Pharmacokinetic Modeling Approach Research Articles

Precision dosing of methadone during pregnancy: A pharmacokinetics virtual clinical trials study

BackgroundMethadone use for the management of opioid dependency during pregnancy is commonplace. Methadone levels are altered during pregnancy due to changes in maternal physiology. Despite this, a paucity of data exist regarding the most appropriate optimal dosing regimens during pregnancy. MethodsThis study applied a pharmacokinetic modeling approach to examine gestational changes in R- and S-methadone concentrations in maternal plasma and fetal (cord) blood. This study did so to derive a theoretical optimal dosing regimen during pregnancy, and to identify the impact of Cytochromes P450 (CYP) 2B6 and 2C19 polymorphisms on methadone maternal and fetal pharmacokinetics. ResultsThe study noted significant decreases in maternal R- and S-methadone plasma concentrations during gestation, with concomitant increases in fetal levels. At a dose of 90 mg once daily, 75% (R-) and 94% (S-) of maternal methadone trough levels were below the lower therapeutic window at term (week 40). The developed optimal dosing regimen escalated doses to 110 mg by week 5, followed by 10 mg increments every 5 weeks up to a maximum of 180 mg once daily near term. This increase resulted in 27% (R-) and 11% (S-) of subjects with trough levels below the lower therapeutic window at term. CYP2B6 poor metabolizers (PM) and either CYP2C19 extensive metabolizers (EM), PM, or ultra-rapid (UM) metabolizer phenotypes demonstrated statistically significant increases in concentrations when compared to their matched CYP2B6 EM counterparts. ConclusionsSpecific and gestation-dependent dose titrations are required during pregnancy to reduce the risks associated with illicit drug use and to maintain fetal safety.

Drug Clearance in Neonates: A Combination of Population Pharmacokinetic Modelling and Machine Learning Approaches to Improve Individual Prediction.

Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex problems in the current era of big data. The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates. Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods. The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods. A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.

A physiologically-based pharmacokinetic model to describe antisense oligonucleotide distribution after intrathecal administration

Antisense oligonucleotides (ASOs) are promising therapeutic agents for a variety of neurodegenerative and neuromuscular disorders, e.g., Alzheimer's, Parkinson's and Huntington's diseases, spinal muscular atrophy (SMA) and amyotrophic lateral sclerosis (ALS), caused by genetic abnormalities or increased protein accumulation. The blood-brain barrier (BBB) represents a challenge to the delivery of systemically administered ASOs to the relevant sites of action within the central nervous system (CNS). Intrathecal (IT) delivery, in which drugs are administered directly into the cerebrospinal fluid (CSF) space, enables to bypass the BBB. Several IT-administered ASO therapeutics have already demonstrated clinical effect, e.g., nusinersen (SMA) and tofersen (ALS). Due to novelty of IT dosing for ASOs, very limited pharmacokinetic (PK) data is available and only a few modeling reports have been generated. The objective of this work is to advance fundamental understanding of whole-body distribution of IT-administered ASOs. We propose a physiologically-based pharmacokinetic modeling approach to describe the distribution along the neuroaxis based on PK data from non-human primate (NHP) studies. We aim to understand the key processes that drive and limit ASO access to the CNS target tissues. To elucidate the trade-off between parameter identifiability and physiological plausibility of the model, several alternative model structures were chosen and fitted to the NHP data. The model analysis of the NHP data led to important qualitative conclusions that can inform projection to human. In particular, the model predicts that the maximum total exposure in the CNS tissues, including the spinal cord and brain, is achieved within two days after the IT injection, and the maximum amount absorbed by the CNS tissues is about 4% of the administered IT dose. This amount greatly exceeds the CNS exposures delivered by systemic administration of ASOs. Clearance from the CNS is controlled by the rate of transfer from the CNS tissues back to CSF, whereas ASO degradation in tissues is very slow and can be neglected. The model also describes local differences in ASO concentration emerging along the spinal CSF canal. These local concentrations need to be taken into account when scaling the NHP model to human: due to the lengthier human spinal column, inhomogeneity along the spinal CSF may cause even higher gradients and delays potentially limiting ASO access to target CNS tissues.

Drug-Drug Interaction Between Oxycodone and Diazepam by a Combined in Silico Pharmacokinetic and Pharmacodynamic Modeling Approach.

Opioids and benzodiazepines have complex drug-drug interactions (DDIs), which serve as an important source of adverse drug effects. In this work, we predicted the DDI between oxycodone (OXY) and diazepam (DZP) in the human body by applying in silico pharmacokinetic (PK) and pharmacodynamic (PD) modeling and simulation. First, we studied the PK interaction between OXY and DZP with a physiologically based pharmacokinetic (PBPK) model. Second, we applied molecular modeling techniques including molecular docking, molecular dynamics (MD) simulation, and the molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) free energy method to predict the PD-DDI between these two drugs. The PK interaction between OXY and DZP predicted by the PBPK model was not obvious. No significant interaction was observed between the two drugs at normal doses, though very high doses of DZP demonstrated a non-negligible inhibitory effect on OXY metabolism. On the contrary, the molecular modeling study shows that DZP has potential to compete with OXY at the same binding pocket of the active μ-opioid receptor (MOR) and κ-opioid receptor (KOR). MD simulation and MM-PBSA calculation results demonstrated that there is likely a synergetic effect between OXY and DZP binding to opioid receptors, as OXY is likely to target the active MOR while DZP selectively binds to the active KOR. Thus, pharmacokinetics contributes slightly to the DDI between OXY and DZP although an overdose of DZP has been brought to attention. Pharmacodynamics is likely to play a more important role than pharmacokinetics in revealing the mechanism of DDI between OXY and DZP.

Interaction between Omeprazole and Gliclazide in Relation to CYP2C19 Phenotype.

The antidiabetic drug gliclazide is partly metabolized by CYP2C19, the main enzyme involved in omeprazole metabolism. The aim of the study was to explore the interaction between omeprazole and gliclazide in relation to CYP2C19 phenotype using physiologically based pharmacokinetic (PBPK) modeling approach. Developed PBPK models were verified using in vivo pharmacokinetic profiles obtained from a clinical trial on omeprazole-gliclazide interaction in healthy volunteers, CYP2C19 normal/rapid/ultrarapid metabolizers (NM/RM/UM). In addition, the association of omeprazole cotreatment with gliclazide-induced hypoglycemia was explored in 267 patients with type 2 diabetes (T2D) from the GoDARTS cohort, Scotland. The PBPK simulations predicted 1.4–1.6-fold higher gliclazide area under the curve (AUC) after 5-day treatment with 20 mg omeprazole in all CYP2C19 phenotype groups except in poor metabolizers. The predicted gliclazide AUC increased 2.1 and 2.5-fold in intermediate metabolizers, and 2.6- and 3.8-fold in NM/RM/UM group, after simulated 20-day dosing with 40 mg omeprazole once and twice daily, respectively. The predicted results were corroborated by findings in patients with T2D which demonstrated 3.3-fold higher odds of severe gliclazide-induced hypoglycemia in NM/RM/UM patients concomitantly treated with omeprazole. Our results indicate that omeprazole may increase exposure to gliclazide and thus increase the risk of gliclazide-associated hypoglycemia in the majority of patients.

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 CLRpost-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.

Results of a Dose‐Finding Phase 1b Study of Subcutaneous Atezolizumab in Patients With Locally Advanced or Metastatic Non–Small Cell Lung Cancer

Intravenous (IV) atezolizumab is approved for non–small cell lung and other cancers. Subcutaneous (SC) atezolizumab coformulated with recombinant human hyaluronidase, a permeation enhancer for SC dispersion and absorption, is being developed to improve treatment options, reduce burden, and increase efficiency for patients and practitioners. IMscin001 (NCT03735121), a 2‐part, open‐label, global, multicenter, phase 1b/3 study, is evaluating the pharmacokinetics (PK), safety, and efficacy of SC atezolizumab. The part 1 (phase 1b) objective was determination of an SC atezolizumab dose yielding a serum trough concentration (Ctrough) comparable with IV. Patients enrolled in 3 cohorts received SC atezolizumab 1800 mg (thigh) once (cohort 1), 1200 mg (thigh) every 2 weeks for 3 cycles (cohort 2), or 1800 mg (abdomen) every 3 weeks cycle 1, then cycles 2 and 3 (thigh) every 3 weeks (cohort 3). In subsequent cycles, IV atezolizumab 1200 mg every 3 weeks was administered until loss of clinical benefit. SC atezolizumab 1800 mg every 3 weeks and 1200 mg every 2 weeks provided similar Ctrough and area under the curve values in cycle 1 to the corresponding IV atezolizumab reference, was well tolerated, and exhibited a safety profile consistent with the established IV formulation. Exposure following SC injection in the abdomen was lower (20%, 28%, and 27% for Ctrough, maximum concentration, and area under the concentration‐time curve from time 0 to day 21, respectively) than in the thigh. Part 1 SC and IV PK data were analyzed using a population PK modeling approach, followed by simulations. Part 2 (phase 3) will now be initiated to demonstrate that SC atezolizumab PK exposure is not lower than that of IV.

Simulation of the In Vivo Fate of Polymeric Nanoparticles Traced by Environment-Responsive Near-Infrared Dye: A Physiologically Based Pharmacokinetic Modelling Approach.

The application of physiologically based pharmacokinetic models to nanoparticles is still very restricted and challenging, owing to the complicated in vivo transport mechanisms involving nanoparticles, including phagocytosis, enhanced permeability and retention effects, cellular recognition, and internalisation, enzymatic degradation, lymphatic transport, and changes in physical properties. In our study, five nanoparticle formulations were synthesised using polycaprolactone as a framework material and methoxy poly (ethylene glycol)-poly(ε-caprolactone) as a long-circulating decorating material, as well as types of environmentally responsive near-infrared aza-boron-dipyrromethene dyes. According to quantification data and direct visualisation involving specific organs, a phagocytosis physiologically based pharmacokinetic model was developed to describe the dynamics of nanoparticles within and between organs in mice, considering cellular mechanisms involving phagocytosis and enhanced permeability and retention effects. Our results offer a better understanding of the in vivo fate of polymeric nanoparticles.

Pharmacokinetics of the CYP3A4 and CYP2B6 Inducer Carbamazepine and Its Drug-Drug 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) parent−metabolite model of carbamazepine and its metabolite carbamazepine-10,11-epoxide, including carbamazepine autoinduction, to be applied for drug–drug interaction (DDI) prediction. The model was developed in PK-Sim, using a total of 92 plasma concentration−time profiles (dosing range 50–800 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.

Evaluating clinical impact of a shortened infusion duration for ramucirumab: a model-based approach

PurposeWe investigated the impact of infusion duration (30 and 60 min) on the pharmacokinetic profile of ramucirumab using a population pharmacokinetic (PopPK) modeling approach. We also assessed the relationship between infusion rate and incidence of immediate infusion-related reactions (IRRs; occurring on the day of administration) using ramucirumab phase II/III study data.MethodsThe impact of different infusion durations (30 vs. 60 min) on the time-course of ramucirumab concentration profiles were evaluated using a PopPK model, established using ramucirumab pharmacokinetic data from 2522 patients. Logistic regression was used to evaluate the association between ramucirumab infusion rate and incidence of immediate IRRs in clinical trials.ResultsRamucirumab time-course concentration profiles were equivalent following a 30- or 60-min infusion. In the pooled clinical study dataset, 254 of 3216 (7.9%) patients receiving ramucirumab experienced at least one immediate IRR (any grade). When grouped according to infusion rate quartile, the incidence of immediate IRRs (any grade or grade ≥ 3) was similar across quartiles; findings were confirmed in sensitivity analyses. The risk of immediate IRRs was not found to be associated with infusion rate based on multivariate logistic analysis.ConclusionShortening the infusion duration of ramucirumab from 60 to 30 min has no impact on ramucirumab exposure. Analysis of trial data found no relationship between an increased risk of immediate IRRs and a faster infusion rate. Such a change in infusion duration is unlikely to affect the clinical efficacy or overall safety profile of ramucirumab.

A Population Pharmacokinetic Modeling Approach to Determine the Efficacy of Intravenous Amikacin in Children with Cystic Fibrosis.

In children with cystic fibrosis (CF), the currently recommended amikacin dose ranges between 30 and 35 mg/kg/d; however, data supporting this dosing efficacy are lacking. In this article, the objectives were to develop a nonparametric pharmacokinetic population model for amikacin in children with CF and investigate the efficacy and toxicity at different dose rates for distinct minimum inhibitory concentration (MIC) clinical breakpoints using Monte Carlo simulations. Data from 94 children with CF (613 serum concentrations) from the Bordeaux University Hospital's CF-centre were analyzed. After determination of nonparametric pharmacokinetic population model parameters and associated influent covariates in Pmetrics, 1000 Monte Carlo simulations were performed for 7 different dose rates between 30 and 60 mg/kg/d, to predict the probability of obtaining peak serum amikacin ≥10 × MIC and trough level ≤2.5 mg/L, for MIC values between 1 and 16 mg/L. The median (min-max) age and weight were 10 (0.3-17) years and 29 (6-71) kg, respectively, with only 2 children younger than 1 year of age. Body weight and creatinine clearance significantly impacted the amikacin volume of distribution and clearance. The mean relative bias/root mean squared error between observed and individual predicted concentrations was -0.68%/8.1%. Monte Carlo simulations showed that for sensitive bacteria with MICs ≤ 4, 30 mg/kg/d was most appropriate for a 100% success rate; for bacteria with MICs ≥ 8 [eg, Pseudomonas aeruginosa (MICamikacin = 8)], a dose of at least 40 mg/kg/d allowed a high success probability (90%), with a trough level below 2.5 mg/L. For intermediate pathogens, a dose of at least 40 mg/kg/d can improve efficacy, with an acceptable calculated residual trough level in cases of normal or hyperfiltration. Because amikacin undergoes renal clearance, which is immature until 1 year of age, dosing recommendations for this age group may be markedly high, warranting cautious interpretation.

Development of a Physiologically Based Pharmacokinetic Model for Prediction of Ethanol Concentration-Time Profile in Different Organs.

A physiologically based pharmacokinetic (PBPK) modeling approach was used to simulate the concentration-time profile of ethanol (EtOH) in stomach, duodenum, plasma and other tissues upon consumption of beer and whiskey under fasted and fed conditions. A full PBPK model was developed for EtOH using the advanced dissolution, absorption and metabolism (ADAM) model fully integrated into the Simcyp Simulator® 15 (Simcyp Ltd., Sheffield, UK). The prediction performance of the developed model was verified and the EtOH concentration-time profile in different organs was predicted. Simcyp simulation showed ≤ 2-fold difference in values of EtOH area under the concentration-time curve (AUC) in stomach and duodenum as compared to the observed values. Moreover, the simulated EtOH maximum concentration (Cmax), time to reach Cmax (Tmax) and AUC in plasma were comparable to the observed values. We showed that liver is exposed to the highest EtOH concentration, faster than other organs (Cmax=839.50mg/L and Tmax=0.53h), while brain exposure of EtOH (AUC=1139.43mg·h/L) is the highest among all other organs. Sensitivity analyses (SAs) showed direct proportion of EtOH rate and extent of absorption with administered EtOH dose and inverse relationship with gastric emptying time (GE) and steady-state volume of distribution (Vss). The current PBPK model approach might help with designing in vitro experiments in the area of alcohol organ damage or alcohol-drug interaction studies.

Physiologically Based Pharmacokinetic Modeling Approach to Identify the Drug–Drug Interaction Mechanism of Nifedipine and a Proton Pump Inhibitor, Omeprazole

Proton pump inhibitors (PPIs) can affect the intragastric release of other drugs from their dosage forms by elevating the gastric pH. They may also influence drug absorption and metabolism by interacting with P-glycoprotein or with the cytochrome P450 (CYP) enzyme system. Nifedipine is a Biopharmaceutics Classification System (BCS) class II drug with low solubility across physiologic pH and high permeability. Previous studies have demonstrated that drug-drug interaction (DDI) existed between omeprazole and nifedipine with significantly increased systemic exposure of nifedipine in subjects after pre-treatment for 7days with omeprazole compared to the subjects without omeprazole treatment. It was shown that omeprazole not only induced an increase in intragastric pH, but also inhibited the CYP3A4 activity, while CYP3A4-mediated oxidation is the main metabolic pathway of nifedipine. The purpose of this study is to apply a physiologically based pharmacokinetic (PBPK) modeling approach to investigate the DDI mechanism for an immediate release formulation of nifedipine with omeprazole. A previously published model for omeprazole was modified to integrate metabolites and to update CYP inhibition based on the most updated published in vitro data. We simulated the nifedipine pharmacokinetics in healthy subjects with or without the multiple-dose pretreatment of omeprazole (20mg) following oral administrations of immediate-release (IR) (10mg) nifedipine. Nifedipine solubility at different pHs was used to simulate the nifedipine pharmacokinetics for both clinical arms. Multiple sensitivity analyses were performed to understand the impact of gastric pH and the CYP3A4-mediated gut and liver first pass metabolism on the overall nifedipine pharmacokinetics. The developed PBPK model properly described the pharmacokinetics of nifedipine and predicted the inhibitory effect of multiple-dose omeprazole on CYP3A4 activity. With the incorporation of the physiologic effect of omeprazole on both gastric pH and CYP3A4 to the PBPK model, the verified PBPK model allows evaluating the impact of the increase in gastric pH and/or CYP3A4 inhibition. The simulated results show that the nifedipine metabolic inhibition by omeprazole may play an important role in the DDI between nifedipine and omeprazole for IR nifedipine formulation. The developed full PBPK model with the capability to simulate DDI by considering gastric pH change and metabolic inhibition provides a mechanistic understanding of the observed DDI of nifedipine with a PPI, omeprazole.

Development of a Pharmacokinetic Model of Transplacental Transfer of Metformin to Predict In Vivo Fetal Exposure.

Two types of systems are used in ex vivo human placental perfusion studies to predict fetal drug exposures, that is, closed systems with recirculation of the maternal and fetal buffer and open systems using a single-pass mode without recirculation. The in vivo fetal/maternal (F:M) ratio of metformin, a cationic drug that crosses the placenta, is consistent with that reported in an open system ex vivo but not with that in a closed system. In the present study, we aimed to develop a pharmacokinetic (PK) model of transplacental transfer of metformin to predict in vivo fetal exposure to metformin and to resolve the apparent inconsistency between open and closed ex vivo systems. The developed model shows that the difference between open and closed systems is due to the difference in the time required to achieve the steady state. The model-predicted F:M ratio (approx. 0.88) is consistent with reported in vivo values [mean (95% confidence interval): 1.10 (0.69-1.51)]. The model incorporates bidirectional transport via organic cation transporter 3 (OCT3) at the basal plasma membrane, and simulations indicate that the use of trimethoprim (an OCT3 inhibitor) to prevent microbial growth in the placenta ex vivo has a negligible effect on the overall maternal-to-fetal and fetal-to-maternal clearances. The model could successfully predict in vivo fetal exposure using ex vivo human placental perfusion data from both closed and open systems. This transplacental PK modeling approach is expected to be useful for evaluating human fetal exposures to other poorly permeable compounds, besides metformin. SIGNIFICANCE STATEMENT: We developed a pharmacokinetic model of transplacental transfer of metformin, used to treat gestational diabetes mellitus, in order to predict in vivo fetal exposure and resolve the discrepancy between reported findings in open and closed ex vivo perfusion systems. The discrepancy is due to a difference in the time required to reach the steady state. The model can predict in vivo fetal exposure using data from both closed and open systems.

External Evaluation of Population Pharmacokinetics and Pharmacodynamics in Linezolid-Induced Thrombocytopenia: The Transferability of Published Models to Different Hospitalized Patients.

The objective of this study was to perform an external evaluation of published linezolid population pharmacokinetic and pharmacodynamic models, to evaluate the predictive performance using an independent data set. Another aim was to offer an elegant environment for display and simulation of both the concentration and platelet count after linezolid administration. We performed a systematic literature search in PubMed for all studies evaluating the population pharmacokinetic and pharmacodynamic parameters of linezolid in patients and selected the models to be used for the external validation. The bias of predictions was visually evaluated by plotting prediction errors (PEs) and relative PEs. The precision of prediction was evaluated by calculating the mean absolute error (MAE), root mean squared error (RMSE), and mean relative error (MRE). Three articles (models A, B, and C) provided linezolid-induced platelet dynamic models using population pharmacokinetic and pharmacodynamic modeling approaches. The PE and relative PE of both linezolid concentrations and platelet counts for models A and C showed similar predictive distributions. With respect to the prediction accuracy of total linezolid concentration, the MAE, RMSE, and MRE of population prediction values for model C was the smallest. The comparison of the MAE, RMSE, and MRE of patient-individual prediction values for the 3 pharmacodynamic models revealed no large differences. We confirmed the transferability of published population pharmacokinetic and pharmacodynamic models and showed that they were suitable for extrapolation to other hospitals and/or patients. This study also introduced application software based on model C for the therapeutic drug monitoring of linezolid.

Physiologically Based Pharmacokinetic Modeling of Central Nervous System Pharmacokinetics of CDK4/6 Inhibitors to Guide Selection of Drug and Dosing Regimen for Brain Cancer Treatment.

A better understanding of the human central nervous system (CNS) pharmacokinetics is critical to the selection of the right drug and refinement of dosing regimen for more effective treatment of primary and metastatic brain cancer. Using the physiologically‐based pharmacokinetic (PBPK) modeling approach, we systematically compared the CNS pharmacokinetics of three cyclin D‐cyclin dependent kinase 4 and 6 (CDK4/6) inhibitors (ribociclib, palbociclib, and abemaciclib) in patients with cancer. A PBPK model platform was developed and verified for predicting plasma and CNS pharmacokinetics. Target engagement ratio (TER), defined as the ratio of the average steady‐state unbound drug brain concentration to the in vitro half‐maximal inhibitory concentration (IC50) for CDK4/6 inhibition, was used as a crude predictor of efficacy. As compared with ribociclib and palbociclib, abemaciclib penetrated into the human brain to a larger extent, but at a slower rate, and was retained in the brain longer. Following the standard dosing regimens, the predicted CDK4/6 TERs were 26/5.2 for abemaciclib, 2.4/0.62 for ribociclib, and 0.36/0.27 for palbociclib. Simulations suggested that abemaciclib achieved comparable TERs following twice daily or daily dosing; ribociclib may sufficiently inhibit both CDK4 and CDK6 at the maximum tolerated dose; whereas, palbociclib achieved TERs < 0.5 even at a dose 50% higher than the standard dose. In conclusion, the PBPK modeling, supported by available preclinical and clinical evidence, suggests that abemaciclib is the best CDK4/6 inhibitor for brain cancer treatment, whereas palbociclib is not recommended. The model refined dosing regimen is 300 mg daily on a 4‐weeks‐on schedule for abemaciclib, and 900 mg daily on a 3‐weeks‐on/1‐week‐off schedule for ribociclib.

Toward Greater Insights on Applications of Modeling and Simulation in Pregnancy.

Pregnant women are often excluded from routine clinical trials. Consequently, appropriate dosing regimens for majority of drugs are unknown in this population, which may lead to unexpected safety issue or insufficient efficacy in this un-studied population. Establishing evidence through the conduct of clinical studies in pregnancy is still a challenge. In recent decades, physiologically-based pharmacokinetic (PBPK) modeling has proven to be useful to support dose selection under various clinical scenarios, such as renal and/or liver impairment, drug-drug interactions, and extrapolation from adult to children. By integrating gestational-dependent physiological characteristics and drug-specific information, PBPK models can be used to predict PK during pregnancy. Population pharmacokinetic (PopPK) modeling approach also could complement pregnancy clinical studies by its ability to analyze sparse sampling data. In the past five years, PBPK and PopPK approaches for pregnancy have made significant progress. We reviewed recent progress, challenges and potential solutions for the application of PBPK, PopPK, and exposure-response analysis in clinical drug development for pregnancy.

Population Pharmacokinetics of Candesartan in Patients with Chronic Heart Failure.

Heart failure (HF) causes pathological changes in multiple organs, thus affecting the pharmacokinetics (PK) of drugs. The aim of this study was to investigate the PK of candesartan in patients with HF while examining significant covariates and their related impact on estimated clearance using a population PK (Pop‐PK) modeling approach. Data from a prospective, multicenter study were used. Modeling and simulations were conducted using Nonlinear Mixed‐Effects Modeling (NONMEM) and R software. A total of 281 white patients were included to develop the Pop‐PK model. The final model developed for apparent oral clearance (CL/F) included weight, estimated glomerular filtration rate (eGFR), and diabetes, which partly explained its interindividual variability. The mean CL/F value estimated was 7.6 L/h (1.7–22.6 L/h). Simulations revealed that an important decrease in CL/F (> 25%) is obtained with the combination of the factors retained in the final model. Considering these factors, a more individualized approach of candesartan dosing should be investigated in patients with HF.

Evaluation of the Impacts of Formulation Parameters on the Pharmacokinetics and Bioequivalence of Risperidone Orodispersible Film: a Physiologically Based Pharmacokinetic Modeling Approach.

The purpose of this study was to investigate the impacts of the formulation parameters on the pharmacokinetics and bioequivalence of risperidone orodispersible film (ODF) using physiologically based pharmacokinetic model. The pharmacokinetic profiles of two risperidone ODFs, which exhibit different in vitro dissolution, were examined in Beagle dogs after supralingual administration. Subsequently, a physiologically based pharmacokinetic (PBPK) model was constructed to evaluate the in vivo performance of risperidone ODF. The parameter sensitivity analysis (PSA) was used to access the impacts of formulation parameters on the pharmacokinetics of risperidone. Moreover, the validated PBPK model was applied to predict human pharmacokinetic profiles and examine the bioequivalence of these two ODFs. These two ODFs displayed similar risperidone pharmacokinetic profiles in dogs. The parameter sensitivity analysis indicated that the changes in the solubility, particle size, particle density, and diffusion coefficient did not have obvious influence on the in vivo properties of risperidone ODF. Alternation of the in vitro complete dissolution time in water from 15 to 30min led to a 30% decrease in Cmax and 20% of increase in Tmax. AUC0-∞ would be decreased if risperidone was not fully released within 1h. As both ODFs completely released risperidone within 15min, the difference in the extent of in vivo absorption, intestinal regional absorption location, and plasma concentration-time curves between these two ODFs was almost negligible. Consequently, a bioequivalence was foreseen in humans. The in vitro cumulative dissolution percentage in water at 15min was found to be the major determinant on the in vivo properties of risperidone ODF. PBPK modeling appears to be an innovative strategy to guide the development of risperidone ODF.

Prediction of maternal pharmacokinetics using physiologically based pharmacokinetic models: assessing the impact of the longitudinal changes in the activity of CYP1A2, CYP2D6 and CYP3A4 enzymes during pregnancy

Concerns over gestational effects on the disposition of drugs has highlighted the need for a better understanding of drug distribution and elimination during pregnancy. This study aimed at predicting maternal drug kinetics using a physiologically based pharmacokinetic (PBPK) modelling approach focusing on the observed gestational changes in three important Cytochrome P450 metabolizing enzymes, namely, CYP1A2, CYP2D6 and CYP3A4 at different gestational weeks (GWs). The Pregnancy PBPK model within the Simcyp Simulator V19 was used to predict the pharmacokinetics of sensitive probes to these enzymes; namely caffeine, theophylline, metoprolol, propranolol, paroxetine, midazolam, nifedipine and rilpivirine. PBPK model predictions were compared against clinical data collated from multiple studies for each compound to cover a wide spectrum of gestational ages. Pregnancy PBPK model predictions were within 2-fold error and indicated that CYP1A2 activity is approximately 0.70, 0.44 and 0.30 fold of the non-pregnant level at the end of the first, second and third trimesters, respectively. On the other hand, CYP2D6 activity increases by 1.36, 2.16 and 3.10 fold of the non-pregnant level at the end of the first, second and third trimesters, respectively. Likewise, CYP3A4 activity increases by 1.25, 1.75 and 2.32 fold of the non-pregnant level at the end of the first, second and third trimesters, respectively. The enzymes activity have been qualified throughout pregnancy. Quantified changes in drug dosing are most relevant during the third trimester, especially for drugs that are mainly eliminated by CYP1A2, CYP2D6 and CYP3A4 enzymes. The provided functions describing the continuous changes to the activity of these enzymes during pregnancy are important when modelling long term pharmacokinetic studies where longitudinal modelling or time-varying covariates are used.