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.
Registration
This event is free to attend for both Members of PSI and Non-Members. To register your place, please click here.
Overview
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 details
Speaker
Biography
Phil Kay
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.
Chandramouli Ramnarayanan
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.
Scientific Meetings
Joint PSI/EFSPI Pre-Clinical SIG Webinar: Efficient R&D: SVEM and Advanced DOE in Preclinical Toxicity Testing
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.
Registration
This event is free to attend for both Members of PSI and Non-Members. To register your place, please click here.
Overview
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 details
Speaker
Biography
Phil Kay
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.
Chandramouli Ramnarayanan
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.
Training Courses
Joint PSI/EFSPI Pre-Clinical SIG Webinar: Efficient R&D: SVEM and Advanced DOE in Preclinical Toxicity Testing
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.
Registration
This event is free to attend for both Members of PSI and Non-Members. To register your place, please click here.
Overview
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 details
Speaker
Biography
Phil Kay
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.
Chandramouli Ramnarayanan
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.
Journal Club
Joint PSI/EFSPI Pre-Clinical SIG Webinar: Efficient R&D: SVEM and Advanced DOE in Preclinical Toxicity Testing
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.
Registration
This event is free to attend for both Members of PSI and Non-Members. To register your place, please click here.
Overview
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 details
Speaker
Biography
Phil Kay
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.
Chandramouli Ramnarayanan
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.
Webinars
Joint PSI/EFSPI Pre-Clinical SIG Webinar: Efficient R&D: SVEM and Advanced DOE in Preclinical Toxicity Testing
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.
Registration
This event is free to attend for both Members of PSI and Non-Members. To register your place, please click here.
Overview
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 details
Speaker
Biography
Phil Kay
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.
Chandramouli Ramnarayanan
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.
Careers Meetings
Joint PSI/EFSPI Pre-Clinical SIG Webinar: Efficient R&D: SVEM and Advanced DOE in Preclinical Toxicity Testing
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.
Registration
This event is free to attend for both Members of PSI and Non-Members. To register your place, please click here.
Overview
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 details
Speaker
Biography
Phil Kay
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.
Chandramouli Ramnarayanan
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.
Upcoming Events
PSI Mentoring 2025
Date: Ongoing 6 month cycle beginning late April/early May 2024
Are you a member of PSI looking to further your career or help develop others - why not sign up to the PSI Mentoring scheme? You can expand your network, improve your leadership skills and learn from more senior colleagues in the industry.
PSI Introduction to Industry Training (ITIT) Course - 2025/2026
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.
Joint PSI/EFSPI Visualisation SIG 'Wonderful Wednesday' Webinars
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.
Overview of the day:
09:00 - Welcome, introduction, and networking
09:25 - Presentation from UCL and Strathclyde on programme delivery
09:45 - Presentation on evidence portfolios from Rachael
10:30 - Q&A Panel of current apprentices
11:10 - Presentation from Liam - A Balancing Act: My Journey from Programmer to Statistician
11:40 - Apprenticeship feedback and networking
12:10 - Close
Presentations and timings are subject to change.
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 interactive online training workshop providing an in-depth review of the estimand framework as laid out by ICH E9(R1) addendum with inputs from estimand experts, case studies, quizzes and opportunity for discussions. You will develop an estimand in a therapeutic area of interest to your company. In an online break-out room, you will join a series of team discussions to implement the estimand framework in a case study, aligning estimands, design, conduct, analysis, (assumptions + sensitivity analyses) to the clinical objective and therapeutic setting.
Maths Meets Medicine: Exploring Careers in the Pharmaceutical Industry
This session will showcase how careers in pharmaceutical statistics can be both rewarding and impactful, with a focus on how mathematics is integral to the development of medicines. Students will hear from industry experts, explore diverse career paths, and learn why continuing to study math is key to unlocking exciting opportunities in the healthcare sector.
Dissolution Testing: Time for Statistical (r)Evolution
Webinar dedicated to the topic of dissolution of oral solid dosage forms; opportunity to hear from statisticians working in the CMC field, with open question and answers.
In addition, the CMC Statistical Network Europe special interest group will discuss advocacy opportunities, have your say to contribute to the future direction.
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 networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
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 BioMarin internship programme will enable students to gain valuable experience and knowledge of the processes and systems within BioMarin, whilst gaining an insight into the pharmaceutical/biotech industry.