Trial and error: The costly consequence of failing to acknowledge non-adherence in dose optimisation
AARDEX Group
AI-powered SaaS Platform Revolutionizing Clinical Trials Through Better Drug Exposure
By the time a new drug therapy reaches a Phase II clinical trial much is already known, but much remains to be understood before it can progress to market.
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Dose optimisation is a core focus at this point in the product lifecycle. Having established a safe dosage range in earlier Phase I studies, as well as possible side effects, the drug can now be exposed to a larger population, and critical data must be gleaned on the optimal combination of dose and dosing regimen required to achieve maximum efficacy and deliver positive patient outcomes.[1]
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Boiled down to its basics, this is a straightforward exercise in risk/benefit analysis: trial sponsors are focused on finding the ‘Goldilocks’ zone where all variables are balanced harmoniously. If the dose it set too low, there is a risk that efficacy is lost; if the dose is set too high, there is a risk that patients will be unnecessarily exposed to adverse effects that are certainly unwanted, sometimes intolerable and, at worst, harmful.
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Within this context, the dose-optimisation task facing pharmaceutical companies in Phase II trials seems clear and obvious: to arrive at a conclusive dose of the drug which, when taken faithfully by patients, will elicit the desired target level of exposure. And yet there is an unwelcome elephant in this particular room in the form of non-adherence.?
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Non-adherence is a known, if not necessarily accepted, phenomenon among both trial participants and, indeed, patients themselves.[2] Its presence in trials creates a situation where dosing is often set at an above-optimal level, since the data being returned is not entirely accurate in relation to the dose-exposure-response relationship. Put simply, evidence of participants’ actual adherence to the dosing regimen can be classified as ‘soft’ rather than ‘hard’.
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The implications for trial sponsors can be costly since failing to arrive at an optimised dose can lead to lengthy delays or calls by regulators for elements of the trial to be repeated or, at worst, the failure of the trial itself. Indeed, analysis from one study highlights that ‘uncertainties relating to dose selection’ was cited as a cause of delays or denials in 16% of unsuccessful first-time applications for new drugs by the US Food & Drug Administration (FDA) between 2000 and 2012.[3]
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But even when dose selection does not cause a trial to fail, and a drug secures regulatory approval, issues around dosing do not necessarily go away. One particular problem is the fact that dosing levels are frequently set above optimal levels. This is often motivated by the objective of maximising efficacy, but without sufficient robust dose-response data, the risk remains that patients are subjected to excessive levels of toxicity.
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Aware of this problem, and keen to mitigate against it, prescribers can sometimes respond by setting dosing levels below the approved amounts. While this is done in the interest of patients, a reliance on personal judgement over solid trial data on the exposure-efficacy-toxicity dynamic can lead to underdosing and unnecessarily poor outcomes. More significantly, there is the potential for regulators to implement post-approval reductions to the approved dose to enact a more fundamental recalibration of the target dose.[4]
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These measures underline the limitations in current approaches to determining optimal dosing in clinical trials, and the need for greater scrutiny to be applied in the pre-approval stage. Arguably, the area where this issue is most problematic is oncology, where patients can potentially be exposed to uncomfortable levels of toxicity if doses are over the optimal threshold.
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The FDA has looked to instigate reform here through The Oncology Center of Excellence (OCE) Project Optimus, which has been developed in acknowledgement of the fact that “doses and schedules are inadequately characterized in oncology drug development”. Based on current approaches, sys the FDA, there is potential for patients to experience additional toxicity without additional efficacy or, more seriously, to be exposed to toxicities that can be classified as “severe”, “intolerable” or “irreversible”.[5]
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Project Optimus should be applauded and supported for its aim of identifying the optimal dose for human prescription drugs during trials. In doing so, it should also encourage greater appreciation of the problem of non-adherence in achieving this important goal. When administering doses in unsupervised environments, such as the home, 50% of study participants deviate from the protocol, whether by reducing or increasing the dose, interrupting the treatment schedule, or discontinuing treatment altogether. However, these participants are understandably keen to continue their involvement and keep receiving treatment, creating a conflict of interest when it comes to accurately recording such deviation via self-reports or pill-count methods. As a result, dose-response and exposure-response data can be flawed, and so too are judgements regarding the optimal dose.
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Consider, by contrast, a scenario where adherence is 100%. Here, it is possible to clearly identify the lowest level of dose required to achieve peak efficacy and, therefore, to elicit an accurate measure of true toxicity exposure at this peak. Any dosing above this ‘sweet spot’ will, naturally, result in increased toxicity levels, although it will not necessarily increase efficacy. Despite accepted thinking, it does not follow that ‘more equals more’.
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In the real world, the perfect scenario described above is likely to be skewed by hidden non-adherence. And if trial co-ordinators are ignorant of its presence, they will be unaware that both efficacy and toxicity are being underreported. In this version of events, it will appear that a far higher dose is required to achieve peak efficacy. However, in ratcheting up the dose to this point, the resulting levels of toxicity will also be far higher, introducing the potential for regulators to question the dose and demand further exploration - or for the trial to be declared a failure.
In reality, the only way to make this porous situation more watertight is to get a more accurate measure of dose exposure and response. Flawed self-reporting methods must be replaced by digital tools that provide unbiased, independent and verifiable data on how much of a drug product has been administered and when. Bringing this level of oversight to a trial brings an important return on investment, empowering patients through data-driven feedback, improving analysis, and enhancing critical judgements regarding dose optimisation.
Crucially, this optimised version of dose optimisation can help avoid the unnecessary yet damaging situation where a trial is delayed or aborted because of failings in dose selection. At Phase II, with so much work having been put into a potential therapy up to this point, the focus should be on bringing the benefits of a drug to market faster and not managing the painful fallout that comes from employing fundamentally flawed methods of measuring adherence.??