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Chhatrapati Bisen A, Nashik Sanap S, Agrawal S, Biswas A, Sankar Bhatta R. Chemical metabolite synthesis and profiling: Mimicking in vivo biotransformation reactions. Bioorg Chem 2023; 139:106722. [PMID: 37453238 DOI: 10.1016/j.bioorg.2023.106722] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/13/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023]
Abstract
Biotransformation was previously viewed as merely the structural characterization of drug metabolites, and it was performed only when drug candidates entered clinical development. The synthesis of drug metabolites is crucial to the drug development process because it generates either pharmacologically active, inactive, or reactive molecules and hence their characterization and comprehensive pharmacological evaluation is necessary. The chemical metabolite synthesis is very challenging due to the complex structures of many drug molecules, presence of multiple stereocenters, poor reaction yields, and the formation of unwanted by-products. Drug metabolites and their chemical synthesis have immense significance in the drug discovery process. The chemical synthesis of metabolites facilitates on- or off-target pharmacological and toxicological evaluations at the easiest. In a broader view metabolite could be a target lead molecule for drug design, toxic reactive metabolites, pharmaceutical standards for bioanalytical methods, etc. Collectively these metabolite information dossiers will aid regulatory agencies such as the EMA and FDA in maintaining strict vigilance over drug manufacturers with regard to the safety of NCE's and their hidden metabolites. Herein, we are presenting a systematic compilation of chemical and biocatalytic strategies reported to date for pharmaceutical drug metabolite synthesis. This review report is very useful for the laboratory synthesis of new drug metabolites, and their preclinical biological evaluation could aid in the detection of early threats (alerts) in drug discovery, eliminate the toxicity profile, explore newer pharmacology, and delivering a promising and safe drug candidate to humankind.
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Affiliation(s)
- Amol Chhatrapati Bisen
- Pharmaceutics & Pharmacokinetics Division, CSIR-Central Drug Research Institute, Lucknow 226031, India; Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, India
| | - Sachin Nashik Sanap
- Pharmaceutics & Pharmacokinetics Division, CSIR-Central Drug Research Institute, Lucknow 226031, India; Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, India
| | - Sristi Agrawal
- Pharmaceutics & Pharmacokinetics Division, CSIR-Central Drug Research Institute, Lucknow 226031, India; Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, India
| | - Arpon Biswas
- Pharmaceutics & Pharmacokinetics Division, CSIR-Central Drug Research Institute, Lucknow 226031, India
| | - Rabi Sankar Bhatta
- Pharmaceutics & Pharmacokinetics Division, CSIR-Central Drug Research Institute, Lucknow 226031, India.
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Weston DJ, Dave M, Colizza K, Thomas S, Tomlinson L, Gregory R, Beaumont C, Pirhalla J, Dear GJ. A Discovery Biotransformation Strategy: Combining In Silico Tools with High-Resolution Mass Spectrometry and Software-Assisted Data Analysis for High-Throughput Metabolism. Xenobiotica 2022; 52:928-942. [PMID: 36227740 DOI: 10.1080/00498254.2022.2136042] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
Understanding compound metabolism in early drug discovery aids medicinal chemistry in designing molecules with improved safety and ADME properties. While advancements in metabolite prediction brings increasedconfidence, structural decisions require experimental data. In vitro metabolism studies using liquid chromatography and high-resolution mass spectrometry (LC-MS) are generally resource intensive and performed on very few compounds, limiting the chemical space that can be examined.Here, we describe a novel metabolism strategy increasing compound throughput using residual in vitro clearance samples conducted at drug concentrations of 0.5 µM. Analysis by robust UHPLC separation and accurate-mass MS detection ensures major metabolites are identified from a single injection. In silico prediction (parent cLogD) tailors chromatographic conditions, with data-dependent MS/MS targeting predicted metabolites. Software-assisted data mining, structure elucidation and automatic reporting are used.Confidence in the globally-aligned workflow is demonstrated with sixteen marketed drugs. The approach is now implemented routinely across our laboratories. To date, the success rate for identification of at least one major metabolite is 85%. The utility of these data has been demonstrated across multiple projects, allowing earlier medicinal chemistry decisions to increase efficiency and impact of the design-make-test cycle; thus improving the translatability of early in vitro metabolism data.
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Affiliation(s)
- Daniel J Weston
- GSK, DMPK, Disposition and Biotransformation, Gunnels Wood Road, Stevenage, SG1 2NY, UK
| | - Mehul Dave
- GSK, DMPK, Disposition and Biotransformation, Gunnels Wood Road, Stevenage, SG1 2NY, UK
| | - Kevin Colizza
- GSK, DMPK, Disposition and Biotransformation, 1250 S. Collegeville Road., Collegeville, PA 19426, USA
| | - Steve Thomas
- GSK, DMPK, Disposition and Biotransformation, Gunnels Wood Road, Stevenage, SG1 2NY, UK
| | - Laura Tomlinson
- GSK, DMPK, Discovery DMPK, Gunnels Wood Road, Stevenage, SG1 2NY, UK
| | - Richard Gregory
- GSK, DMPK, Discovery DMPK, Gunnels Wood Road, Stevenage, SG1 2NY, UK
| | - Claire Beaumont
- GSK, DMPK, Disposition and Biotransformation, Gunnels Wood Road, Stevenage, SG1 2NY, UK
| | - Jill Pirhalla
- GSK, DMPK, Disposition and Biotransformation, 1250 S. Collegeville Road., Collegeville, PA 19426, USA
| | - Gordon J Dear
- GSK, DMPK, Disposition and Biotransformation, Gunnels Wood Road, Stevenage, SG1 2NY, UK
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Esposito S, Orsatti L, Pucci V. Subcutaneous Catabolism of Peptide Therapeutics: Bioanalytical Approaches and ADME Considerations. Xenobiotica 2022; 52:828-839. [PMID: 36039395 DOI: 10.1080/00498254.2022.2119180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
Many peptide drugs such as insulin and glucagon-like peptide (GLP-1) analogues are successfully administered subcutaneously (SC). Following SC injection, peptides may undergo catabolism in the SC compartment before entering systemic circulation, which could compromise their bioavailability and in turn affect their efficacy.This review will discuss how both technology and strategy have evolved over the past years to further elucidate peptide SC catabolism.Modern bioanalytical technologies (particularly liquid chromatography-high-resolution mass spectrometry) and bioinformatics platforms for data mining has prompted the development of in silico, in vitro and in vivo tools for characterizing peptide SC catabolism to rapidly address proteolytic liabilities and, ultimately, guide the design of peptides with improved SC bioavailability.More predictive models able to recapitulate the interplay between SC catabolism and other factors driving SC absorption are highly desirable to improve in vitro/in vivo correlations.We envision the routine incorporation of in vitro and in vivo SC catabolism studies in ADME screening funnels to develop more effective peptide drugs for SC delivery.
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Zhu C, Lai G, Jin Y, Xu D, Chen J, Jiang X, Wang S, Liu G, Xu N, Shen R, Wang L, Zhu M, Wu C. Suspect screening and untargeted analysis of veterinary drugs in food by LC-HRMS: Application of background exclusion-dependent acquisition for retrospective analysis of unknown xenobiotics. J Pharm Biomed Anal 2022; 210:114583. [PMID: 35033942 DOI: 10.1016/j.jpba.2022.114583] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 12/07/2021] [Accepted: 01/05/2022] [Indexed: 01/08/2023]
Abstract
The presence of veterinary drug and pesticide residues in food products pose considerable threats to human health. Monitoring of these residues in food is mainly carried out using targeted analysis by triple quadrupole mass spectrometry. However, these methods are not suitable for suspect screening and untargeted analysis of unknowns. The main objectives of this study were to develop a new high-resolution mass spectrometry (HRMS)-based analytical strategy for retrospective analysis of suspect and unknown xenobiotics and to evaluate its performance in the tentative identification of 48 veterinary drugs as "unknowns" spiked in a pork sample. In the analysis, a newly developed background exclusion data-dependent acquisition (BE-DDA) technique was employed to trigger the product ion (MS/MS) spectral acquisition of the "unknowns", and an in-house precise-and-thorough background-subtraction (PATBS) technique was applied to detect these "unknowns". Results showed that untargeted data mining of the acquired LC-MS dataset by PATBS was able to find all the 48 veterinary drugs and 46 of them were triggered by BE-DDA to generate accurate MS/MS spectra. The dataset of recorded accurate full-scan mass and MS/MS spectra of all the xenobiotics of the test pork sample is defined as the xenobiotics profile. Searching the xenobiotic profile of the test pork sample using mass spectral data of selected veterinary drugs (as suspects) from the mzCloud spectral library led to the correct hits. Searching against the mzCloud spectral library using the mass spectral data of selected individual veterinary drugs (as unknowns) from the xenobiotics profile tentatively confirmed their identities. In contrast, analysis of the same sample using ion intensity-data dependent acquisition only recorded the MS/MS spectra for 34 veterinary drugs. In addition, a data independent acquisition method enabled the acquisition of the fragment spectra for 44 veterinary drugs, but their spectral data displayed only one or a few true product ions of individual analytes of interest along with many fragments from coeluted biological components and background noises. This study demonstrates that this analytical strategy has a potential to become a practical tool for the retrospective suspect screening and untargeted analysis of unknown xenobiotics in a biological sample such as veterinary drugs and pesticides in food products.
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Affiliation(s)
- Chunyan Zhu
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, China
| | - Guoyin Lai
- Xiamen Customs Technology Center, Xiamen, China
| | - Ying Jin
- Department of Cardiology, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Dunming Xu
- Xiamen Customs Technology Center, Xiamen, China
| | - Jiayun Chen
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, China
| | - Xiaojuan Jiang
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, China
| | - Suping Wang
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, China
| | | | | | - Rong Shen
- School of Medicine, Xiamen University, Xiamen, China
| | - Luxiao Wang
- Xiamen Customs Technology Center, Xiamen, China
| | - Mingshe Zhu
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, China; MassDefect Technologies, Princeton, NJ, USA.
| | - Caisheng Wu
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, China.
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