By Aneesh Argikar, PhD
The disposition of drugs in adult population can be different compared to pediatric population. The notion of “one size fits all” is a misnomer from a drug development perspective for pediatric population. One of the most important reasons for the disparity in disposition between the two populations is the expression levels of proteins that affect the Absorption, Distribution, Metabolism and Excretion (ADME) of drugs. These proteins usually encompass drug metabolizing enzymes (DMEs) and drug transporters. The change in expression of these proteins due to age, disease, enzyme/transporter inhibition and induction produces a significant impact on pharmacokinetics (exposure) and pharmacodynamics (response) of drugs. Hence, consideration of ontogeny or maturation of DMEs and transporters is extremely important for drug development of drugs meant for pediatric populations. A plethora of research has already been performed on investigating the expression and maturation of cytochrome P450 enzymes. The importance of transporters as determinants of drug disposition has been an evolving area since the last decade and the importance of transporter ontogeny is now being recognized and evaluated in greater detail1,2,3.
As per the FDA guidance, OATP1B1, OATP1B3, OATP2B1, NTCP, P-glycoprotein (P-gp), BCRP, MRP2, MRP3, BSEP, MATE1, MATE-2K are considered extremely relevant in drug disposition. The transporter expression levels of multiple liver transporters have been recently evaluated using tandem mass spectrometry. The expression levels of most of the liver transporters were found to be different between neonates and adults. For instance, the transporter expression levels for OCT1, OATP1B3 and P-gp were different between neonates (20-40% of adult expression), infants (40-60% of adult expression), and adults1. Additionally, the mRNA expression levels of liver and intestinal proteins were evaluated using real-time reverse-transcription polymerase chain reaction (real-time RT-PCR)2. A significant difference in the mRNA levels of some efflux and uptake transporters was found between neonates and adults.
Physiologically-based pharmacokinetic models (PBPK) models are mechanistic models that use data such as transporter expression levels, binding constants, maximum velocity of reaction (Vmax) and concentration at which half-maximum velocity of reaction is attained (Michaelis-Menten constant) to predict the concentration-time profile of drugs. These models include transporter expression levels as age dependent parameters, providing much improved predictions in pediatric populations. Thus, the knowledge of differences in the expression level of transporters provide a significant advantage in predicting drug concentrations in pediatric populations.
Using mechanistic modeling approaches will lead to more accurate predictions of systemic exposure in pediatric population. As PBPK modeling takes center stage to help predict drug concentrations in human population and as research moves towards using mechanistic modeling and simulation techniques, the use of simpler and less precise prediction techniques to scale-down doses from adult to pediatric population should be avoided; especially for drugs that are transporter substrates.
Introducing our new team member for Pharmacometrics,
Narasimha Midde, PhD
In light of our mission to bring better medicines to children, KinderPharm continues to add new employees to ensure the ability to deliver the best services and solutions to our clients.
We are pleased to introduce Dr. Narasimha Midde, PhD who has recently joined KinderPharm as a Senior Scientist of Pharmacometrics.
Prior to joining KinderPharm, Dr. Midde spent 7 years working in Clinical Pharmacology and Pharmaceutical Sciences. Dr. Midde’s most recent work was conducted at the University of Tennesse Health Science Center applying Population PK and PK/PD model based evaluation of HIV drugs and nanoformulations. Dr. Midde is a strong Pharmacokineticist with extensive experience and good knowledge in preclinical (rodents) PKPD evaluation of NCEs/drugs for neurological and infectious diseases; well versed with in vitro drug-drug interaction (DDI), cell culture, and enzyme kinetic studies; PK data analysis using Phoenix Winnonlin and statistical tools; Extensive experience with LC-MS/MS method development and validation for small molecules and their metabolites in preclinical and clinical samples.