For about a decade, early clinical development in oncology is facing new challenges. This is due to two main reasons. The first one is linked to the developed molecular targeted agents (MTA) themselves for which the maximum tolerated dose (MTD) is no longer the only dose of interest. The second reason is related to the fact that costs and attrition rates are huge. When selecting a dose, evidence of early activity signal becomes required for future engagements. This implies the need to handle both toxicity and activity endpoints in the analysis and also in the dose escalation design of early-phase trials. We propose a model-based design taking into account both safety and activity for dose escalation. The proposed model involves a bivariate Gaussian latent variable depending on a monotonic toxicity curve and a quadratic activity curve. This model is fitted under the Bayesian framework that allows the incorporation of prior information. The predictive distributions of dose-response curves are used to lead the dose recommendation. Uncertainty in the dose-response relationship is taken into account to calculate the probability of being an over-toxic or a target dose. The proposed design is compared to two other widely used methods.