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Samizo S, Kaneko H. Predictive Modeling of HMG-CoA Reductase Inhibitory Activity and Design of New HMG-CoA Reductase Inhibitors. ACS OMEGA 2023; 8:27247-27255. [PMID: 37546661 PMCID: PMC10399166 DOI: 10.1021/acsomega.3c02567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 06/30/2023] [Indexed: 08/08/2023]
Abstract
As blood cholesterol increases, it accumulates in the intima of blood vessels, elevating the risk of atherosclerosis and coronary artery disease. Drugs that inhibit enzymes essential for cholesterol synthesis are effective in improving blood cholesterol levels. Statins are used to treat hypercholesterolemia as they inhibit 3-hydroxyl-3-methylglutaryl coenzyme A (HMG-CoA) reductase (HMGR), the rate-limiting enzyme in cholesterol synthesis. Statins are known to exert their effects by translocating to the liver, where they are taken up by the organic anion transporting polypeptide 1B1 (OATP1B1). Therefore, we hypothesized that a compound with high HMGR inhibitory activity and high affinity for OATP1B1 would be an excellent new therapeutic agent for hypercholesterolemia with increased liver selectivity and fewer side effects. In this study, we developed two models for predicting HMGR inhibitory activity and OATP1B1 affinity to propose the chemical structure of a new therapeutic agent for hypercholesterolemia with both high inhibitory activity and high liver selectivity. HMGR inhibitory activity and OATP1B1 affinity prediction models were constructed with high prediction accuracy for the test data: r2 = 0.772 and 0.768, respectively. New chemical structures were then input into these models to search for candidate compounds. We found compounds with higher HMGR inhibitory activity and OATP1B1 affinity than rosuvastatin, the most recently developed statin drug, and compounds that did not have a common structure of statins with high HMGR inhibitory activity.
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Costales C, Lin J, Kimoto E, Yamazaki S, Gosset JR, Rodrigues AD, Lazzaro S, West MA, West M, Varma MVS. Quantitative prediction of breast cancer resistant protein mediated drug-drug interactions using physiologically-based pharmacokinetic modeling. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:1018-1031. [PMID: 34164937 PMCID: PMC8452302 DOI: 10.1002/psp4.12672] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 05/18/2021] [Accepted: 05/24/2021] [Indexed: 12/11/2022]
Abstract
Quantitative assessment of drug‐drug interactions (DDIs) involving breast cancer resistance protein (BCRP) inhibition is challenged by overlapping substrate/inhibitor specificity. This study used physiologically‐based pharmacokinetic (PBPK) modeling to delineate the effects of inhibitor drugs on BCRP‐ and organic anion transporting polypeptide (OATP)1B‐mediated disposition of rosuvastatin, which is a recommended BCRP clinical probe. Initial static model analysis using in vitro inhibition data suggested BCRP/OATP1B DDI risk while considering regulatory cutoff criteria for a majority of inhibitors assessed (25 of 27), which increased rosuvastatin plasma exposure to varying degree (~ 0–600%). However, rosuvastatin area under plasma concentration‐time curve (AUC) was minimally impacted by BCRP inhibitors with calculated G‐value (= gut concentration/inhibition potency) below 100. A comprehensive PBPK model accounting for intestinal (OATP2B1 and BCRP), hepatic (OATP1B, BCRP, and MRP4), and renal (OAT3) transport mechanisms was developed for rosuvastatin. Adopting in vitro inhibition data, rosuvastatin plasma AUC changes were predicted within 25% error for 9 of 12 inhibitors evaluated via PBPK modeling. This study illustrates the adequacy and utility of a mechanistic model‐informed approach in quantitatively assessing BCRP‐mediated DDIs.
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Affiliation(s)
- Chester Costales
- Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Worldwide R&D, Pfizer Inc, Groton, CT, USA
| | - Jian Lin
- Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Worldwide R&D, Pfizer Inc, Groton, CT, USA
| | - Emi Kimoto
- Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Worldwide R&D, Pfizer Inc, Groton, CT, USA
| | - Shinji Yamazaki
- Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Worldwide R&D, Pfizer Inc, San Diego, CA, USA
| | - James R Gosset
- Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Worldwide R&D, Pfizer Inc, Cambridge, MA, USA
| | - A David Rodrigues
- Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Worldwide R&D, Pfizer Inc, Groton, CT, USA
| | - Sarah Lazzaro
- Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Worldwide R&D, Pfizer Inc, Groton, CT, USA
| | - Mark A West
- Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Worldwide R&D, Pfizer Inc, Groton, CT, USA
| | - Michael West
- Discovery Science, Medicine Design, Worldwide R&D, Pfizer Inc, Groton, CT, USA
| | - Manthena V S Varma
- Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Worldwide R&D, Pfizer Inc, Groton, CT, USA
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Li Y, Meng Q, Yang M, Liu D, Hou X, Tang L, Wang X, Lyu Y, Chen X, Liu K, Yu AM, Zuo Z, Bi H. Current trends in drug metabolism and pharmacokinetics. Acta Pharm Sin B 2019; 9:1113-1144. [PMID: 31867160 PMCID: PMC6900561 DOI: 10.1016/j.apsb.2019.10.001] [Citation(s) in RCA: 126] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Revised: 08/23/2019] [Accepted: 09/09/2019] [Indexed: 12/15/2022] Open
Abstract
Pharmacokinetics (PK) is the study of the absorption, distribution, metabolism, and excretion (ADME) processes of a drug. Understanding PK properties is essential for drug development and precision medication. In this review we provided an overview of recent research on PK with focus on the following aspects: (1) an update on drug-metabolizing enzymes and transporters in the determination of PK, as well as advances in xenobiotic receptors and noncoding RNAs (ncRNAs) in the modulation of PK, providing new understanding of the transcriptional and posttranscriptional regulatory mechanisms that result in inter-individual variations in pharmacotherapy; (2) current status and trends in assessing drug-drug interactions, especially interactions between drugs and herbs, between drugs and therapeutic biologics, and microbiota-mediated interactions; (3) advances in understanding the effects of diseases on PK, particularly changes in metabolizing enzymes and transporters with disease progression; (4) trends in mathematical modeling including physiologically-based PK modeling and novel animal models such as CRISPR/Cas9-based animal models for DMPK studies; (5) emerging non-classical xenobiotic metabolic pathways and the involvement of novel metabolic enzymes, especially non-P450s. Existing challenges and perspectives on future directions are discussed, and may stimulate the development of new research models, technologies, and strategies towards the development of better drugs and improved clinical practice.
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Affiliation(s)
- Yuhua Li
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510275, China
- The First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - Qiang Meng
- College of Pharmacy, Dalian Medical University, Dalian 116044, China
| | - Mengbi Yang
- School of Pharmacy, the Chinese University of Hong Kong, Hong Kong, China
| | - Dongyang Liu
- Drug Clinical Trial Center, Peking University Third Hospital, Beijing 100191, China
| | - Xiangyu Hou
- Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Lan Tang
- School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Xin Wang
- School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Yuanfeng Lyu
- School of Pharmacy, the Chinese University of Hong Kong, Hong Kong, China
| | - Xiaoyan Chen
- Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Kexin Liu
- College of Pharmacy, Dalian Medical University, Dalian 116044, China
| | - Ai-Ming Yu
- UC Davis School of Medicine, Sacramento, CA 95817, USA
| | - Zhong Zuo
- School of Pharmacy, the Chinese University of Hong Kong, Hong Kong, China
| | - Huichang Bi
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510275, China
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Alam K, Crowe A, Wang X, Zhang P, Ding K, Li L, Yue W. Regulation of Organic Anion Transporting Polypeptides (OATP) 1B1- and OATP1B3-Mediated Transport: An Updated Review in the Context of OATP-Mediated Drug-Drug Interactions. Int J Mol Sci 2018. [PMID: 29538325 PMCID: PMC5877716 DOI: 10.3390/ijms19030855] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Organic anion transporting polypeptides (OATP) 1B1 and OATP1B3 are important hepatic transporters that mediate the uptake of many clinically important drugs, including statins from the blood into the liver. Reduced transport function of OATP1B1 and OATP1B3 can lead to clinically relevant drug-drug interactions (DDIs). Considering the importance of OATP1B1 and OATP1B3 in hepatic drug disposition, substantial efforts have been given on evaluating OATP1B1/1B3-mediated DDIs in order to avoid unwanted adverse effects of drugs that are OATP substrates due to their altered pharmacokinetics. Growing evidences suggest that the transport function of OATP1B1 and OATP1B3 can be regulated at various levels such as genetic variation, transcriptional and post-translational regulation. The present review summarizes the up to date information on the regulation of OATP1B1 and OATP1B3 transport function at different levels with a focus on potential impact on OATP-mediated DDIs.
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Affiliation(s)
- Khondoker Alam
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73117, USA.
| | - Alexandra Crowe
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73117, USA.
| | - Xueying Wang
- Center for Computational Biology and Bioinformatics, Indiana Institute of Personalized Medicine, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
| | - Pengyue Zhang
- Center for Computational Biology and Bioinformatics, Indiana Institute of Personalized Medicine, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
| | - Kai Ding
- Department of Biostatistics and Epidemiology, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73126, USA.
| | - Lang Li
- Center for Computational Biology and Bioinformatics, Indiana Institute of Personalized Medicine, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
- Department of Biomedical Informatics, Ohio State University, Columbus, OH 43210, USA.
| | - Wei Yue
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73117, USA.
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