scholarly journals Mechanistic Modeling of Placental Drug Transfer in Humans: How Do Differences in Maternal/Fetal Fraction of Unbound Drug and Placental Influx/Efflux Transfer Rates Affect Fetal Pharmacokinetics?

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiaomei I. Liu ◽  
Dionna J. Green ◽  
John N. van den Anker ◽  
Natella Y. Rakhmanina ◽  
Homa K. Ahmadzia ◽  
...  
Keyword(s):  
Drug Exposure ◽  
Umbilical Vein ◽  
Basolateral Membrane ◽  
Mechanistic Modeling ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Clearance Ratio ◽  
Drug Transfer ◽  
Binding Characteristics ◽  
Pbpk Models

Background: While physiologically based pharmacokinetic (PBPK) models generally predict pharmacokinetics in pregnant women successfully, the confidence in predicting fetal pharmacokinetics is limited because many parameters affecting placental drug transfer have not been mechanistically accounted for.Objectives: The objectives of this study were to implement different maternal and fetal unbound drug fractions in a PBPK framework; to predict fetal pharmacokinetics of eight drugs in the third trimester; and to quantitatively investigate how alterations in various model parameters affect predicted fetal pharmacokinetics.Methods: The ordinary differential equations of previously developed pregnancy PBPK models for eight drugs (acyclovir, cefuroxime, diazepam, dolutegravir, emtricitabine, metronidazole, ondansetron, and raltegravir) were amended to account for different unbound drug fractions in mother and fetus. Local sensitivity analyses were conducted for various parameters relevant to placental drug transfer, including influx/efflux transfer clearances across the apical and basolateral membrane of the trophoblasts.Results: For the highly-protein bound drugs diazepam, dolutegravir and ondansetron, the lower fraction unbound in the fetus vs. mother affected predicted pharmacokinetics in the umbilical vein by ≥10%. Metronidazole displayed blood flow-limited distribution across the placenta. For all drugs, umbilical vein concentrations were highly sensitive to changes in the apical influx/efflux transfer clearance ratio. Additionally, transfer clearance across the basolateral membrane was a critical parameter for cefuroxime and ondansetron.Conclusion: In healthy pregnancies, differential protein binding characteristics in mother and fetus give rise to minor differences in maternal-fetal drug exposure. Further studies are needed to differentiate passive and active transfer processes across the apical and basolateral trophoblast membrane.

scholarly journals Mechanistic Coupling of a Novel in silico Cotyledon Perfusion Model and a Physiologically Based Pharmacokinetic Model to Predict Fetal Acetaminophen Pharmacokinetics at Delivery

2021 ◽  
Vol 9 ◽  
Author(s):  
Paola Mian ◽  
Bridget Nolan ◽  
John N. van den Anker ◽  
Kristel van Calsteren ◽  
Karel Allegaert ◽  
...  
Keyword(s):  
Clinical Studies ◽  
In Silico ◽  
Pbpk Model ◽  
Umbilical Vein ◽  
Whole Body ◽  
Model Parameters ◽  
Drug Transfer ◽  
Physiologically Based Pharmacokinetic ◽  
Perfusion Model ◽  
Physiologically Based

Little is known about placental drug transfer and fetal pharmacokinetics despite increasing drug use in pregnant women. While physiologically based pharmacokinetic (PBPK) models can help in some cases to shed light on this knowledge gap, adequate parameterization of placental drug transfer remains challenging. A novel in silico model with seven compartments representing the ex vivo cotyledon perfusion assay was developed and used to describe placental transfer and fetal pharmacokinetics of acetaminophen. Unknown parameters were optimized using observed data. Thereafter, values of relevant model parameters were copied to a maternal-fetal PBPK model and acetaminophen pharmacokinetics were predicted at delivery after oral administration of 1,000 mg. Predictions in the umbilical vein were evaluated with data from two clinical studies. Simulations from the in silico cotyledon perfusion model indicated that acetaminophen accumulates in the trophoblasts; simulated steady state concentrations in the trophoblasts were 4.31-fold higher than those in the perfusate. The whole-body PBPK model predicted umbilical vein concentrations with a mean prediction error of 24.7%. Of the 62 concentration values reported in the clinical studies, 50 values (81%) were predicted within a 2-fold error range. In conclusion, this study presents a novel in silico cotyledon perfusion model that is structurally congruent with the placenta implemented in our maternal-fetal PBPK model. This allows transferring parameters from the former model into our PBPK model for mechanistically exploring whole-body pharmacokinetics and concentration-effect relationships in the placental tissue. Further studies should investigate acetaminophen accumulation and metabolism in the placenta as the former might potentially affect placental prostaglandin synthesis and subsequent fetal exposure.

Mechanistic Modeling of Soluble Aβ Dynamics and Target Engagement in the Brain by Anti-Aβ mAbs in Alzheimer’s Disease

2020 ◽  
Vol 17 (4) ◽  
pp. 393-406
Author(s):  
Gregory Z. Ferl ◽  
Reina N. Fuji ◽  
Jasvinder K. Atwal ◽  
Tony Sun ◽  
Saroja Ramanujan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Amyloid Β ◽  
Mechanistic Modeling ◽  
Model Parameters ◽  
Target Engagement ◽  
Binding Characteristics ◽  
The Impact ◽  
The Brain

Background: Anti-amyloid-β (Aβ) monoclonal antibodies (mAbs) are currently in development for treating Alzheimer’s disease. Objectives: To address the complexity of Aβ target engagement profiles, improve the understanding of crenezumab Pharmacokinetics (PK) and Aβ Pharmacodynamics (PD) in the brain, and facilitate comparison of anti-Aβ therapies with different binding characteristics. Methods: A mechanistic mathematical model was developed describing the distribution, elimination, and binding kinetics of anti-Aβ mAbs and Aβ (monomeric and oligomeric forms of Aβ1-40 and Aβ1-42) in the brain, Cerebrospinal Fluid (CSF), and plasma. Physiologically meaningful values were assigned to the model parameters based on the previous data, with remaining parameters fitted to clinical measurements of Aβ concentrations in CSF and plasma, and PK/PD data of patients undergoing anti-Aβ therapy. Aβ target engagement profiles were simulated using a Monte Carlo approach to explore the impact of biological uncertainty in the model parameters. Results: Model-based estimates of in vivo affinity of the antibody to monomeric Aβ were qualitatively consistent with the previous data. Simulations of Aβ target engagement profiles captured observed mean and variance of clinical PK/PD data. Conclusion: This model is useful for comparing target engagement profiles of different anti-Aβ therapies and demonstrates that 60 mg/kg crenezumab yields a significant increase in Aβ engagement compared with lower doses of solanezumab, supporting the selection of 60 mg/kg crenezumab for phase 3 studies. The model also provides evidence that the delivery of sufficient quantities of mAb to brain interstitial fluid is a limiting step with respect to the magnitude of soluble Aβ oligomer neutralization.

scholarly journals P01 Application of feto-placental-maternal physiologically-based pharmacokinetic model to predict tenofovir concentration during pregnancy

2019 ◽  
Vol 104 (6) ◽  
pp. e17.2-e18
Author(s):  
K Abduljalil ◽  
TN Johnson ◽  
M Jamei
Keyword(s):  
Tenofovir Disoproxil Fumarate ◽  
Drug Exposure ◽  
Pbpk Model ◽  
Model Parameters ◽  
List Type ◽  
Large Variability ◽  
Pbpk Models ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
Fetal Organs

BackgroundTenofovir is a drug used in combination with other anti-HIV drugs to treat patients with HIV-1 infection. It is used during pregnancy to reduce the risk of HIV transmission to the child. The aim of this work is to use a Physiologically-Based Pharmacokinetic (PBPK) model for prediction of maternal and fetal tenofovir concentration at birth.MethodsA full Feto-Placental-Maternal PBPK model that includes placenta as a 3-comparment permeability limited organ and 14 compartments for different fetal organs was developed using physiological1,2 and drug specific parameters3 to predict tenofovir concentration in 50 virtual pregnant mothers at term after single administration of 600 mg of tenofovir disoproxil fumarate (272 mg tenofovir). The mechanistic model implemented using the Simcyp Lua interface within the Simcyp Simulator. Fetal as well as maternal tissue to plasma ratio values were predicted using the Rodgers & Rowland method with a scalar of 1.5. Predictions of tenofovir maternal and fetal plasma concentration were compared to reported observations.4ResultsIn spite of the large variability in the observed data, the model adequately replicated the maternal as well as fetal clinical observations.4 The placenta transfer by cotyledon was changed 10 times the mean reported value from perfusion experiment.5 All other model parameters were calculated using bottom-up approach.The maternal predicted-to-observed ratio for AUC24hr and Cmax was 1.13 and 1.08, respectively. The predicted fetal exposure was well predicted within the 5th and 95th percentiles and was 0.51 of maternal exposure (AUC24h), the reported value is 0.60.4ConclusionThe developed feto-placental-maternal PBPK models can be used to predict drug exposure in fetal organs during in utero growth. The inter-subject variability can be predicted incorporating both the drug physicochemical properties and system (placental, maternal and fetal) parameters.ReferencesAbduljalil, et al. Clin Pharmacokinet 2018;57(9):1149–1171.Abduljalil, et al. Clin Pharmacokinet 2019;58:235–262Gilead Sciences, Inc. Product Information: tenofovir disoproxil fumarate (VIREAD) tablets.Hirt D, et al., Clin Pharmacol Ther 2009; 85: 182–9.De Sousa Mendes, et al., Br J Clin Pharmacol 2016;81(4):646–57.Disclosure(s)Nothing to disclose

Methodological Approaches to Evaluate Fetal Drug Exposure

2019 ◽  
Vol 25 (5) ◽  
pp. 496-504 ◽  
Author(s):  
Naïm Bouazza ◽  
Frantz Foissac ◽  
Déborah Hirt ◽  
Saïk Urien ◽  
Sihem Benaboud ◽  
...  
Keyword(s):  
Cord Blood ◽  
Drug Exposure ◽  
Placental Transfer ◽  
Ex Vivo ◽  
Pharmacokinetic Analysis ◽  
Population Pharmacokinetic ◽  
Pbpk Models ◽  
Drug Concentrations ◽  
Fetal Drug Exposure

Background: Drug prescriptions are usual during pregnancy, however, women and their fetuses still remain an orphan population with regard to drugs efficacy and safety. Most xenobiotics diffuse through the placenta and some of them can alter fetus development resulting in structural abnormalities, growth or functional deficiencies. Methods: To summarize the different methodologies developed towards the prediction of fetal drug exposure. Results: Neonatal cord blood concentration is the most specific measurement of the transplacental drug transfer at the end of pregnancy. Using the cord blood and mother drug concentrations altogether, drug exchanges between the mother and fetus can be modeled and quantified via a population pharmacokinetic analysis. Thereafter, it is possible to estimate the fetus exposure and the fetus-to-mother exposure ratio. However, the prediction of placental transfer before any administration to pregnant women is desirable. Animal studies remain difficult to interpret due to structural and functional inter-species placenta differences. The ex-vivo perfusion of the human placental cotyledon is the method of reference to study the human placental transfer of drugs because it is thought to mimic the functional placental tissue. However, extrapolation of data to in vivo situation remains difficult. Some research groups have extensively worked on physiologically based models (PBPK) to predict fetal drug exposure and showed very encouraging results. Conclusion: PBPK models appeared to be a very promising tool in order to predict fetal drug exposure in-silico. However, these models mainly picture the end of pregnancy and knowledge regarding both, development of the placental permeability and transporters is strongly needed.

scholarly journals Optimal Investment and Proportional Reinsurance in a Regime-Switching Market Model under Forward Preferences

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1610
Author(s):  
Katia Colaneri ◽  
Alessandra Cretarola ◽  
Benedetta Salterini
Keyword(s):  
Stock Prices ◽  
Regime Switching ◽  
Common Factor ◽  
Optimal Investment ◽  
Investment Strategy ◽  
Market Model ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Insurance Company ◽  
Optimal Investment Strategy

In this paper, we study the optimal investment and reinsurance problem of an insurance company whose investment preferences are described via a forward dynamic exponential utility in a regime-switching market model. Financial and actuarial frameworks are dependent since stock prices and insurance claims vary according to a common factor given by a continuous time finite state Markov chain. We construct the value function and we prove that it is a forward dynamic utility. Then, we characterize the optimal investment strategy and the optimal proportional level of reinsurance. We also perform numerical experiments and provide sensitivity analyses with respect to some model parameters.

scholarly journals Cost Effectiveness of a Home-Based Intervention That Helps Functionally Vulnerable Older Adults Age in Place at Home

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Eric Jutkowitz ◽  
Laura N. Gitlin ◽  
Laura T. Pizzi ◽  
Edward Lee ◽  
Marie P. Dennis
Keyword(s):  
Cost Effectiveness ◽  
Community Dwelling ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Potential Cost ◽  
Functional Difficulties ◽  
Age In Place ◽  
Home Based ◽  
Occupational And Physical Therapy ◽  
Home Based Intervention

Evaluating cost effectiveness of interventions for aging in place is essential for adoption in service settings. We present the cost effectiveness of Advancing Better Living for Elders (ABLE), previously shown in a randomized trial to reduce functional difficulties and mortality in 319 community-dwelling elders. ABLE involved occupational and physical therapy sessions and home modifications to address client-identified functional difficulties, performance goals, and home safety. Incremental cost-effectiveness ratio (ICER), expressed as additional cost to bring about one additional year of life, was calculated. Two models were then developed to account for potential cost differences in implementing ABLE. Probabilistic sensitivity analyses were conducted to account for variations in model parameters. By two years, there were 30 deaths (9: ABLE; 21: control). Additional costs for 1 additional year of life was $13,179 for Model 1 and $14,800 for Model 2. Investment in ABLE may be worthwhile depending on society's willingness to pay.

scholarly journals Inverse mechanistic modeling of transdermal drug delivery for fast identification of optimal model parameters

2020 ◽  
Author(s):  
Thijs Defraeye ◽  
Flora Bahrami ◽  
Rene M Rossi
Keyword(s):  
Drug Delivery ◽  
Inverse Modeling ◽  
Transdermal Drug Delivery ◽  
Drug Transport ◽  
Mechanistic Model ◽  
Mechanistic Modeling ◽  
Drug Uptake ◽  
Added Value ◽  
Model Parameters ◽  
Transdermal Drug Delivery Systems

Transdermal drug delivery systems are a key technology to administer drugs with a high first-pass effect in a non-invasive and controlled way. Physics-based modeling and simulation are on their way to become a cornerstone in the engineering of these healthcare devices since it provides a unique complementarity to experimental data and insights. Simulations enable to virtually probe the drug transport inside the skin at each point in time and space. However, the tedious experimental or numerical determination of material properties currently forms a bottleneck in the modeling workflow. We show that multiparameter inverse modeling to determine the drug diffusion and partition coefficients is a fast and reliable alternative. We demonstrate this strategy for transdermal delivery of fentanyl. We found that inverse modeling reduced the normalized root mean square deviation of the measured drug uptake flux from 26 to 9%, when compared to the experimental measurement of all skin properties. We found that this improved agreement with experiments was only possible if the diffusion in the reservoir holding the drug was smaller than the experimentally-measured diffusion coefficients suggested. For indirect inverse modeling, which systematically explores the entire parametric space, 30 000 simulations were required. By relying on direct inverse modeling, we reduced the number of simulations to be performed to only 300, so a factor 100 difference. The modeling approach's added value is that it can be calibrated once in-silico for all model parameters simultaneously by solely relying on a single measurement of the drug uptake flux evolution over time. We showed that this calibrated model could accurately be used to simulate transdermal patches with other drug doses. We showed that inverse modeling is a fast way to build up an accurate mechanistic model for drug delivery. This strategy opens the door to clinically-ready therapy that is tailored to patients.

scholarly journals Global sensitivity analysis of parameter uncertainty in landscape evolution models

2018 ◽  
Vol 11 (12) ◽  
pp. 4873-4888 ◽  
Author(s):  
Christopher J. Skinner ◽  
Tom J. Coulthard ◽  
Wolfgang Schwanghart ◽  
Marco J. Van De Wiel ◽  
Greg Hancock
Keyword(s):  
Sensitivity Analysis ◽  
Objective Function ◽  
Landscape Evolution ◽  
Relative Sensitivity ◽  
Function Approach ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Model Function ◽  
Sediment Yields ◽  
Statistical Measures

Abstract. The evaluation and verification of landscape evolution models (LEMs) has long been limited by a lack of suitable observational data and statistical measures which can fully capture the complexity of landscape changes. This lack of data limits the use of objective function based evaluation prolific in other modelling fields, and restricts the application of sensitivity analyses in the models and the consequent assessment of model uncertainties. To overcome this deficiency, a novel model function approach has been developed, with each model function representing an aspect of model behaviour, which allows for the application of sensitivity analyses. The model function approach is used to assess the relative sensitivity of the CAESAR-Lisflood LEM to a set of model parameters by applying the Morris method sensitivity analysis for two contrasting catchments. The test revealed that the model was most sensitive to the choice of the sediment transport formula for both catchments, and that each parameter influenced model behaviours differently, with model functions relating to internal geomorphic changes responding in a different way to those relating to the sediment yields from the catchment outlet. The model functions proved useful for providing a way of evaluating the sensitivity of LEMs in the absence of data and methods for an objective function approach.

Development of Nonlinear Coupled Mode Bushing Model Based on the Bouc-Wen Hysteretic Model

2007 ◽  
Author(s):  
Jin-Kyu Ok ◽  
Jeong-Hyun Sohn ◽  
Wan-Suk Yoo
Keyword(s):  
Sensitivity Analyses ◽  
Model Parameters ◽  
Hysteretic Model ◽  
Torsional Mode ◽  
Coupled Mode ◽  
Sensitivity Indices ◽  
Rubber Bushing ◽  
Hysteretic Characteristics ◽  
Vehicle Suspension System ◽  
And Behavior

In this paper, a coupled bushing model for vehicle dynamics analysis based on the Bouc-Wen hysteretic model is proposed. Bushing components of a vehicle suspension system are tested to capture the nonlinear and behavior of the typical rubber bushing elements using MTS machine. Test results are used to define the parameters of the Bouc-Wen bushing model. The Bouc-Wen model is employed to represent the hysteretic characteristics of the bushing. A coupled relation for radial mode and torsional mode are suggested. Model parameters are obtained by using the genetic algorithm, and sensitivity indices of parameters are also extracted from the sensitivity analyses. ADAMS program is used for the identification process and VisualDOC program is employed to find the optimal parameters of the proposed model. A half-car simulation is carried out to validate the proposed bushing model.

scholarly journals Use of Preclinical Data for Selection of a Phase II/III Dose for Evernimicin and Identification of a Preclinical MIC Breakpoint

2001 ◽  
Vol 45 (1) ◽  
pp. 13-22 ◽  
Author(s):  
G. L. Drusano ◽  
S. L. Preston ◽  
C. Hardalo ◽  
R. Hare ◽  
C. Banfield ◽  
...  
Keyword(s):  
Protein Binding ◽  
Healthy Volunteers ◽  
Phase Ii ◽  
Drug Exposure ◽  
Clinical Isolates ◽  
Model Parameters ◽  
Test Drug ◽  
Dose Selection ◽  
Preclinical Data

ABSTRACT One of the most challenging issues in the design of phase II/III clinical trials of antimicrobial agents is dose selection. The choice is often based on preclinical data from pharmacokinetic (PK) studies with animals and healthy volunteers but is rarely linked directly to the target organisms except by the MIC, an in vitro measure of antimicrobial activity with many limitations. It is the thesis of this paper that rational dose-selection decisions can be made on the basis of the pharmacodynamics (PDs) of the test agent predicted by a mathematical model which uses four data sets: (i) the distribution of MICs for clinical isolates, (ii) the distribution of the values of the PK parameters for the test drug in the population, (iii) the PD target(s) developed from animal models of infection, and (iv) the protein binding characteristics of the test drug. In performing this study with the new anti-infective agent evernimicin, we collected a large number (n = 4,543) of recent clinical isolates of gram-positive pathogens (Streptococcus pneumoniae,Enterococcus faecalis and Enterococcus faecium, and Staphylococcus aureus) and determined the MICs using E-test methods (AB Biodisk, Stockholm, Sweden) for susceptibility to evernimicin. Population PK data were collected from healthy volunteers (n = 40) and patients with hypoalbuminemia (n = 12), and the data were analyzed by using NPEM III. PD targets were developed with a neutropenic murine thigh infection model with three target pathogens: S. pneumoniae (n = 5), E. faecalis(n = 2), and S. aureus (n= 4). Drug exposure or the ratio of the area under the concentration-time curve/MIC (AUC/MIC) was found to be the best predictor of microbiological efficacy. There were three possible microbiological results: stasis of the initial inoculum at 24 h (107 CFU), log killing (pathogen dependent, ranging from 1 to 3 log10), or 90% maximal killing effect (90%E max). The levels of protein binding in humans and mice were similar. The PK and PD of 6 and 9 mg of evernimicin per kg of body weight were compared; the population values for the model parameters and population covariance matrix were used to generate five Monte Carlo simulations with 200 subjects each. The fractional probability of attaining the three PD targets was calculated for each dose and for each of the three pathogens. All differences in the fractional probability of attaining the target AUC/MIC in this PD model were significant. For S. pneumoniae, the probability of attaining all three PD targets was high for both doses. For S. aureus and enterococci, there were increasing differences between the 6- and 9-mg/kg evernimicin doses for reaching the 2 log killing (S. aureus), 1 log killing (enterococci), or 90%E max AUC/MIC targets. This same approach may also be used to set preliminary in vitro MIC breakpoints.

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