Experimental anticancer agents have a history of failing in the late stages of clinical development, which has led to significantly increased losses to stakeholders during the drug development process. A bioinformatics-based approach to predict and derisk a drug development program can save time, effort, and expenses resulting from failure of experimental anticancer agents in preclinical/early clinical stages. We present a two-step in silico ensemble method, involving the comparison of localized gene expression from surrounding tissue with tumor tissue, and subsequent correlation with patient survival data, which can help predict safety and efficacy for siRNA-based drug delivery to internal cancer tissues. This is achieved by reducing the possible off-target effects due to reduced or minimal expression of the drug target in surrounding tissue, and increasing survival probability for patients whose cancers can be controlled/eliminated by siRNA-mediated inhibition of drug target. This kind of approach can be useful for more efficient drug development efforts in oncology through reduction of investment in expensive experimentation during the discovery and preclinical phases; and ultimately support the intended clinical trial design.