In-Depth Analysis of the Selection of PBPK Modeling Tools: Bibliometric and Social Network Analysis of the Open Systems Pharmacology Community

Since the Open Source Initiative laid the foundation for the open source software environment in 1998, the popularity of free and open source software has been steadily increasing. Model-informed drug discovery and development (MID3), a key component of pharmaceutical research and development, heavily makes use of computational models which can be developed using various software including the Open Systems Pharmacology (OSP) software (PK-Sim/MoBi), a free and open source software tool for physiologically based pharmacokinetic (PBPK) modeling. In this study, we aimed to investigate the impact, application areas, and reach of the OSP software as well as the relationships and collaboration patterns between organizations having published OSP-related articles between 2017 and 2023. Therefore, we conducted a bibliometric analysis of OSP-related publications and a social network analysis of the organizations with which authors of OSP-related publications were affiliated. On several levels, we found evidence for a significant growth in the size of the OSP community as well as its visibility in the MID3 community since OSP's establishment in 2017. Specifically, the annual publication rate of PubMed-indexed PBPK-related articles using the OSP software outpaced that of PBPK-related articles using any software. Our bibliometric analysis and network analysis demonstrated that the expansion of the OSP community was predominantly driven by new authors and organizations without prior connections to the community involving the generation of research clusters de novo and an overall diversification of the network. These findings suggest an ongoing evolution of the OSP community toward a more segmented, diverse, and inclusive network.

Probabilistic risk assessment of residential exposure to electric arc furnace steel slag using Bayesian model of relative bioavailability and PBPK modeling of manganese

Electric arc furnace (EAF) slag is a coproduct of steel production used primarily for construction purposes. Some applications of EAF slag result in residential exposures by incidental ingestion and inhalation of airborne dust. To evaluate potential health risks, an EAF slag characterization program was conducted to measure concentrations of metals and leaching potential (including oral bioaccessibility) in 38 EAF slag samples. Arsenic, hexavalent chromium, iron, vanadium, and manganese (Mn) were identified as constituents of interest (COIs). Using a probabilistic risk assessment (PRA) approach, estimated distributions of dose for COIs were assessed, and increased cancer risks and noncancer hazard quotients (HQs) at the 50th and 95th percentiles were calculated. For the residents near slag-covered roads, cancer risk and noncancer HQs were <1E - 6 and 1, respectively. For residential driveway or landscape exposure, at the 95th percentile, cancer risks were 1E - 6 and 7E - 07 based on oral exposure to arsenic and hexavalent chromium, respectively. HQs ranged from 0.07 to 2 with the upper bound due to ingestion of Mn among children. To expand the analysis, a previously published physiologically based pharmacokinetic (PBPK) model was used to estimate Mn levels in the globus pallidus for both exposure scenarios and further evaluate the potential for Mn neurotoxicity. The PBPK model estimated slightly increased Mn in the globus pallidus at the 95th percentile of exposure, but concentrations did not exceed no-observed-adverse-effect levels for neurological effects. Overall, the assessment found that the application of EAF slag in residential areas is unlikely to pose a health hazard or increased cancer risk.

Research Landscape of Physiologically Based Pharmacokinetic Model Utilization in Different Fields: A Bibliometric Analysis (1999-2023)

PURPOSE

The physiologically based pharmacokinetic (PBPK) modeling has received increasing attention owing to its excellent predictive abilities. However, there has been no bibliometric analysis about PBPK modeling. This research aimed to summarize the research development and hot points in PBPK model utilization overall through bibliometric analysis.

METHODS

We searched for publications related to the PBPK modeling from 1999 to 2023 in the Web of Science Core Collection (WoSCC) database. The Microsoft Office Excel, CiteSpace and VOSviewers were used to perform the analyses.

RESULTS

A total of 4,649 records from 1999 to 2023 were identified, and the largest number of publications focused in the period 2018-2023. The United States was the leading country, and the Environmental Protection Agency (EPA) was the leading institution. The journal Drug Metabolism and Disposition published and co-cited the most articles. Drug-drug interactions, special populations, and new drug development are the main topics in this research field.

CONCLUSION

We first visualize the research landscape and hotspots of the PBPK modeling through bibliometric methods. Our study provides a better understanding for researchers, especially beginners about the dynamization of PBPK modeling and presents the relevant trend in the future.

Forecasting Fetal Buprenorphine Exposure through Maternal-Fetal Physiologically Based Pharmacokinetic Modeling

Buprenorphine readily crosses the placenta, and with greater prenatal exposure, neonatal opioid withdrawal syndrome (NOWS) likely grows more severe. Current dosing strategies can be further improved by tailoring doses to expected NOWS severity. To allow the conceptualization of fetal buprenorphine exposure, a maternal-fetal physiologically based pharmacokinetic (PBPK) model for sublingual buprenorphine was developed using Simcyp (v21.0). Buprenorphine transplacental passage was predicted from its physicochemical properties. The maternal-fetal PBPK model integrated reduced transmucosal absorption driven by lower salivary pH and induced metabolism observed during pregnancy. Maternal pharmacokinetics was adequately predicted in the second trimester, third trimester, and postpartum period, with the simulated area under the curve from 0 to 12 h, apparent clearance, and peak concentration falling within the 1.25-fold prediction error range. Following post hoc adjustment of the likely degree of individual maternal sublingual absorption, umbilical cord blood concentrations at delivery (n = 21) were adequately predicted, with a geometric mean ratio between predicted and observed fetal concentrations of 1.15 and with 95.2% falling within the 2-fold prediction error range. The maternal-fetal PBPK model developed in this study can be used to forecast fetal buprenorphine exposure and would be valuable to investigate its correlation to NOWS severity.

Next generation risk assessment: an ab initio case study to assess the systemic safety of the cosmetic ingredient, benzyl salicylate, after dermal exposure

We performed an ab initio next-generation risk assessment (NGRA) for a fragrance ingredient, benzyl salicylate (BSal), to demonstrate how cosmetic ingredients can be evaluated for systemic toxicity endpoints based on non-animal approaches. New approach methodologies (NAMs) used to predict the internal exposure included skin absorption assays, hepatocyte metabolism, and physiologically based pharmacokinetic (PBPK) modeling, and potential toxicodynamic effects were assessed using pharmacology profiling, ToxProfiler cell stress assay, transcriptomics in HepG2 and MCF-7 cells, ReproTracker developmental and reproductive toxicology (DART) assays, and cytotoxicity assays in human kidney cells. The outcome of the NGRA was compared to that of the traditional risk assessment approach based on animal data. The identification of the toxicologically critical entity was a critical step that directed the workflow and the selection of chemicals for PBPK modeling and testing in bioassays. The traditional risk assessment and NGRA identified salicylic acid (SA) as the "toxdriver." A deterministic PBPK model for a single-day application of 1.54 g face cream containing 0.5% BSal estimated the Cmax for BSal (1 nM) to be much lower than that of its major in vitro metabolite, SA (93.2 nM). Therefore, SA was tested using toxicodynamics bioassays. The lowest points of departure (PoDs) were obtained from the toxicogenomics assays. The interpretation of these results by two companies and methods were similar (SA only results in significant gene deregulation in HepG2 cells), but PoD differed (213 μM and 10.6 µM). A probabilistic PBPK model for repeated applications of the face cream estimated the highest Cmax of SA to be 630 nM. The resulting margins of internal exposure (MoIE) using the PoDs were 338 and 16, which were more conservative than those derived from external exposure and in vivo PoDs (margin of safety values were 9,705). In conclusion, both traditional and ab initio NGRA approaches concluded that the daily application of BSal in a cosmetic leave-on face cream at 0.5% is safe for humans. The processing and interpretation of toxicogenomics data can lead to different PoDs, which can subsequently affect the calculation of the MoIE. This case study supports the use of NAMs in a tiered NGRA ab initio approach.

Can 3D Printed Tablets Be Bioequivalent and How to Test It: A PBPK Model Based Virtual Bioequivalence Study for Ropinirole Modified Release Tablets

As the field of personalized dosing develops, the pharmaceutical manufacturing industry needs to offer flexibility in terms of tailoring the drug release and strength to the individual patient's needs. One of the promising tools which have such capacity is 3D printing technology. However, manufacturing small batches of drugs for each patient might lead to huge test burden, including the need to conduct bioequivalence trials of formulations to support the change of equipment or strength. In this paper we demonstrate how to use 3D printing in conjunction with virtual bioequivalence trials based on physiologically based pharmacokinetic (PBPK) modeling. For this purpose, we developed 3D printed ropinirole formulations and tested their bioequivalence with the reference product Polpix. The Simcyp simulator and previously developed ropinirole PBPK model were used for the clinical trial simulations. The Weibull-fitted dissolution profiles of test and reference formulations were used as inputs for the model. The virtual bioequivalence trials were run using parallel design. The study power of 80% was reached using 125 individuals. The study demonstrated how to use PBPK modeling in conjunction with 3D printing to test the virtual bioequivalence of newly developed formulations. This virtual experiment demonstrated the bioequivalence of one of the newly developed formulations with a reference product available on a market.

Physiologically Based Pharmacokinetic Modeling of the Drug-Drug Interaction Between CYP3A4 Substrate Glasdegib and Moderate CYP3A4 Inducers in Lieu of a Clinical Study

Glasdegib (DAURISMO) is a hedgehog pathway inhibitor approved for the treatment of acute myeloid leukemia (AML). Cytochrome P450 3A4 (CYP3A4) has been identified as a major metabolism and clearance pathway for glasdegib. The role of CYP3A4 in the clearance of glasdegib has been confirmed with clinical drug-drug interaction (DDI) studies following the coadministration of glasdegib with the strong CYP3A4 inhibitor ketoconazole and the strong inducer rifampin. To evaluate potential drug interactions with CYP3A4 modulators, the coadministration of glasdegib with a moderate CYP3A4 inducer, efavirenz, was evaluated using physiologically based pharmacokinetic (PBPK) modeling using the Simcyp simulator. The glasdegib compound file was developed using measured physicochemical properties, data from human intravenous and oral pharmacokinetics, absorption, distribution, metabolism, and excretion studies, and in vitro reaction phenotyping results. The modeling assumptions, model parameters, and assignments of fractional CYP3A4 metabolism were verified using results from clinical pharmacokinetics (PK) and DDI studies with ketoconazole and rifampin. The verified glasdegib and efavirenz compound files, the latter of which was available in the Simcyp simulator, were used to estimate the potential impact of efavirenz on the PK of glasdegib. PBPK modeling predicted a glasdegib area under the concentration-time curve ratio of 0.45 and maximum plasma concentration ratio of 0.75 following coadministration with efavirenz. The PBPK results, in lieu of a formal clinical study, informed the drug label, with the recommendation to double the clinical dose of glasdegib when administered in conjunction with a moderate CYP3A4 inducer, followed by a resumption of the original dose 7 days post-discontinuation.

Estimation of volatile organic compound exposure concentrations and time to reach a specific dermal absorption using physiologically based pharmacokinetic modeling

A procedure was proposed to estimate dermal exposures based on a physiologically based pharmacokinetic (PBPK) model developed in rats. The study examined vapor concentrations ranging from 500 to 10,000 ppm for dibromomethane and 2,500 to 40,000 ppm for bromochloromethane. These concentrations were reconstructed based on chemical blood levels measured in 4 hr, with errors varying from 0.0% to 52.0%. The PBPK approach adequately predicted the blood concentrations and helped simulate contaminant transport through the stratum corneum and distribution in the body compartments. The proposed technique made it possible to estimate the skin absorption time (SAT) obtained from acute inhalation toxicity data. An inverse relationship exists between the SAT and exposure concentration. The method can be helpful in toxicology and risk assessment of hazardous volatile organic compounds.

The Complexity of Bariatric Patient's Pharmacotherapy: Sildenafil Biopharmaceutics and Pharmacokinetics before vs. after Gastric Sleeve/Bypass

Postbariatric altered gastrointestinal (GI) anatomy/physiology may significantly harm oral drug absorption and overall bioavailability. In this work, sildenafil, the first phosphodiesterase-5 (PDE5) inhibitor, was investigated for impaired postbariatric solubility/dissolution and absorption; this research question is of particular relevance since erectile dysfunction (ED) is associated with higher body mass index (BMI). Sildenafil solubility was determined both in vitro and ex vivo, using pre- vs. postsurgery gastric contents aspirated from patients. Dissolution tests were done in conditions mimicking the stomach before surgery, after sleeve gastrectomy (post-SG, pH 5), and after one anastomosis gastric bypass (post-OAGB, pH 7). Finally, these data were included in physiologically based pharmacokinetic (PBPK) modelling (GastroPlus®) to simulate sildenafil PK before vs. after surgery. pH-dependent solubility was demonstrated with low solubility (0.3 mg/mL) at pH 7 vs. high solubility at pH 1-5, which was also confirmed ex vivo with much lower solubility values in postbariatric gastric samples. Hampered dissolution of all sildenafil doses was obtained under post-OAGB conditions compared with complete (100%) dissolution under both presurgery and post-SG conditions. PBPK simulations revealed delayed sildenafil absorption in postbariatric patients (increased tmax) and reduced Cmax, especially in post-OAGB patients, relative to a presurgery state. Hence, the effect of bariatric surgery on sildenafil PK is unpredictable and may depend on the specific bariatric procedure. This mechanistically based analysis suggests a potentially undesirable delayed onset of action of sildenafil following gastric bypass surgery.

Characterization of P-glycoprotein orthologs from human, sheep, pig, dog, and cat

The ATP-binding cassette transporter P-glycoprotein (P-gp) limits the oral bioavailability of many drugs. Although P-gp has been well studied in humans and mice, little is known about the substrate specificities of many of its species orthologs. To address this, we performed in vitro analysis of P-gp transporter function using HEK293 cells stably expressing human, ovine, porcine, canine, and feline P-gp. We also employed a human physiologically based pharmacokinetic (PBPK) model to assess variations in digoxin exposure resulting from altered P-gp function. Compared to human P-gp, sheep P-gp had significantly less digoxin efflux (2.3-fold ±0.04 vs. 1.8-fold ±0.03, p < .0001) and all species orthologs had significantly less quinidine efflux compared with human P-gp (p < .05). Human P-gp also had significantly greater efflux of talinolol compared to sheep and dog P-gp (1.9-fold ±0.04 vs. 1.6-fold ±0.06, p = .003 and 1.6-fold ±0.05, p = .0002, respectively). P-gp expression protected all lines against paclitaxel-induced toxicity, with sheep P-gp being significantly less protective. The inhibitor verapamil demonstrated dose-dependent inhibition of all P-gp orthologs. Finally, a PBPK model showed digoxin exposure was sensitive to altered P-gp activity. Overall, our study found that species differences in this major drug transporter exist and that the appropriate species ortholog of P-gp should be evaluated during veterinary drug development.

Role of Modeling and Simulation in Preclinical and Clinical Long-Acting Injectable Drug Development

Innovations in the field of long-acting injectable drug development are increasingly being reported. More advanced in vitro and in vivo characterization can improve our understanding of the injection space and aid in describing the long-acting injectable (LAI) drug's behavior at the injection site more mechanistically. These innovations may enable unlocking the potential of employing a model-based framework in the LAI preclinical and clinical space. This review provides a brief overview of the LAI development process before delving deeper into the current status of modeling and simulation approaches in characterizing the preclinical and clinical LAI pharmacokinetics, focused on aqueous crystalline suspensions. A closer look is provided on in vitro release methods, available biopharmaceutical models and reported in vitro/in vivo correlations (IVIVCs) that may advance LAI drug development. The overview allows identifying the opportunities for use of model-informed drug development approaches and potential gaps where further research may be most warranted. Continued investment in improving our understanding of LAI PK across species through translational approaches may facilitate the future development of LAI drug products.

Physiologically Based Pharmacokinetic Modeling for Prediction of 5-FU Pharmacokinetics in Cancer Patients with Hepatic Impairment After 5-FU and Capecitabine Administration

PURPOSE

5-fluorouracil (5-FU) and its prodrug capecitabine are commonly prescribed anti-tumor medications. We aimed to establish physiologically based pharmacokinetic (PBPK) models of capecitabine-metabolites and 5-FU-metabolites to describe their pharmacokinetics in tumor and plasma of cancer patients with liver impairment.

METHODS

Models including the cancer compartment were developed in PK-Sim® and MoBi® and evaluated by R programming language with 25 oral capecitabine and 18 intravenous 5-FU studies for cancer patients with and without liver impairment.

RESULTS

The PBPK models were constructed successfully as most simulated Cmax and AUClast were within two-fold error of observed values. The simulated alterations of tumor 5-FU Cmax and AUClast in cancer patients with severe liver injury compared with normal liver function were 1.956 and 3.676 after oral administration of capecitabine, but no significant alteration was observed after intravenous injection of 5-FU. Besides, 5-FU concentration in tumor tissue increases with higher tumor blood flow but not tumor size. Sensitivity analysis revealed that dihydropyrimidine dehydrogenase (DPD) and other metabolic enzymes' activity, capecitabine intestinal permeability and plasma protein scale factor played a vital role in tumor and plasma 5-FU pharmacokinetics.

CONCLUSIONS

PBPK model prediction suggests no dosage adaption of capecitabine or 5-FU is required for cancer patients with hepatic impairment but it would be reduced when the toxic reaction is observed. Furthermore, tumor blood flow rate rather than tumor size is critical for 5-FU concentration in tumor. In summary, these models could predict pharmacokinetics of 5-FU in tumor in cancer patients with varying characteristics in different scenarios.

Liver and Plasma Concentrations of Food Chemicals after Virtual Oral Doses Extrapolated Using in silico Estimated Input Pharmacokinetic Parameters to Confirm Reported Liver Toxicity in Rats

The estimation of health risks of chemical substances was historically investigated using animal studies; however, current research focuses on reducing the number of animal experiments. The toxicity of chemicals in fish screening systems is reportedly correlated with their hydrophobicity. The inverse relationship between absorption rates (intestinal cell permeability) and virtual hepatic/plasma pharmacokinetics of diverse chemicals has been previously evaluated by modeling oral administration in rats. In the current study, internal exposures, i.e., virtual maximum plasma concentrations (C_max) and areas under the concentration-time curves (AUC), of 56 food chemicals with reported hepatic lowest-observed-effect levels (LOELs) of ≤1000 mg/kg/day in rats were pharmacokinetically modeled using in silico estimated input pharmacokinetic parameters. After a virtual single oral dose of 1.0 mg/kg of 56 food chemicals, the output C_max and AUC values in rat plasma generated by modeling using the corresponding in silico estimated input parameters were not significantly correlated with the reported hepatic LOEL values. However, significant inverse relationships between hepatic/plasma concentrations of selected lipophilic food chemicals (i.e., octanol-water partition coefficient logP >1) using forward dosimetry and reported LOEL values (≤300 mg/kg/day) were observed (n = 14, r = -0.52-0.66, p ≤ 0.05). This simple modeling approach, which uses no experimental pharmacokinetic data, has the potential to play a significant role in reducing the use of animals to estimate toxicokinetics or internal exposures of lipophilic food components after oral doses. Therefore, these methods are valuable for estimating hepatic toxicity by using forward dosimetry in animal toxicity experiments.

Use of Physiologically Based Pharmacokinetic Modeling for Hepatically Cleared Drugs in Pregnancy: Regulatory Perspective

Physiologically based pharmacokinetic modeling could be used to predict changes in exposure during pregnancy and possibly inform medicine use in pregnancy in situations in which there is currently limited or no available clinical PK data. The Medicines and Healthcare Product Regulatory Agency has been evaluating the available models for a number of medicines cleared by hepatic clearance mechanisms. Models were evaluated for metoprolol, tacrolimus, clindamycin, ondansetron, phenytoin, caffeine, fluoxetine, clozapine, carbamazepine, metronidazole, and paracetamol. The hepatic metabolism through cytochrome P450 (CYP) contributes significantly to the elimination of these drugs, and available knowledge of CYP changes during pregnancy has been implemented in the existing pregnancy physiology models. In general, models were able to capture trends in exposure changes in pregnancy to some extent, but the magnitude of pharmacokinetic change for these hepatically cleared drugs was not captured in each case, nor were models always able to capture overall exposure in the populations. A thorough evaluation was hampered by the lack of clinical data for drugs cleared by a specific clearance pathway. The limited clinical data, as well as complex elimination pathways involving CYPs, uridine 5'-diphospho-glucuronosyltransferase and active transporter for many drugs, currently limit the confidence in the prospective use of the models. Pregnancy-related changes in uridine 5'-diphospho-glucuronosyltransferase and transport functions are emerging, and incorporation of such changes in current physiologically based pharmacokinetic modeling software is in progress. Filling this gap is expected to further enhance predictive performance of models and increase the confidence in predicting PK changes in pregnant women for hepatically cleared drugs.

Utility of physiologically based pharmacokinetic modeling to predict inter-antibody variability in monoclonal antibody pharmacokinetics in mice

In this investigation, we tested the hypothesis that a physiologically based pharmacokinetic (PBPK) model incorporating measured in vitro metrics of off-target binding can largely explain the inter-antibody variability in monoclonal antibody (mAb) pharmacokinetics (PK). A diverse panel of 83 mAbs was evaluated for PK in wild-type mice and subjected to 10 in vitro assays to measure major physiochemical attributes. After excluding for target-mediated elimination and immunogenicity, 56 of the remaining mAbs with an eight-fold variability in the area under the curve (A U C 0 - 672 h : 1.74 × 106 -1.38 × 107 ng∙h/mL) and 10-fold difference in clearance (2.55-26.4 mL/day/kg) formed the training set for this investigation. Using a PBPK framework, mAb-dependent coefficients F1 and F2 modulating pinocytosis rate and convective transport, respectively, were estimated for each mAb with mostly good precision (coefficient of variation (CV%) <30%). F1 was estimated to be the mean and standard deviation of 0.961 ± 0.593, and F2 was estimated to be 2.13 ± 2.62. Using principal component analysis to correlate the regressed values of F1/F2 versus the multidimensional dataset composed of our panel of in vitro assays, we found that heparin chromatography retention time emerged as the predictive covariate to the mAb-specific F1, whereas F2 variability cannot be well explained by these assays. A sigmoidal relationship between F1 and the identified covariate was incorporated within the PBPK framework. A sensitivity analysis suggested plasma concentrations to be most sensitive to F1 when F1 > 1. The predictive utility of the developed PBPK model was evaluated against a separate panel of 14 mAbs biased toward high clearance, among which area under the curve of PK data of 12 mAbs was predicted within 2.5-fold error, and the positive and negative predictive values for clearance prediction were 85% and 100%, respectively. MAb heparin chromatography assay output allowed a priori identification of mAb candidates with unfavorable PK.

Integration of In Vitro and In Vivo Models to Predict Cellular and Tissue Dosimetry of Nanomaterials Using Physiologically Based Pharmacokinetic Modeling

Nanomaterials (NMs) have been increasingly used in a number of areas, including consumer products and nanomedicine. Target tissue dosimetry is important in the evaluation of safety, efficacy, and potential toxicity of NMs. Current evaluation of NM efficacy and safety involves the time-consuming collection of pharmacokinetic and toxicity data in animals and is usually completed one material at a time. This traditional approach no longer meets the demand of the explosive growth of NM-based products. There is an emerging need to develop methods that can help design safe and effective NMs in an efficient manner. In this review article, we critically evaluate existing studies on in vivo pharmacokinetic properties, in vitro cellular uptake and release and kinetic modeling, and whole-body physiologically based pharmacokinetic (PBPK) modeling studies of different NMs. Methods on how to simulate in vitro cellular uptake and release kinetics and how to extrapolate cellular and tissue dosimetry of NMs from in vitro to in vivo via PBPK modeling are discussed. We also share our perspectives on the current challenges and future directions of in vivo pharmacokinetic studies, in vitro cellular uptake and kinetic modeling, and whole-body PBPK modeling studies for NMs. Finally, we propose a nanomaterial in vitro to in vivo extrapolation via physiologically based pharmacokinetic modeling (Nano-IVIVE-PBPK) framework for high-throughput screening of target cellular and tissue dosimetry as well as potential toxicity of different NMs in order to meet the demand of efficient evaluation of the safety, efficacy, and potential toxicity of a rapidly increasing number of NM-based products.

Utilization of Physiologically Based Pharmacokinetic Modeling in Pharmacokinetic Study of Natural Medicine: An Overview

Natural medicine has been widely used for clinical treatment and health care in many countries and regions. Additionally, extracting active ingredients from traditional Chinese medicine and other natural plants, defining their chemical structure and pharmacological effects, and screening potential druggable candidates are also uprising directions in new drug research and development. Physiologically based pharmacokinetic (PBPK) modeling is a mathematical modeling technique that simulates the absorption, distribution, metabolism, and elimination of drugs in various tissues and organs in vivo based on physiological and anatomical characteristics and physicochemical properties. PBPK modeling in drug research and development has gradually been recognized by regulatory authorities in recent years, including the U.S. Food and Drug Administration. This review summarizes the general situation and shortcomings of the current research on the pharmacokinetics of natural medicine and introduces the concept and the advantages of the PBPK model in the study of pharmacokinetics of natural medicine. Finally, the pharmacokinetic studies of natural medicine using the PBPK models are summed up, followed by discussions on the applications of PBPK modeling to the enzyme-mediated pharmacokinetic changes, special populations, new drug research and development, and new indication adding for natural medicine. This paper aims to provide a novel strategy for the preclinical research and clinical use of natural medicine.

The LEAP Process: Streamlining the Development of Long-Acting Products and Formulations for Infectious Diseases

Developing long-acting products and formulations for infectious diseases is a nontrivial undertaking that is frequently classified as high risk and low reward by the pharmaceutical industry. The Long-Acting/Extended Release Antiretroviral Research Resource Program (LEAP) was founded in 2015 with the support of the National Institutes of Health to encourage, promote, and accelerate the development of such products. Assessment methodology for any new proposal brought to this group is part of a framework-the LEAP Process-that includes a landscape analysis of what is currently available in the public domain. This is followed by in silico modeling and simulation offered as a service to the relevant scientific community. A variety of preclinical and clinical outcome metrics are applied to each new agent as part of a continuous feedback loop to improve product characteristics. This allows us to catalog knowledge gaps and barriers that can be addressed by engaged stakeholders. Results are communicated in scientific articles, reviews, and position papers. This undertaking serves to de-risk discovery, development, and implementation by bridging the gaps between academic, regulatory, and industrial investigators, and by engaging those in the community who will be the eventual users of these medicines. The LEAP Process has supported formulations now approved for human immunodeficiency virus, as well as products in clinical and preclinical development for tuberculosis and hepatitis viruses B and C.

Considerations and Challenges in the Remdesivir COVID-19 Pediatric Development Program

The US Food and Drug Administration is committed to the development of effective antiviral regimens for pediatric patients with coronavirus disease 2019 (COVID-19), including infants and neonates. On April 25, 2022, the approved indication of remdesivir (RDV) was expanded to include pediatric patients 28 days and older and weighing at least 3 kg with positive results of direct severe acute respiratory syndrome coronavirus 2 viral testing, who are: Hospitalized, or Not hospitalized and have mild to moderate COVID-19 and are at high risk for progression to severe COVID-19, including hospitalization or death. Given the similar course of COVID-19 in adults and pediatric patients, the approval of RDV for use in pediatric patients is supported by the safety and efficacy data from adequate and well-controlled phase 3 trials in adults and adolescents; and by the safety and pharmacokinetic data from a single-arm, open-label, phase 2/3 pediatric clinical trial of 53 pediatric patients at least 28 days of age and weighing at least 3 kg with confirmed severe acute respiratory syndrome coronavirus 2 infection and mild, moderate, or severe COVID-19. At the time of the April 25, 2022, approval action, the US Food and Drug Administration also revoked the emergency use authorization for RDV that previously covered this pediatric population. This article summarizes key issues and regulatory considerations involved in the RDV COVID-19 pediatric development program, including the evolution of the emergency use authorization issued for RDV as results from registrational studies became available, and discusses lessons learned.

Updated in Silico Prediction Methods for Fractions Absorbed and Key Input Parameters of 355 Disparate Chemicals for Physiologically Based Pharmacokinetic Models for Time-Dependent Plasma Concentrations after Virtual Oral Doses in Humans

Human metabolic profiles for substances such as toxic food-derived compounds are usually allometrically extrapolated from traditionally determined in vivo rat concentration profiles. To evaluate internal exposures in humans without any reference to experimental data, physiologically based pharmacokinetic (PBPK) modeling could be used if the model input parameters could be estimated in silico. This approach would simplify the use of PBPK models for forward dosimetry after oral doses. In this study, the in silico estimation of input parameters for PBPK models (i.e., fraction absorbed × intestinal availability, absorption rate constants, and volumes of the systemic circulation) was updated for an panel of 355 chemicals (212 previously analyzed and 143 additional substances) using a light gradient boosting machine learning algorithms (LightGBM) based on between 11 and 29 in silico-calculated chemical descriptors. Simplified human PBPK models were then used to calculate virtual maximum plasma concentrations (C_max) and areas under the concentration-time curve (AUC) based on two sets of input parameters, i.e., traditionally derived values from in vivo data and those calculated in silico using the current updated systems. Both sets of C_max and AUC data were well correlated (r = 0.87 and r = 0.73, respectively; p < 0.01, n = 355). Therefore, input parameters for human PBPK models for a diverse range of compounds could be successfully estimated using chemical descriptors and in silico tools. This approach to pharmacokinetic modeling has potential for application in computational toxicology and in the clinical setting for assessing the potential risk of general chemicals.

Application of Physiologically Based Pharmacokinetic Modeling in Preclinical Studies: A Feasible Strategy to Practice the Principles of 3Rs

Pharmacokinetic characterization plays a vital role in drug discovery and development. Although involving numerous laboratory animals with error-prone, labor-intensive, and time-consuming procedures, pharmacokinetic profiling is still irreplaceable in preclinical studies. With physiologically based pharmacokinetic (PBPK) modeling, the in vivo profiles of drug absorption, distribution, metabolism, and excretion can be predicted. To evaluate the application of such an approach in preclinical investigations, the plasma pharmacokinetic profiles of seven commonly used probe substrates of microsomal enzymes, including phenacetin, tolbutamide, omeprazole, metoprolol, chlorzoxazone, nifedipine, and baicalein, were predicted in rats using bottom-up PBPK models built with in vitro data alone. The prediction's reliability was assessed by comparison with in vivo pharmacokinetic data reported in the literature. The overall predicted accuracy of PBPK models was good with most fold errors within 2, and the coefficient of determination (R2) between the predicted concentration data and the observed ones was more than 0.8. Moreover, most of the observation dots were within the prediction span of the sensitivity analysis. We conclude that PBPK modeling with acceptable accuracy may be incorporated into preclinical studies to refine in vivo investigations, and PBPK modeling is a feasible strategy to practice the principles of 3Rs.

Predicting Nanoparticle Delivery to Tumors Using Machine Learning and Artificial Intelligence Approaches

Background

Low delivery efficiency of nanoparticles (NPs) to the tumor is a critical barrier in the field of cancer nanomedicine. Strategies on how to improve NP tumor delivery efficiency remain to be determined.

Methods

This study analyzed the roles of NP physicochemical properties, tumor models, and cancer types in NP tumor delivery efficiency using multiple machine learning and artificial intelligence methods, using data from a recently published Nano-Tumor Database that contains 376 datasets generated from a physiologically based pharmacokinetic (PBPK) model.

Results

The deep neural network model adequately predicted the delivery efficiency of different NPs to different tumors and it outperformed all other machine learning methods; including random forest, support vector machine, linear regression, and bagged model methods. The adjusted determination coefficients (R2) in the full training dataset were 0.92, 0.77, 0.77 and 0.76 for the maximum delivery efficiency (DEmax), delivery efficiency at 24 h (DE24), at 168 h (DE168), and at the last sampling time (DETlast). The corresponding R2 values in the test dataset were 0.70, 0.46, 0.33 and 0.63, respectively. Also, this study showed that cancer type was an important determinant for the deep neural network model in predicting the tumor delivery efficiency across all endpoints (19-29%). Among all physicochemical properties, the Zeta potential and core material played a greater role than other properties, such as the type, shape, and targeting strategy.

Conclusion

This study provides a quantitative model to improve the design of cancer nanomedicine with greater tumor delivery efficiency. These results help to improve our understanding of the causes of low NP tumor delivery efficiency. This study demonstrates the feasibility of integrating artificial intelligence with PBPK modeling approaches to study cancer nanomedicine.

Use of Physiologically Based Pharmacokinetic Modeling to Evaluate the Impact of Chronic Kidney Disease on CYP3A4-Mediated Metabolism of Saxagliptin

We characterized the impact of chronic kidney disease (CKD) on the cytochrome P450 (CYP) 3A4–mediated metabolism of saxagliptin to its metabolite, 5‐hydroxysaxagliptin, using a physiologically based pharmacokinetic (PBPK) model. A PBPK model of saxagliptin and its CYP3A4 metabolite, 5‐hydroxysaxagliptin, was constructed and validated for oral doses ranging from 5 to 100 mg. The observed ratios of area under the plasma concentration–time curve (AUC) and maximum plasma concentration (C_max) between healthy subjects and subjects with CKD were compared with those predicted using PBPK model simulations. Simulations were performed with virtual CKD populations having decreased CYP3A4 activity (ie, 64%‐75% of the healthy subjects’ CYP3A4 abundance) and preserved CYP3A4 activity (ie, 100% of the healthy subjects’ CYP3A4 abundance). We found that simulations using decreased CYP3A4 activity generally overpredicted the ratios of saxagliptin AUC and C_max in CKD compared with those using preserved CYP3A4 activity. Similarly, simulations using decreased CYP3A4 activity underpredicted the ratio of 5‐hydroxysaxagliptin AUC in moderate and severe CKD compared with simulations using preserved CYP3A4 activity. These findings suggest that decreased CYP3A4 activity in CKD underpredicts saxagliptin clearance compared with that observed clinically. Preserving CYP3A4 activity in CKD more closely estimates saxagliptin clearance and 5‐hydroxysaxagliptin exposure changes observed in vivo. Our findings suggest that there is no clinically meaningful impact of CKD on the metabolism of saxagliptin by CYP3A4. Since saxagliptin is not a highly sensitive substrate and validated probe for CYP3A4, this work represents a case study of a CYP3A4 substrate‐metabolite pair and is not a generalization for all CYP3A4 substrates.

Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Monoclonal Antibody Tumor Disposition

The prediction of monoclonal antibody (mAb) disposition within solid tumors for individual patients is difficult due to inter-patient variability in tumor physiology. Improved a priori prediction of mAb pharmacokinetics in tumors may facilitate the development of patient-specific dosing protocols and facilitate improved selection of patients for treatment with anti-cancer mAb. Here, we report the use of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), with tumor penetration of the contrast agent gadobutrol used as a surrogate, to improve physiologically based pharmacokinetic model (PBPK) predictions of cetuximab pharmacokinetics in epidermal growth factor receptor (EGFR) positive xenografts. In the initial investigations, mice bearing Panc-1, NCI-N87, and LS174T xenografts underwent DCE-MRI imaging with the contrast agent gadobutrol, followed by intravenous dosing of an 125Iodine-labeled, non-binding mAb (8C2). Tumor concentrations of 8C2 were determined following the euthanasia of mice (3 h-6 days after 8C2 dosing). Potential predictor relationships between DCE-MRI kinetic parameters and 8C2 PBPK parameters were evaluated through covariate modeling. The addition of the DCE-MRI parameter Ktrans alone or Ktrans in combination with the DCE-MRI parameter Vp on the PBPK parameters for tumor blood flow (QTU) and tumor vasculature permeability (σTUV) led to the most significant improvement in the characterization of 8C2 pharmacokinetics in individual tumors. To test the utility of the DCE-MRI covariates on a priori prediction of the disposition of mAb with high-affinity tumor binding, a second group of tumor-bearing mice underwent DCE-MRI imaging with gadobutrol, followed by the administration of 125Iodine-labeled cetuximab (a high-affinity anti-EGFR mAb). The MRI-PBPK covariate relationships, which were established with the untargeted antibody 8C2, were implemented into the PBPK model with considerations for EGFR expression and cetuximab-EGFR interaction to predict the disposition of cetuximab in individual tumors (a priori). The incorporation of the Ktrans MRI parameter as a covariate on the PBPK parameters QTU and σTUV decreased the PBPK model prediction error for cetuximab tumor pharmacokinetics from 223.71 to 65.02%. DCE-MRI may be a useful clinical tool in improving the prediction of antibody pharmacokinetics in solid tumors. Further studies are warranted to evaluate the utility of the DCE-MRI approach to additional mAbs and additional drug modalities.

Dissolution Challenges Associated with the Surface pH of Drug Particles: Integration into Mechanistic Oral Absorption Modeling

The present work aimed to differentiate between in vitro dissolution profiles of ibuprofen as input for GastroPlus™ and to see the impact on systemic exposure. In vitro dissolution profiles of ibuprofen obtained under low- and high-buffered dissolution media were used as input using the z-factor approach. In a second step, a customized surface pH calculator was applied to predict the surface pH of ibuprofen under these low- and high-buffered dissolution conditions. These surface pH values were adopted in GastroPlus™ and simulations were performed to predict the systemic outcome. Simulated data were compared with systemic data of ibuprofen obtained under fasted state conditions in healthy subjects. The slower dissolution rate observed when working under low-buffered conditions nicely matched with the slower dissolution rate as observed during the clinical aspiration study and was in line with the systemic exposure of the drug. Finally, a population simulation was performed to explore the impact of z-factor towards bioequivalence (BE) criteria (so-called safe space). Concerning future perspectives, the customized calculator should be developed in such a way to make it possible to predict the dissolution rate (being informed by the particle size distribution) which, in its turn, can be used as a surrogate to predict the USP2 dissolution curve. Subsequently, validation can be done by using this profile as input for PBPK platforms.

Development of Physiologically Based Pharmacokinetic Model for Orally Administered Fexuprazan in Humans

Fexuprazan is a new drug candidate in the potassium-competitive acid blocker (P-CAB) family. As proton pump inhibitors (PPIs), P-CABs inhibit gastric acid secretion and can be used to treat gastric acid-related disorders such as gastroesophageal reflux disease (GERD). Physiologically based pharmacokinetic (PBPK) models predict drug interactions as pharmacokinetic profiles in biological matrices can be mechanistically simulated. Here, we propose an optimized and validated PBPK model for fexuprazan by integrating in vitro, in vivo, and in silico data. The extent of fexuprazan tissue distribution in humans was predicted using tissue-to-plasma partition coefficients in rats and the allometric relationships of fexuprazan distribution volumes (V_SS) among preclinical species. Urinary fexuprazan excretion was minimal (0.29-2.02%), and this drug was eliminated primarily by the liver and metabolite formation. The fraction absorbed (Fa) of 0.761, estimated from the PBPK modeling, was consistent with the physicochemical properties of fexuprazan, including its in vitro solubility and permeability. The predicted oral bioavailability of fexuprazan (38.4-38.6%) was within the range of the preclinical datasets. The C_max, AUC_last, and time-concentration profiles predicted by the PBPK model established by the learning set were accurately predicted for the validation sets.

Interaction between Omeprazole and Gliclazide in Relation to CYP2C19 Phenotype

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

Migalastat Tissue Distribution: Extrapolation From Mice to Humans Using Pharmacokinetic Modeling and Comparison With Agalsidase Beta Tissue Distribution in Mice

Approved therapies for Fabry disease (FD) include migalastat, an oral pharmacological chaperone, and agalsidase beta and agalsidase alfa, 2 forms of enzyme replacement therapy. Broad tissue distribution may be beneficial for clinical efficacy in FD, which has severe manifestations in multiple organs. Here, migalastat and agalsidase beta biodistribution were assessed in mice and modeled using physiologically based pharmacokinetic (PBPK) analysis, and migalastat biodistribution was subsequently extrapolated to humans. In mice, migalastat concentration was highest in kidneys and the small intestine, 2 FD-relevant organs. Agalsidase beta was predominantly sequestered in the liver and spleen (organs unaffected in FD). PBPK modeling predicted that migalastat 123 mg every other day resulted in concentrations exceeding the in vitro half-maximal effective concentration in kidneys, small intestine, skin, heart, and liver in human subjects. However, extrapolation of mouse agalsidase beta concentrations to humans was unsuccessful. In conclusion, migalastat may distribute to tissues that are inaccessible to intravenous agalsidase beta in mice, and extrapolation of mouse migalastat concentrations to humans showed adequate tissue penetration, particularly in FD-relevant organs.

Pharmacokinetic Characterization of Supinoxin and Its Physiologically Based Pharmacokinetic Modeling in Rats

Supinoxin is a novel anticancer drug candidate targeting the Y593 phospho-p68 RNA helicase, by exhibiting antiproliferative activity and/or suppression of tumor growth. This study aimed to characterize the in vitro and in vivo pharmacokinetics of supinoxin and attempt physiologically based pharmacokinetic (PBPK) modeling in rats. Supinoxin has good permeability, comparable to that of metoprolol (high permeability compound) in Caco-2 cells, with negligible net absorptive or secretory transport observed. After an intravenous injection at a dose range of 0.5–5 mg/kg, the terminal half-life (i.e., 2.54–2.80 h), systemic clearance (i.e., 691–865 mL/h/kg), and steady state volume of distribution (i.e., 2040–3500 mL/kg) of supinoxin remained unchanged, suggesting dose-independent (i.e., dose-proportional) pharmacokinetics for the dose ranges studied. After oral administration, supinoxin showed modest absorption with an absolute oral bioavailability of 56.9–57.4%. The fecal recovery following intravenous and oral administration was 16.5% and 46.8%, respectively, whereas the urinary recoveries in both administration routes were negligible. Supinoxin was mainly eliminated via NADPH-dependent phase I metabolism (i.e., 58.5% of total clearance), while UDPGA-dependent phase II metabolism appeared negligible in the rat liver microsome. Supinoxin was most abundantly distributed in the adipose tissue, gut, and liver among the nine major tissues studied (i.e., the brain, liver, kidneys, heart, lungs, spleen, gut, muscles, and adipose tissue), and the tissue exposure profiles of supinoxin were well predicted with physiologically based pharmacokinetics.

Physiologically Based Pharmacokinetic Modeling of Bupropion and Its Metabolites in a CYP2B6 Drug-Drug-Gene Interaction Network

The noradrenaline and dopamine reuptake inhibitor bupropion is metabolized by CYP2B6 and recommended by the FDA as the only sensitive substrate for clinical CYP2B6 drug-drug interaction (DDI) studies. The aim of this study was to build a whole-body physiologically based pharmacokinetic (PBPK) model of bupropion including its DDI-relevant metabolites, and to qualify the model using clinical drug-gene interaction (DGI) and DDI data. The model was built in PK-Sim® applying clinical data of 67 studies. It incorporates CYP2B6-mediated hydroxylation of bupropion, metabolism via CYP2C19 and 11β-HSD, as well as binding to pharmacological targets. The impact of CYP2B6 polymorphisms is described for normal, poor, intermediate, and rapid metabolizers, with various allele combinations of the genetic variants CYP2B6*1, *4, *5 and *6. DDI model performance was evaluated by prediction of clinical studies with rifampicin (CYP2B6 and CYP2C19 inducer), fluvoxamine (CYP2C19 inhibitor) and voriconazole (CYP2B6 and CYP2C19 inhibitor). Model performance quantification showed 20/20 DGI ratios of hydroxybupropion to bupropion AUC ratios (DGI AUCHBup/Bup ratios), 12/13 DDI AUCHBup/Bup ratios, and 7/7 DDGI AUCHBup/Bup ratios within 2-fold of observed values. The developed model is freely available in the Open Systems Pharmacology model repository.

Internal Threshold of Toxicological Concern (iTTC): Where We Are Today and What Is Possible in the Near Future

The Threshold of Toxicological Concern (TTC) is a risk assessment tool for evaluating low-level exposure to chemicals with limited toxicological data. A next step in the ongoing development of TTC is to extend this concept further so that it can be applied to internal exposures. This refinement of TTC based on plasma concentrations, referred to as internal TTC (iTTC), attempts to convert the chemical-specific external NOAELs (in mg/kg/day) in the TTC database to an estimated internal exposure. A multi-stakeholder collaboration formed, with the aim of establishing an iTTC suitable for human safety risk assessment. Here, we discuss the advances and future directions for the iTTC project, including: (1) results from the systematic literature search for metabolism and pharmacokinetic data for the 1,251 chemicals in the iTTC database; (2) selection of ~350 chemicals that will be included in the final iTTC; (3) an overview of the in vitro caco-2 and in vitro hepatic metabolism studies currently being generated for the iTTC chemicals; (4) demonstrate how PBPK modeling is being utilized to convert a chemical-specific external NOAEL to an internal exposure; (5) perspective on the next steps in the iTTC project.

Applications of Physiologically Based Pharmacokinetic Modeling of Rivaroxaban-Renal and Hepatic Impairment and Drug-Drug Interaction Potential

The non–vitamin K antagonist oral anticoagulant rivaroxaban is used in several thromboembolic disorders. Rivaroxaban is eliminated via both metabolic degradation and renal elimination as unchanged drug. Therefore, renal and hepatic impairment may reduce rivaroxaban clearance, and medications inhibiting these clearance pathways could lead to drug‐drug interactions. This physiologically based pharmacokinetic (PBPK) study investigated the pharmacokinetic behavior of rivaroxaban in clinical situations where drug clearance is impaired. A PBPK model was developed using mass balance and bioavailability data from adults and qualified using clinically observed data. Renal and hepatic impairment were simulated by adjusting disease‐specific parameters, and concomitant drug use was simulated by varying enzyme activity in virtual populations (n = 1000) and compared with pharmacokinetic predictions in virtual healthy populations and clinical observations. Rivaroxaban doses of 10 mg or 20 mg were used. Mild to moderate renal impairment had a minor effect on area under the concentration‐time curve and maximum plasma concentration of rivaroxaban, whereas severe renal impairment caused a more pronounced increase in these parameters vs normal renal function. Area under the concentration‐time curve and maximum plasma concentration increased with severity of hepatic impairment. These effects were smaller in the simulations compared with clinical observations. AUC and C_max increased with the strength of cytochrome P450 3A4 and P‐glycoprotein inhibitors in simulations and clinical observations. This PBPK model can be useful for estimating the effects of impaired drug clearance on rivaroxaban pharmacokinetics. Identifying other factors that affect the pharmacokinetics of rivaroxaban could facilitate the development of models that approximate real‐world pharmacokinetics more accurately.

Physiologically-Based Pharmacokinetic/Pharmacodynamic Model of MBQ-167 to Predict Tumor Growth Inhibition in Mice

MBQ-167 is a dual inhibitor of the Rho GTPases Rac and Cdc42 that has shown promising results as an anti-cancer therapeutic at the preclinical stage. This drug has been tested in vitro and in vivo in metastatic breast cancer mouse models. The aim of this study is to develop a physiologically based pharmacokinetic/pharmacodynamic (PBPK-PD) model of MBQ-167 to predict tumor growth inhibition following intraperitoneal (IP) administration in mice bearing Triple Negative and HER2+ mammary tumors. PBPK and Simeoni tumor growth inhibition (TGI) models were developed using the Simcyp V19 Animal Simulator. Our developed PBPK framework adequately describes the time course of MBQ-167 in each of the mouse tissues (e.g., lungs, heart, liver, kidneys, spleen, plasma) and tumor, since the predicted results were consistent with the experimental data. The developed PBPK-PD model successfully predicts tumor shrinkage in HER2+ and triple-negative breast tumors after the intraperitoneal administration of 1 and 10 mg/kg body weight (BW) dose level of MBQ-167 three times a week. The findings from this study suggest that MBQ-167 has a higher net effect and potency inhibiting Triple Negative mammary tumor growth compared to HER2+ and that liver metabolism is the major route of elimination of this drug.

Incorporating Ontogeny in Physiologically Based Pharmacokinetic Modeling to Improve Pediatric Drug Development: What We Know About Developmental Changes in Membrane Transporters

Developmental changes in the biological processes involved in the disposition of drugs, such as membrane transporter expression and activity, may alter the drug exposure and clearance in pediatric patients. Physiologically based pharmacokinetic (PBPK) models take these age-dependent changes into account and may be used to predict drug exposure in children. As a result, this mechanistic-based tool has increasingly been applied to improve pediatric drug development. Under the Prescription Drug User Fee Act VI, the US Food and Drug Administration has committed to facilitate the advancement of PBPK modeling in the drug application review process. Yet, significant knowledge gaps on developmental biology still exist, which must be addressed to increase the confidence of prediction. Recently, more data on ontogeny of transporters have emerged and supplied a missing piece of the puzzle. This article highlights the recent findings on the ontogeny of transporters specifically in the intestine, liver, and kidney. It also provides a case study that illustrates the utility of incorporating this information in predicting drug exposure in children using a PBPK approach. Collaborative work has greatly improved the understanding of the interplay between developmental physiology and drug disposition. Such efforts will continue to be needed to address the remaining knowledge gaps to enhance the application of PBPK modeling in drug development for children.

A Physiologically Based Pharmacokinetic Model of Vismodegib: Deconvoluting the Impact of Saturable Plasma Protein Binding, pH-Dependent Solubility and Nonsink Permeation

Vismodegib displays unique pharmacokinetic characteristics including saturable plasma protein binding to alpha-1 acid glycoprotein (AAG) and apparent time-dependent bioavailability leading to non-linear PK with dose and time, significantly faster time to steady-state and lower than predicted accumulation. Given these unique characteristics, a PBPK model was developed to explore mechanistic insights into saturable protein binding and complex oral absorption processes and de-convolute the impact of these independent non-linear processes on vismodegib exposure. Simcyp V18 was used for model development; oral absorption was characterized using the multi-layer gut wall (M-ADAM) model and mechanistic permeability model, incorporating transport across an unstirred boundary layer (UBL) between the luminal fluid and enterocyte in each segment of the gastrointestinal tract. PBPK simulations were compared with observed PK data from clinical trials in oncology patients and healthy subjects. Saturation of vismodegib protein binding to AAG led to substantially lower total drug accumulation, time to steady-state, and Css_total. For free exposure, Css_free and accumulation were unchanged, but time to steady-state was substantially reduced. Vismodegib oral absorption declined with both dose and dosing frequency; the concentration gradient driving vismodegib oral absorption declined with multiple doses, leading to a 32% decrease in vismodegib f_a from first dose to steady-state. Fed simulations suggested that increased solubility and dissolution are partially offset by reduced permeability across the UBL due to slower diffusion of micelle-bound drug. This work demonstrates the value of PBPK modeling to simultaneously capture and de-convolute multi-faceted absorption and disposition processes and provide mechanistic insights for compounds with complex pharmacokinetics.

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine

Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products. Special attention must be paid toward safe design approaches for nanomaterial-based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products. This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine. Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)-based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described. The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure-activity relationships at nanoscale (nano-QSAR) are revisited. The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation.

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

The purpose of this study was to investigate the impacts of the formulation parameters on the pharmacokinetics and bioequivalence of risperidone orodispersible film (ODF) using physiologically based pharmacokinetic model. The pharmacokinetic profiles of two risperidone ODFs, which exhibit different in vitro dissolution, were examined in Beagle dogs after supralingual administration. Subsequently, a physiologically based pharmacokinetic (PBPK) model was constructed to evaluate the in vivo performance of risperidone ODF. The parameter sensitivity analysis (PSA) was used to access the impacts of formulation parameters on the pharmacokinetics of risperidone. Moreover, the validated PBPK model was applied to predict human pharmacokinetic profiles and examine the bioequivalence of these two ODFs. These two ODFs displayed similar risperidone pharmacokinetic profiles in dogs. The parameter sensitivity analysis indicated that the changes in the solubility, particle size, particle density, and diffusion coefficient did not have obvious influence on the in vivo properties of risperidone ODF. Alternation of the in vitro complete dissolution time in water from 15 to 30 min led to a 30% decrease in C_max and 20% of increase in T_max. AUC_0-∞ would be decreased if risperidone was not fully released within 1 h. As both ODFs completely released risperidone within 15 min, the difference in the extent of in vivo absorption, intestinal regional absorption location, and plasma concentration-time curves between these two ODFs was almost negligible. Consequently, a bioequivalence was foreseen in humans. The in vitro cumulative dissolution percentage in water at 15 min was found to be the major determinant on the in vivo properties of risperidone ODF. PBPK modeling appears to be an innovative strategy to guide the development of risperidone ODF.

Ethosome-Derived Invasomes as a Potential Transdermal Delivery System for Vardenafil Hydrochloride: Development, Optimization and Application of Physiologically Based Pharmacokinetic Modeling in Adults and Geriatrics

Aim

The aim of the current work was to develop vardenafil hydrochloride (VRD)-loaded ethosome-derived invasomes as a possible transdermal system which could be used for patients suffering from pulmonary arterial hypertension.

Methods

VRD-loaded ethosomes were developed at three concentrations of phosphatidylcholine (5, 10 and 15 mg/mL) and three percentages of ethanol (20%, 30% and 40%, v/v). The best achieved VRD-loaded ethosomes (ETH9) were optimized to invasomes via incorporation of terpenes (limonene, cineole and a 1:1 mixture) at three concentrations (0.5%, 1% and 2%, v/v). All systems were evaluated for vesicle size, zeta potential, drug entrapment efficiency (EE%), cumulative drug permeated percentages after 0.5hrs (Q_0.5h) and 12hrs (Q_12h) and steady-state flux (J_ss). The optimized system (ETH9-INV8) was further characterized for morphology, histopathology and confocal laser scanning microscopy (CLSM). Physiologically based pharmacokinetic (PBPK) modeling was employed to estimate VRD pharmacokinetic parameters from the optimized transdermal system and an oral aqueous drug dispersion, in adults and geriatrics.

Results

The optimized invasomal system (ETH9-INV8) was characterized with spherical vesicles (159.9 nm) possessing negative zeta potential (-20.3 mV), promising EE% (81.3%), low Q_0.5h (25.4%), high Q_12h (85.3%) and the largest steady-state flux (6.4 µg.cm^-2h^-1). Following a leave-on period of 12hrs in rats, it showed minor histopathologic changes. CLSM studies proved its ability to deeply permeate rat skin. Lower C_max values, delayed T_max estimates and greater AUC_0-24h folds in adults and geriatrics (≈ 2.18 and 1.69, respectively) were estimated following the transdermal application of ETH9-INV8 system.

Conclusion

ETH9-INV8 is a promising transdermal system for VRD.

Changes in perampanel levels during de-induction: Simulations following carbamazepine discontinuation

Objective

To evaluate the time course of changes in perampanel levels when co‐administered with carbamazepine, and following carbamazepine discontinuation, using a physiologically based pharmacokinetic (PBPK) model.

Methods

The PBPK model was developed, verified using clinical PK data, and used to simulate the effect of abrupt discontinuation and down‐titration (75 mg twice daily [bid]/wk) of co‐administered carbamazepine 300 mg bid on the PK of perampanel once daily (qd). Perampanel dose tapering (8‐4 mg) and up‐titration (2‐6 mg) were simulated during abrupt carbamazepine 300 mg bid discontinuation to identify a titration schedule that minimizes changes in perampanel plasma concentrations.

Results

The PBPK model accurately reproduced perampanel plasma concentration‐time profiles from clinical studies in single‐ and multiple‐dose regimen simulations, including multiple‐dose carbamazepine co‐administration. The time course of return to pre‐induced perampanel levels occurred more slowly following carbamazepine down‐titration (~48 days after first down‐titration) vs abrupt discontinuation (~25 days). Perampanel dose tapering (8‐4 mg) at abrupt carbamazepine discontinuation produced minimal changes in steady‐state concentrations, which returned to the levels observed during carbamazepine co‐administration in ~15 days from the time of carbamazepine discontinuation. When perampanel was up‐titrated in the presence of carbamazepine, return to steady state occurred more slowly when carbamazepine was down‐titrated weekly (~45 days) vs abrupt discontinuation (~24 days).

Conclusion

This PBPK model simulated and predicted optimal perampanel dose tapering and up‐titration schedules for maintaining perampanel levels during conversion to monotherapy. These results may guide physicians when managing conversion from perampanel polytherapy with concomitant enzyme‐inducing anti‐seizure medications to monotherapy.

Pharmacokinetics of Gepotidacin in Renal Impairment

Gepotidacin is a novel triazaacenaphthylene bacterial topoisomerase inhibitor. In this phase 1, nonrandomized, open-label, parallel-group, multicenter, multipart study, the pharmacokinetics, safety, and tolerability of a single intravenous (IV) dose of gepotidacin 750 mg over 2 hours were evaluated in subjects with normal renal function, in those with moderate and severe renal impairment, and in end-stage renal disease (ESRD) on and not on dialysis. Administration of IV gepotidacin 750 mg was safe and generally tolerated in the study subjects. Dosing in severe renal impairment with and without hemodialysis resulted in significant increases in plasma drug levels and decreases in clearance. The geometric mean elimination half-life (t_½ ) was minimally impacted (range 9.45 to 11.5 hours) in all the renal-impairment groups relative to normal renal function. Regardless of renal function, urine gepotidacin concentrations remained considerably high over a 12-hour period. Saliva concentrations displayed a linear relationship with plasma concentrations. The t_½ in saliva was not impacted in the moderate-impairment and ESRD subjects and was comparable to t_½ in plasma. Over a 4-hour dialysis, approximately 6% of the gepotidacin dose was removed. Overall, subjects with severe renal impairment and ESRD with and without hemodialysis may require adjustment in dose or dosing frequency.

Impact of stressors in the aviation environment on xenobiotic dosimetry in humans: physiologically based prediction of the effect of +Gz-forces

The application of physiologically based modeling approaches in evaluating health risks in diverse environments is limited by scarcity of comprehensive reviews detailing how physiological parameters are altered due to stressors. A modern high-performance aviation environment in particular has the potential for simultaneous exposure to chemical and non-chemical stressors which may interact via non-chemical stressor-mediated pharmacokinetic alterations. To support physiologically based pharmacokinetic (PBPK) modeling of in-flight disposition inhaled chemicals, literature review, and synthesis was conducted to determine the impact of gravitational (+Gz) forces on PBPK modeling inputs. Specifically, changes in cardiac output and related parameters heart rate and stroke volume, breathing frequency, tidal volume, and pulmonary and alveolar ventilation rate in vivo were extracted from 36 publications and related mathematically to +Gz intensity. A scenario was simulated where a pilot performing test flights might inhale organic chemicals at the occupational exposure guideline level while experiencing sustained, elevated +Gz. Peak arterial blood concentrations of 1,2,4-trimethylbenzene during a 1 h-flight at +4 Gz were predicted to increase 2-fold relative to would occur on the ground under baseline conditions. This case study demonstrates the potential value of scenario-specific physiological information in assessing changes in risk-relevant internal dosimetry, providing better information for potential risk management actions.

Impact of stressors in the aviation environment on xenobiotic dosimetry in humans: physiologically based prediction of the effect of barometric pressure or altitude

Standard health risks from volatile organic compounds (VOCs) are generally interpreted at ambient environmental conditions. The aim of this study was to develop a strategy for using physiologically based pharmacokinetic (PBPK) modeling to compare known risks in the general population to calculated risks in pilots experiencing pressure-based stressors. PBPK models facilitate these comparisons by prediction of how target-tissue specific doses are altered when a stressor, such as high altitude, produces changes in physiological parameters. Cardiac output, regional blood flow, and alveolar ventilation rate following acute exposure to altitude ranging from moderate to extremely high were estimated from published data from 52 groups of human subjects. Scenarios where pilots might inhale toluene, 1,2,4-trimethylbenzene (1,2,4-TMB), or cyclohexane during routine military flight training were simulated. At the recommended Threshold Limit Values (TLV), arterial blood concentrations were predicted to be higher for exposure at 15000 ft (4572 m) than at sea level. The differences were greater for toluene and TMB, which have higher blood: air and fat: blood partition coefficients than less lipophilic cyclohexane. In summary, quantitative approaches to internal dosimetry prediction that take advantage of existing knowledge of physiological changes induced by occupational stressors possess potential as tools in performing a human health risk assessment.

Meta-Analysis of Nanoparticle Delivery to Tumors Using a Physiologically Based Pharmacokinetic Modeling and Simulation Approach

Numerous studies have engineered nanoparticles with different physicochemical properties to enhance the delivery efficiency to solid tumors, yet the mean and median delivery efficiencies are only 1.48% and 0.70% of the injected dose (%ID), respectively, according to a study using a nonphysiologically based modeling approach based on published data from 2005 to 2015. In this study, we used physiologically based pharmacokinetic (PBPK) models to analyze 376 data sets covering a wide range of nanomedicines published from 2005 to 2018 and found mean and median delivery efficiencies at the last sampling time point of 2.23% and 0.76%ID, respectively. Also, the mean and median delivery efficiencies were 2.24% and 0.76%ID at 24 h and were decreased to 1.23% and 0.35%ID at 168 h, respectively, after intravenous administration. While these delivery efficiencies appear to be higher than previous findings, they are still quite low and represent a critical barrier in the clinical translation of nanomedicines. We explored the potential causes of this poor delivery efficiency using the more mechanistic PBPK perspective applied to a subset of gold nanoparticles and found that low delivery efficiency was associated with low distribution and permeability coefficients at the tumor site (P < 0.01). We also demonstrate how PBPK modeling and simulation can be used as an effective tool to investigate tumor delivery efficiency of nanomedicines.

PBPK modeling characterization of potential acute impairment effects from inhalation of ethanol during e-cigarette use

Objective: Ethanol is used as a solvent for flavoring chemicals in some electronic cigarette (e-cigarette) liquids (e-liquids). However, there are limited data available regarding the effects of inhalation of ethanol on blood alcohol concentration (BAC) during e-cigarette use. In this study, a modified physiologically based pharmacokinetic (PBPK) model for inhalation of ethanol was used to estimate the BAC time-profile of e-cigarette users who puffed an e-liquid containing 23.5% ethanol. Materials and Methods: A modified PBPK model for inhalation of ethanol was developed. Use characteristics were estimated based on first-generation and second-generation e-cigarette topography parameters. Three representative use-case puffing profiles were modeled: a user that took many, short puffs; a typical user with intermediate puff counts and puff durations; and a user that took fewer, long puffs. Results and Discussion: The estimated peak BACs for these three user profiles were 0.22, 0.22, and 0.30 mg/L for first-generation devices, respectively, and 0.85, 0.58, and 0.34 mg/L for second-generation devices, respectively. Additionally, peak BACs for individual first-generation users with directly measured puffing parameters were estimated to range from 0.06 to 0.67 mg/L. None of the scenarios modeled predicted a peak BAC result that approached toxicological or regulatory thresholds that would be associated with physiological impairment (roughly 0.01% or 100 mg/L). Conclusions: The approach used in this study, combining a validated PBPK model for a toxicant with peer-reviewed topographical parameters, can serve as a screening-level exposure assessment useful for evaluation of the safety of e-liquid formulations. Abbreviations: BAC: blood alcohol concentration; e-cigarette: electronic cigarette; e-liquid: e-cigarette liquid or propylene glycol and/or vegetable glycerin-based liquid; HS-GC-FID: headspace gas chromatography with flame-ionization detection; HS-GC-MS: headspace gas chromatography-mass spectrometry; PBPK: physiologically based pharmacokinetic; C_air: puff concentration expressed as ppm; C_air,mass: ethanol air concentration expressed on a mass basis; C_v: ethanol concentration in the venous blood; ρ: density; EC: ethanol concentration in the liquid; PLC: liquid consumption per puff; PAV: air volume of the puff; C_air,mass: puff concentration expressed as ppm; MW: molecular weight; P: pressure; T: temperature; PK: pharmacokinetic.

Development of Prodrugs for PDT-Based Combination Therapy Using a Singlet-Oxygen-Sensitive Linker and Quantitative Systems Pharmacology

Photodynamic therapy (PDT) has become an effective treatment for certain types of solid tumors. The combination of PDT with other therapies has been extensively investigated in recent years to improve its effectiveness and expand its applications. This focused review summarizes the development of a prodrug system in which anticancer drugs are activated locally at tumor sites during PDT treatment. The development of a singlet-oxygen-sensitive linker that can be conveniently conjugated to various drugs and efficiently cleaved to release intact drugs is recapitulated. The initial design of prodrugs, preliminary efficacy evaluation, pharmacokinetics study, and optimization using quantitative systems pharmacology is discussed. Current treatment optimization in animal models using physiologically based a pharmacokinetic (PBPK) modeling approach is also explored.

Impact of Intracellular Concentrations on Metabolic Drug-Drug Interaction Studies

Accurate prediction of drug-drug interactions (DDI) is a challenging task in drug discovery and development. It requires determination of enzyme inhibition in vitro which is highly system-dependent for many compounds. The aim of this study was to investigate whether the determination of intracellular unbound concentrations in primary human hepatocytes can be used to bridge discrepancies between results obtained using human liver microsomes and hepatocytes. Specifically, we investigated if Kp_uu could reconcile differences in CYP enzyme inhibition values (K_i or IC₅₀). Firstly, our methodology for determination of Kp_uu was optimized for human hepatocytes, using four well-studied reference compounds. Secondly, the methodology was applied to a series of structurally related CYP2C9 inhibitors from a Roche discovery project. Lastly, the Kp_uu values of three commonly used CYP3A4 inhibitors—ketoconazole, itraconazole, and posaconazole—were determined and compared to compound-specific hepatic enrichment factors obtained from physiologically based modeling of clinical DDI studies with these three compounds. Kp_uu obtained in suspended human hepatocytes gave good predictions of system-dependent differences in vitro. The Kp_uu was also in fair agreement with the compound-specific hepatic enrichment factors in DDI models and can therefore be used to improve estimations of enrichment factors in physiologically based pharmacokinetic modeling.

Preincubation With Everolimus and Sirolimus Reduces Organic Anion-Transporting Polypeptide (OATP)1B1- and 1B3-Mediated Transport Independently of mTOR Kinase Inhibition: Implication in Assessing OATP1B1- and OATP1B3-Mediated Drug-Drug Interactions

Organic anion transporting polypeptides (OATP)1B1 and OATP1B3 mediate hepatic uptake of many drugs including lipid-lowering statins. Current studies determined the OATP1B1/1B3-mediated drug-drug interaction (DDI) potential of mammalian target of rapamycin (mTOR) inhibitors, everolimus and sirolimus, using R-value and physiologically based pharmacokinetic models. Preincubation with everolimus and sirolimus significantly decreased OATP1B1/1B3-mediated transport even after washing and decreased inhibition constant values up to 8.3- and 2.9-fold for OATP1B1 and both 2.7-fold for OATP1B3, respectively. R-values of everolimus, but not sirolimus, were greater than the FDA-recommended cutoff value of 1.1. Physiologically based pharmacokinetic models predict that everolimus and sirolimus have low OATP1B1/1B3-mediated DDI potential against pravastatin. OATP1B1/1B3-mediated transport was not affected by preincubation with INK-128 (10 μM, 1 h), which does however abolish mTOR kinase activity. The preincubation effects of everolimus and sirolimus on OATP1B1/1B3-mediated transport were similar in cells before preincubation with vehicle control or INK-128, suggesting that inhibition of mTOR activity is not a prerequisite for the preincubation effects observed for everolimus and sirolimus. Nine potential phosphorylation sites of OATP1B1 were identified by phosphoproteomics; none of these are the predicted mTOR phosphorylation sites. We report the everolimus/sirolimus-preincubation-induced inhibitory effects on OATP1B1/1B3 and relatively low OATP1B1/1B3-mediated DDI potential of everolimus and sirolimus.

Assessment of Biopharmaceutical Performance of Supersaturating Formulations of Carbamazepine in Rats Using Physiologically Based Pharmacokinetic Modeling

There is an overgrowing emphasis on supersaturating drug delivery systems (SDDS) with increase in number of poorly water-soluble compounds. However, biopharmaceutical performance from these formulations is limited by phase transformation to stable crystalline form due to their high-energy physical form. In the present study, in vitro kinetic solubility in water and dissolution in biorelevant medium integrated with in silico physiologically based pharmacokinetic (PBPK) modeling was used to predict biopharmaceutical performance of SDDS of poorly water-soluble compound, carbamazepine (CBZ). GastroPlus™ with advanced compartmental absorption and transit model was used as a simulation tool for the study. Wherein, the model was developed using physicochemical properties of CBZ and disposition parameters obtained after intravenous administration of CBZ (20 mg/kg) into Sprague-Dawley (SD) rats. Biorelevant medium was selected by screening different dissolution media for their capability to predict oral plasma concentration-time profile of marketed formulation of CBZ. In vivo performance of SDDS was predicted with the developed model and compared to observed plasma concentration-time profile obtained after oral administration of SDDS into SD rats (20 mg/kg). The predictions, with strategy of using kinetic solubility and dissolution in the selected biorelevant medium, were consistent with observed biopharmaceutical performance of SDDS. Additionally, phase transformation of CBZ during gastrointestinal transit of formulations was evaluated and correlated with in vivo dissolution deconvoluted by Loo-Reigelman analysis.

Simulation-Based Analysis of the Impact of Renal Impairment on the Pharmacokinetics of Highly Metabolized Compounds

Renal impairment (RI) is a highly prevalent disease which can alter the pharmacokinetics (PK) of xenobiotics, including those that are predominately metabolized. The expression and activity of drug metabolizing enzymes (DMEs) and protein binding of compounds has been demonstrated to be affected in RI. A simulation based approach allows for the characterization of the impact of changes in these factors on the PK of compounds which are highly metabolized and allows for improved prediction of PK in RI. Simulations with physiologically based pharmacokinetic (PBPK) modeling was utilized to define the impact of these factors in PK in RI for a model substrate, nifedipine. Changes in fraction unbound and DME expression/activity had profound effects on PK in RI. Increasing fraction unbound and DME expression resulted in a reduction in exposure of nifedipine, while the reduction of DME activity resulted in an increase in exposure. In vitro and preclinical data were utilized to inform simulations for nifedipine, sildenafil and zidovudine. Increasing fraction unbound and changes in the expression/activity of DMEs led to improved predictions of PK. Further characterization of the impact of RI on these factors is warranted in order to better inform a priori predictions of PK in RI.

Breast Cancer and Exposure to Organochlorines in the CECILE Study: Associations with Plasma Levels Measured at the Time of Diagnosis and Estimated during Adolescence

Exposure to environmental chemicals with hormonal effects, such as organochlorine compounds (OCs), during developmental periods of breast cells may have an impact on the incidence of breast cancer later in life. However, the assessment of exposure to these chemicals that occurred in early life at the time of breast cancer development in adult women is a difficult challenge in epidemiological studies. Plasma levels of the OCs p,p'-dichlorodiphenyl dichloroethene (DDE) and polychlorinated biphenyl congener 153 (PCB153) were measured in 695 cases and 1055 controls of a population-based case-control study conducted in France (CECILE study). Based on these values, we used a physiologically-based pharmacokinetic (PBPK) model to estimate PCB153 levels at age 11⁻20 years when the women were adolescents. Overall, there was no clear association between breast cancer risk and measured levels of DDE and PCB153 at the time of diagnosis, but there was a trend of decreasing odds ratios of breast cancer with increasing DDE and PCB153 levels in women aged 50 years and over. The PBPK model revealed that PCB153 concentrations estimated during adolescence were highest in the youngest women born after 1960 who reached adolescence at a time when environmental contamination was maximum, and very low in the oldest women who attained adolescence before the contamination peak. Negative associations between breast cancer and PCB153 estimates during adolescence were also found. The negative associations between DDE and PCB153 levels measured at the time of diagnosis or estimated during adolescence in our study were unexplained. Further investigations are needed to clarify whether this finding is real or related to study artifacts. However, this study suggests that using PBPK models in epidemiological studies to back-estimate OC exposures during early life stages may be useful to address critical questions on cancer development.