What Will Drug Development Look Like in 2036?

Dajun Yang, Chairman & CEO, Ascentage Pharma Group International

The boundary between small molecules and biologics will become obsolete so that the modality divide will no longer exist. Protein degraders, molecular glues, and other next-generation small molecule technologies are already reaching targets and mechanisms that were previously the exclusive domain of antibodies and cell therapies. We’re witnessing the dismantling of the assumption that the complexity of a disease target determines the complexity of the drug required to address it. That is no longer true. In ten years, I expect the choice of therapeutic modality to be dictated entirely by the biology of the problem rather than by historical convention or the limitations of prior technology. What will matter is whether a therapy reaches the right target, in the right cell, at the right time, with an acceptable safety profile. The patients who stand to benefit most from this shift are those whose diseases were previously considered beyond reach.

Miguel Forte, President, ISCT, board member of ARM and CEO of Kiji Therapeutics

The merging of improved gene editing capabilities and AI will drastically transform the field over the next decade. Development will be proactive and by design rather than exploratory. AI models will be able to modulate and reduce experimental need.

Andres Sirulnik, SVP, Hematology Clinical Development Unit Head, Regeneron

In 10 years, we will be realising and capitalising on the promise of AI across all areas of drug development, including operations and big data. I expect AI to play an increasingly important role across drug development, from target identification and hypothesis generation to clinical trial design, patient recruitment, and data analysis.

I also expect that gene therapy approaches will be a standard part of drug development for both rare disease indications and common conditions. We’re experiencing the beginning stages of this transition in real-time as the regulatory environment evolves to match the pace of technological innovation and manufacturing capacity improves.

Christine Allen, CEO & Co-Founder, Intrepid Labs, Professor, University of Toronto

In 10 years, drug development will be far less fragmented. Instead of moving through a series of disconnected handoffs, from discovery to formulation to manufacturing to clinical development, we will see more end-to-end integration powered by AI.

The real opportunity is not simply to apply AI to individual steps, but to connect decisions across the entire development pathway. That means using data and advanced models to design better molecules, formulations, processes, and clinical strategies in a more coordinated way. Just as importantly, it means keeping domain experts, clinicians, regulators, and patients actively involved in the process.

I do not believe the future of drug development lies in AI replacing human expertise. I believe it is AI amplifying human expertise. The companies that get this right will make better decisions earlier, reduce unnecessary iteration, and develop medicines in ways that are faster, more rigorous, and more centred on patients.

Daria Donati, Chief Scientific Officer of Genomic Medicine at Cytiva

Drug development will become far more data-driven, adaptive, and integrated across disciplines.

I expect three major shifts:

Real-time learning systems, where manufacturing and clinical data continuously inform each other
Digital twins of processes and potentially patients, enabling predictive design rather than empirical iteration
A move from “products” to “platform-enabled therapies”, where development cycles are significantly shorter

In cell and gene therapy specifically, we will move closer to treating the process as part of the product and designing both simultaneously from the outset.

Daniel Vitt, Chief Executive Officer of Immunic Therapeutics

In 10 years, I expect drug development to become significantly more efficient, particularly in early discovery. AI, better data integration, and improved biomarkers will help researchers make better decisions earlier, while adaptive development pathways could speed clinical development and support more personalized therapies. What won't change is the need to demonstrate safety and efficacy in patients. The greatest advances will come from improving efficiency around those fundamentals rather than replacing them.

Ali Pashazadeh, CEO of Treehill Partners

The geographic centre of gravity. Within a decade, I expect the majority of early-stage drug development – from target identification through to human proof of concept – will be conducted in Asia. Not because of lower costs alone, but because the operational model emerging there is simply more efficient and more commercially disciplined. Western pharma will remain dominant in later-stage development, regulatory navigation, and most importantly commercialization in the major markets – which is where a large portion of value is being captured and humongous investment is required to build a presence. But the notion that innovation originates in Boston and Cambridge and is then licensed globally will be inverted. The BMS-Hengrui deal is not an outlier. It is the beginning of a structural pattern. The companies and advisors that understand this will thrive. Those that are still treating it as a curiosity will find themselves checkmated.

Renee Aguiar-Lucander, CEO, Hansa Biopharma

I hope regulatory review times will have fallen by around 30%. We will also be much better at predicting drug efficacy using real-world evidence, while AI will make clinical datasets more accessible and easier to analyse. Gene therapy will be more commonplace. We will be able to prevent more diseases.

Advances in diagnostics and predictive tools will allow us to assess compounds and their potential efficacy much earlier in development. Much of early drug development may have shifted to China, while later-stage trials will be conducted across a wider range of countries. The US still remains the highest value market but is closely followed by India and China due to volume. Europe has become a generics market.

Michael May, CEO, CCRM

The development of future medicines will be driven by data, AI and quantum platforms through much greater use of in silico modelling. In addition, the products will be validated by data-enabled, non-animal (human organoid) disease models. While laboratory validation will always remain essential, the transformation will be so stark that “techbio” may be used as much as “biotech” to describe our industry.

Rab Prinjha, Chief Research and Development Officer, Curve Therapeutics

I anticipate seeing a very significant revolution in AI-driven molecule design, accelerating the successful use of AI-guided design-make-test cycles in the next few years. If we do this well in more disease-relevant mammalian cell-based systems, we can anticipate a meaningful reduction in target attrition.

Jane Rhodes, Chief Executive Officer, AstronauTx

Two things are in motion that I believe will dramatically change the landscape - (1) in-silico modeling and prediction: The application of AI, ML and data analytics is allowing us to model every aspect of drug discovery and development and this field is growing at a phenomenal pace, and we will become more reliant on predictive models and reduce the requirement for wet lab experimentation. (2) A shift from treatment to prevention, especially with respect to brain health: Some of the most challenging diseases of our time are chronic neurodegenerative diseases that have the potential to be delayed or prevented if addressed early. Just as cardiovascular health and more recently metabolic health can now be addressed preventatively - so too will brain health. At AstronauTx we are targeting improvement is sleep quality as a therapeutic approach to treating neurological disease for precisely this reason - sleep quality is a modifiable risk factor for chronic neurological disease and by improving sleep quality we believe we can delay the onset and progression of Alzheimer’s disease and other neurological conditions.

Anders Nykjaer, Chief Scientific Officer, Vesper Bio

I think drug development will look fundamentally different. AI will be embedded across the entire value chain – from biological modelling, target identification, and hit finding to ADMET prediction and adaptive trial design. AI-enabled clinical trials will likely become smaller, smarter, and more adaptive because we will be better at selecting the right patients and measuring meaningful biomarkers and biological responses earlier. Ultimately, the time from understanding disease biology to target identification, clinical validation, and regulatory approval should be substantially reduced. It will markedly benefit human health.

Claudia Zylberberg, co-founder and board chair of ARScience Bio, founder and chair of Akron Bio, and co-founder and board chair of Kosten Digital

I believe the distance between discovery and patient access will shorten dramatically.

Today, it can take years, or sometimes decades, for a scientific breakthrough to reach the patients who need it. In the future, advances in AI, digital infrastructure, manufacturing technologies, real-world data, and regulatory innovation will help compress that timeline significantly.

Drug development will become far more interconnected and collaborative. The traditional boundaries between research, manufacturing, clinical care, reimbursement, and patient engagement will continue to blur. Data will flow more seamlessly across the ecosystem, enabling faster learning and better decision-making.

I also believe patients will become much more active participants in their healthcare journey. They will be better informed, more engaged in treatment decisions, and empowered by technologies that allow them to contribute to and benefit from a more personalized healthcare experience.

Some of the systems that have historically resisted change will have no choice but to evolve. Innovation is moving too quickly for stakeholders to operate independently. The future will require greater synchronization between innovators, providers, manufacturers, regulators, and payors, all working toward the same goal.

Most importantly, living longer and living better will increasingly become the expectation rather than the exception. Advanced therapies, precision medicine, AI, and preventive approaches will not only help us treat disease but help us maintain health and quality of life for longer periods of time.

The biggest transformation will not be a single technology, it will be the creation of a more connected healthcare ecosystem where discoveries reach patients faster, treatments become more personalized, and better outcomes are accessible to more people around the world.

The rest of the world will have to adapt to this impactful change.

Jason Bock, CEO of CTMC

In 10 years, I think drug development for advanced medicines will be much more integrated, much more data-rich, and much faster-cycle.

Today, the industry often treats discovery, process development, clinical translation, manufacturing, and commercialization as sequential steps. For complex biologics and especially cell therapies, that model is too slow. The future will be more iterative. We will design the product and the process together. We will use richer analytics to understand the relationship between starting material, manufacturing conditions, product attributes, and clinical outcomes. We will use digital systems and AI not as buzzwords, but as tools to connect development decisions across the full lifecycle.

The analogy I often think about is monoclonal antibodies. Early antibodies were powerful but difficult to develop and manufacture. Over time, the field learned to engineer not just for binding or potency, but also for manufacturability, stability, expression, delivery, and commercial scalability. Cell therapy will go through a similar evolution. We will engineer the product for the patient, but also for the process.

That shift will change everything. The best cell therapies of the future will not just have better biology; they will be designed from the beginning to be made, measured, delivered, and scaled.

Ali Tavassoli, Professor of Chemical Biology, University of Southampton; and Former Chief Scientific Officer and Co-founder, Curve Therapeutics

The timeline from idea to clinical candidate. The five-to-seven years it currently takes to go from a validated target to a molecule ready for the clinic is going to compress dramatically; not because of any single breakthrough technology, but because the whole stack is improving in parallel. Human genetics and functional genomics are sharpening target validation before a single molecule gets made. Generative models are reshaping how we design molecules and explore chemical space. Automated synthesis and high-throughput biology are collapsing cycle times in the lab. Human-relevant screening platforms such as organoids, patient-derived systems, in vitro disease models are letting us ask better questions earlier. Together these approaches will change what is possible.

What I do not think will happen is AI replacing chemists or biologists. The discipline-specific judgement that takes a decade to develop is not going away, and the projects that succeed will still be the ones run by people who deeply understand the biology and the chemistry. What will change is that those people will be doing in two or three years what previously took six or seven. The bottleneck will move, probably to clinical development, where human biology sets a pace that no algorithm can shortcut. That is where the next generation of innovation will need to focus.