Proton-transfer compounds with 4-amino-N-(4,6-dimethylpyrimidin-2-yl)benzenesulfonamide (sulfamethazine): the structures and hydrogen bonding in the salts with 5-nitrosalicylic acid and picric acid

Differences in Coformer Interactions of the 2,4-Diaminopyrimidines Pyrimethamine and Trimethoprim

The identification and study of supramolecular synthons is a fundamental task in the design of pharmaceutical cocrystals. The malaria drug pyrimethamine (pyr) and the antibiotic trimethoprim (tmp) are both 2,4-diaminopyrimidine derivatives, providing the same C-NH2/N=C/C-NH2 and C-NH2/N=C interaction sites. In this article, we analyze and compare the synthons observed in the crystal structures of tmp and pyr cocrystals and molecular salts with sulfamethazine (smz), α-ketoglutaric acid (keto), oxalic acid (ox), sebacic acid (seb), and azeliac acid (az). We show that the same coformer interacts with different binding sites of the 2,4-diaminopyrimidine ring in the respective tmp and pyr cocrystals or binds at the same site but gives H bonding patterns with different graph set notions. Pyr·smz·CH3OH is the first crystal structure in which the interaction of the sulfa drug at the C-NH2/N=C/C-NH2 site with three parallel NH2···N, N···NHsulfonamide, and NH2···O=S H bonds is observed. The main synthon in (tmp+)(keto-).0.5H2O and (tmp+)2(ox2-)·2CH3OH is the motif of fused R 2 1(6) and R 1 2(5) rings instead of the R 2 2(8) motif typically observed in tmp+ and pyr+ carboxylates. Tmp/az is a rare example of cocrystal-salt polymorphism where the two solid-state forms have the same composition, stoichiometry, and main synthon. Theoretical calculations were performed to understand the order of stability, which is tmp·az cocrystal > (tmp+)(az-) salt. Finally, two three-component tmp/sulfa drug/carboxylate cocrystals with a unique ternary synthon are described.

An alternative to the goodness of fit

An alternative measure to the goodness of fit (GoF) is developed and applied to experimental data. The alternative goodness of fit squared (aGoFs) demonstrates that the GoF regularly fails to provide evidence for the presence of systematic errors, because certain requirements are not met. These requirements are briefly discussed. It is shown that in many experimental data sets a correlation between the squared residuals and the variance of observed intensities exists. These correlations corrupt the GoF and lead to artificially reduced values in the GoF and in the numerical value of thewR(F2). Remaining systematic errors in the data sets are veiled by this mechanism. In data sets where these correlations do not appear for the entire data set, they often appear for the decile of largest variances of observed intensities. Additionally, statistical errors for the squared goodness of fit, GoFs, and the aGoFs are developed and applied to experimental data. This measure shows how significantly the GoFs and aGoFs deviate from the ideal value one.

Pharmaceutical crystalline complexes of sulfamethazine with saccharin: same interaction site but different ionization states

Sulfamethazine (SMT) can form either 1 : 1 salt or 1 : 1 cocrystal with saccharin (SAC). The two crystalline complexes possess the same main intermolecular interaction site except the locations of the acidic proton are different.

More about residual values

The usual residual values are complemented by expectation values based solely on the experimental data and the number of model parameters. These theoreticalRvalues serve as benchmark values when all of the basic assumptions for a least-squares refinement,i.e.no systematic errors and a fully adequate model capable of describing the data, are fulfilled. The prediction ofRvalues as presented here is applicable to any field where model parameters are fitted to data with known precision. For crystallographic applications,F2-based residual benchmark values are given. They depend on the first and second moments of variance, intensity and significance distributions, 〈σ2〉, 〈Io2〉, 〈Io2/σ2〉. Possible applications of the theoreticalRvalues are, for example, as a data-quality measure or the detection of systematic deviations between experimental data and model predicted data, although the theoreticalRvalues cannot identify the origin of these systematic deviations. The change inRvalues due to application of a weighting scheme is quantified with the theoreticalRvalues.