Bonn, Bonn 53121, Germany
| | - Lan Phuong Vu
- Pharmaceutical Institute, Pharmaceutical & Medicinal Chemistry, University of Bonn, Bonn 53121, Germany
| | - Renato H. Weiße
- Institute of Bioanalytical Chemistry, Center for Biotechnology and Biomedicine, Leipzig University, Leipzig 04103, Germany
| | - Ulrike Bartz
- Department of Natural Sciences, University of Applied Sciences Bonn-Rhein-Sieg, Rheinbach 53359, Germany
| | - Norbert Sträter
- Institute of Bioanalytical Chemistry, Center for Biotechnology and Biomedicine, Leipzig University, Leipzig 04103, Germany
| | - Christa E. Müller
- Pharmaceutical Institute, Pharmaceutical & Medicinal Chemistry, University of Bonn, Bonn 53121, Germany
| | - Michael Gütschow
- Pharmaceutical Institute, Pharmaceutical & Medicinal Chemistry, University of Bonn, Bonn 53121, Germany
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2
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Mostofian B, Martin HJ, Razavi A, Patel S, Allen B, Sherman W, Izaguirre JA. Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods. J Chem Inf Model 2023; 63:5408-5432. [PMID: 37602861 PMCID: PMC10498452 DOI: 10.1021/acs.jcim.3c00603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Indexed: 08/22/2023]
Abstract
The therapeutic approach of targeted protein degradation (TPD) is gaining momentum due to its potentially superior effects compared with protein inhibition. Recent advancements in the biotech and pharmaceutical sectors have led to the development of compounds that are currently in human trials, with some showing promising clinical results. However, the use of computational tools in TPD is still limited, as it has distinct characteristics compared with traditional computational drug design methods. TPD involves creating a ternary structure (protein-degrader-ligase) responsible for the biological function, such as ubiquitination and subsequent proteasomal degradation, which depends on the spatial orientation of the protein of interest (POI) relative to E2-loaded ubiquitin. Modeling this structure necessitates a unique blend of tools initially developed for small molecules (e.g., docking) and biologics (e.g., protein-protein interaction modeling). Additionally, degrader molecules, particularly heterobifunctional degraders, are generally larger than conventional small molecule drugs, leading to challenges in determining drug-like properties like solubility and permeability. Furthermore, the catalytic nature of TPD makes occupancy-based modeling insufficient. TPD consists of multiple interconnected yet distinct steps, such as POI binding, E3 ligase binding, ternary structure interactions, ubiquitination, and degradation, along with traditional small molecule properties. A comprehensive set of tools is needed to address the dynamic nature of the induced proximity ternary complex and its implications for ubiquitination. In this Perspective, we discuss the current state of computational tools for TPD. We start by describing the series of steps involved in the degradation process and the experimental methods used to characterize them. Then, we delve into a detailed analysis of the computational tools employed in TPD. We also present an integrative approach that has proven successful for degrader design and its impact on project decisions. Finally, we examine the future prospects of computational methods in TPD and the areas with the greatest potential for impact.
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Affiliation(s)
- Barmak Mostofian
- OpenEye, Cadence Molecular Sciences, Boston, Massachusetts 02114 United States
| | - Holli-Joi Martin
- Laboratory
for Molecular Modeling, Division of Chemical Biology and Medicinal
Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599 United States
| | - Asghar Razavi
- ENKO
Chem, Inc, Mystic, Connecticut 06355 United States
| | - Shivam Patel
- Psivant
Therapeutics, Boston, Massachusetts 02210 United States
| | - Bryce Allen
- Differentiated
Therapeutics, San Diego, California 92056 United States
| | - Woody Sherman
- Psivant
Therapeutics, Boston, Massachusetts 02210 United States
| | - Jesus A Izaguirre
- Differentiated
Therapeutics, San Diego, California 92056 United States
- Atommap
Corporation, New York, New York 10013 United States
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3
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Mareş C, Udrea AM, Şuţan NA, Avram S. Bioinformatics Tools for the Analysis of Active Compounds Identified in Ranunculaceae Species. Pharmaceuticals (Basel) 2023; 16:842. [PMID: 37375790 DOI: 10.3390/ph16060842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 05/30/2023] [Accepted: 05/31/2023] [Indexed: 06/29/2023] Open
Abstract
The chemical compounds from extracts of three Ranunculaceae species, Aconitum toxicum Rchb., Anemone nemorosa L. and Helleborus odorus Waldst. & Kit. ex Willd., respectively, were isolated using the HPLC purification technique and analyzed from a bioinformatics point of view. The classes of compounds identified based on the proportion in the rhizomes/leaves/flowers used for microwave-assisted extraction and ultrasound-assisted extraction were alkaloids and phenols. Here, the quantifying of pharmacokinetics, pharmacogenomics and pharmacodynamics helps us to identify the actual biologically active compounds. Our results showed that (i) pharmacokinetically, the compounds show good absorption at the intestinal level and high permeability at the level of the central nervous system for alkaloids; (ii) regarding pharmacogenomics, alkaloids can influence tumor sensitivity and the effectiveness of some treatments; (iii) and pharmacodynamically, the compounds of these Ranunculaceae species bind to carbonic anhydrase and aldose reductase. The results obtained showed a high affinity of the compounds in the binding solution at the level of carbonic anhydrases. Carbonic anhydrase inhibitors extracted from natural sources can represent the path to new drugs useful both in the treatment of glaucoma, but also of some renal, neurological and even neoplastic diseases. The identification of natural compounds with the role of inhibitors can have a role in different types of pathologies, both associated with studied and known receptors such as carbonic anhydrase and aldose reductase, as well as new pathologies not yet addressed.
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Affiliation(s)
- Cătălina Mareş
- Department of Anatomy, Animal Physiology and Biophysics, University of Bucharest, 91-95 Splaiul Independentei, 050095 Bucharest, Romania
| | - Ana-Maria Udrea
- Laser Department, National Institute for Laser, Plasma and Radiation Physics, Atomistilor 409, 077125 Magurele, Romania
- Research Institute of the University of Bucharest-ICUB, University of Bucharest, 91-95 Splaiul Independentei, 050095 Bucharest, Romania
| | - Nicoleta Anca Şuţan
- Department of Natural Sciences, University of Piteşti, 1 Targul din Vale Str., 110040 Pitesti, Romania
| | - Speranţa Avram
- Department of Anatomy, Animal Physiology and Biophysics, University of Bucharest, 91-95 Splaiul Independentei, 050095 Bucharest, Romania
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4
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Conn JM, Carter JW, Conn JJA, Subramanian V, Baxter A, Engkvist O, Llinas A, Ratkova EL, Pickett SD, McDonagh JL, Palmer DS. Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models. J Chem Inf Model 2023; 63:1099-1113. [PMID: 36758178 PMCID: PMC9976279 DOI: 10.1021/acs.jcim.2c01189] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state of the art, the American Chemical Society organized a "Second Solubility Challenge" in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019 but which have not previously been reported. These models were based on computationally inexpensive molecular descriptors and traditional machine learning algorithms and were trained on a relatively small data set of 300 molecules. In the second part of the article, to test the hypothesis that predictions would improve with more advanced algorithms and higher volumes of training data, we compare these original predictions with those made after the deadline using deep learning models trained on larger solubility data sets consisting of 2999 and 5697 molecules. The results show that there are several algorithms that are able to obtain near state-of-the-art performance on the solubility challenge data sets, with the best model, a graph convolutional neural network, resulting in an RMSE of 0.86 log units. Critical analysis of the models reveals systematic differences between the performance of models using certain feature sets and training data sets. The results suggest that careful selection of high quality training data from relevant regions of chemical space is critical for prediction accuracy but that other methodological issues remain problematic for machine learning solubility models, such as the difficulty in modeling complex chemical spaces from sparse training data sets.
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Affiliation(s)
- Jonathan
G. M. Conn
- Department
of Pure and Applied Chemistry, University
of Strathclyde, Thomas Graham Building, 295 Cathedral Street, Glasgow G1 1XL, U.K.
| | - James W. Carter
- Department
of Pure and Applied Chemistry, University
of Strathclyde, Thomas Graham Building, 295 Cathedral Street, Glasgow G1 1XL, U.K.
| | - Justin J. A. Conn
- Department
of Pure and Applied Chemistry, University
of Strathclyde, Thomas Graham Building, 295 Cathedral Street, Glasgow G1 1XL, U.K.
| | - Vigneshwari Subramanian
- Drug
Metabolism and Pharmacokinetics, Research and Early Development, Respiratory & Immunology, BioPharmaceuticals R&D,
AstraZeneca, Pepparedsleden 1, SE-431 83 Göteborg, Sweden
| | - Andrew Baxter
- GSK
Medicines Research Centre, Gunnels Wood Road, Stevenage SG1 2NY, U.K.
| | - Ola Engkvist
- Medicinal
Chemistry, Research and Early Development, Cardiovascular, Renal and
Metabolism (CVRM), BioPharmaceuticals R&D,
AstraZeneca, SE-431 50 Göteborg, Sweden,Department
of Computer Science and Engineering, Chalmers
University of Technology, SE-412 96 Göteborg, Sweden
| | - Antonio Llinas
- Drug
Metabolism and Pharmacokinetics, Research and Early Development, Respiratory & Immunology, BioPharmaceuticals R&D,
AstraZeneca, Pepparedsleden 1, SE-431 83 Göteborg, Sweden
| | - Ekaterina L. Ratkova
- Medicinal
Chemistry, Research and Early Development, Cardiovascular, Renal and
Metabolism (CVRM), BioPharmaceuticals R&D,
AstraZeneca, SE-431 50 Göteborg, Sweden
| | - Stephen D. Pickett
- Computational
Sciences, GlaxoSmithKline R&D Pharmaceuticals, Stevenage SG1 2NY, U.K.
| | - James L. McDonagh
- IBM Research
Europe, Hartree Centre, SciTech Daresbury, Warrington, Cheshire WA4 4AD, U.K.
| | - David S. Palmer
- Department
of Pure and Applied Chemistry, University
of Strathclyde, Thomas Graham Building, 295 Cathedral Street, Glasgow G1 1XL, U.K.,E-mail:
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5
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Oja M, Sild S, Piir G, Maran U. Intrinsic Aqueous Solubility: Mechanistically Transparent Data-Driven Modeling of Drug Substances. Pharmaceutics 2022; 14:pharmaceutics14102248. [PMID: 36297685 PMCID: PMC9611068 DOI: 10.3390/pharmaceutics14102248] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 10/12/2022] [Accepted: 10/18/2022] [Indexed: 11/07/2022] Open
Abstract
Intrinsic aqueous solubility is a foundational property for understanding the chemical, technological, pharmaceutical, and environmental behavior of drug substances. Despite years of solubility research, molecular structure-based prediction of the intrinsic aqueous solubility of drug substances is still under active investigation. This paper describes the authors’ systematic data-driven modelling in which two fit-for-purpose training data sets for intrinsic aqueous solubility were collected and curated, and three quantitative structure–property relationships were derived to make predictions for the most recent solubility challenge. All three models perform well individually, while being mechanistically transparent and easy to understand. Molecular descriptors involved in the models are related to the following key steps in the solubility process: dissociation of the molecule from the crystal, formation of a cavity in the solvent, and insertion of the molecule into the solvent. A consensus modeling approach with these models remarkably improved prediction capability and reduced the number of strong outliers by more than two times. The performance and outliers of the second solubility challenge predictions were analyzed retrospectively. All developed models have been published in the QsarDB.org repository according to FAIR principles and can be used without restrictions for exploring, downloading, and making predictions.
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Affiliation(s)
| | | | | | - Uko Maran
- Correspondence: ; Tel.: +372-7-375-254; Fax: +372-7-375-264
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6
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Deng C, Liang L, Xing G, Hua Y, Lu T, Zhang Y, Chen Y, Liu H. Multi-channel GCN ensembled machine learning model for molecular aqueous solubility prediction on a clean dataset. Mol Divers 2022:10.1007/s11030-022-10465-x. [PMID: 35739374 DOI: 10.1007/s11030-022-10465-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 05/19/2022] [Indexed: 10/17/2022]
Abstract
This study constructed a new aqueous solubility dataset and a solubility regression model which was ensembled by GCN and machine learning models. Aqueous solubility is a key physiochemical property of small molecules in drug discovery. In the past few decades, there have been many studies about solubility prediction. However, many of these studies have high root mean squared error (RMSE). Meanwhile, their dataset always contains salt compounds and solubility data obtained from different experimental conditions. In this paper, we constructed a clean dataset with 2609 compounds, which was small but contains only solubility records without salts at the same temperatures (25 °C). Here, we applied graph convolutional neural network (GCN) to construct an aqueous solubility prediction model. To enhance the performance of the model, the molecular MACCS key fingerprints and physiochemical descriptors were also combined with the GCN model to build a multi-channel model. Additionally, the authors also built two machine learning models (support vector regression and gradient boost decision tree) and assembled them to the GCN model to improve the root mean squared error (RMSE = 0.665). Finally, comparative experiments have shown that our framework achieved the best performance on ESOL dataset (RMSEval = 0.56, RMSEtest = 0.44) and surpassed four established software on aqueous solubility prediction of new compounds.
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Affiliation(s)
- Chenglong Deng
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Li Liang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Guomeng Xing
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Yi Hua
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Tao Lu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.,State Key Laboratory of Natural Medicines, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China
| | - Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.
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7
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Wang Z, Cherukupalli S, Xie M, Wang W, Jiang X, Jia R, Pannecouque C, De Clercq E, Kang D, Zhan P, Liu X. Contemporary Medicinal Chemistry Strategies for the Discovery and Development of Novel HIV-1 Non-nucleoside Reverse Transcriptase Inhibitors. J Med Chem 2022; 65:3729-3757. [PMID: 35175760 DOI: 10.1021/acs.jmedchem.1c01758] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Currently, HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs) are a major component of the highly active anti-retroviral therapy (HAART) regimen. However, the occurrence of drug-resistant strains and adverse reactions after long-term usage have inevitably compromised the clinical application of NNRTIs. Therefore, the development of novel inhibitors with distinct anti-resistance profiles and better pharmacological properties is still an enormous challenge. Herein, we summarize state-of-the-art medicinal chemistry strategies for the discovery of potent NNRTIs, such as structure-based design strategies, contemporary computer-aided drug design, covalent-binding strategies, and the application of multi-target-directed ligands. The strategies described here will facilitate the identification of promising HIV-1 NNRTIs.
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Affiliation(s)
- Zhao Wang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, 44 West Culture Road, 250012 Jinan, Shandong, P.R. China
| | - Srinivasulu Cherukupalli
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, 44 West Culture Road, 250012 Jinan, Shandong, P.R. China
| | - Minghui Xie
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, 44 West Culture Road, 250012 Jinan, Shandong, P.R. China
| | - Wenbo Wang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, 44 West Culture Road, 250012 Jinan, Shandong, P.R. China
| | - Xiangyi Jiang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, 44 West Culture Road, 250012 Jinan, Shandong, P.R. China
| | - Ruifang Jia
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, 44 West Culture Road, 250012 Jinan, Shandong, P.R. China
| | - Christophe Pannecouque
- Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy, K.U. Leuven, Herestraat 49 Postbus 1043 (09.A097), B-3000 Leuven, Belgium
| | - Erik De Clercq
- Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy, K.U. Leuven, Herestraat 49 Postbus 1043 (09.A097), B-3000 Leuven, Belgium
| | - Dongwei Kang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, 44 West Culture Road, 250012 Jinan, Shandong, P.R. China.,China-Belgium Collaborative Research Center for Innovative Antiviral Drugs of Shandong Province, 44 West Culture Road, 250012 Jinan, Shandong, P.R. China
| | - Peng Zhan
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, 44 West Culture Road, 250012 Jinan, Shandong, P.R. China.,China-Belgium Collaborative Research Center for Innovative Antiviral Drugs of Shandong Province, 44 West Culture Road, 250012 Jinan, Shandong, P.R. China
| | - Xinyong Liu
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, 44 West Culture Road, 250012 Jinan, Shandong, P.R. China.,China-Belgium Collaborative Research Center for Innovative Antiviral Drugs of Shandong Province, 44 West Culture Road, 250012 Jinan, Shandong, P.R. China
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8
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Structural modification aimed for improving solubility of lead compounds in early phase drug discovery. Bioorg Med Chem 2022; 56:116614. [DOI: 10.1016/j.bmc.2022.116614] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 12/15/2021] [Accepted: 01/06/2022] [Indexed: 12/19/2022]
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9
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Sorkun MC, Koelman JVA, Er S. Pushing the limits of solubility prediction via quality-oriented data selection. iScience 2021; 24:101961. [PMID: 33437941 PMCID: PMC7788089 DOI: 10.1016/j.isci.2020.101961] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 11/18/2020] [Accepted: 12/15/2020] [Indexed: 01/19/2023] Open
Abstract
Accurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.
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Affiliation(s)
- Murat Cihan Sorkun
- DIFFER - Dutch Institute for Fundamental Energy Research, De Zaale 20, 5612 AJ Eindhoven, the Netherlands
- CCER - Center for Computational Energy Research, De Zaale 20, 5612 AJ Eindhoven, the Netherlands
- Department of Applied Physics, Eindhoven University of Technology, 5600 MB Eindhoven, the Netherlands
| | - J.M. Vianney A. Koelman
- DIFFER - Dutch Institute for Fundamental Energy Research, De Zaale 20, 5612 AJ Eindhoven, the Netherlands
- CCER - Center for Computational Energy Research, De Zaale 20, 5612 AJ Eindhoven, the Netherlands
- Department of Applied Physics, Eindhoven University of Technology, 5600 MB Eindhoven, the Netherlands
| | - Süleyman Er
- DIFFER - Dutch Institute for Fundamental Energy Research, De Zaale 20, 5612 AJ Eindhoven, the Netherlands
- CCER - Center for Computational Energy Research, De Zaale 20, 5612 AJ Eindhoven, the Netherlands
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10
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QSPR models for water solubility of ammonium hexafluorosilicates: analysis of the effects of hydrogen bonds. Struct Chem 2020. [DOI: 10.1007/s11224-020-01652-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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11
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Falcón-Cano G, Molina C, Cabrera-Pérez MÁ. ADME prediction with KNIME: In silico aqueous solubility consensus model based on supervised recursive random forest approaches. ADMET AND DMPK 2020; 8:251-273. [PMID: 35300309 PMCID: PMC8915604 DOI: 10.5599/admet.852] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 08/01/2020] [Indexed: 12/12/2022] Open
Abstract
In-silico prediction of aqueous solubility plays an important role during the drug discovery and development processes. For many years, the limited performance of in-silico solubility models has been attributed to the lack of high-quality solubility data for pharmaceutical molecules. However, some studies suggest that the poor accuracy of solubility prediction is not related to the quality of the experimental data and that more precise methodologies (algorithms and/or set of descriptors) are required for predicting aqueous solubility for pharmaceutical molecules. In this study a large and diverse database was generated with aqueous solubility values collected from two public sources; two new recursive machine-learning approaches were developed for data cleaning and variable selection, and a consensus model based on regression and classification algorithms was created. The modeling protocol, which includes the curation of chemical and experimental data, was implemented in KNIME, with the aim of obtaining an automated workflow for the prediction of new databases. Finally, we compared several methods or models available in the literature with our consensus model, showing results comparable or even outperforming previous published models.
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Affiliation(s)
- Gabriela Falcón-Cano
- Unit of Modeling and Experimental Biopharmaceutics. Centro de Bioactivos Químicos. Universidad Central "Marta Abreu" de las Villas. Santa Clara 54830, Villa Clara, Cuba
| | | | - Miguel Ángel Cabrera-Pérez
- Unit of Modeling and Experimental Biopharmaceutics. Centro de Bioactivos Químicos. Universidad Central "Marta Abreu" de las Villas. Santa Clara 54830, Villa Clara, Cuba.,Department of Pharmacy and Pharmaceutical Technology, University of Valencia, Burjassot 46100, Valencia, Spain.,Department of Engineering, Area of Pharmacy and Pharmaceutical Technology, Miguel Hernández University, 03550 Sant Joan d'Alacant, Alicante, Spain
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12
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Fundamental aspects of DMPK optimization of targeted protein degraders. Drug Discov Today 2020; 25:969-982. [PMID: 32298797 DOI: 10.1016/j.drudis.2020.03.012] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 03/03/2020] [Accepted: 03/16/2020] [Indexed: 12/30/2022]
Abstract
Targeted protein degraders are an emerging modality. Their properties fall outside the traditional small-molecule property space and are in the 'beyond rule of 5' space. Consequently, drug discovery programs focused on developing orally bioavailable degraders are expected to face complex drug metabolism and pharmacokinetics (DMPK) challenges compared with traditional small molecules. Nevertheless, little information is available on the DMPK optimization of oral degraders. Therefore, in this review, we discuss our experience of these DMPK optimization challenges and present methodologies and strategies to overcome the hurdles dealing with this new small-molecule modality, specifically in developing oral degraders to treat cancer.
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