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Medications, including many that are widely used in children, are rarely tested in children. Their safety and effectiveness have not been established in children or have been established only for children of a certain ages. For example, 53 of 140 new medications were labeled for use in children when they were initially approved. Much of pediatric practice, particularly in hospitals and by specialists, has involved “offlabel” use of medications. As a consequence, about one-third of the drugs prescribed in office-based pediatric practices and about two-thirds of those prescribed to children in hospital settings are unlicensed or off-label. This proportion reaches 90% in intensive care units. Thus, accurate information about dosage in different age groups is particularly important, otherwise, children might be over or under treated. The safety of a drug in children often cannot be extrapolated from data in adults; medications may be more or less toxic in children and require specific studies. The effectiveness of a drug can be extrapolated from an adult when no relevant differences can be anticipated in disease, disease progression or exposure-effect relationships. Such data are usually supplemented with pharmacokinetic studies. Drug clearance (CL) is a principle PK parameter determining the age-dependent difference. Therefore, predicting pediatric clearance accurately is crucial for appropriate pediatric dosage regimen. Body weight normalized clearance is widely used because of its simplicity but it does not account the maturation of enzymatic and organ functions. As a result, children is often overdosed because of the over estimation of clearance, especially in neonates and young infants. Recently, anticipation of clearance is built based on more complex mathematical models, which could take a data or knowledge-driven approach by employing either observed data (top-down) or knowledge of human body (bottomup). These two approaches depend on different starting information and are likely to be used in conjunction with each other for the purposes of defining pediatric dosing guidelines. This research first focused on the bottom-up, mechanistic models for predicting age-dependent hepatic and renal elimination. The maturation of specific cytochrome P450 enzymes was modeled based the in vitro ontogeny data from hepatic microsomal studies. The age-related function then was incorporated into a well-stirred liver model with the developmental changes of other physiological factors, achieving the extrapolation from in vitro to in vivo. The model predictions using this physiologybased approach proved to be more accurate and precise than the allometric functions, in neonates and young infants less than 1 year of old for renal and hepatic enzyme eliminated drugs. In the top-down approach, population analysis is commonly used in pediatric PK studies because of heterogeneity of the study population and sparse sampling. The covariates effects such as age and size was examined in simple exponential relationships. However, these estimates, despite statistical validation in the observed population, may not suffice to predict parameters distribution and drug exposure in a new population. A more complex covariate-parameter relationship was explored to describe the maturation process and changes in physiological functions, based on the clearance values of renal and hepatic eliminated probe drugs. In these cases, the maturation information derived from metabolic probes could be used as system specific covariate models for other drugs via the same elimination routes in neonates

Prediction of Heptatic and Renal Clearance in Pediatric Populations: A &quot;Bottom-Up&quot; Approach Versus &quot;Top-Down&quot; Recognition of Covariates