Event

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

Date: Tuesday 18th November 2025
Time: 14:00 - 15:00 GMT | 9:00 - 10:00 EST (US)
Location: Online via Zoom
Speakers: Davit Sargsyan

Who is this event intended for?: Statisticians and Scientists involved or interested in randomization techniques in pre-clinical and early clinical studies.
What is the benefit of attending?: Hearing about modern randomization methods.

Cost

This webinar is free to both Members of PSI and Non-Members.

Registration

To register for this event, please click here

Overview

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 details

Speaker

Biography

Abstract
Davit_Headshot_2025
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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