Assessing transporter-mediated rifampin-linezolid interaction using physiologically-based pharmacokinetic modelling.

The potential of inhibitory metabolites of perpetrator drugs to contribute to drug-drug interactions (DDIs) is uncommon and underestimated. However, the occurrence of unexpected DDI suggests the potential contribution of metabolites to the observed DDI. The aim of this study was to develop a physiologically-based pharmacokinetic (PBPK) model for bupropion and its three primary metabolites—hydroxybupropion, threohydrobupropion and erythrohydrobupropion—based on a mixed “bottom-up” and “top-down” approach and to contribute to the understanding of the involvement and impact of inhibitory metabolites for DDIs observed in the clinic. PK profiles from clinical researches of different dosages were used to verify the bupropion model. Reasonable PK profiles of bupropion and its metabolites were captured in the PBPK model. Confidence in the DDI prediction involving bupropion and co-administered CYP2D6 substrates could be maximized. The predicted maximum concentration (Cmax) area under the concentration-time curve (AUC) values and Cmax and AUC ratios were consistent with clinically observed data. The addition of the inhibitory metabolites into the PBPK model resulted in a more accurate prediction of DDIs (AUC and Cmax ratio) than that which only considered parent drug (bupropion) P450 inhibition. The simulation suggests that bupropion and its metabolites contribute to the DDI between bupropion and CYP2D6 substrates. The inhibitory potency from strong to weak is hydroxybupropion, threohydrobupropion, erythrohydrobupropion, and bupropion, respectively. The present bupropion PBPK model can be useful for predicting inhibition from bupropion in other clinical studies. This study highlights the need for caution and dosage adjustment when combining bupropion with medications metabolized by CYP2D6. It also demonstrates the feasibility of applying the PBPK approach to predict the DDI potential of drugs undergoing complex metabolism, especially in the DDI involving inhibitory metabolites.

Pomotrelvir is a new chemical entity and potent direct-acting antiviral inhibitor of the main protease of coronaviruses. Here the cytochrome P450 (CYP)-mediated drug-drug interaction (DDI) potential of pomotrelvir was evaluated for major CYP isoforms, starting with in vitro assays followed by the basic static model assessment. The identified CYP3A4-mediated potential DDIs were evaluated clinically at a supratherapeutic dose of 1050 mg twice daily (b.i.d.) of pomotrelvir, including pomotrelvir coadministration with ritonavir (strong inhibitor of CYP3A4) or midazolam (sensitive substrate of CYP3A4). Furthermore, a physiologically-based pharmacokinetic (PBPK) model was developed within the Simcyp Population-based Simulator using in vitro and in vivo information and validated with available human pharmacokinetic (PK) data. The PBPK model was simulated to assess the DDI potential for CYP isoforms that pomotrelvir has shown a weak to moderate DDI in vitro and for CYP3A4 at the therapeutic dose of 700 mg b.i.d. To support the use of pomotrelvir in women of childbearing potential, the impact of pomotrelvir on the exposure of the representative oral hormonal contraceptive drugs ethinyl estradiol and levonorgestrel was assessed using the PBPK model. The overall assessment suggested weak inhibition of pomotrelvir on CYP3A4 and minimal impact of a strong CYP3A4 inducer or inhibitor on pomotrelvir PK. Therefore, pomotrelvir is not anticipated to have clinically meaningful DDIs at the clinical dose. These comprehensive in vitro, in clinic, and in silico efforts indicate that the DDI potential of pomotrelvir is minimal, so excluding patients on concomitant medicines in clinical studies would not be required.

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.

GDC-0810 was under development as an oral anti-cancer drug for the treatment of estrogen receptor-positive breast cancer as a single agent or in combination. In vitro data indicated that GDC-0810 is a potent inhibitor of OATP1B1/1B3. To assess clinical risk, a PBPK model was developed to predict the transporter drug-drug interaction (tDDI) between GDC-0810 and pravastatin in human. The PBPK model was constructed in Simcyp® by integrating in vitro and in vivo data for GDC-0810. The prediction of human pharmacokinetics (PK) was verified using GDC-0810 phase I clinical PK data. The Simcyp transporter DDI model was verified using known OATP1B1/1B3 inhibitors (rifampicin, cyclosporine and gemfibrozil) and substrate (pravastatin), prior to using the model to predict GDC-0810 tDDI. The effect of GDC-0810 on pravastatin PK was then predicted based on the proposed clinical scenarios. Sensitivity analysis was conducted on the parameters with uncertainty. The developed PBPK model described the PK profile of GDC-0810 reasonably well. In the tDDI verification, the model reasonably predicted pravastatin tDDI caused by rifampicin and gemfibrozil OATP1B1/3 inhibition but under-predicted tDDI caused by cyclosporine. The effect of GDC-0810 on pravastatin PK was predicted to be low to moderate (pravastatin Cmax ratios 1.01-2.05 and AUC ratio 1.04-2.23). The observed tDDI (Cmax ratio 1.20 and AUC ratio 1.41) was within the range of the predicted values. This work demonstrates an approach using a PBPK model to prospectively assess tDDI caused by a new chemical entity as an OATP1B1/3 uptake transporter inhibitor to assess clinical risk and to support development strategy.

A physiologically based pharmacokinetic (PBPK) model was developed to evaluate and predict (1) the effect of concomitant cytochrome P450 3A (CYP3A) inhibitors or inducers on the exposures of zanubrutinib, (2) the effect of zanubrutinib on the exposures of CYP3A4, CYP2C8, and CYP2B6 substrates, and (3) the impact of gastric pH changes on the pharmacokinetics of zanubrutinib. The model was developed based on physicochemical and in vitro parameters, as well as clinical data, including pharmacokinetic data in patients with B‐cell malignancies and in healthy volunteers from two clinical drug‐drug interaction (DDI) studies of zanubrutinib as a victim of CYP modulators (itraconazole, rifampicin) or a perpetrator (midazolam). This PBPK model was successfully validated to describe the observed plasma concentrations and clinical DDIs of zanubrutinib. Model predictions were generally within 1.5‐fold of the observed clinical data. The PBPK model was used to predict untested clinical scenarios; these simulations indicated that strong, moderate, and mild CYP3A inhibitors may increase zanubrutinib exposures by approximately four‐fold, two‐ to three‐fold, and <1.5‐fold, respectively. Strong and moderate CYP3A inducers may decrease zanubrutinib exposures by two‐ to three‐fold or greater. The PBPK simulations showed that clinically relevant concentrations of zanubrutinib, as a DDI perpetrator, would have no or limited impact on the enzyme activity of CYP2B6 and CYP2C8. Simulations indicated that zanubrutinib exposures are not impacted by acid‐reducing agents. Development of a PBPK model for zanubrutinib as a DDI victim and perpetrator in parallel can increase confidence in PBPK models supporting zanubrutinib label dose recommendations.

Physiologically based pharmacokinetic (PBPK) modeling of drug disposition and drug-drug interactions (DDIs) has become a key component of drug development. PBPK modeling has also been considered as an approach to predict drug disposition in special populations. However, whether models developed and validated in healthy populations can be extrapolated to special populations is not well established. The goal of this study was to determine whether a drug-specific PBPK model validated using healthy populations could be used to predict drug disposition in specific populations and in organ impairment patients. A full PBPK model of atomoxetine was developed using a training set of pharmacokinetic (PK) data from CYP2D6 genotyped individuals. The model was validated using drug-specific acceptance criteria and a test set of 14 healthy subject PK studies. Population PBPK models were then challenged by simulating the effects of ethnicity, DDIs, pediatrics, and renal and hepatic impairment on atomoxetine PK. Atomoxetine disposition was successfully predicted in 100% of healthy subject studies, 88% of studies in Asians, 79% of DDI studies, and 100% of pediatric studies. However, the atomoxetine area under the plasma concentration versus time curve (AUC) was overpredicted by 3- to 4-fold in end stage renal disease and hepatic impairment. The results show that validated PBPK models can be extrapolated to different ethnicities, DDIs, and pediatrics but not to renal and hepatic impairment patients, likely due to incomplete understanding of the physiologic changes in these conditions. These results show that systematic modeling efforts can be used to further refine population models to improve the predictive value in this area.

Background/Objectives: SPT-07A, a D-borneol, is currently being developed in China for the treatment of ischemic stroke. We aimed to create a whole-body physiologically-based pharmacokinetic (PBPK) model to predict the pharmacokinetics of SPT-07A in rats, dogs, and humans. Methods: The in vitro metabolism of SPT-07A was studied using hepatic, renal, and intestinal microsomes. The pharmacokinetics of SPT-07A in rats were simulated using the developed PBPK model and in vitro data. Following validation using pharmacokinetic data in rats, the developed PBPK model was scaled up to dogs and humans. Results: Data from hepatic microsomes revealed that SPT-07A was primarily metabolized by UDP-glucuronosyltransferase (UGTs). Glucuronidation of SPT-07A also occurred in the kidney and intestine. The in vitro to in vivo extrapolation analysis showed that hepatic clearance of SPT-07A in rats, dogs, and humans accounted for 62.2%, 87.3%, and 76.5% of the total clearance, respectively. The renal clearance of SPT-07A in rats, dogs, and humans accounted for 32.6%, 12.7%, and 23.1% of the total clearance, respectively. Almost all of the observed concentrations of SPT-07A following single or multi-dose to rats, dogs, and humans were within the 5th-95th percentiles of simulations from 100 virtual subjects. Sensitivity analysis showed that hepatic metabolic velocity, renal metabolic velocity, and hepatic blood flow remarkably affected the exposure to SPT-07A in humans. Dedrick plots were also used to predict the pharmacokinetics of SPT-07A in humans. Prediction accuracy using the PBPK model is superior to that of Dedrick plots. Conclusions: We elucidate UGT-mediated SPT-07A metabolism in the liver, kidney, and intestine of rats, dogs, and humans. The pharmacokinetics of SPT-07A were successfully simulated using the developed PBPK model.

Membrane transporters play an important role in intestinal absorption, distribution and clearance of drugs. Additionally transporters along with enzymes regulate tissue exposures (e.g. liver, kidney and brain), which are important for safety and efficacy considerations. Early identification of transporters involved guides generation of in vitro and in vivo data needed to gain mechanistic understanding on the role of transporters in organ clearance, tissue exposures and enables development of physiological-based pharmacokinetic (PBPK) models. A lot of progress has been made in developing several in vitro assay systems and mechanistic in silico models to determine kinetic parameters for transporters, which are incorporated into PBPK models. Although, intrinsic clearance and inhibition data from in vitro systems generally tend to underpredict in vivo clearance and magnitude of drug-drug interactions (DDIs), empirical scaling factors derived from a sizable dataset are often used to offset underpredictions. PBPK models are increasing used to predict the impact of transporters on intestinal absorption, clearance, victim and perpetrator DDIs prior to first in human clinical trials. The models are often refined when clinical data is available and are used to predict pharmacokinetics in untested scenarios such as the impact of polymorphisms, ontogeny, ethnicity, disease states and DDIs with other perpetrator drugs. The aim of this review is to provide an overview of (i) regulatory requirements around transporters, (ii) in vitro systems and their limitations in predicting transporter mediated drug disposition and DDIs, (iii) PBPK modelling tactics and case studies used for internal decision making and/or for regulatory submissions.

The new adjuvant chemotherapy of docetaxel, epirubicin, and cyclophosphamide has been recommended for treating breast cancer. It is necessary to investigate the potential drug-drug Interactions (DDIs) since they have a narrow therapeutic window in which slight differences in exposure might result in significant differences in treatment efficacy and tolerability. To guide clinical rational drug use, this study aimed to evaluate the DDI potentials of docetaxel, cyclophosphamide, and epirubicin in cancer patients using physiologically based pharmacokinetic (PBPK) models. The GastroPlus™ was used to develop the PBPK models, which were refined and validated with observed data. The established PBPK models accurately described the pharmacokinetics (PKs) of three drugs in cancer patients, and the predicted-to-observed ratios of all the PK parameters met the acceptance criterion. The PBPK model predicted no significant changes in plasma concentrations of these drugs during co-administration, which was consistent with the observed clinical phenomenon. Besides, the verified PBPK models were then used to predict the effect of other Cytochrome P450 3A4 (CYP3A4) inhibitors/inducers on these drug exposures. In the DDI simulation, strong CYP3A4 modulators changed the exposure of three drugs by 0.71-1.61 fold. Therefore, patients receiving these drugs in combination with strong CYP3A4 inhibitors should be monitored regularly to prevent adverse reactions. Furthermore, co-administration of docetaxel, cyclophosphamide, or epirubicin with strong CYP3A4 inducers should be avoided. In conclusion, the PBPK models can be used to further investigate the DDI potential of each drug and to develop dosage recommendations for concurrent usage by additional perpetrators or victims.

Systems pharmacokinetic (PK) models that can characterize and predict whole body disposition of antibody-drug conjugates (ADCs) are needed to support (i) development of reliable exposure-response relationships for ADCs and (ii) selection of ADC targets with optimal tumor and tissue expression profiles. Towards this goal, we have developed a translational physiologically based PK (PBPK) model for ADCs, using T-DM1 as a tool compound. The preclinical PBPK model was developed using rat data. Biodistribution of DM1 in rats was used to develop the small molecule PBPK model, and the PK of conjugated trastuzumab (i.e., T-DM1) in rats was characterized using platform PBPK model for antibody. Both the PBPK models were combined via degradation and deconjugation processes. The degradation of conjugated antibody was assumed to be similar to a normal antibody, and the deconjugation of DM1 from T-DM1 in rats was estimated using plasma PK data. The rat PBPK model was translated to humans to predict clinical PK of T-DM1. The translation involved the use of human antibody PBPK model to characterize the PK of conjugated trastuzumab, use of allometric scaling to predict human clearance of DM1 catabolites, and use of monkey PK data to predict deconjugation of DM1 in the clinic. PBPK model-predicted clinical PK profiles were compared with clinically observed data. The PK of total trastuzumab and T-DM1 were predicted reasonably well, and slight systemic deviations were observed for the PK of DM1-containing catabolites. The ADC PBPK model presented here can serve as a platform to develop models for other ADCs.

Physiologically-based pharmacokinetic (PBPK) modeling has become the established method for predicting human pharmacokinetics (PK) and drug-drug interactions (DDI). The number of drugs cleared by non-CYP enzyme metabolism has increased steadily and to date, there is no consolidated overview of PBPK modeling for drugs cleared by non-CYP enzymes. This review aims to describe the state-of-the-art for PBPK modeling for drugs cleared via non-CYP enzymes, to identify successful strategies, to describe gaps and to provide suggestion to overcome them. To this end, we conducted a detailed literature search and found 58 articles published before the 1st of January 2023 containing 95 examples of clinical PBPK models for 62 non-CYP enzyme substrates. Reviewed articles covered the drug clearance by uridine 5'-diphospho-glucuronosyltransferases (UGTs), aldehyde oxidase (AO), flavin-containing monooxygenases (FMOs), sulfotransferases (SULTs) and carboxylesterases (CES), with UGT2B7, UGT1A9, CES1, FMO3 and AO being the enzymes most frequently involved. In vitro-in vivo extrapolation (IVIVE) of intrinsic clearance and the bottom-up PBPK modeling involving non-CYP enzymes remains challenging. We observed that the middle-out modeling approach was applied in 80% of the cases, with metabolism parameters optimized in 73% of the models. Our review could not identify a standardized approach used for model optimization based on clinical data, with manual optimization employed most frequently. Successful development of models for UGT2B7, UGT1A9, CES1, and FMO3 substrates provides a foundation for other drugs metabolized by these enzymes and guides the way forward in creating PBPK models for other enzymes in these families. Significance Statement Our review charts the rise of PBPK modeling for drugs cleared by non-CYP enzymes. Analyzing 58 articles and 62 non-CYP enzyme substrates, we found that UGTs, AO, FMOs, SULTs, and CES were the main enzyme families involved and that UGT2B7, UGT1A9, CES1, FMO3 and AO are the individual enzymes with the strongest PBPK modeling precedents. Approaches established for these enzymes can now be extended to additional substrates and to drugs metabolized by enzymes that are similarly well characterized.

The activity changes of cytochrome P450 (CYP450) enzymes, along with the complicated medication scenarios in diabetes mellitus (DM) patients, result in the unanticipated pharmacokinetics (PK), pharmacodynamics (PD), and drug-drug interactions (DDIs). Physiologically based pharmacokinetic (PBPK) modeling has been a useful tool for assessing the influence of disease status on CYP enzymes and the resulting DDIs. This work aims to develop a novel diabetic PBPK population model to facilitate the prediction of PK and DDI in DM patients. First, mathematical functions were constructed to describe the demographic and non-CYP physiological characteristics specific to DM, which were then incorporated into the PBPK model to quantify the net changes in CYP enzyme activities by comparing the PK of CYP probe drugs in DM versus non-DM subjects. The results show that the enzyme activity is reduced by 32.3% for CYP3A4/5, 39.1% for CYP2C19, and 27% for CYP2B6, while CYP2C9 activity is enhanced by 38% under DM condition. Finally, the diabetic PBPK model was developed through integrating the DM-specific CYP activities and other parameters and was further used to perform PK simulations under 12 drug combination scenarios, among which 3 combinations were predicted to result in significant PK changes in DM, which may cause DDI risks in DM patients. The PBPK modeling applied herein provides a quantitative tool to assess the impact of disease factors on relevant enzyme pathways and potential disease-drug-drug-interactions (DDDIs), which may be useful for dosing regimen optimization and minimizing the DDI risks associated with the treatment of DM.

Allosteric oncogenic mutations occur outside the canonical ATP-binding site of EGFR and HER2, for which there are no approved single agent therapies that target this family. BDTX-189 is a potent, selective, irreversible inhibitor of the family of nearly 50 allosteric EGFR and HER2 mutant variants. The goal of this translational analysis was to predict the clinical pharmacokinetic (PK) profile of BDTX-189 utilizing in vitro data on the absorption, distribution and metabolism of BDTX-189 as well as in vivo PK data in preclinical species. The prospective PK modeling was conducted prior to initiation of a Phase 1/2 study, to provide predictions of clinical exposures and active dose range. A challenge in the design of irreversible inhibitors with optimal PK properties is the lack of reliable methods to predict their disposition and elimination in human. The PK of covalent drugs is often driven by extrahepatic elimination pathways, and therefore conventional approaches to predict human clearance using human hepatocytes or allometric scaling can lead to poor predictive accuracy. We employed a novel physiologically-based PK (PBPK) modeling strategy that accounted for compound-specific determinants of BDTX-189 metabolism and disposition. PK studies following intravenous (IV) and oral (PO) administration were conducted in preclinical species as well as in vitro studies to understand the ADME properties of BDTX-189. These preclinical data formed the basis of a PBPK modeling approach to predict the likely PK profile of BDTX-189 in human. The mechanistic assumptions used in the final models were able to recapitulate the observed animal PK after both IV and PO administration and thus predictions utilizing similar assumptions for human were considered plausible. Taken together with BDTX-189 exposure-response data in mouse models of anti-tumor efficacy, this enabled the prediction of potentially active dose levels in patients. Preclinical PBPK modeling indicated that BDTX-189 would be readily orally absorbed with a short elimination half-life (approximately 2 hours) while maintaining suppression of ErbB pathway biomarkers over the dosing interval, consistent with the irreversible mechanism of action and the desired ‘hit-and-run' PK/pharmacodynamic (PD) profile. Active dose levels in human were projected to be in the 400 - 800 mg QD range, based on the exposure - tumor growth inhibition relationship in multiple mouse PDX models harboring ErbB allosteric mutations. This study demonstrates that a PBPK modeling approach and an understanding of the determinants of clearance can provide an effective framework for preclinical-to-clinical translation. BDTX-189 is currently under clinical evaluation in the ongoing MasterKey-01 trial (NCT04209465), and clinical PK will be reported in due course. Citation Format: Giorgio Ottaviani, Matthew O'Connor, Alexander Flohr, Darlene Romashko, Alexander Mayweg, Elizabeth Buck, Nigel Waters. Prospective preclinical modeling to estimate clinical pharmacokinetics and doses of BDTX-189, an inhibitor of allosteric ErbB mutations in advanced solid malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB127.

Zastaprazan (JP-1366) is a new potassium-competitive acid blocker being developed for treating gastrointestinal reflux disease. It is an orally administered small molecule that inhibits gastric H+ and K+-ATPases differently from proton pump inhibitors, which act quickly and have dose-dependent effects on acid secretion. Celecoxib, a selective cyclooxygenase 2 inhibitor, will likely be used with zastaprazan in clinical settings and trials. The objective of current physiologically based pharmacokinetic (PBPK) modeling study is to predict drug-drug interaction (DDI) risk between zastaprazan (perpetrator) and celecoxib (victim). A human PBPK model for zastaprazan was built using experimental physicochemical properties and <i>in silico</i> predictions. The model was optimized with clinical pharmacokinetic (PK) data from a phase 1 study (Protocol No. JP-1366-105). The PBPK model for celecoxib was constructed using the data from previous studies and <i>in silico</i> predictions. The final PBPK model encompassing zastaprazan and celecoxib was used to quantitatively predicted DDI risks in humans. The final PBPK models accurately predicted zastaprazan's PK profiles after single dose in human, and it also well predicted plasma celecoxib concentrations over time. At doses of 20 mg of zastaprazan citrate (JAQBO^® tablet) and 200 mg of celecoxib, multiple oral doses of zastaprazan every 24 hours for 7 days did not increase celecoxib's area under the curve (AUC) and maximum plasma concentration (C_max), with ratios of 1 in both AUC and C_max, indicating no effect of zastaprazan on celecoxib's PK. The PBPK modeling approach provides scientific predictions of DDIs between zastaprazan and celecoxib, guiding future clinical development.

Conducting clinical trials to understand the exposure risk/benefit relationship of cannabis use is not always feasible. Alternatively, physiologically based pharmacokinetic (PBPK) models can be used to predict exposure of the psychoactive cannabinoid (-)-Δ9-tetrahydrocannabinol (THC) and its active metabolite 11-hydroxy-Δ9-tetrahydrocannabinol (11-OH-THC). Here, we first extrapolated in vitro mechanistic pharmacokinetic information previously quantified to build a linked THC/11-OH-THC PBPK model and verified the model with observed data after intravenous and inhalation administration of THC in a healthy, nonpregnant population. The in vitro to in vivo extrapolation of both THC and 11-OH-THC disposition was successful. The inhalation bioavailability (Finh) of THC after inhalation was higher in chronic versus casual cannabis users (Finh = 0.35 and 0.19, respectively). Sensitivity analysis demonstrated that 11-OH-THC but not THC exposure was sensitive to alterations in hepatic intrinsic clearance of the respective compound. Next, we extrapolated the linked THC/11-OH-THC PBPK model to pregnant women. Simulations showed that THC plasma area under the curve (AUC) does not change during pregnancy, but 11-OH-THC plasma AUC decreases by up to 41%. Using a maternal-fetal PBPK model, maternal and fetal THC serum concentrations were simulated and compared with the observed THC serum concentrations in pregnant women at term. To recapitulate the observed THC fetal serum concentrations, active placental efflux of THC needed to be invoked. In conclusion, we built and verified a linked THC/11-OH-THC PBPK model in healthy nonpregnant population and demonstrated how this mechanistic physiologic and pharmacokinetic platform can be extrapolated to a special population, such as pregnant women. SIGNIFICANCE STATEMENT: Although the pharmacokinetics of cannabinoids have been extensively studied clinically, limited mechanistic pharmacokinetic models exist. Here, we developed and verified a physiologically based pharmacokinetic (PBPK) model for (-)-Δ9-tetrahydrocannabinol (THC) and its active metabolite, 11-hydroxy-Δ9-tetrahydrocannabinol (11-OH-THC). The PBPK model was verified in healthy, nonpregnant population after intravenous and inhalation administration of THC, and then extrapolated to pregnant women. The THC/11-OH-THC PBPK model can be used to predict exposure in special populations, predict drug-drug interactions, or impact of genetic polymorphism.