**4. Conclusion**

212 Toxicity and Drug Testing

simulation between each drug and related complexing agent. Trapani et al. (2005) have developed a QSPR model for the correlation of the ratio of the total versus intrinsic solubilities of 25 drugs in the presence of 2-hydroxypropyl-β-cyclodextrin as following:

log 3.766 0.182 0.150 log 0.00683 0.0844

*<sup>S</sup> CMR C P TPSA*

log 1.827 0.00508 0.0122 0.179 log 0.00547

where *CMR* is calculated molecular refractivity, *TPSA* is total polar surface area, *δtot* total solubility parameter, *MW* is molecular weight, and *MV* is molecular volume. Equation 34 was derived using a MLR method and equation 35 was derived using a PLS method

However, as mentioned earlier, none of these models can be applied directly for solubility prediction in the presence of complexing agents and intrinsic solubility is required for all of

There is almost a large number of software for solubility prediction. A thorough review of these software was provided in an article (Jouyban et al., 2008). In this chapter, more useful solubility prediction applications and those which are newly developed or related with drug

ACD/Solubility DB predicts aqueous solubility at different pH with an accuracy of average error of 0.47±0.67 (in decimal logarithm) for solubility prediction of 1125 compounds

ACD/DMSO Solubility predicts whether a compound is soluble (a result of 1) or insoluble (a result of 0) in DMSO. Using a hybrid model of logistic regression with PLS method, its predictive model was trained with solubility related physicochemical parameters, and considering the effects of charged groups, atom chains, and ring scaffolds. It provides 30% high reliability, 70% moderate reliability and <1% low reliability in prediction, with an

Simulations plus' ADMET predictor™, predicts aqueous solubility using 2D and 3D descriptors as input data with average error of 0.432 and 0.423 in logarithm scale for 2817 and 711 number of compounds in train and test sets, respectively (ADMET Predictor™). It can also predict the solubility in biorelevant medium of the fasted state simulated gastric fluid (FaSSGF), the fasted state simulated intestinal fluid (FaSSIF), and the fed state simulated intestinal fluid (FeSSIF). Its average errors in logarithm scale for FaSSGF are 0.510 and 0.470 for 137 and 20 compounds, respectively. Its average errors in logarithm scale for FaSSIF are 0.469 and 0.417 for 141 and 16 compounds, respectively. Its average errors in logarithm scale for FeSSIF are 0.424 and 0.409 for 136 and 21 compounds, respectively. These predictive tools are designed using 2D descriptors as inputs and ADMET Modeler's ANNE

*Total tot*

*Total <sup>S</sup> MW MV C P TPSA*

(34)

(35)

0

*S*

0

*S*

(Trapani et al., 2005).

**3.7 Available software** 

design and development is discussed.

*Complex*

and

them.

(ACD/Labs).

*Complex*

2 2

*R NQ*

2 2

*R NQ*

0.793 , 25 , 0.711

0.763 , 25 , 0.605

overall accuracy of 82% in correct prediction (Japertas et al.).

Although preparation of a drug solution is a simple procedure, the associated problems are still a challenging subject in the pharmaceutical area. Brief review of its importance, various experimental and computational methods to determine the solubility and a number of more common methods to alter the solubility are discussed in this chapter. A comprehensive compilation of aqueous solubility data of chemical/pharmaceutical compounds is available from a reference work of Yalkowsky et al. 2010. The solubility data of pharmaceuticals in organic mono-solvents and also aqueous and non-aqueous solvent mixtures are compiled in a recent work (Jouyban, 2009).
