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Singh AV, Bhardwaj P, Laux P, Pradeep P, Busse M, Luch A, Hirose A, Osgood CJ, Stacey MW. AI and ML-based risk assessment of chemicals: predicting carcinogenic risk from chemical-induced genomic instability. FRONTIERS IN TOXICOLOGY 2024; 6:1461587. [PMID: 39659701 PMCID: PMC11628524 DOI: 10.3389/ftox.2024.1461587] [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: 07/08/2024] [Accepted: 11/11/2024] [Indexed: 12/12/2024] Open
Abstract
Chemical risk assessment plays a pivotal role in safeguarding public health and environmental safety by evaluating the potential hazards and risks associated with chemical exposures. In recent years, the convergence of artificial intelligence (AI), machine learning (ML), and omics technologies has revolutionized the field of chemical risk assessment, offering new insights into toxicity mechanisms, predictive modeling, and risk management strategies. This perspective review explores the synergistic potential of AI/ML and omics in deciphering clastogen-induced genomic instability for carcinogenic risk prediction. We provide an overview of key findings, challenges, and opportunities in integrating AI/ML and omics technologies for chemical risk assessment, highlighting successful applications and case studies across diverse sectors. From predicting genotoxicity and mutagenicity to elucidating molecular pathways underlying carcinogenesis, integrative approaches offer a comprehensive framework for understanding chemical exposures and mitigating associated health risks. Future perspectives for advancing chemical risk assessment and cancer prevention through data integration, advanced machine learning techniques, translational research, and policy implementation are discussed. By implementing the predictive capabilities of AI/ML and omics technologies, researchers and policymakers can enhance public health protection, inform regulatory decisions, and promote sustainable development for a healthier future.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Preeti Bhardwaj
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Peter Laux
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Prachi Pradeep
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Madleen Busse
- Department of Biological Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Andreas Luch
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Akihiko Hirose
- Chemicals Evaluation and Research Institute, Tokyo, Japan
| | - Christopher J. Osgood
- Department of Biological Sciences, Old Dominion University, Norfolk, VA, United States
| | - Michael W. Stacey
- Frank Reidy Research Center for Bioelectrics, Old Dominion University, Norfolk, VA, United States
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2
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Chiera F, Costa G, Alcaro S, Artese A. An overview on olfaction in the biological, analytical, computational, and machine learning fields. Arch Pharm (Weinheim) 2024:e2400414. [PMID: 39439128 DOI: 10.1002/ardp.202400414] [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: 05/24/2024] [Revised: 09/24/2024] [Accepted: 09/26/2024] [Indexed: 10/25/2024]
Abstract
Recently, the comprehension of odor perception has advanced, unveiling the mysteries of the molecular receptors within the nasal passages and the intricate mechanisms governing signal transmission between these receptors, the olfactory bulb, and the brain. This review provides a comprehensive panorama of odors, encompassing various topics ranging from the structural and molecular underpinnings of odorous substances to the physiological intricacies of olfactory perception. It extends to elucidate the analytical methods used for their identification and explores the frontiers of computational methodologies.
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Affiliation(s)
- Federica Chiera
- Dipartimento di Scienze della Salute, Campus "S. Venuta", Università degli Studi "Magna Græcia" di Catanzaro, Catanzaro, Italy
| | - Giosuè Costa
- Dipartimento di Scienze della Salute, Campus "S. Venuta", Università degli Studi "Magna Græcia" di Catanzaro, Catanzaro, Italy
- Net4Science S.r.l., Università degli Studi "Magna Græcia" di Catanzaro, Catanzaro, Italy
| | - Stefano Alcaro
- Dipartimento di Scienze della Salute, Campus "S. Venuta", Università degli Studi "Magna Græcia" di Catanzaro, Catanzaro, Italy
- Net4Science S.r.l., Università degli Studi "Magna Græcia" di Catanzaro, Catanzaro, Italy
- Associazione CRISEA - Centro di Ricerca e Servizi Avanzati per l'Innovazione Rurale, Loc. Condoleo, Belcastro, Italy
| | - Anna Artese
- Dipartimento di Scienze della Salute, Campus "S. Venuta", Università degli Studi "Magna Græcia" di Catanzaro, Catanzaro, Italy
- Net4Science S.r.l., Università degli Studi "Magna Græcia" di Catanzaro, Catanzaro, Italy
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3
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Banerjee A, Kar S, Roy K, Patlewicz G, Charest N, Benfenati E, Cronin MTD. Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure-activity relationship (q-RASAR) with the application of machine learning. Crit Rev Toxicol 2024; 54:659-684. [PMID: 39225123 DOI: 10.1080/10408444.2024.2386260] [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: 06/03/2024] [Revised: 07/25/2024] [Accepted: 07/25/2024] [Indexed: 09/04/2024]
Abstract
This article aims to provide a comprehensive critical, yet readable, review of general interest to the chemistry community on molecular similarity as applied to chemical informatics and predictive modeling with a special focus on read-across (RA) and read-across structure-activity relationships (RASAR). Molecular similarity-based computational tools, such as quantitative structure-activity relationships (QSARs) and RA, are routinely used to fill the data gaps for a wide range of properties including toxicity endpoints for regulatory purposes. This review will explore the background of RA starting from how structural information has been used through to how other similarity contexts such as physicochemical, absorption, distribution, metabolism, and elimination (ADME) properties, and biological aspects are being characterized. More recent developments of RA's integration with QSAR have resulted in the emergence of novel models such as ToxRead, generalized read-across (GenRA), and quantitative RASAR (q-RASAR). Conventional QSAR techniques have been excluded from this review except where necessary for context.
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Affiliation(s)
- Arkaprava Banerjee
- Department of Pharmaceutical Technology, Drug Theoretics and Cheminformatics (DTC) Laboratory, Jadavpur University, Kolkata, India
| | - Supratik Kar
- Department of Chemistry and Physics, Chemometrics & Molecular Modeling Laboratory, Kean University, Union, NJ, USA
| | - Kunal Roy
- Department of Pharmaceutical Technology, Drug Theoretics and Cheminformatics (DTC) Laboratory, Jadavpur University, Kolkata, India
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Nathaniel Charest
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Emilio Benfenati
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
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4
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Blanco MJ, Buskes MJ, Govindaraj RG, Ipsaro JJ, Prescott-Roy JE, Padyana AK. Allostery Illuminated: Harnessing AI and Machine Learning for Drug Discovery. ACS Med Chem Lett 2024; 15:1449-1455. [PMID: 39291033 PMCID: PMC11403745 DOI: 10.1021/acsmedchemlett.4c00260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Revised: 08/15/2024] [Accepted: 08/20/2024] [Indexed: 09/19/2024] Open
Abstract
In the past several years there has been rapid adoption of artificial intelligence (AI) and machine learning (ML) tools for drug discovery. In this Microperspective, we comment on recent AI/ML applications to the discovery of allosteric modulators, focusing on breakthroughs with AlphaFold, structure-based drug discovery (SBDD), and medicinal chemistry applications. We discuss how these technologies are facilitating drug discovery and the remaining challenges to identify allosteric binding sites and ligands.
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Affiliation(s)
- Maria-Jesus Blanco
- Atavistik Bio, 101 Cambridgepark Drive, Cambridge, Massachusetts 02140, United States
| | - Melissa J Buskes
- Atavistik Bio, 101 Cambridgepark Drive, Cambridge, Massachusetts 02140, United States
| | - Rajiv G Govindaraj
- Atavistik Bio, 101 Cambridgepark Drive, Cambridge, Massachusetts 02140, United States
| | - Jonathan J Ipsaro
- Atavistik Bio, 101 Cambridgepark Drive, Cambridge, Massachusetts 02140, United States
| | - Joann E Prescott-Roy
- Atavistik Bio, 101 Cambridgepark Drive, Cambridge, Massachusetts 02140, United States
| | - Anil K Padyana
- Atavistik Bio, 101 Cambridgepark Drive, Cambridge, Massachusetts 02140, United States
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Cardoso Rial R. AI in analytical chemistry: Advancements, challenges, and future directions. Talanta 2024; 274:125949. [PMID: 38569367 DOI: 10.1016/j.talanta.2024.125949] [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: 12/28/2023] [Revised: 03/09/2024] [Accepted: 03/17/2024] [Indexed: 04/05/2024]
Abstract
This article explores the influence and applications of Artificial Intelligence (AI) in analytical chemistry, highlighting its potential to revolutionize the analysis of complex data sets and the development of innovative analytical methods. Additionally, it discusses the role of AI in interpreting large-scale data and optimizing experimental processes. AI has been fundamental in managing heterogeneous data and in advanced analysis of complex spectra in areas such as spectroscopy and chromatography. The article also examines the historical development of AI in chemistry, its current challenges, including the interpretation of AI models and the integration of large volumes of data. Finally, it forecasts future trends and the potential impact of AI on analytical chemistry, emphasizing the need for ethical and secure approaches in the use of AI.
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Affiliation(s)
- Rafael Cardoso Rial
- Federal Institute of Mato Grosso do Sul, 79750-000, Nova Andradina, MS, Brazil.
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6
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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.
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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.
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Karasev DA, Sobolev BN, Filimonov DA, Lagunin A. Prediction of viral protease inhibitors using proteochemometrics approach. Comput Biol Chem 2024; 110:108061. [PMID: 38574417 DOI: 10.1016/j.compbiolchem.2024.108061] [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: 12/28/2023] [Revised: 03/21/2024] [Accepted: 03/23/2024] [Indexed: 04/06/2024]
Abstract
Being widely accepted tools in computational drug search, the (Q)SAR methods have limitations related to data incompleteness. The proteochemometrics (PCM) approach expands the applicability area by using description for both protein and ligand structures. The PCM algorithms are urgently required for the development of new antiviral agents. We suggest the PCM method using the TLMNA descriptors, combining the MNA descriptors of ligands and protein sequence N-grams. Our method was validated on the viral chymotrypsin-like proteases and their ligands. We have developed an original protocol allowing us to collect a comprehensive set of 15 protein sequences and more than 9000 ligands from the ChEMBL database. The N-grams were derived from the 3D-based alignment, accurately superposing ligand-binding regions. In testing the ligand set in SAR mode with MNA descriptors, an accuracy above 0.95 was determined that shows the perspective of the antiviral drug search in virtual chemical libraries. The effective PCM models were built with the TLMNA descriptor. The strong validation procedure with pair exclusion simulated the prediction of interactions between the new ligands and new targets, resulting in accuracy estimation up to 0.89. The PCM approach shows slightly lower accuracy caused by more uncertainty compared with SAR, but it overcomes the problem of data incompleteness.
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Affiliation(s)
- Dmitry A Karasev
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia.
| | - Boris N Sobolev
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia
| | - Dmitry A Filimonov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia
| | - Alexey Lagunin
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia; Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow 117997, Russia
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8
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Jovanović M, Radan M, Čarapić M, Filipović N, Nikolic K, Crevar M. Application of parallel artificial membrane permeability assay technique and chemometric modeling for blood-brain barrier permeability prediction of protein kinase inhibitors. Future Med Chem 2024; 16:873-885. [PMID: 38639375 PMCID: PMC11373572 DOI: 10.4155/fmc-2023-0390] [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: 12/18/2023] [Accepted: 03/26/2024] [Indexed: 04/20/2024] Open
Abstract
Aim: This study aims to investigate the passive diffusion of protein kinase inhibitors through the blood-brain barrier (BBB) and to develop a model for their permeability prediction. Materials & methods: We used the parallel artificial membrane permeability assay to obtain logPe values of each of 34 compounds and calculated descriptors for these structures to perform quantitative structure-property relationship modeling, creating different regression models. Results: The logPe values have been calculated for all 34 compounds. Support vector machine regression was considered the most reliable, and CATS2D_09_DA, CATS2D_04_AA, B04[N-S] and F07[C-N] descriptors were identified as the most influential to passive BBB permeability. Conclusion: The quantitative structure-property relationship-support vector machine regression model that has been generated can serve as an efficient method for preliminary screening of BBB permeability of new analogs.
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Affiliation(s)
- Milan Jovanović
- University of Belgrade - Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Vojvode Stepe 450, P.O.Box 146, 11221, Belgrade, Serbia
- University of Belgrade - "VINCA" Institute of Nuclear Sciences - National Institute of the Republic of Serbia, Department of Molecular Biology & Endocrinology, Mike Petrovica Alasa 12-14, Vinca, 11351, Belgrade, Serbia
| | - Milica Radan
- Institute for Medicinal Plant Research "Dr. Josif Pančić", Tadeuša Košćuška 1, Belgrade, 11000, Serbia
| | - Marija Čarapić
- Medicines & Medical Devices Agency of Serbia, Vojvode Stepe 458, 11000, Belgrade, Serbia
| | - Nenad Filipović
- University of Belgrade - Faculty of Agriculture, Nemanjina 6, 11000, Belgrade, Serbia
| | - Katarina Nikolic
- University of Belgrade - Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Vojvode Stepe 450, P.O.Box 146, 11221, Belgrade, Serbia
| | - Milkica Crevar
- University of Belgrade - Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Vojvode Stepe 450, P.O.Box 146, 11221, Belgrade, Serbia
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Nandi S, Bhaduri S, Das D, Ghosh P, Mandal M, Mitra P. Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence. Mol Pharm 2024; 21:1563-1590. [PMID: 38466810 DOI: 10.1021/acs.molpharmaceut.3c01161] [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] [Indexed: 03/13/2024]
Abstract
Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.
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Affiliation(s)
- Suvendu Nandi
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Soumyadeep Bhaduri
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Debraj Das
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Priya Ghosh
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Mahitosh Mandal
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
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10
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Maddeboina K, Yada B, Kumari S, McHale C, Pal D, Durden DL. Recent advances in multitarget-directed ligands via in silico drug discovery. Drug Discov Today 2024; 29:103904. [PMID: 38280625 DOI: 10.1016/j.drudis.2024.103904] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 01/11/2024] [Accepted: 01/23/2024] [Indexed: 01/29/2024]
Abstract
To combat multifactorial refractory diseases, such as cancer, cardiovascular, and neurodegenerative diseases, multitarget drugs have become an emerging area of research aimed at 'synthetic lethality' (SL) relationships associated with drug-resistance mechanisms. In this review, we discuss the in silico design of dual and triple-targeted ligands, strategies by which specific 'warhead' groups are incorporated into a parent compound or scaffold with primary inhibitory activity against one target to develop one small molecule that inhibits two or three molecular targets in an effort to increase potency against multifactorial diseases. We also discuss the analytical exploration of structure-activity relationships (SARs), physicochemical properties, polypharmacology, scaffold feature extraction of US Food and Drug Administration (FDA)-approved multikinase inhibitors (MKIs), and updates regarding the clinical status of dual-targeted chemotypes.
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Affiliation(s)
- Krishnaiah Maddeboina
- Molecular Targeted Therapeutics Laboratory, Levine Cancer Institute/Atrium Health, Charlotte, NC 28204, USA; Department of Biochemistry, Atrium Health Wake Forest Baptist Comprehensive Cancer Center, Wake Forest University School of Medicine, Winston Salem, NC 27157, USA.
| | - Bharath Yada
- Molecular Targeted Therapeutics Laboratory, Levine Cancer Institute/Atrium Health, Charlotte, NC 28204, USA
| | - Shikha Kumari
- Department of Chemistry, Yale University, 225 Prospect Street, New Haven, CT 06520, USA
| | - Cody McHale
- Molecular Targeted Therapeutics Laboratory, Levine Cancer Institute/Atrium Health, Charlotte, NC 28204, USA
| | - Dhananjaya Pal
- Molecular Targeted Therapeutics Laboratory, Levine Cancer Institute/Atrium Health, Charlotte, NC 28204, USA
| | - Donald L Durden
- Molecular Targeted Therapeutics Laboratory, Levine Cancer Institute/Atrium Health, Charlotte, NC 28204, USA; Department of Biochemistry, Atrium Health Wake Forest Baptist Comprehensive Cancer Center, Wake Forest University School of Medicine, Winston Salem, NC 27157, USA.
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11
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Gil-Pichardo A, Sánchez-Ruiz A, Colmenarejo G. Analysis of metabolites in human gut: illuminating the design of gut-targeted drugs. J Cheminform 2023; 15:96. [PMID: 37833792 PMCID: PMC10571276 DOI: 10.1186/s13321-023-00768-y] [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: 08/17/2023] [Accepted: 10/06/2023] [Indexed: 10/15/2023] Open
Abstract
Gut-targeted drugs provide a new drug modality besides that of oral, systemic molecules, that could tap into the growing knowledge of gut metabolites of bacterial or host origin and their involvement in biological processes and health through their interaction with gut targets (bacterial or host, too). Understanding the properties of gut metabolites can provide guidance for the design of gut-targeted drugs. In the present work we analyze a large set of gut metabolites, both shared with serum or present only in gut, and compare them with oral systemic drugs. We find patterns specific for these two subsets of metabolites that could be used to design drugs targeting the gut. In addition, we develop and openly share a Super Learner model to predict gut permanence, in order to aid in the design of molecules with appropriate profiles to remain in the gut, resulting in molecules with putatively reduced secondary effects and better pharmacokinetics.
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Affiliation(s)
- Alberto Gil-Pichardo
- Biostatistics and Bioinformatics Unit, IMDEA Food, CEI UAM+CSIC, 28049, Madrid, Spain
| | - Andrés Sánchez-Ruiz
- Biostatistics and Bioinformatics Unit, IMDEA Food, CEI UAM+CSIC, 28049, Madrid, Spain
| | - Gonzalo Colmenarejo
- Biostatistics and Bioinformatics Unit, IMDEA Food, CEI UAM+CSIC, 28049, Madrid, Spain.
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