1
|
Adhikari N, Ayyannan SR. Development and validation of machine learning models for the prediction of SH-2 containing protein tyrosine phosphatase 2 inhibitors. Mol Divers 2024; 28:1889-1905. [PMID: 37552436 DOI: 10.1007/s11030-023-10710-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 07/31/2023] [Indexed: 08/09/2023]
Abstract
Discovery and development of a new drug to the market is a highly challenging and resource consuming process. Although, modern drug discovery technologies have enabled the rapid identification of lead compounds, translation of the lead compounds into successful clinical candidates remains a big challenge. In recent years, the availability of massive structural and biological data of diverse small molecules and macromolecules has helped the researchers to deep mine the multidimensional data with the help of artificial intelligence-based predictive tools to draw useful insights on the structural features of biological or therapeutic significance. The aim of this study was to utilize the available data on small molecule (SH2)-containing protein tyrosine phosphatase 2 (SHP2) inhibitors to build and develop machine learning (ML) models that can predict the SHP2 inhibitory potential of new compounds. The dataset contained 2739 unique small molecule SHP2 inhibitors obtained from the BindingDB, ChEMBL and recent literature. After curation of the data, the predictive models such as XGBoost, K nearest neighbours, neural networks were developed and validated through a tenfold cross-validation testing procedure. Out of the seven models developed, the XGBoost model showed an excellent performance with ROC AUC score of 0.96 and accuracy of 0.97 on the test data. Moreover, the Shapley Additive Explanations method was applied to assess a more in-depth understanding of the influence of variables on the model's predictions. In summary, the XGBoost model developed in this study can be useful in the identification of novel SHP2 inhibitors and therefore, can accelerate the discovery of novel therapeutics for cancer therapy.
Collapse
Affiliation(s)
- Nilanjan Adhikari
- Pharmaceutical Chemistry Research Laboratory II, Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, UP, 221005, India
| | - Senthil Raja Ayyannan
- Pharmaceutical Chemistry Research Laboratory II, Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, UP, 221005, India.
| |
Collapse
|
2
|
Nayak SS, Naidu A, Sudhakaran SL, Vino S, Selvaraj G. Prospects of Novel and Repurposed Immunomodulatory Drugs against Acute Respiratory Distress Syndrome (ARDS) Associated with COVID-19 Disease. J Pers Med 2023; 13:664. [PMID: 37109050 PMCID: PMC10142859 DOI: 10.3390/jpm13040664] [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: 02/13/2023] [Revised: 04/09/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
Acute respiratory distress syndrome (ARDS) is intricately linked with SARS-CoV-2-associated disease severity and mortality, especially in patients with co-morbidities. Lung tissue injury caused as a consequence of ARDS leads to fluid build-up in the alveolar sacs, which in turn affects oxygen supply from the capillaries. ARDS is a result of a hyperinflammatory, non-specific local immune response (cytokine storm), which is aggravated as the virus evades and meddles with protective anti-viral innate immune responses. Treatment and management of ARDS remain a major challenge, first, because the condition develops as the virus keeps replicating and, therefore, immunomodulatory drugs are required to be used with caution. Second, the hyperinflammatory responses observed during ARDS are quite heterogeneous and dependent on the stage of the disease and the clinical history of the patients. In this review, we present different anti-rheumatic drugs, natural compounds, monoclonal antibodies, and RNA therapeutics and discuss their application in the management of ARDS. We also discuss on the suitability of each of these drug classes at different stages of the disease. In the last section, we discuss the potential applications of advanced computational approaches in identifying reliable drug targets and in screening out credible lead compounds against ARDS.
Collapse
Affiliation(s)
- Smruti Sudha Nayak
- Department of Bio-Sciences, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Akshayata Naidu
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Sajitha Lulu Sudhakaran
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Sundararajan Vino
- Department of Bio-Sciences, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Gurudeeban Selvaraj
- Centre for Research in Molecular Modeling, Department of Chemistry and Biochemistry, Concordia University-Loyola Campus, Montreal, QC H4B 1R6, Canada
| |
Collapse
|
3
|
Ogawa K, Nakamura S, Oguri H, Ryu K, Yoneda T, Hosoki R. Effective Search of Triterpenes with Anti-HSV-1 Activity Using a Classification Model by Logistic Regression. Front Chem 2021; 9:763794. [PMID: 34796164 PMCID: PMC8593400 DOI: 10.3389/fchem.2021.763794] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 10/11/2021] [Indexed: 11/13/2022] Open
Abstract
Natural products are an excellent source of skeletons for medicinal seeds. Triterpenes and saponins are representative natural products that exhibit anti-herpes simplex virus type 1 (HSV-1) activity. However, there has been a lack of comprehensive information on the anti-HSV-1 activity of triterpenes. Therefore, expanding information on the anti-HSV-1 activity of triterpenes and improving the efficiency of their exploration are urgently required. To improve the efficiency of the development of anti-HSV-1 active compounds, we constructed a predictive model for the anti-HSV-1 activity of triterpenes by using the information obtained from previous studies using machine learning methods. In this study, we constructed a binary classification model (i.e., active or inactive) using a logistic regression algorithm. As a result of the evaluation of predictive model, the accuracy for the test data is 0.79, and the area under the curve (AUC) is 0.86. Additionally, to enrich the information on the anti-HSV-1 activity of triterpenes, a plaque reduction assay was performed on 20 triterpenes. As a result, chikusetsusaponin IVa (11: IC50 = 13.06 μM) was found to have potent anti-HSV-1 with three potentially anti-HSV-1 active triterpenes. The assay result was further used for external validation of predictive model. The prediction of the test compounds in the activity test showed a high accuracy (0.83) and AUC (0.81). We also found that this predictive model was found to be able to successfully narrow down the active compounds. This study provides more information on the anti-HSV-1 activity of triterpenes. Moreover, the predictive model can improve the efficiency of the development of active triterpenes by integrating many previous studies to clarify potential relationships.
Collapse
Affiliation(s)
- Keiko Ogawa
- Laboratory of Regulatory Science, College of Pharmaceutical Sciences, Ritsumeikan University, Kusatsu, Japan
| | - Seikou Nakamura
- Department of Pharmacognosy, Kyoto Pharmaceutical University, Kyoto, Japan
| | - Haruka Oguri
- Laboratory of Regulatory Science, College of Pharmaceutical Sciences, Ritsumeikan University, Kusatsu, Japan
| | - Kaori Ryu
- Department of Pharmacognosy, Kyoto Pharmaceutical University, Kyoto, Japan
| | - Taichi Yoneda
- Department of Pharmacognosy, Kyoto Pharmaceutical University, Kyoto, Japan
| | - Rumiko Hosoki
- Laboratory of Regulatory Science, College of Pharmaceutical Sciences, Ritsumeikan University, Kusatsu, Japan
| |
Collapse
|
4
|
Huang Y, Wang J, Wang S, Xu X, Qin W, Wen Y, Zhao YH, Martyniuk CJ. Discrimination of active and inactive substances in cytotoxicity based on Tox21 10K compound library: Structure alert and mode of action. Toxicology 2021; 462:152948. [PMID: 34530041 DOI: 10.1016/j.tox.2021.152948] [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: 07/16/2021] [Revised: 08/28/2021] [Accepted: 09/08/2021] [Indexed: 10/20/2022]
Abstract
In vitro cytotoxicity assay is an ideal alternative method for the in vivo toxicity in the risk assessment of pollutants in environment. However, modes of action (MOAs) of cytotoxicity have not been investigated for a wide range of compounds. In this paper, binomial and recursive partitioning analysis were carried out between the cytotoxicity and molecular descriptors for 8981 compounds. The results showed that cytotoxicity is strongly related to the chemical hydrophobicity and excess molar refraction, indicating the bio-uptake and chemical-receptor interaction through π and n electron pair play important roles in the cytotoxicity. The decision tree derived from recursive partitioning analysis revealed that the studied compounds could be divided into 25 groups and their structural characteristics could be used as structure alert to identify active and inactive compounds in cytotoxicity. The descriptors used in the decision tree revealed that chemical ionization and bioavailability could affect the cytotoxicity for ionizable and highly hydrophobic compounds. Comparison of MOAs based on Verhaar's classification scheme showed that many inert or less inert compounds were inactive substance, and many reactive or specifically-acting compounds were active substances in the cytotoxicity. In vitro toxicity assay instead of in vivo toxicity assay can be used in the environmental hazard and risk assessment of organic pollutants. The descriptors used in the binomial equation and decision tree reveal that chemical hydrophobicity, ionization and solubility play very important roles for identification of active and inactive compounds. The results obtained in this paper are valuable for understanding the modes of action in cytotoxicity and in vivo-in vitro toxicity relationship.
Collapse
Affiliation(s)
- Ying Huang
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
| | - Jia Wang
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
| | - Shuo Wang
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
| | - Xiaotian Xu
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
| | - Weichao Qin
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
| | - Yang Wen
- Key Laboratory of Environmental Materials and Pollution Control, The Education Department of Jilin Province, School of Environmental Science and Engineering, Jilin Normal University, Siping, Jilin 136000, PR China.
| | - Yuan H Zhao
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China.
| | - Christopher J Martyniuk
- Center for Environmental and Human Toxicology, Department of Physiological Sciences, College of Veterinary Medicine, UF Genetics Institute, Interdisciplinary Program in Biomedical Sciences Neuroscience, University of Florida, Gainesville, FL, 32611, USA
| |
Collapse
|
5
|
Gul S, Rahim F, Isin S, Yilmaz F, Ozturk N, Turkay M, Kavakli IH. Structure-based design and classifications of small molecules regulating the circadian rhythm period. Sci Rep 2021; 11:18510. [PMID: 34531414 PMCID: PMC8445970 DOI: 10.1038/s41598-021-97962-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 08/27/2021] [Indexed: 11/09/2022] Open
Abstract
Circadian rhythm is an important mechanism that controls behavior and biochemical events based on 24 h rhythmicity. Ample evidence indicates disturbance of this mechanism is associated with different diseases such as cancer, mood disorders, and familial delayed phase sleep disorder. Therefore, drug discovery studies have been initiated using high throughput screening. Recently the crystal structures of core clock proteins (CLOCK/BMAL1, Cryptochromes (CRY), Periods), responsible for generating circadian rhythm, have been solved. Availability of structures makes amenable core clock proteins to design molecules regulating their activity by using in silico approaches. In addition to that, the implementation of classification features of molecules based on their toxicity and activity will improve the accuracy of the drug discovery process. Here, we identified 171 molecules that target functional domains of a core clock protein, CRY1, using structure-based drug design methods. We experimentally determined that 115 molecules were nontoxic, and 21 molecules significantly lengthened the period of circadian rhythm in U2OS cells. We then performed a machine learning study to classify these molecules for identifying features that make them toxic and lengthen the circadian period. Decision tree classifiers (DTC) identified 13 molecular descriptors, which predict the toxicity of molecules with a mean accuracy of 79.53% using tenfold cross-validation. Gradient boosting classifiers (XGBC) identified 10 molecular descriptors that predict and increase in the circadian period length with a mean accuracy of 86.56% with tenfold cross-validation. Our results suggested that these features can be used in QSAR studies to design novel nontoxic molecules that exhibit period lengthening activity.
Collapse
Affiliation(s)
- Seref Gul
- Department of Chemical and Biological Engineering, Koc University, Rumelifeneri Yolu, Sariyer, Istabul, Turkey
| | - Fatih Rahim
- Department of Industrial Engineering, Koc University, Rumelifeneri Yolu, Sariyer, Istabul, Turkey
| | - Safak Isin
- Department of Molecular Biology and Genetics, Rumelifeneri Yolu, Sariyer, Istabul, Turkey
| | - Fatma Yilmaz
- Department of Molecular Biology and Genetics, Gebze Technical University, Gebze, 41400, Kocaeli, Turkey
| | - Nuri Ozturk
- Department of Molecular Biology and Genetics, Gebze Technical University, Gebze, 41400, Kocaeli, Turkey
| | - Metin Turkay
- Department of Industrial Engineering, Koc University, Rumelifeneri Yolu, Sariyer, Istabul, Turkey.
| | - Ibrahim Halil Kavakli
- Department of Chemical and Biological Engineering, Koc University, Rumelifeneri Yolu, Sariyer, Istabul, Turkey.
- Department of Molecular Biology and Genetics, Rumelifeneri Yolu, Sariyer, Istabul, Turkey.
| |
Collapse
|
6
|
Predicting blood-to-plasma concentration ratios of drugs from chemical structures and volumes of distribution in humans. Mol Divers 2021; 25:1261-1270. [PMID: 33569705 PMCID: PMC8342319 DOI: 10.1007/s11030-021-10186-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 01/18/2021] [Indexed: 11/05/2022]
Abstract
Abstract Despite their importance in determining the dosing regimen of drugs in the clinic, only a few studies have investigated methods for predicting blood-to-plasma concentration ratios (Rb). This study established an Rb prediction model incorporating typical human pharmacokinetics (PK) parameters. Experimental Rb values were compiled for 289 compounds, offering reliable predictions by expanding the applicability domain. Notably, it is the largest list of Rb values reported so far. Subsequently, human PK parameters calculated from plasma drug concentrations, including the volume of distribution (Vd), clearance, mean residence time, and plasma protein binding rate, as well as 2702 kinds of molecular descriptors, were used to construct quantitative structure–PK relationship models for Rb. Among the evaluated PK parameters, logVd correlated best with Rb (correlation coefficient of 0.47). Thus, in addition to molecular descriptors selected by XGBoost, logVd was employed to construct the prediction models. Among the analyzed algorithms, artificial neural networks gave the best results. Following optimization using six molecular descriptors and logVd, the model exhibited a correlation coefficient of 0.64 and a root-mean-square error of 0.205, which were superior to those previously reported for other Rb prediction methods. Since Vd values and chemical structures are known for most medications, the Rb prediction model described herein is expected to be valuable in clinical settings. Graphical abstract ![]()
Supplementary informations The online version of this article (10.1007/s11030-021-10186-7) contains supplementary material, which is available to authorized users.
Collapse
|
7
|
Piperine: A comprehensive review of methods of isolation, purification, and biological properties. MEDICINE IN DRUG DISCOVERY 2020. [DOI: 10.1016/j.medidd.2020.100027] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
|
8
|
Sakagami H. Introduction to the Special Issue "Biological Efficacy of Natural and Chemically Modified Products against Oral Inflammatory Lesions". MEDICINES 2019; 6:medicines6020052. [PMID: 31035315 PMCID: PMC6630427 DOI: 10.3390/medicines6020052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 04/25/2019] [Indexed: 11/16/2022]
Abstract
This editorial is a brief introduction to the Special Issue of “Biological Efficacy of Natural and Chemically Modified Products against Oral Inflammatory Lesions”. From the natural resources and chemical modifications of the backbone structures of natural products, various attractive substances with new biological functions were excavated. Best fit combination of these materials may contribute in the treatment of oral diseases.
Collapse
Affiliation(s)
- Hiroshi Sakagami
- Meikai University Research Institute of Odontology (M-RIO), 1-1 Keyakidai, Sakado, Saitama 350-0283, Japan.
| |
Collapse
|