Recall that principal components are, by construction, orthogonal. Your original data has a rank of 73 at most, so you cannot derive more than 73 principal components from it. In fact you will lose a degree of freedom yielding 72 PC's.
But what in the world do you plan on doing with 72 principal components?
I can't suggest whether to go this route without knowing your use case, but using a handful of principal components (the first 5-10 for instance) out of your 72 is done in some cases. Things can go wrong in PCA, even for the first few PC's, if your eigenvalues/scree plot do not show those first PC's having much larger eigenvalues than the others.
There is no way for PCA to give you a 73 by 436 score matrix. You could force factor analysis to do it, but I don't think you would yield anything useful. If going the PCA route, you could bootstrap several estimates to test the stability of your first few principal components.