World of Molecules

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Development of Complex Curricula for Molecular Bionics and Infobionics Programs within a consortial* framework**

Consortium leader

PETER PAZMANY CATHOLIC UNIVERSITY

Consortium members

SEMMELWEIS UNIVERSITY, DIALOG CAMPUS PUBLISHER

The Project has been realised with the support of the European Union and has been co-financed by the European Social Fund ***

**Molekuláris bionika és Infobionika Szakok tananyagának komplex fejlesztése konzorciumi keretben

PETER PAZMANY CATHOLIC UNIVERSITY SEMMELWEIS

UNIVERSITY

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Drug research and development:

some current aspects

Compiled by dr. Péter Mátyus

with contribution by dr. Gábor Krajsovszky Formatted by dr. Balázs Balogh

World of Molecules

(Molekulák Világa )

(Gyógyszerkutatás és fejlesztés: néhány aktuális kérdés)

World of Molecules: Drug research

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Terminology

NDA (New Drug Application)

– Is the vehicle in the US through which drug sponsors formally propose that the FDA approve a new pharmaceutical for sale and marketing

– Information provided should permit FDA reviewers to establish the following:

• Drug safety and effectiveness in its proposed uses(s)

• Benefits of the drug outweighing the risks

• Labeling (package insert)

• Methods used in manufacturing (Good Manufacturing Practices – GMP) and to preserve drug’s identity, strenght, quality and purity

– The drug can be legally marketed in the US starting the day an approval of an NDA is obtained

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Terminology

ANDA (Abbreviated New Drug Application)

– For generic drugs that have already been approved via an NDA submitted by another maker (clinical trials normally not required) – Biological drugs, including most recombinant proteins are

considered ineligible for an ANDA under current US law.

NME (New Medical Entity)

– For small organic molecules (generally from synthesis) used in medical treatment

BLA (Biological License Application)

– For biologics (vaccines and many recombinant proteins) used in medical treatment

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Registering a new drug: drug review steps (FDA)

Preclinical Drug Discovery a. Preclinical (animal) testing. Pre-IND. An investigational new drug

application (IND)outlines what the sponsor of a new drug proposes for human testing in clinical trials.

b. Clinical (human) testing.

Phase 1studies (typically involve 20 to 80 people).

Phase 2studies (typically involve a few dozen to about 300 people).

Phase 3studies (typically involve several hundred to about 3,000 people).

c. The pre-NDA period, just before a New Drug Application (NDA) is submitted. A common time for the FDA and drug sponsors to meet.

d. Submission of an NDA is the formal step asking the FDA to considera drug for marketing approval. After an NDA is received, the FDA has 60 days to decide whether to file it so it can be reviewed.

e. If the FDA files the NDA, an FDA review team is assignedto evaluate the sponsor's research on the drug's safety and effectiveness. The FDA reviews information that goes on a drug's professional labeling (information on how to use the drug).

f. The FDA inspects the facilitieswhere the drug will be manufactured as part of the approval process.

Clinical Development

Pre-NDA period

File submission

FDA Review

Inspections

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The productivity of drug research

Despite dramatic increases in R and D investment, the promise of the genomics revolution, and the remarkable array of new technical tools available to the

discovery scientits, the record of industry productivity over the past decade as measured by approvals has, if anything, declined.

Ann. Rep. Med. Chem., 38, 383 (2003)

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Historical Perspective

• The US FDA approved 17 new molecular entities (NMEs) and 2 biologic license applications (BLAs) in 2007, the lowest number recorded since 1983.

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NME Productivity

“.. the bar for regulatory approval has been raised. If an NME is not a first-in-class breakthrough, FDA is increasingly

requesting evidence demonstrating a benfit to a specific`population” (Zimulti)

“The agency’s increasing aversion to risk is also evident”...

“..increasing difficulty of negotiating me-too drugs”...

Nat. Biotech. 2, 137 (2008) “Raising the game”

Annual FDA approvals of NMEs

Year NMEs approved BLAs approved

2007 15 4

2006 17 4

2005 8 2

2004 31 5

2003 21 6

2002 16 7

2001 24 5

2000 27 2

1999 35 3

1998 30 7

1997 39 6

1996 53 3

NMEs, New Medical Entities (not including BLAs);

BLAs, Biologic license applications. Source:

FDA

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New combinations,

indications, formulations &

delivery route

New biologicals Small organic molecules

Natural products Diagnostics and antipoison

Enantiomers and prodrugs of known drugs

Amlodipine/Olmesartan Eculizimab Aliskiren Temsirolimus Hydroxycobalamin Paliperidone

MDTS Estradiol MPE-Epoetin β Maraviroc Retapamulin Gliolan Levocetirizine

Ketoconazole Lanreotide Nilotinib Trabectedin Gadoversetamide Armodafinil

Raloxifene α-Epoetin Lapatinib Anidulafungin Lisdexamfetamine

Zoledronic acid Cervarix Raltegravir Azithromycin Nelarabin

Fluticasone furoate Mecasermin Fenofibrate Topotecan Transdermal Rivastigmine Abacept Ambrisentan Ixabepilone

Histrelin Panitumumab Sapropterin

i.d. Lidocaine Doripenem

Vildagliptin/Metformin Orlistat

Pioglizatone/Metformin Vildagliptin

Olanzepine

Rotigotine Hydroxyurea

2007 Combined FDA and EMEA approved drugs

Red:approved by both FDA and EMEA (11) Blue:approved by FDA only (20)

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The productivity gap

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True NCE‘s in 2007

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EMEA-FDA Approvals 2007

First-in-class molecules contributing to the total NME output is higher.

(in 2007: 6 out of 15; 2006: 5 out of 18; 2005: 7 out of 18)

New indications, formulations, delivery route, combinations of known drugs) and other drugs (enantiomers, metabolites and “me-too”) constitute the majority of the new drug approvals.

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Nature Rev. Drug Discov. 3, 711-715 (2004)

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Phase Events involved Objectives

Pharmaceutical phase Selection of the administration route Preparation of the most appropriate pharmaceutical formulation

Optimize distribution Facilitate absorption Eliminate unwanted organoleptic properties

Pharmacokinetic phase Fate of the drug in the organism:

absorption, distribution metabolism, excretion (’ADME’)

Control the bioavailability, i.e. the ratio of the

administered dose to the concentration at the site of action, as a function of time

Pharmacodynamic phase Quality of the drug-receptor interaction Nature and intensity of the biological response

Maximal activity Maximal selectivity Minimal toxicity

The three phases that govern the activity of a drug

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Potency Absortion In silico models

Metabolic stability

Selectivity

Solubility Absortion/

permeation

Activity (whole cell)

Activity (tissue) PK

(casette)

Efficacy (animal model) PK

(discrete)

In vitro In vivo

Cell toxicity

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Biological Space

» Consider only proteins

» 20 amino acids

» average size of a protein is 300 residues ~10³⁹⁰

» fortunately, human genome encodes ~10⁵ proteins

C. Dobson, Nature, 432824-828 (2004)

Chemical & Biological Space Arithmetic

Chemical space

» Consider only C, N, O, S

» allow branching

» allow exo double bond systems

» eliminate unfeasible multiple bonds ~10²³

» allow for rings at all branch points ~10⁶³

» eliminate unfeasible rings & unfeasible ring systems

10⁶⁰

C. McMartin et. al, Med.Res. Rev. 16, 3-50 (1996)

10¹¹-10¹² stars in our galaxy 10¹¹-10¹²galaxies

10²²-10²⁴stars in the universe

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The role of Medicinal Chemistry

How to find the proper compounds?

All possible combinations of drug like compounds comprising CHNSPClBrF with mw<500 Da are 10⁶²(or 10²⁰⁰!)

Screening one million compounds in one second,

then its exploration would require 10³⁷ times the age of the universe!

Even if half of the known compounds were drugs (very optimistic assumption!) this would produce 10⁷ molecules. Therefore, at most

10^-53% of the possible drug like molecules are known. To discover a new

blockbuster in such a universe, there should be little surprise if it does not work.

Tools? No uniform solution!!!

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Drug Discovery

pharmacophore type of activity (selectivity, toxicity)

molecular structure

physicochemical properties

&

pharmacokinetic properties

degree of activity

&

duration of action

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I. DRUG – RECEPTOR/ENZYME INTERACTION

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D + R DR DR* response

D + R K

_as

DR K

_DR

DRG* response

K

_i

= [ligand] [receptor] / [ligand·receptor]

Drug-Receptor Interactions

Thermodynamics

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Drug-Receptor Interactions

PRODUCTIVE

1. Electrostatic interactions εr

q_iq_j

2. Inductive interactions a) in ligand or receptor

b) between ligand and receptor α α

r⁶ r⁴

~ ~

3. Non-polar interactions α₁α₂

r⁴

I₁I₂ I₁+I₂ 4. Hydrogen bond

5. Hydrophobic interactions

COSTS 1. Entropic

losses of rotational, translational and conformational freedom

2. Enthalpic - desolvation

- higher energy conformation ΔG = ΔH - TΔS = -RTlnK_as

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ΔH

_DW

ΔS

_int

ΔS

_rt

ΔH

_RW

ΔS

_W

ΔS

_vib

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K

_i

= 10

^-9

M = 1 nM ΔG = -51 kJ/mol

The interaction of most ligands with their binding sites can be characterized in terms of a binding affinity (K_i). Binding affinity is most commonly determined using a radiolabeled ligand, known as hot ligand. Homologous competitive binding experiments involve binding-site competition between a hot ligand and a cold ligand (untagged ligand).

Binding free energy (Gibbs free energy) is enthalpy minus the product of

thermodynamic temperature and entropy. It was formerly called free energy or free enthalpy.

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The role of stereoisomerism

1. Configuration 2. Conformation

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Me

C

CO₂H H

OH

Me

C

OH H

CO₂H

Enzime Enzime

S-enantiomer R-enantiomer

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2. 3D QSAR semi- and fully-quantitative e.g. pharmacophore or CoMFA

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N N

N H

O

Cl

N

N O

Me

Cl

(a) (b)

Ce L₁

1 2

5 4 7 6

A¹

A²

L₁

L₂

H ¹ H ²

BzR agonist pharmacophore model

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O N

N S

N N O

O H H

PLS

activity = a S001 + b S002 + … + m S999 + n E001 + … + z E999 + y Contour plots

The CoMFA procedure

Activity S001 … S999 E001 … E999 Compd 1

Compd 2 Compd 3

…

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Pharmaceutical industry:

discovery technologies

Combinatorial (high throughput) chemistry High throughput screening

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Combinatorial chemistry

Many hits, few leads and even fewer development

compounds, none so far that have reached advanced stages of clinical trials.

Disappointing results DDT, 4, 447-448 (1999)

Drug discovery cannot be reduced to a simple

‘synthesize-and-test lottery’!

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HTS

The attempt to replace the quality of scientific arguments by the sheer quantity of data as expressed in HTS or ultra-HTS in the past has failed. An approach that is based on a much broader understanding of biochemical and genetic mechanisms of diseases appears to represent the necessary correction.

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Cost of HTS

It is ca. 3.17$/compound screened (labor and facility costs represent the greater proportion than consumable and reagents).

A large pharma prosecutes ca. 65 HTS campaigns (400.000 compounds per screen) yearly; it costs 82.4$! (ca 10 % of the total cost of bringing a NCE to market)

1:67 survival rate for early stage research projects achieving new drug application (NDA) – Successes?!

It is predicted that an annual yield of 0.3 NCE per company (23 large pharma companies) will result from current HTS activities. This level is an order of magnitude below the current predictions for sustained pharma profitability...

Nevirapine (Boehringer) is the one cited example that was discovered as a result of HTS.

The reasons for deficit are cultural and organizational

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Synthesis Test

3D Structure

(homology modell) Ligand-design

Synthesized compound

3D Structure of the protein

Ligand proposal Bioactive

compound

Structure-Based Drug Design

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1. De Novo design

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De novo design

a) building

O H N

O H

O H N

S O H

O H N H S

N O

O H

O H N

?

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O H N O

O H O

H N

De novo design

b) docking

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CLONIDINE

α

₂

Adrenoceptor Imidazoline I

₁

receptor

α

_2A

α

_2B

α

_2C

GASTROPROTECTION

? ^?

? World of Molecules: Drug research

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Molecular modelling of α

₂

-adrenoceptors a comparative study on the receptor subtypes

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A structure of a cationic 7-TM receptor

‘SIDE VIEW’ ‘TOP VIEW’

Cell membrane

EXTRACELLULAR VOLUME binding site

Extracellular loop (ECL)

Intracellular loop (ICL)

INTRACELLULAR VOLUME

Transmembrane domain (TM)

N-terminal

C-terminal

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Homology modelling of the receptor

The atomic resolution structures of the α₂-adrenoceptors are not yet known in atomic details.

¾The template structure was bovine rhodopsine

SwissProt Expasy database code: 1U19, resolution 2.2 Å

¾Alignement the target to the template with BioEdit

¾ Modell generation (100-100 for each subtype) with Modeller

¾ Validation (eg. Ramachandran plots) with WhatCheck and iMolTalk

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Homology modelling of the receptor

α_2Amodell α_2B modell α_2Cmodell

1U19 (template)

High similarity between subtypes: >50% between A,B, and C (total)

>90% in the TM region,

For α_2Amodel, s. P. Mátyus et al. ChemMedChem,2007.

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Identifying the active site

We can indentify the active site through

¾ docking known ligands

¾ analysing site-directed mutagenesis data

identifying the binding pocket

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Identifying the active site

We can identify the active site from

¾ mutagenesis data (only for subtype A, for B and C “by similarity”)

key residues conserved

region

(near to the active site)

Xhaard et al. J Stuct Biol, 150, 126 (2005)

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Identifying the active site

¾ indentifying the binding pocket (find common geometrical forms with PocketFinder program)

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Identifying the active site

¾ blind docking (find the energetically best position of the ligand without prior knowledge of the binding site)

Hetenyi et al.FEBS Lett, 580, 1447 (2006)

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subtype A: Asp-113, Ser-200, Ser-204 subtype B: Asp-92, Ser-176, Ser-180 subtype C: Asp-131, Ser-214, Ser-218

Identifying the active site

The key residues of the binding sites are:

¾ the key residues

¾ the relative orientation and

¾ the position of the key residues

The subtype selectivity can not be explained by the are very similar

are the same World of Molecules: Drug research

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Active site of subtype A

position of the key residues entrance of the funnel

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Active site of subtype A

entrance of the funnel

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Active site of subtype A

the binding site hydrophobic pocket near

to the binding site

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Computational docking

Computational docking is an in silico tool which can be used to analyze receptor-ligand interaction.

It allows us to

¾ indentify the position of the active site and the key residues (if it is unknow)

¾ determine the optimal position of ligands (best conformation and position)

¾ analyze the interactions between the receptor and the ligands

¾ compute the binding free energy of ligands

¾ make structural predictions for better ligands

¾ screen large molecular database for potential ligands (HTS) World of Molecules: Drug research

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Computational docking

Procedure of docking with AutoDock 4.0

¾ preparing the receptor

¾ partial charges, flexible side chains

¾ preparing the ligands

¾ partial charges, flexible torsions

¾ docking to the receptor: find the optimal (minimal binding free energy) position and conformation of the ligand(s)

¾ scoring

¾ refinement (if necessary)

Morris et al. J Comp Chem, 19, 1639 (1998)

500 independent run with LGA population size: 300

max. num. ener. evals: 3·10⁶ step size: 0.2Å, 5°

grid space: 0.375Å

grid size: 25Å· 25Å· 25Å Parameters:

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Results of computational docking

OH

CH₃ NH₃⁺

O H N

H

NH⁺ O

H

O H

N H

N H₊ NH

N N

Br

NH

Cl Cl

N H

NH⁺

N H₊

N H CH₃ CH₃ C

H₃

OH

NH₂⁺

O H

OH

CH₃ NH

N

NH₂⁺

NH₂ Cl

Cl

NH NH₂⁺

O NH₂ Cl

Cl F

Cl

NH⁺ N

O H

NH₃⁺ OH

NH⁺ N H

OH CH₃ CH₃

C H₃

CH₃ C H₃

NH N H+

O

N H+

N H NH CH₃

CH₃

N

S

N NH₃⁺

CH₂

N H+

NH

The set of the docked ligands – 18 agonists with binding data (pK_i)

A54741 α-methyl-noradrenaline brimonidine clonidine dexmedetomidine

adrenaline guanabenzamidine guanfacine ICI106270

noradrenaline oxymethazoline rilmenidine ST-91 talipexol

NH N H₊

S CH₃

NH⁺ N H CH₃

CH₃ C

H₃

N H+

NH

naphazoline

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Computational docking to subtype A

Best docking conformation of clonidine and adrenaline (subtype A)

H-bond between

•charged N (protonated N of imidazoline ring) of ligand and side chain of Asp-113

H-bond between

•charged N (amino group) and side chain of Asp-113

•catecholic hydroxyl(s) and Ser-200 and (or) Ser-204

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Computational docking to subtype B and C

Best docking conformation of clonidine – subtype B and C World of Molecules: Drug research

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Experimental and in-silico results

Calculated and experimental binding free energies of of 18 ligands World of Molecules: Drug research

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Experimental and in-silico results

Calculated and experimental binding free energies of 18 ligands,

A good correlation was found between the experimental and calculated free energies of binding

18 61

. 126

4928 . 5

) 16230 . 1 9140 . 8 ( 2520

. 11

) 1961 . 0 2070 . 2

( ⁽ ^, ⁾

exp) , (

=

± +

Δ

=

±

= Δ

N F

t G

t

G_b^A _b^A^calc

0.888 r²

18 06

. 105

578 . 109

) 00923 . 0 0115 . 1

( ⁽ ^, ⁾

exp) , (

=

Δ

=

±

= Δ

N F

G t

G_b^B _b^B^calc

0.887 r²

18 356

. 49

363 . 84

) 1286 . 0 0845 . 1

( ⁽ ^, ⁾

exp) , (

=

Δ

=

±

= Δ

N F

G t

G_b^C _b^C^calc

0.790 r²

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Subtype selectivity

¾ the size of the channel that leads to the active site

the size and the structure are almost the same (min. diameter approx. 8Å)

¾ the size of the binding pocket

there are only small differences (482-619 Å) The subtype selectivity could be dependent on

subtype A, 584 Å³ subtype B, 619 Å³ subtype C, 482 Å³

(63)

Subtype selectivity

¾ key residues

the key aspartic acid and serine residues are in very similar positions with very similar orientations

¾ neighbouring residues (within 8 Å)

¾ size and structure of the hydrophobic pocket are very similar A subtype selectivity could be dependent on the

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Subtype selectivity

No significant structural differences have been identified

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Regulatory authorities issued recommendations for the establishment of cardiac safety during preclinical drug development: ICH S7B

Mechanism of action:

• blocking the rapidly activating component of the delayed rectifier potassium current, termed I_Kr

• ion channel protein is encoded by the human ether-a-go-go related gene (hERG)

• drugs that induced TdP in patients were shown to be potent hERG blockers, but not all hERG blockers prolong the QT-interval and induce TdP in humans

Preclinical hERG studies should be accomplished in GLP environment by:

Ether-a-go-go gene found in the Drosophila fly and named in the 1960s by William D. Kaplan Flies with mutations in this gene are anaesthetized with ether, their legs start to shake, like the dancing then popular at the Whisky A Go-Go nightclub in West Hollywood, California

Application of SAR models

in cardiovascular safety pharmacology

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Evolution of hERG values in a Roche project after the implementation of a predictive project specific model—the model consists of only 2 calculated parameters such as the number of hydrogen-bond acceptors and the hydrophobic surface area. The initial model was trained with 11 molecules and validated with more than 100 additional results (r² = 0.812; q² = 0.732; RMSE = 0.371).

Muller, L. et al. (2007) Strategies for using computational toxicology methods in pharmaceutical R&D. In Computational Toxicology-Risk Assessment for Pharmaceutical and Environmental Chemicals (Ekins, S., ed.), pp. 545–579, Wiley

Application of SAR models in safety pharmacology

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Bioavailability

MOLECULE, DRUG + BODY

Physiological Factors Mebrane transport GI motility

Stomach emptying Disease state

Physicochemical Properties Lipophilicitiy

Solubility Ionization

Molecular size and shape Pharmacokinetic Factors GI and liver metabolism (first-pass effect)

Chemical instability

Distribution & elimination Formulation

Crystal form (polymorphism) Particle size

Enhancers

Dissolution rate

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Early ADME studies

•In silico drug-like properties

•Absorption potential (permeability, Caco-2/MDCK)

•Physicochemical profile (solubility, lipophilicity)

•In vitro metabolism

•Rate of metabolism (microsomes, hepatocytes)

•Involvement of enzymes using cDNAs, Supermix

•Potential for drug-drug interactions (fluorescent probe inhibiton assays)

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Hydrophobicity is the association of non-polar groups or molecules in an aqueous environment which arises from the tendency of water to exclude non-polar molecules

Lipophilicity represents the affinity of a molecule or a moiety for a lipophilic environment

Hydrophobicity is used to describe molecular surface properties and is associated with dissolution/solubility of a compound. Solubility is inversely correlated with lipophilicity.

Molecular size and hydrogen bonding capacity of a drug molecule are the major components of lipophilicity

Log P or log D can be written as a linear combination of the size of a molecule and World of Molecules: Drug research

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Oral absorption

Log D values > 0 are often correlated to almost or complete absorption World of Molecules: Drug research

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Correlation between octanol/water and cyclohexane/water partition coefficients. It shows a general positive correlation (r = 0.458, SD = 1.19, F = 98; n = 118) between the solubility of a solute in octanol and its solubility in cyclohexane.

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When series of structurally related compounds are examined individually (B), there are very strong positive correlations between octanol and cyclohexane solubilities for n-alcohols (methanol, ethanol, propanol, butanol, pentanol, hexanol, octanol; r = 0.996, SD = 0.15, F = 663; n = 7) and N-alkyl-2-methyl-3-hydroxy-pyridin-4-ones (HPOs; r = 0.940, SD = 0.159, F = 251; n = 18). Data from Oldendorf (1974), Hansch and Leo (1979), Cornford et al. (1982), Dobbin et al. (1993), Grattonet al. (1997), and Habgoodet al. (1997).

Habgood M.D. et al. Cell. Mol. Neurobio. 20, 231-253 (2000)

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Effect of molecular weight on blood–brain barrier permeability surface area products (logPS). A range of structurally diverse compounds was selected from the literature (Gratton et al., 1997; Habgood et al., 1997; Meulemans et al., 1988) and includes both lipid-soluble and lipid-insoluble compounds (logP_oct, 4.0 to –3.7). For this set of compounds, there does not appear to be any significant correlation between blood–brain barrier permeability and molecular size (r = 0.105, SD = 2.05, F= 0.4; n = 35).

(74)

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Effect of molecular weight on octanol/water partition coefficients. A shows the correlation between logP_oct values and molecular weight for a range of structurally diverse compounds selected from the literature (Hansch and Leo, 1979; Gratton et al., 1997; Cornford et al., 1982; Oldendorf, 1974; Young et al., 1988; Dobbin et al., 1993). In a number of cases, several logP_oct values have been published for the same compound, and since the conditions under which each of these partitions were measured were not given, a mean of all available published partition values for that compound was used. Overall the molecular size of a solute does not appear to have a significant influence on its lipid solubility (r = 0.565, SD = 1.29, F = 46; n = 101). Habgood M.D. et al. Cell. Mol. Neurobio.20, 231-253 (2000)

(75)

However, if the data for series of structurally related compounds are examined individually (B), molecular size appears to have a very strong positive influence on lipid solubility. Data are shown for a series of n-alcohols (methanol, ethanol, propanol, butanol, pentanol, hexanol and octanol; r = 0.999, SD = 0.06, F = 3486; n = 7), n-alkyl acids (formic, acetic, propionic, butyric, pentanoic, hexanoic, octanoic, decanoic;

r = 0.983, SD = 0.30, F = 170; n = 8), N-alkyl-3-hydroxy-pyridin-4-ones (r = 0.995, SD = 2.07, F = 1038;

n = 12), N-hydroxyalkyl-2-methyl-3-hydroxypyridin-4-ones (r = 0.993, SD = 2.26, F = 76; n = 3), and

(76)

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Effect of polar/nonpolar group substitutions on the lipid solubility of 3-hydroxypyridinones. Replacing the terminal methyl group of the N-substituted alkyl chain of 1-butyl-2-methyl-3-hydroxy-pyridin-4-one (CP24) with an hydroxyl group (CP41) results in two compounds that have similar molecular weights but very different lipid solubilities. The presence of the additional hydroxyl group not only reduces the lipid solubility, but also markedly reduces the blood–brain barrier permeability of CP41 compared to CP24 (data from Dobbin et al., 1993; Habgood et al., 1997).

Habgood M.D. et al. Cell. Mol. Neurobio. 20, 231-253 (2000)

(77)

log solubility = c + rR₂ + sπ₂^H+ aα₂^H + bβ₂^H + vV_x

logPS = –1.21 + 0.77R₂ – 1.87π₂^H – 2.80β₂^H + 3.31V_x (r = 0.976, SD = 0.48, F = 65; n = 18).

Abraham Solvation Equations

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Oral bioavailability:

Prediction of intestinal absorbtion Rule-of-Five

The ‘rule-of-five’ devised by Lipinski and coworkers at Pfizer from an

analysis of 2245 drugs from the WDI believed to have entered Phase II trials.

The rule-of-five generates an alert (indicating possible absorption problems) for compounds where any two of the following conditions are satisfied:

Molecular weight >500

Number of hydrogen-bond acceptors >10 Number of hydrogen-bond donors >5 Calculated logP >5.0 (if ClogP is used)

or >4.15 (if MlogP is used)

(79)

Limitations of the rule-of-5

The rule-of-5 properties are not independent. Increasing MW often gives better potency due to more unspecific bindig. Howerer, incerasing a compound’s size can by realised by adding more C or halogen atoms, leading to higher CLOGP, or by adding more hetero atoms, leading to higher hydrogen bonding capacity. Higher CLOGP, or lipophilicity, may result in poorer solubility. Higher H-bonding capacity may result in less membrane permeability.

Optimising on log D alone maybe insufficient as well. Since log D is a combination of size and hydrogen bonding, combinations of high MW and extensive H-bonding capacity are possible to yield a log D and solubility in the desired range. However, oral absorption of such compounds will nevertheless be limited because of the high H-bonding capacity. The balance between these properties begins to define the physicochemical space for success.

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Π = K_ow O_Lumen

O_Lumen= max

(

^1, ^Dose_S ^/0.250

)

w

= (

^1, ^4Dose_S

)

w

max

(

^1, _MW*10[0.5-0.01(MP-25)-log K ]

Π

=

^K^ow

max 4Dose

ow

)

log S_w = 0.5 - log K_ow- 0.01(MP - 25)

Yalkowsky S. H. et al.: Pharm.Res., 23, 2475 (2006)

Prediction of intestinal absorbtion: Rule-of-One

absorption parameter K_ow: oversaturation number

(81)

Absorption is most efficient when the absorption parameter, Π, is greater than unity. This most often occurs when the partition coefficient is greater than unity and/or the over-saturation number is equal to unity. Hence, the ‘rule of unity’.

The ‘rule of 5’ states that if two or more of the criteria are met the drug will likely be poorly absorbed.

(The ‘rule of 5’ is named for the fact that each of the four limiting values is a multiple of 5. The rule is based upon inspection of the properties of compounds that have survived to enter phase II efficacy studies.)

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Model ROC AUC Sensitiv. Specificity p values^a of differences from experimental

data

Perfect model 1.0 1.0 1.0

‘Rule of 5’ 0.68 0.98 0.37 0.005 (Significant)^b

‘Rule of unity’ 0.87 0.97 0.78 0.289 (Not significatnt)^b

Log K_ow 0.84 0.98 0.70 0.039 (Significant)^b

No relationship 0.5 0.5 0.50

ap values calculated using McNemar’s tests.

b Level of significance <0.05.

Statistical Evaluation of the Models

Yalkowsky S. H. et al.: Pharm.Res., 23, 2475 (2006)

(83)

For efficient absorption the ‘rule of 5’ requires that log K_ow be less than 5.0, whereas the ‘rule of unity’ suggests that it be greater than 1.0.

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Leadlike properties and druglike properties, although not mutually exclusive, are significantly different

(85)

Caco-2 permeability

it is primarily dependent on the desolvation potential of the polar functional groups in a molecule and only secondarily dependent on its lipophilicity.

The PSA is defined as that part of the molecular surface that arises from oxygen or nitrogen atoms, or hydrogen atoms attached to nitrogen or oxygen atoms.

Predicting Caco-2 cell permeability

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Absorption across the gastrointestinal tract

The compound

i) has to have sufficient hydrophilicity to dissolve in the aqueous phase and ii) sufficicent lipophilicity to ‘dissolve’ within the lipid core of the cell’s

membranes.

(87)

Prodrug for Increased Water Solubility

poor water solubility corticosteroid

Choice of water solubilizing group: The ester must be stable enough in water for a shelf life of > 2 years (13 year half-life), but must be hydrolyzed in vivo with a half-life < 10 minutes.

for aqueous injection or opthalmic use

Prodrug forms

R O

CH₃ O

H CH₃OH O

OR'

prednisolone (R = R’ = H)

methylprednisolone (R = CH₃, R’ = H)

CCH₂CH₂CO₂Na O

R' =

R' = PO₃Na₂

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The prediction of brain penetration

Log P value ca. 2 is often said to be optimal

(89)

MW should be below 450 (most CNS drugs are relatively small in size, MW around 350), while for GI absorption a limit of 500 has been suggested

The total polar surface area, a measure for hydrogen bonding capacity, should be below 90

A well-known rule-of thumb that optimal CNS drug have a log D/P of ca.2

Octanol/water partition coefficients do not always correlate well with brain uptake solvent systems; alkane/water systems have been suggested (cyclohexane) it was demonstrated that the difference between octanol/water and alkane/water partition coefficients,Δlog P, gives satisfactory correlations with BBB crossing

(alkane/water partition coefficient measurements are not simple, and often solubility limited)

Brain targeting

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Predicting logBB

logBB is the ratio of the steady-state concentrations of the drug molecule between the brain and the blood. Published values of logBB range from approximately -2.00 to +1.00.

Compounds with logBB >0.3 cross the BBB readily, while those with logBB <-1.0 are only poorly distributed to the brain.

LogBB = -0.0148 x PSA + 0.152 x ClogP + 0.139

Here, PSA is the single-conformer PSA and ClogP is the calculated octanol- water partition coefficient.

Calculation is very rapid and is fully automated, requiring no human intervention.

(91)

Making amines more lipophilic

Imine (Schiff base) prodrug

hydrolyze imine and amide to GABA inside brain

progadibe

F

OH

N NH₂

O

Cl

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Prodrug for Site Specificity

The blood-brain barrier prevents hydrophilic molecules from entering the brain, unless actively transported. The anticonvulsant drug vigabatrin crosses poorly.

A glyceryl lipid (R = linolenoyl) containing one GABA ester and one vigabatrin ester was 300 times more potent in vivo than vigabatrin.

O

OCOR

NH₂ O

O

NH₂

(93)

Oxidation strategy

Pralidoxime chloride is an antidote for nerve poisons.

It reacts with acetylcholinesterase that has been inactivated by organophosphorus toxins.

N⁺ CH₃

N Cl^- OH

pralidoxime chloride

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To increase the permeability of pralidoxime into the CNS, the pyridinium ring was reduced

Similar to the reversible redox drug delivery strategy for getting drugs into the brain by attaching them to a dihydronicotinic acid, hydrophobic A crosses the blood-brain barrier; oxidation to B prevents efflux from brain.

oxidation into brain

A

B

N

N OH CH₃

N⁺

N OH CH₃

Cl^-

(95)

Decarboxylation Activation

An imbalance in the inhibitory neurotransmitter dopamine and the excitatory neurotransmitter acetylcholine produces movement disorders, e.g. Parkinson’s disease.

In Parkinson’s there is a loss of dopaminergic neurons and a low dopamine concentration.

Dopamine treatment does not work because it cannot cross blood-brain barrier, but there is an active transport system for L-dopa (levodopa, R = COOH).

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Does not reverse the disease, only slows progression.

After crossing blood-brain barrier

L-aromatic amino acid decarboxylase (PLP)

dopamine (R = H)

O H

NH₂ H R

levodopa (R = COOH)

World of Molecules

Drug research and development:

some current aspects

Compiled by dr. Péter Mátyus

with contribution by dr. Gábor Krajsovszky Formatted by dr. Balázs Balogh

World of Molecules

Terminology

Terminology

The productivity of drug research

Historical Perspective

NME Productivity

2007 Combined FDA and EMEA approved drugs

The productivity gap

True NCE‘s in 2007

EMEA-FDA Approvals 2007

The three phases that govern the activity of a drug

Chemical & Biological Space Arithmetic

The role of Medicinal Chemistry

Drug Discovery

pharmacophore type of activity (selectivity, toxicity)

molecular structure

physicochemical properties

&

pharmacokinetic properties

degree of activity

&

duration of action

I. DRUG – RECEPTOR/ENZYME INTERACTION

D + R DR DR* response

D + R K

DR K

DRG* response

K

= [ligand] [receptor] / [ligand·receptor]

Drug-Receptor Interactions

Drug-Receptor Interactions

ΔH

ΔS

ΔS

ΔH

ΔS

ΔS

K

= 10

M = 1 nM ΔG = -51 kJ/mol

The role of stereoisomerism

1. Configuration 2. Conformation

Enzime Enzime

2. 3D QSAR semi- and fully-quantitative e.g. pharmacophore or CoMFA

N N

N H

O

Cl

N

N O

Me

Cl

(a) (b)

H 1 H 2

BzR agonist pharmacophore model

The CoMFA procedure

Pharmaceutical industry:

discovery technologies

Combinatorial chemistry

Drug discovery cannot be reduced to a simple

‘synthesize-and-test lottery’!

HTS

Cost of HTS

Structure-Based Drug Design

1. De Novo design

De novo design

a) building

De novo design

b) docking

CLONIDINE

α

Adrenoceptor Imidazoline I

receptor

α

α

H ¹ H ²