Integration of pharmacogenetics and pharmacogenomics in drug development: implications for regulatory and medical decision making in pediatric diseases.

This article aims to provide an overview of the current situation regarding pharmacogenetic and pharmacogenomic (PG) studies in pediatrics, with a special focus on the role of PG data in the regulatory decision-making process. Despite the gap in pharmacogenetic research due to the lack of translational studies in adults and children, several technologies exist in drug development and biomarkers validation, which could supply valuable information concerning labeling and dosing recommendations. If performed under strict good clinical practice quality criteria, such findings could be included in the submission package of new chemical entities and used as additional information for prescribers, supporting further evaluation and understanding of the efficacy and safety profile of new medicines. Even though regulatory authorities may be aware of the potential role of PG in medical practice and guidances are available about the integration of PG in drug development, most data obtained from PG studies are not used by prescribers. The challenge is to better understand whether PG markers can be used to assess potential differences in drug response during the clinical program, so PG data can be integrated into the regulatory decision-making process, enabling the introduction of labeling information that promotes optimal dosing in the pediatric population.

Morphine glucuronidation in preterm neonates, infants and children younger than 3 years.

BACKGROUND AND OBJECTIVE: A considerable amount of drug use in children is still unlicensed or off-label. In order to derive rational dosing schemes, the influence of aging on glucuronidation capacity in newborns, including preterms, infants and children under the age of 3 years was studied using morphine and its major metabolites as a model drug. METHODS: A population pharmacokinetic model was developed with the nonlinear mixed-effects modelling software NONMEM V, on the basis of 2159 concentrations of morphine and its glucuronides from 248 infants receiving intravenous morphine ranging in bodyweight from 500 g to 18 kg (median 2.8 kg). The model was internally validated using normalized prediction distribution errors. RESULTS: Formation clearances of morphine to its glucuronides and elimination clearances of the glucuronides were found to be primarily influenced by bodyweight, which was parameterized using an allometric equation with an estimated exponential scaling factor of 1.44. Additionally, a postnatal age of less than 10 days was identified as a covariate for formation clearance to the glucuronides, independent of birthweight or postmenstrual age. Distribution volumes scaled linearly with bodyweight. CONCLUSIONS: Model-based simulations show that in newborns, including preterms, infants and children under the age of 3 years, a loading dose in microg/kg and a maintenance dose expressed in microg/kg1.5/h, with a 50% reduction of the maintenance dose in newborns younger than 10 days, results in a narrow range of morphine and metabolite serum concentrations throughout the studied age range. Future pharmacodynamic investigations are needed to reveal target concentrations in this population, after which final dosing recommendations can be made.

Pharmacogenetics and paediatric drug development: issues and consequences to labelling and dosing recommendations.

The area of pharmacogenetics (PGt) is evolving rapidly. However, ongoing efforts in this field are not aligned with the requirements for the inclusion of clinically relevant findings into the label, especially with reference to paediatric indications. Clinical research in children poses unique issues from a practical and technical perspective, but many challenges can be overcome by applying advanced study design and data analysis methods. When investigating the role of PGt factors on treatment effect, all features that influence drug response must be taken into account. Yet, PGt often has a privileged status in research protocols, with PGt factors evaluated independently from other determinants of response, instead of being regarded as other demographic or clinical covariates (e.g., age, renal function). At present, guidelines to incorporate PGt findings into label statements are lacking in part because this is a new and incompletely understood area. This situation is no longer acceptable. To achieve the potential that PGt can offer to drug development and ultimately to drug prescription, academia, industry and regulatory agencies need to pool resources on the revision of study design and data analysis requisites, bringing in model-based methodologies to enable accurate interpretation of results and provide appropriate labelling recommendations.