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Paliwal A, Jain S, Kumar S, Wal P, Khandai M, Khandige PS, Sadananda V, Anwer MK, Gulati M, Behl T, Srivastava S. Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine. Expert Opin Drug Metab Toxicol 2024; 20:181-195. [PMID: 38480460 DOI: 10.1080/17425255.2024.2330666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 03/11/2024] [Indexed: 03/22/2024]
Abstract
INTRODUCTION Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties. AREAS COVERED The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged. EXPERT OPINION AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.
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Affiliation(s)
- Ajita Paliwal
- Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, Greater Noida, India
| | - Smita Jain
- Department of Pharmacy, Banasthali Vidyapith, Banasthali, India
| | - Sachin Kumar
- Department of Pharmacology, Delhi Pharmaceutical Sciences and Research University (DPSRU), New Delhi, India
| | - Pranay Wal
- Department of Pharmacy, Pranveer Singh Institute of Technology, Pharmacy, Kanpur, India
| | - Madhusmruti Khandai
- Department of Pharmacy, Royal College of Pharmacy and Health Sciences, Berahmpur, India
| | - Prasanna Shama Khandige
- NGSM Institute of Pharmaceutical Sciences, Department of Pharmacology, Manglauru, NITTE (Deemed to be University), Manglauru, India
| | - Vandana Sadananda
- AB Shetty Memorial Institute of Dental Sciences, Department of Conservative Dentistry and Endodontics, NITTE (Deemed to be University), Mangaluru, India
| | - Md Khalid Anwer
- Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
| | - Monica Gulati
- School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, India
- ARCCIM, Health, University of Technology, Sydney, Ultimo, Australia
| | - Tapan Behl
- Amity School of Pharmaceutical Sciences, Amity University, Mohali, Punjab, India
| | - Shriyansh Srivastava
- Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, Greater Noida, India
- Department of Pharmacology, Delhi Pharmaceutical Sciences and Research University (DPSRU), New Delhi, India
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Jian J, He D, Gao S, Tao X, Dong X. Pharmacokinetics in Pharmacometabolomics: Towards Personalized Medication. Pharmaceuticals (Basel) 2023; 16:1568. [PMID: 38004434 PMCID: PMC10675232 DOI: 10.3390/ph16111568] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 10/19/2023] [Accepted: 10/27/2023] [Indexed: 11/26/2023] Open
Abstract
Indiscriminate drug administration may lead to drug therapy results with varying effects on patients, and the proposal of personalized medication can help patients to receive effective drug therapy. Conventional ways of personalized medication, such as pharmacogenomics and therapeutic drug monitoring (TDM), can only be implemented from a single perspective. The development of pharmacometabolomics provides a research method for the realization of precise drug administration, which integrates the environmental and genetic factors, and applies metabolomics technology to study how to predict different drug therapeutic responses of organisms based on baseline metabolic levels. The published research on pharmacometabolomics has achieved satisfactory results in predicting the pharmacokinetics, pharmacodynamics, and the discovery of biomarkers of drugs. Among them, the pharmacokinetics related to pharmacometabolomics are used to explore individual variability in drug metabolism from the level of metabolism of the drugs in vivo and the level of endogenous metabolite changes. By searching for relevant literature with the keyword "pharmacometabolomics" on the two major literature retrieval websites, PubMed and Web of Science, from 2006 to 2023, we reviewed articles in the field of pharmacometabolomics that incorporated pharmacokinetics into their research. This review explains the therapeutic effects of drugs on the body from the perspective of endogenous metabolites and pharmacokinetic principles, and reports the latest advances in pharmacometabolomics related to pharmacokinetics to provide research ideas and methods for advancing the implementation of personalized medication.
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Affiliation(s)
- Jingai Jian
- School of Medicine, Shanghai University, Shanghai 200444, China; (J.J.); (D.H.)
| | - Donglin He
- School of Medicine, Shanghai University, Shanghai 200444, China; (J.J.); (D.H.)
| | - Songyan Gao
- Institute of Translational Medicine, Shanghai University, Shanghai 200444, China;
| | - Xia Tao
- Department of Pharmacy, Changzheng Hospital, Second Military Medical University, Shanghai 200003, China
| | - Xin Dong
- School of Medicine, Shanghai University, Shanghai 200444, China; (J.J.); (D.H.)
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He X, Yang X, Yan X, Huang M, Xiang Z, Lou Y. Individualized Dosage of Tacrolimus for Renal Transplantation Patients Based on Pharmacometabonomics. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27113517. [PMID: 35684454 PMCID: PMC9182099 DOI: 10.3390/molecules27113517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/19/2022] [Accepted: 05/27/2022] [Indexed: 11/16/2022]
Abstract
The clinical pharmacodynamics of tacrolimus in renal transplant patients has significant interindividual variability. T lymphocytes were selected to study the pharmacodynamic response of tacrolimus, which was significantly correlated with renal function and the outcome of renal transplant patients. Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectroscopy (UPLC/Q-TOF-MS) was performed to obtain the metabolic profiles of 109 renal transplant patients. A partial least squares (PLS) model was constructed to screen potential biomarkers that could predict the efficacy of tacrolimus. Multinomial logistic regression analysis established a bridge that could quantify the relationship between the efficacy of tacrolimus and biomarkers. The results showed a good correlation between endogenous molecules and the efficacy of tacrolimus. Metabolites such as serum creatinine, mesobilirubinogen, L-isoleucine, 5-methoxyindoleacetate, eicosapentaenoic acid, N2-succinoylarginine, tryptophyl-arginine, and butyric acid were indicated as candidate biomarkers. In addition, the key biomarkers could correctly predict the efficacy of tacrolimus with an accuracy of 82.5%. Finally, we explored the mechanism of individual variation by pathway analysis, which showed that amino acid metabolism was significantly related to the efficacy of tacrolimus. Moreover, orthogonal partial least squares discriminant analysis (OPLS-DA) showed that there was no difference in key metabolites among different pharmacodynamic groups at 1 month and 3 months after dose adjustment, suggesting that pharmacometabonomics is a useful tool to predict individual differences in pharmacodynamics and thus to facilitate individualized drug therapy.
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Affiliation(s)
- Xiaoying He
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, Department of Clinical Pharmacy, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou 310000, China; (X.H.); (X.Y.); (X.Y.)
| | - Xi Yang
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, Department of Clinical Pharmacy, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou 310000, China; (X.H.); (X.Y.); (X.Y.)
| | - Xiaoting Yan
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, Department of Clinical Pharmacy, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou 310000, China; (X.H.); (X.Y.); (X.Y.)
| | - Mingzhu Huang
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, Department of Clinical Pharmacy, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou 310000, China; (X.H.); (X.Y.); (X.Y.)
- Correspondence: (M.H.); (Z.X.); (Y.L.); Tel.: +86-571-8723-6871 (Y.L.)
| | - Zheng Xiang
- School of Pharmaceutical Sciences, Zhejiang University City College, Hangzhou 310000, China
- Correspondence: (M.H.); (Z.X.); (Y.L.); Tel.: +86-571-8723-6871 (Y.L.)
| | - Yan Lou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, Department of Clinical Pharmacy, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou 310000, China; (X.H.); (X.Y.); (X.Y.)
- Correspondence: (M.H.); (Z.X.); (Y.L.); Tel.: +86-571-8723-6871 (Y.L.)
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Du P, Liu L, Hu T, An Z. Integrative Analysis of Pharmacokinetic and Metabolomic Profiles for Predicting Metabolic Phenotype and Drug Exposure Caused by Sotorasib in Rats. Front Oncol 2022; 12:778035. [PMID: 35449573 PMCID: PMC9017425 DOI: 10.3389/fonc.2022.778035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 02/28/2022] [Indexed: 11/23/2022] Open
Abstract
Sotorasib is a novel targeted inhibitor of Kirsten rat sarcoma (KRAS) (G12C) that has shown exciting tumor-suppressing effects not only for single targeted agents but also for combination with immune checkpoint inhibitors. However, no integrative analysis of the pharmacokinetics (PK) and pharmacometabolomics (PM) of sotorasib has been reported to date. In the present study, a sensitive and robust high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS/MS) method was firstly developed and fully validated for the quantitation of sotorasib in rat plasma. After one-step protein precipitation, sotorasib and an internal standard (carbamazepine) were separated on a Waters XBrige C18 column (50 mm × 2.1 mm, 3.5 μm) and analyzed in electrospray ionization positive ion (ESI+) mode. The optimized method was fully validated according to guidance and was successfully applied for the PK study of sotorasib at a dose of 10 mg/kg. In addition, a longitudinal and transversal PM was employed and correlated with PK using partial least squares model and Pearson’s analysis. With multivariate statistical analysis, the selected six (AUC model) and nine (Cmax model) metabolites completely distinguished the high- and low-exposure groups after sotorasib treatment, which indicates that these potential biomarkers can predict drug exposure or toxicity. The results of this study will not only shed light on how sotorasib disturbs the metabolic profiles and the relationship between PK and PM but also offer meaningful references for precision therapy in patients with the KRAS (G12C) mutation.
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Affiliation(s)
- Ping Du
- Department of Pharmacy, Research Unit, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Lihong Liu
- Department of Pharmacy, Research Unit, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Ting Hu
- Department of Pharmacy, Research Unit, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Zhuoling An
- Department of Pharmacy, Research Unit, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
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Du P, Wang G, Hu T, Li H, An Z. Integration Analysis of Pharmacokinetics and Metabolomics to Predict Metabolic Phenotype and Drug Exposure of Remdesivir. Front Pharmacol 2022; 12:779135. [PMID: 35069201 PMCID: PMC8766850 DOI: 10.3389/fphar.2021.779135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 12/06/2021] [Indexed: 12/24/2022] Open
Abstract
Remdesivir has displayed pharmacological activity against SARS-CoV-2. However, no pharmacometabolomics (PM) or correlation analysis with pharmacokinetics (PK) was revealed. Rats were intravenously administered remdesivir, and a series of blood samples were collected before and after treatment. Comprehensive metabolomics profile and PK were investigated and quantitated simultaneously using our previous reliable HPLC-MS/MS method. Both longitudinal and transversal metabolic analyses were conducted, and the correlation between PM and PK parameters was evaluated using Pearson's correlation analysis and the PLS model. Multivariate statistical analysis was employed for discovering candidate biomarkers which predicted drug exposure or toxicity of remdesivir. The prominent metabolic profile variation was observed between pre- and posttreatment, and significant changes were found in 65 metabolites. A total of 15 metabolites-12 carnitines, one N-acetyl-D-glucosamine, one allantoin, and one corticosterone-were significantly correlated with the concentration of Nuc (active metabolite of remdesivir). Adenosine, spermine, guanosine, sn-glycero-3-phosphocholine, and l-homoserine may be considered potential biomarkers for predicting drug exposure or toxicity. This study is the first attempt to apply PM and PK to study remdesivir response/toxicity, and the identified candidate biomarkers might be used to predict the AUC and Cmax, indicating capability of discriminating good or poor responders. Currently, this study originally offers considerable evidence to metabolite reprogramming of remdesivir and sheds light on precision therapy development in fighting COVID-19.
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Affiliation(s)
- Ping Du
- Department of Pharmacy/Phase I Clinical Trial and Research Unit, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | | | | | | | - Zhuoling An
- Department of Pharmacy/Phase I Clinical Trial and Research Unit, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
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6
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Du P, Wang GY, Zhao R, An ZL, Liu LH. Eicosanoid Metabolomic Profile of Remdesivir Treatment in Rat Plasma by High-Performance Liquid Chromatography Mass Spectrometry. Front Pharmacol 2021; 12:747450. [PMID: 34658883 PMCID: PMC8511316 DOI: 10.3389/fphar.2021.747450] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 08/17/2021] [Indexed: 01/14/2023] Open
Abstract
Remdesivir, a nucleotide analog prodrug, has displayed pharmacological activity against SARS-CoV-2. Recently, eicosanoids are widely involved in regulating immunity and inflammation for COVID-19 patients. Rats were intravenously administered remdesivir at a dose of 5 mg/kg, and series of blood samples were collected before and after treatment. Targeted metabolomics regarding the eicosanoid profile were investigated and quantitated simultaneously using the previously reported reliable HPLC-MS/MS method. Additionally, interplay relationship between metabolomics and pharmacokinetic parameters was performed using the Pearson correlation analysis and PLS model. For the longitudinal metabolomics of remdesivir, metabolic profiles of the same rat were comparatively substantial at discrete sampling points. The metabolic fingerprints generated by individual discrepancy of rats were larger than metabolic disturbance caused by remdesivir. As for the transversal metabolomics, the prominent metabolic profile variation was observed between the baseline and treatment status. Except for TXB2, the inflammatory- and immunology-related eicosanoids of resolvin D2, 5-HEPE, 5-HETE, and DHA were significantly disturbed and reduced after single administration of remdesivir (p < 0.05, p < 0.001). Moreover, the metabolite of PGE2 correlated with GS-441524 (active metabolite of remdesivir) concentration and pharmacokinetic parameters of Cmax, AUC0-t, AUC0-infinity, and CL significantly. Eicosanoid metabolic profiles of remdesivir at both longitudinal and transversal levels were first revealed using the robust HPLC-MS/MS method. This initial observational eicosanoid metabolomics may lighten the therapy for fighting COVID-19 and further provide mechanistic insights of SARS-CoV-2 virus infection.
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Affiliation(s)
- Ping Du
- *Correspondence: Ping Du, ; Li-hong Liu,
| | | | | | | | - Li-hong Liu
- Department of Pharmacy, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
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Huang Q, Cao L, Luo N, Qian H, Wei M, Xue L, Zhou Q, Zou B, Tan L, Chu Y, Ma X, Wang C, Wu H, Zhang L, Sun L, Li D, Fan X, Miao L, Zhou G. Predicting Range of Initial Warfarin Dose Based on Pharmacometabolomic and Genetic Inputs. Clin Pharmacol Ther 2021; 110:1585-1594. [PMID: 34460938 DOI: 10.1002/cpt.2407] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 08/22/2021] [Indexed: 12/29/2022]
Abstract
Anticoagulation response to warfarin during the initial stage of therapy varies among individuals. In this study, we aimed to combine pharmacometabolomic and pharmacogenetic data to predict interindividual variation in warfarin response, and, on this basis, suggest an initial daily dose range. The baseline metabolic profiles, genotypes, and clinical information of 160 patients with heart valve disease served as the variables of the function of the last international normalized ratio measured before a patient's discharge (INRday7 ) to screen for potential biomarkers. The partial least-squares model showed that two baseline metabolites (uridine and guanosine), one single-nucleotide variation (VKORC1), and four clinical parameters (weight, creatinine level, amiodarone usage, and initial daily dose) had good predictive power for INRday7 (R2 = 0.753 for the training set, 0.643 for the test set). With these biomarkers, a machine learning algorithm (two-dimensional linear discriminant analysis-multinomial logit model) was used to predict the subgroups with extremely warfarin-sensitive or less warfarin-sensitive patients with a prediction accuracy of 91% for the training set and 90% for the test set, indicating that individual responses to warfarin could be effectively predicted. Based on this model, we have successfully designed an algorithm,"IniWarD," for predicting an effective dose range in the initial 7-day warfarin therapy. The results indicate that the daily dose range suggested by the IniWarD system is more appropriate than that of the conventional genotype-based method, and the risk of bleeding or thrombus due to warfarin could thus be avoided.
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Affiliation(s)
- Qing Huang
- Department of Clinical Pharmacy, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science & Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,National Medical Products Administration, Key Laboratory for Impurity Profile of Chemical Drugs, Jiangsu Institute for Food and Drug Control, Nanjing, China
| | - Ling Cao
- National Medical Products Administration, Key Laboratory for Impurity Profile of Chemical Drugs, Jiangsu Institute for Food and Drug Control, Nanjing, China
| | - Nan Luo
- Department of Clinical Pharmacy, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science & Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Hanyu Qian
- National Medical Products Administration, Key Laboratory for Impurity Profile of Chemical Drugs, Jiangsu Institute for Food and Drug Control, Nanjing, China
| | - Meng Wei
- Department of Clinical Pharmacy, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science & Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
| | - Ling Xue
- Department of Clinical Pharmacology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Qiang Zhou
- Department of Clinical Pharmacy, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science & Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
| | - Bingjie Zou
- Department of Clinical Pharmacy, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science & Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Li Tan
- National Medical Products Administration, Key Laboratory for Impurity Profile of Chemical Drugs, Jiangsu Institute for Food and Drug Control, Nanjing, China
| | - Yanan Chu
- Department of Clinical Pharmacy, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science & Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
| | - Xueping Ma
- Department of Clinical Pharmacy, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science & Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
| | - Changtian Wang
- Department of Cardio-Thoracic Surgery, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
| | - Haiwei Wu
- Department of Cardio-Thoracic Surgery, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
| | - Lei Zhang
- Department of Cardio-Thoracic Surgery, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
| | - Lei Sun
- Department of Cardio-Thoracic Surgery, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
| | - Demin Li
- Department of Cardio-Thoracic Surgery, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
| | - Xialei Fan
- National Medical Products Administration, Key Laboratory for Impurity Profile of Chemical Drugs, Jiangsu Institute for Food and Drug Control, Nanjing, China
| | - Liyan Miao
- Department of Clinical Pharmacology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Guohua Zhou
- Department of Clinical Pharmacy, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science & Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Department of Cardio-Thoracic Surgery, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
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An Z, Wang X, Li P, He J, Liu L. Exploring the metabolic characteristics and pharmacokinetic variation of paroxetine in healthy volunteers using a pharmacometabonomic approach. J Pharm Biomed Anal 2021; 204:114224. [PMID: 34265484 DOI: 10.1016/j.jpba.2021.114224] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 06/17/2021] [Accepted: 06/19/2021] [Indexed: 01/07/2023]
Abstract
The pharmacokinetic parameters of paroxetine vary between individuals, leading to differences in efficacy. The focus of our research was to predict responses to paroxetine using a pharmacometabonomic approach combining metabolic phenotypes and pharmacokinetic parameters. The pharmacokinetics of 12 healthy volunteers treated with paroxetine over two cycles was determined using high-performance liquid chromatography tandem mass spectrometry. Metabolic profiling and phenotyping were performed on the blood samples that remained after pharmacokinetic studies, using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry. Thirty-nine metabolites (p < 0.05) increased or decreased after treatment with paroxetine. Vitamin B6 metabolism; valine, leucine, and isoleucine biosynthesis; phenylalanine metabolism; pantothenate and coenzyme A biosynthesis; tyrosine metabolism; and glyoxylate and dicarboxylate metabolism were likely to be relevant for the effects of paroxetine. The two-stage partial least squares (PLS) strategy was used to screen potential biomarkers and predict the pharmacokinetic parameters of paroxetine. An orthogonal PLS discriminant analysis strategy was then applied to separate the high- and low-value groups based on the screened biomarkers. Pearson correlation test and receiver operating characteristic curve analysis confirmed the key prediction biomarkers. Seven common biomarkers were able to predict both the area under the curve (AUC) and the maximum concentration (Cmax): cortisone, l-tyrosine, phenylpyruvate, l-valine, 2-oxoglutarate, l-lactate, and glycerate. Furthermore, homoprotocatechuate and l-glutamate were unique biomarkers for AUC, and citicoline and fumarate were unique biomarkers for Cmax. The selected biomarkers were able to predict the AUC and Cmax and discriminate good responders from poor responders to paroxetine treatment. This trial was registered with http://www.chinadrugtrials.org.cn/ (no. CTR20171590).
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Affiliation(s)
- Zhuoling An
- Pharmacy Department of Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, PR China
| | - Xiangyi Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100050, PR China
| | - Pengfei Li
- Pharmacy Department of Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, PR China
| | - Jiuming He
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100050, PR China.
| | - Lihong Liu
- Pharmacy Department of Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, PR China.
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