Christopher Conway has extensive experience in both small molecule and Biologics drug discovery.
Credit: Bio X Cell
Preclinical drug discovery is no longer built around a small set of standardized animal models and off-the-shelf reagents. As organoids, new approach methodologies (NAMs), AI-guided workflows, and increasingly complex biologics move into mainstream use, the experimental systems used to generate early data have become more sensitive — and less forgiving — of variability. Tools that were once taken for granted are now influencing experimental outcomes in measurable ways.
These shifts are forcing researchers to re-examine long-held assumptions about reproducibility, comparability, and what it really means for a reagent to be “fit for purpose.” To explore how those expectations are changing for antibodies in particular, DDN spoke with Christopher Conway, CEO of Bio X Cell. In this conversation, Conway reflected on how evolving models and therapeutic formats are reshaping the role antibodies play in preclinical research — and why quality and consistency can no longer be treated as secondary considerations.
What has changed in preclinical research that makes antibody quality and consistency more consequential to drug discovery outcomes than ever before?
Preclinical research has shifted from relatively simple, reductionist systems to highly complex, human-relevant models such as organoids, NAMs, and increasingly AI-guided workflows. In these systems, variability that was once tolerated is now amplified. Antibody quality and consistency have become far more consequential because they directly influence biological interpretation, not just signal detection.
As models better reflect true physiology, any inconsistency in reagents, including activity, purity, or formulation, can distort outcomes and undermine translational relevance. In many ways, antibody quality is now a first-order variable in drug discovery rather than a background assumption.
As drug discovery moves toward more human-relevant models like organoids and NAMs, what new demands does that place on the tools researchers rely on?
Human-relevant models are more sensitive to subtle biological perturbations and place much higher demands on reagents. Researchers now require tools that are:
- Functionally consistent, not just structurally defined
- Free of confounding impurities such as endotoxin or carrier proteins
- Optimized for biological context, not repurposed from other applications
These models expose differences that were previously masked, so reagents must be designed for precision and reproducibility in complex systems.
How important is it that antibodies behave consistently across animal models, human organoids, and tissue slices — and what enables that consistency?
Consistency across systems is critical because drug discovery increasingly depends on translating findings from model systems to human biology. If an antibody behaves differently across animal models, organoids, and tissue systems, it becomes difficult to interpret whether outcomes reflect biology or reagent variability.
Consistency is enabled by:
- Stable, well-controlled production processes
- Defined formulations optimized for functional use
- Rigorous validation across relevant biological contexts
Ultimately, reproducibility across systems builds confidence that observed effects are real and translatable.
What invisible factors most often corrupt in vivo data?
The invisible factors that most often corrupt in vivo data are typically not the intended biology, but the reagents themselves. These include endotoxin contamination, aggregation or instability, the presence of carrier proteins or stabilizers, and lot-to-lot variability in functional activity. Together, these factors can trigger immune responses, alter pharmacodynamics, or introduce unintended signaling, leading to false positive or false negative conclusions that are often difficult to detect.
How do you see NAMs and in vivo studies interacting to advance translational workflows, and what quality considerations will remain essential across both systems?
NAMs and in vivo studies are becoming complementary rather than competing approaches. NAMs provide human-relevant mechanistic insight and the ability to quickly screen many samples, while in vivo studies remain essential for systemic and whole-organism validation. Across both systems, quality considerations remain constant, including functional reproducibility, controlled formulation, and biological compatibility. As workflows integrate these approaches, reliable, consistent reagents are what make translation between models possible.
What’s the biggest misconception about recombinant antibodies?
The biggest misconception is that recombinant antibodies are inherently superior simply because they are sequence-defined. Sequence information matters, but on its own it doesn’t guarantee that an antibody will behave consistently or perform well in complex biological systems. Like any other reagent, recombinant antibodies still need to be designed for the specific job they’re meant to do, manufactured under tightly controlled conditions, and carefully validated for their intended biological use. Without that, a defined sequence alone does not ensure reliable results.
Clinical pipelines are rapidly moving toward bispecifics and Fc-engineered antibodies. Why hasn’t preclinical tooling kept pace?
Clinical innovation has outpaced preclinical tooling, largely because tool development has traditionally prioritized availability over functional fidelity. As bispecifics and Fc-engineered antibodies introduce more complex mechanisms of action, preclinical tools have struggled to keep pace. For a long time, traditional antibody formats were considered “good enough” for earlier models, so there was little pressure to improve them. The added complexity of newer formats demanded more sophisticated production and validation than many tools were built for. At the same time, the industry underestimated just how strongly reagent quality can shape outcomes in complex biological systems. Now, there is growing recognition that preclinical tools need to evolve in step with therapeutic innovation.
As multispecifics advance in the clinic, what does the preclinical research community need in terms of comparator tools to run meaningful head-to-head studies?
The preclinical community needs standardized, in vivo-relevant multispecific comparator tools that let researchers benchmark their molecules against the right monotherapies, combinations, and clinical-like formats in the same model system. Just as important, those reagents need to be highly reproducible, low-variability, and available in formats that mirror biology closely enough to make head-to-head data truly interpretable.
In practice, that means tools that help answer a simple question: is the multispecific creating real biological advantage, or just adding complexity? That’s where translational comparators and well-characterized controls become essential for faster go/no-go decisions and cleaner science.
Why are recombinant antibodies becoming foundational infrastructure for reproducible preclinical research — and what does a defined-sequence approach actually mean for experimental outcomes?
Recombinant antibodies are becoming foundational because they introduce the concept of a defined, reproducible starting point. A defined sequence allows researchers to eliminate one major source of variability and build experiments on a stable foundation.
For experimental outcomes, this means:
- Reduced lot-to-lot variability
- Greater reproducibility across labs and studies
- Improved ability to extend findings over time
However, the full value is only realized when sequence definition is paired with consistent manufacturing and formulation.
If you could change one assumption researchers make about antibodies today, what would it be?
I’d change the assumption that all antibodies are interchangeable. In reality, small differences in format, isotype, affinity, and epitope can completely change how an antibody behaves in vitro and in vivo, so validation and context matter as much as the target itself.
For researchers, the big shift is to think less about “an antibody to the target” and more about “the right antibody for the biology and the study design.” That mindset leads to cleaner data, better comparators, and more meaningful translational decisions.
