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Wu F, Zhou Y, Li L, Shen X, Chen G, Wang X, Liang X, Tan M, Huang Z. Computational Approaches in Preclinical Studies on Drug Discovery and Development. Front Chem 2020; 8:726. [PMID: 33062633 PMCID: PMC7517894 DOI: 10.3389/fchem.2020.00726] [Citation(s) in RCA: 106] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 07/14/2020] [Indexed: 12/11/2022] Open
Abstract
Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.
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Affiliation(s)
- Fengxu Wu
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, China
| | - Yuquan Zhou
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
| | - Langhui Li
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Xianhuan Shen
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Ganying Chen
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
| | - Xiaoqing Wang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Xianyang Liang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
| | - Mengyuan Tan
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Zunnan Huang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
- Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang, China
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Romański M, Zacharzewska A, Teżyk A, Główka FK. In Vivo Red Blood Cells/Plasma Partition Coefficient of Treosulfan and Its Active Monoepoxide in Rats. Eur J Drug Metab Pharmacokinet 2019. [PMID: 29542019 PMCID: PMC6133075 DOI: 10.1007/s13318-018-0469-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
BACKGROUND AND OBJECTIVES Treosulfan is a prodrug applied in the treatment of ovarian cancer and conditioning prior to stem cell transplantation. So far, the bioanalysis of treosulfan in either whole blood or red blood cells (RBC) has not been carried out. In this work, the RBC/plasma partition coefficient (Ke/p) of treosulfan and its active monoepoxide was determined for the first time. METHODS Male and female 10-week-old Wistar rats (n = 6/6) received an intraperitoneal injection of treosulfan at the dose of 500 mg/kg body weight. The concentrations of treosulfan and its monoepoxide in plasma (Cp) and RBC were analyzed with a validated HPLC-MS/MS method. RESULTS The mean Ke/p of treosulfan and its monoepoxide were 0.74 and 0.60, respectively, corresponding to the blood/plasma partition coefficient of 0.88 and 0.82. The Spearman test demonstrated that the Ke/p of the prodrug correlated with its Cp, but no correlation between the Ke/p and Cp of the active monoepoxide was observed. CONCLUSIONS Treosulfan and its monoepoxide achieve higher concentrations in plasma than in RBC; therefore, the choice of plasma for bioanalysis is rational as compared to whole blood. The distribution of treosulfan into RBC may be a saturable process at therapeutic concentrations.
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Affiliation(s)
- Michał Romański
- Department of Physical Pharmacy and Pharmacokinetics, Poznan University of Medical Sciences, 6 Święcickiego Street, 60-781, Poznan, Poland
| | - Anna Zacharzewska
- Department of Physical Pharmacy and Pharmacokinetics, Poznan University of Medical Sciences, 6 Święcickiego Street, 60-781, Poznan, Poland
| | - Artur Teżyk
- Department of Forensic Medicine, Poznan University of Medical Sciences, 6 Święcickiego Street, 60-781, Poznan, Poland
| | - Franciszek K Główka
- Department of Physical Pharmacy and Pharmacokinetics, Poznan University of Medical Sciences, 6 Święcickiego Street, 60-781, Poznan, Poland.
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Determination of prodrug treosulfan and its biologically active monoepoxide in rat plasma, liver, lungs, kidneys, muscle, and brain by HPLC–ESI–MS/MS method. J Pharm Biomed Anal 2017; 140:122-129. [DOI: 10.1016/j.jpba.2017.03.023] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Revised: 03/08/2017] [Accepted: 03/14/2017] [Indexed: 01/07/2023]
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