Effect of Obesity on the Exposure of Long-acting Cabotegravir and Rilpivirine: A Modeling Study.

BACKGROUND: Obesity is increasingly prevalent among people with human immunodeficiency virus (HIV, PWH). Obesity can reduce drug exposure; however, limited data are available for long-acting (LA) antiretrovirals. We performed in silico trials using physiologically based pharmacokinetic (PBPK) modeling to determine the effect of obesity on the exposure of LA cabotegravir and rilpivirine after the initial injection and after multiple injections. METHODS: Our PBPK model was verified against available clinical data for LA cabotegravir and rilpivirine in normal weight/ overweight (body mass index [BMI] <30 kg/m2) and in obese (BMI >30 kg/m2). Cohorts of virtual individuals were generated to simulate the exposure of LA cabotegravir/rilpivirine up to a BMI of 60 kg/m2. The fold change in LA cabotegravir and rilpivirine exposures (area under the curve [AUC]) and trough concentrations (Cmin) for monthly and bimonthly administration were calculated for various BMI categories relative to normal weight (18.5-25 kg/m2). RESULTS: Obesity was predicted to impact more cabotegravir than rilpivirine with a decrease in cabotegravir AUC and Cmin of >35% for BMI >35 kg/m2 and in rilpivirine AUC and Cmin of >18% for BMI >40 kg/m2 at steady-state. A significant proportion of morbidly obese individuals were predicted to have both cabotegravir and rilpivirine Cmin below the target concentration at steady-state with the bimonthly administration, but this was less frequent with the monthly administration. CONCLUSIONS: Morbidly obese PWH are at risk of presenting suboptimal Cmin for cabotegravir/rilpivirine after the first injection but also at steady-state particularly with the bimonthly administration. Therapeutic drug monitoring is advised to guide dosing interval adjustment.

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.

Assessing and mitigating pH-mediated DDI risks in drug development - formulation approaches and clinical considerations.

pH-mediated drug-drug interactions (DDI) is a prevalent DDI in drug development, especially for weak base compounds with highly pH-dependent solubility. FDA has released a guidance on the evaluation of pH-mediated DDI assessments using in vitro testing and clinical studies. Currently, there is no common practice of ways of testing across the academia and industry. The development of biopredictive method and physiologically-based biopharmaceutics modeling (PBBM) approaches to assess acid-reducing agent (ARA)-DDI have been proven with accurate prediction and could decrease drug development burden, inform clinical design and potentially waive clinical studies. Formulation strategies and careful clinical design could help mitigate the pH-mediated DDI to avoid more clinical studies and label restrictions, ultimately benefiting the patient. In this review paper, a detailed introduction on biorelevant dissolution testing, preclinical and clinical study requirement and PBPK modeling approaches to assess ARA-DDI are described. An improved decision tree for pH-mediated DDI is proposed. Potential mitigations including clinical or formulation strategies are discussed.

Styrene lung cancer risk assessment: an alternative evaluation of human lung cancer risk assuming mouse lung tumors are potentially human relevant and operating by a threshold-based non-genotoxic mode of action.

Rodent inhalation studies indicate styrene is a mouse lung-specific carcinogen. Mode-of-action (MOA) analyses indicate that the lung tumors cannot be excluded as weakly quantitatively relevant to humans due to shared oxidative metabolites detected in rodents and humans. However, styrene also is not genotoxic following in vivo dosing. The objective of this review was to characterize occupational and general population cancer risks by conservatively assuming mouse lung tumors were relevant to humans but operating by a non-genotoxic MOA. Inhalation cancer values reference concentrations for respective occupational and general population exposures (RfCcar-occup and RfCcar-genpop) were derived from initial benchmark dose (BMD) modeling of mouse inhalation tumor dose-response data. An overall lowest BMDL10 of 4.7 ppm was modeled for lung tumors, which was further duration- and dose-adjusted by physiologically based pharmacokinetic (PBPK) modeling to derive RfCcar-occup/genpop values of 6.2 ppm and 0.8 ppm, respectively. With the exception of open-mold fiber reinforced composite workers not using personal protective equipment (PPE), the RfCcar-occup/genpop values are greater than typical occupational and general population human exposures, thus indicating styrene exposures represent a low potential for human lung cancer risk. Consistent with this conclusion, a review of styrene occupational epidemiology did not support a conclusion of an association between styrene exposure and lung cancer occurrence, and further supports a conclusion that the conservatively derived RfCcar-occup is lung cancer protective.

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.

Investigating pharmacokinetic profiles of Centella asiatica using machine learning and PBPK modelling.

Physiologically based pharmacokinetic (PBPK) modeling serves as a valuable tool for determining the distribution and disposition of substances in the body of an organism. It involves a mathematical representation of the interrelationships among crucial physiological, biochemical, and physicochemical parameters. A lack of the values of pharmacokinetic parameters can be challenging in constructing a PBPK model. Herein, we propose an artificial intelligence framework to evaluate a key pharmacokinetic parameter, the intestinal effective permeability (Peff). The publicly available Peff dataset was utilized to develop regression machine learning models. The XGBoost model demonstrates the best test accuracy of R-squared (R2, coefficient of determination) of 0.68. The model is then applied to compute the Peff of asiaticoside and madecassoside, the parent compounds found in Centella asiatica. Subsequently, PBPK modeling was conducted to evaluate the biodistribution of the herbal substances following oral administration in a rat model. The simulation results were evaluated and validated, which agreed with the existing in vivo studies in rats. This in silico pipeline presents a potential approach for investigating the pharmacokinetic parameters and profiles of drugs or herbal substances, which can be used independently or integrated into other modeling systems.

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

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

In Vitro In Vivo Extrapolation and Bioequivalence Prediction for Immediate-Release Capsules of Cefadroxil Based on a Physiologically-Based Pharmacokinetic ACAT Model.

Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic concept, which helps to judge the effects of biopharmceutical properties of drug product such as in vitro dissolution on its pharmacokinetic and in vivo performance. With the application of virtual bioequivalence (VBE) study, the drug product development using model-based approach can help in evaluating the possibility of extending BCS-based biowaiver. Therefore, the current study was intended to develop PBPK model as well as in vitro in vivo extrapolation (IVIVE) for BCS class III drug i.e. cefadroxil. A PBPK model was created in GastroPlus™ 9.8.3 utilizing clinical data of immediate-release cefadroxil formulations. By the examination of simulated and observed plasma drug concentration profiles, the predictability of the proposed model was assessed for the prediction errors. Furthermore, mechanistic deconvolution was used to create IVIVE, and the plasma drug concentration profiles and pharmacokinetic parameters were predicted for different virtual formulations with variable cefadroxil in vitro release. Virtual bioequivalence study was also executed to assess the bioequivalence of the generic verses the reference drug product (Duricef®). The developed PBPK model satisfactorily predicted Cmax and AUC0-t after cefadroxil single and multiple oral dose administrations, with all individual prediction errors within the limits except in a few cases. Second order polynomial correlation function obtained accurately predict in vivo drug release and plasma concentration profile of cefadroxil test and reference (Duricef®) formulation. The VBE study also proved test formulation bioequivalent to reference formulation and the statistical analysis on pharmacokinetic parameters reported 90% confidence interval for Cmax and AUC0-t in the FDA acceptable limits. The analysis found that a validated and verified PBPK model with a mechanistic background is as a suitable approach to accelerate generic drug development.

Management of Drug-Drug Interactions Between Long-Acting Cabotegravir and Rilpivirine and Comedications With Inducing Properties: A Modeling Study.

BACKGROUND: Long-acting (LA) intramuscular cabotegravir and rilpivirine are prone to drug-drug interactions (DDI). However, given the long dosing interval, the conduct of clinical DDIs studies with LA antiretrovirals is challenging. We performed virtual clinical DDI studies using physiologically based pharmacokinetic (PBPK) modeling to provide recommendations for the management of DDIs with strong or moderate inducers such as rifampicin or rifabutin. METHODS: Each DDI scenario included a cohort of virtual individuals (50% female) between 20 and 50 years of age with a body mass index of 18-30 kg/m2. Cabotegravir and rilpivirine were given alone and in combination with rifampicin or rifabutin. The predictive performance of the PBPK model to simulate cabotegravir and rilpivirine pharmacokinetics after oral and intramuscular administration and to reproduce DDIs with rifampicin and rifabutin was first verified against available observed clinical data. The verified model was subsequently used to simulate unstudied DDI scenarios. RESULTS: At steady state, the strong inducer rifampicin was predicted to decrease the area under the curve (AUC) of LA cabotegravir by 61% and rilpivirine by 38%. An increase in the dosing frequency did not overcome the DDI with rifampicin. The moderate inducer rifabutin was predicted to reduce the AUC of LA cabotegravir by 16% and rilpivirine by 18%. The DDI with rifabutin can be overcome by administering LA cabotegravir/rilpivirine monthly together with a daily oral rilpivirine dose of 25 mg. CONCLUSIONS: LA cabotegravir/rilpivirine should be avoided with strong inducers but coadministration with moderate inducers is possible by adding oral rilpivirine daily dosing to the monthly injection.

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 AUC0-∞ 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 AUC0-∞ 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.

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.

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.

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.

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).

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 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.

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.

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.

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.

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.