- Research & Development
Adaptive clinical trial success calls for flexible approachGareth Macdonald — 15 Mar 2021
Adaptive clinical trials have accelerated COVID-19 vaccine development and could make drug R&D more efficient but running such studies is complex and challenging and sponsors need to prepare, say experts
The use of adaptive clinical trials is increasing thanks, in part, to regulatory support. In 2018 for example, the US Food and Drug Administration (FDA) released guidelines (1) on adaptive trials as part of what Scott Gottlieb, then Agency Commissioner, said was a wider effort to modernize clinical research.
“By modernizing our approach to the design of clinical trials, we can make drug development more efficient and less costly while also increasing the amount of information we can learn about a new product’s safety and benefits,” Gottlieb wrote.
But regulatory support is not the only factor according to study expert (2) Dr Philip Pallmann from the centre for trials research at Cardiff University in Wales, who says increased funding opportunities and new management technologies have also increased their popularity.
“Adaptive clinical trials have certainly become a lot more common across the board in the last 10-15 years,” he says. “Knowledge about these designs is spreading among trial methodologists and clinicians. More user-friendly software to design and analyse data from these trials has been developed.
“Public funding bodies such as NIHR and MRC are more open to funding adaptive trials, and sometimes they even actively encourage the use of novel adaptive designs. Regulatory agencies such as FDA, EMA and MHRA have clarified that they are happy to accept data from adaptive trials and published guidance for investigators.”
This view is shared by Thomas Underwood from biometrics focused clinical research organization (CRO) Quanticate who says “frequentist models have their place, but adaptive designs can provide more flexible and efficiency in terms of trial duration and performing interim analysis.
“Adaptive designs allow for more flexibility in the trial conduct, you can perform an interim analysis to see results sooner than a traditional method. You can also make changes accordingly so there is less burden or patients, less patient recruitment and the ability to stop a failing drug sooner to help reduce costs for drug developers.”
The coronavirus pandemic has had an impact, Pallmann says, citing the need for faster vaccine studies as the major driver.
“The COVID pandemic has given adaptive trials another push, as high-quality trials were needed that provide scientific rigour yet can give results more quickly than traditional clinical trials and are highly adaptable when new potential treatments need to be tested.”
A study (3) published last autumn also noted the impact the coronavirus pandemic is having on the use of adaptive trials.
According to the authors, “Traditional randomized controlled trials provide strong experimental evidence, however, tend to be slow, inflexible, and have limited generalizability.
“Adaptive and pragmatic designs meet our ethical obligation during the SARS-CoV-2 pandemic to balance the rapidly changing standards of care with speed, agility, and scientific rigor as we seek the best treatments for our patients. The age of the pragmatic, adaptive clinical trial has come.”
In addition to SARS-CoV-2 vaccine development, adaptive trials have also been used to assess potential treatments for COVID-19.
For example, a study (4) recently published in the Lancet used an adaptive design to show that azithromycin, an antibiotic with potential antiviral and anti-inflammatory properties, offers no benefits to patients hospitalised with COVID-19.
The main advantage of an adaptive clinical trial is the ability to look at data before the study is completed, which has been particularly important during the pandemic.
“In a non-adaptive trial all outcome data (which tell you whether an experimental treatment is beneficial or not) is analysed in one go at the end of the trial. By contrast, in an adaptive trial, the investigators can perform interim analyses of the outcome data during the trial and, depending on the result of an interim analysis, make changes to the ongoing trial,” Pallmann says.
This affords sponsors the ability to stop the trial if there is already enough evidence showing that the experimental treatment is beneficial or if experimental treatment turns out to be less beneficial than expected and it would be pointless to continue.
Pallmann says the ability to look at data early also allows sponsors to increase the recruitment target when it turns out that the experimental treatment is promising but a larger sample of patients is needed to demonstrate its benefit convincingly.
“Or you can narrow down recruitment to a sub-population of patients, for example those with a certain biomarker, for whom the experimental treatment looks more promising than others,” he explains.
Likewise, adaptive designs can be used to assess several different experimental treatments - or several doses of the same experimental treatment – and select only the most promising treatment or dose for further study.
The key is that such modifications are planned, Pallmann says: “Importantly, these options to make changes are built into the initial design of the trial from the very beginning, and ‘trigger points’ for making changes are pre-defined.
“This approach to trial design has been described as ‘planning to be flexible’, which isn't the same as just making changes on the go; the latter would raise questions about the validity and integrity of the findings obtained from the trial.”
So adaptive trial designs have significant potential benefits. But knowing when to use them instead of traditional randomized, controlled study designs can be a challenge, according to Pallmann who says not every trial needs to be adaptive, and for some trials a simpler design is preferable.
“The overarching rationale is to increase efficiency in clinical trials,” he says. “Stopping treatments or entire trials early means resources - time, money, patients – aren’t wasted on studying treatments with little to no benefit further, and treatments with a clear benefit can be made available to patients outside the trial quicker.”
Adaptive designs are also suitable when there is uncertainty when a trial is designed with regard to determining the optimal dose of a treatment, Pallmann adds.
“It is wise to begin the trial with several doses and once enough information has been collected, select the best dose for further study. This also means fewer patients in the trial will be exposed to less beneficial treatments or doses, or fewer patients who are less likely to benefit from a treatment will be recruited into the trial, which is a great ethical advantage.”
Planning is critical to success in all types of clinical research. However, even for sponsors used to running traditional studies, developing adaptive trial protocols can be a challenge.
According to a recent study (5) sponsors ranked the “cost and time to adaptively design trials, unclear rationales for using Ads, and, most importantly, a lack of education regarding adaptive designs” as perceived barriers.
Such concerns are understandable, according to Pallmann, who says sponsors more used to traditional trials often lack the specialist expertise required for adaptive studies.
“The statistical design and analysis are usually more complex than for a non-adaptive trial, which means you need a trial statistician with expertise in adaptive designs. Sometimes they need to run simulations to work out how an adaptive trial design would perform under different scenarios, which can be time-consuming.”
Avoiding potential bias is another consideration. In traditional research the very concept of early data analysis is anathema, with the perception being that such action can influence the results.
Companies running adaptive trials have even more need to guard against introducing bias, says Pallmann, explaining that working with independent experts is the most effective way of ensuring early data analysis does not impact the results.
“The risk can be minimised by making sure interim analyses are carried out by an independent statistician and interim results are kept confidential, only reviewed by an independent data monitoring committee that decides whether to make a change or not, and not leaked to investigators as this knowledge might influence their behaviour.”
For Quanticate’s Thomas Underwood, reducing bias is achieved at the trial planning stage: “Since you are looking at the data before the end of the trial and making decisions based on that data, you need to ensure that you preserve the integrity and validity of the trial. This is why you need to pre-specify the changes that may be made based on pre-defined decision rules. We need to make sure data for interim analyses are collected, analysed and stored correctly and in accordance with good clinical practice at every stage.
“You also need to ensure the interim analysis is conducted in a way that preserves the blind for the study team. Reviewing observed data at each interim analysis requires careful thought to avoid introducing bias into the trial.”
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