Drug Repositioning Platforms to Predict Drug- Disease ... · Drug Repositioning Screen 1. Sivanesan Dakshanamurthy et al. J. Med. Chem. 2012, 55, 6832−6848 (triggered hundreds of

Drug Repositioning Platforms to Predict Drug-Disease Signatures

Experimental Methods

• Transcriptome matching, cell based models

• Phenotypic screening

• Protein-protein interaction assays

• Drug annotation technologies

• Gene activity mapping

• In-vivo animal models

• Observational studies

• Experimental medicine (case based studies)

Drug Repositioning Screen

Typical Experiment screening(mostly Irrational)

Cons of Experiment Screening

o Experimentally testing drugs against all targets is extremely expensive, and technically not achievable

o Even after finding a ‘hit’ the rational mechanism of action must still be revealed and tested

o Not every drug is available commercially, If available, is expensive from the branded company

o Set up an assay from scratch is a daunting task, even after setup, not guarantee FDA drugs are going to work

o The financial cost per assay run, and, maintaining the target immobilization may alter binding site properties

o The amount of potential druggable targets is exponentially increasing, creating the vast possible drug-target interactions space explore with expt. is a challenge.

Target basedProtein binding site based similarity Protein-Ligand Docking Simulations

Chemical centricPhysico-chemical properties Chemical similarity Shape similarity

Other methods Drug–disease relationships, Drug regulated gene expression, Side effect profile, Literature Mining of small-molecules

In-Silico Methods

Computer Based screening(Rational)

Drug Repositioning ScreenN

ot

Structure based

fit ligands into the ”real” atomic structure of the protein

| P ><SF > < S6/M2 >

KcsA 65|ALWWSVETATTVGYGDLYPVTLWGRLVAVVVMVAGITSFGLVTAALATWF------- VGREQE

MthK 364|SLYWTFVTIATVGYGDYSPSTPLGMYFTVTLIVLGIGTFAVAVERLLEFL--------INREQM

KvAP 198|ALWWAVVTATTVGYGDVVPATPIGKVIGIAVMLTGISALTLLIGTVSNMFGKIL----VGEPEP

ratKv1.2 364|AFWWAVVSMTTVGYGDMVPTTIGGKIVGSLCAIAGVLTIALPVPVIVSNFNYFYHRETEGEEQA

hKv1.3 382|AFWWAVVTMTTVGYGDMHPVTIGGKIVGSLCAIAGVLTIALPVPVIVSNFNYFYHRETEGEEQS

hKv1.5 470|AFWWAVVTMTTVGYGDMRPITVGGKIVGSLCAIAGVLTIALPVPVIVSNFNYFYHRETDHEEPA

hKv2.1 363|SFWWATITMTTVGYGDIYPKTLLGKIVGGLCCIAGVLVIALPIPIIVNNFSEFYKEQKRQEKAI

or use homology model

Docking, MD simulations

Docking = virtual screening gold standard


Poor correlation of Ki values with docking score (2,335 holo protein structures)

Poor correlation of IC50 values with docking score (2,335 holo protein structures)

Virtual Screening Performance (existing computer screening methods)

Naiem T, Oakland P, Byers S, Dakshanamurthy S. Comb. Chem. & High Thro. Scr. 2015 (Most accessed article))


• Inadequate treatment of solvent effect: Solvent water and counter ions are needed to treat proper volume, change shape of the binding site, bridging interaction

• Conformational change and entropy not included

• Good affinity prediction not necessarily leads to correct biological binding mode

• Inadequate treatment of protein (and drug) dynamicity i.e. docking treat protein as rigid

• All these factors contribute to high false positives and false negatives in virtual screening

Challenges to Virtual Screening

Given low hit rate,

Need a New Comprehensive Method?

o TMFS method using Proteo-chemometric approach combines ligand and protein centric to predict high hit rate.

o TMFS method is combined to systems medicine, patient survival data with gene expressions to predict mechanism of action and make drug-disease relationships for new therapeutics (standalone or combination)


1. Sivanesan Dakshanamurthy et al. J. Med. Chem. 2012, 55, 6832−6848 (triggered hundreds of press releases)2. Naiem T, Oakland P, Byers S, Dakshanamurthy S. Comb. Chem. & High Thro. Scr. 2015 (Most accessed article)3. Assefnia et al. Cadherin-11 in poor prognosis malignancies and rheumatoid arthritis: common target, common therapies. Oncotarget. 2014 Mar 30;5(6):1458-74.

TMFS Method

Train: Train the drugs -> Ref. ligands, proteins (topology, properties), expt. data

Match: Match their topology, properties

Fit: Fit the data with expt. Data, andintegrate it with systems medicine pipeline

Streamline: Streamline the hits using the TMFS score


‘Z’ score rank ordered viz. top 1 through top 40 target hits for

each drug

TMFS Method Algorithm

)(),()],(),([),(1

1

1

1

OLICCSXffYZj

n

lcnlcilp

i

ilpk

normalized docking score normalized Euclidean

distance scores –protein pocket, FDA drugs, Ref. native ligands

Ligand based descriptor score–Ligand shape, physico-chemical properties of FDA drugs, Ref. native ligands

Ligand topology descriptor score–surface properties of FDA drugs, Ref. native ligands

Ligand - Protein contact pointsscore

CS (OLIC) = S (OLIC-R) - S (OLIC-T)

contact point scoreof FDA drugs

contact point scoreof reference ligands


RePurposeVS Method

TMFS Method Algorithm

1. Sivanesan Dakshanamurthy et al. J. Med. Chem. 2012, 55, 6832−6848 (triggered hundreds of press releases)2. Naiem T, Oakland P, Byers S, Dakshanamurthy S. Comb. Chem. & High Thro. Scr. 2015 (Most accessed article))

RePurposeVS Z-rank score

1. Sivanesan Dakshanamurthy et al. J. Med. Chem. 2012, 55, 6832−6848 (triggered hundreds of press releases)2. Naiem T, Oakland P, Byers S, Dakshanamurthy S. Comb. Chem. & High Thro. Scr. 2015 (Most accessed article))

Example: Sutent (Sunitinib) -> predicted alternate targets

Performance of the RepurposeVS

o 37% increase in enrichment at the top 1% compared to GLIDE (commercial software)

o 48% increase in enrichment at the top 5% compared to GLIDE.

Alternative Targets of FDA Blockbuster Drugs

Application: Drug Repositioning

Sutent (Sunitinib) is predicted to hit the greatestnumber of protein targets followed by Alimta…….

FDA Blockbuster Alternate Targets

Prograf, Valcote, Concerta, Sifrol, Niaspan, Exelon, Evodart, Sevorane, and Klacid have no hits

Repurposing Potential New Disease Classes

• Anti-neoplastic drugs have the greatest repurposing potential• Anti-infective drugs have low repurposing potential• Anti-psychotic, anti-bacterial drugs have modest repurposing potential

100)(1

xnEnZnYnXnB

nYnBnAPCP

i jijijijiji

jijiji

Validation of Predictions

where i < # drugs, j < #targets

To calculate the percent correctly predicted (PCP) targets,

o RpurposeVS predicts drug-target associations with greater than 91% accuracy for the majority of drugs

A. Predicted number of (A) KEGG pathways

B. GO molecular functions affected by each drug

NextGenRepurposeVS: Connected to Molecular Mechanistic Pathways and Disease Associations

Subset of drug-function-pathway-disease network

MAPK network, many drugs target multiple disease classes, central pathway in pathogenesis of many diseases.

Experiment Validation I

o Mebendazole binds with some kinases at affinity ranging from 5µM to 90nM

o Mebendazole as a Therapeutic Option in Drug Resistant Melanoma in-vitro and in-vivo

o Mebendazole program are planned for dose escalation clinical trial with new formulations


Experimental Validation I

100-500 nM

500-1000 nM

1000+ nM

P38α

JNK3

JNK2

CDK7

DYRK1B

ABL1

VEGFR2 KIT

PDGFRα PDGFRβ

o As predicted by TMFS, Mebendazole inhibits several kinases at an affinity ranging from 15µM to 90nM

1. Sivanesan Dakshanamurthy et al. J. Med. Chem. 2012, 55, 6832−6848 (triggered hundreds of press releases)2. Naiem T, Oakland P, Byers S, Dakshanamurthy S. Comb. Chem. & High Thro. Scr. 2015 (Most accessed article)

o Celecoxib (1.42 μM), and Dimethyl Celecoxib (1.56 μM) interacted with CDH11

o Celecoxib and DMC completed early stage preclinical studies in-vivo model

Experiment Validation II

1. Sivanesan Dakshanamurthy et al. J. Med. Chem. 2012, 55, 6832−6848 (triggered hundreds of press releases)2. Assefnia et al. Cadherin-11 in poor prognosis malignancies and rheumatoid arthritis: common target, common therapies. Oncotarget. 2014 Mar 30;5(6):1458-74.

Celecoxib and DiMethyl Celecoxib binding to CDH11

(G) Gel Analysis: Celecoxib reduces the CDH11 dimer fraction (Native gel comparison of cadherin-11 in the absence (left) and presence (right) of celecoxib) (H) SPR assay: Celecoxib (1.42 μM), and Dimethyl Celecoxib (1.56 μM) interacted with CDH11

1. Sivanesan Dakshanamurthy et al. J. Med. Chem. 2012, 55, 6832−6848 (triggered hundreds of press releases)2. Assefnia et al. Cadherin-11 in poor prognosis malignancies and rheumatoid arthritis: common target, common therapies. Oncotarget. 2014 Mar 30;5(6):1458-74.

CDH11 CDH11

Summary

•TMFS method hit rate is greater than 91% accuracy for the majority of drugs.

•Anti-hookworm medication Mebendazole repurposed to specific kinases for possible drug resistant melanoma.

•Anti-inflammatory drug Celecoxib and DMC repurposed to CDH11, can be used for rheumatoid arthritis, IBD, and poor prognosis malignancies for which no targeted therapies exist.

•NextGenRePurposeVS provided new disease connections for many drugs and insight into their mechanism of action.

• Several drugs has been validated in-vitro and in-vivo, provided the validity of our screening tools; TMFS, RePurposeVS and NextGenRepurpose.

Documents

Drug Repositioning Platforms to Predict Drug- Disease ... · Drug Repositioning Screen 1. Sivanesan Dakshanamurthy et al. J. Med. Chem. 2012, 55, 6832−6848 (triggered hundreds of