Application of stochastic simulation-estimation approach to the optimization of Pharmacokinetic studies design in the context of paediatric extrapolation: A step toward better decision making for drug sponsors and regulators

Application of stochastic simulation-estimation approach to the optimization of Pharmacokinetic studies design in the context of paediatric extrapolation: A step toward better decision making for drug sponsors and regulators | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Application of stochastic simulation-estimation approach to the optimization of Pharmacokinetic studies design in the context of paediatric extrapolation: A step toward better decision making for drug sponsors and regulators Happy Phanio Djokoto, Lana Ernst, Jean-Michel Dogné, Flora T. Musuamba This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4356168/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Extrapolation-based approaches are widely used in the context of paediatric drug development. Most of the times, inference is made on the (favourable)[ 1 ] benefit/risk balance (BRB) based on the similarity pharmacokinetics (PK) exposures between adults and children. This PK-based extrapolation approach necessitates generation of PK data in children. Given the ethical and practical challenges inherent to conducting clinical trials in children, it is crucial to ensure that the collected data are relevant and informative. In the present work, we propose a stochastic simulation-estimation-based approach to ensure the optimality of the key study design factors (number of patients, number of samples and sampling times) for conduct of PK studies in the context of paediatric extrapolation. Using 3 case-studies including a monoclonal antibody administered subcutaneously and two small molecules with intravenous and oral administration routes, we illustrate how stochastic simulation estimation (SSE) can be used in the context of drug development, to meet regulatory requirements. The present research demonstrates how the design of a paediatric study can be optimized before data collection based on the available data from the adult drug development, that often precedes children’s. Our results show how, for each of the 3 case-drugs, using a simulation-based approach, paediatric PK study can be designed ensuring that model parameters precision and accuracy would be under 30% and 20% respectively. These results provide useful information for drug sponsors and regulators as far as extrapolation in smaller populations such as paediatric is concerned. Stochastic simulations estimation Pharmacokinetics Paediatrics design optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4356168","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299907324,"identity":"d7c5e6ce-7937-48eb-ae3e-fbd9e9568018","order_by":0,"name":"Happy Phanio Djokoto","email":"","orcid":"","institution":"University of Namur","correspondingAuthor":false,"prefix":"","firstName":"Happy","middleName":"Phanio","lastName":"Djokoto","suffix":""},{"id":299907326,"identity":"71dfe90d-75bb-472b-9bae-05b7d3010619","order_by":1,"name":"Lana Ernst","email":"","orcid":"","institution":"University of Namur","correspondingAuthor":false,"prefix":"","firstName":"Lana","middleName":"","lastName":"Ernst","suffix":""},{"id":299907328,"identity":"638b011d-79c8-4e42-85cf-ee7c99bbb90e","order_by":2,"name":"Jean-Michel Dogné","email":"","orcid":"","institution":"University of Namur","correspondingAuthor":false,"prefix":"","firstName":"Jean-Michel","middleName":"","lastName":"Dogné","suffix":""},{"id":299907329,"identity":"7fe327fc-6c72-46d2-9d79-fbf71ab1849e","order_by":3,"name":"Flora T. 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Most of the times, inference is made on the (favourable)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] benefit/risk balance (BRB) based on the similarity pharmacokinetics (PK) exposures between adults and children. This PK-based extrapolation approach necessitates generation of PK data in children. Given the ethical and practical challenges inherent to conducting clinical trials in children, it is crucial to ensure that the collected data are relevant and informative.\u003c/p\u003e \u003cp\u003eIn the present work, we propose a stochastic simulation-estimation-based approach to ensure the optimality of the key study design factors (number of patients, number of samples and sampling times) for conduct of PK studies in the context of paediatric extrapolation. Using 3 case-studies including a monoclonal antibody administered subcutaneously and two small molecules with intravenous and oral administration routes, we illustrate how stochastic simulation estimation (SSE) can be used in the context of drug development, to meet regulatory requirements. The present research demonstrates how the design of a paediatric study can be optimized before data collection based on the available data from the adult drug development, that often precedes children\u0026rsquo;s. Our results show how, for each of the 3 case-drugs, using a simulation-based approach, paediatric PK study can be designed ensuring that model parameters precision and accuracy would be under 30% and 20% respectively. 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