Mechanistic Physiologically Based Pharmacokinetic Research Articles

A whole-body mechanistic physiologically-based pharmacokinetic modeling of intravenous iron.

Iron is essential for every cell of the mammalian organism. Iron deficiency is a major public health issue worldwide. Intravenous (IV) iron therapy has been used to treat anemia. However, IV iron therapy is known far away from ideal because the quantitative relationship between the pharmacokinetics and biodistribution of IV iron under different iron statuses remains unclear. Patients are known to suffer adverse effects from excessive iron accumulation. Our objective was to develop a physiologically based pharmacokinetic (PBPK) model of iron in mice and validate its application for predicting iron disposition in rats and humans. Previously published data on iron were collected for constructing the PBPK model of iron in mice, and then extrapolated to rats and humans based on physiologically and chemically specific parameters relevant to each species. The PBPK model characterized the distribution of iron in mice successfully. The model based on extrapolation to rats accurately simulated the ferric carboxymaltose (FCM) PK profiles in rat tissues. Similarly, the observed and simulated serum PK of FCM in humans were in reasonable agreement. This mechanistic whole-body PBPK model is useful for understanding and predicting iron effects on different species. It also establishes a foundation for future research that incorporates iron kinetics and biodistribution, along with related clinical experiments. This approach could lead to the development of effective and personalized iron deficiency anemia treatments.

Development of a physiologically-based pharmacokinetic model to simulate the pharmacokinetics of intramuscular antiretroviral drugs.

There is growing interest in the use of long-acting (LA) injectable drugs to improve treatment adherence. However, their long elimination half-life complicates the conduct of clinical trials. Physiologically-based pharmacokinetic (PBPK) modeling is a mathematical tool that allows to simulate unknown clinical scenarios for LA formulations. Thus, this work aimed to develop and verify a mechanistic intramuscular PBPK model. The framework describing the release of a LA drug from the depot was developed by including both the physiology of the injection site and the physicochemical properties of the drug. The framework was coded in Matlab® 2020a and implemented in our existing PBPK model for the verification step using clinical data for LA cabotegravir, rilpivirine, and paliperidone. The model was considered verified when the simulations were within twofold of observed data. Furthermore, a local sensitivity analysis was conducted to assess the impact of various factors relevant for the drug release from the depot on pharmacokinetics. The PBPK model was successfully verified since all predictions were within twofold of observed clinical data. Peak concentration, area under the concentration-time curve, and trough concentration were sensitive to media viscosity, drug solubility, drug density, and diffusion layer thickness. Additionally, inflammation was shown to impact the drug release from the depot. The developed framework correctly described the release and the drug disposition of LA formulations upon intramuscular administration. It can be implemented in PBPK models to address pharmacological questions related to the use of LA formulations.

Development of a PBPK model to quantitatively understand absorption and disposition mechanism and support future clinical trials for PB-201.

PB-201 is the second glucokinase activator in the world to enter the phase III clinical trials for the treatment of type 2 diabetes mellitus (T2DM). Combined with the efficacy advantages and the friendly absorption, distribution, metabolism, and excretion characteristics, the indication population of PB-201 will be broad. Because the liver is the primary organ for PB-201 elimination, and the elderly account for 20% of patients with T2DM, it is essential to estimate PB-201 exposure in specific populations to understand the pharmacokinetic characteristics and avoid hypoglycemia. Despite the limited contribution of CYP3A4 to PB-201 metabolism in vivo, the dual effects of nonspecific inhibitors/inducers on PB-201 (substrate for CYP3A4 and CYP2C9 isoenzymes) exposure under fasted and fed states also need to be evaluated to understand potential risks of combination therapy. To grasp the unknown information, the physiologically-based pharmacokinetic (PBPK) model was first developed and the influence of internal and external factors on PB-201 exposure was evaluated. Results are shown that the predictive performance of the mechanistic PBPK model meets the predefined criteria, and can accurately capture the absorption and disposition characteristics. Impaired liver function and age-induced changes in physiological factors may significantly increase the exposure under fasted state by 36%-158% and 48%-82%, respectively. The nonspecific inhibitor (fluconazole) and inducer (rifampicin) may separately increase/decrease PB-201 systemic exposure by 44% and 58% under fasted state, and by 78% and 47% under fed state. Therefore, the influence of internal and external factors on PB-201 exposure deserves attention, and the precision dose can be informed in future clinical studies based on the predicted results.

Development of a pediatric physiologically-based pharmacokinetic model to support recommended dosing of atezolizumab in children with solid tumors.

Background: Atezolizumab has been studied in multiple indications for both pediatric and adult patient populations. Generally, clinical studies enrolling pediatric patients may not collect sufficient pharmacokinetic data to characterize the drug exposure and disposition because of operational, ethical, and logistical challenges including burden to children and blood sample volume limitations. Therefore, mechanistic modeling and simulation may serve as a tool to predict and understand the drug exposure in pediatric patients. Objective: To use mechanistic physiologically-based pharmacokinetic (PBPK) modeling to predict atezolizumab exposure at a dose of 15 mg/kg (max 1,200 mg) in pediatric patients to support dose rationalization and label recommendations. Methods: A minimal mechanistic PBPK model was used which incorporated age-dependent changes in physiology and biochemistry that are related to atezolizumab disposition such as endogenous IgG concentration and lymph flow. The PBPK model was developed using both in vitro data and clinically observed data in adults and was verified across dose levels obtained from a phase I and multiple phase III studies in both pediatric patients and adults. The verified model was then used to generate PK predictions for pediatric and adult subjects ranging from 2- to 29-year-old. Results: Individualized verification in children and in adults showed that the simulated concentrations of atezolizumab were comparable (76% within two-fold and 90% within three-fold, respectively) to the observed data with no bias for either over- or under-prediction. Applying the verified model, the predicted exposure metrics including Cmin, Cmax, and AUCtau were consistent between pediatric and adult patients with a geometric mean of pediatric exposure metrics between 0.8- to 1.25-fold of the values in adults. Conclusion: The results show that a 15 mg/kg (max 1,200 mg) atezolizumab dose administered intravenously in pediatric patients provides comparable atezolizumab exposure to a dose of 1,200 mg in adults. This suggests that a dose of 15 mg/kg will provide adequate and effective atezolizumab exposure in pediatric patients from 2- to 18-year-old.

Prediction of pyrotinib exposure based on physiologically-based pharmacokinetic model and endogenous biomarker.

Pyrotinib, a novel irreversible epidermal growth factor receptor dual tyrosine kinase inhibitor, is mainly (about 90%) eliminated through cytochrome P450 (CYP) 3A mediated metabolism in vivo. Meanwhile, genotype is a key factor affecting pyrotinib clearance and 4β-hydroxycholesterol is an endogenous biomarker of CYP3A activity that can indirectly reflect the possible pyrotinib exposure. Thus, it is necessary to evaluate the clinical drug-drug interactions (DDI) between CYP3A perpetrators and pyrotinib, understand potential exposure in specific populations including liver impairment and geriatric populations, and explore the possible relationships among pyrotinib exposure, genotypes and endogenous biomarker. Physiologically-based pharmacokinetic (PBPK) model can be used to replace prospective DDI studies and evaluate external and internal factors that may influence system exposure. Herein, a basic PBPK model was firstly developed to evaluate the potential risk of pyrotinib coadministration with strong inhibitor and guide the clinical trial design. Subsequently, the mechanistic PBPK model was established and used to quantitatively estimate the potential DDI risk for other CYP3A modulators, understand the potential exposure of specific populations, including liver impairment and geriatric populations. Meanwhile, the possible relationships among pyrotinib exposure, genotypes and endogenous biomarker were explored. With the help of PBPK model, the DDI clinical trial of pyrotinib coadministration with strong inhibitor has been successfully completed, some DDI clinical trials may be waived based on the predicted results and clinical trials in specific populations can be reasonably designed. Moreover, the mutant genotypes of CYP3A4*18A and CYP3A5*3 were likely to have a limited influence on pyrotinib clearance, and the genotype-independent linear correlation coefficient between endogenous biomarker and system exposure was larger than 0.6. Therefore, based on the reliable predicted results and the linear correlations between pyrotinib exposure and endogenous biomarker, dosage adjustment of pyrotinib can be designed for clinical practice.

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.

Mechanistic PBPK Modelling to Predict the Advantage of the Salt Form of a Drug When Dosed with Acid Reducing Agents

Acid reducing agents (ARAs) reduce the dissolution rate of weakly basic drugs in the stomach potentially leading to lower bioavailability. Formulating the API as a rapidly dissolving salt is one strategy employed to reduce the impact of ARAs on dissolution of such drugs. In the present work, a model drug was selected with an immediate release formulation of the free base dosed in both the absence and presence of the ARA famotidine. In the latter case, bioavailability is restricted and several salt formulations were investigated. To simulate these drug products a mechanistic physiologically based pharmacokinetic (PBPK) model was built using the Simcyp Simulator, which illustrates the advantage of formulating an API as a salt compared to the free base form. The simulations use a mechanistic salt model utilising knowledge of the solubility product which was applied to predict the salt advantage. The developed PBPK model exemplifies that it can be critical to account for the surface pH and solubility when modelling the dissolution of low pKa bases and their salts in the gastric environment. In particular, the mechanistic salt model can be used to aid in screening and salt form selection where the aim is to mitigate effects of ARAs.

1315. No Dose Adjustment of Metformin with Fostemsavir Coadministration Based on Mechanistic Static and Physiologically Based Pharmacokinetic Models

BackgroundFostemsavir (FTR) is an oral prodrug of the first-in-class attachment inhibitor temsavir (TMR) which is being evaluated in patients with multidrug resistant HIV-1 infection. In vitro studies indicated that TMR and its 2 major metabolites are inhibitors of organic cation transporters (OCT)1, OCT2, and multidrug and toxin extrusion transporters (MATEs). To assess the clinical relevance, of OCT and MATE inhibition, mechanistic static DDI prediction with calculated Imax,u/IC50 ratios was below the cut-off limits for a DDI flag based on FDA guidelines and above the cut-off limits for MATEs based on EMA guidelines.MethodsMetformin is a commonly used probe substrate for OCT1, OCT2 and MATEs. To predict the potential for a drug interaction between TMR and metformin, a physiologically based pharmacokinetic (PBPK) model for TMR was developed based on its physicochemical properties, in vitro and in vivo data. The model was verified and validated through comparison with clinical data. The TMR PBPK model accurately described AUC and Cmax within 30% of the observed data for single and repeat dose studies with or without food. The SimCYP models for metformin and ritonavir were qualified using literature data before applications of DDI prediction for TMRResultsTMR was simulated at steady state concentrations after repeated oral doses of FTR 600 mg twice daily which allowed assessment of the potential OCT1, OCT2, and MATEs inhibition by TMR and metabolites. No significant increase in metformin systemic exposure (AUC or Cmax) was predicted with FTR co-administration. In addition, a sensitivity analysis was conducted for either hepatic OCT1 Ki, or renal OCT2 and MATEs Ki values. The model output indicated that, a 10-fold more potent Ki value for TMR would be required to have a ~15% increase in metformin exposureConclusionBased on mechanistic static models and PBPK modeling and simulation, the OCT1/2 and MATEs inhibition potential of TMR and its metabolites on metformin pharmacokinetics is not clinically significant. No dose adjustment of metformin is necessary when co-administered with FTRDisclosuresXiusheng Miao, PhD, GlaxoSmithKline (Employee) Mindy Magee, Doctor of Pharmacy, GlaxoSmithKline (Employee, Shareholder) Peter D. Gorycki, BEChe, MSc, PhD, GSK (Employee, Shareholder) Katy P. Moore, PharmD, RPh, ViiV Healthcare (Employee)

Use of Physiologically Based Pharmacokinetic (PBPK) Modeling for Predicting Drug-Food Interactions: an Industry Perspective

The effect of food on pharmacokinetic properties of drugs is a commonly observed occurrence affecting about 40% of orally administered drugs. Within the pharmaceutical industry, significant resources are invested to predict and characterize a clinically relevant food effect. Here, the predictive performance of physiologically based pharmacokinetic (PBPK) food effect models was assessed via de novo mechanistic absorption models for 30 compounds using controlled, pre-defined in vitro, and modeling methodology. Compounds for which absorption was known to be limited by intestinal transporters were excluded in this analysis. A decision tree for model verification and optimization was followed, leading to high, moderate, or low food effect prediction confidence. High (within 0.8- to 1.25-fold) to moderate confidence (within 0.5- to 2-fold) was achieved for most of the compounds (15 and 8, respectively). While for 7 compounds, prediction confidence was found to be low (> 2-fold). There was no clear difference in prediction success for positive or negative food effects and no clear relationship to the BCS category of tested drug molecules. However, an association could be demonstrated when the food effect was mainly related to changes in the gastrointestinal luminal fluids or physiology, including fluid volume, motility, pH, micellar entrapment, and bile salts. Considering these findings, it is recommended that appropriately verified mechanistic PBPK modeling can be leveraged with high to moderate confidence as a key approach to predicting potential food effect, especially related to mechanisms highlighted here.

Predicting renal clearance of morphine and morphine‐6‐glucuronide using the “organ‐on‐a‐chip” technology and physiologically‐based pharmacokinetic modeling

BackgroundRenal elimination of xenobiotics consists of dynamic sequential and parallel processes including glomerular filtration, active secretion, and active and passive reabsorption. Conventional ways to predict renal clearance involve allometric scaling and in vitro‐to‐in vivo extrapolation using cell lines. However, static predictions using these methods are often not quantitatively accurate because of the differences in transporter expression and function between species and systems, and the lack of dynamics of reabsorption and concentration gradients that exist in the kidney. We hypothesized that a 3D vascularized human proximal tubule microphysiological system (VPT‐MPS) together with physiologically‐based pharmacokinetic (PBPK) modeling could be used to accurately predict human renal clearance. The goal of this study was to test this hypothesis using morphine and its 6‐glucuronide (M6G) as model compounds and to predict their renal clearances from data collected from our VPT‐MPS 1 using our mechanistic kidney PBPK model 2.MethodsHuman proximal tubule epithelial cells (PTECs) collected from three donors were seeded in the 3D VPT‐MPS. Passive diffusion and active secretion clearances of morphine and M6G were measured using the 3D VPT‐MPS in the presence and absence of probenecid (OAT inhibitor) and tetraethyl ammonium (OCT inhibitor). Similarly, passive diffusion and active secretion clearances across PTECs for morphine and M6G were measured in a 2D transwell system. The measured clearances were used with our verified mechanistic kidney PBPK model to predict the renal clearances of morphine and M6G. The predicted renal clearances were compared to the observed in vivo renal clearances to define the accuracy of the predictions.ResultsThe clearances across PTECs in the 3D VPT‐MPS for morphine and M6G were significantly reduced in the presence of probenecid and tetraethyl ammonium. In the 3D VPT‐MPS system, the passive diffusion clearances of morphine and M6G were 0.8 (range: 0.4 – 1.2) and 0.3 (range: 0.2 – 0.7) μL/h, respectively, while the active secretion clearances of morphine and M6G were 2.6 (range: 1.4 – 7.9) and 0.9 (range: 0.04 – 9.3) μL/h, respectively. These results showed a significant active secretion for both morphine and M6G that is mediated by OAT and OCT transporters. The predicted renal clearances using these measured clearances and our mechanistic kidney PBPK model were 7.6 ± 3.0 L/h for morphine and 6.4 ± 1.5 L/h for M6G. These predicted renal clearances were within 2‐fold of the observed population mean renal clearances demonstrating successful in vitro‐to‐in vivo prediction of renal clearance using our method.ConclusionThis study shows that the 3D VPT‐MPS together with mechanistic PBPK modeling can accurately predict renal clearance of drugs that are eliminated through both passive and active processes.Support or Funding InformationNIH UG3TR002158‐02

Mechanistic PBPK Modeling of Urine pH Effect on Renal and Systemic Disposition of Methamphetamine and Amphetamine.

The effect of urine pH on renal excretion and systemic disposition has been observed for many drugs and metabolites. When urine pH is altered, tubular ionization, passive reabsorption, renal clearance, and systemic exposure of drugs and metabolites may all change dramatically, raising clinically significant concerns. Surprisingly, the urine pH effect on drug disposition is not routinely explored in humans, and regulatory agencies have neither developed guidance on this issue nor required industry to conduct pertinent human trials. In this study, we hypothesized that physiologically based pharmacokinetic (PBPK) modeling could be used as a cost-effective method to examine potential urine pH effect on drug and metabolite disposition. Our previously developed and verified mechanistic kidney model was integrated with a full-body PBPK model to simulate renal clearance and area under the plasma concentration-time curve (AUC) with varying urine pH statuses using methamphetamine and amphetamine as model compounds. We first developed and verified drug models for methamphetamine and amphetamine under normal urine pH condition [absolute average fold error (AAFE) < 1.25 at study level]. Then, acidic and alkaline urine scenarios were simulated. Our simulation results show that the renal excretion and plasma concentration-time profiles for methamphetamine and amphetamine could be recapitulated under different urine pH (AAFE < 2 at individual level). The methamphetamine-amphetamine parent-metabolite full-body PBPK model also successfully simulated amphetamine plasma concentration-time profiles (AAFE < 1.25 at study level) and amphetamine/methamphetamine urinary concentration ratios (AAFE < 2 at individual level) after dosing methamphetamine. This demonstrates that our mechanistic PBPK model can predict urine pH effect on systemic and urinary disposition of drugs and metabolites. SIGNIFICANCE STATEMENT: Our study shows that integrating mechanistic kidney model with full-body physiologically based pharmacokinetic model can predict the magnitude of alteration in renal excretion and area under the plasma concentration-time curve (AUC) of drugs and metabolites when urine pH is changed. This provides a cost-effective method to evaluate the likelihood of renal and systemic disposition changes due to varying urine pH. This is important because multiple drugs and diseases can alter urine pH, leading to quantitatively and clinically significant changes in drug and metabolite disposition that may require adjustment of therapy.

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

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

Bridging in vitro dissolution and in vivo exposure for acalabrutinib. Part II. A mechanistic PBPK model for IR formulation comparison, proton pump inhibitor drug interactions, and administration with acidic juices

Acalabrutinib (Calquence®) 100 mg (bid) has received accelerated approval by FDA for the treatment of adult patients with mantle cell lymphoma (MCL) who have received at least one prior therapy. Acalabrutinib is a substrate of PgP and CYP3A4, with a significant fraction of drug metabolized by first pass gut extraction and 25% absolute bioavailability. The absorption of acalabrutinib is affected by stomach pH, with lower pharmacokinetic exposure observed following co-administration with proton pump inhibitors.During dissolution at pH values below its highest basic pKa, the two basic moieties of acalabrutinib react with protons from the aqueous solution, leading to a higher pH at the drug surface than in the bulk solution. A batch-specific product particle size distribution (P-PSD), was derived from dissolution data using a mechanistic model that was based on the understanding of surface pH and the contribution of micelles to the dissolution rate. P-PSD values obtained for various batches of acalabrutinib products in simple buffers, or in complex fluids such as fruit juices, were successfully integrated into a physiologically based pharmacokinetic (PBPK) model developed using GastroPlus v9.0™. The integrated model allowed the prediction of clinical pharmacokinetics under normal physiological stomach pH conditions as well as following treatment with proton pump inhibitors. The model also accounted for lower pharmacokinetic exposure that was observed when acalabrutinib was co-administered with the acidic beverages, grapefruit juice, (which contains CYP3A inhibitors), and orange drink (which does not contain CYP3A inhibitors), relative to administration with water.The integration of dissolution data in the PBPK model enables mechanistic understanding and the establishment of more robust in vitro-in vivo correlations (IVIVC) under a variety of conditions. The model can then distinguish the interplay between dissolution and first pass extraction and how in vivo stomach pH, saturation of gut PgP, and saturation or inhibition of gut CYP3A4, will impact the pharmacokinetics of acalabrutinib.

Physiologically Based Pharmacokinetic Model of Itraconazole and Two of Its Metabolites to Improve the Predictions and the Mechanistic Understanding of CYP3A4 Drug-Drug Interactions.

Physiologically based pharmacokinetic (PBPK) modeling for itraconazole using a bottom-up approach is challenging, not only due to complex saturable pharmacokinetics (PK) and the presence of three metabolites exhibiting CYP3A4 inhibition, but also because of discrepancies in reported in vitro data. The overall objective of this study is to provide a comprehensive mechanistic PBPK model for itraconazole in order to increase the confidence in its drug-drug interaction (DDI) predictions. To achieve this, key in vitro and in vivo data for itraconazole and its major metabolites were generated. These data were crucial to developing a novel bottom-up PBPK model in Simcyp (Simcyp Ltd., Certara, Sheffield, United Kingdom) for itraconazole and two of its major metabolites: hydroxy-itraconazole (OH-ITZ) and keto-itraconazole (keto-ITZ). Performance of the model was validated using prespecified acceptance criteria against different dosing regimens, formulations for 29 PK, and DDI studies with midazolam and other CYP3A4 substrates. The main outcome is an accurate PBPK model that simultaneously predicts the PK profiles of itraconazole, OH-ITZ, and keto-ITZ. In addition, itraconazole DDIs with midazolam and other CYP3A4 substrates were successfully predicted within a 2-fold error. Prediction precision and bias of DDI expressed as geometric mean fold error were for the area under the concentration-time curve and peak concentration, 1.06 and 0.96, respectively. To conclude, in this paper a comprehensive data set for itraconazole and its metabolites is provided that enables bottom-up mechanism-based PBPK modeling. The presented model is applicable for studying the contribution from the metabolites and allows improved assessments of itraconazole DDI.

Development of a Physiologically Based Pharmacokinetic Model for Sinogliatin, a First-in-Class Glucokinase Activator, by Integrating Allometric Scaling, In Vitro to In Vivo Exploration and Steady-State Concentration-Mean Residence Time Methods: Mechanistic Understanding of its Pharmacokinetics.

The objective of this study was to develop a physiologically based pharmacokinetic (PBPK) model for sinogliatin (HMS-5552,dorzagliatin) by integrating allometric scaling (AS), in vitro to in vivo exploration (IVIVE), and steady-state concentration-mean residence time (Css-MRT) methods and to provide mechanistic insight into its pharmacokinetic properties in humans. Human major pharmacokinetic parameters were analyzed using AS, IVIVE, and Css-MRT methods with available preclinical in vitro and in vivo data to understand sinogliatin drug metabolism and pharmacokinetic (DMPK) characteristics and underlying mechanisms. On this basis, an initial mechanistic PBPK model of sinogliatin was developed. The initial PBPK model was verified using observed data from a single ascending dose (SAD) study and further optimized with various strategies. The final model was validated by simulating sinogliatin pharmacokinetics under a fed condition. The validated model was applied to support a clinical drug-drug interaction (DDI) study design and to evaluate the effects of intrinsic (hepatic cirrhosis, genetic) factors on drug exposure. The two-species scaling method using rat and dog data (TS-rat,dog) was the best AS method in predicting human systemic clearance in the central compartment (CL). The IVIVE method confirmed that sinogliatin was predominantly metabolized by cytochrome P450 (CYP) 3A4. The Css-MRT method suggested dog pharmacokinetic profiles were more similar to human pharmacokinetic profiles. The estimated CL using the AS and IVIVE approaches was within 1.5-fold of that observed. The Css-MRT method in dogs also provided acceptable prediction of human pharmacokinetic characteristics. For the PBPK approach, the 90% confidence intervals (CIs) of the simulated maximum concentration (Cmax), CL, and area under the plasma concentration-time curve (AUC) of sinogliatin were within those observed and the 90% CI of simulated time to Cmax (tmax) was closed to that observed for a dose range of 5-50 mg in the SAD study. The final PBPK model was validated by simulating sinogliatin pharmacokinetics with food. The 90% CIs of the simulated Cmax, CL, and AUC values for sinogliatin were within those observed and the 90% CI of the simulated tmax was partially within that observed for the dose range of 25-200mg in the multiple ascending dose (MAD) study. This PBPK model selected a final clinical DDI study design with itraconazole from four potential designs and also evaluated the effects of intrinsic (hepatic cirrhosis, genetic) factors on drug exposure. Sinogliatin pharmacokinetic properties were mechanistically understood by integrating all four methods and a mechanistic PBPK model was successfully developed and validated using clinical data. This PBPK model was applied to support the development of sinogliatin.

Mechanistic Physiologically Based Pharmacokinetic Modeling of the Dissolution and Food Effect of a Biopharmaceutics Classification System IV Compound—The Venetoclax Story

Venetoclax, a selective B-cell lymphoma-2 inhibitor, is a biopharmaceutics classification system class IV compound. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model to mechanistically describe absorption and disposition of an amorphous solid dispersion formulation of venetoclax in humans. A mechanistic PBPK model was developed incorporating measured amorphous solubility, dissolution, metabolism, and plasma protein binding. A middle-out approach was used to define permeability. Model predictions of oral venetoclax pharmacokinetics were verified against clinical studies of fed and fasted healthy volunteers, and clinical drug interaction studies with strong CYP3A inhibitor (ketoconazole) and inducer (rifampicin). Model verification demonstrated accurate prediction of the observed food effect following a low-fat diet. Ratios of predicted versus observed Cmax and area under the curve of venetoclax were within 0.8- to 1.25-fold of observed ratios for strong CYP3A inhibitor and inducer interactions, indicating that the venetoclax elimination pathway was correctly specified. The verified venetoclax PBPK model is one of the first examples mechanistically capturing absorption, food effect, and exposure of an amorphous solid dispersion formulated compound. This model allows evaluation of untested drug-drug interactions, especially those primarily occurring in the intestine, and paves the way for future modeling of biopharmaceutics classification system IV compounds.

The Use of In Vitro Data and Physiologically-Based Pharmacokinetic Modeling to Predict Drug Metabolite Exposure: Desipramine Exposure in Cytochrome P4502D6 Extensive and Poor Metabolizers Following Administration of Imipramine.

Major circulating drug metabolites can be as important as the drugs themselves in efficacy and safety, so establishing methods whereby exposure to major metabolites following administration of parent drug can be predicted is important. In this study, imipramine, a tricyclic antidepressant, and its major metabolite desipramine were selected as a model system to develop metabolite prediction methods. Imipramine undergoes N-demethylation to form the active metabolite desipramine, and both imipramine and desipramine are converted to hydroxylated metabolites by the polymorphic enzyme CYP2D6. The objective of the present study is to determine whether the human pharmacokinetics of desipramine following dosing of imipramine can be predicted using static and dynamic physiologically-based pharmacokinetic (PBPK) models from in vitro input data for CYP2D6 extensive metabolizer (EM) and poor metabolizer (PM) populations. The intrinsic metabolic clearances of parent drug and metabolite were estimated using human liver microsomes (CYP2D6 PM and EM) and hepatocytes. Passive diffusion clearance of desipramine, used in the estimation of availability of the metabolite, was predicted from passive permeability and hepatocyte surface area. The predicted area under the curve (AUCm/AUCp) of desipramine/imipramine was 12- to 20-fold higher in PM compared with EM subjects following i.v. or oral doses of imipramine using the static model. Moreover, the PBPK model was able to recover simultaneously plasma profiles of imipramine and desipramine in populations with different phenotypes of CYP2D6. This example suggested that mechanistic PBPK modeling combined with information obtained from in vitro studies can provide quantitative solutions to predict in vivo pharmacokinetics of drugs and major metabolites in a target human population.

A Bottom-Up Whole-Body Physiologically Based Pharmacokinetic Model to Mechanistically Predict Tissue Distribution and the Rate of Subcutaneous Absorption of Therapeutic Proteins.

The ability to predict subcutaneous (SC) absorption rate and tissue distribution of therapeutic proteins (TPs) using a bottom-up approach is highly desirable early in the drug development process prior to clinical data being available. A whole-body physiologically based pharmacokinetic (PBPK) model, requiring only a few drug parameters, to predict plasma and interstitial fluid concentrations of TPs in humans after intravenous and subcutaneous dosing has been developed. Movement of TPs between vascular and interstitial spaces was described by considering both convection and diffusion processes using a 2-pore framework. The model was optimised using a variety of literature sources, such as tissue lymph/plasma concentration ratios in humans and animals, information on the percentage of dose absorbed following SC dosing via lymph in animals and data showing loss of radiolabelled IgG from the SC dosing site in humans. The resultant model was used to predict t max and plasma concentration profiles for 12 TPs (molecular weight 8–150 kDa) following SC dosing. The predicted plasma concentration profiles were generally comparable to observed data. t max was predicted within 3-fold of reported values, with one third of the predictions within 0.8–1.25-fold. There was no systematic bias in simulated C max values, although a general trend for underprediction of t max was observed. No clear trend between prediction accuracy of t max and TP isoelectric point or molecular size was apparent. The mechanistic whole-body PBPK model described here can be applied to predict absorption rate of TPs into blood and movement into target tissues following SC dosing.Electronic supplementary materialThe online version of this article (doi:10.1208/s12248-015-9819-4) contains supplementary material, which is available to authorized users.

Physiologically Based Pharmacokinetic Modelling to Predict Single- and Multiple-Dose Human Pharmacokinetics of Bitopertin

Bitopertin (RG1678) is a glycine reuptake inhibitor currently in phase 3 trials for treatment of schizophrenia. This paper describes the use of physiologically based pharmacokinetic (PBPK) modelling and preclinical data to gain insights into and predict bitopertin clinical pharmacokinetics. Simulations of pharmacokinetics were initiated early in the drug discovery stage by integrating physicochemical properties and in vitro measurements into a PBPK rat model. Comparison of pharmacokinetics predicted by PBPK modelling with those measured after intravenous and oral dosing in rats and monkeys showed a good match and thus increased confidence that a similar approach could be applied for human prediction. After comparison of predicted plasma concentrations with those measured after single oral doses in the first clinical study, the human model was refined and then applied to simulate multiple-dose pharmacokinetics. Clinical plasma concentrations measured were in good agreement with PBPK predictions. Predicted area under the plasma concentration-time curve (AUC) was within twofold of the observed mean values for all dose levels. Maximum plasma concentration (C max) at higher doses was well predicted but approximately twofold below observed values at the lower doses. A slightly less than dose-proportional increase in both AUC and C max was observed, and model simulations indicated that when the dose exceeded 50mg, solubility limited the fraction of dose absorbed. Refinement of the absorption model with additional solubility and permeability measurements further improved the match of simulations to observed single-dose data. Simulated multiple-dose pharmacokinetics with the refined model were in good agreement with observed data. Clinical pharmacokinetics of bitopertin can be well simulated with a mechanistic PBPK model. This model supports further clinical development and provides a valuable repository for pharmacokinetic knowledge gained about the molecule.

BioDMET: a physiologically based pharmacokinetic simulation tool for assessing proposed solutions to complex biological problems

We developed a detailed, whole-body physiologically based pharmacokinetic (PBPK) modeling tool for calculating the distribution of pharmaceutical agents in the various tissues and organs of a human or animal as a function of time. Ordinary differential equations (ODEs) represent the circulation of body fluids through organs and tissues at the macroscopic level, and the biological transport mechanisms and biotransformations within cells and their organelles at the molecular scale. Each major organ in the body is modeled as composed of one or more tissues. Tissues are made up of cells and fluid spaces. The model accounts for the circulation of arterial and venous blood as well as lymph. Since its development was fueled by the need to accurately predict the pharmacokinetic properties of imaging agents, BioDMET is more complex than most PBPK models. The anatomical details of the model are important for the imaging simulation endpoints. Model complexity has also been crucial for quickly adapting the tool to different problems without the need to generate a new model for every problem. When simpler models are preferred, the non-critical compartments can be dynamically collapsed to reduce unnecessary complexity. BioDMET has been used for imaging feasibility calculations in oncology, neurology, cardiology, and diabetes. For this purpose, the time concentration data generated by the model is inputted into a physics-based image simulator to establish imageability criteria. These are then used to define agent and physiology property ranges required for successful imaging. BioDMET has lately been adapted to aid the development of antimicrobial therapeutics. Given a range of built-in features and its inherent flexibility to customization, the model can be used to study a variety of pharmacokinetic and pharmacodynamic problems such as the effects of inter-individual differences and disease-states on drug pharmacokinetics and pharmacodynamics, dosing optimization, and inter-species scaling. While developing a tool to aid imaging agent and drug development, we aimed at accelerating the acceptance and broad use of PBPK modeling by providing a free mechanistic PBPK software that is user friendly, easy to adapt to a wide range of problems even by non-programmers, provided with ready-to-use parameterized models and benchmarking data collected from the peer-reviewed literature.Electronic supplementary materialThe online version of this article (doi:10.1007/s10928-011-9229-x) contains supplementary material, which is available to authorized users.