Husna Rahim, PharmD & Archana Gurudu
Translating Adult-Derived Pharmacogenomic Data to Pediatric Populations. Can pharmacogenomic (PGx) data derived from adult-studied populations translate to pediatric patients? While PGx data from adults serves as a universal baseline for interpreting genetic variants across all populations, it may not fully account for the unique physiological and developmental differences in pediatric patients. Understanding developmental shifts throughout childhood and implementing modern solutions like advanced computational modeling and routine PGx testing are essential to overcome these challenges.
Enzyme activity levels fluctuate dynamically throughout childhood. Developmental differences in gene expression impact pediatric pharmacokinetics and pharmacodynamics in ways distinct from adults (Leeder 2014). For example, enzyme activity levels—such as CYP2D6 and CYP2C19—fluctuate dynamically throughout childhood, creating unique challenges when compounded by genetic polymorphisms (Bousman, 2023). The Clinical Pharmacogenetics Implementation Consortium (CPIC) has recommendations for gene-drug pairs, but relies heavily on adult data, underscoring the need for additional pediatric validation (Kim et al., 2015).
AI and PBPK modeling can generate simulations for real time clinical decision-making. Physiologically based pharmacokinetic (PBPK) modeling uses patient-specific data, including genomics and stages of enzyme maturity (ontogenesis), to predict pediatric drug exposure based on validated adult models. Adults and children exhibit different levels of clearance and bioavailability, which are captured in these models. Additionally, PBPK models assist in assessing drug-drug interactions (DDIs), especially critical in neonates and infants where empirical DDI data remains scarce (Maglalang, et al. 2024).
Real-time data-sharing lowers barriers to implementing precision medicine. Furthermore, implementing genetic testing into routine pediatric care can not only help overcome the limitations of current PGx data but also enable clinicians to proactively adjust medication regimens based on individual genetic profiles (Jordan et al., 2023). Real-time data-sharing frameworks further facilitate access to accumulated PGx evidence, lowering barriers to implementing precision medicine for children across healthcare settings (Green et al., 2016).
Learn more about UGenome’s Personalized Medication Service, ProPEx, or contact UGenome. You can also find case studies for UGenome’s bioinformatics services Metabolite Identification, Bone Metastasis Risk Analysis in Breast Cancer, Survival Analysis with gene signatures in cancer
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