Successful Prediction of Human Pharmacokinetics by Improving Calculation Processes of Physiologically Based Pharmacokinetic Approach

The physiologically based pharmacokinetics (PBPK) model is a major mechanistic approach for predicting human pharmacokinetics (PK) using drug-specific and physiological parameters but has been difficult to use for human PK prediction with acceptable accuracy. Here, we report a newly developed PBPK approach that incorporates the mechanism of albumin-mediated membrane penetration in the liver and interspecies correlation for unbound tissue fractions. To verify the utility of our PBPK approach, we used 12 drugs that are mainly eliminated by hepatic metabolism to compare the prediction accuracy with a conventional PBPK approach and to observe human PK parameters. We found the predictive accuracy for total clearance (CL_tot), distribution volume at the steady state (V_ss), elimination half-life (t_1/2), and plasma concentration at the last measurable time point (C_last) of our PBPK approach to show better absolute average fold error and percentage within 2-fold error (1.6-1.8 and 67%-92%, respectively) compared with values obtained from the conventional PBPK approach (2.1-2.4 and 42%-67%, respectively). As our approach can use parameters obtained in early drug screening, it could help accelerate successful nomination of drug candidates by optimizing the pharmacokinetics of new chemical entities by directly using predicted human PK profiles.     

Physiologically Based Pharmacokinetic Modeling to Supplement Nilotinib Pharmacokinetics and Confirm Dose Selection in Pediatric Patients

In adult patients, nilotinib is indicated for chronic myeloid leukemia at an approved oral dose of 300 or 400 mg BID. Physiologically based pharmacokinetic (PBPK) model was developed to describe and supplement limited PK data in the pediatric population ranging from 2 to less than 6 years of age and ultimately inform dosing regimen. An adult Simcyp PBPK model was established and verified with clinical pharmacokinetic data after a single or multiple oral doses of 400 mg nilotinib (230 mg/m²). The model was then applied to a pediatric PBPK model, taking account of ontogeny profiles of metabolizing enzymes and pediatric physiological parameters. The model was further verified using observed pediatric PK data in 12- to <18-year-old and from 6- to <12-year-old patients. The PBPK models were able to recover, describe, and supplement the limited nilotinib concentration-time data profile in 2- to <6-year-old patients after a single dose and C_min,ss after BID dosing. The exposure (C_max,ss, C_min,ss, and AUC_tau,ss) was predicted to be similar across age groups. PBPK model simulations confirmed that body surface area–normalized dosing regimen of 230 mg/m² is considered appropriate for pediatric patients >2 to <18 years of age.     

The Quantitative Prediction of CYP-mediated Drug Interaction by Physiologically Based Pharmacokinetic Modeling

PURPOSE: The objective is to confirm if the prediction of the drug-drug interaction using a physiologically based pharmacokinetic (PBPK) model is more accurate. In vivo Ki values were estimated using PBPK model to confirm whether in vitro Ki values are suitable. METHOD: The plasma concentration-time profiles for the substrate with coadministration of an inhibitor were collected from the literature and were fitted to the PBPK model to estimate the in vivo Ki values. The AUC ratios predicted by the PBPK model using in vivo Ki values were compared with those by the conventional method assuming constant inhibitor concentration. RESULTS: The in vivo Ki values of 11 inhibitors were estimated. When the in vivo Ki values became relatively lower, the in vitro Ki values were overestimated. This discrepancy between in vitro and in vivo Ki values became larger with an increase in lipophilicity. The prediction from the PBPK model involving the time profile of the inhibitor concentration was more accurate than the prediction by the conventional methods. CONCLUSION: A discrepancy between the in vivo and in vitro Ki values was observed. The prediction using in vivo Ki values and the PBPK model was more accurate than the conventional methods.     

Relative Bioavailability Risk Assessment: A Systematic Approach to Assessing In Vivo Risk Associated With CM&C-Related Changes

Relative bioavailability (RBA) studies are often carried out to bridge changes made between drug products used for clinical studies. In this work, we describe the development of a risk assessment (RA) tool that comprehensively and objectively assesses the risk of noncomparable in vivo performance associated with Chemistry, Manufacturing, and Controls (CM&C)-related changes. The RA tool is based on a risk grid that provides a quantitative context to facilitate discussions to determine the need for an in vivo RBA study. Relevant regulatory guidances and the required in vitro and in silico absorption modeling data, on which the RA is based, are discussed. In addition, an analysis of previously executed RBA studies at Eli Lilly and Company over a period of several years is presented. The risk grid incorporates individual risk factors for a given study and provides a recommendation on the risk associated with bypassing an RBA study. The outcome of an RA results in 1 of 3 possible risk zones; lower tier risk, intermediate tier risk, and upper tier risk. In cases where the outcome from the RA falls into the intermediate tier risk zone, further in depth data analysis is required.     

Integrating Drug- and Formulation-Related Properties With Gastrointestinal Tract Variability Using a Product-Specific Particle Size Approach: Case Example Ibuprofen

In the present study, an in vitro–in vivo extrapolation of dissolution integrated to a physiologically based pharmacokinetics modeling approach, considering a product-specific particle size distribution and a self-buffering effect of the drug, is introduced and appears to be a promising translational modeling strategy to support drug product development, manufacturing changes and setting clinically relevant specifications for immediate release formulations containing ibuprofen and other weak acids with similar properties.     

曲妥珠单抗-药物共轭物(T-DM1)临床前药物代谢动力学研究及人体药物代谢动力学预测

本试验通过临床前药物代谢动力学(PK)和毒理学研究结果,对曲妥珠单抗-药物共轭物(T-DM1)的人体PK特性进行了预测,并探讨了目前广泛采用的预测方法的不足和可能的解决途径。本试验首先进行了动物试验,包括大鼠急性毒性试验和食蟹猴PK试验,通过试验获得了T-DM1的总抗体、偶联抗体和游离小分子药物emtansine(DM1)的PK参数,随后基于这些参数,使用异速增长模型和种属-时间不变法,对总抗体和偶联抗体的人体PK特性进行了预测。此外,通过参考近年的一些相关研究,探讨了如何基于动物生理药动学(PBPK)模型,更科学地预测抗体偶联药物中小分子药物的人体PK和分布特性。     

Physiologically based pharmacokinetic modeling to predict complex drug–drug interactions: a case study of AZD2327 and its metabolite,competitive and time‐dependent CYP3A inhibitors

4‐{(R)‐(3‐Aminophenyl)[4‐(4‐fluorobenzyl)‐piperazin‐1‐yl]methyl}‐N,N‐diethylbenzamide (AZD2327) is a highly potent and selective agonist of the δ ‐opioid receptor. AZD2327 and N‐deethylated AZD2327 (M1) are substrates of cytochrome P450 3A (CYP3A4) and comprise a complex multiple inhibitory system that causes competitive and time‐dependent inhibition of CYP3A4. The aim of the current work was to develop a physiologically based pharmacokinetic (PBPK) model to predict quantitatively the magnitude of CYP3A4 mediated drug–drug interaction with midazolam as the substrate. Integrating in silico, in vitro and in vivo PK data, a PBPK model was successfully developed to simulate the clinical accumulation of AZD2327 and its primary metabolite. The inhibition of CYP3A4 by AZD2327, using midazolam as a probe drug, was reasonably predicted. The predicted maximum concentration (C_max) and area under the concentration–time curve (AUC) for midazolam were increased by 1.75 and 2.45‐fold, respectively, after multiple dosing of AZD2327, indicating no or low risk for clinically relevant drug–drug interactions (DDI). These results are in agreement with those obtained in a clinical trial with a 1.4 and 1.5‐fold increase in C_max and AUC of midazolam, respectively. In conclusion, this model simulated DDI with less than a two‐fold error, indicating that complex clinical DDI associated with multiple mechanisms, pathways and inhibitors (parent and metabolite) can be predicted using a well‐developed PBPK model. Copyright © 2015 John Wiley & Sons, Ltd.     

Fifty-Eight Years and Counting: High-Impact Publishing in Computational Pharmaceutical Sciences and Mechanism-Based Modeling

With this issue of the Journal of Pharmaceutical Sciences, we celebrate the nearly 6 decades of contributions to mechanistic-based modeling and computational pharmaceutical sciences. Along with its predecessor, The Journal of the American Pharmaceutical Association: Scientific Edition first published in 1911, JPharmSci has been a leader in the advancement of pharmaceutical sciences beginning with its inaugural edition in 1961. As one of the first scientific journals focusing on pharmaceutical sciences, JPharmSci has established a reputation for publishing high-quality research articles using computational methods and mechanism-based modeling. The journal’s publication record is remarkable. With over 15,000 articles, 3000 notes, and more than 650 reviews from industry, academia, and regulatory agencies around the world, JPharmSci has truly been the leader in advancing pharmaceutical sciences.     

Physiologically Based Pharmacokinetic Modeling of Drug Disposition in Rat and Human: A Fuzzy Arithmetic Approach

PURPOSE: Probabilistic methods are insufficient for dealing with the vagueness inherent in human judgment of minimal data available during early drug development. We sought to use fuzzy set theory as a basis for quantifying and propagating vague judgment in a physiologically based pharmacokinetic (PBPK) model for diazepam disposition. MATERIALS AND METHODS: First, using diazepam distribution data in rat tissues and fuzzy regression, we estimated fuzzy rat tissue-to-plasma partition coefficients (Kp's). We scaled the coefficients prior to human PBPK modeling. Next, we constructed the fuzzy set of hepatic intrinsic clearance (CLint) by integrating CLint values measured in vitro from human hepatocytes. Finally, we used these parameters, and other physiological and biochemical information, to predict human diazepam disposition. We compared the simulated plasma kinetics with published concentration-time profiles. RESULTS: We successfully identified rat Kp's by fuzzy regression. The predicted rat tissue concentration-time contours enveloped the animal tissue distribution data. For the human PBPK model, the mean in vivo plasma concentrations were contained in the simulated concentration-time envelopes. CONCLUSIONS: We present a novel computational approach for handling information paucity in PBPK models using fuzzy arithmetic. Our methodology can model the vagueness associated with human perception and interpretation of minimal drug discovery data.     

Predicting age-appropriate pharmacokinetics of six volatile organic compounds in the rat utilizing physiologically based pharmacokinetic modeling.

The capability of physiologically based pharmacokinetic models to incorporate age-appropriate physiological and chemical-specific parameters was utilized to predict changes in internal dosimetry for six volatile organic compounds (VOCs) across different ages of rats. Typical 6-h animal inhalation exposures to 50 and 500 ppm perchloroethylene, trichloroethylene, benzene, chloroform, methylene chloride, or methyl ethyl ketone (MEK) were simulated for postnatal day 10 (PND10), 2-month-old (adult), and 2-year-old (aged) rats. With the exception of MEK, predicted venous blood concentrations of VOCs in the aged rat were equal or up to 1.5-fold higher when compared to the adult rat at both exposure levels, whereas levels were predicted to be up to 3.8-fold higher in the case of PND10 at 50 ppm. Predicted blood levels of MEK were similar in the adult and aged rat, but were more than 5-fold and 30-fold greater for PND10 rats at 500 and 50 ppm, respectively, reflecting high water solubility along with lower metabolic capability and faster ventilation rate per unit body weight (BW) of PND10 animals. Steady-state blood levels of VOCs, simulated by modeling constant exposure, were predicted to be achieved in the order PND10 > adult > aged, largely due to increasing fat volume. The dose metric, total amount metabolized per unit liver volume was generally much lower in PND10 than in adult rats. The blood:air partition coefficient, fat volume, and fat blood flow were identified as critical determinants for the predicted differences in venous blood concentrations between the adult and aged. The lower metabolic capability, largely due to a smaller liver size, and faster ventilation rate per unit BW of PND10 animals contribute the most to the differences between PND10 and adult rats. This study highlights the pharmacokinetic differences and the relevant parameters that may contribute to differential susceptibility to the toxic effects of VOCs across life stages of the rat.     

A Physiologically Based Pharmacokinetic Modeling Approach to Predict Drug-Drug Interactions of Buprenorphine After Subcutaneous Administration of CAM2038 With Perpetrators of CYP3A4

CAM2038, FluidCrystal injection depot, is an extended release formulation of buprenorphine given subcutaneously every 1 week (Q1W) or every 4 weeks (Q4W). The purpose of this research was to predict the magnitude of drug-drug interaction (DDI) after coadministration of a strong CYP3A4 inducer or inhibitor using physiologically based pharmacokinetic (PBPK) modeling. A PBPK model was developed for CAM2038 based on the previously published buprenorphine PBPK model after intravenous and sublingual administration and the PK profiles after subcutaneous administration of CAM2038 from 2 phase I clinical trials. The strong CYP3A4 inhibitor ketoconazole was predicted to increase the buprenorphine exposure by 35% for the Q1W formulation and 34% for Q4W formulation, respectively. Also, the strong CYP3A4 inducer rifampin was predicted to decrease the buprenorphine exposure by 26% for both the Q1W and Q4W formulations. The results provided insight into the potential DDI effect for CAM2038 and suggested a lack of clinically meaningful DDI when CAM2038 is coadministered with CYP3A4 inhibitor or inducer. Therefore, no dose adjustment is required when CAM2038 is coadministered with CYP3A4 perpetrators.     

Challenges and opportunities with modelling and simulation in drug discovery and drug development

The benefits of modelling and simulation at the pre-clinical stage of drug development can be realized through formal and realistic integration of data on physicochemical properties, pharmacokinetics, pharmacodynamics, formulation and safety. Such data integration and the powerful combination of physiologically based pharmacokinetic (PBPK) with pharmacokinetic–pharmacodynamic relationship (PK/PD) models provides the basis for quantitative outputs allowing comparisons across compounds and resulting in improved decision-making during the selection process. Such PBPK/PD evaluations provide crucial information on the potency and safety of drug candidates in vivo and the bridging of the PK/PD concept established during the pre-clinical phase to clinical studies. Modelling and simulation is required to address a number of key questions at the various stages of the drug-discovery and -development process. Such questions include the following. (1) What is the expected human PK profile for potential clinical candidate(s)? (2) Is this profile and its associated PD adequate for the given indication? (3) What is the optimal dosing schedule with respect to safety and efficacy? (4) Is a food effect expected? (5) How can formulation be improved and what is the potential benefit? (6) What is the expected variability and uncertainty in the predictions?     

Physiologically Based Pharmacokinetic Modeling of a Homologous Series of Barbiturates in the Rat: A Sensitivity Analysis

Sensitivity analysis studies the effects of the inherent variability and uncertainty in model parameters on the model outputs and may be a useful tool at all stages of the pharmacokinetic modeling process. The present study examined the sensitivity of a whole-body physiologically based pharmacokinetic (PBPK) model for the distribution kinetics of nine 5-n-alkyl-5-ethyl barbituric acids in arterial blood and 14 tissues (lung, liver, kidney, stomach, pancreas, spleen, gut, muscle, adipose, skin, bone, heart, brain, testes) after iv bolus administration to rats. The aims were to obtain new insights into the model used, to rank the model parameters involved according to their impact on the model outputs and to study the changes in the sensitivity induced by the increase in the lipophilicity of the homologues on ascending the series. Two approaches for sensitivity analysis have been implemented. The first, based on the Matrix Perturbation Theory, uses a sensitivity index defined as the normalized sensitivity of the 2-norm of the model compartmental matrix to perturbations in its entries. The second approach uses the traditional definition of the normalized sensitivity function as the relative change in a model state (a tissue concentration) corresponding to a relative change in a model parameter. Autosensitivity has been defined as sensitivity of a state to any of its parameters; cross-sensitivity as the sensitivity of a state to any other states' parameters. Using the two approaches, the sensitivity of representative tissue concentrations (lung, liver, kidney, stomach, gut, adipose, heart, and brain) to the following model parameters: tissue-to-unbound plasma partition coefficients, tissue blood flows, unbound renal and intrinsic hepatic clearance, permeability surface area product of the brain, have been analyzed. Both the tissues and the parameters were ranked according to their sensitivity and impact. The following general conclusions were drawn: (i) the overall sensitivity of the system to all parameters involved is small due to the weak connectivity of the system structure; (ii) the time course of both the auto- and cross-sensitivity functions for all tissues depends on the dynamics of the tissues themselves, e.g., the higher the perfusion of a tissue, the higher are both its cross-sensitivity to other tissues' parameters and the cross-sensitivities of other tissues to its parameters; and (iii) with a few exceptions, there is not a marked influence of the lipophilicity of the homologues on either the pattern or the values of the sensitivity functions. The estimates of the sensitivity and the subsequent tissue and parameter rankings may be extended to other drugs, sharing the same common structure of the whole body PBPK model, and having similar model parameters. Results show also that the computationally simple Matrix Perturbation Analysis should be used only when an initial idea about the sensitivity of a system is required. If comprehensive information regarding the sensitivity is needed, the numerically expensive Direct Sensitivity Analysis should be used.     

Reducing Whole Body Physiologically Based Pharmacokinetic Models Using Global Sensitivity Analysis: Diazepam Case Study

Abtract There are situations in drug development where one may wish to reduce the dimensionality and complexity of whole body physiologically based pharmacokinetic models. A technique for formal reduction of such models, based on global sensitivity analysis, is suggested. Using this approach mean and variance of tissue(s) and/or blood concentrations are preserved in the reduced models. Extended Fourier amplitude sensitivity test (FAST), a global sensitivity technique, takes a sampling approach, acknowledging parameter variability and uncertainty, to calculate the impact of parameters on concentration variance. We used existing literature rules for formal model reduction to identify all possible smaller dimensionally models. To discriminate among those competing mechanistic models extended FAST was used, whereby we treated model structural uncertainty as another factor contributing to the overall uncertainty. A previously developed 14 compartment whole body physiologically based model for diazepam disposition in rat was reduced to three alternative reduced models, with preserved arterial mean and variance concentration profiles.     

Bioequivalence Comparison of Pediatric Dasatinib Formulations and Elucidation of Absorption Mechanisms Through Integrated PBPK Modeling

SPRYCEL^® (Dasatinib) is a Biopharmaceutical Classification System II weakly basic drug that exhibits strong pH-dependent solubility. Dasatinib is currently presented in 2 drug product formulations as an adult immediate release tablet and a pediatric powder for oral suspension. A bioequivalence study comparing the formulations in adult healthy subjects found that overall exposure (AUC_0-24) from suspension treatments was ～9% to 13% lower, Cmax was similar, and median Tmax from powder for oral suspension was ～30 min earlier. To understand the mechanism contributing to this behavior, a combination of biorelevant dissolution studies and physiologically based pharmacokinetic modeling was used to simulate in vivo performance. In vitro biorelevant dissolution confirmed that the rate and extent of release was similar between tablet and suspension formulations (>90% release within first 15 min). Physiologically based pharmacokinetic parameter sensitivity analysis demonstrated particular sensitivity to dosage form gastric residence time. A 12% higher AUC_0-24 was simulated for tablet dosage forms with 10 to 15 min longer gastric transit relative to solutions or suspensions of small particulates (rapid gastric emptying). The corresponding narrow simulated Cmax range also agreed with observed tablet and suspension bioequivalence data. The unique physicochemical properties, absorption characteristics, and inherent differences in dosage form transit behavior are attributed to influence the dasatinib bioequivalence.     

Fuzzy Simulation of Pharmacokinetic Models: Case Study of Whole Body Physiologically Based Model of Diazepam

The aim of the present study is to develop and implement a methodology that accounts for parameter variability and uncertainty in the presence of qualitative and semi-quantitative information (fuzzy simulations) as well as when some parameters are better quantitatively defined than others (fuzzy-probabilistic approach). The fuzzy simulations method consists of (i) representing parameter uncertainty and variability by fuzzy numbers and (ii) simulating predictions by solving the pharmacokinetic model. The fuzzy-probabilistic approach includes an additional transformation between fuzzy numbers and probability density functions. To illustrate the proposed method a diazepam WBPBPK model was used where the information for hepatic intrinsic clearance determined by in vitro-in vivo scaling was semi-quantitative. The predicted concentration time profiles were compared with those resulting from a Monte Carlo simulation. Fuzzy simulations can be used as an alternative to Monte Carlo simulation.     

Prediction of ARA/PPI Drug-Drug Interactions at the Drug Discovery and Development Interface

Advances in understanding of human disease have prompted the U.S. Food and Drug Administration to classify certain molecules as “break-through therapies,” providing an accelerated review that may potentially enhance the quality of patient lives. With this designation come compressed timelines to develop drug products, which are not only suitable for clinic trials but can also be approved and brought to the market rapidly. Early risk identification for decreased oral absorption due to drug-drug interactions with proton pump inhibitors (PPIs) or acid-reducing agents (ARAs) is paramount to an effective drug product development strategy. An early ARA/PPI drug-drug interaction (DDI) risk identification strategy has been developed using physiologically based absorption modeling that readily integrates ADMET predictor generated in silico estimates or measured in vitro solubility, permeability, and ionization constants. Observed or predicted pH-solubility profile data along with pKas and drug dosing parameters were used to calculate a fraction of drug absorbed ratio in absence and presence of ARAs/PPIs. An integrated physiologically based pharmacokinetic absorption model using GastroPlus? with pKa values fitted to measured pH-solubility profile data along with measured permeability data correctly identified the observed ARA/PPI DDI for 78% (16/22) of the clinical studies. Formulation strategies for compounds with an anticipated pH-mediated DDI risk are presented.     

Integration of Precipitation Kinetics From an In Vitro,Multicompartment Transfer System and Mechanistic Oral Absorption Modeling for Pharmacokinetic Prediction of Weakly Basic Drugs

Solubility, dissolution, and precipitation in the gastrointestinal tract can be critical for the oral bioavailability of weakly basic drugs. To understand the dissolution and precipitation during the transfer out of the stomach into the intestine, a multicompartment transfer system was developed by modifying a conventional dissolution system. This transfer system included gastric, intestinal, sink and supersaturation, and reservoir compartments. Simulated gastric fluid and fasted state simulated intestinal fluid were used in the gastric and intestinal compartment, respectively, to mimic fasted condition. The new transfer system was evaluated based on 2 model weak bases, dipyridamole and ketoconazole. Traditional 2-stage dissolution using 250 mL of simulated gastric fluid media, followed by 250 mL of fasted state simulated intestinal fluid, was used as a reference methodology to compare dissolution and precipitation results. An in silico model was built using R software suite to simulate the in vitro time-dependent dissolution and precipitation process when formulations were tested using the transfer system. The precipitation rate estimated from the in vitro data was then used as the input for absorption and pharmacokinetic predictions using GastroPlus. The resultant simulated plasma concentration profiles were generally in good agreement with the observed clinical data, supporting the translatability of the transfer system in vitro precipitation kinetics to in vivo.     

A Refined Developability Classification System

In 2010, the Developability Classification System (DCS) was proposed. The DCS was designed to close the gap between the biopharmaceutics classification system, which is aimed at guiding regulatory decisions about well-characterized drugs, and the need for early evaluation of drug candidates with respect to their suitability for oral delivery. The DCS applied solubility in fasted state simulated intestinal fluid to estimate intestinal solubility, assessed the compensatory nature of permeability and solubility during oral absorption and provided a way of estimating the critical the particle size at which dissolution becomes rate-limiting to absorption. Building on this framework, a refined developability classification system (rDCS) is now proposed. The rDCS is stratified into standard investigations applied to all candidates, and customized investigations. Standard investigation of solubility and permeability can be performed according to in-house methods, and the results compared with standard data sets of fasted state human intestinal fluid solubility and human effective jejunal permeability, which have been generated specifically for rDCS. Customized investigations are triggered when there is potential for supersaturation/precipitation (weak bases; salts of weak acids) and to assess dissolution versus permeation limited absorption. In addition, the rDCS offers facile visualization of the results, enabling pragmatic comparison of drug candidates and formulation approaches.