1

Pharmaceutical Industry

2

World industry: (2008)

Min

ing

and

Oi l

War

ind.

Ente

rtai

nmen

t

Phar

ma

Medical ind.

Straighforward researchBig R&D budget

Food

Sma l

l mac

hine

Toba

coo

14.3 14.1 11.1 8.7 7.3 7.1 6.8 6.1

Mac

hine

ind.

5.8

Total income: ~ 640 billion $ (+10 %)Total profit: ~ 90 billion $ (P/I = 15%)R&D: ~ 30 billion $ (30%)

Manhattan project: 22 B $ / 5 yearApollo program: 98 B $ / 14 year

Hung GDP 140 billion $ Hung budget 60 billion $OTKA 0.4 billion $

ca. 2 million employee200 thousend R&D(!!!)

%

Tele

com

. Com

p .

R&D30%

New Investments30%

Market-ing

10%

Others10%

Divident20%

3

Largest Pharmaceutical Companies (2012)2012 Name Global Income Profit Value P/B % Empl.

1 Pfizer 34 67.4 B 10 B 165.4 B 14.8 119 200

2 Novartis 62 58.6 B 9.1 B 150.4 B 15.5 86 600

3 Sanofi 78 43.2 B 7.4 B 103.3 B 17.1 103 483

4 Merck 80 48.0 B 6.3 B 115.8 B 13.1 78 604

5 Roche 103 45.3 B 10 B 152.0 B 23.0 99 495

6 GSK 119 42.5 B 8.2 B 111.8 B 19.3 98 200

7 Abbott L 127 38.9 B 4.7 B 93.4 B 12.1 67 400

8 AstraZ 142 32.4 B 9.6 B 58.0 B 29.6 68 697

9 Eli Lilly 208 24.3 B 4.3 B 46.6 B 17.7 58 900

10 BMS 217 21.2 B 3.7 B 56.0 B 17.4 50 527

11 TEVA 223 17.2 B 2.6 B 41.0 B 15.1 42 000

20-25 Servier - 3.8 B 0.8 B - 21.0 25 000

Exon 1 433.5 B 41.1B 407.4 B 9.5 600 000

JP Morgan 2 110.8 B 19.0 B 170.1 B 17.2 ?

>100 Richter >2000 0.8 B 0.25 B 2.24 B 30 Kb.6 000

www.fortune.com

ACADEMICRESEARCH

AkadémiaiKutatóintézetek,

egyetemek

PROFIT

TAXES

TAXESTENDERS

SUPPORT

RESU

LTS

DRUGPRODUCTIONRESEARCH DEVELOPMENT

B

AD( )n

CLINICS

GOVERMENT

AcademyReserch Inst.,Universities

PHARMACEUTICAL COMPANY

5

Diseases

Symptom Reason

BODY LEVEL

ORGAN LEVEL

CEL LULARLEVEL

MOLECULARLEVEL

Moderntherapy

Surgery

Immunology

Medicines

sickness, head-acheFever, etc.

symptomatic treatment

„DOGMA”:Most of the diseases

has molecular background!

Old therapy

Medication:Influencing

Molecular targetsActivation, inhibition

6

MOLECULAR TARGET BASED TREATMENT

error

Cellular Information Network

7

Card iovas cu lar d i s eas e ( thrombos is , a r ter ios c le ros i s , … )

Metabol i c d i s eas e(d iabete s , oste oporos i s , ster i l i ty, hormones , obes i ty, … )

Centra l ne r vous system (de pre ss ion , psych iat r i c d i s e as e … )

Contag ion (bacte r ia , v i rus , fungous , o ther paras i tes , … )

Onko log y

Respiratory and immunolog y d i s eas e (astma, copd, … )

Out

er re

ason

Inne

r rea

son

(life

styl

e, h

ered

itary

)Therapeutic area:

8

DRUG RESEARCH

Discovery

Development

Basic research

Drug store

B

AD( )n

Clinics

MARKETING

DRUG RESEARCH

Drug candidate

drug substance

DRUG

Diseases ReceptorsEnzymes Drug product

Biol.target

PRECLINICS PHASE I PHASE II PHASE IIIResearch LICENC

HTS

Lead opt.

Lead

cpd

Chemical dev.

Production of Clinical batches

Cand

idat

e

Industrial batches

marketing

Synt

hetic

rout

e

tech

nolo

gy

appr

oval

Dru

g pr

oduc

t

In vivo, animal trials

Healthy people

100

diseased people

100-5000 person

Real population

1000-50000 patients, many countries, many race

SPEED QUALITY ECONOMY

Life cycle of drugs

In vitro

Industrial Dev.

9

DISCOVERY DEVELOPMENT PRODUCTIONS

CLINICS

20 4 5 7-8 11-132 2 1 2-3 4-5

Dru

g

Dru

g su

bsta

nce

10

Discovery: 2–3 years

Goal: find an active compound

high throughput screening (HTS)

~10 lead compound (2-4 clusters)

105 compound

101–102 compound (several clusters)

Candidate 2-3 cpds

optimization

optimization

B

AD( )n

Development

Input: receptor with known effect

target

target

11

Bioactivity

Selectivity

Side-effect

ADME

Toxicologicalprofile

Genotoxicologicalprofile

Marketing

Pharmaceuticalproperties

Drugmolecule

Series1

0

5

Series1

0

5

Series1

0

5

RealOptimal Poor

AdsorptionDistributionMetabolismElimination

12

Preclinical development : 2 years

Synthesis optimizationScale-up

Formulation

Physical quality development

analitics

Salt formation, polymorphism, Particle size

stabilization, solubility increase

clinics

Defined crystal form

Drug product

Drug substance

Input: candidate (2-3 cpds)Goal: develop the drug

Kg scale

13

Impurity profileToxicologyof impurities

Supplier

TechnologyEconomy

Ecology

Marketing

Pharmaceuticalproperties

Drugsubstance

Series1

0

5

Series1

0

5

Series1

0

5

ProductivitySafetyRobusstness

RealOptimal Poor

AccuracyReliablility

Non-toxicToxicGenotoxic

MarketableNon-marketable

Economically friendlyRisky

MarketableNon-marketable

14

Clinical trials: 6–8 years

Phase I

Phase II

Phase III

Treatment dose determinationSpecial diseased people

Normal diseased peopleDrug efficiency

Real sample of the population

Maximal dose and side-effects Healthy volunteers

50 – 100 people

200 – 1000 people

1000 – 5000 people

Phase IIa

Phase IIb

10000 – 50000 people

Phase IV Requiest

uncertainty: 90%

uncertainty : 75%

uncertainty : 40%

uncertainty : 10%Approval

Proof of concept

15

What is the DRUG

O

OH

O

O

Chemical structure

Chemical synthesis, technology

Known impurity profile

Defined particle size distribution, polymorphism

Known biological effect, toxicity

Known metabolism, pharmacokinetics

Industrial capacity, starting materials, suppliers

Marketing, Market

Patents, etc.

Strategy

R&D PROFIT

Intelectual properties and patents

marketing

~ 25 years

~ 12 years ~ 12 years~ 1-2 years

~ 1 billion $ ~ 200-300 million $/year

0 2 4 6 8 10

109 $

Mar

ketin

g

Pha

se I

II.

Pha

se I

.

Pha

se I

I.

Pre

clin

ical

Dev

Dis

cove

ryExp

en

se

year

109 $

Discovery

Marketing

Preclinical Dev

Phase III.

Phase II.

Phase I.

25%

25%

20%

15% 10%5%

STAR

T

END

OF

THE

ORI

GIN

AL

1. PATENT

16

17

Typical life-cycle of a drug

12 y 25 y

R & D GENERIC COMPANIESGOLDEN AGEMARKETING

Intelectual properties and patents

Profi

t/ye

arIn

vest

/yea

r

18

Typical life-cycle of a company

12 y 25 y

Block blusters

Overall Profitability

Profi

t/ye

ar

Loosers

19

0

15

30

45

60

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

Pat

ents

0

17

34

R=

D exp

ense

Billio

n $)

Drug research

1965 1970 1975 1980 1985 1990 1995 20000.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Eg

y g

yóg

ysze

r ku

tatá

sára

szá

nt

öss

zeg

(m

illiá

rd$)

problem

redgreen = blue

1980 – 200 million $1990 – 500 million $2000 – 1000 million $2010 – 1700 million $

R$D expense for one drug product

20

Dyrk 1A inhibitor(Onkology, Apoptosis)

0 2 4 6 8 10 12 14

Research Clinics Filing

NNHN

O

N

Cl

Cl

Cl

X

No in vivo activity

-AnimalToxicity

-ADMEMetabolism

Development Market

RimonabantSanofi

Genotoxicity Risky productSide-product

(incr. numb. of suicide)

FALSE in DRUG COMPANIES 1.

3 billions $

-No humanActivity-Toxicity

Worked at molecular levelDoes not work at cellular level

- No profit- No market

21

Selective V2-vasopressin Antagonist (Triggers Excretion of Water Only)

NS

O

O N

OO

O

NH

O

O

SatavaptamSanofi

0 2 4 6 8 10 12 14

N

HO

Cl

O

NH

O

TolvaptamOtsuka

Research Clinics & Development Filing

FALSE in DRUG COMPANIES 2.

FASTER

TOO SLOW

22

D2/D3 receptor antagonist (Antidepressant)

0 2 4 6 8 10 12 14

Research Clinics & Development Filing

N

N

HN

O

N

Cl

Cl

CariprazineRichter

N

N

Cl

Cl

OHN O

AripiprazolOtsuka

Original Generic

Original

16

FALSE in DRUG COMPANIES 3.

Patent exp.

23

Resources to obtain the same results

idő

n ow

20251900 1925 1950 1975 2000 2025

2025

computations

experiments

Inve

sted

res

ourc

e

24

0

5

10

15

20

25

30

35

40

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

log Size (Å)

log

Ma

ss

(D

alt

on

s)

H(1926)

Peptides (2006)Protein (2026)

Protein Complex (2036)

Mitocondria (2046)

Cell

CHEMISTRY

BIOLOGY

Tumor Organs

Homo sapiens

Drugs(1986)

25

Discovery Development Clinics

Reaction mechanismAb-initio

„Statistics”

Intensive useMany methods

Rare useSeveral methods

No application

Modeling Docking

Virtual screeningQSAR

PharmacophoreSimilarityDynamicsHomology

etc…

26

Discovery research

Structure-based method(direct drug design)

Ligand-based method(indirect drug design)

-Known protein structure-Known ligand structure-Known protein-ligand structure-Known interactions

-Protein structure not known-Known ligand structures-Protein-ligand structure not known-Interactions not known

Docking Pharmacophoremodelling

QSAR*

*Quantitative structure-activity relationship

20 000 pdb 100 000 protein

27

Act

ivity

Parameters

EXAMPLE 1.

parameter = length of the chainresponse = activity

What is the appropriate length?

QSAR = a theoretical method, using experimental values

28

X

What is the appropriate chemical moiety

New candidate

EXAMPLE 1.

LogPLogDpKaPSA

29

Ligand-based method (QSAR)

Correlation between calculated properties of molecules and their experimentally determined biological activity, may be derived. These QSAR relationships in turn may be used to predict the activity of new analogs.

Small group of known

active compounds

Selection of N parameter

MathematicalModel

N-dimensional Training set

Test set

WorksNO YES

Data set

MathematicalModel

N-dimensional

New analogues

Larger group of known

active and inactive compounds

Large data setwith unknown

activity

Mathematical method, based on experimental data

Filtering

30

Many variables or Parameters or descriptors

parameters: MW, lenghtpolarity

(descriptors) hydrophobicitydistancesteric effectaromaticityhydrogen bondingnumber of heteroatomLogPLogDpKanumber of HBDnumber of HBAnumber of OH groupsHOMO-LUMO gapetc

Ligand-based method (QSAR)Many dimensional

relationship

Few thousands descriptorsexist

31

Limitations 11. More than one maxima on the N-dim surface2. No direct function from the set of descriptors to chemical structure = more solution3. Difficult curve fitting4. Large error in the measured values, 3. Difficult to see the border of the inactive and active

DescriptorHomo-Lumo gapHydrophobicity

Polar surface areaLogP

Activ

ity

error

32

Act

ivity

Descriptor A

bioa

ctivity

decriptor A)

Limitations 2

bioa

ctivity

decriptor A)

33

Descriptor A

Act

ivity

Outlier ???

Limitations 3

34

Active non-active bordersActives Inactives

Limitations 4

35

1950 1960 1970 1980 1990 2000 2010

num

ber o

f sto

k or

bab

y

Years

stork baby

Limitations 5

False correlations

36

Molecules may be used to derive a pharmacophore model that defines the minimum necessary structural characteristics a molecule must possess in order to bind to the target.In other words, a model of the biological target may be built based on the knowledge of what binds to it, and this model in turn may be used to design new molecular entities that interact with the target.

Ligand-based methodPharmacophore modelling

37

pharmacophores

Goal: To find the suitable chemical structure

d2 d1

Interactions: Polar, Apolar, HB, Aromatic, Ionic

Ligand-based method (PM)

38

Example 1

NH+

CO2H

CH3H

NH

NH+H

CH3

OH

OH

PHARMACOPHORE MODEL

L-LIPOPHILIC SITE

D-H- BOND DONOR

PD-PROTONATED H- BOND DONOR

d1

d2 d3

d1

d2 d3

d1

d2 d3

Ligand-based method (PM)

39

Example 2

C7OH

OH

A

D

BC1

MeO OMe

ClClCl

BA

O

OC7OH

OHOH

A

B

C1

O

NMe2

OH

A B

CL

LL d1

d2

d3L

LL

d1

d2

d3

L

LL

d1

d2

d3

L

L

L

d1 d2

d3

L

LL

d1

d2

d3

"Pharmacophore"

Ligand-based method (PM)

40

Example: Receptor Map for Opiate Narcotics

*6.5Å

7.5-8.5Å

Flat surface for aromatic ring

Cavity for part of piperidine ring

Focus of charge

Anionic site

R1R2

R3

3D structure basedPharmacophore search

Morphine, Codeine, Heroin

41

Small group of known

active compounds

Selection of N-pharmaco-

phores

MathematicalModel

N-dimensional Training set

Test set

WorksNO YES

Data set

MathematicalModel

N-dimensional

New analogues

Larger group of known

active and inactive compounds

Large data setwith unknown

activity

Mathematical method, based on experimental data

Filtering

Ligand-based method (PM)Pharmacophore filtering

42

Identify the binding regions Evaluate their disposition in space

Search for molecules in the library

Reverse Pharmacophore modelingStructure Based Ligand Design

Building Molecules at the Binding Site

43

Structure Based Ligand Design

O

NH

O

H

O

NH

?

O

O

O

H

O

NH

NSO

O

H

O

NH

O

H

O

NHS?

?

O

H

O

NH

??

?

OO

H

O

NH

DockingBuilding

Linking

Reverse Pharmacophore modeling

44

O

O

O

H

HO

O

O

HH

OO

OH

OO

OH

O

O

OH

OH

O

DB Search

Define PharmacophoreLigandDesign

Structure based ligand designReverse Pharmacophore modeling

0

45

Protein (receptor or enzyme) - drug interaction

+

Interaction: - hidrofobic - hidrofilic - polar - apolar - hydrogen bonding - aromatic – aromatic

DGbinding

docking

Docking

46

Approaches to Docking

• Qualitative– Geometric– shape complementarity and fitting

• Quantitative– Energy Calculations– determine minimum energy structures– free energy measure

• Hybrid– Geometric and energy complementarity– 2 phase process: rigid and flexible docking

47

1. Rigid Docking• Shape-complementarity method: find

binding mode(s) without any steric clashes

• Only 6-degrees of freedom (translations and rotations)

• Move ligand to binding site and monitor the decrease in the energy

• Only non-bonded terms remain in the energy term

• Try to find a good steric match between ligand and receptor

Fit into basket

-Fast, effective, (million cpds/day) -Inaccurate

48

2. Flexible Docking I. (FlexX, AutoDock)

• Dock flexible ligands into binding pocket of rigid protein

• Binding site broken down into regions of possible interactions

binding site from X-ray

hydrophobic

H-bonds

parameterised binding site

1. 2.

49

• Then dock the molecule into pocket by matching up interactions with ligand

• Uses “random” translation, rotation, and torsion, and look for a better binding mode.

parameterised binding site docked ligand

2. Flexible Docking II. (FlexX, AutoDock)

-Slower (thousends cpds/day) -More accurate

3. 4.

50

• Even though we have considered the ligand to be flexible, the active site was kept as a rigid structure.

• The side chains of the protein in the vicinity of the active site should be flexible, but computationally more expensive.

2. Flexible Docking III. (Glide)

51

Incremental Construction (FlexX)

• A piecewise assembly of ligand within the active site.

• Generate rigid fragments by scissoring the rotatable bonds of known ligands.

• Dock the fragments one by one starting from the larger fragment

• Assemble the whole ligand by reconnecting them and repeat the docking process

52

Free Energy of Binding• Dock ligand into pseudo-intercalation

site– Manual, automatic, and flexible

ligand docking• Energy minimize to determine DG complex • Determine DGligand

_=interaction energy of ligand with surroundings when explicitly solvated

Gbinding = Hinteraction – T Sconf + Gsolvent

Force Field Relaxed Fixed

Calculate the change in binding H and „guess” the change in free energy (G)

+

DGbinding

docking

53

Detailed calculations on all possibilities would be very expensive

The major challenge is to identify the best position and orientation of the ligand in the binding site of the target.

This is done by scoring or ranking of the various possibilities, which are based on empirical parameters, knowledge based on using rigorous calculations.

Need for Scoring of Fitness point

54

To find the appropriate skeleton

Lead compound

Virtual screening

B

AD( )n

105–106 db compounds

DOCKING

10–102 db compounds

1. part 2. part 3. part

Rigid parts

rough appropriate arrangement

B

AD( )n

105–106

Low level MM High level MM

First selection Precise docking parameters DGbinding

10104

lead compound

10 sec / compound1 sec / compound

min-hour / compound102

MM molecular mechanics