BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH PRODID:-//Telerik Inc.//Sitefinity CMS 15.4//EN BEGIN:VTIMEZONE TZID:UTC BEGIN:STANDARD DTSTART;VALUE=DATE:20250101 TZNAME:UTC TZOFFSETFROM:+0000 TZOFFSETTO:+0000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DESCRIPTION:Date: \;Tuesday 27th June 2023\nTime: \;14:00-15:00 BST | 15:00-16:00 CET\nSpeakers: \;Weiliang Qiu and Cheng Wenren\n\nWho i s this event intended for? \;Statisticians in the Pharmaceutical Indus try.\nWhat is the benefit of attending? \;EC50\, the concentration of a drug that induces a response halfway between the baseline and maximum\, is a key quantity to evaluate drug potency. In this talk\, attendees will hear from Weiliang and Cheng who will be presenting their investigations o n EC50 estimation based on multi-donor dose-response data via different ap proaches.\nRegistration\nRegistration for this webinar is free to both Mem bers of PSI and Non-Members.\nPlease \;click here \;to register.\n \nOverview\nWeiliang Qiu1\, Cheng Wenren1\, Tamara Slavnic2\, Els Pattyn1\ , Luc Essermeant1\n1Non-Clinical Efficacy &\; Safety\, Early Developmen t &\; Research\, Biostatistics &\; Programming\, Sanofi\n2 IT&\;M Stats.\nDose&ndash\;response relationships are important in assessing the efficacy and potency of drugs\, which can usually be characterized by a 4 -parameter logistic (4-PL) model: EC50\, slope\, lower asymptote\, and upp er asymptote. EC50\, the concentration of a drug that induces a response h alfway between the baseline and maximum\, is a key quantity to evaluate dr ug potency. For multi-donor dose-response data\, it is often the interest to estimate the overall EC50 and its 95% confidence interval (CI). A few m ulti-donor EC50 estimation methods have been proposed in literature. Jiang and Kopp-Schneider (2014) systematically compared meta-analysis and nonli near mixed-effects approaches and concluded that meta-analysis approach is simple and robust to summarize EC50 estimates from multiple experiments\, especially suited in the case of small number of experiments\, while nonl inear mixed-effects approach has issue of convergence failure probably due to overparameterization. In this talk\, we investigated ways to improve n onlinear mixed-effects approach to alleviates its issue of convergence fai lure.\nWeiliang Qiu\, Els Pattyn\, Cheng Wenren and Luc Essermeant are San ofi employees and may hold shares and/or stock options in the company. Tam ara Slavnic has nothing to disclose.\nSpeaker details\n\n\n\n \n \n \n Speaker\n \n \n Biography\n \n \n \n \n \n Weiliang Qiu\n \n \n Weili ang Qiu is a Non-Clinical Efficacy and Safety statistician expert leader a t Sanofi and is passionate about using statistics knowledge to help improv e the lives of patients. He obtained PhD degree in Statistics from the Uni versity of British Columbia in 2004 and have worked at Brigham and Women's Hospital/Harvard Medical School for 14 years since 2004. \n We iliang joined Sanofi Non-Clinical Efficacy and Safety (NCES) team in 2018 and provided statistical supports for non-clinical studies in a variety of therapeutic areas\, such as translational sciences\, rare and neurologica l diseases\, immunology and inflammation\, immuno-oncology\, and Genome Me dicine Unit. He also works with the NCES team to develop and implement inn ovative statistical methods to analyze the data from these studies.\n \n \n \n \n \n Cheng Wenren\n \n \n Cheng Wenren is a Princip al Statistician in the Non-Clinical Efficacy &\; Safety (NCES) team at Sanofi. Prior to joining Sanofi in 2021\, Cheng worked as a CMC Statistici an at Bristol-Myers Squibb. Cheng earned his PhD in Statistics from Bowlin g Green State University in 2014\, where his thesis focused on "Mixed mode l selection based on the conceptual predictive statistic".\n \n \n \n\n \;\n DTEND:20230627T140000Z DTSTAMP:20260418T195929Z DTSTART:20230627T130000Z LOCATION: SEQUENCE:0 SUMMARY:PSI Pre-Clinical SIG Webinar: An investigation to improve nonlinear mixed-effects approach for EC50 estimation based on multi-donor dose-resp onse data UID:RFCALITEM639121391696922094 X-ALT-DESC;FMTTYPE=text/html:Date: \;Tuesday 27th June 2023
\nTime: \;14:00-15:00 BST | 15:00-16:00 CE T
\nSpeakers: \;Weiliang Qiu and Cheng Wenren
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\nWho is this event intended for? \;Statis ticians in the Pharmaceutical Industry.
\nWhat is the benefit of attending? \;EC50\, the concentration of a drug that indu ces a response halfway between the baseline and maximum\, is a key quantit y to evaluate drug potency. In this talk\, attendees will hear from Weilia ng and Cheng who will be presenting their investigations on EC50 estimatio n based on multi-donor dose-response data via different approaches.
\ n

Registration

\nRegistration for this webinar is free to both Memb ers of PSI and Non-Members.
\nPlease \;click here \; to register.
\n
\n

Overview

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Weiliang Qiu1\, Cheng We nren1\, Tamara Slavnic2\, Els Pattyn1\, Luc Essermeant1
\n1Non-Clinic al Efficacy &\; Safety\, Early Development &\; Research\, Biostatist ics &\; Programming\, Sanofi
\n2 IT&\;M Stats.

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Dose&nda sh\;response relationships are important in assessing the efficacy and pot ency of drugs\, which can usually be characterized by a 4-parameter logist ic (4-PL) model: EC50\, slope\, lower asymptote\, and upper asymptote. EC5 0\, the concentration of a drug that induces a response halfway between th e baseline and maximum\, is a key quantity to evaluate drug potency. For m ulti-donor dose-response data\, it is often the interest to estimate the o verall EC50 and its 95% confidence interval (CI). A few multi-donor EC50 e stimation methods have been proposed in literature. Jiang and Kopp-Schneid er (2014) systematically compared meta-analysis and nonlinear mixed-effect s approaches and concluded that meta-analysis approach is simple and robus t to summarize EC50 estimates from multiple experiments\, especially suite d in the case of small number of experiments\, while nonlinear mixed-effec ts approach has issue of convergence failure probably due to overparameter ization. In this talk\, we investigated ways to improve nonlinear mixed-ef fects approach to alleviates its issue of convergence failure.

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Wei liang Qiu\, Els Pattyn\, Cheng Wenren and Luc Essermeant are Sanofi employ ees and may hold shares and/or stock options in the company. Tamara Slavni c has nothing to disclose.

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Speaker details

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Speaker< /strong>

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Biography

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weiliangedit
\n Weiliang Qiu

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Weiliang Qiu is a Non-Clinical Efficacy and Safety statistician expert leader at Sanofi and is passionate about using statistics knowledge to help improve the lives of patients. H e obtained PhD degree in Statistics from the University of British Columbi a in 2004 and have worked at Brigham and Women's Hospital/Harvard Medical School for 14 years since 2004.

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Weiliang joined Sanof i Non-Clinical Efficacy and Safety (NCES) team in 2018 and provided statis tical supports for non-clinical studies in a variety of therapeutic areas\ , such as translational sciences\, rare and neurological diseases\, immuno logy and inflammation\, immuno-oncology\, and Genome Medicine Unit. He als o works with the NCES team to develop and implement innovative statistical methods to analyze the data from these studies.

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chengedit
\n Cheng Wenr en

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Cheng Wenren is a Principal Statistician in the Non-Clinical Efficacy &\; Safety (NCES) team at Sanofi. Prior to joini ng Sanofi in 2021\, Cheng worked as a CMC Statistician at Bristol-Myers Sq uibb. Cheng earned his PhD in Statistics from Bowling Green State Universi ty in 2014\, where his thesis focused on "Mixed model selection based on t he conceptual predictive statistic".

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