Roginolisib

On International Clinical Trials Day, industry experts speak to DDW about the current state of play within global clinical trials. Liam Tremble: A lot has happened in clinical development over the last few years. Several national agencies were gutted and policies such as MFN are resulting in fundamental re-evaluation of company portfolios. This period has also created a lot of opportunity. The US FDA been granting National Priority Review Vouchers and have been making further commitments on requiring only one pivotal trial. Recent data on the number of Phase I studies being conducted in China is likely to drive further reform that allows us to bring treatments to patients more efficiently. Kirsty Wydenbach: We are encouraged by the increasing willingness of regulators to adopt more science-driven, flexible approaches. Greater acceptance of NAMs has the potential to improve translational relevance while also reducing time and cost, and streamlined review processes can help promising therapies move through development and regulatory assessment with greater predictability. The real challenge will be achieving consistent global implementation, which needs stronger collaboration between regulators and industry. We also need to maintain clear evidentiary standards, so innovation can advance without compromising scientific rigor, data quality, or patient safety. These reforms could ultimately support more sustainable and patient-focused drug development. Ian McGowan: We are encouraged by regulatory acceptance of NAMs as enabling tools for smarter, more predictive drug development. One of the biggest challenges with clinical development is entering human trials without translational preclinical data that reliably predicts human outcomes, as uncertainty around human relevance remains a major cause of failure. We have addressed this by generating robust safety and efficacy data in ex vivo human organ models that closely reflect clinical conditions, reducing biological uncertainty early. Combined with more efficient regulatory pathways, stronger preclinical-to-clinical translation supports better trial design, lower downstream risk, and more successful clinical programmes. LT: It is having a very significant impact. Selecting the right patient population and even the right dosing strategy can be informed by big data. There are an increasing number of service providers who have the kind of data and skills needed to provide tailored solutions. Augmenting datasets is particularly valuable during earlier stages of clinical research. Ultimately, the success we are looking for will manifest as more successful Phase III programs with bigger effect sizes for patients, there is a lag period before we will see this data mature but I am confident we will see this soon. KW: AI and automation are improving clinical development, but not by replacing experts. The biggest gains come from helping experienced teams make better decisions faster. Clinical development rarely fails because of a lack of data – it fails because studies are designed around weak assumptions, poorly defined patient populations or fragmented decision-making. Technology can reduce those failures through better prediction, simulation, and operational execution. This being said, the tech-based outputs are only as good as the input data and underlying clinical thinking. Better tools do not remove the need for judgement, they increase the value of it. IM: Technology is increasingly improving clinical trial success by strengthening decisions made long before trials begin. AI, automation, and advanced analytics enhance data integration, model selection, and hypothesis testing across preclinical and clinical stages. When combined with human relevant experimental systems, these technologies help identify promising candidates earlier and derisk development pathways. Rather than replacing clinical execution, they improve its foundation – supporting smarter trial design, more appropriate endpoints, and better patient selection. The result is a more evidence driven development process with a higher likelihood of clinical success. LT: Big pharma is certainly looking to in licence more late-stage programmes than ever before which also means they are willing to invest in mid to late-stage programmes. Early-stage science relies on specialist VCs and it can be challenging to get the levels of investment needed. But a good idea, an experienced management team, and a clear exit strategy will find the financing it needs. KW: In recent years the market has become more disciplined. Strong science still attracts capital, but investors are increasingly asking a different question: can this team convert interesting biology into a clinically differentiated medicine? That shifts the focus from novelty alone to development strategy and execution. The industry has spent years rewarding narratives before mechanisms, and ambition before evidence. The companies attracting attention today combine differentiated science with credible plans for generating decision-grade clinical data quickly. Investors are not just looking for potential upside, but for teams that can deliver on reducing uncertainty and compound learning over time. IM: While late-stage value inflection remains important, there is growing investor recognition that strong, differentiated early-stage science is critical to long term success. Investors increasingly look for platforms and technologies that demonstrate clear biological relevance, translational robustness, and the potential to reduce downstream risk. High quality preclinical data generated in human relevant systems is becoming a key differentiator. This shift reflects a broader understanding that better early decisions lead to more capital efficient development and ultimately stronger outcomes at later stages. LT: We are at a crossroads moment. Artificial Intelligence is the buzzword in every workplace, I think most of us are seeing greater integration every single week. In a GCP environment things move slower, but particularly in Europe there is an existential crisis that our healthcare models are unable to support the scale of R&D needed to bring new medicines, and with policies such as MFN we may quickly lose access to existing and emerging therapies. Everyone can feel the system is ready for innovation because ultimately, we need to find a way to bring better medicines to current and future generations of patients. KW: One of the most encouraging shifts is the growing focus on decision quality, not just development speed. Faster trials only matter if they ask the right questions in the right patient population. We are also seeing movement towards more continuous, learning-focused development systems. The FDA’s recent pilot exploring real-time clinical trial data analysis reflects this direction of travel. Combined with better biomarkers, digital tools, and real-world data, this creates opportunities to identify signals earlier and improve how evidence is generated and interpreted. Ultimately, the goal is better medicines reaching patients sooner. IM: One of the most promising signs of progress is the increasing alignment between biology, technology, and trial design. Better use of translational data, biomarkers, and patient stratification is improving the relevance of clinical endpoints and reducing variability. This progress is rooted in stronger preclinical foundations that better reflect human biology, enabling clearer hypotheses entering the clinic. As a result, trials are becoming more focused, more informative, and better equipped to answer the critical questions needed to advance development. Paramjit Kaur: One of the most encouraging signs is the shift toward biology‑first trials that integrate translational science from the outset. This is improving confidence in early‑stage signals and supporting faster, more informed development decisions. For example, roginolisib’s clinical strategy has been built around a deep understanding of disease biology, mechanism‑specific biomarkers, and combination potential, allowing us to assess relevance well beyond traditional response metrics. The growing alignment between clinical, translational and regulatory thinking is helping companies design trials that are not only faster, but far more predictive of long‑term clinical and commercial success. LT: Speaking from an oncology perspective it is now standard for Phase I protocols to be written in a way which allows them to expand to include what we previously would have considered a Phase II trial. Some of the biggest breakthroughs in haematology over the last decade have been based off adaptive Phase I study design. Concerted efforts to develop novel endpoints like minimal residual disease in myeloma patients are reducing trial timelines by years. Michael Grant: These approaches have made studies more targeted, flexible, and ultimately more clinically meaningful. Smarter use of biomarkers and patient selection helps identify the populations most likely to benefit, while adaptive methods allow earlier learning and more informed decisions during development. Decentralised elements can improve recruitment, retention, and patient experience – particularly where geography or disease burden are barriers to participation. More thoughtful endpoint selection is also helping generate evidence that is more relevant to clinical practice and patient outcomes. Together this is leading to stronger data, more efficient development programmes, and the potential for increased probability of success. IM: Increased focus on disease pathogenesis, including the biological pathways through which disease evolves, have facilitated the development of highly targeted drugs that maximize clinical efficacy while minimizing off target toxicity. These insights also provide the opportunity to enrich study populations with patients most likely to benefit from the experimental drug. This leads to smaller trials, faster clinical development, and more homogeneous data. PK: Smarter trial design has become central to improving clinical success rates. Biomarker‑driven patient selection, in particular, allows for earlier and clearer signals of efficacy while reducing unnecessary exposure. With roginolisib, we have prioritised enrolling biologically defined patient populations where PI3K‑δ inhibition is most relevant, supported by pharmacodynamic biomarkers that inform dose and scheduling decisions. Adaptive elements allow protocols to evolve as data emerges, improving efficiency and decision‑making. LT: The biggest challenge is always timelines. Trials take time, particularly during the set up. Getting the protocol designed properly is essential, and experience is essential if designing a global trial that can be conducted seamlessly across various regions of the world. MG: One of the biggest challenges is balancing increasing scientific and operational complexity with the need to deliver studies efficiently from a time and capital perspective. Recruitment and retention remain difficult, particularly in competitive indications and rare diseases, while global studies also face regulatory variability, site capacity constraints, and growing data management demands. Sponsors are under pressure to generate high-quality evidence faster, but without compromising patient safety or trial integrity. Ensuring protocols remain feasible for sites and accessible for patients is critical, particularly as studies incorporate more advanced technologies and increasingly targeted populations. IM: One of the biggest challenges is the increasing cost and complexity of running clinical trials, particularly the rising costs of CROs. Contract negotiation timelines with clinical sites are also lengthy, delaying study start-up after regulatory approval. Competition for experienced sites, patient populations, and investigators is intense, especially in specialized therapeutic areas. Together, these factors increase operational risk, prolong recruitment and development timelines, and add pressure on biotech companies to secure funding while maintaining high-quality execution. Close collaboration with experienced investigators and investigator-driven trials can help reduce costs, accelerate execution, and provide access to leading clinical expertise and high-performing sites. LT: Innovation. So many technologies are coming of age. But I think rare disease is somewhere we may see particular progress. The level of global integration and communication is supporting medical engagement across the world and so pharma know where to find them, and likewise patients know so much about what is going on and often know about a clinical trial before it has even been announced. We can make trials so much more efficient by performing them in the right locations. MG: Advances in biomarkers, genomics, AI-powered analytics, and real-world data are helping us design smarter studies and identify the right patients earlier. At the same time, decentralised approaches and digital tools are improving accessibility and patient engagement. There is also growing collaboration between regulators, industry, and healthcare systems to modernise development pathways. Together, these changes have the potential to accelerate access to innovative therapies while generating stronger and more clinically meaningful evidence. IM: What excites me most is how clinical trials are becoming more adaptive, data driven, and aligned with real world patient needs. Advances in trial design, patient stratification, and endpoint selection are enabling clearer insights earlier during execution. Combined with increasing regulatory flexibility and improved use of digital tools, this allows sponsors to make better informed decisions throughout development. As trials continue to evolve toward greater relevance, efficiency, and patient centricity, we have a meaningful opportunity to improve success rates and accelerate the delivery of impactful therapies. DDW spoke to Liam Tremble, Principal Scientist at Poolbeg Pharma, Kirsty Wydenbach, Head of Regulatory & Drug Development Clinician at Weatherden, Michael Grant, Senior Drug Development Clinician at Weatherden, Ian McGowan, Chief Medical Officer at Synklino, and Paramjit Kaur, VP Clinical Sciences at iOnctura. 4 June 2026 3 June 2026 3 June 2026 2 June 2026 · Diana Turner 25 May 2026 · Diana Turner 12 May 2026 · Diana Turner