What you are terming 'main effects' are not really that in the usual sense. You cannot interpret lower-order effects (e.g., "main" effects) without simultaneously taking into account any higher-order (e.g. interaction) effects that are constructed of terms from lower-order effects. My Regression Modeling Strategies course notes have a detailed example of interpretation for a simple example where age has a linear effect and interacts with sex. It shows you how to do the composite "chunk" tests that are meaningful and are independent of how the variable are coded. For example, the age effect is the combined age and age x sex effects, which tests whether age is associated with Y for either sex. The sex effect is the chunk test for sex + age x sex interaction and tests whether there is a difference between the sexes at any age.

To get specific meaningful estimates, you form contrasts, e.g. difference between male and female at the median age.

Don't use statistical significance to choose a model. Stick with pre-specification, with an exception being this: if you do a chunk test of all interaction effects combined and the multiple degree of freedom test for all interactions yields p=0.4, you can fairly safely drop all the interaction terms. Some statisticians use AIC in making this decision.

The R rms package anova and summary functions do all this automatically, as shown in the detailed case studies in my notes. For more resources see http://fharrell.com/links.

What you are terming 'main effects' are not really that in the usual sense. You cannot interpret lower-order effects (e.g., "main" effects) without simultaneously taking into account any higher-order (e.g. interaction) effects that are constructed of terms from lower-order effects. My Regression Modeling Strategies course notes have a detailed example of interpretation for a simple example where age has a linear effect and interacts with sex. It shows you how to do the composite "chunk" tests that are meaningful and are independent of how the variable are coded. For example, the age effect is the combined age and age x sex effects, which tests whether age is associated with Y for either sex. The sex effect is the chunk test for sex + age x sex interaction and tests whether there is a difference between the sexes at any age.

To get specific meaningful estimates, you form contrasts, e.g. difference between male and female at the median age.

Don't use statistical significance to choose a model. Stick with pre-specification, with an exception being this: if you do a chunk test of all interaction effects combined and the multiple degree of freedom test for all interactions yields p=0.4, you can fairly safely drop all the interaction terms. Some statisticians use AIC in making this decision.

The R rms package anova and summary functions do all this automatically, as shown in the detailed case studies in my notes. For more resources see http://hbiostat.org.