Sponsored Content by TTP plcReviewed by Maria OsipovaJul 15 2026
In this interview, Greg Walkowiak explores how organoids are unlocking new possibilities in modern drug discovery by improving disease modelling within DMTA-driven research workflows.
Artificial intelligence (AI) is accelerating the generation of new compounds. How is this changing the priorities within drug discovery?
As AI and automation continue to increase the speed at which new compounds can be generated, the primary challenge in drug discovery is shifting from molecule generation to improving the quality of decision-making. Producing larger numbers of compounds only adds value if they can be evaluated rapidly using biological models that accurately reflect human physiology.
Organoids represent one of the most promising solutions to this challenge. However, to fulfill that promise, they must evolve beyond bespoke laboratory models and become scalable, standardized platforms suitable for industrial drug discovery.
Why are organoids becoming increasingly important within the DMTA cycle?
Organoids are steadily establishing themselves as a valuable testing methodology within the design, make, test, and analyze (DMTA) cycle that underpins modern drug discovery.
By occupying the space between the relative simplicity of spheroids and the complexity and cost of in vivo models, organoids offer an attractive balance. Their ability to generate data that is more representative of human biology than conventional two-dimensional cell cultures and many animal models makes them particularly valuable during lead optimization, where efficacy must be carefully balanced against potential toxicity.
Liver organoids, for example, have the potential to identify hepatotoxicity much earlier in the discovery process, before substantial time and resources have been invested in a lead series.
Similarly, tumor organoids, particularly those derived directly from patients, can provide more predictive insights into therapeutic response in oncology. Intestinal or gut organoids may also offer a more human-relevant platform for assessing drug absorption, barrier integrity, and gastrointestinal toxicity than traditional two-dimensional assays.
How are organoid technologies continuing to evolve?
Organoids are becoming increasingly sophisticated through advances in three-dimensional tissue architecture, incorporation of multiple cell types, inclusion of physiologically relevant structures, and improved replication of realistic cellular behaviors.
They also offer considerable potential for advancing personalized medicine. Patient-derived organoids can capture individual differences in disease characteristics and drug response, allowing therapies to be evaluated in models that more closely reflect individual patients.
Over the past 15 years, organ-on-chip technologies have also attracted significant interest because they enable researchers to control and monitor important physiological parameters, including fluid flow, mechanical forces, and tissue interfaces.
Following the passage of the FDA Modernization Act 2.0 in 2022, regulatory agencies and funding organizations have increasingly supported organoids as part of the broader family of new approach methodologies (NAMs) designed to reduce reliance on animal testing throughout drug discovery and development.
Could organoids play a larger role than simply supporting later stages of testing?
Absolutely. Restricting organoid use to the later stages of the DMTA cycle significantly underestimates their potential.
Organoids provide deeper insights into organ function, translational biomarkers, and drug toxicity, meaning they have the capacity to eliminate unsuitable lead candidates much earlier in the discovery process before more expensive stages of development begin. This aligns closely with the industry's "fail fast, fail cheap" philosophy.
In addition, employing more predictive biological models at an earlier stage could reduce the likelihood of false negatives, which remain a concern when less robust assay systems are used.
Organoid testing can help improve decision-making. A candidate compound that performs well in a conventional cell-based assay but shows early signs of liver toxicity or poor efficacy in a human-relevant organoid model could be deprioritized before proceeding to expensive animal studies and advanced development programs.
If organoids offer so many advantages, what is preventing wider adoption?
The fundamental challenge is scalability.
Future DMTA workflows will require organoids to be produced in large volumes, at speed, and with levels of consistency that current manufacturing methods cannot provide. If organoids are to become a routine component of industrial drug discovery, they must transition from bespoke biological models into standardized, industrially manufactured testing platforms.
At present, organoids are generally produced in relatively small batches by highly skilled biologists, with outcomes often influenced by individual expertise and laboratory-specific practices
While this approach is appropriate for exploratory research, it is inherently difficult to scale. Future DMTA workflows may require thousands of highly comparable organoids to be generated on demand: traditional artisanal manufacturing methods were never designed to achieve this.
The absence of automation also contributes to significant challenges in achieving consistency. Variation in organoid quality from one batch to another can result from differences in the starting biological materials, but it is also influenced by variations in how individual researchers perform the manufacturing process.
These differences are often amplified by laboratory-specific protocols and quality control standards.
How could automation help overcome these limitations?
Automating organoid manufacturing has the potential to address both scalability and consistency simultaneously.
The situation closely resembles the evolution of the biologics industry several decades ago. Just as biologics transitioned from small-scale laboratory production to tightly controlled industrial manufacturing, organoids may require a similar transformation if they are to become dependable tools within mainstream drug discovery.
Such an approach could enable the production of hundreds or even thousands of genetically matched liver, gut, or tumor organoids manufactured to common specifications and deployed across multiple screening campaigns. The resulting datasets would be significantly more consistent than those generated using today's predominantly manual processes.
To support automated organoid manufacturing, microfluidic technologies could be combined with robotics and other automated liquid-handling systems to reduce both labor requirements and operator variability during the early stages of organoid production.
Similarly, AI-enabled imaging techniques and other automated functional assays could reduce the reliance on human judgment during both in-process monitoring and final quality control assessments.
Beyond improving efficiency, perhaps the greatest advantage of automation is that it shifts organoid manufacturing away from a process dependent upon individual expertise toward one governed by repeatable manufacturing controls.
In the long term, this increase in reproducibility may prove even more valuable than the labor savings achieved through automation.
Even if organoid manufacturing becomes industrialized, will that alone solve the scalability challenge?
Not entirely. Although industrial-scale manufacturing represents an essential step forward, it does not fully address the challenges associated with broader organoid adoption.
One of the key limitations is that organoids typically require weeks or even months to mature, while also having a relatively limited period during which they remain viable for experimentation.
Could organizations simply forecast demand well in advance?
One possible strategy would be to predict testing requirements several months in advance. However, this approach is difficult to reconcile with the responsive and agile workflows that modern DMTA systems are designed to support.
Large-scale manufacturing also introduces another important consideration: balancing production with demand. As in any mature manufacturing environment, production and consumption need to be decoupled.
Consequently, storage becomes a critical enabling technology, allowing organoids to be manufactured in bulk, quality controlled, and deployed as needed.
In principle, this could help research teams obtain qualified batches of liver, kidney, or other organoids in much the same way they currently procure cell lines or assay reagents.
This would eliminate weeks of preparation time from screening programs while making advanced biological models available whenever required.
What storage methods are currently used for organoids?
The most widely used method for biological storage today is controlled-rate freezing, which employs cryoprotective agents such as DMSO to minimize damage caused by ice crystal formation.
Preserving mature organoids presents a greater challenge than preserving individual cells because organoids are large, structurally complex three-dimensional tissues.
Different cell populations within a single organoid may respond differently during freezing and thawing, and organoids frequently require a recovery period before they are suitable for experimental use.
There are still opportunities to optimize existing cryopreservation methods, both by refining preservation protocols and by identifying more effective cryoprotective compounds.
An alternative approach, known as vitrification, employs higher concentrations of cryoprotectants together with ultra-rapid freezing to eliminate ice crystal formation entirely. Although promising, additional research is needed to overcome challenges associated with osmotic stress and the potential toxicity of cryoprotective agents.
Ultimately, the long-term goal is to manage organoids less like bespoke biological experiments and more like standardized laboratory reagents. This would require that organoids be manufactured, qualified, stored, and distributed as needed, enabling far more agile and responsive drug discovery workflows.
Looking ahead, what will determine whether organoids become mainstream tools in drug discovery?
In summary, organoids–and, by extension, organ-on-chip technologies–offer tremendous opportunities within the DMTA cycle because they have the potential to refine lead candidates during the earliest stages of testing, thereby reducing costs later in the development process.
Realizing this potential will require a transition away from small-scale, artisanal manufacturing toward automated, industrial-scale production methods that more closely resemble those used for biologics and other laboratory reagents.
The primary goal is not to create the most complex biological model achievable, but rather to produce organoids that are sufficiently predictive, highly reproducible, and available at the scale required by modern DMTA workflows.
In many situations, consistency may prove to be more valuable than achieving the highest possible level of biological complexity.
What will ultimately determine the future role of organoids in drug discovery?
The long-term success of organoids will depend not solely on advances in biology, but equally on advances in manufacturing.
Once organoids can be produced, qualified, stored, and distributed with the same reliability as other essential laboratory consumables, they will transition from specialized research tools into core infrastructure supporting modern drug discovery.
When this is made possible, organoids and organ-on-chip technologies will become central components of the DMTA data engine, generating standardized, high-quality, and human-relevant datasets that support faster and more confident decision-making throughout drug discovery.
As artificial intelligence becomes increasingly embedded within discovery workflows, the ability to generate these large, consistent datasets may ultimately prove just as valuable as the biological models themselves.
How does TTP help pharmaceutical and biotechnology companies accelerate drug discovery?
TTP partners with pharmaceutical and biotechnology companies to accelerate drug discovery by addressing bottlenecks across the DMTA cycle.
The drug discovery tools team develops bespoke technologies that enable faster experimentation, higher-quality data generation, and more effective decision-making.
Drawing upon expertise in biology, chemistry, automation, microfluidics, software, and instrumentation, the team works with clients to solve complex challenges spanning synthesis, purification, screening, organoid systems, assay development, and integrated laboratory workflows.
Whether the objective is optimizing an existing workflow or developing an entirely new discovery platform, TTP collaborates with organizations to increase DMTA iteration velocity and unlock the full potential of AI-enabled drug discovery.
Acknowledgments
Produced using materials originally authored by Greg Walkowiak.
About Greg Walkowiak 
Greg Walkowiak is a Consultant and Project Leader at TTP, where he develops tools for drug discovery, laboratory automation, and bioprocess monitoring. He holds a PhD in Analytical Science and has a background in protein biochemistry and biotechnology. Before joining TTP, Greg worked in drug discovery roles, gaining experience in assay technologies and research automation. At TTP, he leads projects at the intersection of biology, instrumentation, and automation hardware, translating complex processes into instruments and workflows that enable novel, data-rich approaches to discovery and life sciences research.
About TTP plc
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