Date: Tuesday 8th October 2024
Time: 14:00-15:00 BST | 15:00-16:00 CEST | 09:00-10:00 ET
Location: Online via Zoom
Speakers: Phil Kay (JMP) and Chandramouli Ramnarayanan (JMP).
Who is this event intended for? Statisticians and data scientists working on pre-clinical experiments in the pharmaceutical industry.
What is the benefit of attending? Learning about experimenting with maximum efficiency i.e. the least number of experiments.
This event is free to attend for both Members of PSI and Non-Members. To register your place, please click here.
Advances in AI, lab automation and closed-loop optimisation promise big productivity gains for pharma R&D. But experimenting with maximum efficiency - that is, the least number of runs - will always be important. This is especially true where there is an ethical imperative, such as in pre-clinical animal experiments.
Recent advances in statistical Design Of Experiments (DOE) including Definitive Screening Designs (DSDs) and the broader class of Orthogonal Minimally Aliasing Response Surface (OMARS) designs have given us new options for understanding complex systems with small experiments. Self-Validated Ensemble Modelling (SVEM) is an innovative analysis approach that applies ideas from Machine Learning to small data from designed experiments. In this presentation we will show how SVEM works and how it can overcome common challenges when you are building useful models of complex systems from small experiments.
We will illustrate this with a case study example on testing the toxicity of an oncology formulation in a preclinical setting. This (simulated) study examines the impact of various formulation factors and their interactions on the toxicity and efficacy of a new oncology drug cocktail using a rat model. Key factors include the concentrations of Erlotinib, Cisplatin, and Dexamethasone, with responses measured such as tumor inhibition rate, overall survival rate, and toxicity indicators. Additionally, the pH of the formulation and particle size were evaluated. Responses measured include efficacy, represented by tumor inhibition rate, and various toxicity parameters, such as overall survival rate, hepatic toxicity, renal toxicity, cardiac toxicity, and hematological toxicity.
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Speaker |
Biography |
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Phil leads a global team with a mission to spread the use and impact of data analytics amongst scientists and engineers at some of the world’s largest chemical, pharmaceutical, semiconductor and consumer product companies. Earlier in his career he gained a passion for statistical design and analysis of experiments while working as a development chemist at FujiFilm. Phil has a master’s degree in applied statistics and a master’s and PhD in chemistry. He is a chartered chemist and chair of the RSC process chemistry and technology interest group. |
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Chandra (Chandramouli Ramnarayanan), PhD, is a member of the Global Technical Enablement team at JMP Statistical Discovery LLC, a SAS Institute subsidiary. He provides technical support to leading health and life sciences clients and contributes to product development. Previously, Chandra held senior roles in quality assurance within the pharmaceutical sector and served as a professor of pharmaceutical quality assurance. With a PhD in Pharmaceutical Sciences, over 30 publications, and certifications in SAS® Programming, PRINCE2 Agile®, and ITIL 4. |
Date: Tuesday 8th October 2024
Time: 14:00-15:00 BST | 15:00-16:00 CEST | 09:00-10:00 ET
Location: Online via Zoom
Speakers: Phil Kay (JMP) and Chandramouli Ramnarayanan (JMP).
Who is this event intended for? Statisticians and data scientists working on pre-clinical experiments in the pharmaceutical industry.
What is the benefit of attending? Learning about experimenting with maximum efficiency i.e. the least number of experiments.
This event is free to attend for both Members of PSI and Non-Members. To register your place, please click here.
Advances in AI, lab automation and closed-loop optimisation promise big productivity gains for pharma R&D. But experimenting with maximum efficiency - that is, the least number of runs - will always be important. This is especially true where there is an ethical imperative, such as in pre-clinical animal experiments.
Recent advances in statistical Design Of Experiments (DOE) including Definitive Screening Designs (DSDs) and the broader class of Orthogonal Minimally Aliasing Response Surface (OMARS) designs have given us new options for understanding complex systems with small experiments. Self-Validated Ensemble Modelling (SVEM) is an innovative analysis approach that applies ideas from Machine Learning to small data from designed experiments. In this presentation we will show how SVEM works and how it can overcome common challenges when you are building useful models of complex systems from small experiments.
We will illustrate this with a case study example on testing the toxicity of an oncology formulation in a preclinical setting. This (simulated) study examines the impact of various formulation factors and their interactions on the toxicity and efficacy of a new oncology drug cocktail using a rat model. Key factors include the concentrations of Erlotinib, Cisplatin, and Dexamethasone, with responses measured such as tumor inhibition rate, overall survival rate, and toxicity indicators. Additionally, the pH of the formulation and particle size were evaluated. Responses measured include efficacy, represented by tumor inhibition rate, and various toxicity parameters, such as overall survival rate, hepatic toxicity, renal toxicity, cardiac toxicity, and hematological toxicity.
|
Speaker |
Biography |
|
|
Phil leads a global team with a mission to spread the use and impact of data analytics amongst scientists and engineers at some of the world’s largest chemical, pharmaceutical, semiconductor and consumer product companies. Earlier in his career he gained a passion for statistical design and analysis of experiments while working as a development chemist at FujiFilm. Phil has a master’s degree in applied statistics and a master’s and PhD in chemistry. He is a chartered chemist and chair of the RSC process chemistry and technology interest group. |
|
|
Chandra (Chandramouli Ramnarayanan), PhD, is a member of the Global Technical Enablement team at JMP Statistical Discovery LLC, a SAS Institute subsidiary. He provides technical support to leading health and life sciences clients and contributes to product development. Previously, Chandra held senior roles in quality assurance within the pharmaceutical sector and served as a professor of pharmaceutical quality assurance. With a PhD in Pharmaceutical Sciences, over 30 publications, and certifications in SAS® Programming, PRINCE2 Agile®, and ITIL 4. |
Date: Tuesday 8th October 2024
Time: 14:00-15:00 BST | 15:00-16:00 CEST | 09:00-10:00 ET
Location: Online via Zoom
Speakers: Phil Kay (JMP) and Chandramouli Ramnarayanan (JMP).
Who is this event intended for? Statisticians and data scientists working on pre-clinical experiments in the pharmaceutical industry.
What is the benefit of attending? Learning about experimenting with maximum efficiency i.e. the least number of experiments.
This event is free to attend for both Members of PSI and Non-Members. To register your place, please click here.
Advances in AI, lab automation and closed-loop optimisation promise big productivity gains for pharma R&D. But experimenting with maximum efficiency - that is, the least number of runs - will always be important. This is especially true where there is an ethical imperative, such as in pre-clinical animal experiments.
Recent advances in statistical Design Of Experiments (DOE) including Definitive Screening Designs (DSDs) and the broader class of Orthogonal Minimally Aliasing Response Surface (OMARS) designs have given us new options for understanding complex systems with small experiments. Self-Validated Ensemble Modelling (SVEM) is an innovative analysis approach that applies ideas from Machine Learning to small data from designed experiments. In this presentation we will show how SVEM works and how it can overcome common challenges when you are building useful models of complex systems from small experiments.
We will illustrate this with a case study example on testing the toxicity of an oncology formulation in a preclinical setting. This (simulated) study examines the impact of various formulation factors and their interactions on the toxicity and efficacy of a new oncology drug cocktail using a rat model. Key factors include the concentrations of Erlotinib, Cisplatin, and Dexamethasone, with responses measured such as tumor inhibition rate, overall survival rate, and toxicity indicators. Additionally, the pH of the formulation and particle size were evaluated. Responses measured include efficacy, represented by tumor inhibition rate, and various toxicity parameters, such as overall survival rate, hepatic toxicity, renal toxicity, cardiac toxicity, and hematological toxicity.
|
Speaker |
Biography |
|
|
Phil leads a global team with a mission to spread the use and impact of data analytics amongst scientists and engineers at some of the world’s largest chemical, pharmaceutical, semiconductor and consumer product companies. Earlier in his career he gained a passion for statistical design and analysis of experiments while working as a development chemist at FujiFilm. Phil has a master’s degree in applied statistics and a master’s and PhD in chemistry. He is a chartered chemist and chair of the RSC process chemistry and technology interest group. |
|
|
Chandra (Chandramouli Ramnarayanan), PhD, is a member of the Global Technical Enablement team at JMP Statistical Discovery LLC, a SAS Institute subsidiary. He provides technical support to leading health and life sciences clients and contributes to product development. Previously, Chandra held senior roles in quality assurance within the pharmaceutical sector and served as a professor of pharmaceutical quality assurance. With a PhD in Pharmaceutical Sciences, over 30 publications, and certifications in SAS® Programming, PRINCE2 Agile®, and ITIL 4. |
Date: Tuesday 8th October 2024
Time: 14:00-15:00 BST | 15:00-16:00 CEST | 09:00-10:00 ET
Location: Online via Zoom
Speakers: Phil Kay (JMP) and Chandramouli Ramnarayanan (JMP).
Who is this event intended for? Statisticians and data scientists working on pre-clinical experiments in the pharmaceutical industry.
What is the benefit of attending? Learning about experimenting with maximum efficiency i.e. the least number of experiments.
This event is free to attend for both Members of PSI and Non-Members. To register your place, please click here.
Advances in AI, lab automation and closed-loop optimisation promise big productivity gains for pharma R&D. But experimenting with maximum efficiency - that is, the least number of runs - will always be important. This is especially true where there is an ethical imperative, such as in pre-clinical animal experiments.
Recent advances in statistical Design Of Experiments (DOE) including Definitive Screening Designs (DSDs) and the broader class of Orthogonal Minimally Aliasing Response Surface (OMARS) designs have given us new options for understanding complex systems with small experiments. Self-Validated Ensemble Modelling (SVEM) is an innovative analysis approach that applies ideas from Machine Learning to small data from designed experiments. In this presentation we will show how SVEM works and how it can overcome common challenges when you are building useful models of complex systems from small experiments.
We will illustrate this with a case study example on testing the toxicity of an oncology formulation in a preclinical setting. This (simulated) study examines the impact of various formulation factors and their interactions on the toxicity and efficacy of a new oncology drug cocktail using a rat model. Key factors include the concentrations of Erlotinib, Cisplatin, and Dexamethasone, with responses measured such as tumor inhibition rate, overall survival rate, and toxicity indicators. Additionally, the pH of the formulation and particle size were evaluated. Responses measured include efficacy, represented by tumor inhibition rate, and various toxicity parameters, such as overall survival rate, hepatic toxicity, renal toxicity, cardiac toxicity, and hematological toxicity.
|
Speaker |
Biography |
|
|
Phil leads a global team with a mission to spread the use and impact of data analytics amongst scientists and engineers at some of the world’s largest chemical, pharmaceutical, semiconductor and consumer product companies. Earlier in his career he gained a passion for statistical design and analysis of experiments while working as a development chemist at FujiFilm. Phil has a master’s degree in applied statistics and a master’s and PhD in chemistry. He is a chartered chemist and chair of the RSC process chemistry and technology interest group. |
|
|
Chandra (Chandramouli Ramnarayanan), PhD, is a member of the Global Technical Enablement team at JMP Statistical Discovery LLC, a SAS Institute subsidiary. He provides technical support to leading health and life sciences clients and contributes to product development. Previously, Chandra held senior roles in quality assurance within the pharmaceutical sector and served as a professor of pharmaceutical quality assurance. With a PhD in Pharmaceutical Sciences, over 30 publications, and certifications in SAS® Programming, PRINCE2 Agile®, and ITIL 4. |
Date: Tuesday 8th October 2024
Time: 14:00-15:00 BST | 15:00-16:00 CEST | 09:00-10:00 ET
Location: Online via Zoom
Speakers: Phil Kay (JMP) and Chandramouli Ramnarayanan (JMP).
Who is this event intended for? Statisticians and data scientists working on pre-clinical experiments in the pharmaceutical industry.
What is the benefit of attending? Learning about experimenting with maximum efficiency i.e. the least number of experiments.
This event is free to attend for both Members of PSI and Non-Members. To register your place, please click here.
Advances in AI, lab automation and closed-loop optimisation promise big productivity gains for pharma R&D. But experimenting with maximum efficiency - that is, the least number of runs - will always be important. This is especially true where there is an ethical imperative, such as in pre-clinical animal experiments.
Recent advances in statistical Design Of Experiments (DOE) including Definitive Screening Designs (DSDs) and the broader class of Orthogonal Minimally Aliasing Response Surface (OMARS) designs have given us new options for understanding complex systems with small experiments. Self-Validated Ensemble Modelling (SVEM) is an innovative analysis approach that applies ideas from Machine Learning to small data from designed experiments. In this presentation we will show how SVEM works and how it can overcome common challenges when you are building useful models of complex systems from small experiments.
We will illustrate this with a case study example on testing the toxicity of an oncology formulation in a preclinical setting. This (simulated) study examines the impact of various formulation factors and their interactions on the toxicity and efficacy of a new oncology drug cocktail using a rat model. Key factors include the concentrations of Erlotinib, Cisplatin, and Dexamethasone, with responses measured such as tumor inhibition rate, overall survival rate, and toxicity indicators. Additionally, the pH of the formulation and particle size were evaluated. Responses measured include efficacy, represented by tumor inhibition rate, and various toxicity parameters, such as overall survival rate, hepatic toxicity, renal toxicity, cardiac toxicity, and hematological toxicity.
|
Speaker |
Biography |
|
|
Phil leads a global team with a mission to spread the use and impact of data analytics amongst scientists and engineers at some of the world’s largest chemical, pharmaceutical, semiconductor and consumer product companies. Earlier in his career he gained a passion for statistical design and analysis of experiments while working as a development chemist at FujiFilm. Phil has a master’s degree in applied statistics and a master’s and PhD in chemistry. He is a chartered chemist and chair of the RSC process chemistry and technology interest group. |
|
|
Chandra (Chandramouli Ramnarayanan), PhD, is a member of the Global Technical Enablement team at JMP Statistical Discovery LLC, a SAS Institute subsidiary. He provides technical support to leading health and life sciences clients and contributes to product development. Previously, Chandra held senior roles in quality assurance within the pharmaceutical sector and served as a professor of pharmaceutical quality assurance. With a PhD in Pharmaceutical Sciences, over 30 publications, and certifications in SAS® Programming, PRINCE2 Agile®, and ITIL 4. |
Date: Tuesday 8th October 2024
Time: 14:00-15:00 BST | 15:00-16:00 CEST | 09:00-10:00 ET
Location: Online via Zoom
Speakers: Phil Kay (JMP) and Chandramouli Ramnarayanan (JMP).
Who is this event intended for? Statisticians and data scientists working on pre-clinical experiments in the pharmaceutical industry.
What is the benefit of attending? Learning about experimenting with maximum efficiency i.e. the least number of experiments.
This event is free to attend for both Members of PSI and Non-Members. To register your place, please click here.
Advances in AI, lab automation and closed-loop optimisation promise big productivity gains for pharma R&D. But experimenting with maximum efficiency - that is, the least number of runs - will always be important. This is especially true where there is an ethical imperative, such as in pre-clinical animal experiments.
Recent advances in statistical Design Of Experiments (DOE) including Definitive Screening Designs (DSDs) and the broader class of Orthogonal Minimally Aliasing Response Surface (OMARS) designs have given us new options for understanding complex systems with small experiments. Self-Validated Ensemble Modelling (SVEM) is an innovative analysis approach that applies ideas from Machine Learning to small data from designed experiments. In this presentation we will show how SVEM works and how it can overcome common challenges when you are building useful models of complex systems from small experiments.
We will illustrate this with a case study example on testing the toxicity of an oncology formulation in a preclinical setting. This (simulated) study examines the impact of various formulation factors and their interactions on the toxicity and efficacy of a new oncology drug cocktail using a rat model. Key factors include the concentrations of Erlotinib, Cisplatin, and Dexamethasone, with responses measured such as tumor inhibition rate, overall survival rate, and toxicity indicators. Additionally, the pH of the formulation and particle size were evaluated. Responses measured include efficacy, represented by tumor inhibition rate, and various toxicity parameters, such as overall survival rate, hepatic toxicity, renal toxicity, cardiac toxicity, and hematological toxicity.
|
Speaker |
Biography |
|
|
Phil leads a global team with a mission to spread the use and impact of data analytics amongst scientists and engineers at some of the world’s largest chemical, pharmaceutical, semiconductor and consumer product companies. Earlier in his career he gained a passion for statistical design and analysis of experiments while working as a development chemist at FujiFilm. Phil has a master’s degree in applied statistics and a master’s and PhD in chemistry. He is a chartered chemist and chair of the RSC process chemistry and technology interest group. |
|
|
Chandra (Chandramouli Ramnarayanan), PhD, is a member of the Global Technical Enablement team at JMP Statistical Discovery LLC, a SAS Institute subsidiary. He provides technical support to leading health and life sciences clients and contributes to product development. Previously, Chandra held senior roles in quality assurance within the pharmaceutical sector and served as a professor of pharmaceutical quality assurance. With a PhD in Pharmaceutical Sciences, over 30 publications, and certifications in SAS® Programming, PRINCE2 Agile®, and ITIL 4. |
An introductory course giving an overview of the pharmaceutical industry and the drug development process as a whole, aimed at those with 1-3 years' experience. It comprises of six 2-day sessions covering a range of topics including Research and Development, Toxicology, Data Management and the Role of a CRO, Clinical Trials, Reimbursement, and Marketing.
Our monthly webinar explores examples of innovative data visualisations relevant to our day to day work. Each month a new dataset is provided from a clinical trial or other relevant example, and participants are invited to submit a graphic that communicates interesting and relevant characteristics of the data.
The book club’s usual focus is to read and discuss professional development books. In this short format event you can more easily develop you career without the commitment of reading the whole book - simply listen to the 1-hour long podcast before joining the interactive session on 21 May.
This webinar is organised by the RWD SIG and the Historical Data SIG. We will review recent methods, applications, and tools of integrating subject-level-data from clinical trial with external data using Bayesian methods and/or causal inference methods.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
This will be a 45 minute webinar which will explain the topic presented in the published paper, ‘Applying the Estimand Framework to Clinical Pharmacology Trials with a Case Study in Bioequivalance’. There will be 15 minutes for a panel Q&A with some of the authors following the presentation.
This 1-hour webinar will be an opportunity to hear about the methodology and first results of the iRISE consortium. iRISE is working towards a better understanding of reproducibility and the interventions that work to improve it. At the end of the presentation there will also be the opportunity to ask questions.
This one-day event focuses on the comprehensive management of transitioning to R/Open-Source, addressing the challenges and providing actionable insights. Attendees will participate in sessions covering essential topics such as training best practices, creating strategic plans, making the case to senior management, and managing both statistical and programming aspects of the transition.
Develop your non-technical skills by reading The Art of Explanation by Ros Atkins and joining the Sept-Dec 2025 book club. You will be invited to join facilitated discussions of the concepts and ideas and apply skills from the book in-between sessions.
This course is aimed at biostatisticians with no or some pediatric drug development experience who are interested to further their understanding. We will give you an introduction to the pediatric drug development landscape. This will include identifying the key regulations and processes governing pediatric development, a discussion on the needs and challenges when conducting pediatric research and a focus on the ways to overcome these challenges from a statistical perspective.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
The program will feature insightful sessions led by distinguished invited speakers, alongside a poster session showcasing the latest advancements in the field. Further details will be provided.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
This is an exciting, new opportunity for an experienced Statistician looking to take the next step in their career. Offered as a remote or hybrid position aligned with our site in Harrogate, North Yorkshire.
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