Translational two-pore PBPK model to characterize whole-body disposition of different-size endogenous and exogenous proteins

Two-pore physiologically based pharmacokinetic (PBPK) modeling has demonstrated its potential in describing the pharmacokinetics (PK) of different-size proteins. However, all existing two-pore models lack either diverse proteins for validation or interspecies extrapolation. To fill the gap, here we have developed and optimized a translational two-pore PBPK model that can characterize plasma and tissue disposition of different-size proteins in mice, rats, monkeys, and humans. Datasets used for model development include more than 15 types of proteins: IgG (150 kDa), F(ab)2 (100 kDa), minibody (80 kDa), Fc-containing proteins (205, 200, 110, 105, 92, 84, 81, 65, or 60 kDa), albumin conjugate (85.7 kDa), albumin (67 kDa), Fab (50 kDa), diabody (50 kDa), scFv (27 kDa), dAb2 (23.5 kDa), proteins with an albumin-binding domain (26, 23.5, 22, 16, 14, or 13 kDa), nanobody (13 kDa), and other proteins (110, 65, or 60 kDa). The PBPK model incorporates: (i) molecular weight (MW)-dependent extravasation through large and small pores via diffusion and filtration, (ii) MW-dependent renal filtration, (iii) endosomal FcRn-mediated protection from catabolism for IgG and albumin-related modalities, and (iv) competition for FcRn binding from endogenous IgG and albumin. The finalized model can well characterize PK of most of these proteins, with area under the curve predicted within two-fold error. The model also provides insights into contribution of renal filtration and lysosomal degradation towards total elimination of proteins, and contribution of paracellular convection/diffusion and transcytosis towards extravasation. The PBPK model presented here represents a cross-modality, cross-species platform that can be used for development of novel biologics.

Whole-Body Disposition and Physiologically Based Pharmacokinetic Modeling of Adeno-Associated Viruses and the Transgene Product

To facilitate model-informed drug development (MIDD) of adeno-associated virus (AAV) therapy, here we have developed a physiologically based pharmacokinetic (PBPK) model for AAVs following preclinical investigation in mice. After 2E11 Vg/mouse dose of AAV8 and AAV9 encoding a monoclonal antibody (mAb) gene, whole-body disposition of both the vector and the transgene mAb was evaluated over 3 weeks. At steady-state, the following tissue-to-blood (T/B) concentration ratios were found for AAV8/9: ∼50 for liver; ∼10 for heart and muscle; ∼2 for brain, lung, kidney, adipose, and spleen; ≤1 for bone, skin, and pancreas. T/B values for mAb were compared with the antibody biodistribution coefficients, and five different clusters of organs were identified based on their transgene expression profile. All the biodistribution data were used to develop a novel AAV PBPK model that incorporates: (i) whole-body distribution of the vector; (ii) binding, internalization, and intracellular processing of the vector; (iii) transgene expression and secretion; and (iv) whole-body disposition of the secreted transgene product. The model was able to capture systemic and tissue PK of the vector and the transgene-produced mAb reasonably well. Pathway analysis of the PBPK model suggested that liver, muscle, and heart are the main contributors for the secreted transgene mAb. Unprecedented PK data and the novel PBPK model developed here provide the foundation for quantitative systems pharmacology (QSP) investigations of AAV-mediated gene therapies. The PBPK model can also serve as a quantitative tool for preclinical study design and preclinical-to-clinical translation of AAV-based gene therapies.

Toxicity and risk priority ranking of polybrominated diphenyl ethers (PBDEs): A relative receptor-bound concentration approach

Toxicity and risk priority ranking of chemicals are crucial to management and decision-making. In this work, we develop a new mechanistic ranking approach of toxicity and risk priority ranking for polybrominated diphenyl ethers (PBDEs) based on receptor-bound concentration (RBC). Based on the binding affinity constant predicted using molecular docking, internal concentration converted from human biomonitoring data via PBPK model, and the receptor concentration derived from the national center for biotechnology information (NCBI) database, the RBC of 49 PBDEs binding to 24 nuclear receptors were calculated. 1176 RBC results were successfully obtained and analyzed. High brominated PBDEs, including BDE-201, BDE-205, BDE-203, BDE-196, BDE-183, BDE-206, BDE-207, BDE-153, BDE-208, BDE-204, BDE-197, and BDE-209, exerted more potent than low brominated congeners (BDE-028, BDE-047, BDE-099, and BDE-100) at the same daily intake dose in terms of toxicity ranking. For risk ranking, with human biomonitoring serum data, the relative RBC of BDE-209 was significantly greater than that of any others. For receptor prioritization, constitutive androstane receptor (CAR), retinoid X receptor alpha (RXRA), and liver X receptor alpha (LXRA) may be the sensitive targets for PBDEs to trigger effects in the liver. In summary, high brominated PBDEs are more potent than low brominated congeners, thus, besides BDE-047 and BDE-099, BDE-209 should be priority controlled. In conclusion, this study provides a new approach for toxicity and risk ranking of groups of chemicals, which can readily be used for others.

Human health risk assessment of 6:2 Cl-PFESA through quantitative in vitro to in vivo extrapolation by integrating cell-based assays, an epigenetic key event, and physiologically based pharmacokinetic modeling

Human health risk assessment of chemicals is essential but often relies on time-consuming and animal and labor-extensive procedures. Here, we develop a population-based, quantitative in vitro to in vivo extrapolation (QIVIVE) approach which depended on cellular effects monitored by in vitro assays, considered chemical internal concentration determined by LC-MS/MS, extrapolated into in vivo target tissue concentration through physiologically based pharmacokinetic (PBPK) modelling, and assessed populational health risk using in silico modelling. By applying this QIVIVE approach to 6:2 chlorinated polyfluorinated ether sulfonate (6:2 Cl-PFESA), as a representative of the emerging pollutants, we find that 6:2 Cl-PFESA disturbed lipid homeostasis in HepG2 cells through enhancement of lipid accumulation and fatty acid β-oxidation, during which miR-93-5p served as a key event towards toxicity and thus, could serve as an efficient toxicity marker for risk assessment; further, the disruption potency of lipid homeostasis of 6:2 Cl-PFESA for the most of studied populations in China might be of moderate concern. Together, our approach improved the reliability of QIVIVE during human health risk assessment, which can readily be used for other chemicals.

Modeling digestion, absorption, and ketogenesis after administration of tricaprilin formulations to humans

At present, tricaprilin is used as a ketogenic source for the management of mild to moderate Alzheimer's disease. After administration of the medium-chain triglyceride, tricaprilin is hydrolyzed to octanoic acid and further metabolized to ketones, acting as an alternative energy substrate for the brain. In this investigation, we developed a physiologically-based biopharmaceutics model simulating in vivo processes following the peroral administration of tricaprilin. The model includes multiple data sources to establish a partially verified framework for the simulation of plasma profiles. The input parameters were identified based on existing literature data and in vitro digestion studies. Model validation was conducted using the data from a phase I clinical trial. A partial parameter sensitivity analysis elucidated various influences on the plasma ketone levels that are mainly responsible for the therapeutic effects of tricaprilin. Based on our findings, we concluded that dispersibility and lipolysis of tricaprilin together with the gastric emptying patterns are limiting ketogenesis, while other steps such as the conversion of octanoic acid to ketone bodies play a minor role only.

Development of a multi-route physiologically based pharmacokinetic (PBPK) model for nanomaterials: a comparison between a traditional versus a new route-specific approach using gold nanoparticles in rats

Background

Physiologically based pharmacokinetic (PBPK) modeling is an important tool in predicting target organ dosimetry and risk assessment of nanoparticles (NPs). The methodology of building a multi-route PBPK model for NPs has not been established, nor systematically evaluated. In this study, we hypothesized that the traditional route-to-route extrapolation approach of PBPK modeling that is typically used for small molecules may not be appropriate for NPs. To test this hypothesis, the objective of this study was to develop a multi-route PBPK model for different sizes (1.4–200 nm) of gold nanoparticles (AuNPs) in adult rats following different routes of administration (i.e., intravenous (IV), oral gavage, intratracheal instillation, and endotracheal inhalation) using two approaches: a traditional route-to-route extrapolation approach for small molecules and a new approach that is based on route-specific data that we propose to be applied generally to NPs.

Results

We found that the PBPK model using this new approach had superior performance than the traditional approach. The final PBPK model was optimized rigorously using a Bayesian hierarchical approach with Markov chain Monte Carlo simulations, and then converted to a web-based interface using R Shiny. In addition, quantitative structure–activity relationships (QSAR) based multivariate linear regressions were established to predict the route-specific key biodistribution parameters (e.g., maximum uptake rate) based on the physicochemical properties of AuNPs (e.g., size, surface area, dose, Zeta potential, and NP numbers). These results showed the size and surface area of AuNPs were the main determinants for endocytic/phagocytic uptake rates regardless of the route of administration, while Zeta potential was an important parameter for the estimation of the exocytic release rates following IV administration.

Conclusions

This study suggests that traditional route-to-route extrapolation approaches for PBPK modeling of small molecules are not applicable to NPs. Therefore, multi-route PBPK models for NPs should be developed using route-specific data. This novel PBPK-based web interface serves as a foundation for extrapolating to other NPs and to humans to facilitate biodistribution estimation, safety, and risk assessment of NPs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12989-022-00489-4.

Translating dosimetry of Dibenzo[def,p]chrysene (DBC) and metabolites across dose and species using physiologically based pharmacokinetic (PBPK) modeling

Dibenzo[def,p]chrysene (DBC) is an environmental polycyclic aromatic hydrocarbon (PAH) that causes tumors in mice and has been classified as a probable human carcinogen by the International Agency for Research on Cancer. Animal toxicity studies often utilize higher doses than are found in relevant human exposures. Additionally, like many PAHs, DBC requires metabolic bioactivation to form the ultimate toxicant, and species differences in DBC metabolism have been observed. To understand the implications of dose and species differences, a physiologically based pharmacokinetic model (PBPK) for DBC and major metabolites was developed in mice and humans. Metabolism parameters used in the model were obtained from experimental in vitro metabolism assays in mice and human hepatic microsomes. PBPK model simulations were evaluated against mice dosed with 15 mg/kg DBC by oral gavage and human volunteers orally microdosed with 29 ng of DBC and. DBC and its primary metabolite DBC-11,12-diol were measured in blood of mice and humans, while in urine, the majority of DBC metabolites were conjugated DBC-11,12-diol, conjugated DBC tetrols, and unconjugated DBC tetrols. The PBPK model was able to predict the time course concentrations of DBC and DBC-11,12-diol, and other DBC metabolites in blood and urine of human volunteers and mice with reasonable accuracy. The agreement between the simulations of our model and measured pharmacokinetic data in mice and human studies demonstrate the success and versatility of our model for interspecies extrapolation and applicability for different doses. Furthermore, our simulations show that internal dose metrics used for risk assessment do not necessarily scale allometrically, and that PBPK modeling provides a reliable approach to appropriately account for interspecies differences in metabolism and physiology.

An integrated exposure and pharmacokinetic modeling framework for assessing population-scale risks of phthalates and their substitutes

To effectively incorporate in vitro-in silico-based methods into the regulation of consumer product safety, a quantitative connection between product phthalate concentrations and in vitro bioactivity data must be established for the general population. We developed, evaluated, and demonstrated a modeling framework that integrates exposure and pharmacokinetic models to convert product phthalate concentrations into population-scale risks for phthalates and their substitutes. A probabilistic exposure model was developed to generate the distribution of multi-route exposures based on product phthalate concentrations, chemical properties, and human activities. Pharmacokinetic models were developed to simulate population toxicokinetics using Bayesian analysis via the Markov chain Monte Carlo method. Both exposure and pharmacokinetic models demonstrated good predictive capability when compared with worldwide studies. The distributions of exposures and pharmacokinetics were integrated to predict the population distributions of internal dosimetry. The predicted distributions showed reasonable agreement with the U.S. biomonitoring surveys of urinary metabolites. The "source-to-outcome" local sensitivity analysis revealed that food contact materials had the greatest impact on body burden for di(2-ethylhexyl) adipate (DEHA), di-2-ethylhexyl phthalate (DEHP), di(isononyl) cyclohexane-1,2-dicarboxylate (DINCH), and di(2-propylheptyl) phthalate (DPHP), whereas the body burden of diethyl phthalate (DEP) was most sensitive to the concentration in personal care products. The upper bounds of predicted plasma concentrations showed no overlap with ToxCast in vitro bioactivity values. Compared with the in vitro-to-in vivo extrapolation (IVIVE) approach, the integrated modeling framework has significant advantages in mapping product phthalate concentrations to multi-route risks, and thus is of great significance for regulatory use with a relatively low input requirement. Further integration with new approach methodologies will facilitate these in vitro-in silico-based risk assessments for a broad range of products containing an equally broad range of chemicals.

Physiologically Based Biopharmaceutics Modeling to Demonstrate Virtual Bioequivalence and Bioequivalence Safe-space for Ribociclib which has Permeation Rate-controlled Absorption

A physiologically based biopharmaceutics model (PBBM) was developed to support formulation development of ribociclib, an orally bioavailable selective CDK4/6 inhibitor. Ribociclib is a weak base with moderate permeability and complete in vitro dissolution under stomach pH. GastroPlus™ was used to simulate the pharmacokinetics (PK) in healthy volunteers after capsule dosing. Simulations showed rapid, complete dissolution in human stomach without intestinal precipitation and with permeation-controlled absorption. Permeability was identified as controlling the systemic exposure. PBBM predicted bioequivalence (BE) between capsule and tablet in healthy volunteers, despite non-similarity between in vitro dissolution kinetics (f2<50). BE was verified in a clinical study. Then virtual bioequivalence (VBE) simulations predicted comparable PK in cancer patients between capsule and tablet of commercial batch, which was also confirmed in a clinical study. Finally, virtual trial simulations using virtual batches with slower dissolution were used to define an in vitro BE safe-space for tablets, where BE is expected. PBBM can identify drugs with permeability-controlled absorption for which formulation optimization can focus more on manufacturability rather than dissolution. PBBM can be used to predict BE study outcomes, define clinically relevant specification and BE safe-space, superseding dissolution similarity f2 criteria.

Biorelevant in vitro Tools and in silico Modeling to Assess pH-Dependent Drug-drug Interactions for Salts of Weak Acids: Case Example Potassium Raltegravir

BACKGROUND

Early assessment of pH-dependent drug-drug-interactions (DDIs) for salts of poorly soluble weakly acidic compounds offers various advantages for patient safety, the pharmaceutical industry, and regulatory bodies. Biorelevant media and tests reflecting physiological changes during acid-reducing agent (ARA) co-administration can be used to explore and predict the extent of the pH effect during therapy with ARAs.

METHODS

Solubility, one-stage and two-stage dissolution of tablets containing potassium raltegravir, the marketed salt form of this poorly soluble, weakly acidic drug, was investigated using biorelevant media specially designed to reflect administration without and during ARA co-therapy. The dissolution data were then converted into parameters suitable for input into an in silico model (Simcyp™) and the simulated plasma profiles were compared with available pharmacokinetic (PK) data from the literature.

RESULTS

Dissolution of the potassium raltegravir formulation in media reflecting ARA co-administration, and thus elevated gastric pH, was faster and more complete than in experiments reflecting the low gastric pH observed in the absence of ARA co-administration. Simulations using data from dissolution experiments with ARA media appropriately bracketed the in vivo data for ARA co-administration in healthy volunteers.

CONCLUSION

Dissolution data from in vitro experiments in biorelevant media reflecting physiological changes due to ARA co-administration provide valuable information about potassium raltegravir's behavior during concomitant ARA therapy. The approach may also be suitable for salts forms of other poorly soluble, weakly acidic drugs.

Evaluating the impact of acid-reducing agents on drug absorption using biorelevant in vitro tools and PBPK modeling - case example dipyridamole

BACKGROUND

In vitro and in silico methods have become an essential tool in assessing metabolic drug-drug interactions (DDI) and avoiding reduced efficacy and increased side-effects. Another important type of DDI is the impact of acid-reducing agent (ARA) co-therapy on drug pharmacokinetics due to changes in gastric pH, especially for poorly soluble weakly basic drugs.

METHODS

One-stage, two-stage and transfer dissolution experiments with dipyridamole tablets using novel biorelevant media representing the ARA effect were conducted and the results were coupled with a PBPK model. Clinical pharmacokinetic data were compared with the simulations from the PBPK model and with output from TIM-1 experiments, an evolved in vitro system which aims to simulate the physiology in the upper GI tract.

RESULTS

Two-stage and transfer experiments confirmed that these in vitro set-ups tend to overestimate the extent of dipyridamole precipitation occurring in the intestines in vivo. Consequently, data from one-stage dissolution testing under elevated gastric pH conditions were used as an input for PBPK modeling of the ARA/dipyridamole interaction. Using media representing the ARA effect in conjunction with the PBPK model, the ARA effect observed in vivo was successfully bracketed. As an alternative, the TIM-1 system with gastric pH values adjusted to simulate ARA pre-treatment can be used to forecast the ARA effect on dipyridamole pharmacokinetics.

CONCLUSION

Drug-drug interactions of dipyridamole with ARA were simulated well with a combination of dissolution experiments using biorelevant media representing the gastric environment after an ARA treatment together with the PBPK model. Adjustment of the TIM-1 model to reflect ARA-related changes in gastric pH was also successful in forecasting the interaction. Further testing of both approaches for predicting ARA-related DDIs using a wider range of drugs should be conducted to verify their utility for this purpose.

Prediction of plasma profiles of a weakly basic drug after administration of omeprazole using PBPK modeling

BACKGROUND

Oral medicines must release the drug appropriately in the GI tract in order to assure adequate and reproducible absorption. Disease states and co-administration of drugs may alter GI physiology and therefore the release profile of the drug. Acid-reducing agents (ARAs), especially proton pump inhibitors (PPIs), are frequently co-administered during various therapies. As orally administered drugs are frequently poorly soluble weak bases, PPI co-administration raises the risk of pH-induced drug-drug interactions (DDIs) and the potential for changes in the therapeutic outcome.

METHODS

This research compared the dissolution data of a poorly soluble weakly basic drug ("PSWB 001") from capsules in standard fasted state biorelevant media (FaSSGF, FaSSIF V1 and FaSSIF V2), water and recently devised media representing gastric conditions under various levels of PPI co-administration. An in silico simulation model, based on Simcyp software, was developed to compare simulated plasma profiles with clinical data.

RESULTS

PSWB 001 capsules showed rapid and complete dissolution in acidic conditions representing gastric fluids, whereas limited dissolution was observed in deionized water, media representing PPI co-administration and in two biorelevant media representing fluids in the upper small intestine. Buffer capacity and the presence of native surfactants were shown to be important factors in the in vitro dissolution of PSWB 001. The data from in vitro experiments were used in conjunction with the in silico simulation model, which correctly predicted the plasma profiles of PSWB 001 when administered without PPIs, as well as bracketing the PPI effect observed in vivo.

CONCLUSIONS

Recently developed biorelevant media representing gastric conditions under PPI therapy, combined with PBPK modeling, were able to bracket the observed plasma profiles of PSWB 001. These media may also be useful for predicting PPI effects for other poorly soluble, weakly basic drugs.

Evaluation of Prediction Accuracy for Volume of Distribution in Rat and Human Using In Vitro, In Vivo, PBPK and QSAR Methods

Volume of distribution at steady state (V_ss) is an important pharmacokinetic parameter of a drug candidate. In this study, V_ss prediction accuracy was evaluated by using: (1) seven methods for rat with 56 compounds, (2) four methods for human with 1276 compounds, and (3) four in vivo methods and three Kp (partition coefficient) scalar methods from scaling of three preclinical species with 125 compounds. The results showed that the global QSAR models outperformed the PBPK methods. Tissue fraction unbound (f_u,t) method with adipose and muscle also provided high V_ss prediction accuracy. Overall, the high performing methods for human V_ss prediction are the global QSAR models, Øie-Tozer and equivalency methods from scaling of preclinical species, as well as PBPK methods with Kp scalar from preclinical species. Certain input parameter ranges rendered PBPK models inaccurate due to mass balance issues. These were addressed using appropriate theoretical limit checks. Prediction accuracy of tissue Kp were also examined. The f_u,t method predicted Kp values more accurately than the PBPK methods for adipose, heart and muscle. All the methods overpredicted brain Kp and underpredicted liver Kp due to transporter effects. Successful V_ss prediction involves strategic integration of in silico, in vitro and in vivo approaches.

Improved Prediction of the Drug-Drug Interactions of Pemafibrate Caused by Cyclosporine A and Rifampicin via PBPK Modeling: Consideration of the Albumin-Mediated Hepatic Uptake of Pemafibrate and Inhibition Constants With Preincubation Against OATP1B

Pemafibrate (PMF) is highly albumin-bound (>99.8%) and a substrate for hepatic uptake transporters (OATP1B) and CYP enzymes. Here, we developed a PBPK model of PMF to capture drug-drug interactions (DDI) incurred by cyclosporine (CsA) and rifampicin (RIF), the two OATP1B inhibitors. Initial PBPK modeling of PMF utilized in vitro hepatic uptake clearance (PS_inf) obtained in the absence of albumin, but failed in capturing the blood PMF pharmacokinetic (PK) profiles. Based on the results that in vitro PS_inf of unbound PMF was enhanced in the presence of albumin, we applied the albumin-facilitated dissociation model and the resulting PS_inf parameters improved the prediction of the blood PMF PK profiles. In refining our PBPK model toward improved prediction of the observed DDI data (PMF co-administered with single dosing of CsA or RIF; PMF following multiple RIF dosing), we adjusted the previously obtained in vivo OATP1B inhibition constants (K_i,OATP1B) of CsA or RIF for pitavastatin by correcting for substrate-dependency. We also incorporated the induction of OATP1B and CYP enzymes after multiple RIF dosing. Sensitivity analysis informed that the higher gastrointestinal absorption rate constant could further improve capturing the observed DDI data, suggesting the possible inhibition of intestinal ABC transporter(s) by CsA or RIF.

Best Practices in the Development and Validation of Physiologically Based Biopharmaceutics Modeling. A Workshop Summary Report

This workshop report summarizes the proceedings of Day 2 of a three-day workshop on "Current State and Future Expectations of Translational Modeling Strategies toSupportDrug Product Development, Manufacturing Changes and Controls". From a drug product quality perspective, physiologically based biopharmaceutics modeling (PBBM) is a tool to link variations in the drug product quality attributes to in vivo outcomes enabling the establishment of clinically relevant drug product specifications (CRDPS). Day 2 of the workshop focused on best practices in developing, verifying and validating PBBM. This manuscript gives an overview of podium presentations and summarizes breakout (BO) session discussions related to (1) challenges and opportunities for using PBBM to assess the clinical impact of formulation and manufacturing changes on the in vivo performance of a drug product, (2) best practices to account for parameter uncertainty and variability during model development, (3) best practices in the development, verification and validation of PBBM and (4) opportunities and knowledge gaps related to leveraging PBBM for virtual bioequivalence simulations.

Physiologically Based Pharmacokinetic Model of the DPP-4 Inhibitor Linagliptin to Describe its Nonlinear Pharmacokinetics in Humans

Linagliptin, a dipeptidyl peptidase (DPP)-4 inhibitor, for type 2 diabetes exhibits nonlinear plasma protein binding in the therapeutic concentration range due to its high affinity binding to the pharmacological target DPP-4, and its pharmacokinetics both in plasma and urine is also nonlinear. The purpose of the present study was to explain the nonlinear pharmacokinetic profiles using a physiologically based pharmacokinetic (PBPK) model with saturable binding of linagliptin to soluble and membrane-bound DPP-4 in blood and organs including kidneys. The model was first fitted to previously reported full-scale plasma concentrations and urinary excretion data at 4 intravenous (iv) dose levels. Additional fitting to the data from 4 oral (po) dose levels was then performed to yield the final iv-po based model including gastrointestinal absorption-associated parameters. Data from [¹⁴C]linagliptin mass balance study were also used for optimizing parameters related to enterohepatic circulation. The PBPK model was thus constructed and well describes the nonlinear pharmacokinetic profiles of linagliptin in both plasma and urine, demonstrating that the nonlinear pharmacokinetics are fully explained by its specific binding to target protein. The present study thus introduces the involvement of target-mediated disposition for linagliptin in humans.

The Provisional No-Effect Threshold of Sugar Alcohols on Oral Drug Absorption Estimated by Physiologically Based Biopharmaceutics Model

Sugar alcohols reduce oral drug bioavailability by osmotic effects, but the magnitude of these effects differs among different drugs. This study aimed to identify the drug-related critical attributes of osmotic effects and estimate the impact of a "practical" sugar alcohol dose on the pharmacokinetics of various molecules using modeling and simulation approaches. We developed a physiologically based biopharmaceutics model that considers the dose-dependent effects of sugar alcohols on the gastrointestinal physiology. The developed model captured the effects of sugar alcohols on ranitidine hydrochloride, metoprolol tartrate, theophylline, cimetidine, and lamivudine. Sensitivity analysis provided quantitative insights into the effects of sugar alcohols dependent on different drug permeability. In addition, our developed model indicated for the first time that a high systemic elimination rate is crucial for the reduction in maximum plasma concentration even for highly permeable drugs. Nonetheless, mannitol/sorbitol level of less than 400 mg had minor effects on the pharmacokinetics of the most sensitive drugs, indicating a provisional no-effect threshold dose. This mechanistic approach provides comprehensive estimation of osmotic effects on variety of drugs. Subsequently, these findings may invoke scientific discussion on the criteria for excipient changes in the context of biowaiver guidelines (e.g. biopharmaceutics classification system-based biowaiver).

Probabilistic human health risk assessment of perfluorooctane sulfonate (PFOS) by integrating in vitro, in vivo toxicity, and human epidemiological studies using a Bayesian-based dose-response assessment coupled with physiologically based pharmacokinetic (PBPK) modeling approach

BACKGROUND

Environmental exposure to perfluorooctane sulfonate (PFOS) is associated with various adverse outcomes in humans. However, risk assessment for PFOS with the traditional risk estimation method is faced with multiple challenges because there are high variabilities and uncertainties in its toxicokinetics and toxicity between species and among different types of studies.

OBJECTIVES

This study aimed to develop a robust probabilistic risk assessment framework accounting for interspecies and inter-experiment variabilities and uncertainties to derive the human equivalent dose (HED) and reference dose for PFOS.

METHODS

A Bayesian dose-response model was developed to analyze selected 34 critical studies, including human epidemiological, animal in vivo, and ToxCast in vitro toxicity datasets. The dose-response results were incorporated into a multi-species physiologically based pharmacokinetic (PBPK) model to reduce the toxicokinetic/toxicodynamic variabilities. In addition, a population-based probabilistic risk assessment of PFOS was performed for Asian, Australian, European, and North American populations, respectively, based on reported environmental exposure levels.

RESULTS

The 5th percentile of HEDs derived from selected studies was estimated to be 21.5 (95% CI: 10.6-36.3) ng/kg/day. After exposure to environmental levels of PFOS, around 50% of the population in all studied populations would likely have >20% of increase in serum cholesterol, but the effects on other endpoints were estimated to be minimal (<10% changes). There was a small population (~10% of the population) that was highly sensitive to endocrine disruption and cellular response by environmental PFOS exposure.

CONCLUSION

Our results provide insights into a complete risk characterization of PFOS and may help regulatory agencies in the reevaluation of PFOS risk. Our new probabilistic approach can conduct dose-response analysis of different types of toxicity studies simultaneously and this method could be used to improve risk assessment for other perfluoroalkyl substances (PFAS).

Physiologically Based Pharmacokinetic Modelling of Glycopyrronium in Patients With Renal Impairment

Glycopyrronium bromide, a synthetic anticholinergic agent used to treat patients with chronic obstructive pulmonary disease (COPD), is eliminated from the body by renal excretion and therefore systemic exposure is expected to be increased in patients with decreasing renal function. Despite enrollment of patients with decreasing renal function to evaluate the impact of renal impairment on the pharmacokinetics of glycopyrronium in clinical studies, no patients with severe renal impairment were included. A physiologically based pharmacokinetic (PBPK) model was developed in patients with COPD with normal renal function and used to predict systemic exposure of glycopyrronium in patients with severe renal impairment. The model accurately predicted plasma concentration-time profiles in patients with normal renal function, and mild and moderate renal impairment; the predicted and observed AUC and C_max in these populations were similar. Compared to patients with normal renal function, a 1.20-, 1.45-, and 1.59-fold increase AUC was predicted in patients with mild, moderate, and severe renal impairment, respectively, suggesting dose adjustment is not necessary in patients with renal impairment. In conclusion, PBPK models, verified with clinical study data from patients with normal renal function, can potentially be used to predict the pharmacokinetics and recommended dose adjustment for patients with renal impairment.

Bayesian evaluation of a physiologically based pharmacokinetic (PBPK) model for perfluorooctane sulfonate (PFOS) to characterize the interspecies uncertainty between mice, rats, monkeys, and humans: Development and performance verification

A challenge in the risk assessment of perfluorooctane sulfonate (PFOS) is the large interspecies differences in its toxicokinetics that results in substantial uncertainty in the dosimetry and toxicity extrapolation from animals to humans. To address this challenge, the objective of this study was to develop an open-source physiologically based pharmacokinetic (PBPK) model accounting for species-specific toxicokinetic parameters of PFOS. Considering available knowledge about the toxicokinetic properties of PFOS, a PBPK model for PFOS in mice, rats, monkeys, and humans after intravenous and oral administrations was created. Available species-specific toxicokinetic data were used for model calibration and optimization, and independent datasets were used for model evaluation. Bayesian statistical analysis using Markov chain Monte Carlo (MCMC) simulation was performed to optimize the model and to characterize the uncertainty and interspecies variability of chemical-specific parameters. The model predictions well correlated with the majority of datasets for all four species, and the model was validated with independent data in rats, monkeys, and humans. The model was applied to predict human equivalent doses (HEDs) based on reported points of departure in selected critical toxicity studies in rats and monkeys following U.S. EPA's guidelines. The lower bounds of the model-derived HEDs were overall lower than the HEDs estimated by U.S. EPA (e.g., 0.2 vs. 1.3 μg/kg/day based on the rat plasma data). This integrated and comparative analysis provides an important step towards improving interspecies extrapolation and quantitative risk assessment of PFOS, and this open-source model provides a foundation for developing models for other perfluoroalkyl substances.

Physiologically based pharmacokinetic-quantitative systems toxicology and safety (PBPK-QSTS) modeling approach applied to predict the variability of amitriptyline pharmacokinetics and cardiac safety in populations and in individuals

The physiologically based pharmacokinetic (PBPK) models allow for predictive assessment of variability in population of interest. One of the future application of PBPK modeling is in the field of precision dosing and personalized medicine. The aim of the study was to develop PBPK model for amitriptyline given orally, predict the variability of cardiac concentrations of amitriptyline and its main metabolite-nortriptyline in populations as well as individuals, and simulate the influence of those xenobiotics in therapeutic and supratherapeutic concentrations on human electrophysiology. The cardiac effect with regard to QT and RR interval lengths was assessed. The Emax model to describe the relationship between amitriptyline concentration and heart rate (RR) length was proposed. The developed PBPK model was used to mimic 29 clinical trials and 19 cases of amitriptyline intoxication. Three clinical trials and 18 cases were simulated with the use of PBPK-QSTS approach, confirming lack of cardiotoxic effect of amitriptyline in therapeutic doses and the increase in heart rate along with potential for arrhythmia development in case of amitriptyline overdose. The results of our study support the validity and feasibility of the PBPK-QSTS modeling development for personalized medicine.

Physiologically based pharmacokinetic (PBPK) modeling of human lactational transfer of methylmercury in China

Methylmercury can readily cross the human placental barrier and the blood-brain barrier and cause damage to the vulnerable developing brains of the fetus and infants. Most of the previous studies on the maternal transfer of methylmercury to the next generation have focused on the prenatal period. In this study, human physiologically based pharmacokinetic (PBPK) models of methylmercury were established for breastfeeding mothers and suckling infants based on the existing model prototypes of previous studies. Relevant parameters of the models were modified, and the validation was conducted based on measured data in North China. The models could effectively describe the human lactational transfer of methylmercury, including the time-dependent methylmercury levels in different tissues and organs of the breastfeeding mothers and suckling infants. The results indicated that 77.2% and 14.9% of methylmercury were excreted via hair and breast milk, respectively, from breastfeeding mothers during the first year after delivery. Meanwhile, 79.2% was excreted from the suckling infants during the first year after delivery via hair. Lactational transfer of methylmercury was considered an important pathway of methylmercury exposure for the breastfeeding infants, which accounted for approximately 80% of the accumulated adverse impacts at the early stages of human development.

Quantitative bias analysis of a reported association between perfluoroalkyl substances (PFAS) and endometriosis: The influence of oral contraceptive use

An association between serum levels of perfluoroalkyl substances (PFAS) and endometriosis has recently been reported in an epidemiologic study. Oral contraceptive use to treat dysmenorrhea (pelvic pain associated with endometriosis) could potentially influence this association by reducing menstrual fluid loss, a route of excretion for PFAS. In this study, we aimed to evaluate the influence of differential oral contraceptive use on the association between PFAS and endometriosis. We used a published life-stage physiologically based pharmacokinetic (PBPK) model to simulate plasma levels of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) from birth to age at study participation (range 18-44years). In the simulated population, PFAS level distributions matched those for controls in the epidemiologic study. Prevalence and geometric mean duration (standard deviation [SD]) of oral contraceptive use in the simulated women were based on data from the National Health and Nutrition Examination Survey; among the women with endometriosis the values were, respectively, 29% and 6.8 (3.1) years; among those without endometriosis these values were 18% and 5.3 (2.8) years. In simulations, menstrual fluid loss (ml/cycle) in women taking oral contraceptives was assumed to be 56% of loss in non-users. We evaluated the association between simulated plasma PFAS concentration and endometriosis in the simulated population using logistic regression. Based on the simulations, the association between PFAS levels and endometriosis attributable to differential contraceptive use had an odds ratio (95% CI) of 1.05 (1.02, 1.07) for a log_e unit increase in PFOA and 1.03 (1.02, 1.05) for PFOS. In comparison, the epidemiologic study reported odds ratios of 1.62 (0.99, 2.66) for PFOA and 1.25 (0.87, 1.80) for PFOS. Our results suggest that the influence of oral contraceptive use on the association between PFAS levels and endometriosis is relatively small.

Physiologically based pharmacokinetic modeling of (18)F-SiFAlin-Asp3-PEG1-TATE in AR42J tumor bearing mice

PURPOSE

Peptide receptor radionuclide therapy (PRRT) is commonly performed in the treatment of neuroendocrine tumors (NET), where somatostatin analogs (DOTATATE) are radiolabeled with (90)Y, (68)Ga or (111)In for pre-therapeutic and therapeutic purposes. Quantitative evaluation of the biokinetic data can be performed by using physiologically based pharmacokinetic (PBPK) models. Knowledge about the biodistribution in a pre-clinical setting would allow optimizing the translation from bench to bedside. The aim of this study was to develop a PBPK model to describe the biodistribution of a novel sst2-targeting radiotracer.

METHODS

Biokinetic data of six mice after injection of (18)F-SiFAlin-Asp3-PEG1-TATE were investigated using two PBPK models. The PBPK models describe the biodistribution of the tracer in the tumor, kidneys, liver, remainder and whole body via blood flow to these organs via absorption, distribution, metabolism and excretion. A recently published sst2 PBPK model for humans (model 1) was used to describe the data. Physiological information in this model was adapted to that of a mouse. Model 1 was further modified by implementing receptor-mediated endocytosis (model 2). Model parameters were fitted to the biokinetic data of each mouse. Model selection was performed by calculating Akaike weights wi using the corrected Akaike Information Criterion (AICc).

RESULTS

The implementation of receptor-mediated endocytosis considerably improved the description of the biodistribution (Akaike weights w1=0% and w2=100% for model 1 and 2, respectively). The resulting time-integrated activity coefficients determined by model 2 were for tumor (0.05 ± 0.02) h, kidneys (0.11 ± 0.01) h and liver (0.02 ± 0.01) h.

CONCLUSION

Simply downscaling a human PBPK model does not allow for an accurate description of (18)F-SiFAlin-Asp3-PEG1-TATE in mice. Biokinetics of this tracer can be accurately and adequately described using a physiologically based pharmacokinetic model including receptor-mediated endocytosis. Thus, an optimized translation from bench to bedside is possible.

Dependence of treatment planning accuracy in peptide receptor radionuclide therapy on the sampling schedule

Background

Peptide receptor radionuclide therapy (PRRT) plays an important role in the treatment of neuroendocrine tumors (NET). Pre-therapeutic dosimetry using the area under the measured time-activity curve (AUC) is important. The sampling schedule for this dosimetry determines the accuracy and reliability of the obtained AUC.

The aim of this study was to investigate the effect of reduced number of measurement points (i.e., gamma camera image acquisition or serum measurements) on treatment planning accuracy in PRRT using ¹¹¹In-labeled-diethylenetriaminopentaacetic acid-octreotide (DTPAOC; Octreoscan™).

Methods

Pre-therapeutic biokinetic data of 15 NET patients were investigated using a recently developed physiologically based pharmacokinetic (PBPK) model. Two parameter sets were determined (standard or iterative approach) and used for calculation of time-integrated activity coefficients (TIACs) for the tumor, kidneys, liver, spleen, serum, and whole body. TIACs obtained using the full data sets were used as reference. To evaluate the effect of sampling on individual treatment planning, reduced sampling schedules were generated omitting either 1, 2, 3, or 4 organ and serum measurements or all serum measurements for each patient. Relative deviations (RDs) between these and reference TIACs were calculated and used as criterion for treatment planning accuracy. An RD < 0.1 was considered acceptable.

Results

When omitting serum measurements, TIAC accuracy remained acceptable (RD < 0.1) for the standard approach. The kidney TIACs could be estimated for both approaches with acceptable RDs using two time points (t = 4 h; 2 d); tumor RDs were <0.3. The iterative approach reduced the range of RD, but did not further reduce the number of needed measurement points (i.e., to achieve an RD <0.1). For both approaches RDs for liver, spleen and whole body were larger than 0.1. However, in the clinical setting these RDs are less relevant as liver and spleen are not organs at risk due to the low absorbed doses.

Conclusions

When using a priori information of a PBPK model structure combined with Bayesian information about PBPK model parameter distribution, the administered activity could be determined with acceptable accuracy using only two time points (4 h, 2 d) and thus allow a considerable reduction of needed data for individual dosimetry.