1
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Duan Y, Yang X, Zeng X, Wang W, Deng Y, Cao D. Enhancing Molecular Property Prediction through Task-Oriented Transfer Learning: Integrating Universal Structural Insights and Domain-Specific Knowledge. J Med Chem 2024; 67:9575-9586. [PMID: 38748846 DOI: 10.1021/acs.jmedchem.4c00692] [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: 06/14/2024]
Abstract
Precisely predicting molecular properties is crucial in drug discovery, but the scarcity of labeled data poses a challenge for applying deep learning methods. While large-scale self-supervised pretraining has proven an effective solution, it often neglects domain-specific knowledge. To tackle this issue, we introduce Task-Oriented Multilevel Learning based on BERT (TOML-BERT), a dual-level pretraining framework that considers both structural patterns and domain knowledge of molecules. TOML-BERT achieved state-of-the-art prediction performance on 10 pharmaceutical datasets. It has the capability to mine contextual information within molecular structures and extract domain knowledge from massive pseudo-labeled data. The dual-level pretraining accomplished significant positive transfer, with its two components making complementary contributions. Interpretive analysis elucidated that the effectiveness of the dual-level pretraining lies in the prior learning of a task-related molecular representation. Overall, TOML-BERT demonstrates the potential of combining multiple pretraining tasks to extract task-oriented knowledge, advancing molecular property prediction in drug discovery.
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Affiliation(s)
- Yanjing Duan
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha Hunan 410013, P. R. China
| | - Xixi Yang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410013, P. R. China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410013, P. R. China
| | - Wenxuan Wang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha Hunan 410013, P. R. China
| | - Youchao Deng
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha Hunan 410013, P. R. China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha Hunan 410013, P. R. China
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR 999077, P. R. China
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2
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Parchmann N, Hansen D, Orzechowski M, Steger F. An ethical assessment of professional opinions on concerns, chances, and limitations of the implementation of an artificial intelligence-based technology into the geriatric patient treatment and continuity of care. GeroScience 2024:10.1007/s11357-024-01229-6. [PMID: 38834930 DOI: 10.1007/s11357-024-01229-6] [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: 04/05/2024] [Accepted: 05/27/2024] [Indexed: 06/06/2024] Open
Abstract
With the introduction of an artificial intelligence-based dashboard into the clinic, the project SURGE-Ahead responds to the importance of improving perioperative geriatric patient treatment and continuity of care. The use of artificial intelligence to process and analyze data automatically, aims at an evidence-based evaluation of the patient's health condition and recommending treatment options. However, its development and introduction raise ethical questions. To ascertain professional perspectives on the clinical use of the dashboard, we have conducted 19 semi-structured qualitative interviews with head physicians, computer scientists, jurists, and ethicists. The application of a qualitative content analysis and thematic analysis enabled the detection of main ethical concerns, chances, and limitations. These ethical considerations were categorized: changes of the patient-physician relationship and the current social reality are expected, causing de-skilling and an active participation of the artificial intelligence. The interviewees anticipated a redistribution of human resources, time, knowledge, and experiences as well as expenses and financing. Concerns of privacy, accuracy, transparency, and explainability were stated, and an insufficient data basis, an intensifying of existing inequalities and systematic discrimination considering a fair access emphasized. Concluding, the patient-physician relationship, social reality, redistribution of resources, fair access, as well as data-related aspects of the artificial intelligence-based system could conflict with the ethical principles of autonomy, non-maleficence, beneficence, and social justice. To respond to these ethical concerns, a responsible use of the dashboard and a critical verification of therapy suggestions is mandatory, and the application limited by questions at the end of life and taking life-changing decisions.
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Affiliation(s)
- Nina Parchmann
- Institute of the History, Philosophy and Ethics of Medicine, Ulm University, Oberberghof 7, 89081, Ulm, Baden-Wuerttemberg, Germany.
| | - David Hansen
- Institute of the History, Philosophy and Ethics of Medicine, Ulm University, Oberberghof 7, 89081, Ulm, Baden-Wuerttemberg, Germany
| | - Marcin Orzechowski
- Institute of the History, Philosophy and Ethics of Medicine, Ulm University, Oberberghof 7, 89081, Ulm, Baden-Wuerttemberg, Germany
| | - Florian Steger
- Institute of the History, Philosophy and Ethics of Medicine, Ulm University, Oberberghof 7, 89081, Ulm, Baden-Wuerttemberg, Germany
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3
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Carrera-Pacheco SE, Mueller A, Puente-Pineda JA, Zúñiga-Miranda J, Guamán LP. Designing cytochrome P450 enzymes for use in cancer gene therapy. Front Bioeng Biotechnol 2024; 12:1405466. [PMID: 38860140 PMCID: PMC11164052 DOI: 10.3389/fbioe.2024.1405466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 04/30/2024] [Indexed: 06/12/2024] Open
Abstract
Cancer is a significant global socioeconomic burden, as millions of new cases and deaths occur annually. In 2020, almost 10 million cancer deaths were recorded worldwide. Advancements in cancer gene therapy have revolutionized the landscape of cancer treatment. An approach with promising potential for cancer gene therapy is introducing genes to cancer cells that encode for chemotherapy prodrug metabolizing enzymes, such as Cytochrome P450 (CYP) enzymes, which can contribute to the effective elimination of cancer cells. This can be achieved through gene-directed enzyme prodrug therapy (GDEPT). CYP enzymes can be genetically engineered to improve anticancer prodrug conversion to its active metabolites and to minimize chemotherapy side effects by reducing the prodrug dosage. Rational design, directed evolution, and phylogenetic methods are some approaches to developing tailored CYP enzymes for cancer therapy. Here, we provide a compilation of genetic modifications performed on CYP enzymes aiming to build highly efficient therapeutic genes capable of bio-activating different chemotherapeutic prodrugs. Additionally, this review summarizes promising preclinical and clinical trials highlighting engineered CYP enzymes' potential in GDEPT. Finally, the challenges, limitations, and future directions of using CYP enzymes for GDEPT in cancer gene therapy are discussed.
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Affiliation(s)
- Saskya E. Carrera-Pacheco
- Centro de Investigación Biomédica (CENBIO), Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador
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Ali Z, Rehman W, Rasheed L, Alzahrani AY, Ali N, Hussain R, Emwas AH, Jaremko M, Abdellattif MH. New 1,3,4-Thiadiazole Derivatives as α-Glucosidase Inhibitors: Design, Synthesis, DFT, ADME, and In Vitro Enzymatic Studies. ACS OMEGA 2024; 9:7480-7490. [PMID: 38405480 PMCID: PMC10882623 DOI: 10.1021/acsomega.3c05854] [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: 08/09/2023] [Revised: 12/14/2023] [Accepted: 12/15/2023] [Indexed: 02/27/2024]
Abstract
Diabetes is an emerging disorder in the world and is caused due to the imbalance of insulin production as well as serious effects on the body. In search of a better treatment for diabetes, we designed a novel class of 1,3,4-thiadiazole-bearing Schiff base analogues and assessed them for the α-glucosidase enzyme. In the series (1-12), compounds are synthesized and 3 analogues showed excellent inhibitory activity against α-glucosidase enzymes in the range of IC50 values of 18.10 ± 0.20 to 1.10 ± 0.10 μM. In this series, analogues 4, 8, and 9 show remarkable inhibition profile IC50 2.20 ± 0.10, 1.10 ± 0.10, and 1.30 ± 0.10 μM by using acarbose as a standard, whose IC50 is 11.50 ± 0.30 μM. The structure of the synthesized compounds was confirmed through various spectroscopic techniques, such as NMR and HREI-MS. Additionally, molecular docking, pharmacokinetics, cytotoxic evaluation, and density functional theory study were performed to investigate their behavior.
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Affiliation(s)
- Zahid Ali
- Department
of Chemistry, Hazara University, Mansehra 21120, Pakistan
| | - Wajid Rehman
- Department
of Chemistry, Hazara University, Mansehra 21120, Pakistan
| | - Liaqat Rasheed
- Department
of Chemistry, Hazara University, Mansehra 21120, Pakistan
| | - Abdullah Y. Alzahrani
- Department
of Chemistry, Faculty of Science and Arts, King Khalid University, Mohail, Assir 61421, Saudi Arabia
| | - Nawab Ali
- Shanghai
Key Laboratory of Functional Materials Chemistry, School of Chemistry
and Molecular Engineering, East China University
of Science and Technology, Meilong Road130, Shanghai 200237, PR China
| | - Rafaqat Hussain
- Department
of Chemistry, Hazara University, Mansehra 21120, Pakistan
| | - Abdul-Hamid Emwas
- Core
Laboratories, King Abdullah University of
Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Mariusz Jaremko
- Biological
and Environmental Science and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Magda H. Abdellattif
- Department
of Chemistry, Sciences College, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
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5
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Xiong J, Cui R, Li Z, Zhang W, Zhang R, Fu Z, Liu X, Li Z, Chen K, Zheng M. Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application. Acta Pharm Sin B 2024; 14:623-634. [PMID: 38322350 PMCID: PMC10840476 DOI: 10.1016/j.apsb.2023.10.008] [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: 07/02/2023] [Revised: 09/07/2023] [Accepted: 10/11/2023] [Indexed: 02/08/2024] Open
Abstract
Aldehyde oxidase (AOX) is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics. AOX-mediated metabolism can result in unexpected outcomes, such as the production of toxic metabolites and high metabolic clearance, which can lead to the clinical failure of novel therapeutic agents. Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability. In this study, we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism. AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction, while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both tasks. AOMP significantly outperformed the benchmark methods in both cross-validation and external testing. Using AOMP, we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability, which were validated through in vitro experiments. Furthermore, for the convenience of the community, we established the first online service for AOX metabolism prediction based on AOMP, which is freely available at https://aomp.alphama.com.cn.
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Affiliation(s)
- Jiacheng Xiong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Rongrong Cui
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhaojun Li
- College of Computer and Information Engineering, Dezhou University, Dezhou 253023, China
- AI Department, Suzhou Alphama Biotechnology Co., Ltd., Suzhou 215000, China
| | - Wei Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Runze Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zunyun Fu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaohong Liu
- AI Department, Suzhou Alphama Biotechnology Co., Ltd., Suzhou 215000, China
| | - Zhenghao Li
- Shanghai Institute for Advanced Immunochemical Studies, and School of Life Science and Technology, ShanghaiTech University, Shanghai 200031, China
| | - Kaixian Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing 210023, China
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6
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Cui S, Gao Y, Huang Y, Shen L, Zhao Q, Pan Y, Zhuang S. Advances and applications of machine learning and deep learning in environmental ecology and health. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 335:122358. [PMID: 37567408 DOI: 10.1016/j.envpol.2023.122358] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 08/02/2023] [Accepted: 08/08/2023] [Indexed: 08/13/2023]
Abstract
Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image recognition and prediction) and risk assessment and management in environmental ecology and health (EEH). Considering the rapid growth and increasing complexity of data in EEH, it is of significance to summarize recent advances and applications of ML and DL in EEH. This review summarized the basic processes and fundamental algorithms of the ML and DL modeling, and indicated the urgent needs of ML and DL in EEH. Recent research hotspots such as environmental ecology and restoration, environmental fate of new pollutants, chemical exposures and risks, chemical hazard identification and control were highlighted. Various applications of ML and DL in EEH demonstrate their versatility and technological revolution, and present some challenges. The perspective of ML and DL in EEH were further outlined to promote the innovative analysis and cultivation of the ML-driven research paradigm.
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Affiliation(s)
- Shixuan Cui
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China
| | - Yuchen Gao
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Yizhou Huang
- Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China
| | - Lilai Shen
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Qiming Zhao
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Yaru Pan
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Shulin Zhuang
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China.
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7
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Wang B, Guo J, Liu X, Yu Y, Wu J, Wang Y. Prediction of the effects of small molecules on the gut microbiome using machine learning method integrating with optimal molecular features. BMC Bioinformatics 2023; 24:338. [PMID: 37697256 PMCID: PMC10496404 DOI: 10.1186/s12859-023-05455-1] [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: 05/10/2023] [Accepted: 08/25/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND The human gut microbiome (HGM), consisting of trillions of microorganisms, is crucial to human health. Adverse drug use is one of the most important causes of HGM disorder. Thus, it is necessary to identify drugs or compounds with anti-commensal effects on HGM in the early drug discovery stage. This study proposes a novel anti-commensal effects classification using a machine learning method and optimal molecular features. To improve the prediction performance, we explored combinations of six fingerprints and three descriptors to filter the best characterization as molecular features. RESULTS The final consensus model based on optimal features yielded the F1-score of 0.725 ± 0.014, ACC of 82.9 ± 0.7%, and AUC of 0.791 ± 0.009 for five-fold cross-validation. In addition, this novel model outperformed the prior studies by using the same algorithm. Furthermore, the important chemical descriptors and misclassified anti-commensal compounds are analyzed to better understand and interpret the model. Finally, seven structural alerts responsible for the chemical anti-commensal effect are identified, implying valuable information for drug design. CONCLUSION Our study would be a promising tool for screening anti-commensal compounds in the early stage of drug discovery and assessing the potential risks of these drugs in vivo.
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Affiliation(s)
- Binyou Wang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China
- School of Pharmacy, Southwest Medical University, Luzhou, 646000, China
| | - Jianmin Guo
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China
| | - Xiaofeng Liu
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China
| | - Yang Yu
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China
- Key Laboratory of Medical Electrophysiology, Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China
| | - Jianming Wu
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China.
- School of Pharmacy, Southwest Medical University, Luzhou, 646000, China.
- Key Laboratory of Medical Electrophysiology, Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China.
- Sichuan Key Medical Laboratory of New Drug Discovery and Druggability Evaluation, Luzhou Key Laboratory of Activity Screening and Druggability Evaluation for Chinese Materia Medica, School of Pharmacy, Southwest Medical University, Luzhou, 646000, China.
| | - Yiwei Wang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China.
- School of Pharmacy, Southwest Medical University, Luzhou, 646000, China.
- Key Laboratory of Medical Electrophysiology, Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, China.
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8
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Li L, Lu Z, Liu G, Tang Y, Li W. Machine Learning Models to Predict Cytochrome P450 2B6 Inhibitors and Substrates. Chem Res Toxicol 2023; 36:1332-1344. [PMID: 37437120 DOI: 10.1021/acs.chemrestox.3c00065] [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: 07/14/2023]
Abstract
Cytochrome P450 2B6 (CYP2B6) is responsible for the metabolism of ∼7% of marketed drugs. The in vitro drug interaction studies guidance for industry issued by the FDA stipulates that drug sponsors need to evaluate whether the investigated drugs interact with the major drug-metabolizing P450s including CYP2B6. Therefore, there has been greater attention to the development of predictive models for CYP2B6 inhibitors and substrates. In this study, conventional machine learning and deep learning models were developed to predict CYP2B6 inhibitors and substrates. Our results showed that the best CYP2B6 inhibitor model yielded the AUC values of 0.95 and 0.75 with the 10-fold cross-validation and the test set, respectively, and the best CYP2B6 substrate model produced the AUC values of 0.93 and 0.90 with the 10-fold cross-validation and the test set, respectively. The generalization ability of the CYP2B6 inhibitor and substrate models was assessed by using the external validation sets. Several significant substructural fragments relevant to CYP2B6 inhibitors and substrates were detected via frequency substructure analysis and information gain. In addition, the applicability domain of the models was defined by employing a nonparametric method based on the probability density distribution. We anticipate that our results would be useful for the prediction of potential CYP2B6 inhibitors and substrates in the early stage of drug discovery.
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Affiliation(s)
- Longqiang Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zhou Lu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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Sun L, Mi K, Hou Y, Hui T, Zhang L, Tao Y, Liu Z, Huang L. Pharmacokinetic and Pharmacodynamic Drug-Drug Interactions: Research Methods and Applications. Metabolites 2023; 13:897. [PMID: 37623842 PMCID: PMC10456269 DOI: 10.3390/metabo13080897] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 08/26/2023] Open
Abstract
Because of the high research and development cost of new drugs, the long development process of new drugs, and the high failure rate at later stages, combining past drugs has gradually become a more economical and attractive alternative. However, the ensuing problem of drug-drug interactions (DDIs) urgently need to be solved, and combination has attracted a lot of attention from pharmaceutical researchers. At present, DDI is often evaluated and investigated from two perspectives: pharmacodynamics and pharmacokinetics. However, in some special cases, DDI cannot be accurately evaluated from a single perspective. Therefore, this review describes and compares the current DDI evaluation methods based on two aspects: pharmacokinetic interaction and pharmacodynamic interaction. The methods summarized in this paper mainly include probe drug cocktail methods, liver microsome and hepatocyte models, static models, physiologically based pharmacokinetic models, machine learning models, in vivo comparative efficacy studies, and in vitro static and dynamic tests. This review aims to serve as a useful guide for interested researchers to promote more scientific accuracy and clinical practical use of DDI studies.
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Affiliation(s)
- Lei Sun
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
| | - Kun Mi
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MOA Laboratory for Risk Assessment of Quality and Safety of Livestock and Poultry Products, Huazhong Agricultural University, Wuhan 430000, China
| | - Yixuan Hou
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
| | - Tianyi Hui
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
| | - Lan Zhang
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
| | - Yanfei Tao
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
| | - Zhenli Liu
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
- MOA Laboratory for Risk Assessment of Quality and Safety of Livestock and Poultry Products, Huazhong Agricultural University, Wuhan 430000, China
| | - Lingli Huang
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
- MOA Laboratory for Risk Assessment of Quality and Safety of Livestock and Poultry Products, Huazhong Agricultural University, Wuhan 430000, China
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10
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Sienkiewicz-Oleszkiewicz B, Salamonowicz-Bodzioch M, Słonka J, Kałwak K. Antifungal Drug-Drug Interactions with Commonly Used Pharmaceutics in European Pediatric Patients with Acute Lymphoblastic Leukemia. J Clin Med 2023; 12:4637. [PMID: 37510753 PMCID: PMC10380616 DOI: 10.3390/jcm12144637] [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: 05/17/2023] [Revised: 06/26/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
Leukemia is one of the leading childhood malignancies, with acute lymphoblastic leukemia (ALL) being the most common type. Invasive fungal disease is a concerning problem also at pediatric hemato-oncology units. Available guidelines underline the need for antifungal prophylaxis and give recommendations for proper treatment in various clinical scenarios. Nonetheless, antifungal agents are often involved in drug-drug interaction (DDI) occurrence. The prediction of those interactions in the pediatric population is complicated because of the physiological differences in adults, and the lack of pharmacological data. In this review, we discuss the potential DDIs between antifungal agents and commonly used pharmaceutics in pediatric hemato-oncology settings, with special emphasis on the use of liposomal amphotericin B and ALL treatment. We obtained information from Micromedex® and Drugs.com® interaction checking databases and checked the EudraVigilance® database to source the frequency of severe adverse drug reactions that resulted from antifungal drug interactions. Several major DDIs were identified, showing a favorable safety profile of echinocandins and liposomal amphotericin B. Interestingly, although there are numerous available drug interaction checking tools facilitating the identification of potential serious DDIs, it is important to use more than one tool, as the presented searching results may differ between particular checking programs.
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Affiliation(s)
- Beata Sienkiewicz-Oleszkiewicz
- Department of Clinical Pharmacology, Faculty of Pharmacy, Wrocław Medical University, ul. Borowska 211a, 50-556 Wrocław, Poland
| | - Małgorzata Salamonowicz-Bodzioch
- Department and Clinic of Pediatric Oncology, Hematology and Bone Marrow Transplantation, Wrocław Medical University, Borowska 213, 50-556 Wrocław, Poland
| | - Justyna Słonka
- Gilead Sciences Poland Sp. z o.o., ul. Postepu 17A, 02-676 Warsaw, Poland
| | - Krzysztof Kałwak
- Department and Clinic of Pediatric Oncology, Hematology and Bone Marrow Transplantation, Wrocław Medical University, Borowska 213, 50-556 Wrocław, Poland
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Wojtowicz K, Nowicki M. The characterization of the sensitive ovarian cancer cell lines A2780 and W1 in response to ovarian CAFs. Biochem Biophys Res Commun 2023; 662:1-7. [PMID: 37088000 DOI: 10.1016/j.bbrc.2023.04.059] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 04/25/2023]
Abstract
PURPOSE The cancer-associated fibroblasts (CAFs) are one of the most abundant components of the tumor microenvironment (TME). CAFs have been implicated in tumor progression, extracellular matrix (ECM) remodeling, and treatment resistance. Drug resistance is the primary limiting factor in achieving cures for patients with cancer, particularly ovarian cancer. Therefore, inhibiting CAFs can be an effective strategies for cancer treatment. In this research, we studied whether CAFs have an influence on drug-sensitive ovarian cancer cells to become more resistant. We examined the influence of CAFs on genes and proteins expression changes in sensitive ovarian cancer cells. We prepared a 3D co-culture to investigate the role of CAFs on cancer cell morphology. METHODS Here, we performed a detailed analysis of drug-sensitive ovarian cancer cell lines (A2780 and W1) and the influence of ovarian CAFs on the A2780 and W1 cells morphology, genes and proteins expression. The 2D and 3D cultures, genes expression analysis (TaqMan qPCR), and proteins expression (Western blot analysis) were assessed in this study. RESULTS We observed upregulation of ABCC5, CYP2C8, CYP2C9, and DHFR mRNA in cell lines supplemented by CAFs medium. We showed fibronectin overexpression and COL3A1 downregulation after supplementation with CAFs. Co-culturing with CAFs prevented the formation of spheroids in 3D conditions. CONCLUSION We demonstrated that the process of drug resistance in ovarian cancer cells is launched by CAFs. CAFs not only simulate cancer cells to produce drug transporters and specific enzymes production, but also remodel the TME to increase drug resistance. We believe that cancer progression and migration is due to the CAFs po-tumorigenic activity.
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Affiliation(s)
- Karolina Wojtowicz
- Department of Histology and Embryology, Poznan University of Medical Sciences, Poznan, Poland.
| | - Michał Nowicki
- Department of Histology and Embryology, Poznan University of Medical Sciences, Poznan, Poland
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Alrowais F, Alotaibi SS, Hilal AM, Marzouk R, Mohsen H, Osman AE, Alneil AA, Eldesouki MI. Clinical Decision Support Systems to Predict Drug-Drug Interaction Using Multilabel Long Short-Term Memory with an Autoencoder. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2696. [PMID: 36768060 PMCID: PMC9916256 DOI: 10.3390/ijerph20032696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 01/24/2023] [Accepted: 01/26/2023] [Indexed: 06/18/2023]
Abstract
Big Data analytics is a technique for researching huge and varied datasets and it is designed to uncover hidden patterns, trends, and correlations, and therefore, it can be applied for making superior decisions in healthcare. Drug-drug interactions (DDIs) are a main concern in drug discovery. The main role of precise forecasting of DDIs is to increase safety potential, particularly, in drug research when multiple drugs are co-prescribed. Prevailing conventional method machine learning (ML) approaches mainly depend on handcraft features and lack generalization. Today, deep learning (DL) techniques that automatically study drug features from drug-related networks or molecular graphs have enhanced the capability of computing approaches for forecasting unknown DDIs. Therefore, in this study, we develop a sparrow search optimization with deep learning-based DDI prediction (SSODL-DDIP) technique for healthcare decision making in big data environments. The presented SSODL-DDIP technique identifies the relationship and properties of the drugs from various sources to make predictions. In addition, a multilabel long short-term memory with an autoencoder (MLSTM-AE) model is employed for the DDI prediction process. Moreover, a lexicon-based approach is involved in determining the severity of interactions among the DDIs. To improve the prediction outcomes of the MLSTM-AE model, the SSO algorithm is adopted in this work. To assure better performance of the SSODL-DDIP technique, a wide range of simulations are performed. The experimental results show the promising performance of the SSODL-DDIP technique over recent state-of-the-art algorithms.
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Affiliation(s)
- Fadwa Alrowais
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Saud S. Alotaibi
- Department of Information Systems, College of Computing and Information System, Umm Al-Qura University, Makkah 24211, Saudi Arabia
| | - Anwer Mustafa Hilal
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al Kharj 16436, Saudi Arabia
| | - Radwa Marzouk
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Heba Mohsen
- Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo 11835, Egypt
| | - Azza Elneil Osman
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al Kharj 16436, Saudi Arabia
| | - Amani A. Alneil
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al Kharj 16436, Saudi Arabia
| | - Mohamed I. Eldesouki
- Department of Information System, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al Kharj 16436, Saudi Arabia
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Artificial Intelligence and Data Mining for the Pharmacovigilance of Drug-Drug Interactions. Clin Ther 2023; 45:117-133. [PMID: 36732152 DOI: 10.1016/j.clinthera.2023.01.002] [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: 10/02/2022] [Revised: 12/15/2022] [Accepted: 01/09/2023] [Indexed: 02/01/2023]
Abstract
Despite increasing mechanistic understanding, undetected and underrecognized drug-drug interactions (DDIs) persist. This elusiveness relates to an interwoven complexity of increasing polypharmacy, multiplex mechanistic pathways, and human biological individuality. This persistent elusiveness motivates development of artificial intelligence (AI)-based approaches to enhancing DDI detection and prediction capabilities. The literature is vast and roughly divided into "prediction" and "detection." The former relatively emphasizes biological and chemical knowledge bases, drug development, new drugs, and beneficial interactions, whereas the latter utilizes more traditional sources such as spontaneous reports, claims data, and electronic health records to detect novel adverse DDIs with authorized drugs. However, it is not a bright line, either nominally or in practice, and both are in scope for pharmacovigilance supporting signal detection but also signal refinement and evaluation, by providing data-based mechanistic arguments for/against DDI signals. The wide array of intricate and elegant methods has expanded the pharmacovigilance tool kit. How much they add to real prospective pharmacovigilance, reduce the public health impact of DDIs, and at what cost in terms of false alarms amplified by automation bias and its sequelae are open questions. (Clin Ther. 2023;45:XXX-XXX) © 2023 Elsevier HS Journals, Inc.
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Sinha K, Ghosh J, Sil PC. Machine Learning in Drug Metabolism Study. Curr Drug Metab 2022; 23:CDM-EPUB-128463. [PMID: 36578255 DOI: 10.2174/1389200224666221227094144] [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: 06/28/2022] [Revised: 10/27/2022] [Accepted: 11/01/2022] [Indexed: 12/30/2022]
Abstract
Metabolic reactions in the body transform the administered drug into metabolites. These metabolites exhibit diverse biological activities. Drug metabolism is the major underlying cause of drug overdose-related toxicity, adversative drug effects and the drug's reduced efficacy. Though metabolic reactions deactivate a drug, drug metabolites are often considered pivotal agents for off-target effects or toxicity. On the other side, in combination drug therapy, one drug may influence another drug's metabolism and clearance and is thus considered one of the primary causes of drug-drug interactions. Today with the advancement of machine learning, the metabolic fate of a drug candidate can be comprehensively studied throughout the drug development procedure. Naïve Bayes, Logistic Regression, k-Nearest Neighbours, Decision Trees, different Boosting and Ensemble methods, Support Vector Machines and Artificial Neural Network boosted Deep Learning are some machine learning algorithms which are being extensively used in such studies. Such tools are covering several attributes of drug metabolism, with an emphasis on the prediction of drug-drug interactions, drug-target-interactions, clinical drug responses, metabolite predictions, sites of metabolism, etc. These reports are crucial for evaluating metabolic stability and predicting prospective drug-drug interactions, and can help pharmaceutical companies accelerate the drug development process in a less resource-demanding manner than what in vitro studies offer. It could also help medical practitioners to use combinatorial drug therapy in a more resourceful manner. Also, with the help of the enormous growth of deep learning, traditional fields of computational drug development like molecular interaction fields, molecular docking, quantitative structure-to-activity relationship (QSAR) studies and quantum mechanical simulations are producing results which were unimaginable couple of years back. This review provides a glimpse of a few contextually relevant machine learning algorithms and then focuses on their outcomes in different studies.
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Affiliation(s)
| | | | - Parames C Sil
- Division of Molecular Medicine, Bose Institute, Kolkata-700054
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Wu J, Wang J, Wu Z, Zhang S, Deng Y, Kang Y, Cao D, Hsieh CY, Hou T. ALipSol: An Attention-Driven Mixture-of-Experts Model for Lipophilicity and Solubility Prediction. J Chem Inf Model 2022; 62:5975-5987. [PMID: 36417544 DOI: 10.1021/acs.jcim.2c01290] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Lipophilicity (logD) and aqueous solubility (logSw) play a central role in drug development. The accurate prediction of these properties remains to be solved due to data scarcity. Current methodologies neglect the intrinsic relationships between physicochemical properties and usually ignore the ionization effects. Here, we propose an attention-driven mixture-of-experts (MoE) model named ALipSol, which explicitly reproduces the hierarchy of task relationships. We adopt the principle of divide-and-conquer by breaking down the complex end point (logD or logSw) into simpler ones (acidic pKa, basic pKa, and logP) and allocating a specific expert network for each subproblem. Subsequently, we implement transfer learning to extract knowledge from related tasks, thus alleviating the dilemma of limited data. Additionally, we substitute the gating network with an attention mechanism to better capture the dynamic task relationships on a per-example basis. We adopt local fine-tuning and consensus prediction to further boost model performance. Extensive evaluation experiments verify the success of the ALipSol model, which achieves RMSE improvement of 8.04%, 2.49%, 8.57%, 12.8%, and 8.60% on the Lipop, ESOL, AqSolDB, external logD, and external logS data sets, respectively, compared with Attentive FP and the state-of-the-art in silico tools. In particular, our model yields more significant advantages (Welch's t-test) for small training data, implying its high robustness and generalizability. The interpretability analysis proves that the atom contributions learned by ALipSol are more reasonable compared with the vanilla Attentive FP, and the substitution effects in benzene derivatives agreed well with empirical constants, revealing the potential of our model to extract useful patterns from data and provide guidance for lead optimization.
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Affiliation(s)
- Jialu Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058Zhejiang, P. R. China.,CarbonSilicon AI Technology Co., Ltd, Hangzhou, 310018Zhejiang, P. R. China
| | - Junmei Wang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania15261, United States
| | - Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058Zhejiang, P. R. China.,CarbonSilicon AI Technology Co., Ltd, Hangzhou, 310018Zhejiang, P. R. China
| | - Shengyu Zhang
- Tencent Quantum Laboratory, Tencent, Shenzhen, 518057Guangdong, P. R. China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, 310018Zhejiang, P. R. China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058Zhejiang, P. R. China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410004Hunan, P. R. China
| | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058Zhejiang, P. R. China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058Zhejiang, P. R. China
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