Multi-phase multi-layer mechanistic dermal absorption (MPML MechDermA) model to predict local and systemic exposure of drug products applied on skin.

Physiologically-based pharmacokinetic models combine knowledge about physiology, drug product properties, such as physicochemical parameters, absorption, distribution, metabolism, excretion characteristics, formulation attributes, and trial design or dosing regimen to mechanistically simulate drug pharmacokinetics (PK). The current work describes the development of a multiphase, multilayer mechanistic dermal absorption (MPML MechDermA) model within the Simcyp Simulator capable of simulating uptake and permeation of drugs through human skin following application of drug products to the skin. The model was designed to account for formulation characteristics as well as body site- and sex- population variability to predict local and systemic bioavailability. The present report outlines the structure and assumptions of the MPML MechDermA model and includes results from simulations comparing absorption at multiple body sites for two compounds, caffeine and benzoic acid, formulated as solutions. Finally, a model of the Feldene (piroxicam) topical gel, 0.5% was developed and assessed for its ability to predict both plasma and local skin concentrations when compared to in vivo PK data.

Estimation of Ontogeny Functions for Renal Transporters Using a Combined Population Pharmacokinetic and Physiology-Based Pharmacokinetic Approach: Application to OAT1,3.

To date, information on the ontogeny of renal transporters is limited. Here, we propose to estimate the in vivo functional ontogeny of transporters using a combined population pharmacokinetic (popPK) and physiology-based pharmacokinetic (PBPK) modeling approach called popPBPK. Clavulanic acid and amoxicillin were used as probes for glomerular filtration, combined glomerular filtration, and active secretion through OAT1,3, respectively. The predictive value of the estimated OAT1,3 ontogeny function was assessed by PBPK predictions of renal clearance (CLR) of other OAT1,3 substrates: cefazolin and piperacillin. Individual CLR post-hoc values, obtained from a published popPK model on the concomitant use of clavulanic acid and amoxicillin in critically ill children between 1 month and 15 years, were used as dependent variables in the popPBPK analysis. CLR was re-parameterized according to PBPK principles, resulting in the estimation of OAT1,3-mediated intrinsic clearance (CLint,OAT1,3,invivo) and its ontogeny. CLint,OAT1,3,invivo ontogeny was described by a sigmoidal function, reaching half of adult level around 7 months of age, comparable to findings based on renal transporter-specific protein expression data. PBPK-based CLR predictions including this ontogeny function were reasonably accurate for piperacillin in a similar age range (2.5 months-15 years) as well as for cefazolin in neonates as compared to published data (%RMSPE of 21.2 and 22.8%, respectively and %PE within ±50%). Using this novel approach, we estimated an in vivo functional ontogeny profile for CLint,OAT1,3,invivo that yields accurate CLR predictions for different OAT1,3 substrates across different ages. This approach deserves further study on functional ontogeny of other transporters.

The Influence of Drug Properties and Ontogeny of Transporters on Pediatric Renal Clearance through Glomerular Filtration and Active Secretion: a Simulation-Based Study.

Glomerular filtration (GF) and active tubular secretion (ATS) contribute to renal drug elimination, with the latter remaining understudied across the pediatric age range. Therefore, we systematically analyzed the influence of transporter ontogeny on the relative contribution of GF and ATS to renal clearance CLR for drugs with different properties in children. A physiology-based model for CLR in adults was extrapolated to the pediatric population by including maturation functions for the system-specific parameters. This model was used to predict GF and ATS for hypothetical drugs with a range of drug-specific properties, including transporter-mediated intrinsic clearance (CLint,T) values, that are substrates for renal secretion transporters with different ontogeny patterns. To assess the impact of transporter ontogeny on ATS and total CLR, a percentage prediction difference (%PD) was calculated between the predicted CLR in the presence and absence of transporter ontogeny. The contribution of ATS to CLR ranges between 41 and 90% in children depending on fraction unbound and CLint,T values. If ontogeny of renal transporters is < 0.2 of adult values, CLR predictions are unacceptable (%PD > 50%) for the majority of drugs regardless of the pediatric age. Ignoring ontogeny patterns of secretion transporters increasing with age in children younger than 2 years results in CLR predictions that are not systematically acceptable for all hypothetical drugs (%PD > 50% for some drugs). This analysis identified for what drug-specific properties and at what ages the contribution of ATS on total pediatric CLR cannot be ignored. Drugs with these properties may be sensitive in vivo probes to investigate transporter ontogeny.

The Predictive Value of Glomerular Filtration Rate-Based Scaling of Pediatric Clearance and Doses for Drugs Eliminated by Glomerular Filtration with Varying Protein-Binding Properties.

INTRODUCTION: For drugs eliminated by glomerular filtration (GF), clearance (CL) is determined by GF rate (GFR) and the unbound fraction of the drug. When predicting CL of GF-eliminated drugs in children, instead of physiologically based pharmacokinetic (PBPK) methods that consider changes in both GFR and protein binding, empiric bodyweight-based methods are often used. In this article, we explore the predictive value of scaling using a GFR function, and compare the results with linear and allometric scaling methods for drugs with different protein-binding properties. METHODS: First, different GFR maturation functions were compared to identify the GFR function that would yield the most accurate GFR predictions across the pediatric age range compared with published pediatric inulin/mannitol CL values. Subsequently, the accuracy of pediatric CL scaling using this GFR maturation function was assessed and compared with PBPK CL predictions for hypothetical drugs binding, to varying extents, to serum albumin or α-acid glycoprotein across the pediatric age range. Additionally, empiric bodyweight-based methods were assessed. RESULTS: The published GFR maturation functions yielded comparable maturation profiles, with the function reported by Salem et al. leading to the most accurate predictions. On the basis of this function, GFR-based scaling yields reasonably accurate (percentage prediction error ≤ 50%) pediatric CL values for all drugs, except for some drugs highly bound to AGP in neonates. Overall, this method was more accurate than linear or 0.75 allometric bodyweight-based scaling. CONCLUSION: When scaling CL and dose by GFR function, maturational changes in plasma protein concentrations impact GF minimally, making this method a superior alternative to empiric bodyweight-based scaling.

Amikacin Pharmacokinetics To Optimize Dosing in Neonates with Perinatal Asphyxia Treated with Hypothermia.

Aminoglycoside pharmacokinetics (PK) is expected to change in neonates with perinatal asphyxia treated with therapeutic hypothermia (PATH). Several amikacin dosing guidelines have been proposed for treating neonates with (suspected) septicemia; however, none provide adjustments for cases of PATH. Therefore, we aimed to quantify the differences in amikacin PK between neonates with and without PATH to propose suitable dosing recommendations. Based on amikacin therapeutic drug monitoring data collected retrospectively from neonates with PATH, combined with a published data set, we assessed the impact of PATH on amikacin PK by using population modeling. Monte Carlo and stochastic simulations were performed to establish amikacin exposures in neonates with PATH after dosing according to the current guidelines and according to proposed model-derived dosing guidelines. Amikacin clearance was decreased 40.6% in neonates with PATH, with no changes in volume of distribution. Simulations showed that increasing the dosing interval by 12 h results in a decrease in the percentage of neonates reaching toxic trough levels (>5 mg/liter), from 40 to 76% to 14 to 25%, while still reaching efficacy targets compared to the results of current dosing regimens. Based on this study, a 12-h increase in the amikacin dosing interval in neonates with PATH is proposed to correct for the reduced clearance, yielding safe and effective exposures. As amikacin is renally excreted, further studies into other renally excreted drugs may be required, as their clearance may also be impaired.

Target-Site Investigation for the Plasma Prolactin Response: Mechanism-Based Pharmacokinetic-Pharmacodynamic Analysis of Risperidone and Paliperidone in the Rat.

To understand the drivers in the biological system response to dopamine D2 receptor antagonists, a mechanistic semiphysiologically based (PB) pharmacokinetic-pharmacodymanic (PKPD) model was developed to describe prolactin responses to risperidone (RIS) and its active metabolite paliperidone (PAL). We performed a microdialysis study in rats to obtain detailed plasma, brain extracellular fluid (ECF), and cerebrospinal fluid (CSF) concentrations of PAL and RIS. To assess the impact of P-glycoprotein (P-gp) functioning on brain distribution, we performed experiments in the absence or presence of the P-gp inhibitor tariquidar (TQD). PK and PKPD modeling was performed by nonlinear mixed-effect modeling. Plasma, brain ECF, and CSF PK values of RIS and PAL were well described by a 12-compartmental semi-PBPK model, including metabolic conversion of RIS to PAL. P-gp efflux functionality was identified on brain ECF for RIS and PAL and on CSF only for PAL. In the PKPD analysis, the plasma drug concentrations were more relevant than brain ECF or CSF concentrations to explain the prolactin response; the estimated EC50 was in accordance with reports in the literature for both RIS and PAL. We conclude that for RIS and PAL, the plasma concentrations better explain the prolactin response than do brain ECF or CSF concentrations. This research shows that PKPD modeling is of high value to delineate the target site of drugs.