In Silico Methods for ADMET and Solubility Prediction

Dr John MitchellUniversity of St Andrews

Outline

• Part 1: Computational Toxicology• Part 2: Aqueous Solubility

1. Toxicological Relationships Between Proteins Obtained From

a Molecular Spam Filter

Florian Nigsch & John Mitchell

Now at Novartis Institutes, Boston

Spam

• Unsolicited (commercial) email

• Approx. 90% of all email traffic is spam

• Where are the legitimate messages?

• Filtering

Analogy to Drug Discovery

• Huge number of possible candidates• Virtual screening to help in selection process

Properties of Drugs

• High affinity to protein target

• Soluble• Permeable• Absorbable• High bioavailability• Specific rate of metabolism• Renal/hepatic clearance?

• Volume of distribution?• Low toxicity• Plasma protein binding?• Blood-Brain-Barrier

penetration?• Dosage (once/twice daily?)• Synthetic accessibility• Formulation (important in

development)

Multiobjective Optimisation

Bioactivity Synthetic accessibility

Permeability

Toxicity

Metabolism

Solubility

Huge number of candidates …

Multiobjective Optimisation

Bioactivity Synthetic accessibility

Permeability

Toxicity

Metabolism

Solubility

U S E L E

S SDrug

Huge number of candidates … most of which are useless!

Feature Space - Chemical Space

m = (f1,f2,…,fn)

f1

f2

f3

Feature spaces of high dimensionality

COX2

f2

f3

f1

DHFR

CDK1CDK2

Features of Molecules

Based on circular fingerprints

Combinations of Features

Combinations of molecular features to account for synergies.

Winnow Algorithm

Protein Target Prediction

• Which protein does a given molecule bind to?• Virtual Screening• Multiple endpoint drugs - polypharmacology• New targets for existing drugs• Prediction of adverse drug reactions (ADR)

– Computational toxicology

Predicted Protein Targets

• Selection of 233 classes from the MDL Drug Data Report

• ~90,000 molecules• 15 independent

50%/50% splits into training/test set

Predicted Protein Targets

Cumulative probability of correct prediction within the three top-ranking predictions: 82.1% (±0.5%)

Computational Toxicology

• Model for target prediction

• Annotated library of toxic molecules– MDL Toxicity database– ~150,000 molecules

• For each molecule we predict the likely target

• Correlations between predicted protein targets and known toxicity codes– Canonical (23)– Full (490)

Toxicological Relationships Outline (1)

• Protein target prediction allows us to link (predictively) 150,000 toxic organic molecules to 233 specific protein targets

• Each target is treated as a single protein, although may be sets of related proteins

• Toxicological databases link (experimentally) these 150,000 molecules to 23 toxicity classes

• Combining these two sources of data matches the 233 proteins with the 23 toxicity classes

Toxicity Annotations

CANONICAL TOXICITY CODES (23)

FULL TOXICITY CODES (490)Y41 : Glycolytic < Metabolism (intermediary) < Biochemical

Toxicological Relationships Outline (2)

• For each protein target, we have a profile of association with the 23 toxicity classes

• Proteins with similar profiles are clustered together

• We demonstrate that these clusters of proteins can be physiologically meaningful.

Predictions Obtained

L70 - Changes in liver weight<LiverY07 - Hepatic microsomal oxidase<Enzyme inhibitionM30 - Other changes<Kidney, Urether, and BladderL30 - Other changes<Liver

Target Prediction Highest ranking one IS predicted protein targetProtein code j

Toxicity codes i

Result matrix R = (rij)rij incremented for each prediction.

( )Protein targetsToxcodes

r11 r12

r21

…

Proteins by Toxicity

• Cardiac - G1. Kainic acid receptor2. Adrenergic alpha23. Phosphodiesterase III4. cAMP Phosphodiesterase5. O6-Alkylguanine-DNA

alkyltransferase

• Vascular - H1. Angiotensin II AT22. Dopamine (D2)3. Bombesin4. Adrenergic alpha25. 5-HT antagonist

Top 5 Proteins by Toxicity

68 distinct proteins for 23 toxicity classes, i.e., 3 proteins per canonical toxicity code.

Lanosterol 14alpha-Methyl Demethylase 5 Glucose-6-phosphate Translocase 4 IL-6 4 Benzodiazepine Antagonist 3 Kainic Acid Receptor 3

Proteins and their connectivities

Correlations between proteins: 233 by 233 correlation matrix

Cluster 1 (proteins 6-11)

Correlation Between Proteins

• Carbonic Anhydrase Inhibitor

• Estrogen Receptor Modulator

• LHRH Agonist• Aromatase Inhibitor• Cysteine Protease

Inhibitor• DHFR Inhibitor

Cluster 1• Within-cluster

correlation (without auto-correlation)

r = 0.95

Proteins involved in breast cancer

Cluster 1

Proteins involved in breast cancer

Cluster 1

CA

ER LHRH

Aromatase Cysteine Prot.

DHFR

Tissue-specific transcripts of human steroid sulfatase are under control of estrogen signaling pathways in breast carcinoma, Zaichuk 2007 “aim of this study was to characterize carbonic anhydrase II (CA2), as novel estrogen responsive gene” Caldarelli 2005 The Transactivation Domain AF-2

but not the DNA-Binding Domain of the Estrogen Receptor Is Required to Inhibit Differentiation of Avian Erythroid Progenitors, Marieke von Lindern 1998

This led to premature expression of CAII, a possible explanation for the toxic effects of overexpressed ER.

Cathepsin L Gene Expression and Promoter Activation in Rodent Granulosa Cells, Sriraman 2004showed that cathepsin L expression in granulosa cells of small, growing follicles in- creased in periovulatory follicles after human chorionic gonadotropin stimulation.

Controversies of adjuvant endocrine treatment for breast cancer and recommendations of the 2007 St Gallen conference, Rabaglio 2007

Merchenthaler 2005

Summary of aromatase inhibitor trials: The past and future, Goss 2007 Regulation of collagenolytic cysteine protease

synthesis by estrogen in osteoclasts, Furuyama 2000

Antimalarials?

Induction by estrogens of methotrexate resistance in MCF-7 breast cancer cells, Thibodeau 1998

Literature-based links between these proteins

Breast Cancer Proteins

Cluster 4

This cluster links treatment of stomach ulcers to loss of bone mass!

Proton Pump Inhibitors etc.

Correlation above 0.98

Proton Pump Inhibitors etc.

Correlation above 0.99

Correlation above 0.98

Proton Pump Inhibitors etc.

• Proton pump inhibitors used to limit production of gastric acid

• PTH is important in the developent/regulation of osteoclasts (cells for bone resorption)

• PTH controls levels of Ca2+ in the blood; increased PTH levels are associated with age-related decrease of bone mass

Recent clinical studies showed increased risk of hip fractures resulting from long-term use of proton pump inhibitors. Hence link between PTH and proton pump inhibitors.

PTH = Parathyroid hormone (84 aa mini-protein)

Conclusions from Part 1

• Successful adaptation of algorithm formerly not used in chemoinformatics

• Can find correct protein targets for molecules• Hence link proteins together via ligand-binding properties and

associations of ligands with toxicities• Identify clinically relevant toxicological relationships between

proteins

2. In silico calculation of aqueous solubility

Dr John MitchellUniversity of St Andrews

Our Methods …

(a) Random Forest (informatics)

References

We want to construct a model that will predict solubility for druglike molecules …

We don’t expect our model either to use real physics and chemistry or to be easily interpretable …

We do expect it to be fast and reasonably accurate …

Our Random Forest Model …

Machine Learning Method

Random Forest

Random Forest for Predicting Solubility

• Dataset is partitioned into consecutively smaller subsets (of similar solubility)

• Each partition is based upon the value of one descriptor

• The descriptor used at each split is selected so as to minimise the MSE

• High predictive accuracy• Includes descriptor selection• No training problems – largely immune from

overfitting• “Out-of-bag” validation – using those

molecules not in the bootstrap samples.

Leo Breiman, "Random Forests“, Machine Learning 45, 5-32 (2001).

A Forest of Regression Trees

DatasetLiterature Data• Compiled from Huuskonen dataset and AquaSol database – pharmaceutically relevant molecules• All molecules solid at room temperature• n = 988 molecules• Training = 658 molecules• Test = 330 molecules• MOE descriptors 2D/3D

Datasets compiled from diverse literature data may have significant random and systematic errors.

● Intrinsic aqueous solubility – the thermodynamic solubility of the neutral form in unbuffered water at 25oC

RMSE(te)=0.69r2(te)=0.89Bias(te)=-0.04

RMSE(tr)=0.27r2(tr)=0.98Bias(tr)=0.005

RMSE(oob)=0.68r2(oob)=0.90Bias(oob)=0.01

DS Palmer et al., J. Chem. Inf. Model., 47, 150-158 (2007)

These results are competitive with any other informatics or QSPR solubility prediction method

Random Forest: Solubility Results

Part 2a, Solubility by Random Forest: Conclusions

● Random Forest gives an RMS error of 0.69 logS units.

● These results are competitive with any other informatics or QSPR solubility prediction method.

● The nature of the model is predictive, without offering much insight.

Our Methods …

(b) Thermodynamic Cycle (A hybrid of theoretical chemistry & informatics)

Reference

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 don’t expect it to be fast by informatics or QSPR standards, but it should be reasonably accurate …

Our Thermodynamic Cycle method …

We may need to include some empirical parameters…

For this study 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

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

Gsub comes mostly from lattice energy minimisation based on the experimental crystal structure.

Gsolv comes from 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.

Gtr comes from ClogP

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

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.

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?

● 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

…

Results from Theoretical Calculations

…

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

Results from Hybrid Model

This regression equation gives r2=0.77 and RMSE=0.71

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.

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

Ulatt

Ssub & b_rotR

Gsolv & ClogP

Part 2b, Solubility by TD Cycle: Conclusions

● We have a hybrid part-theoretical, part-empirical method.

● An interesting idea, but relatively low throughput - and an experimental (or possibly predicted?) crystal structure is needed.

● Similarly accurate to pure QSPR for a druglike set.

● Instructive to compare with literature of theoretical solubility studies.

Thanks• Unilever• Dr Florian Nigsch

• Pfizer & PIPMS• Dr Dave Palmer• Pfizer (Dr Iñaki Morao, Dr Nick Terrett & Dr Hua Gao)