Echoing agreement with all previous posters. The good news is you ran a factorial, so you might still have sufficient treatments for a fractional factorial. Here is my advice for missing data in an experiment.
1. As datadad points out, what did you learn from the treatments which did not produce an output (this may be the most informative part of the experiment)? Was it a result of the treatment or did something else happened?
2. Can you develop an alternative response variable? Another way to measure the experimental units.
3. You might be able to use regression with the remaining data to create a model that will predict the missing data points (you will need to cognizant of DFs available)
4. Use the average of the other treatments for the missing data point (this tends to normalize that particular treatment effect)
5. Had you predicted the results á priori, use your predicted result.
6. Do 3-5 and substitute values for the missing data point. perform fit model analysis and look to see how well those analysis agree. If there is reasonable agreement, you can probably be confident in your analysis. If not, you might need to re-run the missing treatments coupled with some you do have data for (to estimate block effect).
"All models are wrong, some are useful" G.E.P. Box