A critical early step in drug discovery is the screening of a chemical library. Typically, promising compounds are identified in a primary screen and then more fully characterized in a dose-response analysis with 7-10 data points per compound. Here, we describe a robust microfluidic approach that increases the number of data points to approximately 10,000 per compound. The system exploits Taylor-Aris dispersion to create concentration gradients, which are then segmented into picoliter microreactors by droplet-based microfluidics. The large number of data points results in IC 50 values that are highly precise ( 2.40% at 95% confidence) and highly reproducible (CV 2.45%, n 16). In addition, the high resolution of the data reveals complex dose-response relationships unambiguously. We used this system to screen a chemical library of 704 compounds against protein tyrosine phosphatase 1B, a diabetes, obesity, and cancer target. We identified a number of novel inhibitors, the most potent being sodium cefsulodine, which has an IC 50 of 27 0.83 μM.high-throughput screening | HTS | small molecule library I n the early 16th century the Swiss chemist Paracelsus declared "all substances are poisons, there is none which is not a poison; only the right dose makes a substance non-poisonous." This idea that the biological effects of a chemical compound are dependent upon its concentration was quantified by A. V. Hill in 1910 (1). However, despite the fact that compounds can display complex concentration-dependent relationships, varying in potency, efficacy, and steepness of response, usually just a single measurement at a single concentration (approximately 10 μM) is obtained for each compound in the chemical library during a primary drug screen, even when using state-of-the-art robotic microplate-based screening systems. This results in high numbers of false positives and false negatives (2), as well as the inability to identify subtle, complex pharmacology, such as partial agonism or antagonism. Even when dose-response curves are generated during a quantitative primary screen (3) or, more typically, during the follow-up of a single-point screen, the time and cost limitations mean that the curves typically contain only 7-10 data points each. With ≤10 nonduplicated data points, and as many as four adjustable nonlinear parameters (e.g., in the four-parameter Hill function), the results are highly sensitive to the data quality: For example, the presence of a single outlying data point can substantially alter the fit of the data, unless the outliers are identified and removed (4).We have developed a system that uses droplet-based microfluidics to generate high-quality dose-response data during drug screening. Droplet-based microfluidics is itself a new technology for creating and manipulating picoliter-volume aqueous droplets that function as independent microreactors (for a review see ref. 5). As a result of the miniaturization inherent in this approach, our system (Fig. 1) is capable of generating doseresponse curves at materiall...

The DLS-VS strategy was developed as an integrated method for identifying chemical modulators for orphan GPCRs. It combines differential low-throughput screening (DLS) and virtual screening (VS). The two cascaded techniques offer complementary advantages and allow the experimental testing of a minimal number of compounds. First, DLS identifies modulators specific for the considered receptor among a set of receptors, through the screening of a small library with diverse chemical compounds. Then, an active molecular model of the receptor is built by homology to a validated template, and it is progressively refined by rotamers modification for key side-chains, by VS of the already screened library, and by iterative selection of the model generating the best enrichment. The refined active model is finally used for the VS of a large chemical library and the selection of a small set of compounds for experimental testing. Applied to the orphan receptor GPR34, the DLS-VS strategy combined the experimental screening of 20 000 compounds and the virtual screening of 1 250 000 compounds. It identified one agonist and eight inverse agonists, showing a high chemical diversity. We describe the method. The strategy can be applied to other GPCRs.

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