Options for Automated Solubility Measurement In
Discovery and Developments:
Science Issues and Practical Solutions
Arnon Chait
ANALIZA, Inc.
1
OUTLINE
Solubility: the basics
 Solubility assays
 Particles in solution: why should we care?
 Focus: elemental solubility assay

– How does it work?
– Sample data for pH, excipient screening
DMSO effect:
The Good, the Bad, and the Ugly
 How do we deal with impure samples?

2
Assay Effects: Summary (1)
Turbidity
Large
DRY
INCREASE
Small
Absorbance
Variable/
unknown
DMSO
Elemental
3
Indirect
Direct
Assay Effects: Summary (2)

Solid particles are an integral part of
the solubility assay
– Particles are always present
– They must be present for turbidity to work
– They are artifacts in absorbance/elemental
assays

The effects of particles on the data
must be considered when examining
the data
– Subtle to substantial influence on quality of
results
4
Assay Effects: Summary (3)

DMSO effect can be very large, and is
unpredictable

Starting material has significant
influence on the data

DMSO effect can not be neglected, even
at small amounts

Late discovery and development should
avoid DMSO if at all possible
5
A Simple Concept
Solubility = Concentration of a
dissolved compound in equilibrium with
its solid
 But:

– Which solid?
 Equilibrium
(most stable form) vs. apparent
(other forms)
– Which solvent?
 Buffers
(intrinsic?) and co-solvents (kinetic)
– Which equilibrium?
 Time
6
(kinetic) and temperature
But It’s All in the Details
Conceptually a very easy experiment to
conduct
 But:

– We don’t always have the right form (discovery)
– We don’t always have time to wait for
equilibrium
– Everything matters:
Probably one of the most sensitive
thermodynamic experiments to conduct
– Different users refers to same term with
different meanings
7
Who Are The Users?

Discovery:
– Obtain a measure of solubility of relevance to HTS and early
ADMET
 Can we determine if the compound will crash out of
solution?
– Obtain ADMET information of relevance for selecting
amongst hits

Development:
– Early lead characterization
– Lead optimization
– Formulation excipient selection
8
Who Are The Users? (2)

Will the compound crash out?  solubility

Everyone else needs good data:
– For promoting a compound: binning may be
sufficient
– For lead optimization:
– For formulation development:
– For anything regulatory:
9
accurate data
accurate data
accurate data
How Does a Compound Come Out of
Solution?

Homogeneous vs.
heterogeneous
nucleation
G
Hard spheres
Insulin
r
r*
YBCuO
Courtesy: Rick Rogers, NASA GRC

Growth by dislocations
(crystalline) vs.
amorphous
http://monet.physik.unibas.ch/~lang/gallery/gallery.htm
10
Solid Forms
 Amorphous

Crystalline
Small molecules:


Multiple crystalforms
Amorphous is rare
Biomolecules:


11
Single crystalform
Amorphous is the norm
Key Differentiating Factor

Are you measuring
the actual
concentration?
Elemental:
Yes, directly
Elemental Concentration:
Carbon
Nitrogen
...
No standards / calibration
Evaporative Light
Scattering
No standards / calibration
Absorbance:
Yes, indirectly
Static Light Scattering
Turbidity:
No, solubility is
inferred from dilution
factor off a standard
12
Compound specific
Absorbance
What Controls the Size Distribution in Time?
Ostwald coarsening:

Particles nucleate at a minimum radius (critical radius)

Growth is an interplay between:
oversaturation level in the liquid
gain/loss from dissolving/growing neighbors particles

The particle size distribution is a solution this interplay
Lifshitz-Slezov and Marqusee and Ross theories predict a
continuous growth of larger particles at the expense of
smaller ones:
f (r , t ) t
13
1/3
But Why Should We Care?
We Can Always Filter the Particles

You can NOT filter all the particles
– You must have solid at solubility

Filtering simply places a dynamic cutoff limit to
the size distribution function – but it will again
change in time!
Time

Log(Size)

14
Log(Size)
Filtering is necessary to reduce the potentially
deleterious effects of particles
Take Home Message #1

Particles are a fact of life:
without them - we have no solubility

Particle size is changing dynamically

You can only interfere – for a while with the natural size distribution by
filtering

Each assay technique is sensitive to
particles to a different degree
15
Optical Properties of Particles in Solution
Gold particles
Scattering is
very small in
comparison with
absorbance
when the solid
particles are
small
Van de Hulst, 1957
16
Measured
Signal
Can We Predict Optical Effects of Particles
In Practice?

Light scattering depends on:
– Angle, refractive indices, sizes, cross section
efficiencies, concentration, wave length, structure
factor, time… - in a nonlinear and non-intuitive way!

Plus:
– Structure of the compound in solution changes with
concentration (monomers  dimers  trimers  …)
– Static light scattering is VERY sensitive to
everything:
 Free surface shape, scratches, dust, fingerprints, …

17
Too complex to predict in practice!
A Little Experiment in Light Scattering (1)
18
Can You Tell The Saturated Solution?
Saturated solutions
Phosphate buffer, pH 11
1 week incubation
Left to Right:
ChlorpromazineHCl
Bendroflumethiazide
Clofazimine
Bifonazole
ThioridazineHCl
TriflupromazineHCl
Nifedipine
Perphenazine
PromazineHCl
19
So Let’s Filter the Stuff

Dynamic Light Scattering:
Bendroflumethiazide, saturated, filtered
20
Filter,
m
Particle size,
nm
Polydispersity
0.2
481 – 4,000
0.226
0.45
270
0.096
1
543 - 2,000
0.327
Particle Effects in Optical Assays (1)

Turbidity:
– Relies on particles to produce scattering
– No filtering is desired
– Particle evolution is unpredictable

Absorbance:
– Scattering is an undesired artifact
I 0  I scattered  Itransmitted
– Filtering is desired
– Scattering contribution to data is unpredictable
21
Particle Effects in Optical Assays (2)
No particles:

No scattering

Beer’s law works
Is
Particles in solution:
22

Scattering

Beer’s law does
not work
It
I0
A ~ C

Determine from dissolved
samples (undersaturated)

Use
 in saturated solutions
The Ideal Experiment
70
60
Analytical Signal
50
All of these data
have particle
effects!
40
30
20
10
0
0
20
40
60
Caffeine concentration, mg/ml
23
80
100
Saturation is a Difficult Place To Assay
100
Analytical Signal
80
60
40
nm solids artifacts:
20
- Assay specific
- Significant for optical techniques
0
0
20
40
60
Caffeine concentration, mg/ml
24
80
100
Take Home Message #2
Particles will affect solubility data:

Turbidity:
Increase, perhaps significantly

Absorbance:
Increase, unpredictably

Elemental:
Increase, more predictably
25
What Do We Want to Study?
pH
Co-Solvent
/Excipient
Time
Solubility
Temperature
Salt
DMSO
26
Crystalform
How We Chose to Do the Job:
Automated Solubility Workstation

Philosophy of Design:
–
–
–
–
–
–
–
–
27
Accuracy is everything
Assay flexibility is a must
Medium throughput is acceptable (up to 250/day)
System must be compatible with dry and DMSOdissolved
Minimize number of assumptions
Native assay relies on elemental assays: no
standards required
Eliminate the need for experienced chemist by
using intelligent software
Simplify and modernize everything significant
Solubility: Where Do You Spend Your Time?

Weighing

Preparing standards (or believing
nominal library concentrations)

Deciphering data – trapping errors
28
Streamlined Solubility Assay

Weighing:
– Optional. Useful only for detecting
insufficient source material (undersaturated
solution)

Preparing standards (or believing
nominal library concentrations)
– Eliminated. You only need MW and #of
nitrogens

Deciphering data – trapping errors
– Intelligent data analysis, smart error
detection, automated reporting
29
Two Options on One Platform
Native Assay:

Equimolar nitrogen detection

Dry or dissolved sample

No calibration required
Optional Assay:
 Millipore MultiScreen®
solubility plates
30

DMSO dissolved samples

UV assay
Millipore MultiScreen®
Solubility Protocol
The ASolW
Native Assay
31
ADW - Automated Discovery Workstation
ANALIZA’s
ADW
In-house
Development
System
32
ASolW – Automated Solubility Workstation
33
Total Nitrogen Detection
1050
R  N  O2  NO  products
NO  O3  NO2*  O2  h (600  900nm)

Exceptions:
–
–
–
–
34
N2 – nothing is detected!
Nitrosamine (for GC)
Azides (1/3 response)
Double bonds (partial
response)
Separate
calibration
Effect of Nitrogen Structure
Compound
# N atoms
caffeine
4
HEPES
2
propranolol HCl
1
sulfanilamide
2
tris base
1
benzocaine
1
verapamil HCl
2
nicotinamide
2
alprenolol HCl
1
trimethoprim
4
phenacetin
1
Compound
antipyrine
allopurinol
35
# N atoms
2
4
Adjacent N?
None
None
None
None
None
None
None
None
None
None
None
mg/ml
Adjacent N?
mg/ml
2
2
1.35
6.29
2.52
2.07
4.97
0.60
0.47
2.65
1.67
0.12
0.17
Average
yield:
1.98
0.08
Yield %
94.7
90.9
96.5
98.3
100.4
100.2
86.0
99.7
102.7
86.7
110.1
96.6%
Yield %
82.1
76.4
Concentration Assayed by ASolW system, mg/ml
Assay Using Nitrogen Content
20
15
10
5
0
0
5
10
15
Nominal Caffeine Concentration, mg/ml
36
20
Dynamic Range
Analytical signal +/- 1 sd (n=3)
100
10
1
0.1
0.01
1
10
100
ppm N of calibrator
37
1000
10000
Accuracy
1 - Clofazimine; 2 - Nifedipine; 3 - Bendroflumethiazide; 4 - Perphenazine;
5 - Nitroflurazone; 6 - Butamben; 7 - Nitrofurantoin; 8 - Hydroflumethiazide;
9 - Allopurinol; 10 - Tolazamide; 11 - Phenacetin; 12 - Sulfanilamide
12
11
ASolW, mg/ml
Solubility Measured by ASolW, mg/ml
0.8
10
8
0.6
10
0.4
8
3
7
0.2
0.0
2
9
4
6
6 5
12
1
0.0
0.2
0.4
0.6
0.8
Literature Data
4
2
10
9
0
0
11
Sexp = 0.036(±0.018) + 1.005(±0.008)*Slit
N = 12; r2 = 0.9993; s = 0.0596,
2
4
6
Solubility of Compounds in Water (Literature Data), mg/ml
38
8
Data Dispersion
relative standard deviation, CV, % (n=3)
100
10
1
0.1
0.01
1
10
100
ppm N of calibrator
39
1000
10000
Repeated Measurements
0.5
Solubility, mg/ml
0.4
Average = 0.264
Upper Limit = 0.283
0.3
Lower Limit = 0.245
0.2
0.1
0.0
0
5
10
15
Run #
Allopurinol in 0.15M NaCl in 0.01M Universal buffer, pH 6.6
40
20
Effect of Filter Type on Concentration
Normalized concentration (to PTFE)
1.6
bendroflumethiazide
0.023mg/ml
1.4
butamben
0.130 mg/ml
1.2
acetazolamide
0.986 mg/ml
benzocaine
0.925 mg/ml
benzthiazide
0.066 mg/ml
1.0
0.8
0.6
nylon
PTFE 0.45
PTFE 0.2
PES
PP
PVDF
0.4
0.2
0.0
41
ANALIZA
recommends
PTFE 0.45
Solubility-pH Profiles
3.5
Solubility mg/ml
3.0
2.5
2.0
benzocaine
1.5
1.0
butamben
0.5
bendroflumethiazide
0.0
0
2
4
6
pH
0.01M universal buffer
42
8
10
12
Excipients/Co-Solvent Effects (1)
Solubility with PEG600
18
clofazimine
16
bifonazole
nifedipine
14
mg/ml
12
10
8
6
4
2
0
0
20
40
60
% PEG 600
43
80
100
120
Excipients/Co-Solvent Effects (2)
Solubility with Tween80
1.4
clofazimine
bifonazole
nifedipine
1.2
Solubility mg/ml
1
0.8
0.6
0.4
0.2
0
0
2
4
6
8
10
% Tween
44
12
14
16
18
What is the Effect of DMSO?

DMSO is a fact of life during screening

DMSO solubility is a current issue in
HTS
e.g. K. Balakin, “DMSO Solubility and Bioscreening”,
Current Drug Discovery, (August 03)

Key Issue: How did you start?
– DMSO-dissolved sample: NO solid
Precipitating solid at saturation  crystalline form
Difficult experiment requiring high concentrations
– DMSO added to solid form
45
DMSO – Time Experiments
30 mM DMSO stock solutions, 20-fold dilution
 Powder with buffer or with 5% DMSO
 50 mM phosphate buffer at pH 11

46
Source
% DMSO
Incubation
Time, hrs.
Powder
0
20
DMSO stock
5
20
DMSO stock
5
1
Powder
0
1
Powder
5
20
Test Compounds – Solubility (mg/ml)
9 verapamilHCl
1 bendroflumethiazide
2 nifedipine
0.0064
0.3507
10 clofazimine
0.0006
11 bifonazole
0.0009
12 clomipramineHCl
0.0004
13 labetalolHCl
0.5946
0.0013
3 nitrofurazone
0.1615
4 perphenazine
0.0006
5 trimethoprim
0.3440
6 nafoxidineHCl
0.0040
14 chlorpromazineHCl
0.0023
7 nadolol
0.0038
15 triflupromazineHCl
0.0013
8 imipramineHCl
0.0007
16 thioridazineHCl
0.0007
47
Solubility Relative to Total Compound Concentration
Group I: Fully Dissolved
48
Powder in Buffer alone, 20 hr incubation
30 mM DMSO stock, diluted 20-fold in buffer
Powder in buffer with 5% DMSO
Plot 1 Upper Control Line
1.2
1.0
0.8
0.6
0.4
0.2
0.0
#1
#5
#13
Group II: Similar Results
Powder in Buffer alone, 20 hr incubation
30 mM DMSO stock, diluted 20-fold in buffer
Powder in buffer with 5% DMSO
Plot 1 Upper Control Line
Solubility Relative to Powder in Buffer Alone
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
#3
49
#7
#11
#14
#15
Group III: Significant DMSO Stock Effect
Solubility Relative to Powder in Buffer Alone
40
Powder in Buffer alone, 20 hr incubation
30 mM DMSO stock, diluted 20-fold in buffer
Powder in buffer with 5% DMSO
Plot 1 Lower Control Line
30
20
10
0
#4
50
#2
#6
#10
#16
Group IV: Significant DMSO Effect
Solubility Relative to Powder in Buffer Alone
25
Powder in Buffer alone, 20 hr incubation
30 mM DMSO stock, diluted 20-fold in buffer
Powder in buffer with 5% DMSO
Plot 1 Upper Control Line
20
15
10
5
0
#4
51
#8
#9
#12
Powder Plus DMSO: Reasonable at Low %
pH 7.4
14
Solubility with buffer plus DMSO, mg/ml
Allopurinol
Bendroflumethiazide
Butamben
Clofazimine
Nitrofurazone
Theophylline
Nifedipine
Perphenazine
Phenacetin
Sulfanilamide
Trimethoprim
Solubility (2% DMSO) = Solubility (buffer) * 1.524
12
10
PBS vs. PBS + 2%DMSO
PBS vs. PBS + 1%DMSO
8
6
Solubility (1% DMSO) = Solubility (buffer) * 1.064
4
2
0
0
2
4
6
Solubility with buffer alone, mg/ml
52
8
10
Powder Plus DMSO: Reasonable at Low %
Solubility with buffer plus DMSO, mg/ml
0.5
PBS vs. PBS + 2%DMSO
PBS vs. PBS + 1%DMSO
0.4
0.3
0.2
0.1
0.0
0.0
0.1
0.2
0.3
Solubility with buffer alone, mg/ml
53
0.4
0.5
Kinetic Solubility from DMSO stock, mg/ml
DMSO Stock Effect is Still Significant at 1%
1.4
1.2
1.0
8
9
0.8
2
0.6
0.4
4
0.2
6
7
1
0.0
0.0
3
5
0.2
0.4
0.6
0.8
1.0
Equilibrium Solubility in PBS/ 1% DMSO
54
1
2
3
4
5
6
7
8
9
1.2
bendroflumethiazide
benzocaine
benzthiazide
butamben
nifedipine
nitrofurazone
perphenazine
phenacetin
trimethoprim
Take Home Message #3

DMSO effect is unpredictable

DMSO effect is most pronounced when starting
material is DMSO-dissolved

DMSO effect is still significant and unpredictable
in practice, even at low DMSO %, when starting
from DMSO-dissolved samples

DMSO-dissolved samples are fine for early
discovery questions, but can not be relied on for
later discovery and development
55
How Do We Deal With Impure Samples?

Impurities are a fact of life:
– Combi libraries are 90-95% pure
– Lead synthesis is similarly pure

Turbidity:
– No need, usually, but you don’t know what
came out of solution

Absorbance:
– Use spectral techniques – not trivially

Elemental:
– Clean sample: inline column + fast gradient
56
Final Thoughts

If you need good numbers, you have to
watch your steps:
– Particle effects in optical methods
– Filter effects
– DMSO effects when starting from dissolved
samples

Elemental method provides for automated,
accurate determinations with full
accessibility to later discovery/development
questions: pH, temperature, salts, etc…
57
Coming Up: Integrating ADMET (1)
Hardware is expensive
 Software is very expensive
 Gold-standard assays are always that
 Only big pharma can own “one of each”
 In silico predictive analysis works much
better on same series, if properly trained
 Chemists will use your data if it is GOOD,
USEFUL, COMPREHENSIVE, and EASILY
AVAILABLE

58
Coming Up: Integrating ADMET (2)
Automated Discovery Workstation:
 Multi-assay on the same hardware platform:
–
–
–
–





59
Solubility (dry/DMSO, pH, salts,…)
LogD (pH)
BBB (near term GIT) permeability
Coming: Tox (cardiac), protein binding
Nothing proprietary, no magic, only goldstandard assays
Never have to weigh or prepare standards
Never have to believe your library concentration
Optional on-line sample cleanup
Future integration with predictive software
Thank You
Acknowledgements
Padetha Tin
Pam Lechner
Alexander Belgovskiy
Boris Zaslavsky
60