Whole-body Physiologically Based Pharmacokinetic Model Research Articles

Determination of the Number of Tissue Groups of Kinetically Distinct Transit Time in Whole-Body Physiologically Based Pharmacokinetic (PBPK) Models II: Practical Application of Tissue Lumping Theories for Pharmacokinetics of Various Compounds.

In our companion paper, we described the theoretical basis for tissue lumping in whole-body physiologically based pharmacokinetic (WB-PBPK) models and found that Kdet, a coefficient for determining the number of tissue groups of distinct transit time in WB-PBPK models, was related to the fractional change in the terminal slope (FCT) when tissues were progressively lumped from the longest transit time to shorter ones. This study was conducted to identify the practical threshold of Kdet by applying the lumping theory to plasma/blood concentration-time relationships of 113 model compounds collected from the literature. We found that drugs having Kdet < 0.3 were associated with FCT < 0.1 even when all peripheral tissues were lumped, resulting in comparable plasma concentration-time profiles between one-tissue minimal PBPK (mPBPK) and WB-PBPK models. For drugs with Kdet ≥ 1, WB-PBPK profiles appeared similar with two-tissue mPBPK models by applying the rule of FCT < 0.1 for lumping slowly equilibrating tissues. The two-tissue mPBPK model also appeared appropriate in terms of concentration-time profiles for drugs with 0.3 ≤ Kdet < 1, although, some compounds (15.9% of the total cases), but not all, in this range showed a slight (maximum of 18.9% of the total AUC) deviation from WB-PBPK models, indicating that the two-tissue model, with caution, could still be used for those cases. Comparison of kinetic parameters between traditional (model-fitting) and current (theoretical calculation) mPBPK analyses revealed their significant correlations. Collectively, these observations suggest that the number of tissue groups could be determined based on the Kdet/FCT criteria, and plasma concentration-time profiles from WB-PBPK could be calculated using equations significantly less complex.

Determination of the Number of Tissue Groups of Kinetically Distinct Transit Time in Whole-Body Physiologically Based Pharmacokinetic (PBPK) Models I: Theoretical Consideration of Bottom-Up Approach of Lumping Tissues in Whole-Body PBPK.

Minimal physiologically based pharmacokinetic (mPBPK) models, consisting of system-specific (e.g., tissue volume and blood flow) and drug-related (e.g., tissue-to-plasma partition coefficient) parameters, are practically useful for pharmacokinetic analyses. However, biopharmaceutical principles were not clear on how peripheral tissues, adopted in whole-body physiologically based pharmacokinetic (WB-PBPK) models, could be kinetically consolidated into one or two tissue groups in the mPBPK models. In this theoretical examination, we studied the relationship between the progressive tissue lumping in the direction from the longest mean transit time (MTTmax) to the shorter one(s) and the slopes of the terminal (λter)/distributional phases, assuming tissues with comparable MTTs are kinetically combined. The appropriateness of lumping was ascertained by evaluating the impact of difference in tissue MTTs during the lumping on the analytical solution of WB-PBPK models. We found that the ratio of MTTmax to the mean residence time in the body, viz., Kdet, is related to the change in λter by the progressive lumping and can serve as an index for the robustness of λter. Calculations with two extreme cases revealed that, for caffeine at Kdet < 0.03, the change in λter was minimal even when all peripheral tissues were collectively lumped, whereas for artesunic acid at Kdet > 50, the tissue of MTTmax could not be kinetically combined even with the tissue having the second-longest MTT without significantly affecting λter. Therefore, we proposed Kdet as an index for the robustness of λter during tissue lumping and for the number of tissue groups with distinct transit times in WB-PBPK.

Renal Transporter-Mediated Drug-Biomarker Interactions of the Endogenous Substrates Creatinine and N1 -Methylnicotinamide: A PBPK Modeling Approach.

Endogenous biomarkers for transporter-mediated drug-drug interaction (DDI) predictions represent a promising approach to facilitate and improve conventional DDI investigations in clinical studies. This approach requires high sensitivity and specificity of biomarkers for the targets of interest (e.g., transport proteins), as well as rigorous characterization of their kinetics, which can be accomplished utilizing physiologically-based pharmacokinetic (PBPK) modeling. Therefore, the objective of this study was to develop PBPK models of the endogenous organic cation transporter (OCT)2 and multidrug and toxin extrusion protein (MATE)1 substrates creatinine and N1 -methylnicotinamide (NMN). Additionally, this study aimed to predict kinetic changes of the biomarkers during administration of the OCT2 and MATE1 perpetrator drugs trimethoprim, pyrimethamine, and cimetidine. Whole-body PBPK models of creatinine and NMN were developed utilizing studies investigating creatinine or NMN exogenous administration and endogenous synthesis. The newly developed models accurately describe and predict observed plasma concentration-time profiles and urinary excretion of both biomarkers. Subsequently, models were coupled to the previously built and evaluated perpetrator models of trimethoprim, pyrimethamine, and cimetidine for interaction predictions. Increased creatinine plasma concentrations and decreased urinary excretion during the drug-biomarker interactions with trimethoprim, pyrimethamine, and cimetidine were well-described. An additional inhibition of NMN synthesis by trimethoprim and pyrimethamine was hypothesized, improving NMN plasma and urine interaction predictions. To summarize, whole-body PBPK models of creatinine and NMN were built and evaluated to better assess creatinine and NMN kinetics while uncovering knowledge gaps for future research. The models can support investigations of renal transporter-mediated DDIs during drug development.

Impact of route-dependent phase-II gut metabolism and enterohepatic circulation on the bioavailability and systemic disposition of resveratrol in rats and humans: A comprehensive whole body physiologically-based pharmacokinetic modeling

Resveratrol, a natural polyphenolic phytoalexin, is a dietary supplement that improves the outcomes of metabolic, cardiovascular, and other age-related diseases due to its diverse pharmacological activities. Although there have been several preclinical and clinical investigations of resveratrol, the contributions of gut phase-II metabolism and enterohepatic circulation to the oral bioavailability and pharmacokinetics of resveratrol remain unclear. Furthermore, a physiologically-based pharmacokinetic (PBPK) model that accurately describes and predicts the systemic exposure profiles of resveratrol in clinical settings has not been developed. Experimental data were acquired from several perspectives, including in vitro protein binding and blood distribution, in vitro tissue S9 metabolism, in situ intestinal perfusion, and in vivo pharmacokinetics and excretion studies. Using these datasets, an in-house whole-body PBPK model incorporating route-dependent phase-II (glucuronidation and sulfation) gut metabolism and enterohepatic circulation processes was constructed and optimized for chemical-specific parameters. The developed PBPK model aligned with the observed systemic exposure profiles of resveratrol in single and multiple dosing regimens with an acceptable accuracy of 0.538–0.999-fold errors. Furthermore, the model simulations elucidated the substantial contribution of gut first-pass metabolism to the oral bioavailability of resveratrol and suggested differential effects of enterohepatic circulation on the systemic exposure of resveratrol between rats and humans. After partial modification and verification, our proposed PBPK model would be valuable to optimize dosage regimens and predict food-drug interactions with resveratrol-based natural products in various clinical scenarios.

Mechanistic Modeling of Central Nervous System Pharmacokinetics and Target Engagement of HER2 Tyrosine Kinase Inhibitors to Inform Treatment of Breast Cancer Brain Metastases.

This study evaluated the central nervous system (CNS) pharmacokinetics and target engagement of lapatinib, neratinib, and tucatinib in patients with cancer, using a physiologically based pharmacokinetic (PBPK) modeling approach. Drug-specific parameters for in vitro metabolism, binding to plasma proteins and brain tissues, transcellular passive permeability, and interactions with efflux transporters were determined. Whole-body PBPK models integrated with a 4-compartment permeability-limited brain model 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 (Css,ave,br) to in vitro IC50 for HER2 inhibition, was used as a predictor of intracranial efficacy. PBPK models predicted that following 1 cycle of standard dosing, tucatinib and lapatinib achieved similar Css,ave,br (14.5 vs. 16.8 nmol/L), while neratinib Css,ave,br (0.68 nmol/L) was 20-fold lower. Tucatinib and neratinib were equally potent for HER2 inhibition (IC50, 6.9 vs. 5.6 nmol/L), while lapatinib was less potent (IC50, 109 nmol/L). The model-predicted population mean TER in the human normal brain was 2.1 for tucatinib, but < 0.20 for lapatinib and neratinib. The PBPK modeling suggests that tucatinib induces sufficient HER2 inhibition (TER > 2.0) in not only brain metastases with a disrupted blood-brain barrier (BBB), but also micrometastases where the BBB largely remains intact. These findings, in line with available clinical pharmacokinetics and efficacy data, support the therapeutic value of tucatinib for treatment of brain metastases and warrant further clinical investigation for the prevention of brain metastases in patients with HER2-positive breast cancer.

Prediction of lung exposure to anti-tubercular drugs using plasma pharmacokinetic data: Implications for dose selection

The development of novel candidate molecules for tuberculosis remains challenging, as drug distribution into the target tissue is not fully characterised in preclinical models of infection. Often antitubercular human dose selection is derived from pharmacokinetic data in plasma. Here, we explore whether whole-body physiologically-based pharmacokinetic (PBPK) modelling enables the prediction of lung exposure to anti-tubercular drugs in humans. Whole-body PBPK models were developed for rifampicin, isoniazid, pyrazinamide, and ethambutol using plasma data in mice as basis for the prediction of lung exposure. Model parameters were subsequently used to extrapolate disposition properties from mouse and determine lung:plasma ratio in humans. Model predictions were compared to biopsy data from patients. Predictions were deemed adequate if they fell within two-fold range of the observations. The concentration vs time profiles in lung were adequately predicted in mice. Isoniazid and pyrazinamide lung exposures were predicted to be comparable to plasma levels, whereas ethambutol lung exposure was predicted to be higher than in plasma. Lung:plasma ratio in humans could be reasonably predicted from preclinical data, but was highly dependent on the distribution model. This analysis showed that plasma pharmacokinetics may be used in conjunction with PBPK modelling to derive lung tissue exposure in mice and humans during early lead optimisation phase. However, the impact of uncertainty in predicted tissue exposure due to distribution should be always investigated through a sensitivity analysis when only plasma data is available. Despite these limitations, insight into lung tissue distribution represents a critical step for the dose rationale in tuberculosis patients.

PBPK Modelling of Dexamethasone in Patients With COVID-19 and Liver Disease

The aim of the study was to apply Physiologically-Based Pharmacokinetic (PBPK) modelling to predict the effect of liver disease (LD) on the pharmacokinetics (PK) of dexamethasone (DEX) in the treatment of COVID-19. A whole-body PBPK model was created to simulate 100 adult individuals aged 18–60 years. Physiological changes (e.g., plasma protein concentration, liver size, CP450 expression, hepatic blood flow) and portal vein shunt were incorporated into the LD model. The changes were implemented by using the Child-Pugh (CP) classification system. DEX was qualified using clinical data in healthy adults for both oral (PO) and intravenous (IV) administrations and similarly propranolol (PRO) and midazolam (MDZ) were qualified with PO and IV clinical data in healthy and LD adults. The qualified model was subsequently used to simulate a 6 mg PO and 20 mg IV dose of DEX in patients with varying degrees of LD, with and without shunting. The PBPK model was successfully qualified across DEX, MDZ and PRO. In contrast to healthy adults, the simulated systemic clearance of DEX decreased (35%–60%) and the plasma concentrations increased (170%–400%) in patients with LD. Moreover, at higher doses of DEX, the AUC ratio between healthy/LD individuals remained comparable to lower doses. The exposure of DEX in different stages of LD was predicted through PBPK modelling, providing a rational framework to predict PK in complex clinical scenarios related to COVID-19. Model simulations suggest dose adjustments of DEX in LD patients are not necessary considering the low dose administered in the COVID-19 protocol.

Predicting Drug-Drug Interactions between Rifampicin and Ritonavir-Boosted Atazanavir Using PBPK Modelling.

ObjectivesThe aim of this study was to simulate the drug–drug interaction (DDI) between ritonavir-boosted atazanavir (ATV/r) and rifampicin (RIF) using physiologically based pharmacokinetic (PBPK) modelling, and to predict suitable dose adjustments for ATV/r for the treatment of people living with HIV (PLWH) co-infected with tuberculosis.MethodsA whole-body DDI PBPK model was designed using Simbiology 9.6.0 (MATLAB R2019a) and verified against reported clinical data for all drugs administered alone and concomitantly. The model contained the induction mechanisms of RIF and ritonavir (RTV), the inhibition effect of RTV for the enzymes involved in the DDI, and the induction and inhibition mechanisms of RIF and RTV on the uptake and efflux hepatic transporters. The model was considered verified if the observed versus predicted pharmacokinetic values were within twofold. Alternative ATV/r dosing regimens were simulated to achieve the trough concentration (Ctrough) clinical cut-off of 150 ng/mL.ResultsThe PBPK model was successfully verified according to the criteria. Simulation of different dose adjustments predicted that a change in regimen to twice-daily ATV/r (300/100 or 300/200 mg) may alleviate the induction effect of RIF on ATV Ctrough, with > 95% of individuals predicted to achieve Ctrough above the clinical cut-off.ConclusionsThe developed PBPK model characterized the induction-mediated DDI between RIF and ATV/r, accurately predicting the reduction of ATV plasma concentrations in line with observed clinical data. A change in the ATV/r dosing regimen from once-daily to twice-daily was predicted to mitigate the effect of the DDI on the Ctrough of ATV, maintaining plasma concentration levels above the therapeutic threshold for most patients.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40262-021-01067-1.

Physiologically Based Pharmacokinetic Modeling of Rosuvastatin to Predict Transporter-Mediated Drug-Drug Interactions

PurposeTo build a physiologically based pharmacokinetic (PBPK) model of the clinical OATP1B1/OATP1B3/BCRP victim drug rosuvastatin for the investigation and prediction of its transporter-mediated drug-drug interactions (DDIs).MethodsThe Rosuvastatin model was developed using the open-source PBPK software PK-Sim®, following a middle-out approach. 42 clinical studies (dosing range 0.002–80.0 mg), providing rosuvastatin plasma, urine and feces data, positron emission tomography (PET) measurements of tissue concentrations and 7 different rosuvastatin DDI studies with rifampicin, gemfibrozil and probenecid as the perpetrator drugs, were included to build and qualify the model.ResultsThe carefully developed and thoroughly evaluated model adequately describes the analyzed clinical data, including blood, liver, feces and urine measurements. The processes implemented to describe the rosuvastatin pharmacokinetics and DDIs are active uptake by OATP2B1, OATP1B1/OATP1B3 and OAT3, active efflux by BCRP and Pgp, metabolism by CYP2C9 and passive glomerular filtration. The available clinical rifampicin, gemfibrozil and probenecid DDI studies were modeled using in vitro inhibition constants without adjustments. The good prediction of DDIs was demonstrated by simulated rosuvastatin plasma profiles, DDI AUClast ratios (AUClast during DDI/AUClast without co-administration) and DDI Cmax ratios (Cmax during DDI/Cmax without co-administration), with all simulated DDI ratios within 1.6-fold of the observed values.ConclusionsA whole-body PBPK model of rosuvastatin was built and qualified for the prediction of rosuvastatin pharmacokinetics and transporter-mediated DDIs. The model is freely available in the Open Systems Pharmacology model repository, to support future investigations of rosuvastatin pharmacokinetics, rosuvastatin therapy and DDI studies during model-informed drug discovery and development (MID3).

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

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.

A physiologically based pharmacokinetic (PBPK) model to describe organ distribution of 68Ga-DOTATATE in patients without neuroendocrine tumors

BackgroundPhysiologically based pharmacokinetic (PBPK) models combine drug-specific information with prior knowledge on the physiology and biology at the organism level. Whole-body PBPK models contain an explicit representation of the organs and tissue and are a tool to predict pharmacokinetic behavior of drugs. The aim of this study was to develop a PBPK model to describe organ distribution of 68Ga-DOTATATE in a population of patients without detectable neuroendocrine tumors (NETs).MethodsClinical 68Ga-DOTATATE PET/CT data from 41 patients without any detectable somatostatin receptor (SSTR) overexpressing tumors were included. Scans were performed at 45 min (range 30–60 min) after intravenous bolus injection of 68Ga-DOTATATE. Organ (spleen, liver, thyroid) and blood activity levels were derived from PET scans, and corresponding DOTATATE concentrations were calculated. A whole-body PBPK model was developed, including an internalization reaction, receptor recycling, enzymatic reaction for intracellular degradation and renal clearance. SSTR2 expression was added for several organs. Input parameters were fixed or estimated using a built-in Monte Carlo algorithm for parameter identification.Results68Ga-DOTATATE was administered with a median peptide amount of 12.3 µg (range 8.05–16.9 µg) labeled with 92.7 MBq (range 43.4–129.9 MBq). SSTR2 amounts for spleen, liver and thyroid were estimated at 4.40, 7.80 and 0.0108 nmol, respectively. Variability in observed organ concentrations was best described by variability in SSTR2 expression and differences in administered peptide amounts.ConclusionsTo conclude, biodistribution of 68Ga-DOTATATE was described with a whole-body PBPK model, where tissue distribution was mainly determined by variability in SSTR2 organ expression and differences in administered peptide amounts.

Developing a physiologically based pharmacokinetic model of apixaban to predict scenarios of drug–drug interactions, renal impairment and paediatric populations

To develop a physiologically based pharmacokinetic (PBPK) model for apixaban, an oral anticoagulant with a narrow therapeutic index, and to predict PK profiles and potential drug-drug interactions (DDIs) in patients with renal impairment and paediatrics. A whole-body apixaban PBPK model was developed and validated in SimCYP for healthy adults with or without interacting drugs. The model was extended to renal impairment and paediatrics. Observed PK data in adults were compared with predicted data. The effect of renal function, age and DDIs on apixaban PK was investigated. The PBPK model successfully predicted the PK of apixaban alone and under the influence of interacting drugs. For patients with renal impairment, the PBPK model successfully predicted the fold change in each impairment group; inhibitory DDI and renal impairment had a synergistic effect on the increase of apixaban exposure (e.g., almost 3-fold increase of AUC in ketoconazole + severe renal impairment group). For infants younger than 1year, the exposure of apixaban decreased with increased weight-normalized clearance. For newborn infants, AUC of apixaban was >2-fold higher than that in children older than 1year. Meanwhile, the effect of DDI seems to be weakened while the effect of renal impairment might be enhanced in infants younger than 1year. Our study provides a reasonable approach to estimate the dose adjustment for the first use of apixaban in special populations with complex situations, which has the opportunity to make the clinical practice much safer.

Development and Evaluation of a Physiologically Based Pharmacokinetic Drug-Disease Model of Propranolol for Suggesting Model Informed Dosing in Liver Cirrhosis Patients.

AimThe study was aimed to understand the underlying causes for the differences in propranolol pharmacokinetics (PK) between healthy and cirrhosis populations by using a systematic whole-body physiologically based pharmacokinetic (PBPK) model-building approach for suggesting model informed propranolol dosing in liver cirrhosis patients with different stages of disease severity.MethodsA whole-body PBPK model was developed by using population simulator PK-Sim® by using reported physicochemical and clinical data for propranolol in healthy and liver cirrhosis populations. The model evaluation was done by visual verification and comparison of PK parameters using their observed/predicted ratios (Robs/pred).ResultsThe developed model has effectively described the disposition of propranolol after intravenous and oral application in healthy and liver cirrhosis populations. All the model predictions were comparable to the observed clinical data and the Robs/pred for all the PK parameters were within a 2-fold range. A significant increase in plasma concentration of propranolol and decrease in drug clearance was observed in progressive stages of liver cirrhosis. The developed model after evaluation with the reported clinical PK data was used for suggesting model informed propranolol dosing in different stages of liver cirrhosis based on systemic unbound drug concentration.ConclusionThe developed PBPK model has successfully described propranolol PK in healthy and cirrhosis populations after IV and oral administration. The evaluated PBPK propranolol-cirrhosis model can have many implications in predicting propranolol dosing in liver cirrhosis patients with different stages of disease severity.

Estimation of main and total effect of PBPK parameters in Meningioma patients

This study aims to identify the most important parameters in the Physiologically-Based Pharmacokinetic (PBPK) model of Peptide Receptor Radionuclide Therapy (PRRT). By knowing the size of the contribution of physiological parameters to the PBPK model, it can reduce the variability of the absorbed dose (AD) in organs at risk, such as the kidney and tumor between individuals. The small variability has the potential to increase the accuracy of planning individual radionuclide therapy treatments. This study uses the extended Fourier Amplitude Sensitivity Test (eFAST) Global Sensitivity Analysis method, the best variance-based global method in analyzing the PBPK model. A whole-body PBPK model that has been developed for treatment planning in PRRT therapy for meningioma patients (n = 7). The parameters of interest analyzed were organ receptor densities Rdens, organ flows f, organ release rates, and peptide binding rate. AD as the desired output from the eFAST algorithm by calculating Si and STi from each AD Kidney and AD Tumor. All parameters of interest are converted into the lognormal distribution. The sampling strategy based on eFAST sampling, the interference factor is equal to 4. To see the convergence of the convergence value of Si and STi , a simulation was performed with a total evaluation of 129, 257, 513, 1025, 2049, 4097, and 8193. The results of the simulation, inter-individual variability of tumor AD (coefficient of variation CV up to CV = 73%) was higher than that organ at risk (e.g. kidneys CV around 22%). Based on GSA analysis, the most important parameter determined the AD of tumors, tumors receptor density (Si = 0.8, S Ti = 0.93), kidneys AD was kidneys receptor density (Si = 0.66, STi = 0.71). After validating Si by fixing every parameter considered important, the results can reduce the CV of the kidney AD from 22% to 1%, with a decrease in CV presentation of around 95%. CV AD tumor 1 was reduced by 68% from CV 44% to 14%, and CV in tumor AD 2 from 72% to 17% with a reduced CV presentation of about 77%. It was concluded that receptor measurement is important because it can improve the accuracy of radionuclide therapy treatment.

Important pharmacokinetic parameters for individualization of 177 Lu-PSMA therapy: A global sensitivity analysis for a physiologically-based pharmacokinetic model.

The knowledge of the contribution of anatomical and physiological parameters to interindividual pharmacokinetic differences could potentially be used to improve individualized treatment planning for radionuclide therapy. The aim of this study was therefore to identify the physiologically based pharmacokinetic (PBPK) model parameters that determine the interindividual variability of absorbed doses (ADs) to kidneys and tumor lesions in therapy with 177 Lu-labeled PSMA-targeting radioligands. A global sensitivity analysis (GSA) with the extended Fourier Amplitude Sensitivity Test (eFAST) algorithm was performed. The whole-body PBPK model for PSMA-targeting radioligand therapy from our previous studies was used in this study. The model parameters of interest (input of the GSA) were the organ receptor densities [R0 ], the organ blood flows f, and the organ release rates λ. These parameters were systematically sampled NE times according to their distribution in the patient population. The corresponding pharmacokinetics were simulated and the ADs (model output) to kidneys and tumor lesions were collected. The main effect and total effect were calculated using the eFAST algorithm based on the variability of the model output: The main effect of input parameter represents the reduction in variance of the output if the "true" value of parameter would be known. The total effect of an input parameter represents the proportion of variance remaining if the "true" values of all other input parameters except for are known. The numbers of samples NE were increased up to 8193 to check the stability (i.e., convergence) of the calculated main effects and total effects . From the simulations, the relative interindividual variability of ADs in the kidneys (coefficient of variation CV=31%) was lower than that of ADs in the tumors (CV up to 59%). Based on the GSA, the most important parameters that determine the ADs to the kidneys were kidneys flow ( =0.36, =0.43) and kidneys receptor density ( =0.25, =0.30). Tumor receptor density was identified as the most important parameter determining the ADs to tumors ( and up to 0.72). The results suggest that an accurate measurement of receptor density and flow before therapy could be a promising approach for developing an individualized treatment with 177 Lu-labeled PSMA-targeting radioligands.

A Physiologically-Based Pharmacokinetic (PBPK) Model Network for the Prediction of CYP1A2 and CYP2C19 Drug-Drug-Gene Interactions with Fluvoxamine, Omeprazole, S-mephenytoin, Moclobemide, Tizanidine, Mexiletine, Ethinylestradiol, and Caffeine.

Physiologically-based pharmacokinetic (PBPK) modeling is a well-recognized method for quantitatively predicting the effect of intrinsic/extrinsic factors on drug exposure. However, there are only few verified, freely accessible, modifiable, and comprehensive drug–drug interaction (DDI) PBPK models. We developed a qualified whole-body PBPK DDI network for cytochrome P450 (CYP) CYP2C19 and CYP1A2 interactions. Template PBPK models were developed for interactions between fluvoxamine, S-mephenytoin, moclobemide, omeprazole, mexiletine, tizanidine, and ethinylestradiol as the perpetrators or victims. Predicted concentration–time profiles accurately described a validation dataset, including data from patients with genetic polymorphisms, demonstrating that the models characterized the CYP2C19 and CYP1A2 network over the whole range of DDI studies investigated. The models are provided on GitHub (GitHub Inc., San Francisco, CA, USA), expanding the library of publicly available qualified whole-body PBPK models for DDI predictions, and they are thereby available to support potential recommendations for dose adaptations, support labeling, inform the design of clinical DDI trials, and potentially waive those.

Dose prediction for repurposing nitazoxanide in SARS-CoV-2 treatment or chemoprophylaxis.

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has been declared a global pandemic and urgent treatment and prevention strategies are needed. Nitazoxanide, an anthelmintic drug, has been shown to exhibit in vitro activity against SARS‐CoV‐2. The present study used physiologically based pharmacokinetic (PBPK) modelling to inform optimal doses of nitazoxanide capable of maintaining plasma and lung tizoxanide exposures above the reported SARS‐CoV‐2 EC90.MethodsA whole‐body PBPK model was validated against available pharmacokinetic data for healthy individuals receiving single and multiple doses between 500 and 4000 mg with and without food. The validated model was used to predict doses expected to maintain tizoxanide plasma and lung concentrations above the EC90 in >90% of the simulated population. PopDes was used to estimate an optimal sparse sampling strategy for future clinical trials.ResultsThe PBPK model was successfully validated against the reported human pharmacokinetics. The model predicted optimal doses of 1200 mg QID, 1600 mg TID and 2900 mg BID in the fasted state and 700 mg QID, 900 mg TID and 1400 mg BID when given with food. For BID regimens an optimal sparse sampling strategy of 0.25, 1, 3 and 12 hours post dose was estimated.ConclusionThe PBPK model predicted tizoxanide concentrations within doses of nitazoxanide already given to humans previously. The reported dosing strategies provide a rational basis for design of clinical trials with nitazoxanide for the treatment or prevention of SARS‐CoV‐2 infection. A concordant higher dose of nitazoxanide is now planned for investigation in the seamless phase I/IIa AGILE trial.

Ethambutol disposition in humans: Challenges and limitations of whole-body physiologically-based pharmacokinetic modelling in early drug development

Whole-body physiologically based pharmacokinetic (WB-PBPK) models have become an important tool in drug development, as they enable characterization of pharmacokinetic profiles across different organs based on physiological (systems-specific) and physicochemical (drug-specific) properties. However, it remains unclear which data are needed for accurate predictions when applying the approach to novel candidate molecules progressing into the clinic. In this work, as case study, we investigated the predictive performance of WB-PBPK models both for prospective and retrospective evaluation of the pharmacokinetics of ethambutol, considering scenarios that reflect different stages of development, including settings in which the data are limited to in vitro experiments, in vivo preclinical data, and when some clinical data are available. Overall, the accuracy of PBPK model-predicted systemic and tissue exposure was heavily dependant on prior knowledge about the eliminating organs. Whilst these findings may be specific to ethambutol, the challenges and potential limitations identified here may be relevant to a variety of drugs, raising questions about (1) the minimum requirements for prospective use of WB-PBPK models during the characterization of drug disposition and (2) implication of uncertainty for dose selection in humans.

Physiologically Based Pharmacokinetic Modeling and Tissue Distribution Characteristics of SHetA2 in Tumor-Bearing Mice.

The orally available novel small molecule SHetA2 is the lead sulfur-containing heteroarotinoid that selectively inhibits cancer cells over normal cells, and is currently under clinical development for anticancer treatment and cancer prevention. The objective of this study was to assess and characterize the tissue distribution of SHetA2 in tumor-bearing mice by developing a physiologically based pharmacokinetic (PBPK) model. An orthotopic SKOV3 ovarian cancer xenograft mouse model was used to most accurately mimic the ovarian cancer tumor microenvironment in the peritoneal cavity. SHetA2 concentrations in plasma and 14 different tissues were measured at various time points after a single intravenous dose of 10mg/kg and oral dose of 60mg/kg, and these data were used to develop a whole-body PBPK model. SHetA2 exhibited a multi-exponential plasma concentration decline with an elimination half-life of 4.5h. Rapid and extensive tissue distribution, which was best described by a perfusion rate-limited model, was observed with the tissue-to-plasma partition coefficients (kp = 1.4-21.2). The PBPK modeling estimated the systemic clearance (76.4mL/h) from circulation as a main elimination pathway of SHetA2. It also indicated that the amount absorbed into intestine was the major determining factor for the oral bioavailability (22.3%), while the first-pass loss from liver and intestine contributed minimally (< 1%). Our results provide an insight into SHetA2 tissue distribution characteristics. The developed PBPK model can be used to predict the drug exposure at tumors or local sites of action for different dosing regimens and scaled up to humans to correlate with efficacy.

Development and evaluation of a harmonized whole body physiologically based pharmacokinetic (PBPK) model for flutamide in rats and its extrapolation to humans

By their definition, inadvertent exposure to endocrine disrupting compounds (EDCs) intervenes with the endocrine signalling system, even at low dose. On the one hand, some EDCs are used as important pharmaceutical drugs that one would not want to dismiss. On the other hand, these pharmaceutical drugs are having off-target effects and increasingly significant exposure to the general population with unwanted health implications. Flutamide, one of the top pharmaceutical products marketed all over the world for the treatment of prostate cancer, is also a pollutant. Its therapeutic action mainly depends on targeting the androgen receptors and inhibiting the androgen action that is essential for growth and survival of prostate tissue. Currently flutamide is of concern with respect to its categorization as an endocrine disruptor.In this work we have developed a physiologically based pharmacokinetic (PBPK) model of flutamide that could serve as a standard tool for its human risk assessment. First we built the model for rat (where many parameters have been measured). The rat PBPK model was extrapolated to human where the re-parameterization involved human-specific physiology, metabolic kinetics derived from in-vitro studies, and the partition coefficient same as the rat model. We have harmonized the model by integrating different sets of in-vitro, in-vivo and physiological data into a PBPK model. Then the model was used to simulate different exposure scenarios and the results were compared against the observed data. Both uncertainty and sensitivity analysis was done.Since this new whole-body PBPK model can predict flutamide concentrations not only in plasma but also in various organs, the model may have clinical applications in efficacy and safety assessment of flutamide. The model can also be used for reverse dosimetry in the context of interpreting the available biomonitoring data to estimate the degree to which the population is currently being exposed, and a tool for the pharmaceutical companies to validate the estimated Permitted Daily Exposure (PDE) for flutamide.