Large Row-Constrained Supersaturated Designs for High-throughput Screening

TL;DR

This paper introduces a new class of row-constrained supersaturated designs tailored for high-throughput screening, improving experimental efficiency and statistical power in drug discovery processes.

Contribution

It develops a computational method for constructing row-constrained supersaturated designs and demonstrates their superiority over existing methods through simulations and real data application.

Findings

Proposed designs outperform existing methods in simulations

New designs accommodate biological constraints effectively

Application to drug discovery data validates practical utility

Abstract

High-throughput screening, in which multiwell plates are used to test large numbers of compounds against specific targets, is widely used across many areas of the biological sciences and most prominently in drug discovery. We propose a statistically principled approach to these screening experiments, using the machinery of supersaturated designs and the Lasso. To accommodate limitations on the number of biological entities that can be applied to a single microplate well, we present a new class of row-constrained supersaturated designs. We develop a computational procedure to construct these designs, provide some initial lower bounds on the average squared off-diagonal values of their main-effects information matrix, and study the impact of the constraint on design quality. We also show via simulation that the proposed constrained row screening method is statistically superior to existing methods and demonstrate the use of the new methodology on a real drug-discovery system.