No population left behind: Improving paediatric drug safety using informatics and systems biology

Giangreco, Nicholas; Elias, Jonathan E.; Tatonetti, Nicholas P.

doi:10.1111/bcp.14705

Cited by 11 publications

(8 citation statements)

References 112 publications

(171 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data is a burgeoning area with a significant potential to impact pharmacometrics. Giangreco et al 14…”

Section: Leveraging Artificial Intelligence Machine Learning and Bigmentioning

confidence: 99%

“…Leveraging Artificial Intelligence, Machine Learning and Big Data is a burgeoning area with a significant potential to impact pharmacometrics. Giangreco et al 14 provide a vision for improving drug safety in paediatrics using state of the art approaches that combine informatics and systems biology to address continuing challenges and leverage machine learning techniques that can identify risk in this area. Wang et al 15 introduce natural language processing strategies for identifying potential pharmacokinetic drug interactions through logical operators evaluating online information providing the basis for hypothesis generation and more specific exploration using translational informatics approaches.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Perspectives on the past, present, and future of pharmacometrics

Bies

Wright

2022

Brit J Clinical Pharma

View full text Add to dashboard Cite

In October 1966, the futuristic television show Star Trek aired an episode called "Miri" in which the crew of the starship Enterprise became trapped on a planet in the middle of a viral pandemic. The ship's doctor, clearly well trained in medical chemistry, drug delivery, and biopharmaceutics, quickly developed a remedy. However, the science officer was not able to access the ships computer to conduct the necessary simulations to find a safe and effective dose. They were forced to test possible doses using a trial and error approach, nearly poisoning a crew member in the process. Unknowingly, the writers of this episode correctly predicted the important place that pharmacometrics would eventually assume in drug development.Pharmacometrics is the use of mathematical models to describe and predict drug response. It is now an integral part of Phase 1, 2, and 3 data analysis and an important methodology for preclinical and postmarketing trials. As stated by Gobburu 1 in his insightful commentary

show abstract

“…Data is a burgeoning area with a significant potential to impact pharmacometrics. Giangreco et al 14…”

Section: Leveraging Artificial Intelligence Machine Learning and Bigmentioning

confidence: 99%

mentioning

confidence: 99%

Perspectives on the past, present, and future of pharmacometrics

Bies

Wright

2022

Brit J Clinical Pharma

View full text Add to dashboard Cite

show abstract

“…However, the current pediatric drug safety landscape, including clinical trials, is limited as it rarely includes children and relies on extrapolation from adults. Children have immature organ function and a different spectrum of diseases compared with adults, and it is proposed that pediatric drug safety should comprehensively consider children’s systems biology ( Giangreco et al, 2022 ). Hence, accurate methods for post-marketing drug safety surveillance and signal detection of DIC in children are urgently needed.…”

Section: Introductionmentioning

confidence: 99%

Signal Detection of Pediatric Drug–Induced Coagulopathy Using Routine Electronic Health Records

Nie

Jia

et al. 2022

Front. Pharmacol.

View full text Add to dashboard Cite

Background: Drug-induced coagulopathy (DIC) is a severe adverse reaction and has become a significantly increased clinical problem in children. It is crucial to the detection of the DIC safety signal for drug post-marketing scientific supervision purposes. Therefore, this study aimed to detect potential signals for DIC in children using the routine electronic medical record (EMR) data.Methods: This study extracted EMR data from Beijing Children’s Hospital between 2009 and 2020. A two-stage modeling method was developed to detect the signal of DIC. We calculated the crude incidence by mining cases of coagulopathy to select the potential suspected drugs; then, propensity score-matched retrospective cohorts of specific screened drugs from the first stage were constructed and estimated the odds ratio (OR) and 95% confidence interval (CI) using conditional logistic regression models. The current literature evidence was used to assess the novelty of the signal.Results:In the study, from a total of 340 drugs, 22 drugs were initially screened as potentially inducing coagulopathy. In total, we identified 19 positive DIC associations. Of these, potential DIC risk of omeprazole (OR: 2.23, 95% CI: 1.88–2.65), chlorpheniramine (OR:3.04, 95% CI:2.56–3.60), and salbutamol sulfate (OR:1.36, 95% CI:1.07–1.73) were three new DIC signals in both children and adults. Twelve associations between coagulopathy and drugs, meropenem (OR: 3.38, 95% CI: 2.72–4.20), cefoperazone sulbactam (OR: 2.80, 95% CI: 2.30–3.41), fluconazole (OR: 2.11, 95% CI: 1.71–2.59), voriconazole (OR: 2.82, 95% CI: 2.20–3.61), ambroxol hydrochloride (OR: 2.12, 95% CI: 1.74–2.58), furosemide (OR: 2.36, 95% CI: 2.08–2.67), iodixanol (OR: 2.21, 95% CI: 1.72–2.85), cefamandole (OR: 1.82, 95% CI: 1.56–2.13), ceftizoxime (OR: 1.95, 95% CI: 1.44–2.63), ceftriaxone (OR: 1.95, 95% CI: 1.44–2.63), latamoxef sodium (OR: 1.76, 95% CI: 1.49–2.07), and sulfamethoxazole (OR: 1.29, 95% CI: 1.01–1.64), were considered as new signals in children.Conclusion: The two-stage algorithm developed in our study to detect safety signals of DIC found nineteen signals of DIC, including twelve new signals in a pediatric population. However, these safety signals of DIC need to be confirmed by further studies based on population study and mechanism research.

show abstract

“…Adverse drug events (ADEs) in children are common and can result in injury and death 1,2 . Clinical trials rarely include children 3 and pediatric-specific trials are limited in identifying possible ADEs in the population 4 . Pediatric drug safety studies can evaluate large numbers of ADEs from the population 5 but current methodologies are limited in their ability to identify the mechanisms that drive pediatric ADEs 6 .…”

Section: Introductionmentioning

confidence: 99%

Evaluating risk detection methods to uncover ontogenic-mediated adverse drug effect mechanisms in children

Giangreco

Tatonetti

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Identifying adverse drugs effects (ADEs) in children is essential for preventing disability and death from marketed drugs. At the same time, however, detection is challenging due to dynamic biological processes during growth and maturation, called ontogeny, that alter pharmacokinetics and pharmacodynamics. As a result, current data mining methodologies have been limited to event surveillance and have not focused on investigating adverse event mechanisms. There is an opportunity to design data mining methodologies to identify and evaluate drug event patterns within observational databases for ontogenic-mediated adverse event mechanisms. The first step of which is to establish statistical models that can identify temporal trends of adverse effects across childhood. Results: Using simulation, we evaluated a population stratification method (the proportional reporting ratio or PRR) and a population modeling method (the generalized additive model or GAM) to identify and quantify ADE risk at varying reporting rates and dynamics. We found that GAMs showed improved performance over the PRR in detecting dynamic drug event reporting across child developmental stages. Moreover, GAMs exhibited normally distributed and robust ADE risk estimation at all development stages by sharing information across child development stages. Conclusions: Our study underscores the opportunity for using population modeling techniques, which leverages drug event reporting across development stages, to identify adverse drug effect risk resulting from ontogenic mechanisms.

show abstract

No population left behind: Improving paediatric drug safety using informatics and systems biology

Cited by 11 publications

References 112 publications

Perspectives on the past, present, and future of pharmacometrics

Perspectives on the past, present, and future of pharmacometrics

Signal Detection of Pediatric Drug–Induced Coagulopathy Using Routine Electronic Health Records

Evaluating risk detection methods to uncover ontogenic-mediated adverse drug effect mechanisms in children

Contact Info

Product

Resources

About