Pre-Clinical SIG Webinar: Modern Algorithms for Animal Randomization in Preclinical Studies

PSI Introduction to Industry Training (ITIT) Course - 2026/2027

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

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AIMS SIG - Open-Source Lunch Bites

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PSI Career Young Virtual Event (Q2 2026)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

PSI Book Club: The AI Con – Joint with ASA Book Club

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Causal Inference SIG and EFSPI Methods Leaders: Modern Covariate Adjustment in Clinical Trials

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PSI Book Club: Change: How organisations achieve hard-to-image results in uncertain and volatile times

Organizations have to adapt to the transforming landscape of our industry to ensure they continue to be successful in the future. Many of us are feeling the impact of organizational change. By reading John P Kotter’s book we can understand about organizational change and learn how to thrive, rather than just survive, through change. Change, by John P Kotter (and his team), is a summary of all that he has learned over his decades of research and leading change. His book describes why many current approaches to change are inadequate and explains why new solutions need to give people a voice and a role in a new, change-embracing organization. Develop your understanding of organisational change and become empowered to be part of your organisation’s change, by reading Change by John P Kotter and joining the Sept-Dec 2025 book club. You will be invited to join facilitated discussions of the concepts and ideas and apply knowledge from the book in-between sessions.

PSI Career Young Virtual Event (Q3 2026)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

PSI Training Course: Propensity Scores: Practical Application in Non-randomised Studies

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PSI Career Young Virtual Event (Q4 2026)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

Speaker	Biography	Abstract
Davit Sargsyan, Johnson & Johnson	Davit Sargsyan is an associate director in a nonclinical statistics group at J&J Innovative Medicine, mainly supporting Immunology Discover R&D. He received an MS in statistics and completed his PhD in pharmaceutical sciences at Rutgers University. To date, Davit has co-published over 70 scientific articles in peer-reviewed journals on topics ranging from clinical outcomes of cardiovascular patients using a state-wide hospital admission data registry to natural compounds testing in in vivo and in vitro models. Davit’s research at Rutgers School of Pharmacy concentrated on computational methods and visualization of omics data studying the effect of dietary phytochemicals on epigenome, transcriptome and microbiome.	Modern Algorithms for Animal Randomization in Preclinical Studies In both, clinical and preclinical studies randomizing subjects to treatment groups is a key part of the study design. This step minimizes the risk of confounding. For large studies, complete randomization is often enough to ensure that treatment groups are similar. However, for small clinical trials and especially for in vivo studies this approach is riskier. More elaborate randomization techniques can be applied for those studies such as randomized block design. However, the best results are achieved when algorithmic approach to subject allocation is taken. One such approach is based on genetic algorithm that is rooted in Theory of Evolution. The methodology minimizes the fitness function criteria for partitions of a dataset into balanced subgroups. The performance of the algorithm was compared to random allocation and the exhaustive search using synthetic and real-world data. The results showed that experimental groups created by this algorithm were more homogeneous compared those created by exhaustive search. Additionally, this algorithm is significantly less expensive computationally compared to the exhaustive search, and the efficiency gains increase rapidly as the number of subjects and design factors increase. We will discuss the theory behind this approach as well as the extensions and variations of the algorithm such as simulated annealing.

Speaker	Biography	Abstract
Davit Sargsyan, Johnson & Johnson	Davit Sargsyan is an associate director in a nonclinical statistics group at J&J Innovative Medicine, mainly supporting Immunology Discover R&D. He received an MS in statistics and completed his PhD in pharmaceutical sciences at Rutgers University. To date, Davit has co-published over 70 scientific articles in peer-reviewed journals on topics ranging from clinical outcomes of cardiovascular patients using a state-wide hospital admission data registry to natural compounds testing in in vivo and in vitro models. Davit’s research at Rutgers School of Pharmacy concentrated on computational methods and visualization of omics data studying the effect of dietary phytochemicals on epigenome, transcriptome and microbiome.	Modern Algorithms for Animal Randomization in Preclinical Studies In both, clinical and preclinical studies randomizing subjects to treatment groups is a key part of the study design. This step minimizes the risk of confounding. For large studies, complete randomization is often enough to ensure that treatment groups are similar. However, for small clinical trials and especially for in vivo studies this approach is riskier. More elaborate randomization techniques can be applied for those studies such as randomized block design. However, the best results are achieved when algorithmic approach to subject allocation is taken. One such approach is based on genetic algorithm that is rooted in Theory of Evolution. The methodology minimizes the fitness function criteria for partitions of a dataset into balanced subgroups. The performance of the algorithm was compared to random allocation and the exhaustive search using synthetic and real-world data. The results showed that experimental groups created by this algorithm were more homogeneous compared those created by exhaustive search. Additionally, this algorithm is significantly less expensive computationally compared to the exhaustive search, and the efficiency gains increase rapidly as the number of subjects and design factors increase. We will discuss the theory behind this approach as well as the extensions and variations of the algorithm such as simulated annealing.

Speaker	Biography	Abstract
Davit Sargsyan, Johnson & Johnson	Davit Sargsyan is an associate director in a nonclinical statistics group at J&J Innovative Medicine, mainly supporting Immunology Discover R&D. He received an MS in statistics and completed his PhD in pharmaceutical sciences at Rutgers University. To date, Davit has co-published over 70 scientific articles in peer-reviewed journals on topics ranging from clinical outcomes of cardiovascular patients using a state-wide hospital admission data registry to natural compounds testing in in vivo and in vitro models. Davit’s research at Rutgers School of Pharmacy concentrated on computational methods and visualization of omics data studying the effect of dietary phytochemicals on epigenome, transcriptome and microbiome.	Modern Algorithms for Animal Randomization in Preclinical Studies In both, clinical and preclinical studies randomizing subjects to treatment groups is a key part of the study design. This step minimizes the risk of confounding. For large studies, complete randomization is often enough to ensure that treatment groups are similar. However, for small clinical trials and especially for in vivo studies this approach is riskier. More elaborate randomization techniques can be applied for those studies such as randomized block design. However, the best results are achieved when algorithmic approach to subject allocation is taken. One such approach is based on genetic algorithm that is rooted in Theory of Evolution. The methodology minimizes the fitness function criteria for partitions of a dataset into balanced subgroups. The performance of the algorithm was compared to random allocation and the exhaustive search using synthetic and real-world data. The results showed that experimental groups created by this algorithm were more homogeneous compared those created by exhaustive search. Additionally, this algorithm is significantly less expensive computationally compared to the exhaustive search, and the efficiency gains increase rapidly as the number of subjects and design factors increase. We will discuss the theory behind this approach as well as the extensions and variations of the algorithm such as simulated annealing.

Speaker	Biography	Abstract
Davit Sargsyan, Johnson & Johnson	Davit Sargsyan is an associate director in a nonclinical statistics group at J&J Innovative Medicine, mainly supporting Immunology Discover R&D. He received an MS in statistics and completed his PhD in pharmaceutical sciences at Rutgers University. To date, Davit has co-published over 70 scientific articles in peer-reviewed journals on topics ranging from clinical outcomes of cardiovascular patients using a state-wide hospital admission data registry to natural compounds testing in in vivo and in vitro models. Davit’s research at Rutgers School of Pharmacy concentrated on computational methods and visualization of omics data studying the effect of dietary phytochemicals on epigenome, transcriptome and microbiome.	Modern Algorithms for Animal Randomization in Preclinical Studies In both, clinical and preclinical studies randomizing subjects to treatment groups is a key part of the study design. This step minimizes the risk of confounding. For large studies, complete randomization is often enough to ensure that treatment groups are similar. However, for small clinical trials and especially for in vivo studies this approach is riskier. More elaborate randomization techniques can be applied for those studies such as randomized block design. However, the best results are achieved when algorithmic approach to subject allocation is taken. One such approach is based on genetic algorithm that is rooted in Theory of Evolution. The methodology minimizes the fitness function criteria for partitions of a dataset into balanced subgroups. The performance of the algorithm was compared to random allocation and the exhaustive search using synthetic and real-world data. The results showed that experimental groups created by this algorithm were more homogeneous compared those created by exhaustive search. Additionally, this algorithm is significantly less expensive computationally compared to the exhaustive search, and the efficiency gains increase rapidly as the number of subjects and design factors increase. We will discuss the theory behind this approach as well as the extensions and variations of the algorithm such as simulated annealing.

Speaker	Biography	Abstract
Davit Sargsyan, Johnson & Johnson	Davit Sargsyan is an associate director in a nonclinical statistics group at J&J Innovative Medicine, mainly supporting Immunology Discover R&D. He received an MS in statistics and completed his PhD in pharmaceutical sciences at Rutgers University. To date, Davit has co-published over 70 scientific articles in peer-reviewed journals on topics ranging from clinical outcomes of cardiovascular patients using a state-wide hospital admission data registry to natural compounds testing in in vivo and in vitro models. Davit’s research at Rutgers School of Pharmacy concentrated on computational methods and visualization of omics data studying the effect of dietary phytochemicals on epigenome, transcriptome and microbiome.	Modern Algorithms for Animal Randomization in Preclinical Studies In both, clinical and preclinical studies randomizing subjects to treatment groups is a key part of the study design. This step minimizes the risk of confounding. For large studies, complete randomization is often enough to ensure that treatment groups are similar. However, for small clinical trials and especially for in vivo studies this approach is riskier. More elaborate randomization techniques can be applied for those studies such as randomized block design. However, the best results are achieved when algorithmic approach to subject allocation is taken. One such approach is based on genetic algorithm that is rooted in Theory of Evolution. The methodology minimizes the fitness function criteria for partitions of a dataset into balanced subgroups. The performance of the algorithm was compared to random allocation and the exhaustive search using synthetic and real-world data. The results showed that experimental groups created by this algorithm were more homogeneous compared those created by exhaustive search. Additionally, this algorithm is significantly less expensive computationally compared to the exhaustive search, and the efficiency gains increase rapidly as the number of subjects and design factors increase. We will discuss the theory behind this approach as well as the extensions and variations of the algorithm such as simulated annealing.

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