In drug development, assessment of the dose–response relationship gives essential insight into which doses are efficacious, yet safe. These relationships are often estimated by parametric, nonlinear models. A hurdle for trials that aim to estimate these models, is that the optimal study design depends on the unknown model parameters. A solution is to use adaptive designs. Ideally, such procedures evaluate a safety and an efficacy endpoint to get the best understanding of the therapeutic window. Many such designs have already been proposed. However, the outcomes are typically required to be of a specific type and the correlation between them has to be modeled explicitly. We propose a more general and simpler approach. Separate models can be specified for each endpoint, without restrictions on the type of model or outcome. The dependence between the outcomes is considered, without having to specify its structure. A new penalty function is introduced to balance the need for optimal precision and safe dose assignments. The method is illustrated by an example study in depression. Simulation results indicate that the adaptive design is more efficient than a fixed design. Use of the penalty function decreases estimation efficiency, but increases dose assignment to favorable doses. Supplementary materials for this article are available online.