PRED for BLQ-like observations

Hello The NONMEM Users, When we use M3-like approach, the outputs has PRED for non-missing observations and something else for BLQ (is that PRED=CUMD?). As in the diagnostic figures PRED for BLQs looks like noise, I remove them. It is not always perfect, but OK in for most frequent cases. When we use count data such as a scale with few possible values (for example, 0, 1, 2, 3, 4, 5), it makes more sense to use PHI function (home-made likelihood) for all observations rather than to treat the count as a continuous variable an apply M3-like approach to 1 and 5 while only (as we know, they are like LLOQ and ULOQ). In this case, all PRED values look like noise. A hard way to replace the noise with PRED value is to simulate PRED for each point and merge them with the DV and IPRED data. Is there an easy way? (The model runs well and better than when the count is treated as a continuous variable.) Thanks! Pavel