Logistics
in an integrated circuit – the authors christened the trend ‘Eroom’s Law’.
Great Eroom has assigned a grim fate. In 2015, the Organisation for Economic Cooperation and Development (OECD) warned that, without reform, healthcare in advanced economies could be unaffordable by the middle of this century. In most accounts, it is digitalisation that will prove to be pharma and healthcare’s deliverance. And yet, if that were going to be the case, one would expect the very rise of computers – and Moore’s Law with them – would be visible in the pharmaceutical industry’s productivity statistics. It’s not.
“I think it’s something of the past, this traditional trial design with huge patient populations. The uncertainties are much higher now.”
Jörg Mahlich
Instead, what the graphs make painfully clear is the ever-increasing failure rate of clinical research, especially in phase 3. As Jörg Mahlich, market access and government affairs lead at Miltenyi Biomedicine, points out, the vast majority of these unsuccessful trials, and particularly those run by the largest companies, look much as they might have in 1950 – too prescribed and inflexible to properly benefit from all the innovations of the intervening years. That’s despite the fact advances in precision medicine are shrinking available patient populations and increasing the difficulty of measuring clinical effects. “I think it’s something of the past, this traditional trial design with huge patient populations,” says Mahlich. “The uncertainties are much higher now.” For him, as well as a growing number of people across the industry and its regulators, it’s more considered adaptive trial protocols, rather than blind faith in technology, that will enable the industry to deal with its mounting uncertainties and address the spiralling costs of drug development.
The selling point 32– ENACT 24 42%
The reduction in patient sampling that would have found the same result for the ProFHER trial.
The need to quickly evaluate treatments and vaccines for Covid-19 has led to a notable uptick in the use of adaptive designs over the past two years, but many companies are yet to be convinced that they have benefits besides speed – particularly given the extra expenses and complexities more flexible parameters can create for logistics and operations teams. That said, recent research has shown that these may be less burdensome than previously feared: in October 2021, the University of Sheffield’s Costing Adaptive Trials (CAT) project reported that the median increase in costs for adaptive trials over traditional alternatives was between 2–4% for all scenarios, meaning resource
requirements “should not be a barrier to adaptive designs being cost-effective to use in practice”. That’s an important finding, but even before the pandemic, the FDA, EMA and other regulators were publishing adaptive pathway guidelines that stressed their potential for decreasing the cost of pharmaceutical R&D. Mahlich links his interest in boosting pharmaceutical productivity with adaptive trials to the efforts of former FDA commissioner Scott Gottlieb, noting that he was one of the first to push for more innovative trial designs in the belief that sustainable healthcare systems would be better served by lower R&D costs than tightly regulated medicine prices, which could dissuade innovation. “The major costs in pharmaceuticals are in R&D, obviously,” says Mahlich, “and bringing those costs down makes a big impact on productivity – and eventually on prices as well.” Or, as Gottlieb himself put it in 2019, “Using more modern approaches to clinical trials, we can lower the cost of developing new drugs.” That’s exactly what Mahlich and his co-authors set out to prove in their 2021 Health Economics Review article ‘Can adaptive clinical trials help to solve the productivity crisis of the pharmaceutical industry?’. Working primarily from data published in the 2016 DiMasi et al paper that pegged the average drug development cost at $2.56bn (in 2013 USD), they estimated that greater use of adaptive trial designs could increase phase 3 success rates from 63% to between a “conservative” 70% and a “plausible” 80%, simply by reducing attrition rates. Whereas high attrition rates would necessarily doom a traditional clinical trial, adaptive designs enable investigators to re-estimate sample sizes and recruit more patients after interim analyses, enabling them to continue the study and show a treatment effect that may be of slightly lower statistical significance than anticipated but is still clinically meaningful.
In the ‘plausible’ (variation 1) scenario, Mahlich and his co-authors found that greater use of adaptive trials would reduce attrition rates from 38% to 20%, pushing overall clinical success rates up from 11.8% to 15.8%. Although the CAT project found that sample size re-estimation was the costliest element of adaptive trials – requiring a median of 26.5% more resources – using it to prevent the termination of a trial due to attrition issues can create far larger savings. Indeed, as successfully mitigating attrition rates means sponsors avoid wasting the money spent on a trial up to that point and don’t need to double their investment by restarting with a new protocol, even moderate changes in overall clinical success rates translate to very large savings. The 4% variation 1 increase would reduce the average cost of developing a new drug by 14.4%, from $2.56bn to $2.19bn. Even in the more cautious variation 2 model, costs would fall by 6.6% to $2.39bn.
Clinical Trials Insight /
www.worldpharmaceuticals.net
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