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Mangalpady SS, Peña-Corona SI, Borbolla-Jiménez F, Kaverikana R, Shetty S, Shet VB, Almarhoon ZM, Calina D, Leyva-Gómez G, Sharifi-Rad J. Arnicolide D: a multi-targeted anticancer sesquiterpene lactone-preclinical efficacy and mechanistic insights. NAUNYN-SCHMIEDEBERG'S ARCHIVES OF PHARMACOLOGY 2024:10.1007/s00210-024-03095-7. [PMID: 38652277 DOI: 10.1007/s00210-024-03095-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 04/11/2024] [Indexed: 04/25/2024]
Abstract
Arnicolide D, a potent sesquiterpene lactone from Centipeda minima, has emerged as a promising anticancer candidate, demonstrating significant efficacy in inhibiting cancer cell proliferation, inducing apoptosis, and suppressing metastasis across various cancer models. This comprehensive study delves into the molecular underpinnings of Arnicolide D's anticancer actions, emphasizing its impact on key signaling pathways such as PI3K/AKT/mTOR and STAT3, and its role in modulating cell cycle and survival mechanisms. Quantitative data from preclinical studies reveal Arnicolide D's dose-dependent cytotoxicity against cancer cell lines, including nasopharyngeal carcinoma, triple-negative breast cancer, and human colon carcinoma, showcasing its broad-spectrum anticancer potential. Given its multifaceted mechanisms and preclinical efficacy, Arnicolide D warrants further investigation in clinical settings to validate its therapeutic utility against cancer. The evidence presented underscores the need for rigorous pharmacokinetic and toxicological studies to establish safe dosing parameters for future clinical trials.
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Affiliation(s)
- Shivaprasad Shetty Mangalpady
- Department of Chemistry, NMAM Institute of Technology (NMAMIT), Nitte (Deemed to Be University), Nitte, Mangaluru, India
| | - Sheila I Peña-Corona
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, 04510, Ciudad de Mexico, Mexico
| | - Fabiola Borbolla-Jiménez
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, 04510, Ciudad de Mexico, Mexico
| | - Rajesh Kaverikana
- Department of Pharmacology, NGSM Institute of Pharmaceuticals, Nitte (Deemed to Be University), Mangaluru, India
| | - Shobhitha Shetty
- Department of Chemistry, A.J. Institute of Engineering & Technology, Mangaluru, India
| | - Vinayaka Babu Shet
- Department of Biotechnology Engineering, NMAM Institute of Technology (NMAMIT), Nitte (Deemed to Be University), Mangaluru, India
| | - Zainab M Almarhoon
- Department of Chemistry, College of Science, King Saud University, P. O. Box 2455, 11451, Riyadh, Saudi Arabia
| | - Daniela Calina
- Department of Clinical Pharmacy, University of Medicine and Pharmacy of Craiova, 200349, Craiova, Romania.
| | - Gerardo Leyva-Gómez
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, 04510, Ciudad de Mexico, Mexico.
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2
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Słota D, Jampilek J, Sobczak-Kupiec A. Targeted Clindamycin Delivery Systems: Promising Options for Preventing and Treating Bacterial Infections Using Biomaterials. Int J Mol Sci 2024; 25:4386. [PMID: 38673971 PMCID: PMC11050486 DOI: 10.3390/ijms25084386] [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: 03/20/2024] [Revised: 04/11/2024] [Accepted: 04/13/2024] [Indexed: 04/28/2024] Open
Abstract
Targeted therapy represents a real opportunity to improve the health and lives of patients. Developments in this field are confirmed by the fact that the global market for drug carriers was worth nearly $40 million in 2022. For this reason, materials engineering and the development of new drug carrier compositions for targeted therapy has become a key area of research in pharmaceutical drug delivery in recent years. Ceramics, polymers, and metals, as well as composites, are of great interest, as when they are appropriately processed or combined with each other, it is possible to obtain biomaterials for hard tissues, soft tissues, and skin applications. After appropriate modification, these materials can release the drug directly at the site requiring a therapeutic effect. This brief literature review characterizes routes of drug delivery into the body and discusses biomaterials from different groups, options for their modification with clindamycin, an antibiotic used for infections caused by aerobic and anaerobic Gram-positive bacteria, and different methods for the final processing of carriers. Examples of coating materials for skin wound healing, acne therapy, and bone tissue fillers are given. Furthermore, the reasons why the use of antibiotic therapy is crucial for a smooth and successful recovery and the risks of bacterial infections are explained. It was demonstrated that there is no single proven delivery scheme, and that the drug can be successfully released from different carriers depending on the destination.
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Affiliation(s)
- Dagmara Słota
- Department of Materials Science, Faculty of Materials Engineering and Physics, KrakowUniversity of Technology, 37 Jana Pawła II Av., 31-864 Krakow, Poland;
| | - Josef Jampilek
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University, Ilkovicova 6, 842 15 Bratislava, Slovakia
- Department of Chemical Biology, Faculty of Science, Palacky University, Slechtitelu 27, 783 71 Olomouc, Czech Republic
| | - Agnieszka Sobczak-Kupiec
- Department of Materials Science, Faculty of Materials Engineering and Physics, KrakowUniversity of Technology, 37 Jana Pawła II Av., 31-864 Krakow, Poland;
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Biehn SE, Goncalves LM, Lehmann J, Marty JD, Mueller C, Ramirez SA, Tillier F, Sage CR. BioPrint meets the AI age: development of artificial intelligence-based ADMET models for the drug-discovery platform SAFIRE. Future Med Chem 2024; 16:587-599. [PMID: 38372202 DOI: 10.4155/fmc-2024-0007] [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: 01/08/2024] [Accepted: 02/08/2024] [Indexed: 02/20/2024] Open
Abstract
Background: To prioritize compounds with a higher likelihood of success, artificial intelligence models can be used to predict absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of molecules quickly and efficiently. Methods: Models were trained with BioPrint database proprietary data along with public datasets to predict various ADMET end points for the SAFIRE platform. Results: SAFIRE models performed at or above 75% accuracy and 0.4 Matthew's correlation coefficient with validation sets. Training with both proprietary and public data improved model performance and expanded the chemical space on which the models were trained. The platform features scoring functionality to guide user decision-making. Conclusion: High-quality datasets along with chemical space considerations yielded ADMET models performing favorably with utility in the drug discovery process.
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Affiliation(s)
- Sarah E Biehn
- Eurofins DiscoveryAI, Eurofins Panlabs, Inc., Saint Charles, MO 63304, USA
| | | | - Juerg Lehmann
- Eurofins DiscoveryAI, Eurofins Panlabs, Inc., Saint Charles, MO 63304, USA
| | - Jessica D Marty
- Eurofins DiscoveryAI, Eurofins Panlabs, Inc., Saint Charles, MO 63304, USA
| | - Christoph Mueller
- Eurofins DiscoveryAI, Eurofins Panlabs, Inc., Saint Charles, MO 63304, USA
| | - Samuel A Ramirez
- Eurofins DiscoveryAI, Eurofins Panlabs, Inc., Saint Charles, MO 63304, USA
| | - Fabien Tillier
- Eurofins DiscoveryAI, Eurofins Panlabs, Inc., Saint Charles, MO 63304, USA
| | - Carleton R Sage
- Eurofins DiscoveryAI, Eurofins Panlabs, Inc., Saint Charles, MO 63304, USA
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4
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Knox C, Wilson M, Klinger C, Franklin M, Oler E, Wilson A, Pon A, Cox J, Chin NE, Strawbridge S, Garcia-Patino M, Kruger R, Sivakumaran A, Sanford S, Doshi R, Khetarpal N, Fatokun O, Doucet D, Zubkowski A, Rayat D, Jackson H, Harford K, Anjum A, Zakir M, Wang F, Tian S, Lee B, Liigand J, Peters H, Wang RQ, Nguyen T, So D, Sharp M, da Silva R, Gabriel C, Scantlebury J, Jasinski M, Ackerman D, Jewison T, Sajed T, Gautam V, Wishart D. DrugBank 6.0: the DrugBank Knowledgebase for 2024. Nucleic Acids Res 2024; 52:D1265-D1275. [PMID: 37953279 PMCID: PMC10767804 DOI: 10.1093/nar/gkad976] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 10/09/2023] [Accepted: 10/16/2023] [Indexed: 11/14/2023] Open
Abstract
First released in 2006, DrugBank (https://go.drugbank.com) has grown to become the 'gold standard' knowledge resource for drug, drug-target and related pharmaceutical information. DrugBank is widely used across many diverse biomedical research and clinical applications, and averages more than 30 million views/year. Since its last update in 2018, we have been actively enhancing the quantity and quality of the drug data in this knowledgebase. In this latest release (DrugBank 6.0), the number of FDA approved drugs has grown from 2646 to 4563 (a 72% increase), the number of investigational drugs has grown from 3394 to 6231 (a 38% increase), the number of drug-drug interactions increased from 365 984 to 1 413 413 (a 300% increase), and the number of drug-food interactions expanded from 1195 to 2475 (a 200% increase). In addition to this notable expansion in database size, we have added thousands of new, colorful, richly annotated pathways depicting drug mechanisms and drug metabolism. Likewise, existing datasets have been significantly improved and expanded, by adding more information on drug indications, drug-drug interactions, drug-food interactions and many other relevant data types for 11 891 drugs. We have also added experimental and predicted MS/MS spectra, 1D/2D-NMR spectra, CCS (collision cross section), RT (retention time) and RI (retention index) data for 9464 of DrugBank's 11 710 small molecule drugs. These and other improvements should make DrugBank 6.0 even more useful to a much wider research audience ranging from medicinal chemists to metabolomics specialists to pharmacologists.
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Affiliation(s)
- Craig Knox
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Mike Wilson
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Christen M Klinger
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Mark Franklin
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Eponine Oler
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Alex Wilson
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Allison Pon
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Jordan Cox
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Na Eun (Lucy) Chin
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Seth A Strawbridge
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Marysol Garcia-Patino
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Ray Kruger
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Aadhavya Sivakumaran
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Selena Sanford
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Rahil Doshi
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Nitya Khetarpal
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Omolola Fatokun
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Daphnee Doucet
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Ashley Zubkowski
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Dorsa Yahya Rayat
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Hayley Jackson
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Karxena Harford
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Afia Anjum
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Mahi Zakir
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Fei Wang
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Siyang Tian
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Brian Lee
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Jaanus Liigand
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Harrison Peters
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Ruo Qi (Rachel) Wang
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Tue Nguyen
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Denise So
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Matthew Sharp
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Rodolfo da Silva
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Cyrella Gabriel
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Joshua Scantlebury
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Marissa Jasinski
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - David Ackerman
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Timothy Jewison
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Tanvir Sajed
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Vasuk Gautam
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB T6G 2H1, Canada
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 1C9, Canada
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5
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Chen M, Yang J, Tang C, Lu X, Wei Z, Liu Y, Yu P, Li H. Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches. Curr Top Med Chem 2024; 24:222-242. [PMID: 38083894 DOI: 10.2174/0115680266280005231207105900] [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: 09/19/2023] [Revised: 11/02/2023] [Accepted: 11/10/2023] [Indexed: 05/04/2024]
Abstract
Quantitative Structure-Property Relationship (QSPR) employs mathematical and statistical methods to reveal quantitative correlations between the pharmacokinetics of compounds and their molecular structures, as well as their physical and chemical properties. QSPR models have been widely applied in the prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). However, the accuracy of QSPR models for predicting drug ADMET properties still needs improvement. Therefore, this paper comprehensively reviews the tools employed in various stages of QSPR predictions for drug ADMET. It summarizes commonly used approaches to building QSPR models, systematically analyzing the advantages and limitations of each modeling method to ensure their judicious application. We provide an overview of recent advancements in the application of QSPR models for predicting drug ADMET properties. Furthermore, this review explores the inherent challenges in QSPR modeling while also proposing a range of considerations aimed at enhancing model prediction accuracy. The objective is to enhance the predictive capabilities of QSPR models in the field of drug development and provide valuable reference and guidance for researchers in this domain.
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Affiliation(s)
- Meilun Chen
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Changsha, Hunan, 410013, China
| | - Jie Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Changsha, Hunan, 410013, China
| | - Chunhua Tang
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Changsha, Hunan, 410013, China
| | - Xiaoling Lu
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Changsha, Hunan, 410013, China
| | - Zheng Wei
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Changsha, Hunan, 410013, China
| | - Yijie Liu
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Changsha, Hunan, 410013, China
| | - Peng Yu
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Changsha, Hunan, 410013, China
| | - HuanHuan Li
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Changsha, Hunan, 410013, China
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6
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Dudas B, Miteva MA. Computational and artificial intelligence-based approaches for drug metabolism and transport prediction. Trends Pharmacol Sci 2024; 45:39-55. [PMID: 38072723 DOI: 10.1016/j.tips.2023.11.001] [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: 08/02/2023] [Revised: 11/09/2023] [Accepted: 11/09/2023] [Indexed: 01/07/2024]
Abstract
Drug metabolism and transport, orchestrated by drug-metabolizing enzymes (DMEs) and drug transporters (DTs), are implicated in drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Reliable and precise predictions of DDIs and ADRs are critical in the early stages of drug development to reduce the rate of drug candidate failure. A variety of experimental and computational technologies have been developed to predict DDIs and ADRs. Recent artificial intelligence (AI) approaches offer new opportunities for better predicting and understanding the complex processes related to drug metabolism and transport. We summarize the role of major DMEs and DTs, and provide an overview of current progress in computational approaches for the prediction of drug metabolism, transport, and DDIs, with an emphasis on AI including machine learning (ML) and deep learning (DL) modeling.
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Affiliation(s)
- Balint Dudas
- Université Paris Cité, CNRS UMR 8038 CiTCoM, Inserm U1268 MCTR, Paris, France
| | - Maria A Miteva
- Université Paris Cité, CNRS UMR 8038 CiTCoM, Inserm U1268 MCTR, Paris, France.
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7
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Woodson ME, Mottaleb MA, Murelli RP, Tavis JE. In vitro evaluation of tropolone absorption, metabolism, and clearance. Antiviral Res 2023; 220:105762. [PMID: 37996012 PMCID: PMC10843707 DOI: 10.1016/j.antiviral.2023.105762] [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: 09/22/2023] [Revised: 11/13/2023] [Accepted: 11/17/2023] [Indexed: 11/25/2023]
Abstract
Tropolone compounds can inhibit hepatitis B virus (HBV) replication at sub-micromolar levels and are synergistic upon co-treatment with nucleos(t)ide analog drugs. However, only a few compounds within this chemotype have been screened for their pharmacological properties. Here, we chose 36 structurally diverse tropolones from six subclasses to characterize their in vitro pharmacological parameters. All compounds were more soluble in pHs that reflect the gastrointestinal tract (pH 5 and 6.5) than plasma (pH 7.4). Those compounds that had solubility limits >100 μM were tested in a passive permeability assay, and there was no general trend in the compounds' passive permeability at any pH. Twenty-nine compounds with the best absorption parameters were tested in HEK293 cells to assess potential cytotoxicity; measured toxicities were similar to those in the hepatic HepDES19 cells used for screening (R2 = 0.55). Sixteen representative compounds were tested against five major CYP450 isoforms and there was no substantial inhibition by any compound against any of the enzymes tested (<50%). The t1/2 values of 15 compounds were determined in the microsome stability assay and 12 compounds were evaluated in plasma protein binding assays to assess factors affecting their rate of clearance. All compounds with detectable analyte peaks had t1/2 > 30 min, and while 4 of 12 had statistically significant decreased potency in conditions with increased albumin concentrations, only one compound's potency was biologically significant. These data indicate that the tropolones have pharmacological characteristics that reflect approved drugs and inform future structure activity relationships during drug design.
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Affiliation(s)
- Molly E Woodson
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, USA; Institute for Drug and Biotherapeutic Innovation, Saint Louis University, St. Louis, MO, USA
| | - M Abdul Mottaleb
- Institute for Drug and Biotherapeutic Innovation, Saint Louis University, St. Louis, MO, USA
| | - Ryan P Murelli
- Department of Chemistry and Biochemistry, Brooklyn College, City University of New York, Brooklyn, NY, USA; The Graduate Center, City University of New York, New York, NY, USA
| | - John E Tavis
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, USA; Institute for Drug and Biotherapeutic Innovation, Saint Louis University, St. Louis, MO, USA.
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8
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Abdullahi M, Uzairu A, Shallangwa GA, Mamza PA, Ibrahim MT, Chandra A, Goel VK. Molecular modelling studies of substituted indole derivatives as novel influenza a virus inhibitors. J Biomol Struct Dyn 2023:1-20. [PMID: 37964590 DOI: 10.1080/07391102.2023.2280735] [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: 09/25/2023] [Accepted: 11/01/2023] [Indexed: 11/16/2023]
Abstract
The emergence of drug-resistant strains motivate researchers to find new innovative anti-IAV candidates with a different mode of action. In this work, molecular modelling strategies, such as 2D-QSAR, 3D-QSAR, molecular docking, molecular dynamics, FMOs, and ADMET were applied to some substituted indoles as IAV inhibitors. The best-developed 2D-QSAR models, MLR (Q2 = 0.7634, R2train = 0.8666) and ANN[4-3-1] (Q2 = 0.8699, R2train = 0.8705) revealed good statistical validation for the inhibitory response predictions. The 3D-QSAR models, CoMFA (Q2 = 0.504, R2train = 0.805) and CoMSIA/SEDHA (Q2 = 0.619, R2train = 0.813) are selected as the best 3D models following the global thresholds. In addition, the contour maps generated from the CoMFA and CoMSIA models illustrate the relationship between the molecular fields and the inhibitory effects of the studied molecules. The results of the studies led to the design of five new molecules (24a-e) with enhanced anti-IAV activities and binding potentials using the most active molecule (24) as the template scaffold. The conformational stability of the best-designed molecules with the NA protein showed hydrophobic and H-bonds with the key residues from the molecular dynamics simulations of 100 ns. Furthermore, the global reactivity indices from the DFT calculations portrayed the relevance of 24c in view of its smaller band gap as also justified by our QSAR and molecular simulation studies.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Mustapha Abdullahi
- Department of Chemistry, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Nigeria
- Department of Pure and Applied Chemistry, Faculty of Physical Sciences, Kaduna State University, Kaduna, Nigeria
| | - Adamu Uzairu
- Department of Chemistry, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Nigeria
| | - Gideon Adamu Shallangwa
- Department of Chemistry, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Nigeria
| | - Paul Andrew Mamza
- Department of Chemistry, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Nigeria
| | - Muhammad Tukur Ibrahim
- Department of Chemistry, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Nigeria
| | - Anshuman Chandra
- School of Physical Science, Jawaharlal Nehru University, New Delhi, India
| | - Vijay Kumar Goel
- School of Physical Science, Jawaharlal Nehru University, New Delhi, India
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9
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Keefer CE, Chang G, Di L, Woody NA, Tess DA, Osgood SM, Kapinos B, Racich J, Carlo AA, Balesano A, Ferguson N, Orozco C, Zueva L, Luo L. The Comparison of Machine Learning and Mechanistic In Vitro-In Vivo Extrapolation Models for the Prediction of Human Intrinsic Clearance. Mol Pharm 2023; 20:5616-5630. [PMID: 37812508 DOI: 10.1021/acs.molpharmaceut.3c00502] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/11/2023]
Abstract
Accurate prediction of human pharmacokinetics (PK) remains one of the key objectives of drug metabolism and PK (DMPK) scientists in drug discovery projects. This is typically performed by using in vitro-in vivo extrapolation (IVIVE) based on mechanistic PK models. In recent years, machine learning (ML), with its ability to harness patterns from previous outcomes to predict future events, has gained increased popularity in application to absorption, distribution, metabolism, and excretion (ADME) sciences. This study compares the performance of various ML and mechanistic models for the prediction of human IV clearance for a large (645) set of diverse compounds with literature human IV PK data, as well as measured relevant in vitro end points. ML models were built using multiple approaches for the descriptors: (1) calculated physical properties and structural descriptors based on chemical structure alone (classical QSAR/QSPR); (2) in vitro measured inputs only with no structure-based descriptors (ML IVIVE); and (3) in silico ML IVIVE using in silico model predictions for the in vitro inputs. For the mechanistic models, well-stirred and parallel-tube liver models were considered with and without the use of empirical scaling factors and with and without renal clearance. The best ML model for the prediction of in vivo human intrinsic clearance (CLint) was an in vitro ML IVIVE model using only six in vitro inputs with an average absolute fold error (AAFE) of 2.5. The best mechanistic model used the parallel-tube liver model, with empirical scaling factors resulting in an AAFE of 2.8. The corresponding mechanistic model with full in silico inputs achieved an AAFE of 3.3. These relative performances of the models were confirmed with the prediction of 16 Pfizer drug candidates that were not part of the original data set. Results show that ML IVIVE models are comparable to or superior to their best mechanistic counterparts. We also show that ML IVIVE models can be used to derive insights into factors for the improvement of mechanistic PK prediction.
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Affiliation(s)
- Christopher E Keefer
- Translational Modeling and Simulation, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States
| | - George Chang
- Translational Modeling and Simulation, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States
| | - Li Di
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States
| | - Nathaniel A Woody
- Translational Modeling and Simulation, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States
| | - David A Tess
- Translational Modeling and Simulation, Pfizer Worldwide Research and Development, Cambridge, Massachusetts 02139, United States
| | - Sarah M Osgood
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States
| | - Brendon Kapinos
- Discovery Sciences, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States
| | - Jill Racich
- Discovery Sciences, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States
| | - Anthony A Carlo
- Discovery Sciences, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States
| | - Amanda Balesano
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States
| | - Nicholas Ferguson
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States
| | - Christine Orozco
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States
| | - Larisa Zueva
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States
| | - Lina Luo
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States
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10
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Tran TTV, Tayara H, Chong KT. Recent Studies of Artificial Intelligence on In Silico Drug Absorption. J Chem Inf Model 2023; 63:6198-6211. [PMID: 37819031 DOI: 10.1021/acs.jcim.3c00960] [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: 10/13/2023]
Abstract
Absorption is an important area of research in pharmacochemistry and drug development, because the drug has to be absorbed before any drug effects can occur. Furthermore, the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profile of drugs can be directly and considerably altered by modulating factors affecting absorption. Many drugs in development fail because of poor absorption. The research and continuous efforts of researchers in recent years have brought many successes and promises in drug absorption property prediction, especially in silico, which helps to reduce the time and cost significantly for screening undesirable drug candidates. In this report, we explicitly provide an overview of recent in silico studies on predicting absorption properties, especially from 2019 to the present, using artificial intelligence. Additionally, we have collected and investigated public databases that support absorption prediction research. On those grounds, we also proposed the challenges and development directions of absorption prediction in the future. We hope this review can provide researchers with valuable guidelines on absorption prediction to facilitate the development of newer approaches in drug discovery.
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Affiliation(s)
- Thi Tuyet Van Tran
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
- Faculty of Information Technology, An Giang University, Long Xuyen 880000, Vietnam
- Vietnam National University, Ho Chi Minh City, Ho Chi Minh 700000, Vietnam
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Kil To Chong
- Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
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11
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Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, Aldairem A, Alrashed M, Bin Saleh K, Badreldin HA, Al Yami MS, Al Harbi S, Albekairy AM. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC MEDICAL EDUCATION 2023; 23:689. [PMID: 37740191 PMCID: PMC10517477 DOI: 10.1186/s12909-023-04698-z] [Citation(s) in RCA: 76] [Impact Index Per Article: 76.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 09/19/2023] [Indexed: 09/24/2023]
Abstract
INTRODUCTION Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI's role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools. RESEARCH SIGNIFICANCE This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI's significance in healthcare and supports healthcare organizations in effectively adopting AI technologies. MATERIALS AND METHODS The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application. RESULTS Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust. CONCLUSION AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.
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Affiliation(s)
- Shuroug A Alowais
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia.
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia.
| | - Sahar S Alghamdi
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
- Department of Pharmaceutical Sciences, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Nada Alsuhebany
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Tariq Alqahtani
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
- Department of Pharmaceutical Sciences, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Abdulrahman I Alshaya
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Sumaya N Almohareb
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Atheer Aldairem
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Mohammed Alrashed
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Khalid Bin Saleh
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Hisham A Badreldin
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Majed S Al Yami
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Shmeylan Al Harbi
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Abdulkareem M Albekairy
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
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Singh S, Kumar R, Payra S, Singh SK. Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery. Cureus 2023; 15:e44359. [PMID: 37779744 PMCID: PMC10539991 DOI: 10.7759/cureus.44359] [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] [Accepted: 07/31/2023] [Indexed: 10/03/2023] Open
Abstract
Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep learning, and natural language processing. These advancements have greatly influenced drug discovery, development, and precision medicine. AI algorithms analyze vast biomedical data identifying potential drug targets, predicting efficacy, and optimizing lead compounds. AI has diverse applications in pharmacological research, including target identification, drug repurposing, virtual screening, de novo drug design, toxicity prediction, and personalized medicine. AI improves patient selection, trial design, and real-time data analysis in clinical trials, leading to enhanced safety and efficacy outcomes. Post-marketing surveillance utilizes AI-based systems to monitor adverse events, detect drug interactions, and support pharmacovigilance efforts. Machine learning models extract patterns from complex datasets, enabling accurate predictions and informed decision-making, thus accelerating drug discovery. Deep learning, specifically convolutional neural networks (CNN), excels in image analysis, aiding biomarker identification and optimizing drug formulation. Natural language processing facilitates the mining and analysis of scientific literature, unlocking valuable insights and information. However, the adoption of AI in pharmacological research raises ethical considerations. Ensuring data privacy and security, addressing algorithm bias and transparency, obtaining informed consent, and maintaining human oversight in decision-making are crucial ethical concerns. The responsible deployment of AI necessitates robust frameworks and regulations. The future of AI in pharmacological research is promising, with integration with emerging technologies like genomics, proteomics, and metabolomics offering the potential for personalized medicine and targeted therapies. Collaboration among academia, industry, and regulatory bodies is essential for the ethical implementation of AI in drug discovery and development. Continuous research and development in AI techniques and comprehensive training programs will empower scientists and healthcare professionals to fully exploit AI's potential, leading to improved patient outcomes and innovative pharmacological interventions.
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Affiliation(s)
- Shruti Singh
- Department of Pharmacology, All India Institute of Medical Sciences, Patna, IND
| | - Rajesh Kumar
- Department of Pharmacology, All India Institute of Medical Sciences, Patna, IND
| | - Shuvasree Payra
- Department of Pharmacology, All India Institute of Medical Sciences, Patna, IND
| | - Sunil K Singh
- Department of Pharmacology, All India Institute of Medical Sciences, Patna, IND
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Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP. Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics 2023; 15:1916. [PMID: 37514102 PMCID: PMC10385763 DOI: 10.3390/pharmaceutics15071916] [Citation(s) in RCA: 34] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 06/28/2023] [Accepted: 07/04/2023] [Indexed: 07/30/2023] Open
Abstract
Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.
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Affiliation(s)
- Lalitkumar K Vora
- School of Pharmacy, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK
| | - Amol D Gholap
- Department of Pharmaceutics, St. John Institute of Pharmacy and Research, Palghar 401404, Maharashtra, India
| | - Keshava Jetha
- Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
- Ph.D. Section, Gujarat Technological University, Ahmedabad 382424, Gujarat, India
| | | | - Hetvi K Solanki
- Pharmacy Section, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
| | - Vivek P Chavda
- Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
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Mousa S, Sarfraz M, Mousa WK. The Interplay between Gut Microbiota and Oral Medications and Its Impact on Advancing Precision Medicine. Metabolites 2023; 13:metabo13050674. [PMID: 37233715 DOI: 10.3390/metabo13050674] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 05/14/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023] Open
Abstract
Trillions of diverse microbes reside in the gut and are deeply interwoven with the human physiological process, from food digestion, immune system maturation, and fighting invading pathogens, to drug metabolism. Microbial drug metabolism has a profound impact on drug absorption, bioavailability, stability, efficacy, and toxicity. However, our knowledge of specific gut microbial strains, and their genes that encode enzymes involved in the metabolism, is limited. The microbiome encodes over 3 million unique genes contributing to a huge enzymatic capacity, vastly expanding the traditional drug metabolic reactions that occur in the liver, manipulating their pharmacological effect, and, ultimately, leading to variation in drug response. For example, the microbial deactivation of anticancer drugs such as gemcitabine can lead to resistance to chemotherapeutics or the crucial role of microbes in modulating the efficacy of the anticancer drug, cyclophosphamide. On the other hand, recent findings show that many drugs can shape the composition, function, and gene expression of the gut microbial community, making it harder to predict the outcome of drug-microbiota interactions. In this review, we discuss the recent understanding of the multidirectional interaction between the host, oral medications, and gut microbiota, using traditional and machine-learning approaches. We analyze gaps, challenges, and future promises of personalized medicine that consider gut microbes as a crucial player in drug metabolism. This consideration will enable the development of personalized therapeutic regimes with an improved outcome, ultimately leading to precision medicine.
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Affiliation(s)
- Sara Mousa
- College of Pharmacy, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
| | - Muhammad Sarfraz
- College of Pharmacy, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
| | - Walaa K Mousa
- College of Pharmacy, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
- College of Pharmacy, Mansoura University, Mansoura 35516, Egypt
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