Drug discovery strategy final draft

Drug Discovery

Anaeli Shockey

Delaine M. Zayas-Bazán

Nicollle A. Rosa

Zuleika Velázquez

Student Mentor: Mr. Carlos Castroda

Introduction • Food and Drug Administration (FDA)– Drug Discovery and Development– Clinical Trials– FDA Reviews and New Drug Application (NDA)– Manufacturing

• Considered Parameters– Absorption– Distribution– Metabolism– Excretion

• What is In silico drug discovery?

. Pharmacophore

identification and Pharmacophore Model

Generation (LigandScout)

Identification of Top-hits and potential

Lead Compounds. (Ranking of binding

energies)

Drug Discovery Strategy

Primary Sequence Analysis; degree

conservation (NCBI/Swiss-Prot)

Biological Problem (Biomedically Relevant Condition or Process)

Identification of optimal target (s)

for drug development

Identification of compounds that fulfill requirements of Pharmacophore model

Filtering Small chemical

compoundsDatabases

Target Analysis Number, quality and distance of “hot spots’

3D Structurewww.pdb.org

PyMol

BioAssay

Secondary Screening (AutoDock Vina)

Primary Screening:Pharmacophore Model

(ZINCPharmer)

High AffinityLead

Compounds

Further refinement of Pharmacophore

Model

FTMap and In Silico

screening ofchemical probes

Therapeutically relevant protein

targets

Docking/screening of Filtered Databases

B

C

D

A

Identification of Top-hits and potential

Lead Compounds. (Ranking of binding

energies)

Drug Discovery Strategy

Identification of compounds that fulfill requirements of Pharmacophore model

BioAssay

Secondary Screening (AutoDock Vina)

High AffinityLead

Compounds

Further refinement of

Pharmacophore Model

Docking/screening of

Filtered Databases

D

Work Plan

Work Plan• Run the Docking/Screening (“AutoDock Vina”)

– This is needed for the analysis of the top hits

– It is achieved by the utilization of the program Auto Dock• Download Results and Ranking of Top Hits

– For this, the programs CyberDuck and Excel were used.

– The results were downloaded and then opened with Excel to sort by affinity

– Select the drugs with the highest affinity

– Look for information about the drugs and take the pictures

Work Plan• Analyze Interactions

–Open Autodock and let the program analyze the results

–Take pictures of the interactions

Name AffinityZINC06716957 -11.4

ZINC14880002 -11.4

ZINC22940637 -10.6

CID_64143_Nelfinavir -10.5

Zinc14879987_Tipranavir -10.5

ZINC02570819 -10.4

ZINC00896717 -10.4

ZINC03951740 -10.4

zinc_3951740_lopinavir -10.4

ZINC22448696 -10.2

DMP -10

ZINC03914169 -10

ZINC52955754 -10

Zinc22448696_indinavir -10

Identification of Top-hitsSteps One and Two

Nilotinib• ZINC06716957 -11.4

• Tyrosine kinase inhibitor

• Chronic myelogenous leukemia treatment

• Hydrochloride monohydrate salt

• Oral ingestion

Lopinavir

• zinc_3951740_lopinavir -10.4

• Antiretroviral of the protease inhibitor class

• Used with ritonavir (protease inhibitor)

• Oral ingestion

Ergoloid• ZINC00896717 -10.4

• Mixture of methanesulfonate salts

• Used to treat dementia and age-related cognitive impairments

• Also used in a patients recovery after stroke

• Oral ingestion and parenteral

Zafirlukast• ZINC14880002 -11.4

• Oral ingestion

• Leukotriene receptor antagonist

• Inhibits what causes inflammation in respiratory system

Images of the Drug’s Interactions with the amino acids of the Proteases of HIV

Images of the Drug’s Interactions with the amino acids of the Proteases of HIV

• Due to technical difficulties with the program, the images of the other three drugs’ interactions with the amino acids of the proteases could not be presented.

• This images composed the last step: the analysis of the results.

Conclusion• The fourteen drugs with the highest affinity were

chosen.• These range from -10 to -11.4• Nilotinib, Lopinavir, Ergoloid, and Zafirlukast

were evaluated using AutoDock Vina, CyberDuck and Excel.

• The drugs with the highest affinity to the HIV related protein are Zafirlukast and Nilotinib.