Clinical Trial Trends Shaping Modern Drug Development

Clinical trial trends in 2026 are being shaped by a practical question, which asks how sponsors can run studies that are faster, more inclusive, and more data-rich without adding unnecessary risk or burden.

Technology still matters, but the main test is whether it supports better trial design, stronger oversight, and more reliable evidence. In this article, we look at the trends affecting the future of clinical trials, from AI and decentralised models to real-world evidence, RBQM, advanced therapies, and outsourcing models.

In Brief

• Clinical trial trends in 2026 are less about adopting new methods and more about using them where they fit the trial question.

• Decentralised models, AI, RWE, PROs, and adaptive designs can support modern trials where they are governed carefully.
• These trends show up in study design, recruitment, site readiness, data management, RBQM, outsourcing, and digital data oversight.
• The main risk is treating technology or novel designs as shortcuts rather than planning for validation, accountability, and data quality.
• The article explains how sponsors and CROs can think more clearly about trial methods, participant burden, and operational risk.
  
  

Participant Experience, Retention, and Decentralised Models

One of the most significant shifts in recent years has been the rapid adoption of decentralised clinical trials (DCTs), a trend accelerated by the COVID-19 pandemic. Decentralised and hybrid trial models are increasingly used where they fit the protocol, patient population and data collection requirements. They can enable remote participation through technologies such as wearable devices, electronic clinical outcome assessment tools and mobile health platforms. Decentralised trials not only expand the breadth of data collected but also support patient-centricity by making trials more convenient and accessible. As a result, sponsors are increasingly partnering with contract research organisations (CROs) that provide full DCT support, including remote monitoring, home-based care, and digital platforms for real-time data capture.

The value of decentralised and hybrid approaches depends on whether they burden without weakening data quality. Remote visits, home health support and wearable-enabled data collection can help patients take part with fewer site visits, but they can also create new questions around device validation, protocol consistency, data volume and the reliability of remotely collected assessments. These issues need to be considered during study design rather than treated as operational details later.

ICH E6(R3), adopted in 2025, reinforces proportionate, risk-based approaches and quality by design principles that are relevant to modern trial conduct, including the use of technology where it is appropriate and controlled. By actively engaging patients in trial design and decision-making, studies can become more aligned with patient priorities, while supporting trial conduct and data integrity.

Participant experience also has a direct operational effect. High travel burden, complex visit schedules, reimbursement delays and unclear study communications can affect recruitment, retention and data completeness. Site burden matters too. If protocols are difficult to deliver, sites may underperform, recruitment can slow and deviations may increase. For this reason, patient-centric trial design should be treated as part of operational planning, not only as a patient engagement principle.

Integration of Artificial Intelligence (AI), Automation, and Machine Learning (ML)

Machine learning and AI is more commonly being used to support trial design, recruitment, and operational oversight. The clearest use cases are usually targeted rather than broad. These may include protocol review, site selection support, recruitment feasibility, data review, medical coding assistance, safety signal detection and risk-based monitoring. In each case, the value depends on data quality, model validation, documented decision-making and appropriate human review.

Automation and AI is also being applied to clinical data management, with workflow tools increasingly used to support data cleaning, edit checks, medical coding, data transformation and submission preparation. SDTM, or the Study Data Tabulation Model, is a CDISC data standard rather than an automation tool. Automation can support SDTM mapping and dataset creation, but the standard itself defines how clinical trial data should be structured for submission.

However, AI and automation should not be treated as a shortcut around clinical, statistical or regulatory judgement. Generative AI tools can produce incorrect or unsupported outputs, and predictive models may perform poorly if trained on incomplete, biased or non-representative data. Sponsors and CROs therefore need clear governance around where AI or automation in clinical trials is used, how outputs are checked, who remains accountable and how model-supported decisions are documented. Automated workflows also need validation, version control, audit trails, exception management and human review where decisions affect patient safety, data quality or regulatory submissions.

The Growing Role of Real-World Evidence and Patient-Reported Outcomes

Real-world evidence (RWE) is increasingly used as a complimentary evidence source in modern clinical development. By utilising health data from sources like electronic health records (EHRs), insurance claims, wearable devices, patient registries, and observational studies, RWE can provide insight into drug effectiveness, safety, and treatment patterns across diverse populations. With rising trial costs and the growing number of rare disease studies, RWE offers sponsors a useful way to complement randomised controlled trials (RCTs). Regulatory bodies such as the FDA are becoming more accepting of Real-World Data (RWD) to support drug approvals, particularly in challenging areas like oncology and rare diseases, where traditional patient recruitment is difficult.

In 2026, RWE is better framed as part of a broader evidence strategy rather than as a replacement for controlled trial evidence. It can help contextualise how treatments are used in routine care, support post-market surveillance and provide insight into groups that may be underrepresented in traditional trials. CROs are now expected to have RWE capabilities, incorporating EHRs, patient registries, and other RWD sources into trial design and data collection.

Patient-reported outcomes (PROs) are also becoming more important in the evidence mix. PROs capture information directly from patients about symptoms, functioning, treatment burden or quality of life. They can be particularly useful when traditional clinical endpoints do not fully reflect whether a treatment makes a meaningful difference to daily life. As with RWE, the value of PROs depends on choosing measures that are validated, relevant to the patient population and suitable for the way the trial is delivered.

Adaptive, Bayesian, and Master Protocol Designs

The increase in the use of RWE, observational studies and advancements in technology to capture more real-time data, are driving the need for more flexible and adaptive trial designs. Rather than moving away from RCTs as the ‘gold standard’, the industry is making greater use of fit-for-purpose designs that can answer specific clinical questions more efficiently where the scientific and regulatory rationale supports them.

One significant limitation of RCT designs is the inability to adjust treatment allocation probabilities, whereas adaptive clinical trial designs, and especially response adaptive randomisation (RAR) designs, offer the flexibility to change treatment allocation based on observed performance. If one treatment is seen to outperform others, this type of trial design can allow patients to be allocated to the better performing treatment halfway through the trial. This may benefit patients and sponsors in some settings, but it also requires careful statistical planning, operational control, and regulatory engagement.

Another promising development in early-phase clinical trials is the introduction of Bayesian Optimal Interval (BOIN) designs. BOIN designs provide a more efficient framework for dose-finding trials by allowing for flexible decision-making, ensuring that the optimal dose is found more quickly and with fewer patients.

Master protocols are also becoming more visible in modern clinical development. Umbrella, basket and platform trials allow related questions to be studied within a shared protocol structure, which can support efficiency in areas such as oncology, rare disease and precision medicine. These designs can reduce duplication, but they also require careful planning around governance, statistical assumptions, operational logistics and data interpretation.

In addition, regulatory initiatives like the FDA's Project Optimus are encouraging a shift away from traditional dose-escalation designs that rely on identifying the Maximum Tolerated Dose (MTD). This approach, historically used in oncology trials, often prioritises higher doses that maximise efficacy, but also come with significant toxicity risks. Project Optimus is pushing for dose optimisation strategies that put patient safety and long-term outcomes at the forefront, which could change the way dose-finding studies are designed.

RAR, BOIN designs, master protocols, and initiatives like Project Optimus represent just a few of the novel approaches that move beyond a one-size-fits-all approach to trial design.

The Expansion from Risk-Based Monitoring (RBM) to Risk-Based Quality Management (RBQM)

Over the past few years, we’ve seen a notable shift in the industry, moving on from Risk-Based Monitoring (RBM) to Risk-Based Quality Management (RBQM), which is a more comprehensive approach. The ICH GCP E6(R2) Addendum initially drove the adoption of a risk-based approach to clinical trials, and while RBM was already gaining traction prior to the release of these guidelines in 2017, the industry has since progressed to apply these risk-based principles across all aspects of trial management, not just monitoring.

What Does a Risk-Based Approach Look Like Today?

A modern risk-based approach includes several key components:

Risk Assessment
Conducted during pre-study start-up and continuously reassessed throughout the trial to identify and mitigate potential risks.

Protocol and Investigational Plan
A well-designed and detailed protocol, based on the risk assessment, ensures the trial is designed to minimise risks and optimise data quality.

Risk-Based Monitoring Plan
While monitoring remains important, it is now integrated within a broader RBQM framework, focusing on overall trial quality, patient safety, and regulatory compliance.

These components work in tandem to implement risk controls and take corrective actions as required during the trial, all of which are documented in an auditable log. This broader remit has led the industry to develop from RBM to RBQM, which incorporates a more holistic view of trial oversight.

Since 2017, the role of RBQM has continued to expand due to several factors:

ICH E6 (R3) Guidelines
ICH E6(R3) now reinforces quality by design (QbD), proportionate approaches and risk-based principles across trial planning, conduct and oversight. This makes RBQM relevant from protocol design through to close-out, rather than only during monitoring.

Decentralised and Hybrid Trials
The acceleration of decentralised and hybrid trial models has necessitated new risk assessment strategies. RBQM frameworks now account for the complexities of remote data collection, trial oversight, and patient engagement, ensuring high data quality in diverse trial environments.

Vendor and Supplier Oversight
As clinical data is increasingly collected from multiple external sources, such as CROs and other third-party vendors, maintaining control over data quality has become critical. RBQM places a strong focus on ensuring that sponsors remain accountable for the quality of the data, even when outsourced, and that vendors comply with risk management protocols.

Site and investigator readiness should also be part of this risk-based view. A site that looks suitable on paper may still face constraints around staff capacity, patient access, competing studies, technology set-up or assessment training. Treating site readiness as an ongoing operational factor, rather than a start-up checkpoint, can help reduce delays, protocol deviations and variability in data collection.

Rare Disease, Paediatric, and Advanced Therapy Studies

The rare disease sector is experiencing significant growth, driven by scientific progress, greater use of advanced therapies and continued unmet need in small patient populations.

Gene therapy trials, particularly in the rare disease space, often require specific regulatory pathways and specialised trial designs due to the small patient populations and the complexity of the treatments. These trials also face significant challenges in patient recruitment and the logistics of gene therapy manufacturing.

For rare disease, paediatric and advanced therapy studies, the main challenge is often not only finding enough participants. Sponsors also need endpoints that are sensitive enough to detect meaningful change, data management approaches that can cope with limited and heterogeneous data, and long-term follow-up plans that are proportionate to the therapy and patient population. In paediatric studies, developmental stage, caregiver-reported outcomes and assent or consent considerations can add further complexity.

For cell and gene therapies, site capability also matters. Trials may require specialist storage, handling, administration, patient monitoring, safety reporting and coordination across manufacturing and clinical teams. These requirements should be planned early because they can affect feasibility, site selection, timelines and the quality of long-term evidence.

Industry Emphasis on Diversity and Inclusion

Diversity in clinical trials is a top priority. Regulatory bodies like the FDA have introduced guidance and policy expectations intended to support clinical trials that better represent diverse populations. Historically, trials have often lacked participation from racial minorities, women, older adults, and other underrepresented groups, leading to gaps in the understanding of how therapies perform across different demographics. Sponsors and CROs are now tasked with implementing targeted strategies to improve diversity in patient recruitment, ensuring that trial data is reflective of the real-world populations these therapies will serve. Greater diversity in clinical trials will not only improve the accuracy of results but also foster equity in healthcare by ensuring therapies are safe and effective for all patient groups.

For this section to remain accurate, any claim about FDA Diversity Action Plans should be checked against the latest FDA guidance status before publication. A cautious position is to say that sponsors are under increasing pressure to plan for representative enrolment, justify enrolment goals and address barriers to participation, without overstating the status or scope of any single requirement.

Functional Service Provision (FSP), Hybrid Outsourcing, and CRO Partnerships

Traditionally, FSP involved vendors providing dedicated functional teams to support specific services, offering sponsors flexibility while maintaining some internal control over trial management.

As we reflect on recent outsourcing patterns, we’ve seen this prediction materialise, with smaller biotech companies increasingly adopting FSP models. This shift has been driven by the need for flexibility, cost control, and the ability to scale services quickly, which FSP models can provide. At the same time, hybrid models, which combine elements of both FSP and full-service outsourcing, have gained popularity. These hybrid approaches allow sponsors to tailor their outsourcing strategies to specific needs, giving them the agility of FSP while still benefiting from the comprehensive support of full-service partnerships.

In terms of revenue distribution, while FSP continues to be a significant portion of CROs' revenue, the lines between FSP and full-service outsourcing are blurring. As sponsors balance cost pressure, internal control and access to specialist expertise, CROs that can offer expertise in both areas, as well as flexible hybrid solutions will be well-positioned.

The Growing Priority of Cybersecurity and Data Privacy

As decentralised trials and wearable technologies become more prevalent in clinical research, the volume of data being collected and transmitted has significantly increased. With this rise in data streams comes heightened concerns around cybersecurity and data privacy. Ensuring the security of sensitive patient information is more critical than ever, particularly as clinical trials rely on digital tools and remote data collection.

Regulatory bodies, such as the GDPR in Europe and HIPAA in the United States, impose strict guidelines on the management and protection of personal health data. It’s important to partner with CROs that have well-defined data privacy and cybersecurity measures in place, as they’re better equipped to meet these regulatory standards. Sponsors are increasingly seeking CROs with proven in-depth expertise in secure data handling to mitigate risks of data breaches, non-compliance, and reputational damage.

As we move ahead, the emphasis on data integrity and patient privacy is expected to grow, with the ability to safeguard personal health data while managing decentralised trials being a regulatory requirement.

Cybersecurity should also be linked to vendor oversight. Modern trials often rely on CROs, technology vendors, eCOA providers, EDC systems, wearable devices, imaging vendors and laboratories. Sponsors remain accountable for clinical data integrity even when collection or processing is outsourced, so privacy, access control, audit trails, data transfer processes and supplier oversight should be part of the trial’s risk management approach.

Conclusion

The central point is that trends should not be adopted because they are fashionable. Sponsors and CROs need to choose methods that fit the trial question, patient population, site network, data requirements and regulatory context. In practice, the future of clinical trials will depend less on any single technology and more on how well teams manage quality, evidence generation, participant burden and operational risk.

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