As 2026 approaches, the clinical research and development landscape is evolving beyond traditional scientific innovation with strategic, operational and technological transformation converging to ensure exciting therapeutic advances reach impacted patients. 

In a time when all clinical trial sponsors weigh tough decisions regarding where to strategically allocate resources, early-stage biotechnology companies face mounting pressure to de-risk development. Further, the surge in anti-obesity research underscores the need for patient-centric trial designs that address motivational and logistical barriers to retention. Meanwhile, artificial intelligence and machine learning have moved from concept to reality in the past few years to accelerate clinical documentation, automate trial processes and enable adaptive strategies. 

Together, these trends signal a R&D future in which success hinges on integrating science with strategy, empathy and intelligent tech-enabled processes.

Strategic de-risk for early-stage biotech sponsors

Early-stage biotech companies face a conundrum: they hold therapeutic breakthrough potential but operate under intense uncertainty and capital constraints. The journey from lead candidate to proof-of-concept can be long, costly and potentially fraught with risk. Success rates for moving from Phase I to regulatory approval remain below 10%. In this environment, de-risking strategies are essential to attract investment and accelerate innovation, especially considering that investors may favor assets with clinical progress over those in pre-clinical stages.  

For one, biotechs may struggle to articulate a compelling product vision early enough and with strategic clarity. A dynamic Target Product Profile (TPP) is critical for defining differentiation, the unmet need and commercial viability while also aligning internal leadership and teams with external stakeholders, including investors. Companies that delay TPP development may risk misalignment between clinical outputs and market expectations, which can undermine investor confidence.

As an operational planning component, a robust clinical development plan will help with scenario modeling across multiple pathways and balance time, cost and risk. For resource-limited biotechs, this provides flexibility to help them navigate regulatory complexity and competitive landscapes. Incorporating adaptive trial designs and real-world data strengthens decision-making capabilities and demonstrates strategic foresight to investors.

Additionally, quantifying value is key for helping this sponsor segment go from scientific uncertainty to logical investment. An expected net present value framework helps do that by integrating timelines, costs, probabilities of success and revenue projections. Scenario modeling can range from aggressive “fast-to-market” strategies to conservative “fail-fast” approaches to help companies rationalize trade-offs and individualize pitches to complement investor priorities.

To succeed in 2026 and beyond, embracing earlier commercial thinking will benefit this sponsor segment. This means better prioritizing indications based on market opportunity, engaging payers and providers to validate assumptions and continuously refining the TPP as evidence evolves. 

Beyond the molecule: what matters in anti-obesity trials 

The anti-obesity treatment pipeline is booming, with more than 120 investigational molecules in development. In today’s competitive landscape, trial success hinges not only on innovative therapies but on the ability to keep patients engaged over extended study periods. However, anti-obesity trials face unique hurdles, and overcoming these challenges is critical for market differentiation. 

The engagement gap

Anti-obesity trials can be uniquely demanding. Their protocols demand multifaceted lifestyle changes, such as dietary restrictions, exercise regimens and frequent assessments alongside managing adverse effects like nausea or fatigue. Integrating these requirements into real-world routines can create stress and non-adherence.

Participants may enter the trial with optimism, but some may experience motivation fatigue given: 

• A sustained commitment of up to 12 months or longer. 
• Potential for weight loss progress slowing down, weight-loss plateaus or lack of response to treatment. If assigned to the placebo group, individuals may lose interest as they experience slower or less weight loss, etc. 
• Operational hurdles, including transportation issues, multiple site visits, missing work for site visits and tech requirements, can create friction. 
• The burden of documentation demands, including logs, symptom tracking and wearable data. 

These factors can negatively impact engagement and related data integrity needed for regulatory evaluation. 

What patients need to stay interested

Anti-obesity trial sponsors need to better embed the patient voice earlier in protocol design to improve feasibility and downstream engagement. Patient boards can enhance study startup and reduce amendments while addressing real-world barriers like travel, scheduling, cultural considerations and complex documentation activities.

For patients, trials will need to start with appropriate expectations. Transparent communications and conversations about gradual weight loss and potential for plateaus help normalize what may come from the journey. Additionally, site teams that highlight health improvements beyond scale readings, such as better energy levels, quality of sleep, clinical lab values and more, give participants alternative markers of success to consider. 

Also, personalized lifestyle support is critical. In many cases, registered dietitians and lifestyle counselors translate protocol demands into practical strategies. These experts can provide guidance for sustainable healthy habit formation while also addressing emotional and logistical needs. To keep patients connected, telehealth visits, concierge scheduling, in-app reminders and other digital tools can help reduce their operational burdens while allowing sponsors to gauge self-reported data. When paired with motivational messaging and milestone tracking, these tools turn engagement into partnership.

It is equally important for sponsors to recognize the value of community partnerships and culturally competent outreach to connect and build trust among underrepresented populations. Embedded site models,  which run trials in familiar care settings and take advantage of existing patient-provider relationships, can improve continuity and accountability.

What sites need to enhance patient support 

Sites are integral for improving patient engagement in anti-obesity trials, so staff training needs to go beyond protocol mechanics to include instruction about empathetic communication and behavioral insights. Study coordinators need to be able to spot early signs of disengagement, such as missed diary entries, shorter responses and changes in tone, and quickly intervene.

To understand the unique patient support needs, trial sponsors should prioritize site partners with proven obesity trial expertise and embedded care models. A thoughtful site selection approach can help enhance patient enrollment and the needed retention. 

More than scientific rigor, anti-obesity trials will require patient-centric strategies that anticipate participants’ motivational, emotional and practical needs to ensure engagement throughout the journey. 

AI/ML: growing trial transformation in 2026 and beyond

It is no surprise that AI/ML are poised to redefine clinical trial operations as sponsors dive deep into 2026 plans. But how, specifically? 

One of the most immediate impacts of AI/ML will be in document generation. For example, essential trial documents, such as informed consent forms, clinical study reports and protocols, have potential to be prepared up to 50% quicker and with fewer errors and greater consistency. With AI-driven tools, sponsors may be able to: 

• Automatically draft protocol amendments based on prior versions and regulatory guidance. 
• Generate informed consent forms tailored for readability and compliance. 

As regulators begin deploying their own AI systems to assess a growing number of submissions, document formats may move from traditional narratives to data-rich, insight-driven structures to enable faster review and deeper analytics.

Beyond documentation, 2026 will mark the early stages of substantive process automation in trial startup and conduct. Intelligent agents will increasingly be used to tackle complex use cases, including site feasibility analysis via predictive models, automated patient recruitment with electronic health records and real-world data and risk-based monitoring powered by advanced detection algorithms. These advances will help sponsors reduce cycle times and mitigate operational risk.

Perhaps most transformative will be the emergence of life sciences foundation models, which are large-scale AI models trained on diverse biomedical and clinical datasets. These models will start informing strategic decisions across R&D, such as: 

• Protocol design optimization using historical trial outcomes. 
• Adaptive trial strategies that can adjust enrollment or dosing based on interim data. 

The message for sponsors is clear: AI/ML is no longer a future concept but is instead a near-term operational reality that will require updated strategic frameworks focused on data readiness, evolving governance and various tech-enabled workflows. 

Clarity, flexibility and readiness 

The coming year will challenge trial sponsors to think about R&D differently. Risk mitigation for emerging biotechs, patient engagement in obesity trials and AI-driven efficiencies are not siloed priorities. Rather, they represent a broader shift toward holistic innovation in strategy for every point of drug development. 

Debra Gerlach, MA, Director, Strategy and Solution Design, Patient and Site Centric Solutions, IQVIA
Debra has more than two decades of clinical research industry experience, including 16 years in global patient recruitment and site engagement. She brings that experience to her current role to lead strategic initiatives across multiple therapeutic areas, including oncology, rare diseases, pediatrics, gastroenterology, infectious disease and endocrinology, using decentralized trials, direct outreach, home nursing, site resourcing and other innovative approaches. 

Anna Haught, Director of Operations, Avacare
Anna brings more than 20 years of clinical research industry experience to the Director of Operations role with Avacare, a clinical research network. She began her clinical research career as a dietician, supporting multiple sites and patients within Arizona. In her time with Avacare, Anna has the unique opportunity to help empower individuals to take control of their health through meaningful and measurable clinical research. 

Meg Hooton, President, IQVIA Biotech
Megan Hooton serves as President of IQVIA Biotech, a specialized business unit within IQVIA dedicated to providing clinical development solutions for biotech and emerging biopharma customers. With more than 30 years of experience in managing global research and development operations across pharmaceutical and biotech sectors, Meg brings a wealth of operational and cross-cultural expertise to her role.

Raja Shankar, Vice President, Machine Learning, Research and Development Solutions, IQVIA
Raja is determined to change healthcare with the power of AI, leading the team to create new narratives that fully leverage AI’s potential to reshape the industry from R&D through to commercialization. Raja brings together diverse technical and strategic capabilities, including Machine Learning, Deep Learning, Generative AI, Product Development, Life Sciences Expertise and Business Consulting Skills.