CoMFA analysis of tgDHFR and rlDHFR based on antifolates with 6-5 fused ring system using the all-orientation search (AOS) routine and a modified cross-validated r(2)-guided region selection (q(2)-GRS) routine and its initial application

Abstract

We report the development of CoMFA analysis models that correlate the 3D chemical structures of 80 compounds with 6-5 fused ring system synthesized in our laboratory and their inhibitory potencies against tgDHFR and rlDHFR. In addition to conventional CoMFA analysis, we used two routines available in the literature aimed at the optimization of CoMFA: all-orientation search (AOS) and cross-validated r(2)-guided region selection (q(2)-GRS) to further optimize the models. During this process, we identified a problem associated with q(2)-GRS routine and modified using two strategies. Thus, for the inhibitory activity against each enzyme (tgDHFR and rlDHFR), five CoMFA models were developed using the conventional CoMFA, AOS optimized CoMFA, the original q(2)-GRS optimized CoMFA and the modified q(2)-GRS optimized CoMFA using the first and the second strategy. In this study, we demonstrate that the modified q(2)-GRS routines are superior to the original routine. On the basis of the steric contour maps of the models, we designed four new compounds in the 2,4-diamino-5-methyl-6-phenylsulfanyl-substituted pyrrolo[2,3-d]pyrimidine series. As predicted, the new compounds were potent and selective inhibitors of tgDHFR. One of them, 2,4-diamino-5-methyl-6-(2',6'-dimethylphenylthio)pyrrolo[2,3-d]pyrimidine, is the first 6-5 fused ring system compound with nanomolar tgDHFR inhibitory activity. The HCl salt of this compound was also prepared to increase solubility. Both forms of the drug were tested in vivo in a Toxoplasma gondii infection mouse model. The results indicate that both forms were active with the HCl salt significantly more potent than the free base.

Figure 1

The structure and pcDHFR bound conformation of 81

Figure 2

The compound aggregate. (A) front view; (B) top view.

Figure 3

Front view of the q²-GRS grids (black), Apparently, these grids do not coincide with that of the conventional CoMFA, and the distances between the grid points at the opposite side of the border (purple) of two adjacent subregions can be much less then 1.0 Å.

Figure 4

A comparison of the contour maps of the tgDHFR training set generated with the conventional CoMFA (left) and with the original q²-GRS with the cutoff set at −1000 .0 (so that all subregions are selected) (right). The areas of the q²-GRS contour maps where the grid points are very close to each other (These areas are not present in the conventional CoMFA maps) are highlighted with the white symbols. (A) The steric contour maps, (B) The electrostatic contour maps.

Figure 5

Front view of the modified q²-GRS grids using the first strategy (blue), which coincide with that of the conventional CoMFA (green)

Figure 6

A comparison of the contour maps of the tgDHFR training set generated with the conventional CoMFA (left) and with the first strategy modified original q²-GRS with the cutoff set at −1000 .0 (so that all subregions are selected) (right). superimposed on their respective region box(es) (cyan) (A) The steric contour maps, (B) The electrostatic contour maps.

Figure 7

Front view of the modified q²-GRS subregions (purple) and grids (green) using the second strategy.

Figure 8

Comparison of the contour maps of the tgDHFR training set generated with the conventional CoMFA (left) and with the second strategy modified original q²-GRS with the cutoff set at −1000 .0 (so that all subregions are selected) (right) (A) The steric contour maps, (B) The electrostatic contour maps.

Figure 9

Predictions for the training (●) and test (○) sets for tgDHFR inhibitory activities. The solid line is the regression line for the training set predictions whereas the dotted lines indicate the ±1.0 log margins.

Figure 9

Predictions for the training (●) and test (○) sets for tgDHFR inhibitory activities. The solid line is the regression line for the training set predictions whereas the dotted lines indicate the ±1.0 log margins.

Figure 9

Predictions for the training (●) and test (○) sets for tgDHFR inhibitory activities. The solid line is the regression line for the training set predictions whereas the dotted lines indicate the ±1.0 log margins.

Figure 10

Predictions for the training (●) and test (○) sets for rlDHFR inhibitory activities. The solid line is the regression line for the training set predictions whereas the dotted lines indicate the ±1.0 log margins.

Figure 10

Predictions for the training (●) and test (○) sets for rlDHFR inhibitory activities. The solid line is the regression line for the training set predictions whereas the dotted lines indicate the ±1.0 log margins.

Figure 10

Predictions for the training (●) and test (○) sets for rlDHFR inhibitory activities. The solid line is the regression line for the training set predictions whereas the dotted lines indicate the ±1.0 log margins.

Figure 11

Predictions for the training (●) and test (○) sets for rlDHFR inhibitory activities (Three outliers were dropped from the training set). The solid line is the regression line for the training set predictions whereas the dotted lines indicate the ±1.0 log margins.

Figure 11

Predictions for the training (●) and test (○) sets for rlDHFR inhibitory activities (Three outliers were dropped from the training set). The solid line is the regression line for the training set predictions whereas the dotted lines indicate the ±1.0 log margins.

Figure 11

Predictions for the training (●) and test (○) sets for rlDHFR inhibitory activities (Three outliers were dropped from the training set). The solid line is the regression line for the training set predictions whereas the dotted lines indicate the ±1.0 log margins.

Figure 12

(A) Orthogonal view of the steric fields generated with the second strategy modified q²-GRS CoMFA model of tgDHFR: yellow indicates regions where bulky groups decrease activity, whereas green indicates regions where bulky groups increase activity. (B) Orthogonal view of the electrostatic fields generated with the second strategy modified q²-GRS CoMFA model of tgDHFR: red indicates regions where more negatively charged groups increase activity, whereas blue indicates regions where more positively charged groups increase activity.

Figure 13

(A) Orthogonal view of the steric fields generated with AOS CoMFA model of rlDHFR (after dropping three outliers from the training set): yellow indicates regions where bulky groups decrease activity, whereas green indicates regions where bulky groups increase activity. (B) Orthogonal view of the electrostatic fields generated with the AOS CoMFA model of rlDHFR (after dropping three outliers from the training set): red indicates regions where more negatively charged groups increase activity, whereas blue indicates regions where more positively charged groups increase activity.

Figure 14

(A) Stereo view of the steric fields of the tgDHFR CoMFA model with the structure of compound 62, there are green (steric favorable) regions near both sides of the phenyl moiety (B) Stereo view of the steric fields of the rlDHFR CoMFA model with the structure of compound 60, only one side of the phenyl moiety is close to a green (steric favorable) region.

Scheme 1