CPT: Pharmacometrics & Systems Pharmacology最新文献

{"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}

{"title":"Local depletion of large molecule drugs due to target binding in tissue interstitial space","authors":"Tatiana Zasedateleva,&nbsp;Stephan Schaller,&nbsp;Elizabeth C. M. de Lange,&nbsp;Wilhelmus E. A. de Witte","doi":"10.1002/psp4.13262","DOIUrl":"10.1002/psp4.13262","url":null,"abstract":"<p>Drug–target binding determines a drug's pharmacodynamics but can also have a profound impact on a drug's pharmacokinetics, known as target-mediated drug disposition (TMDD). TMDD models describe the influence of drug–target binding and target turnover on unbound drug concentrations and are frequently used for biologics and drugs with nonlinear plasma pharmacokinetics. For drug targets expressed in tissues, the effect of TMDD may not be detected when analyzing plasma concentration curves, but it might still affect tissue concentrations and occupancy. This review aimed to investigate the likeliness of such a scenario by reviewing the literature for a typical range of TMDD parameter values and their impact on local drug concentrations and target occupancy in a whole-body PBPK model with TMDD. Our analysis demonstrated that tissue drug concentrations are impacted and significantly depleted in many physiological scenarios. In contrast, the effect on plasma concentrations is much lower, specifically for smaller organs with lower perfusion. Moreover, in scenarios with fast internalization of the drug–target complex, the distribution of large molecules from plasma to tissue interstitial space emerges as a rate-limiting step for the drug–target interaction. These factors may lead to overpredicting local drug concentrations when considering only plasma pharmacokinetics. A sensitivity analysis revealed the high and not always intuitive impact of drug-specific parameters, including the drug molecule hydrodynamic radius, dissociation constant (<i>K</i>_d), drug–target complex internalization rate constant (<i>k</i>_int), and target dissociation rate constant (<i>k</i>_off), on the drug's pharmacokinetics. Our analysis demonstrated that tissue TMDD needs to be considered even if plasma pharmacokinetics are linear.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 12","pages":"2068-2086"},"PeriodicalIF":3.1,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13262","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616195","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":"Population pharmacokinetics and pharmacodynamics of edoxaban in pediatric patients.","authors":"Peng Zou, Akhilesh Atluri, Peter Chang, Michael Goedecke, Tarek A Leil","doi":"10.1002/psp4.13248","DOIUrl":"https://doi.org/10.1002/psp4.13248","url":null,"abstract":"<p><p>Edoxaban is an orally active inhibitor of activated factor X (FXa). Population pharmacokinetic (PK) and pharmacodynamic (PD) analyses were performed to characterize the PK and PK-PD relationships of edoxaban in pediatric patients to identify the covariates that may contribute to inter-subject variability in PK and PD of edoxaban in pediatric patients, and to compare the PK and PD data between pediatric and adult patients. The pediatric PK of edoxaban was best described by a two-compartment model with transit compartments, first-order oral absorption, and linear elimination. The estimated glomerular filtration rate (eGFR), body weight, and post-menstrual age were the significant covariates explaining variability in edoxaban PK among pediatric patients. A function based on renal maturation was applied to edoxaban clearance. The clearance for a 70 kg patient with an eGFR of 110 mL/min/1.73 m² was estimated to be 42.9 L/h (CV ~ 31.8%). PK simulation showed that exposures across five pediatric age groups were comparable to that in adult patients receiving 60 mg once daily dose. The PK-PD relationship for anti-factor Xa was best fit with an E_max (8.65 IU/mL) model with an EC₅₀ of 631 ng/mL. The PK-PD relationships for activated partial thromboplastin time and prothrombin time were best fit with linear models (slopes of 0.0467, and 0.0415 s mL/ng, respectively). In addition, due to the small number of efficacy and safety events, an exploratory analysis did not detect a correlation between efficacy events (recurrent venous thromboembolism) or safety events (clinically relevant bleeding) and edoxaban exposure.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616217","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":"Physiologically based pharmacokinetic modeling of drug–drug interactions between ritonavir-boosted atazanavir and rifampicin in pregnancy","authors":"Shakir Atoyebi,&nbsp;Maiara Camotti Montanha,&nbsp;Ritah Nakijoba,&nbsp;Catherine Orrell,&nbsp;Henry Mugerwa,&nbsp;Marco Siccardi,&nbsp;Paolo Denti,&nbsp;Catriona Waitt","doi":"10.1002/psp4.13268","DOIUrl":"10.1002/psp4.13268","url":null,"abstract":"<p>Ritonavir-boosted atazanavir (ATV/r) and rifampicin are mainstays of second-line antiretroviral and multiple anti-TB regimens, respectively. Rifampicin induces CYP3A4, a major enzyme involved in atazanavir metabolism, causing a drug–drug interaction (DDI) which might be exaggerated in pregnancy. Having demonstrated that increasing the dose of ATV/r from once daily (OD) to twice daily (BD) in non-pregnant adults can safely overcome this DDI, we developed a pregnancy physiologically based pharmacokinetic (PBPK) model to explore the impact of pregnancy. Predicted pharmacokinetic parameters were validated with separate clinical datasets of ATV/r alone (NCT03923231) and rifampicin alone in pregnant women. The pregnancy model was considered validated when the absolute average fold error (AAFE) for <i>C</i>_trough and AUC_0-24 of both drugs were &lt;2 when comparing predicted vs. observed data. Thereafter, predicted atazanavir <i>C</i>_trough was compared against its protein-adjusted IC₉₀ (14 ng/mL) when simulating the co-administration of ATV/r 300/100 mg OD and rifampicin 600 mg OD. Pregnancy was predicted to increase the rifampicin DDI effect on atazanavir. For the dosing regimens of ATV/r 300/100 mg OD, ATV/r 300/200 mg OD, and ATV/r 300/100 mg BD (all with rifampicin 600 mg OD), predicted atazanavir <i>C</i>_trough was above 14 ng/mL in 29%, 71%, and 100%; and 32%, 73% and 100% of the population in second and third trimesters, respectively. Thus, PBPK modeling suggests ATV/r 300/100 mg BD could maintain antiviral efficacy when co-administered with rifampicin 600 mg OD in pregnancy. Clinical studies are warranted to confirm safety and efficacy in pregnancy.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 11","pages":"1967-1977"},"PeriodicalIF":3.1,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13268","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616216","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":"Quantitative pharmacology of dual-targeted bicistronic CAR-T-cell therapy using multiscale mechanistic modeling.","authors":"Mei-Chi Su, Agnish Dey, Erfan Maddah, Ganesh M Mugundu, Aman P Singh","doi":"10.1002/psp4.13259","DOIUrl":"https://doi.org/10.1002/psp4.13259","url":null,"abstract":"<p><p>Despite the initial success of single-targeted chimeric-antigen receptor (CAR) T-cell therapy in hematological malignancies, its long-term effectiveness is often hindered by antigen heterogeneity and escape. As a result, there is a growing interest in cell therapies targeting multiple antigens (≥2). However, the dose-exposure-response relationship and specific factors influencing the pharmacology of dual-targeted CAR-T-cell therapy remain unclear. In this study, we have developed a multiscale cellular kinetic-pharmacodynamic (CK-PD) model using case studies from CD19/CD22 and GPRC5D/BCMA autologous CAR-Ts. Initially, an in vitro tumor-killing model characterized the impact of individual binder affinities and their contribution to overall potency across varying (1) effector: target (ET) ratios and (2) tumor-associated antigen (TAA) expressing cell lines. Subsequently, an integrated CK-PD model was developed in pediatric acute lymphoblastic leukemia (ALL) patients, which accounted for CAR-T-cell product composition and relative antigen abundance in patients' tumor burden to characterize patient-level multiphasic cellular kinetics using multiple bioanalytical assays (e.g., flow and qPCR-based readouts). Global sensitivity analysis highlighted relative antigen expression, maximum killing rate constant, and CAR-T expansion rate constant as major determinants for observed exposure of dual-targeted CAR-T-cell therapy. This modeling framework could facilitate dose-optimization and construct refinement for dual-targeted bicistronic CAR-T-cell therapies, serving as a valuable tool for both forward and reverse translation in drug development.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142590404","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":"An integrated quantitative systems pharmacology virtual population approach for calibration with oncology efficacy endpoints.","authors":"Nathan Braniff, Tanvi Joshi, Tyler Cassidy, Michael Trogdon, Rukmini Kumar, Kamrine Poels, Richard Allen, Cynthia J Musante, Blerta Shtylla","doi":"10.1002/psp4.13270","DOIUrl":"https://doi.org/10.1002/psp4.13270","url":null,"abstract":"<p><p>In drug development, quantitative systems pharmacology (QSP) models are becoming an increasingly important mathematical tool for understanding response variability and for generating predictions to inform development decisions. Virtual populations are essential for sampling uncertainty and potential variability in QSP model predictions, but many clinical efficacy endpoints can be difficult to capture with QSP models that typically rely on mechanistic biomarkers. In oncology, challenges are particularly significant when connecting tumor size with time-to-event endpoints like progression-free survival while also accounting for censoring due to consent withdrawal, loss in follow-up, or safety criteria. Here, we expand on our prior work and propose an extended virtual population selection algorithm that can jointly match tumor burden dynamics and progression-free survival times in the presence of censoring. We illustrate the core components of our algorithm through simulation and calibration of a signaling pathway model that was fitted to clinical data for a small molecule targeted inhibitor. This methodology provides an approach that can be tailored to other virtual population simulations aiming to match survival endpoints for solid-tumor clinical datasets.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142590403","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}