Meropenem

There are a number of examples in the literature describing statistical analyses of MIC90 raw data (arithmetic mean, geometric mean, median, mode, etc.) to enable rank-ordering compounds with the use of the geometric mean being somewhat common. Therefore, we have also included the geometric mean in our analysis for comparison (Table 1). As was the case with the BMIC⁹⁰ parameter, confidence intervals for the geometric mean values are also quite wide and not useful with regard to rank-ordering analogs. As alluded to above, antibacterial research teams often make decisions around compound progression based on MIC⁹⁰ data. Rank-ordering this set of analogs based solely on the MIC⁹⁰ data would provide the following: 2 = 3 > 1 = 4 = 5 > BAL30072 > Meropenem. By using the parameters described above, we can attempt to provide a greater degree of separation. Recall that the FDR values strongly suggest that MIC⁹⁰ values for all compounds are statistically different with 1 and 4 being the exceptions. With this in mind, recognizing the ranking trends observed for the BMIC⁹⁰, NPS and geometric mean values could lead to the following rank-order: 2 > 3 > 1≈4 > 5 > BAL30072 > Meropenem. Obviously, given the wide confidence intervals associated with the BMIC90 and geometric mean values, this ranking cannot be stated with 100% confidence. Drug research teams regularly make decisions with regard to compound advancement based on a variety of information (potency, pharmacokinetics, safety, stability, ease of synthesis, etc.). The methods described above were designed to illustrate potential trends in potency data for single experiments; there is an inherent risk that the analysis could occasionally mislead. That said, the reality of antibacterial drug research involves teams making decisions based on MIC⁹⁰ data, and while the methods described herein do not enable decisions with 100% confidence, we are hopeful that they may lead to better informed, higher quality decisions.