56 research outputs found
Mathematical characterization of skin absorption
Work on the mathematical modeling and data analysis of skin absorption with focus on finite dosing is presented within this thesis. It could be shown that correction of experimental data based on the area of application should be taken into consideration depending on the drug's lipophilic character. Next, the influence of the lateral compartment for finite dose in-vitro skin mass balance was examined with the help of three mathematical models that could satisfyingly predict or describe the experimental data. Moreover, for two model drugs high amounts of substance could be found in the lateral skin compartment. Finally, a diffusion model was developed to predict in-vivo concentration-depth profiles (C-DP) with model input parameters gathered from in-vitro experiments. In addition, work on prediction of keratin binding is presented using an extended database. Furthermore, it could be shown that skin topography might have a considerable impact on skin absorption. Finally, a mathematical equation to correct Raman signal attenuation to improve non-invasive C-DPs of human skin is presented. In summary, it can be stated that the concepts and investigations at hand could improve experimental data, experimental setups and mathematical models in the field of skin absorption.Arbeiten auf dem Gebiet der mathematischen Modellierung und Analyse von Hautabsorption, mit einem Fokus auf finite Dosierung, werden in dieser Arbeit vorgestellt. Es konnte gezeigt werden, dass abhĂ€ngig von der Lipophilie der Substanz, die Korrektur experimenteller Daten, basierend auf der ApplikationsflĂ€che, in Betracht gezogen werden sollte. Der Einfluss des lateralen Kompartiments auf die in-vitro Massenbilanz bei finiter Dosierung wurde mit Hilfe dreier mathematischer Modelle untersucht, die experimentelle Daten befriedigend vorhersagen oder beschreiben konnten. FĂŒr zwei Modellsubstanzen konnten hohe Substanzmengen im lateralen Hautkompartiment gefunden werden. Zuletzt wurde ein Diffusionsmodell, basierend auf aus in-vitro Experimenten stammenden Modelleingabeparametern, entwickelt, um in-vivo Konzentrations-Schicht-Tiefenprofile (KST) vorherzusagen. In ErgĂ€nzung werden Arbeiten auf dem Gebiet der Vorhersage von Keratinbindung mit Hilfe einer erweiterten Datenbank vorgestellt. AuĂerdem konnte gezeigt werden, dass die Hauttopographie Auswirkungen auf die Hautabsorption haben kann. AbschlieĂend wird eine mathematische Gleichung zur SignaldĂ€mpfungskorrektur bei Raman-Spektroskopie vorgestellt, um nichtinvasive Messungen von KST der menschlichen Haut zu verbessern. Zusammenfassend lĂ€sst sich sagen, dass die hier gezeigten Konzepte und Untersuchungen zur Verbesserung experimenteller Daten, Versuche und mathematischer Modelle im Bereich der Hautabsorption beigetragen konnten
Physiologically Based Precision Dosing Approach for Drug-Drug-Gene Interactions: A Simvastatin Network Analysis
Drugâdrug interactions (DDIs) and drugâgene interactions (DGIs) are well known mediators for adverse drug reactions (ADRs), which are among the leading causes of death in many countries. Because physiologically based pharmacokinetic (PBPK) modeling has demonstrated to be a valuable tool to improve pharmacotherapy affected by DDIs or DGIs, it might also be useful for precision dosing in extensive interaction network scenarios. The presented work proposes a novel approach to extend the prediction capabilities of PBPK modeling to complex drugâdrugâgene interaction (DDGI) scenarios. Here, a wholeâbody PBPK network of simvastatin was established, including three polymorphisms (SLCO1B1 (rs4149056), ABCG2 (rs2231142), and CYP3A5 (rs776746)) and four perpetrator drugs (clarithromycin, gemfibrozil, itraconazole, and rifampicin). Exhaustive network simulations were performed and ranked to optimize 10,368 DDGI scenarios based on an exposure marker cost function. The derived dose recommendations were translated in a digital decision support system, which is available at simvastatin.precisiondosing.de. Although the network covers only a fraction of possible simvastatin DDGIs, it provides guidance on how PBPK modeling could be used to individualize pharmacotherapy in the future. Furthermore, the network model is easily extendable to cover additional DDGIs. Overall, the presented work is a first step toward a vision on comprehensive precision dosing based on PBPK models in daily clinical practice, where it could drastically reduce the risk of ADRs
Physiologically-based pharmacokinetic modeling of dextromethorphan to investigate interindividual variability within CYP2D6 activity score groups
This study provides a whole-body physiologically-based pharmacokinetic (PBPK) model of dextromethorphan and its metabolites dextrorphan and dextrorphan O-glucuronide for predicting the effects of cytochrome P450 2D6 (CYP2D6) drug-gene interactions (DGIs) on dextromethorphan pharmacokinetics (PK). Moreover, the effect of interindividual variability (IIV) within CYP2D6 activity score groups on the PK of dextromethorphan and its metabolites was investigated. A parent-metabolite-metabolite PBPK model of dextromethorphan, dextrorphan, and dextrorphan O-glucuronide was developed in PK-Sim and MoBi. Drug-dependent parameters were obtained from the literature or optimized. Plasma concentration-time profiles of all three analytes were gathered from published studies and used for model development and model evaluation. The model was evaluated comparing simulated plasma concentration-time profiles, area under the concentration-time curve from the time of the first measurement to the time of the last measurement (AUClast) and maximum concentration (Cmax) values to observed study data. The final PBPK model accurately describes 28 population plasma concentration-time profiles and plasma concentration-time profiles of 72 individuals from four cocktail studies. Moreover, the model predicts CYP2D6 DGI scenarios with six of seven DGI AUClast and seven of seven DGI Cmax ratios within the acceptance criteria. The high IIV in plasma concentrations was analyzed by characterizing the distribution of individually optimized CYP2D6 kcat values stratified by activity score group. Population simulations with sampling from the resulting distributions with calculated log-normal dispersion and mean parameters could explain a large extent of the observed IIV. The model is publicly available alongside comprehensive documentation of model building and model evaluation
Data Digitizing: Accurate and Precise Data Extraction for Quantitative Systems Pharmacology and Physiologically-Based Pharmacokinetic Modeling
In quantitative systems pharmacology (QSP) and physiologically-based pharmacokinetic (PBPK) modeling, data digitizing is a valuable tool to extract numerical information from published data presented as graphs. To quantify their relevance, a literature search revealed a remarkable mean increase of 16% per year in publications citing digitizing software together with QSP or PBPK. Accuracy, precision, confounder influence, and variability were investigated using scaled median symmetric accuracy (ζ), thus finding excellent accuracy (mean ζ = 0.99%). Although significant, no relevant confounders were found (mean ζ ± SD circles = 0.69% ± 0.68% vs. triangles = 1.3% ± 0.62%). Analysis of 181 literature peak plasma concentration values revealed a considerable discrepancy between reported and post hoc digitized data with 85% having ζ > 5%. Our findings suggest that data digitizing is precise and important. However, because the greatest pitfall comes from pre-existing errors, we recommend always making published data available as raw values
Physiologically Based Pharmacokinetic Modeling of Bergamottin and 6,7âDihydroxybergamottin to Describe CYP3A4 Mediated GrapefruitâDrug Interactions
Grapefruit is a moderate to strong inactivator of CYP3A4, which metabolizes up to 50% of marketed drugs. The
inhibitory effect is mainly attributed to furanocoumarins present in the fruit, irreversibly inhibiting preferably intestinal
CYP3A4 as suicide inhibitors. Effects on CYP3A4 victim drugs can still be measured up to 24hours after grapefruit
juice (GFJ) consumption. The current study aimed to establish a physiologically-based pharmacokinetic (PBPK)
grapefruit-drug interaction model by modeling the relevant CYP3A4 inhibiting ingredients of the fruit to simulate
and predict the effect of GFJ consumption on plasma concentration-time profiles of various CYP3A4 victim drugs.
The grapefruit model was developed in PK-Sim and coupled with previously developed PBPK models of CYP3A4
substrates that were publicly available and already evaluated for CYP3A4-mediated drugâdrug interactions. Overall,
43 clinical studies were used for model development. Models of bergamottin (BGT) and 6,7-dihydroxybergamottin
(DHB) as relevant active ingredients in GFJ were established. Both models include: (i) CYP3A4 inactivation informed
by in vitro parameters, (ii) a CYP3A4 mediated clearance estimated during model development, as well as (iii) passive
glomerular filtration. The final model successfully describes interactions of GFJ ingredients with 10 different CYP3A4
victim drugs, simulating the effect of the CYP3A4 inactivation on the victimsâ pharmacokinetics as well as their main
metabolites. Furthermore, the model sufficiently captures the time-dependent effect of CYP3A4 inactivation as well
as the effect of grapefruit ingestion on intestinal and hepatic CYP3A4 concentrations
Pharmacokinetics of the CYP3A4 and CYP2B6 Inducer Carbamazepine and Its DrugâDrug Interaction Potential: A Physiologically Based Pharmacokinetic Modeling Approach
The anticonvulsant carbamazepine is frequently used in the long-term therapy of epilepsy
and is a known substrate and inducer of cytochrome P450 (CYP) 3A4 and CYP2B6. Carbamazepine
induces the metabolism of various drugs (including its own); on the other hand, its metabolism can
be affected by various CYP inhibitors and inducers. The aim of this work was to develop a physiologically based pharmacokinetic (PBPK) parentâmetabolite model of carbamazepine and its metabolite
carbamazepine-10,11-epoxide, including carbamazepine autoinduction, to be applied for drugâdrug
interaction (DDI) prediction. The model was developed in PK-Sim, using a total of 92 plasma
concentrationâtime profiles (dosing range 50â800 mg), as well as fractions excreted unchanged in
urine measurements. The carbamazepine model applies metabolism by CYP3A4 and CYP2C8 to
produce carbamazepine-10,11-epoxide, metabolism by CYP2B6 and UDP-glucuronosyltransferase
(UGT) 2B7 and glomerular filtration. The carbamazepine-10,11-epoxide model applies metabolism by
epoxide hydroxylase 1 (EPHX1) and glomerular filtration. Good DDI performance was demonstrated
by the prediction of carbamazepine DDIs with alprazolam, bupropion, erythromycin, efavirenz and
simvastatin, where 14/15 DDI AUClast ratios and 11/15 DDI Cmax ratios were within the prediction
success limits proposed by Guest et al. The thoroughly evaluated model will be freely available in
the Open Systems Pharmacology model repository
Physiologically Based Pharmacokinetic Modeling of Bupropion and Its Metabolites in a CYP2B6 Drug-Drug-Gene Interaction Network
The noradrenaline and dopamine reuptake inhibitor bupropion is metabolized by CYP2B6
and recommended by the FDA as the only sensitive substrate for clinical CYP2B6 drugâdrug interaction (DDI) studies. The aim of this study was to build a whole-body physiologically based
pharmacokinetic (PBPK) model of bupropion including its DDI-relevant metabolites, and to qualify
the model using clinical drugâgene interaction (DGI) and DDI data. The model was built in PK-SimÂź
applying clinical data of 67 studies. It incorporates CYP2B6-mediated hydroxylation of bupropion,
metabolism via CYP2C19 and 11ÎČ-HSD, as well as binding to pharmacological targets. The impact
of CYP2B6 polymorphisms is described for normal, poor, intermediate, and rapid metabolizers,
with various allele combinations of the genetic variants CYP2B6*1, *4, *5 and *6. DDI model performance was evaluated by prediction of clinical studies with rifampicin (CYP2B6 and CYP2C19
inducer), fluvoxamine (CYP2C19 inhibitor) and voriconazole (CYP2B6 and CYP2C19 inhibitor).
Model performance quantification showed 20/20 DGI ratios of hydroxybupropion to bupropion
AUC ratios (DGI AUCHBup/Bup ratios), 12/13 DDI AUCHBup/Bup ratios, and 7/7 DDGI AUCHBup/Bup
ratios within 2-fold of observed values. The developed model is freely available in the Open Systems
Pharmacology model repository
In VitroâIn Silico Modeling of Caffeine and Diclofenac Permeation in Static and Fluidic Systems with a 16HBE Lung Cell Barrier
Static in vitro permeation experiments are commonly used to gain insights into the permeation properties of drug substances but exhibit limitations due to missing physiologic cell stimuli.
Thus, fluidic systems integrating stimuli, such as physicochemical fluxes, have been developed.
However, as fluidic in vitro studies display higher complexity compared to static systems, analysis
of experimental readouts is challenging. Here, the integration of in silico tools holds the potential
to evaluate fluidic experiments and to investigate specific simulation scenarios. This study aimed
to develop in silico models that describe and predict the permeation and disposition of two model
substances in a static and fluidic in vitro system. For this, in vitro permeation studies with a 16HBE
cellular barrier under both static and fluidic conditions were performed over 72 h. In silico models
were implemented and employed to describe and predict concentrationâtime profiles of caffeine and
diclofenac in various experimental setups. For both substances, in silico modeling identified reduced
apparent permeabilities in the fluidic compared to the static cellular setting. The developed in vitroâin
silico modeling framework can be expanded further, integrating additional cell tissues in the fluidic
system, and can be employed in future studies to model pharmacokinetic and pharmacodynamic
drug behavior
Renal Transporter-Mediated Drug-Biomarker Interactions of the Endogenous Substrates Creatinine and N1 -Methylnicotinamide : A PBPK Modeling Approach
Endogenous biomarkers for transporter-mediated drug-drug interaction (DDI) predictions represent a promising approach to facilitate and improve conventional DDI investigations in clinical studies. This approach requires high sensitivity and specificity of biomarkers for the targets of interest (e.g., transport proteins), as well as rigorous characterization of their kinetics, which can be accomplished utilizing physiologically-based pharmacokinetic (PBPK) modeling. Therefore, the objective of this study was to develop PBPK models of the endogenous organic cation transporter (OCT)2 and multidrug and toxin extrusion protein (MATE)1 substrates creatinine and N1-methylnicotinamide (NMN). Additionally, this study aimed to predict kinetic changes of the biomarkers during administration of the OCT2 and MATE1 perpetrator drugs trimethoprim, pyrimethamine, and cimetidine. Whole-body PBPK models of creatinine and NMN were developed utilizing studies investigating creatinine or NMN exogenous administration and endogenous synthesis. The newly developed models accurately describe and predict observed plasma concentration-time profiles and urinary excretion of both biomarkers. Subsequently, models were coupled to the previously built and evaluated perpetrator models of trimethoprim, pyrimethamine, and cimetidine for interaction predictions. Increased creatinine plasma concentrations and decreased urinary excretion during the drug-biomarker interactions with trimethoprim, pyrimethamine, and cimetidine were well-described. An additional inhibition of NMN synthesis by trimethoprim and pyrimethamine was hypothesized, improving NMN plasma and urine interaction predictions. To summarize, whole-body PBPK models of creatinine and NMN were built and evaluated to better assess creatinine and NMN kinetics while uncovering knowledge gaps for future research. The models can support investigations of renal transporter-mediated DDIs during drug development
A Physiologically Based Pharmacokinetic and Pharmacodynamic Model of the CYP3A4 Substrate Felodipine for DrugâDrug Interaction Modeling
The antihypertensive felodipine is a calcium channel blocker of the dihydropyridine type,
and its pharmacodynamic effect directly correlates with its plasma concentration. As a sensitive
substrate of cytochrome P450 (CYP) 3A4 with high first-pass metabolism, felodipine shows low oral
bioavailability and is susceptible to drugâdrug interactions (DDIs) with CYP3A4 perpetrators. This
study aimed to develop a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD)
parentâmetabolite model of felodipine and its metabolite dehydrofelodipine for DDI predictions. The
model was developed in PK-SimÂź and MoBiÂź using 49 clinical studies (94 plasma concentrationâtime
profiles in total) that investigated different doses (1â40 mg) of the intravenous and oral adminis tration of felodipine. The final model describes the metabolism of felodipine to dehydrofelodipine
by CYP3A4, sufficiently capturing the first-pass metabolism and the subsequent metabolism of
dehydrofelodipine by CYP3A4. Diastolic blood pressure and heart rate PD models were included,
using an Emax function to describe the felodipine concentrationâeffect relationship. The model was
tested in DDI predictions with itraconazole, erythromycin, carbamazepine, and phenytoin as CYP3A4
perpetrators, with all predicted DDI AUClast and Cmax ratios within two-fold of the observed values.
The model will be freely available in the Open Systems Pharmacology model repository and can be
applied in DDI predictions as a CYP3A4 victim drug
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