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Building a unified design-make-test-decide
approach to drug discovery
Uniting scientists, data, and decisions in a connected environment transforms fragmented
workflows into fast, insight-driven discovery.
Falling behind
Drug discovery has long followed a core
management, and data analysis in one shared
on emerging modalities
loop: design a molecule, synthesize it, test its
environment. Researchers can register com
biological properties, and analyze the results
The challenges of fragmentation are par
pounds,
manage assays, view results, and
to inform the next round of design. This itera
ticularly
evident in the development of
explore trends, with all data linked to its
tive
process, known as the design-make-test
new therapeutic modalities, such as ADCs,
experimental context. Built-in dashboards
analyze
(DMTA) cycle, has driven decades of
which involve multidisciplinary effort
help scientists quickly see what’s working
innovation in small-molecule therapeutics.
across several scientific domains. ADC
and what’s not, while compatibility with
The rise of modalities like antibody-drug
development begins with immunologists
other software allows teams to keep using
conjugates (ADCs) and engineered cell ther
and pharmacologists, who identify target
the tools they rely on.
apies
has added new layers of complexity
antigens, immunize animals, and generate
In complex therapeutic programs like
to this process. With specialized teams,
hybridomas. Next, protein chemists screen
ADC development, such integration is trans
diverse data types, and scattered systems,
the resulting antibodies and design expres
formative.
Instead of relying on disconnect
modern discovery programs face increas
sion
constructs. Assay scientists then
ed
systems, Signals One provides a unifie
ing
challenges in transitioning smoothly
assess these candidates through binding
workspace where each team stays aligned.
through the DMTA cycle and capturing timely
and functional assays to evaluate effica
Pharmacologists track immunization proto
insights to guide decisions. In this landscape,
and safety. Finally, medicinal chemists
cols
in the system, and assay scientists record
a more integrated and fluid approach is
design and synthesize cytotoxic payloads
screening data in real time for immediate com
becoming essential.
and linkers to conjugate to the antibodies.
parison
across clones. Protein engineers can
Each step generates rich datasets that
track sequences, manage constructs, and
The cost of fragmentation
inform subsequent decisions. But in tradi
visualize expression data — all in one con
In many organizations, the path from idea to
tional
environments, these workflows often
nected
workspace, while chemists capture
candidate is slowed by fragmentation. Sci
operate in isolation. Data may be stored
structure and assay data alongside earlier
entists
working across chemistry, biology,
in different systems with limited traceabil
results. As ADC constructs are synthesized
pharmacology, and informatics often rely
ity
or visibility across teams. Redundant
and tested, all related data — design history,
on separate systems that don’t communicate
work may occur because earlier results
conjugation details, in vitro assays — remain
with one another. A chemist might log com
are difficult to access or interpret, and
connected. Throughout, team members share
pound
designs in one platform, a biologist
promising candidates may be deprioritized
a complete view of each candidate’s jour
may record assay results in another, and a
due to a lack of integrated context. These
ney,
enabling fast, informed decisions and
data scientist might analyze outcomes using
inefficiencies cost time, raise development
workflow adjustments.
a third. The gaps between these systems are
costs, and limit the team’s ability to adapt
Better systems for better science
often bridged with spreadsheets, manual
based on emerging data.
exports, and email threads, creating a work
As scientific questions grow more com
Evolving the cycle:
flow
riddled with inefficiencie
plex
and discovery programs draw on a
design-make-test-decide
This fragmentation can have real conse
wider range of expertise, the systems that
quences.
Without a shared system, teams
To meet the demands of modern drug discov
support research increasingly shape what
may overlook important links between how
ery,
the team at Revvity Signals introduced
science looks like. The right tools do more
a molecule is built and how it behaves in
an evolved framework: the design-make-test
than improve speed and efficiency — they
biological tests. Valuable patterns can go
decide
(DMTD) cycle. This updated model
influence how teams define problems, share
unnoticed, and decisions may be made with
emphasizes decision-making as a central
insights, and pursue new ideas. When infra
out
the full picture.
and continuous part of the process. Rather
structure
keeps pace with the complexity of
A case study at AstraZeneca showed that
than treating data analysis as a retrospective
modern science, it creates space for break
by streamlining the DMTA cycle through
activity, the DMTD cycle encourages real-time
throughs
that might otherwise be missed.
cross-functional collaboration, digital tooling,
decisions that are directly informed by ongo
Frameworks like DMTD, built within Signals
and cohesive workflows, the team achieved
ing
design and experimentation.
One, connect people, data, and decisions —
a 4 6 p ercent reduction i n c ycle t ime a nd
Central to this vision is Signals One™, a
helping teams unlock their full potential.
cut the cost per candidate by 50 percent.
unified, cloud-native SaaS software solu
Reference
Plowright, A. T. et al. Hypothesis driven drug design:
These outcomes highlight not only the draw
tion
built to support the full DMTD cycle. It
improving quality and effectiveness of the design
backs
of fragmentation but also the value
brings together tools for molecule design,
make-test-analyse cycle. Drug Discovery Today
of connected systems.
synthesis planning, reagent tracking, assay
17, 56–62 (2012).
