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11

Pharmacokinetic Modeling and Drug Design

V. Frecer

Department of Physical Chemistry of Drugs

Faculty of Pharmacy, Comenius University in Bratislava

Elective subject, year 4, SS, 2L/1S

2019-2020

Lecture 1, 2, 3

2

List of lectures:

• Lecture 1,2 - Introduction to pharmacokinetics: Transport and fate ofa drug in organism; Drug design and development; Molecular structure and pharmacokinetic parameters

• Lecture 3 - Physicochemical principles of drug distribution

• Lecture 4 - Pharmacokinetic models of drug disposition

• Lecture 5 - Pharmacokinetic compartment models

• Lecture 6 - Nonlinear pharmacokinetic models

• Lecture 7 - Perfusion pharmacokinetic models

• Lecture 8 - Physiological pharmacokinetic models

• Lecture 9 - Pharmacokinetic models of drug-receptor binding

• Lecture 10 - Methods of prediction of transport properties of compounds

Contents

2

3

Literature

Recommended literature:

• ATKINS, Peter W. – DE PAULA, Julio: Physical Chemistry: Thermodynamics, Structure, and Change, 10th Ed., Oxford University Press, Oxford, UK, 2014.

• BOROUJERDI, Mehdi: Pharmacokinetics and Toxicokinetics, CRC Press, Boca Raton, FL, U.S.A. 2015.

• JAMBHEKAR, Sunil S. - BREEN, Philip J.: Basic Pharmacokinetics, 2nd Ed., Pharmaceutical Press, London, UK, 2012.

• KERNS, Edward H. - DI, Li: Drug-like Properties: Concepts, Structure Design and Methods, Elsevier, Burlington, MA, U.S.A., 2008.

• PATRICK, Graham L.: An Introduction to Medicinal Chemistry, 5th Ed., Oxford University Press, Oxford, UK, 2013.

4

Lecture 1

Introduction to Pharmacokinetics:

Transport and fate of a drug in organism

Drug design and development

Molecular structure and pharmacokinetic parameters

3

5

Pharmacokinetics

Pharmacokinetics – study of time course of concentrations of a drug and

its metabolites in various organs of the body

Absorption Distribution Metabolism Excretion

Elimination

Administrationp.o.i.m.s.c.

Blood

circulation Liver

biotrans-formation

Excretion

urinestool

organs

tissues

receptors

i.v.

absorption

6

Pharmacokinetic terms

Basic pharmacokinetic terms:

Absorption – transfer of a drug from the site of administration into the systemic

circulation; fraction of a dose, which passes the gut wall into the circulation (in %)

Distribution – process during which a drug disperses or partitions from the blood

into tissues and organs of the body

Metabolism – chemical conversion of a drug in the living organism to other

compounds through biochemical processes catalyzed by enzymes

Excretion – elimination, clearance – process during which the organism gets rid

of a drug or its metabolites through expelling from the body;

amount of blood cleared of the drug in an unit of time (in ml/s)

Dose – amount of drug (in g, ml, ...) administered to achieve

a therapeutic effect

Oral bioavailability – fraction of a dose of drug which

will reach the systemic circulation

(after the first-pass metabolism in the liver)

Distribution volume – ratio of the dose and initial

concentration of a drug in the plasma

4

7

Transport and fate of a drug in the organism

absorption

metabolism

distribution

binding

Excretion

drug drug

dissolved

bound

metabolite free

drug bound

free

cleared

cleared

GITskin

muscleslungs

body fluidsand tissues

urinestoolbile

breathsaliva, sweat

biologiceffect

Pharmacodyna-mic phase

interaction drug-receptor

at site of action

drug available for biological

action

bioavailability

Pharmacokinetic phase

absorption

distribution

metabolism

excretion

drug binding

drug available for absorption

pharmaceutical availability

Pharmaceutic phase

disintegration of drug form

dissolution of released active

substance

extra vascularadministrationof drug dose

Interaction of a drug with an organism:

8

Transport of drugs across biological membranes

Model of cellular

membrane:

Fluid bilayerof phospholipids

(phosphatidylcholine)with proteins

thickness: 75 – 100 nm

membranechannels - pores

diameter:~4 nm Bates TR, Gibaldi M. Biopharmaceutics. Lea & Febiger, Philadelphia, USA, 1970.

Transport of drugs:

passive diffusion of molecules across membranes (lipophilic compounds, non-dissociated polar molecules, logPo,w, pKa), driving force: concentration gradient

passage of compounds through pores in cellular membrane (soluble electrolytes and ions with dimensions up to the pore diameter, dependent of the charge in the pore opening, d < 4 nm, Mw < 200 Da)

transport facilitated by specific carriers (active transport against conc. and electrochem. grad., facilitated diffusion in direction of conc. and electrochem. g.)

pinocytosis (large molecules engulfed by the membrane)

5

9

Dissociation and distribution of drugs

B[100]

OH- BH+

[1]

Total: [101]

B[100]

BH+ OH-

[1]

Total: [101]

pH = 7,0

weak base pKa =5,0 weak base pKa =5,0

B[100]

OH- BH+

[1]

Total: [101]

B[100]

BH+ OH-

[10000]

Total: [10100]

pH = 7,0 pH = 7,0 pH = 3,0

Dissociation: acid AH + H2O H3O+ + A- base B + H2O BH+ + OH-

pKa=-log[H3O+][A-]/[AH] Henderson-Hasselbach: pKa – pH = log[AH]/[A-]

pKa – pH = log[BH+]/[B]

pKa – pH = 5 – 7 = -2 acid conc. [A-] = 100 and [AH] = 1

base conc. [BH+] = 1 and [B] = 100 (pKb = 14 – pKa = 9)

pKa – pH = 5 – 3 = 2 acid conc. [A-] = 1 and [AH] = 100

base conc. [BH+] = 100 and [B] = 1 (pKb = 14 – pKa = 9)

Diffusion: across membranes only the neutral non-dissociated form, equilibrium in each phase

10

Absorption of drugs

Absorption of drugs from GIT into systemic circulation

Absorption depends on the structure and physicochemical properties of molecules

Lipinski „Rule of five“ - oral bioavailability: Mw < 500 Da, 5 HB prot. don.,

10 HB prot. accep., logPo/w 5 (2245 per oral drugs from WDI, 1997)

Properties, which determine ADME

- molecular structure (composition, topology, 3D-str.)

- molecular mass Mw,

- polar and hydrophobic molecular surface, Ap An,

- number of hydrogen bonds, HBpd and HBpa,

- number of rotatable bonds, Nrot,

- partitioning coefficient octanol/water, logPo/w,

- solubility in water, logSw,

- blood/brain partitioning coef., logPBB,

- permeability of Caco-2 cells,

- binding to serum proteins, logKsp,

- binding to serum albumines, logKhsa,

- number of possible metabolic reactions

Lipinski CR. et al. Adv. Drug Deliv. Rev. 23, 3-25 (1997).

absorption of molecules of a drug in small intestine

molecular structure determines physicochemical properties of compounds

6

11

Significance of ADME properties for drug design

Role of ADME/Tox properties in termination of drug design projects:

ADME

- unfavorable pharmacokinetic profile (39 %)

Toxicity

- toxicity in animals (11 %)

- harmful side effects in man (10 %)

12

Prediction of ADME properties of drugs

Calculation of physicochemical properties determining ADME (W. Jorgensen)

- partitioning coefficient octanol/water logPo/w, QikProp

- water solubility logSw,

- blood/brain distribution coefficient logPBB,

- binding to serum albumins logKhsa,

Physicochemical properties (descriptors) calculated with help of molecular mechanics (MM), quantum chemistry (QM), computer simulations, ... are correlated with experimental quantities (logPo/w, logSw, ...) for large sets of compounds (700 molecules, including 500 drugs)

QikProp, Schrödinger, release 2016-4 , LLC, New York, NY, U.S.A.

2016.

Duffy EM, Jorgensen WL. J. Am. Chem. Soc. 122, 2878-2888 (2000).

Jorgensen WL, Duffy EM. Bioorg. Med. Chem. Lett. 10, 1155-1158

(2000).

7

1313

Stages of drug development

identify disease

isolate proteininvolved in disease

find a molecule effective

against protein

preclinical testing

compound synthesis scale up

drug formulation

human clinical trialsFDA approval

drug in clinical practice

explore molecular basis of disease (biochemistry, pharmacological target

identification, …)

isolate protein, determine crystal structure, protein homology modeling,

binding site determination, …

rational drug design, combinatorial chemistry, virtual screening, QSAR,

lead compound identification, …

development of a new drug: ~15 years, costs: >800 mil. US$

14

selectivity

efficacytoxicity

metabolism

absorptionexcretion

distribution

potentialdrug

Requirements for new drugs

• genetics

• cell and molecular biology

• bioinformatics

• structural biology

• biochemistry

• computer-assisted drug design

• medicinal chemistry

• toxicology

• chemical technology

• clinical pharmacology

• medicine

• ...

Drug discovery – multidisciplinary research

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151515151515

Role of computations in drug development

Identification and validation of site of action

Genetics, Molecular biology

Bioinformatics

Determination of 3D structure

Crystallographic analysis

NMR spectra, Homology modeling

Medicinal chemistry

Organic synthesis

Combinatorial chemistry

Peptide chemistry

Biological testing

High throughput screening

in vitro, in vivo screening

Molecular design

Molecular modeling

Computational chemistry

Computer graphics

Optimization of hits

QSAR, Toxicology, ADME

Clinical tests

Pharmacology, Pharmacokin.

Finding of active ligands

Screening of databases

16161616

Force field-based simulations

Quantumchemical calculations

QSAR analyses

Homology protein modeling

Molecular diversity

Computer-assisted combinatorial chemistry

In silico screening

Docking of small molecules

Prediction of ligand-receptor binding affinity

Pharmacophore models

ADME properties prediction

Solvent effect calculations

Data mining

Bioinformatics

Chemiformatics

Molecular graphics

…

Computational methods in biomedical research

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17

Combinatorial (parallel)

synthesis (same chemistry)

• in solid phase

• in solution

103-105 compunds/experiment

Traditional (serial) synthesis

1 researcher =

~50 compounds/year

A + B AB

BmA1 B1

A2 B2

A3 B3

. .

. .

An(acids)

Bm(amines) n x m (amides)

B1 B2

A2

A1

An

HTS

Traditional and combinatorial drug design

181818

Design of virtual combinatorial libraries

3D structure

of protein

pharmaco-

phore

protein

family

no

information

focused diverse

Info

rmati

on

ab

ou

t ta

rget

Design structure

based

Design based

on pharmacophore

Targeted Sets

Initial libraries

(diversity lib.)

Library size

Chemical space: ~1050 - 1080 compounds Arpád Furka, 1981

(existence of Universe since Big bang: ~5.1018 s)

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191919

Strategies of rational drug design

1919

Drug design Active compound known?

Yes No

3D structure

of receptor known ?

No

A

Pharmacophore-based design, QSAR

Small focused libraries

Random screening,

HTS

Large diversity libraries

Yes

B

Structure-based design

Small focused libraries

C

Fragment-based design

Fragment libraries

Traditional design:

individual compounds

combinatorial

design:

libraries of compounds

2020

A) Ligand-based drug design (LBDD)

• Unknown 3D structure of receptor

• Design based on known ligands:

- ligand database searching (pharmacophore)

- quantitative structure-activity relationships (QSAR)

- 3D-QSAR analysis (CoMFA)

donor

acceptor

negative charge

hydrophobic

Free – Wilson QSAR analysis: biological activity is determined by contributions from all substituents

3D contour map in the vicinity of coumarine and inhibitors of cytochrome P450 2A5

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2121

Pharmacophore-based design

Pharmacophore – spatial arrangement of a collection of groups or molecular fragments, which determine its biological activity

Utilization:

- Database searching–• for new chemical structures,

which correspond to pharmacophore

donor

acceptor

charged-neg.

hydrophobic

Pharmacophore of thymidine monophosphate kinase of M. Tuberculosis

Overlap with designed inhibitor

Keita M, et al.: RSC Adv. 4(99), 55853-55866 (2014).

2222

Quantitative structure-activity relationships (QSAR)

• QSAR: chemical, physical, biological properties (activity) – encoded in chemical structure of molecules

- similar molecules ~ similar properties, diverse molecules ~ diverse prop.

- model of biological activity

biological effect = f(molecular structure)

* mechanism of action

* prediction of activities of analogs

Molecular property Descriptor

Lipophilicity p, logPo/w, Rt

Steric properties Mw, Vm, shadow ind., 3D des.

Electronic prop. q, sd, EHOMO, ELUMO, E.A.

Structure, topology HB, W, logZ, ,

Biological effect Descriptor

Binding to receptor structural, electronic, topologic

Oral bioavailability lipophilicity, structural, steric

Metabolic activation structural, electronic

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2323

• Methods of QSAR:

- L.P. Hammett, R.W. Taft dissociation constants of aromatic acids

pK = pKo - .s

- S.M. Free, J.W. Wilson biol. activity: sum of contributions of substituents

log(1/C) = a.(contrib.R1)+b.(contrib.R2)+...

- C. Hansch, A. Leo biol. activity: lipophilicity, steric, electronic prop.

log(1/C) = a.π2 + b.π + c.Es +d.s + e

Quantitative structure-activity relationships (QSAR)

Procedure:iterative approach to

selection of descriptorsand QSAR model

Methods:genetic algorithms

neural networksmachine learning

etc.

2424

3D Quantitative structure-activity relat. (3D-QSAR)

• QSAR models:

- 2D-QSAR models – descriptors derived from molecular structure

- 3D-QSAR models (CoMFA, CoMSIA, CoMMA,...) descriptors – molecular fields (MEP, hydrophobic interactions, ...) in 3D space around molecule

CoMFA models depend on the quality of overlap of compared molecules:

overlap with pharmacophoreoverlap of atoms

overlap of molecular shapes overlap of molecular fields

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2525

B) Structure-based design (SBDD)

3D structure of pharmacological target (site of action):

• X-ray structures of protein targets (Protein Data Bank, 149,000 3D str. of macromolecules, Feb. 2019)

• NMR structures of proteins in solution

• homology-based protein models

Design of ligands:

• in situ modificat. of known ligands/substrates

• docking and in silico screen. of new molecules

Influenza A virus N1 neuraminidase–oseltamivir (2HU0) hemagglutinin IAV H18N11 (4MC5)

2626

Structure-based drug design (SBDD)

Interactions of ligand in binding site of a receptor, ligand design:

• complementarity of L and R (steric, molecular fields)

• desolvation of L and R

• entropic effects

A B

C D

www.kubinyi.de

binding site ofreceptor

ligand 1

Estimate of binding affinity of ligand to receptor:

Calculation of Gibbs free energy of LR complex formation

DGo = -RT lnKi

molecularfields

ligand 2

shape of binding site structure of binding site

complementarity of ligand and binding site

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2727

Structure-based drug design (SBDD)

Factors that contribute to recognition and binding of a ligand:

π-π interactions

hydrophobiccontacts

explicitwater molecules

hydrogen bonds(directional interactions)

steric interactions

flexibility of ligandand receptor

desolvation of ligandand receptor

peptidomimetic inhibitor of plasmepsin II

2828

Protein homology (comparative) modeling

Availability of 3D structures of macromolecules (proteins) is limited

• PDB (160,000 X-ray and NMR atomic resolution structures), 148,000 proteins, only 30000 sequences <30% identity

• similar a.a. sequence similar 3D structure (particularly overall fold) similar function of protein

• protein structures are more conserved than sequences amongst homologous proteins (up to 30% sequence identity)

• homology model building of target protein

- template proteins selection (detection of distant evolutionary relationships)

- target-templates sequences alignment (BLAST, FASTA, PFAM, protein threading RaptorX,…)

- target-templates structural alignment and model building (backbone generation, loop modeling, side chains conform., refinement)(Modeller, Jackal, SCRWL, SwissModel, …

- model assessment (What If, ProCheck, PSQS, Ramachandran plot,…)

15

2929

Protein homology (comparative) modeling

Sali A, Kuriyan J.: Trends Biochem. Sci. 22, M20–M24 (1999)

Quality of homology models depends mainly on:

- selection of template proteins

- alignment and sequence identity totemplates

- resolution of template structures

- method of modeling and refinement

3030

Binding affinity of ligand to receptor

Models of ligand-receptor binding:

- lock and key

- induced fit theory

ligand (L) + receptor (R) ligand-receptor (LR) complex

+

Pharmacodynamic effect of ligand:

kas kf kdis [LR] [L]L + R LR LR* Kd = —— Rel. response = —— = ————

kdis k-f kas [Ro] [L] + Kd

binding affinity pharmacol. dissociation effect depends on dose Leffect constant

[L] – concentration of free ligand

Binding affinity of L: lnKd = DGo/RT property of complex LR

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3131

Prediction of binding affinity of ligand

Molecular modeling – calculation of binding affinity – modeling of LRaq

pKd = -log10Kd = aDGcom,aq/2,303RT + b

Thermodynamic cycle:

DGcom,g

Lg + Rg LRg gas phase

Gsol(L) Gsol(R) Gsol(LR)

DGcom,aq

Laq + Raq LRaq aqueous solution

Gibbs free energy of formation of LRaq:

DGcom,aq = [Gg(LR) - Gg (L) - Gg (R)] + [Gsol(LR) - Gsol(L) - Gsol(R)]

where:Gg = Etot + thermal corrections of internal energy and entropy

3232

Calculations of ligand-receptor interactions

• Energies of molecules and intermolecular interactions:

- quantum mechanical methods (QM)

- molecular mechanics methods (MM, force fields)

- hybrid QM/MM methods

- scoring functions

• Molecular simulations, statistically averaged quantities:

- MC simulations, MD simulations

- conformational searching

• Solvation:

- explicit models (discrete solvent molecules)

- implicit models (GBSA - Still et al., PB - Honig et al., PCM - Miertuš et al.)

• Models of estimate of DGcom,aq used in drug research:

- MM-PBSA/GBSA (Kollman et al.)

- LIE (Åqvist et al.)

- MM-PCM (Frecer, Miertuš)

- …

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3333

Methods of computational chemistry

Quantum chemical methods (ab initio, semiempiric, DFT methods)

Schrödinger equation

ĤΨ(r1,…,rn,R1,…,RN) = EΨ(r1,…,rn,R1,…,RN)

Ĥ – Hamilton operator of total e., Ψ – wave function (observable physicalquantities), E – energy of molecule, ri, Ri – radius vectors of electrons and nuclei

I. R. Levine

hyper surface E(r,R)

Science 2007;315:1561-1565.

propanal

343434

Methods of computational chemistry

Molecular mechanics - potential energy of molecule

U(x1,…,x3N) = ½∑vkv(l-lo)2 + ½∑uku(θ-θo)

2 +

+ ½∑tVnt[1+cos(nω-γ)] +

+ ½∑i∑j(qiqj/εrij) +

+ ½∑i∑j[Aij/rij12-Bij/rij

6] +Force field constants: parameterized against experiment and ab initio calculations

+ ½∑i∑j[Cij/rij12-Dij/rij

1 0]

Molecules 2014;19(10):15735

bonding

nonbonding

electrostatic

dispersion-repulsion

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3535

Methods of computational chemistry

Hybrid quantum mechanical/molecular mechanical methods (QM/MM):

• Binding site – QM region

Schrödinger equation: ĤΨ = EΨ

• Bulk protein – MM region

molecular mechanics: E = bonding + nonbonding terms

• Solvent – MM region

- explicit hydration (MM)

- implicit hydration – classical models (Born, Jano, PCM, PB, GBSA, ....)

• Link between QM and MM regions of protein (dummy atoms, frontier atomic orbitals, ...)

Nobel price for chemistry 2013: QM/MM methodsM. Karplus, A. Warshel, M. Levitt

3636

Polarizable Continuum Model of solvation (PCM)

MEP on molecular surface of imidazole

Solvent – continuous homogeneous polarizable medium (dielectric constant)

Reaction field – elst. interaction with the molecule incavity

Quantum chemical model a:

Gsol,els = <Ψ|Ĥo+VRF|Ψ> - <Ψo|Ĥo|Ψo> - ½ <Ψ|VRF|Ψ>

Classical model b:

Gsol,els = ½ j (qj VRF,j – mj ●ERF,j)

a Miertus S, Scrocco E, Tomasi J.: Chem. Phys. 55(1), 117-129 (1981).

Miertus S, Tomasi J.: Chem. Phys. 65(2), 239-245 (1982).

b Frecer V, Miertus S.: Int. J. Quant. Chem. 42(5), 1449-1469 (1991).

Miertus S, Frecer V.: J. Math. Chem. 10(1), 183-204 (1992).

vdW cavity - aminopropanone

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3737

Molecular simulations

• Experimental biological activities:

Ki, IC50 – thermodynamic quantities observed on large ensembles of molecules

(V = 1μL, c = 1 μM 6·1011 molecules of enzyme at [T, p])

(macroscopic quantities averaged over a set of most probable configurations of statistical ensemble at thermal equilibrium )

• Prediction of Ki, IC50 through calculations of L-R binding affinity:

lnKi = - DG°bin/RT

averaging of DG°bin over statistical ensemble of LR configurations DG°bin

• Calculation of thermodynamic quantities - molecular simulations:

- Monte Carlo simulations

- molecular dynamics

dihydrofolate reductase in a periodic box of water

3838

Monte Carlo simulations

• MC simulations:

- sampling of configurational space by generating states Ai (random changes of internal coordinates)

- calculation of averaged phys. chem. quantities: A = 1/n·∑i

nAi

- configuration Ai+1 is generated from configuration Ai (Markov chain)

- configuration Ai+1 is accepted to statistical ensemble if: Ei+1 < Ei

if : Ei+1 > Ei random number generator (0<R<1)

Boltzmann probability of state Ai+1 (Metropolis importance sampling)

• R > exp[-(Ei+1-Ei)/kT] Ai+1 is accepted

• R < exp[-(Ei+1-Ei)/kT] Ai+1 is rejected

Tapia L, et al.: Bioinformatics 23(13), i539-i548 (2007).

evolution of population of conformations during

protein folding; Monte Carlo simulation

20

3939

Molecular dynamics

• Molecular dynamics:

- method of statistical mechanics: simulation of evolution of complex systems, calculation of time-averaged physicochemical quantities

- generation of MD trajectories (system configurations) – numerical integration of Newton equations of motion (dt = 1 fs) in a constantly changing force field:

fxi = miaxi = mi(d2xi/dt2) = -∂U(x1,…,x3N)/∂xi

axi(t) = fxi/mi vxi(t+dt) = vxi(t) + axi(t)·dt

xi(t+dt) = xi(t) + vi(t)·dt + ½ ai(t)·dt2

U(x1,…,x3N) – potential energy (FF)

xi = xi(t), vi = vi(t) i= 1, ... N

trajectory

van Eijk M, et al.: J. Biol. Chem. 287, 26666-26677 (2012).

time-course of RMSD of a loop of porcine and human surfactant protein D (collectin)

4040

Molecular dynamics

• Applications of molecular dynamics:

- calculation of thermodynamic quantities of systems - ensemble averaged values

Ā = 1/n·∑i

nA(ti)

- kinetic quantities

- conformational transitions

- refinement of structures

- tracking of dynamic behavior of macromolecules: thermal fluctuations

Software: CHARMM, AMBER, GROMOS, NAMD, GROMACS, MacroModel, Tinker, ...

two snapshots of a protein in different times of an MD simulation

a consequence of thermal fluctuations is that proteins are “breathing”

which can lead to transitions between various conformations

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4141

Docking

• Virtual (in silico) screening of libraries of compounds:

- 3D structure of macromolecular receptor

- binding site of ligand

- docking: generation of ligand poses in bindingsite of receptor

- scoring functions

- ranking of poses and ligands

- selection of perspective ligands for synthesis and activity testing

Plasmodium falciparum enoyl-acyl carrier protein reductase

active site ofPfENROverlap of generated poses of

selected analogs of triclosan

Frecer V., Megnassan E., Miertus S. Eur. J. Med. Chem. 44(7), 3009-3019 (2009).

4242

Docking

• Scoring functions:

- force field-based (AMBER, CHARMM, MMFF, OPLS, ..)physically correct, suitable for organic molecules

- empirical based on frequency of occurrence of specific types of interactions

- knowledge-based - obtained from analysis of crystal structures of LRcomplexes, prefer frequently occurring interactions and geometries

• Famous scoring functions:

AutoDock, LigScore, ChemScore, GlideScore, PLP, PMF, LUDI, FlexXMMFF(tot, vdW), OPLS(tot, vdW), HINT, ICM, Validate, DrugScore, ...

• Docking to rigid or flexible receptor

Kitchen, D. B., et al.: Nat. Rev. Drug Discov. 3, 935-949 (2004).

Moitessier, N. et al.: Brit. J. Pharmacol. 153, 7-26 (2008).

22

4343

C) Fragment-based drug design (FBDD)

• Ligand is constructed from fragments in the binding site of receptor

- fragment - small molecule (<300 Da), weaker binding in a pocket of the active site, fragments are linked into a larger molecule (500-700 Da)

- positions and orientations of fragments are retained in molecules

Hajduk PJ, M, et al.: J. Am. Chem. Soc. 119(25), 5818-5827 (1997).

Ki = 17 mM Ki = 20 μM

Ki = 15 nM

SAR by NMRdesign of nonpetidic

inhibitors of metalloprotease

stromelysin

fragment 1 fragment 2

linker

www.kubinyi.de

4444

Fragment-based drug design (FBDD)

Hunk A, et al.: Angew. Chemie Int. Ed. 48(45), 8452-8456 (2009).

inhibitor panthotenate synthase of M.

tuberculosisdesigned by FBDD

• FBDD includes 3 steps:

- design of high quality library of fragments

- docking, scoring and ranking of fragments

- augmenting, combination and joining of fragments into final ligand

• Software for FBDD:

- GRID, MCSS, SPROUT, MUSIC, LUDI, SkelGen, Superstar, SEED, FFLD, GANDI, eHiTS, Caveat, HOOK, Recore, Allegrow, Confirm, MED-SuMo, LEGEND, GROWMOL, LigBuilder, SMoG, HOOK, PRO_LIGAND, SPLICE/RACHEL, CLIX, LORE, GEMINI, ...

23

45

Examples of design of bioactive compounds by molecular modeling

46

Kubinyi H., In: Computer Applications in Pharmaceutical Research and Development, Ekins S. (Ed.), John Wiley & Sons, Inc., (2006).

Drugs developed by means of molecular modeling:

Dorzolamide (Trusopt – Merck, 1995)Inhibitor of carbonic anhydrase therapy of glaucoma, occular drops

Imatinib (STI-571, Gleevec, Glivec – Novartis, 2001)

Inhibitor of tyrosine kinase, cancer therapy (chronic myelogenic leukemia)

Nelfinavir (AG1343, Viracept - Aguron Pharm., Pfizer, 1997)

Inhibitor of aspartic protease of HIV-1 virustherapy of HIV-1 infection and AIDS

Examples of successful molecular design of drugs

24

47

Examples of drug design projects

4848

Design of inhibitors of protease of HIV-1 virus

Peptidomimetic inhibitors of aspartic protease of HIV-1 (HIV PR)

Crystal structure of HIV PR with inhibitor XV-638 (1BV9)

HIV PR essential enzymenecessary for viral maturation

selected inhibitors of HIV PRapproved by FDA

HIV-1 virus

25

4949

Drug resistance of HIV-1 virus

mutations that cause drug resistance of HIV-1 to PR inhibitors

Viral resistance to drug occurs under selective pressure:

- known HIV PR mutations

R8K, R8Q, V32I, K45I, M46I/M46L/M46F, I47V, G48V, F53L, A71V, V82A/V82I/V82T, I84V, L89M and their combinations

Constant need to develop new antiviral compounds, which act on wider spectrum of HIV PR mutants

50

Peptidomimetic inhibitors of HIV PR

Three series of C2-symmetric HIV PR inhibitors:

- contain non-cleavable isosteres of peptide bond

(prepared in collaboration with University of Trieste, Prof. F. Benedetti)

dihydroxyethylenediamine core--Phe-Ψ[CHOH-CHOH]-Phe--

hydroxyethylenediamine core--Phe-Ψ[CH2-CHOH]-Phe--

Dihydroxyethylenediamine core--Phe-Ψ[CHOH-CHOH]-Pro--

Lopinavir, Abbott Laboratories

26

5151

Peptidomimetic inhibitors of HIV PR

--Phe-Ψ[CHOH-CHOH]-Phe– inhibitors designed to compensate the V82A, V82I, V82F mutations of HIV PR

10 structures proposed

Frecer V, Miertus S, Tossi A, Romeo D. Drug Des. Disc. 15(4), 211-231 (1998).

Burello E, Bologa C, Frecer V, Miertus S. Mol. Phys. 100(19) 3187-3198 (2002).

5252

hydroxyetylénediamínové jadro --Phe-Ψ[CH2-CHOH]-Phe--

32 štruktúr navrhnutých

Frecer V. Miertus S. Macromol. Chem. Phys. 203(10-11), 1650-1657 (2002).

Frecer V, Jedinak A, Tossi A, Berti F, Benedetti F, Romeo D, Miertus S. Lett. Drug Des. Disc. 2(8), 638-646 (2005).

T17 v aktívnom centre HIV PR

Peptidomimetické inhibítory HIV PR

27

53535353

Dihydroxyethylenediamine core --Phe-Ψ[CHOH-CHOH]-Pro--

~100 structures proposed

Frecer V. Miertus S.: Macromol. Chem. Phys. 203(10-11), 1650-1657 (2002).

Frecer V, Jedinak A, Tossi A, Berti F, Benedetti F, Romeo D, Miertus S.: Lett. Drug Des. Disc. 2(8), 638-646 (2005).

Frecer V, Berti F, Benedetti F, Miertus S.: J. Mol. Graphics Modell. 27(3), 376-378 (2008).

FP23 - best drug candidate

FP23 in catalytic center of HIV PR

favorable ADME properties

Peptidomimetic inhibitors of HIV PR

54

Experimental verification of predicted inhibitory potencies of designed peptidomimetics towards the HIV PR: group of Profs. Benedetti and Berti, University of Trieste, Italy

Confirmed inhibition constants in the low nanomolar conventration range

Synthesis and testing of inhibitors of HIV PR

Frecer V, Jedinák A, Tossi A, Berti F, Benedetti F, Romeo D, Miertus S. Lett. Drug Des. Disc. 2(8), 638-646 (2005).

Berti F, Frecer V, Miertus S.: Curr. Pharm. Des. 20(3), 3398-3411 (2014).

28

5555

drug-likecharacter

of designed inhibitors

Confirmed inhibition constants in low nanomolar concentration range

Frecer V, Berti F, Benedetti F, Miertus S. J. Mol. Graphics Modell. 27(3), 376-378 (2008).

Berti F, Frecer V, Miertus S.: Curr. Pharm. Des. 20(3), 3398-3411 (2014).

Synthesis and testing of inhibitors of HIV PR

Experimental verification of predicted inhibitory potencies of designed peptidomimetics towards the HIV PR: group of Profs. Benedetti and Berti, University of Trieste, Italy

56

Thank you

29

5757

Collaborators:ICS-UNIDO, UCM-Trnava University of TriesteS. Miertus D. RomeoJ. Miertus F. Benedetti

F. BertiInternational Fellows A. Tossi E. Burello (Italy) S. PriclS. DeNardi (Italy) P. BraiucaC. Bologa (USA) A. Jedinák (Slovakia) National University of SingaporeA. Nair (India) J.L. DingE. Megnassan (Ivory Coast) B. HoM. Keita (Ivory Coast) D.H.P. LowM. Kabeláč (Czech Rep.) D. Cerin (Slovenia) University of MilanR. Kothamarti (India) P. SeneciL. Owono (Cameroon) T. Udommaneethanakit (Thailand) Xeptagen, VeniceT. Rungrotmongkol (Thailand) G. FassinaM. Malaisree (Thailand) P. PengoD. Kong (China) L. Beneduce

5858

Plasmodium falciparum – virulentná forma malárie v krajinách tretieho sveta

Rezistentné formy P. falciparum – potrebné nové “dostupné” liečivá zacielené proti novým farmakologickým targetom

Target - enzýmy FAS-II biochemickej syntézy mastných kyselín

enoyl-acyl carrier protein reduktáza esenciálny enzým FAS-II pathway (PfENR)

kryštálová štruktúra PfENR s peptidomimetickým inhibítorom TCL11

Freundlich JS, et al. J. Biol. Chem. 282(35), 25436-25444 (2007).

séria PfENR inhibítorov odvodených od triclosanu, aktívne v nanomolárnych koncentráciách

Príklad 2. SBDD: Kombinatorický dizajn inhibítorov

Anopheles gambiae

30

5959

- predpoveď väzbovej afinity: kotvenie inhibítorov do kryštálovej štruktúry PfENR-TCL11 (IC50 =76 nM)

- QSAR model pre tréningový set: pIC50 = -logIC50 = a.LUDI + btarget specific scoring function LUDI (prispôsobená pre PfENR)

- predpoveď ADME vlastností

- QSAR model aplikovaný na in silico skríning kombinatorickej knižniceanalógov

inhibítory PfENR tréningového setu

pIC50 = -6.3473 + 0.0069·LUDI

n = 16, R2 = 0.83, F-test = 65.7

Enoyl-acyl carrier protein reduktáza - P. falciparum

aktívne centrum PfENR

V. Frecer, E. Megnassan, S. Miertus, Eur. J. Med. Chem. 44(7), 3009-3019 (2009).

6060

Kombinatorická knižnica inhibítorov

60

Peptidomimetický skafold, 3 R-groups

počiatočná diversity library

40 (R1) x 23 (R2) x 11 (R3) =

= knižnica10120 analógov

zacielená knižnica

8 (R1) x 5 (R2) x 3 (R3) =

= knižnica120 analógov

R-groups zacielenej (výslednej) knižnice

Kombinatorická knižnica - 3 substitučné miesta R1, R2, R3

8 R1 5 R2 3 R3

31

61

Navrhnuté inhibítory PfENR

Vybrané štruktúry najlepších kandidátov na inhibítory PfENR

Frecer V, Megnassan E, Miertus S. Eur. J. Med. Chem. 44(7), 3009-3019 (2009).

TCL-40-9-11 preložená s TCL11

Predpovedaná IC50 = 13 nM

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