Across-Species Meta-Analysis of Betamethasone Pharmacokinetics Utilizing a Minimal PBPK Model.

The aim of this study was to develop a two-pore minimum physiologically-based pharmacokinetic (mPBPK) model in describing the pharmacokinetic (PK) of therapeutic monoclonal antibody (TMAb) in human subjects. PK data used in this study were endogenous/exogenous native IgG and two TMAbs (palivizumab and Motavizumab-YTE) in normal volunteer or familial hypercatabolic hypoproteinemia (FIHH) patient. Several important components were implemented to overcome the limitations of the early mPBPK model, e.g. two-pore model to describe the transcapillary transport of IgG from vascular to interstitial space. Six mPBPK models with different osmotic reflection coefficient (OFC) of transcapillary transport, endocytosis rates (ETR) and plasma clearance for the TMAbs/IgG were tested and the best model was selected using AICc values. The final model consisted of different OFC and ETR values for native IgG and TMAbs, supporting the hypothesis that the dynamics in the endosomal space had an important role in the compliant FcRn salvage mechanism to determine the clearance of TMAbs. The estimated FcRn concentration of FIHH subjects was 2.72μmol/l. The final two-pore mPBPK model has a better performance for native IgG than previously developed mPBPK model. The final two-pore mPBPK model not only overcome the limitations of the early mPBPK model but also has a better performance to describe the disposition of the IgG antibody in human subjects.

Minimal physiologically-based pharmacokinetic (mPBPK) models are an alternative to full physiologically-based pharmacokinetic (PBPK) models as they offer reduced complexity while maintaining the physiological interpretation of key model components. Full PBPK models have been developed for pregnancy, but a mPBPK model eases the ability to perform a "top-down" meta-analysis melding all available pharmacokinetic (PK) data in the mother and fetus. Our hybrid mPBPK model consists of mPBPK models for the mother and fetus with connection by the placenta. This model was applied to describe the rich PK data of antenatal corticosteroid betamethasone (BET) jointly with the limited data for dexamethasone (DEX) in the mother and fetus. Physiologic model parameters were obtained from the literature while drug-dependent parameters were estimated by the simultaneous fitting of all available data for DEX and BET. Maternal clearances of DEX and BET confirmed the literature values, and the expected fetal-to-maternal plasma ratios ranged from 0.3 to 0.4 for both drugs. Simulations of maternal plasma concentrations for the dosing regimens of BET and DEX recommended by the World Health Organization based on our findings revealed up to 60% lower exposures than found in nonpregnant women and offers a means of devising alternative dosing regimens. Our hybrid mPBPK model and meta-analysis approach could facilitate assessment of other classes of drugs indicated for the treatment of pregnant women.

The pharmacokinetic (PK) parameters of dexamethasone (DEX) in 11 species were collected from the literature and clearances (CL) assessed by basic allometric methods, and concentration-time course profiles were fitted using two PK models incorporating physiological or allometric scaling. Plots of log CL vs. log body weights (BW) correlated reasonably with R2 =0.91, with a maximum ratio of actual to fitted CL of 6 (for pig). A minimal physiologically-based pharmacokinetic (mPBPK) model containing blood and two lumped tissue compartments and integrated utilization of physiological parameters was compared to an allometric two-compartment model (a2CM). The plasma PK profiles of DEX from 11 species were analyzed jointly, with the mPBPK model having conserved partition coefficients (Kp ), physiologic blood and tissue volumes, and species-specific CL values. The DEX PK profiles were reasonably captured by the mPBPK model for 9 of 11 species in the joint analysis with three fitted parameters (besides CL) including an overall tissue-to-plasma partition coefficient of 1.07. The a2CM with distribution CL and central and peripheral volumes scaled allometrically fitted the plasma concentration profiles similarly but required a total of six parameters (besides CL). Overall, the literature reported that DEX CL values exhibit moderate variability (mean=0.64L/h/kg; coefficient of variation=105%), but distribution parameters were largely conserved across most species.

There is a growing interest in the use of physiologically based pharmacokinetic (PBPK) models as clinical pharmacology drug development tools. In PBPK modeling, not every organ or physiological parameter is required, leading to the development of a minimal PBPK (mPBPK) model, which is simple and efficient. The objective of this study was to streamline mPBPK modeling approaches and enable straightforward prediction of clearance of protein-based products in children. Four mPBPK models for scaling clearance from adult to children were developed and evaluated on Excel spreadsheets using (1) liver and kidneys; (2) liver, kidneys, and skin; (3) liver, kidneys, skin, and lymph; and (4) interstitial, lymph, and plasma volume. There were 35 therapeutic proteins with a total of 113 observations across different age groups (premature neonates to adolescents). For monoclonal and polyclonal antibodies, more than 90% of observations were within a 0.5- to 2-fold prediction error for all 4 methods. For nonantibodies, 79% to 100% of observations were within the 0.5- to 2-fold prediction error for the 4 different methods. Methods 1 and 4 provided the best results, >90% of the total observations were within the 0.5- to 2-fold prediction error for all 3 classes of protein-based products across a wide age range. The precision of clearance prediction was comparatively lower in children ≤2 years of age vs older children (>2 years of age) with methods 1 and 4 predicting 80% to 100% and 75% to 90% of observations within the 0.5- to 2-fold prediction error, respectively. The results of the study indicated that mPBPK models can be developed on spreadsheets, with acceptable performance for prediction of clearance.

The objective of this study was to systematically assess literature datasets and quantitatively analyze metformin PK in plasma and some tissues of nine species. The pharmacokinetic (PK) parameters and profiles of metformin in nine species were collected from the literature. Based on a simple allometric scaling, the systemic clearances () of metformin in these species highly correlate with body weight () (R2 = 0.85) and are comparable to renal plasma flow in most species except for rabbit and cat. Reported volumes of distribution () varied appreciably (0.32 to 10.1 L/kg) among species. Using the physiological and anatomical variables for each species, a minimal physiologically based pharmacokinetic (mPBPK) model consisting of blood and two tissue compartments (Tissues 1 and 2) was used for modeling metformin PK in the nine species. Permeability-limited distribution (low and ) and a single tissue-to-plasma partition coefficient () value for Tissues 1 and 2 were applied in the joint mPBPK fitting. Nonlinear regression analysis for common tissue distribution parameters along with species-specific values reasonably captured the plasma PK profiles of metformin across most species, except for rat and horse with later time deviations. In separate fittings of the mPBPK model to each species, Tissue 2 was considered as slowly-equilibrating compartment consisting of muscle and skin based on in silico calculations of the mean transit times through tissues. The well-fitted mPBPK model parameters for absorption and disposition PK of metformin for each species were compared with in vitro/in vivo results found in the literature with regard to the physiological details and physicochemical properties of metformin. Bioavailability and absorption rates decreased with the increased among the species. Tissues such as muscle dominate metformin distribution with low permeability and partitioning while actual tissue concentrations found in rats and mice show likely transporter-mediated uptake in liver, kidney, and gastrointestinal tissues. Metformin has diverse pharmacologic actions, and this assessment revealed allometric relationships in its absorption and renal clearance but considerable variability in actual and modeled tissue distribution probably caused by transporter differences.

Intratumoral pharmacokinetic (PK) and pharmacodynamic (PD) heterogeneity contribute to variability in NB tumor response to chemotherapy and can be responsible for tumor relapse. Herein we propose to develop a whole body PBPK model with an individualized tumor compartment to derive individual tumor specific concentration-time profiles for the NB standard of care drug TPT. This model can then relate intratumoral heterogeneity in tumor blood flow to PD response and antitumor effects. PK studies of TPT (0.6, 1.25, 5, and 20 mg/kg, IV bolus) will be performed in CD1 nude mice (n = 3 mice/time point) bearing orthotopic NB (NB5) xenograft. Blood samples will be collected at predetermined time points using cardiac puncture, and plasma separated and stored until analysis. Animals will be perfused using saline solution to remove residual blood, and tissue samples including tumor, muscle, adipose, bone, liver, gallbladder, kidney, spleen, lungs, brain, heart, duodenum, and large intestine collected. TPT concentrations in plasma and tissue homogenate samples will be quantified using a validated HPLC fluorescence spectrophotometry method. Tumor samples will be divided into two sections each, one for TPT quantification and one for immunohistochemistry of PD markers for DNA damage (γ-H2AX) and apoptosis (CASP3). A cohort of mice will be used to quantify tumor blood flow using contrast-enhanced ultrasound (CEUS) using MicroMarker® microbubbles prior to dosing the mice for the PK study. TPT plasma and tissue concentration-time data will be used to develop the whole-body PBPK model with an individualized tumor compartment using NONMEM. Individual tumor perfusion data obtained using CEUS will be combined with the PBPK model to derive tumor specific concentration-time profiles. A preliminary study conducted in non-tumor bearing mice receiving TPT 5 mg/kg showed that TPT plasma and tissue concentration-time data were reasonably described by our PBPK model. As expected from our previous studies, the brain tissue was found to have the lowest exposure to TPT with a brain to plasma partition coefficient (Kp,brain ∼ 8%). We also observed high permeability of TPT (Kp > 1) into the gallbladder, duodenum, large intestine, spleen, liver and kidney. In future we will study the correlations between individual tumor concentrations based on our comprehensive PBPK model and γ-H2AX and CASP3 activity. Citation Format: Yogesh T. Patel, Megan O. Jacus, Abbas Shirinifard, Abigail D. Davis, Suresh Thiagarajan, Stacy L. Throm, Vinay M. Daryani, Andras Sablauer, Clinton F. Stewart. Development of a whole body physiologically-based pharmacokinetic (PBPK) model with individualized tumor compartment for topotecan (TPT) in mice bearing neuroblastoma (NB). [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4519. doi:10.1158/1538-7445.AM2015-4519

Objective: Preclinical studies have demonstrated that CFAK-C4 has anti-tumor efficacy in a variety of malignancies. To maximize its efficacy, it is necessary to understand the pharmacokinetic (PK) properties of CFAK-C4 in the body. A PK study was conducted to characterize CFAK-C4 disposition in plasma and various tissues, including brain, heart, liver, lung, muscle, spleen, and sternum. Subsequently, a physiologically-based pharmacokinetic (PBPK) model was developed to simultaneously characterize and predict plasma and tissue CFAK-C4 concentrations and thus help guide future dosing strategies alone and in combination. Methods: Female CD-1 mice received a single IP injection of CFAK-C4 with a dose of 50mg/kg, and then plasma and tissue samples were collected at serial time points after injection. Three mice were sacrificed at each time point. CFAK-C4 concentrations were determined by a validated LC-MS/MS method. Noncompartmental PK analysis was performed using WinNonlin (Pharsight, Version 5.3) for PK parameters. CFAK-C4 concentration-time profiles were fitted with a PBPK model composed of compartments for plasma, all the measured tissues, peritoneum, and a remainder compartment which represented all other tissues where CFAK-C4 was not measured, using ADAPT5 (BMSR, USC). The PBPK model was assessed by goodness-of-fit plots together with agreement of estimated parameters with noncompartmental analysis. Results: CFAK-C4 concentrations followed a monoexponential decay in plasma, while there was a longer elimination phase observed in tissues. As a result, CFAK-C4 concentration-time profiles in plasma and tissues were simultaneously fitted into a plasma-flow-rate-limited PBPK model successfully. Partition coefficients (Kp), as a measure of the extent of tissue distribution, and plasma clearance (Cl) were estimated by the PBPK model, while the volumes of distribution and plasma flow rates of tissues were fixed to physiological values. The model predicts that CFAK-C4 can be well distributed to various tissues quickly, and Cmax is achieved within half an hour after IP injection. The estimated Cl, 0.111 (±7.4%) l/h, was similar to the value from non-compartmental analysis (NCA) (0.0925 L/h). The model also predicts CFAK-C4 has the highest penetration to lung, with a Kp of 28.1 (±15.2%), followed by brain, with a Kp of 16.6 (±11.6%), and it has the lowest penetration to muscle, with a Kp of 3.6 (±8.3%). Conclusions: The wide tissue distribution of CFAK-C4 provides a great advantage for maximizing its anti-tumor efficacy. The PBPK model predicts CFAK-C4 plasma and tissue concentrations reasonably well. This PBPK model will be used as a tool to build PK/PD models to characterize the temporal relationship between CFAK-C4 pharmacokinetics and its antitumor efficacy and thus help decide future dosing strategies for the treatment of various malignancies. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3786. doi:1538-7445.AM2012-3786

Background: Significant hysteresis between plasma concentration and target inhibition at the effect site (e.g., tumor) is a frequent observation, commonly described mathematically by connecting the central (i.e., plasma) compartment to an ‘effect compartment’ by a ‘link’ which causes the concentration in the latter to be delayed relative to the plasma. The result is a direct response between effect-compartment concentration and target inhibition. A significant drawback is that the effect compartment cannot be observed (making it impossible to validate) and has no physiological interpretation (rendering communication with other disciplines difficult). We develop a novel approach that is more physiologically meaningful, provides more-precise model parameter estimates, and gives insight into the physico-chemical factors limiting distribution into the tumor. Method: We orally administered single doses of several compounds (including Crizotinib, AZD3463, and others) targeting ALK to mice bearing tumors derived from the DEL and H3122 non-small-cell lung cancer line, at several dose levels. At 6, 24, and 48 hours post-dose, we measured the plasma and tumor concentrations of each compound and associated target inhibition (phosphorylated ALK, pALK) in the tumor. pALK inhibition shows a direct response not to plasma, but to tumor concentration, indicating that the delay is distributional in nature. We constructed a miniature physiologically-based pharmacokinetic (mPBPK) model consisting of a central compartment and a tumor of fixed physiological volume. pALK inhibition was modeled as a direct Emax response to tumor concentration. For each compound, we simultaneously fitted the mPBPK model to the naïve-pooled plasma and tumor concentrations, as well as pALK, using all available dose levels. Beyond the standard PK and PD parameters (Emax, E0, IC50) we also fitted the tumor partition constant Kp, and tumor blood flow rate Qt. For comparison, we fitted a standard effect-compartment (‘link’) model to the plasma concentrations and pALK levels to the same data. Results: For each compound, we computed unbound EC50 for both effect-compartment and mPBPK models. We found that while the point estimates largely agree, the mPBPK model delivers more-precise estimates (typically 50% lower CV%). We attribute this to its use of additional data (tumor concentration) to constrain the model, which more than compensates for the additional parameters in the mPBPK model. We find that there is broad consistency in estimates of tumor flow rate Qt across the compounds studied, indicating that distribution from plasma to site of action is limited by blood flow, rather than by permeability. Additionally, we found that the greater physiological interpretability of the mPBPK model enhances cross-functional communication within project teams. Citation Format: Francis D. Gibbons, Dan Widzowski, Minhui Shen, Jane Cheng, Lisa Drew, Jamal C. Saeh, Douglas Ferguson. Miniaturized PBPK model improves pharmacodynamic characterization and physiological interpretability for compounds with profound hysteresis in tumor. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3362. doi:10.1158/1538-7445.AM2013-3362

Pharmacokinetic (PK) data for levamisole, an important immunostimulant and antiparasitic agent, were identified in 18 species providing sufficient PK data following oral (PO) and/or intravenous (IV) administration for assessment and comparison. Pharmacokinetic parameters were sought in all species for traditional allometric assessment. Among these, 2 bird and 6 mammalian species provided sufficient data for joint modeling using traditional compartmental PK and minimal physiologically-based pharmacokinetic (mPBPK) methods. Simple allometric scaling was first used examine clearance (CL), steady-state volume of distribution (V ss ), absorption rate constant (k a ), and bioavailability (F) in relation to body weight (BW) across species. The V ss correlated well with BW (Parameter = α·BW b ) with b = 0.89 (R2 = 0.81) whereas CL (b = 0.26, R2 = 0.46), k a (b = 0.25, R2 = 0.14), and F (b = 0.08, R2 = 0.70) showed weaker correlations with ducks appearing as outliers for CL. Biexponential PK profiles were adequately captured using an allometric two-compartment model (2CM). Joint fitting of IV PK data from 8 species to a generalized mPBPK model, incorporating unified distribution parameters (e.g., tissue partition coefficient K p and fractional distribution parameter f d ), yielded good performance across species. The mPBPK model assuming high tissue permeability and species-specific K p values for pig and chicken (K p, pig and K p, chicken ) best described the observed profiles. Oral bioavailability(F) was highly consistent across all species (50-80%), with the exception of goats. This study demonstrates that levamisole with rapid absorption and extensive metabolism exhibits largely consistent PK properties across species. Minimal PBPK modeling offers advantageous comparison of interspecies determinants of levamisole PK. The online version contains supplementary material available at 10.1007/s40005-025-00770-6.

Assessing Liver-to-Plasma Partition Coefficients and In Silico Calculation Methods: When Does the Hepatic Model Matter in PBPK?

ABSTRACTDSTA4637A, a THIOMAB™ antibody-antibiotic conjugate targeting Staphylococcus aureus, has shown promising bactericidal activity in a mouse model. DSTA4637A consists of a monoclonal anti-S. aureus antibody with an average of two rifalogue antibiotic molecules, dmDNA31, linked to its light chains. The goal of this study was to develop a minimal physiologically-based pharmacokinetic (mPBPK) model to characterize the pharmacokinetic (PK) properties of three analytes of DSTA4637A (i.e., total antibody, antibody-conjugated dmDNA31, and unconjugated dmDNA31) in mice, and to predict pharmacokinetics of DSTA4637A analytes in humans, as well as to provide an initial assessment for potential PK drug-drug interactions (DDI) in clinical trials via cross-species scaling of the mPBPK model. In the proposed model, selected organs, including heart, liver, and kidney, were connected anatomically with plasma and lymph flows. Mouse plasma and tissue concentrations of the three analytes of DSTA4637A were fitted simultaneously to estimate the PK parameters. Cross-species scaling of the model was performed by integrating allometric scaling and human physiological parameters. The final mPBPK model was able to successfully capture PK profiles of three DSTA4637A analytes in mouse plasma and in investigated organs. The model predicted a steady-state peak unbound dmDNA31 concentration lower than 5% of the IC50 of dmDNA31 towards cytochrome P450 following 100 mg/kg weekly intravenous dose, which suggests a low risk of PK DDI in humans for DSTA4637A with co-administered cytochrome P450 substrates. The proposed mPBPK modeling and cross-species scaling approaches provide valuable tools that facilitate the understanding and translation of DSTA4637A disposition from preclinical species to humans.

The objective of this study was to compare the predictive performance of an allometric model with that of a physiologically based pharmacokinetic (PBPK) model to predict clearance or area under the concentration-time curve (AUC) of drugs in subjects from neonates to adolescents. From the literature, 10 studies were identified in which clearance or AUC of drugs from neonates to adolescents was predicted by PBPK models. In these published studies, drugs were given to children either by intravenous or oral route. The allometric model was an age-dependent exponent (ADE) model for the prediction of clearance across the age groups. The predicted clearance or AUC values from the PBPK and ADE models were compared with the experimental values. The acceptable prediction error was the percentage of subjects within an 0.5- to 2-fold or 0.5- to 1.5-fold prediction error. There were 73 drugs with a total of 372 observations. From PBPK and allometric models, 91.1% and 90.6% of observations were within 0.5- to 2-fold prediction error, respectively. For children ≤2 years old (n = 130), PBPK and allometric models had 89% and 87% of observations within the 0.5- to 2-fold prediction error, respectively. This study indicates that the predictive power of PBPK and allometric models was essentially similar for the prediction of clearance or AUC in pediatric subjects ranging from neonates to adolescents.

A parent-metabolite population pharmacokinetic (popPK) model of iberdomide and its pharmacologically active metabolite (M12) was developed and the influence of demographic and disease-related covariates on popPK parameters was assessed based on data from 3 clinical studies of iberdomide (dose range, 0.1-6mg) in healthy subjects (n= 81) and patients with relapsed and refractory multiple myeloma (n245). Nonlinear mixed effects modelling was used to develop the popPK model based on data from 326 subjects across 3 clinical studies. The pharmacokinetics (PK) of iberdomide were adequately described with a 2-compartment model with first-order absorption and elimination. A first-order conversion rate was used to link the 1-compartment linear elimination metabolite model with the parent model. Subject type (multiple myeloma patients vs. healthy subject) was a statistically significant covariate on apparent clearance and apparent volume of distribution for the central compartment, suggesting different PK between patients with multiple myeloma and healthy subjects. Aspartate aminotransferase and sex were statistically but not clinically relevant covariates on apparent clearance. Metabolite (M12) PK tracked the PK of iberdomide. The metabolite to parent ratio was consistent across doses and combinations. The parent-metabolite population PK model adequately described the time course PK data of iberdomide and M12. Iberdomide and M12 PK exposure were not complicated by demographic factors (age [19-82 y], body weight [41-172 kg], body surface area [1.4-2.7m2 ], body mass index [16.4-59.3kg/m2 ]), combination (in combination with dexamethasone and daratumumab), mild hepatic, or mild and moderate renal impairments. The model can be used to guide the dosing strategy for special patient population and inform future iberdomide study design.

ABSTRACTThe aim of this study was to investigate neonatal Fc receptor (FcRn) concentration developmental pharmacology in adult and pediatric subjects using minimal physiologically-based pharmacokinetic (mPBPK) modelling. Three types of pharmacokinetic (PK) data for three agents (endogenous/exogenous native IgG, bevacizumab and palivizumab) were used. The adult group contained six subjects with weights from 50 to 100 kg. For pediatric subjects, seven age groups were assumed, with five subjects each having the weight of 95%, 75%, 50%, 25% and 5% percentile of the population. A first evidence-based rating system to evaluate the quality of the source data used to derive pediatric-specific mPBPK model parameter was proposed. A stepwise approach was used to examine the best combination of age/weight effect on the parameters of the mPBPK model in adult and pediatric subjects. IgG synthesis rate (Ksyn), extravasation rate (ER) and FcRn were fitted simultaneously to the PK of bevacizumab and native-IgG in both adult and pediatric. All fitting showed good fits based on the graphs and the coefficient of variation of the fitted parameters (< 50%). Estimated weight-normalized Ksyn increased while weight-normalized FcRn and ER decreased with increasing age. The age and weight effect on FcRn were successfully estimated from the data. The final mPBPK model developed with native IgG and bevacizumab was able to predict the PK of palivizumab in pediatric subjects. Implementation of the mPBPK model enables us to analyze the relationships of age, weight, FcRn, ER and Ksyn in both adult and pediatric subject. This information may benefit the understanding of complex interaction between the FcRn developmental pharmacology and PK parameters, and improve the prediction of the antibody disposition in pediatric subjects.

Background:Tofacitinib is an oral JAK inhibitor that is being investigated for juvenile idiopathic arthritis (JIA).Objectives:To describe tofacitinib pharmacokinetics (PK) in patients with JIA, identify potential covariates accounting for variability in exposure, assess the formulation effect of oral solution vs tablet and propose a simplified dosing regimen.Methods:This was a pooled analysis of data from 3 tofacitinib clinical studies in patients with JIA aged 2−&lt;18 years: a Phase 1, open-label (OL), non-randomised study (NCT01513902); a Phase 3, randomised, double-blind, placebo-controlled, withdrawal study (NCT02592434); and an OL long-term extension study (NCT01500551). Tofacitinib was dosed at 5 mg twice daily (BID) in patients ≥40 kg or at body weight (BW)-based lower doses BID in patients &lt;40 kg, to achieve average concentrations (Cavg) comparable with those in patients receiving 5 mg BID. A sparse PK sampling scheme was applied, and the plasma samples were assayed using a validated, sensitive and specific high-performance liquid chromatography tandem mass spectrometric method (lower limit of quantification = 0.100 ng/mL). A nonlinear mixed-effects modelling approach was used for the population PK model, and population parameter variability was assumed to be log-normally distributed. Covariates relating to patient demographics, disease characteristics, concomitant medications and formulation (oral solution vs tablet) were selected using a stepwise covariate modelling approach, and parameter-covariate relationships were evaluated using stepwise forward-inclusion (p&lt;0.05) backward-deletion (p&lt;0.001) procedures. The effect of time-varying BW on oral clearance (CL/F) and apparent volume of distribution (V/F) was characterised using an allometric model. Final model quality was assessed by Visual Predictive Checks (VPCs).Results:Of 246 patients in the analysis, 74.0% were female; 87.8% were white, 2% were black, 10.2% were ‘other’ races and no patients were Asian. Median (range) BW was 46.3 (11.1−121.8) kg. Initially, 100 patients received oral solution and 146 patients received tablets; 11 patients switched formulations during the studies. A one compartment disposition model with first-order absorption and a lag time sufficiently described the data. Final estimates for CL/F, V/F and the first-order absorption rate constant (ka) for tablets were 26.1 L/hr, 89.2 L and 2.78 hr-1, respectively. The only statistically significant covariate was a formulation effect on ka. All parameters were estimated adequately. Estimated allometric exponents were 0.310 for CL/F and 0.537 for V/F. Absorption was described with an estimated lag time of 0.186 hr, and the oral solution had a 1.64-fold faster absorption rate vs the tablet. VPCs sufficiently described the observed data over time, across BWs and ages. Given the PK characterisation and variability in patients with JIA, a simplified dosing scheme was proposed, targeting Cavgvalues equivalent to those in patients receiving 5 mg BID: 3.2 mg BID solution in patients 10−&lt;20 kg; 4 mg BID solution in patients 20−&lt;40 kg; and 5 mg BID tablet or solution in patients ≥40 kg.Conclusion:Tofacitinib population PK in patients with JIA were adequately described by a one compartment model parameterised in terms of CL/F, V/F and first-order absorption with a lag time. Drug absorption from the oral solution was faster than from the tablet. Tofacitinib does not require dose modification or restrictions for any covariates, except BW, to account for differences in Cavg. Based on the results of this analysis, a simplified BW-based dosing regimen was proposed.Acknowledgments:Study sponsored by Pfizer Inc. Medical writing support was provided by Sarah Piggott of CMC Connect and funded by Pfizer Inc.Disclosure of Interests:Camille Vong Shareholder of: Pfizer Inc, at time of analysis, Employee of: Pfizer Inc, at time of analysis, Xiaoxing Wang Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Anasuya Hazra Shareholder of: Pfizer Inc, at time of analysis, Employee of: Pfizer Inc, at time of analysis, Arnab Mukherjee Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Timothy Nicholas Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Cheng Chang Shareholder of: Pfizer Inc, Employee of: Pfizer Inc