Modern machine learning methods, and their use alongside established paradigms such as Quality by Design, have the potential to fundamentally change the way bioprocesses are developed. In particular, horizontal knowledge transfer methods, which seek to exploit data from historical processes to facilitate process development for a new product, provide an opportunity to rethink process development workflows. In this work, we firstly assess the potential of two knowledge transfer approaches, meta learning and one-hot encoding, in combination with Gaussian process (GP) models. We compare their performance to GPs developed only on data of the new process. Using simulated mammalian cell cultivation data, we observe that both knowledge transfer approaches outperform the individual-product approach. In the second part, we address the question whether experiments for a new product could be designed more effectively by exploiting existing knowledge. In particular, we suggest to specifically design few runs for the novel product to calibrate knowledge transfer models, a task that we coin calibration design. We propose a novel, customised metric to identify a set of calibration design runs, which exploits differences in process evolutions of historical products. In two simulated case studies, we observed that training with calibration designs yields similar test set errors compared to common approaches of Design of Experiments. However, much fewer experiments are needed for the former, suggesting an interesting alternative for future bioprocess development. Overall, the results suggest that process development could be significantly streamlined when systematically carrying knowledge from one product to the next.