Personalized Chronomodulated 5-Fluorouracil Treatment: A Physiologically-Based Pharmacokinetic Precision Dosing Approach for Optimizing Cancer Therapy.

The discovery of circadian clock genes greatly amplified the study of diurnal variations impacting cancer therapy, transforming it into a rapidly growing field of research. Especially, use of chronomodulated treatment with 5-fluorouracil (5-FU) has gained significance. Studies indicate high interindividual variability (IIV) in diurnal variations in dihydropyrimidine dehydrogenase (DPD) activity - a key enzyme for 5-FU metabolism. However, the influence of individual DPD chronotypes on chronomodulated therapy remains unclear and warrants further investigation. To optimize precision dosing of chronomodulated 5-FU, this study aims to: (i) build physiologically-based pharmacokinetic (PBPK) models for 5-FU, uracil, and their metabolites, (ii) assess the impact of diurnal variation on DPD activity, (iii) estimate individual DPD chronotypes, and (iv) personalize chronomodulated 5-FU infusion rates based on a patient's DPD chronotype. Whole-body PBPK models were developed with PK-Sim(R) and MoBi(R). Sinusoidal functions were used to incorporate variations in enzyme activity and chronomodulated infusion rates as well as to estimate individual DPD chronotypes from DPYD mRNA expression or DPD enzymatic activity. Four whole-body PBPK models for 5-FU, uracil, and their metabolites were established utilizing data from 41 5-FU and 10 publicly available uracil studies. IIV in DPD chronotypes was assessed and personalized chronomodulated administrations were developed to achieve (i) comparable 5-FU peak plasma concentrations, (ii) comparable 5-FU exposure, and (iii) constant 5-FU plasma levels via "noise cancellation" chronomodulated infusion. The developed PBPK models capture the extent of diurnal variations in DPD activity and can help investigate individualized chronomodulated 5-FU therapy through testing alternative personalized dosing strategies.

A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug-Drug Interaction Perpetrators.

The antifungal ketoconazole, which is mainly used for dermal infections and treatment of Cushing's syndrome, is prone to drug-food interactions (DFIs) and is well known for its strong drug-drug interaction (DDI) potential. Some of ketoconazole's potent inhibitory activity can be attributed to its metabolites that predominantly accumulate in the liver. This work aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model of ketoconazole and its metabolites for fasted and fed states and to investigate the impact of ketoconazole's metabolites on its DDI potential. The parent-metabolites model was developed with PK-Sim® and MoBi® using 53 plasma concentration-time profiles. With 7 out of 7 (7/7) DFI AUClast and DFI Cmax ratios within two-fold of observed ratios, the developed model demonstrated good predictive performance under fasted and fed conditions. DDI scenarios that included either the parent alone or with its metabolites were simulated and evaluated for the victim drugs alfentanil, alprazolam, midazolam, triazolam, and digoxin. DDI scenarios that included all metabolites as reversible inhibitors of CYP3A4 and P-gp performed best: 26/27 of DDI AUClast and 21/21 DDI Cmax ratios were within two-fold of observed ratios, while DDI models that simulated only ketoconazole as the perpetrator underperformed: 12/27 DDI AUClast and 18/21 DDI Cmax ratios were within the success limits.

Physiologically Based Precision Dosing Approach for Drug-Drug-Gene Interactions: A Simvastatin Network Analysis.

Drug-drug interactions (DDIs) and drug-gene 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 drug-drug-gene interaction (DDGI) scenarios. Here, a whole-body 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 Metoprolol Enantiomers and α-Hydroxymetoprolol to Describe CYP2D6 Drug-Gene Interactions.

The beta-blocker metoprolol (the sixth most commonly prescribed drug in the USA in 2017) is subject to considerable drug-gene interaction (DGI) effects caused by genetic variations of the CYP2D6 gene. CYP2D6 poor metabolizers (5.7% of US population) show approximately five-fold higher metoprolol exposure compared to CYP2D6 normal metabolizers. This study aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model to predict CYP2D6 DGIs with metoprolol. The metoprolol (R)- and (S)-enantiomers as well as the active metabolite α-hydroxymetoprolol were implemented as model compounds, employing data of 48 different clinical studies (dosing range 5-200 mg). To mechanistically describe the effect of CYP2D6 polymorphisms, two separate metabolic CYP2D6 pathways (α-hydroxylation and O-demethylation) were incorporated for both metoprolol enantiomers. The good model performance is demonstrated in predicted plasma concentration-time profiles compared to observed data, goodness-of-fit plots, and low geometric mean fold errors of the predicted AUClast (1.27) and Cmax values (1.23) over all studies. For DGI predictions, 18 out of 18 DGI AUClast ratios and 18 out of 18 DGI Cmax ratios were within two-fold of the observed ratios. The newly developed and carefully validated model was applied to calculate dose recommendations for CYP2D6 polymorphic patients and will be freely available in the Open Systems Pharmacology repository.

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.

Population Pharmacokinetics of Mefloquine Intermittent Preventive Treatment for Malaria in Pregnancy in Gabon.

Mefloquine was evaluated as an alternative for intermittent preventive treatment of malaria in pregnancy (IPTp) due to increasing resistance against the first-line drug sulfadoxine-pyrimethamine (SP). This study determined the pharmacokinetic characteristics of the mefloquine stereoisomers and the metabolite carboxymefloquine (CMQ) when given as IPTp in pregnant women. Also, the relationship between plasma concentrations of the three analytes and cord samples was evaluated, and potential covariates influencing the pharmacokinetic properties were assessed. A population pharmacokinetic analysis was performed with 264 pregnant women from a randomized controlled trial evaluating a single and a split-dose regimen of two 15-mg/kg mefloquine doses at least 1 month apart versus SP-IPTp. Both enantiomers of mefloquine and its carboxy-metabolite (CMQ), measured in plasma and cord samples, were applied for pharmacokinetic modelling using NONMEM 7.3. Both enantiomers and CMQ were described simultaneously by two-compartment models. In the split-dose group, mefloquine bioavailability was significantly increased by 5%. CMQ induced its own metabolism significantly. Maternal and cord blood concentrations were significantly correlated (r2 = 0.84) at delivery. With the dosing regimens investigated, prophylactic levels are not constantly achieved. A modeling tool for simulation of the pharmacokinetics of alternative mefloquine regimens is presented. This first pharmacokinetic characterization of mefloquine IPTp indicates adequate exposure in both mefloquine regimens; however, concentrations at delivery were below previously suggested threshold levels. Our model can serve as a valuable tool for researchers and clinicians to develop and optimize alternative dosing regimens for IPTp in pregnant women.

Inhibition of Cyclin-Dependent Kinase 5: A Strategy to Improve Sorafenib Response in Hepatocellular Carcinoma Therapy.

Therapeutic options for patients with advanced-stage hepatocellular carcinoma (HCC) are very limited. The only approved first-line treatment is the multi-tyrosine kinase inhibitor sorafenib, which shows low response rates and severe side effects. In particular, the compensatory activation of growth factor receptors leads to chemoresistance and limits the clinical impact of sorafenib. However, combination approaches to improve sorafenib have failed. Here we investigate the inhibition of cyclin-dependent kinase 5 (Cdk5) as a promising combination strategy to improve sorafenib response in HCC. Combination of sorafenib with Cdk5 inhibition (genetic knockdown by short hairpin RNA or CRISPR/Cas9 and pharmacologic inhibition) synergistically impaired HCC progression in vitro and in vivo by inhibiting both tumor cell proliferation and migration. Importantly, these effects were mediated by a mechanism for Cdk5: A liquid chromatography-tandem mass spectrometry-based proteomic approach revealed that Cdk5 inhibition interferes with intracellular trafficking, a process crucial for cellular homeostasis and growth factor receptor signaling. Cdk5 inhibition resulted in an accumulation of enlarged vesicles and respective cargos in the perinuclear region, considerably impairing the extent and quality of growth factor receptor signaling. Thereby, Cdk5 inhibition offers a comprehensive approach to globally disturb growth factor receptor signaling that is superior to specific inhibition of individual growth factor receptors. Conclusion: Cdk5 inhibition represents an effective approach to improve sorafenib response and to prevent sorafenib treatment escape in HCC. Notably, Cdk5 is an addressable target frequently overexpressed in HCC, and with Dinaciclib, a clinically tested Cdk5 inhibitor is readily available. Thus, our study provides evidence for clinically evaluating the combination of sorafenib and Dinaciclib to improve the therapeutic situation for patients with advanced-stage HCC.

Can toxicokinetics of (synthetic) cannabinoids in pigs after pulmonary administration be upscaled to humans by allometric techniques?

Being advertised and distributed as attractive substitutes of cannabis, synthetic cannabinoids (SC) are gaining increasing relevance in forensic and clinical toxicology. As no data from controlled human studies are available, SC are sold and consumed without the knowledge of their toxicokinetic (TK) and toxicodynamic properties. Hence, animal models coupled with mathematical approaches should be used to ascertain those properties. Therefore, a controlled pig TK study allowing for extrapolation to human data was performed. For this purpose, eleven pigs received a single pulmonary dose of 200 µg/kg body weight each of Δ9-tetrahydrocannabinol (THC), 4-ethylnaphthalene-1-yl-(1-pentylindole-3-yl)methanone (JWH-210) as well as 2-(4-methoxyphenyl)-1-(1-pentyl-indole-3-yl)methanone (RCS-4) via an ultrasonic nebulizer. Blood and urine samples were repeatedly drawn over 8 h. Serum-concentration-time profiles of the parent compounds were determined using LC-MS/MS. Urine specimens were analyzed by LC-HR-MS/MS in order to elucidate the main metabolites. Maximum serum concentrations were reached 10-15 min after beginning of nebulization and amounted to 66 ±â€¯36 ng/mL for THC, 41 ±â€¯11 ng/mL for JWH-210, and 34 ±â€¯8.9 ng/mL for RCS-4. The serum-concentration-time profiles of THC, JWH-210, and RCS-4 were best described by three-compartment models with first order absorption and elimination processes. Absorption from the lungs to serum was modeled by first-order processes. The determination of the bioavailability yielded 23.0%, 24.2%, and 45.7% for THC, JWH-210, and RCS-4, respectively. Furthermore, the developed THC model was upscaled to humans using allometric scaling techniques. A successful prediction of human concentration-time profiles could be done. Also the metabolic patterns were in good agreement with human data. In conclusion, these findings are the first reported regarding the TK properties of SC after pulmonary administration to pigs. The presented method of TK serves as an appropriate predictor of human TK of cannabinoids.

How to disentangle psychobiological stress reactivity and recovery: A comparison of model-based and non-compartmental analyses of cortisol concentrations.

This article seeks to address the prevailing issue of how to measure specific process components of psychobiological stress responses. Particularly the change of cortisol secretion due to stress exposure has been discussed as an endophenotype of many psychosomatic health outcomes. To assess its process components, a large variety of non-compartmental parameters (i.e., composite measures of substance concentrations at different points in time) like the area under the concentration-time curve (AUC) are commonly utilized. However, a systematic evaluation and validation of these parameters based on a physiologically plausible model of cortisol secretion has not been performed so far. Thus, a population pharmacokinetic (mixed-effects stochastic differential equation) model was developed and fitted to densely sampled salivary cortisol data of 10 males from Montreal, Canada, and sparsely sampled data of 200 mixed-sex participants from Dresden, Germany, who completed the Trier Social Stress Test (TSST). Besides the two major process components representing (1) stress-related cortisol secretion (reactivity) and (2) cortisol elimination (recovery), the model incorporates two additional, often disregarded components: (3) the secretory delay after stress onset, and (4) deviations from the projected steady-state concentration due to stress-unrelated fluctuations of cortisol secretion. The fitted model (R2 = 99%) was thereafter used to investigate the correlation structure of the four individually varying, and readily interpretable model parameters and eleven popular non-compartmental parameters. Based on these analyses, we recommend to use the minimum-maximum cortisol difference and the minimum concentration as proxy measures of reactivity and recovery, respectively. Finally, statistical power analyses of the reactivity-related sex effect illustrate the consequences of using impure non-compartmental measures of the different process components that underlie the cortisol stress response.

A physiologically based pharmacokinetic (PBPK) parent-metabolite model of the chemotherapeutic zoptarelin doxorubicin-integration of in vitro results, Phase I and Phase II data and model application for drug-drug interaction potential analysis.

PURPOSE: Zoptarelin doxorubicin is a fusion molecule of the chemotherapeutic doxorubicin and a luteinizing hormone-releasing hormone receptor (LHRHR) agonist, designed for drug targeting to LHRHR positive tumors. The aim of this study was to establish a physiologically based pharmacokinetic (PBPK) parent-metabolite model of zoptarelin doxorubicin and to apply it for drug-drug interaction (DDI) potential analysis. METHODS: The PBPK model was built in a two-step procedure. First, a model for doxorubicin was developed, using clinical data of a doxorubicin study arm. Second, a parent-metabolite model for zoptarelin doxorubicin was built, using clinical data of three different zoptarelin doxorubicin studies with a dosing range of 10-267 mg/m2, integrating the established doxorubicin model. DDI parameters determined in vitro were implemented to predict the impact of zoptarelin doxorubicin on possible victim drugs. RESULTS: In vitro, zoptarelin doxorubicin inhibits the drug transporters organic anion-transporting polypeptide 1B3 (OATP1B3) and organic cation transporter 2 (OCT2). The model was applied to evaluate the in vivo inhibition of these transporters in a generic manner, predicting worst-case scenario decreases of 0.5% for OATP1B3 and of 2.5% for OCT2 transport rates. Specific DDI simulations using PBPK models of simvastatin (OATP1B3 substrate) and metformin (OCT2 substrate) predict no significant changes of the plasma concentrations of these two victim drugs during co-administration. CONCLUSIONS: The first whole-body PBPK model of zoptarelin doxorubicin and its active metabolite doxorubicin has been successfully established. Zoptarelin doxorubicin shows no potential for DDIs via OATP1B3 and OCT2.

Pharmacokinetics of (synthetic) cannabinoids in pigs and their relevance for clinical and forensic toxicology.

Synthetic cannabinoids (SCs) are gaining increasing importance in clinical and forensic toxicology. They are consumed without any preclinical safety studies. Thus, controlled human pharmacokinetic (PK) studies are not allowed, although being relevant for interpretation of analytical results in cases of misuse or poisoning. As alternative, in a controlled animal experiment, six pigs per drug received a single intravenous dose of 200µg/kg BW each of Δ(9)-tetrahydrocannabinol (THC), 4-ethylnaphthalen-1-yl-(1-pentylindol-3-yl)methanone (JWH-210), or 2-(4-methoxyphenyl)-1-(1-pentyl-indol-3-yl)methanone (RCS-4). In addition, six pigs received a combination of the three drugs with the identical dose each. The drugs were determined in serum using LC-MS/MS. A population (pop) PK analysis revealed that a three-compartment model described best the PK data of all three cannabinoids. Central volumes of distribution were estimated at 0.29L/kg, 0.20L/kg, and 0.67L/kg for THC, JWH-210, and RCS-4, respectively. Clearances were 0.042L/min/kg, 0.048L/min/kg, and 0.093L/min/kg for THC, JWH-210, and RCS-4, respectively. The popPK THC pig model was upscaled to humans using allometric techniques. Comparison with published human data revealed that the concentration-time profiles could successfully be predicted. These findings indicate that pigs in conjunction with PK modeling technique may serve as a tool for prediction of human PK of SCs.