criblages et méthodologies in silico · dominique douguet criblages et méthodologies in silico...

Dominique Douguet

Criblages et Méthodologies

In silico

Ecole Thématique de Criblage 2-4 Octobre 2018 Carry-Le-Rouet

Institut de Pharmacologie Moléculaire et Cellulaire, UMR 7275 CNRS - Université Nice Sophia Antipolis, France

Genomic platform, ion channels, small G proteins, vesicular

transport, immunology

Pharmacology &

Neurosciences

ChemInfoScreen

Chimiothèque Nationale ChemBioScreen

ChemInfoScreen ADME-Tox Evaluation

Hit-to-Lead optimization programs to Explore and Cure living systems

• HTS assay optimization • Hit Identification

• Pharmacokinetics • Structure-Activity Relationships (SAR)

ChemInfoScreen: Cheminformatics Platform

To date: 8 sites + Coordinating site at UGCN

Nice

Strasbourg

Marseille

Paris

Orléans

Montpellier

UGCN (Philippe Jauffret) CBS (Gilles Labesse)

CRCM (Xavier Morelli)

IPMC (Dominique Douguet)

ICOA (Pascal Bonnet)

BFA (Pierre Tufféry) Institut Pasteur (Olivier Sperandio)

LIT (Didier Rognan) CMC (Alexandre Varnek)

~ 10-14 years/~1 Billion $

Pathology

Identified Target

Protein Sequence

3D Structure

Known Ligands Yes No

Identified Hits

Clinical trials

Approved drug

Lead Optimisation

Target Drug Discovery (TDD)

IR FRISBI

Medicinal Chemistry

.~ 10-14 years/~1 Billion $

Pathology

Identified Target

Protein Sequence

3D Structure


Identified Hits

Clinical trials

Approved drug

Lead Optimisation


Chimiothèque Nationale

ChemBioScreen

ChemInfoScreen

ADME-Tox

Medicinal Chemistry

IR FRISBI


Pathology

Identified Target

Protein Sequence

3D Structure


Identified Hits

Clinical trials

Approved drug

Lead Optimisation


Virtual Screenings


ChemBioScreen

Libraries Design

ChemInfoScreen

Raw data analysis

and SAR building

Synthesis

Bio-Profiling & ADME-Tox

Prediction

CN subset approved drugs commercial cpds

ligand-based structure-based

ADME-Tox prioritizing cpds prioritizing reactants scaffold hopping

Metabolism (site, CYP450s…) Off-targets

PAINS/reactivity alert analogs in catalogs properties prediction LogP, Sol.,Kd, Kon/Koff

Medicinal Chemistry

IR FRISBI


Pathology

Identified Target

Protein Sequence

3D Structure


Identified Hits

Clinical trials

Approved drug

Lead Optimisation


Virtual Screenings


ChemBioScreen

ChemInfoScreen


ADME-Tox

Docking

Ligand- and Structure-based Screenings

3D experimental structure/model of the target

Ligand-based Structure-based

3D • Pharmacophore

• Shape

2D/3D QSAR model (requires a large dataset)

N Ar

Od1 = 9-10 Å

d2 = 3-4 Å

d3 = 6-7 Å

bit set if the feature is present

Known ligands 2D • Graph/substructure • Fingerprint (eg: ECFP4)

Medicinal Chemistry

IR FRISBI


Pathology

Identified Target

Protein Sequence

3D Structure


Identified Hits

Clinical trials

Approved drug

Lead Optimisation


Virtual Screenings


ChemBioScreen

Libraries Design

ChemInfoScreen



ADME-Tox

Chemical space

1020-1060 ‘druglike’ molecules Chemical universe

Weininger D., Encyclopedia of Computational Chemistry, Vol 8,p1056; Bohacek RS. et al., Med. Res. Rev., 1996; Ertl P., J.Chem.Inf.Comput. Sci., 2003. ‘Druglike’: C, N, O, S, P, H, Cl, Br, F, I and MW ≤ 500 (Dobson C.M., Nature, 2004); Walters W.P., J. Med. Chem., 2018.

« The chemist as astronaut: Searching for biologically useful space in the chemical universe » D. Triggle, Biochem.Pharmacol., 2009.

Bar

naby

Rop

er

Chemical Space & Screening

Dune of Pilat

atoms in the Universe1

1060 1080 1020 1017 108

● CAS: ~100.106 (organics/inorganics) ● Dedicated to Pharmacology: Commercial: 106 (screening libraries) Naturals: 106 (theoretically) < 0.1.106 (isolated (10%)3) Toxins: 20.106 (theoretically) ~0.2.106 (UniProtKB (1%)4) Drugs: < 2000 FDA approved small-molecule drug structures (MW ≤ 2000)

isolated molecules ‘druglike’ molecules2 sand grains seconds

age of the Earth

2 ‘Druglike’: C, N, O, S, P, H, Cl, Br, F, I and MW ≤ 500 (Dobson C.M., Nature, 2004) 3 Harvey A., Drug Discovery Today, 2000 1 Source: C. Magnan, Collège de France, http://www.lacosmo.com/dixpuissance80.html 4 Zhang Y, Dongwuxue Yanjiu, 2015

- What is the usable size of a chemical library?

- Experimental High Throughput Screening (HTS) * A screening campaign may assay up to 500 000 compounds / week a low cost estimate ~ 0.40 $ / compound 1 (1 million compounds = 400 000 $) (includes cost of the chemical synthesis, high-throughput-screening disposables, capital costs and human resources)

several side issues: molecule re-supply, solubility, chemical stability, presence of PAINS (false positives)… as well as the management of waste products !

* It is commonly accepted that the suitable size of a library is ~250 000 to optimize the likelihood of finding a hit 2,3

Chemical Space Chemical space

1 Lipinski C. and Hopkins A., Nature, 2004, 432, 855-860. 3 Baell J, ACS Med Chem Lett, 2018. 2 Hibert M. and Haiech J., médecine/sciences, 2000, 16, 1332-9.

Pyridoxine (13 atoms)

Chemical Space Chemical space

- What is the usable size of a chemical library?

- Virtual Screening * Building chemical structures Example of the GDB-131 database: (13 atoms [C, N, O, S, Cl]) (<< mean drug size) - Combinatorial enumeration of structures - 3D Building, minimizing and validating structures

Results: 910 111 673 structures 39 882 (h) CPU time (= 1661 days of computation on 1 processor) ~0.16s /molecule (540 000 molecules / day / processor)

* Evaluating properties and/or interactions (e.g.: calculating the binding free energy ∆G of a ligand-protein complex)

- Using empiric method (docking method): ~20s to 3 min /molecule followed by visual inspection

- Using Molecular Dynamic (MD): hours to few days of calculation /molecule

1 Blum LC, Reymond JL.., J Am Chem Soc. 2009, 31(25), 8732-3.

Medicinal Chemistry

IR FRISBI


Pathology

Identified Target

Protein Sequence

3D Structure


Identified Hits

Clinical trials

Approved drug

Lead Optimisation


Virtual Screenings


ChemBioScreen

Libraries Design

ChemInfoScreen

Raw data analysis

and SAR building



ADME-Tox

PAINS/reactivity alert analogs in catalogs properties prediction LogP, Sol.,Kd, Kon/Koff


Pathology

Identified Target

Protein Sequence

3D Structure


Identified Hits

Clinical trials

Approved drug

Lead Optimisation


Hit-to-Lead

Lead Optimization

Drug

MW [1-200] LogP [0.5-4]

A hit ~ a molecule with µM range of activity

MW < 500 LogP < 5 nbHA<5, nbHD<10


Pathology

Identified Target

Protein Sequence

3D Structure


Identified Hits

Clinical trials

Approved drug

Lead Optimisation


Hit-to-Lead

Drug-like hits

Lead-like hits

High affinity hits

> 0.1 µM MW > 350 LogP > 3

> 0.1 µM MW < 350 LogP < 3 (polar)

<< 0.1 µM MW >> 350 LogP < 3

MW LogP

unfavored

Lead Optimization

Drug

MW [1-200] LogP [0.5-4]

A hit ~ a molecule with µM range of activity MW

LogP

Teague et al., Angew. Chem. Int. Ed., 1999



Pathology

Identified Target

Protein Sequence

3D Structure


Identified Hits

Clinical trials

Approved drug

Lead Optimisation


Hit-to-Lead

Drug-like hits

Lead-like hits

High affinity hits

> 0.1 µM MW > 350 LogP > 3

> 0.1 µM MW < 350 LogP < 3 (polar)

<< 0.1 µM MW >> 350 LogP < 3

MW LogP

unfavored

Lead Optimization

Drug

MW [1-200] LogP [0.5-4]


LogP

LE > 0.35 ; LLE > 5 ; PFI < 7 LE = pX50*1.37 /#heavy atoms (kcal/mol/atom) LipE = LLE = pX50 - cLogP PFI = Chrom LogDpH7.4 + #Ar rings iPFI = Chrom LogP + #Ar rings

Leeson and Springthorpe, Nat Rev Drug Discov, 2007. Leeson and Young, ACS Med. Chem. Lett., 2015. Young and Leeson, J. med. Chem., 2018.




Pathology

Identified Target

Protein Sequence

3D Structure


Identified Hits

Clinical trials

Approved drug

Lead Optimisation


Hit-to-Lead

Drug-like hits

Lead-like hits

High affinity hits

> 0.1 µM MW > 350 LogP > 3

> 0.1 µM MW < 350 LogP < 3 (polar)

<< 0.1 µM MW >> 350 LogP < 3

MW LogP

unfavored

Lead Optimization

Drug

MW [1-200] LogP [0.5-4]


LogP




Identifying good – progressable - Hits


Drug e 3D

Searches by: Names Substructures keywords Target name…

http://chemoinfo.ipmc.cnrs.fr

Pharmacokinetic data set

rings , fused rings and acyclics ( linkers and substituants)

Drug-like Fragments and Frameworks

(Bemis & Murcko definition)

X : anchoring point for substituents

Tota

l num

ber o

f dru

gs c

onta

inin

g th

e fr

amew

ork

10 … 496

… …

…

Framework type

1939 1939

1946 1942

1940 1949

1946

1939 1939

1951

1953

1945

1960

24 54

Most populated frameworks in approved drugs

Drug frameworks

47% structures are represented by only 24 “frameworks”

Most frameworks are unique (represented by only 1 drug structure)

Pihan et al., Bioinformatics, 2012; Douguet D., ACS Med Chem Lett, 2018. http://chemoinfo.ipmc.cnrs.fr

Tota

l num

ber

Decade Decade

Tota

l num

ber

After 1980s a larger number of new frameworks … but most populated frameworks are oldest ones

Most populated frameworks in approved drugs

Drug frameworks


Drug Frameworks

37 drugs

38 drugs

41 drugs

47 drugs

48 drugs

49 drugs

69 drugs

91 drugs

227 drugs

Sulfapyridine (1939) (Unknown target; Estrogen receptor (Diethylstilbestrol))

Histamine (1939) (H1 receptor) Angiotensin-converting enzyme (Captopril))

Mephenytoin (1946) (Nav ion channel) HIV reverse transcriptase (Zidovudine))

Chloroquine (1949) (Lactate dehydrogenase; Beta adrenergic receptor (Propranolol))

Theophylline (1940) (Phosphodiesterase; Calcium-activated K+ (SK) channel (Riluzole))

Meperidine (1942) (Mu type opioid receptor; Neprylisin(Sacubitril))

Diphenydramine (1946) (H1 receptor; Prostaglandin H synthase (Amfenac/Nepafenac))

Desoxycorticosterone (1939) (Aldosterone synthase; Glucocorticoid receptor (Halobetasol))

Butabarbital (1939) (GABA receptor; 20S proteasome (Ixazomib))

e-Drug3D: release of July 2016 (1557 princeps / 1822 different structures) - 1189 different scaffolds (out of 1697) - 512 different frameworks Source: http://chemoinfo.ipmc.cnrs.fr ; Pihan et al., Bioinformatics, 2012.


Drug Frameworks

Cephalothin (1974) (Penicillin-Binding Protein)

Imipramine (1959) (Noradrelanine Transporter)

Folic acid (1946) (vitamine B9; DHFR (Methotrexate))

12 drugs

11 drugs

11 drugs

12 drugs

13 drugs

14 drugs

15 drugs

18 drugs

24 drugs

Sulfathiazole (1945) (Unknown target; Dihydropteroate synthase (Sulfamethizole); NKCC1, CFTR (Furosemide))

Prochlorperazine (1956) (D2 Dopamine receptor)

Thiamine (1953) (Vitamine B1; Alpha adrenergic receptor (Clonidine)

Vidarabine (1976) (Adenosine deaminase)

Diazepam (1963) (GABA receptor)

Promethazine (1951) (H1 receptor)


Drug Frameworks

Clomiphene (1967) (Estrogen receptor)

11 drugs

Phenoxybenzamine (1953) (Alpha adrenergic receptor; Platelet glycoprotein (Tirofiban)) 10 drugs

Triamcinolone acetonide (1960) (Glucocorticoid receptor) 10 drugs

10 drugs

Tropicamide (1960) (Muscarinic acetylcholine receptor; Noradrenaline transporter (Benzphetamine))

10 drugs

Benzquinamide (1974) (P-glycoprotein receptor; Cannabinoid receptor (Dronabinol))

∑ (represented drugs) = 828/1822 = 45.4% of drug structures are represented by 23 frameworks

The simplest frameworks appeared first and are the most populated


Drug Frameworks

Cycrimine () (Muscarinic acetylcholine receptor M1)

2 drugs

Clidinium () (Muscarinic acetylcholine receptor)

2 drugs

Deslanoside () (Sodium/potassium ATPase)

1 drug

2 drugs

2 drugs Clidinium () (Muscarinic acetylcholine receptor)

2 drugs

Meclocycline () & Methacycline () (Ribosome)

Protokylol () (Beta 1/Beta 2 adrenergic receptor)

Methixene () (Muscarinic acetylcholine receptor)

2 drugs

Discontinued

Pentolinium () (antihypertensive)

1 drug

Pyrvinium () (anthelmintic)

Quinestrol () (Estrogen receptor)

Prazepam () (GABA receptor)

Trilostane () (Estrogen receptor)

Dezocine () (Kappa/Mu opioid receptor)

Hetacillin () (Penicillin-Binding Proetins 1A/1B)

Amdinocillin () (Penicillin-Binding Proetins 2B)

Candicidin () large polyene structure (membrane)

Ceruletide () large structure (Cholecystokinin type A)

Hexafluorenium () (Cholinesterase)

Viomycin () large ring (70S ribosome)

Beta carotene () (Beta carotene monooxygenase)

Saralasin () (Angiotensin II receptor)

Gentian violet () (NADPH oxidase)


Drug Frameworks

Discontinued Frame 13 Dicumarol (1944) (Xanthine oxidase)

Frame 14 Metocurine/Tubocarine (1945) (5-HT3 receptor)

Frame 41 Très proche de frame 1 (sulfapyridine) Hydroxystilbamidine (1953) antiparasitic (Unknown target)

Frame 47 Ambenonium (1956) (Cholinesterase)

Frame 53 Rescinnamine (1956) (Angiotensin-converting enzyme)

Frame 56 Diphenidol (1967)/Oxyphencyclimine (Muscarinic acetylcholine receptor)

Frame 64 Biperiden (1959) (Muscarinic acetylcholine receptor)

Frame 66 Benzthiazide (1960) (Antihypertensive)

Frame74 Chlorprothixene (1967) (D2 Dopamine receptor)

Frame 79 Cyclothiazide (1982) (Glutamate receptor 2)

Frame 90 Testolactone (1969) (CYP450 19-aromatase)

Frame 98 Pentagastrin (1974) (Cholecystokinin type B receptor)

Frame 100 Mazindol (1973) (Noradrenaline & Dopamine transporter)

Frame 119 Guanadrel (1982) (Antihypertensive)

Frame 131 Antazoline (1990) (Cav channel)

Frame 136 Bepridil (1990) (Ca channel)

Frame 161 Doxacurium (1991) (Muscarinic acetylcholine receptor M1)

Frame 173 Trovafloxacin/Alatrofloxacin (1997) (DNA gyrase)

Frame 174 Cisapride (1993) (5-HT4 receptor)

Frame 175 Levocabastine (1993) (Histamine H1 receptor)

Frame 213 Troglitazone (1997) (PPAR gamma)

Frame 239 Pemirolast (1999) (antiinflammatory)

Frame 245 Telithromycin (2004) large ring (50S ribosome)


Drug Frameworks

Discontinued

Frame 297 Cyclacillin / Methicillin (1979) (Penicillin-Binding Proetins 1A/1B)

Frame 303 Troleandomycin (1969) large ring (ribosome)

Frame 304 Novobiocin (1964) (DNA gyrase)

Frame 306 Cephapirin (1974) (Penicillin-Binding Proetins 1A/1B)

Frame 309 Ticarcillin (1976) (Penicillin-Binding Proetins 1A/1B)

Frame 314 Cefoperazone/Cefpiramide (1982) (Penicillin-Binding Proetins 1A/1B)

Frame 315 Azlocillin/Mezlocillin (1981) (Penicillin-Binding Proetins 1A/1B)

Frame 325 Cefmetazole (1989) (Penicillin-Binding Proetins 1A/1B)

Frame 327 Dirithromycin (1995) large ring (ribosome)

Frame 343 Guanethidine (1960) (Nitric oxide synthase)

Frame 346 Bitolterol (1984) (Beta 2 adrenergic receptor)

Frame 377 Hydrocortisone cypionate (1955) (Glucocorticoid receptor)

Frame 380 Nandrolone phenpropionate (1959) (Androgen receptor)

Frame 381 Sulfaphenazole (1974) (CYP450)

Frame 386 Protirelin (1976) (Hormone analog)

Frame 392 Nalmefene (1995) (Opioid receptor)

Frame 396 Plicamycin (1970) (Unknown target)

Frame 397 Carbenicillin indanyl (1972) (Penicillin-Binding Proetins)

Frame 417 Telaprevir (2011) (NS3/4A protease)

rings , fused rings and acyclics ( linkers and substituants)

(Bemis & Murcko definition)

X : anchoring point for substituents

Drug-like Fragments and Frameworks

Privileged Structures/Fragments

Mean number of : - legos in drug structures - legos in frameworks (rings + linkers) - substituants in drug structures


Statistics on approved drugs*: Evolution of drug properties

Year

Mol

ecul

ar W

eigh

t

Year

Pola

r Sur

face

Are

a

Physico-Chemical Properties

*e-Drug3D: release of March 2015 (1746 different structures)

Statistics on approved drugs*: Evolution of drug properties

Physico-Chemical Properties

*e-Drug3D: release of March 2015 (1746 different structures) ** Fsp3 = Number of C(sp3) / Number of C

Year

LogP

Year

Fsp3

*

Privileged Structures/Fragments

Drug structures have gained weight over the years and are more complex (more legos)

An increase in the complexity of new frameworks (highly branched structures)

On average, the number of legos in a drug = 5.3 - 3.6 legos in the framework (rings + linkers) - 1.7 legos in ’decoration’ (substituants)

Example: Venetoclax (2016) (protein-protein inhibitor)



Pathology

Identified Target

Protein Sequence

3D Structure


Identified Hits

Clinical trials

Approved drug

Lead Optimisation


Hit-to-Lead

MW < 500 LogP < 5 nbHA<5 nbHD<10

Drug-like hits

Lead-like hits

High affinity hits

> 0.1 µM MW > 350 LogP > 3

> 0.1 µM MW < 350 LogP < 3 (polar)

<< 0.1 µM MW >> 350 LogP < 3

MW LogP

unfavored

Lead Optimization

Drug

MW [1-200] LogP [0.5-4]


LogP




Identifying good – progressable - Hits

Lead-like compounds: MW 150-350 LogP 3 Rings 1-4 Hbond donor (nbHD) <5 Hbond acceptor (nbHA) <8 Exclude PAINS (Pan-Assay Interference compounds) -> false positive hits = apparent biological activity molecules interfere with the assays (aggregation, micelle, autofluorescence… ) interfere with the function of the protein (chemical reactivity (aldehydes, epoxides, acid halide…), metal chelation, redox activity…) e.g.: phenotypic assay & amphiphilic molecules (!! non specific activity through membrane binding) PAINS classes: rhodanines, quinones, cathechols… are well known frequent hitters Reproducible activity with Re-synthesized or Repurified molecule Additional biological assays (SPR, structural biology…) Demonstration of Structure-Activity Relationships (SAR) and Hit-to-Lead optimization

Good pract ices for HIT Ident ificat ion

O

O

S

N SO

OO

O O

OOcurcumin

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