au
th
or
eL
ib
ra
ry
by
GDD/TRD/PHAD/CPP
ES
C
M
ID
“Drug-Likeness”:
Physicochemical Properties in
Small-Molecule Drug Discovery
©
Andrea Decker, PhD
Bootcamp on Medicinal Chemistry @ESCMID/ASM Conference
Lisbon, September 2018
28
au
th
or
Agenda
by
• Introduction “Drug-Likeness”
ra
ry
& Properties
M
ID
eL
ib
• Ionization
• Lipophilicity
• Solubility
©
ES
C
• Summary & Conclusion
PK
eL
ib
PD
ra
ry
by
au
th
or
What Makes a Small-Molecule into
a Successful Drug?
Tox
©
ES
C
M
ID
Dev
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
3
“Drug-likeness”:
Intrinsic, structural
properties that produce
acceptable PK &Tox
Causes for failure in the clinic
au
th
or
Advantages of “Good”
PK Properties
by
2000-2010
ES
C
M
ID
eL
ib
ra
ry
1991 and 2000
©
Kola & Landis: Nat. Rev. Drug Discov. 2004; 3(8); 711.
Waring et al.:
Nat. Rev. Drug Discov.
2015; 14(7); 475.
 Control PK properties early to avoid failure later
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
4
au
th
or
Structure → Properties → Profile
chemical
stability
MW
eL
M
ES
C
H
N
OH
metabolic
stability
Off-target effects
logP
lipophilicity
O
LD50
# of aromatic rings
polarity
polar surface area
half-life
permeability
ID
flexibility
structureCl
bioavailability
ib
# of rotatable bonds
# of H-bonding
acceptors & donors
target
property
profile
solubility
shape
sp3 fraction
clearance
ra
ry
size
by
Molecular Physicochemical Biochemical Pharmacokinetics (PK)
properties
Properties
Properties
& Toxicity
logD
pKa
plasma protein
binding
ionization
©
Cl
 Modify structure to obtain desired target property profile
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
5
©
ES
C
M
ID
eL
ib
ra
ry
by
au
th
or
Drug Discovery is a
Multiparameter Optimization
 Many parameters are of interest, most are interconnected
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
6
28
au
th
or
Agenda
by
• Introduction “Drug-Likeness”
ra
ry
& Properties
M
ID
eL
ib
• Ionization
• Lipophilicity
• Solubility
©
ES
C
• Summary & Conclusion
Why We Care About Ionization
au
th
or
aqueous solubility (log scale)
solubility
PhysChem Properties
(solubility, logD,...)
by
neutral
eL
ib
pKa
ID
selectivity ratio
selectivity @5µM
acidic
ES
C
M
Toxicity
(selectivity, offtarget binding,...)
basic neutral
Formulation
(solubility, salt formation,...)
©
basic neutral
hepatic clearance
clearance mL/min/kg
basic
ra
ry
acidic
Activity, potency
(on-target binding,...)
acidic
PK properties
(permeability,
metabolism,...)
 Has major effect on many properties and parameters
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
8
graphs from Charifson & Walters: J Med Chem 2014; 57(23): 9701.
au
th
or
Ionization: Some Statistics
Oral marketed drugs
Ionizable
Neutral
Always ionized
Others (salts/mixtures)
5%
by
4%
ra
ry
12%
ES
C
M
ID
eL
ib
79%
©
 Most drugs are ionizable
 Bases are most common
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
9
Manallack et al.: Chem Soc Rev. 2013; 42(2); 485.
au
th
or
pKa: The Fundamentals
• Ionization (protonation/deprotonation) can be considered
HA
H++ A-
Bases*: BH+
B + H+
eL
Acids:
ib
ra
ry
by
as one of the simplest chemical (fast equilibrium) reaction
ID
𝑯𝑯𝑯𝑯
𝒑𝒑𝒑𝒑𝒂𝒂 = 𝒑𝒑𝒑𝒑 + 𝒍𝒍𝒍𝒍𝒍𝒍 −
𝑨𝑨
M
ES
C
𝑯𝑯+ 𝑨𝑨−
𝑲𝑲𝒂𝒂 =
𝑯𝑯𝑯𝑯
𝒑𝒑𝒑𝒑𝒂𝒂 = −𝐥𝐥𝐥𝐥𝐥𝐥 𝟏𝟏𝟏𝟏 𝑲𝑲𝒂𝒂
𝒑𝒑𝒑𝒑 = −𝐥𝐥𝐥𝐥𝐥𝐥 𝟏𝟏𝟏𝟏 [𝑯𝑯+ ]
©
 When 𝒑𝒑𝒑𝒑 = 𝒑𝒑𝒑𝒑𝒂𝒂 then [𝑨𝑨− ] = 𝑨𝑨𝑨𝑨 or [𝑩𝑩𝑩𝑩+ ] = 𝑩𝑩
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
10
* For bases, the conjugated acid is considered and its pKa is used
Distribution of species (%)
BH+
Cl
NH2
+
O
H 2N
B
HN
ID
O
O
Atenolol
base with pKa = 9.5
ES
C
OH
80.0
AH
A-
60.0
by
H
N
-
ib
Diclofenac
acid with pKa = 4.0
O
40.0
20.0
O
OH
0.0
1.0
3.0
100.0
Distribution of species (%)
Cl
O
ra
ry
H
N
Cl
NH2
OH
M
Cl
100.0
A-
O
eL
AH
au
th
or
Ionization depends on pH and pKa
5.0
7.0
9.0
pH (concentration scale)
11.0
BH+
80.0
B
60.0
40.0
20.0
0.0
1.0
3.0
5.0
7.0
9.0
pH (concentration scale)
11.0
©
 When 𝒑𝒑𝒑𝒑 < 𝒑𝒑𝒑𝒑𝒂𝒂 , then species protonated (HA or BH+)
 When 𝒑𝒑𝒑𝒑 > 𝒑𝒑𝒑𝒑𝒂𝒂 , then species deprotonated (A- or B)
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
11
au
th
or
Zwitterions
Ciprofloxacin: acidic pKa= 6.2 + basic pKa = 8.6
cation: ZH2+
by
zwitterion: ZH±
+
+
H 2N
H 2N
N
N
O
F
OH
O
O
N
O
F
-
O
O
-
eL
ib
O
O
F
HN
N
N
ra
ry
N
anion: Z-
©
~ pH=6 50% + 50% + 0%
~ pH=7.4 5% + 90% + 5%
~ pH=9
0% + 30% + 70%
Distribution of species (%)
ES
C
M
ID
• Zwitterion: pseudo neutral, simultaneously charged + & isoelectric point
• Multiple species co-exist
100.0
80.0
ZH±
Z-
60.0
40.0
20.0
0.0
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
12
ZH2+
1.0
3.0
5.0
7.0
9.0
pH (concentration scale)
11.0
by
au
th
or
pH- & Ionization-Dependent
Property: Solubility
Atenolol
ra
ry
Diclofenac
O
ib
H
N
OH
eL
Cl
acid
pKa = 4.0
base
pKa = 9.5
ES
C
M
ID
Cl
©
 Ionized species have a higher
aqueous solubility than neutral ones
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
13
NH2
HN
O
O
OH
ib
ra
ry
by
Simple membrane model:
phospholipid bilayer
Permeability
Diclofenac
eL
pH
base
pKa = 10.6
ES
C
M
ID
Permeability
Desipramine
fraction ionized
URL: https://cnx.org/contents/FPtK1zmh@
8.108:q2X995E3@12/The-Cell-Membrane;
Author: OpenStax
acid
pKa = 4.0
fraction ionized
au
th
or
pH- & Ionization-Dependent
Property: Permeability
©
pH
 Ionized species have a lower
passive diffusion permeability than neutral ones
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
14
adapted from: Wohnsland & Faller: J Med Chem 2001; 44(6): 923.
28
au
th
or
Agenda
by
• Introduction “Drug-Likeness”
ra
ry
& Properties
M
ID
eL
ib
• Ionization
• Lipophilicity
• Solubility
©
ES
C
• Summary & Conclusion
au
th
or
Why We Care About Lipophilicity
• Has major effect on many PK and toxicity properties, as
ra
ry
by
well as pharmacological activity
solubility
• Is a basic structural property
metabolic
stability
ib
eL
• “lipophilic”:
ES
C
M
ID
greek λίπος "fat" & φίλος "friendly"
lipophilicity
protein
binding
distribution
©
 logPoctanol/water :
common estimate of lipophilicity
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
16
permeability
toxicity
au
th
or
logPoctanol/water: The Fundamentals
ib
eL
octanol
M
ID
model for biological
membranes (lipid
systems)
ra
ry
by
• instrinsic structural property of the molecule
• partition coefficient P:
ratio of concentrations of neutral species in two phases
O
H
ES
C
H
water
X0
oct
X0water
𝐥𝐥𝐥𝐥𝐥𝐥 𝑷𝑷 = 𝐥𝐥𝐥𝐥𝐥𝐥 𝟏𝟏𝟏𝟏
𝑿𝑿𝟎𝟎 𝒐𝒐𝒐𝒐𝒐𝒐
𝑿𝑿𝟎𝟎 𝒘𝒘
©
 logP often used for structure-property-relationship
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
17
au
th
or
logD: The Fundamentals
XH+water
ID
M
ES
C
𝒑𝒑𝒑𝒑𝒂𝒂 = 𝐥𝐥𝐥𝐥𝐥𝐥 𝟏𝟏𝟏𝟏
𝑿𝑿𝑿𝑿+
𝑿𝑿𝟎𝟎 + 𝑯𝑯+
X0oct
ib
eL
XH+oct
ra
ry
by
• logD combines lipophilicity (logP) & ionizability (pKa)
X0water
𝐥𝐥𝐥𝐥𝐥𝐥 𝑷𝑷 = 𝐥𝐥𝐥𝐥𝐥𝐥 𝟏𝟏𝟏𝟏
𝐥𝐥𝐥𝐥𝐥𝐥 𝑫𝑫 = 𝐥𝐥𝐥𝐥𝐥𝐥 𝟏𝟏𝟏𝟏
𝑿𝑿𝟎𝟎 𝒐𝒐𝒐𝒐𝒐𝒐
𝑿𝑿𝟎𝟎 𝒘𝒘
𝑿𝑿𝑿𝑿+ 𝒐𝒐𝒐𝒐𝒐𝒐 + 𝑿𝑿𝟎𝟎 𝒐𝒐𝒐𝒐𝒐𝒐
𝑿𝑿𝑿𝑿+ 𝒘𝒘 + 𝑿𝑿𝟎𝟎 𝒘𝒘
©
 logD is pH dependent  logD@pHxyz
 logD may be better “predictor” for in-vivo properties
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
18
au
th
or
High-Throughput logP & logD@pH7.4
In-House Statistics
ES
C
©
logP
M
ID
eL
ib
ra
ry
by
• >7’000 logP & >11’000 logD datapoints (2015-2017)
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
19
logD
au
th
or
logP & log D: Examples
ra
ry
by
Atenolol
logP = 0.8
logD@7.4 = -0.2
ES
C
M
ID
eL
ib
Chlorzoxazone
logP = 2.1
logD@6.8 = 2.1
Diclofenac
logP = 4.4
logD@7.4 = 1.4
©
Rifampicin / Rifampin
clogP = 3.7
logD@7.4 = 0.9
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
20
Atazanavir
logP = 4.0
logD@7.4 = 4.0
Lasofoxifene
logP = 5.8
logD@6.8 = 3.3
28
au
th
or
Agenda
by
• Introduction “Drug-Likeness”
ra
ry
& Properties
M
ID
eL
ib
• Ionization
• Lipophilicity
• Solubility
©
ES
C
• Summary & Conclusion
au
th
or
Why We Care About Solubility
by
• One of the most important properties in drug development
ra
ry
• 70% of all sales for oral delivery
eL
ib
• Key parameter for bioavailability
ID
• Required for intravenous dosing
absorption in
small intestines
M
• Required for robust readout of bio-, PK-,
ES
C
toxicity-assays
©
solubility
absorption
bioavailability
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
22
pharmacological
efficacy & safety
au
th
or
Solubility in Drug Discovery:
Dissolving Pebbles?
M
ES
C
marble
14 mg / L
sand
10 mg / L
(50 grains)
x 17,000,000
©
x 100,000,000
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
23
Daktarin (Miconazole)
1.4 mg/L
(neutral form)
ID
salt
350 g / L
(23 table spoons)
eL
sugar
2 kg / L
(666 sugar cubes)
ib
ra
ry
by
• Comparison on how much dissolves in 1L of water
x 500
Cordarone (Amiodarone)
0.004 mg/L
(neutral form)
au
th
or
High-Throughput Eq. Solubility pH6.8
NIBR In-House Statistics
ra
ry
Project A
Project B
Project C
...
...
...
...
M
ID
eL
ib
pH6.8
by
• >30’000 compounds measured by HTSol over two years
©
ES
C
≤10 uM
≈ ≤ 5 mg/L
10−100 uM ≈ 5−50 mg/L
>100 uM ≈ >50 mg/L
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
24
au
th
or
How the Physical Environment
Can Affect Solubility
Solvent matrix
ib
ra
ry
by
e.g. which solvent, pure or
mixture, additives,....
theo. max.:
10-fold per pH unit
M
Temperature
ID
eL
1- to >1000-fold
pH
Solid state form
e.g. amorphous or crystalline
©
ES
C
< 2-fold for ∆T~15°C
2- to >1000-fold
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
25
au
th
or
How Structural Properties Can
Affect Solubility
ib
ra
ry
by
Solute – Solvent
Interactions  logP
10-fold ↑
(lipophilicity)
 melting point
ES
C
M
ID
max. 10-fold ↑ per unit ↓
10-fold ↑ per 100ºC ↓
(empirical)*
eL
 pKa
©
Ionization
see for example: B. Faller, P. Ertl, Advanced Drug Delivery Reviews 59 (2007) 533-545
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
26
per log-unit ↓
(empirical)*
Solid State
Interactions
(crystal packing)
*according to empirical “General Solubility
Equation” by Y. Ran, N. Jain, S.H. Yalkowski, J.
Chem. Inf. Model. 41 (2001) 1208–1217
au
th
or
Strategies to Improve Solubility:
Example A
R1
R1
N
N
R3
by
N
R2
R4
ra
ry
R4
ib
Cpd A
solubility of neutral [mM]
M
fraction ionized @pH6.8
ES
C
logP
melting point Tm
N
F
Cpd C
0.01
0.02
0.10
0.01
0.02
0.10
0.0
0.0
0.0
3.5
3.1
3.1
189ºC
157ºC
111ºC
ID
solubility @pH6.8 [mM]
R3
R4
Cpd B
eL
Property
N
R2
N
R2
R3
N
O
N
N
F
R1
N
©
 Decrease in logP and melting point improves solubility
 Trends may be envisioned qualitatively, not quantitatively
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
27
R
N
R
N
by
R
au
th
or
Strategies to Improve Solubility:
Example B
ra
ry
Cl
solubility of neutral [mM]
M
fraction ionized @pH6.8
ES
C
logP
melting point Tm
OH
Cpd C
0.001
0.002
0.25
0.001
0.21
0.25
0.0
1.0
0.0
4.5
3.8
3.2
187ºC
225ºC
158ºC
ID
solubility @pH6.8 [mM]
N
Cpd B
eL
Cpd A
O
O
R2
ib
N
Property
NH
NH
NH
N
©
 Properties are interconnected
 Structural changes affect one or multiple properties
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
28
28
au
th
or
Agenda
by
• Introduction “Drug-Likeness”
ra
ry
& Properties
M
ID
eL
ib
• Ionization
• Lipophilicity
• Solubility
©
ES
C
• Summary & Conclusion
by
Oral
au
th
or
What is the “Best” PhysChem Target
Property Profile?
Antibiotics
ra
ry
IV
injection
ES
C
M
Topical
ID
eL
ib
Target
Property
Profile
Intraocular
into
brain
©
 Desired target property profile depends also on route
of administration and target location
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
30
au
th
or
Summary & Conclusion
ib
ra
ry
by
• A successful drug needs high potency/activity AND
good properties
• PK & physchem properties should be considered early
on, e.g.
ES
C
M
ID
eL
• Ionization
• Lipophilicity
• Solubility
©
• Desired target property profile depends also on route of
administration and target location
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
30
31
32
©
ID
M
ES
C
Thank you!
Questions?
ra
ry
ib
eL
by
au
th
or
au
th
or
by
ra
ry
Andrea Decker, Ph.D.
Fellow, Chemical and Pharmaceutical Profiling
eL
ib
Novartis Global Drug Development
Technical Research & Development
CH-4002 Basel
Switzerland
©
ES
C
M
ID
+41 79 831 00 97
andrea.decker@novartis.com
34
au
th
or
by
ra
ry
ib
eL
M
ID
Backup / Additional Material
©
ES
C
• Literature references
• Details & additional information
•
•
au
th
or
Literature references
Benet, L. Z., et al. (2016). "BDDCS, the Rule of 5 and drugability." Adv Drug Deliv Rev.
Broccatelli, F., et al. (2018). "Why Decreasing Lipophilicity Alone Is Often Not a Reliable Strategy for Extending IV Half-life." ACS Medicinal
Chemistry Letters 9(6): 522-527.
Cavalluzzi, M. M., et al. (2017). "Ligand efficiency metrics in drug discovery: the pros and cons from a practical perspective." Expert Opinion
on Drug Discovery 12(11): 1087-1104.
•
Charifson, P. S. and W. P. Walters (2014). "Acidic and Basic Drugs in Medicinal Chemistry: A Perspective." J Med Chem 57(23): 97019717.
•
Daina, A., et al. (2017). "SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of
small molecules." Sci Rep 7: 42717.
•
•
•
Eder, J., et al. (2014). "The discovery of first-in-class drugs: origins and evolution." Nat Rev Drug Discov 13(8): 577-587.
•
Gunaydin, H., et al. (2018). "Strategy for Extending Half-life in Drug Design and Its Significance." ACS Medicinal Chemistry Letters 9(6):
528-533.
•
•
Harrison, R. K. (2016). "Phase II and phase III failures: 2013-2015." Nat Rev Drug Discov 15(12): 817-818.
•
•
•
Hopkins, A. L., et al. (2014). "The role of ligand efficiency metrics in drug discovery." Nat Rev Drug Discov 13(2): 105-121.
ra
ry
ib
Faller, B. and P. Ertl (2007). "Computational approaches to determine drug solubility." Adv Drug Deliv Rev 59(7): 533-545.
ID
eL
Gleeson, M. P., et al. (2011). "Probing the links between in vitro potency, ADMET and physicochemical parameters." Nat Rev Drug Discov
10(3): 197-208.
M
Hill, A. P. and R. J. Young (2010). "Getting physical in drug discovery: a contemporary perspective on solubility and hydrophobicity." Drug
Discov Today 15(15–16): 648-655.
ES
C
Kola, I. and J. Landis (2004). "Can the pharmaceutical industry reduce attrition rates?" Nat Rev Drug Discov 3(8): 711-716.
Kramer, C., et al. (2018). "Learning Medicinal Chemistry Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Rules from
Cross-Company Matched Molecular Pairs Analysis (MMPA)." J Med Chem 61(8): 3277-3292.
Leeson, P. D. (2016). "Molecular inflation, attrition and the rule of five." Adv Drug Deliv Rev.
Leeson, P. D. (2018). "Impact of Physicochemical Properties on Dose and Hepatotoxicity of Oral Drugs." Chem Res Toxicol.
Leeson, P. D., et al. (2011). "Impact of ion class and time on oral drug molecular properties." Med. Chem. Commun. 2(2): 91-105.
©
•
•
•
•
by
•
Lipinski, C. A. (2016). "Rule of five in 2015 and beyond: Target and ligand structural limitations, ligand chemistry structure and drug
discovery project decisions." Adv Drug Deliv Rev.
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
35
au
th
or
Literature references
Lipinski, C. A., et al. (2012). "Experimental and computational approaches to estimate solubility and permeability in drug discovery and
development settings." Adv Drug Deliv Rev 64: 4-17.
•
Lovering, F., et al. (2009). "Escape from flatland: increasing saturation as an approach to improving clinical success." J Med Chem 52(21):
6752-6756.
•
•
Manallack, D. T., et al. (2013). "The significance of acid/base properties in drug discovery." Chem Soc Rev 42(2): 485-496.
•
Meanwell, N. A. (2011). "Improving drug candidates by design: a focus on physicochemical properties as a means of improving compound
disposition and safety." Chem Res Toxicol 24(9): 1420-1456.
•
Meanwell, N. A. (2016). "Improving Drug Design: An Update on Recent Applications of Efficiency Metrics, Strategies for Replacing
Problematic Elements, and Compounds in Nontraditional Drug Space." Chem Res Toxicol 29(4): 564-616.
•
Mignani, S., et al. (2018). "Present drug-likeness filters in medicinal chemistry during the hit and lead optimization process: how far can they
be simplified?" Drug Discov Today 23(3): 605-615.
•
Ran, Y., et al. (2001). "Prediction of Aqueous Solubility of Organic Compounds by the General Solubility Equation (GSE)." Journal of
Chemical Information and Computer Sciences 41(5): 1208-1217.
•
•
•
•
•
Shultz, M. D. (2014). "Improving the plausibility of success with inefficient metrics." ACS Med Chem Lett 5(1): 2-5.
•
Wohnsland, F. and B. Faller (2001). "High-Throughput Permeability pH Profile and High-Throughput Alkane/Water log P with Artificial
Membranes." J Med Chem 44(6): 923-930.
•
Wong, H., et al. (2017). "Translational pharmacokinetic-pharmacodynamic analysis in the pharmaceutical industry: an IQ Consortium PK-PD
Discussion Group perspective." Drug Discov Today.
•
Young, R. J., et al. (2011). "Getting physical in drug discovery II: the impact of chromatographic hydrophobicity measurements and
aromaticity." Drug Discov Today 16(17-18): 822-830.
by
•
ID
eL
ib
ra
ry
Manallack, D. T., et al. (2018). "The influence and manipulation of acid/base properties in drug discovery." Drug Discovery Today:
Technologies.
Tsopelas, F., et al. (2017). "Lipophilicity and biomimetic properties to support drug discovery." Expert Opin Drug Discov: 1-12.
M
Vistoli, G., et al. (2008). "Assessing drug-likeness--what are we missing?" Drug Discov Today 13(7-8): 285-294.
Ward, S. E. and P. Beswick (2014). "What does the aromatic ring number mean for drug design?" Expert Opin Drug Discov 9(9): 995-1003.
©
ES
C
Waring, M. J., et al. (2015). "An analysis of the attrition of drug candidates from four major pharmaceutical companies." Nat Rev Drug Discov
14(7): 475-486.
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
36
•
•
•
•
ra
ry
Exploratory
Candidates
Iterative
process
Characterization
M
ID
Discovery
Clinical Readiness
eL
ib
Assays & compounds
ES
C
Leads
©
Optimization
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
37
iterative
dynamic
responsive
multidimensional
by
Targets & pathways
au
th
or
Overview of Novartis Drug Discovery
and Development Pathway
Preclinical
Clinical
Evaluation
Clinical
H
O
H
N
N+
H
N
HO
HO
HO
Morphine: pKa’s : B (8.2) – A (9.3)
XH2+
80.0
60.0
40.0
20.0
0.0
1.0
ib
Ampholyte: "neutral" form exists at IEP
without formal charges
H 2N
H 2N
HN
O
F
O
OH
F
N
M
N
N
ES
C
N
100.0
O
O
O
-
N
N
O
F
O
O
-
Ciprofloxacin: pKa’s : A (6.2) – B (8.6)
©
Zwitterion: pseudo neutral form at IEP (pH=7.4)
 simultaneously + & - charged
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
38
Distribution of species (%)
+
+
5.0
7.0
9.0
pH (concentration scale)
11.0
Z-
ID
ZH±
3.0
IEP: Isoelectric point
eL
ZH2+
X-
XH
by
O
H
100.0
X-
ra
ry
O
O
XH
Distribution of species (%)
HO
XH2+
HO
au
th
or
Ampholytes and Zwitterions
ZH2+
80.0
ZH±
Z-
60.0
40.0
20.0
0.0
1.0
3.0
5.0
7.0
9.0
pH (concentration scale)
11.0
Trichlormethiazide
logP = 0.97
eL
ib
Acetazolamide
logP = -0.12
ID
𝑿𝑿𝒐𝒐𝒐𝒐𝒐𝒐
= 101 = 10
𝑿𝑿𝒘𝒘
𝑋𝑋𝑜𝑜𝑜𝑜𝑜𝑜 = 𝟏𝟏𝟏𝟏 ∗ 𝑋𝑋𝑤𝑤
Chlorzoxazone
logP = 2.1
Lasofoxifene
logP = 5.84
logP = 2
logP = 6
𝑿𝑿𝒐𝒐𝒐𝒐𝒐𝒐
= 102 = 100
𝑿𝑿𝒘𝒘
𝑋𝑋𝑜𝑜𝑜𝑜𝑜𝑜 = 𝟏𝟏𝟏𝟏𝟏𝟏 ∗ 𝑋𝑋𝑤𝑤
©
ES
C
𝑿𝑿𝒐𝒐𝒐𝒐𝒐𝒐
= 100 = 1
𝑿𝑿𝒘𝒘
logP = 1
M
logP = 0
𝑋𝑋𝑜𝑜𝑜𝑜𝑜𝑜 = 𝑋𝑋𝑤𝑤
ra
ry
by
au
th
or
logP: The Fundamentals
Logarithmic Scale
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
39
𝑿𝑿𝒐𝒐𝒐𝒐𝒐𝒐
= 106 = 1′ 000′000
𝑿𝑿𝒘𝒘
𝑋𝑋𝑜𝑜𝑜𝑜𝑜𝑜 = 𝟏𝟏𝟏𝟏𝟔𝟔 ∗ 𝑋𝑋𝑤𝑤
au
th
or
pKa measurement:
Instrumentation
Capillaries, for adding reagents
ib
ra
ry
by
UV Dip Probe.
ES
C
– UV-base titration
eL
M
• Automated titrator
ID
Sirius Analytical Ltd. http://www.sirius-analytical.com
Automatic
overhead
Stirrer
– Potentiometric titration
• More than just pKa determination
©
(logP, Intrinsic solubility,
Supersaturation ratio, pHmax...)
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
40
Glass vial,
4 ml total
capacity
Electronic
thermometer pH electrode,
diameter 3mm
Sirius Analytical Ltd. http://www.sirius-analytical.com
au
th
or
ES
C
M
ID
eL
ib
ra
ry
by
LogD: pH Dependence
Acids (AH)
©
logD ≤ logP (always!)
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
41
Curr Drug Metab.; 2008 Nov; 9(9):869-78
au
th
or
ES
C
M
ID
eL
ib
ra
ry
by
LogD: pH Dependence
Bases (B)
©
logD ≤ logP (always!)
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
42
Curr Drug Metab.; 2008 Nov; 9(9):869-78
au
th
or
Lipophilicity:
How it can be predicted
• Many commercial software packages:
ra
ry
by
– clogP (BioByte): tried and tested, based on old data
– Moka, ACD-labs, ADMET predictor, ChemDraw, COSMO-RS, ...
ib
• Internal model:
eL
– NIBR:logP: machine learning model, trained on internal and
P Gedeck, et al.; unpublished data
ES
C
M
ID
external data (>15k cpd’s)
Easy to calculate
Easy to measure
logP vs. logD
©
Difficult to measure
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
43
Difficult to calculate
au
th
or
clogP (BioByte)
Comparison to Experimental logP
ID
eL
ib
ra
ry
predicted Biobyte clogP
by
Difference
no qualified values
~3700 data points
ES
C
M
•
•
experimental logP
©
 Relatively large error
 Not very reliable prediction (project dependent)
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
44
eL
by
ib
ra
ry
predicted NIBR:logP
predicted Biobyte clogP
au
th
or
clogP (Biobyte) vs. NIBR:logP
Comparison to Experimental logP
13
13 %
%
40
40 %
%
33
33 %
%
23
23 %
%
35
35 %
%
27
27 %
%
ES
C
M
ID
20
20 %
%
99 %
%
experimental logP
experimental logP
©
 Highly improved performance compared to clogP
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
45
au
th
or
Solubility: How it can be predicted
by
• In-silico prediction of solubility VERY DIFFICULT
• Many approaches and methods
ib
ra
ry
– Fitted equations or estimates;
e.g. “General Solubility Equation”:
eL
Yalkowsky et al.; 1980 J. Pharm. Sci. 69(8): 912-922.
Jain et al.; 2001 J. Pharm. Sci 90(2): 234-252.
ES
C
M
ID
– Physics-based models
– Machine-learning models
 State-of-the-art:
©
0.7-1.0 log unit (i.e. x10) accuracy
Drug Likeness: PhysChem Properties / Andrea Decker / Lisbon, September 2018
46
From Skyner, R. E., et al. (2015). Phys
Chem Chem Phys 17(9): 6174-6191.