With the advent of the various pediatric initiatives and regulatory requirements by the FDA, such as the “Best Pharmaceuticals for Children Act (BPCA)” and the “Pediatric Research Equity Act (PREA) as well as similar pediatric legislation introduced by the EMA in Europe (such as REGULATION (EC) No 1902/2006), pharmaceutical companies are now obliged to formally address the needs of children during the clinical development of a new drug.
As the importance of conducting pediatric clinical trials has become increasingly recognized, the historical perspective of “protecting children from research” has been largely replaced by the notion of “protecting children through research”. This is especially so when one considers the inherent risks involved in prescribing an “off-label”, extemporaneously formulated drug to a child, often without any formal knowledge of the drug’s safety and efficacy. In addition, there is often an incomplete understanding of the appropriate dose and dosing regimen to use in children at different ages and stages of maturation.
Various methods to scale “adult doses” to children have been used over the years. These have included empirical allometric scaling methods based on bodyweight or body surface area. Although allometric scaling is generally acceptable in adolescents and older children, the predictive power of this method can be compromised in neonates, infants and young children. There are a number of reasons for this. Apart from differences in bodyweight, drug disposition can be significantly influenced by factors such as body composition (fat, protein, water content etc), plasma protein binding, enzyme (CYP and transporter) expression and the maturation in hepatic and renal function with age. Ignoring these factors can lead to either an over-prediction or under-prediction of the dose and exposure depending on the drug in question. So how can pharmacometric approaches such as Physiologically Based PK (PBPK) modeling help?
PBPK modeling is an advanced PK modeling & simulation approach that integrates the physicochemical properties of the molecule with the fundamental processes involved in drug absorption, distribution metabolism and elimination (ADME) using physiologically and anatomically relevant computer-based models.
In contrast to more empirical PK modeling techniques, PBPK modeling takes into account factors such as drug solubility, lipophilicity, ionization, and dissolution together with enzymology (e.g. CYP substrate/inhibitor data), transporter, and protein binding characteristics of the molecule to build a predictive PK model.
Variability in PK can be evaluated by creating virtual pediatric patient populations from the underlying databases of demographic, physiological and anatomical information within the software. This allows factors such as variability in body composition, organ weight, tissue perfusion, blood & lymph flow, gastric emptying time, GI transit and organ (renal/hepatic) function on drug disposition to be evaluated.
The models also have the ability to assess the impact of differential gene expression profiles of multiple uptake and efflux transporters and drug metabolism enzymes in various organs and tissues. In addition, the formulation and in vitro dissolution characteristics of a molecule can be incorporated into the model to assess how these characteristics are likely to influence the rate and extent of drug absorption in both fed and fasted conditions.
Because of these powerful capabilities, PBPK modeling is emerging as a key enabling technology in pediatric drug development and the importance and potential of this approach has been highlighted in a several recent pediatric Regulatory Guidances, reviews and position papers from the FDA, EMA and industry.
Why is modeling and simulation so important in pediatric drug research? The short answer is, that as a research community, we need to minimize the number of children that are required to participate in clinical trials whilst maximizing the information we can derive from such studies. Pharmacometric modeling and simulation approaches are uniquely suited to such a task.
For example, under certain circumstances where the disease can be shown to be the same in adults and children, and an exposure-response (PK/PD) relationship can be demonstrated in adults, it may be possible to reduce the number of children that are required to participate in a trial by extrapolating the efficacy data from adults through pharmacokinetic “exposure-matching” principles. Indeed, the 2014 FDA Guidance for Industry on “General Clinical Pharmacology Considerations for Pediatric Studies for Drugs and Biological Products” provides a framework and algorithm for pediatric extrapolation based on PK. Similar approaches are also accepted by the EMA, and extrapolation methods are now an integral part of pediatric study plan templates (iPSPs and PIPs) in both the US and EU.
Another important pharmacometric technique, “Population PK Modeling”, is also used extensively in pediatric drug development. Through the use of Population PK modeling it is possible to obtain detailed pharmacokinetic information in children from a limited number of “sparse blood samples”. This is an important consideration from both an ethical and practical perspective as it helps reduce barriers to enrollment by reducing the number of invasive procedures (blood draws) as well as minimizing the volume and frequency of blood sampling.
Given the growing number of trials that are now being performed to support pediatric labeling, it is envisioned that these powerful pharmacometric techniques will become even more widely adopted and accepted by industry as a critical component in pediatric drug development.
Contributed by Dr Martin Graham PhD, President & CEO, KinderPharm, Exton PA.
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