CASE STUDY

Hit to development
candidate in 10
months: Rapid
discovery of a novel,
potent MALT1 inhibitor
Digital chemistry platform provides scale and accuracy to drive
high precision molecular design

8.2 billion

78

10 months

compounds
computationally evaluatedtotal compounds
synthesized in lead series

to discovery ofdevelopment candidate“The ability to leverage thecomputational platform
to rapidly identify not justone, but several novel,
highly potent series withwell-balanced properties
is unique in my many years
experience in industry.”— Zhe Nie, Project Lead
Executive Director, Medicinal Chemistry,
Schrödinger Therapeutics Group

Target

MALT1, protease

ProgramType

Schrödinger proprietary
program, small molecule

Indication

Relapsed or refractory
B-cell lymphoma, chronic
lymphocytic leukemia

StagePhase 1 clinical trial

DESIGN CHALLENGE
Mucosa-associated Lymphoid Tissue
Lymphoma Translocation Protein 1 (MALT1)
is a genetically validated target for thetreatment of diseases associated withlymphocyte regulation. MALT1 consists ofthree domains: a paracaspase proteasedomain, an Ig3 domain, and a linking
helix. First generation MALT1 inhibitors
consisted of large peptidomimetics
targeting the protease domain; due to theirpoor drug-like properties, none made it
into the clinic. Second generation MALT1
inhibitors targeting an allosteric region
at the interface of the caspase-like andIg3 domains have been more successful,
resulting in a clinical stage compound.
Significant challenges exist in optimizing
the properties of second generation MALT1
inhibitors, specifically permeability, efflux,and solubility, while maintaining on-target
potency. The aim of this program wasto discover a potent inhibitor with goodoverall drug-like properties to support
combinations with standard of care agentsfor treatment of relapsed or refractory
B-cell malignancies.

Scale and accuracy of digital assays drives
efficient DMTA cycles
Finding a novel molecule with the right balance of on-target affinity and desired
physicochemical properties is the essential challenge of every drug discovery
program. In principle, increasing the number of rationally designed compounds
assessed across these various properties increases the odds of success. Designing
molecules in silico — with the speed and accuracy to traverse billions of molecules— is the guiding ethos of Schrödinger’s digital chemistry strategy. Specifically, thisproject combines rigorous physics-based modeling with machine learning (ML),predictive ADMET models, and data analytics to search and triage a chemical spaceconsisting of more than 8B compounds. Ultimately, execution of this strategy enabled
the identification of multiple novel series.
First, the team performed structure-activity relationship (SAR) analysis of existingchemical matter, followed by computational assessment of the allosteric binding siteusing WaterMap. As a result, the team identified a number of displaceable highenergy water molecules in regions of the binding site that provided an opportunity togain potency while exploring different chemotypes.1
Schrödinger’s drug discovery team used this information to drive the evaluation ofbillions of compounds via a De Novo Design strategy for iterative large-scale designand scoring. This strategy included synthetically-aware, reaction-based enumeration,
crowdsourced medicinal chemistry ideation, and FEP+ for free energy perturbation
modeling. The accuracy and utility of FEP+ as a computational assay for theprediction of relative binding energies of molecules has been validated extensively,
generating predictions within one kcal/mol of experimental values on average.2 By
combining FEP+ with high performance cloud computing and machine learning(Active Learning FEP+), over 1,700 molecules were evaluated in the first three monthsof the project. All ideas and corresponding modeled data crowdsourced by the team
were captured and analyzed with LiveDesign, a best-in-class, modeling-enabledcollaborative enterprise platform for real-time project ideation (Figure 1). In less thanthree months, with fewer than 50 total compounds synthesized, the team was able
to identify two novel and distinct series of highly potent MALT1 inhibitors, affording
progression to in vivo testing.

PREDICT
Digital assays forpotency, solubility (FEP+);models for permeability
and clearance (ADMETModeling)

DESIGNSAR analysis
of publishedchemicalmatter

Crystallization& structure
modeling

Crowdsourced
medicinal chemistryideation

CAPTURE,COLLABORATE& ANALYZE

Synthetically-awarelarge-scale chemical
enumeration & ﬁltering

Figure 1: Modeling strategy and design-make-test-analyze(DMTA) cycle employed for MALT1 inhibitor program.

MAKE —TEST
Synthesis &experimental
assaysDevelopmentcandidate2

Once potent chemical series were identified, the teamfocused on tuning physicochemical properties to meet thetarget product profile (TPP). Specifically, they employed asingle multi-parameter optimization (MPO) score to triage
molecules rapidly based on their predicted ability to satisfy
the TPP. Calculation of the MPO score was based on values
derived from predictive models for solubility, permeability,selectivity, and potency. Using this strategy the design teamassessed over 5,000 ideas and identified 43 compounds
that met the program’s criteria. A handful progressed tosynthesis and experimental testing, reducing cost andtime significantly.
Within 10 months and a total of 78 compounds
synthesized in the lead series (and 129 compounds
program wide), the project team identified a potential bestin-class MALT1 inhibitor with balanced properties and ontarget activity, SGR-1505 (Figure 2). SGR-1505 demonstrates
strong positive effects in patient-derived B-cell tumor
models both as a single agent as well as in combination
with existing standard of care (Figure 3).

Criteria

JNJ-67856633

ONO-7018

JanssenOno

SGR-1505

MALT1 BiochemicalPotency
MALT1-mediated Cell
Growth Inhibition
Clearance (humanliver microsome)
Permeability/Efflux
Improved byformulation

Solubility
Plasma Clearance(mouse/rat/dog/cyno)
Oral Bioavailability
hERG Inhibition
Cytochrome P450
Inhibition
Figure 2: Comparison of SGR-1505 with competitor’s
MALT1 inhibitors; all competitor data was internallygenerated by contract research organizations.





Overcoming the MPO
challenge by tuning potency,solubility, and permeability
simultaneously












Figure 3: SGR-1505 shows strong anti-tumor activity in ABC-DLBCL
tumor models as single agent and combination therapy in vivo.3

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Enabling digital technologies to drive discovery programs
FEP+
Digital assay for predicting
protein-ligand binding
across broad chemical
space at an accuracy
matching experimental
methods

De Novo Design
Workflow
Ultra-large scalechemical space
exploration combining
multiple compoundenumeration strategies
with an advanced
filtering cascadeWaterMap

LiveDesign

Calculation of thepositions and energies ofwater sites in a protein
binding pocket

Collaborative enterprise
informatics platform
for centralizing accessto virtual and wet lab
project data and powerfulcomputational predictions

Software and services to meet your organization’s needs
Industry-Leading Software Platform
Deploy digital drug discovery workflows using a comprehensive and user-friendly platform for molecularmodeling, design, and collaboration.

Research Enablement Services
Leverage Schrödinger’s team of expert computational scientists to advance your projects through key stages
in the drug discovery process.

Scientific and Technical Support
Access expert support, educational materials, and training resources designed for both novice andexperienced users.

References
1.

Calculating Water Thermodynamics in the Binding Site of Proteins — Applications of WaterMap toDrug Discovery. Cappel et al. Curr. Top. Med. Chem. 2017, 17(23), 2586-2598.

2.

Advancing Drug Discovery Through Enhanced Free Energy Calculations. Abel et al. Acc. Chem. Res. 2017,50, 7, 1625–1632.

3.

Characterization of Potent Paracaspase MALT1 Inhibitors for Hematological Malignancies. Yin et al.
ASH Presentation 2021.

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