Solubility is an important issue in drug discovery and a major source of attrition

2. Solubility is an important issue in drug discovery and a major source of attrition This is expensive for the pharma industry A good model for predicting the solubility of druglike molecules would be very valuable.

3. How should we approach the prediction/estimation/calculation of the aqueous solubility of druglike molecules? Two (apparently) fundamentally different approaches

4. Modelling in Chemistry PHYSICS-BASED ab initio Density Functional Theory Car-Parrinello Fluid Dynamics AM1, PM3 etc. Molecular Dynamics DPD Monte Carlo Docking 2-D QSAR/QSPR Machine Learning CoMFA EMPIRICAL NON-ATOMISTIC ATOMISTIC

5. LOW THROUGHPUT ab initio Density Functional Theory Car-Parrinello Fluid Dynamics AM1, PM3 etc. Molecular Dynamics DPD Monte Carlo Docking 2-D QSAR/QSPR Machine Learning CoMFA HIGH THROUGHPUT

6. THEORETICAL CHEMISTRY ab initio Density Functional Theory Car-Parrinello Fluid Dynamics AM1, PM3 etc. Molecular Dynamics DPD NO FIRM BOUNDARIES! Monte Carlo Docking 2-D QSAR/QSPR Machine Learning CoMFA INFORMATICS

7. Although their scientific domains overlap, they represent distinct philosophies …

8. The Two Faces of Computational Chemistry Theoretical Chemistry Informatics

9. Informatics “The problem is too difficult to solve using physics and chemistry, so we will design a black box to link structure and solubility”

10. Informatics and Empirical Models • In general, Informatics methods represent phenomena mathematically, but not in a physics-based way. • Inputs and output model are based on an empirically parameterised equation or more elaborate mathematical model. • Do not attempt to simulate reality. • Usually High Throughput.

11. Towards Solubility Calculation: Informatics Techniques ab initio Density Functional Theory Car-Parrinello Fluid Dynamics AM1, PM3 etc. Molecular Dynamics DPD Monte Carlo Docking 2-D QSAR/QSPR (regression) Machine Learning (Support Vector Machine, Random Forest, Neural Network etc.) CoMFA

12. Theoretical Chemistry “The problem is difficult, but by making suitable approximations we can solve it at reasonable cost based on our understanding of physics and chemistry”

13. Theoretical Chemistry • Calculations and simulations based on real physics. • Calculations are either quantum mechanical or use parameters derived from quantum mechanics. • Attempt to model or simulate reality. • Usually Low Throughput.

14. Drug Disc.Today, 10 (4), 289 (2005)

15. Towards Solubility Calculation: Theoretical Chemistry Techniques ab initio Density Functional Theory Car-Parrinello Fluid Dynamics AM1, PM3 etc. Molecular Dynamics DPD Monte Carlo Docking 2-D QSAR/QSPR Machine Learning CoMFA

16. The state of the art is that … … solubility has proved a difficult property to calculate. It involves different phases (solid & solution) and different substances (solute and solvent), and both enthalpy & entropy are important. The theoretical approaches are generally based around thermodynamic cycles and involve some empirical element.

17. The state of the art is that … … there has been solid progress but no solution. Some examples of good papers in the area …

23. Now to our own work … We want to construct a theoretical model that will predict solubility for druglike molecules … We expect our model to use real physics and chemistry and to give some insight … We may need to include some empirical parameters… We don’t expect it to be fast by QSPR standards, but it should be reasonably accurate …

24. Reference

25. Supersaturated Solution 8 Intrinsic solubility values Subsaturated Solution Where do the data come from?

26. ● With DMSO (normal method in industry) Kinetic Solubility ● Equilibria reached? ● Filtering Big errors ● Detection by UV-Vis Chromophores needed Solubility measurements Classical MethodShake Flask Method ● Many published variations of this method Datasets compiled from diverse literature data may have significant random and systematic errors.

27. Ka K0 ANa A- ……….Na+ AH AH Intrinsic solubility-Of an ionisable compound is the thermodynamic solubility of the free acid or base form (Horter, D, Dressman, J. B., Adv. Drug Deliv. Rev., 1997, 25, 3-14) S0 is essentially the solubility of the neutral form only.

28. Diclofenac Supersaturated Solution 8 Intrinsic solubility values Subsaturated Solution ● First precipitation – Kinetic Solubility (Not in Equilibrium) ● Thermodynamic Solubility through “Chasing Equilibrium”- Intrinsic Solubility (In Equilibrium) Supersaturation Factor SSF = Skin – S0 In Solution Powder Error less than 0.05 log units !!!! ●We continue “Chasing equilibrium” until a specified number of crossing points have been reached ● A crossing point represents the moment when the solution switches from a saturated solution to a subsaturated solution; no change in pH, gradient zero, no re-dissolving nor precipitating…. SOLUTION IS IN EQUILIBRIUM “CheqSol” * A. Llinàs, J. C. Burley, K. J. Box, R. C. Glen and J. M. Goodman. Diclofenac solubility: independent determination of the intrinsic solubility of three crystal forms. J. Med. Chem. 2007, 50(5), 979-983

29. For this study we (Toni Llinàs) measured 30 solubilities using the CheqSol method and took another 30 from other high quality studies (Bergstrom & Rytting). We use a Sirius glpKa instrument

30. Our goal is to ask …

31. Can we use theoretical chemistry to calculate solubility via a thermodynamic cycle?

33. Gsubfrom lattice energy & an entropy term (DMAREL based on B3LYP/6-31G*) Gsolvfrom a semi-empirical solvation model (SCRF B3LYP/6-31G* in Jaguar) Gtr from ClogP (i.e., different kinds of theoretical/computational methods, albeit with consistent functional and basis set )

35. Gsub = Hsub – TSsub Hsub = -Ulatt – 2RT Ssub = Strans,gas + Srot,gas – Sphonon,xl Ulatt and Sphonon,xl are obtained from DMAREL, using B3LYP/6-31G*. Intramolecular vibrational entropy is assumed to cancel.

37. Gsolvfrom a semi-empirical solvation model (SCRF B3LYP/6-31G* in Jaguar) This is likely to be the least accurate term in our equation. We also tried SM5.4 with AM1 & PM3 in Spartan, with similar results.

39. Gtr from ClogP ClogP is a fragment-based (informatics) method of estimating the octanol-water partition coefficient.

40. What Error is Acceptable? • For typically diverse sets of druglike molecules, a “good” QSPR will have an RMSE ≈ 0.7 logS units. • An RMSE > 1.0 logS unit is probably unacceptable. • This corresponds to an error range of 4.0 to 5.7 kJ/mol in Gsol.

41. What Error is Acceptable? • A useless model would have an RMSE close to the SD of the test set logS values: ~ 1.4 logS units; • The best possible model would have an RMSE close to the SD resulting from the experimental error in the underlying data: ~ 0.5 logS units?

42. Results from Theoretical Calculations

43. Results from Theoretical Calculations ● Direct calculation was a nice idea, but didn’t quite work – errors larger than QSPR ● “Why not add a correction factor to account for the difference between the theoretical methods?” ● This was originally intended to calibrate the different theoretical approaches, but …

44. Results from Theoretical Calculations ● Direct calculation was a nice idea, but didn’t quite work – errors larger than QSPR ● “Why not add a correction factor to account for the difference between the theoretical methods?” ● This was originally intended to calibrate the different theoretical approaches, but …

45. Results from Theoretical Calculations ● Direct calculation was a nice idea, but didn’t quite work – errors larger than QSPR ● “Why not add a correction factor to account for the difference between the theoretical methods?” ● This was originally intended to calibrate the different theoretical approaches, but …

46. … ● Within a week this had become a hybrid method, essentially a QSPR with the theoretical energies as descriptors

47. Results from Hybrid Model

48. We find that Gsolv is poorly correlated with logS and fails to appear in the regression equation. B_rotR is the proportion of bonds that are rotatable and describes the propensity of flexible molecules to be more soluble. We can also write an almost equivalent equation in the form …

49. This regression equation gave r2=0.77 and RMSE=0.71

50. How Well Did We Do? • For a training-test split of 34:26, we obtain an RMSE of 0.71 logS units for the test set. • This is comparable with the performance of “pure” QSPR models. • This corresponds to an error of about 4.0 kJ/mol in Gsol.