CPT: Pharmacometrics & Systems Pharmacology最新文献

{"title":"Exploration of the potential impact of batch-to-batch variability on the establishment of pharmacokinetic bioequivalence for inhalation powder drug products.","authors":"Shuhui Li, Kairui Feng, Jieon Lee, Yuqing Gong, Fang Wu, Bryan Newman, Miyoung Yoon, Lanyan Fang, Liang Zhao, Jogarao V S Gobburu","doi":"10.1002/psp4.13276","DOIUrl":"https://doi.org/10.1002/psp4.13276","url":null,"abstract":"<p><p>Batch-to-batch variability in inhalation powder has been identified as a potential challenge in the development of generic versions. This study explored the impact of batch-to-batch variability on the probability of establishing pharmacokinetic (PK) bioequivalence (BE) in a two-sequence, two-period (2 × 2) crossover study. A model-based parametric simulation approach was employed, incorporating batch-to-batch variability through the relative bioavailability (RBA) ratio. In the absence of batch variability, recruiting a total of 48 subjects in a 2 × 2 crossover study with the reference formulation resulted in a 95% probability of concluding BE. However, this probability decreased to 80% with a 5% batch difference in RBA and further declined to 30% with a 10% batch difference. With a 10% batch difference, the required number of subjects to achieve an 80% probability of concluding BE increased to 84. When considering product differences between the reference and the test formulations, an additional 10% batch difference reduced the study power from 97% to 30% for a T/R bioavailability ratio of 100% in a 2 × 2 crossover study with 48 subjects. As a result, the substantial impact of batch-to-batch variability on the study power and type I error of the PK BE study may pose significant challenges for the development of generic Advair Diskus due to its degree of PK batch-to-batch variability. Therefore, alternative PK BE study designs and guidelines are needed to adequately address the influence of batch-to-batch variability in products like Advair Diskus.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142686439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical study design strategies to mitigate confounding effects of time-dependent clearance on dose optimization of therapeutic antibodies.","authors":"Jeffrey R Proctor, Harvey Wong","doi":"10.1002/psp4.13280","DOIUrl":"https://doi.org/10.1002/psp4.13280","url":null,"abstract":"<p><p>Time-dependent pharmacokinetics (TDPK) is a frequent confounding factor that misleads exposure-response (ER) analysis of therapeutic antibodies, where a decline in clearance results in increased drug exposure over time in patients who respond to therapy, causing a false-positive ER finding. The object of our simulation study was to explore the influence of clinical trial designs on the frequency of false-positive ER findings. Two previously published population PK models representative of slow- (pembrolizumab) and fast-onset (rituximab) TDPK were used to simulate virtual patient cohorts with time-dependent clearance and the frequency of false-positive ER findings. The impact of varying the number of dose groups, dose range, and sample size was evaluated over time. Study designs with a single tested dose level showed a high probability of showing a false-positive ER finding. When TDPK has a slow onset, use of exposure measures from early timepoints in ER analysis significantly reduces the risk of a false-positive, while with fast onset it did not. Randomization of patients to two dose levels greatly reduced the risk, with a threefold or greater dose range offering the greatest benefit. The likelihood of false-positive increases with a larger sample size, where greater care should be taken to identify confounding factors. Clinical trial simulation supports that appropriate clinical study design and analysis with adequate dose exploration can reduce but cannot entirely eliminate the risk of misleading ER findings.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142686438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Population pharmacokinetics of selexipag for dose selection and confirmation in pediatric patients with pulmonary arterial hypertension.","authors":"Lene Nygaard Axelsen, Anne Kümmel, Juan Jose Perez Ruixo, Alberto Russu","doi":"10.1002/psp4.13231","DOIUrl":"https://doi.org/10.1002/psp4.13231","url":null,"abstract":"<p><p>Selexipag is an oral selective prostacyclin receptor agonist approved for the treatment of pulmonary arterial hypertension (PAH) in adults. To date, no treatment targeting the prostacyclin pathway is approved for pediatric patients. Our goal is to identify a pediatric dose regimen that results in comparable exposures to selexipag and its active metabolite JNJ-68006861 as those shown to be efficacious in adult PAH patients. Extrapolation from the population pharmacokinetic (PK) model developed in adults (GRIPHON study; NCT01106014) resulted in the definition of three different pediatric body weight groups (≥9 to <25 kg, ≥25 to <50 kg, and ≥50 kg) with corresponding starting doses (100, 150, and 200 μg twice daily) and maximum allowed doses (800, 1200, and 1600 μg twice daily). The proposed pediatric dose regimen was subsequently tested in a clinical study (NCT03492177), including 63 pediatric PAH patients ≥2 to <18 years of age and a body weight range of 9.9-93.5 kg. The body weight-adjusted dose regimen for selexipag resulted in comparable systemic exposures to selexipag and its active metabolite in pediatric patients as previously observed in adult PAH patients. Updating the adult selexipag population PK model provided overall consistent parameters and confirmed that the PK characteristics of selexipag and its active metabolite were comparable between pediatric and adult patients. The presented selexipag dose regimen for pediatric PAH patients is considered appropriate for continuing the clinical evaluation of the safety and efficacy of selexipag in pediatric patients ≥2 years of age.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142686440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exposure-response modeling of liver fat imaging endpoints in non-alcoholic fatty liver disease populations administered ervogastat alone and co-administered with clesacostat.","authors":"Jim H Hughes, Neeta B Amin, Jessica Wojciechowski, Manoli Vourvahis","doi":"10.1002/psp4.13275","DOIUrl":"https://doi.org/10.1002/psp4.13275","url":null,"abstract":"<p><p>Non-alcoholic fatty liver disease and non-alcoholic steatohepatitis describe a collection of liver conditions characterized by the accumulation of liver fat. Despite biopsy being the reference standard for determining the severity of disease, non-invasive measures such as magnetic resonance imaging proton density fat fraction (MRI-PDFF) and FibroScan® controlled attenuation parameter (CAP™) can be used to understand longitudinal changes in steatosis. The aim of this work was to describe the exposure-response relationship of ervogastat with or without clesacostat on steatosis, through population pharmacokinetic/pharmacodynamic (PK/PD) modeling of both liver fat measurements simultaneously. Population pharmacokinetic and exposure-response models using individual predictions of average concentrations were used to describe ervogastat/clesacostat PKPD. Due to both liver fat endpoints being continuous-bounded outcomes on different scales, a dynamic transform-both-sides approach was used to link a common latent factor representing liver fat to each endpoint. Simultaneous modeling of both MRI-PDFF and CAP™ was successful with both measurements being adequately described by the model. The clinical trial simulation was able to adequately predict the results of a recent Phase 2 study, where subjects given ervogastat/clesacostat 300/10 mg BID for 6 weeks had a LS means and model-predicted median (95% confidence intervals) percent change from baseline MRI-PDFF of -45.8% and -45.6% (-61.6% to -31.8%), respectively. Simultaneous modeling of both MRI-PDFF and CAP™ was successful with both measurements being adequately described. By describing the underlying changes of steatosis with a latent variable, this model may be extended to describe biopsy results from future studies.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142675257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of a fully mechanistic physiologically based pharmacokinetics model of absorption to support predictions of milk concentrations in breastfeeding women and the exposure of infants: A case study for albendazole","authors":"Susan Cole,&nbsp;Maria Malamatari,&nbsp;Andrew Butler,&nbsp;Mahnoor Arshad,&nbsp;Essam Kerwash","doi":"10.1002/psp4.13260","DOIUrl":"10.1002/psp4.13260","url":null,"abstract":"<p>Due to limited non-clinical and clinical data, European guidance recommends to discontinue breastfeeding when taking albendazole. The aim of this study was to consider the use of PBPK modeling to support the expected exposure in breastfed infants. A fully mechanistic PBPK approach was used to provide quantitative predictions of albendazole and its main active metabolite, albendazole sulfoxide, concentrations in plasma and breast milk of lactating women. The model predicted the exposure in adults and the large food effect, however, it does not predict all the clinical data for the exposure in children. Milk/plasma ratio predictions were also largely over-predicted for this lipophilic compound, but not for the less lipophilic metabolite. Predictions using the observed ratio and a worse-case exposure based on <i>C</i>_max predictions, suggest doses to children through milk will be low. However, more clinical data are required before full exposure predictions can be made to breastfed infants.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 11","pages":"1990-2001"},"PeriodicalIF":3.1,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13260","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A theoretical systems chronopharmacology approach for COVID-19: Modeling circadian regulation of lung infection and potential precision therapies.","authors":"Yu-Yao Tseng","doi":"10.1002/psp4.13277","DOIUrl":"https://doi.org/10.1002/psp4.13277","url":null,"abstract":"<p><p>The COVID-19 pandemic, caused by SARS-CoV-2, has underscored the urgent need for innovative therapeutic approaches. Recent studies have revealed a complex interplay between the circadian clock and SARS-CoV-2 infection in lung cells, opening new avenues for targeted interventions. This systems pharmacology study investigates this intricate relationship, focusing on the circadian protein BMAL1. BMAL1 plays a dual role in viral dynamics, driving the expression of the viral entry receptor ACE2 while suppressing interferon-stimulated antiviral genes. Its critical position at the host-pathogen interface suggests potential as a therapeutic target, albeit requiring a nuanced approach to avoid disrupting essential circadian regulation. To enable precise tuning of potential interventions, we constructed a computational model integrating the lung cellular clock with viral infection components. We validated this model against literature data to establish a platform for drug administration simulation studies using the REV-ERB agonist SR9009. Our simulations of optimized SR9009 dosing reveal circadian-based strategies that potentially suppress viral infection while minimizing clock disruption. This quantitative framework offers insights into the viral-circadian interface, aiming to guide the development of chronotherapy-based antivirals. More broadly, it underscores the importance of understanding the connections between circadian timing, respiratory viral infections, and therapeutic responses for advancing precision medicine. Such approaches are vital for responding effectively to the rapid spread of coronaviruses like SARS-CoV-2.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142675250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent applications of pharmacometrics and systems pharmacology approaches to improve and optimize drug therapy for pregnant and lactating women","authors":"Priya Jayachandran,&nbsp;Jane Knöchel,&nbsp;Brian Cicali,&nbsp;Karen Rowland Yeo","doi":"10.1002/psp4.13269","DOIUrl":"10.1002/psp4.13269","url":null,"abstract":"&lt;p&gt;Drug exposure to a fetus during pregnancy or an infant during breastfeeding remains a key concern for women of reproductive age, and this risk potential has led to the exclusion or under-representation of pregnant and lactating women in clinical trials. When included, studies have typically been underpowered or key biomarkers have been omitted. Ideally, robust data on drug exposure in mothers, fetuses, and breastfeeding infants are required to perform appropriate safety and efficacy assessments to make informed decisions regarding medication use in pregnant and lactating women. The US Food and Drug Administration (FDA) and the International Council of Harmonization (ICH) have recently released initiatives such as the Diversity Action Plan (DAP) (https://www.fda.gov/media/179593/download) and the &lt;i&gt;E21 Efficacy Guidelines for Inclusion of Pregnant and Breastfeeding Individuals in Clinical Trials&lt;/i&gt; (https://database.ich.org/sites/default/files/ICH_E21_Final_Concept_Paper_2023_1106_MCApproved.pdf), which are changing the frontiers of inclusion. These regulatory initiatives are providing the impetus for the conduct of more clinical pregnancy and lactation studies by pharmaceutical companies. While the ethical, operational, enrollment, and study design challenges in study conduct are significant, they offer an opportunity for pharmacometrics and systems pharmacology (PSP) to play a key role in making clinical studies more inclusive and supporting clinical data to inform the drug label. This themed issue in &lt;i&gt;CPT: Pharmacometrics and Systems Pharmacology&lt;/i&gt; on pregnancy and lactation offers perspectives on regulatory drivers for drug research in pregnant and lactating women, improves our understanding of non-clinical safety data to inform drug exposure in lactation, and spotlights recent quantitative applications in pharmacometrics and physiologically-based pharmacokinetic (PBPK) modeling to optimize drug therapy for pregnant and lactating women.&lt;/p&gt;&lt;p&gt;In 2022, the FDA published the draft &lt;i&gt;Diversity Plans to Improve Enrollment of Participants from Underrepresented Racial and Ethnic Populations in Clinical Trials Guidance for Industry&lt;/i&gt; (https://www.fda.gov/media/179593/download). While emphasizing race and ethnicity, the FDA encouraged sponsors also to submit plans for other underrepresented populations defined by pregnancy and lactation status. This year, the draft guidance was superseded by the draft &lt;i&gt;Diversity Action Plans to Improve Enrollment of Participants from Underrepresented Populations in Clinical Studies&lt;/i&gt;, which calls to action improved enrollment of participants from underrepresented populations in clinical studies. Complementary to the FDA DAP, the ICH released the E21 final concept paper (2023) focusing on a global framework and best practices for inclusion of pregnant and lactating women in clinical trials.&lt;/p&gt;&lt;p&gt;The ICH E21 guideline uses the ICH E11 guidance for pediatrics as its foundation. In their perspective, Copp","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 11","pages":"1815-1819"},"PeriodicalIF":3.1,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13269","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142647306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-dimensional neural ordinary differential equations accounting for inter-individual variability implemented in Monolix and NONMEM.","authors":"Dominic Stefan Bräm, Bernhard Steiert, Marc Pfister, Britta Steffens, Gilbert Koch","doi":"10.1002/psp4.13265","DOIUrl":"https://doi.org/10.1002/psp4.13265","url":null,"abstract":"<p><p>Neural ordinary differential equations (NODEs) are an emerging machine learning (ML) method to model pharmacometric (PMX) data. Combining mechanism-based components to describe \"known parts\" and neural networks to learn \"unknown parts\" is a promising ML-based PMX approach. In this work, the implementation of low-dimensional NODEs in two widely applied PMX software packages (Monolix and NONMEM) is explained. Inter-individual variability is introduced to NODEs and proposals for the practical implementation of NODEs in such software are presented. The potential of such implementations is shown on various demonstrational datasets available in the Monolix model library, including pharmacokinetic (PK), pharmacodynamic (PD), target-mediated drug disposition (TMDD), and survival analyses. All datasets were fitted with NODEs in Monolix and NONMEM and showed comparable results to classical modeling approaches. Model codes for demonstrated PK, PKPD, TMDD applications are made available, allowing a reproducible and straight-forward implementation of NODEs in available PMX software packages.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142647302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of breakthrough bleeding model of combined-oral contraceptives utilizing model-based meta-analysis","authors":"Huili Chen,&nbsp;Dain Chun,&nbsp;Karthik Lingineni,&nbsp;Serge Guzy,&nbsp;Rodrigo Cristofoletti,&nbsp;Joachim Hoechel,&nbsp;Tianze Jiao,&nbsp;Brian Cicali,&nbsp;Valvanera Vozmediano,&nbsp;Stephan Schmidt","doi":"10.1002/psp4.13261","DOIUrl":"10.1002/psp4.13261","url":null,"abstract":"<p>Breakthrough bleeding (BTB) is a common side effect of hormonal contraception and is thought to impact adherence to combined oral contraceptives (COCs) but respective dose–response relationships are not yet fully understood. Therefore, the objective of this model-based meta-analysis (MBMA) was to establish dose–response for COCs containing different progestin/EE combinations using BTB as the pharmacodynamic endpoint. Data from 25 studies containing BTB information of 4 progestins (desogestrel, drospirenone, gestodene, and levonorgestrel) in combination with ethinyl estradiol (EE) at various dose levels was used for this analysis. The results of our MBMA show that BTB is significantly increased upon initiation of COC use but subsides over time. The time needed for BTB to return to baseline depends on the EE dose and differs marginally between progestins during the initial months of use at the same EE dose. BTB typically returns to baseline within 3 months at the highest (30 μg) dose, whereas it can take significantly longer to reestablish a regular bleeding pattern at lower EE doses (15 and 20 μg), irrespective of the progestin used. The dose–response relationships established for BTB across different progestin/EE combinations can now be used to support the selection of optimal COC dosing/treatment regimens and serve as the scientific basis for evaluating the impact of clinically relevant factors, including drug–drug interactions and demographics, on BTB.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 11","pages":"2016-2025"},"PeriodicalIF":3.1,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13261","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142647341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence modeling of biomarker-based physiological age: Impact on phase 1 drug-metabolizing enzyme phenotypes.","authors":"Amruta Gajanan Bhat, Murali Ramanathan","doi":"10.1002/psp4.13273","DOIUrl":"https://doi.org/10.1002/psp4.13273","url":null,"abstract":"<p><p>Age and aging are important predictors of health status, disease progression, drug kinetics, and effects. The purpose was to develop ensemble learning-based physiological age (PA) models for evaluating drug metabolism. National Health and Nutrition Examination Survey (NHANES) data were modeled with ensemble learning to obtain two PA models, PA-M1 and PA-M2. PA-M1 included body composition, blood and urine biomarkers, and disease variables as predictors. PA-M2 had blood and urine-derived variables as predictors. Activity phenotypes for cytochrome-P450 (CYP) CYP2E1, CYP1A2, CYP2A6, xanthine oxidase (XO), and N-acetyltransferase-2 (NAT-2) and telomere attrition were assessed. Bayesian networks were used to obtain mechanistic systems pharmacology model structures for PA. The study included n = 22,307 NHANES participants (51.5% female, mean age 46.0 years, range: 18-79 years). The PA-M1 and PA-M2 distributions had greater dispersion across age strata with a right skew for younger age strata and a left skew for older age strata. There was no evidence of algorithmic bias based on sex or race/ethnicity. Klotho, lean body mass, glycohemoglobin, and systolic blood pressure were the top four predictors for PA-M1. Glycohemoglobin, serum creatinine, total cholesterol, and urine creatinine were the top four predictors for PA-M2. The models also performed satisfactorily in independent validation. Model-predicted PA was associated with CYP2E1, CYP1A2, CYP2A6, XO, and NAT-2 activity. Telomere attrition was associated with greater PA-M1 and PA-M2. Ensemble learning models provide robust assessments of PA from easily obtained blood and urine biomarkers. PA is associated with Phase I drug-metabolizing enzyme phenotypes.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}