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Mansouri K, Taylor K, Auerbach S, Ferguson S, Frawley R, Hsieh JH, Jahnke G, Kleinstreuer N, Mehta S, Moreira-Filho JT, Parham F, Rider C, Rooney AA, Wang A, Sutherland V. Unlocking the Potential of Clustering and Classification Approaches: Navigating Supervised and Unsupervised Chemical Similarity. ENVIRONMENTAL HEALTH PERSPECTIVES 2024; 132:85002. [PMID: 39106156 DOI: 10.1289/ehp14001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2024]
Abstract
BACKGROUND The field of toxicology has witnessed substantial advancements in recent years, particularly with the adoption of new approach methodologies (NAMs) to understand and predict chemical toxicity. Class-based methods such as clustering and classification are key to NAMs development and application, aiding the understanding of hazard and risk concerns associated with groups of chemicals without additional laboratory work. Advances in computational chemistry, data generation and availability, and machine learning algorithms represent important opportunities for continued improvement of these techniques to optimize their utility for specific regulatory and research purposes. However, due to their intricacy, deep understanding and careful selection are imperative to align the adequate methods with their intended applications. OBJECTIVES This commentary aims to deepen the understanding of class-based approaches by elucidating the pivotal role of chemical similarity (structural and biological) in clustering and classification approaches (CCAs). It addresses the dichotomy between general end point-agnostic similarity, often entailing unsupervised analysis, and end point-specific similarity necessitating supervised learning. The goal is to highlight the nuances of these approaches, their applications, and common misuses. DISCUSSION Understanding similarity is pivotal in toxicological research involving CCAs. The effectiveness of these approaches depends on the right definition and measure of similarity, which varies based on context and objectives of the study. This choice is influenced by how chemical structures are represented and the respective labels indicating biological activity, if applicable. The distinction between unsupervised clustering and supervised classification methods is vital, requiring the use of end point-agnostic vs. end point-specific similarity definition. Separate use or combination of these methods requires careful consideration to prevent bias and ensure relevance for the goal of the study. Unsupervised methods use end point-agnostic similarity measures to uncover general structural patterns and relationships, aiding hypothesis generation and facilitating exploration of datasets without the need for predefined labels or explicit guidance. Conversely, supervised techniques demand end point-specific similarity to group chemicals into predefined classes or to train classification models, allowing accurate predictions for new chemicals. Misuse can arise when unsupervised methods are applied to end point-specific contexts, like analog selection in read-across, leading to erroneous conclusions. This commentary provides insights into the significance of similarity and its role in supervised classification and unsupervised clustering approaches. https://doi.org/10.1289/EHP14001.
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Affiliation(s)
- Kamel Mansouri
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Kyla Taylor
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Scott Auerbach
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Stephen Ferguson
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Rachel Frawley
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Jui-Hua Hsieh
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Gloria Jahnke
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Nicole Kleinstreuer
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Suril Mehta
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - José T Moreira-Filho
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Fred Parham
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Cynthia Rider
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Andrew A Rooney
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Amy Wang
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Vicki Sutherland
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
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2
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Wang N, Wu X, Liang J, Liu B, Wang B. Molecular design of hydroxamic acid-based derivatives as urease inhibitors of Helicobacter pylori. Mol Divers 2024:10.1007/s11030-024-10914-9. [PMID: 39020133 DOI: 10.1007/s11030-024-10914-9] [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: 04/27/2024] [Accepted: 06/08/2024] [Indexed: 07/19/2024]
Abstract
Helicobacter pylori is the main causative agent of gastric cancer, especially non-cardiac gastric cancers. This bacterium relies on urease producing much ammonia to colonize the host. Herein, the study provides valuable insights into structural patterns driving urease inhibition for high-activity molecules designed via exploring known inhibitors. Firstly, an ensemble model was devised to predict the inhibitory activity of novel compounds in an automated workflow (R2 = 0.761) that combines four machine learning approaches. The dataset was characterized in terms of chemical space, including molecular scaffolds, clustering analysis, distribution for physicochemical properties, and activity cliffs. Through these analyses, the hydroxamic acid group and the benzene ring responsible for distinct activity were highlighted. Activity cliff pairs uncovered substituents of the benzene ring on hydroxamic acid derivatives are key structures for substantial activity enhancement. Moreover, 11 hydroxamic acid derivatives were designed, named mol1-11. Results of molecular dynamic simulations showed that the mol9 exhibited stabilization of the active site flap's closed conformation and are expected to be promising drug candidates for Helicobacter pylori infection and further in vitro, in vivo, and clinical trials to demonstrate in future.
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Affiliation(s)
- Na Wang
- College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China
| | - Xiaoyan Wu
- College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China
| | - Jianhuai Liang
- College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China
| | - Boping Liu
- College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China.
| | - Bingfeng Wang
- College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China.
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3
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Juárez-Mercado KE, Avellaneda-Tamayo JF, Villegas-Quintero H, Chávez-Hernández AL, López-López CD, Medina-Franco JL. Food Chemicals and Epigenetic Targets: An Epi Food Chemical Database. ACS OMEGA 2024; 9:25322-25331. [PMID: 38882162 PMCID: PMC11170626 DOI: 10.1021/acsomega.4c03321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 05/09/2024] [Accepted: 05/14/2024] [Indexed: 06/18/2024]
Abstract
There is increasing awareness of epigenetics's importance in understanding disease etiologies and developing novel therapeutics. An increasing number of publications in the past few years reflect the renewed interest in epigenetic processes and their relationship with food chemicals. However, there needs to be a recent study that accounts for the most recent advances in the area by associating the chemical structures of food and natural product components with their biological activity. Here, we analyze the status of food chemicals and their intersection with natural products in epigenetic research. Using chemoinformatics tools, we compared quantitatively the chemical contents, structural diversity, and coverage in the chemical space of food chemicals with reported epigenetic activity. As part of this work, we built and curated a compound database of food and natural product chemicals annotated with structural information, an epigenetic target activity profile, and the main source of the food chemical or natural product, among other relevant features. The compounds are cross-linked with identifiers from other major public databases such as FooDB and the collection of open natural products, COCONUT. The compound database, the "Epi Food Chemical Database", is accessible in HTML and CSV formats at https://github.com/DIFACQUIM/Epi_food_Chemical_Database.
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Affiliation(s)
- K Eurídice Juárez-Mercado
- DIFACQUIM Research Group. Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510 Ciudad de México, Mexico
| | - Juan F Avellaneda-Tamayo
- DIFACQUIM Research Group. Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510 Ciudad de México, Mexico
| | - Hassan Villegas-Quintero
- DIFACQUIM Research Group. Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510 Ciudad de México, Mexico
| | - Ana L Chávez-Hernández
- DIFACQUIM Research Group. Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510 Ciudad de México, Mexico
| | | | - José L Medina-Franco
- DIFACQUIM Research Group. Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510 Ciudad de México, Mexico
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4
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Díaz-Rojas M, González-Andrade M, Aguayo-Ortiz R, Rodríguez-Sotres R, Pérez-Vásquez A, Madariaga-Mazón A, Mata R. Discovery of inhibitors of protein tyrosine phosphatase 1B contained in a natural products library from Mexican medicinal plants and fungi using a combination of enzymatic and in silico methods*. Front Pharmacol 2023; 14:1281045. [PMID: 38027024 PMCID: PMC10644722 DOI: 10.3389/fphar.2023.1281045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 10/13/2023] [Indexed: 12/01/2023] Open
Abstract
This work aimed to discover protein tyrosine phosphatase 1B (PTP1B) inhibitors from a small molecule library of natural products (NPs) derived from selected Mexican medicinal plants and fungi to find new hits for developing antidiabetic drugs. The products showing similar IC50 values to ursolic acid (UA) (positive control, IC50 = 26.5) were considered hits. These compounds were canophyllol (1), 5-O-(β-D-glucopyranosyl)-7-methoxy-3',4'-dihydroxy-4-phenylcoumarin (2), 3,4-dimethoxy-2,5-phenanthrenediol (3), masticadienonic acid (4), 4',5,6-trihydroxy-3',7-dimethoxyflavone (5), E/Z vermelhotin (6), tajixanthone hydrate (7), quercetin-3-O-(6″-benzoyl)-β-D-galactoside (8), lichexanthone (9), melianodiol (10), and confusarin (11). According to the double-reciprocal plots, 1 was a non-competitive inhibitor, 3 a mixed-type, and 6 competitive. The chemical space analysis of the hits (IC50 < 100 μM) and compounds possessing activity (IC50 in the range of 100-1,000 μM) with the BIOFACQUIM library indicated that the active molecules are chemically diverse, covering most of the known Mexican NPs' chemical space. Finally, a structure-activity similarity (SAS) map was built using the Tanimoto similarity index and PTP1B absolute inhibitory activity, which allows the identification of seven scaffold hops, namely, compounds 3, 5, 6, 7, 8, 9, and 11. Canophyllol (1), on the other hand, is a true analog of UA since it is an SAR continuous zone of the SAS map.
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Affiliation(s)
- Miriam Díaz-Rojas
- Facultad de Química, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | | | - Rodrigo Aguayo-Ortiz
- Facultad de Química, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | | | | | - Abraham Madariaga-Mazón
- Instituto de Química Unidad Mérida and Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas Unidad Mérida, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Rachel Mata
- Facultad de Química, Universidad Nacional Autónoma de México, Mexico City, Mexico
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5
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Sahoo AK, Baskaran SP, Chivukula N, Kumar K, Samal A. Analysis of structure-activity and structure-mechanism relationships among thyroid stimulating hormone receptor binding chemicals by leveraging the ToxCast library. RSC Adv 2023; 13:23461-23471. [PMID: 37546222 PMCID: PMC10401517 DOI: 10.1039/d3ra04452a] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 07/31/2023] [Indexed: 08/08/2023] Open
Abstract
The thyroid stimulating hormone receptor (TSHR) is crucial in thyroid hormone production in humans, and dysregulation in TSHR activation can lead to adverse health effects such as hypothyroidism and Graves' disease. Further, animal studies have shown that binding of endocrine disrupting chemicals (EDCs) with TSHR can lead to developmental toxicity. Hence, several such chemicals have been screened for their adverse physiological effects in human cell lines via high-throughput assays in the ToxCast project. The invaluable data generated by the ToxCast project has enabled the development of toxicity predictors, but they can be limited in their predictive ability due to the heterogeneity in structure-activity relationships among chemicals. Here, we systematically investigated the heterogeneity in structure-activity as well as structure-mechanism relationships among the TSHR binding chemicals from ToxCast. By employing a structure-activity similarity (SAS) map, we identified 79 activity cliffs among 509 chemicals in TSHR agonist dataset and 69 activity cliffs among 650 chemicals in the TSHR antagonist dataset. Further, by using the matched molecular pair (MMP) approach, we find that the resultant activity cliffs (MMP-cliffs) are a subset of activity cliffs identified via the SAS map approach. Subsequently, by leveraging ToxCast mechanism of action (MOA) annotations for chemicals common to both TSHR agonist and TSHR antagonist datasets, we identified 3 chemical pairs as strong MOA-cliffs and 19 chemical pairs as weak MOA-cliffs. In conclusion, the insights from this systematic investigation of the TSHR binding chemicals are likely to inform ongoing efforts towards development of better predictive toxicity models for characterization of the chemical exposome.
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Affiliation(s)
- Ajaya Kumar Sahoo
- The Institute of Mathematical Sciences (IMSc) Chennai 600113 India
- Homi Bhabha National Institute (HBNI) Mumbai 400094 India
| | - Shanmuga Priya Baskaran
- The Institute of Mathematical Sciences (IMSc) Chennai 600113 India
- Homi Bhabha National Institute (HBNI) Mumbai 400094 India
| | - Nikhil Chivukula
- The Institute of Mathematical Sciences (IMSc) Chennai 600113 India
- Homi Bhabha National Institute (HBNI) Mumbai 400094 India
| | - Kishan Kumar
- The Institute of Mathematical Sciences (IMSc) Chennai 600113 India
| | - Areejit Samal
- The Institute of Mathematical Sciences (IMSc) Chennai 600113 India
- Homi Bhabha National Institute (HBNI) Mumbai 400094 India
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6
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Paulus J, Nachtigall B, Meyer P, Sewald N. RGD Peptidomimetic MMAE-Conjugate Addressing Integrin αVβ3-Expressing Cells with High Targeting Index. Chemistry 2023; 29:e202203476. [PMID: 36454662 DOI: 10.1002/chem.202203476] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/01/2022] [Accepted: 12/01/2022] [Indexed: 12/03/2022]
Abstract
Small molecule-drug conjugates (SMDCs) mimicking the RGD sequence (-Arg-Gly-Asp-) with a non-peptide moiety require a pharmacophore-independent attachment site. A library of 36 sulfonamide-modified RGD mimetics with nM to pM affinity for integrin αV β3 was synthesized and analysed via DAD mapping. The best structure of the conjugable RGD mimetic was used and a linker was attached to an aromatic ring by Negishi cross-coupling. The product retained high affinity and selectivity for integrin αV β3 . The conjugable RGD mimetic was then attached to an enzymatically cleavable GKGEVA linker equipped with a self-immolative PABC and the antimitotic drug monomethyl auristatin E (MMAE). The resulting SMDC preferred binding to integrin αV β3 over α5 β1 in a ratio of 1 : 4519 (ELISA) and showed selectivity for αV β3 -positive WM115 cells over αV β3 -negative M21-L cells in the in vitro cell adhesion assay as well as in cell viability assays with a targeting index of 134 (M21-L/WM115).
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Affiliation(s)
- Jannik Paulus
- Organic and Bioorganic Chemistry, Faculty of Chemistry, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany
| | - Beate Nachtigall
- Organic and Bioorganic Chemistry, Faculty of Chemistry, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany
| | - Peter Meyer
- Organic and Bioorganic Chemistry, Faculty of Chemistry, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany
| | - Norbert Sewald
- Organic and Bioorganic Chemistry, Faculty of Chemistry, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany
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7
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Medina‐Franco JL, Chávez‐Hernández AL, López‐López E, Saldívar‐González FI. Chemical Multiverse: An Expanded View of Chemical Space. Mol Inform 2022; 41:e2200116. [PMID: 35916110 PMCID: PMC9787733 DOI: 10.1002/minf.202200116] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 08/01/2022] [Indexed: 12/30/2022]
Abstract
Technological advances and practical applications of the chemical space concept in drug discovery, natural product research, and other research areas have attracted the scientific community's attention. The large- and ultra-large chemical spaces are associated with the significant increase in the number of compounds that can potentially be made and exist and the increasing number of experimental and calculated descriptors, that are emerging that encode the molecular structure and/or property aspects of the molecules. Due to the importance and continued evolution of compound libraries, herein, we discuss definitions proposed in the literature for chemical space and emphasize the convenience, discussed in the literature to use complementary descriptors to obtain a comprehensive view of the chemical space of compound data sets. In this regard, we introduce the term chemical multiverse to refer to the comprehensive analysis of compound data sets through several chemical spaces, each defined by a different set of chemical representations. The chemical multiverse is contrasted with a related idea: consensus chemical space.
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Affiliation(s)
- José L. Medina‐Franco
- DIFACQUIM research group, Department of Pharmacy, School of ChemistryNational Autonomous University of MexicoMexico City04510Mexico
| | - Ana L. Chávez‐Hernández
- DIFACQUIM research group, Department of Pharmacy, School of ChemistryNational Autonomous University of MexicoMexico City04510Mexico
| | - Edgar López‐López
- Department of PharmacologyCenter for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV)Mexico City07360Mexico
| | - Fernanda I. Saldívar‐González
- DIFACQUIM research group, Department of Pharmacy, School of ChemistryNational Autonomous University of MexicoMexico City04510Mexico
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8
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Liu W, Jiang J, Lin Y, You Q, Wang L. Insight into Thermodynamic and Kinetic Profiles in Small-Molecule Optimization. J Med Chem 2022; 65:10809-10847. [PMID: 35969687 DOI: 10.1021/acs.jmedchem.2c00682] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Structure-activity relationships (SARs) and structure-property relationships (SPRs) have been considered the most important factors during the drug optimization process. For medicinal chemists, improvements in the potencies and druglike properties of small molecules are regarded as their major goals. Among them, the binding affinity and selectivity of small molecules on their targets are the most important indicators. In recent years, there has been growing interest in using thermodynamic and kinetic profiles to analyze ligand-receptor interactions, which could provide not only binding affinities but also detailed binding parameters for small-molecule optimization. In this perspective, we are trying to provide an insight into thermodynamic and kinetic profiles in small-molecule optimization. Through a highlight of strategies on the small-molecule optimization with specific cases, we aim to put forward the importance of structure-thermodynamic relationships (STRs) and structure-kinetic relationships (SKRs), which could provide more guidance to find safe and effective small-molecule drugs.
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Affiliation(s)
- Wei Liu
- State Key Laboratory of Natural Medicines and Jiangsu Key Laboratory of Drug Design and Optimization, China Pharmaceutical University, Nanjing 210009, China.,Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Jingsheng Jiang
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Yating Lin
- State Key Laboratory of Natural Medicines and Jiangsu Key Laboratory of Drug Design and Optimization, China Pharmaceutical University, Nanjing 210009, China.,Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Qidong You
- State Key Laboratory of Natural Medicines and Jiangsu Key Laboratory of Drug Design and Optimization, China Pharmaceutical University, Nanjing 210009, China.,Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Lei Wang
- State Key Laboratory of Natural Medicines and Jiangsu Key Laboratory of Drug Design and Optimization, China Pharmaceutical University, Nanjing 210009, China.,Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
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9
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Paulus J, Sewald N. Synthesis and Evaluation of a Non-Peptide Small-Molecule Drug Conjugate Targeting Integrin αVβ3. Front Chem 2022; 10:869639. [PMID: 35480387 PMCID: PMC9035832 DOI: 10.3389/fchem.2022.869639] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 02/28/2022] [Indexed: 01/16/2023] Open
Abstract
An integrin αVβ3-targeting linear RGD mimetic containing a small-molecule drug conjugate (SMDC) was synthesized by combining the antimitotic agent monomethyl auristatin E (MMAE), an enzymatically cleavable Val-Ala-PABC linker with a linear conjugable RGD mimetic. The structure proposal for the conjugable RGD mimetic was suggested upon the DAD mapping analysis of a previously synthesized small-molecule RGD mimetic array based on a tyrosine scaffold. Therefore, a diversifying strategy was developed as well as a novel method for the partial hydrogenation of pyrimidines in the presence of the hydrogenolytically cleavable Cbz group. The small-molecule RGD mimetics were evaluated in an ELISA-like assay, and the structural relationships were analyzed by DAD mapping revealing activity differences induced by structural changes as visualized in dependence on special structural motifs. This provided a lead structure for generation of an SMDC containing the antimitotic drug MMAE. The resulting SMDC containing a linear RGD mimetic was tested in a cell adhesion and an in vitro cell viability assay in comparison to reference SMDCs containing cRGDfK or cRADfK as the homing device. The linear RGD SMDC and the cRGDfK SMDC inhibited adhesion of αVβ3-positive WM115 cells to vitronectin with IC50 values in the low µM range, while no effect was observed for the αVβ3-negative M21-L cell line. The cRADfK SMDC used as a negative control was about 30-fold less active in the cell adhesion assay than the cRGDfK SMDC. Conversely, both the linear RGD SMDC and the cRGDfK SMDC are about 55-fold less cytotoxic than MMAE against the αVβ3-positive WM115 cell line with IC50 values in the nM range, while the cRADfK SMDC is 150-fold less cytotoxic than MMAE. Hence, integrin binding also influences the antiproliferative activity giving a targeting index of 2.8.
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10
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Naveja JJ, Vogt M. Automatic Identification of Analogue Series from Large Compound Data Sets: Methods and Applications. Molecules 2021; 26:5291. [PMID: 34500724 PMCID: PMC8433811 DOI: 10.3390/molecules26175291] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 08/27/2021] [Accepted: 08/28/2021] [Indexed: 01/21/2023] Open
Abstract
Analogue series play a key role in drug discovery. They arise naturally in lead optimization efforts where analogues are explored based on one or a few core structures. However, it is much harder to accurately identify and extract pairs or series of analogue molecules in large compound databases with no predefined core structures. This methodological review outlines the most common and recent methodological developments to automatically identify analogue series in large libraries. Initial approaches focused on using predefined rules to extract scaffold structures, such as the popular Bemis-Murcko scaffold. Later on, the matched molecular pair concept led to efficient algorithms to identify similar compounds sharing a common core structure by exploring many putative scaffolds for each compound. Further developments of these ideas yielded, on the one hand, approaches for hierarchical scaffold decomposition and, on the other hand, algorithms for the extraction of analogue series based on single-site modifications (so-called matched molecular series) by exploring potential scaffold structures based on systematic molecule fragmentation. Eventually, further development of these approaches resulted in methods for extracting analogue series defined by a single core structure with several substitution sites that allow convenient representations, such as R-group tables. These methods enable the efficient analysis of large data sets with hundreds of thousands or even millions of compounds and have spawned many related methodological developments.
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Affiliation(s)
- José J. Naveja
- Instituto de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico;
| | - Martin Vogt
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5-6, 53115 Bonn, Germany
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11
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López-López E, Cerda-García-Rojas CM, Medina-Franco JL. Tubulin Inhibitors: A Chemoinformatic Analysis Using Cell-Based Data. Molecules 2021; 26:2483. [PMID: 33923169 PMCID: PMC8123128 DOI: 10.3390/molecules26092483] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 04/18/2021] [Accepted: 04/20/2021] [Indexed: 12/14/2022] Open
Abstract
Inhibiting the tubulin-microtubules (Tub-Mts) system is a classic and rational approach for treating different types of cancers. A large amount of data on inhibitors in the clinic supports Tub-Mts as a validated target. However, most of the inhibitors reported thus far have been developed around common chemical scaffolds covering a narrow region of the chemical space with limited innovation. This manuscript aims to discuss the first activity landscape and scaffold content analysis of an assembled and curated cell-based database of 851 Tub-Mts inhibitors with reported activity against five cancer cell lines and the Tub-Mts system. The structure-bioactivity relationships of the Tub-Mts system inhibitors were further explored using constellations plots. This recently developed methodology enables the rapid but quantitative assessment of analog series enriched with active compounds. The constellations plots identified promising analog series with high average biological activity that could be the starting points of new and more potent Tub-Mts inhibitors.
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Affiliation(s)
- Edgar López-López
- Departamento de Química y Programa de Posgrado en Farmacología, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Apartado 14-740, Mexico City 07000, Mexico;
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Carlos M. Cerda-García-Rojas
- Departamento de Química y Programa de Posgrado en Farmacología, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Apartado 14-740, Mexico City 07000, Mexico;
| | - José L. Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
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12
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Hu H, Bajorath J. Increasing the public activity cliff knowledge base with new categories of activity cliffs. Future Sci OA 2020; 6:FSO472. [PMID: 32518687 PMCID: PMC7273365 DOI: 10.2144/fsoa-2020-0020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Aim: Extending the public knowledge base of activity cliffs (ACs) with new categories of ACs having special structural characteristics. Methodology: Dual-site ACs, isomer ACs and ACs with privileged substructures are described and their systematic identification is detailed. Exemplary results & data: More than 7400 new ACs belonging to different categories with activity against more than 200 targets were identified and are made publicly available. Limitations & next steps: For dual-site ACs, limited numbers of isomers are available as structural analogs for rationalizing contributions to AC formation. The search for such analogs will continue. In addition, the target distribution of ACs containing privileged substructures will be further analyzed. Activity cliffs (ACs) are formed by small molecules that have very similar structures, are active against the same biological target, but have a large difference in potency against their target. Accordingly, ACs are of interest in medicinal chemistry because they reveal small structural changes that greatly influence the potency of active compounds. This information can be used for compound optimization. Computational methods are applied to search for ACs in large compound databases. Here, we further extend the public AC knowledge base with new categories of ACs having special structural characteristics.
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Affiliation(s)
- Huabin Hu
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology & Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, Bonn D-53113, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology & Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, Bonn D-53113, Germany
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13
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López-López E, Rabal O, Oyarzabal J, Medina-Franco JL. Towards the understanding of the activity of G9a inhibitors: an activity landscape and molecular modeling approach. J Comput Aided Mol Des 2020; 34:659-669. [PMID: 32060676 DOI: 10.1007/s10822-020-00298-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 02/07/2020] [Indexed: 11/26/2022]
Abstract
In this work, we analyze the structure-activity relationships (SAR) of epigenetic inhibitors (lysine mimetics) against lysine methyltransferase (G9a or EHMT2) using a combined activity landscape, molecular docking and molecular dynamics approach. The study was based on a set of 251 G9a inhibitors with reported experimental activity. The activity landscape analysis rapidly led to the identification of activity cliffs, scaffolds hops and other active an inactive molecules with distinct SAR. Structure-based analysis of activity cliffs, scaffold hops and other selected active and inactive G9a inhibitors by means of docking followed by molecular dynamics simulations led to the identification of interactions with key residues involved in activity against G9a, for instance with ASP 1083, LEU 1086, ASP 1088, TYR 1154 and PHE 1158. The outcome of this work is expected to further advance the development of G9a inhibitors.
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Affiliation(s)
- Edgar López-López
- Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, 04510, Mexico City, Mexico
| | - Obdulia Rabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research, CIMA, University of Navarra, Pio XII, 55, 31008, Pamplona, Spain
| | - Julen Oyarzabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research, CIMA, University of Navarra, Pio XII, 55, 31008, Pamplona, Spain
| | - José L Medina-Franco
- Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, 04510, Mexico City, Mexico.
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Activity Landscape and Molecular Modeling to Explore the SAR of Dual Epigenetic Inhibitors: A Focus on G9a and DNMT1. Molecules 2018; 23:molecules23123282. [PMID: 30544967 PMCID: PMC6321328 DOI: 10.3390/molecules23123282] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2018] [Revised: 12/09/2018] [Accepted: 12/10/2018] [Indexed: 11/17/2022] Open
Abstract
In this work we discuss the insights from activity landscape, docking and molecular dynamics towards the understanding of the structure-activity relationships of dual inhibitors of major epigenetic targets: lysine methyltransferase (G9a) and DNA methyltranferase 1 (DNMT1). The study was based on a novel data set of 50 published compounds with reported experimental activity for both targets. The activity landscape analysis revealed the presence of activity cliffs, e.g., pairs of compounds with high structure similarity but large activity differences. Activity cliffs were further rationalized at the molecular level by means of molecular docking and dynamics simulations that led to the identification of interactions with key residues involved in the dual activity or selectivity with the epigenetic targets.
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Abstract
INTRODUCTION Activity landscapes (ALs) are representations and models of compound data sets annotated with a target-specific activity. In contrast to quantitative structure-activity relationship (QSAR) models, ALs aim at characterizing structure-activity relationships (SARs) on a large-scale level encompassing all active compounds for specific targets. The popularity of AL modeling has grown substantially with the public availability of large activity-annotated compound data sets. AL modeling crucially depends on molecular representations and similarity metrics used to assess structural similarity. Areas covered: The concepts of AL modeling are introduced and its basis in quantitatively assessing molecular similarity is discussed. The different types of AL modeling approaches are introduced. AL designs can broadly be divided into three categories: compound-pair based, dimensionality reduction, and network approaches. Recent developments for each of these categories are discussed focusing on the application of mathematical, statistical, and machine learning tools for AL modeling. AL modeling using chemical space networks is covered in more detail. Expert opinion: AL modeling has remained a largely descriptive approach for the analysis of SARs. Beyond mere visualization, the application of analytical tools from statistics, machine learning and network theory has aided in the sophistication of AL designs and provides a step forward in transforming ALs from descriptive to predictive tools. To this end, optimizing representations that encode activity relevant features of molecules might prove to be a crucial step.
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Affiliation(s)
- Martin Vogt
- a Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry , Rheinische Friedrich-Wilhelms-Universität , Bonn , Germany
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16
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Chávez-Villarreal KG, García A, Romo-Mancillas A, Garza-González E, de Torres NW, Miranda LD, Moo-Puc RE, Chale-Dzul J, del Rayo Camacho-Corona M. Synthesis, antimycobacterial evaluation, and QSAR analysis of meso-dihydroguaiaretic acid derivatives. Med Chem Res 2018. [DOI: 10.1007/s00044-017-2125-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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17
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In search of selective 11β-HSD type 1 inhibitors without nephrotoxicity: An approach to resolve the metabolic syndrome by virtual based screening. ARAB J CHEM 2018. [DOI: 10.1016/j.arabjc.2015.08.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
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18
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Naveja JJ, Oviedo-Osornio CI, Medina-Franco JL. Computational Methods for Epigenetic Drug Discovery: A Focus on Activity Landscape Modeling. COMPUTATIONAL MOLECULAR MODELLING IN STRUCTURAL BIOLOGY 2018; 113:65-83. [DOI: 10.1016/bs.apcsb.2018.01.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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19
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Naveja JJ, Norinder U, Mucs D, López-López E, Medina-Franco JL. Chemical space, diversity and activity landscape analysis of estrogen receptor binders. RSC Adv 2018; 8:38229-38237. [PMID: 35559115 PMCID: PMC9089822 DOI: 10.1039/c8ra07604a] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 11/05/2018] [Indexed: 11/21/2022] Open
Abstract
Understanding the structure–activity relationships (SAR) of endocrine-disrupting chemicals has a major importance in toxicology. Despite the fact that classifiers and predictive models have been developed for estrogens for the past 20 years, to the best of our knowledge, there are no studies of their activity landscape or the identification of activity cliffs. Herein, we report the first SAR of a public dataset of 121 chemicals with reported estrogen receptor binding affinities using activity landscape modeling. To this end, we conducted a systematic quantitative and visual analysis of the chemical space of the 121 chemicals. The global diversity of the dataset was characterized by means of Consensus Diversity Plot, a recently developed method. Adding pairwise activity difference information to the chemical space gave rise to the activity landscape of the data set uncovering a heterogeneous SAR, in particular for some structural classes. At least eight compounds were identified with high propensity to form activity cliffs. The findings of this work further expand the current knowledge of the underlying SAR of estrogenic compounds and can be the starting point to develop novel and potentially improved predictive models. Global diversity and activity landscape analysis of endocrine-disrupting chemicals identifies activity cliffs that are rationalized at the structure level.![]()
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Affiliation(s)
- J. Jesús Naveja
- Department of Pharmacy
- School of Chemistry
- Universidad Nacional Autónoma de México
- Mexico City
- Mexico
| | - Ulf Norinder
- Swetox
- Karolinska Institutet
- Unit of Toxicology Sciences
- SE-151 36 Södertälje
- Sweden
| | - Daniel Mucs
- Swetox
- Karolinska Institutet
- Unit of Toxicology Sciences
- SE-151 36 Södertälje
- Sweden
| | - Edgar López-López
- Department of Pharmacy
- School of Chemistry
- Universidad Nacional Autónoma de México
- Mexico City
- Mexico
| | - Josė L. Medina-Franco
- Department of Pharmacy
- School of Chemistry
- Universidad Nacional Autónoma de México
- Mexico City
- Mexico
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20
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Balachandran PV, Xue D, Theiler J, Hogden J, Gubernatis JE, Lookman T. Importance of Feature Selection in Machine Learning and Adaptive Design for Materials. MATERIALS DISCOVERY AND DESIGN 2018. [DOI: 10.1007/978-3-319-99465-9_3] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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21
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Naveja JJ, Oviedo-Osornio CI, Trujillo-Minero NN, Medina-Franco JL. Chemoinformatics: a perspective from an academic setting in Latin America. Mol Divers 2017; 22:247-258. [PMID: 29204824 DOI: 10.1007/s11030-017-9802-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Accepted: 11/26/2017] [Indexed: 12/13/2022]
Abstract
This perspective discusses the current progress of a chemoinformatics group in a major university in Latin America. Three major aspects are discussed in a critical manner: research, education, and collaboration with industry and other public research networks. It is also presented an overview of the progress in applied research and development of research concepts. Efforts to teach chemoinformatics at the undergraduate and graduate levels are discussed. It is addressed how the partnership with industry and other not-for-profit research institutions not only brings additional sources of funding but, more importantly, increases the impact of the multidisciplinary work and offers the students to be exposed to other research environments. We also discuss the main perspectives and challenges that remain to be addressed in these settings.
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Affiliation(s)
- J Jesús Naveja
- School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México, Avenida Universidad 3000, 04510, Mexico City, Mexico.,PECEM, Facultad de Medicina, Universidad Nacional Autónoma de México, Avenida Universidad 3000, 04510, Mexico City, Mexico
| | - C Iluhí Oviedo-Osornio
- School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México, Avenida Universidad 3000, 04510, Mexico City, Mexico
| | - Nicole N Trujillo-Minero
- School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México, Avenida Universidad 3000, 04510, Mexico City, Mexico
| | - José L Medina-Franco
- School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México, Avenida Universidad 3000, 04510, Mexico City, Mexico.
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22
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González-Medina M, Méndez-Lucio O, Medina-Franco JL. Activity Landscape Plotter: A Web-Based Application for the Analysis of Structure-Activity Relationships. J Chem Inf Model 2017; 57:397-402. [PMID: 28234475 DOI: 10.1021/acs.jcim.6b00776] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Activity landscape modeling is a powerful method for the quantitative analysis of structure-activity relationships. This cheminformatics area is in continuous growth, and several quantitative and visual approaches are constantly being developed. However, these approaches often fall into disuse due to their limited access. Herein, we present Activity Landscape Plotter as the first freely available web-based tool to automatically analyze structure-activity relationships of compound data sets. Based on the concept of activity landscape modeling, the online service performs pairwise structure and activity relationships from an input data set supplied by the user. For visual analysis, Activity Landscape Plotter generates Structure-Activity Similarity and Dual-Activity Difference maps. The user can interactively navigate through the maps and export all the pairwise structure-activity information as comma delimited files. Activity Landscape Plotter is freely accessible at https://unam-shiny-difacquim.shinyapps.io/ActLSmaps /.
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Affiliation(s)
- Mariana González-Medina
- School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México , Avenida Universidad 3000, Mexico City 04510, Mexico
| | - Oscar Méndez-Lucio
- School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México , Avenida Universidad 3000, Mexico City 04510, Mexico
| | - José L Medina-Franco
- School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México , Avenida Universidad 3000, Mexico City 04510, Mexico
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23
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Saldívar-González FI, Naveja JJ, Palomino-Hernández O, Medina-Franco JL. Getting SMARt in drug discovery: chemoinformatics approaches for mining structure–multiple activity relationships. RSC Adv 2017. [DOI: 10.1039/c6ra26230a] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
In light of the high relevance of polypharmacology, multi-target screening is a major trend in drug discovery.
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Affiliation(s)
- Fernanda I. Saldívar-González
- Facultad de Química
- Departamento de Farmacia
- Universidad Nacional Autónoma de México
- Avenida Universidad 3000
- Mexico City 04510
| | - J. Jesús Naveja
- Facultad de Química
- Departamento de Farmacia
- Universidad Nacional Autónoma de México
- Avenida Universidad 3000
- Mexico City 04510
| | - Oscar Palomino-Hernández
- Facultad de Química
- Departamento de Farmacia
- Universidad Nacional Autónoma de México
- Avenida Universidad 3000
- Mexico City 04510
| | - José L. Medina-Franco
- Facultad de Química
- Departamento de Farmacia
- Universidad Nacional Autónoma de México
- Avenida Universidad 3000
- Mexico City 04510
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24
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García-Sánchez MO, Cruz-Monteagudo M, Medina-Franco JL. Quantitative Structure-Epigenetic Activity Relationships. CHALLENGES AND ADVANCES IN COMPUTATIONAL CHEMISTRY AND PHYSICS 2017. [DOI: 10.1007/978-3-319-56850-8_8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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25
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Molecular Modeling and Chemoinformatics to Advance the Development of Modulators of Epigenetic Targets: A Focus on DNA Methyltransferases. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2016; 105:1-26. [PMID: 27567482 DOI: 10.1016/bs.apcsb.2016.05.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
In light of the emerging field of Epi-informatics, ie, computational methods applied to epigenetic research, molecular docking, and dynamics, pharmacophore and activity landscape modeling and QSAR play a key role in the development of modulators of DNA methyltransferases (DNMTs), one of the major epigenetic target families. The increased chemical information available for modulators of DNMTs has opened up the avenue to explore the epigenetic relevant chemical space (ERCS). Herein, we discuss recent progress on the identification and development of inhibitors of DNMTs as potential epi-drugs and epi-probes that have been driven by molecular modeling and chemoinformatics methods. We also survey advances on the elucidation of their structure-activity relationships and exploration of ERCS. Finally, it is illustrated how computational approaches can be applied to identify modulators of DNMTs in food chemicals.
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26
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Banerjee J, Yongye AB, Chang YP, Gyanda R, Medina-Franco JL, Armishaw CJ. Design and synthesis of α-conotoxin GID analogues as selective α4β2 nicotinic acetylcholine receptor antagonists. Biopolymers 2016; 102:78-87. [PMID: 24122487 DOI: 10.1002/bip.22413] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2013] [Revised: 08/30/2013] [Accepted: 09/09/2013] [Indexed: 01/01/2023]
Abstract
The α4β2 nicotinic acetylcholine receptor (nAChR) is an important target for currently approved smoking cessation therapeutics. However, the development of highly selective α4β2 nAChR antagonists remains a significant challenge. α-Conotoxin GID is an antagonist of α4β2 nAChRs, though it is significantly more potent toward the α3β2 and α7 subtypes. With the goal of obtaining further insights into α-conotoxin GID/nAChR interactions that could lead to the design of GID analogues with improved affinity for α4β2 nAChRs, we built a homology model of the GID/α4β2 complex using an X-ray co-crystal structure of an α-conotoxin/acetylcholine binding protein (AChBP) complex. Several additional interactions that could potentially enhance the affinity of GID for α4β2 nAChRs were observed in our model, which led to the design and synthesis of 22 GID analogues. Seven analogues displayed inhibitory activity toward α4β2 nAChRs that was comparable to GID. Significantly, both GID[A10S] and GID[V13I] demonstrated moderately improved selectivity toward α4β2 over α3β2 when compared with GID, while GID[V18N] exhibited no measurable inhibitory activity for the α3β2 subtype, yet retained inhibitory activity for α4β2. In this regard, GID[V18N] is the most α4β2 nAChR selective α-conotoxin analogue identified to date.
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Affiliation(s)
- Jayati Banerjee
- Torrey Pines Institute for Molecular Studies, Port St. Lucie, FL, 34987
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27
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Activity and property landscape modeling is at the interface of chemoinformatics and medicinal chemistry. Future Med Chem 2016; 7:1197-211. [PMID: 26132526 DOI: 10.4155/fmc.15.51] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Property landscape modeling (PLM) methods are at the interface of experimental sciences and computational chemistry. PLM are becoming a common strategy to describe systematically structure-property relationships of datasets. Thus far, PLM have been used mainly in medicinal chemistry and drug discovery. Herein, we survey advances on key topics on PLM with emphasis on questions often raised regarding the outcomes of the property landscape studies. We also emphasize on concepts of PLM that are being extended to other experimental areas beyond drug discovery. Topics discussed in this paper include applications of PLM to further characterize protein-ligand interactions, the utility of PLM as a quantitative and descriptive approach, and the statistical validation of property cliffs.
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28
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Activity landscape analysis of novel 5$$\upalpha $$-reductase inhibitors. Mol Divers 2016; 20:771-80. [DOI: 10.1007/s11030-016-9659-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Accepted: 01/12/2016] [Indexed: 01/21/2023]
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29
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Rivera-Borroto OM, García-de la Vega JM, Marrero-Ponce Y, Grau R. Relational Agreement Measures for Similarity Searching of Cheminformatic Data Sets. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2016; 13:158-67. [PMID: 26886740 DOI: 10.1109/tcbb.2015.2424435] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Research on similarity searching of cheminformatic data sets has been focused on similarity measures using fingerprints. However, nominal scales are the least informative of all metric scales, increasing the tied similarity scores, and decreasing the effectivity of the retrieval engines. Tanimoto's coefficient has been claimed to be the most prominent measure for this task. Nevertheless, this field is far from being exhausted since the computer science no free lunch theorem predicts that "no similarity measure has overall superiority over the population of data sets". We introduce 12 relational agreement (RA) coefficients for seven metric scales, which are integrated within a group fusion-based similarity searching algorithm. These similarity measures are compared to a reference panel of 21 proximity quantifiers over 17 benchmark data sets (MUV), by using informative descriptors, a feature selection stage, a suitable performance metric, and powerful comparison tests. In this stage, RA coefficients perform favourably with repect to the state-of-the-art proximity measures. Afterward, the RA-based method outperform another four nearest neighbor searching algorithms over the same data domains. In a third validation stage, RA measures are successfully applied to the virtual screening of the NCI data set. Finally, we discuss a possible molecular interpretation for these similarity variants.
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30
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Carrió P, Sanz F, Pastor M. Toward a unifying strategy for the structure-based prediction of toxicological endpoints. Arch Toxicol 2015; 90:2445-60. [PMID: 26553148 DOI: 10.1007/s00204-015-1618-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Accepted: 10/19/2015] [Indexed: 01/13/2023]
Abstract
Most computational methods used for the prediction of toxicity endpoints are based on the assumption that similar compounds have similar biological properties. This principle can be exploited using computational methods like read across or quantitative structure-activity relationships. However, there is no general agreement about which method is the most appropriate for quantifying compound similarity neither for exploiting the similarity principle in order to obtain reliable estimations of the compound properties. Moreover, optimal similarity metrics and modeling methods might depend on the characteristics of the endpoints and training series used in each case. This study describes a comparative analysis of the predictive performance of diverse similarity metrics and modeling methods in toxicological applications. A collection of two quantitative (n = 660, n = 1114) and three qualitative (n = 447, n = 905, n = 1220) datasets representing very different endpoints of interest in drug safety evaluation and rigorous methods were used to estimate the external predictive ability in each case. The results confirm that no single approach produces the best results in all instances, and the best predictions were obtained using different tools in different situations. The trends observed in this study were exploited to propose a unifying strategy allowing the use of the most suitable method for every compound. A comparison of the quality of the predictions obtained by the unifying strategy with those obtained by standard prediction methods confirmed the usefulness of the proposed approach.
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Affiliation(s)
- Pau Carrió
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra, Carrer Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Ferran Sanz
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra, Carrer Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Manuel Pastor
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra, Carrer Dr. Aiguader 88, 08003, Barcelona, Spain.
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31
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Naveja JJ, Medina-Franco JL. Activity landscape of DNA methyltransferase inhibitors bridges chemoinformatics with epigenetic drug discovery. Expert Opin Drug Discov 2015; 10:1059-70. [DOI: 10.1517/17460441.2015.1073257] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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32
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Pérez-Villanueva J, Méndez-Lucio O, Soria-Arteche O, Medina-Franco JL. Activity cliffs and activity cliff generators based on chemotype-related activity landscapes. Mol Divers 2015; 19:1021-35. [PMID: 26150300 DOI: 10.1007/s11030-015-9609-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2015] [Accepted: 06/24/2015] [Indexed: 12/26/2022]
Abstract
Activity cliffs have large impact in drug discovery; therefore, their detection and quantification are of major importance. This work introduces the metric activity cliff enrichment factor and expands the previously reported activity cliff generator concept by adding chemotype information to representations of the activity landscape. To exemplify these concepts, three molecular databases with multiple biological activities were characterized. Compounds in each database were grouped into chemotype classes. Then, pairwise comparisons of structure similarities and activity differences were calculated for each compound and used to construct chemotype-based structure-activity similarity (SAS) maps. Different landscape distributions among four major regions of the SAS maps were observed for different subsets of molecules grouped in chemotypes. Based on this observation, the activity cliff enrichment factor was calculated to numerically detect chemotypes enriched in activity cliffs. Several chemotype classes were detected having major proportion of activity cliffs than the entire database. In addition, some chemotype classes comprising compounds with smooth structure activity relationships (SAR) were detected. Finally, the activity cliff generator concept was applied to compounds grouped in chemotypes to extract valuable SAR information.
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Affiliation(s)
- Jaime Pérez-Villanueva
- División de Ciencias Biológicas y de la Salud, Departamento de Sistemas Biológicos, Universidad Autónoma Metropolitana Unidad Xochimilco (UAM-X), 04960, Mexico, DF, Mexico.
| | - Oscar Méndez-Lucio
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México (UNAM), 04510, Mexico, DF, Mexico.,Unilever Centre for Molecular Science Informatics Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Olivia Soria-Arteche
- División de Ciencias Biológicas y de la Salud, Departamento de Sistemas Biológicos, Universidad Autónoma Metropolitana Unidad Xochimilco (UAM-X), 04960, Mexico, DF, Mexico
| | - José L Medina-Franco
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México (UNAM), 04510, Mexico, DF, Mexico
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Fleeman R, LaVoi TM, Santos RG, Morales A, Nefzi A, Welmaker GS, Medina-Franco JL, Giulianotti MA, Houghten RA, Shaw LN. Combinatorial Libraries As a Tool for the Discovery of Novel, Broad-Spectrum Antibacterial Agents Targeting the ESKAPE Pathogens. J Med Chem 2015; 58:3340-55. [PMID: 25780985 DOI: 10.1021/jm501628s] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Mixture based synthetic combinatorial libraries offer a tremendous enhancement for the rate of drug discovery, allowing the activity of millions of compounds to be assessed through the testing of exponentially fewer samples. In this study, we used a scaffold-ranking library to screen 37 different libraries for antibacterial activity against the ESKAPE pathogens. Each library contained between 10000 and 750000 structural analogues for a total of >6 million compounds. From this, we identified a bis-cyclic guanidine library that displayed strong antibacterial activity. A positional scanning library for these compounds was developed and used to identify the most effective functional groups at each variant position. Individual compounds were synthesized that were broadly active against all ESKAPE organisms at concentrations <2 μM. In addition, these compounds were bactericidal, had antibiofilm effects, showed limited potential for the development of resistance, and displayed almost no toxicity when tested against human lung cells and erythrocytes. Using a murine model of peritonitis, we also demonstrate that these agents are highly efficacious in vivo.
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Affiliation(s)
| | - Travis M LaVoi
- ∥Torrey Pines Institute for Molecular Studies, Port St. Lucie, Florida 34987, United States
| | - Radleigh G Santos
- ∥Torrey Pines Institute for Molecular Studies, Port St. Lucie, Florida 34987, United States
| | - Angela Morales
- ∥Torrey Pines Institute for Molecular Studies, Port St. Lucie, Florida 34987, United States
| | - Adel Nefzi
- ∥Torrey Pines Institute for Molecular Studies, Port St. Lucie, Florida 34987, United States
| | - Gregory S Welmaker
- ∥Torrey Pines Institute for Molecular Studies, Port St. Lucie, Florida 34987, United States
| | - José L Medina-Franco
- ⊥Facultad de Química, Departamento de Farmacia, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico
| | - Marc A Giulianotti
- ∥Torrey Pines Institute for Molecular Studies, Port St. Lucie, Florida 34987, United States
| | - Richard A Houghten
- ∥Torrey Pines Institute for Molecular Studies, Port St. Lucie, Florida 34987, United States
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DE LA CRUZ-HERNANDEZ ERICK, MEDINA-FRANCO JOSELUIS, TRUJILLO JAENAI, CHAVEZ-BLANCO ALMA, DOMINGUEZ-GOMEZ GUADALUPE, PEREZ-CARDENAS ENRIQUE, GONZALEZ-FIERRO AURORA, TAJA-CHAYEB LUCIA, DUEÑAS-GONZALEZ ALFONSO. Ribavirin as a tri-targeted antitumor repositioned drug. Oncol Rep 2015; 33:2384-92. [DOI: 10.3892/or.2015.3816] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2014] [Accepted: 01/23/2015] [Indexed: 11/06/2022] Open
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Méndez-Lucio O, Kooistra AJ, Graaf CD, Bender A, Medina-Franco JL. Analyzing Multitarget Activity Landscapes Using Protein–Ligand Interaction Fingerprints: Interaction Cliffs. J Chem Inf Model 2015; 55:251-62. [DOI: 10.1021/ci500721x] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Oscar Méndez-Lucio
- Centre
for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Albert J. Kooistra
- Division
of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for
Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Chris de Graaf
- Division
of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for
Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Andreas Bender
- Centre
for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - José L. Medina-Franco
- Facultad
de Química, Departamento de Farmacia, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico
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Naveja JJ, Medina-Franco JL. Activity landscape sweeping: insights into the mechanism of inhibition and optimization of DNMT1 inhibitors. RSC Adv 2015. [DOI: 10.1039/c5ra12339a] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Inhibitors of DNA methyltransferases have distinct structure–activity relationships as revealed by the activity landscape sweeping study discussed in this work.
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Affiliation(s)
- J. Jesús Naveja
- Facultad de Química
- Departamento de Farmacia
- Universidad Nacional Autónoma de México
- México
- México
| | - José L. Medina-Franco
- Facultad de Química
- Departamento de Farmacia
- Universidad Nacional Autónoma de México
- México
- México
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Medina-Franco JL, Méndez-Lucio O, Dueñas-González A, Yoo J. Discovery and development of DNA methyltransferase inhibitors using in silico approaches. Drug Discov Today 2014; 20:569-77. [PMID: 25526932 DOI: 10.1016/j.drudis.2014.12.007] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2014] [Revised: 11/19/2014] [Accepted: 12/10/2014] [Indexed: 01/08/2023]
Abstract
Multiple strategies have evolved during the past few years to advance epigenetic compounds targeting DNA methyltransferases (DNMTs). Significant progress has been made in HTS, lead optimization and determination of 3D structures of DNMTs. In light of the emerging concept of epi-informatics, computational approaches are employed to accelerate the development of DNMT inhibitors helping to screen chemical databases, mine the DNMT-relevant chemical space, uncover SAR and design focused libraries. Computational methods also synergize with natural-product-based drug discovery and drug repurposing. Herein, we survey the latest developments of in silico approaches to advance epigenetic drug and probe discovery targeting DNMTs.
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Affiliation(s)
- José L Medina-Franco
- Facultad de Química, Departamento de Farmacia, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico.
| | - Oscar Méndez-Lucio
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Alfonso Dueñas-González
- Unidad de Investigación Biomédica en Cáncer, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México and Instituto Nacional de Cancerología, Av. San Fernando 22, Mexico City 14080, Mexico
| | - Jakyung Yoo
- Life Science Research Institute, Daewoong Pharmaceutical Co. Ltd., 72 Dugye-Ro, Pogok-Eup, Gyeonggi-do 449-814, Republic of Korea
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Kuyoc-Carrillo VF, Medina-Franco JL. Progress in the Analysis of Multiple Activity Profile of Screening Data Using Computational Approaches. Drug Dev Res 2014; 75:313-23. [DOI: 10.1002/ddr.21209] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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Rojas-Aguirre Y, Medina-Franco JL. Analysis of structure-Caco-2 permeability relationships using a property landscape approach. Mol Divers 2014; 18:599-610. [DOI: 10.1007/s11030-014-9514-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2013] [Accepted: 02/28/2014] [Indexed: 12/14/2022]
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Guha R, Medina-Franco JL. On the validity versus utility of activity landscapes: are all activity cliffs statistically significant? J Cheminform 2014; 6:11. [PMID: 24694189 PMCID: PMC4021161 DOI: 10.1186/1758-2946-6-11] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2013] [Accepted: 03/25/2014] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Most work on the topic of activity landscapes has focused on their quantitative description and visual representation, with the aim of aiding navigation of SAR. Recent developments have addressed applications such as quantifying the proportion of activity cliffs, investigating the predictive abilities of activity landscape methods and so on. However, all these publications have worked under the assumption that the activity landscape models are "real" (i.e., statistically significant). RESULTS The current study addresses for the first time, in a quantitative manner, the significance of a landscape or individual cliffs in the landscape. In particular, we question whether the activity landscape derived from observed (experimental) activity data is different from a randomly generated landscape. To address this we used the SALI measure with six different data sets tested against one or more molecular targets. We also assessed the significance of the landscapes for single and multiple representations. CONCLUSIONS We find that non-random landscapes are data set and molecular representation dependent. For the data sets and representations used in this work, our results suggest that not all representations lead to non-random landscapes. This indicates that not all molecular representations should be used to a) interpret the SAR and b) combined to generate consensus models. Our results suggest that significance testing of activity landscape models and in particular, activity cliffs, is key, prior to the use of such models.
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Affiliation(s)
- Rajarshi Guha
- NIH Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - José L Medina-Franco
- Circuito Exterior, Instituto de Química, Universidad Nacional Autónoma de México, Ciudad Universitaria, México D.F. 04510, Mexico ; Current address: Mayo Clinic, 13400 East Shea Boulevard, Scottsdale, AZ 85259, USA
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Rationalization of activity cliffs of a sulfonamide inhibitor of DNA methyltransferases with induced-fit docking. Int J Mol Sci 2014; 15:3253-61. [PMID: 24566147 PMCID: PMC3958909 DOI: 10.3390/ijms15023253] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2014] [Revised: 02/12/2014] [Accepted: 02/14/2014] [Indexed: 12/04/2022] Open
Abstract
Inhibitors of human DNA methyltransferases (DNMT) are of increasing interest to develop novel epi-drugs for the treatment of cancer and other diseases. As the number of compounds with reported DNMT inhibition is increasing, molecular docking is shedding light to elucidate their mechanism of action and further interpret structure–activity relationships. Herein, we present a structure-based rationalization of the activity of SW155246, a distinct sulfonamide compound recently reported as an inhibitor of human DNMT1 obtained from high-throughput screening. We used flexible and induce-fit docking to develop a binding model of SW155246 with a crystallographic structure of human DNMT1. Results were in excellent agreement with experimental information providing a three-dimensional structural interpretation of ‘activity cliffs’, e.g., analogues of SW155246 with a high structural similarity to the sulfonamide compound, but with no activity in the enzymatic assay.
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Medina-Franco JL, Méndez-Lucio O, Martinez-Mayorga K. The Interplay Between Molecular Modeling and Chemoinformatics to Characterize Protein–Ligand and Protein–Protein Interactions Landscapes for Drug Discovery. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2014; 96:1-37. [DOI: 10.1016/bs.apcsb.2014.06.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Chemoinformatic characterization of activity and selectivity switches of antiprotozoal compounds. Future Med Chem 2013; 6:281-94. [PMID: 24279680 DOI: 10.4155/fmc.13.173] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Benzimidazole derivatives are promising compounds for the treatment of parasitic infections. The structure-activity relationships of 91 benzimidazoles with activity against Trichomonas vaginalis and Giardia intestinalis were analyzed using a novel activity landscape modeling approach. RESULTS We identified two prominent cases of 'activity switches' and 'selectivity switches' where two R group substitutions in the benzimidazole scaffold completely invert the activity and selectivity pattern for T. vaginalis and G. intestinalis. CONCLUSION A chemoinformatic methodology was used to rapidly identify discrete structural changes around the central scaffold that are associated with large changes in biological activity for each parasite. The structure-activity relationships for the benzimidazole derivatives is smooth for both protozoan with few but markedly important activity cliffs.
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Santos RG, Giulianotti MA, Houghten RA, Medina-Franco JL. Conditional probabilistic analysis for prediction of the activity landscape and relative compound activities. J Chem Inf Model 2013; 53:2613-25. [PMID: 23971977 PMCID: PMC3850180 DOI: 10.1021/ci400243e] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Structure-property relationships and structure-activity relationships play an important role in many research areas, such as medicinal chemistry and drug discovery. Such methods, however, have focused on providing post-hoc descriptions of such relationships based on known data. The ability for these descriptions to remain relevant when considering compounds of unknown activity, and thus the prediction of activity and property landscapes using existing data, remains little explored. In this study, we present a novel method of evaluating the ability of a compound comparison methodology to provide accurate information about a set of unknown compounds and also explore the ability of these predicted activity landscapes to prioritize active compounds over inactive. These methods are applied to three distinct and diverse sets of compounds, each with activity data for multiple targets, for a total of eight target-compound set pairs. Six methodologically distinct compound comparison methods were evaluated. We show that overall, all compound comparison methods provided an improvement in structure-activity relationship prediction over random and were able to prioritize compounds in a superior manner to random sampling, but the degree of success and therefore applicability varied markedly.
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Affiliation(s)
- Radleigh G. Santos
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987
| | - Marc A. Giulianotti
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987
| | - Richard A. Houghten
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987
| | - José L. Medina-Franco
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987
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Medina-Franco JL, Aguayo-Ortiz R. Progress in the Visualization and Mining of Chemical and Target Spaces. Mol Inform 2013; 32:942-53. [DOI: 10.1002/minf.201300041] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2013] [Accepted: 05/06/2013] [Indexed: 01/15/2023]
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Medina-Franco JL, Edwards BS, Pinilla C, Appel JR, Giulianotti MA, Santos RG, Yongye AB, Sklar LA, Houghten RA. Rapid scanning structure-activity relationships in combinatorial data sets: identification of activity switches. J Chem Inf Model 2013; 53:1475-85. [PMID: 23705689 DOI: 10.1021/ci400192y] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
We present a general approach to describe the structure-activity relationships (SAR) of combinatorial data sets with activity for two biological endpoints with emphasis on the rapid identification of substitutions that have a large impact on activity and selectivity. The approach uses dual-activity difference (DAD) maps that represent a visual and quantitative analysis of all pairwise comparisons of one, two, or more substitutions around a molecular template. Scanning the SAR of data sets using DAD maps allows the visual and quantitative identification of activity switches defined as specific substitutions that have an opposite effect on the activity of the compounds against two targets. The approach also rapidly identifies single- and double-target R-cliffs, i.e., compounds where a single or double substitution around the central scaffold dramatically modifies the activity for one or two targets, respectively. The approach introduced in this report can be applied to any analogue series with two biological activity endpoints. To illustrate the approach, we discuss the SAR of 106 pyrrolidine bis-diketopiperazines tested against two formylpeptide receptors obtained from positional scanning deconvolution methods of mixture-based libraries.
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Affiliation(s)
- José L Medina-Franco
- Torrey Pines Institute for Molecular Studies, Port St. Lucie, Florida 34987, USA.
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Ramírez-Espinosa JJ, García-Jiménez S, Rios MY, Medina-Franco JL, López-Vallejo F, Webster SP, Binnie M, Ibarra-Barajas M, Ortiz-Andrade R, Estrada-Soto S. Antihyperglycemic and sub-chronic antidiabetic actions of morolic and moronic acids, in vitro and in silico inhibition of 11β-HSD 1. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2013; 20:571-6. [PMID: 23453304 DOI: 10.1016/j.phymed.2013.01.013] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2012] [Revised: 12/17/2012] [Accepted: 01/27/2013] [Indexed: 05/20/2023]
Abstract
Morolic (1) and moronic (2) acids are the main constituents of acetonic extract from Phoradendron reichenbachianum (Loranthaceae), a medicinal plant used in Mexico for the treatment of diabetes. The aim of the current study was to establish the sub-acute antidiabetic and antihyperlipidemic effects of compounds 1 and 2 over non insulin-dependent diabetic rat model. Also, to determine the antihyperglycemic action on normoglycemic rats by oral glucose tolerance test. Daily-administered morolic (1) and moronic (2) acids (50 mg/kg) significantly lowered the blood glucose levels at 60% since first day until tenth day after treatment than untreated group (p<0.05). Moreover, analyzed blood samples obtained from diabetic rats indicated that both compounds diminished plasmatic concentration of cholesterol (CHO) and triglycerides (TG), returning them to normal levels (p<0.05). Also, pretreatment with 50 mg/kg of each compound induced significant antihyperglycemic effect after glucose and sucrose loading (2 g/kg) compared with control group (p<0.05). In vitro studies showed that compounds 1 and 2 induced inhibition of 11β-HSD 1 activity at 10 μM. However, in silico analysis of the pentaclyclic triterpenic acids on 11β-HSD 1 revealed that all compounds had high docking scores and important interactions with the catalytic site allowing them to inhibit 11β-HSD 1 enzyme. In conclusion, morolic and moronic acids have shown sustained antidiabetic and antihyperglycemic action possibly mediated by an insulin sensitization with consequent changes of glucose, cholesterol and triglycerides, in part mediated by inhibition of 11β-HSD 1 as indicated by in vitro and in silico studies.
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Bergner A, Parel SP. Hit Expansion Approaches Using Multiple Similarity Methods and Virtualized Query Structures. J Chem Inf Model 2013; 53:1057-66. [DOI: 10.1021/ci400059p] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Affiliation(s)
- Andreas Bergner
- BioFocus, Chesterford Research
Park, Saffron Walden, Essex CB10 1XL, United Kingdom
| | - Serge P. Parel
- BioFocus, Chesterford Research
Park, Saffron Walden, Essex CB10 1XL, United Kingdom
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Yongye AB, Medina-Franco JL. Systematic characterization of structure-activity relationships and ADMET compliance: a case study. Drug Discov Today 2013; 18:732-9. [PMID: 23583765 DOI: 10.1016/j.drudis.2013.04.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2012] [Revised: 03/18/2013] [Accepted: 04/04/2013] [Indexed: 01/29/2023]
Abstract
Traditionally, activity landscape modeling has been focused on analyzing SAR, despite the fact that lead optimization in drug discovery involves concurrent enhancements of activity and ADMET properties of leads. As a case study, we discuss the systematic analysis of activity landscapes, incorporating ADMET considerations, using a dataset of 166 compounds screened for kappa-opioid receptor activity. Pairwise MACCS/Tanimoto structure similarities, property similarities utilizing 33 ADMET descriptors and a 35-dimensional 'violation bit vector' representing drug-likeness are analyzed. We address the question about the range of ADMET property violations that arise from structural changes, subtle and significant. Pairs of compounds are identified bearing identical, comparable and significantly different drug-likeness in the three informative regions of structure-activity landscapes.
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Affiliation(s)
- Austin B Yongye
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, FL 34987, USA.
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