High Recall but too low Precision result in imbalanced data
Given that both the f1-score and PR AUC are very low even for the prevalence of ~0.45%, it can not be deduced if the limitations are imposed by the nature of the data or the model (features plus the algorithm used).
In order to build a better understanding and to resolve the issue, I would suggest to break the problem into two parts:

Build a model that works for the selected features. For this purpose, you may try creating a slightly balanced dataset 80-20? both for training and testing. Once you are satisfied with the performance of your approach, move to 2 below
Use your original imbalanced dataset and see if the situation is any better. If not, it is now clearer that the issue is with the imbalanced nature of the data, and you should try all standard techniques for dealing with imbalanced classes. I hope this helps you, because otherwise only options at hand are synthesizing or collecting more data for the minority class.