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5. Cannabidiol Exposure Through Maternal Marijuana Use: Predictions in Breastfed Infants

Author

Yeung, Cindy HT, Bertrand, Kerri A, Best, Brookie M, Capparelli, Edmund, Chambers, Christina D, Hajducek, Dagmar M, Hamadeh, Abdullah, Ito, Shinya, Momper, Jeremiah D, and Edginton, Andrea N

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Pediatric, Vaccine Related, Pediatric Research Initiative, Prevention, Aetiology, 2.2 Factors relating to the physical environment, Female, Infant, Humans, Child, Breast Feeding, Cannabidiol, Marijuana Use, Milk, Human, Pharmacology & Pharmacy, Pharmacology and pharmaceutical sciences
Abstract

Background and objectiveKnowledge about exposure to cannabidiol (CBD) in breastfed infants can provide an improved understanding of potential risk. The aim was to predict CBD exposure in breastfed infants from mothers taking CBD and CBD-containing products.MethodsCannabidiol concentrations in milk previously attained from data collected through an existing human milk research biorepository were used to simulate infant doses and identify subgroups. A developed pediatric physiologically based pharmacokinetic model produced virtual breastfed infants administered the simulated CBD doses. Predicted breastfed infant exposures and upper area under the curve ratios were compared to the lowest therapeutic dose for approved indications in children.ResultsThe existing human milk research biorepository contained 200 samples from 181 unique breastfeeding mothers for whom self-reported administration data and CBD concentrations had previously been measured. Samples that were above the lower limit of quantification with only one maternal administration type revealed that administration type, i.e., joint/blunt or edible versus oil or pipe, resulted in significantly different subgroups in terms of milk concentrations. Resulting simulated infant doses (ng/kg) were described by lognormal distributions with geometric means and geometric standard deviations: 0.61 ± 2.41 all concentrations, 0.10 ± 0.37 joint/blunt or edible, and 2.23 ± 8.15 oil or pipe. Doses administered to breastfed infants had exposures magnitudes lower than exposures in children aged 4-11 years administered the lowest therapeutic dose for approved indications, and low upper area under the curve ratios.ConclusionsBased on real-world use, breastfeeding infants are predicted to receive very small exposures of CBD through milk. Studies examining adverse reactions will provide further insight into potential risk.

Published
2023

7. Addressing maternal medication use during breastfeeding using clinical resources and a novel physiologically based pharmacokinetic model-derived metric: A qualitative study

Author

Yeung, Cindy Hoi Ting, Houle, Sherilyn KD, Anderson, Philip O, Best, Brookie M, Dubinsky, Samuel, and Edginton, Andrea N

Subjects
Paediatrics, Biomedical and Clinical Sciences, Pediatric, Good Health and Well Being, maternal medication use, breastfeeding resources, physiologically-based pharmacokinetic (PBPK) modeling, healthcare provider interviews, thematic analysis, infant health, Paediatrics and Reproductive Medicine, Other Medical and Health Sciences
Abstract

IntroductionBreastfeeding has major benefits to the maternal-infant dyad and yet healthcare providers have expressed uncertainty about advocating breastfeeding when mothers are taking medications. The tendency for some providers to be more cautious in their advising approach is likely a consequence of limited, unfamiliar, and unreliable existing information on medication use during lactation. A novel risk metric termed the Upper Area Under the Curve Ratio (UAR) was developed to overcome existing resource shortcomings. However, the perception and use of the UAR in practice by providers is not known. The aim of this study was to understand existing resource use and potential UAR use in practice, their advantages and disadvantages, and areas of improvement for the UAR.MethodsHealthcare providers mainly practicing in California with experience advising on medication use during lactation were recruited. One-on-one semi-structured interviews that included questions on current practices when advising medication use during breastfeeding, and approaches to a given a scenario with and without information about the UAR were conducted. The Framework Method was applied for data analysis to construct themes and codes.ResultsTwenty-eight providers representing multiple professions and disciplines were interviewed. Six main themes emerged: (1) Current Practice Approaches, (2) Advantages of Existing Resources, (3) Disadvantages of Existing Resources, (4) Advantages of the UAR, (5) Disadvantages of the UAR, and (6) Strategies to Improve the UAR. Overall, 108 codes were identified that illustrated theme topics ranging from a general lack of metric use to the realities of advising. A workflow describing current practice approaches connected all other themes. Almost all disadvantages of existing resources could be overcome by advantages of other resources and the UAR. Several improvements to the UAR were identified to address its shortcomings.ConclusionThrough interviews with providers who use resources to advise on medication use during breastfeeding, an improved understanding of current practice approaches and accessed resources was ascertained. Ultimately, it was found that the UAR would confer multiple benefits over existing resources, and improvements of the UAR were identified. Future work should focus on implementing the suggested recommendations to ensure optimal uptake of the UAR to improve advising practices.

Published
2023

19. Physiologically‐based pharmacokinetic modeling of pantoprazole to evaluate the role of CYP2C19 genetic variation and obesity in the pediatric population.

Author

Thompson, Elizabeth J., Jeong, Angela, Helfer, Victória E., Shakhnovich, Valentina, Edginton, Andrea, Balevic, Stephen J., James, Laura P., Collier, David N., Anand, Ravinder, Gonzalez, Daniel, Benjamin, Daniel K., Kennel, Phyllis, Alderman, Cheryl, Sund, Zoe, Opel, Kylie, Beci, Rose, Hornik, Chi D., Kearns, Gregory L., Paul, Ian M., and Sullivan, Janice

Subjects
CHILDHOOD obesity, BODY weight, PROTON pump inhibitors, CHILD patients, BODY size, OVERWEIGHT children
Abstract

Pantoprazole is a proton pump inhibitor indicated for the treatment of gastroesophageal reflux disease, a condition that disproportionately affects children with obesity. Appropriately dosing pantoprazole in children with obesity requires understanding the body size metric that best guides dosing, but pharmacokinetic (PK) trials using traditional techniques are limited by the need for larger sample sizes and frequent blood sampling. Physiologically‐based PK (PBPK) models are an attractive alternative that can account for physiologic‐, genetic‐, and drug‐specific changes without the need for extensive clinical trial data. In this study, we explored the effect of obesity on pantoprazole PK and evaluated label‐suggested dosing in this population. An adult PBPK model for pantoprazole was developed using data from the literature and accounting for genetic variation in CYP2C19. The adult PBPK model was scaled to children without obesity using age‐associated changes in anatomical and physiological parameters. Lastly, the pediatric PBPK model was expanded to children with obesity. Three pantoprazole dosing strategies were evaluated: 1 mg/kg total body weight, 1.2 mg/kg lean body weight, and US Food and Drug Administration‐recommended weight‐tiered dosing. Simulated concentration–time profiles from our model were compared with data from a prospective cohort study (PAN01; NCT02186652). Weight‐tiered dosing resulted in the most (>90%) children with pantoprazole exposures in the reference range, regardless of obesity status or CYP2C19 phenotype, confirming results from previously published population PK models. PBPK models may allow for the efficient study of physiologic and developmental effects of obesity on PK in special populations where clinical trial data may be limited. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Canadian clinical experience on switching from standard half‐life recombinant factor VIII (rFVIII), octocog alfa, to extended half‐life rFVIII, damoctocog alfa pegol, in persons with haemophilia A ≥ 12 years followed in a Comprehensive Hemophilia Care Program in Canada

Author

Matino, Davide, primary, Germini, Federico, additional, Chan, Anthony K. C., additional, Decker, Kay, additional, Iserman, Emma, additional, Chelle, Pierre, additional, Edginton, Andrea N., additional, Oladoyinbo, Olayide, additional, Trinari, Elisabetta, additional, Keepanasseril, Arun, additional, and Iorio, Alfonso, additional

Published
2024
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27. Maximum likelihood estimation of renal transporter ontogeny profiles for pediatric PBPK modeling

Author

Hunt, J. Porter, Dubinsky, Samuel, Mcknite, Autumn M., Cheung, Kit Wun Kathy, van Groen, Bianca D., Giacomini, Kathleen M., de Wildt, Saskia N., Edginton, Andrea N., Watt, Kevin M., Hunt, J. Porter, Dubinsky, Samuel, Mcknite, Autumn M., Cheung, Kit Wun Kathy, van Groen, Bianca D., Giacomini, Kathleen M., de Wildt, Saskia N., Edginton, Andrea N., and Watt, Kevin M.

Abstract

Optimal treatment of infants with many renally cleared drugs must account for maturational differences in renal transporter (RT) activity. Pediatric physiologically-based pharmacokinetic (PBPK) models may incorporate RT activity, but this requires ontogeny profiles for RT activity in children, especially neonates, to predict drug disposition. Therefore, RT expression measurements from human kidney postmortem cortical tissue samples were normalized to represent a fraction of mature RT activity. Using these data, maximum likelihood estimated the distributions of RT activity across the pediatric age spectrum, including preterm and term neonates. PBPK models of four RT substrates (acyclovir, ciprofloxacin, furosemide, and meropenem) were evaluated with and without ontogeny profiles using average fold error (AFE), absolute average fold error (AAFE), and proportion of observations within the 5-95% prediction interval. Novel maximum likelihood profiles estimated ontogeny distributions for the following RT: OAT1, OAT3, OCT2, P-gp, URAT1, BCRP, MATE1, MRP2, MRP4, and MATE-2 K. Profiles for OAT3, P-gp, and MATE1 improved infant furosemide and neonate meropenem PBPK model AFE from 0.08 to 0.70 and 0.53 to 1.34 and model AAFE from 12.08 to 1.44 and 2.09 to 1.36, respectively, and improved the percent of data within the 5-95% prediction interval from 48% to 98% for neonatal ciprofloxacin simulations, respectively. Even after accounting for other critical population-specific maturational differences, novel RT ontogeny profiles substantially improved neonatal PBPK model performance, providing validated estimates of maturational differences in RT activity for optimal dosing in children.

Published
2024

28. Development of a Plasminogen Population PK model supporting prophylactic replacement therapy for Plasminogen deficient patients within the WAPPS‐Hemo platform.

Author

Chelle, Pierre, Hajducek, Dagmar, Thibaudeau, Karen, Hobson, Nicholas, Iorio, Alfonso, Shapiro, Amy, and Edginton, Andrea

Subjects
PLASMINOGEN, BLOOD coagulation factor IX, BLOOD coagulation factor VIII, DATABASES, RARE diseases
Abstract

Introduction: Plasminogen deficiency is an ultra rare disease whose patients may develop ligneous lesions if untreated. Prophylactic replacement therapy with plasma derived plasminogen, Ryplazim, is efficient in treating lesions and could benefit from pharmacokinetic (PK) tailoring. Aim: The objectives of this study are to develop, evaluate and integrate into the WAPPS‐Hemo platform a Population PK model supporting prophylactic replacement therapy for Plasminogen deficient patients. Methods: Population PK modelling and evaluations followed the same protocol performed for factor VIII and IX concentrates. Limited sampling analysis used dosing and sampling scenarios in accordance with recommended treatment for Ryplazim. Results: The population PK model, derived from 16 participants included in previous clinical studies, was a 2‐compartment model whose variability was best described by fat‐free mass. Evaluations showed that the model described well the data and Bayesian forecasting in limited sampling environment led to acceptable precision for PK parameters relevant to plasminogen treatment. Conclusion: The model was integrated into the WAPPS‐Hemo webservice to help individualize prophylactic treatment in plasminogen deficient patients. Prospective PK data to be collected through the WAPPS‐Hemo database will be used to better understand plasminogen PK and improve patient care. [ABSTRACT FROM AUTHOR]

Published
2024
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31. Development and Validation of a Population-Pharmacokinetic Model for Rurioctacog Alfa Pegol (Adynovate®): A Report on Behalf of the WAPPS-Hemo Investigators Ad Hoc Subgroup

Author

Chelle, Pierre, Yeung, Cindy H. T., Croteau, Stacy E., Lissick, Jennifer, Balasa, Vinod, Ashburner, Christina, Park, Young Shil, Bonanad, Santiago, Megías-Vericat, Juan Eduardo, Nagao, Azusa, Wynn, Tung, Corrales-Medina, Fernando, Tran, Huyen, Sharathkumar, Anjali, Chitlur, Meera, Sarmiento, Samuel, Edginton, Andrea, and Iorio, Alfonso

Published
2020
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32. Using pharmacokinetics for tailoring prophylaxis in people with hemophilia switching between clotting factor products: A scoping review

Author

Yu, Jacky K., Iorio, Alfonso, Edginton, Andrea N., Ahuja, Sanjay, Álvarez Román, Ma Teresa, Arrieta, Ma E., Arola, Mikko, Barillari, Giovanni, Balasa, Vinod, Belletrutti, Mark, Berrueco Moreno, Ruben, Beurrier, Philippe, Bidlingmaier, Cristoph, Blanchette, Victor, Blatny, Jan, Bonanad, Santiago, Brose, Kelsey, Brown, Deborah, Byant, Paulette C., Canaro, Mariana, Carvalho, Manuela, Catarino, Cristina, Chitlur, Meera, Cockrell, Erin, Chowdary, Pratima, Cnossen, Marjon, Collins, Peter, Coppens, Michial, Croteau, Stacy, Cultrera, Dorina, de Cristofaro, Raimundo, de Raucourt, Emmauelle, Desprez, Dominique, Dunn, Amy, El‐Ekiabi, Magda, Faganel Kotnik, Barbara, Fischer, Kathleen, Frotscher, Brigit, Garbiero, Susana, Garrido Ruiz, Raquel, Gill, Joan, Gomez del Castillo, Carmen, Gottstein, Saskia, Lassandro, Giuseppe, Giordano, Paola, Hart, Daniel, Hegemann, Inga, Hermans, Cedric, Hua, Baolai, Hwang, Nina, Jackson, Shannon, James, Paula, Katsarou, Olga, Kavakli, Kaan, Kempton, Christine, Kentouche, Karim, Khan, Osman, Kobelt, Rainer, Kruse‐Jarres, Rebecca, Laane, Edward, Larson, Eric, Lassila, Riitta, Lee, Adrienne, Poon, Man‐Chiu, Lissick, Jennifer, Langstrom, Satu, Mahlangu, Johnny, Makris, Michael, Marchesini, Emmanuela, Mateo, Jose, Marco Vera, Pacual, Martorell, Marta, Matsushita, Tadashi, McCrae, Simon, Mignot‐Castellano, Eva, Montcrieff, Caitlin, Maes, Philip, Mondelars, Veerle, Bekart, Marlies, Mora, Elena, Cristóbal Morales, Juan, Mourey, Guillaume, Bertrand, Marie Ann, Napolitano, Mariasanta, Siragusa, Sergio, Negrier, Claude, Neme, Daniela, Niinimaki, Ritta, Oldenburg, Johannes, Albert, Thilo, Ornstein, Deborah, Ozelo, Margarete, Panetta, John Carl, Neufeld, Ellis J., P'Ng, Stephanie, Peerlinck, Kathelijne, Pollio, Berardino, Pouplard, Claire, Gruel, Yves, Prezotti, Alessandra, Price, Vicky, Primacakti, Fitri, Puyade, Mathieu, Radossi, Paolo, Raffini, Leslie, Ragni, Margaret, Rangarajan, Savita, Reding, Mark T., Reid, Robin, Restrepo, Jose, Ramirez, Jose, Recht, Michael, Rodriguez Lopez, Manuel, Ruiz‐Sàez, Arlette, Saleh, Mahasen, Shapiro, Amy, Sharathkumar, Anjali, Selmeczi, Anna, Simpson, Mindy, Singleton, Tami, Cruz, Maria Sol, Soto, Veronica, Steele, MacGregor, Streif, Werner, Sun, Hao Wei, Ritchie, Bruce, Sun, Jing, Feng, Xiaqin, Suzuki, Takashi, Nagao, Asuza, Takemoto, Cliff, Tapp, Heather, Teitel, Jerry, Tinmouth, Alan, Thornburg, Courtney, Tosseto, Alberto, Turnstall, Oliver, Vezina, Catherine, Warren, Beth, Wheeler, Allison, Wilches Gutierrez, Juan D., Wu, John K.M., Wynn, Tung, Yang, Renchi, Young, Guy, Zanon, Ezio, and Zupan, Irena

Published
2019
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37. Performing and interpreting individual pharmacokinetic profiles in patients with Hemophilia A or B: Rationale and general considerations

Author

Iorio, Alfonso, Edginton, Andrea N., Blanchette, Victor, Blatny, Jan, Boban, Ana, Cnossen, Marjon, Collins, Peter, Croteau, Stacy E., Fischer, Katheljin, Hart, Daniel P., Ito, Shinya, Korth‐Bradley, Joan, Lethagen, Stefan, Lillicrap, David, Makris, Mike, Mathôt, Ron, Morfini, Massimo, Neufeld, Ellis J., and Spears, Jeffrey

Published
2018
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42. Development of a Minimalistic Physiologically Based Pharmacokinetic (mPBPK) Model for the Preclinical Development of Spectinamide Antibiotics

Author

Parmar, Keyur R., primary, Lukka, Pradeep B., additional, Wagh, Santosh, additional, Temrikar, Zaid H., additional, Liu, Jiuyu, additional, Lee, Richard E., additional, Braunstein, Miriam, additional, Hickey, Anthony J., additional, Robertson, Gregory T., additional, Gonzalez-Juarrero, Mercedes, additional, Edginton, Andrea, additional, and Meibohm, Bernd, additional

Published
2023
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43. Point/Counterpoint: Are Outstanding Leaders Born or Made?

Author

Boerma, Marjan, Coyle, Elizabeth A., Dietrich, Michael A., Dintzner, Matthew R., Drayton, Shannon J., Early, Johnnie L., II, Edginton, Andrea N., Horlen, Cheryl K., Kirkwood, Cynthia K., Lin, Anne Y.F., Rager, Michelle L., Shah-Manek, Bijal, Welch, Adam C., and Williams, Nancy Toedter

Published
2017
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46. Data from Targeting Mitochondria with Avocatin B Induces Selective Leukemia Cell Death

Author

Lee, Eric A., primary, Angka, Leonard, primary, Rota, Sarah-Grace, primary, Hanlon, Thomas, primary, Mitchell, Andrew, primary, Hurren, Rose, primary, Wang, Xiao Ming, primary, Gronda, Marcela, primary, Boyaci, Ezel, primary, Bojko, Barbara, primary, Minden, Mark, primary, Sriskanthadevan, Shrivani, primary, Datti, Alessandro, primary, Wrana, Jeffery L., primary, Edginton, Andrea, primary, Pawliszyn, Janusz, primary, Joseph, Jamie W., primary, Quadrilatero, Joe, primary, Schimmer, Aaron D., primary, and Spagnuolo, Paul A., primary

Published
2023
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47. Supplementary Methods and References from Targeting Mitochondria with Avocatin B Induces Selective Leukemia Cell Death

Author

Lee, Eric A., primary, Angka, Leonard, primary, Rota, Sarah-Grace, primary, Hanlon, Thomas, primary, Mitchell, Andrew, primary, Hurren, Rose, primary, Wang, Xiao Ming, primary, Gronda, Marcela, primary, Boyaci, Ezel, primary, Bojko, Barbara, primary, Minden, Mark, primary, Sriskanthadevan, Shrivani, primary, Datti, Alessandro, primary, Wrana, Jeffery L., primary, Edginton, Andrea, primary, Pawliszyn, Janusz, primary, Joseph, Jamie W., primary, Quadrilatero, Joe, primary, Schimmer, Aaron D., primary, and Spagnuolo, Paul A., primary

Published
2023
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48. Supplementary Tables S1-S3 from Targeting Mitochondria with Avocatin B Induces Selective Leukemia Cell Death

Author

Lee, Eric A., primary, Angka, Leonard, primary, Rota, Sarah-Grace, primary, Hanlon, Thomas, primary, Mitchell, Andrew, primary, Hurren, Rose, primary, Wang, Xiao Ming, primary, Gronda, Marcela, primary, Boyaci, Ezel, primary, Bojko, Barbara, primary, Minden, Mark, primary, Sriskanthadevan, Shrivani, primary, Datti, Alessandro, primary, Wrana, Jeffery L., primary, Edginton, Andrea, primary, Pawliszyn, Janusz, primary, Joseph, Jamie W., primary, Quadrilatero, Joe, primary, Schimmer, Aaron D., primary, and Spagnuolo, Paul A., primary

Published
2023
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49. Supplementary Figure Legends from Targeting Mitochondria with Avocatin B Induces Selective Leukemia Cell Death

Author

Lee, Eric A., primary, Angka, Leonard, primary, Rota, Sarah-Grace, primary, Hanlon, Thomas, primary, Mitchell, Andrew, primary, Hurren, Rose, primary, Wang, Xiao Ming, primary, Gronda, Marcela, primary, Boyaci, Ezel, primary, Bojko, Barbara, primary, Minden, Mark, primary, Sriskanthadevan, Shrivani, primary, Datti, Alessandro, primary, Wrana, Jeffery L., primary, Edginton, Andrea, primary, Pawliszyn, Janusz, primary, Joseph, Jamie W., primary, Quadrilatero, Joe, primary, Schimmer, Aaron D., primary, and Spagnuolo, Paul A., primary

Published
2023
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50. Supplementary Figures S1-S9 from Targeting Mitochondria with Avocatin B Induces Selective Leukemia Cell Death

Author

Lee, Eric A., primary, Angka, Leonard, primary, Rota, Sarah-Grace, primary, Hanlon, Thomas, primary, Mitchell, Andrew, primary, Hurren, Rose, primary, Wang, Xiao Ming, primary, Gronda, Marcela, primary, Boyaci, Ezel, primary, Bojko, Barbara, primary, Minden, Mark, primary, Sriskanthadevan, Shrivani, primary, Datti, Alessandro, primary, Wrana, Jeffery L., primary, Edginton, Andrea, primary, Pawliszyn, Janusz, primary, Joseph, Jamie W., primary, Quadrilatero, Joe, primary, Schimmer, Aaron D., primary, and Spagnuolo, Paul A., primary

Published
2023
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