Revisiting Nonlinear Bosentan Pharmacokinetics by Physiologically Based Pharmacokinetic Modeling: Target Binding, Albeit Not a Major Contributor to Nonlinearity, Can Offer Prediction of Target Occupancy

When to consider intra-target microdosing: physiologically based pharmacokinetic modeling approach to quantitatively identify key factors for observing target engagement

Intra-Target Microdosing (ITM), integral to Phase 0 clinical studies, offers a novel approach in drug development, effectively bridging the gap between preclinical and clinical phases. This methodology is especially relevant in streamlining early drug development stages. Our research utilized a Physiologically Based Pharmacokinetic (PBPK) model and Monte Carlo simulations to examine factors influencing the effectiveness of ITM in achieving target engagement. The study revealed that ITM is capable of engaging targets at levels akin to systemically administered therapeutic doses for specific compounds. However, we also observed a notable decrease in the probability of success when the predicted therapeutic dose exceeds 10 mg. Additionally, our findings identified several critical factors affecting the success of ITM. These encompass both lower dissociation constants, higher systemic clearance and an optimum abundance of receptors in the target organ. Target tissues characterized by relatively low blood flow rates and high drug clearance capacities were deemed more conducive to successful ITM. These insights emphasize the necessity of taking into account each drug's unique pharmacokinetic and pharmacodynamic properties, along with the physiological characteristics of the target tissue, in determining the suitability of ITM.

After 50 Years of Hepatic Clearance Models, Where Should We Go from Here? Improvements and Implications for Physiologically Based Pharmacokinetic Modeling

There is overwhelming preference for application of the unphysiologic, well-stirred model (WSM) over the parallel tube model (PTM) and dispersion model (DM) to predict hepatic drug clearance, CLH , despite that liver blood flow is dispersive and closer to the DM in nature. The reasoning is the ease in computation relating the hepatic intrinsic clearance ( CLint ), hepatic blood flow ( QH ), unbound fraction in blood ( fub ) and the transmembrane clearances ( CLin and CLef ) to CLH for the WSM. However, the WSM, being the least efficient liver model, predicts a lower EH that is associated with the in vitro CLint ( Vmax / Km ), therefore requiring scale-up to predict CLH in vivo. By contrast, the miniPTM, a three-subcompartment tank-in-series model of uniform enzymes, closely mimics the DM and yielded similar patterns for CLint versus EH , substrate concentration [S] , and KL / B , the tissue to outflow blood concentration ratio. We placed these liver models nested within physiologically based pharmacokinetic models to describe the kinetics of the flow-limited, phenolic substrate, harmol, using the WSM (single compartment) and the miniPTM and zonal liver models (ZLMs) of evenly and unevenly distributed glucuronidation and sulfation activities, respectively, to predict CLH For the same, given CLint ( Vmax and Km ), the WSM again furnished the lowest extraction ratio ( EH,WSM = 0.5) compared with the miniPTM and ZLM (>0.68). Values of EH,WSM were elevated to those for EH, PTM and EH, ZLM when the Vmax s for sulfation and glucuronidation were raised 5.7- to 1.15-fold. The miniPTM is easily manageable mathematically and should be the new normal for liver/physiologic modeling. SIGNIFICANCE STATEMENT: Selection of the proper liver clearance model impacts strongly on CLH predictions. The authors recommend use of the tank-in-series miniPTM (3 compartments mini-parallel tube model), which displays similar properties as the dispersion model (DM) in relating CLint and [ S ] to CLH as a stand-in for the DM, which best describes the liver microcirculation. The miniPTM is readily modified to accommodate enzyme and transporter zonation.

Elucidating nonlinear pharmacokinetics of telmisartan: Integration of target-mediated drug disposition and OATP1B3-mediated hepatic uptake in a physiologically based model

Telmisartan, a selective inhibitor of angiotensin II receptor type 1 (AT1), demonstrates nonlinear pharmacokinetics (PK) when orally administered in ascending doses to healthy volunteers, but the underlying mechanisms remain unclear. This study presents a physiologically based pharmacokinetic model integrated with target-mediated drug disposition (TMDD-PBPK model) to explore the mechanism of its nonlinear PK. We employed the Cluster-Gauss Newton method for top-down analysis, estimating the in vivo Km,OATP1B3 (Michaelis-Menten constant for telmisartan hepatic uptake via Organic Anion Transporting Polypeptide 1B3) to be 2.0-5.7 nM. This range is significantly lower than the reported in vitro value of 810 nM, obtained in 0.3% human serum albumin (HSA) conditions. Further validation was achieved through in vitro assessment in plated human hepatocytes with 4.5% HSA, showing a Km of 4.5 nM. These results underscore the importance of albumin-mediated uptake effect for the hepatic uptake of telmisartan. Our TMDD-PBPK model, developed through a "middle-out" approach, underwent sensitivity analysis to identify key factors in the nonlinear PK of telmisartan. We found that the nonlinearity in the area under the concentration-time curve (AUC) and/or maximum concentration (Cmax) of telmisartan is sensitive to Km,OATP1B3 across all dosages. Additionally, the dissociation constant (Kd) for telmisartan binding to the AT1 receptor, along with its receptor abundance, notably influences PK at lower doses (below 20 mg). In conclusion, the nonlinear PK of telmisartan appears primarily driven by hepatic uptake saturation across all dose ranges and by AT1-receptor binding saturation, notably at lower doses.

Beyond the basics: A deep dive into parameter estimation for advanced PBPK and QSP models

Physiologically-based pharmacokinetic (PBPK) models and quantitative systems pharmacology (QSP) models have contributed to drug development strategies. The parameters of these models are commonly estimated by capturing observed values using the nonlinear least-squares method. Software packages for PBPK and QSP modeling provide a range of parameter estimation algorithms. To choose the most appropriate method, modelers need to understand the basic concept of each approach. This review provides a general introduction to the key points of parameter estimation with a focus on the PBPK and QSP models, and the respective parameter estimation algorithms. The latter part assesses the performance of five parameter estimation algorithms - the quasi-Newton method, Nelder-Mead method, genetic algorithm, particle swarm optimization, and Cluster Gauss-Newton method - using three examples of PBPK and QSP modeling. The assessment revealed that some parameter estimation results were significantly influenced by the initial values. Moreover, the choice of algorithms demonstrating good estimation results heavily depends on factors such as model structure and the parameters to be estimated. To obtain credible parameter estimation results, it is advisable to conduct multiple rounds of parameter estimation under different conditions, employing various estimation algorithms.

Kinetic modeling of the plasma pharmacokinetic profiles of ADAMTS13 fragment and its Fc-fusion counterpart in mice

Introduction: Fusion of the fragment crystallizable (Fc) to protein therapeutics is commonly used to extend the circulation time by enhancing neonatal Fc-receptor (FcRn)-mediated endosomal recycling and slowing renal clearance. This study applied kinetic modeling to gain insights into the cellular processing contributing to the observed pharmacokinetic (PK) differences between the novel recombinant ADAMTS13 fragment (MDTCS) and its Fc-fusion protein (MDTCS-Fc). Methods: For MDTCS and MDTCS-Fc, their plasma PK profiles were obtained at two dose levels following intravenous administration of the respective proteins to mice. The plasma PK profiles of MDTCS were fitted to a kinetic model with three unknown protein-dependent parameters representing the fraction recycled (FR) and the rate constants for endocytosis (kup, for the uptake into the endosomes) and for the transfer from the plasma to the interstitial fluid (kpi). For MDTCS-Fc, the model was modified to include an additional parameter for binding to FcRn. Parameter optimization was done using the Cluster Gauss-Newton Method (CGNM), an algorithm that identifies multiple sets of approximate solutions ("accepted" parameter sets) to nonlinear least-squares problems. Results: As expected, the kinetic modeling results yielded the FR of MDTCS-Fc to be 2.8-fold greater than that of MDTCS (0.8497 and 0.3061, respectively). In addition, MDTCS-Fc was predicted to undergo endocytosis (the uptake into the endosomes) at a slower rate than MDTCS. Sensitivity analyses identified the association rate constant (kon) between MDTCS-Fc and FcRn as a potentially important factor influencing the plasma half-life in vivo. Discussion: Our analyses suggested that Fc fusion to MDTCS leads to changes in not only the FR but also the uptake into the endosomes, impacting the systemic plasma PK profiles. These findings may be used to develop recombinant protein therapeutics with extended circulation time.

Analysis on dynamic changes of etizolam and its metabolites and exploration of its development prospect using UPLC-Q-exactive-MS

As one of the most widely abused designer benzodiazepines in the world, etizolam has been found in many cases in many countries. In this study, UPLC-Q-Exactive-MS was used for the first time to establish a dynamic change model of etizolam and its metabolites in rats. Compared with previous studies, the detection sensitivity and reproducibility of the instrument were higher. In the experiment, we optimized the traditional pharmacokinetic model based on Gauss function. According to the significant difference of etizolam in the plasma elimination phase of rats, a new pharmacokinetic model based on Lorentz function was established to describe the dynamic changes of etizolam more rigorously, which made the error effects lower and the accuracy of the pharmacokinetic parameters was improved. At the same time, the pharmacokinetic parameters of etizolam were compared with four other designer benzodiazepines reported in previous studies in rats, and we found the direct reason for the popularity of etizolam in the NPS market and explored the future development of etizolam for the first time. In addition, 21 metabolites were found through rat experiments to effectively detect etizolam abuse for a long time, of which 4 metabolites had the longest detection window and could be used as long-acting metabolites for experiments, which greatly prolongs the detection window and extends the time range in which etizolam was detected in real cases. This study is the first to conduct a systematic and comprehensive study on the metabolism and pharmacokinetics of etizolam and find out the direct reason for the prevalence of etizolam abuse, and we also discuss the development trend of etizolam in the future market of new psychoactive substances, which is beneficial for forensic experts to assess the trend of drug abuse and can provide reference for relevant drug control and drug treatment.

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

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

Cluster Gauss-Newton method for a quick approximation of profile likelihood: With application to physiologically-based pharmacokinetic models

Physiologically-based pharmacokinetic (PBPK) models can be challenging to work with because they can have too many parameters to identify from observable data. The profile likelihood method can help solve this issue by determining parameter identifiability and confidence intervals, but it involves repetitive parameter optimizations that can be time-consuming. The Cluster Gauss-Newton method (CGNM) is a parameter estimation method that efficiently searches through a wide range of parameter space. In this study, we propose a method that approximates the profile likelihood by reusing intermediate computation results from CGNM, allowing us to obtain the upper bounds of the profile likelihood without conducting additional model evaluation. This method allows us to quickly draw approximate profile likelihoods for all unknown parameters. Additionally, the same approach can be used to draw two-dimensional profile likelihoods for all parameter combinations within seconds. We demonstrate the effectiveness of this method on three PBPK models.

A 20-Year Research Overview: Quantitative Prediction of Hepatic Clearance Using the In Vitro-In Vivo Extrapolation Approach Based on Physiologically Based Pharmacokinetic Modeling and Extended Clearance Concept

Understanding the extended clearance concept and establishing a physiologically based pharmacokinetic (PBPK) model are crucial for investigating the impact of changes in transporter and metabolizing enzyme abundance/functions on drug pharmacokinetics in blood and tissues. This mini-review provides an overview of the extended clearance concept and a PBPK model that includes transporter-mediated uptake processes in the liver. In general, complete in vitro and in vivo extrapolation (IVIVE) poses challenges due to missing factors that bridge the gap between in vitro and in vivo systems. By considering key in vitro parameters, we can capture in vivo pharmacokinetics, a strategy known as the top-down or middle-out approach. We present the latest progress, theory, and practice of the Cluster Gauss-Newton method, which is used for middle-out analyses. As examples of poor IVIVE, we discuss "albumin-mediated hepatic uptake" and "time-dependent inhibition" of OATP1Bs. The hepatic uptake of highly plasma-bound drugs is more efficient than what can be accounted for by their unbound concentration alone. This phenomenon is referred to as "albumin-mediated" hepatic uptake. IVIVE was improved by measuring hepatic uptake clearance in vitro in the presence of physiologic albumin concentrations. Lastly, we demonstrate the application of Cluster Gauss-Newton method-based analysis to the target-mediated drug disposition of bosentan. Incorporating saturable target binding and OATP1B-mediated hepatic uptake into the PBPK model enables the consideration of nonlinear kinetics across a wide dose range and the prediction of receptor occupancy over time. SIGNIFICANCE STATEMENT: There have been multiple instances where researchers' endeavors to unravel the underlying mechanism of poor in vitro-in vivo extrapolation have led to the discovery of previously undisclosed truths. These include 1) albumin-mediated hepatic uptake, 2) the target-mediated drug disposition in small molecules, and 3) the existence of a trans-inhibition mechanism by inhibitors for OATP1B-mediated hepatic uptake of drugs. Consequently, poor in vitro-in vivo extrapolation and the subsequent inquisitiveness of scientists may serve as a pivotal gateway to uncover hidden mechanisms.

Predicting In Vivo Target Occupancy (TO) Profiles via Physiologically Based Pharmacokinetic-TO Modeling of Warfarin Pharmacokinetics in Blood: Importance of Low Dose Data and Prediction of Stereoselective Target Interactions

Warfarin is well recognized for its high-affinity and capacity-limited binding to the pharmacological target and undergoes target-mediated drug disposition. Here, we developed a physiologically based pharmacokinetic (PBPK) model that incorporated saturable target binding and other reported hepatic disposition components of warfarin. The PBPK model parameters were optimized by fitting to the reported blood pharmacokinetic (PK) profiles of warfarin with no stereoisomeric separation after oral dosing of racemic warfarin (0.1, 2, 5, or 10 mg) using the Cluster Gauss-Newton method (CGNM). The CGNM-based analysis yielded multiple "accepted" sets for six optimized parameters, which were then used to simulate the warfarin blood PK and in vivo target occupancy (TO) profiles. When further analyses examined the impact of dose selection on uncertainty in parameter estimation by the PBPK modeling, the PK data from 0.1 mg dose (well below target saturation) was important in practically identifying the target binding-related parameters in vivo. When stereoselective differences were incorporated for both hepatic disposition and target interactions, our PBPK modeling predicted that R-warfarin (of slower clearance and lower target affinity than S-warfarin) contributes to TO prolongation after oral dosing of racemic warfarin. Our results extend the validity of the approach by which the PBPK-TO modeling of blood PK profiles can yield TO prediction in vivo (applicable to the drugs with targets of high affinity and abundance and limited distribution volume via nontarget interactions). Our findings support that model-informed dose selection and PBPK-TO modeling may aid in TO and efficacy assessment in preclinical and clinical phase 1 studies. SIGNIFICANCE STATEMENT: The current physiologically based pharmacokinetic modeling incorporated the reported hepatic disposition components and target binding of warfarin and analyzed the blood pharmacokinetic (PK) profiles from varying warfarin doses, practically identifying target binding-related parameters in vivo. By implementing the stereoselective differences between R- and S-warfarin, our analysis predicted the role of R-warfarin in prolonging overall target occupancy. Our results extend the validity of analyzing blood PK profiles to predict target occupancy in vivo, which may guide efficacy assessment.

Physiologically-based pharmacokinetic model-based translation of OATP1B-mediated drug-drug interactions from coproporphyrin I to probe drugs

The accurate prediction of OATP1B-mediated drug-drug interactions (DDIs) is challenging for drug development. Here, we report physiologically-based pharmacokinetic (PBPK) model analysis for clinical DDI data generated in heathy subjects who received oral doses of cyclosporin A (CysA; 20 and 75 mg) as an OATP1B inhibitor, and the probe drugs (pitavastatin, rosuvastatin and valsartan). PBPK models of CysA and probe compounds were combined assuming inhibition of hepatic uptake of endogenous coproporphyrin I (CP-I) by CysA. In vivo K_i of unbound CysA for OATP1B (K_i,OATP1B ), and the overall intrinsic hepatic clearance per body weight of CP-I (CL_int,all,unit ) were optimized to account for the CP-I data (K_i,OATP1B , 0.657 ± 0.048 nM; CL_int,all,unit , 57.0 ± 6.3 L/h/kg). DDI simulation using K_i,OATP1B reproduced the dose-dependent effect of CysA (20 and 75 mg) and the dosing interval (1 h and 3 h) on the time profiles of blood concentrations of pitavastatin and rosuvastatin, but DDI simulation using in vitro K_i,OATP1B failed. The Cluster Gauss-Newton method was used to conduct parameter optimization using 1,000 initial parameter sets for the seven pharmacokinetic parameters of CP-I (β, CL_int,all , F_a F_g , R_dif , f_bile , f_syn , and v_syn ), and K_i,OATP1B , and K_i,MRP2 of CysA. Based on the accepted 498 parameter sets, the range of CL_int,all and K_i,OATP1B was narrowed, with coefficients of variation (CVs) of 9.3% and 11.1%, respectively, indicating that these parameters were practically identifiable. These results suggest that PBPK model analysis of CP-I is a promising translational approach to predict OATP1B-mediated DDIs in drug development.