1
|
Meng L, Zhou B, Liu H, Chen Y, Yuan R, Chen Z, Luo S, Chen H. Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:174201. [PMID: 38936709 DOI: 10.1016/j.scitotenv.2024.174201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 06/17/2024] [Accepted: 06/20/2024] [Indexed: 06/29/2024]
Abstract
Perfluorinated and perfluoroalkyl substances (PFASs), encompassing a vast array of isomeric chemicals, are recognized as typical emerging contaminants with direct or potential impacts on human health and the ecological environment. With the complex and elusive toxicological profiles of PFASs, machine learning (ML) has been increasingly employed in their toxicity studies due to its proficiency in prediction and data analytics. This integration is poised to become a predominant trend in environmental toxicology, propelled by the swift advancements in computational technology. This review diligently examines the literature to encapsulate the varied objectives of employing ML in the toxicity studies of PFASs: (1) Utilizing ML to establish Quantitative Structure-Activity Relationship (QSAR) models for PFASs with diverse toxicity endpoints, facilitating the targeted toxicity prediction of unidentified PFASs; (2) Investigating and substantiating the Adverse Outcome Pathway (AOP) through the synergy of ML and traditional toxicological methods, with this refining the toxicity assessment framework for PFASs; (3) Dissecting and elucidating the features of established ML models to advance Open Research into the toxicity of PFASs, with a primary focus on determinants and mechanisms. The discourse extends to an in-depth examination of ML studies, segregating findings based on their distinct application trajectories. Given that ML represents a nascent paradigm within PFASs research, this review delineates the collective challenges encountered in the ML-mediated study of PFAS toxicity and proffers strategic guidance for ensuing investigations.
Collapse
Affiliation(s)
- Lingxuan Meng
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Beihai Zhou
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Haijun Liu
- School of Resources and Environment, Anqing Normal University, Anqing, China.
| | - Yuefang Chen
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
| | - Rongfang Yuan
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Zhongbing Chen
- Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 16500 Praha-Suchdol, Czech Republic.
| | - Shuai Luo
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Huilun Chen
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
| |
Collapse
|
2
|
Wu S, Li SX, Qiu J, Zhao HM, Li YW, Feng NX, Liu BL, Cai QY, Xiang L, Mo CH, Li QX. Accurate Prediction of Rat Acute Oral Toxicity and Reference Dose for Thousands of Polycyclic Aromatic Hydrocarbon Derivatives Based on Chemometric QSAR and Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024. [PMID: 39137267 DOI: 10.1021/acs.est.4c03966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
Acute oral toxicity is currently not available for most polycyclic aromatic hydrocarbons (PAHs), especially their derivatives, because it is cost-prohibitive to experimentally determine all of them. Here, quantitative structure-activity relationship (QSAR) models using machine learning (ML) for predicting the toxicity of PAH derivatives were developed, based on oral toxicity data points of 788 individual substances of rats. Both the individual ML algorithm gradient boosting regression trees (GBRT) and the stacking ML algorithm (extreme gradient boosting + GBRT + random forest regression) provided the best prediction results with satisfactory determination coefficients for both cross-validation and the test set. It was found that those PAH derivatives with fewer polar hydrogens, more large-sized atoms, more branches, and lower polarizability have higher toxicity. Software based on the optimal ML-QSAR model was successfully developed to expand the application potential of the developed model, obtaining reliable prediction of pLD50 values and reference doses for 6893 external PAH derivatives. Among these chemicals, 472 were identified as moderately or highly toxic; 10 out of them had clear environment detection or use records. The findings provide valuable insights into the toxicity of PAHs and their derivatives, offering a standard platform for effectively evaluating chemical toxicity using ML-QSAR models.
Collapse
Affiliation(s)
- Shuang Wu
- Guangdong Provincial Research Center for Environment Pollution Control and Remediation Materials, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Shi-Xin Li
- Guangdong Provincial Research Center for Environment Pollution Control and Remediation Materials, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Jing Qiu
- Guangdong Provincial Research Center for Environment Pollution Control and Remediation Materials, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Hai-Ming Zhao
- Guangdong Provincial Research Center for Environment Pollution Control and Remediation Materials, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Yan-Wen Li
- Guangdong Provincial Research Center for Environment Pollution Control and Remediation Materials, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Nai-Xian Feng
- Guangdong Provincial Research Center for Environment Pollution Control and Remediation Materials, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Bai-Lin Liu
- Guangdong Provincial Research Center for Environment Pollution Control and Remediation Materials, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Quan-Ying Cai
- Guangdong Provincial Research Center for Environment Pollution Control and Remediation Materials, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Lei Xiang
- Guangdong Provincial Research Center for Environment Pollution Control and Remediation Materials, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Ce-Hui Mo
- Guangdong Provincial Research Center for Environment Pollution Control and Remediation Materials, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Qing X Li
- Department of Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, Honolulu, Hawaii, 96822, United States
| |
Collapse
|
3
|
Kim D, Cho S, Jeon JJ, Choi J. Inhalation Toxicity Screening of Consumer Products Chemicals using OECD Test Guideline Data-based Machine Learning Models. JOURNAL OF HAZARDOUS MATERIALS 2024; 478:135446. [PMID: 39154469 DOI: 10.1016/j.jhazmat.2024.135446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Revised: 07/24/2024] [Accepted: 08/05/2024] [Indexed: 08/20/2024]
Abstract
This study aimed to screen the inhalation toxicity of chemicals found in consumer products such as air fresheners, fragrances, and anti-fogging agents submitted to K-REACH using machine learning models. We manually curated inhalation toxicity data based on OECD test guideline 403 (Acute inhalation), 412 (Sub-acute inhalation), and 413 (Sub-chronic inhalation) for 1709 chemicals from the OECD eChemPortal database. Machine learning models were trained using ten algorithms, along with four molecular fingerprints (MACCS, Morgan, Topo, RDKit) and molecular descriptors, achieving F1 scores ranging from 51 % to 91 % in test dataset. Leveraging the high-performing models, we conducted a virtual screening of chemicals, initially applying them to data-rich chemicals generally used in occupational settings to determine the prediction uncertainty. Results showed high sensitivity (75 %) but low specificity (23 %), suggesting that our models can contribute to conservative screening of chemicals. Subsequently, we applied the models to consumer product chemicals, identifying 79 as of high concern. Most of the prioritized chemicals lacked GHS classifications related to inhalation toxicity, even though they were predicted to be used in many consumer products. This study highlights a potential regulatory blind spot concerning the inhalation risk of consumer product chemicals while also indicating the potential of artificial intelligence (AI) models to aid in prioritizing chemicals at the screening level.
Collapse
Affiliation(s)
- Donghyeon Kim
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Soyoung Cho
- Department of Statistics, University of Seoul, Seoul 02504, Republic of Korea
| | - Jong-June Jeon
- Department of Statistics, University of Seoul, Seoul 02504, Republic of Korea.
| | - Jinhee Choi
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea.
| |
Collapse
|
4
|
Cao A, Zhang L, Bu Y, Sun D. Machine Learning Prediction of On/Off Target-driven Clinical Adverse Events. Pharm Res 2024; 41:1649-1658. [PMID: 39095534 DOI: 10.1007/s11095-024-03742-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: 02/20/2024] [Accepted: 07/06/2024] [Indexed: 08/04/2024]
Abstract
OBJECTIVE Currently, 90% of clinical drug development fails, where 30% of these failures are due to clinical toxicity. The current extensive animal toxicity studies are not predictive of clinical adverse events (AEs) at clinical doses, while current computation models only consider very few factors with limited success in clinical toxicity prediction. We aimed to address these issues by developing a machine learning (ML) model to directly predict clinical AEs. METHODS Using a dataset with 759 FDA-approved drugs with known AEs, we first adapted the ConPLex ML model to predict IC50 values of these FDA-approved drugs against their on-target and off-target binding among 477 protein targets. Subsequently, we constructed a new ML model to predict clinical AEs using IC50 values of 759 drugs' primary on-target and off-target effects along with tissue-specific protein expression profiles. RESULTS The adapted ConPLex model predicted drug-target interactions for both on- and off-target effects, as shown by co-localization of the 6 small molecule kinase inhibitors with their respective kinases. The coupled ML models demonstrated good predictive capability of clinical AEs, with accuracy over 75%. CONCLUSIONS Our approach provides a new insight into the mechanistic understanding of in vivo drug toxicity in relationship with drug on-/off-target interactions. The coupled ML models, once validated with larger datasets, may offer advantages to directly predict clinical AEs using in vitro/ex vivo and preclinical data, which will help to reduce drug development failure due to clinical toxicity.
Collapse
Affiliation(s)
- Albert Cao
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI, 48109, United States
- Centennial High School, Ellicott City, MD, 21042, United States
| | - Luchen Zhang
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI, 48109, United States
| | - Yingzi Bu
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI, 48109, United States
- Michigan Institute for Computational Discovery & Engineering, University of Michigan, Ann Arbor, MI, 48109, United States
| | - Duxin Sun
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI, 48109, United States.
- Duxin Sun, 1600 Huron Parkway, North Campus Research Complex, Building 520, Ann Arbor, MI, 48109, United States.
| |
Collapse
|
5
|
Alvarez-Mora I, Arturi K, Béen F, Buchinger S, El Mais AER, Gallampois C, Hahn M, Hollender J, Houtman C, Johann S, Krauss M, Lamoree M, Margalef M, Massei R, Brack W, Muz M. Progress, applications, and challenges in high-throughput effect-directed analysis for toxicity driver identification - is it time for HT-EDA? Anal Bioanal Chem 2024:10.1007/s00216-024-05424-4. [PMID: 38992177 DOI: 10.1007/s00216-024-05424-4] [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: 04/30/2024] [Revised: 06/21/2024] [Accepted: 06/24/2024] [Indexed: 07/13/2024]
Abstract
The rapid increase in the production and global use of chemicals and their mixtures has raised concerns about their potential impact on human and environmental health. With advances in analytical techniques, in particular, high-resolution mass spectrometry (HRMS), thousands of compounds and transformation products with potential adverse effects can now be detected in environmental samples. However, identifying and prioritizing the toxicity drivers among these compounds remain a significant challenge. Effect-directed analysis (EDA) emerged as an important tool to address this challenge, combining biotesting, sample fractionation, and chemical analysis to unravel toxicity drivers in complex mixtures. Traditional EDA workflows are labor-intensive and time-consuming, hindering large-scale applications. The concept of high-throughput (HT) EDA has recently gained traction as a means of accelerating these workflows. Key features of HT-EDA include the combination of microfractionation and downscaled bioassays, automation of sample preparation and biotesting, and efficient data processing workflows supported by novel computational tools. In addition to microplate-based fractionation, high-performance thin-layer chromatography (HPTLC) offers an interesting alternative to HPLC in HT-EDA. This review provides an updated perspective on the state-of-the-art in HT-EDA, and novel methods/tools that can be incorporated into HT-EDA workflows. It also discusses recent studies on HT-EDA, HT bioassays, and computational prioritization tools, along with considerations regarding HPTLC. By identifying current gaps in HT-EDA and proposing new approaches to overcome them, this review aims to bring HT-EDA a step closer to monitoring applications.
Collapse
Affiliation(s)
- Iker Alvarez-Mora
- Department of Exposure Science, Helmholtz Centre for Environmental Research, UFZ, Leipzig, Germany.
- Research Centre for Experimental Marine Biology and Biotechnology (PIE), University of the Basque Country (UPV/EHU), Plentzia, Basque Country, Spain.
| | - Katarzyna Arturi
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
| | - Frederic Béen
- KWR Water Research Institute, Nieuwegein, the Netherlands
- Chemistry for Environment and Health, Amsterdam Institute for Life and Environment (A-LIFE), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Sebastian Buchinger
- Department of Biochemistry and Ecotoxicology, Federal Institute of Hydrology (BfG), Koblenz, Germany
| | | | | | - Meike Hahn
- Department of Biochemistry and Ecotoxicology, Federal Institute of Hydrology (BfG), Koblenz, Germany
| | - Juliane Hollender
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
- Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, Zürich, Switzerland
| | - Corine Houtman
- Chemistry for Environment and Health, Amsterdam Institute for Life and Environment (A-LIFE), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- The Water Laboratory, Haarlem, the Netherlands
| | - Sarah Johann
- Department of Evolutionary Ecology and Environmental Toxicology, Goethe University Frankfurt, Frankfurt Am Main, Germany
| | - Martin Krauss
- Department of Exposure Science, Helmholtz Centre for Environmental Research, UFZ, Leipzig, Germany
| | - Marja Lamoree
- Chemistry for Environment and Health, Amsterdam Institute for Life and Environment (A-LIFE), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Maria Margalef
- Chemistry for Environment and Health, Amsterdam Institute for Life and Environment (A-LIFE), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Riccardo Massei
- Department of Monitoring and Exploration Technologies, Research Data Management Team (RDM), Helmholtz Centre for Environmental Research, UFZ, Leipzig, Germany
- Department of Ecotoxicology, Group of Integrative Toxicology (iTox), Helmholtz Centre for Environmental Research, UFZ, Leipzig, Germany
| | - Werner Brack
- Department of Exposure Science, Helmholtz Centre for Environmental Research, UFZ, Leipzig, Germany
- Department of Evolutionary Ecology and Environmental Toxicology, Goethe University Frankfurt, Frankfurt Am Main, Germany
| | - Melis Muz
- Department of Exposure Science, Helmholtz Centre for Environmental Research, UFZ, Leipzig, Germany
| |
Collapse
|
6
|
Zhou Y, Ning C, Tan Y, Li Y, Wang J, Shu Y, Liang S, Liu Z, Wang Y. ToxMPNN: A deep learning model for small molecule toxicity prediction. J Appl Toxicol 2024; 44:953-964. [PMID: 38409892 DOI: 10.1002/jat.4591] [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: 11/08/2023] [Revised: 01/23/2024] [Accepted: 02/02/2024] [Indexed: 02/28/2024]
Abstract
Machine learning (ML) has shown a great promise in predicting toxicity of small molecules. However, the availability of data for such predictions is often limited. Because of the unsatisfactory performance of models trained on a single toxicity endpoint, we collected toxic small molecules with multiple toxicity endpoints from previous study. The dataset comprises 27 toxic endpoints categorized into seven toxicity classes, namely, carcinogenicity and mutagenicity, acute oral toxicity, respiratory toxicity, irritation and corrosion, cardiotoxicity, CYP450, and endocrine disruption. In addition, a binary classification Common-Toxicity task was added based on the aforementioned dataset. To improve the performance of the models, we added marketed drugs as negative samples. This study presents a toxicity predictive model, ToxMPNN, based on the message passing neural network (MPNN) architecture, aiming to predict the toxicity of small molecules. The results demonstrate that ToxMPNN outperforms other models in capturing toxic features within the molecular structure, resulting in more precise predictions with the ROC_AUC testing score of 0.886 for the Toxicity_drug dataset. Furthermore, it was observed that adding marketed drugs as negative samples not only improves the predictive performance of the binary classification Common-Toxicity task but also enhances the stability of the model prediction. It shows that the graph-based deep learning (DL) algorithms in this study can be used as a trustworthy and effective tool to assess small molecule toxicity in the development of new drugs.
Collapse
Affiliation(s)
- Yini Zhou
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, China
- Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, China
- Peptide and Small Molecule Drug R&D Platform, Furong Laboratory, Hunan Normal University, Changsha, China
| | - Chao Ning
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, China
- Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, China
- Peptide and Small Molecule Drug R&D Platform, Furong Laboratory, Hunan Normal University, Changsha, China
| | - Yijun Tan
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, China
| | - Yaqi Li
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, China
- Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, China
- Peptide and Small Molecule Drug R&D Platform, Furong Laboratory, Hunan Normal University, Changsha, China
| | - Jiaxu Wang
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, China
- Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, China
- Peptide and Small Molecule Drug R&D Platform, Furong Laboratory, Hunan Normal University, Changsha, China
| | - Yuanyuan Shu
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, China
- Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, China
- Peptide and Small Molecule Drug R&D Platform, Furong Laboratory, Hunan Normal University, Changsha, China
| | - Songping Liang
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, China
- Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, China
- Peptide and Small Molecule Drug R&D Platform, Furong Laboratory, Hunan Normal University, Changsha, China
| | - Zhonghua Liu
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, China
- Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, China
- Peptide and Small Molecule Drug R&D Platform, Furong Laboratory, Hunan Normal University, Changsha, China
| | - Ying Wang
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, China
- Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, China
- Peptide and Small Molecule Drug R&D Platform, Furong Laboratory, Hunan Normal University, Changsha, China
| |
Collapse
|
7
|
Singh S, Kachhawaha K, Singh SK. Comprehensive approaches to preclinical evaluation of monoclonal antibodies and their next-generation derivatives. Biochem Pharmacol 2024; 225:116303. [PMID: 38797272 DOI: 10.1016/j.bcp.2024.116303] [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: 12/24/2023] [Revised: 05/03/2024] [Accepted: 05/17/2024] [Indexed: 05/29/2024]
Abstract
Biotherapeutics hold great promise for the treatment of several diseases and offer innovative possibilities for new treatments that target previously unaddressed medical needs. Despite successful transitions from preclinical to clinical stages and regulatory approval, there are instances where adverse reactions arise, resulting in product withdrawals. As a result, it is essential to conduct thorough evaluations of safety and effectiveness on an individual basis. This article explores current practices, challenges, and future approaches in conducting comprehensive preclinical assessments to ensure the safety and efficacy of biotherapeutics including monoclonal antibodies, toxin-conjugates, bispecific antibodies, single-chain antibodies, Fc-engineered antibodies, antibody mimetics, and siRNA-antibody/peptide conjugates.
Collapse
Affiliation(s)
- Santanu Singh
- Laboratory of Engineered Therapeutics, School of Biochemical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Kajal Kachhawaha
- Laboratory of Engineered Therapeutics, School of Biochemical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Sumit K Singh
- Laboratory of Engineered Therapeutics, School of Biochemical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.
| |
Collapse
|
8
|
Abou Hajal A, Al Meslamani AZ. Overcoming barriers to machine learning applications in toxicity prediction. Expert Opin Drug Metab Toxicol 2024; 20:549-553. [PMID: 38088128 DOI: 10.1080/17425255.2023.2294939] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 12/11/2023] [Indexed: 07/25/2024]
Affiliation(s)
- Abdallah Abou Hajal
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates
| | - Ahmad Z Al Meslamani
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates
| |
Collapse
|
9
|
Amorim AM, Piochi LF, Gaspar AT, Preto A, Rosário-Ferreira N, Moreira IS. Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction. Chem Res Toxicol 2024; 37:827-849. [PMID: 38758610 PMCID: PMC11187637 DOI: 10.1021/acs.chemrestox.3c00352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 05/19/2024]
Abstract
The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.
Collapse
Affiliation(s)
- Ana M.
B. Amorim
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PhD
Programme in Biosciences, Department of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PURR.AI,
Rua Pedro Nunes, IPN Incubadora, Ed C, 3030-199 Coimbra, Portugal
| | - Luiz F. Piochi
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - Ana T. Gaspar
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - António
J. Preto
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PhD Programme
in Experimental Biology and Biomedicine, Institute for Interdisciplinary
Research (IIIUC), University of Coimbra, Casa Costa Alemão, 3030-789 Coimbra, Portugal
| | - Nícia Rosário-Ferreira
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - Irina S. Moreira
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| |
Collapse
|
10
|
Toni E, Ayatollahi H, Abbaszadeh R, Fotuhi Siahpirani A. Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review. Pharmaceuticals (Basel) 2024; 17:795. [PMID: 38931462 PMCID: PMC11206653 DOI: 10.3390/ph17060795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Drug safety relies on advanced methods for timely and accurate prediction of side effects. To tackle this requirement, this scoping review examines machine-learning approaches for predicting drug-related side effects with a particular focus on chemical, biological, and phenotypical features. METHODS This was a scoping review in which a comprehensive search was conducted in various databases from 1 January 2013 to 31 December 2023. RESULTS The results showed the widespread use of Random Forest, k-nearest neighbor, and support vector machine algorithms. Ensemble methods, particularly random forest, emphasized the significance of integrating chemical and biological features in predicting drug-related side effects. CONCLUSIONS This review article emphasized the significance of considering a variety of features, datasets, and machine learning algorithms for predicting drug-related side effects. Ensemble methods and Random Forest showed the best performance and combining chemical and biological features improved prediction. The results suggested that machine learning techniques have some potential to improve drug development and trials. Future work should focus on specific feature types, selection techniques, and graph-based methods for even better prediction.
Collapse
Affiliation(s)
- Esmaeel Toni
- Medical Informatics, Student Research Committee, Iran University of Medical Sciences, Tehran, Iran 14496-14535;
| | - Haleh Ayatollahi
- Medical Informatics, Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran 1996-713883
| | - Reza Abbaszadeh
- Pediatric Cardiology, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran 19956-14331;
| | - Alireza Fotuhi Siahpirani
- Systems Biology and Bioinformatics, Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran 14176-14411;
| |
Collapse
|
11
|
Cavasotto CN, Di Filippo JI, Scardino V. Lessons learnt from machine learning in early stages of drug discovery. Expert Opin Drug Discov 2024; 19:631-633. [PMID: 38727031 DOI: 10.1080/17460441.2024.2354279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 05/08/2024] [Indexed: 05/22/2024]
Affiliation(s)
- Claudio N Cavasotto
- Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), CONICET-Universidad Austral, Pilar, Buenos Aires, Argentina
- Facultad de Ciencias Biomédicas, Universidad Austral, Pilar, Buenos Aires, Argentina
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Argentina
| | - Juan I Di Filippo
- Facultad de Ciencias Biomédicas, Universidad Austral, Pilar, Buenos Aires, Argentina
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Argentina
- Meton AI, Inc, Wilmington, DE, USA
| | - Valeria Scardino
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Argentina
- Meton AI, Inc, Wilmington, DE, USA
| |
Collapse
|
12
|
Saravanan KM, Wan JF, Dai L, Zhang J, Zhang JZH, Zhang H. A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity. Methods 2024; 226:164-175. [PMID: 38702021 DOI: 10.1016/j.ymeth.2024.04.020] [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: 09/20/2023] [Revised: 04/01/2024] [Accepted: 04/28/2024] [Indexed: 05/06/2024] Open
Abstract
Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.
Collapse
Affiliation(s)
- Konda Mani Saravanan
- Department of Biotechnology, Bharath Institute of Higher Education and Research, Chennai 600073, Tamil Nadu, India
| | - Jiang-Fan Wan
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Drug Evaluation and Inspection of NMPA, Shenzhen 518000, China
| | - Liujiang Dai
- Guangdong Immune Cell Therapy Engineering and Technology Research Center, Center for Protein and Cell-Based Drugs, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jiajun Zhang
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; College of Science, Hunan University of Technology and Business, Changsha 410205, China
| | - John Z H Zhang
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Haiping Zhang
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
| |
Collapse
|
13
|
Chen C, Lavezzi SM, McDougall D. The estimation and translation uncertainties in applying NOAEL to clinical dose escalation. Clin Transl Sci 2024; 17:e13831. [PMID: 38808564 PMCID: PMC11134224 DOI: 10.1111/cts.13831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/08/2024] [Accepted: 04/30/2024] [Indexed: 05/30/2024] Open
Abstract
The systemic exposure at the no-observed-adverse-effect-level (NOAEL) estimated from animals is an important criterion commonly applied to guard the safety of participants in clinical trials of investigational drugs. However, the discrepancy in toxicity profile between species is widely recognized. The objective of the work reported here was to assess, via simulation, the level of uncertainty in the NOAEL estimated from an animal species and the effectiveness of applying its associated exposure value to minimizing the toxicity risk to human. Simulations were conducted for dose escalation of an investigational new chemical entity with hypothetical exposure-response models for the dose-limiting toxicity under a variety of conditions, in terms of between-species relative sensitivity to the toxicity and the between-subject variability in the key parameters determining the sensitivity and pharmacokinetics. Results show a high uncertainty in the NOAEL estimation. Notably, even when the animal species and humans are assumed to have the same sensitivity, which may not be realistic, limiting clinical dose to the exposure at the NOAEL that has been identified in the animals carries a high risk of either causing toxicity or under-dosing, hence undermining the therapeutic potential of the drug candidate. These findings highlight the importance of understanding the mechanism of the toxicity profile and its cross-species translatability, as well as the importance of understanding the dose requirement for achieving adequate pharmacology.
Collapse
Affiliation(s)
- Chao Chen
- Clinical Pharmacology Modelling and SimulationGSKLondonUK
| | - Silvia Maria Lavezzi
- Clinical Pharmacology, Modelling and SimulationParexel InternationalDublinIreland
| | - David McDougall
- Clinical Pharmacology, Modelling and SimulationParexel InternationalBrisbaneQueenslandAustralia
| |
Collapse
|
14
|
Tortora F, Guerrera V, Lettieri G, Febbraio F, Piscopo M. Prediction of Pesticide Interactions with Proteins Involved in Human Reproduction by Using a Virtual Screening Approach: A Case Study of Famoxadone Binding CRBP-III and Izumo. Int J Mol Sci 2024; 25:5790. [PMID: 38891976 PMCID: PMC11171824 DOI: 10.3390/ijms25115790] [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: 05/07/2024] [Revised: 05/21/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
In recent years, the awareness that pesticides can have other effects apart from generic toxicity is growing. In particular, several pieces of evidence highlight their influence on human fertility. In this study, we investigated, by a virtual screening approach, the binding between pesticides and proteins present in human gametes or associated with reproduction, in order to identify new interactions that could affect human fertility. To this aim, we prepared ligand (pesticides) and receptor (proteins) 3D structure datasets from online structural databases (such as PubChem and RCSB), and performed a virtual screening analysis using Autodock Vina. In the comparison of the predicted interactions, we found that famoxadone was predicted to bind Cellular Retinol Binding Protein-III in the retinol-binding site with a better minimum energy value of -10.4 Kcal/mol and an RMSD of 3.77 with respect to retinol (-7.1 Kcal/mol). In addition to a similar network of interactions, famoxadone binding is more stabilized by additional hydrophobic patches including L20, V29, A33, F57, L117, and L118 amino acid residues and hydrogen bonds with Y19 and K40. These results support a possible competitive effect of famoxadone on retinol binding with impacts on the ability of developing the cardiac tissue, in accordance with the literature data on zebrafish embryos. Moreover, famoxadone binds, with a minimum energy value between -8.3 and -8.0 Kcal/mol, to the IZUMO Sperm-Egg Fusion Protein, interacting with a network of polar and hydrophobic amino acid residues in the cavity between the 4HB and Ig-like domains. This binding is more stabilized by a predicted hydrogen bond with the N185 residue of the protein. A hindrance in this position can probably affect the conformational change for JUNO binding, avoiding the gamete membrane fusion to form the zygote. This work opens new interesting perspectives of study on the effects of pesticides on fertility, extending the knowledge to other typologies of interaction which can affect different steps of the reproductive process.
Collapse
Affiliation(s)
- Fabiana Tortora
- Institute of Genetics and Biophysics “Adriano Buzzati Traverso”, National Research Council (CNR), Via P. Castellino 111, 80131 Naples, Italy;
- Institute of Biochemistry and Cell Biology, National Research Council (CNR), Via P. Castellino 111, 80131 Naples, Italy
| | - Valentina Guerrera
- Institute of Biochemistry and Cell Biology, National Research Council (CNR), Via P. Castellino 111, 80131 Naples, Italy
| | - Gennaro Lettieri
- Department of Biology, University of Naples Federico II, Via Cinthia, 21, 80126 Naples, Italy (M.P.)
| | - Ferdinando Febbraio
- Institute of Biochemistry and Cell Biology, National Research Council (CNR), Via P. Castellino 111, 80131 Naples, Italy
| | - Marina Piscopo
- Department of Biology, University of Naples Federico II, Via Cinthia, 21, 80126 Naples, Italy (M.P.)
| |
Collapse
|
15
|
Zi J, Barker J, Zi Y, MacIsaac HJ, Zhou Y, Harshaw K, Chang X. Assessment of estrogenic potential from exudates of microcystin-producing and non-microcystin-producing Microcystis by metabolomics, machine learning and E-screen assay. JOURNAL OF HAZARDOUS MATERIALS 2024; 470:134170. [PMID: 38613957 DOI: 10.1016/j.jhazmat.2024.134170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/18/2024] [Accepted: 03/28/2024] [Indexed: 04/15/2024]
Abstract
Cyanobacterial blooms, often dominated by Microcystis aeruginosa, are capable of producing estrogenic effects. It is important to identify specific estrogenic compounds produced by cyanobacteria, though this can prove challenging owing to the complexity of exudate mixtures. In this study, we used untargeted metabolomics to compare components of exudates from microcystin-producing and non-microcystin-producing M. aeruginosa strains that differed with respect to their ability to produce microcystins, and across two growth phases. We identified 416 chemicals and found that the two strains produced similar components, mainly organoheterocyclic compounds (20.2%), organic acids and derivatives (17.3%), phenylpropanoids and polyketides (12.7%), benzenoids (12.0%), lipids and lipid-like molecules (11.5%), and organic oxygen compounds (10.1%). We then predicted estrogenic compounds from this group using random forest machine learning. Six compounds (daidzin, biochanin A, phenylethylamine, rhein, o-Cresol, and arbutin) belonging to phenylpropanoids and polyketides (3), benzenoids (2), and organic oxygen compound (1) were tested and exhibited estrogenic potency based upon the E-screen assay. This study confirmed that both Microcystis strains produce exudates that contain compounds with estrogenic properties, a growing concern in cyanobacteria management.
Collapse
Affiliation(s)
- Jinmei Zi
- Yunnan Collaborative Innovation Center for Plateau Lake Ecology and Environmental Health, College of Agronomy and Life Sciences, Kunming University, Kunming 650214, China; Great Lakes Institute for Environmental Research, University of Windsor, Windsor, Ontario N9B 3P4, Canada
| | - Justin Barker
- Great Lakes Institute for Environmental Research, University of Windsor, Windsor, Ontario N9B 3P4, Canada; Maps, Data, and Government Information Centre, Trent University, Peterborough, Ontario K9L 0G2, Canada
| | - Yuanyan Zi
- Great Lakes Institute for Environmental Research, University of Windsor, Windsor, Ontario N9B 3P4, Canada
| | - Hugh J MacIsaac
- Great Lakes Institute for Environmental Research, University of Windsor, Windsor, Ontario N9B 3P4, Canada; School of Ecology and Environmental Sciences, Yunnan University, Kunming 650091, China
| | - Yuan Zhou
- The Ecological and Environmental Monitoring Station of DEEY in Kunming, Kunming 650228, China; School of Ecology and Environmental Sciences, Yunnan University, Kunming 650091, China
| | - Keira Harshaw
- Great Lakes Institute for Environmental Research, University of Windsor, Windsor, Ontario N9B 3P4, Canada
| | - Xuexiu Chang
- Yunnan Collaborative Innovation Center for Plateau Lake Ecology and Environmental Health, College of Agronomy and Life Sciences, Kunming University, Kunming 650214, China; Great Lakes Institute for Environmental Research, University of Windsor, Windsor, Ontario N9B 3P4, Canada.
| |
Collapse
|
16
|
Li H, Li H, Zuo N, Lang D, Du W, Zhang P, Pan B. Can the concentration of environmentally persistent free radicals describe its toxicity to Caenorhabditis elegans? Evidence provided by neurotoxicity and oxidative stress. JOURNAL OF HAZARDOUS MATERIALS 2024; 469:133823. [PMID: 38442598 DOI: 10.1016/j.jhazmat.2024.133823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/04/2024] [Accepted: 02/16/2024] [Indexed: 03/07/2024]
Abstract
Environmentally persistent free radicals (EPFRs) are emerging pollutants stabilized on or inside particles. Although the toxicity of EPFR-containing particles has been confirmed, the conclusions are always ambiguous because of the presence of various compositions. A clear dose-response relationship was always challenged by the fact that the concentrations of these coexisted components simultaneously changed with EPFR concentrations. Without these solid dose-response pieces of evidence, we could not confidently conclude the toxicity of EPFRs and the description of potential EPFR risks. In this study, we established a particle system with a fixed catechol concentration but different reaction times to obtain particles with different EPFR concentrations. Caenorhabditis elegans (C. elegans) in response to different EPFR concentrations was systematically investigated at multiple biological levels, including behavior observations and biochemical and transcriptome analyses. Our results showed that exposure to EPFRs disrupted the development and locomotion of C. elegans. EPFRs cause concentration-dependent neurotoxicity and oxidative damage to C. elegans, which could be attributed to reactive oxygen species (ROS) promoted by EPFRs. Furthermore, the expression of key genes related to neurons was downregulated, whereas antioxidative genes were upregulated. Overall, our results confirmed the toxicity from EPFRs and EPFR concentration as a rational parameter to describe the extent of toxicity.
Collapse
Affiliation(s)
- Huijie Li
- Yunnan Provincial Key Lab of Soil Carbon Sequestration and Pollution Control, Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Hao Li
- Yunnan Provincial Key Lab of Soil Carbon Sequestration and Pollution Control, Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China.
| | - Ning Zuo
- Yunnan Research Academy of Eco-environmental Science, Kunming 650034, China
| | - Di Lang
- Yunnan Provincial Key Lab of Soil Carbon Sequestration and Pollution Control, Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Wei Du
- Yunnan Provincial Key Lab of Soil Carbon Sequestration and Pollution Control, Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Peng Zhang
- Yunnan Provincial Key Lab of Soil Carbon Sequestration and Pollution Control, Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Bo Pan
- Yunnan Provincial Key Lab of Soil Carbon Sequestration and Pollution Control, Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China.
| |
Collapse
|
17
|
Zhang S, Zhao D, Cui Q. Gap-Δenergy, a New Metric of the Bond Energy State, Assisting to Predict Molecular Toxicity. ACS OMEGA 2024; 9:17839-17847. [PMID: 38680329 PMCID: PMC11044234 DOI: 10.1021/acsomega.3c07682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 04/02/2024] [Accepted: 04/05/2024] [Indexed: 05/01/2024]
Abstract
Molecular toxicity is a critical feature of drug development. It is thus very important to develop computational models to evaluate the toxicity of small molecules. The accuracy of toxicity prediction largely depends on the quality of molecular representation; however, current methods for this purpose do not address this issue well. Here, we introduce a new metric, gap-Δenergy, which is designed to quantify the intermolecular bond energy difference with atom distance. We next find significant variations in the gap-Δenergy distribution among different types of molecules. Moreover, we show that this metric is able to distinguish the toxic small molecules. We collected data sets of toxic and exogenous small molecules and presented a novel index, namely, global toxicity, to evaluate the overall toxicity of molecules. Based on molecular descriptors and the proposed gap-Δenergy metric, we further constructed machine learning models that were trained with 7816 small molecules. The XGBoost-based model achieved the best performance with an AUC score of 0.965 and an F1 score of 0.849 on the test set (1954 small molecules), which outperformed the model that did not use gap-Δenergy features, with a sensitivity score increase of 3.2%.
Collapse
Affiliation(s)
- Senpeng Zhang
- Department of Biomedical
Informatics, State Key Laboratory of Vascular Homeostasis and Remodeling,
School of Basic Medical Sciences, Peking
University, 38 Xueyuan Rd, Beijing 100191, People’s Republic
of China
| | - Dongyu Zhao
- Department of Biomedical
Informatics, State Key Laboratory of Vascular Homeostasis and Remodeling,
School of Basic Medical Sciences, Peking
University, 38 Xueyuan Rd, Beijing 100191, People’s Republic
of China
| | | |
Collapse
|
18
|
Anandhi G, Iyapparaja M. Systematic approaches to machine learning models for predicting pesticide toxicity. Heliyon 2024; 10:e28752. [PMID: 38576573 PMCID: PMC10990867 DOI: 10.1016/j.heliyon.2024.e28752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 03/13/2024] [Accepted: 03/24/2024] [Indexed: 04/06/2024] Open
Abstract
Pesticides play an important role in modern agriculture by protecting crops from pests and diseases. However, the negative consequences of pesticides, such as environmental contamination and adverse effects on human and ecological health, underscore the importance of accurate toxicity predictions. To address this issue, artificial intelligence models have emerged as valuable methods for predicting the toxicity of organic compounds. In this review article, we explore the application of machine learning (ML) for pesticide toxicity prediction. This review provides a detailed summary of recent developments, prediction models, and datasets used for pesticide toxicity prediction. In this analysis, we compared the results of several algorithms that predict the harmfulness of various classes of pesticides. Furthermore, this review article identified emerging trends and areas for future direction, showcasing the transformative potential of machine learning in promoting safer pesticide usage and sustainable agriculture.
Collapse
Affiliation(s)
- Ganesan Anandhi
- Department of Smart Computing, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - M. Iyapparaja
- Department of Smart Computing, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| |
Collapse
|
19
|
Joshi S, Joshi R, Jadhao M. A simple dual responsive chemosensor for selective sensing of Cs + for environmental monitoring and mimicking molecular logic gates. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 306:123580. [PMID: 37922850 DOI: 10.1016/j.saa.2023.123580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 11/07/2023]
Abstract
Detection of toxic metals is of vital importance to safeguard both public health and the ecosystem. Herein, we investigate the newly designed and synthesised isoxazole-based azo dye, (E)-cyclopentyl(5-((5-(4-fluorophenyl) isoxazole-3-yl) diazenyl)-2-hydroxyphenyl) methanone (FPAZ), as a dual chromogenic and fluorogenic sensor. FPAZ demonstrates high selectivity, reusability and ultra-sensitivity towards Cs+ ions manifested through naked eye detection in aqueous medium by employing simple and economic optical spectroscopy techniques. The color change from colourless to dark yellow and enhancement of fluorescence intensity reveal about FPAZ-Cs+ complexation by UV-Vis and fluorescence spectroscopy respectively. The complexation is also supported by DFT calculations. The LOD is estimated to be 0.476 µM, which by far, is the lowest LOD obtained for Cs+ detection. Further, FPAZ is fabricated with various flexible materials (paper, cotton, non-woven fabric) which provide information about on-site Cs+ ion contamination by means of change in relative RGB values using a handy smart-phone camera. Besides this, the logic gate as IMPLICATION and INHIBIT is designed employing Cs+ and Cl- ions as inputs and absorbance maxima as output. Overall, the developed chemosensor is simple, quick, and more promising than previously reported systems, as it does not need any chemical modification, expensive instruments, or expertise.
Collapse
Affiliation(s)
- Supriya Joshi
- Institute of Chemical Technology Mumbai Marathwada Campus Jalna, Maharashtra 431203, India
| | - Ritika Joshi
- Institute of Chemical Technology Mumbai Marathwada Campus Jalna, Maharashtra 431203, India; Department of Chemistry, S.B.E.S. College of Science, Aurangabad, Maharashtra 431001, India
| | - Manojkumar Jadhao
- Institute of Chemical Technology Mumbai Marathwada Campus Jalna, Maharashtra 431203, India.
| |
Collapse
|
20
|
Cichońska A, Ravikumar B, Rahman R. AI for targeted polypharmacology: The next frontier in drug discovery. Curr Opin Struct Biol 2024; 84:102771. [PMID: 38215530 DOI: 10.1016/j.sbi.2023.102771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/30/2023] [Accepted: 12/20/2023] [Indexed: 01/14/2024]
Abstract
In drug discovery, targeted polypharmacology, i.e., targeting multiple molecular targets with a single drug, is redefining therapeutic design to address complex diseases. Pre-selected pharmacological profiles, as exemplified in kinase drugs, promise enhanced efficacy and reduced toxicity. Historically, many of such drugs were discovered serendipitously, limiting predictability and efficacy, but currently artificial intelligence (AI) offers a transformative solution. Machine learning and deep learning techniques enable modeling protein structures, generating novel compounds, and decoding their polypharmacological effects, opening an avenue for more systematic and predictive multi-target drug design. This review explores the use of AI in identifying synergistic co-targets and delineating them from anti-targets that lead to adverse effects, and then discusses advances in AI-enabled docking, generative chemistry, and proteochemometric modeling of proteome-wide compound interactions, in the context of polypharmacology. We also provide insights into challenges ahead.
Collapse
|
21
|
Arora S, Satija S, Mittal A, Solanki S, Mohanty SK, Srivastava V, Sengupta D, Rout D, Arul Murugan N, Borkar RM, Ahuja G. Unlocking The Mysteries of DNA Adducts with Artificial Intelligence. Chembiochem 2024; 25:e202300577. [PMID: 37874183 DOI: 10.1002/cbic.202300577] [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: 08/16/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 10/25/2023]
Abstract
Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade-off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e. g., mutations) or non-genetic (e. g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)-based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI-based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts' identification, prediction of biochemical routes for adduct formation, adducts' half-life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI-based approaches in DNA adduct biology.
Collapse
Affiliation(s)
- Sakshi Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Shiva Satija
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Aayushi Mittal
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Saveena Solanki
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Sanjay Kumar Mohanty
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Vaibhav Srivastava
- Division of Glycoscience, Department of Chemistry CBH School, Royal Institute of Technology (KTH) AlbaNova University Center, 10691, Stockholm, Sweden
| | - Debarka Sengupta
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Diptiranjan Rout
- Department of Transfusion Medicine National Cancer Institute, AIIMS, New Delhi, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110608, India
| | - Natarajan Arul Murugan
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Roshan M Borkar
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER)-Guwahati, Sila Katamur Halugurisuk P.O.: Changsari, Dist, Guwahati, Assam, 781101, India
| | - Gaurav Ahuja
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| |
Collapse
|
22
|
Habiballah S, Heath LS, Reisfeld B. A deep-learning approach for identifying prospective chemical hazards. Toxicology 2024; 501:153708. [PMID: 38104655 DOI: 10.1016/j.tox.2023.153708] [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: 10/18/2023] [Revised: 12/11/2023] [Accepted: 12/13/2023] [Indexed: 12/19/2023]
Abstract
With the aim of helping to set safe exposure limits for the general population, various techniques have been implemented to conduct risk assessments for chemicals and other environmental stressors; however, none of these tools facilitate the identification of completely new chemicals that are likely hazardous and elicit an adverse biological effect. Here, we detail a novel in silico, deep-learning framework that is designed to systematically generate structures for new chemical compounds that are predicted to be chemical hazards. To assess the utility of the framework, we applied the tool to four endpoints related to environmental toxicants and their impacts on human and animal health: (i) toxicity to honeybees, (ii) immunotoxicity, (iii) endocrine disruption via ER-α antagonism, and (iv) mutagenicity. In addition, we characterized the predicted potency of these compounds and examined their structural relationship to existing chemicals of concern. As part of the array of emerging new approach methodologies (NAMs), we anticipate that such a framework will be a significant asset to risk assessors and other environmental scientists when planning and forecasting. Though not in the scope of the present study, we expect that the methodology detailed here could also be useful in the de novo design of more environmentally-friendly industrial chemicals.
Collapse
Affiliation(s)
- Sohaib Habiballah
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80523-1370, USA
| | - Lenwood S Heath
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061-0106, USA
| | - Brad Reisfeld
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80523-1370, USA; Colorado School of Public Health, Colorado State University, Fort Collins, CO 80523-1612, USA.
| |
Collapse
|
23
|
Akili AWR, Hardianto A, Latip J, Permana A, Herlina T. Virtual Screening and ADMET Prediction to Uncover the Potency of Flavonoids from Genus Erythrina as Antibacterial Agent through Inhibition of Bacterial ATPase DNA Gyrase B. Molecules 2023; 28:8010. [PMID: 38138500 PMCID: PMC10745610 DOI: 10.3390/molecules28248010] [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: 08/21/2023] [Revised: 10/02/2023] [Accepted: 10/02/2023] [Indexed: 12/24/2023] Open
Abstract
The emergence of antimicrobial resistance due to the widespread and inappropriate use of antibiotics has now become the global health challenge. Flavonoids have long been reported to be a potent antimicrobial agent against a wide range of pathogenic microorganisms in vitro. Therefore, new antibiotics development based on flavonoid structures could be a potential strategy to fight against antibiotic-resistant infections. This research aims to screen the potency of flavonoids of the genus Erythrina as an inhibitor of bacterial ATPase DNA gyrase B. From the 378 flavonoids being screened, 49 flavonoids show potential as an inhibitor of ATPase DNA gyrase B due to their lower binding affinity compared to the inhibitor and ATP. Further screening for their toxicity, we identified 6 flavonoids from these 49 flavonoids, which are predicted to have low toxicity. Among these flavonoids, erystagallin B (334) is predicted to have the best pharmacokinetic properties, and therefore, could be further developed as new antibacterial agent.
Collapse
Affiliation(s)
- Abd. Wahid Rizaldi Akili
- Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Padjadjaran, Jatinangor 45363, Indonesia; (A.W.R.A.); (A.H.); (A.P.)
| | - Ari Hardianto
- Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Padjadjaran, Jatinangor 45363, Indonesia; (A.W.R.A.); (A.H.); (A.P.)
| | - Jalifah Latip
- Department of Chemical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 46300, Malaysia;
| | - Afri Permana
- Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Padjadjaran, Jatinangor 45363, Indonesia; (A.W.R.A.); (A.H.); (A.P.)
| | - Tati Herlina
- Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Padjadjaran, Jatinangor 45363, Indonesia; (A.W.R.A.); (A.H.); (A.P.)
| |
Collapse
|
24
|
Durai P, Lee SJ, Lee JW, Pan CH, Park K. Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors. J Cheminform 2023; 15:86. [PMID: 37742003 PMCID: PMC10517535 DOI: 10.1186/s13321-023-00760-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 09/12/2023] [Indexed: 09/25/2023] Open
Abstract
Machine learning-based chemical screening has made substantial progress in recent years. However, these predictions often have low accuracy and high uncertainty when identifying new active chemical scaffolds. Hence, a high proportion of retrieved compounds are not structurally novel. In this study, we proposed a strategy to address this issue by iteratively optimizing an evolutionary chemical binding similarity (ECBS) model using experimental validation data. Various data update and model retraining schemes were tested to efficiently incorporate new experimental data into ECBS models, resulting in a fine-tuned ECBS model with improved accuracy and coverage. To demonstrate the effectiveness of our approach, we identified the novel hit molecules for the mitogen-activated protein kinase kinase 1 (MEK1). These molecules showed sub-micromolar affinity (Kd 0.1-5.3 μM) to MEKs and were distinct from previously-known MEK1 inhibitors. We also determined the binding specificity of different MEK isoforms and proposed potential docking models. Furthermore, using de novo drug design tools, we utilized one of the new MEK inhibitors to generate additional drug-like molecules with improved binding scores. This resulted in the identification of several potential MEK1 inhibitors with better binding affinity scores. Our results demonstrated the potential of this approach for identifying novel hit molecules and optimizing their binding affinities.
Collapse
Affiliation(s)
- Prasannavenkatesh Durai
- Natural Product Informatics Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea
| | - Sue Jung Lee
- Natural Product Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea
| | - Jae Wook Lee
- Natural Product Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea
| | - Cheol-Ho Pan
- Natural Product Informatics Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea
| | - Keunwan Park
- Natural Product Informatics Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea.
- Department of YM-KIST Bio-Health Convergence, Yonsei University, Wonju, 26493, Republic of Korea.
| |
Collapse
|
25
|
Shamsian S, Sokouti B, Dastmalchi S. Benchmarking different docking protocols for predicting the binding poses of ligands complexed with cyclooxygenase enzymes and screening chemical libraries. BIOIMPACTS : BI 2023; 14:29955. [PMID: 38505677 PMCID: PMC10945300 DOI: 10.34172/bi.2023.29955] [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: 06/22/2023] [Revised: 08/09/2023] [Accepted: 08/23/2023] [Indexed: 03/21/2024]
Abstract
Introduction Non-steroidal anti-inflammatory drugs (NSAIDs) constitute an important class of pharmaceuticals acting on cyclooxygenase COX-1 and COX-2 enzymes. Due to their numerous severe side effects, it is necessary to search for new selective, safe, and effective anti-inflammatory drugs. In silico design of novel therapeutics plays an important role in nowadays drug discovery pipelines. In most cases, the design strategies require the use of molecular docking calculations. The docking procedure may require case-specific condition for a successful result. Additionally, many different docking programs are available, which highlights the importance of identifying the most proper docking method and condition for a given problem. Methods In the current work, the performances of five popular molecular docking programs, namely, GOLD, AutoDock, FlexX, Molegro Virtual Docker (MVD) and Glide to predict the binding mode of co- crystallized inhibitors in the structures of known complexes available for cyclooxygenases were evaluated. Furthermore, the best performers, Glide, AutoDock, GOLD and FlexX, were further evaluated in docking-based virtual screening of libraries consisted of active ligands and decoy molecules for cyclooxygenase enzymes and the obtained docking scores were assessed by receiver operating characteristics (ROC) analysis. Results The results of docking experiments indicated that Glide program outperformed other docking programs by correctly predicting the binding poses (RMSD less than 2 Å) of all studied co-crystallized ligands of COX-1 and COX-2 enzymes (i.e., the performance was 100%). However, the performances of the other studied docking methods for correctly predicting the binding poses of the ligands were between 59% to 82%. Virtual screening results treated by ROC analysis revealed that all tested methods are useful tools for classification and enrichment of molecules targeting COX enzymes. The obtained AUCs range between 0.61-0.92 with enrichment factors of 8 - 40 folds. Conclusion The obtained results support the importance of choosing appropriate docking method for predicting ligand-receptor binding modes, and provide specific information about docking calculations on COXs ligands.
Collapse
Affiliation(s)
- Sara Shamsian
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, 5165665931, Iran
- Department of Medicinal Chemistry, School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, 5166414766, Iran
| | - Babak Sokouti
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, 5165665813, Iran
| | - Siavoush Dastmalchi
- Department of Medicinal Chemistry, School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, 5166414766, Iran
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, 5165665813, Iran
- Faculty of Pharmacy, Near East University, POBOX:99138, Nicosia, North Cyprus, Mersin 10, Turkey
| |
Collapse
|
26
|
Sinha K, Ghosh N, Sil PC. A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies. Chem Res Toxicol 2023; 36:1174-1205. [PMID: 37561655 DOI: 10.1021/acs.chemrestox.2c00375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.
Collapse
Affiliation(s)
- Krishnendu Sinha
- Department of Zoology, Jhargram Raj College, Jhargram 721507, West Bengal, India
| | - Nabanita Ghosh
- Department of Zoology, Maulana Azad College, Kolkata 700013, West Bengal, India
| | - Parames C Sil
- Division of Molecular Medicine, Bose Institute, Kolkata 700054, West Bengal, India
| |
Collapse
|
27
|
Ketkar R, Liu Y, Wang H, Tian H. A Benchmark Study of Graph Models for Molecular Acute Toxicity Prediction. Int J Mol Sci 2023; 24:11966. [PMID: 37569341 PMCID: PMC10418346 DOI: 10.3390/ijms241511966] [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: 06/21/2023] [Revised: 07/21/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
With the wide usage of organic compounds, the assessment of their acute toxicity has drawn great attention to reduce animal testing and human labor. The development of graph models provides new opportunities for acute toxicity prediction. In this study, five graph models (message-passing neural network, graph convolution network, graph attention network, path-augmented graph transformer network, and Attentive FP) were applied on four toxicity tasks (fish, Daphnia magna, Tetrahymena pyriformis, and Vibrio fischeri). With the lowest prediction error, Attentive FP was reported to have the best performance in all four tasks. Moreover, the attention weights of the Attentive FP model helped to construct atomic heatmaps and provide good explainability.
Collapse
Affiliation(s)
- Rajas Ketkar
- Yale College, Yale University, New Haven, CT 06520, USA
| | - Yue Liu
- Department of Chemistry, University of Washington, Seattle, WA 98195, USA
| | - Hengji Wang
- Department of Physics, University of Washington, Seattle, WA 98195, USA
| | - Hao Tian
- Department of Chemistry, Southern Methodist University, Dallas, TX 75275, USA
| |
Collapse
|
28
|
Hu Y, Ren Q, Liu X, Gao L, Xiao L, Yu W. In Silico Prediction of Human Organ Toxicity via Artificial Intelligence Methods. Chem Res Toxicol 2023. [PMID: 37300507 DOI: 10.1021/acs.chemrestox.2c00411] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Unpredicted human organ level toxicity remains one of the major reasons for drug clinical failure. There is a critical need for cost-efficient strategies in the early stages of drug development for human toxicity assessment. At present, artificial intelligence methods are popularly regarded as a promising solution in chemical toxicology. Thus, we provided comprehensive in silico prediction models for eight significant human organ level toxicity end points using machine learning, deep learning, and transfer learning algorithms. In this work, our results showed that the graph-based deep learning approach was generally better than the conventional machine learning models, and good performances were observed for most of the human organ level toxicity end points in this study. In addition, we found that the transfer learning algorithm could improve model performance for skin sensitization end point using source domain of in vivo acute toxicity data and in vitro data of the Tox21 project. It can be concluded that our models can provide useful guidance for the rapid identification of the compounds with human organ level toxicity for drug discovery.
Collapse
Affiliation(s)
- Yuxuan Hu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Qiuhan Ren
- School of Science, China Pharmaceutical University, Nanjing 211198, China
| | - Xintong Liu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Liming Gao
- School of Science, China Pharmaceutical University, Nanjing 211198, China
| | - Lecheng Xiao
- School of Pharmacy, China Pharmaceutical University, Nanjing 211198, China
| | - Wenying Yu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| |
Collapse
|
29
|
Patlewicz G, Paul-Friedman K, Houck K, Zhang L, Huang R, Xia M, Brown J, Simmons SO. Evaluating the utility of a high throughput thiol-containing fluorescent probe to screen for reactivity: A case study with the Tox21 library. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2023; 26:10.1016/j.comtox.2023.100271. [PMID: 37388277 PMCID: PMC10304587 DOI: 10.1016/j.comtox.2023.100271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
High-throughput screening (HTS) assays for bioactivity in the Tox21 program aim to evaluate an array of different biological targets and pathways, but a significant barrier to interpretation of these data is the lack of high-throughput screening (HTS) assays intended to identify non-specific reactive chemicals. This is an important aspect for prioritising chemicals to test in specific assays, identifying promiscuous chemicals based on their reactivity, as well as addressing hazards such as skin sensitisation which are not necessarily initiated by a receptor-mediated effect but act through a non-specific mechanism. Herein, a fluorescence-based HTS assay that allows the identification of thiol-reactive compounds was used to screen 7,872 unique chemicals in the Tox21 10K chemical library. Active chemicals were compared with profiling outcomes using structural alerts encoding electrophilic information. Random Forest classification models based on chemical fingerprints were developed to predict assay outcomes and evaluated through 10-fold stratified cross validation (CV). The mean CV Balanced Accuracy of the validation set was 0.648. The model developed shows promise as a tool to screen untested chemicals for their potential electrophilic reactivity based solely on chemical structural features.
Collapse
Affiliation(s)
- Grace Patlewicz
- Center for Computational Toxicology & Exposure (CCTE), U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, 27709, USA
| | - Katie Paul-Friedman
- Center for Computational Toxicology & Exposure (CCTE), U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, 27709, USA
| | - Keith Houck
- Center for Computational Toxicology & Exposure (CCTE), U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, 27709, USA
| | - Li Zhang
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, Bethesda, MD 20892, USA
| | - Ruili Huang
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, Bethesda, MD 20892, USA
| | - Menghang Xia
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, Bethesda, MD 20892, USA
| | - Jason Brown
- Center for Computational Toxicology & Exposure (CCTE), U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, 27709, USA
| | - Steven O. Simmons
- Center for Computational Toxicology & Exposure (CCTE), U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, 27709, USA
| |
Collapse
|
30
|
Cavasotto CN, Di Filippo JI. The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking. J Chem Inf Model 2023; 63:2267-2280. [PMID: 37036491 DOI: 10.1021/acs.jcim.2c01471] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Structure-based virtual screening methods are, nowadays, one of the key pillars of computational drug discovery. In recent years, a series of studies have reported docking-based virtual screening campaigns of large databases ranging from hundreds to thousands of millions compounds, further identifying novel hits after experimental validation. As these larg-scale efforts are not generally accessible, machine learning-based protocols have emerged to accelerate the identification of virtual hits within an ultralarge chemical space, reaching impressive reductions in computational time. Herein, we illustrate the motivation and the problem behind the screening of large databases, providing an overview of key concepts and essential applications of machine learning-accelerated protocols, specifically concerning supervised learning methods. We also discuss where the field stands with these novel developments, highlighting possible insights for future studies.
Collapse
Affiliation(s)
- Claudio N Cavasotto
- Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), CONICET-Universidad Austral, Av. Juan Domingo Perón 1500, B1629AHJ Pilar, Argentina
- Facultad de Ciencias Biomédicas, and Facultad de Ingeniería, Universidad Austral, Av. Juan Domingo Perón 1500, B1629AHJ Pilar, Argentina
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Av. Juan Domingo Perón 1500, B1629AHJ Pilar, Argentina
| | - Juan I Di Filippo
- Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), CONICET-Universidad Austral, Av. Juan Domingo Perón 1500, B1629AHJ Pilar, Argentina
- Facultad de Ciencias Biomédicas, and Facultad de Ingeniería, Universidad Austral, Av. Juan Domingo Perón 1500, B1629AHJ Pilar, Argentina
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Av. Juan Domingo Perón 1500, B1629AHJ Pilar, Argentina
| |
Collapse
|
31
|
Nishimura Y, Kudoh T, Komada M. Editorial: Model organisms in predictive toxicology 2022. Front Pharmacol 2023; 14:1205945. [PMID: 37201026 PMCID: PMC10185904 DOI: 10.3389/fphar.2023.1205945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 04/25/2023] [Indexed: 05/20/2023] Open
Affiliation(s)
- Yuhei Nishimura
- Department of Integrative Pharmacology, Mie University Graduate School of Medicine, Tsu, Mie, Japan
- *Correspondence: Yuhei Nishimura,
| | - Tetsuhiro Kudoh
- Department of Biosciences, University of Exeter, Exeter, United Kingdom
| | - Munekazu Komada
- Mammalian Embryology, Department of Life Science, Faculty of Science and Engineering, Kindai University, Higashiosaka, Osaka, Japan
| |
Collapse
|