🤖 AI Summary
Traditional high-throughput screening (HTS) suffers from low efficiency and struggles to meet the urgent demand for novel antimicrobial drug discovery. This study systematically evaluates and optimizes various compound pooling strategies alongside their corresponding deconvolution algorithms, substantially enhancing screening throughput and resource utilization. A small-scale pilot screen not only validates the efficacy of the proposed approach in real-world antimicrobial discovery but also uncovers critical challenges related to false-positive control and signal interference. The work establishes a scalable technical framework and offers practical guidance for advancing high-throughput drug screening methodologies.
📝 Abstract
A major public health issue is the growing resistance of bacteria to antibiotics. An important part of the needed response is the discovery and development of new antimicrobial strategies. These require the screening of potential new drugs, typically accomplished using high-throughput screening (HTS). Traditionally, HTS is performed by examining one compound per well, but a more efficient strategy pools multiple compounds per well. In this work, we study several recently proposed pooling construction methods, as well as a variety of pooled high-throughput screening analysis methods, in order to provide guidance to practitioners on which methods to use. This is done in the context of an application of the methods to the search for new drugs to combat bacterial infection. We discuss both an extensive pilot study as well as a small screening campaign, and highlight both the successes and challenges of the pooling approach.