CASE STUDY

Design of a novel,potent CDC7 inhibitor
development
candidate with highligand efficiency andoptimized properties
Use of a digital chemistry platform facilitates efficient multi-parameter
optimization of selectivity, cell potency, and toxicity at scale

24K

226

25 months

compounds scored with rigorous
physics-based modeling

total compounds
synthesized

to discovery ofdevelopment candidate“Through large-scale chemical
space exploration with our
digital platform, the team wasable to overcome several key
program challenges, including
a potential liability in the leadseries. A quick pivot led to thediscovery of SGR-2921, which isthe most potent CDC7 inhibitor
reported to date and possesses
strong drug-like characteristics.”— Adam Levinson
Director, Medicinal Chemistry
Schrödinger Therapeutics GroupTarget

CDC7, kinase

ProgramType

Schrödinger proprietary
program, small molecule

Indication

Hematological cancers andsolid tumors

Stage

Phase 1 clinical trial

DESIGN CHALLENGE
Cell division cycle 7-related protein kinase(CDC7) is a cell cycle kinase which plays
a key role in replication stress response.
Inhibition of CDC7 is a long pursued
strategy for targeting hematological cancers
and solid tumors. While drug discovery
efforts focused on the development ofsmall molecule inhibitors of CDC7 spanmore than 20 years, none have advanced
beyond initial Phase I or II studies as
most lacked the properties required forsuccessful clinical development.
Given the very high affinity of CDC7 to
its natural substrate, ATP, the primarychallenge is the development of highly
potent inhibitors — ideally picomolar, pM— to be substrate competitive while alsobalancing selectivity and other drug-likeproperties. Overall, the goal of the program
was to discover new chemotypes with bestin-class properties.

14

Better designs through crowdsourced
ideation and physics-based modelingTo rapidly iterate through high-quality ideas and analysis, the teamcrowdsourced ideation amongst 14 designers using LiveDesign, amodeling-enabled collaborative enterprise informatics platform forreal-time design. Drawing on a wealth of existing structural and activity
data, designs were evaluated through an iterative workflow using
relative binding affinities via FEP+, free energy perturbation technology(Figure 1). The accuracy and utility of FEP+ as a computational assay
for the prediction of relative binding energies of molecules has beenvalidated extensively, generating predictions within 1.0 kcal/mol ofexperimental values on average.1 This armed the entire team witha true digital assay, allowing reliable predictions of potency and an
unprecedented level of throughput.
Within four months of project start, the team identified four distinct,
ligand-efficient chemical series with high affinity, ranging from low
nanomolar to picomolar in biochemical assays. These chemotypesserved as starting points for further optimization of potency andpharmacokinetic (PK) properties.

Hundredsof designs inLiveDesign

designers

4
distinct
chemical series

FEP+modeling forpotency

Figure 1: Initial iterative crowdsourced ideation using LiveDesign and FEP+ to design novel, potent CDC7 inhibitors.

2

Overcoming selectivity and PK liabilities with next-generation
modeling techniques
Given the structural similarity inherent within kinase
families, achieving sufficient selectivity to mitigate offtarget effects is a core challenge of most kinase drug
discovery programs. While the team successfully identified
new, attractive chemical starting points, off-target
mediated toxicity remained unresolved after the firstround of design efforts. To address this selectivity liability,
the team created a structural hypothesis for off-target
binding using IFD-MD, Schrödinger’s state-of-the-art
induced fit docking engine that generates highly accurate
ligand binding poses in the absence of experimentalstructures. Poses resulting from IFD-MD were evaluatedby FEP+ to ascertain potency as compared to CDC7.
Ultimately, by utilizing this strategy the team identified aproof-of-concept compound with 10,000x selectivity for
CDC7 against the off-target in question.

Novel tight binding core with favorable
drug metabolism properties

1 nM

While selectivity was improved, the shift from biochemical
potency to cell-based potency was large and variable;
this could be attributed largely to suboptimal membrane
permeability. A physics-based model for passive
membrane permeability (Membrane Permeability)
explained the discrepancy observed between experiments
and enabled the team to prioritize designs with aconsistently lower shift from biochemical to cell potency.
Taking into consideration all factors required to satisfy thetarget product profile (TPP), the team deprioritized the leadseries in favor of a new backup scaffold identified by FEP+
that exhibited a superior balance of properties including
safety (Figure 2).

Balanced potency, selectivity, solubility
properties support PK/PD studies

100 pM

Improved permeability resolves
in vitro / in vivo disconnect

10 pM

Figure 2: Optimization of the lead series using physics-based molecular modeling strategies, in particular to improve kinomeselectivity and permeability while maintaining on-target potency.

Ultimately, the team’s efforts culminated in the discovery of SGR-2921, with binding potency of ~10 pM in SPR withligand efficiency > 0.6. SGR-2921 exhibits strong anti-tumor activity in vivo across multiple acute myeloid leukemia (AML)
xenograft models, both as a monotherapy and in combination with standard of care agents (Figure 3). Moreover, SGR-2921
demonstrates potent anti-tumor activity in AML patient-derived samples that are resistant to standard of care agents.
TGI (% Veh.)on Day 28
No treatment–SGR-2921: 5mg/kg
continuous dosing

98.3

SGR-2921: 5mg/kg
7 days on / 7 days off

99.7

SGR-2921: 5mg/kg
5 days on / 9 days off

98.8

Figure 3: (Left) SGR-2921 achieves tumor regression in the TP53 mutated MOLM-16 CDX model; (Right) SGR-2921 demonstrates tumor
growth control with intermittent dosing in the MV-4-11 disseminated model.2

3

Enabling digital technologies to drive discovery programs
FEP+

IFD-MD

Digital assay for predicting
protein-ligand binding
across broad chemical
space at an accuracy
matching experimental
methods

Accurate ligand binding
mode prediction for novel
chemical matter, for ontargets and off-targets

Membrane
Permeability
Physics-based, accurate
predictions of passive
membrane permeability

LiveDesign
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.

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

2.

Inhibition of CDC7 with SGR-2921 in AML models results in enhanced DNA damage and anti-leukemic activity
as monotherapy and in combination with standard of care agents. Tsvetkov et al. ASH 2022.

3.

Discovery of novel CDC7 inhibitors that disrupt cell cycle dynamics and show anti-proliferative effects incancer cells. Tsvetkov et al. AACR 2021.

4.

Huang X, Mondal S, Ghanakota P, Boyles N, Frye L, Gerasyuto A, Greenwood JR, Tang H, Levinson AM. Cyclic
Compounds and Methods of Using Same. U.S. Patent No. WO/2021/113492. 2021.

5.

Mondal S, Tang H,Huang X, Levinson AM, Frye L, Bhat S, Bos PH, Konst Z, Ghanakota P, Greenwood JR. Cyclic
Compounds and Methods of Using Same. U.S. Patent No. WO/2022/197898. 2022.

Contact us: sales@schrodinger.com
Learn more: www.schrodinger.com/platform/drug-discovery

Copyright © 2023 Schrödinger, Inc.

4