Physiologically Based Pharmacokinetic Modeling for Maribavir to Inform Dosing in Drug-Drug Interaction Scenarios with CYP3A4 Inducers and Inhibitors

Maribavir, an orally available antiviral agent, has been approved in multiple countries for the treatment of patients with refractory post-transplant cytomegalovirus (CMV) infection and/or disease. Maribavir is primarily metabolized by CYP3A4; coadministration with CYP3A4 inducers and inhibitors may significantly alter maribavir exposure, thereby affecting its efficacy and safety. The effect of CYP3A4 inducers and inhibitors on maribavir exposure was evaluated based on a drug-drug interaction (DDI) study and physiologically-based pharmacokinetic (PBPK) modeling. The effect of rifampin (a strong inducer of CYP3A4 and moderate inducer of CYP1A2), administered at a 600 mg dose once daily, on maribavir pharmacokinetics was assessed in a clinical phase 1 DDI study in healthy participants. A full PBPK model for maribavir was developed and verified using in vitro and clinical pharmacokinetic data from phase 1 studies. The verified PBPK model was then used to simulate maribavir DDI interactions with various CYP3A4 inducers and inhibitors. The DDI study results showed that coadministration with rifampin decreased the maribavir maximum plasma concentration (Cmax), area under the plasma concentration-time curve (AUC), and trough concentration (Ctrough) by 39%, 60%, and 82%, respectively. Based on the results from the clinical DDI study, the coadministration of maribavir with rifampin is not recommended. The PBPK model did not predict a clinically significant effect of CYP3A4 inhibitors on maribavir exposure; however, it predicted that strong or moderate CYP3A4 inducers, including carbamazepine, efavirenz, phenobarbital, and phenytoin, may reduce maribavir exposure to a clinically significant extent, and may prompt the consideration of a maribavir dosing increase, in accordance with local approved labels and/or regulations.

Enhanced extracellular vesicles mediated uttroside B (Utt-B) delivery to Hepatocellular carcinoma cell: Pharmacokinetics based on PBPK modelling

Our prior investigation has confirmed that the anti-hepatocellular carcinoma activity of the plant saponin, specifically Uttroside B (Utt-B), derived from the leaves of Solanum nigrum Linn. This study concentrated on formulating a novel biocompatible nanocarrier utilizing Extracellular vesicles (EVs) to enhance the delivery of plant saponin into cells. The physicochemical attributes of Extracellular Vesicles/UttrosideB (EVs/Utt-B) were comprehensively characterized through techniques such as Transmission Electron Microscopy (TEM) and Fourier-transform infrared spectroscopy (FTIR). Despite the promising therapeutic potential of this uttroside B, mechanistic know-how about its entry into cells is still in its infancy. Our research sheds light on the extracellular vesicle-mediated mechanism facilitating the entry of the saponin into cells, a phenomenon confirmed through the use of by confocal microscopy. We further analysed drug-releasing kinetics and simulated the Pharmacokinetics by PBPK modelling. The simulated pharmacokinetics revealed the bioavailability of Uttroside-B in oral administration against intravenous administration.

Drug Exposure of Long-Acting Cabotegravir and Rilpivirine in Older People With Human Immunodeficiency Virus: A Pharmacokinetic Modeling Study

Background

The life expectancy of people with human immunodeficiency virus (PWH) has significantly increased, thanks to combined antiretrovirals with improved potency and tolerability. One further step has been achieved with the development of long-acting (LA) injectable antiretrovirals, which allow for infrequent dosing. However, the pharmacokinetics of LA antiretrovirals has been poorly characterized in older PWH, as they are generally excluded from trials. We performed virtual studies using physiologically based pharmacokinetic (PBPK) modeling to determine the anticipated exposure of LA cabotegravir/rilpivirine in older individuals.

Methods

Our PBPK model was verified against available observed data for LA cabotegravir and rilpivirine. Cohorts of virtual individuals aged 20-50, 50-65, or 65-85 years were generated to simulate the exposure of LA cabotegravir/rilpivirine for each age group. The fold changes in trough concentration (Cmin) and in drug exposure (area under the time-concentration curve [AUC]) were determined for older relative to young individuals.

Results

The verified PBPK models predicted an increase in exposure within the 0.8-1.25 fold range for monthly LA cabotegravir/rilpivirine. The Cmin and AUC were predicted to be 29% and 26% higher in older compared with young adults for LA cabotegravir administered bimonthly (every 2 months) and 46% and 41% higher for LA rilpivirine bimonthly. The Cmin and AUC of LA cabotegravir and rilpivirine were predicted to be modestly increased in female compared with male individuals for all age groups.

Conclusions

LA cabotegravir/rilpivirine exposure and trough concentrations are predicted to be higher in older than in young PWH; thus, older adults could have a lower risk to present suboptimal concentrations during the dosing interval.

Physiologically-based pharmacokinetic modeling and dosing optimization of cefotaxime in preterm and term neonates

BACKGROUND

Cefotaxime is commonly used in treating bacterial infections in neonates. To characterize the pharmacokinetic process in neonates and evaluate different recommended dosing schedules of cefotaxime, a physiologically-based pharmacokinetic (PBPK) model of cefotaxime was established in adults and scaled to neonates.

METHODS

A whole-body PBPK model was built in PK-SIM® software. Three elimination pathways are composed of enzymatic metabolism in the liver, passive filtration through glomerulus, and active tubular secretion mediated by renal transporters. The ontogeny information was applied to account for age-related changes in cefotaxime pharmacokinetics. The established models were verified with realistic clinical data in adults and pediatric populations. Simulations in neonates were conducted and 100% of the dosing interval where the unbound concentration in plasma was above the minimum inhibitory concentration (fT>MIC) was selected as the target index for dosing regimen evaluation.

RESULTS

The developed PBPK models successfully described the pharmacokinetic process of cefotaxime in adults and were scaled to the pediatric population. Good verification results were achieved in both adults' and neonates' PBPK models, indicating a good predictive performance. The optimal dosage regimen of cefotaxime was proposed according to the postnatal age (PNA) and gestational age (GA) of neonates. For preterm neonates (GA < 36 weeks), dosages of 25 mg/kg every 8 hours in PNA 0-6 days and 25 mg/kg every 6 hours in PNA 7-28 days were suggested. For term neonates (GA ≥ 36 weeks), dosages of 33 mg/kg every 8 hours in PNA 0-6 days and 33 mg/kg every 6 hours in PNA 7-28 days were recommended.

CONCLUSIONS

Our study may provide useful experience in practicing PBPK model-informed precision dosing in the pediatric population.

Physiologically Based Pharmacokinetic (PBPK) Modeling to Predict CYP3A-Mediated Drug Interaction between Saxagliptin and Nicardipine: Bridging Rat-to-Human Extrapolation

The aim of this study was to predict the cytochrome P450 3A (CYP3A)-mediated drug-drug interactions (DDIs) between saxagliptin and nicardipine using a physiologically based pharmacokinetic (PBPK) model. Initially, in silico and in vitro parameters were gathered from experiments or the literature to construct PBPK models for each drug in rats. These models were integrated to predict the DDIs between saxagliptin, metabolized via CYP3A2, and nicardipine, exhibiting CYP3A inhibitory activity. The rat DDI PBPK model was completed by optimizing parameters using experimental rat plasma concentrations after co-administration of both drugs. Following co-administration in Sprague-Dawley rats, saxagliptin plasma concentration significantly increased, resulting in a 2.60-fold rise in AUC, accurately predicted by the rat PBPK model. Subsequently, the workflow of the rat PBPK model was applied to humans, creating a model capable of predicting DDIs between the two drugs in humans. Simulation from the human PBPK model indicated that nicardipine co-administration in humans resulted in a nearly unchanged AUC of saxagliptin, with an approximate 1.05-fold change, indicating no clinically significant changes and revealing a lack of direct translation of animal interaction results to humans. The animal-to-human PBPK model extrapolation used in this study could enhance the reliability of predicting drug interactions in clinical settings where DDI studies are challenging.

Predicting pharmacodynamic effects through early drug discovery with artificial intelligence-physiologically based pharmacokinetic (AI-PBPK) modelling

A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model links the concentration-time profile of a drug with its therapeutic effects based on the underlying biological or physiological processes. Clinical endpoints play a pivotal role in drug development. Despite the substantial time and effort invested in screening drugs for favourable pharmacokinetic (PK) properties, they may not consistently yield optimal clinical outcomes. Furthermore, in the virtual compound screening phase, researchers cannot observe clinical outcomes in humans directly. These uncertainties prolong the process of drug development. As incorporation of Artificial Intelligence (AI) into the physiologically based pharmacokinetic/pharmacodynamic (PBPK) model can assist in forecasting pharmacodynamic (PD) effects within the human body, we introduce a methodology for utilizing the AI-PBPK platform to predict the PK and PD outcomes of target compounds in the early drug discovery stage. In this integrated platform, machine learning is used to predict the parameters for the model, and the mechanism-based PD model is used to predict the PD outcome through the PK results. This platform enables researchers to align the PK profile of a drug with desired PD effects at the early drug discovery stage. Case studies are presented to assess and compare five potassium-competitive acid blocker (P-CAB) compounds, after calibration and verification using vonoprazan and revaprazan.

Physiologically based radiopharmacokinetic (PBRPK) modeling to simulate and analyze radiopharmaceutical therapies: studies of non-linearities, multi-bolus injections, and albumin binding

BACKGROUND

We aimed to develop a publicly shared computational physiologically based pharmacokinetic (PBPK) model to reliably simulate and analyze radiopharmaceutical therapies (RPTs), including probing of hot-cold ligand competitions as well as alternative injection scenarios and drug designs, towards optimal therapies.

RESULTS

To handle the complexity of PBPK models (over 150 differential equations), a scalable modeling notation called the "reaction graph" is introduced, enabling easy inclusion of various interactions. We refer to this as physiologically based radiopharmacokinetic (PBRPK) modeling, fine-tuned specifically for radiopharmaceuticals. As three important applications, we used our PBRPK model to (1) study the effect of competition between hot and cold species on delivered doses to tumors and organs at risk. In addition, (2) we evaluated an alternative paradigm of utilizing multi-bolus injections in RPTs instead of prevalent single injections. Finally, (3) we used PBRPK modeling to study the impact of varying albumin-binding affinities by ligands, and the implications for RPTs. We found that competition between labeled and unlabeled ligands can lead to non-linear relations between injected activity and the delivered dose to a particular organ, in the sense that doubling the injected activity does not necessarily result in a doubled dose delivered to a particular organ (a false intuition from external beam radiotherapy). In addition, we observed that fractionating injections can lead to a higher payload of dose delivery to organs, though not a differential dose delivery to the tumor. By contrast, we found out that increased albumin-binding affinities of the injected ligands can lead to such a differential effect in delivering more doses to tumors, and this can be attributed to several factors that PBRPK modeling allows us to probe.

CONCLUSIONS

Advanced computational PBRPK modeling enables simulation and analysis of a variety of intervention and drug design scenarios, towards more optimal delivery of RPTs.

Local Identifiability Analysis, Parameter Subset Selection and Verification for a Minimal Brain PBPK Model

Physiologically-based pharmacokinetic (PBPK) modeling is important for studying drug delivery in the central nervous system, including determining antibody exposure, predicting chemical concentrations at target locations, and ensuring accurate dosages. The complexity of PBPK models, involving many variables and parameters, requires a consideration of parameter identifiability; i.e., which parameters can be uniquely determined from data for a specified set of concentrations. We introduce the use of a local sensitivity-based parameter subset selection algorithm in the context of a minimal PBPK (mPBPK) model of the brain for antibody therapeutics. This algorithm is augmented by verification techniques, based on response distributions and energy statistics, to provide a systematic and robust technique to determine identifiable parameter subsets in a PBPK model across a specified time domain of interest. The accuracy of our approach is evaluated for three key concentrations in the mPBPK model for plasma, brain interstitial fluid and brain cerebrospinal fluid. The determination of accurate identifiable parameter subsets is important for model reduction and uncertainty quantification for PBPK models.

PBPK Modeling of PAXLOVIDTM: Incorporating Rotamer Conversion Kinetics to Advanced Dissolution and Absorption Model

PAXLOVID™ is a combination medicine of nirmatrelvir tablets co-packaged with ritonavir tablets. Nirmatrelvir is a peptidomimetic inhibitor of SARS-CoV2 main protease (Mpro), developed for the treatment of COVID-19. Ritonavir is co-administered as a pharmacokinetics (PK) enhancer to inhibit CYP3A mediated metabolism increasing exposures of nirmatrelvir. In the solid form, nirmatrelvir exists in a stable single conformational state (ANTI form). However, nirmatrelvir exhibits atropisomerism in solution whereby upon dissolution the ANTI rotational isomer reversibly converts to another conformation state (SYN form). Nirmatrelvir rotamer conversion follows pseudo first order kinetics with a conversion half-life of approximately 15 min in aqueous solutions, which is on a similar time scale of diffusion mediated dissolution from the solid form. In vitro dissolution studies further indicated that rotamer conversion is one of the processes controlling nirmatrelvir dissolution. It was hypothesized that rotamer conversion kinetics would affect oral absorption of nirmatrelvir in vivo. Consequently, a physiologically based pharmacokinetic (PBPK) model for Paxlovid was developed in Simcyp™ using the advanced dissolution, absorption, and metabolism model (ADAM) by incorporating rotamer conversion kinetics to achieve a more mechanistic description of nirmatrelvir oral absorption. The results demonstrate that the established absorption model with rotamer kinetics adequately described observed clinical data from various nirmatrelvir doses, dosage forms, and dosing regimens. The predicted vs. observed AUCinf and Cmax ratios were within 2-fold. The model has been internally used to inform clinical studies and dose recommendations for pediatrics.

Teaching of drug disposition using physiologically based pharmacokinetic modeling software: GastroPlus as an educational tool

Physiologically based pharmacokinetic (PBPK) modeling requires an understanding of chemical, physiologic, and pharmacokinetic principles. Active learning with PBPK modeling software (GastroPlus) may be useful to teach these scientific principles while also teaching software operation. To examine this issue, a graduate-level course was designed using learning objectives in science, software use, and PBPK model application. These objectives were taught through hands-on PBPK modeling to answer clinically relevant questions. Students demonstrated proficient use of software, based on their responses to these questions, and showed an improved understanding of scientific principles on a pre- and post-course assessment. These outcomes support the effectiveness of simultaneous teaching of interdependent software and science.NEW & NOTEWORTHY Physiologically based pharmacokinetic (PBPK) modeling is a major growth area in drug development, regulatory submissions, and clinical applications. There is a demand for experts in this area with multidisciplinary backgrounds. In this article, we describe a course designed to teach PBPK modeling and the underlying scientific principles in parallel.

Animal-free assessment of developmental toxicity: Combining PBPK modeling with the ReproTracker assay

in vitro screening platforms to assess teratogenic potential of compounds are emerging rapidly. ReproTracker is a human induced pluripotent stem cells (hiPSCs)-based biomarker assay that is shown to identify the teratogenicity potential of new pharmaceuticals and chemicals reliably. In its current state, the assay is limited to identifying the potential teratogenic effects and does not immediately quantify a clinical dose relevant to the exposure of chemicals or drugs observable in mothers or fetuses. The goal of this study was to evaluate whether the ReproTracker assay can be extrapolated in vivo and quantitatively predict developmental toxicity exposure levels of two known human teratogens, thalidomide, and carbamazepine. Here, we utilized Physiologically Based Pharmacokinetic (PBPK) modeling to describe the pharmacokinetic behavior of these compounds and conducted an in vitro to in vivo extrapolation (IVIVE) approach to predict human equivalent effect doses (HEDs) that correspond with in vitro concentrations potentially associated with adverse outcomes in ReproTracker. The HEDs derived from the ReproTracker concentration predicted to cause developmental toxicity were close to the reported teratogenic human clinical doses and the HED derived from the rat or rabbit developmental toxicity study. The ReproTracker derived-HED revealed to be sensitive and protective of humans. Overall, this pilot study demonstrated the importance of integrating PBPK model in extrapolating and assessing developmental toxicity in vitro. The combination of these tools demonstrated that they could improve the safety assessment of drugs and chemicals without animal testing.

In vitro and in vivo pharmacokinetic characterization, chiral conversion and PBPK scaling towards human PK simulation of S-MRI-1867, a drug candidate for Hermansky-Pudlak syndrome pulmonary fibrosis

Hermansky-Pudlak syndrome (HPS) is a rare autosomal recessive disorder that affects lysosome-related organelles, often leading to fatal pulmonary fibrosis (PF). The search for a treatment for HPS pulmonary fibrosis (HPSPF) is ongoing. S-MRI-1867, a dual cannabinoid receptor 1 (CB1R)/inducible nitric oxide synthase (iNOS) inhibitor, has shown great promise for the treatment of several fibrotic diseases, including HPSPF. In this study, we investigated the in vitro ADME characteristics of S-MRI-1867, as well as its pharmacokinetic (PK) properties in mice, rats, dogs, and monkeys. S-MRI-1867 showed low aqueous solubility (< 1 µg/mL), high plasma protein binding (>99%), and moderate to high metabolic stability. In its preclinical PK studies, S-MRI-1867 exhibited moderate to low plasma clearance (CLp) and high steady-state volume of distribution (Vdss) across all species. Despite the low solubility and P-gp efflux, S-MRI-1867 showed great permeability and metabolic stability leading to a moderate bioavailability (21-60%) across mouse, rat, dog, and monkey. Since the R form of MRI-1867 is CB1R-inactive, we investigated the potential conversion of S-MRI-1867 to R-MRI-1867 in mice and found that the chiral conversion was negligible. Furthermore, we developed and validated a PBPK model that adequately fits the PK profiles of S-MRI-1867 in mice, rats, dogs, and monkeys using various dosing regimens. We employed this PBPK model to simulate the human PK profiles of S-MRI-1867, enabling us to inform human dose selection and support the advancement of this promising drug candidate in the treatment of HPSPF.

Digitalizing the TIM-1 Model Using Computational Approaches─Part Two: Digital TIM-1 Model in GastroPlus

A TIM-1 model is an in vitro gastrointestinal (GI) simulator considering crucial physiological parameters that will affect the in vivo drug release process. The outcome of these experiments can indicate the critical bioavailability attributes (CBAs) that will impact the fraction absorbed in vivo. The model is widely used in the nonclinical stage of drug product development to assess the bioaccessible fraction of drugs for numerous candidate formulations. In this work, we developed a digital TIM-1 model in the GastroPlus platform. In a first step, we performed validation experiments to assess the luminal concentrations and bioaccessible fractions for two marker compounds. The digital TIM-1 was able to adequately reflect the luminal concentrations and bioaccessible fractions of these markers under different prandial conditions, confirming the appropriate integration of mass transfer in the TIM-1 model. In a second set of experiments, a case example with PF-07059013 was performed, where luminal concentrations and bioaccessible fractions were predicted for 200 and 1000 mg doses under fasted and achlorhydric conditions. Experimental and simulated data pointed out that the achlorhydric effect was more pronounced at the 1000 mg dose, showing a solubility-limited dissolution and, consequently, decreased bioaccessible fraction. Toward future applications, the digital TIM-1 model will be thoroughly applied to explore a link between in vitro and in vivo outcomes based on more case examples with model compounds with the access of TIM-1 and plasma data. Ideally, this digital TIM-1 can be directly used in GastroPlus to explore an in vitro-in vivo correlation (IVIVC) between the fraction dissolved (digital TIM-1 settings) and the fraction absorbed (human PBPK settings).

Digitalizing the TIM-1 Model using Computational Approaches-Part One: TIM-1 Data Explorer

The TIM-1 gastrointestinal model is one of the most advanced in vitro systems currently available for biorelevant dissolution testing. This technology, the initial version of which was developed nearly 30 years ago and has been subject to a number of significant updates over this period, simulates the dynamic environment of the human gastrointestinal tract, including pH, transfer times, secretion of bile, enzymes, and electrolytes. In the pharmaceutical industry, the TIM-1 system is used to support drug product design and provide a biopredictive assessment of drug product performance. Typically, the bioaccessibility data sets generated by TIM-1 experiments are used to qualitatively compare formulation performance, and the use of bioaccessibility data as inputs for physiologically based pharmacokinetic (PBPK) modeling for quantitative predictions is limited. To expand the utility of the TIM-1 model beyond standard bioaccessibility measurements (which define the fraction available for absorption), we have developed a computational tool, TIM-1 Data Explorer, to describe the fluid and mass balance within the TIM-1 system. The use of this tool allows a detailed inspection and in-depth interpretation of the experimental data. In addition to mass balance calculation, this model also can be used to describe the critical processes a drug substance would undergo during a TIM-1 experiment, such as dissolution, precipitation on transfer from the stomach to duodenum, and redissolution. The TIM-1 Data Explorer was validated in two case studies. In the first case study with paracetamol, we have shown the ability of the simulator to adequately describe mass transfer events within the TIM-1 system, and in the second study with a weakly basic in-house compound, PF-07059013, the TIM-1 Data Explorer was successfully used to describe dissolution and precipitation processes.

Physiologically Based Pharmacokinetics Modeling in the Neonatal Population-Current Advances, Challenges, and Opportunities

Physiologically based pharmacokinetic (PBPK) modeling is an approach to predicting drug pharmacokinetics, using knowledge of the human physiology involved and drug physiochemical properties. This approach is useful when predicting drug pharmacokinetics in under-studied populations, such as pediatrics. PBPK modeling is a particularly important tool for dose optimization for the neonatal population, given that clinical trials rarely include this patient population. However, important knowledge gaps exist for neonates, resulting in uncertainty with the model predictions. This review aims to outline the sources of variability that should be considered with developing a neonatal PBPK model, the data that are currently available for the neonatal ontogeny, and lastly to highlight the data gaps where further research would be needed.

Increasing confidence in new approach methodologies for inhalation risk assessment with multiple end point assays using 5-day repeated exposure to 1,3-dichloropropene

New Approach Methodologies (NAMs) are being widely used to reduce, refine, and replace, animal use in studying toxicology. For respiratory toxicology, this includes both in silico and in vitro alternatives to replace traditional in vivo inhalation studies. 1,3-Dichloropropene (1,3-DCP) is a volatile organic compound that is widely used in agriculture as a pre-planting fumigant. Short-term exposure of humans to 1,3-DCP can result in mucous membrane irritation, chest pain, headache, and dizziness. In our previous work, we exposed differentiated cells representing different parts of the respiratory epithelium to 1,3-DCP vapor, measured cytotoxicity, and did In Vitro to In Vivo Extrapolation (IVIVE). We have extended our previous study with 1,3-DCP vapors by conducting transcriptomics on acutely exposed nasal cultures and have implemented a separate 5-day repeated exposure with multiple endpoints to gain further molecular insight into our model. MucilAir™ Nasal cell culture models, representing the nasal epithelium, were exposed to six sub-cytotoxic concentrations of 1,3-DCP vapor at the air-liquid interface, and the nasal cultures were analyzed by different methodologies, including histology, transcriptomics, and glutathione (GSH) -depletion assays. We observed the dose-dependent effect of 1,3-DCP in terms of differential gene expression, change in cellular morphology from pseudostratified columnar epithelium to squamous epithelium, and depletion of GSH in MucilAir™ nasal cultures. The MucilAir™ nasal cultures were also exposed to 3 concentrations of 1,3-DCP using repeated exposure 4 h per day for 5 days and the histological analyses indicated changes in cellular morphology and a decrease in ciliated bodies and an increase in apoptotic bodies, with increasing concentrations of 1,3-DCP. Altogether, our results suggest that sub-cytotoxic exposures to 1,3-DCP lead to several molecular and cellular perturbations, providing significant insight into the mode-of-action (MoA) of 1,3-DCP using an innovative NAM model.

PBPK Modeling of Azithromycin Systemic Exposure in a Roux-en-Y Gastric Bypass Surgery Patient Population

In this investigation, PBPK modeling using the Simcyp® Simulator was performed to evaluate whether Roux-en-Y gastric bypass (RYGB) surgery impacts the oral absorption and bioavailability of azithromycin. An RYGB surgery patient population was adapted from the published literature and verified using the same probe medications, atorvastatin and midazolam. Next, a PBPK model of azithromycin was constructed to simulate changes in systemic drug exposure after the administration of different oral formulations (tablet, suspension) to patients pre- and post-RYGB surgery using the developed and verified population model. Clinically observed changes in azithromycin systemic exposure post-surgery following oral administration (single-dose tablet formulation) were captured using PBPK modeling based on the comparison of model-predicted exposure metrics (Cmax, AUC) to published clinical data. Model simulations predicted a 30% reduction in steady-state AUC after surgery for three- and five-day multiple dose regimens of an azithromycin tablet formulation. The relative bioavailability of a suspension formulation was 1.5-fold higher than the tablet formulation after multiple dosing. The changes in systemic exposure observed after surgery were used to evaluate the clinical efficacy of azithromycin against two of the most common pathogens causing community acquired pneumonia based on the corresponding AUC24/MIC pharmacodynamic endpoint. The results suggest lower bioavailability of the tablet formulation post-surgery may impact clinical efficacy. Overall, the research demonstrates the potential of a PBPK modeling approach as a framework to optimize oral drug therapy in patients post-RYGB surgery.

Applications of Modeling and Simulation Approaches in Support of Drug Product Development of Oral Dosage Forms and Locally Acting Drug Products: a Symposium Summary

The number of modeling and simulation applications, including physiologically based pharmacokinetic (PBPK) models, physiologically based biopharmaceutics modeling (PBBM), and empirical models, has been constantly increasing along with the regulatory acceptance of these methodologies. While aiming at minimizing unnecessary human testing, these methodologies are used today to support the development and approval of novel drug products and generics. Modeling approaches are leveraged today for assessing drug-drug interaction, informing dose adjustments in renally or hepatically impaired patients, perform dose selection in pediatrics and pregnant women and diseased populations, and conduct biopharmaceutics-related assessments such as establish clinically relevant specifications for drug products and achieve quality assurance throughout the product life cycle. In the generics space, PBPK analyses are utilized toward virtual bioequivalence assessments within the scope of alternative bioequivalence approaches, product-specific guidance development, and food effect assessments among others. Case studies highlighting the evolving and expanding role of modeling and simulation approaches within the biopharmaceutics space were presented at the symposium titled "Model Informed Drug Development (MIDD): Role in Dose Selection, Vulnerable Populations, and Biowaivers - Chemical Entities" and Prologue "PBPK/PBBM to inform the Bioequivalence Safe Space, Food Effects, and pH-mediated DDIs" at the American Association of Pharmaceutical Scientists (AAPS) PharmSci 360 Annual Meeting in Boston, MA, on October 16-19, 2022, and are summarized here.

A Machine Learning Framework to Improve Rat Clearance Predictions and Inform Physiologically Based Pharmacokinetic Modeling

During drug discovery and development, achieving appropriate pharmacokinetics is key to establishment of the efficacy and safety of new drugs. Physiologically based pharmacokinetic (PBPK) models integrating in vitro-to-in vivo extrapolation have become an essential in silico tool to achieve this goal. In this context, the most important and probably most challenging pharmacokinetic parameter to estimate is the clearance. Recent work on high-throughput PBPK modeling during drug discovery has shown that a good estimate of the unbound intrinsic clearance (CLint,u,) is the key factor for useful PBPK application. In this work, three different machine learning-based strategies were explored to predict the rat CLint,u as the input into PBPK. Therefore, in vivo and in vitro data was collected for a total of 2639 proprietary compounds. The strategies were compared to the standard in vitro bottom-up approach. Using the well-stirred liver model to back-calculate in vivo CLint,u from in vivo rat clearance and then training a machine learning model on this CLint,u led to more accurate clearance predictions (absolute average fold error (AAFE) 3.1 in temporal cross-validation) than the bottom-up approach (AAFE 3.6-16, depending on the scaling method) and has the advantage that no experimental in vitro data is needed. However, building a machine learning model on the bias between the back-calculated in vivo CLint,u and the bottom-up scaled in vitro CLint,u also performed well. For example, using unbound hepatocyte scaling, adding the bias prediction improved the AAFE in the temporal cross-validation from 16 for bottom-up to 2.9 together with the bias prediction. Similarly, the log Pearson r2 improved from 0.1 to 0.29. Although it would still require in vitro measurement of CLint,u., using unbound scaling for the bottom-up approach, the need for correction of the fu,inc by fu,p data is circumvented. While the above-described ML models were built on all data points available per approach, it is discussed that evaluation comparison across all approaches could only be performed on a subset because ca. 75% of the molecules had missing or unquantifiable measurements of the fraction unbound in plasma or in vitro unbound intrinsic clearance, or they dropped out due to the blood-flow limitation assumed by the well-stirred model. Advantageously, by predicting CLint,u as the input into PBPK, existing workflows can be reused and the prediction of the in vivo clearance and other PK parameters can be improved.

Development and Verification of a Japanese Pediatric Physiologically Based Pharmacokinetic Model with Emphasis on Drugs Eliminated by Cytochrome P450 or Renal Excretion

Physiologically based pharmacokinetic (PBPK) models are useful in bridging drug exposure in different ethnic groups, and there is increasing regulatory application of this approach in adults. Reported pediatric PBPK models tend to focus on the North European population, with few examples in other ethnic groups. This study describes the development and verification of a Japanese pediatric PBPK population. The development of the model was based on the existing North European pediatric population. Japanese systems and clinical data were collated from public databases and the literature, and the underlying demographics and equations were optimized so that physiological outputs represented the Japanese pediatric population. The model was tested using 14 different small molecule drugs, eliminated by a variety of pathways, including cytochrome P450 3A4 (CYP3A4) metabolism and renal excretion. Given the limitations of the clinical data, the overall performance of the model was good, with 44/62 predictions for PK parameters (area under the plasma drug concentration-time curve, AUC; maximum serum concentration, Cmax ; clearance, CL) being within 0.8- to 1.25-fold, 56/62 within 0.67- to 1.5-fold, and 61/62 within 0.5- to 2.0-fold of the observed values. Specific results for the 5 CYP3A4 substrates showed 20/31 cases were predicted within 0.8- to 1.25-fold, 27/31 within 0.67- to 1.5-fold, and all were within 0.5- to 2.0-fold of the observed values. Given the increased regulatory use of pediatric PBPK in drug development, expanding these models to other ethnic groups are important. Considering qualifying these models based on the context of use, there is a need to expand on the current research to include a larger range of drugs with different elimination pathways. Collaboration among academic, industry, model providers, and regulators will facilitate further development.

Physiologically Based Pharmacokinetic Modeling of Extracellular Vesicles

Extracellular vesicles (EVs) are lipid membrane bound-cell-derived structures that are a key player in intercellular communication and facilitate numerous cellular functions such as tumor growth, metastasis, immunosuppression, and angiogenesis. They can be used as a drug delivery platform because they can protect drugs from degradation and target specific cells or tissues. With the advancement in the technologies and methods in EV research, EV-therapeutics are one of the fast-growing domains in the human health sector. Therapeutic translation of EVs in clinics requires assessing the quality, safety, and efficacy of the EVs, in which pharmacokinetics is very crucial. We report here the application of physiologically based pharmacokinetic (PBPK) modeling as a principal tool for the prediction of absorption, distribution, metabolism, and excretion of EVs. To create a PBPK model of EVs, researchers would need to gather data on the size, shape, and composition of the EVs, as well as the physiological processes that affect their behavior in the body. The PBPK model would then be used to predict the pharmacokinetics of drugs delivered via EVs, such as the rate at which the drug is absorbed and distributed throughout the body, the rate at which it is metabolized and eliminated, and the maximum concentration of the drug in the body. This information can be used to optimize the design of EV-based drug delivery systems, including the size and composition of the EVs, the route of administration, and the dose of the drug. There has not been any dedicated review article that describes the PBPK modeling of EV. This review provides an overview of the absorption, distribution, metabolism, and excretion (ADME) phenomena of EVs. In addition, we will briefly describe the different computer-based modeling approaches that may help in the future of EV-based therapeutic research.

The In Vitro, In Vivo, and PBPK Evaluation of a Novel Lung-Targeted Cardiac-Safe Hydroxychloroquine Inhalation Aerogel

Hydroxychloroquine (HCQ) was repurposed for COVID-19 treatment. Subtherapeutic HCQ lung levels and cardiac toxicity of oral HCQ were overcome by intratracheal (IT) administration of lower HCQ doses. The crosslinker-free supercritical fluid technology (SFT) produces aerogels and impregnates them with drugs in their amorphous form with efficient controlled release. Mechanistic physiologically based pharmacokinetic (PBPK) modeling can predict the lung's epithelial lining fluid (ELF) drug levels. This study aimed to develop a novel HCQ SFT formulation for IT administration to achieve maximal ELF levels and minimal cardiac toxicity. HCQ SFT formulation was prepared and evaluated for physicochemical, in vitro release, pharmacokinetics, and cardiac toxicity. Finally, the rat HCQ ELF concentrations were predicted using PBPK modeling. HCQ was amorphous after loading into the chitosan-alginate nanoporous microparticles (22.7±7.6 μm). The formulation showed a zero-order release, with only 40% released over 30 min compared to 94% for raw HCQ. The formulation had a tapped density of 0.28 g/cm3 and a loading efficiency of 35.3±1.3%. The IT administration of SFT HCQ at 1 mg/kg resulted in 23.7-fold higher bioavailability, fourfold longer MRT, and eightfold faster absorption but lower CK-MB and LDH levels than oral raw HCQ at 4 mg/kg. The PBPK model predicted 6 h of therapeutic ELF levels for IT SFT HCQ and a 100-fold higher ELF-to-heart concentration ratio than oral HCQ. Our findings support the feasibility of lung-targeted and more effective SFT HCQ IT administration for COVID-19 compared to oral HCQ with less cardiac toxicity. Graphical abstract.

Physiologically-Based Pharmacokinetic Modeling of PAXLOVID™ with First-Order Absorption Kinetics

PURPOSE

PAXLOVID™ is nirmatrelvir tablets co-packaged with ritonavir tablets. Ritonavir is used as a pharmacokinetics (PK) enhancer to reduce metabolism and increase exposure of nirmatrelvir. This is the first disclosure of Paxlovid physiologically-based pharmacokinetic (PBPK) model.

METHODS

Nirmatrelvir PBPK model with first-order absorption kinetics was developed using in vitro, preclinical, and clinical data of nirmatrelvir in the presence and absence of ritonavir. Clearance and volume of distribution were derived from nirmatrelvir PK obtained using a spray-dried dispersion (SDD) formulation where it is considered to be dosed as an oral solution, and absorption is near complete. The fraction of nirmatrelvir metabolized by CYP3A was estimated based on in vitro and clinical ritonavir drug-drug interaction (DDI) data. First-order absorption parameters were established for both SDD and tablet formulation using clinical data. Nirmatrelvir PBPK model was verified with both single and multiple dose human PK data, as well as DDI studies. Simcyp® first-order ritonavir compound file was also verified with additional clinical data.

RESULTS

The nirmatrelvir PBPK model described the observed PK profiles of nirmatrelvir well with predicted AUC and Cmax values within ± 20% of the observed. The ritonavir model performed well resulting in predicted values within twofold of observed.

CONCLUSIONS

Paxlovid PBPK model developed in this study can be applied to predict PK changes in special populations, as well as model the effect of victim and perpetrator DDI. PBPK modeling continues to play a critical role in accelerating drug discovery and development of potential treatments for devastating diseases such as COVID-19. NCT05263895, NCT05129475, NCT05032950 and NCT05064800.

Can oral toxicity data for PFAS inform on toxicity via inhalation?

Per- and poly-fluoroalkyl substances (PFAS) are ubiquitous in the environment and are detected in wildlife and humans. With respect to human exposure, studies have shown that ingestion is the primary route of exposure; however, in certain settings, exposure via inhalation could also be a significant source of exposure. While many studies examined toxicity of PFAS via ingestion, limited information is available for PFAS toxicity via the inhalation route, translating into a lack of exposure guidelines. Consequently, this article examined whether route-to-route extrapolation to derive guidelines for inhalation exposure is appropriate for PFAS. Perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) were used as exemplary PFAS given the abundance of toxicity data for these two compounds. Our evaluation determined that available toxicity and toxicokinetic data support route-to-route extrapolation for PFAS in order to derive inhalation-based standards. Results from this analysis suggest that an air concentration of 7.0 × 10-5  mg/m3 (or 0.07 μg/m3 ) would be an appropriate RfC for PFOA and PFOS assuming the 2016 EPA RfD of 0.00002 mg/kg-day, whereas use of the interim RfDs proposed in 2022 of 1.5 × 10-9 and 7.9 × 10-9  mg/kg would yield much lower RfCs of 5.25 × 10-9 and 2.77 × 10-8  mg/m3 (or 5.25 × 10-6 and 2.77 × 10-5 μg/m3 ) for PFOA and PFOS, respectively.

Advanced In Vivo Prediction by Introducing Biphasic Dissolution Data into PBPK Models

Coupling biorelevant in vitro dissolution with in silico physiological-based pharmacokinetic (PBPK) tools represents a promising method to describe and predict the in vivo performance of drug candidates in formulation development including non-passive transport, prodrug activation, and first-pass metabolism. The objective of the present study was to assess the predictability of human pharmacokinetics by using biphasic dissolution results obtained with the previously established BiPHa+ assay and PBPK tools. For six commercial drug products, formulated by different enabling technologies, the respective organic partitioning profiles were processed with two PBPK in silico modeling tools, namely PK-Sim and GastroPlus^®, similar to extended-release dissolution profiles. Thus, a mechanistic dissolution/precipitation model of the assessed drug products was not required. The developed elimination/distribution models were used to simulate the pharmacokinetics of the evaluated drug products and compared with available human data. In essence, an in vitro to in vivo extrapolation (IVIVE) was successfully developed. Organic partitioning profiles obtained from the BiPHa+ dissolution analysis enabled highly accurate predictions of the pharmacokinetic behavior of the investigated drug products. In addition, PBPK models of (pro-)drugs with pronounced first-pass metabolism enabled adjustment of the solely passive diffusion predicting organic partitioning profiles, and increased prediction accuracy further.

Impact of Obesity on the Drug-Drug Interaction Between Dolutegravir and Rifampicin or Any Other Strong Inducers

Background

Obesity is increasingly prevalent among people with HIV. Obesity can impact drug pharmacokinetics and consequently the magnitude of drug-drug interactions (DDIs) and, thus, the related recommendations for dose adjustment. Virtual clinical DDI studies were conducted using physiologically based pharmacokinetic (PBPK) modeling to compare the magnitude of the DDI between dolutegravir and rifampicin in nonobese, obese, and morbidly obese individuals.

Methods

Each DDI scenario included a cohort of virtual individuals (50% female) between 20 and 50 years of age. Drug models for dolutegravir and rifampicin were verified against clinical observed data. The verified models were used to simulate the concurrent administration of rifampicin (600 mg) at steady state with dolutegravir (50 mg) administered twice daily in normal-weight (BMI 18.5-30 kg/m²), obese (BMI 30-40 kg/m²), and morbidly obese (BMI 40-50 kg/m²) individuals.

Results

Rifampicin was predicted to decrease dolutegravir area under the curve (AUC) by 72% in obese and 77% in morbidly obese vs 68% in nonobese individuals; however, dolutegravir trough concentrations were reduced to a similar extent (83% and 85% vs 85%). Twice-daily dolutegravir with rifampicin resulted in trough concentrations always above the protein-adjusted 90% inhibitory concentration for all BMI groups and above the 300 ng/mL threshold in a similar proportion for all BMI groups.

Conclusions

The combined effect of obesity and induction by rifampicin was predicted to further decrease dolutegravir exposure but not the minimal concentration at the end of the dosing interval. Thus, dolutegravir 50 mg twice daily with rifampicin can be used in individuals with a high BMI up to 50 kg/m².

Prediction of CYP-Mediated Drug Interaction Using Physiologically Based Pharmacokinetic Modeling: A Case Study of Salbutamol and Fluvoxamine

Drug-drug interactions (DDIs) represent a significant concern in healthcare, particularly for patients undergoing polytherapy. DDIs can lead to a range of outcomes, from decreased therapeutic effectiveness to adverse effects. Salbutamol, a bronchodilator recommended for the treatment of respiratory diseases, is metabolized by cytochrome P450 (CYP) enzymes, which can be inhibited or induced by co-administered drugs. Studying DDIs involving salbutamol is crucial for optimizing drug therapy and preventing adverse outcomes. Here, we aimed to investigate CYP-mediated DDIs between salbutamol and fluvoxamine through in silico approaches. The physiologically based pharmacokinetic (PBPK) model of salbutamol was developed and validated using available clinical PK data, whereas the PBPK model of fluvoxamine was previously verified by GastroPlus. Salbutamol-fluvoxamine interaction was simulated according to different regimens and patient's characteristics (age and physiological status). The results demonstrated that co-administering salbutamol with fluvoxamine enhanced salbutamol exposure in certain situations, especially when fluvoxamine dosage increased. To sum up, this study demonstrated the utility of PBPK modeling in predicting CYP-mediated DDIs, making it a pioneer in PK DDI research. Furthermore, this study provided insights into the relevance of regular monitoring of patients taking multiple medications, regardless of their characteristics, to prevent adverse outcomes and for the optimization of the therapeutic regimen, in cases where the therapeutic benefit is no longer experienced.

Model-Based Analysis of In Vivo Release Data of Levonorgestrel Implants: Projecting Long-Term Systemic Exposure

Levonorgestrel (LNG) is a progestin used in many contraceptive formulations, including subcutaneous implants. There is an unmet need for developing long-acting formulations for LNG. To develop long-acting formulations, release functions need to be investigated for LNG implant. Therefore, a release model was developed and integrated into an LNG physiologically-based pharmacokinetic (PBPK) model. Utilizing a previously developed LNG PBPK model, subcutaneous administration of 150 mg LNG was implemented into the modeling framework. To mimic LNG release, ten functions incorporating formulation-specific mechanisms were explored. Release kinetic parameters and bioavailability were optimized using Jadelle^® clinical trial data (n = 321) and verified using two additional clinical trials (n = 216). The First-order release and Biexponential release models showed the best fit with observed data, the adjusted R-squared (R²) value is 0.9170. The maximum released amount is approximately 50% of the loaded dose and the release rate is 0.0009 per day. The Biexponential model also showed good agreement with the data (adjusted R² = 0.9113). Both models could recapitulate observed plasma concentrations after integration into the PBPK simulations. First-order and Biexponential release functionality may be useful in modeling subcutaneous LNG implants. The developed model captures central tendency of the observed data as well as variability of release kinetics. Future work focuses on incorporating various clinical scenarios into model simulations, including drug-drug interactions and a range of BMIs.

Developing a Formulation Strategy Coupled with PBPK Modeling and Simulation for the Weakly Basic Drug Albendazole

Albendazole (ABZ) is a weakly basic drug that undergoes extensive presystemic metabolism after oral administration and converts to its active form albendazole sulfoxide (ABZ_SO). The absorption of albendazole is limited by poor aqueous solubility, and dissolution is the rate-limiting step in the overall exposure of ABZ_SO. In this study, PBPK modeling was used to identify formulation-specific parameters that impact the oral bioavailability of ABZ_SO. In vitro experiments were carried out to determine pH solubility, precipitation kinetics, particle size distribution, and biorelevant solubility. A transfer experiment was conducted to determine the precipitation kinetics. A PBPK model for ABZ and ABZ_SO was developed using the Simcyp™ Simulator based on parameter estimates from in vitro experiments. Sensitivity analyses were performed to assess the impact of physiological parameters and formulation-related parameters on the systemic exposure of ABZ_SO. Model simulations predicted that increased gastric pH significantly reduced ABZ absorption and, subsequently, ABZ_SO systemic exposure. Reducing the particle size below 50 µm did not improve the bioavailability of ABZ. Modeling results illustrated that systemic exposure of ABZ_SO was enhanced by increasing solubility or supersaturation and decreasing the drug precipitation of ABZ at the intestinal pH level. These results were used to identify potential formulation strategies to enhance the oral bioavailability of ABZ_SO.

Physiologically-based pharmacokinetic modeling-guided rational combination of tacrolimus and voriconazole in patients with different CYP3A5 and CYP2C19 alleles

The drug-drug interactions (DDIs) between tacrolimus and voriconazole are highly variable among individuals. We aimed to develop a physiologically based pharmacokinetic (PBPK) model to predict the DDIs in people with different CYP3A5 and CYP2C19 alleles. First, pharmacokinetic data of humans receiving tacrolimus with or without voriconazole from the literature were used to construct and validate the PBPK model. Thereafter, we developed a model incorporating the metabolism of voriconazole mediated by CYP2C19 and the inhibitory effect of voriconazole on CYP3A4/5. Finally, the model was used to evaluate the dose adjustment of tacrolimus in people with different CYP3A5 and CYP2C19 alleles. When tacrolimus was administered alone (3 mg PO, single dose), the predicted AUC_0-∞ of tacrolimus in CYP3A5 nonexpressers (19.22) was 3.5-fold higher than that in expressers (5.48). Following voriconazole (200 mg PO, bid) administration in human with different CYP2C19 genotypes, the AUC_0-∞ of tacrolimus increased by 5.1- to 8.3-fold in CYP3A5 expressers and by 5.3- to 10.2-fold in CYP3A5 nonexpressers. The lower the gene expression level of CYP2C19 in the population, the higher the exposure to tacrolimus. When tacrolimus was combined with voriconazole (200 mg, bid; 400 mg, bid, on Day 1), the final model simulations suggested that the dose regimen of tacrolimus should be regulated to 0.15 mg/kg/day (qd) in CYP3A5 expressers with different CYP2C19 genotypes. For CYP3A5 nonexpressers, the dosing schedule of tacrolimus should be modified to 0.05 mg/kg/24 h for patients with 2C19 EM, 0.05 mg/kg/48 h for 2C19 IM and 0.05 mg/kg/72 h for 2C19 PM. In conclusion, a PBPK model with CYP3A5 and CYP2C19 polymorphisms was successfully established, providing more insights regarding the DDIs between tacrolimus and voriconazole to guide the clinical use of tacrolimus.

Quantitative Characterization of Clinically Relevant Drug-Metabolizing Enzymes and Transporters in Rat Liver and Intestinal Segments for Applications in PBPK Modeling

Rats are extensively used as a preclinical model for assessing drug pharmacokinetics (PK) and tissue distribution; however, successful translation of the rat data requires information on the differences in drug metabolism and transport mechanisms between rats and humans. To partly fill this knowledge gap, we quantified clinically relevant drug-metabolizing enzymes and transporters (DMETs) in the liver and different intestinal segments of Sprague-Dawley rats. The levels of DMET proteins in rats were quantified using the global proteomics-based total protein approach (TPA) and targeted proteomics. The abundance of the major DMET proteins was largely comparable using quantitative global and targeted proteomics. However, global proteomics-based TPA was able to detect and quantify a comprehensive list of 66 DMET proteins in the liver and 37 DMET proteins in the intestinal segments of SD rats without the need for peptide standards. Cytochrome P450 (Cyp) and UDP-glycosyltransferase (Ugt) enzymes were mainly detected in the liver with the abundance ranging from 8 to 6502 and 74 to 2558 pmol/g tissue. P-gp abundance was higher in the intestine (124.1 pmol/g) as compared to that in the liver (26.6 pmol/g) using the targeted analysis. Breast cancer resistance protein (Bcrp) was most abundant in the intestinal segments, whereas organic anion transporting polypeptides (Oatp) 1a1, 1a4, 1b2, and 2a1 and multidrug resistance proteins (Mrp) 2 and 6 were predominantly detected in the liver. To demonstrate the utility of these data, we modeled digoxin PK by integrating protein abundance of P-gp and Cyp3a2 into a physiologically based PK (PBPK) model constructed using PK-Sim software. The model was able to reliably predict the systemic as well as tissue concentrations of digoxin in rats. These findings suggest that proteomics-informed PBPK models in preclinical species can allow mechanistic PK predictions in animal models including tissue drug concentrations.

Physiologically Based Absorption Modelling to Explore the Formulation and Gastric pH Changes on the Pharmacokinetics of Acalabrutinib

Acalabrutinib, a selective Bruton's tyrosine kinase inhibitor, is a biopharmaceutics classification system class II drug. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model to mechanistically describe absorption of immediate release capsule formulation of acalabrutinib in humans. Integration of in vitro biorelevant measurements, dissolution studies and in silico modelling provided clinically relevant inputs for the mechanistic absorption PBPK model. The batch specific dissolution data were integrated in two ways, by fitting a diffusion layer model scalar to the drug product dissolution with integration of drug substance laser diffraction particle size data, or by fitting a product particle size distribution to the dissolution data. The latter method proved more robust and biopredictive. In both cases, the drug surface solubility was well predicted by the Simcyp simulator. The model using the product particle size distribution (P-PSD) for each clinical batch adequately captured the PK profiles of acalabrutinib and its active metabolite. Average fold errors were 0.89 for both Cmax and AUC, suggesting good agreement between predicted and observed PK values. The model also accurately predicted pH-dependent drug-drug interactions between omeprazole and acalabrutinib, which was similar across all clinical formulations. The model predicted acalabrutinib geometric mean AUC ratios (with omeprazole vs acalabrutinib alone) were 0.51 and 0.68 for 2 batches of formulations, which are close to observed values of 0.43 and 0.51~0.63, respectively. The mechanistic absorption PBPK model could be potentially used for future applications such as optimizing formulations or predicting the PK for different batches of the drug product.

Physiologically based Pharmacokinetic Models under the Prism of the Finite Absorption Time Concept

To date, mechanistic modeling of oral drug absorption has been achieved via the use of physiologically based pharmacokinetic (PBPK) modeling, and more specifically, physiologically based biopharmaceutics model (PBBM). The concept of finite absorption time (FAT) has been developed recently and the application of the relevant physiologically based finite time pharmacokinetic (PBFTPK) models to experimental data provides explicit evidence that drug absorption terminates at a specific time point. In this manuscript, we explored how PBBM and PBFTPK models compare when applied to the same dataset. A set of six compounds with clinical data from immediate-release formulation were selected. Both models resulted in absorption time estimates within the small intestinal transit time, with PBFTPK models generally providing shorter time estimates. A clear relationship between the absorption rate and the product of permeability and luminal concentration was observed, in concurrence with the fundamental assumptions of PBFTPK models. We propose that future research on the synergy between the two modeling approaches can lead to both improvements in the initial parameterization of PBPK/PBBM models but to also expand mechanistic oral absorption concepts to more traditional pharmacometrics applications.

Use of physiological based pharmacokinetic modeling for cross-species prediction of pharmacokinetic and tissue distribution profiles of a novel niclosamide prodrug

Introduction: Niclosamide (Nc) is an FDA-approved anthelmintic drug that was recently identified in a drug repurposing screening to possess antiviral activity against SARS-CoV-2. However, due to the low solubility and permeability of Nc, its in vivo efficacy was limited by its poor oral absorption. Method: The current study evaluated a novel prodrug of Nc (PDN; NCATS-SM4705) in improving in vivo exposure of Nc and predicted pharmacokinetic profiles of PDN and Nc across different species. ADME properties of the prodrug were determined in humans, hamsters, and mice, while the pharmacokinetics (PK) of PDN were obtained in mice and hamsters. Concentrations of PDN and Nc in plasma and tissue homogenates were measured by UPLC-MS/MS. A physiologically based pharmacokinetic (PBPK) model was developed based on physicochemical properties, pharmacokinetic and tissue distribution data in mice, validated by the PK profiles in hamsters and applied to predict pharmacokinetic profiles in humans. Results: Following intravenous and oral administration of PDN in mice, the total plasma clearance (CL_p) and volume of distribution at steady-state (Vd_ss) were 0.061-0.063 L/h and 0.28-0.31 L, respectively. PDN was converted to Nc in both liver and blood, improving the systemic exposure of Nc in mice and hamsters after oral administration. The PBPK model developed for PDN and in vivo formed Nc could adequately simulate plasma and tissue concentration-time profiles in mice and plasma profiles in hamsters. The predicted human CL_p/F and Vd_ss/F after an oral dose were 2.1 L/h/kg and 15 L/kg for the prodrug respectively. The predicted Nc concentrations in human plasma and lung suggest that a TID dose of 300 mg PDN would provide Nc lung concentrations at 8- to 60-fold higher than in vitro IC₅₀ against SARS-CoV-2 reported in cell assays. Conclusion: In conclusion, the novel prodrug PDN can be efficiently converted to Nc in vivo and improves the systemic exposure of Nc in mice after oral administration. The developed PBPK model adequately depicts the mouse and hamster pharmacokinetic and tissue distribution profiles and highlights its potential application in the prediction of human pharmacokinetic profiles.

Impact of Multiple Concomitant CYP3A Inhibitors on Venetoclax Pharmacokinetics: A PBPK and Population PK-Informed Analysis

Venetoclax is an approved, orally bioavailable, B-cell lymphoma type 2 (BCL-2) inhibitor that is primarily metabolized by cytochrome P450 3A (CYP3A). Polypharmacy is common in patients undergoing treatment for hematological malignancies such as acute myeloid leukemia or chronic lymphocytic leukemia, and although venetoclax exposure has been well characterized with 1 concomitant CYP3A inhibitor, complex drug-drug interactions (DDIs) involving more than 1 inhibitor have not been systematically evaluated. Here, we aimed to describe the potential impact of multiple concomitant CYP3A inhibitors on venetoclax pharmacokinetics (PK) using physiologically based pharmacokinetic (PBPK) and population PK modeling. The modeling approaches were informed by clinical data in the presence of single or multiple CYP3A inhibitors, and the effects of 1 or more inhibitors were systematically considered within these modeling frameworks. The PBPK modeling approach was independently validated against clinical data involving more than 1 CYP3A inhibitor along with CYP3A substrates other than venetoclax. Both approaches indicated that combining a strong CYP3A inhibitor with another competitive CYP3A inhibitor does not seem to result in any additional increase in venetoclax exposure, beyond what would be expected with a strong inhibitor alone. This suggests that the current dose reductions recommended for venetoclax would be appropriate even when 2 or more CYP3A inhibitors are taken concomitantly. However, the results indicate that the involvement of time-dependent inhibition might lead to additional inhibitory effects over and above the effect of a single strong CYP3A inhibitor. Thus, the clinical management of such interactions must consider the underlying mechanism of the interactions.

Pharmacokinetic Basis for Using Saliva Matrine Concentrations as a Clinical Compliance Monitoring in Antitumor B Chemoprevention Trials in Humans

This study reports the first clinical evidence of significantly high secretion of matrine in a multi-component botanical (Antitumor B, ATB) into human saliva from the systemic circulation. This is of high clinical significance as matrine can be used as a monitoring tool during longitudinal clinical studies to overcome the key limitation of poor patient compliance often reported in cancer chemoprevention trials. Both matrine and dictamine were detected in the saliva and plasma samples but only matrine was quantifiable after the oral administration of ATB tablets (2400 mg) in 8 healthy volunteers. A significantly high saliva/plasma ratios for C_max (6.5 ± 2.0) and AUC_0-24 (4.8 ± 2.0) of matrine suggested an active secretion in saliva probably due to entero-salivary recycling as evident from the long half-lives (t_1/2 plasma = 10.0 ± 2.8 h, t_1/2 saliva = 13.4 ± 6.9 h). The correlation between saliva and plasma levels of matrine was established using a population compartmental pharmacokinetic co-model. Moreover, a species-relevant PBPK model was developed to adequately describe the pharmacokinetic profiles of matrine in mouse, rat, and human. In conclusion, matrine saliva concentrations can be used as an excellent marker compound for mechanistic studies of active secretion of drugs from plasma to saliva as well as monitor the patient's compliance to the treatment regimen in upcoming clinical trials of ATB.

Predicting transporter mediated drug-drug interactions via static and dynamic physiologically based pharmacokinetic modeling: A comprehensive insight on where we are now and the way forward

The greater utilization and acceptance of physiologically-based pharmacokinetic (PBPK) modeling to evaluate the potential metabolic drug-drug interactions is evident by the plethora of literature, guidance's, and regulatory dossiers available in the literature. In contrast, it is not widely used to predict transporter-mediated DDI (tDDI). This is attributed to the unavailability of accurate transporter tissue expression levels, the absence of accurate in vitro to in vivo extrapolations (IVIVE), enzyme-transporter interplay, and a lack of specific probe substrates. Additionally, poor understanding of the inhibition/induction mechanisms coupled with the inability to determine unbound concentrations at the interaction site made tDDI assessment challenging. Despite these challenges, continuous improvements in IVIVE approaches enabled accurate tDDI predictions. Furthermore, the necessity of extrapolating tDDI's to special (pediatrics, pregnant, geriatrics) and diseased (renal, hepatic impaired) populations is gaining impetus and is encouraged by regulatory authorities. This review aims to visit the current state-of-the-art and summarizes contemporary knowledge on tDDI predictions. The current understanding and ability of static and dynamic PBPK models to predict tDDI are portrayed in detail. Peer-reviewed transporter abundance data in special and diseased populations from recent publications were compiled, enabling direct input into modeling tools for accurate tDDI predictions. A compilation of regulatory guidance's for tDDI's assessment and success stories from regulatory submissions are presented. Future perspectives and challenges of predicting tDDI in terms of in vitro system considerations, endogenous biomarkers, the use of empirical scaling factors, enzyme-transporter interplay, and acceptance criteria for model validation to meet the regulatory expectations were discussed.

NAM-based Prediction of Point-of-contact Toxicity in the Lung: A Case Example With 1,3-dichloropropene

Time, cost, ethical, and regulatory considerations surrounding in vivo testing methods render them insufficient to meet existing and future chemical safety testing demands. There is a need for the development of in vitro and in silico alternatives to replace traditional in vivo methods for inhalation toxicity assessment. Exposures of differentiated airway epithelial cultures to gases or aerosols at the air-liquid interface (ALI) can assess tissue responses and in vitro to in vivo extrapolation can align in vitro exposure levels with in-life exposures expected to give similar tissue exposures. Because the airway epithelium varies along its length, with various regions composed of different cell types, we have introduced a known toxic vapor to five human-derived, differentiated, in vitro airway epithelial cell culture models-MucilAir of nasal, tracheal, or bronchial origin, SmallAir, and EpiAlveolar-representing five regions of the airway epithelium-nasal, tracheal, bronchial, bronchiolar, and alveolar. We have monitored toxicity in these cultures 24hours after acute exposure using an assay for transepithelial conductance (for epithelial barrier integrity) and the lactate dehydrogenase (LDH) release assay (for cytotoxicity). Our vapor of choice in these experiments was 1,3-dichloropropene (1,3-DCP). Finally, we have developed an airway dosimetry model for 1,3-DCP vapor to predict in vivo external exposure scenarios that would produce toxic local tissue concentrations as determined by in vitro experiments. Measured in vitro points of departure (PoDs) for all tested cell culture models were similar. Calculated rat equivalent inhaled concentrations varied by model according to position of the modeled tissue within the airway, with nasal respiratory tissue being the most proximal and most sensitive tissue, and alveolar epithelium being the most distal and least sensitive tissue. These predictions are qualitatively in accordance with empirically determined in vivo PoDs. The predicted PoD concentrations were close to, but slightly higher than, PoDs determined by in vivo subchronic studies.

Predicting fetal exposure of crizotinib during pregnancy: Combining human ex vivo placenta perfusion data with physiologically-based pharmacokinetic modeling

Commercially available physiologically-based pharmacokinetic (PBPK) modeling platforms increasingly allow estimations of fetal exposure to xenobiotics. We aimed to explore a physiology-based approach in which literature data from ex vivo placenta perfusion studies are used to parameterize Simcyp's pregnancy-PBPK (p-PBPK) model, taking crizotinib as an example. First, a physiologically-based semi-mechanistic placenta (PBMP) model was developed in MATLAB to analyze placenta perfusion data of crizotinib. Mixed-effects modeling was performed to derive intrinsic unbound clearance values across the maternal-placental barrier and fetal-placental barrier. Values were then used for parameterization of the p-PBPK model. The PBMP model adequately described the perfusion data. Clearance was estimated to be 71 mL/min and 535 mL/min for the maternal placental uptake and efflux, and 8 mL/min and 163 mL/min for fetal placental uptake and efflux, respectively. For oral dosing of 250 mg twice daily, p-PBPK modeling predicted a C_max and AUC_0-τ of 0.08 mg/L and 0.78 mg/L*h in the umbilical vein at steady-state, respectively. In placental tissue, a C_max of 5.04 mg/L was predicted. In conclusion, PBMP model-based data analysis and the associated p-PBPK modeling approach illustrate how ex vivo placenta perfusion data may be used for fetal exposure predictions.

Physiologically-Based Pharmacokinetic Modeling to Predict Methylphenidate Exposure Affected by Interplay Among Carboxylesterase 1 Pharmacogenetics, Drug-Drug Interactions, and Sex

BACKGROUND AND OBJECTIVE

The pharmacokinetics (PK) of methylphenidate (MPH) differ significantly among individuals. Carboxylesterase 1 (CES1) is the primary enzyme metabolizing MPH, and its function is affected by genetic variants, drug-drug interaction (DDI), and sex. The object of this study is to evaluate CES1 pharmacogenetics as related to MPH metabolism using human liver samples and develop a physiologically-based pharmacokinetic (PBPK) modeling approach to investigate the influence of CES1 genotypes and other factors on MPH PK.

METHODS

The effect of the CES1 variant G143E (rs71647871) on MPH metabolism was studied utilizing 102 individual human liver S9 (HLS9) fraction samples. PBPK models were developed using the population-based PBPK software PK-Sim® by incorporating the HLS9 incubation data. The established models were applied to simulate MPH PK profiles under various clinical scenarios, including different genotypes, drug-alcohol interactions, and the difference between males and females.

RESULTS

The HLS9 incubation study showed that subjects heterozygous for the CES1 variant G143E metabolized MPH at a rate of approximately 50% of that in non-carriers. The developed PBPK models successfully predicted the exposure alteration of MPH from the G143E genetic variant, ethanol-MPH DDI, and sex. Importantly, the study suggests that male G143E carriers who are alcohol consumers are at a higher risk of MPH overexposure.

CONCLUSION

PBPK modeling provides a means for better understanding the mechanisms underlying interindividual variability in MPH PK and PD and could be utilized to develop a safer and more effective MPH pharmacotherapy regimen.

Case study on the impact of the source of metabolism parameters in next generation physiologically based pharmacokinetic models: Implications for occupational exposures to trimethylbenzenes

Physiologically based pharmacokinetic (PBPK) models are a means of making important linkages between exposure assessment and in vitro toxicity. A key constraint on rapid application of PBPK models in risk assessment is traditional reliance on substance-specific in vivo toxicokinetic data to evaluate model quality. Bounding conditions, in silico, in vitro, and chemical read-across approaches have been proposed as alternative sources for metabolic clearance estimates. A case study to test consistency of predictive ability across these approaches was conducted using trimethylbenzenes (TMB) as prototype chemicals. Substantial concordance was found among TMB isomers with respect to accuracy (or inaccuracy) of approaches to estimating metabolism; for example, the bounding conditions never reproduced the human in vivo toxicokinetic data within two-fold. Using only approaches that gave acceptable prediction of in vivo toxicokinetics for the source compound (1,2,4-TMB) substantially narrowed the range of plausible internal doses for a given external dose for occupational, emergency response, and environmental/community health risk assessment scenarios for TMB isomers. Thus, risk assessments developed using the target compound models with a constrained subset of metabolism estimates (determined for source chemical models) can be used with greater confidence that internal dosimetry will be estimated with accuracy sufficient for the purpose at hand.

Usefulness of Optimized Human Fecal Material in Simulating the Bacterial Degradation of Sulindac and Sulfinpyrazone in the Lower Intestine

The first aim of this study was to evaluate the usefulness of optimized human fecal material in simulating sulforeductase activity in the lower intestine by assessing bacterial degradation of sulindac and sulfinpyrazone, two sulforeductase substrates. The second aim was to evaluate the usefulness of drug degradation half-life generated in simulated colonic bacteria (SCoB) in informing PBPK models. Degradation experiments of sulfinpyrazone and of sulindac in SCoB were performed under anaerobic conditions using recently described methods. For sulfinpyrazone, the abundance of clinical data allowed for construction of a physiologically based pharmacokinetic (PBPK) model and evaluation of luminal degradation clearance determined from SCoB data. For sulindac, the availability of sulindac sulfide and sulindac sulfone standards allowed for evaluating the formation of the main metabolite, sulindac sulfide, during the experiments in SCoB. Both model compounds degraded substantially in SCoB. The PBPK model was able to adequately capture exposure of sulfinpyrazone and its sulfide metabolite in healthy subjects, in ileostomy and/or colectomy subjects, and in healthy subjects pretreated with metoclopramide by implementing degradation half-lives in SCoB to calculate intrinsic colon clearance. Degradation rates of sulindac and formation rates of sulindac sulfide in SCoB were almost identical, in line with in vivo data suggesting the sulindac sulfide is the primary metabolite in the lower intestine. Experiments in SCoB were useful in simulating sulforeductase related bacterial degradation activity in the lower intestine. Degradation half-life calculated from experiments in SCoB is proven useful for informing a predictive PBPK model for sulfinpyrazone.

Opportunities and challenges of physiologically based pharmacokinetic modeling in drug delivery

Physiologically based pharmacokinetic (PBPK) modeling is an important in silico tool to bridge drug properties and in vivo PK behaviors during drug development. Over the recent decade, the PBPK method has been largely applied to drug delivery systems (DDS), including oral, inhaled, transdermal, ophthalmic, and complex injectable products. The related therapeutic agents have included small-molecule drugs, therapeutic proteins, nucleic acids, and even cells. Simulation results have provided important insights into PK behaviors of new dosage forms, which strongly support drug regulation. In this review, we comprehensively summarize recent progress in PBPK applications in drug delivery, which shows large opportunities for facilitating drug development. In addition, we discuss the challenges of applying this methodology from a practical viewpoint.

Physiologically based pharmacokinetic modeling and simulations to inform dissolution specifications and clinical relevance of release rates on elagolix exposure

The aim of this analysis was to use a physiologically based pharmacokinetic (PBPK) model to predict the impact of changes in dissolution rates on elagolix exposures and define clinically relevant acceptance criteria for dissolution. Varying in vitro dissolution profiles were utilized in a PBPK model to describe the absorption profiles of elagolix formulations used in Phase 3 clinical trials and for the to be marketed commercial formulations. Single dose studies of 200 mg elagolix formulations were used for model verification under fasted conditions. Additional dissolution scenarios were evaluated to assess the impact of dissolution rates on elagolix exposures. Compared to the Phase 3 clinical trial formulation, sensitivity analysis on dissolution rates suggested that a hypothetical scenario of ∼75% slower dissolution rate would result in 14% lower predicted elagolix plasma exposures, however, the predicted exposures are still within the bioequivalence boundaries of 0.8-1.25 for both C_max and AUC. A clinically verified PBPK model of elagolix was utilized to evaluate the impact of wider dissolution specifications on elagolix plasma exposures. The simulation results indicated that a slower in vitro dissolution profile, would not have a clinically significant impact on elagolix exposures. These model results informed the setting of wider dissolution specifications without requiring in vivo studies.

Blood-brain barrier crossing using magnetic stimulated nanoparticles

Due to the low permeability and high selectivity of the blood-brain barrier (BBB), existing brain therapeutic technologies are limited by the inefficient BBB crossing of conventional drugs. Magnetic nanoparticles (MNPs) have shown great potential as nano-carriers for efficient BBB crossing under the external static magnetic field (SMF). To quantify the impact of SMF on MNPs' in vivo dynamics towards BBB crossing, we developed a physiologically based pharmacokinetic (PBPK) model for intraperitoneal (IP) injected superparamagnetic iron oxide nanoparticles coated by gold and conjugated with poly (ethylene glycol) (PEG) (SPIO-Au-PEG NPs) in mice. Unlike most reported PBPK models that ignore brain permeability, we first obtained the brain permeabilities with and without SMF by determining the concentration of SPIO-Au-PEG NPs in the cerebral blood and brain tissue. This concentration in the brain was simulated by the advection-diffusion equations and was numerically solved in COMSOL Multiphysics. The results from the PBPK model after incorporating the brain permeability showed a good agreement (regression coefficient R² = 0.848) with the in vivo results, verifying the capability of using the proposed PBPK model to predict the in vivo biodistribution of SPIO-Au-PEG NPs under the exposure to SMF. Furthermore, the in vivo results revealed that the distribution coefficient from blood to brain under the exposure to SMF (4.01%) is slightly better than the control group (3.68%). In addition, the modification of SPIO-Au-PEG NPs with insulin (SPIO-Au-PEG-insulin) showed an improvement of the brain bioavailability by 24.47% in comparison to the non-insulin group. With the SMF stimulation, the brain bioavailability of SPIO-Au-PEG-insulin was further improved by 3.91% compared to the group without SMF. The PBPK model and in vivo validation in this paper lay a solid foundation for future study on non-invasive targeted drug delivery to the brain.

Development and application of a physiologically based pharmacokinetic model for orbifloxacin in crucian carp (Carassius auratus)

A flow-limited physiologically based pharmacokinetic (PBPK) model consisting of seven compartments was established for orbifloxacin in crucian carp to predict drug concentrations after intravenous or intramuscular injections. Physiological and anatomical parameters, including tissue weights and blood flow through different tissues, were obtained from previous literature. The tissue/plasma partition coefficients for orbifloxacin were calculated using the area method or parameter optimization. In addition, their values were 0.9326, 1.1204, 1.1644, 1.3514, and 2.0057 in the liver, skin, muscle, kidney, and the rest of the body compartment, respectively. Based on the current PBPK model, orbifloxacin concentrations were predicted and compared with those previously reported for further validation. In addition, the mean absolute percentage error (MAPE) values were also calculated, with values ranging from 10.21% in plasma to 42.37% in kidneys, indicating acceptable predictions for all tissues and plasma. A local sensitivity analysis was performed, which showed that the parameters related to elimination and distribution were most influential on orbifloxacin concentrations in muscle. This model was finally used to predict plasma and tissue concentrations after multiple intramuscular dosing. The current PBPK model provided a valuable tool for predicting the tissue residues of orbifloxacin in crucian carp following intramuscular injection.

Physiologically Based Pharmacokinetic Modeling of Metformin in Children and Adolescents with Obesity

Childhood obesity continues to rise in the United States, and with it the off-label use of metformin for weight loss. The influence of age and obesity on the drug's disposition and exposure has not previously been studied using a mechanistic framework. Here, an adult physiologically based pharmacokinetic (PBPK) model of metformin was scaled to pediatric populations without obesity, with overweight / obesity, and with severe obesity; a published virtual population of children and adolescents with obesity was leveraged during model evaluation. When the pediatric model was simulated in groups 10 - 18 y of age, oral clearance (CL/F) following 1,000 mg of metformin was higher (∼1200 mL/min) in those with obesity and severe obesity compared to the groups without and with overweight (∼1000 mL/min). In addition, simulated AUC in older children and adolescents with obesity and severe obesity was comparable to that in adults with a similar dose-exposure relationship. Overall, simulations using the pediatric PBPK model support the use of adult doses of metformin in older children and adolescents with obesity. Moreover, the virtual population of children and adolescents with obesity offers a valuable tool to facilitate development of pediatric PBPK models for studying populations with obesity and, in turn, contribute information to inform drug labeling in this special population. This article is protected by copyright. All rights reserved.

Review of paediatric gastrointestinal physiology relevant to the absorption of orally administered medicines

Despite much progress in regulations to improve paediatric drug development, there remains a significant need to develop better medications for children. For the design of oral dosage forms, a detailed understanding of the specific gastrointestinal (GI) conditions in children of different age categories and how they differ from GI conditions in adults is essential. Several review articles have been published addressing the ontogeny of GI characteristics, including luminal conditions in the GI tract of children. However, the data reported in most of these reviews are of limited quality because (1) information was cited from very old publications and sometimes low quality sources, (2) data gaps in the original data were filled with textbook knowledge, (3) data obtained on healthy and sick children were mixed, (4) average data obtained on groups of patients were mixed with data obtained on individual patients, and (5) results obtained using investigative techniques that may have altered the outcome of the respective studies were considered. Consequently, many of these reviews draw conclusions that may be incorrect. The aim of the present review was to provide a comprehensive and updated overview of the available original data on the ontogeny of GI luminal conditions relevant to oral drug absorption in the paediatric population. To this end, the PubMed and Web of Science metadatabases were searched for appropriate studies that examined age-related conditions in the oral cavity, esophagus, stomach, small intestine, and colon. Maturation was observed for several GI parameters, and corresponding data sets were identified for each paediatric age group. However, it also became clear that the ontogeny of several GI traits in the paediatric population is not yet known. The review article provides a robust and valuable data set for the development of paediatric in vitro and in silico biopharmaceutical tools to support the development of age-appropriate dosage forms. In addition, it provides important information on existing data gaps and should provide impetus for further systematic and well-designed in vivo studies on GI physiology in children of specific age groups in order to close existing knowledge gaps and to sustainably improve oral drug therapy in children.

Assessment of Ethanol Exposure from Hand Sanitizer Use and Potential for Developmental Toxicity in Nursing Infants

Ingestion of ethanol during pregnancy is known to have detrimental effects on the fetus. Although the potential developmental effects of maternal ethanol intake during lactation are less well characterized, public health guidelines recommend avoidance of alcohol or, if alcohol is consumed, to allow for 1–2 h to pass before nursing. A proposal to classify ethanol as potentially harmful to breast‐fed children warrants an investigation of the potential adverse neurodevelopmental effects of low‐dose ethanol exposure during lactation. There currently are no studies that have examined neurodevelopmental outcomes from lactational exposure to ethanol from the use of topical products that contain ethanol, such as alcohol‐based hand sanitizers (ABHS). Furthermore, the epidemiological literature of lactational ethanol exposures from maternal alcohol consumption is limited in design, provides equivocal evidence of neurological effects in infants, and is insufficient to characterize a dose–response relationship for developmental effects. Toxicological studies that observed neurodevelopmental effects in pups from ethanol via lactation did so at exceedingly high doses that also caused maternal toxicity. In this investigation, blood ethanol concentrations (BECs) of breastfeeding women following typical‐to‐intense ABHS use were computationally predicted and compared to health benchmarks to quantify the risk for developmental outcomes. Margins of 2.2 to 1000 exist between BECs associated with ABHS use compared to BECs associated with neurotoxicity adverse effect levels in the toxicology literature or oral ethanol intake per public health guidelines. Neurodevelopmental effects are not likely to occur in infants due to ABHS use by breastfeeding women, even when ABHSs are used at intense frequencies.

Adverse neurodevelopmental effects of ethanol exposure are supported by toxicological studies; ethanol as a potential “lactation hazard” needs evaluation. A lactation hazard may not be applicable for all ethanol‐containing products, such as hand sanitizers. Furthermore, internal dose assessments of hand sanitizer use are lacking. Computationally modeled blood ethanol concentrations following hand sanitizer use are lower than concentrations associated with toxicological and guideline benchmarks. Therefore, there is low potential for the use of ethanol‐containing hand sanitizers to be a lactation hazard.

Plasma concentration profiles for hepatotoxic pyrrolizidine alkaloid senkirkine in humans extrapolated from rat data sets using a simplified physiologically based pharmacokinetic model

AIM

The main aim of the current study was to obtain forward dosimetry assessments of pyrrolizidine alkaloid senkirkine plasma and liver concentrations by setting up a human physiologically based pharmacokinetic (PBPK) model based on the limited information available.

BACKGROUND

The risks associated with plant-derived pyrrolizidine alkaloids as natural toxins have been assessed.

OBJECTIVE

The pyrrolizidine alkaloid senkirkine was investigated because it was analyzed in a European transcriptomics study of natural hepatotoxins and in a study of the alkaloidal constituents of traditional Japanese food plants Petasites japonicus. The in silico human plasma and liver concentrations of senkirkine were modeled using doses reported for acute-term toxicity in humans.

METHODS

Using a simplified PBPK model established using rat pharmacokinetic data, forward dosimetry was conducted. Since in vitro rat and human intrinsic hepatic clearances were similar; an allometric scaling approach was applied to rat parameters to create a human PBPK model.

RESULTS

After oral administration of 1.0 mg/kg in rats in vivo, water-soluble senkirkine was absorbed and cleared from plasma to two orders of magnitude below the maximum concentration in 8 h. Human in silico senkirkine plasma concentration curves were generated after virtual daily oral administrations of 3.0 mg/kg senkirkine (the dose involved in an acute fatal hepatotoxicity case). A high concentration of senkirkine in the culture medium caused in vitro hepatotoxicity as evidenced by lactate dehydrogenase leakage from human hepatocyte-like HepaRG cells.

CONCLUSION

Higher virtual concentrations of senkirkine in human liver and plasma than those in rat plasma were estimated using the current rat and human PBPK models. Current simulations suggest that if P. japonicus (a water-soluble pyrrolizidine alkaloid-producing plant) is ingested daily as food, hepatotoxic senkirkine could be continuously present in human plasma and liver.