An Application of a Physiologically Based Pharmacokinetic Approach to Predict Ceftazidime Pharmacokinetics in a Pregnant Population.

Physiological changes during pregnancy can alter maternal and fetal drug exposure. The objective of this work was to predict maternal and umbilical ceftazidime pharmacokinetics during pregnancy. Ceftazidime transplacental permeability was predicted from its physicochemical properties and incorporated into the model. Predicted concentrations and parameters from the PBPK model were compared to the observed data. PBPK predicted ceftazidime concentrations in non-pregnant and pregnant subjects of different gestational weeks were within 2-fold of the observations, and the observed concentrations fell within the 5th-95th prediction interval from the PBPK simulations. The calculated transplacental clearance (0.00137 L/h/mL of placenta volume) predicted an average umbilical cord-to-maternal plasma ratio of 0.7 after the first dose, increasing to about 1.0 at a steady state, which also agrees well with clinical observations. The developed maternal PBPK model adequately predicted the observed exposure and kinetics of ceftazidime in the pregnant population. Using a verified population-based PBPK model provides valuable insights into the disposition of drug concentrations in special individuals that are otherwise difficult to study and, in addition, offers the possibility of supplementing sparse samples obtained in vulnerable populations with additional knowledge, informing the dosing adjustment and study design, and improving the efficacy and safety of drugs in target populations.

Incorporation and Performance Verification of Hepatic Portal Blood Flow Shunting in Minimal and Full PBPK Models of Liver Cirrhosis.

Patho-physiological changes in liver cirrhosis create portacaval shunts that allow blood flow to bypass the hepatic portal vein into the systemic circulation affecting drug pharmacokinetics (PKs). The objectives of this work were to implement a physiologically-based pharmacokinetic (PBPK) framework describing shunted blood flows in virtual patients with differing degrees of liver cirrhosis; and to assess the minimal and full PBPK model's performance using drugs with intermediate to high hepatic extraction. Single dose concentration-time profiles and PK parameters for oral ibrutinib, midazolam, propranolol, and buspirone were simulated in healthy volunteers (HVs) and subjects with cirrhosis (Child-Pugh severity score (CP-A, CP-B, or CP-C)). Model performance was verified by comparing predicted to observed fold-changes in PK parameters between HVs and cirrhotic subjects. The verified model was used to simulate the PK changes for simvastatin in patients with cirrhosis. The predicted area under the curve ratios (AUCCirr :AUCHV ) for ibrutinib were 3.38, 6.87, and 11.46 using the minimal PBPK model with shunt and 1.61, 2.58, and 4.33 without the shunt, these compared with observed values of 4.33, 8.14, and 9.04, respectively. For ibrutinib, propranolol, and buspirone, including a shunt in the PBPK model improved the prediction of the AUCCirr :AUCHV and maximum plasma concentration ratios (CmaxCirr :CmaxHV ). For midazolam, an intermediate extraction drug, the differences were less clear. Simulated simvastatin dose adjustments in cirrhosis suggested that 20 mg in CP-A and 10 mg in CP-B could be used clinically. A mechanistic model-informed understanding of the anatomic and pathophysiology of cirrhosis will facilitate improved dose prediction and adjustment in this vulnerable population.

Physiologically-based pharmacokinetic modeling to predict drug-drug interaction of enzalutamide with combined P-gp and CYP3A substrates.

Enzalutamide is known to strongly induce cytochrome P450 3A4 (CYP3A4). Furthermore, enzalutamide showed induction and inhibition of P-glycoprotein (P-gp) in in vitro studies. A clinical drug-drug interaction (DDI) study between enzalutamide and digoxin, a typical P-gp substrate, suggested enzalutamide has weak inhibitory effect on P-gp substrates. Direct oral anticoagulants (DOACs), such as apixaban and rivaroxaban, are dual substrates of CYP3A4 and P-gp, and hence it is recommended to avoid co-administration of these DOACs with combined P-gp and strong CYP3A inducers. Enzalutamide's net effect on P-gp and CYP3A for apixaban and rivaroxaban plasma exposures is of interest to physicians who treat patients for venous thromboembolism with prostate cancer. Accordingly, a physiologically-based pharmacokinetic (PBPK) analysis was performed to predict the magnitude of DDI on apixaban and rivaroxaban exposures in the presence of 160 mg once-daily dosing of enzalutamide. The PBPK models of enzalutamide and M2, a major metabolite of enzalutamide which also has potential to induce CYP3A and P-gp and inhibit P-gp, were developed and verified as perpetrators of CYP3A-and P-gp-mediated interaction. Simulation results predicted a 31% decrease in AUC and no change in Cmax for apixaban and a 45% decrease in AUC and a 25% decrease in Cmax for rivaroxaban when 160 mg multiple doses of enzalutamide were co-administered. In summary, enzalutamide is considered to decrease apixaban and rivaroxaban exposure through the combined effects of CYP3A induction and net P-gp inhibition. Concurrent use of these drugs warrants careful monitoring for efficacy and safety.

Pharmacokinetics under the COVID-19 storm.

AIMS: The storm-like nature of the health crises caused by COVID-19 has led to unconventional clinical trial practices such as the relaxation of exclusion criteria. The question remains: how can we conduct diverse trials without exposing subgroups of populations to potentially harmful drug exposure levels? The aim of this study was to build a knowledge base of the effect of intrinsic/extrinsic factors on the disposition of several repurposed COVID-19 drugs. METHODS: Physiologically based pharmacokinetic (PBPK) models were used to study the change in the pharmacokinetics (PK) of drugs repurposed for COVID-19 in geriatric patients, different race groups, organ impairment and drug-drug interactions (DDIs) risks. These models were also used to predict epithelial lining fluid (ELF) exposure, which is relevant for COVID-19 patients under elevated cytokine levels. RESULTS: The simulated PK profiles suggest no dose adjustments are required based on age and race for COVID-19 drugs, but dose adjustments may be warranted for COVID-19 patients also exhibiting hepatic/renal impairment. PBPK model simulations suggest ELF exposure to attain a target concentration was adequate for most drugs, except for hydroxychloroquine, azithromycin, atazanavir and lopinavir/ritonavir. CONCLUSION: We demonstrate that systematically collated data on absorption, distribution, metabolism and excretion, human PK parameters, DDIs and organ impairment can be used to verify simulated plasma and lung tissue exposure for drugs repurposed for COVID-19, justifying broader patient recruitment criteria. In addition, the PBPK model developed was used to study the effect of age and ethnicity on the PK of repurposed drugs, and to assess the correlation between lung exposure and relevant potency values from in vitro studies for SARS-CoV-2.

Prediction of basic drug exposure in milk using a lactation model algorithm integrated within a physiologically based pharmacokinetic model.

Medication use during breastfeeding can be a matter of concern due to unintended infant exposure to drugs through breast milk. The available information relating to the safety of most medications is limited and may vary. More precise information is needed regarding the safety to the newborn or infants of the medications taken by the mother during breastfeeding. Physiologically based Pharmacokinetic Model (PBPK) approaches can be utilized to predict the drug exposure in the milk of breastfeeding women and can act as a supporting tool in the risk assessment of feeding infants. This study aims to assess the predictive performance of an integrated 'log transformed phase-distribution' lactation model within a PBPK platform. The model utilizes the physicochemical properties of four basic drugs, namely tramadol, venlafaxine, fluoxetine, and paroxetine, and analyses the milk compositions to predict the milk-to-plasma (M/P) ratio. The M/P prediction model was incorporated within the Simcyp Simulator V20 to predict the milk exposure and to estimate the likely infant dose for these drugs. The PBPK models adequately predicted the maternal plasma exposure, M/P ratio, and the infant daily dose to within two-fold of the clinically observed values for all four compounds. Integration of the lactation model within PBPK models facilitates the prediction of drug exposure in breast milk. The developed model can inform the design of lactation studies and assist with the neonatal risk assessment after maternal exposure to such environmental chemicals or basic drugs which diffuse passively into the milk.

Non-specific binding of compounds in in vitro metabolism assays: a comparison of microsomal and hepatocyte binding in different species and an assessment of the accuracy of prediction models.

Non-specific binding in in vitro metabolism systems leads to an underestimation of the true intrinsic metabolic clearance of compounds being studied. Therefore in vitro binding needs to be accounted for when extrapolating in vitro data to predict the in vivo metabolic clearance of a compound. While techniques exist for experimentally determining the fraction of a compound unbound in in vitro metabolism systems, early in drug discovery programmes computational approaches are often used to estimate the binding in the in vitro system.Experimental fraction unbound data (n = 60) were generated in liver microsomes (fumic) from five commonly used pre-clinical species (rat, mouse, dog, minipig, monkey) and humans. Unbound fraction in incubations with mouse, rat or human hepatocytes was determined for the same 60 compounds. These data were analysed to determine the relationship between experimentally determined binding in the different matrices and across different species. In hepatocytes there was a good correlation between fraction unbound in human and rat (r2=0.86) or mouse (r2=0.82) hepatocytes. Similar correlations were observed between binding in human liver microsomes and microsomes from rat, mouse, dog, Göttingen minipig or monkey liver microsomes (r2 of >0.89, n = 51 - 52 measurements in different species). Physicochemical parameters (logP, pKa and logD) were predicted for all evaluated compounds. In addition, logP and/or logD were measured for a subset of compounds.Binding to human hepatocytes predicted using 5 different methods was compared to the measured data for a set of 59 compounds. The best methods evaluated used measured microsomal binding in human liver microsomes to predict hepatocyte binding. The collated physicochemical data were used to predict the human fumic using four different in silico models for a set of 53-60 compounds. The correlation (r2) and root mean square error between predicted and observed microsomal binding was 0.69 & 0.20, 0.47 & 0.23, 0.56 & 0.21 and 0.54 & 0.26 for the Turner-Simcyp, Austin, Hallifax-Houston and Poulin models, respectively. These analyses were extended to include measured literature values for binding in human liver microsomes for a larger set of compounds (n=697). For the larger dataset of compounds, microsomal binding was well predicted for neutral compounds (r2=0.67 - 0.70) using the Poulin, Austin, or Turner-Simcyp methods but not for acidic or basic compounds (r2<0.5) using any of the models. While the lipophilicity-based models can be used, the in vitro binding should be measured for compounds where more certainty is needed, using appropriately calibrated assays and possibly established weak, moderate, and strong binders as reference compounds to allow comparison across databases.

Mechanistic Modeling of In Vitro Skin Permeation and Extrapolation to In Vivo for Topically Applied Metronidazole Drug Products Using a Physiologically Based Pharmacokinetic Model.

Physiologically based pharmacokinetic (PBPK) modeling has increasingly been employed in dermal drug development and regulatory assessment, providing a framework to integrate relevant information including drug and drug product attributes, skin physiology parameters, and population variability. The current study aimed to develop a stepwise modeling workflow with knowledge gained from modeling in vitro skin permeation testing (IVPT) to describe in vivo exposure of metronidazole locally in the stratum corneum following topical application of complex semisolid drug products. The initial PBPK model of metronidazole in vitro skin permeation was developed using infinite and finite dose aqueous metronidazole solution. Parameters such as stratum corneum lipid-water partition coefficient (Ksclip/water) and stratum corneum lipid diffusion coefficient (Dsclip) of metronidazole were optimized using IVPT data from simple aqueous solutions (infinite) and MetroGel (10 mg/cm2 dose application), respectively. The optimized model, when parameterized with physical and structural characteristics of the drug products, was able to accurately predict the mean cumulative amount permeated (cm2/h) and flux (µg/cm2/h) profiles of metronidazole following application of different doses of MetroGel and MetroCream. Thus, the model was able to capture the impact of differences in drug product microstructure and metamorphosis of the dosage form on in vitro metronidazole permeation. The PBPK model informed by IVPT study data was able to predict the metronidazole amount in the stratum corneum as reported in clinical studies. In summary, the proposed model provides an enhanced understanding of the potential impact of drug product attributes in influencing in vitro skin permeation of metronidazole. Key kinetic parameters derived from modeling the metronidazole IVPT data improved the predictions of the developed PBPK model of in vivo local metronidazole concentrations in the stratum corneum. Overall, this work improves our confidence in the proposed workflow that accounts for drug product attributes and utilizes IVPT data toward improving predictions from advanced modeling and simulation tools.

Application of a Physiologically Based Pharmacokinetic Approach to Predict Theophylline Pharmacokinetics Using Virtual Non-Pregnant, Pregnant, Fetal, Breast-Feeding, and Neonatal Populations.

Perinatal pharmacology is influenced by a myriad of physiological variables that are changing dynamically. The influence of these covariates has not been assessed systemically. The objective of this work was to use theophylline as a model drug and to predict its pharmacokinetics before, during (including prediction of the umbilical cord level), and after pregnancy as well as in milk (after single and multiple doses) and in neonates using a physiological-based pharmacokinetic (PBPK) model. Neonatal theophylline exposure from milk consumption was projected in both normal term and preterm subjects. Predicted infant daily doses were calculated using theophylline average and maximum concentration in the milk as well as an estimate of milk consumption. Predicted concentrations and parameters from the PBPK model were compared to the observed data. PBPK predicted theophylline concentrations in non-pregnant and pregnant populations at different gestational weeks were within 2-fold of the observations and the observed concentrations fell within the 5th-95th prediction interval from the PBPK simulations. The PBPK model predicted an average cord-to-maternal plasma ratio of 1.0, which also agrees well with experimental observations. Predicted postpartum theophylline concentration profiles in milk were also in good agreement with observations with a predicted milk-to-plasma ratio of 0.68. For an infant of 2 kg consuming 150 ml of milk per day, the lactation model predicted a relative infant dose (RID) of 12 and 17% using predicted average (Cavg,ss) and maximum (Cmax,ss) concentration in milk at steady state. The maximum RID of 17% corresponds to an absolute infant daily dose of 1.4 ± 0.5 mg/kg/day. This dose, when administered as 0.233 mg/kg every 4 h, to resemble breastfeeding frequency, resulted in plasma concentrations as high as 3.9 (1.9-6.8) mg/L and 2.8 (1.3-5.3) (5th-95th percentiles) on day 7 in preterm (32 GW) and full-term neonatal populations.

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

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

Understanding Interindividual Variability in the Drug Interaction of a Highly Extracted CYP1A2 Substrate Tizanidine: Application of a Permeability-Limited Multicompartment Liver Model in a Population Based Physiologically Based Pharmacokinetic Framework.

Tizanidine, a centrally acting skeletal muscle relaxant, is predominantly metabolized by CYP1A2 and undergoes extensive hepatic first-pass metabolism after oral administration. As a highly extracted drug, the systemic exposure to tizanidine exhibits considerable interindividual variability and is altered substantially when coadministered with CYP1A2 inhibitors or inducers. The aim of the current study was to compare the performance of a permeability-limited multicompartment liver (PerMCL) model, which operates as an approximation of the dispersion model, and the well stirred model (WSM) for predicting tizanidine drug-drug interactions (DDIs). Physiologically based pharmacokinetic models were developed for tizanidine, incorporating the PerMCL model and the WSM, respectively, to simulate the interaction of tizanidine with a range of CYP1A2 inhibitors and inducers. Whereas the WSM showed a tendency to underpredict the fold change of tizanidine area under the plasma concentration-time curve (AUC ratio) in the presence of perpetrators, the use of PerMCL model increased precision (absolute average-fold error: 1.32-1.42 versus 1.58) and decreased bias (average-fold error: 0.97-1.25 versus 0.63) for the predictions of mean AUC ratios as compared with the WSM. The PerMCL model captured the observed range of individual AUC ratios of tizanidine as well as the correlation between individual AUC ratios and CYP1A2 activities without interactions, whereas the WSM was not able to capture these. The results demonstrate the advantage of using the PerMCL model over the WSM in predicting the magnitude and interindividual variability of DDIs for a highly extracted sensitive substrate tizanidine. SIGNIFICANCE STATEMENT: This study demonstrates the advantages of the PerMCL model, which operates as an approximation of the dispersion model, in mitigating the tendency of the WSM to underpredict the magnitude and variability of DDIs of a highly extracted CYP1A2 substrate tizanidine when it is administered with CYP1A2 inhibitors or inducers. The physiologically based pharmacokinetic modeling approach described herein is valuable to the understanding of drug interactions of highly extracted substrates and the source of its interindividual variability.

Population PBPK modeling using parametric and nonparametric methods of the Simcyp Simulator, and Bayesian samplers.

Physiologically-based pharmacokinetic (PBPK) models usually include a large number of parameters whose values are obtained using in vitro to in vivo extrapolation. However, such extrapolations can be uncertain and may benefit from inclusion of evidence from clinical observations via parametric inference. When clinical interindividual variability is high, or the data sparse, it is essential to use a population pharmacokinetics inferential framework to estimate unknown or uncertain parameters. Several approaches are available for that purpose, but their relative advantages for PBPK modeling are unclear. We compare the results obtained using a minimal PBPK model of a canonical theophylline dataset with quasi-random parametric expectation maximization (QRPEM), nonparametric adaptive grid estimation (NPAG), Bayesian Metropolis-Hastings (MH), and Hamiltonian Markov Chain Monte Carlo sampling. QRPEM and NPAG gave consistent population and individual parameter estimates, mostly agreeing with Bayesian estimates. MH simulations ran faster than the others methods, which together had similar performance.

A Mechanistic Absorption and Disposition Model of Ritonavir to Predict Exposure and Drug-Drug Interaction Potential of CYP3A4/5 and CYP2D6 Substrates.

BACKGROUND AND OBJECTIVES: Due to health authority warnings and the recommended limited use of ketoconazole as a model inhibitor of cytochrome P450 (CYP) 3A4 in clinical drug-drug interaction (DDI) studies, there is a need to search for alternatives. Ritonavir is a strong inhibitor for CYP3A4/5-mediated DDIs and has been proposed as a suitable alternative to ketoconazole. It can also be used as a weak inhibitor for CYP2D6-mediated DDIs. Most of the currently available physiologically based pharmacokinetic (PBPK) inhibitor models developed for predicting DDIs use first-order absorption models, which do not mechanistically capture the effect of formulations on the systemic exposure of the inhibitor. Thus, the main purpose of the current study was to verify the predictive performance of a mechanistic absorption and disposition model of ritonavir when it was applied to the inhibition of CYP2D6 and CYP3A4/5 by ritonavir. METHODS: A PBPK model that incorporates formulation characteristics and enzyme kinetic parameters for post-absorptive pharmacokinetic processes of ritonavir was constructed. Key absorption-related parameters in the model were determined using mechanistic modelling of in vitro biopharmaceutics experiments. The model was verified for systemic exposure and DDI risk assessment using clinical observations from 13 and 18 studies, respectively. RESULTS: Maximal inhibition of hepatic (3.53% of the activity remaining) and gut (5.16% of the activity remaining) CYP3A4 activity was observed when ritonavir was orally administered in doses of 100 mg or higher. The PBPK model accurately described the concentrations of ritonavir in the different simulated studies. The prediction accuracy for maximum concentration (Cmax) and area under the plasma concentration versus time curve (AUC) were assessed. The bias (average fold error, AFE) for the prediction of Cmax and AUC was 0.92 and 1.06, respectively, and the precision (absolute average fold error, AAFE) was 1.29 and 1.23, respectively. The PBPK model predictions for all Cmax and AUC ratios when ritonavir was used as an inhibitor of CYP metabolism fell within twofold of the clinical observations. The prediction accuracy for Cmax and AUC ratios had a bias (AFE) of 0.85 and 0.99, respectively, and a precision (AAFE) of 1.21 and 1.33, respectively. CONCLUSIONS: The current model, which incorporates formulation characteristics and mechanistic disposition parameters, can be used to assess the DDI potential of CYP3A4/5 and CYP2D6 substrates administered with a twice-daily dose of 100 mg of ritonavir for 14 days.

Prediction of Maternal and Fetoplacental Concentrations of Cefazolin, Cefuroxime, and Amoxicillin during Pregnancy Using Bottom-Up Physiologically Based Pharmacokinetic Models.

Concerns over maternal and fetal drug exposures highlight the need for a better understanding of drug distribution into the fetus through the placental barrier. This study aimed to predict maternal and fetal drug disposition using physiologically based pharmacokinetic (PBPK) modeling. The detailed maternal-placental-fetal PBPK model within the Simcyp Simulator V20 was used to predict the maternal and fetoplacental exposure of cefazolin, cefuroxime, and amoxicillin during pregnancy and at delivery. The mechanistic dynamic model includes physiologic changes of the maternal, fetal, and placental parameters over the course of pregnancy. Placental kinetics were parametrized using permeability parameters determined from the physicochemical properties of these compounds. Then, the PBPK predictions were compared with the observed data. Fully bottom-up fetoplacental PBPK models were developed for cefuroxime, cefazolin, and amoxicillin without any parameter fitting. Predictions in nonpregnant subjects and in pregnant subjects fall within 2-fold of the observed values. Predictions matched observed pharmacokinetic data reported in nine maternal (five fetoplacental) studies for cefuroxime, 10 maternal (five fetoplacental) studies for cefazolin, and six maternal (two fetoplacental) studies for amoxicillin. Integration of the fetal and maternal system parameters within PBPK models, together with compound-related parameters used to calculate placental permeability, facilitates and extends the applications of the maternal-placental-fetal PBPK model. The developed model can also be used for designing clinical trials and prospectively used for maternal-fetal risk assessment after maternally administered drugs or unintended exposure to environmental toxicants. SIGNIFICANCE STATEMENT: This study investigates the performance of an integrated maternal-placental-fetal PBPK model to predict maternal and fetal tissue exposure of renally eliminated antibiotics that cross the placenta through a passive diffusion mechanism. The transplacental permeability clearance was predicted from the drug physicochemical properties. Results demonstrate that the PBPK approach can facilitate the prediction of maternal and fetal drug exposure simultaneously at any gestational age to support its use in the maternal-fetal exposure assessments.

Prediction of Maternal and Fetal Acyclovir, Emtricitabine, Lamivudine, and Metformin Concentrations during Pregnancy Using a Physiologically Based Pharmacokinetic Modeling Approach.

BACKGROUND: Concerns over maternal and fetal drug exposure during pregnancy highlight the need for improved understanding of drug distribution to the fetus through the placental barrier. OBJECTIVE: Our objective was to predict maternal and fetal drug disposition using a physiologically based pharmacokinetic (PBPK) modeling approach. METHODS: We used the detailed maternal-placental-fetal PBPK model within the Simcyp Simulator V20 to predict the maternal and fetal drug exposure of acyclovir, emtricitabine, lamivudine, and metformin during pregnancy and at delivery. The dynamic model includes gestational changes to the maternal, fetal, and placental physiological parameters. Placental kinetics were parameterized using published ex vivo data for these four compounds. Amniotic data were included where available. PBPK predictions were compared with the observed data using twofold criteria. RESULTS: Maternal-fetal PBPK models were developed completely from the bottom up without any parameter adjustments. The PBPK model-predicted exposures matched the observed maternal and umbilical exposure for acyclovir (six maternal studies, all of which all reported umbilical exposure), emtricitabine (six maternal studies, of which four reported umbilical exposure), lamivudine, (five maternal studies, of which four reported umbilical exposure), and metformin (seven studies, of which six reported umbilical exposure). Predicted pharmacokinetic parameters were within twofold of the observed values. CONCLUSION: Integration of fetal and maternal system parameters within PBPK models, together with experimental data from ex vivo placental perfusion studies, facilitated and extended the application of the pregnancy PBPK model. Such models can also be used inform clinical trials and maternal/fetal risk assessment following maternally administered drugs or unintended exposure to environmental toxicants.

Development of physiologically-based pharmacokinetic models for standard of care and newer tuberculosis drugs.

Tuberculosis (TB) remains a global health problem and there is an ongoing effort to develop more effective therapies and new combination regimes that can reduce duration of treatment. The purpose of this study was to demonstrate utility of a physiologically-based pharmacokinetic modeling approach to predict plasma and lung concentrations of 11 compounds used or under development as TB therapies (bedaquiline [and N-desmethyl bedaquiline], clofazimine, cycloserine, ethambutol, ethionamide, isoniazid, kanamycin, linezolid, pyrazinamide, rifampicin, and rifapentine). Model accuracy was assessed by comparison of simulated plasma pharmacokinetic parameters with healthy volunteer data for compounds administered alone or in combination. Eighty-four percent (area under the curve [AUC]) and 91% (maximum concentration [Cmax ]) of simulated mean values were within 1.5-fold of the observed data and the simulated drug-drug interaction ratios were within 1.5-fold (AUC) and twofold (Cmax ) of the observed data for nine (AUC) and eight (Cmax ) of the 10 cases. Following satisfactory recovery of plasma concentrations in healthy volunteers, model accuracy was assessed further (where patients' with TB data were available) by comparing clinical data with simulated lung concentrations (9 compounds) and simulated lung: plasma concentration ratios (7 compounds). The 5th-95th percentiles for the simulated lung concentration data recovered between 13% (isoniazid and pyrazinamide) and 88% (pyrazinamide) of the observed data points (Am J Respir Crit Care Med, 198, 2018, 1208; Nat Med, 21, 2015, 1223; PLoS Med, 16, 2019, e1002773). The impact of uncertain model parameters, such as the fraction of drug unbound in lung tissue mass (fumass ), is discussed. Additionally, the variability associated with the patient lung concentration data, which was sparse and included extensive within-subject, interlaboratory, and experimental variability (as well interindividual variability) is reviewed. All presented models are transparently documented and are available as open-source to aid further research.

Mechanistic PBPK Modelling to Predict the Advantage of the Salt Form of a Drug When Dosed with Acid Reducing Agents.

Acid reducing agents (ARAs) reduce the dissolution rate of weakly basic drugs in the stomach potentially leading to lower bioavailability. Formulating the API as a rapidly dissolving salt is one strategy employed to reduce the impact of ARAs on dissolution of such drugs. In the present work, a model drug was selected with an immediate release formulation of the free base dosed in both the absence and presence of the ARA famotidine. In the latter case, bioavailability is restricted and several salt formulations were investigated. To simulate these drug products a mechanistic physiologically based pharmacokinetic (PBPK) model was built using the Simcyp Simulator, which illustrates the advantage of formulating an API as a salt compared to the free base form. The simulations use a mechanistic salt model utilising knowledge of the solubility product which was applied to predict the salt advantage. The developed PBPK model exemplifies that it can be critical to account for the surface pH and solubility when modelling the dissolution of low pKa bases and their salts in the gastric environment. In particular, the mechanistic salt model can be used to aid in screening and salt form selection where the aim is to mitigate effects of ARAs.

Prediction of drug concentrations in milk during breastfeeding, integrating predictive algorithms within a physiologically-based pharmacokinetic model.

There is a risk of exposure to drugs in neonates during the lactation period due to maternal drug intake. The ability to predict drugs of potential hazards to the neonates would be useful in a clinical setting. This work aimed to evaluate the possibility of integrating milk-to-plasma (M/P) ratio predictive algorithms within the physiologically-based pharmacokinetic (PBPK) approach and to predict milk exposure for compounds with different physicochemical properties. Drug and physiological milk properties were integrated to develop a lactation PBPK model that takes into account the drug ionization, partitioning between the maternal plasma and milk matrices, and drug partitioning between the milk constituents. Infant dose calculations that take into account maternal and milk physiological variability were incorporated in the model. Predicted M/P ratio for acetaminophen, alprazolam, caffeine, and digoxin were 0.83 ± 0.01, 0.45 ± 0.05, 0.70 ± 0.04, and 0.76 ± 0.02, respectively. These ratios were within 1.26-fold of the observed ratios. Assuming a daily milk intake of 150 ml, the predicted relative infant dose (%) for these compounds were 4.0, 6.7, 9.9, and 86, respectively, which correspond to a daily ingestion of 2.0 ± 0.5 mg, 3.7 ± 1.2 µg, 2.1 ± 1.0 mg, and 32 ± 4.0 µg by an infant of 5 kg bodyweight. Integration of the lactation model within the PBPK approach will facilitate and extend the application of PBPK models during drug development in high-throughput screening and in different clinical settings. The model can also be used in designing lactation trials and in the risk assessment of both environmental chemicals and maternally administered drugs.

Ocular pharmacokinetics of atenolol, timolol and betaxolol cocktail: Tissue exposures in the rabbit eye.

Quantitative understanding of pharmacokinetics of topically applied ocular drugs requires more research to further understanding and to eventually allow predictive in silico models to be developed. To this end, a topical cocktail of betaxolol, timolol and atenolol was instilled on albino rabbit eyes. Tear fluid, corneal epithelium, corneal stroma with endothelium, bulbar conjunctiva, anterior sclera, iris-ciliary body, lens and vitreous samples were collected and analysed using LC-MS/MS. Iris-ciliary body was also analysed after intracameral cocktail injection. Non-compartmental analysis was utilized to estimate the pharmacokinetics parameters. The most lipophilic drug, betaxolol, presented the highest exposure in all tissues except for tear fluid after topical administration, followed by timolol and atenolol. For all drugs, iris-ciliary body concentrations were higher than that of the aqueous humor. After topical instillation the most hydrophilic drug, atenolol, had 3.7 times higher AUCiris-ciliary body than AUCaqueous humor, whereas the difference was 1.4 and 1.6 times for timolol and betaxolol, respectively. This suggests that the non-corneal route (conjunctival-scleral) was dominating the absorption of atenolol, while the corneal route was more important for timolol and betaxolol. The presented data increase understanding of ocular pharmacokinetics of a cocktail of drugs and provide data that can be used for quantitative modeling and simulation.

Fetal Physiologically Based Pharmacokinetic Models: Systems Information on Fetal Cardiac Output and Its Distribution to Different Organs during Development.

BACKGROUND AND OBJECTIVE: Fetal circulation is unique and the parameters describing hemodynamic status during development are critical for constructing a fetal physiologically based pharmacokinetic model. To date, a comprehensive review of circulatory changes during fetal development, with a specific focus on developing these models, has not been reported. The objective of this work was to collate, analyze, and mathematically describe physiological information on fetal cardiac output and tissue blood flows during development. METHODS: A comprehensive literature search was carried out to collate and evaluate the changes to fetal cardiac output and fetal tissue blood flows during growth. The collated data were assessed, integrated, and analyzed to establish continuous mathematical functions describing the average parameter changes and variability during development. RESULTS: Data were available for fetal cardiac output (14 Doppler studies), blood flow through the fetal umbilical vein (15 studies), ductus venosus (6 studies), liver veins (5 studies), brain (4 studies), lungs (5 studies), and kidneys (2 studies). Fetal cardiac output is described as either an age- or weight-dependent function. The latter is preferred as it generates an individualized cardiac output that is correlated to the fetal body weight. Blood flow as a proportion of fetal cardiac output to the liver, placenta, brain, kidneys, and lungs was age varying, whilst for the adipose, bone, heart, muscle, and skin the blood flow proportions were fixed. The pattern of change (with respect to direction and pace) for each of these parameters was different. CONCLUSIONS: Despite limitations in the availability of some values, the collected data provide a useful resource for fetal physiologically based pharmacokinetic modeling. Potential applications of these data include predicting xenobiotic exposure and risk assessment in the fetus following the administration of maternally dosed drugs or unintended exposure to environmental toxicants.