Abstract
Deciphering the effects of compounds on molecular events within living cells is becoming an increasingly important component of drug discovery. In a model application of the industrial drug discovery process, the authors profiled a panel of 22 compounds using hierarchical cluster analysis of multiparameter high-content screening measurements from nearly 500,000 cells per microplate. RNAi protein knockdown methodology was used with high-content screening to dissect the effects of 2 anticancer drugs on multiple target activities. Camptothecin activated p53 in A549 lung carcinoma cells pretreated with scrambled siRNA, exhibited concentration-dependent cell cycle blocks, and induced moderate microtubule stabilization. Knockdown of camptothecin-induced p53 protein expression with p53 siRNA inhibited the G1/S blocking activity of the drug and diminished its microtubule-stabilizing activity. Paclitaxel activated p53 protein at low concentrations but exhibited G2/M cell cycle blocking activity at higher concentrations where microtubules were stabilized. In cells treated with p53 siRNA, paclitaxel failed to activate p53 protein, but the knockdown did not have a significant effect on the ability of paclitaxel to stabilize microtubules or induce a G2/M cell cycle block. Thus, this model application of the use of RNAi technology within the context of high-content screening shows the potential to provide massive amounts of combinatorial cell biological information on the temporal and spatial responses that cells mount to treatment by promising therapeutic candidates.