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High-throughput screening (HTS) is a widespread method in early drug discovery for identifying promising chemical matter that modulates a target or phenotype of interest. Because HTS campaigns involve screening millions of compounds, it is often desirable to initiate screening with a subset of the full collection. Subsequently, virtual screening methods prioritize likely active compounds in the remaining collection in an iterative process. With this approach, orthogonal virtual screening methods are often applied, necessitating the prioritization of hits from different approaches. Here, we introduce a novel method of fusing these prioritizations and benchmark it prospectively on 17 screening campaigns using virtual screening methods in three descriptor spaces. We found that the fusion approach retrieves 15% to 65% more active chemical series than any single machine-learning method and that appropriately weighting contributions of similarity and machine-learning scoring techniques can increase enrichment by 1% to 19%. We also use fusion scoring to evaluate the tradeoff between screening more chemical matter initially in lieu of replicate samples to prevent false-positives and find that the former option leads to the retrieval of more active chemical series. These results represent guidelines that can increase the rate of identification of promising active compounds in future iterative screens.

Gene silencing by RNA interference has become a powerful tool to help identify genes that regulate biological processes. However, the complexity of the biology probed and the incomplete validation of the reagents used make it difficult to interpret the results of genome-wide siRNA screens. To address this challenge and maximize the return on the efforts required for validating genomic screen hits, the screening strategy must be designed to increase the robustness of the primary screening hits and include assays that inform on the mechanism of action of the knocked-down transcripts. Here, we describe the implementation of a small interfering RNA (siRNA) screen to identify genes that sensitize the effect of poly-(ADP ribose)-polymerase (PARP) inhibitor on cell survival. In the strategy we designed for the primary screen, two biological activities, apoptosis and cell viability, were measured simultaneously at different time points in the presence and absence of a PARP inhibitor (PARPi). The multiplexed assay allowed us to identify PARPi sensitizers induced by both caspase-dependent and independent mechanisms. The multiplexed screening strategy yielded robust primary hits with significant enrichment for DNA repair genes, which were further validated using relevant high-content imaging assays and confirmation of transcript knockdown by real-time PCR (rtPCR).

High-throughput screening (HTS) of large-scale RNA interference (RNAi) libraries has become an increasingly popular method of functional genomics in recent years. Cell-based assays used for RNAi screening often produce small dynamic ranges and significant variability because of the combination of cellular heterogeneity, transfection efficiency, and the intrinsic nature of the genes being targeted. These properties make reliable hit selection in the RNAi screen a difficult task. The use of robust methods based on median and median absolute deviation (MAD) has been suggested to improve hit selection in such cases, but mean and standard deviation (SD)-based methods are still predominantly used in many RNAi HTS. In an experimental approach to compare these 2 methods, a genome-scale small interfering RNA (siRNA) screen was performed, in which the identification of novel targets increasing the therapeutic index of the chemotherapeutic agent mitomycin C (MMC) was sought. MAD values were resistant to the presence of outliers, and the hits selected by the MAD-based method included all the hits that would be selected by SD-based method as well as a significant number of additional hits. When retested in triplicate, a similar percentage of these siRNAs were shown to genuinely sensitize cells to MMC compared with the hits shared between SD- and MAD-based methods. Confirmed hits were enriched with the genes involved in the DNA damage response and cell cycle regulation, validating the overall hit selection strategy. Finally, computer simulations showed the superiority and generality of the MAD-based method in various RNAi HTS data models. In conclusion, the authors demonstrate that the MAD-based hit selection method rescued physiologically relevant false negatives that would have been missed in the SD-based method, and they believe it to be the desirable 1st-choice hit selection method for RNAi screen results.

RNA interference (RNAi), combined with the availability of genome sequences, provides an unprecedented opportunity for the massive and parallel investigations of gene function. Small interfering RNA (siRNA) represents a popular and quick approach of RNAi for in vitro loss-of-function genetic screens. Efficient transfection of siRNA is critical for unambiguous interpretation of screen results and thus overall success of any siRNA screen. A high-throughput, lipid-based transfection method for siRNA was developed that can process eighty 384-well microplates in triplicate (for a total of 30,720 unique transfections) in 8 h. Transfection throughput was limited only by the speed of robotics, whereas the cost of screening was reduced. As a proof of principle, a genome-scale screen with a library of 22,108 siRNAs was performed to identify the genes sensitizing cells to mitomycin C at concentrations of 0, 20, and 60 nM. Transfection efficiency, performances of control siRNAs, and other quality metrics were monitored and demonstrated that the new, optimized transfection protocol produced high-quality results throughout the screen. (Journal of Biomolecular Screening XXXX:xx-xx)