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Ruschkowski BA, Esmaeil Y, Daniel K, Gaudet C, Yeganeh B, Grynspan D, Jankov RP. Thrombospondin-1 Plays a Major Pathogenic Role in Experimental and Human Bronchopulmonary Dysplasia. Am J Respir Crit Care Med 2022; 205:685-699. [PMID: 35021035 DOI: 10.1164/rccm.202104-1021oc] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
RATIONALE Extremely preterm infants develop bronchopulmonary dysplasia (BPD), a chronic lung injury that lacks effective treatment. Thrombospondin-1 is an anti-angiogenic protein that activates TGF-β1, a cytokine strongly linked to both experimental and human BPD. OBJECTIVES 1) To examine effects of inhibiting thrombospondin-1-mediated TGF-β1 activation (LSKL) in neonatal rats with bleomycin-induced lung injury, 2) To examine effects of a thrombospondin-1-mimic (ABT-510) on lung morphology, and 3) To determine whether thrombospondin-1 and related signaling peptides are increased in lungs of human preterm infants at risk for BPD. METHODS From postnatal days 1-14, rat pups received daily i.p. bleomycin (1 mg/kg) or vehicle combined with daily s.c. LSKL (20 mg/kg) or vehicle. Separate animals were treated with vehicle or ABT-510 (30 mg/kg/d). Paraffin-embedded lung tissues from 47 autopsies (controls; death <28 days, n=30 and BPD at risk; death ≥28 days, n=17) performed on infants born <29 completed weeks' gestation were semi-quantified for injury markers (collagen, macrophages, 3-nitrotyrosine), thrombospondin-1 and TGF-β1. MEASUREMENTS AND MAIN RESULTS Bleomycin or ABT-510 increased lung TGF-β1 activity and macrophage influx, caused pulmonary hypertension and led to alveolar and microvascular hypoplasia. Treatment with LSKL partially prevented abnormal lung morphology secondary to bleomycin. Lungs from human infants at-risk for BPD had increased contents of thrombospondin-1 and TGF-β1 when compared to controls. TGF-β1 content correlated with markers of lung injury. CONCLUSIONS Thrombospondin-1 inhibits alveologenesis in neonatal rats, in part via up-regulated activity of TGF-β1. Observations in human lung suggest a similar pathogenic role for thrombospondin-1 in infants at-risk for BPD.
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Affiliation(s)
- Brittany Ann Ruschkowski
- Children's Hospital of Eastern Ontario Research Institute, 274065, Molecular Biomedicine, Ottawa, Ontario, Canada
| | - Yousef Esmaeil
- University of Ottawa, Paediatrics, Ottawa, Ontario, Canada
| | - Kate Daniel
- Children's Hospital of Eastern Ontario Research Institute, 274065, Molecular Biomedicine, Ottawa, Ontario, Canada
| | - Chantal Gaudet
- Children's Hospital of Eastern Ontario Research Institute, 274065, Molecular Biomedicine, Ottawa, Ontario, Canada
| | - Behzad Yeganeh
- Children's Hospital of Eastern Ontario Research Institute, 274065, Molecular Biomedicine, Ottawa, Ontario, Canada
| | - David Grynspan
- University of Ottawa, Paediatrics, Ottawa, Ontario, Canada
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Conklin LS, Hoffman EP, van den Anker J. Developmental Pharmacodynamics and Modeling in Pediatric Drug Development. J Clin Pharmacol 2020; 59 Suppl 1:S87-S94. [PMID: 31502687 DOI: 10.1002/jcph.1482] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 06/17/2019] [Indexed: 12/14/2022]
Abstract
Challenges in pediatric drug development include small patient numbers, limited outcomes research, ethical barriers, and sparse biosamples. Increasingly, pediatric drug development is focusing on extrapolation: leveraging knowledge about adult disease and drug responses to inform projections of drug and clinical trial performance in pediatric subpopulations. Pharmacokinetic-pharmacodynamic (PK-PD) modeling and extrapolation aim to reduce the numbers of patients and data points needed to establish efficacy. Planning for PK-PD and biomarker studies should begin early in the adult drug development program. Extrapolation relies on the assumption that both the underlying disease and the mechanism of action of the drug used to treat that disease are similar in adults and pediatric subpopulations. Clearly, developmental changes in PK and PD need to be considered to enhance the quality of PK-PD modeling and, therefore, increase the success of extrapolation. This article focuses on the influence of differences in PD between adults and pediatric subpopulations that are highly relevant for the use of extrapolation.
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Affiliation(s)
- Laurie S Conklin
- Division of Gastroenterology, Hepatology, and Nutrition, Children's National Health System, Washington, DC, USA.,ReveraGen BioPharma, Rockville, MD, USA
| | - Eric P Hoffman
- ReveraGen BioPharma, Rockville, MD, USA.,Binghamton University-SUNY, School of Pharmacy and Pharmaceutical Sciences, Binghamton, NY, USA
| | - John van den Anker
- ReveraGen BioPharma, Rockville, MD, USA.,Division of Clinical Pharmacology, Children's National Health System, Washington, DC, USA
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Kelly LE, Caswell K, Short MA, Parimi PS, Offringa M, Diacovo T. Response biomarkers in neonatal intervention studies. Pediatr Res 2018; 83:425-430. [PMID: 29278643 DOI: 10.1038/pr.2017.204] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Accepted: 08/06/2017] [Indexed: 12/21/2022]
Abstract
BackgroundUp to 90% of all drugs used in neonatal intensive care units (NICUs) have not been clinically tested for safety and efficacy. To promote drug development for neonates, the pharmaceutical industry is moving toward rigorous testing, necessitating the need to development, and validating biomarkers in neonates to predict their response. The objective of this review is to evaluate the quality of the response biomarker reporting in neonatal clinical trials.MethodsA validated literature search strategy was applied. Prospective neonatal intervention studies reporting response biomarkers published in 2014 were included. The data were extracted independently and in duplicate using a data-extraction form.ResultsFollowing the full-text review, 167 published prospective neonatal trials were included; 35% (59/167) reported the use of response biomarkers. In these 59 trials, we identified 275 biomarkers used to measure the response (pharmacodynamics and safety) reported as primary or secondary outcomes. Heart rate and oxygen saturation were the most commonly reported. Measurement and instrumentation data were often not provided.ConclusionWe identified a huge variability in the selection, measurement, and reporting of neonatal response biomarkers in prospective intervention studies. Reporting initiatives are needed to reduce research waste and improve the reproducibility of biomarker use in neonatal intervention studies.
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Affiliation(s)
- Lauren E Kelly
- Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
| | - Kimberly Caswell
- School of Biological Sciences and Applied Chemistry, Seneca College, Toronto, Canada
| | | | - Prabhu S Parimi
- Department of Neonatology, John's Hopkins All Children's Hospital, St Petersburg, Florida
| | - Martin Offringa
- Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
| | - Thomas Diacovo
- Department of Pediatrics, Pathology and Cell Biology, Columbia University Medical Centre, New York, NY
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Mehrotra N, Bhattaram A, Earp JC, Florian J, Krudys K, Lee JE, Lee JY, Liu J, Mulugeta Y, Yu J, Zhao P, Sinha V. Role of Quantitative Clinical Pharmacology in Pediatric Approval and Labeling. ACTA ACUST UNITED AC 2016; 44:924-33. [PMID: 27079249 DOI: 10.1124/dmd.116.069559] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2016] [Accepted: 04/13/2016] [Indexed: 12/18/2022]
Abstract
Dose selection is one of the key decisions made during drug development in pediatrics. There are regulatory initiatives that promote the use of model-based drug development in pediatrics. Pharmacometrics or quantitative clinical pharmacology enables development of models that can describe factors affecting pharmacokinetics and/or pharmacodynamics in pediatric patients. This manuscript describes some examples in which pharmacometric analysis was used to support approval and labeling in pediatrics. In particular, the role of pharmacokinetic (PK) comparison of pediatric PK to adults and utilization of dose/exposure-response analysis for dose selection are highlighted. Dose selection for esomeprazole in pediatrics was based on PK matching to adults, whereas for adalimumab, exposure-response, PK, efficacy, and safety data together were useful to recommend doses for pediatric Crohn's disease. For vigabatrin, demonstration of similar dose-response between pediatrics and adults allowed for selection of a pediatric dose. Based on model-based pharmacokinetic simulations and safety data from darunavir pediatric clinical studies with a twice-daily regimen, different once-daily dosing regimens for treatment-naïve human immunodeficiency virus 1-infected pediatric subjects 3 to <12 years of age were evaluated. The role of physiologically based pharmacokinetic modeling (PBPK) in predicting pediatric PK is rapidly evolving. However, regulatory review experiences and an understanding of the state of science indicate that there is a lack of established predictive performance of PBPK in pediatric PK prediction. Moving forward, pharmacometrics will continue to play a key role in pediatric drug development contributing toward decisions pertaining to dose selection, trial designs, and assessing disease similarity to adults to support extrapolation of efficacy.
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Affiliation(s)
- Nitin Mehrotra
- Division of Pharmacometrics, Office of Clinical Pharmacology (N.M., A.B., J.C.E., J.F., K.K., J.E.L., J.L., Y.M., J.Y., P.Z., V.S.), and Division of Biometrics VII, Office of Biostatistics (J.Y.L.), Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland
| | - Atul Bhattaram
- Division of Pharmacometrics, Office of Clinical Pharmacology (N.M., A.B., J.C.E., J.F., K.K., J.E.L., J.L., Y.M., J.Y., P.Z., V.S.), and Division of Biometrics VII, Office of Biostatistics (J.Y.L.), Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland
| | - Justin C Earp
- Division of Pharmacometrics, Office of Clinical Pharmacology (N.M., A.B., J.C.E., J.F., K.K., J.E.L., J.L., Y.M., J.Y., P.Z., V.S.), and Division of Biometrics VII, Office of Biostatistics (J.Y.L.), Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland
| | - Jeffry Florian
- Division of Pharmacometrics, Office of Clinical Pharmacology (N.M., A.B., J.C.E., J.F., K.K., J.E.L., J.L., Y.M., J.Y., P.Z., V.S.), and Division of Biometrics VII, Office of Biostatistics (J.Y.L.), Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland
| | - Kevin Krudys
- Division of Pharmacometrics, Office of Clinical Pharmacology (N.M., A.B., J.C.E., J.F., K.K., J.E.L., J.L., Y.M., J.Y., P.Z., V.S.), and Division of Biometrics VII, Office of Biostatistics (J.Y.L.), Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland
| | - Jee Eun Lee
- Division of Pharmacometrics, Office of Clinical Pharmacology (N.M., A.B., J.C.E., J.F., K.K., J.E.L., J.L., Y.M., J.Y., P.Z., V.S.), and Division of Biometrics VII, Office of Biostatistics (J.Y.L.), Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland
| | - Joo Yeon Lee
- Division of Pharmacometrics, Office of Clinical Pharmacology (N.M., A.B., J.C.E., J.F., K.K., J.E.L., J.L., Y.M., J.Y., P.Z., V.S.), and Division of Biometrics VII, Office of Biostatistics (J.Y.L.), Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland
| | - Jiang Liu
- Division of Pharmacometrics, Office of Clinical Pharmacology (N.M., A.B., J.C.E., J.F., K.K., J.E.L., J.L., Y.M., J.Y., P.Z., V.S.), and Division of Biometrics VII, Office of Biostatistics (J.Y.L.), Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland
| | - Yeruk Mulugeta
- Division of Pharmacometrics, Office of Clinical Pharmacology (N.M., A.B., J.C.E., J.F., K.K., J.E.L., J.L., Y.M., J.Y., P.Z., V.S.), and Division of Biometrics VII, Office of Biostatistics (J.Y.L.), Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland
| | - Jingyu Yu
- Division of Pharmacometrics, Office of Clinical Pharmacology (N.M., A.B., J.C.E., J.F., K.K., J.E.L., J.L., Y.M., J.Y., P.Z., V.S.), and Division of Biometrics VII, Office of Biostatistics (J.Y.L.), Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland
| | - Ping Zhao
- Division of Pharmacometrics, Office of Clinical Pharmacology (N.M., A.B., J.C.E., J.F., K.K., J.E.L., J.L., Y.M., J.Y., P.Z., V.S.), and Division of Biometrics VII, Office of Biostatistics (J.Y.L.), Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland
| | - Vikram Sinha
- Division of Pharmacometrics, Office of Clinical Pharmacology (N.M., A.B., J.C.E., J.F., K.K., J.E.L., J.L., Y.M., J.Y., P.Z., V.S.), and Division of Biometrics VII, Office of Biostatistics (J.Y.L.), Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland
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