1
|
Thompson EJ, Wu H, Maharaj A, Edginton AN, Balevic SJ, Cobbaert M, Cunningham AP, Hornik CP, Cohen-Wolkowiez M. Physiologically Based Pharmacokinetic Modeling for Trimethoprim and Sulfamethoxazole in Children. Clin Pharmacokinet 2020; 58:887-898. [PMID: 30840200 DOI: 10.1007/s40262-018-00733-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE The aims of this study were to (1) determine whether opportunistically collected data can be used to develop physiologically based pharmacokinetic (PBPK) models in pediatric patients; and (2) characterize age-related maturational changes in drug disposition for the renally eliminated and hepatically metabolized antibiotic trimethoprim (TMP)-sulfamethoxazole (SMX). METHODS We developed separate population PBPK models for TMP and SMX in children after oral administration of the combined TMP-SMX product and used sparse and opportunistically collected plasma concentration samples to validate our pediatric model. We evaluated predictability of the pediatric PBPK model based on the number of observed pediatric data out of the 90% prediction interval. We performed dosing simulations to target organ and tissue (skin) concentrations greater than the methicillin-resistant Staphylococcus aureus (MRSA) minimum inhibitory concentration (TMP 2 mg/L; SMX 9.5 mg/L) for at least 50% of the dosing interval. RESULTS We found 67-87% and 71-91% of the observed data for TMP and SMX, respectively, were captured within the 90% prediction interval across five age groups, suggesting adequate fit of our model. Our model-rederived optimal dosing of TMP at the target tissue was in the range of recommended dosing for TMP-SMX in children in all age groups by current guidelines for the treatment of MRSA. CONCLUSION We successfully developed a pediatric PBPK model of the combination antibiotic TMP-SMX using sparse and opportunistic pediatric pharmacokinetic samples. This novel and efficient approach has the potential to expand the use of PBPK modeling in pediatric drug development.
Collapse
Affiliation(s)
| | - Huali Wu
- Duke Clinical Research Institute, 300 West Morgan Street, Suite 800, Durham, NC, 27701, USA
| | - Anil Maharaj
- Duke Clinical Research Institute, 300 West Morgan Street, Suite 800, Durham, NC, 27701, USA
| | - Andrea N Edginton
- Duke Clinical Research Institute, 300 West Morgan Street, Suite 800, Durham, NC, 27701, USA
| | - Stephen J Balevic
- Department of Pediatrics, Duke University Medical Center, Durham, NC, USA
- Duke Clinical Research Institute, 300 West Morgan Street, Suite 800, Durham, NC, 27701, USA
| | - Marjan Cobbaert
- Duke Clinical Research Institute, 300 West Morgan Street, Suite 800, Durham, NC, 27701, USA
| | - Anthony P Cunningham
- Duke Clinical Research Institute, 300 West Morgan Street, Suite 800, Durham, NC, 27701, USA
| | - Christoph P Hornik
- Department of Pediatrics, Duke University Medical Center, Durham, NC, USA
- Duke Clinical Research Institute, 300 West Morgan Street, Suite 800, Durham, NC, 27701, USA
| | - Michael Cohen-Wolkowiez
- Department of Pediatrics, Duke University Medical Center, Durham, NC, USA.
- Duke Clinical Research Institute, 300 West Morgan Street, Suite 800, Durham, NC, 27701, USA.
| |
Collapse
|
2
|
Lee AJ, Cunningham AP, Kuchenbaecker KB, Mavaddat N, Easton DF, Antoniou AC. BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface. Br J Cancer 2014; 110:535-45. [PMID: 24346285 PMCID: PMC3899766 DOI: 10.1038/bjc.2013.730] [Citation(s) in RCA: 155] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2013] [Revised: 10/22/2013] [Accepted: 10/25/2013] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) is a risk prediction model that is used to compute probabilities of carrying mutations in the high-risk breast and ovarian cancer susceptibility genes BRCA1 and BRCA2, and to estimate the future risks of developing breast or ovarian cancer. In this paper, we describe updates to the BOADICEA model that extend its capabilities, make it easier to use in a clinical setting and yield more accurate predictions. METHODS We describe: (1) updates to the statistical model to include cancer incidences from multiple populations; (2) updates to the distributions of tumour pathology characteristics using new data on BRCA1 and BRCA2 mutation carriers and women with breast cancer from the general population; (3) improvements to the computational efficiency of the algorithm so that risk calculations now run substantially faster; and (4) updates to the model's web interface to accommodate these new features and to make it easier to use in a clinical setting. RESULTS We present results derived using the updated model, and demonstrate that the changes have a significant impact on risk predictions. CONCLUSION All updates have been implemented in a new version of the BOADICEA web interface that is now available for general use: http://ccge.medschl.cam.ac.uk/boadicea/.
Collapse
Affiliation(s)
- A J Lee
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
| | - A P Cunningham
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
| | - K B Kuchenbaecker
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
| | - N Mavaddat
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
| | - D F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
| | - A C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
| | - The Consortium of Investigators of Modifiers of BRCA1/21
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
| | - The Breast Cancer Association Consortium1
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
| |
Collapse
|
3
|
Antoniou AC, Cunningham AP, Peto J, Evans DG, Lalloo F, Narod SA, Risch HA, Eyfjord JE, Hopper JL, Southey MC, Olsson H, Johannsson O, Borg A, Pasini B, Radice P, Manoukian S, Eccles DM, Tang N, Olah E, Anton-Culver H, Warner E, Lubinski J, Gronwald J, Gorski B, Tryggvadottir L, Syrjakoski K, Kallioniemi OP, Eerola H, Nevanlinna H, Pharoah PDP, Easton DF. Erratum: The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions. Br J Cancer 2008. [PMCID: PMC2441956 DOI: 10.1038/sj.bjc.6604411] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
|
4
|
Antoniou AC, Cunningham AP, Peto J, Evans DG, Lalloo F, Narod SA, Risch HA, Eyfjord JE, Hopper JL, Southey MC, Olsson H, Johannsson O, Borg A, Pasini B, Passini B, Radice P, Manoukian S, Eccles DM, Tang N, Olah E, Anton-Culver H, Warner E, Lubinski J, Gronwald J, Gorski B, Tryggvadottir L, Syrjakoski K, Kallioniemi OP, Eerola H, Nevanlinna H, Pharoah PDP, Easton DF. The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions. Br J Cancer 2008; 98:1457-66. [PMID: 18349832 PMCID: PMC2361716 DOI: 10.1038/sj.bjc.6604305] [Citation(s) in RCA: 345] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Multiple genetic loci confer susceptibility to breast and ovarian cancers. We have previously developed a model (BOADICEA) under which susceptibility to breast cancer is explained by mutations in BRCA1 and BRCA2, as well as by the joint multiplicative effects of many genes (polygenic component). We have now updated BOADICEA using additional family data from two UK population-based studies of breast cancer and family data from BRCA1 and BRCA2 carriers identified by 22 population-based studies of breast or ovarian cancer. The combined data set includes 2785 families (301 BRCA1 positive and 236 BRCA2 positive). Incidences were smoothed using locally weighted regression techniques to avoid large variations between adjacent intervals. A birth cohort effect on the cancer risks was implemented, whereby each individual was assumed to develop cancer according to calendar period-specific incidences. The fitted model predicts that the average breast cancer risks in carriers increase in more recent birth cohorts. For example, the average cumulative breast cancer risk to age 70 years among BRCA1 carriers is 50% for women born in 1920-1929 and 58% among women born after 1950. The model was further extended to take into account the risks of male breast, prostate and pancreatic cancer, and to allow for the risk of multiple cancers. BOADICEA can be used to predict carrier probabilities and cancer risks to individuals with any family history, and has been implemented in a user-friendly Web-based program (http://www.srl.cam.ac.uk/genepi/boadicea/boadicea_home.html).
Collapse
Affiliation(s)
- A C Antoniou
- Cancer Research UK, Genetic Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
5
|
Abstract
Current standard cancer therapies (chemotherapy and radiation) often cause serious adverse off-target effects. Drug design strategies are therefore being developed that will more precisely target cancer cells for destruction while leaving surrounding normal cells relatively unaffected. Telomerase, widely expressed in most human cancers but almost undetectable in normal somatic cells, provides an exciting drug target. This review focuses on recent pharmacogenomic approaches to telomerase inhibition. Antisense oligonucleotides, RNA interference, ribozymes, mutant expression, and the exploitation of differential telomerase expression as a strategy for targeted oncolysis are discussed here in the context of cancer therapeutics. Reports of synergism between telomerase inhibitors and traditional cancer therapeutic agents are also analyzed.
Collapse
MESH Headings
- Drug Design
- Enzyme Inhibitors/pharmacology
- Enzyme Inhibitors/therapeutic use
- Humans
- Neoplasms/drug therapy
- Neoplasms/enzymology
- Neoplasms/pathology
- Oligonucleotides, Antisense/genetics
- Oligonucleotides, Antisense/pharmacology
- Oligonucleotides, Antisense/therapeutic use
- RNA, Antisense/genetics
- RNA, Antisense/pharmacology
- RNA, Antisense/therapeutic use
- RNA, Catalytic/genetics
- RNA, Catalytic/metabolism
- RNA, Untranslated/genetics
- RNA, Untranslated/metabolism
- Telomerase/antagonists & inhibitors
- Telomerase/genetics
- Telomerase/metabolism
Collapse
Affiliation(s)
- A P Cunningham
- Department of Biology, University of Alabama at Birmingham, AL 35294, USA
| | | | | | | | | |
Collapse
|