CPT: Pharmacometrics & Systems Pharmacology最新文献

{"title":"Combining data on the bioavailability of midazolam and physiologically-based pharmacokinetic modeling to investigate intestinal CYP3A4 ontogeny","authors":"Trevor N. Johnson,&nbsp;Hannah K. Batchelor,&nbsp;Jan Goelen,&nbsp;Richard D. Horniblow,&nbsp;Jean Dinh","doi":"10.1002/psp4.13192","DOIUrl":"10.1002/psp4.13192","url":null,"abstract":"<p>Pediatric physiologically-based modeling in drug development has grown in the past decade and optimizing the underlying systems parameters is important in relation to overall performance. In this study, variation of clinical oral bioavailability of midazolam as a function of age is used to assess the underlying ontogeny models for intestinal CYP3A4. Data on midazolam bioavailability in adults and children and different ontogeny patterns for intestinal CYP3A4 were first collected from the literature. A pediatric PBPK model was then used to assess six different ontogeny models in predicting bioavailability from preterm neonates to adults. The average fold error ranged from 0.7 to 1.38, with the rank order of least to most biased model being No Ontogeny &lt; Upreti = Johnson &lt; Goelen &lt; Chen &lt; Kiss. The absolute average fold error ranged from 1.17 to 1.64 with the rank order of most to least precise being Johnson &gt; Upreti &gt; No Ontogeny &gt; Goelen &gt; Kiss &gt; Chen. The optimal ontogeny model is difficult to discern when considering the possible influence of CYP3A5 and other population variability; however, this study suggests that from term neonates and older a faster onset Johnson model with a lower fraction at birth may be close to this. For inclusion in other PBPK models, independent verification will be needed to confirm these results. Further research is needed in this area both in terms of age-related changes in midazolam and similar drug bioavailability and intestinal CYP3A4 ontogeny.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141455798","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":"Impact of obesity and roux-en-Y gastric bypass on the pharmacokinetics of (R)- and (S)-omeprazole and intragastric pH","authors":"Leandro F. Pippa,&nbsp;Valvanera Vozmediano,&nbsp;Lieke Mitrov-Winkelmolen,&nbsp;Daan Touw,&nbsp;Amira Soliman,&nbsp;Rodrigo Cristofoletti,&nbsp;Wilson Salgado Junior,&nbsp;Natalia Valadares de Moraes","doi":"10.1002/psp4.13189","DOIUrl":"10.1002/psp4.13189","url":null,"abstract":"<p>This study employed physiologically-based pharmacokinetic–pharmacodynamics (PBPK/PD) modeling to predict the effect of obesity and gastric bypass surgery on the pharmacokinetics and intragastric pH following omeprazole treatment. The simulated plasma concentrations closely matched the observed data from non-obese, morbidly obese, and post-gastric bypass populations. Obesity significantly reduces CYP3A4 and CYP2C19 activities, as reflected by the metabolic ratio [omeprazole sulphone]/[omeprazole] and [5-hydroxy-omeprazole]/[omeprazole]. The morbidly obese model accounted for the down-regulation of CYP2C19 and CYP3A4 to recapitulate the observed data. Sensitivity analysis showed that intestinal CYP3A4, gastric pH, small intestine bypass, and the delay in bile release do not have a major influence on omeprazole exposure. Hepatic CYP3A4 had a significant impact on the AUC of (<i>S</i>)-omeprazole, while hepatic CYP2C19 affected both (<i>R</i>)- and (<i>S</i>)-omeprazole AUC. After gastric bypass surgery, the activity of CYP3A4 and CYP2C19 is restored. The PBPK model was linked to a mechanism-based PD model to assess the effect of omeprazole on intragastric pH. Following 40 mg omeprazole, the mean intragastric pH was 4.3, 4.6, and 6.6 in non-obese, obese, and post-gastric bypass populations, and the daily time with pH &gt;4 was 14.7, 16.4, and 24 h. Our PBPK/PD approach provides a comprehensive understating of the impact of obesity and weight loss on CYP3A4 and CYP2C19 activity and omeprazole pharmacokinetics. Given that simulated intragastric pH is relatively high in post-RYGB patients, irrespective of the dose of omeprazole, additional clinical outcomes are imperative to assess the effect of proton pump inhibitor in preventing marginal ulcers in this population.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13189","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141455799","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 of valproic acid in children with epilepsy: Implications for dose tailoring when switching from oral syrup to sustained-release tablets","authors":"Wei-Jun Wang,&nbsp;Yue Li,&nbsp;Ya-Hui Hu,&nbsp;Jie Wang,&nbsp;Yuan-Yuan Zhang,&nbsp;Lin Fan,&nbsp;Hao-Ran Dai,&nbsp;Hong-Li Guo,&nbsp;Xuan-Sheng Ding,&nbsp;Feng Chen","doi":"10.1002/psp4.13191","DOIUrl":"10.1002/psp4.13191","url":null,"abstract":"<p>Significant pharmacokinetic (PK) differences exist between different forms of valproic acid (VPA), such as syrup and sustained-release (SR) tablets. This study aimed to develop a population pharmacokinetic (PopPK) model for VPA in children with epilepsy and offer dose adjustment recommendation for switching dosage forms as needed. The study collected 1411 VPA steady-state trough concentrations (<i>C</i>_trough) from 617 children with epilepsy. Using NONMEM software, a PopPK model was developed, employing a stepwise approach to identify possible variables such as demographic information and concomitant medications. The final model underwent internal and external evaluation via graphical and statistical methods. Moreover, Monte Carlo simulations were used to generate a dose tailoring strategy for typical patients weighting 20–50 kg. As a result, the PK characteristics of VPA were described using a one-compartment model with first-order absorption. The absorption rate constant (<i>k</i>_a) was set at 2.64 and 0.46 h⁻¹ for syrup and SR tablets. Body weight and sex were identified as significant factors affecting VPA's pharmacokinetics. The final PopPK model demonstrated acceptable prediction performance and stability during internal and external evaluation. For children taking syrup, a daily dose of 25 mg/kg resulted in the highest probability of achieving the desired target <i>C</i>_trough, while a dose of 20 mg/kg/day was appropriate for those taking SR tablets. In conclusion, we established a PopPK model for VPA in children with epilepsy to tailor VPA dosage when switching between syrup and SR tablets, aiming to improve plasma VPA concentrations fluctuations.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141455800","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":"Physiologically-based pharmacokinetic modeling predicts the drug interaction potential of GLS4 in co-administered with ritonavir","authors":"Zexu Sun,&nbsp;Nan Zhao,&nbsp;Ran Xie,&nbsp;Bo Jia,&nbsp;Junyu Xu,&nbsp;Lin Luo,&nbsp;Yulei Zhuang,&nbsp;Yuyu Peng,&nbsp;Xinchang Liu,&nbsp;Yingjun Zhang,&nbsp;Xia Zhao,&nbsp;Zhaoqian Liu,&nbsp;Yimin Cui","doi":"10.1002/psp4.13184","DOIUrl":"10.1002/psp4.13184","url":null,"abstract":"<p>GLS4 is a first-in-class hepatitis B virus (HBV) capsid assembly modulator (class I) that is co-administered with ritonavir to maintain the anticipated concentration required for the effective antiviral activity of GLS4. In this study, the first physiologically-based pharmacokinetic (PBPK) model for GLS4/ritonavir was successfully developed. The predictive performance of the PBPK model was verified using data from 39 clinical studies, including single-dose, multiple-dose, food effects, and drug–drug interactions (DDI). The PBPK model accurately described the PK profiles of GLS4 and ritonavir, with predicted values closely aligning with observed data. Based on the verified GLS4/ritonavir model, it prospectively predicts the effect of hepatic impairment (HI) and DDI on its pharmacokinetics (PK). Notably, CYP3A4 inducers significantly influenced GLS4 exposure when co-administered with ritonavir; co-administered GLS4 and ritonavir significantly influenced the exposure of CYP3A4 substrates. Additionally, with the severity of HI increased, there was a corresponding increase in the exposure to GLS4 when co-administered with ritonavir. The GLS4/ritonavir PBPK model can potentially be used as an alternative to clinical studies or guide the design of clinical trial protocols.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13184","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731095","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":"Hepatic OATP1B zonal distribution: Implications for rifampicin-mediated drug–drug interactions explored within a PBPK framework","authors":"Mattie Hartauer,&nbsp;William A. Murphy,&nbsp;Kim L. R. Brouwer,&nbsp;Roz Southall,&nbsp;Sibylle Neuhoff","doi":"10.1002/psp4.13188","DOIUrl":"10.1002/psp4.13188","url":null,"abstract":"<p>OATP1B facilitates the uptake of xenobiotics into hepatocytes and is a prominent target for drug–drug interactions (DDIs). Reduced systemic exposure of OATP1B substrates has been reported following multiple-dose rifampicin; one explanation for this observation is OATP1B induction. Non-uniform hepatic distribution of OATP1B may impact local rifampicin tissue concentrations and rifampicin-mediated protein induction, which may affect the accuracy of transporter- and/or metabolizing enzyme-mediated DDI predictions. We incorporated quantitative zonal OATP1B distribution data from immunofluorescence imaging into a PBPK modeling framework to explore rifampicin interactions with OATP1B and CYP substrates. PBPK models were developed for rifampicin, two OATP1B substrates, pravastatin and repaglinide (also metabolized by CYP2C8/CYP3A4), and the CYP3A probe, midazolam. Simulated hepatic uptake of pravastatin and repaglinide increased from the periportal to the pericentral region (approximately 2.1-fold), consistent with OATP1B distribution data. Simulated rifampicin unbound intracellular concentrations increased in the pericentral region (1.64-fold) compared to simulations with uniformly distributed OATP1B. The absolute average fold error of the rifampicin PBPK model for predicting substrate maximal concentration (<i>C</i>_max) and area under the plasma concentration–time curve (AUC) ratios was 1.41 and 1.54, respectively (nine studies). In conclusion, hepatic OATP1B distribution has a considerable impact on simulated zonal substrate uptake clearance values and simulated intracellular perpetrator concentrations, which regulate transporter and metabolic DDIs. Additionally, accounting for rifampicin-mediated OATP1B induction in parallel with inhibition improved model predictions. This study provides novel insight into the effect of hepatic OATP1B distribution on site-specific DDI predictions and the impact of accounting for zonal transporter distributions within PBPK models.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13188","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141426566","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":"Simulating realistic patient profiles from pharmacokinetic models by a machine learning postprocessing correction of residual variability","authors":"Christos Kaikousidis,&nbsp;Robert R. Bies,&nbsp;Aristides Dokoumetzidis","doi":"10.1002/psp4.13182","DOIUrl":"10.1002/psp4.13182","url":null,"abstract":"<p>We address the problem of model misspecification in population pharmacokinetics (PopPK), by modeling residual unexplained variability (RUV) by machine learning (ML) methods in a postprocessing step after conventional model building. The practical purpose of the method is the generation of realistic virtual patient profiles and the quantification of the extent of model misspecification, by introducing an appropriate metric, to be used as an additional diagnostic of model quality. The proposed methodology consists of the following steps: After developing a PopPK model, the individual residual errors <i>IRES = DV–IPRED</i>, are computed, where DV are the observations and IPRED the individual predictions and are modeled by ML to obtain <i>IRES</i>_<i>ML</i>. Correction of the IPREDs can then be carried out as <i>DV</i>_<i>ML</i> <i>= IPRED + IRES</i>_<i>ML</i>. The methodology was tested in a PK study of ropinirole, for which a PopPK model was developed while a second deliberately misspecified model was also considered. Various supervised ML algorithms were tested, among which Random Forest gave the best results. The ML model was able to correct individual predictions as inspected in diagnostic plots and most importantly it simulated realistic profiles that resembled the real data and canceled out the artifacts introduced by the elevated RUV, even in the case of the heavily misspecified model. Furthermore, a metric to quantify the extent of model misspecification was introduced based on the <i>R</i>² between IRES and IRES_ML, following the rationale that the greater the extent of variability explained by the ML model, the higher the degree of model misspecification present in the original model.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13182","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141320673","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":"Is PBPK useful to inform product label on managing clinically significant drug interactions mediated by cytokine release syndrome?","authors":"Xinyuan Zhang,&nbsp;Ping Zhao","doi":"10.1002/psp4.13185","DOIUrl":"10.1002/psp4.13185","url":null,"abstract":"<p>Evaluating drug interactions caused by cytokine release syndrome (CRS) with PBPK (Physiologically Based Pharmacokinetic) modeling has been reported in some bispecific antibody regulatory submissions for 10 years. However, the published regulatory reviews and sponsors' analyses seem to disagree on the roles of PBPK modeling in regulatory decision-making. In this editorial, we reviewed and provided our opinions on the FDA's current practice and sponsors' position in evaluating CRS-mediated drug interactions. We discussed what has been done and what is lacking in the current PBPK approach assessing the CRS-mediated drug interactions and proposed areas to bridge the gaps. And finally, we call to actions to improve the current practice toward a patient-centric clinical pharmacology approach with more quantitative assessment and management of CRS-mediated drug interactions.</p><p>The manuscript by Willemin et al.<span>¹</span> described the use of a PBPK approach to evaluate the effect of elevated IL-6 following the treatment of teclistamab on the PK of CYP enzyme (1A2, 2C9, 2C19, 3A4, 3A5) substrates. This marks the 4th PBPK publication by CPT-PSP of the effect of CRS as a result of biologics-treatment on co-medications that are CYP substrates, after blinatumomab,<span>²</span> mosunetuzumab,<span>³</span> and glofitamab.<span>⁴</span> The scientific community and drug developers are using the PBPK modeling tool to study the effect of CRS on the PK and safety of co-administered CYP substrate drugs. However, there seems to be a gap between the peer-reviewed papers<span>^1-4</span> and the regulatory evaluations<span>^5-8</span> in terms of concluding the impact of PBPK predictions. In this editorial, we examine the gap and share our opinions on the value, expectation, and future of PBPK modeling in this specific area with the aim of increasing awareness, calling for enhanced predictive performance, and ultimately, achieving patient-centric clinical pharmacology.</p><p>Cytokine release syndrome is characterized by the rapid release of pro-inflammatory cytokines and immune cell activation. T cell-engaging bispecific antibodies can cause transient release of cytokines that may potentially suppress CYP450 enzymes. Utilizing the PBPK modeling approach to evaluate the CRS-mediated drug interactions in a regulatory submission can be traced back to the first FDA-approved T-cell-engaging bispecific antibody, blinatumomab, in 2014.<span>⁵</span> Over the past 10 years, a few additional T-cell-engaging bispecific antibodies were approved by FDA (mosunetuzumab, tebentafusp, teclistamab, epcoritamab, glofitamab, and talquetamab). We examined the FDA's biologics license application assessment packages, USPIs (United States Prescribing Information), and relevant PBPK publications to see how drug interactions mediated by CRS were evaluated and reported to healthcare professionals.</p><p>Amon","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13185","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141310294","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":"Quantification of the effect of GLP-1R agonists on body weight using in vitro efficacy information: An extension of the Hall body composition model","authors":"Rolien Bosch,&nbsp;Eric J. G. Sijbrands,&nbsp;Nelleke Snelder","doi":"10.1002/psp4.13183","DOIUrl":"10.1002/psp4.13183","url":null,"abstract":"<p>Obesity has become a major public health concern worldwide. Pharmacological interventions with the glucagon-like peptide-1 receptor agonists (GLP-1RAs) have shown promising results in facilitating weight loss and improving metabolic outcomes in individuals with obesity. Quantifying drug effects of GLP-1RAs on energy intake (EI) and body weight (BW) using a QSP modeling approach can further increase the mechanistic understanding of these effects, and support obesity drug development. An extensive literature-based dataset was created, including data from several diet, liraglutide and semaglutide studies and their effects on BW and related parameters. The Hall body composition model was used to quantify and predict effects on EI. The model was extended with (1) a lifestyle change/placebo effect on EI, (2) a weight loss effect on activity for the studies that included weight management support, and (3) a GLP-1R agonistic effect using in vitro potency efficacy information. The estimated reduction in EI of clinically relevant dosages of semaglutide (2.4 mg) and liraglutide (3.0 mg) was 34.5% and 13.0%, respectively. The model adequately described the resulting change in BW over time. At 20 weeks the change in BW was estimated to be −17% for 2.4 mg semaglutide and −8% for 3 mg liraglutide, respectively. External validation showed the model was able to predict the effect of semaglutide on BW in the STEP 1 study. The GLP-1RA body composition model can be used to quantify and predict the effect of novel GLP-1R agonists on BW and changes in underlying processes using early in vitro efficacy information.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13183","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141310295","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":"SAAM II: A general mathematical modeling rapid prototyping environment","authors":"Simone Perazzolo","doi":"10.1002/psp4.13181","DOIUrl":"10.1002/psp4.13181","url":null,"abstract":"<p>Simulation Analysis and Modeling II (SAAM II) is a graphical modeling software used in life sciences for compartmental model analysis, particularly, but not exclusively, appreciated in pharmacokinetics (PK) and pharmacodynamics (PD), metabolism, and tracer modeling. Its intuitive “circles and arrows” visuals allow users to easily build, solve, and fit compartmental models without the need for coding. It is suitable for rapid prototyping of models for complex kinetic analysis or PK/PD problems, and in educating students and non-modelers. Although it is straightforward in design, SAAM II incorporates sophisticated algorithms programmed in C to address ordinary differential equations, deal with complex systems via forcing functions, conduct multivariable regression featuring the Bayesian maximum a posteriori, perform identifiability and sensitivity analyses, and offer reporting functionalities, all within a single package. After 26 years from the last SAAM II tutorial paper, we demonstrate here SAAM II's updated applicability to current life sciences challenges. We review its features and present four contemporary case studies, including examples in target-mediated PK/PD, CAR-T-cell therapy, viral dynamics, and transmission models in epidemiology. Through such examples, we demonstrate that SAAM II provides a suitable interface for rapid model selection and prototyping. By enabling the fast creation of detailed mathematical models, SAAM II addresses a unique requirement within the mathematical modeling community.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13181","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141305611","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 bootstrapping method to optimize go/no-go decisions from single-arm, signal-finding studies in oncology","authors":"Raunak Dutta,&nbsp;Aparna Mohan,&nbsp;Jacqueline Buros-Novik,&nbsp;Gregory Goldmacher,&nbsp;Omobolaji O. Akala,&nbsp;Brian Topp","doi":"10.1002/psp4.13161","DOIUrl":"10.1002/psp4.13161","url":null,"abstract":"<p>Phase Ib trials are common in oncology development but often are not powered for statistical significance. Go/no-go decisions are largely driven by observed trends in response data. We applied a bootstrapping method to systematically compare tumor dynamic end points to historical control data to identify drugs with clinically meaningful efficacy. A proprietary mathematical model calibrated to phase Ib anti–PD-1 therapy trial data (KEYNOTE-001) was used to simulate thousands of phase Ib trials (<i>n</i> = 30) with a combination of anti–PD-1 therapy and four novel agents with varying efficacy. A redacted bootstrapping method compared these results to a simulated phase III control arm (<i>N</i> = 511) while adjusting for differences in trial duration and cohort size to determine the probability that the novel agent provides clinically meaningful efficacy. Receiver operating characteristic (ROC) analysis showed strong ability to separate drugs with modest (area under ROC [AUROC] = 83%), moderate (AUROC = 96%), and considerable efficacy (AUROC = 99%) from placebo in early-phase trials (<i>n</i> = 30). The method was shown to effectively move drugs with a range of efficacy through an in silico pipeline with an overall success rate of 93% and false-positive rate of 7.5% from phase I to phase III. This model allows for effective comparisons of tumor dynamics from early clinical trials with more mature historical control data and provides a framework to predict drug efficacy in early-phase trials. We suggest this method should be employed to improve decision making in early oncology trials.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141305610","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}