The Pre-Clinical SIG Workshop returns for 2024\, bring ing you a two half-days event\, starting with a course on pre-clinical des ign of experiments by Simon Bate\, GSK. We will learn from him about how t o implement Hasse diagrams and good practices on repeated measures\, as we ll as hear more on how to teach experimental design. In addition\, we will also have speakers on topics such as statistics and genetic toxicology\, the new and promising OMARS DOE and ANOVA-type.
\nAgenda
\nT o download a copy of the Agenda\, please click here.
\nSpeaker details
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| \n \n Following a PhD in experimenta l design at Royal Holloway\, University of London\, Simon started his phar maceutical career in the Discovery Statistics group at GlaxoSmithKline. Th is role involved collaborating with life-scientists\, running a 3-day trai ning course on statistics and conducting statistical research. \nHe has also worked at Huntingdon Life Sciences\, supporting toxi cologists and pharmacologists on the design and analysis of data from regu latory safety assessment and toxicology studies. During this time he also collaborated with a programmer to develop InVivoStat\, a free-to-use stati stical software package for life-scientists and other researchers. InVivoS tat jointly won the 2018 RSS and PSI prize for Statistical Excellence in t he Pharmaceutical Industry. \nRecently he has spent the last 12 years at GSK supporting pre-clinical research and medicine. \ nHe has been a committee member of the PSI Toxicology SIG a nd the NC3Rs (National Centre for the 3Rs) experimental design project gro up and has given external training courses to professional and academic or ganizations\, including the Karolinska Institute\, Paris-Sud University\, UCL\, NC3Rs\, European Summer School in Whole Animal Pharmacology and the British Association for Psychopharmacology. His is co-author of the textbo ok &ldquo\;The Design and Statistical Analysis of Animal Experiments&rdquo \;. \n | \n \n Experimental design plays a fundamental role in im proving both the reliability and reproducibility of all experimentation. I n pre-clinical research it can be argued it plays an even more important r ole due to limited resources\, the need to apply the 3Rs principals and th e underlying complexity of the biological subject matter. \nIn what is planned to be an interactive session\, Simon Bate will pres ent his thoughts and experiences on over 20 years working with life-scient ists and will discuss the strategies he has developed over that time. He w ill offer his ideas on how we can work alongside life scientists\, guiding them through the intricacies of designing experiments. By putting experim ental design at the front and centre of the research process\, he will arg ue that not only will we get more reliable results\, while reducing animal use\, but we can avoid some of the common pitfalls but navigate contentio us issues surrounding research in this area such as the role of p-values\, pseudo-replication and multiple comparison tests. \nThe session will also include a session on how to collaborate and communicate with scientists\, where he will explore several tools available that can aid the scientist in their understanding and application of experimental d esign in practice. \n | \n
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| \n \n David Lovell is Emeritus Reader in Medical Stat istics at St George&rsquo\;s Medical School\, University of London. Previo usly he was Reader in Medical Statistics at the Postgraduate Medical Schoo l\, University of Surrey and an Associate Director and Head of Biostatisti cs support to Clinical Pharmacogenomics at Pfizer Global Research and Deve lopment (PGRD) in Sandwich\, Kent providing data management and statistica l support to pharmacogenetics and genomics. His PhD was from the Departmen t of Human Genetics and Biometry at University College London in 1980. Bef ore joining Pfizer\, David was the Head of the Science Division at BIBRA I nternational\, Carshalton\, which included Molecular Biology\, Genetic Tox icology\, Biostatistics and Computer Services. At BIBRA David managed the statistical and computing group providing specialised statistical support to BIBRA&rsquo\;s Clinical Unit and contract research work. He conducted a nd managed research programmes on genetics\, statistics and quantitative r isk assessment for the EU and UK Government Departments. His research inte rests at BIBRA were in the use of mathematical and statistical methods tog ether with genetic models in the understanding of toxicological mechanisms and risk assessment problems. David had previously been a Senior Research Officer with the MRC Experimental Embryology and Teratology Unit\, a visi ting Postdoctoral Fellow at the NIEHS in North Carolina\, USA\, a Genetici st at the MRC Laboratories\, Carshalton and a Research Assistant in Cytoge netics at Birmingham University. He was Vice Chair of the Scientific Commi ttee of EFSA (the European Food Safety Authority) from 2009-12 and a membe r of the Independent Scientific Advisory Committee (ISAC) for MHRA databas e research from 2006-12. He was Chair of the UK Government&rsquo\;s Adviso ry Committee on Mutagenicity of Chemicals in Food\, Consumer Products and the Environment (COM) and a member of the Committees on Carcinogenicity (C OC) (until April 2021) and is currently a member of the Committee on Toxic ity (COT). He was a member of the Board of the NC3R's (2017 - 2022) and a member of its Grant Assessment Panel (2013-2017). In 2019\, he receved fro m UKEMS the Jim Parry Award to a senior scientist who has made substantial contributions to the field of environmental mutagenesis. \n | \n \n
Genotoxicity testing is an important component in the pre-clinical test ing of drugs and other chemicals. It is 40 years since the first OECD guid eline on bacterial mutagenicity\, the Ames test. The International Worksho ps on Genotoxicity Testing (IWGT) were set up at about the same time to pr ovide a forum for industry\, academic and government experts to meet and r eview regulatory and other topics related to genotoxicity. The 8th Worksho p in August 2022 in Ottawa\, Canada covered a range of topics where statis tical issues arose including statistical approaches and data interpretatio n\, predictivity of in vitro genotoxicity testing and analysis of genotoxi city dose-responses. The relationship between statistical significance and biological importance often arose during the group discussions. This pres entation will outline some of the conclusions reached and indicate some of the challenges facing genotoxicity testing as new methodologies become av ailable and how statistical input can help. \n | \n
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| \n \n Odile Coudert Berthion holds a MSc in biostatistics f rom ISUP (Statistics Institute of Paris Universities) that she validated b y a research master thesis within the department of statistics of the Univ ersity of Glasgow. She has been working for more than 13 years for the Non Clinical Efficacy &\;Safety (NCES) department of Sanofi as a contracto r\, first at Keyrus Life Science then at IT&\;M stats. She had the oppo rtunity to analyze a wide range of studies from various therapeutic areas\ , she also contributed to improve the statistical methods applied in NCES. She has given trainings on mixed models. In November 2023\, she has joine d Ipsen to provide statistical leadership and support to the Research\, Ex ternal Innovation and Early Development (REED). \n | \n
\n In prec linical and research field\, departures from the hypotheses underlying par ametric analyses\, normal distribution and variances homogeneity\, are fre quently encountered\; this is all the more concerning since the sample siz e is mall and the data may be repeated. \nAnova-Type St atistics (ATS) is a non-parametric method used for analyzing longitudinal data from factorial designs without making any assumption on the data dist ribution and the variances homogeneity. Particularly adapted to small samp le sizes\, it remains valid even when there is no variability within some factors levels. The ATS method is based on data distributions instead of p ositional parameters. The distributions and the relative factors effects a re estimated using the ranks amongst all observations. This method can eas ily be implemented using the Mixed procedure of SAS software. \n | \n
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Bernard G Francq is Lead Statistician with GSK Biologicals\, driving st atistical innovation for CMC projects worldwide. He holds a PhD in Statist ics (UCLouvain\, 2013). \nThe world's foremost expert on errors-in-variables (EIV) regressions in method comparison studies\, his work has been awarded Best MSc Thesis Biostatistics (Qué\;telet 2008 \, Belgium)\, Best Chemometrician Prize (Chimiomé\;trie 2009\, Paris )\, and Best Young Researcher (Agrostat 2012\, Paris). His communication s kills have been recognized with the Greenfield Challenge Award (ENBIS 2012 \, Ljubljana)\, and Best Communication Award (ENBIS 2012\, Ljubljana). Rec ent work on tolerance intervals in bridging studies was awarded Best GSK S tatistical Paper (2020). \nBernard's research interests include DOE's\, CMC statistics\, clinical trials\, non-linear mixed models \, tolerance intervals\, and advanced data visualization techniques. He le ctures at UCLouvain and regularly offers trainings to statisticians in the (bio)pharmaceutical industry. \n | \n \n In the past\, screening (which process parameters are impactful) and optimisation were 2 distinct phases performed by 2 different designs of experiments (DoE). Then\, the d efinitive screening designs (DSDs) published approximately 10 years ago at tracted a lot of attention from both statisticians and non-statisticians\, espcially in the pharma industry. The idea is to combine screening and op timisation in a single DoE. This allows to reduce the total number of expe riments and the research development time with a substantial gain in the b udget. Furthermore\, it accelerates the time-to-market\, at least by 2\, o f future drugs and vacccines. \nRecently\, a new type of DoE called OMARS for orthogonal minimally aliased response surface has be en published. These OMARS DoEs outperform DSDs in many criteria. Firstly\, the orthogonality criteria where the independence between main effects is fulfilled\, and also between main effects and interaction (+ quadratic) t erms (also in presence of categorical factors). Secondly\, the projection property where OMARS designs are more likely able to estimate a response s urface model from the selected significant parameters. \nIn this presentation\, we will assess the impact and discuss the deployme nt of OMARS DoEs in the pharma industry. The first case study will illustr ate fermentations on Ambr system for vaccine development (with continous p rocess parameters). The second case study will involve categorical factor for analytical development. We will then discuss the opportunities and dep loyment of OMARS DoEs in non-clinical and pre-clinical phases. \n | \n
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