What Really Happens When I Take a Drug?

Philip E. Bourne University of California San Diego

[email protected] http://www.sdsc.edu/pb

Vancouver April 12, 2012 Big Questions in the Lab {In the spirit of Hamming} 1. Can we improve how science is disseminated and comprehended? 2. What is the ancestry and organization of the structure universe and what can we learn from it? 3. Are there alternative ways to represent from which we can learn something new? 4. What really happens when we take a drug? 5. Can we contribute to the treatment of neglected Erren et al 2007 PLoS Comp. Biol., 3(10): e213 {tropical} diseases?

Motivators Our Motivation

• Tykerb – Breast cancer

• Gleevac – Leukemia, GI cancers

• Nexavar – Kidney and liver cancer

• Staurosporine – natural product – alkaloid – uses many e.g., antifungal antihypertensive

Collins and Workman 2006 Nature Chemical Biology 2 689-700 Motivators Our Broad Approach

• Involves the fields of: – Structural bioinformatics – Cheminformatics – Biophysics – Systems biology – Pharmaceutical chemistry

• L. Xie, L. Xie, S.L. Kinnings and P.E. Bourne 2012 Novel Computational Approaches to Polypharmacology as a Means to Define Responses to Individual Drugs, Annual Review of Pharmacology and Toxicology 52: 361-379 • L. Xie, S.L. Kinnings, L. Xie and P.E. Bourne 2012 Predicting the Polypharmacology of Drugs: Identifying New Uses Through Bioinformatics and Cheminformatics Approaches in Drug Repurposing M. Barrett and D. Frail (Eds.) Wiley and Sons. (available upon request)

Disciplines Touched & 2012 Reviews A Quick Aside – RCSB PDB Pharmacology/Drug View 2012

• Establish linkages to drug resources (FDA, Drug Name Asp Aspirin PubChem, DrugBank, ChEBI, BindingDB etc.)

% Similarity to Has Bound Drug Drug Molecule 100 • Create query capabilities for drug information • Provide superposed views of ligand binding sites • Analyze and display protein-ligand interactions

Mockups of drug view features Led by Peter Rose

RCSB PDB’s Drug Work RCSB PDB Team A Quick Aside PDB Scope/Deliverables

• Part I: small molecule drugs, nutraceuticals, and their targets ( DrugBank) - 2012 • Part II: peptide derived compounds (PRD)- tbd • Part III: toxins and toxin targets (T3DB), human metabolites (HMDB) • Part IV: biotherapeutics, i.e., monoclonal antibodies • Part V: veterinary drugs (FDA Green Book)

RCSB PDB’s Drug Work Our Approach

• We characterize a known protein-ligand binding site from a 3D structure (primary site) and search for similar sites (secondary sites) on a proteome wide scale independent of global structure similarity • We try a static and dynamic network- based approach to understand the implications of drug binding to multiple sites

Methodology

Applications Thus Far

• Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir) • Early detection of side-effects (J&J) • Late detection of side-effects (torcetrapib) • Lead optimization (e.g., SERMs, Optima, Limerick) • Drugomes (TB, P. falciparum, T. cruzi)

Applications Approach - Need to Start with a 3D Drug- Receptor Complex – Either Experimental or Modeled

Generic Name Other Name Treatment PDBid

Lipitor Atorvastatin High cholesterol 1HWK, 1HW8…

Testosterone Testosterone Osteoporosis 1AFS, 1I9J ..

Taxol Paclitaxel Cancer 1JFF, 2HXF, 2HXH

Viagra Sildenafil citrate ED, pulmonary 1TBF, 1UDT, arterial 1XOS.. hypertension

Digoxin Lanoxin Congestive heart 1IGJ failure

Computational Methodology Some Numbers to Show Limitations

TB-drugome Pf- Drugome Target 3996 5491 Target protein in PDB 284 136 Solved structure in PDB 749 333 Reliable homology models 1446 1236 Structure coverage 43.29% 25.02% Drugs 274 321 Drug binding sites 962 1569 A Reverse Engineering Approach to Drug Discovery Across Gene Families

Characterize ligand binding Identify off-targets by ligand site of primary target binding site similarity (Geometric Potential) (Sequence order independent profile-profile alignment) Extract known drugs or inhibitors of the primary and/or off-targets

Search for similar small molecules …

Dock molecules to both primary and off-targets

Statistics analysis of docking score correlations Xie and Bourne 2009 Computational Methodology Bioinformatics 25(12) 305-312 Characterization of the Ligand Binding Site - The Geometric Potential

. Conceptually similar to hydrophobicity or electrostatic potential that is dependant on both global and local environments • Initially assign C atom with a value that is the distance to the environmental boundary

• Update the value with those of surrounding C atoms dependent on distances and orientation – atoms within a 10A radius define i

Pi cos( i) 1.0

GP P neighborsDi 1.0 2.0

Computational Methodology Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9 Discrimination Power of the Geometric Potential

4 binding site non-binding site 3.5

3 • Geometric

2.5 potential can

2 distinguish

1.5 binding and non-binding 1 sites 0.5

0 100 0 0 11 22 33 44 55 66 77 88 99 Geometric Potential Geometric Potential Scale For Residue Clusters

Computational Methodology Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9 Local Sequence-order Independent Alignment with Maximum-Weight Sub-Graph Algorithm

Structure A Structure B

L E R

V K D L

L E R

V K D L

• Build an associated graph from the graph representations of two structures being compared. Each of the nodes is assigned with a weight from the similarity matrix • The maximum-weight clique corresponds to the optimum alignment of the two structures

Computational Methodology Xie and Bourne 2008 PNAS, 105(14) 5441 Similarity Matrix of Alignment

Chemical Similarity • Amino acid grouping: (LVIMC), (AGSTP), (FYW), and (EDNQKRH) • Amino acid chemical similarity matrix

Evolutionary Correlation • Amino acid substitution matrix such as BLOSUM45 • Similarity score between two sequence profiles

i i i i d fa Sb fb Sa i i

fa, fb are the 20 amino acid target frequencies of profile a and b, respectively

Sa, Sb are the PSSM of profile a and b, respectively Computational Methodology Xie and Bourne 2008 PNAS, 105(14) 5441 Scoring The Point is this Approach Can Now be Applied on a Proteome-wide Scale

• Scores for binding site matching by SOIPPA follow an extreme value distribution (EVD). Benchmark studies show that the EVD model performs at least two-orders faster and is more accurate than the non-parametric statistical method in the previous SOIPPA version a) Blosum45 and b) b) McLachlan substitution matrices.

Xie, Xie and Bourne 2009 Bioinformatics 25(12) 305-312 Applications Thus Far

• Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir) • Early detection of side-effects (J&J) • Late detection of side-effects (torcetrapib) • Lead optimization (e.g., SERMs, Optima, Limerick) • Drugomes (TB, P. falciparum, T. cruzi)

Applications Nelfinavir

• Nelfinavir may have the most potent antitumor activity of the HIV protease inhibitors Joell J. Gills et al, Clin Cancer Res, 2007; 13(17) Warren A. Chow et al, The Lancet Oncology, 2009, 10(1) • Nelfinavir can inhibit receptor tyrosine kinase(s) • Nelfinavir can reduce Akt activation

• Our goal: • to identify off-targets of Nelfinavir in the human proteome • to construct an off-target binding network • to explain the mechanism of anti-cancer activity

Possible Nelfinavir Repositioning PLoS Comp. Biol., 2011 7(4) e1002037 Possible Nelfinavir Repositioning drug target

off-target? structural proteome

binding site comparison

1OHR protein ligand docking

MD simulation & MM/GBSA Binding free energy calculation

network construction & mapping

Clinical Outcomes Possible Nelfinavir Repositioning Binding Site Comparison

• 5,985 structures or models that cover approximately 30% of the human proteome are searched against the HIV protease dimer (PDB id: 1OHR)

• Structures with SMAP p-value less than 1.0e-3 were retained for further investigation

• A total 126 structures have significant p-values < 1.0e-3

Possible Nelfinavir Repositioning PLoS Comp. Biol., 2011 2011 7(4) e1002037 Enrichment of Protein Kinases in Top Hits

• The top 7 ranked off-targets belong to the same EC family - aspartyl proteases - with HIV protease

• Other off-targets are dominated by protein kinases (51 off-targets) and other ATP or binding proteins (17 off-targets)

• 14 out of 18 proteins with SMAP p-values < 1.0e-4 are protein kinases

Possible Nelfinavir Repositioning PLoS Comp. Biol., 2011 2011 7(4) e1002037 Distribution of Top Hits on the Human Kinome

p-value < 1.0e-4

p-value < 1.0e-3

Manning et al., Science, 2002, V298, 1912

Possible Nelfinavir Repositioning Interactions between Inhibitors and Epidermal Growth Factor Receptor (EGFR) – 74% of binding site resides are comparable

1. Hydrogen bond with main chain amide of Met793 (without it 3700 fold loss of inhibition) 2. Hydrophobic interactions of aniline/phenyl with gatekeeper Thr790 and other residues

EGFR-DJK EGFR-Nelfinavir Co-crys ligand H-bond: Met793 with benzamide H-bond: Met793 with quinazoline N1 hydroxy O38

DJK = N-[4-(3-BROMO-PHENYLAMINO)-QUINAZOLIN-6-YL]-ACRYLAMIDE Off-target Interaction Network

Identified off-target Pathway Activation

Intermediate protein Cellular effect Inhibition PLoS Comp. Biol., 2011 7(4) e1002037 Possible Nelfinavir Repositioning Other Experimental Evidence to Show Nelfinavir inhibition on EGFR, IGF1R, CDK2 and Abl is Supportive

The inhibitions of Nelfinavir on IGF1R, EGFR, Akt activity were detected by immunoblotting.

The inhibition of Nelfinavir on Akt activity is less than a known PI3K inhibitor

Joell J. Gills et al. Clinic Cancer Research September 2007 13; 5183

Nelfinavir inhibits growth of human melanoma cells by induction of cell cycle arrest

Nelfinavir induces G1 arrest through inhibition of CDK2 activity.

Such inhibition is not caused by inhibition of Akt signaling.

Jiang W el al. Cancer Res. 2007 67(3)

BCR-ABL is a constitutively activated tyrosine kinase that causes chronic myeloid leukemia (CML) Druker, B.J., et al New England Journal of Medicine, 2001. 344(14): p. 1031-1037

Nelfinavir can induce apoptosis in leukemia cells as a single agent Bruning, A., et al. , Molecular Cancer, 2010. 9:19

Nelfinavir may inhibit BCR-ABL Possible Nelfinavir Repositioning Summary

• The HIV-1 drug Nelfinavir appears to be a broad spectrum low affinity kinase inhibitor • Most targets are upstream of the PI3K/Akt pathway • Findings are consistent with the experimental literature • More direct experiment is needed

PLoS Comp. Biol., 2011 2011 7(4) e1002037 Possible Nelfinavir Repositioning

Applications Thus Far

• Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir) • Early detection of side-effects (J&J) • Late detection of side-effects (torcetrapib) • Lead optimization (e.g., SERMs, Optima, Limerick) • Drugomes (TB, P. falciparum, T. cruzi)

Applications Case Study: Torcetrapib Side Effect

• Cholesteryl ester transfer protein (CETP) inhibitors treat cardiovascular disease by raising HDL and lowering LDL cholesterol (Torcetrapib, Anacetrapib, JTT-705).

• Torcetrapib withdrawn due to occasional lethal side effect, severe hypertension.

• Cause of hypertension undetermined; off-target effects suggested.

• Predicted off-targets include metabolic . Renal function is strong determinant of blood pressure. Causal off-targets may be found through modeling kidney metabolism. Constraint-based Metabolic

Modeling Flux space Metabolic network reactions

Steady-state assumption S · v = 0 Perturbation constraint

Flux HEX1 ? PGI ? PFK ? FBA ? TPI ? GAPD ? PGK ? PGM ? ENO ? Matrix representation of network PYK ? Change in system capacity Recon1: A Human Metabolic Network Global Metabolic Map Comprehensively represents Reactions Compounds Pathways (3,311) (2,712) known reactions in human cells (98)

Genes (1,496) Transcripts (1,905) Proteins (2,004) Compartments (7) http://bigg.ucsd.edu (Duarte et al Proc Natl Acad Sci USA 2007) Context-specific Modeling Pipeline

metabolomic biofluid & tissue localization data

metabolic network

gene constrain exchange expression fluxes data preliminary model normalize & set threshold refine model set flux based on GIMME constraints metabolic capabilities influx set minimum objective objective flux function

literature metabolic efflux Predicted Hypertension Causal Drug Off-Targets

Impacts Stronger Functional Reactions Renal Drug Official Off-Target Site Limited by Function in Binding Symbol Protein Prediction Overlap Expression Simulation Affinity Cryptic Genetic Risk Factors Prostacyclin PTGIS x x x x x synthase ACOX1 Acyl CoA oxidase x x x x x

AK3L1 4 x x x x SLC3A1; SLC7A9; SLC7A10; HAO2 Hydroxyacid oxidase 2 x x x x ABCC1 Mitochondrial MT-COI x x x CYP27B1; ABCC1 cytochrome c oxidase I Ubiquinol-cytochrome c UQCRC1 x x x CYP27B1; ABCC1 reductase core protein I *Clinically linked to hypertension. Applications Thus Far

• Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir) • Early detection of side-effects (J&J) • Late detection of side-effects (torcetrapib) • Lead optimization (e.g., SERMs, Optima, Limerick) • Drugomes (TB, P. falciparum, T. cruzi)

Applications The Future as a High Throughput Approach….. The Problem with Tuberculosis

• One third of global population infected • 1.7 million deaths per year • 95% of deaths in developing countries • Anti-TB drugs hardly changed in 40 years • MDR-TB and XDR-TB pose a threat to human health worldwide • Development of novel, effective and inexpensive drugs is an urgent priority

Repositioning - The TB Story The TB-Drugome

1. Determine the TB structural proteome

2. Determine all known drug binding sites from the PDB

3. Determine which of the sites found in 2 exist in 1

4. Call the result the TB-drugome

A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976 1. Determine the TB Structural Proteome

3, 996 2, 266 284

1, 446

• High quality homology models from ModBase (http://modbase.compbio.ucsf.edu) increase structural coverage from 7.1% to 43.3%

A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976 2. Determine all Known Drug Binding Sites in the PDB

• Searched the PDB for protein crystal structures bound with FDA-approved drugs • 268 drugs bound in a total of 931 binding sites

140

120 Acarbose 100 Darunavir Alitretinoin 80 Conjugated 60 estrogens 40 Chenodiol

Methotrexate No. drugs of No. 20

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 No. of drug binding sites

A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976 Map 2 onto 1 – The TB-Drugome http://funsite.sdsc.edu/drugome/TB/

Similarities between the binding sites of M.tb proteins (blue), and binding sites containing approved drugs (red).

From a Drug Repositioning Perspective

• Similarities between drug binding sites and TB proteins are found for 61/268 drugs • 41 of these drugs could potentially inhibit more than one TB protein

20 18 conjugated 16 estrogens & 14 chenodiol methotrexate levothyroxine 12 testosterone 10 raloxifene

8 ritonavir alitretinoin drugs No. No. of 6 4 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 No. of potential TB targets A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976 Top 5 Most Highly Connected Drugs

No. of Drug Intended targets Indications TB proteins connections transthyretin, thyroid adenylyl cyclase, argR, bioD, levothyroxine hypothyroidism, goiter, hormone receptor α & β-1, CRP/FNR trans. reg., ethR, chronic lymphocytic thyroxine-binding globulin, 14 glbN, glbO, kasB, lrpA, nusA, thyroiditis, myxedema coma, mu-crystallin homolog, prrA, secA1, thyX, trans. reg. stupor serum albumin protein alitretinoin retinoic acid receptor RXR-α, adenylyl cyclase, aroG, β & γ, retinoic acid receptor cutaneous lesions in patients bioD, bpoC, CRP/FNR trans. 13 α, β & γ-1&2, cellular retinoic with Kaposi's sarcoma reg., cyp125, embR, glbN, acid-binding protein 1&2 inhA, lppX, nusA, pknE, purN conjugated menopausal vasomotor acetylglutamate kinase, symptoms, osteoporosis, adenylyl cyclase, bphD, estrogens estrogen receptor 10 hypoestrogenism, primary CRP/FNR trans. reg., cyp121, ovarian failure cysM, inhA, mscL, pknB, sigC methotrexate gestational choriocarcinoma, acetylglutamate kinase, aroF, dihydrofolate reductase, chorioadenoma destruens, cmaA2, CRP/FNR trans. reg., 10 serum albumin hydatidiform mole, severe cyp121, cyp51, lpd, mmaA4, psoriasis, rheumatoid arthritis panC, usp raloxifene adenylyl cyclase, CRP/FNR estrogen receptor, estrogen osteoporosis in post- trans. reg., deoD, inhA, pknB, 9 receptor β menopausal women pknE, Rv1347c, secA1, sigC

Vignette within Vignette

• Entacapone and tolcapone shown to have potential for repositioning • Direct mechanism of action avoids M. tuberculosis resistance mechanisms • Possess excellent safety profiles with few side effects – already on the market • In vivo support • Assay of direct binding of entacapone and tolcapone to InhA reveals a possible lead with no chemical relationship to existing drugs

Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423 Summary from the TB Alliance – Medicinal Chemistry

• The minimal inhibitory concentration (MIC) of 260 uM is higher than usually considered • MIC is 65x the estimated plasma concentration • Have other InhA inhibitors in the pipeline

Repositioning - The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423 Acknowledgements

Lei Xie

Li Xie

Roger Chang Bernhard Palsson

Jian Wang Sarah Kinnings

http://funsite.sdsc.edu