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