Is Fragmented Discovery the Silent Saboteur of Complex Medicines?

Introduction: From Breakthrough Science to Breakthrough Execution

Over the past decade, biotechnology has delivered extraordinary scientific advances. Novel targets, sophisticated modalities, and increasingly precise therapeutic strategies are redefining what is possible in medicine. Peptides, oligonucleotides, conjugates, and hybrid modalities are no longer niche technologies; they are becoming central pillars of modern drug discovery.

Yet despite this scientific progress, the industry continues to struggle with a familiar problem: turning promising ideas into successful clinical candidates.

Attrition remains high, timelines remain long, and capital efficiency remains fragile. Many programmes do not fail because the science was wrong, but because risks were discovered too late or decisions were made in isolation, causing development paths to drift without visibility.  

This is where an integrated Design–Make–Test–Analyse (DMTA) framework becomes essential. DMTA aligns chemistry, biology, analytics, and CMC thinking from the outset, ensuring every iteration sharpens insight and reduces uncertainty to generated connected data. By tightening collaboration and eliminating gaps between disciplines, integrated approaches can shorten iteration cycles by up to 40% while improving the overall developability of emerging candidates (McKinsey & Company, The 8 ingredients to biopharma R&D productivity).

In today’s investment climate, the economic environment for biotech has become more demanding. Capital is more selective, and funding milestones are closer together; scientific novelty alone is no longer sufficient. Success now depends on a company’s ability to manage complexity deliberately — to guide programmes along a critical experimental path that balances ambition with realism, speed with robustness, and innovation with scalability. DMTA provides the operating model to achieve this.

In this blog, I will explore the unique challenges that complex modalities introduce into modern biotech drug development and examine how integrated DMTA workflows help surface and address these risks earlier in the journey by delving into each phase of the cycle. By looking at why traditional, fragmented approaches fall short, and how DMTA supports stronger candidates with clearer progression, we set the foundation for understanding why integration is now essential for translational success.

The New Reality of Biotech Drug Development

Biotech drug development now operates under two converging pressures:

1. Therapeutic innovation is becoming increasingly complex. Peptides, oligonucleotides, conjugates, and hybrid modalities promise greater precision and novel mechanisms of action, but they also introduce multi-dimensional challenges in chemistry, biology, analytics, manufacturing, and regulation. These molecules behave differently from traditional small molecules at every stage of development.
2. The economic environment for biotech has changed. Programmes are no longer judged solely on biological novelty, but on their credibility to progress efficiently towards the clinic.

This creates a central challenge for modern biotech:

How do you advance highly complex medicines at pace, without accumulating hidden scientific, technical, and regulatory risk that later destroys value?

Complexity Changes the Nature of Risk

For complex modalities, failure is rarely driven by a single issue. These molecules must simultaneously meet requirements for biological activity, stability, delivery, analytical control, manufacturability, and regulatory acceptability. These properties are tightly coupled; improvements in one might compromise another.

Crucially, many liabilities originate in early design decisions, but only become visible much later — often during scale-up, toxicology, or regulatory preparation, when timelines are compressed and capital is already deployed.

Large-scale analyses of pharmaceutical R&D productivity consistently show that late-stage failure is often caused by issues that could have been identified earlier with better integration and decision-making (Scannell et al.; Paul et al., Nature Reviews Drug Discovery).

The Cost of Fragmentation

Traditional discovery workflows evolved around functional separation: chemistry, biology, analytics, and CMC were treated as independent activities. In outsourced models, this separation is often amplified across multiple vendors.

While this approach can appear efficient in the short term, it obscures the true critical experimental path of a programme:

• Chemistry decisions are made without manufacturability insight
• Biological data is generated without analytical or stability context
• CMC and regulatory considerations are deferred until late development

Regulatory agencies consistently identify missing or delayed CMC and stability data as major contributors to IND and IMPD delays (FDA guidance; EMA Guideline on Chemical and Pharmaceutical Quality Documentation for Investigational Medicinal Products, Rev. 2; Parexel, Optimise Your CMC Strategy: Five Steps to Avoid Regulatory Setbacks).

Fragmentation does not reduce risk — it delays its discovery.

Integrated DMTA: A Framework for Managing Complexity

Design–Make–Test–Analyse (DMTA) is often described as an iterative loop, but for complex medicines, it is better understood as a decision architecture.

DMTA integrates chemistry, biology, analytics, and development insight from the outset. Each cycle is evaluated not simply by activity, but by whether it improves the quality of the next decision.

At CatSci, this philosophy is formalised as Design–Make–Test–Select (DMTS), where each iteration is structured to support robust candidate nomination and IND readiness, rather than incremental optimisation.

Design: Translational Insight from the Start

In complex modalities, design is where molecular architecture, translational behaviour, and development feasibility first converge.

For peptides, oligonucleotides, and bioconjugates, molecular architecture features, such as linker choice, conjugation strategy, and chemical modifications, shape, biological performance, stability, manufacturability, and ultimately clinical viability.

Within a DMTA framework, design becomes a translational discipline that integrates chemical creativity with a deep understanding of how structural decisions propagate through biological systems and development constraints.

This is especially critical for linkers and bioconjugates, which are often the least visible but most consequential components of complex medicines. Linker length, flexibility, chemistry, and cleavage mechanisms directly influence target engagement, cellular uptake, biodistribution, stability, and safety. Decisions made at this stage can determine whether a conjugated molecule behaves as intended in vivo — or fails due to premature release, poor exposure, or unexpected toxicity.

A DMTA-driven design phase integrates SAR insight, linker and bioconjugation strategy, stability and release mechanisms, tissue distribution, synthetic and analytical implications, and anticipation of regulatory expectations from the outset.

Once biological data is generated on a given architecture, linker and conjugation changes are notoriously difficult to make without invalidating prior results. Industry analyses show that many late-stage failures stem not from incorrect biology, but from architectural liabilities that were not interrogated early (Scannell et al.; Paul et al., Nature Reviews Drug Discovery).

By embedding translational and developability insight at this stage, DMTA ensures that scientific ambition is directed toward architectures capable of surviving the full development journey.

Make: Scalability as a Scientific Variable

For peptides, oligonucleotides, and bioconjugates, synthesis is rarely straightforward. Specialised chemistries, sensitive intermediates, and tight impurity tolerances mean that small architectural changes can significantly alter both biological behaviour and analytical tractability, particularly as programmes move toward scale.

In a DMTA framework, the Make phase is structured as an integrated scientific activity, developed in parallel with design and testing rather than as a downstream service.

Route and process choices are selected with iteration and scalability in mind. Chemistry evolves alongside biological understanding, supported by early awareness of impurity formation and analytical detectability.

A peptide or oligonucleotide that performs well at milligram scale but cannot be reliably reproduced or controlled at gram scale is not a viable candidate. Similarly, a bioconjugate whose heterogeneity cannot be resolved analytically introduces unacceptable development and regulatory risk.

Regulatory guidance and industry experience consistently identify CMC deficiencies as major contributors to IND and IMPD delays (FDA CMC guidance; EMA quality documentation guidelines; Parexel). DMTA directly addresses this by embedding CMC awareness into the Make phase from the outset.

At CatSci, execution across small molecules, peptides, oligonucleotides, and conjugates within a single integrated platform preserves learning continuity, accelerates iteration, and reduces cumulative development cost.

Test: Fit-for-Purpose Experimentation to De-Risk Complex Medicines

Testing is often where programmes unintentionally lose focus. Traditional discovery models tend to equate progress with data generation, but data alone does not reduce uncertainty. For peptides, oligonucleotides, and bioconjugates in particular, programmes frequently advance with strong biological signals but limited understanding of factors that ultimately determine development success.

In DMTA, testing is a strategic exercise, explicitly designed to clarify the most consequential uncertainties at each stage. The objective is not exhaustive characterisation, but interrogation of what matters most at each decision point.

Compounds rarely fail because biological activity was never demonstrated; they fail because stability, exposure, formulation sensitivity, analytical fragility, or emerging safety concerns were not understood early enough (Kola & Landis; Cook et al., Nature Reviews Drug Discovery).

DMTA-driven testing prioritises decision-relevant experiments. Early biological assays are complemented by targeted developability assessments that highlight risks associated with degradation pathways, aggregation, heterogeneity, or formulation dependence. Testing strategies evolve with the programme, shifting focus as uncertainties are resolved.

This approach aligns with modern preclinical development, which emphasise fit-for-purpose experimentation, integrated data packages, and mechanistically informed models to support earlier, more confident decisions (FDA guidance; EMA expectations; Mehta K et al., ACS Pharmacology & Translational Science, 2025).

Analyse: From Data Generation to Decision Architecture

Analysis is the stage where complexity is either clarified or compounded.

Within DMTA, analysis functions as a decision architecture that integrates chemistry, biology, analytics, and developability into a coherent view of candidate quality. It makes trade-offs explicit, highlights risks, and ensures that decisions are grounded in evidence.

This approach aligns with regulatory expectations for traceable, decision-ready data packages (FDA; EMA; Cook et al.; Mehta K et al., 2025). Integrated analysis improves scientific understanding, reduces rework, and underpins the cycle-time reductions reported for integrated R&D models (McKinsey & Company).

Analysis within DMTA is therefore not about reporting past work — it enables the next decision with confidence.

Discover how our teams leverage DMTA to streamline oligonucleotide design and testing in our case study.

DMTA, Pace, and Capital Efficiency

The cumulative effect of DMTA integration is structural efficiency. Industry analyses indicate that integrated R&D models can reduce design–make–test cycle times by up to ~40%, largely through eliminating rework and early exposure of risk (McKinsey & Company, The 8 Ingredients to Biopharma R&D Productivity).

More importantly, DMTA improves the quality of candidate nomination, reducing the probability of expensive failure after substantial capital deployment.

Why This Matters for Investment and Value Creation

Value inflection in biotech is increasingly driven by credible execution as much as by scientific novelty. Investors increasingly assess whether programmes understand their risks, have a credible path to IND, and can deliver regulator-aligned data efficiently.

Integrated DMTA workflows directly support these expectations.

They give investors confidence that a programme is not just scientifically compelling, but developmentally sound.

Conclusion: Integration as the New Standard for Success

As the biotechnology industry evolves, one truth is becoming increasingly clear: complex medicines demand integrated thinking.

Design–Make–Test–Analyse provides a framework for meeting this challenge. By aligning scientific ambition with translational reality, regulatory expectations, and economic discipline, DMTA enables teams to move faster without cutting corners.

In today’s biotech landscape, DMTA is not simply a workflow.

It is the operating model of modern drug development.

Design-Make-Test-Analyse-Select at CatSci

At CatSci, we address the challenges of fragmented discovery and development by delivering fully integrated Design-Make-Test-Analyse workflows for peptides, oligonucleotides, and synthetic conjugates. Instead of separating chemistry, biology, analytics, and CMC across different teams or vendors, our multi-disciplinary experts work as one seamless unit. This ensures that every design decision is informed by manufacturability, analytical robustness, biological relevance, and regulatory expectations from the outset.

Where traditional models often generate chemistry without scalability insight, biology without stability context, or defer CMC considerations until after significant investment, our integrated approach brings these factors together early on. This prevents late-stage surprises that frequently delay INDs and inflate costs, enabling risks to be identified and mitigated while there is still time to act.

Crucially, our approach moves beyond iteration. At CatSci, DMTA is formalised as Design-Make-Test-Select, where each cycle is explicitly structured to support confident decision-making, robust candidate nomination, and IND readiness. Rather than endlessly optimising within a circular workflow, our DMTS process creates a deliberate progression towards selection, ensuring that learning is translated into clear go/no-go decisions at the right time.

If you’re exploring how DMTA and purposeful selection could strengthen your programme, we’d be delighted to discuss how we can support you.

References

1. Scannell JW et al., Diagnosing the Decline in Pharmaceutical R&D Efficiency, Nature Reviews Drug Discovery
2. Paul SM et al., How to Improve R&D Productivity, Nature Reviews Drug Discovery
3. Cook D et al., Lessons Learned from Attrition, Nature Reviews Drug Discovery
4. Kola I, Landis J, Can the Pharmaceutical Industry Reduce Attrition?, Nature Reviews Drug Discovery
5. McKinsey & Company, The 8 ingredients to biopharma R&D productivity
6. FDA Guidance for Industry: IND and CMC Development
7. EMA Guideline on Quality, Non-Clinical and Clinical Requirements
8. Parexel, Optimising Your CMC Strategy to Avoid Regulatory Setbacks
9. Mehta K, et al. Modernizing Preclinical Drug Development: The Role of New Approach Methodologies. ACS Pharmacol Transl Sci. 2025 May 29;8(6):1513-1525. doi: 10.1021/acsptsci.5c00162. PMID: 40567279; PMCID: PMC12186754.