Application of PBPK Modeling and Virtual Clinical Study Approaches to Predict the Outcomes of CYP2D6 Genotype-Guided Dosing of Tamoxifen

Applications of model simulation in pharmacological fields and the problems of theoretical reliability

The use of mathematical models has become increasingly prevalent in pharmacological fields, particularly in drug development processes. These models are instrumental in tasks such as designing clinical trials and assessing factors like efficacy, toxicity, and clinical practice. Various types of models have been developed and documented. Nevertheless, emphasizing the reliability of parameter values is crucial, as they play a pivotal role in shaping the behavior of the system. In some instances, parameter values reported previously are treated as fixed values, which can lead to convergence towards values that deviate substantially from those found in actual biological systems. This is especially true when parameter values are determined through fitting to limited observations. To mitigate this risk, the reuse of parameter values from previous reports should be approached with a critical evaluation of their validity. Currently, there is a proposal for a simultaneous search for plausible values for all parameters using comprehensive search algorithms in both pharmacokinetic and pharmacodynamic or systems pharmacological models. Implementing these methodologies can help address issues related to parameter determination. Furthermore, integrating these approaches with methods developed in the field of machine-learning field has the potential to enhance the reliability of parameter values and the resulting model outputs.

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.

Prediction of combination effect of quinidine on the pharmacokinetics of tipepidine using a physiologically based pharmacokinetic model

Tipepidine, an antitussive drug, has been reported to have central pharmacological effects and can be expected to be safely repositioned as treatment for psychiatric disorders. Since tipepidine requires three doses per day, development of a once-daily medication would be highly beneficial. Previously, we reported that combination use with quinidine, a CYP2D6 inhibitor, prolongs the half-life of tipepidine in chimeric mice with humanised liver.In this study, to predict this combination effect in humans, a physiologically based pharmacokinetic (PBPK) model was developed, and quantitative simulation was conducted. The simulation results indicated that concomitant administration of tipepidine with quinidine increased the predicted Cmax, AUC, and t1/2 of tipepidine in the Japanese population by 3.4-, 6.6-, and 2.4-fold, respectively.Furthermore, to compare with another approach that aims to prolong the half-life, the PK profile of tipepidine administered in hypothetical extended-release form was simulated. Extended-release form was predicted to be more influenced by CYP2D6 genotype than combination with quinidine, and the predicted plasma exposure was markedly increased in poor metabolizers, potentially leading to adverse effects.In conclusion, quantitative simulation using the PBPK model suggests the feasibility of the safe repositioning of tipepidine as a once-daily medication in combination with quinidine.

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.

Physiologically-based pharmacokinetic modeling for investigating the effect of simeprevir on concomitant drugs and an endogenous biomarker of OATP1B

The orally available anti-hepatitis C virus (HCV) drug simeprevir exhibits nonlinear pharmacokinetics at the clinical doses due to saturation of cytochrome P450 (CYP) 3A4 metabolism and organic anion transporting peptide (OATP) 1B mediated hepatic uptake. Additionally, simeprevir increases exposures of concomitant drugs by CYP3A4 and OATP1B inhibition. The objective of this study was to develop physiologically-based pharmacokinetic (PBPK) models that could describe drug-drug interactions (DDIs) of simeprevir with concomitant drugs via CYP3A4 and OATP1B inhibition, and also to capture the effects on coproporphyrin-I (CP-I), an endogenous biomarker of OATP1B. PBPK modeling estimated unbound simeprevir inhibitory constant (Ki ) of 2.89 μM against CYP3A4 in the DDI results between simeprevir and midazolam in healthy volunteers. Then, we analyzed the DDIs between simeprevir and atorvastatin, a dual substrate of CYP3A4 and OATP1B, in healthy volunteers, and unbound Ki against OATP1B was estimated to be 0.00347 μM. Finally, we analyzed the increase in the blood level of CP-I by simeprevir to verify the Ki,OATP1B . Because CP-I was measured in subjects with HCV with various hepatic fibrosis state, Monte Carlo simulation was performed to involve the decreases in expression levels of hepatic CYP3A4 and OATP1B and their interindividual variabilities. The PBPK modeling coupled with Monte Carlo simulation using the Ki,OATP1B value obtained from atorvastatin study reasonably recovered the observed relationship between CP-I and simeprevir blood levels. In conclusion, the simeprevir PBPK model developed in this study can quantitatively describe the increase in exposures of concomitant drugs and an endogenous biomarker via inhibition of CYP3A4 and OATP1B.

All Roads Lead to Rome: Enhancing the Probability of Target Attainment with Different Pharmacokinetic/Pharmacodynamic Modelling Approaches

In light of rising antimicrobial resistance and a decreasing number of antibiotics with novel modes of action, it is of utmost importance to accelerate development of novel treatment options. One aspect of acceleration is to understand pharmacokinetics (PK) and pharmacodynamics (PD) of drugs and to assess the probability of target attainment (PTA). Several in vitro and in vivo methods are deployed to determine these parameters, such as time-kill-curves, hollow-fiber infection models or animal models. However, to date the use of in silico methods to predict PK/PD and PTA is increasing. Since there is not just one way to perform the in silico analysis, we embarked on reviewing for which indications and how PK and PK/PD models as well as PTA analysis has been used to contribute to the understanding of the PK and PD of a drug. Therefore, we examined four recent examples in more detail, namely ceftazidime-avibactam, omadacycline, gepotidacin and zoliflodacin as well as cefiderocol. Whereas the first two compound classes mainly relied on the ‘classical’ development path and PK/PD was only deployed after approval, cefiderocol highly profited from in silico techniques that led to its approval. Finally, this review shall highlight current developments and possibilities to accelerate drug development, especially for anti-infectives.

Population Pharmacokinetics of Z-Endoxifen in Patients With Advanced Solid Tumors

The purpose of this study was to develop and validate a population pharmacokinetic model for Z-endoxifen in patients with advanced solid tumors and to identify clinical variables that influence pharmacokinetic parameters. Z-endoxifen-HCl was administered orally once a day on a 28-day cycle (±3 days) over 11 dose levels ranging from 20 to 360 mg. A total of 1256 Z-endoxifen plasma concentration samples from 80 patients were analyzed using nonlinear mixed-effects modeling to develop a population pharmacokinetic model for Z-endoxifen. A 2-compartment model with oral depot and linear elimination adequately described the data. The estimated apparent total clearance, apparent central volume of distribution, and apparent peripheral volume of distribution were 4.89 L/h, 323 L, and 39.7 L, respectively, with weight-effect exponents of 0.75, 1, and 1, respectively. This model was used to explore the effects of clinical and demographic variables on Z-endoxifen pharmacokinetics. Weight, race on clearance, and aspartate aminotransferase on the absorption rate constant were identified as significant covariates in the final model. This novel population pharmacokinetic model provides insight regarding factors that may affect the pharmacokinetics of Z-endoxifen and may assist in the design of future clinical trials.

Unique Genotyping Protocol of CYP2D6 Allele Frequency Using Real Time Quantitative PCR from Japanese Healthy Women

CYP2D6 is an important drug-metabolizing enzyme involved in the metabolism of 20-25% of commonly prescribed drugs. Genetic polymorphism of CYP has clinically significant modifications in patients' drug-metabolizing capacities. Since gene copy number variation (CNV) and single nucleotide polymorphism (SNP) frequently occur in the CYP2D6 gene, which the activity of CYP2D6 particularly depend on the genetic factors. This study aimed to investigate the frequencies of CYP2D6 genotypes in a Japanese female subject of 216 healthy volunteers. The volunteers were genotyped for CNV Exon 9 and four CYP2D6 genetic variants (*2, *5, *10, *14, *41) performed by TaqMan^® genotyping assays. The CNV allele frequencies were 82.9% for two copies, 11.6% for one copy, 4.6% for three copies and 0.9% for zero copy, respectively. The frequencies of CYP2D6*1, *2, *5, *10, *14, and *41 were 38.7, 16.7, 6.3, 34.7, 0.2, and 1.2%, respectively. CYP2D6*5 and *14 were the major defective alleles. However, this genotyping is labor intensive, time consuming, and costly. We report an optimized novel protocol for the determination of CNV and SNP in CYP2D6 gene by real-time quantitative PCR. This can lower the cost and accurately determine CNV and SNP in the CYP2D6 gene with a higher output and enabling reliable estimates of disease prediction in large epidemiological samples.