Aggregates as Promiscuous False Positive HTS Hits

It has been observed that certain compounds appear as frequent hitters in HTS regardless of the biological target, especially when screened at higher than 10 |J.M concentration. Figure 5 shows examples of promiscuous HTS hits (McGovern et al., 2002; McGovern et al., 2003; McGovern and Shoichet, 2003). These spurious hits have low potency at 1-10 |J.M, noncompetitive and reversible inhibition, flat SAR and poor selectivity. The potency increases with incubation time, but decreases with higher enzyme concentration, temperature, ionic strength and with the addition of urea or BSA. The inhibition is not reversible in a dialysis experiment, suggesting formation of aggregates (Feng et al., 2005).

The aggregates of frequent hitters are in the size range of 30-400 nm and they are not micelles or vesicles. Some of the aggregates will pass through a 0.2 |xM filter (200 nm), which is commonly used in solubility assays to separate the insoluble solid from the soluble compounds in solution. It has been speculated that the aggregation is a consequence of compounds being tested at supersaturated concentrations. This can occur when poorly soluble compounds are introduced into aqueous media from DMSO stock solutions. The aggregation state may be a kinetically transient state that precedes crystallization (Lipinski, 2004b). The exact mechanism by which aggregates appear as "actives" in HTS is unclear. They seem to inhibit enzyme activity through adsorption or absorption to the target proteins. Aggregate formation is concentration dependent. The higher the screening concentration, the more likely that compounds will show promiscuous inhibition due to aggregate formation. In a screening study of 1,030 compounds, promiscuous inhibitors dropped from 19% to 1.4%, when the screening concentration was reduced from 30 |J.M to 5 |J.M. Structurally, the

Figure 5. Examples of promiscuous false positive HTS hits.

phony hittters can be drug-like or non-drug-like. Even some marketed drugs form aggregates at high concentrations.

Spurious HTS hits can drain medicinal chemistry resources and downstream biological efforts. Screening at high concentration can be counterproductive in lead discovery. Early elimination of these hits from the screening process, or removal from the screening library can increase discovery productivity. There are several approaches that can be used to recognize and overcome false positive hits due to aggregate formation.

(1) Re-screen hits in the presence and absence of the detergent Triton X-100 (0.1%) (Ryan et al., 2003; Feng et al., 2005). This is the most effective way to break up the aggregates. It has been found that detergent-dependent enzyme assays give the fastest and most reliable single indication of aggregate-based inhibition. Molecules that inhibit only in the absence of detergent are considered likely promiscuous aggregators. The potency of promiscuous inhibitors is reduced in the presence of 0.1% Triton X-100.

(2) Use a dynamic light scattering (DLS) plate reader to measure aggregate formation. The identification of aggregate particle formation using a DLS plate reader seems reliable. However, other aggregation phenomena, such as precipitation, can also lead to light scattering and be mis-identified as aggregation. Precipitation and aggregation are two distinct phenomena. Signals from aggregates can be weak, which can lead to ambiguous results. Molecules with optical properties can interfere with observation in DLS, making the results un-reliable. For example, Congo Red absorbs light at 514.4 nm and interferes with DLS measurement. Congo Red forms aggregates, as observed by transmission electron microscopy (TEM), and shows promiscuous inhibition. However, it does not form particles detectable by DLS due to interference (McGovern et al., 2002; Feng et al., 2005).

(3) Examine the IC50 curves to see if they are very steep compared to normal hits (Figure 6) (McGovern and Shoichet, 2003). Careful examination of IC50 curves for abnormality can help diagnose aggregate formation. Aggregate-based target protein inhibition

Aggregating Hits

Aggregating Hits

Concentration of Inhibitor

Figure 6. Steep concentration dependence of aggregate-based inhibition.

Concentration of Inhibitor

Figure 6. Steep concentration dependence of aggregate-based inhibition.

has a steep dependence on concentration. Screening at lower concentrations, below this steep rise, can minimize false hits from aggregates.

(4) Use computational methods that have been developed to rapidly and automatically identify potential frequent hitters (Roche et al., 2002; Seidler et al., 2003; Feng et al., 2005). Predictive models show some potential for predicting aggregation-based promiscuity in large libraries. A caveat of computational models is that they remain too crude to capture the concentration dependence of aggregate formation.

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