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Dinh J, Johnson TN, Grimstein M, Lewis T. Physiologically Based Pharmacokinetics Modeling in the Neonatal Population-Current Advances, Challenges, and Opportunities. Pharmaceutics 2023; 15:2579. [PMID: 38004559 PMCID: PMC10675397 DOI: 10.3390/pharmaceutics15112579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/24/2023] [Accepted: 10/29/2023] [Indexed: 11/26/2023] Open
Abstract
Physiologically based pharmacokinetic (PBPK) modeling is an approach to predicting drug pharmacokinetics, using knowledge of the human physiology involved and drug physiochemical properties. This approach is useful when predicting drug pharmacokinetics in under-studied populations, such as pediatrics. PBPK modeling is a particularly important tool for dose optimization for the neonatal population, given that clinical trials rarely include this patient population. However, important knowledge gaps exist for neonates, resulting in uncertainty with the model predictions. This review aims to outline the sources of variability that should be considered with developing a neonatal PBPK model, the data that are currently available for the neonatal ontogeny, and lastly to highlight the data gaps where further research would be needed.
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Affiliation(s)
- Jean Dinh
- Certara UK Limited, Sheffield S1 2BJ, UK; (J.D.); (T.N.J.)
| | | | - Manuela Grimstein
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD 20903, USA
| | - Tamorah Lewis
- Pediatric Clinical Pharmacology & Toxicology, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
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Zhang J, Qiu Z, Zhang Y, Wang G, Hao H. Intracellular spatiotemporal metabolism in connection to target engagement. Adv Drug Deliv Rev 2023; 200:115024. [PMID: 37516411 DOI: 10.1016/j.addr.2023.115024] [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: 04/25/2023] [Revised: 07/05/2023] [Accepted: 07/26/2023] [Indexed: 07/31/2023]
Abstract
The metabolism in eukaryotic cells is a highly ordered system involving various cellular compartments, which fluctuates based on physiological rhythms. Organelles, as the smallest independent sub-cell unit, are important contributors to cell metabolism and drug metabolism, collectively designated intracellular metabolism. However, disruption of intracellular spatiotemporal metabolism can lead to disease development and progression, as well as drug treatment interference. In this review, we systematically discuss spatiotemporal metabolism in cells and cell subpopulations. In particular, we focused on metabolism compartmentalization and physiological rhythms, including the variation and regulation of metabolic enzymes, metabolic pathways, and metabolites. Additionally, the intricate relationship among intracellular spatiotemporal metabolism, metabolism-related diseases, and drug therapy/toxicity has been discussed. Finally, approaches and strategies for intracellular spatiotemporal metabolism analysis and potential target identification are introduced, along with examples of potential new drug design based on this.
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Affiliation(s)
- Jingwei Zhang
- State Key Laboratory of Natural Medicines, Key Laboratory of Drug Metabolism & Pharmacokinetics, China Pharmaceutical University, Nanjing, China
| | - Zhixia Qiu
- Center of Drug Metabolism and Pharmacokinetics, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Yongjie Zhang
- Clinical Pharmacokinetics Laboratory, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Guangji Wang
- State Key Laboratory of Natural Medicines, Key Laboratory of Drug Metabolism & Pharmacokinetics, China Pharmaceutical University, Nanjing, China; Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, Research Unit of PK-PD Based Bioactive Components and Pharmacodynamic Target Discovery of Natural Medicine of Chinese Academy of Medical Sciences, China Pharmaceutical University, Nanjing, China.
| | - Haiping Hao
- State Key Laboratory of Natural Medicines, Key Laboratory of Drug Metabolism & Pharmacokinetics, China Pharmaceutical University, Nanjing, China.
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El-Masri H, Paul Friedman K, Isaacs K, Wetmore BA. Advances in computational methods along the exposure to toxicological response paradigm. Toxicol Appl Pharmacol 2022; 450:116141. [PMID: 35777528 PMCID: PMC9619339 DOI: 10.1016/j.taap.2022.116141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/27/2022] [Accepted: 06/23/2022] [Indexed: 10/17/2022]
Abstract
Human health risk assessment is a function of chemical toxicity, bioavailability to reach target biological tissues, and potential environmental exposure. These factors are complicated by many physiological, biochemical, physical and lifestyle factors. Furthermore, chemical health risk assessment is challenging in view of the large, and continually increasing, number of chemicals found in the environment. These challenges highlight the need to prioritize resources for the efficient and timely assessment of those environmental chemicals that pose greatest health risks. Computational methods, either predictive or investigative, are designed to assist in this prioritization in view of the lack of cost prohibitive in vivo experimental data. Computational methods provide specific and focused toxicity information using in vitro high throughput screening (HTS) assays. Information from the HTS assays can be converted to in vivo estimates of chemical levels in blood or target tissue, which in turn are converted to in vivo dose estimates that can be compared to exposure levels of the screened chemicals. This manuscript provides a review for the landscape of computational methods developed and used at the U.S. Environmental Protection Agency (EPA) highlighting their potentials and challenges.
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Affiliation(s)
- Hisham El-Masri
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA.
| | - Katie Paul Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kristin Isaacs
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Barbara A Wetmore
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA
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Kirman CR, Hays SM. Use of Biomarker Data and Relative Potencies of Mutagenic Metabolites to Support Derivation of Cancer Unit Risk Values for 1,3-Butadiene from Rodent Tumor Data. TOXICS 2022; 10:toxics10070394. [PMID: 35878299 PMCID: PMC9316621 DOI: 10.3390/toxics10070394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 07/04/2022] [Accepted: 07/07/2022] [Indexed: 12/10/2022]
Abstract
Unit Risk (UR) values were derived for 1,3-butadiene (BD) based upon its ability to cause tumors in laboratory mice and rats. Metabolism has been established as the significant molecular initiating event of BD’s carcinogenicity. The large quantitative species differences in the metabolism of BD and potency of critical BD epoxide metabolites must be accounted for when rodent toxicity responses are extrapolated to humans. Previously published methods were extended and applied to cancer risk assessments to account for species differences in metabolism, as well as differences in mutagenic potency of BD metabolites within the context of data-derived adjustment factors (DDEFs). This approach made use of biomarker data (hemoglobin adducts) to quantify species differences in the internal doses of BD metabolites experienced in mice, rats, and humans. Using these methods, the dose–response relationships in mice and rats exhibit improved concordance, and result in upper bound UR values ranging from 2.1 × 10−5 to 1.2 × 10−3 ppm−1 for BD. Confidence in these UR values was considered high based on high confidence in the key studies, medium-to-high confidence in the toxicity database, high confidence in the estimates of internal dose, and high confidence in the dose–response modeling.
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Sun MZ, Zheng QC. The regioselectivity of the interaction between dextromethorphan and CYP2D6. Phys Chem Chem Phys 2022; 24:2234-2242. [PMID: 35014636 DOI: 10.1039/d1cp03933d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
CYP2D6 is an important enzyme of the cytochrome P450 superfamily, and catalyzes nearly 25% of the drugs sold in the market. For decades, the interactions and metabolism between CYP2D6 and substrates have been a hot topic. However, the key factors of the catalytic regioselectivity for CYP2D6 still remain controversial. Here, we construct four systems to explore the interaction between dextromethorphan (DM) and CYP2D6. A new binding mode of CYP2D6 is defined, and two key residues (residue Asp301 and residue Glu216) are discovered working simultaneously to stabilize the DM at the reactive site by forming water bridge hydrogen bonds when CYP2D6 binds DM. Our results also indicate that the substrate concentration could mediate the binding mode between the substrate and CYP2D6 by decreasing the volume of the catalytic pocket, which is not conducive to the O-demethylation of DM but benefits the N-demethylation of DM. These results could shed light on the process of CYP2D6 binding to the substrate, and help to better understand the regioselectivity of CYP2D6 catalyzing the substrates.
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Affiliation(s)
- Min-Zhang Sun
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, 2 Liutiao Road, Changchun 130023, P. R. China.
| | - Qing-Chuan Zheng
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, 2 Liutiao Road, Changchun 130023, P. R. China. .,Key Laboratory for Molecular Enzymology and Engineering of the Ministry of Education, Jilin University, Changchun 130012, P. R. China
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Giangreco NP, Elias JE, Tatonetti NP. No population left behind: Improving paediatric drug safety using informatics and systems biology. Br J Clin Pharmacol 2020; 88:1464-1470. [PMID: 33332641 PMCID: PMC8209126 DOI: 10.1111/bcp.14705] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 10/26/2020] [Accepted: 12/05/2020] [Indexed: 12/12/2022] Open
Abstract
Adverse drugs effects (ADEs) in children are common and may result in disability and death. The current paediatric drug safety landscape, including clinical trials, is limited as it rarely includes children and relies on extrapolation from adults. Children are not small adults but go through an evolutionarily conserved and physiologically dynamic process of growth and maturation. Novel quantitative approaches, integrating observations from clinical trials and drug safety databases with dynamic mechanisms, can be used to systematically identify ADEs unique to childhood. In this perspective, we discuss three critical research directions using systems biology methodologies and novel informatics to improve paediatric drug safety, namely child versus adult drug safety profiles, age-dependent drug toxicities and genetic susceptibility of ADEs across childhood. We argue that a data-driven framework that leverages observational data, biomedical knowledge and systems biology modelling will reveal previously unknown mechanisms of pediatric adverse drug events and lead to improved paediatric drug safety.
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Affiliation(s)
- Nicholas P Giangreco
- Department of Biomedical Informatics and Systems Biology, Columbia University, New York, NY, USA
| | - Jonathan E Elias
- Department of Pediatrics, Instructor in Pediatrics, Assistant Medical Director of Information Services, Weill Cornell Medical & NYP Weill Cornell Medical Center, New York, NY, USA
| | - Nicholas P Tatonetti
- Department of Biomedical Informatics and Systems Biology, Columbia University, New York, NY, USA
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