CPT: Pharmacometrics & Systems Pharmacology最新文献

{"title":"Use of Model-Based Meta-Analysis to Inform the Design of Early Clinical Trials of Anti-Amyloid Beta Therapies in Alzheimer's Disease.","authors":"Sagar S Bachhav, Ana Victoria Ponce-Bobadilla, Diana Clausznitzer, Sven Stodtmann, Hao Xiong","doi":"10.1002/psp4.70038","DOIUrl":"https://doi.org/10.1002/psp4.70038","url":null,"abstract":"<p><p>To inform an efficient development of new investigational anti-amyloid beta (anti-Aβ) monoclonal antibodies (mAbs), a modeling-and-simulation-based strategy was proposed. A general modeling framework that links drug exposures to the time course of amyloid plaque removal and amyloid-related imaging abnormalities characterized by edema and effusion (ARIA-E) was developed based on publicly available data on aducanumab, lecanemab, and donanemab. A non-linear mixed effect model with shared model parameters described the dose response data from aducanumab, lecanemab, and donanemab studies after adjusting for different potency for different antibodies, which allowed the rate of amyloid plaque removal to vary by drug. A time-to-event model was developed to describe ARIA-E incidence. The model assumes that ARIA-E incidence rate is dependent on the rate of amyloid plaque removal with a drug-dependent scaling factor linking amyloid plaque removal rate and treatment-dependent hazard. Simulations of amyloid plaque removal and ARIA-E for a hypothetical anti-Aβ mAb based on certain assumptions and scenarios provided insights into possible outcomes. Overall, the meta-analysis of published data on existing anti-Aβ mAbs could be utilized to model exposure-response relationships and the time course of amyloid plaque removal and ARIA-E incidence of new anti-Aβ mAbs and to inform the design of early clinical trials for them.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143980342","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":"Precision Dosing in Presence of Multiobjective Therapies by Integrating Reinforcement Learning and PK-PD Models: Application to Givinostat Treatment of Polycythemia Vera","authors":"Alessandro De Carlo,&nbsp;Elena Maria Tosca,&nbsp;Paolo Magni","doi":"10.1002/psp4.70012","DOIUrl":"10.1002/psp4.70012","url":null,"abstract":"<p>Precision dosing aims to optimize and customize pharmacological treatment at the individual level. The integration of pharmacometric models with Reinforcement Learning (RL) algorithms is currently under investigation to support the personalization of adaptive dosing therapies. In this study, this hybrid technique is applied to the real multiobjective precision dosing problem of givinostat treatment in polycythemia vera (PV) patients. PV is a chronic myeloproliferative disease with an overproduction of platelets (PLT), white blood cells (WBC), and hematocrit (HCT). The therapeutic goal is to simultaneously normalize the levels of these efficacy/safety biomarkers, thus inducing a complete hematological response (CHR). An RL algorithm, Q-Learning (QL), was integrated with a PK-PD model describing the givinostat effect on PLT, WBC, and HCT to derive both an adaptive dosing protocol (QL_pop-agent) for the whole population and personalized dosing strategies by coupling a specific QL-agent to each patient (QL_ind-agents). QL_pop-agent learned a general adaptive dosing protocol that achieved a similar CHR rate (77% vs. 83%) when compared to the actual givinostat clinical protocol on 10 simulated populations. Treatment efficacy and safety increased with a deeper dosing personalization by QL_ind-agents. These QL-based patient-specific adaptive dosing rules outperformed both the clinical protocol and QL_pop-agent by reaching the CHR in 93% of the test patients and completely avoided severe toxicities during the whole treatment period. These results confirm that RL and PK-PD models can be valid tools for supporting adaptive dosing strategies as interesting performances were achieved in both learning a general set of rules and in customizing treatment for each patient.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 6","pages":"1018-1031"},"PeriodicalIF":3.1,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143988875","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 Novel Empirical Autoinduction Model to Characterize the Population Pharmacokinetics and Recommend Dose for Repotrectinib in Adult and Adolescents With Advanced Solid Tumors Harboring ALK, ROS1, or NTRK1-3 Rearrangements.","authors":"Shengnan Du, Zheyi Hu, Jun Shen, Lora Hamuro, Justine Lam, Ming Lu, Li Zhu, Amit Roy, Anna Kondic","doi":"10.1002/psp4.70036","DOIUrl":"https://doi.org/10.1002/psp4.70036","url":null,"abstract":"<p><p>Repotrectinib is approved in the US for treating ROS1-positive non-small cell lung cancer (NSCLC) and solid tumors harboring an NTRK gene fusion. A Population Pharmacokinetic (PopPK) model for repotrectinib was developed using data from 620 adults (118 healthy volunteers and 502 patients) across seven studies and 24 pediatric patients from one study. The PopPK model, a two-compartment model with first-order absorption and an absorption lag time, incorporating a time-varying clearance due to drug-induced autoinduction, adequately described all PK data. Clearance was modeled as a time- and concentration-dependent (Ctrough) autoinduction process, accounting for increased clearance over time. While empirical in nature, this Ctrough-driven autoinduction model effectively described the changes in clearance and avoided the abrupt concentration changes that can occur with discrete dose-driven autoinduction models. Additionally, this approach avoided time-consuming differential equation computations for the semi-mechanistic enzyme turnover autoinduction models. The model estimated that the maximum drug-induced clearance (CLMAX) was 4.9 times the baseline clearance. Body weight (BW) effects on clearance and volume of distribution were estimated as allometric scaling exponents of 0.477 and 0.962, respectively. Age was found to affect CLMAX, with younger patients generally exhibiting higher CLMAX values. Simulations suggested that a flat dosing regimen (e.g., 160 mg QD for 14 days followed by 160 mg BID) provides comparable drug exposures in both adult and adolescent patients. The PopPK model supported the health authority approval of the dosing regimen for repotrectinib in both adult and adolescent patients with NTRK gene fusion-positive solid tumors.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143995965","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":"Interactions of Therapeutic Antibodies With Presynaptically-Released Misfolded Proteins in Neurodegenerative Diseases. A Spatial Monte-Carlo Simulation Study.","authors":"Jamie Goff, Maryam Khalifa, Shaina M Short, Piet H van der Graaf, Hugo Geerts","doi":"10.1002/psp4.70035","DOIUrl":"https://doi.org/10.1002/psp4.70035","url":null,"abstract":"<p><p>The spatial progression hypothesis of misfolded tau and alpha-synuclein proteins in Alzheimer's and Parkinson's Disease proposes the release of proteins from a presynaptic membrane followed by diffusion over the synaptic cleft and uptake by the postsynaptic membrane in the afferent neuron. A number of antibodies aiming to reduce this neuronal uptake by capturing these proteins in the extracellular space are currently in clinical development, so far without much success. For modeling the interaction between antibodies and misfolded proteins in the extremely small synaptic volume with only a few proteins navigating a crowded environment of transsynaptic proteins, traditional assumptions of ordinary differential equations (ODEs) break down. Here we use spatial Monte Carlo calculations of individual molecule trajectories in a realistic geometrical environment using the open-source software Mcell (mcell.org). For several different densities of transsynaptic proteins, we show that due to geometric constraints, less than 0.5% of the antibody in the brain interstitial fluid (ISF) can enter the crowded synaptic cleft. As a consequence, uptake of the seed-competent proteins is reduced by less than 10%, even at the highest concentration and for selective antibodies. Only the seed-competent protein that escapes the synaptic cleft (between 15% and 30%) is captured by the antibody. Given the extremely low penetrance of the antibodies, it is close to impossible for antibodies to interfere with the uptake mechanism that takes place in the synaptic cleft. These simulations using a detailed and realistic biological environment provide a possible explanation for the clinical trial failures of anti-tau and anti-αsynuclein antibodies.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143962776","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 a Heuristic Machine Analogy Method for Model Simplification With an Application to a Large-Scale Model of Gi/Gs Signaling","authors":"Liang Yang,&nbsp;David Finlay,&nbsp;Michelle Glass,&nbsp;Stephen Duffull","doi":"10.1002/psp4.70029","DOIUrl":"10.1002/psp4.70029","url":null,"abstract":"<p>Model simplification is a process to simplify large-scale mathematical models to enable easy applications such as simulation and parameter estimation. A novel heuristic machine analogy method of model simplification was developed and applied to a motivating example of a model for cAMP signaling switch induced by Gi/Gs pathway competition for the CB₁ receptor (consisting of 31 species and 76 parameters) to enable its use in estimation. The method first acquired an understanding of the mechanism by full model simulation, and then the mechanism was abstracted to a machine analogy. The machine analogy included signal start, signal mode selector, signal size regulator, and final effector, representing functions of different parts of the full model. The simplified minimal model (consisting of 11 species and 13 estimated parameters) was used for parameter estimation for Gi/Gs signaling of six CB₁ agonists. The results of the minimal model suggested that six CB₁ agonists have similar ratios of Gi/Gs activation, indicating Gi/Gs preference was more of a system effect rather than a ligand-specific effect. In conclusion, the novel machine analogy method can be used to heuristically simplify a larger-scale model while maintaining the important mechanisms. In the example here, the full Gi/Gs model of CB₁ was successfully simplified, and the results indicated Gi/Gs preference is a system-dependent effect.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 6","pages":"1098-1107"},"PeriodicalIF":3.1,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143982655","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":"Model-Based Meta-Analysis of the Relationship Between Pioglitazone and Histological Outcomes in Metabolic Dysfunction-Associated Steatohepatitis Patients.","authors":"Quyen Thi Tran, Tham Thi Bui, Lien Thi Ngo, Bo Ram Yang, In-Hwan Baek, Van Hung Nguyen, Kyung Ae Lee, Hwi-Yeol Yun, Jung-Woo Chae, Soyoung Lee, Jae Hyun Kim, Woojin Jung","doi":"10.1002/psp4.70034","DOIUrl":"https://doi.org/10.1002/psp4.70034","url":null,"abstract":"<p><p>Given the high prevalence of the population who have metabolic dysfunction-associated steatohepatitis (MASH), interest is growing in MASH-targeted treatments. However, currently, there has been only one regulatory approved drug for MASH (Rezdiffra). Pioglitazone, a commonly used type 2 diabetes mellitus drug, is currently used off-label for the treatment of MASH. Our study aimed to perform a model-based meta-analysis to quantitatively examine the efficacy of pioglitazone in improving histological parameters and liver enzymes in patients with MASH. A comprehensive search was performed in Pubmed and clinicaltrials.gov. We collected histological outcomes (including steatosis, inflammation, ballooning, and fibrosis) and liver enzyme data. Due to sparse data, the gathered histological outcomes were used to generate virtual data. Next, model development for the virtual histological dataset was performed using a logistic model. In addition, Weibull and exponential models were tested to find the best fit for liver enzyme data. Model evaluations were carried out by visual predictive check, bootstrap method, and stacked bar plot. Eight studies with 540 patients were included. A logit model was used to analyze four outcomes. The results showed that using pioglitazone improved all four histological parameters. These effects are dose- and time-dependent under the Emax-time model for steatosis and ballooning, and under the linear relationship for inflammation and fibrosis. For liver enzymes, the Weibull model fitted well for both ALT and AST data. In conclusion, the developed models of pioglitazone may serve as a benchmark to assess the effectiveness of novel MASH-targeted treatments.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143972552","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":"Historical Use of Markov Model and Posterior Predictive Checks in Pharmacometrics","authors":"Pascal Girard,&nbsp;Helen Kastrissios","doi":"10.1002/psp4.70031","DOIUrl":"10.1002/psp4.70031","url":null,"abstract":"&lt;p&gt;We would like to congratulate the authors for their excellent “&lt;i&gt;Tutorial on pharmacometric Markov models&lt;/i&gt;” published in a recent issue of CPT-PSP [&lt;span&gt;1&lt;/span&gt;]. Their publication emphasizes the increasing use of such models in the field of pharmacometrics, is very complete, and presents in one single paper the theoretical framework of discret-time Markov model (DTMM), continuous-time Markov model, and Hidden Markov model.&lt;/p&gt;&lt;p&gt;However, by restricting their Pubmed search to &lt;b&gt;“&lt;/b&gt;Markov pharmacometric” the authors missed two important seminal papers in this field, both authored by Girard, Sheiner, Kastrissios and Blashke, related to the analysis of dosing regimen compliance (or adherence) data and population pharmacokinetic (pop-PK) modeling [&lt;span&gt;2, 3&lt;/span&gt;]. The first paper [&lt;span&gt;2&lt;/span&gt;] addressed via DTMM and pop-PK simulations the question of the loss of information (bias and precision) in pop-PK analysis when using partial information on patients' dose intakes before concentration measurements versus using the full dosing history as provided by an electronic device. The paper concluded that the use of a limited number of dose records (chosen based on an a priori estimate of the half-life of the drug) would be sufficient to get unbiased and precise PK parameter estimates. Interestingly, the sequence of dose intakes was simulated using a DTMM that was calibrated using real data from electronically monitored patients, which to our best knowledge is the first time a Markov model was used in the field of pharmacometrics.&lt;/p&gt;&lt;p&gt;For &lt;i&gt;p&lt;/i&gt;(&lt;i&gt;n&lt;/i&gt;), a Markov model was postulated and logits were derived with proper constraints for &lt;i&gt;n&lt;/i&gt; = 0, 1, or &gt; 1. The full log likelihood was derived, and parameter estimation was performed with the Laplacian method in NONMEM. The covariates, time of the day (morning, mid-day, evening), weekend days, and age were found to be significant. Figure 4 of that paper visualizes observed dosing patterns (corresponding to the number of times the pill bottle was opened by the patients) [&lt;span&gt;3&lt;/span&gt;] (reproduced here as Figure 1). Interestingly, it is quite similar to panels (a) and (b) of Figure 1 of the tutorial paper that shows a visualization to explore the Markovian features of a categorical response, although the latter paper goes one step further by showing a correlation plot of current versus previous response [&lt;span&gt;1&lt;/span&gt;].&lt;/p&gt;&lt;p&gt;The original work [&lt;span&gt;3&lt;/span&gt;] was published in a statistical journal and was written based on a statistical background rather than a pharmacometric one. However, it is an important reminder that our work [&lt;span&gt;2, 3&lt;/span&gt;] was supervised and guided by Prof Lewis B. Sheiner, a giant in the field of pharmacometrics, even before it became a newly coined discipline in 1982 [&lt;span&gt;4&lt;/span&gt;]. Coming back to our original published work [&lt;span&gt;3&lt;/span&gt;], it is also worth noting that this paper was the first time where pharmacometricians used the ‘posterior pr","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 5","pages":"817-818"},"PeriodicalIF":3.1,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802648","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":"Synthetic Data in Healthcare and Drug Development: Definitions, Regulatory Frameworks, Issues","authors":"Giuseppe Pasculli,&nbsp;Marco Virgolin,&nbsp;Puja Myles,&nbsp;Anna Vidovszky,&nbsp;Charles Fisher,&nbsp;Elisabetta Biasin,&nbsp;Miranda Mourby,&nbsp;Francesco Pappalardo,&nbsp;Saverio D'Amico,&nbsp;Mario Torchia,&nbsp;Alexander Chebykin,&nbsp;Vincenzo Carbone,&nbsp;Luca Emili,&nbsp;Daniel Roeshammar","doi":"10.1002/psp4.70021","DOIUrl":"10.1002/psp4.70021","url":null,"abstract":"<p>With the recent and evolving regulatory frameworks regarding the usage of Artificial Intelligence (AI) in both drug and medical device development, the differentiation between data derived from observed (‘true’ or ‘real’) sources and artificial data obtained using process-driven and/or (data-driven) algorithmic processes is emerging as a critical consideration in clinical research and regulatory discourse. We conducted a critical literature review that revealed evidence of the current ambivalent usage of the term “synthetic” (along with derivative terms) to refer to “true/observed” data in the context of clinical trials and AI-generated data (or “artificial” data). This paper, stemming from a critical evaluation of different perspectives captured from the scientific literature and recent regulatory endeavors, seeks to elucidate this distinction, exploring their respective utilities, regulatory stances, and upcoming needs, as well as the potential for both data types in advancing medical science and therapeutic development.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 5","pages":"840-852"},"PeriodicalIF":3.1,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802649","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":"SpatialCNS-PBPK: An R/Shiny Web-Based Application for Physiologically Based Pharmacokinetic Modeling of Spatial Pharmacokinetics in the Human Central Nervous System and Brain Tumors","authors":"Charuka D. Wickramasinghe,&nbsp;Seongho Kim,&nbsp;Jing Li","doi":"10.1002/psp4.70026","DOIUrl":"10.1002/psp4.70026","url":null,"abstract":"<p>Quantitative understanding of drug penetration and exposure in the human central nervous system (CNS) and brain tumors is essential for the rational development of new drugs and optimal use of existing drugs for brain cancer. To address this need, we developed and validated a novel 9-compartment permeability-limited CNS (9-CNS) physiologically based pharmacokinetic (PBPK) model, enabling mechanistic and quantitative prediction of spatial pharmacokinetics for systemically administered small-molecule drugs across different regions of the human brain, cerebrospinal fluid, and brain tumors. To make the 9-CNS model accessible to a broad range of users, we developed the SpatialCNS-PBPK app, a user-friendly, web-based R/Shiny platform built with R and Shiny programming. The app provides key functionalities for model simulation, sensitivity analysis, and pharmacokinetic parameter calculation. This tutorial introduces the development and evaluation of the SpatialCNS-PBPK app, highlights its key features and functions, and provides a step-by-step user guide for practical applications. By enhancing our ability to predict the spatial pharmacokinetics of anticancer drugs in the human CNS and brain tumors, the SpatialCNS-PBPK app serves as an invaluable computational tool and data-driven approach for advancing drug development and optimizing treatment strategies for more effective treatment of brain cancer.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 5","pages":"864-880"},"PeriodicalIF":3.1,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143779362","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":"Harnessing Open-Source Solutions: Insights From the First Open Systems Pharmacology (OSP) Community Conference","authors":"André Dallmann,&nbsp;Denise Feick,&nbsp;Pavel Balazki,&nbsp;Salih Benamara,&nbsp;Rolf Burghaus,&nbsp;Marylore Chenel,&nbsp;Siak-Leng Choi,&nbsp;Henrik Cordes,&nbsp;Mariana Guimarães,&nbsp;Abdullah Hamadeh,&nbsp;Ibrahim Ince,&nbsp;Kathleen M. Job,&nbsp;Tobias Kanacher,&nbsp;Andreas Kovar,&nbsp;Lars Kuepfer,&nbsp;Jörg Lippert,&nbsp;Julia Macente,&nbsp;Nina Nauwelaerts,&nbsp;Christoph Niederalt,&nbsp;Sheila Peters,&nbsp;Susana Proença,&nbsp;Masanobu Sato,&nbsp;Stephan Schaller,&nbsp;Jan Frederik Schlender,&nbsp;Annika Schneider,&nbsp;Erik Sjögren,&nbsp;Juri Solodenko,&nbsp;Alexander Staab,&nbsp;Paul Vrenken,&nbsp;Thomas Wendl,&nbsp;Wilhelmus E. A. de Witte,&nbsp;Donato Teutonico","doi":"10.1002/psp4.70028","DOIUrl":"10.1002/psp4.70028","url":null,"abstract":"<p>In 2017, the free and open-source software Open Systems Pharmacology (OSP) was launched. Since then, OSP has evolved from a small community into a diverse network of stakeholders committed to advancing open-source solutions for model-informed drug development (MIDD). In this context, the first OSP Community Conference was hosted by Novartis in Basel, Switzerland, on October 7–8, 2024, which gathered over 100 attendees from more than 40 institutions. This perspective synthesizes key insights from the conference.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 5","pages":"822-827"},"PeriodicalIF":3.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143771723","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}