CASE STUDY

Morphic Therapeutic
leverages digital chemistrystrategy to design anovel small molecule
inhibitor of α4β7 integrin
Collaborative enterprise platform and physics-based digital assays empowera team of experts to tackle a challenging target — α4β7 integrin

Performed rapid in silico design
cycles with collaborative platform

Balanced potency, selectivity,and permeability using a
structure-based design strategy

By combining Morphic’s
integrin drug hunterswith Schrödinger’s in silico
molecular design technologies,
we were able to successfully
deliver a novel small moleculewith the key potency,selectivity, and PK properties
for this highly validated
biological target where others
had previously failed.”— Matt Bursavich
VP, Head of Chemical Sciences
Morphic Therapeutic

Resulted in a development
candidate currently in Phase 2bTarget

α4β7 integrin

Program Type

Collaborative program,small moleculePartnerMorphic TherapeuticIndication

Inflammatory bowel
diseases (ulcerative colitis,Crohn’s disease)

Stage

Phase 2b clinical trial

DESIGN CHALLENGE
α4β7 integrin is a well validated target for the treatment ofinflammatory bowel diseases (IBD), such as Crohn’s disease
and ulcerative colitis. The approved biologic vedolizumab
is an antibody targeting α4β7 and is delivered via injection
or infusion, severely limiting accessibility and affordability.
An orally bioavailable small molecule therapeutic would
represent an enormous step forward in addressing these
diseases on a global scale. Despite R&D efforts for over 20years, the design of potent and selective small molecule
inhibitors of α4β7 has been unsuccessful due in large partto the daunting task of overcoming the protein’s structuralsimilarity to other integrin family members. Furthermore,
maintaining high potency while balancing optimal physicalproperties in the design of an oral small molecule inhibiting
protein-protein interactions is a formidable challenge.
Morphic Therapeutic, experts in all things integrin,
partnered with Schrödinger’s drug discovery team todesign an orally bioavailable drug selectively targeting
α4β7 integrin to treat inflammatory bowel disease. The
goal of the program was to leverage a digital chemistry
approach, using the power of physics-based predictivemodeling technologies combined with Morphic’s uniqueintegrin biology and chemistry insights, to design a highly
selective α4β7 inhibitor with best-in-class properties.

• Design• Ideation• Collaboration

Driving efficient, data-driven
design cycles with LiveDesign
Dispersed teams and remote work environments canpose a significant obstacle to real-time collaboration ina drug discovery program. LiveDesign, Schrödinger’s
cloud-based enterprise informatics platform, enables
project teams to design compounds, run predictivemodels, and analyze experimentally-generated datain one place, allowing teams to drive design-maketest-analyze (DMTA) cycles either independently or
collaboratively in teams. Centralizing data access anddesign tools within LiveDesign was imperative to theproject team’s ability to tackle their α4β7 integrin inhibitor
design challenge (Figure 1).
Using LiveDesign, the Morphic and Schrödinger teams
efficiently evaluated and triaged thousands of designideas much faster than with traditional methods, with far
fewer meetings required. Using LiveDesign as the dataand predictive modeling platform allowed the teams toutilize a multi-parameter optimization (MPO) strategy
driven by advanced data analytics, accurate biophysicalmodeling of potency and selectivity, and predictivemodels for pharmacokinetic (PK) properties. Enabled byseamless integration of data, visualization, and predictivemodeling, the team rapidly prioritized molecules forsynthesis and testing, which resulted in the identificationof novel chemical matter that combined the key
properties required to overcome all hurdles.

StructuralBiology

Medicinal
ChemistryComputationalChemistry• Crystallography• Physics-Based Modeling• Machine Learning (ML/AI)• High Performance Computing

Synthesis• Synthesis Tracking• Project Management• Data Analysis

Assay Data
DMPK Data

LiveDesign Integration• Project Data
• Structural Biology• Computational Models

Figure 1: Modeling strategy and DMTA cycle employed in LiveDesign for the α4β7 inhibitor program.

2

Gaining potency and balancing ADMET properties
through rigorous computational assays
Through generation of over 100 proprietary co-crystal
structures of inhibitor-bound α4β7 as well as α4β1 — akey off-target — the project team gained significant
novel insight into the structure-activity relationships(SAR) of potency and selectivity. However, balancingPK properties with potency remained unsolved. Toaddress this challenge the team combined advanced
pKa prediction (a parameter known to correlate withoral bioavailability) using quantum mechanics (Jaguar)with free energy perturbation simulations (FEP+) for
accurate potency prediction. The accuracy and utilityof FEP+ as a computational assay for the prediction ofrelative binding energies of molecules has been validated
extensively, generating predictions within 1.0 kcal/mol ofexperimental values on average.122

PotentialCandidates

Most recent compounds20Over 8,500 FEP+ calculations were performed,enabling the team to improve potency 1000-fold, while
simultaneously addressing PK liabilities. In parallel, amembrane permeability prediction model was applied
to each design as part of the MPO strategy. Compounds
that met the computational MPO were then synthesizedin the lab. All resulting data obtained for synthesizedanalogs — both modeled and experimentally derived— were stored and analyzed in LiveDesign. Compounds
that were identified to satisfy potency, selectivity, andpermeability criteria were aggressively pursued withfurther characterization (Figure 2). The team’s efforts
culminated in the design and delivery of MORF-057, aclinical candidate currently in Phase 2b development fortreatment of ulcerative colitis (Figure 3).

Oldest compounds
18

Increasing Permeability

1614121086

MDCK = 5420AJM
042

1

0.6

0.4

0.2

0.1

0.06 0.04

0.02

0.01

0.006 0.004

0.002

Increasing Potency

β7 high effector memory T cells

β7 central memory T cells

120120p=0.0303vs Placebo*110

Median % of baseline

Median % of baseline

0.001

Figure 2: Improving cellular potency andpermeability over time using a hypothesisdriven, property-focused computational
design strategy. The data points in dark blue
represent the early compounds, while thedata points in light blue represent the more
recently designed compounds. The red dashedline marks the MDCK value of 5; above 5
correlates to an increased absorption. AJM(black star) is a competitor compound that isapproved in Japan, requires three doses perday, and is not selective between α4β7 and
α4β1. A promising early compound designed byMorphic (orange star) was further optimized
and resulted in a compound (green star) witha potency 200x greater than AJM. Additional
optimization resulted in a number of potential
candidates in the upper right square,including what would become MORF-057.2

10090*p=0.0317
vs Placebo

11010090Placebo

50 mg BID

100 mg BID

25 mg BID

80

80
Predose

D1

D7

D14Predose

D1

D7

D14Figure 3: MORF-057 demonstrated changes in pharmacodynamic biomarkers consistent with those of approved antibody drugs.33Enabling digital technologies to drive discovery programs
LiveDesign

FEP+

JaguarCollaborative enterprise informatics
platform for centralizing access tovirtual and wet lab project data andpowerful computational predictions

Digital assay for predicting proteinligand binding across broad chemical
space at an accuracy matchingexperimental methods

High performance quantum
mechanics package
enabling pKa prediction

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.

Accelerating first-in-class and best-in-class programs using a large-scale digital chemistry strategy.
Bursavich et al. Endpoints Webinar.

3.

MORF-057, an oral selective α4β7 integrin inhibitor for Inflammatory Bowel Disease, leads to specific target
engagement in a single and multiple ascending dose study in healthy subjects. Ray et al. ECCO 2021.

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