CPT: Pharmacometrics & Systems Pharmacology最新文献_第6页

A sample size analysis of a mathematical model of longitudinal tumor volume and progression-free survival for Bayesian individual dynamic predictions in recurrent high-grade glioma 纵向肿瘤体积和无进展生存的数学模型的样本大小分析，用于贝叶斯个体动态预测复发的高级别胶质瘤。

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-12-30 DOI: 10.1002/psp4.13290

Daniel J. Glazar, Solmaz Sahebjam, Hsiang-Husan M. Yu, Dung-Tsa Chen, Menal Bhandari, Heiko Enderling

{"title":"A sample size analysis of a mathematical model of longitudinal tumor volume and progression-free survival for Bayesian individual dynamic predictions in recurrent high-grade glioma","authors":"Daniel J. Glazar, Solmaz Sahebjam, Hsiang-Husan M. Yu, Dung-Tsa Chen, Menal Bhandari, Heiko Enderling","doi":"10.1002/psp4.13290","DOIUrl":"10.1002/psp4.13290","url":null,"abstract":"Patients with recurrent high-grade glioma (rHGG) have a poor prognosis with median progression-free survival (PFS) of <7 months. Responses to treatment are heterogenous, suggesting a clinical need for prognostic models. Bayesian data analysis can exploit individual patient follow-up imaging studies to adaptively predict the risk of progression. We propose a novel sample size analysis for Bayesian individual dynamic predictions and demonstrate proof of principle. We coupled a nonlinear mixed effects tumor growth inhibition model with a survival model. Longitudinal tumor volumes and time-to-progression were simulated for 2000 in silico rHGG patients. Bayesian individual dynamic predictions of PFS curves were evaluated using area under the receiver operating characteristic curve (AUC) and Brier skill score (BSS). We investigated the effects of sample size on AUC and BSS margins of error. A power law relationship was observed between sample size and margins of error of AUC and BSS. Sample size was also found to be negatively correlated with margins of error and landmark time. We explored the use of this sample size analysis as a clinical look-up table for prospective clinical trial design and retrospective clinical data analysis. Here, we motivate the application of Bayesian individual dynamic predictions as a clinical end point for clinical trial design. Doing so could aid in the development of study protocols with patient-specific adaptations (escalate or de-escalate dose or frequency of drug administration, increase or decrease the frequency of follow-up, or change therapeutic modality) according to patient-specific prognosis. Future developments of this approach will focus on further model development and validation.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 3","pages":"495-509"},"PeriodicalIF":3.1,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13290","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906647","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}

引用次数: 0

QSP Modeling Shows Pathological Synergism Between Insulin Resistance and Amyloid-Beta Exposure in Upregulating VCAM1 Expression at the BBB Endothelium QSP模型显示胰岛素抵抗和β淀粉样蛋白暴露在血脑屏障内皮上调VCAM1表达中的病理协同作用。

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-12-27 DOI: 10.1002/psp4.13296

Zengtao Wang, Vaishnavi Veerareddy, Xiaojiao Tang, Kevin J. Thompson, Sunil Krishnan, Krishna R. Kalari, Karunya K. Kandimalla

{"title":"QSP Modeling Shows Pathological Synergism Between Insulin Resistance and Amyloid-Beta Exposure in Upregulating VCAM1 Expression at the BBB Endothelium","authors":"Zengtao Wang, Vaishnavi Veerareddy, Xiaojiao Tang, Kevin J. Thompson, Sunil Krishnan, Krishna R. Kalari, Karunya K. Kandimalla","doi":"10.1002/psp4.13296","DOIUrl":"10.1002/psp4.13296","url":null,"abstract":"Type 2 diabetes mellitus (T2DM), characterized by insulin resistance, is closely associated with Alzheimer's disease (AD). Cerebrovascular dysfunction is manifested in both T2DM and AD, and is often considered as a pathological link between the two diseases. Insulin signaling regulates critical functions of the blood–brain barrier (BBB), and endothelial insulin resistance could lead to BBB dysfunction, aggravating AD pathology. However, insulin signaling is intrinsically dynamic and involves interactions among numerous molecular mediators. Hence, a mechanistic systems biology model is needed to understand how insulin regulates BBB physiology and the consequences of its impairment in T2DM and AD. In this study, we investigated the pharmacodynamic effect of insulin on the expression of vascular cell adhesion molecule 1 (VCAM1), a marker of cerebrovascular inflammation. Intriguingly, normal insulin concentrations selectively activated the PI3K-AKT pathway, leading to decreased VCAM1 expression. However, exposure to supraphysiological insulin levels, which is present in insulin resistance, activated both PI3K-AKT and MEK–ERK pathways, and increased VCAM1 expression. We developed a mathematical model that adequately described the dynamics of various insulin signaling nodes and VCAM1 expression. Further, the model was integrated with in vitro proteomics and transcriptomics data from AD patients to simulate VCAM1 expression under pathological conditions. This approach allowed us to establish a quantitative systems pharmacology framework to investigate BBB dysfunction in AD and metabolic syndrome, thereby offering opportunities to identify specific disruptions in molecular networks that will enable us to identify novel therapeutic targets.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 3","pages":"561-571"},"PeriodicalIF":3.1,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13296","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892113","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}

引用次数: 0

Disease Progression Mathematical Modeling With a Case Study on Hepatitis B Virus Infection 以乙型肝炎病毒感染为例的疾病进展数学模型。

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-12-27 DOI: 10.1002/psp4.13298

Clémence Boivin-Champeaux, Nieves Velez de Mendizabal, Aksana Jones, Scott Balsitis, Stephan Schmidt, Justin S. Feigelman, Francine Johansson Azeredo

引用次数: 0

Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma 预测头颈部鳞状细胞癌生存结果的机制学习。

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-12-25 DOI: 10.1002/psp4.13294

Kevin Atsou, Anne Auperin, Jôel Guigay, Sébastien Salas, Sebastien Benzekry

{"title":"Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma","authors":"Kevin Atsou, Anne Auperin, Jôel Guigay, Sébastien Salas, Sebastien Benzekry","doi":"10.1002/psp4.13294","DOIUrl":"10.1002/psp4.13294","url":null,"abstract":"We employed a mechanistic learning approach, integrating on-treatment tumor kinetics (TK) modeling with various machine learning (ML) models to address the challenge of predicting post-progression survival (PPS)—the duration from the time of documented disease progression to death—and overall survival (OS) in Head and Neck Squamous Cell Carcinoma (HNSCC). We compared the predictive power of model-derived TK parameters versus RECIST and assessed the efficacy of nine TK-OS ML models against conventional survival models. Data from 526 advanced HNSCC patients treated with chemotherapy and cetuximab in the TPExtreme trial were analyzed using a double-exponential model. TK parameters from the first line and maintenance (TKL1) or after four cycles (TK4) were used to predict PPS and post-cycle 4 OS (OS4), combined with 12 baseline parameters. While ML algorithms underperformed compared to the Cox model for PPS, a random survival forest was superior for OS prediction using TK4 and surpassed RECIST-based metrics. This model demonstrated unbiased OS4 prediction, suggesting its potential for improving HNSCC treatment evaluation.Trial Registration: ClinicalTrials.gov identifier: NCT02268695.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 3","pages":"540-550"},"PeriodicalIF":3.1,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13294","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892778","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}

引用次数: 0

Development of a physiologically-based pharmacokinetic model for Ritonavir characterizing exposure and drug interaction potential at both acute and steady-state conditions 建立基于生理的利托那韦药代动力学模型，以表征急性和稳态条件下的暴露和药物相互作用潜力。

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-12-23 DOI: 10.1002/psp4.13293

Lien Thi Ngo, Woojin Jung, Tham Thi Bui, Hwi-yeol Yun, Jung-woo Chae, Jeremiah D. Momper

{"title":"Development of a physiologically-based pharmacokinetic model for Ritonavir characterizing exposure and drug interaction potential at both acute and steady-state conditions","authors":"Lien Thi Ngo, Woojin Jung, Tham Thi Bui, Hwi-yeol Yun, Jung-woo Chae, Jeremiah D. Momper","doi":"10.1002/psp4.13293","DOIUrl":"10.1002/psp4.13293","url":null,"abstract":"Ritonavir (RTV) is a potent CYP3A inhibitor that is widely used as a pharmacokinetic (PK) enhancer to increase exposure to select protease inhibitors. However, as a strong and complex perpetrator of CYP3A interactions, RTV can also enhance the exposure of other co-administered CYP3A substrates, potentially causing toxicity. Therefore, the prediction of drug–drug interactions (DDIs) and estimation of dosing requirements for concomitantly administered drugs is imperative. In this study, we aimed to develop a physiologically-based PK (PBPK) model for RTV using the PK-sim® software platform. A total of 13 clinical PK studies of RTV covering a wide dose range (100 to 600 mg including both single and multiple dosing), and eight clinical DDI studies with RTV on CYP3A and P-gp substrates, including alprazolam, midazolam, rivaroxaban, clarithromycin, fluconazole, sildenafil, and digoxin were used for the model development and evaluation. Chronopharmacokinetic differences (between morning vs. evening doses) and limitations in parameter estimation for biochemical processes of RTV from in vitro studies were incorporated in the PBPK model. The final developed PBPK model predicted 100% of RTV AUClast and Cmax within a twofold dimension error. The geometric mean fold error (GMFE) from all PK datasets was 1.275 and 1.194, respectively. In addition, 97% of the DDI profiles were predicted with the DDI ratios within a twofold dimension error. The GMFE values from all DDI datasets were 1.297 and 1.212, respectively. Accordingly, this model could be applied to the prediction of DDI profiles of RTV and CYP3A substrates and used to estimate dosing requirements for concomitantly administered drugs.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 3","pages":"523-539"},"PeriodicalIF":3.1,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13293","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876588","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}

引用次数: 0

Mirikizumab pharmacokinetics and exposure–response in pediatric patients with moderate-to-severe ulcerative colitis 米瑞珠单抗在小儿中重度溃疡性结肠炎患者中的药代动力学和暴露反应

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-12-18 DOI: 10.1002/psp4.13286

Yuki Otani, Laiyi Chua, Wendy J. Komocsar, Amy Larkin, Jordan Johns, Xin Zhang

{"title":"Mirikizumab pharmacokinetics and exposure–response in pediatric patients with moderate-to-severe ulcerative colitis","authors":"Yuki Otani, Laiyi Chua, Wendy J. Komocsar, Amy Larkin, Jordan Johns, Xin Zhang","doi":"10.1002/psp4.13286","DOIUrl":"10.1002/psp4.13286","url":null,"abstract":"Mirikizumab is a p19-directed anti-interleukin-23 antibody approved for the treatment of adults with moderate-to-severe ulcerative colitis (UC). Here, we report the first data of mirikizumab pharmacokinetics (PK) and exposure–response (E/R) relationships in pediatric participants (aged 2 to <18 years weighing >10 kg) with moderate-to-severe UC from the phase II, open-label study SHINE-1 (NCT04004611). PK parameters were analyzed using a model developed previously in adults with fixed-exponent allometry for body weight. Serum samples collected from 26 participants during the 12-week induction and 40-week maintenance periods of SHINE-1 were analyzed. Estimated body weight-adjusted systemic clearance, volume of distribution, and subcutaneous bioavailability were 0.021 L/h, 0.069 L/kg, and 49.8%, respectively. Covariate analysis identified no clinically significant covariates other than body weight. In the exposure range studied, E/R analysis using post hoc grouping by average concentration quartile and comparison of observed change from baseline in modified Mayo Score (MMS) at Week 12 with the adult model prediction revealed no obvious E/R relationship in clinical response, clinical remission, or endoscopic response, consistent with observations in adults. The E/R relationship for observed change from baseline in MMS at Week 12 is also similar to the adult model prediction. The PK modeling and E/R analyses suggested optimal doses of intravenous mirikizumab 300 mg for weight >40 kg, 5 mg/kg for weight ≤40 kg every 4 weeks (Q4W) during induction, and subcutaneous mirikizumab 200 mg (>40 kg), 100 mg (>20 to ≤40 kg), or 50 mg (≤20 kg) Q4W during maintenance therapy for pediatric patients with moderate-to-severe UC.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 3","pages":"474-485"},"PeriodicalIF":3.1,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13286","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853499","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}

引用次数: 0

Physiologically based pharmacokinetic modeling and simulation of topiramate in populations with renal and hepatic impairment and considerations for drug–drug interactions 基于生理学的托吡酯在肝肾功能受损人群中的药代动力学建模和模拟，以及对药物间相互作用的考虑。

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-12-15 DOI: 10.1002/psp4.13292

Shuqing Chen, Chaozhuang Shen, Yuchen Tian, Yuhe Peng, Jing Hu, Haitang Xie, Ping Yin

{"title":"Physiologically based pharmacokinetic modeling and simulation of topiramate in populations with renal and hepatic impairment and considerations for drug–drug interactions","authors":"Shuqing Chen, Chaozhuang Shen, Yuchen Tian, Yuhe Peng, Jing Hu, Haitang Xie, Ping Yin","doi":"10.1002/psp4.13292","DOIUrl":"10.1002/psp4.13292","url":null,"abstract":"Topiramate (TPM) is a broad-spectrum antiepileptic drug (AED) commonly prescribed for approved and off-label uses. Routine monitoring is suggested for clinical usage of TPM in special population due to its broad side effect profile. Therefore, it is crucial to further explore its pharmacokinetic characteristics. Physio-chemical properties of TPM were initially determined from online database and further optimized while establishing the PBPK model for healthy adults using the PK-Sim software. The model was then extrapolated to patients with renal impairment and patients who were hepatically impaired. A drug–drug interaction (DDI) model was also built to simulate plasma TPM concentrations while concomitantly used with carbamazepine (CBZ). The goodness-of-fit method and average fold error (AFE) method were used to compare the differences between predicted and observed values to assess the accuracy of the PBPK model. Almost all of the predicted concentration fell within twofold error range of corresponding observed concentrations. The AFE ratio of predicted to observed values of Cmax and AUC0-inf was all within 0.5 and 2. It is recommended that the doses be reduced to 70%, 50%, and 40% of the healthy adult dose for the chronic kidney disease (CKD) stage 3, stage 4, and stage 5 patients, respectively, and reduced to ~70%, and 35% for the Child–Pugh-B, and Child–Pugh C scored patient with hepatic impairment, respectively. If TPM is co-administered with CBZ, increasing TPM doses to 150%–175% of the monotherapy dose is recommended according to model simulation.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 3","pages":"510-522"},"PeriodicalIF":3.1,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13292","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827651","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}

引用次数: 0

Clinical modeling of motor function to predict treatment efficacy and enable in silico treatment comparisons in infantile-onset Pompe disease. 运动功能的临床建模以预测治疗效果，并使婴儿起病庞贝病的计算机治疗比较成为可能。

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-12-13 DOI: 10.1002/psp4.13287

Fatiha Rachedi, Rana Jreich, Susan Sparks, Atef Zaher, Kristina An Haack, Alicia Granados, Zhaoling Meng

{"title":"Clinical modeling of motor function to predict treatment efficacy and enable in silico treatment comparisons in infantile-onset Pompe disease.","authors":"Fatiha Rachedi, Rana Jreich, Susan Sparks, Atef Zaher, Kristina An Haack, Alicia Granados, Zhaoling Meng","doi":"10.1002/psp4.13287","DOIUrl":"https://doi.org/10.1002/psp4.13287","url":null,"abstract":"Infantile-onset Pompe disease (IOPD) is a rare, deadly, quickly-progressing degenerative disease. Even with life-sustaining treatment (e.g., alglucosidase alfa [ALGLU]), many patients experience continued motor impairment. The Mini-COMET trial evaluated avalglucosidase alfa (AVAL) versus ALGLU on motor and other outcomes in IOPD. However, treatment groups were imbalanced at baseline and the trial was not powered to directly compare treatments. To supplement this limited data, we developed a modeling and simulation approach to compare AVAL versus ALGLU head-to-head in in silico (i.e., computer-simulated) trials. We first developed a longitudinal clinical model to establish the relationship between changes in motor function and changes in urinary hexose tetrasaccharide (uHex4), an established biomarker in IOPD. This model was based on pooled data from Mini-COMET (n = 21) and COMET trials (n = 100 patients with late-onset Pompe disease, LOPD). We then conducted in silico trials mimicking Mini-COMET. Simulated trials were informed by motor data generated from the clinical model and uHex4 profiles simulated in a quantitative systems pharmacology model. The virtual IOPD population was based on observed Mini-COMET baseline characteristics but engineered to have well-balanced baseline characteristics across treatment cohorts. In silico trials showed that patients with IOPD would have the greatest improvements in motor function with AVAL 40 mg/kg every other week (Q2W), suboptimal improvement with ALGLU 40 mg/kg Q2W, and no improvement with ALGLU 20 mg/kg Q2W. This study provides information on the relative efficacy of IOPD treatments and mitigates the confounding effects of imbalanced treatment cohorts. Our approach could also be applied in other rare diseases.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142817323","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}

引用次数: 0

A tutorial on pharmacometric Markov models 关于药物计量学马尔可夫模型的教程。

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-12-13 DOI: 10.1002/psp4.13278

Qing Xi Ooi, Elodie Plan, Martin Bergstrand

{"title":"A tutorial on pharmacometric Markov models","authors":"Qing Xi Ooi, Elodie Plan, Martin Bergstrand","doi":"10.1002/psp4.13278","DOIUrl":"10.1002/psp4.13278","url":null,"abstract":"The Markov chain is a stochastic process in which the future value of a variable is conditionally independent of the past, given its present value. Data with Markovian features are characterized by: frequent observations relative to the expected changes in values, many consecutive same-category or similar-value observations at the individual level, and a positive correlation observed between the current and previous values for that variable. In drug development and clinical settings, the data available commonly present Markovian features and are increasingly often modeled using Markov elements or dedicated Markov models. This tutorial presents the main characteristics, evaluations, and applications of various Markov modeling approaches including the discrete-time Markov models (DTMM), continuous-time Markov models (CTMM), hidden Markov models, and item-response theory model with Markov sub-models. The tutorial has a specific emphasis on the use of DTMM and CTMM for modeling ordered-categorical data with Markovian features. Although the main body of this tutorial is written in a software-neutral manner, annotated NONMEM code for all key Markov models is included in the Supplementary Information.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 2","pages":"197-216"},"PeriodicalIF":3.1,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13278","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142817390","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}

引用次数: 0

Advancing pharmacometrics in Africa—Transition from capacity development toward job creation 在非洲推进药物计量学——从能力发展到创造就业的转变。

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-12-09 DOI: 10.1002/psp4.13291

Goonaseelan (Colin) Pillai, Samer Mouksassi, Innocent G. Asiimwe, Craig R. Rayner, Steven Kern, Phumla Sinxadi, Paolo Denti, Eric Decloedt, Catriona Waitt, Bernhards R. Ogutu, Rik de Greef

{"title":"Advancing pharmacometrics in Africa—Transition from capacity development toward job creation","authors":"Goonaseelan (Colin) Pillai, Samer Mouksassi, Innocent G. Asiimwe, Craig R. Rayner, Steven Kern, Phumla Sinxadi, Paolo Denti, Eric Decloedt, Catriona Waitt, Bernhards R. Ogutu, Rik de Greef","doi":"10.1002/psp4.13291","DOIUrl":"10.1002/psp4.13291","url":null,"abstract":"Trained pharmacometricians remain scarce in Africa due to limited training opportunities, lack of a pharmaceutical product development ecosystem, and emigration to high-income countries. The Applied Pharmacometrics Training (APT) fellowship program was established to address these gaps and specifically foster job creation for talent retention. We review the APT program's progress over 3 years and encourage collaboration to enhance local clinical data analysis in Africa. Initiated in 2021 by Pharmacometrics Africa, a non-profit educational entity, with support from partners including the Bill & Melinda Gates Foundation and Certara, the APT program targets African doctoral-level scientists and clinicians. This 6-month program is jointly managed by partners, with Pharmacometrics Africa handling logistics and sponsor liaison. Job creation initiatives include inviting fellows to join consulting teams or local research centers. Over the 3 year reporting period, 177 applications were received, with 27 individuals (41% female, median age 35 years) from nine African countries selected into and completing the full program. The fellows worked on 13 data analysis projects, with six so far being presented at international conferences and/or submitted for publication in peer-reviewed journals. Nine fellows have joined consulting teams or research centers working from offices in Africa. Currently, in the 3rd year, the APT program has demonstrated success in skills development, job creation, and fostering a critical mass of African pharmacometricians. Collaboration is essential for the sustainable advancement of model-informed drug development in Africa.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 3","pages":"407-419"},"PeriodicalIF":3.1,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13291","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142794539","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}

引用次数: 0