TPSA

This function computes the polar surface area of an input SMILES structure. It uses the table for tpsa fragment SMARTS and fragment partial polar surface areas shown in Table A.3. It relies on the count matches function described in Chapter 7. 

Create Function gnova.tpsa(character varying) 

Select sum(psa count matches ( 1, smarts) ) From tpsa  

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Fig. 5.3 Sigmoidal relationship of intestinal absorption with TPSA for 20 representative drugs.

The most significant differences between TPSA and 3D PSA were observed for large macrocycles containing many polar substituents These substituents are usually buried in the center of the ring and are therefore not accessible to solvent. Fragment-based TPSA provided larger values than 3D PSA in such cases. 

Erd, P., http //www.daylight.com/ meetings / emugOO / Ertl/tpsa.c... 

Ertl and co-workers [20] have developed such a method for generating a topological PSA (TPSA) based on 3D PSA values for 43 fragments resulting from an analysis of 34,810 compounds taken from the WDI database. The correlation between PSA and TPSA is very high (r2 = 0.982). 

The overall trend described in this section in going from a more complex and time-consuming computational protocol, such as PSAhydrogen-bonding counts, while still capturing the same information content is a general one that will be an overall theme in this chapter The simpler, the better but do not fumble the ball . 

Of the physicochemical descriptors, lipophilicity (as described by clogP and Topological Polar Surface Area (TPSA) gave the strongest overall correlation to incidence of adverse in vivo outcomes, whether analyzed in terms of free or total drug threshold concentrations. In the case of free drug threshold analysis, a Random Forest statistical method indicated that there was a higher chance of a compound with TPSA <70... 

Partial dependence on dogP free drug Partial dependence TPSA CCG free drug... 

Figure 1 Random Forest models of dogP and TPSA against relative risk.

Table 1 Observed odds for toxicity versus clogP/TPSA. Numbers of compounds in each cohort are in parenthesis...

For modeling the BBB penetration, authors used Abraham s data set of 57 compounds as the training set. The test set consisted of 13 compounds, 7 of which were taken from Abraham s data set and 6 from the data set of Lombardo and workers. In addition to the lipoaffinity descriptor, the other descriptors used by them include molecular weight and TPSA. Two models were developed one based on stepwise MLR and the other one based on ANN. To test the performance of different descriptors, they first carried out a simple LR of the 55 training set compounds (two outliers were removed) using TPSA as the only descriptor (Eq. 41). The equation was comparable to Clark s model (Eq. 33). 

Next, they applied the stepwise MLR to the training set providing TPSA, LA, and MW as the descriptors so as to take into account hydroaffinity, lipoaffinity, and molecular size effects. However, the calculation failed to generate a statistically sound linear equation using all three descriptors. Instead the following linear model was generated (Eq. 42) which was comparable to Clark s Eq. 31 ... 

Incorporation of all three descriptors—TPSA, MW, and LA—into one linear equation by MLR resulted in the following equation (Eq. 43) ... 

Since MW correlates with molecular size, the negative sign of its coefficient indicated indeed that larger molecules have lower permeation rates. The positive coefficient of LA indicated that lipoaffinity enhances the permeation rate, and the negative coefficient of TPSA was in agreement with the perception that hydrophilicity reduces the permeation rate. 

A simple LR of the 78 compounds in the training set using TPSA as the only descriptor resulted in the following equation and statistics (Eq. 58) ... 

On the basis of their results, the authors made the following interpretations. Consistent with literature reports, log D, a measure of lipophilicity, had positive contribution to BBB permeability. TPSA measures a compound s polarity and hydrogen-bonding potential and had negative contribution to log PS. A larger TPSA value usually deters a compound from entering the brain. 

Low PSA increases BBB permeability of compounds. Fragment-based TPSA methodology is also fast and easy to compute for large number of compounds. 

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