1.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí ADMET Prediction Fiction or Reality? Antonio Llinàs Martí The Pfizer Institute for Pharmaceutical Materials Science University of Cambridge

2.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Is ADMET important?

3.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Why to predict Physico-Chemical properties?

4.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí ADMET predictive models ●Linear models MLR (Multiple Linear Regression) PLS (Partial Least Squares) PCR (Principal Components Regression) ●Non Linear models ANN (Artificial Neural Networks) RF (Random Forest) SVM (Support Vector Machines)

5.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí ADMET predictive models Data Set Training Set Test Set (≈30 %) New Data Set Good Model ≈ 1 R 2 ≈ 1 RMSE ≈ 0 BIAS ≈ 0 0.98 R 2 = 0.98 RMSE = 0.27 BIAS = 0.005 0.90 R 2 = 0.90 RMSE = 0.68 BIAS = 0.01 0.78 R 2 = 0.78 RMSE = 0.85 BIAS = 0.1 Building a Model Cross Validation

6.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí ADMET predictive models Building Good Data David Palmer, John Mitchell Unilever Centre For Molecular Informatics, University of Cambridge

7.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Artificial Neural Networks ADMET predictive models Artificial Neural Networks (ANNs) have been used to distinguish drug-like and non-drug-like molecules using a substructural analysis [Jain 1998]. So and Karplus [So 1997] used electrostatic and steric properties at grid points for feeding a genetic artificial neural network in order to develop a QSAR model. Running the network consists of Forward pass: the outputs are calculated and the error at the output units calculated. Backward pass: The output unit error is used to alter weights on the output units. Then the error at the hidden nodes is calculated (by back-propagating the error at the output units through the weights), and the weights on the hidden nodes altered using these values. For each data pair to be learned a forward pass and backwards pass is performed. This is repeated over and over again until the error is at a low enough level (or we give up).

8.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Random Forest ADMET predictive models The sinking of the Titanic Source: "Report on the Loss of the 'Titanic' (S.S.)" (1990), British Board of Trade Inquiry Report (reprint), Gloucester, UK: Allan Sutton Publishing A Decision Tree Forest is an ensemble (collection) of decision trees whose predictions are combined to make the overall prediction for the forest. A decision tree forest is similar to a TreeBoost model in the sense that a large number of trees are grown. However, TreeBoost generates a series of trees with the output of one tree going into the next tree in the series. In contrast, a decision tree forest grows a number of independent trees in parallel, and they do not interact until after all of them have been built. TreeBoost

9.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Support Vector Machines ADMET predictive models The SVM analysis attempts to find a 1-dimensional hyperplane (i.e. a line) that separates the cases based on their target categories. There are an infinite number of possible lines; two candidate lines are shown above. The question is which line is better, and how do we define the optimal line. Rather than fitting nonlinear curves to the data, SVM handles this by using a kernel function to map the data into a different space where a hyperplane can be used to do the separation. The concept of a kernel mapping function is very powerful. It allows SVM models to perform separations even with very complex boundaries such as this one.

10.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Multiple Linear Regression* Log.S = 0.07nHDon (+/-0.018) - 0.21TPSA (+/-0.033) + 0.11MAXDP (+/-0.022) - 0.22n.Ct (+/-0.019) - 0.29KierFlex (+/-0.032) - 0.59SLOGP (+/0.036) - 0.26ATS2m (+/-0.026) + 0.25RBN (+/-0.033) ADMET predictive models Antonio Llinàs Martí * David Palmer, John Mitchell. Unilever Centre For Molecular Informatics, University of Cambridge

11.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Random Forest* RMSE(te)=0.69 R 2 (te)=0.89 Bias(te)=-0.04 RMSE(tr)=0.27 R 2 (tr)=0.98 Bias(tr)=0.005 RMSE(oob)=0.68 R 2 (oob)=0.90 Bias(oob)=0.01 ADMET predictive models Antonio Llinàs Martí * David Palmer, John Mitchell. Unilever Centre For Molecular Informatics, University of Cambridge

12.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Problems with the actual literature data bases i. Egregious errors in reporting data and references Pontolillo, J. and Eganhouse, P., U.S. Department of Interior. U. S. Geological Survey. Water- Resources Investigations Report 01-4201. Reston. Virginia. 2001 ii. Poor data quality and/or inadequate documentation procedures

13.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Problems with the actual literature data bases i. Egregious errors in reporting data and references * S. E. Adams, J. M. Goodman, R. J. Kidd, A. D. McNaught, P. Murray-Rust, F. R. Norton, J. A. Townsend and C. A. Waudby Org. Biomol. Chem. 2004, 2, 3067-3070.

14.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Problems with the actual literature data bases i. Egregious errors in reporting data and references Citation Analysis* C. Lee, W. Yang, R. G. Parr, Phys. Rev. B, 1988, 37, 785 1259. C. Lee, W. Yang, R. G. Parr, Phys. Rev. B, 1988, 37, 785. 9 DUPLICATES FOUND: NEAR MATCHES: 1382. C. Lee, W. Yang, R. G. Parr, Phys. Rev. B, 1988, 37, 785-789. 3008. C. Lee, W. Yang, R. G. Parr, Phys. Rev., 1988, 785-788. 4199. C. Lee, W. Yang, R. Parr, Phys. Rev. B, 1988, 37, 785. 6006. C. Lee, W. Yang, R. G. Parr, Phys. Rev. B, 1998, 37, 785. 9038. C. T. Lee, W. T. Yang, R. G. Parr, Phys. Rev. B, 1988, 37, 785. 9125. C. Lee, W. Yang, R. G. Parr, Phys. Rev. B, 1993, 37, 785. 11481. C. Lee, W. Yang, R. G. Parr, Phys. Rev. B, 1988, 37, 785-789. 10742. C. T. Lee, W. T. Yang, R. G. Parr, Phys. Rev. B, 1988, 37, 785-789. * Bruce Russell, Jonathan Goodman (Unilever Centre For Molecular Informatics, University of Cambridge)

15.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Problems with the actual literature data bases i. Egregious errors in reporting data and references Citation Analysis* 1259. C. Lee, W. Yang, R. G. Parr, Phys. Rev. B, 1988, 37, 785. 9 DUPLICATES FOUND: NEAR MATCHES: 1382. C. Lee, W. Yang, R. G. Parr, Phys. Rev. B, 1988, 37, 785-789. 3008. C. Lee, W. Yang, R. G. Parr, Phys. Rev., 1988, 785-788. 4199. C. Lee, W. Yang, R. Parr, Phys. Rev. B, 1988, 37, 785. 6006. C. Lee, W. Yang, R. G. Parr, Phys. Rev. B, 1998, 37, 785. 9038. C. T. Lee, W. T. Yang, R. G. Parr, Phys. Rev. B, 1988, 37, 785. 9125. C. Lee, W. Yang, R. G. Parr, Phys. Rev. B, 1993, 37, 785. 11481. C. Lee, W. Yang, R. G. Parr, Phys. Rev. B, 1988, 37, 785-789. 10742. C. T. Lee, W. T. Yang, R. G. Parr, Phys. Rev. B, 1988, 37, 785-789. * Bruce Russell, Jonathan Goodman (Unilever Centre For Molecular Informatics, University of Cambridge)

16.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Problems with the actual literature data bases i. Egregious errors in reporting data and references Pontolillo, J. and Eganhouse, P., U.S. Department of Interior. U. S. Geological Survey. Water- Resources Investigations Report 01-4201. Reston. Virginia. 2001 ia. Multi-level referencing

17.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Problems with the actual literature data bases i. Egregious errors in reporting data and references Pontolillo, J. and Eganhouse, P., U.S. Department of Interior. U. S. Geological Survey. Water- Resources Investigations Report 01-4201. Reston. Virginia. 2001 ib. Data errors

18.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí ii. Poor data quality and/or inadequate documentation procedures TemperatureSolubility g/lReference 25 2.132 896.2 [1] [2] [1] Oliveri-Mandala, E. (1926), Gazzetta Chimica Italiana 56, 896-901 [2] Ochsner, A. B., Belloto, R. J., and Sokoloski, T. D. (1985), Journal of Pharmaceutical Sciences 74, 132-135

19.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Solubility: definition Huge range of definitions Kinetic Solubility Thermodynamic Solubility Equilibrium Solubility Apparent Solubility Ionic Solubility Solubility product Intrinsic Solubility Aqueous Solubility Standard Solubility... SwSwSwSw S aq STSTSTST S0S0S0S0 0*S0*0*S0* K0K0K0K0 Ko*Ko*Ko*Ko* K sp

20.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Solubility: definition Solute Electrolyte Non-electrolyteStrongWeak

21.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Solubility: definition Solubility- Concentration of a compound in a saturated solution when excess solid is present Aqueous Solubility- Concentration of a compound in a saturated solution of pure water when excess solid is present. Thermodynamic Solubility- Solubility when the compound in solution is at equilibrium with the solid form. Kinetic Solubility – Solubility at the time when an induced precipitate first appears in a solution 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)

22.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Process of dissolution Step 1 Step 2 Step 3

23.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Factors Influencing Solubility TemperatureSalinitypHDissolved organic matter (DOM) Co-solventsCrystallinityPolymorphism - In general as T Solubility - In general as salinity Activity coef Solubility - Common ion effect Solubility - If the solute is subject to acid/base reactions then pH is vital in determining water solubility. - DOM Solubility - f v Solubility exponentially

24.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Factors Influencing Solubility Temperature Generally: as T increases, solubility increases for solids. The influence of temperature on water solubility can be quantitatively described by the van't Hoff equation as: ln C sat = -  H/(RT) + Const.

25.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Factors Influencing Solubility Salinity As salinity increases, the solubility of neutral organic compounds decreases (activity coefficient increases) K s = Setschenow salt constant (depends on the compound and the salt) The addition of salt makes it more difficult for the organic compound to find a cavity to fit into, because water molecules are “busy” solvating the ions.

26.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Factors Influencing Solubility pH pH effect depends on the structure of the solute. If the solute is subject to acid/base reactions then pH is vital in determining water solubility. The ionized form has much higher solubility than the neutral form. The apparent solubility is higher because it comprises both the ionized and neutral forms. The intrinsic solubility of the neutral form is not affected.

27.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Factors Influencing Solubility Dissolved organic matter (DOM) DOM increases the apparent water solubility Solubility in water in the presence of DOM is given by the relation: C sat,DOM = C sat (1 + [DOM]K DOM ) [DOM] = concentration of DOM in water, kg/L K DOM = DOM/water partition coefficient Again, the intrinsic solubility of the compound is not affected.

28.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Factors Influencing Solubility Co-Solvents Co-solvents increase the solubility of hydrophobic organic chemicals. Co-solvents can completely change the solvation properties of “water” Solubility increases exponentially as cosolvent fraction increases. fv is the volume fraction of co-solvent  i c is the slope term, which depends on the both the cosolvent and solute

29.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Factors Influencing Solubility Crystallinity Crystallinity decreases the apparent solubility Crystallinity (%)Apparent Solubility M 35 C 88.6 36.7 20.8 3.50 x 10 -3 4.39 x 10 -3 5.27 x 10 -3

30.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Factors Influencing Solubility Polymorphism Crystallising into different crystal forms will result in different melting points and solubilities Crystalline Form MPApparent Solubility M 25 C I II III 68 58 42 5.70 x 10 -3 6.30 x 10 -3 7.40 x 10 -3

31.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Solubility measurements Classical Method Shake Flask Method ● Many published variations of this method ● With DMSO (normal method in industry) Kinetic Solubility ● Equilibria reached? ● FilteringBig errors ● Detection by UV-Vis Chromophores needed

32.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí In Solution PowderDiclofenac An Example CheqSol is a new method developed by

33.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí In Solution PowderDiclofenac Precipitation ● As soon as pptate is detected titrant addition stops ● pH keeps going up because AH is removed from solution and A- reacts with H+ to replace the AH lost ● The solution, at this point, is SUPERSATURATED NOT IN EQUILIBRIUM

34.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí In Solution PowderDiclofenac Dissolution ● After pptation is confirmed an aliquot of base is added ● pH goes down because AH (solid) is brought back in solution, AH (ston), generating A- and H+ ● The solution, at this point, is SUBSATURATED NOT IN EQUILIBRIUM

35.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí In Solution PowderDiclofenac ● 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 Supersaturated Solution Subsaturated Solution S i = 1.53 ± 0.15  g/ml

36.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí In Solution PowderDiclofenacCharacterisation NO MATCH !!!!

37.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí In Solution PowderDiclofenacCharacterisation MATCH !!!!

38.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí In Solution PowderDiclofenacCharacterisation Diclofenac Acid C2/c polymorph * Polyhedron (1993), 12, 1361

39.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí In Solution Powder Crystal Crystallisation EtOH, RT X-Ray Single Crystal X-Ray Powder ? Sodium diclofenac pentahydrate P 2(1)DiclofenacCharacterisation * Thanks to John Davies for solving the X-ray structure of this crystal

40.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí In Solution Powder CrystalDiclofenacCharacterisation DSC- MP = 267.4 ˚C TGA- Anhydrous DSC- MP = 263.4 ˚C TGA- Pentahydrate DSC- MP = 180.5 ˚C TGA- Anhydrous

41.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí In Solution Powder Crystal S i = 1.53 ± 0.15 %  g/ml S i = 1.47 ± 0.12 %  g/ml S i = 1.49 ± 0.09 %  g/ml Diclofenac Solubility (25˚C, I= 0.15 M)

42.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí In Solution Powder CrystalDiclofenacComplete Powder XRD- ? Single Crystal XRD- EA- BAD MP (DSC)- 267.4 ˚C TGA- Sodium Salt Anhydrous Solubility – 1.528 ± 0.15%  g/ml Powder XRD- NEW Single Crystal XRD- SOLVED Sodium salt PentahydrateP2(1) EA- OK MP (DSC)- 263.4 ˚C TGA- Sodium Salt Pentahydrate Solubility – 1.472 ± 0.09  g/ml Powder XRD- SIKLIH01 Single Crystal XRD- NO EA- OK MP (DSC)- 180.5 ˚C TGA- Diclofenac Acid Anhydrous Solubility – 1.488 ± 0.12 %  g/ml

43.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs MartíConclusions Predictive ADMET is in its infancy Predictive ADMET is in its infancy Models are not improving Models are not improving Actual databases are no good: bad quality data, no Actual databases are no good: bad quality data, no diverse enough Need of high quality data to build reliable databases Need of high quality data to build reliable databases Need of standardization. Same conditions, same definition, characterisation, and statistical treatment Need of standardization. Same conditions, same definition, characterisation, and statistical treatment Solubility: Intrinsic, 25 ˚C, I = 0.15 M (KCl), purity of starting material >99.5 %, Solid characterisation. Solubility: Intrinsic, 25 ˚C, I = 0.15 M (KCl), purity of starting material >99.5 %, Solid characterisation.

44.  The Pfizer Institute for Pharmaceutical Materials Science UNIVERSITY OF CAMBRIDGE Antonio Llinàs Martí Acknowledgments - University of Cambridge - Pfizer Dr. Hua Gao - Unilever Solubility Team Prof. Robert Glen (Director) Dr. Jonathan Goodman (Group Leader) Dr. John Mitchell (Group Leader) Dr. Antonio Llinàs David Palmer To ALL of YOU - Sirius Analytical Instruments Ltd. Karl Box