Why clinical trials fail - top reasons overviewed

Guest post by Safeer Khan, Lecturer at Department of Pharmaceutical Sciences, Government College University, Lahore, Pakistan

Clinical trials are the backbone of clinical drug development. They are essential for advancing medicine, testing new therapies, and ensuring patient safety before drugs reach the market. Yet, despite enormous investment, approximately 90% of all clinical trials fail to achieve regulatory approval, according to recent industry studies.[1]

Failed trials result in significant financial losses — the average cost of a failed Phase III trial alone can exceed $100 million. Beyond money, the setbacks delay potentially life-saving treatments and raise ethical concerns regarding participant exposure without therapeutic benefit.[2]

For research sponsors, researchers, clinical operations professionals, and regulatory bodies, understanding why clinical trials fail is more important than ever. By identifying common pitfalls and systemic weaknesses, stakeholders can design better studies, enhance patient protection, and improve overall clinical success rates. This blog post takes a deep dive into the leading causes of clinical drug development failures.

Inadequate Target Validation

One of the most common early-stage causes of clinical trial failure is inadequate validation of the drug target. Many programs move into human testing based on promising preclinical data, only to stall or collapse because the underlying biology doesn’t hold up under real-world complexity.

In these cases, the failure isn’t due to flawed execution during the trial — it starts much earlier, during the drug discovery and design phases. Often, the selected target is poorly understood or turns out to be less relevant in humans than in animal models.

Beyond the biology, drug properties themselves can undermine a trial before it truly begins. About 10–15% of clinical drug development failures are attributed to poor drug-like properties [1]. Even a well-targeted drug can fail if it doesn’t reach therapeutic levels in human tissue or if it produces toxic byproducts.

Addressing target validation early and rigorously isn’t a silver bullet, but it’s a crucial first step in reducing the overall rate of clinical drug development failures. Neglecting this phase remains one of the core reasons why clinical trials fail before they truly begin.

Methodological Issues

Another foundational reason why clinical trials fail is poor study design. Methodological flaws can doom a trial to failure regardless of how well it’s executed later.

A major design-related problem is the growing reliance on overly complex protocol. This not only complicates trial operations but also increases the likelihood of protocol deviations, missing data, and patient non-compliance — all of which compromise data integrity. For example, in antidepressant trials, as many as 50% of studied interventions fail to show statistical significance partly because the trial design does not adequately capture the nuances of the patient’s clinical condition [3].

Another common pitfall is restrictive inclusion and exclusion criteria. While tight criteria aim to create a homogenous study population and reduce variability, they often backfire by dramatically shrinking the eligible patient pool. As a result, recruitment slows, prolonging study timelines and inflating costs. Read more about Clinical Trial Eligibility Criteria.

Finally, many protocols are designed with overly optimistic expectations regarding drug efficacy and safety, setting unrealistic benchmarks that become impossible to meet in larger, more diverse patient populations.

Poor Risk Management

Clinical trials inherently involve balancing potential benefits against potential risks. This balance must be continuously assessed, not only at study initiation but throughout its execution. A review found that over 20% of terminated trials were halted because interim data showed the risk-benefit ratio had become unfavorable [4].

Unfortunately, many trials take a reactive rather than proactive approach to risk management. Sponsors often scramble to fix problems after they occur rather than anticipating them from the start. Common disruptors — such as unexpectedly high dropout rates, site staff turnover, protocol deviations, or supply chain interruptions — frequently derail studies that otherwise had solid therapeutic potential.

Poor risk planning doesn’t just increase operational headaches — it directly impacts patient safety, trial validity, and regulatory approval prospects. In today’s regulatory environment, where authorities like the FDA and EMA emphasize the importance of continuous risk evaluation, failure to manage risk proactively remains a major contributor to clinical drug development failures.

In modern research, incorporating AI into clinical trial planning and management enables researchers to predict risks, allocate resources more efficiently, and enhance patient safety [5]. Machine learning tools like the Clinical Trial Risk Tool are transforming how clinical trials are planned and managed.

Recruitment and Retention Challenges

Recruitment and retention challenges are among the most persistent and costly reasons for clinical trial failure. A staggering 55% of clinical trials are prematurely terminated due to poor patient enrollment, according to industry-wide analyses [6].

One major contributor to recruitment failure is the overestimation of eligible participants. Sponsors and investigators often develop overly optimistic recruitment projections based on broad epidemiological data without accounting for real-world limitations.

Patient willingness to participate is another critical factor. Concerns about privacy, exploitation, and the perceived risks of experimental treatments deter many potential participants.

Moreover, physicians are a linchpin in the recruitment process, but they are often an overlooked variable. Physician hesitancy to discuss trial options with eligible patients is a well-documented barrier. Time constraints, administrative burden, lack of financial incentives, or discomfort with unfamiliar protocols can all discourage physicians from referring patients into clinical trials. Moreover, psychological factors, such as a physician’s belief that one treatment is superior, can lead them to favor standard care over enrolling patients in experimental trials.

Financial Constraints and Resource Allocation

Clinical trials are among the most expensive undertakings in biomedical research. On average, bringing a new drug to market can cost $985.3 million [7], with clinical trial expenses accounting for a substantial portion of that figure. An inaccurate budget estimate can cause project delays, cost overruns, or even force a clinical trial to shut down early. See our earlier blog post about the importance of cost estimation in clinical trials.

One of the most common financial pitfalls is underestimating timelines and costs. Early projections often fail to account for inevitable delays in patient recruitment, regulatory approvals, or data processing. As a result, studies frequently exhaust their funding reserves long before reaching critical endpoints.

In many cases, the problem traces back to poor initial planning. Trial budgeting often focuses heavily on direct costs while underestimating indirect expenses like site maintenance, technology infrastructure, and staff turnover. Failure to model the financial impact of slow enrollment, patient dropouts, or protocol amendments can cripple even well-intentioned trials.

Fortunately, modern predictive tools are beginning to mitigate these risks. Solutions like Clinical Trial Risk Tool leverage artificial intelligence (AI) and natural language processing (NLP) to help trial managers forecast costs, enrollment challenges, and risk factors with greater precision. These technologies enable sponsors to make better upfront resource allocation decisions and respond dynamically as the trial progresses.

Operational and Logistical Challenges

Even when scientific hypotheses are strong and funding is secure, operational and logistical challenges can undermine a clinical trial. Inefficiencies in trial conduct remain a major reason why clinical trials fail, especially in multi-center and global studies.

At the site level, problems frequently stem from lack of trained staff, insufficient infrastructure, or low motivation among investigators and coordinators. Trials often depend on a few key individuals at each site — if these people are inexperienced, under-resourced, or disengaged, the trial’s day-to-day execution suffers.

Multi-center trials introduce additional complexity. Inconsistent communication, delays in data transmission, discrepancies in recruitment rates, and variability in protocol adherence between sites are common. Without strong centralized coordination, the variability between centers can introduce major biases into the trial results.

Operational inefficiency is often magnified by a lack of alignment among stakeholders. Investigators, clinical research coordinators, site managers, sponsors, and contract research organizations (CROs) may all interpret protocols differently or prioritize tasks inconsistently. Without clear governance structures and regular cross-functional meetings, misunderstandings and delays become inevitable.

External Factors Influencing Trial Outcomes

Not all clinical trial failures stem from internal flaws. External factors can significantly impact trial outcomes. Recognizing and planning for these forces is essential for understanding why clinical trials fail.

The COVID-19 pandemic exemplifies how external pressures can drastically impact trial recruitment and execution. Staff shortages, shifting hospital priorities, and patient hesitancy to attend non-COVID-related visits caused recruitment delays and protocol deviations across multiple therapeutic areas — even outside infectious diseases [8].

Beyond acute crises, cultural, linguistic, and socioeconomic disparities also exert ongoing influence over trial success. In many regions, cultural perceptions of clinical research can deter participation. Language barriers further complicate recruitment efforts, limiting outreach and comprehension among non-native speaking populations.

Socioeconomic factors, such as lack of access to healthcare or transportation challenges, can prevent otherwise eligible patients from enrolling or remaining in trials. This not only hinders recruitment goals but skews study populations toward more affluent, urban, and often less diverse groups, thereby reducing the generalizability of trial results to real-world settings.

Impact of Disease-Specific Factors

Disease specific factors affecting trial feasibility, patient recruitment, and completion of a study are another important reason of why clinical trial fail. For instance, patient accrual is notoriously difficult in oncology. Clinical cancer trials often have extremely stringent eligibility criteria. The restrictive eligibility requirements that stem from scientific needs compromise the enrollment potential of available patients. In addition, informed consent for oncology trials is usually complex as patients have to understand high risk experimental therapies in miserable emotional states leading to hesitation to participate [9].

In Alzheimer’s diseases, logistical and perceptual barriers predominate. The enrollment process is complicated as patients are unable to provide informed consent independently, requiring the involvement of caregivers or legal representatives. Similarly, long natural history of neurodegenerative diseases require the need for longer study durations, which increase the risk of attrition [10].

In other vulnerable populations, such as those with rare genetic disorders or pediatric illnesses, additional challenges emerge. In these cases, patients and families are usually reluctant to invasive diagnostic procedures necessary for trial eligibility.

Conclusion

Clinical trials are essential for advancing medicine, but the high rate of failure reflects deep, systemic challenges across the research ecosystem. From early-stage issues like inadequate target validation to operational hurdles and external pressures, the reasons why clinical trials fail are complex and interconnected.

Proactive risk management, realistic financial planning, patient-centered strategies, and operational excellence are no longer optional — they are essential for future success. Tools like the Clinical Trial Risk Tool can help sponsors and investigators take proactive steps to reduce failure risk.

References

1. Sun D, Gao W, Hu H, Zhou S. Why 90% of clinical drug development fails and how to improve it? Acta Pharmaceutica Sinica B. 2022;12(7):3049-62.

2. Zarin DA, Goodman SN, Kimmelman J. Harms From Uninformative Clinical Trials. Jama. 2019;322(9):813-4.

3. Santen GW, Horrigan JP, Danhof M, Pasqua OD. From Trial and Error to Trial Simulation. Part 2: An Appraisal of Current Beliefs in the Design and Analysis of Clinical Trials for Antidepressant Drugs. Clinical Pharmacology & Therapeutics. 2009;86(3):255-62.

4. Williams RJ, Tse T, DiPiazza K, Zarin DA. Terminated Trials in the ClinicalTrials.gov Results Database: Evaluation of Availability of Primary Outcome Data and Reasons for Termination. Plos One. 2015;10(5):e0127242.

5. Wood TA, McNair D. Clinical Trial Risk Tool: software application using natural language processing to identify the risk of trial uninformativeness. Gates Open Research. 2023;7(56):56.

6. Wandile PM. Patient recruitment in clinical trials: areas of challenges and success, a practical aspect at the private research site. Journal of Biosciences and Medicines. 2023;11(10):103-13.

7. Wouters OJ, McKee M, Luyten J. Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018. Jama. 2020;323(9):844-53.

8. Hirt J, Rasadurai A, Briel M, Düblin P, Janiaud P, Hemkens LG. Clinical Trial Research on COVID-19 in Germany – A Systematic Analysis. F1000research. 2021;10:913.

9. Tang C, Sherman SI, Price M, Weng J, Davis SE, Hong DS, et al. Clinical Trial Characteristics and Barriers to Participant Accrual: The MD Anderson Cancer Center Experience Over 30 Years, a Historical Foundation for Trial Improvement. Clinical Cancer Research. 2017;23(6):1414-21.

10. Watson JL, Ryan L, Silverberg N, Cahan V, Bernard M. Obstacles and Opportunities in Alzheimer’s Clinical Trial Recruitment. Health Affairs. 2014;33(4):574-9.