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Gonnabathula P, Choi MK, Li M, Kabadi SV, Fairman K. Utility of life stage-specific chemical risk assessments based on New Approach Methodologies (NAMs). Food Chem Toxicol 2024; 190:114789. [PMID: 38844066 DOI: 10.1016/j.fct.2024.114789] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 05/17/2024] [Accepted: 06/03/2024] [Indexed: 06/17/2024]
Abstract
The safety assessments for chemicals targeted for use or expected to be exposed to specific life stages, including infancy, childhood, pregnancy and lactation, and geriatrics, need to account for extrapolation of data from healthy adults to these populations to assess their human health risk. However, often adequate and relevant toxicity or pharmacokinetic (PK) data of chemicals in specific life stages are not available. For such chemicals, New Approach Methodologies (NAMs), such as physiologically based pharmacokinetic (PBPK) modeling, biologically based dose response (BBDR) modeling, in vitro to in vivo extrapolation (IVIVE), etc. can be used to understand the variability of exposure and effects of chemicals in specific life stages and assess their associated risk. A life stage specific PBPK model incorporates the physiological and biochemical changes associated with each life stage and simulates their impact on the absorption, distribution, metabolism, and elimination (ADME) of these chemicals. In our review, we summarize the parameterization of life stage models based on New Approach Methodologies (NAMs) and discuss case studies that highlight the utility of a life stage based PBPK modeling for risk assessment. In addition, we discuss the utility of artificial intelligence (AI)/machine learning (ML) and other computational models, such as those based on in vitro data, as tools for estimation of relevant physiological or physicochemical parameters and selection of model. We also discuss existing gaps in the available toxicological datasets and current challenges that need to be overcome to expand the utility of NAMs for life stage-specific chemical risk assessment.
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Affiliation(s)
- Pavani Gonnabathula
- Division of Biochemical Toxicology, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), Jefferson, AR, 72079, USA
| | - Me-Kyoung Choi
- Division of Biochemical Toxicology, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), Jefferson, AR, 72079, USA
| | - Miao Li
- Division of Biochemical Toxicology, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), Jefferson, AR, 72079, USA
| | - Shruti V Kabadi
- Center for Food Safety and Applied Nutrition (CFSAN), US Food and Drug Administration (FDA), College Park, MD, 20740, USA
| | - Kiara Fairman
- Division of Biochemical Toxicology, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), Jefferson, AR, 72079, USA.
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2
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Malik A, Khan T, Siddique MUM, Faruk A, Sood AK, Bhat ZR. HSPiP, Computational, and Thermodynamic Model-Based Optimized Solvents for Subcutaneous Delivery of Tolterodine Tartrate and GastroPlus-Based In Vivo Prediction in Humans: Part I. AAPS PharmSciTech 2024; 25:93. [PMID: 38693316 DOI: 10.1208/s12249-024-02800-2] [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: 01/04/2024] [Accepted: 03/27/2024] [Indexed: 05/03/2024] Open
Abstract
Tolterodine tartrate (TOTA) is associated with adverse effect, high hepatic access, varied bioavailability, slight aqueous solubility, and short half-life after oral delivery. Hansen solubility parameters (HSP, HSPiP program), experimental solubility (T = 298.2 to 318.2 K and p = 0.1 MPa), computational (van't Hoff and Apelblat models), and thermodynamic models were used to the select solvent(s). HSPiP predicted PEG400 as the most suitable co-solvent based on HSP values (δd = 17.88, δp = 4.0, and δh = 8.8 of PEG400) and comparable to the drug (δd = 17.6, δp = 2.4, and δh = 4.6 of TOTA). The experimental mole fraction solubility of TOTA was maximum (xe = 0.0852) in PEG400 confirming the best fit of the prediction. The observed highest solubility was attributed to the δp and δh interacting forces. The activity coefficient (ϒi) was found to be increased with temperature. The higher values of r2 (linear regression coefficient) and low RMSD (root mean square deviation) indicated a good correlation between the generated "xe" data for crystalline TOTA and the explored models (modified Apelblat and van't Hoff models). TOTA solubility in "PEG400 + water mixture" was endothermic and entropy-driven. IR (immediate release product) formulation can be tailored using 60% PEG400 in buffer solution for 2 mg of TOTA in 0.25 mL (dosing volume). The isotonic binary solution was associated with a pH of 7.2 suitable for sub-Q delivery. The approach would be a promising alternative with ease of delivery to children and aged patients.
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Affiliation(s)
- Abdul Malik
- Department of Pharmaceutics, College of Pharmacy, King Saud University, 11451, Riyadh, Saudi Arabia
| | - Tasneem Khan
- Department of Pharmaceutics, School of Pharmaceutical Education and Research (SPER), Jamia Hamdard, New Delhi, 110062, India.
| | - Mohd Usman Mohd Siddique
- Department of Pharmaceutical Chemistry, Shri Vile Parle Kelavani Mandal's Institute of Pharmacy Dhule, Dhule, MH, 424001, India
| | - Abdul Faruk
- Department of Pharmaceutical Sciences, HNB Garhwal University (A Central University), Srinagar - Garhwal, 246174, Uttarakhand, India
| | - Ashwani Kumar Sood
- Department of Chemistry, Guru Nanak Dev University, Amritsar, 143005, Punjab, India
| | - Zahid Rafiq Bhat
- Department of Molecular and Cellular Oncology, MD Anderson Cancer Centre, Houston, Texas, USA
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Djuris J, Cvijic S, Djekic L. Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration. Pharmaceuticals (Basel) 2024; 17:177. [PMID: 38399392 PMCID: PMC10892858 DOI: 10.3390/ph17020177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/23/2023] [Accepted: 12/29/2023] [Indexed: 02/25/2024] Open
Abstract
The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure-permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques.
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Affiliation(s)
- Jelena Djuris
- Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia; (S.C.); (L.D.)
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Miners JO, Polasek TM, Hulin JA, Rowland A, Meech R. Drug-drug interactions that alter the exposure of glucuronidated drugs: Scope, UDP-glucuronosyltransferase (UGT) enzyme selectivity, mechanisms (inhibition and induction), and clinical significance. Pharmacol Ther 2023:108459. [PMID: 37263383 DOI: 10.1016/j.pharmthera.2023.108459] [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: 02/21/2023] [Revised: 05/18/2023] [Accepted: 05/22/2023] [Indexed: 06/03/2023]
Abstract
Drug-drug interactions (DDIs) arising from the perturbation of drug metabolising enzyme activities represent both a clinical problem and a potential economic loss for the pharmaceutical industry. DDIs involving glucuronidated drugs have historically attracted little attention and there is a perception that interactions are of minor clinical relevance. This review critically examines the scope and aetiology of DDIs that result in altered exposure of glucuronidated drugs. Interaction mechanisms, namely inhibition and induction of UDP-glucuronosyltransferase (UGT) enzymes and the potential interplay with drug transporters, are reviewed in detail, as is the clinical significance of known DDIs. Altered victim drug exposure arising from modulation of UGT enzyme activities is relatively common and, notably, the incidence and importance of UGT induction as a DDI mechanism is greater than generally believed. Numerous DDIs are clinically relevant, resulting in either loss of efficacy or an increased risk of adverse effects, necessitating dose individualisation. Several generalisations relating to the likelihood of DDIs can be drawn from the known substrate and inhibitor selectivities of UGT enzymes, highlighting the importance of comprehensive reaction phenotyping studies at an early stage of drug development. Further, rigorous assessment of the DDI liability of new chemical entities that undergo glucuronidation to a significant extent has been recommended recently by regulatory guidance. Although evidence-based approaches exist for the in vitro characterisation of UGT enzyme inhibition and induction, the availability of drugs considered appropriate for use as 'probe' substrates in clinical DDI studies is limited and this should be research priority.
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Affiliation(s)
- John O Miners
- Discipline of Clinical Pharmacology and Flinders Centre for Innovation in Cancer, Flinders University College of Medicine and Public Health, Flinders University, Adelaide, Australia.
| | - Thomas M Polasek
- Certara, Princeton, NJ, USA; Centre for Medicines Use and Safety, Monash University, Melbourne, Australia
| | - Julie-Ann Hulin
- Discipline of Clinical Pharmacology and Flinders Centre for Innovation in Cancer, Flinders University College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | - Andrew Rowland
- Discipline of Clinical Pharmacology and Flinders Centre for Innovation in Cancer, Flinders University College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | - Robyn Meech
- Discipline of Clinical Pharmacology and Flinders Centre for Innovation in Cancer, Flinders University College of Medicine and Public Health, Flinders University, Adelaide, Australia
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Deepika D, Kumar V. The Role of "Physiologically Based Pharmacokinetic Model (PBPK)" New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3473. [PMID: 36834167 PMCID: PMC9966583 DOI: 10.3390/ijerph20043473] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration-time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.
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Affiliation(s)
- Deepika Deepika
- Environmental Engineering Laboratory, Departament d’Enginyeria Quimica, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalonia, Spain
- Pere Virgili Health Research Institute (IISPV), Hospital Universitari Sant Joan de Reus, Universitat Rovira i Virgili, 43204 Reus, Catalonia, Spain
| | - Vikas Kumar
- Environmental Engineering Laboratory, Departament d’Enginyeria Quimica, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalonia, Spain
- Pere Virgili Health Research Institute (IISPV), Hospital Universitari Sant Joan de Reus, Universitat Rovira i Virgili, 43204 Reus, Catalonia, Spain
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Mikov M, Đanić M, Lazarević S, Pavlović N, Stanimirov B, Al-Salami H, Mooranian A. Editorial: Pharmacokinetic evaluation and modeling of clinically significant drug metabolites, Volume II. Front Pharmacol 2023; 13:1087988. [PMID: 36686693 PMCID: PMC9846551 DOI: 10.3389/fphar.2022.1087988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 12/14/2022] [Indexed: 01/06/2023] Open
Affiliation(s)
- Momir Mikov
- Department of Pharmacology, Toxicology and Clinical Pharmacology, Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia,*Correspondence: Momir Mikov,
| | - Maja Đanić
- Department of Pharmacology, Toxicology and Clinical Pharmacology, Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia
| | - Slavica Lazarević
- Department of Pharmacology, Toxicology and Clinical Pharmacology, Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia
| | - Nebojša Pavlović
- Department of Pharmacy, Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia
| | - Bojan Stanimirov
- Department of Biochemistry, Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia
| | - Hani Al-Salami
- The Biotechnology and Drug Development Research Laboratory, Curtin Medical School & Curtin Health Innovation Research Institute, Curtin University, Bentley, WA, Australia
| | - Armin Mooranian
- The Biotechnology and Drug Development Research Laboratory, Curtin Medical School & Curtin Health Innovation Research Institute, Curtin University, Bentley, WA, Australia,Hearing Therapeutics Department, Ear Science Institute Australia, Queen Elizabeth II Medical Centre, Nedlands, WA, Australia
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Assessing the contribution of UGT isoforms on raltegravir drug disposition through PBPK modeling. Eur J Pharm Sci 2022; 179:106309. [DOI: 10.1016/j.ejps.2022.106309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 09/13/2022] [Accepted: 10/16/2022] [Indexed: 11/24/2022]
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Parrott N, Manevski N, Olivares-Morales A. Can We Predict Clinical Pharmacokinetics of Highly Lipophilic Compounds by Integration of Machine Learning or In Vitro Data into Physiologically Based Models? A Feasibility Study Based on 12 Development Compounds. Mol Pharm 2022; 19:3858-3868. [PMID: 36150125 DOI: 10.1021/acs.molpharmaceut.2c00350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
While high lipophilicity tends to improve potency, its effects on pharmacokinetics (PK) are complex and often unfavorable. To predict clinical PK in early drug discovery, we built human physiologically based PK (PBPK) models integrating either (i) machine learning (ML)-predicted properties or (ii) discovery stage in vitro data. Our test set was composed of 12 challenging development compounds with high lipophilicity (mean calculated log P 4.2), low plasma-free fraction (50% of compounds with fu,p < 1%), and low aqueous solubility. Predictions focused on key human PK parameters, including plasma clearance (CL), volume of distribution at steady state (Vss), and oral bioavailability (%F). For predictions of CL, the ML inputs showed acceptable accuracy and slight underprediction bias [an average absolute fold error (AAFE) of 3.55; an average fold error (AFE) of 0.95]. Surprisingly, use of measured data only slightly improved accuracy but introduced an overprediction bias (AAFE = 3.35; AFE = 2.63). Predictions of Vss were more successful, with both ML (AAFE = 2.21; AFE = 0.90) and in vitro (AAFE = 2.24; AFE = 1.72) inputs showing good accuracy and moderate bias. The %F was poorly predicted using ML inputs [average absolute prediction error (AAPE) of 45%], and use of measured data for solubility and permeability improved this to 34%. Sensitivity analysis showed that predictions of CL limited the overall accuracy of human PK predictions, partly due to high nonspecific binding of lipophilic compounds, leading to uncertainty of unbound clearance. For accurate predictions of %F, solubility was the key factor. Despite current limitations, this work encourages further development of ML models and integration of their results within PBPK models to enable human PK prediction at the drug design stage, even before compounds are synthesized. Further evaluation of this approach with more diverse chemical types is warranted.
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Affiliation(s)
- Neil Parrott
- Pharmaceutical Sciences, Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070 Basel, Switzerland
| | - Nenad Manevski
- Pharmaceutical Sciences, Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070 Basel, Switzerland
| | - Andrés Olivares-Morales
- Pharmaceutical Sciences, Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070 Basel, Switzerland
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Wu C, Li B, Meng S, Qie L, Zhang J, Wang G, Ren CC. Prediction for optimal dosage of pazopanib under various clinical situations using physiologically based pharmacokinetic modeling. Front Pharmacol 2022; 13:963311. [PMID: 36172188 PMCID: PMC9510668 DOI: 10.3389/fphar.2022.963311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/22/2022] [Indexed: 11/13/2022] Open
Abstract
This study aimed to apply a physiologically based pharmacokinetic (PBPK) model to predict optimal dosing regimens of pazopanib (PAZ) for safe and effective administration when co-administered with CYP3A4 inhibitors, acid-reducing agents, food, and administered in patients with hepatic impairment. Here, we have successfully developed the population PBPK model and the predicted PK variables by this model matched well with the clinically observed data. Most ratios of prediction to observation were between 0.5 and 2.0. Suitable dosage modifications of PAZ have been identified using the PBPK simulations in various situations, i.e., 200 mg once daily (OD) or 100 mg twice daily (BID) when co-administered with the two CYP3A4 inhibitors, 200 mg BID when simultaneously administered with food or 800 mg OD when avoiding food uptake simultaneously. Additionally, the PBPK model also suggested that dosing does not need to be adjusted when co-administered with esomeprazole and administration in patients with wild hepatic impairment. Furthermore, the PBPK model also suggested that PAZ is not recommended to be administered in patients with severe hepatic impairment. In summary, the present PBPK model can determine the optimal dosing adjustment recommendations in multiple clinical uses, which cannot be achieved by only focusing on AUC linear change of PK.
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Affiliation(s)
- Chunnuan Wu
- Department of pharmacy, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Bole Li
- Department of pharmacy, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Shuai Meng
- Department of pharmacy, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Linghui Qie
- Department of pharmacy, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Jie Zhang
- Department of pharmacy, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- *Correspondence: Jie Zhang, ; Guopeng Wang, ; Cong Cong Ren,
| | - Guopeng Wang
- Zhongcai Health Biological Technology Development Co., Ltd., Beijing, China
- *Correspondence: Jie Zhang, ; Guopeng Wang, ; Cong Cong Ren,
| | - Cong Cong Ren
- Department of pharmacy, Liaocheng People’s Hospital, Liaocheng, China
- *Correspondence: Jie Zhang, ; Guopeng Wang, ; Cong Cong Ren,
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