Strategies for managing multicollinearity in hierarchical regression

Hi, I am doing a thesis project that involves using hierarchical multiple regression where I am interested in the individual effect of predictors and also the effect of the interaction between the predictors. The problem is that I have a serious problem with multicollinearity (VIF = 206!).

I have read about different options including removing predictors and using mean centre of predictors before creating the interaction variable. Would anyone please be able to suggest the best method for managing multicollinearity?

The concern is that I had already transformed the predictor variables (using square root for some and log transformation for others) before running the regression because of issues with normality, so I don't know if I can then mean centre. Is it possible to mean centre a variable that has been transformed already? If so how would you interpret the results of the regression?

I have never used regression before and have very limited statistics knowledge so all responses would be much appreciated please, however please also keep the responses as simplistic as possible so that I can try to understand it.

Thank you in advance for your help.