High-throughput, high-content cytometry screen predicts mitochondrial toxicity through machine learning-driven analysis of physiological responses.

1. High-throughput, high-content cytometry screen predicts
mitochondrial toxicity through machine learning-driven
analysis of physiological responses.
Andrew A. Bieberich, PhD 1, Bartek Rajwa, PhD 2, Raymond O. Fatig III 1,
Meena L. Narsimhan, PhD 1, Allison Irvine, MS 1, Brad Henke, PhD 3,
T. Vincent Shankey, PhD 1
1. AsedaSciences AG 2. Bindley Bioscience Center, Purdue University 3. Opti-Mol Consulting LLC
❶ Overview
Live-cell assay via automated, multiparametric
flow cytometry.
Data: multiparametric phenotypes are tensors of
compound distances from +/- controls.
Supervised machine learning classifier: compare
test compound phenotypes with training set of
known compounds.
Reference compounds, case studies.
❷ Introduction
❸ Methods: assay assembly
Pharmaceutical R&D uses phenotypic screens to study both mechanism of action and
cell stress potential of candidate compounds. However, screens are often
unidimensional, with low information content. Flow cytometry (FC) is an established
technique for multiparametric phenotypic analysis of single cells, but it is infrequently
used to screen compounds because manually guided data processing prevents rapid,
unbiased analysis and limits reproducibility. However, advances in machine learning
techniques have enabled automated phenotypic feature extraction from
multiparametric data.
The acute cellular stress screen described here interrogates 12 biological parameters
simultaneously, followed by a custom-developed analysis pipeline, which departs from
the tradition of representing “toxicity” by logEC50 values (logarithm of half maximal
effective concentration) for individual phenotypic markers. Instead, an automated
algorithm produces a tensor of distance values, between controls and each compound
dilution series, across all parameters. A supervised machine learning classifier scores
each compound relative to a training set of 370 carefully annotated sets of failed and
on-market pharmaceuticals, known mitochondrial toxins, and environmental toxins.
The assay is capable of recognizing ~50% of toxins present in the test database, which
includes compounds that failed for multiple reasons (organ-specific toxicities, DILI, toxic
metabolites, efficacy, etc.), with a positive predictive value (PPV) of 94%. The overall
performance makes this a unique and ideal screen for early-stage compound
evaluation, modification, and prioritization.
Biomek® 4000
❶ 16 compounds formatted as 10-step 3X dilution
series. Range = 10nM to 200µM. Series are
transferred in duplicate to 384- well format. +/-
controls formatted during same method.
❷ 2-hour incubation (room temperature, dark) to
allow binding equilibrium between test compounds
and serum protein.
Biomek® NXP
❸ HL60 cells deposited, final density 2.5x106/ml.
Final compound concentration range = 5nM to
100µM. Compound exposure 4 hours, 37°C, 5% CO2.
❹ Two alternate stain mixes use subsets of:
MitoSOX™ Red, monobromobimane, calcein AM,
SYTOX™ Red, Vybrant® DyeCycle™ Violet, propidium
iodide, JC-9.
HL60 cells
4 hour compound exposure,
add dye mix.
❹ Methods: data acquisition ❻ Results: reference compounds
COMPOUND
Morphology
CytoplasmicMembraneIntegrity
ReactiveOxygenSpecies
Glutathione
NuclearMembraneIntegrity(1)
CellCycle
NuclearMembraneIntegrity(2)
MitochondrialMembranePotential
SYSTEMETRIC®Probability
SYSTEMETRIC®ProbabilityHeatMap
Class_known
AMIODARONE HCL 0.78 0.47 0.12 0.57 0.12 0.92 0.58 0.74 90.00% yes
ANTIMYCIN A 0.91 0.24 0.75 0.38 0.23 0.37 0.26 0.60 70.00% unk
CHELERYTHRINE CHLORIDE 1.00 0.99 1.00 0.99 0.99 0.85 0.98 1.00 100.00% yes
CHLORPROMAZINE HCL 1.00 1.00 1.00 0.99 0.98 0.81 0.96 0.94 100.00% yes
FCCP 0.99 0.96 0.97 0.92 0.96 0.76 0.68 0.98 100.00% yes
GOSSYPOL 0.63 0.46 0.64 0.49 0.98 0.42 0.47 0.94 90.00% unk
LAPATINIB DITOSYLATE 1.00 1.00 0.73 0.87 0.87 0.92 0.90 1.00 100.00% unk
MASITINIB 1.00 0.99 0.95 0.95 0.33 0.65 0.84 0.99 100.00% unk
MYXOTHIAZOL 1.00 1.00 0.99 0.99 0.99 0.47 0.97 0.99 100.00% yes
NELFINAVIR MESYLATE HYDRATE 0.61 0.27 0.62 0.19 0.52 0.73 0.44 0.85 80.00% unk
OLIGOMYCIN 0.85 0.95 0.86 0.86 0.85 0.30 0.82 0.96 100.00% yes
PIOGLITAZONE 0.13 0.08 0.08 0.48 0.19 0.37 0.09 0.06 10.00% no
ROTENONE 0.84 0.60 0.81 0.56 0.22 0.28 0.52 0.98 90.00% yes
SIMVASTATIN 0.04 0.06 0.07 0.15 0.06 0.07 0.06 0.06 0.00% no
TERFENADINE 1.00 1.00 1.00 1.00 0.98 0.98 0.98 0.99 100.00% yes
VALINOMYCIN 0.90 0.30 0.75 0.67 0.35 0.29 0.39 0.85 80.00% yes
CyAn ADP™
HyperCyt®
HyperCyt® with CyAn ADP™ 3-laser, 9-color flow cytometer
❶ 10,000 cells/well acquired from 384-well platform. Raw FC data
moved to cloud location previously created for plate run.
❷ Plate map with well locations of test compounds and +/- controls
exported from LIMS to cloud location previously created for plate run.
Automated analysis on cloud
❸ When both the 384-well FC data and plate map appear in the
same directory, an automated analysis algorithm is triggered.
❹ A web-based control center enables review and downloading
of results for individual test compounds.
❺ Methods: analysis
Concentration5nM-100µM
Biological
parameters
❶ Tensor for
one compound
❷ Manifold
of tensors
Probabilityof
causingcellstress
Tensor position on hyperplane
❸ Supervised machine learning classifier
using logistic regression
❶ The data for one compound dilution series is a tensor of values representing
the QF distance from each concentration to each of the control compounds, across
all biological parameters.
❷ For the training set of literature annotated compounds, a manifold of tensors
was generated.
❸ A multidimensional classifier was trained to optimize a logistic regression model
so that phenotypes of known cell stressors and mitochondrial toxins are
consistently classified together. Test compound phenotypes are now assigned
probability of class membership using this classifier.
Sixteen reference compounds provide a variety of cell stress phenotypes that are
consistently used for assay quality control SOPs. Most produce phenotypes that
match their known class, meaning that literature annotation provides an
expectation of the observed mitochondrial or other cellular stress level. Five are of
unknown class with respect to annotation; however, these consistently produce
phenotypes which the multidimensional classifier groups with known toxins in the
370 compound training set.
❼ Results: case studies
COMPOUND
Morphology
CytoplasmicMembraneIntegrity
ReactiveOxygenSpecies
Glutathione
NuclearMembraneIntegrity(1)
CellCycle
NuclearMembraneIntegrity(2)
MitochondrialMembranePotential
SYSTEMETRIC®Probability
SYSTEMETRIC®ProbabilityHeatMap
Class_known
TERFENADINE (Seldane) 1.00 1.00 1.00 1.00 0.98 0.98 0.98 0.99 100.00% yes, heart
FEXOFENADINE (Allegra) 0.02 0.01 0.07 0.04 0.12 0.04 0.01 0.03 0.00% no
TOLCAPONE (Tasmar) 0.97 0.60 0.98 0.54 0.30 0.92 0.34 0.29 90.00% yes, liver
ENTACAPONE (Comtan) 0.08 0.52 0.01 0.56 0.43 0.04 0.07 0.04 0.00% no
ALPIDEM (Anaxyl) 0.82 0.10 0.86 0.55 0.63 0.56 0.09 0.19 70.00% yes, liver
ZOLPIDEM (Ambien) 0.04 0.03 0.04 0.27 0.02 0.34 0.01 0.04 0.00% no
NEFAZODONE.HCl (Serzone) 0.99 1.00 1.00 0.98 0.85 0.92 0.91 1.00 100.00% yes, liver
TRAZODONE.HCl (Desyrel) 0.00 0.02 0.01 0.05 0.17 0.01 0.07 0.07 0.00% no
BUSPIRONE.HCl (BuSpar) 0.01 0.04 0.01 0.03 0.01 0.05 0.03 0.05 0.00% no
Development cost saving potential
Terfenadine toxicity arises in people who are unable to metabolize terfenadine into
non-toxic fexofenadine due to drug interactions, age, genetics, or diet. Terfenadine
was withdrawn from market 12 years after release; however, this assay clearly
separates phenotypes caused by terfenadine and fexofenadine, signaling
terfenadine’s risk potential.
In each of the subsequent three compound groups, toxicity has correlated with the
ability to form reactive metabolites and has often been ascribed to metabolism per
se. This assay indicates that mitochondrial/cellular stress is an intrinsic property of
the withdrawn drugs (tolcapone, alpidem, and nefazodone HCl). The compounds in
each group are structurally related to one another.