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Cheung SYA, Hay JL, Lin YW, de Greef R, Bullock J. Pediatric oncology drug development and dosage optimization. Front Oncol 2024; 13:1235947. [PMID: 38348118 PMCID: PMC10860405 DOI: 10.3389/fonc.2023.1235947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 12/29/2023] [Indexed: 02/15/2024] Open
Abstract
Oncology drug discovery and development has always been an area facing many challenges. Phase 1 oncology studies are typically small, open-label, sequential studies enrolling a small sample of adult patients (i.e., 3-6 patients/cohort) in dose escalation. Pediatric evaluations typically lag behind the adult development program. The pediatric starting dose is traditionally referenced on the recommended phase 2 dose in adults with the incorporation of body size scaling. The size of the study is also small and dependent upon the prevalence of the disease in the pediatric population. Similar to adult development, the dose is escalated or de-escalated until reaching the maximum tolerated dose (MTD) that also provides desired biological activities or efficacy. The escalation steps and identification of MTD are often rule-based and do not incorporate all the available information, such as pharmacokinetic (PK), pharmacodynamic (PD), tolerability and efficacy data. Therefore, it is doubtful if the MTD approach is optimal to determine the dosage. Hence, it is important to evaluate whether there is an optimal dosage below the MTD, especially considering the emerging complexity of combination therapies and the long-term tolerability and safety of the treatments. Identification of an optimal dosage is also vital not only for adult patients but for pediatric populations as well. Dosage-finding is much more challenging for pediatric populations due to the limited patient population and differences among the pediatric age range in terms of maturation and ontogeny that could impact PK. Many sponsors defer the pediatric strategy as they are often perplexed by the challenges presented by pediatric oncology drug development (model of action relevancy to pediatric population, budget, timeline and regulatory requirements). This leads to a limited number of approved drugs for pediatric oncology patients. This review article provides the current regulatory landscape, incentives and how they impact pediatric drug discovery and development. We also consider different pediatric cancers and potential clinical trial challenges/opportunities when designing pediatric clinical trials. An outline of how quantitative methods such as pharmacometrics/modelling & simulation can support the dosage-finding and justification is also included. Finally, we provide some reflections that we consider helpful to accelerate pediatric drug discovery and development.
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Quinney SK, Bies RR, Grannis SJ, Bartlett CW, Mendonca E, Rogerson CM, Backes CH, Shah DK, Tillman EM, Costantine MM, Aruldhas BW, Allam R, Grant A, Abbasi MY, Kandasamy M, Zang Y, Wang L, Shendre A, Li L. The MPRINT Hub Data, Model, Knowledge and Research Coordination Center: Bridging the gap in maternal-pediatric therapeutics research through data integration and pharmacometrics. Pharmacotherapy 2023; 43:391-402. [PMID: 36625779 PMCID: PMC10192201 DOI: 10.1002/phar.2765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/13/2022] [Accepted: 12/08/2022] [Indexed: 01/11/2023]
Abstract
Maternal and pediatric populations have historically been considered "therapeutic orphans" due to their limited inclusion in clinical trials. Physiologic changes during pregnancy and lactation and growth and maturation of children alter pharmacokinetics (PK) and pharmacodynamics (PD) of drugs. Precision therapy in these populations requires knowledge of these effects. Efforts to enhance maternal and pediatric participation in clinical studies have increased over the past few decades. However, studies supporting precision therapeutics in these populations are often small and, in isolation, may have limited impact. Integration of data from various studies, for example through physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling or bioinformatics approaches, can augment the value of data from these studies, and help identify gaps in understanding. To catalyze research in maternal and pediatric precision therapeutics, the Obstetric and Pediatric Pharmacology and Therapeutics Branch of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) established the Maternal and Pediatric Precision in Therapeutics (MPRINT) Hub. Herein, we provide an overview of the status of maternal-pediatric therapeutics research and introduce the Indiana University-Ohio State University MPRINT Hub Data, Model, Knowledge and Research Coordination Center (DMKRCC), which aims to facilitate research in maternal and pediatric precision therapeutics through the integration and assessment of existing knowledge, supporting pharmacometrics and clinical trials design, development of new real-world evidence resources, educational initiatives, and building collaborations among public and private partners, including other NICHD-funded networks. By fostering use of existing data and resources, the DMKRCC will identify critical gaps in knowledge and support efforts to overcome these gaps to enhance maternal-pediatric precision therapeutics.
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Affiliation(s)
- Sara K Quinney
- Department of Obstetrics and Gynecology, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Robert R Bies
- Department of Pharmaceutical Sciences, University at Buffalo School of Pharmacy and Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York, USA
- Institute for Computational and Data Sciences, University at Buffalo, State University of New York at Buffalo, Buffalo, New York, USA
| | - Shaun J Grannis
- Department of Family Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, USA
- Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, Indiana, USA
| | - Christopher W Bartlett
- The Steve & Cindy Rasmussen Institute for Genomic Medicine, Battelle Center for Computational Biology, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, Ohio, USA
- Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Eneida Mendonca
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
| | - Colin M Rogerson
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Carl H Backes
- Division of Neonatology, Nationwide Children’s Hospital; Departments of Pediatrics and Obstetrics and Gynecology, The Ohio State University College of Medicine; Center for Perinatal Research and The Ohio Perinatal Research Network, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, USA; The Heart Center at Nationwide Children’s Hospital, Columbus, Ohio, USA
| | - Dhaval K Shah
- Department of Pharmaceutical Sciences, University at Buffalo School of Pharmacy and Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York, USA
| | - Emma M Tillman
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Maged M Costantine
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, The Ohio State University, Columbus, Ohio, USA
| | - Blessed W Aruldhas
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
- Department of Pharmacology and Clinical Pharmacology, Christian Medical College, Vellore, India
| | - Reva Allam
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Amelia Grant
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Mohammed Yaseen Abbasi
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Murugesh Kandasamy
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Yong Zang
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Lei Wang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Aditi Shendre
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA
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Zheng L, Yang H, Dallmann A, Jiang X, Wang L, Hu W. Physiologically Based Pharmacokinetic Modeling in Pregnant Women Suggests Minor Decrease in Maternal Exposure to Olanzapine. Front Pharmacol 2022; 12:793346. [PMID: 35126130 PMCID: PMC8807508 DOI: 10.3389/fphar.2021.793346] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/23/2021] [Indexed: 01/08/2023] Open
Abstract
Pregnancy is accompanied by significant physiological changes that might affect the in vivo drug disposition. Olanzapine is prescribed to pregnant women with schizophrenia, while its pharmacokinetics during pregnancy remains unclear. This study aimed to develop a physiologically based pharmacokinetic (PBPK) model of olanzapine in the pregnant population. With the contributions of each clearance pathway determined beforehand, a full PBPK model was developed and validated in the non-pregnant population. This model was then extrapolated to predict steady-state pharmacokinetics in the three trimesters of pregnancy by introducing gestation-related alterations. The model adequately simulated the reported time-concentration curves. The geometric mean fold error of Cmax and AUC was 1.14 and 1.09, respectively. The model predicted that under 10 mg daily dose, the systematic exposure of olanzapine had minor changes (less than 28%) throughout pregnancy. We proposed that the reduction in cytochrome P4501A2 activity is counteracted by the induction of other enzymes, especially glucuronyltransferase1A4. In conclusion, the PBPK model simulations suggest that, at least at the tested stages of pregnancy, dose adjustment of olanzapine can hardly be recommended for pregnant women if effective treatment was achieved before the onset of pregnancy and if fetal toxicity can be ruled out.
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Affiliation(s)
- Liang Zheng
- Department of Clinical Pharmacology, The Second Hospital of Anhui Medical University, Hefei, China
- Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
| | - Hongyi Yang
- Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
| | - André Dallmann
- Pharmacometrics/Modeling and Simulation, Research and Development, Pharmaceuticals Bayer AG, Leverkusen, Germany
| | - Xuehua Jiang
- Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
| | - Ling Wang
- Department of Clinical Pharmacy and Pharmacy Administration, West China School of Pharmacy, Sichuan University, Chengdu, China
- *Correspondence: Ling Wang, ; Wei Hu,
| | - Wei Hu
- Department of Clinical Pharmacology, The Second Hospital of Anhui Medical University, Hefei, China
- *Correspondence: Ling Wang, ; Wei Hu,
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Tang F, Ng CM, Bada HS, Leggas M. Clinical pharmacology and dosing regimen optimization of neonatal opioid withdrawal syndrome treatments. Clin Transl Sci 2021; 14:1231-1249. [PMID: 33650314 PMCID: PMC8301571 DOI: 10.1111/cts.12994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 12/31/2020] [Accepted: 01/07/2021] [Indexed: 11/26/2022] Open
Abstract
In this paper, we review the management of neonatal opioid withdrawal syndrome (NOWS) and clinical pharmacology of primary treatment agents in NOWS, including morphine, methadone, buprenorphine, clonidine, and phenobarbital. Pharmacologic treatment strategies in NOWS have been mostly empirical, and heterogeneity in dosing regimens adds to the difficulty of extrapolating study results to broader patient populations. As population pharmacokinetics (PKs) of pharmacologic agents in NOWS become more well‐defined and knowledge of patient‐specific factors affecting treatment outcomes continue to accumulate, PK/pharmacodynamic modeling and simulation will be powerful tools to aid the design of optimal dosing regimens at the patient level. Although there is an increasing number of clinical trials on the comparative efficacy of treatment agents in NOWS, here, we also draw attention to the importance of optimizing the dosing regimen, which can be arguably equally important at identifying the optimal treatment agent.
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Affiliation(s)
- Fei Tang
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, Lexington, Kentucky, USA
| | - Chee M Ng
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, Lexington, Kentucky, USA.,NewGround Pharmaceutical Consulting LLC, Foster City, California, USA
| | - Henrietta S Bada
- Department of Pediatrics, College of Medicine, University of Kentucky, Lexington, Kentucky, USA
| | - Markos Leggas
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, Lexington, Kentucky, USA
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