1
|
Chen J, Li X, Tao J, Luo L. Identification of Marine-Derived SLC7A11 Inhibitors: Molecular Docking, Structure-Based Virtual Screening, Cytotoxicity Prediction, and Molecular Dynamics Simulation. Mar Drugs 2024; 22:375. [PMID: 39195490 DOI: 10.3390/md22080375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2024] [Revised: 08/13/2024] [Accepted: 08/16/2024] [Indexed: 08/29/2024] Open
Abstract
The search for anticancer drugs that target ferroptosis is a promising avenue of research. SLC7A11, a key protein involved in ferroptosis, has been identified as a potential target for drug development. Through screening efforts, novel inhibitors of SLC7A11 have been designed with the aim of promoting ferroptosis and ultimately eliminating cancer cells. We initially screened 563 small molecules using pharmacophore and 2D-QSAR models. Molecular docking and ADMET toxicity predictions, with Erastin as a positive control, identified the small molecules 42711 and 27363 as lead compounds with strong inhibitory activity against SLC7A11. Further optimization resulted in the development of a new inhibitor structure (42711_11). Molecular docking and ADMET re-screening demonstrated successful fragment substitution for this small molecule. Final molecular dynamics simulations also confirmed its stable interaction with the protein. These findings represent a significant step towards the development of new therapeutic strategies for ferroptosis-related diseases.
Collapse
Affiliation(s)
- Jiaqi Chen
- The First Clinical College, Guangdong Medical University, Zhanjiang 524023, China
| | - Xuan Li
- The First Clinical College, Guangdong Medical University, Zhanjiang 524023, China
| | - Jiahua Tao
- The First Clinical College, Guangdong Medical University, Zhanjiang 524023, China
| | - Lianxiang Luo
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang 524023, China
| |
Collapse
|
2
|
Toropova AP, Toropov AA, Roncaglioni A, Benfenati E. The System of Self-Consistent Models: QSAR Analysis of Drug-Induced Liver Toxicity. TOXICS 2023; 11:toxics11050419. [PMID: 37235234 DOI: 10.3390/toxics11050419] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/11/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023]
Abstract
Removing a drug-like substance that can cause drug-induced liver injury from the drug discovery process is a significant task for medicinal chemistry. In silico models can facilitate this process. Semi-correlation is an approach to building in silico models representing the prediction in the active (1)-inactive (0) format. The so-called system of self-consistent models has been suggested as an approach for two tasks: (i) building up a model and (ii) estimating its predictive potential. However, this approach has been tested so far for regression models. Here, the approach is applied to building up and estimating a categorical hepatotoxicity model using the CORAL software. This new process yields good results: sensitivity = 0.77, specificity = 0.75, accuracy = 0.76, and Matthew correlation coefficient = 0.51 (all compounds) and sensitivity = 0.83, specificity = 0.81, accuracy = 0.83 and Matthew correlation coefficient = 0.63 (validation set).
Collapse
Affiliation(s)
- Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Alessandra Roncaglioni
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| |
Collapse
|
3
|
Floresta G, Cilibrizzi A, Abbate V, Spampinato A, Zagni C, Rescifina A. FABP4 inhibitors 3D-QSAR model and isosteric replacement of BMS309403 datasets. Data Brief 2018; 22:471-483. [PMID: 30619925 PMCID: PMC6312796 DOI: 10.1016/j.dib.2018.12.047] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 12/12/2018] [Accepted: 12/13/2018] [Indexed: 12/12/2022] Open
Abstract
The data have been obtained from FABP4 inhibitor molecules previously published. The 120 compounds were used to build a 3D-QSAR model. The development of the QSAR model has been undertaken with the use of Forge software using the PM3 optimized structure and the experimental IC50 of each compound. The QSAR model was also employed to predict the activity of 3000 new isosteric derivatives of BMS309403. The isosteric replacement was also validated by the synthesis and the biological screening of three new compounds reported in the related research article “3D-QSAR assisted identification of FABP4 inhibitors: An effective scaffold hopping analysis/QSAR evaluation” (Floresta et al., 2019).
Collapse
Affiliation(s)
- Giuseppe Floresta
- Department of Drug Sciences, University of Catania, V.le A. Doria 6, 95125 Catania, Italy.,Department of Chemical Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy.,Institute of Pharmaceutical Science, King׳s College London, Stamford Street, London SE1 9NH, UK
| | - Agostino Cilibrizzi
- Institute of Pharmaceutical Science, King׳s College London, Stamford Street, London SE1 9NH, UK.,King׳s Forensics, School of Population Health & Environmental Sciences, King׳s College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK
| | - Vincenzo Abbate
- King׳s Forensics, School of Population Health & Environmental Sciences, King׳s College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK
| | - Ambra Spampinato
- Department of Drug Sciences, University of Catania, V.le A. Doria 6, 95125 Catania, Italy
| | - Chiara Zagni
- Department of Drug Sciences, University of Catania, V.le A. Doria 6, 95125 Catania, Italy
| | - Antonio Rescifina
- Department of Drug Sciences, University of Catania, V.le A. Doria 6, 95125 Catania, Italy
| |
Collapse
|
4
|
Floresta G, Cilibrizzi A, Abbate V, Spampinato A, Zagni C, Rescifina A. 3D-QSAR assisted identification of FABP4 inhibitors: An effective scaffold hopping analysis/QSAR evaluation. Bioorg Chem 2018; 84:276-284. [PMID: 30529845 DOI: 10.1016/j.bioorg.2018.11.045] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 11/20/2018] [Accepted: 11/24/2018] [Indexed: 02/09/2023]
Abstract
Following on the recent publication of pharmacologically relevant effects, small molecule inhibitors of adipocyte fatty-acid binding protein 4 (FABP4) have attracted high interest. FABP4 is mainly expressed in macrophages and adipose tissue, where it regulates fatty acid storage and lipolysis, being also an important mediator of inflammation. In this regard, FABP4 recently demonstrated an interesting molecular target for the treatment of type 2 diabetes, other metabolic diseases and some type of cancers. In the past years, hundreds of effective FABP4 inhibitors have been synthesized. In this paper, a quantitative structure-activity relationship (QSAR) model has been produced, in order to predict the bioactivity of FABP4 inhibitors. The methodology has been combined with a scaffold-hopping approach, allowing to identify three new molecules that act as effective inhibitors of this protein. These molecules, synthesized and tested for their FABP4 inhibitor activity, showed IC50 values between 3.70 and 5.59 μM, with a high level of agreement with the predicted values.
Collapse
Affiliation(s)
- Giuseppe Floresta
- Department of Drug Sciences, University of Catania, V.le A. Doria 6, 95125 Catania, Italy; Department of Chemical Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy; Institute of Pharmaceutical Science, King's College London, Stamford Street, London SE1 9NH, UK.
| | - Agostino Cilibrizzi
- Institute of Pharmaceutical Science, King's College London, Stamford Street, London SE1 9NH, UK; King's Forensics, School of Population Health & Environmental Sciences, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK
| | - Vincenzo Abbate
- King's Forensics, School of Population Health & Environmental Sciences, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK
| | - Ambra Spampinato
- Department of Drug Sciences, University of Catania, V.le A. Doria 6, 95125 Catania, Italy
| | - Chiara Zagni
- Department of Drug Sciences, University of Catania, V.le A. Doria 6, 95125 Catania, Italy
| | - Antonio Rescifina
- Department of Drug Sciences, University of Catania, V.le A. Doria 6, 95125 Catania, Italy.
| |
Collapse
|
5
|
Fatemi MH, Abraham MH, Haghdadi M. Prediction of biomagnification factors for some organochlorine compounds using linear free energy relationship parameters and artificial neural networks. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2009; 20:453-465. [PMID: 19916109 DOI: 10.1080/10629360903278776] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Multiple linear regression and artificial neural networks (ANNs) as feature mapping techniques were used for the prediction of the biomagnification factor (BMF) of some organochlorine pollutants. As independent variables, or compound descriptors, the Abraham descriptors often employed in linear free energy relationships were used. Much better results were obtained from the nonlinear ANN model than from multiple linear regression. The average absolute error, average relative error and root mean square error in the calculation of log (BMF) by the ANN model were 0.055, 0.051 and 0.097 for the training set and 0.11, 0.086 and 0.175 for the internal validation set, respectively. The degree of importance of each descriptor was evaluated by carrying out a sensitivity analysis approach for the nonlinear model. The results obtained reveal that the order of importance is the pollutant volume, the pollutant dipolarity/polarizability and the pollutant excess molar refraction. In order to examine the credibility of the obtained ANN model the leave-many-out cross-validation test was applied which gave Q(2)= 0.827 and SPRESS = 0.15. Also the Y-scrambling procedure was applied to the ANN model in order to examine the effect of chance correlations. The results obtained reveal that it is possible to predict the BMFs of organochlorine pollutants using a nonlinear ANN model with Abraham descriptors as inputs.
Collapse
Affiliation(s)
- M H Fatemi
- Department of Chemistry, Mazandaran University, Babolsar, Iran.
| | | | | |
Collapse
|
6
|
Chen HF. Quantitative predictions of gas chromatography retention indexes with support vector machines, radial basis neural networks and multiple linear regression. Anal Chim Acta 2008; 609:24-36. [DOI: 10.1016/j.aca.2008.01.003] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2007] [Revised: 09/17/2007] [Accepted: 01/02/2008] [Indexed: 11/25/2022]
|