BIOINFORMÁTICA APLICADA AL

DISEÑO Y TRANSPORTE DE

DROGAS

Dra. Claudia Machicado R, PhD. MSc.

Sr Clinical Research Associate/Quintiles

Profesor/Proyecto QUIPU

1

Summary2

Bioinformatics

Computer-aided drug design

Examples

Transport of therapeutical molecules

Conclusions

Bioinformatics3

October 29, 2003© Bioinformatics Centre, University of Pune4

Bioinformatics is a scientific discipline that encompasses all aspects of

biological information acquisition, processing, storage, distribution,

analysis and interpretation.

What is

Bioinformatics?

¿Qué es la Bioinformática?

La bioinformática consiste en analizar, comprender y predecir procesosbiológicos con la ayuda de herramientas computacionales.

Predicción observaciónAntes 1953

AhoraATATTGCCGACC

GGCGCCCGGTAC

CTGGCCCATGTC 2009

La ciencia de las predicciones

A. Genómica y proteómica C. Biología de sistemas

D. Bases de datos

La Bioinformática se divide en 4 áreas:

B. Bioinformática estructural

Bioinformatics Applications

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Computer-aided drug design7

Bioinformatics Applications: CADD

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Targeted Medicine

A better understanding of the genetic root cause of

disease is the key to improved diagnosis and treatment of

many complex chronic diseases.

Bioinformatics Applications: CADD

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Bioinformatics Applications: CADD

Bioinformatics Applications: CADD

TACGCTTCCGGATTCAA

transcription

AUGCGAAGGCCUAAGUU

DNA:

RNA:

translation

PIRLMQTSProtein

Amino Acids:

Bioinformatics Applications: CADD

Protein

Small molecule drug

Bioinformatics Applications: CADD

Protein

Small molecule drug

Protein

Protein disabled … disease cured

Linkage between Swiss-Prot-DrugBank-

PubChem-MMDB

(see these

marketed target

links )

(411)

(15728) = 181

(2501)

Bioinformatics Applications: CADD

20/03/2008Dept. of Pharmaceutics

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Bioinformatics Applications: CADD

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Bioinformatics Applications: CADD

17

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Bioinformatics Applications: CADD

20/03/2008Dept. of Pharmaceutics

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Computer-aided drug design20

Important Points in Drug Design based on

Bioinformatics Tools

Chemical Modification of Known Drugs

Drug improvement by chemical modification

Pencillin G -> Methicillin; morphine->nalorphine

Receptor Based drug design

Receptor is the target (usually a protein)

Drug molecule binds to cause biological effects

It is also called lock and key system

Structure determination of receptor is important

Ligand-based drug design

Search a lead compound or active ligand

Structure of ligand guide the drug design process

Casos generales en el diseño de fármacos

Cribado virtual:

método computacional cuya misión es seleccionar los compuestos más prometedores(frente a una diana terapéutica dada) dentro de bases de datos moleculares

Docking:

predicción del modo de unión entre dos moléculas

dockingcribado virtual

de novobúsqueda 3D basada en el

receptorconocida

QSARfarmacóforosbúsqueda en

BD

química combinatoria (QC)cribado masivo (HTS)

desconocida

conocidadesconocidareceptor

ligandoestructura

3D

dockingcribado virtual

de novobúsqueda 3D basada en el

receptorconocida

QSARfarmacóforosbúsqueda en

BD

química combinatoria (QC)cribado masivo (HTS)

desconocida

conocidadesconocidareceptor

ligandoestructura

3D

Important Points in Drug Design based on

Bioinformatics Tools

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Molecular docking

Important Points in Drug Design based on

Bioinformatics Tools

Identify Target Disease

Identify and study the lead compounds

Marginally useful and may have severe side effects

Detect the Molecular Bases for Disease

Detection of drug binding site

Tailor drug to bind at that site

Protein modeling techniques

Traditional Method (brute force testing)

TargetIdentification /Validation

LeadIdentification /Validation

LeadOptimization

Development/Clinical Trials

High Throughput

Screening - HTSSynthesis -

Compounds

& Compound

Libraries

Absorption,

Distribution,

Metabolism &

Excretion ;

Toxicology

Strategy for Drug Discovery

Bioinformatics accelerate this process

ChemInformatics

Chemistry,

Structure-Activity Relationships

Lead optimization

Target &

Assays

Biology/

Bioinformatics

Proteomics

Biology/

Animal study

Clinical trials

Important Points in Drug Design based on

Bioinformatics Tools

Application of Genome

3 billion bases pair

30,000 unique genes

Any gene may be a potential drug target

~500 unique target

Their may be 10 to 100 variants at each target gene

1.4 million SNP

10200 potential small molecules

Fewer than 500 characterized molecular targets

Potential targets : 5,000-10,000

Impact of Structural Genomics on Drug

Discovery

Dry, S. et. al. (2000) Nat. Struc.Biol. 7:976-949.

Important Points in Drug Design based on

Bioinformatics Tools

Refinement of compounds

Refine lead compounds using laboratorytechniques

Greater drug activity and fewer side effects

Compute change required to design betterdrug

Quantitative Structure Activity Relationships(QSAR)

Compute functional group in compound

QSAR compute every possible number

Enormous curve fitting to identify drug activity

chemical modifications for synthesis and testing.

Solubility of Molecule

Drug Testing

Drug Discovery & Development

Identify disease

Isolate proteininvolved in disease (2-3 years)

Find a drug effectiveagainst disease protein(2-4 years)

Preclinical testing(1-3 years)

Formulation

Human clinical trials(2-8 years)

Scale-up

FDA approval(2-3 years)

Techology is impacting this process

Identify disease

Isolate protein

Find drug

Preclinical testing

GENOMICS, PROTEOMICS & BIOPHARM.

HIGH THROUGHPUT SCREENING

MOLECULAR MODELING

VIRTUAL SCREENING

COMBINATORIAL CHEMISTRY

IN VITRO & IN SILICO ADME MODELS

Potentially producing many more targetsand “personalized” targets

Screening up to 100,000 compounds aday for activity against a target protein

Using a computer topredict activity

Rapidly producing vast numbersof compounds

Computer graphics & models help improve activity

Tissue and computer models begin to replace animal testing

High-Throughput Screening

Screening perhaps millions of compounds in a corporate

collection to see if any show activity against a certain disease

protein

HTS (virtual and in vitro) can test 100,000 compounds a day

for activity against a protein target

Toxicity

Traditionally, animals were used for pre-human testing: animal tests are expensive, time consuming and ethically undesirable

In Silico ADME (Absorbtion, Distribution, Metabolism, Excretion) models

ADME techniques help model how the drug will likely act in the body

These methods can be experemental (in vitro) using cellular tissue, or in silico, using computational models

Toxicity

Computational methods can predict compound properties important to ADME, e.g.

LogP, a liphophilicity measure

Solubility

Permeability

Cytochrome p450 metabolism

Means estimates can be made for millions of compounds, helping reduce “attrition” – the failure rate of compounds in late stage

Process network and technology mapping

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Bioinformatics database developed

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1. Therapeutic target database

http://xin.cz3.nus.edu.sg/group/cjttd/ttd.asp

2. Drug adverse reaction target database

http://xin.cz3.nus.edu.sg/group/drt/dart.asp

3. Drug ADME associated protein database

http://xin.cz3.nus.edu.sg/group/admeap/admeap.asp

4. Kinetic data of biomolecular interactions database

http://xin.cz3.nus.edu.sg/group/kdbi.asp

5. Computed ligand binding energy database

http://xin.cz3.nus.edu.sg/group/CLiBE/CLiBE.asp

An Ideal Target

Is generally an enzyme/receptor in a pathwaywhose inhibition leads to either killing a pathogenicorganism (i.e. parasite) or to modify some aspectsof metabolism of body that is functioningabormally.

Normally…

Is essential for the survival of the organism.

Located at a critical step in the metabolic pathway.

Makes the organism vulnerable.

Concentration of target gene product is low.

The enzyme amenable for simple HTS assays

Some interesting targets

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How Bioinformatics can help in Target

Identification?

Homologous & Orthologous genes

Gene Order

Gene Clusters

Molecular Pathways & Wire diagrams

Gene Ontology

Identification of Unique Genes of Parasite as

potential drug target.

What one should look for?

Human

P.F

Mosquito

Proteins that are shared by – All genomes Exclusively by Human & P.f. Exclusively by Human & Mosquito Exclusively by P.f. & Mosquito

Unique proteins in –

Human

P.f. Targets for anti-

malarial drugs

Mosquito

Modeling targets

Methods Sequence Identity with known structures

ab initio 0-20%

Fold recognition 20-35%

Homology Modeling >35%

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Modeling targets

Métodos “de novo”(obtención de nuevas moléculas)

Identificación y producciónde dianas terapéuticas

Estructura 3D de la diana (libre y/o con ligando)

Cristalografía, RMN, modelado por homología

Síntesis

Evaluación

Optimización del candidato

(afinidad, toxicidad, especificidad)

Pruebaspre-clínicas

Pruebasclínicas

Fármaco

Bases de datos deestructuras 3D

Cribado Virtual(docking)

Drug Development Flowchart

Lead Optimization

Lead Lead OptimizationActive site

Drug Discovery Pipeline

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It is of utmost importance to identify lead compounds in the early stages of drug discovery that will be most likely to succeed

Recent study by Tufts Center for the Study of Drug Development showed that bringing one drug to market costs an average of $800M!

5/5,000 potential new drugs tested on animals reach clinical trials, and only one ultimately wins FDA approval

Drug Discovery Pipeline46

Drug Discovery Pipeline47

EJEMPLOS PRÁCTICOS48

ESTRATEGIAS GENÓMICAS PARA DISEÑO

DE DROGAS CONTRA EL VIRUS AH1N1

Maurer-Stroh y cols., 2009. Biology Direct 2009, 4:18.

Brote inicial fue en Marzo 2009 + Bioinformática= vacuna en seis meses (Setiembre 2009)

Base de datos de miles de genomas de AH1N1

Secuencia de Proteína de la Neuraminidasa

Análisis bioinformáticos: Aplicaciones

Predicción de estructura y diseño de drogas específicas

Análisis bioinformáticos

Planque et al., 2008. Autoin. Rev. 7: 473-479

VIH

421-433gp120

Síntesis de péptidos gp120

Estudio genómico y proteómico del VIH

VIH

DEL VIRUS VIH A LA VACUNA

VIH inicio en 1979 + Bioinformática (2008)= vacuna en proceso (2010)

ESTRATEGIAS GENÓMICAS PARA DISEÑO

DE VACUNAS CONTRA EL HIV

Retrociclina

Genoma y proteoma humano

DEL Genoma humano A LA

VACUNA

Venkataraman y Colewhether. PLoS Biology, 2009

Análisis de Secuencias de ADN Humano no codificante

(98%)

Síntesis y producción de Retrociclina Humana

VIH inicio en 1979 + Bioinformática (2009)= vacuna en proceso (2010)

ESTRATEGIAS GENÓMICAS PARA DISEÑO

DE VACUNAS CONTRA EL HIV

¿Cómo la proteómica puede ayudar al pronóstico del cáncer y desarrollo eficiente de drogas?

Encontrado nuevas drogas

(Se requiere drogas específicas para matar células que producen melanomas en un cáncer de piel): diseño óptimo de biomoléculas mediante bioinformática

ESTRATEGIAS GENÓMICAS PARA DISEÑO

DE DROGAS CONTRA EL CÀNCER

Rappuoli R. Vaccine. 2001;19:2688-2691

Expresióny

purificación

proteínas

Inmunización

Escherichia coliGenoma bacteriano

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1,000,000

1,100,0001,200,000

1,300,000

1,400,000

1,500,000

1,600,000

1,700,000

1,800,000

1,900,000

2,000,000

2,100,000

2,200,000IHT-A

IHT-B

IHT-C

1

Se identifican antígenos

Del GENOMA a la VACUNA

Vacuna recombinante

sueroinmunidad protectora

ESTRATEGIAS GENÓMICAS PARA DISEÑO

DE VACUNAS CONTRA BACTERIAS

PATÓGENAS

Transporte de moléculas terapéuticas54

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Drug Delivery Carriers

January 15, 2002: It's like a scene from

the movie "Fantastic Voyage." A tiny

vessel -- far smaller than a human cell --

tumbles through a patient's bloodstream,

hunting down diseased cells and

penetrating their membranes to deliver

precise doses of medicines.

Only this isn't Hollywood. This is real

science.

Right: Tiny capsules much smaller than these blood cells may someday be injected into people's bloodstreams to treat conditions ranging from cancer to radiation damage. Copyright 1999, Daniel Higgins, University of Illinois at Chicago.

http://science.nasa.gov/headlines/y2002/15jan_nano.htm

Diseño de un proteínas transportadoras

Mutaciones truncantes generadoras de cavidades internas

Mutaciones truncantes in silico

Apertura de cavidades internas

Unión de pequeñas moléculas

Lisozima T4 Barnasa Citocromo c peroxidasa

Diseño de un proteínas transportadoras

Trp66

Leu44Ile52

Ile109

Leu105

Mutaciones in silico

truncadas a Ala (Phe)

Método de modelización

Análisis de los modelos

de flavodoxina con

cavidad

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Diseño de un proteínas transportadoras

Bajos valores de rmsd entre la

estructura cristalizada y modelada

Reducción

Teórico CristalModelo

Alta precisión en la predicción

de la forma y tamaño de las

cavidades y de la estructura

global de la proteína

ExpansiónTeórico Cristal

Modelo

Algoritmo de unión a cavidades

Puntuación y lista

de candidatos

Filtro de

volumen

Ligandos

potenciales

Filtros de

polaridad

ACOPLAMIENTO FINO

CERIUS2

CATALYST

AFFINITY

Evaluación del Gu

DBM

250000

5000

100

8

Algoritmo de unión a cavidades

STHGunión

RT

GuniónA exp

Docking

Scoring

Targeted therapeutics must:

1. Diffuse out of vasculature (<20 nm)

2. Recognize target cells and bind with high avidity and specificityto extracellular binding domain

3. Internalize and intracellularly traffic to site of intended action

4. Avoid “normal” tissue

5. Remain intact until reaching its intended site of action

6. Carrier should be stable andbiologically inert

Normal Cell

Cancer Cell

Challenges Facing Targeted Therapeutics

Vessel

Targeted

therapeutic

Especificidad

?

Liberación

?Ingreso

?

Acción intracelular

?

Transporte plasmático

? Antigen diffusion leads to higher carrier binding affinity

Challenges Facing Targeted Therapeutics

Molecular Dynamics

Mixed Quantum Mechanics Molecular Mechanics

Aim 1: Model for Glycocalyx resistance

-- Monte Carlo Simulations to predict nanocarrier binding

Aim 2: Model for Endocytosis

-- Molecular simulations to predict membrane dynamics

GlycocalyxAntibodyCarrierAntigenCell

Glycocalyx on EC

Endocytosis

Interaction of protein carriers with endothelial cell

Glycocalyx Morphology and Length Scales

100 nm1,2,3Glycocalyx

10 nmAntibody

100 nmCarrier

20 nmAntigen

10-20 μmCell

Length Scales

Endocytosis

Ford et al., Nature, 2002

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THANK YOU

E-mail: claudia.machicado@yahoo.com