Profit from prophesy

Clinical trials are characteristically unpredictable. It is rare that things go exactly as planned. Regardless of the uncertainties involved, particularly when working with certain populations and study designs, it is always worth taking the time to create a solid forecast, as these will not only have benefits for the current trial but also those that follow. Costs are always front of mind when making predictions. Sometimes when clinical trial managers make forecasts, they tend to overestimate how much they will spend in an attempt to ‘play it safe’. This can result in company leaders reallocating funds if there is continual underspend. Although underspending and overspending are problematic, the former is generally considered to be the lesser of two evils. Having some wiggle room within a trial is always beneficial but this should be within reasonable limits.

For Danny Lee, associate director of clinical supply at Alkahest, patients should be the first consideration when making a forecast. “It’s important to think first and foremost about the population you’re targeting,” says Lee. “A great way to start is to look at similar clinical trials in the industry as benchmarks, while taking into account the base of patient population in your selected country and the competing trial set.”

Minimising harm to patients is, of course, imperative, and this needs to be anticipated from the start. It is one of the areas where collaboration can be hugely beneficial. “I like to work with the quality team to understand the risk of any trial by doing a risk assessment,” says Lee.

Companies often use risk probabilities and adjustments when forecasting clinical trials. However, for trials that have not yet begun, it is essential they have a risk probability factor that differs from that of active studies. In many cases, a similar risk probability factor will be allocated to a new trial and an ongoing trial. Although an active trial may experience slower progress in patient recruitment, a new trial might also encounter these issues in addition to others, such as site initiation and contract timing. Drawing upon existing data can also be hugely beneficial in producing a realistic forecast. “Benchmarking and historical trial data, from your own or others’ trials, can help you develop bookends of best and worst-case scenarios for drug supply, patient recruitment rates, and a country and site-specific enrolment plan,” Lee explains. “Though hindsight is not always a perfect 20/20 when it comes to forecasting, data from past trials can lend useful and predictive context.”

Real-time feedback is hugely important to take into consideration to ensure that the emerging needs of the trial are responded to. “Real-time feedback allows us to ensure clinical trial supply meets real-world patient recruitment rates,” says Lee. “We can adjust by site or country, but that, of course, requires careful planning and a deep familiarity with your drug.”

To improve the chances of being able to act on this information, it must be well timed. “Creating a strong clinical trial forecast requires timely information, great estimates on patient base and recruitment rates, and agility throughout the forecast,” says Lee. “Being able to stay nimble throughout the trial while efficiently tracking metrics and supply needs comes down to both an art and a science.”

It is also important to be realistic about how much the trial can be adapted once it has started, however. “Real-time feedback may not lead to realtime adjustment,” explains Lee. “Ensuring you meet demand challenges when you have complicated supply or distribution chains makes the task of forecasting more difficult, as you may not be able to adjust or shift supply based on actual recruitment.”

The metrics system

Regardless of the amount of experience of the clinical trial manager, accuracy of the prediction can never be guaranteed. “In forecasting, there is always a good chance you’ll be wrong, but good metrics can keep you within your forecasted demand bands,” says Lee.

One of the main reasons behind missing the mark, according to industry experts, is a lack of understanding of financial accruals. Accruals, which are calculated monthly, estimate the entire value of clinical trial activity, an unequivocally arduous task. These are highly dependent on having appropriate models in place to produce accurate accrual estimates.

Fortunately, there are a number of technologies that can support forecasting efforts. “Today, software exists in the form of apps that serve as supply chain management suites,” says Lee. “This type of digital support can be extremely helpful in forecasting by providing a degree of organisation and automation that can save time and energy.”

There are also a number of best practices to follow to maximise the effectiveness of a prediction. This includes defining what is most important right from the beginning of the process. “One of the most important things you can do is to forecast all of your trial’s highest risk factors according to the phase of the study and ensure you can account for supply within the range of outcomes,” explains Lee. “This sensitivity and range analysis can keep you from stocking out at any location, particularly if you are running a smaller study.”

Forecasting clinical trials can be difficult, but a strategic approach can lead to fewer problems when the trial gets under way.

When forecasting, prioritising which items are most important is also key. It can be easy to get caught up in low-budget items and fail to pay sufficient attention to areas such as site monitoring expenses and investigator grant payments. Although the expenses of low-budget items are likely to change over the course of a trial, they are unlikely to affect the overall budget.

Keeping track of what was predicted can also reap huge rewards, in the short and long term. “I’m a big believer in documenting all of my forecast assumptions, and any revisions to the assumptions, so that the forecast can inform later studies or any documentation needs later on,” says Lee. “Documentation also helps with budgeting and quality; of course, when your forecast changes – which it will – you need to be ready to go back and assess the impact on your build and distribution supplies; there can be a ripple effect.”

Although forecasts are always complex, certain trial types are particularly challenging. “The hardest trials to forecast are those with brand-new drugs, or areas where physicians, sites and countries don’t have a historical base of trials in that therapeutic area,” explains Lee. “You then have to start from scratch and account for more variability.”

Perhaps counter intuitively, larger trials are not necessarily the most difficult. “Smaller studies present unique challenges as well, particularly if each site is distant in geography,” says Lee. “One-treatment therapies are also very hard to forecast, as are those with complicated supply chains or cold chain.”

For those involved in running clinical trials, budgeting and forecasting are inevitably complex in nature. However, with a strategic approach, more accurate forecasts can be produced, easing the clinical trial process as a whole for both the current study and those in the future.

The cost of clinical trials – a study

Background

The increasing cost of clinical research has significant implications for public health, as it affects drug companies’ willingness to undertake clinical trials, which in turn limits patient access to novel treatments. Thus, gaining a better understanding of the key cost drivers of clinical research in the US is important.

Purpose

The study, which was based on a report prepared by Eastern Research Group for the US Department of Health and Human Services, examined different factors, such as therapeutic area, patient recruitment, administrative staff, and clinical procedure expenditures, and each one’s contribution to pharmaceutical clinical trial costs in the US by clinical trial phase.

Methods

The study used aggregate data from three proprietary databases on clinical trial costs provided by Medidata Solutions. Per-study costs were evaluated across therapeutic areas by aggregating detailed (per patient and per site) cost information. Also compared were average expenditures on cost drivers with the use of weighted mean and standard deviation statistics.

Results

The therapeutic area was an important determinant of clinical trial costs by phase. The average cost of a phase-I study conducted at a US site ranged from $1.4 million (pain and anaesthesia) to $6.6 million (immunomodulation), including estimated site overheads and monitoring costs of the sponsoring organisation. A phase-II study costs from $7 million (cardiovascular) to $19.6 million (haematology), whereas a phase-III study cost ranged from$11.5 million (dermatology) to $52.9 (pain and anaesthesia) on average. Across all study phases, and excluding estimated site overhead costs, and costs for sponsors to monitor the study, the top three cost drivers of clinical trial expenditures were clinical procedure costs (15–22%), administrative staff costs (11–29% ) and site monitoring costs (9–14%).

Limitations

The data was from 2004–12 and was not adjusted for inflation. Additionally, the databases used represented a convenience that was non-probability, sample and did not allow for statistically valid estimates of cost drivers. Finally, the data was from trials funded by the global pharmaceutical and biotechnology industry only. Hence, the study findings were limited to that segment.

Conclusion

The therapeutic area being studied, as well as the number and types of clinical procedures involved, were the key drivers of direct costs in phase-I through phase-II studies. Research shows that strategies exist for reducing the price tag of some of these major direct cost components. Therefore, to increase clinical trial efficiency and reduce costs, gaining a better understanding of the key direct cost drivers is an important step.

Source: ‘Key cost drivers of pharmaceutical clinical trials in the United States’, Clinical Trials, Sertkaya, Wong, Jessup and Beleche