The placebo-controlled trial combined tarextumab with etoposide plus either cisplatin or carboplatin in patients with previously untreated extensive-stage SCLC. Tarextumab was one of two drug candidates whose failures within two weeks prompted OncoMed to eliminate half of its workforce.
The company said that tirasemtiv failed to show change from baseline in slow vital capacity SVC following 24 weeks of double-blind, placebo-controlled treatment, then followed up in December with details. How drug failed : Seven patients treated with tozadenant developed sepsis—five of whom died. Five days later, Acorda terminated development of tozadenant.
Of the seven sepsis cases, four were associated with agranulocytosis, two had no white blood cell counts available at the time of the event, and one had a high white blood cell count. Type of drug : First-in-class, highly selective adenosine mimetic targeting the A 1 subreceptor.
Six months later in July, Inotek halted development of trabodenoson after it failed a Phase II fixed-dose combination trial in combination with latanoprost, also in glaucoma, by showing no meaningful clinical advantage in IOP reduction at Day 56, after 4 additional weeks of treatment and night-time dosing. Inotek responded by exploring strategic alternatives, with relatively quick success.
In September, the company announced a merger agreement with gene therapy developer Rocket Pharmaceuticals, a deal that is expected to close in the first quarter of Seattle Genetics also halted enrollment in all other clinical trials assessing the ADC.
Log in to leave a comment. Sign in. Forgot your password? Weak management skills disturb the productive mood. This approach mostly results in a low team engagement with no desire to continue pro-active study participation and adopt a pro-active attitude towards questionable data quality. The surveyees warn that non-involved teams will find it hard to deal with protocol complexity. The surveyed experts stated that the large number of protocol amendments that follow, which are supposed to remove half of the initial endpoints, is a clear indicator of protocol over-complexity.
A challenging situation arises, when project management hits the conflicting interests of research. On the one hand, sites receive recruiting targets; on the other hand, they face limitations of eligibility of volunteers. Often project management finds itself caught between two fires, i. Also in these cases the surveyed observe similar dynamics, such as a significant drop in motivation with no desire to continue pro-active study participation and adopt a pro-active attitude towards questionable data quality.
Another reason mentioned for failure is the lack of well-structured training of study sites. Only a competence-based training and competence-based verification guarantees well-designed standards and reduces study variability. Variability in measuring procedures may be critical for clinical studies. It increases the confidence intervals of key indicators and introduces risks of obtaining results of statistical insignificance. It is very likely that poor training will result in ethical issues and poor data quality.
From time to time breaches occur, either consciously, or sometimes subconsciously. Ethical issues introduce a high risk of trial failure, severely damaging the reputation of all parties involved, i.
Too many industry cases illustrate that alleged short-term gains can rapidly turn into long-term losses. Clinical data build the key evidence and conclusive results of any clinical trial. Therefore, data quality is key to successful study closure. The absence of data quality monitoring during study conduct will understandably have regrettable consequences.
The introduction of noise due to miscalibration of measurement devices, or misunderstanding of measurement methods for example, will negatively influence the resulting confidence intervals and subsequently the critical trial outcomes. Some companies offer a regular manual statistical quality check of raw clinical data; others offer statistical data-surveillance services during a clinical trial.
Regardless of all risks and challenges, project managers need a stroke of luck bringing a clinical trial to a successful end. Sometimes luck comes with experience, with a healthy corporate culture, but mostly it strikes for no reason at all. Apart from the complexity of a project, I wish you a serenity to accept the things you cannot change, the courage to change the things you can, and the wisdom to know where the difference lies.
Well said, the design and eligibility criteria leads to failure of a trial especially where not all team players are involved at the right point and when we are in a hurry to get the trial going setting unrealistic timelines.
These can also lead to unethical conduct of a trial. The above reasons are real and important. I would like to add that the choice of investigators, site personel recently working in clinical research and having not or too little training, poor communication between teams from upper management, project manager having knowledge but having poor management skills, high turnover and tight timelines, work overload for all parties are other reasons for failure.
Beside this very comprehensive overview in the previous messages , the start of the negative outcome of a trial or drugdevelopment process at all , is probably also related on a row of wrong decisions what already starts by misinterpretation of preclinical results. Recruitment and retention can suffer when patients perceive support staff to be unavailable or uninterested, or if they have to interface routinely with new staff [ 33 ]. Encouraging patient trust in the clinical trial process may be expected to lead to better participation [ ].
Incentivizing staff providing funds for enrolling patients has been shown to improve patient recruitment [ 33 ]. Using nurses instead of surgeons physicians to perform recruitment has not evidenced any difference in outcomes; however, cost savings have been realized [ 35 , 40 ] which may be important in supporting recruitment and retention, or other aspects of the clinical trial, indirectly.
Patient recruitment and retention is affected negatively when patients are concerned about being assigned to a control group rather than receiving active study drug. Part of this effect may be due to patients having poor knowledge about placebos [ 55 ] or what specific treatment is given in the control group.
For patients with poor prognoses, the concern may center around not having effective treatment at all. Ulrich et al. Patients reported burdens including potential side effects from treatment, additional tests that would have to be endured, financial concerns including loss of job support and work disruption , and a general worry about the unknown future, including whether or not the study drug assigned would be beneficial. Sometimes patients are not presented with a clear rationale for why their participation is important and receive minimal feedback.
These concerns were echoed in Rosbach and Andersen [ ], which reported on burdens on patients with multimorbidity.
In addition, scientific literacy in the general population is limited, leading to difficulty understanding information associated with a clinical trial [ 9 , 67 ]. Hadden et al. Moreover, even for those who completed a trial, 1 in 6 patients found the ICF vexing. It is of interest to determine if artificial intelligence tools employing sentiment analysis could be useful in crafting the language of the ICF and other materials to provide a more compassionate tone and greater patient confidence [ 38 , 91 , ] in addition to maintaining an appropriate reading level.
Davis et al. There is an impetus for simplifying the ICF, as well as other associated written materials. In addition, Sood et al. Even receiving a simple biosketch card of a healthcare provider has been associated with improved patient satisfaction [ 90 ]. Surveys of patient satisfaction conducted by a personalized health network suggest patients often have a poor experience [ ].
Communicating with the patient is important at all stages of the clinical trial and supports recruitment, enrollment, and retention [ 61 ]. A repeated problematic pattern in the literature is that study centers report fewer eligible patients than anticipated [ 6 , 33 ].
Study centers with a track record of successful performance are historically more likely to meet enrollment targets [ 43 ]. There is considerable literature reporting results from studies in which numerous study sites failed to meet enrollment, or failed to enroll any subject at all [ 64 , 72 , 73 , ].
Levett et al. A site that has historically little focus on clinical trials or presents other non-scientific impediments may lead to low investigator enthusiasm [ 61 ]. Enthusiasm from the lead investigator at a study site was the most important factor associated with positive recruitment across 60 study centers in trial assessing the management of local post-surgical pain [ 26 , 41 , 51 ].
Thoma et al. Slow recruitment may come from an inadequate staff and a lack of prioritizing the clinical trial over day-to-day operations [ ].
It can also come when the investigator has competing trials. When a trial suffers too many dropouts either based on projected or actual enrollment , the trial may become underpowered. Underpowered clinical trials are problematic. The sponsor may adapt to low enrollment by expanding the number of sites perhaps in additional countries, with corresponding costly protocol amendments and delays in further research , increasing funds allocated to the study in an effort to meet minimum enrollment.
By consequence this sometimes necessitates eliminating certain planned tests in order to reallocate available funds. In turn, certain endpoints may have an insufficient sample size to detect an important result. As an example [ 61 ], the STICH trial surgical treatment for ischemic heart failure that studied the effectiveness of coronary bypass surgery in patients with heart failure took place ultimately in 26 countries, in study sites, and involved patients.
Originally, the study was designed to cover 32 study sites in the USA and Canada, but low patient enrollment required expanding the study to sites internationally. After the expansion, 44 sites that had been approved for the trial failed to enroll a patient. The extra costs involved in expanding the trial to numerous sites in various countries meant that funds intended originally for imaging studies had to be diverted.
The imaging studies were removed from the protocol, creating additional expenses for protocol amendments. Importantly, and as mentioned briefly earlier, underpowered trials have also been described as unethical [ 47 ], even though some patients may benefit from the trial, because patients who volunteer to be in the trial are unlikely to know or appreciate that their results will not be likely to contribute to a statistically significant outcome.
Carlisle et al. Thus, poor recruitment, enrollment, and retention remains a primary area of concern for multiple reasons. Formulating a list of factors to consider when designing and executing a clinical trial can provide a foundation for better outcomes. However, not all factors are equally important. A well-structured mathematical framework e. For example, increasing the speed of enrollment leads to faster completion, and may be associated with fewer dropouts, better statistical power, and increased confidence in results.
Enrollment can be accelerated by spending money on recruitment, both in advertising and in having available friendly personnel. Thus, there is a direct trade-off between the speed of enrollment and the cost of executing the trial. Budgets are not unlimited, and therefore various trade-offs need to be considered, including not only the speed of enrollment, but the likelihood of meeting the enrollment goal.
A more-effective study center with a long history of running clinical trials successfully and with a nearby population of prospective participants may be more expensive than another more remote site with less experience. But choosing the cheaper alternative may result in failing to meet recruitment. By consequence, this may necessitate spending more on additional study centers, which come with additional costs of evaluating, training, protocol amendments, and trial execution.
Quantifying these trade-offs can assist with making better decisions. Given the tremendous problem of clinical trials that fail to complete due to poor recruitment, enrollment, and retention, it's of primary importance in designing and executing clinical trials to consider the burden that each patient undergoes, with the belief that retention is correlated negatively with patient burden.
All burdens to the patient should be given attention, but financial impacts deserve special consideration. The financial impact to patients in clinical trials can be easily overlooked while focusing on the objectives, endpoints, and other aspects of a particular trial design. Patients may have out-of-pocket costs when participating in a clinical trial. These include the cost of transportation and lost work, but also medical costs for additional testing. Insurance may not cover medical care beyond that which is deemed routine.
Even when it does, deductibles are often quite high and a given patient may not be able to afford to participate [ 92 ]. In addition, many trials require participants to travel to their specific study centers, even for tests or procedures that could be provided locally [ 92 ], or conducted at home.
Patients may need to relocate close to a study center for some period of time [ 79 ]. The additional cost of participating under these circumstances biases participation to those in higher socioeconomic levels [ 5 , , , ], particularly in oncology studies.
Stump et al. Studies also show that the financial impact of some trials can adversely event patient adherence as well as retention [ 8 , 23 , ]. While some trial participants do need to relocate during a study, many are not willing to do so [ 19 ] and most participate in local trials. Patient recruitment and retention depends in part on the willingness of the participant to travel to and from the local study center [ 96 ].
Transportation is a long-standing particular challenge for elderly participants [ 77 , 83 , 96 , 99 ]. Regardless of patient age, long travel times, particularly in urban areas can dissuade participation. Research that would provide a mathematical function describing the likelihood of patient recruitment or retention as a function of distance to a study center and other factors such as demographics appears missing currently.
However, some related information can provide guidance in the absence of such specific research. Demographic analysis showed that males were willing to travel for longer duration Interestingly, those 65 years of age or older were willing to travel only These data suggest the importance of recruiting patients from proximate vicinities local to study centers when in urban settings.
Moreover, when incorporating older patients it is important to assist in minimizing their total time investment as they may begin from a perspective of being less patient than the average participant. Proper site selection can help minimize long travel times.
In addition, selecting a study site with a nearby larger population pool has been correlated positively, as expected, with the likelihood of meeting recruitment targets [ ]. In their analyzed data, study centers in China and India were more likely to meet recruitment targets, with centers in certain locations in Western Europe and North America being least likely.
It remains an open question as to whether study adherence is equivalent across these sites, and if other factors influencing positive recruitment might be associated with any operational issues.
Artificial intelligence applications offer promise in helping reducing patient time investment regardless of constraints on study site location [ 7 , 20 ].
In particular, evolutionary algorithms [ 11 , 66 ], which use computer simulations of nature's processes of variation and selection to solve problems, can assign the most appropriate study center for each prospective patient in a trial based on patient and study center availability. There is also the opportunity not only to schedule staff to support a clinical trial appropriately [ 12 ] but also to match staff with patients so that patients tend to see familiar faces at each visit and could also request having alternative staff to interact with if desired.
In certain cases, it is possible to schedule study center visits to minimize other conflicts that a patient may have. For example, the burden on a single parent of elementary school children who must come to a study center at 10am is different than the burden for the same procedure scheduled at 4pm, after school has let out for the day. Artificial intelligence software can examine the profile of each study participant and impute the least burdensome times for appointments within the constraints of a study center's activity and the constraints of the protocol e.
The same software can search for opportunities to reschedule patients adaptively when openings develop, making the most efficient use of the clinical trial's time. Effective scheduling also should incorporate the patient's time spent waiting after checking in before being seen. Waiting time has been offered as being associated negatively with patient satisfaction and how patients feel about the quality of their health care [ 28 , 93 , 94 ].
Long waiting times are a source of stress and can leave patients feeling disrespected, which intuitively would be associated with lower retention. The patient's perception of a long waiting time can be reduced by assigning an additional person to facilitate interaction with the patient [ ].
This additional person can also help to relieve the burden on other doctor-office staff who would be dividing their attention between patients receiving routine care and those participating in a trial. Each of the facets of protocol design, execution, and successive trial planning offers opportunities for trading off different concerns, as well as simply making inappropriate judgments leading to poor outcomes.
Study site selection is an important aspect of the clinical trial process. Poor choices can lead directly to study failure, or to a costly exercise of including additional study sites, amended protocols, and the potential for patient populations receiving different treatment regimens.
When possible, having contingency plans to open additional sites, perform extra recruitment, and cover protocol amendments is recommended. The practicality of holding out reserve funds to covers these and other contingencies, however, is case specific. Many factors for study site selection are study specific. Hurtago-Chong et al. It is straightforward to presume that many specific requirements for a study in this specific area would not carry over to a criteria for, say, a study on pediatric oncology.
Still, there are many study-site-related factors that are common to successful trials. Getz [ 42 ] cited research from pharmaceutical companies Lilly and Pfizer, suggesting a correlation between performing well on one trial and performing well on a subsequent trial, as well as the converse of performing poorly on one trial and performing poorly on a subsequent trial. Experience with clinical trials is also important, as experience facilitates effectiveness.
A site that has conducted between 6 and 10 clinical trials has a greater probability of meeting enrollment within the required time than does a site with a history of fewer trials [ 42 ]. An additional indicator is time to enroll the first patient, which is correlated with better overall performance. Data in Ref. In addition, as mentioned earlier, other positive factors include an enthusiastic investigator and experienced and involved staff.
A key item deserving more attention is the minimization of patient burden and maximizing patient appreciation. This encompasses: 1 providing materials that are easy to understand, 2 having empathetic and supportive staff, 3 leadership and enthusiasm from the principal investigator, 4 a schedule time and events that works in synergy with the patient's constraints rather than at odds with those constraints, 5 the opportunity to adaptively reschedule visits and assign appropriate personnel to support participants, 6 trial management software to send effective reminders about visits and protocol adherence via phone, text, or email, including supporting multiple languages in multi-lingual areas [ 14 ], and 7 understanding what the patient's day-to-day experience during the trial is likely to be.
Study support staff should be generally aware of how study participants are feeling during the trial, and seek to minimize patient stress. Hui et al. Patients are more likely to withdraw from a trial when they perceive their condition as not improving, even though this may be anticipated.
Support staff, as well as the investigator, should seek to set patient expectations appropriately and provide appropriate empathy for any burdens that a patient is undergoing during a trial. Patients deserve to have access to reports from studies in which they participated.
Yet, Ziv [ ] reported that, after completion, most studies are not available via open access. Thus, patients have to pay to be able to read a published study, even one in which they have participated.
Having already given much of their time in support of a clinical trial, they may feel disrespected to have to pay to find out what information was discovered during the trial. It would be easy for a sponsor to take the position that the patient has nothing left to offer to the trial after the trial concludes and thus any additional funds required to provide article access or to provide a copy of a publication would be better allocated elsewhere.
This misses the point, however, that by ensuring patient participation from start all the way through publication, the patient may feel more respected and be less likely to dropout. Study designers should employ methods to ensure that study populations are relevant to the real-world population that is intended to benefit from treatment. Eligibility criteria should be reviewed carefully in this regard. Older patients may be viewed, correctly in some cases, as presenting more potential for comorbidities, propensity for adverse events, and for ultimately withdrawing from a trial.
Collecting and reporting data on participation and withdrawal should be more common place in order to assist with a better understanding of how to design trials so that they can complete with representative subpopulations. For example, Hui et al. Determining the repeatability of these factors across different types of trials remains for future work.
Future efforts should also be directed toward improving the efficiency and effectiveness of clinical trials broadly e. Bringing eligibility to the level of the individual holds the promise for establishing greater study drug efficacy but also has the drawback of limiting the available sample size [ ] to more rapidly direct the use of study drug to targets of opportunity. Success depends crucially on identifying genetic features reliably [ 86 , ], which could benefit from establishing collaborative databases for academic research.
It will also be important to determine quality of life measures to better assess the cost effectiveness of these tailored trials. Current data do support the cost effectiveness in terms of additional dollars spent per month of extended life in the case of genetic markers for acute myeloid leukemia [ 52 ] but short-term extended life cf.
This review covers many aspects of clinical study design that can be affected positively by appropriate design considerations and trial execution. Study site selection and addressing patient concerns are two primary areas where the effectiveness of clinical trials can be affected positively.
For convenience, Table 1 offers a summary of the factors associated with problems or challenges that occur when preparing for and executing clinical trials. Some of these issues may not lead directly to the failure of a trial; however, a series of issues can lead to a critical failure.
The table presents the opportunities for improving the likelihood of success and the role that artificial intelligence may play in that improvement. Many of these factors are correlated or interrelated, thus the table is not a substitute for the greater detailed explanation found in the text. A list of factors associated with problems or challenges when preparing for or executing a clinical trial, along with the opportunities for artificial intelligence to help alleviate these issues.
The author thanks E. Daly-DeJoy, M. Nicoletti, F. Rahman, J. Wallace, and T. Walpole for helpful comments that improved the quality of this review. National Center for Biotechnology Information , U. Contemp Clin Trials Commun. Published online Aug 7. David B. Author information Article notes Copyright and License information Disclaimer. Fogel: ia. This article has been cited by other articles in PMC. Abstract Clinical trials are time consuming, expensive, and often burdensome on patients. Background Clinical trials for pharmaceuticals and medical devices offer many opportunities for failure.
Failing to demonstrate efficacy or safety The primary source of trial failure has been and remains an inability to demonstrate efficacy. Financial impact Hwang et al. Patient recruitment Patients are often willing to consent to participation in a clinical trial if they believe that they have an opportunity to receive better treatment or if the results can help others [ 29 , 45 , 89 ]. Additional costs associated with recruitment Beyond remuneration, the additional costs associated with patient recruitment can be difficult to estimate and highly variable, even within the same investigative area [ 21 ].
Respecting the patient's concerns Patient recruitment and retention is affected negatively when patients are concerned about being assigned to a control group rather than receiving active study drug. Poor recruitment, dropouts, and underpowered trials A repeated problematic pattern in the literature is that study centers report fewer eligible patients than anticipated [ 6 , 33 ]. Employing quantitative measures Formulating a list of factors to consider when designing and executing a clinical trial can provide a foundation for better outcomes.
Considering the patient's financial burden Given the tremendous problem of clinical trials that fail to complete due to poor recruitment, enrollment, and retention, it's of primary importance in designing and executing clinical trials to consider the burden that each patient undergoes, with the belief that retention is correlated negatively with patient burden.
Patient time investment While some trial participants do need to relocate during a study, many are not willing to do so [ 19 ] and most participate in local trials.
Discussion Each of the facets of protocol design, execution, and successive trial planning offers opportunities for trading off different concerns, as well as simply making inappropriate judgments leading to poor outcomes. Table 1 A list of factors associated with problems or challenges when preparing for or executing a clinical trial, along with the opportunities for artificial intelligence to help alleviate these issues.
Appropriate statistical analysis NLP of available literature, summarizing statistical methods and associating these methods with successful or failed outcomes. Determination of appropriate sample size Nonlinear modeling, such as with neural networks, to predict patient drop-out rates and better estimate sample size to avoid becoming underpowered. Agent-based modeling to simulate trial before execution. Use of NLP to mine previously published trials to determine sample sizes used in successful trials Reducing likelihood of amendments NLP and knowledge-based processing to present designer with pertinent information to consider.
Inconsistencies in protocol NLP including table-based format to check time and events schedule against text, as well as summary of changes for any amendments. Ineffective site selection Effective measurement of trade-offs for each site Nonlinear modeling, such as with neural networks, to assess trade-offs site history, staff experience, investigator enthusiasm, available population, expected patient burden, and financial impact. Potential use of fuzzy logic to provide linguistic measurement descriptions.
Targeting communication to meet patient profile, including sentiment analysis. Ensuring appropriate eligibility criteria NLP on prior publications to identify suitable criteria, and also criteria associated with other trial failures.
Facilitating locating eligible patients Database coordination, prompting investigators and patients when appropriate trials are available for specific patients. Enrolling patients who are likely to complete the trial NLP and machine learning to profile patients based on prior data on who is more likely to complete a trial, reducing drop-outs. Adapt site visit schedule if possible. Minimize out-of-pocket expenses Systematic review of all patient costs to identify opportunities to minimize impacts.
Increase likelihood of feeling respected Sentiment analysis and other NLP tools applied to all documents provided to patients. Prompts to interacting staff for personalizing interactions. Tailored messaging to participants to increase likelihood of retention. Poor trial execution Automating reporting of events Automated prompting of events for patients and staff, reporting requirements, notes missed events, prompts for required reporting, including protocol deviations and adverse events.
Overall Factor analysis to improve trade-offs based on budget and other constraints Multicriteria decision making based on Pareto analysis or single aggregated evaluation function Valuated State Space to quantify and illuminate trade-offs.
Open in a separate window. Acknowledgments The author thanks E. References 1. Abraha I. Deviation from intention to treat analysis in randomised trials and treatment effect estimates: meta-epidemiology study.
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