Estimating the Costs Associated with Novel Pharmaceutical development: Methods and Limitations.

Data sources for cost analysis of drug development R&D and clinical trials

Cost estimates for pre-clinical and clinical development across the pharmaceutical industry differ based on several factors. One of these is the source of data used by each costing study to inform these estimates. Several studies use private data, which can include confidential surveys filled out by pharmaceutical firms/clinical trial units and random samples from private databases3,9,10,14,15,16. Other studies have based their cost estimates upon publicly available data, such as data from the FDA/national drug regulatory agencies, published peer-reviewed studies, and other online public databases1,2,12,13,17.

Some have questioned the validity of using private surveys from large multinational pharmaceutical companies to inform cost estimates, saying that survey data may be artificially inflated by pharmaceutical companies to justify high therapeutic prices 18,19,20. Another concern is that per trial spending by larger pharmaceutical companies and multinational firms would far exceed the spending of start-ups and smaller firms, meaning cost estimates made based on data from these larger companies would not be representative of smaller firms.

Failure rate of R&D and clinical trial pipelines

Many estimates include the cost of failures, which is especially the case for cost estimates “per approved drug”. As many compounds enter the clinical trial pipeline, the cost to develop one approved drug/compound includes cost of failures by considering the clinical trial success rate and cost of failed compounds. For example, if 100 compounds enter phase I trials, and 2 compounds are approved, the clinical cost per approved drug would include the amount spent on 50 compounds.

The rate of success used can massively impact cost estimates, where a low success rate or high failure rate will lead to much higher costs per approved drug. The overall probability of clinical success may vary by year and has been estimated at a range of values including 7.9%21, 11.83%10, and 13.8%22. There are concerns that some studies suggesting lower success rates have relied on small samples from industry curated databases and are thereby vulnerable to selection bias12,22.

Success rates per phase transition also affects overall costs. When more ultimately unsuccessful compounds enter late clinical trial stages, the higher the costs are per approved compound. In addition, success rates are also dependent on therapeutic area and patient stratification by biomarkers, among other factors. For example, one study estimated the lowest success rate at 1.6% for oncological trials without biomarker use compared with a peak success rate of 85.7% for cardiovascular trials utilising biomarkers22. While aggregate success rates can be used to estimate costs, using specific success rates will be more accurate to estimate the cost of a specific upcoming trial, which could help with budgeting and funding decisions.

Out-of-pocket costs vs capitalised costs & interest rates

Cost estimates also differ due to reporting of out-of-pocket and capitalised costs. An out-of-pocket cost refers to the amount of money spent or expensed on the R&D of a therapeutic. This can include all aspects of setting up therapeutic development, from initial funding in drug discovery/device design, to staff and site costs during clinical trials, and regulatory approval expenses.

The capitalised cost of a new therapeutic refers to the addition of out-of-pocket costs to a yearly interest rate applied to the financial investments funding the development of a new drug. This interest rate, referred to as the discount rate, is determined by (and is typically greater than) the cost of capital for the relevant industry.

Discount rates for the pharmaceutical industry vary between sources and can dramatically alter estimates for capitalised cost, where a higher discount rate will increase capitalised cost. Most studies place the private cost of capital for the pharmaceutical industry to be 8% or higher23,24, while the cost of capital for government is lower at around 3% to 7% for developed countries23,25. Other sources have suggested rates from as high as 13% to as low as zero13,23,26, where the zero cost of capital has been justified by the idea that pharmaceutical firms have no choice but to invest in R&D. However, the mathematical model used in many estimations for the cost of industry capital, the CAPM model, tends to give more conservative estimates23. This would mean the 10.5% discount rate widely used in capitalised cost estimates may in fact result in underestimation.

While there is not a consensus on what discount rate to use, capitalised costs do represent the risks undertaken by research firms and investors. A good approach may be to present both out-of-pocket and capitalised estimated costs, in addition to rates used, justification for the rate used, and the estimates using alternative rates in a sensitivity analysis26.

Costs variation over time

The increase in therapeutic development costs

Generally, there has been a significant increase in the estimated costs to develop a new therapeutic over time26. One study reported an exponential increase of capitalised costs from the 1970s to the mid-2010s, where the total capitalised costs rose annually 8.5% above general inflation from 1990 to 201310. Recent data has suggested that average development costs reached a peak in 2019 and had decreased the following two years9. This recent decrease in costs was associated with slightly reduced cycle times and an increased proportion of infectious disease research, likely in response to the rapid response needed for COVID-19.

Recent cost estimates

Costs can range with more than 100-fold differences for phase III/pivotal trials alone1. One of the more widely cited studies on drug costs used confidential survey data from ten multinational pharmaceutical firms and a random sample from a database of publicly available data10. In 2013, this study estimated the total pre-approval cost at $2.6 billion USD per approved new compound. This was a capitalised cost, and the addition of post-approval R&D costs increased this estimate to $2.87 billion (2013 USD). The out-of-pocket cost per approved new compound was reported at $1.395 billion, of which $965 million were clinical costs and the remaining $430 million were pre-clinical.

Another estimate reported the average cost to develop an asset at $1.296 billion in 20139. Furthermore, it reported that this cost had increased until 2019 at $2.431 billion and had since decreased to $2.376 billion in 2020 and $2.006 billion in 2021. While comparable to the previous out-of-pocket estimate for 2013, this study does not state whether their estimates are out-of-pocket or capitalised, making it difficult to meaningfully compare these estimates.

Figure 1: Recent cost estimates for drug development per approved new compound. “Clinical only” costs include only the costs of phase 0-III clinical trials, while “full” costs include pre-clinical costs. The colour of each bubble indicates the study, while bubble size indicated relative cost. A dashed border indicated the study used private data for their estimations, while a solid border indicates the study utilised publicly available data. Figure represents studies 9, 10, 12, 13 and 17 from the reference list in this report.

Publicly available data of 63 FDA-approved new biologics from 2009-2018 was used to estimate the capitalised (at 10.5%) R&D investment to bring a new drug to market at median of $985.3 million and a mean of $1.3359 billion (inflation adjusted to 2018 USD)12. These data were mostly accessible from smaller firms, smaller trials, first-in-class drugs, and further specific areas. The variation in estimated cost was, through sensitivity analysis, mostly explained by success/failure rates, preclinical expenditures, and cost of capital.

Publicly available data of 10 companies with no other drugs on the market in 2017 was used to estimate out-of-pocket costs for the development of a single cancer drug. This was reported at a median of $648 million and a mean of $719.8 million13. Capitalised costs were also reported using a 7% discount rate, with a median of $754.4 million and mean of $969.4 million. By focusing on data from companies without other drugs on the market, these estimates may better represent the development costs per new molecular entity (NME) for start-ups as the cost of failure of other drugs in the pipeline were included while any costs related to supporting existing on-market drugs were systematically impossible to include.

One study estimated the clinical costs per approved non-orphan drug at $291 million (out-of-pocket) and $412 million (capitalised 10.5%)17. The capitalised cost estimate increased to $489 million when only considering non-orphan NMEs. The difference between these estimates for clinical costs and the previously mentioned estimates for total development costs puts into perspective the amount

spent on pre-clinical trials and early drug development, with one studynoting their pre-clinical estimates comprised 32% of out-of-pocket and 42% of capitalised costs10.

Things to consider about cost estimates

The issue with these estimates is that there are so many differing factors affecting each study. This complicates cost-based pricing discussions, especially when R&D cost estimates can differ orders of magnitude apart. The methodologies used to calculate out-of-pocket costs differ between studies9,17, and the use of differing data sources (public data vs confidential surveys) seem to impact these estimates considerably.

There is also an issue with the transparency of data and methods from various sources in cost estimates. Some of this is a result of using confidential data, where some analyses are not available for public scrutiny8. This study in particular raised questions as estimates were stated without any information about the methodology or data used in the calculation of estimates. The use of confidential surveys of larger companies has also been criticised as the confidential data voluntarily submitted would have been submitted anonymously without independent verification12.

Due to the limited amount of comprehensive and published cost data17, many estimates have no option but to rely on using a limited data set and making some assumptions to arrive at a reasonable estimate. This includes a lack of transparent available data for randomised control trials, where one study reported that only 18% of FDA-approved drugs had publicly available cost data18. Therefore, there is a need for transparent and replicable data in this field to allow for more plausible cost estimates to be made, which in turn could be used to support budget planning and help trial sustainability18,26.

Despite the issues between studies, the findings within each study can be used to gather an idea of trends, cost drivers, and costs specific to company/drug types. For example, studies suggest an increasing overall cost of drug development from 1970 to peak in 201910, with a subsequent decrease in 2020 and 20219.

For a full list of references used in this article, please see the main report here: https://anatomisebiostats.com/biostatistics-blog/how-much-does-developing-a-novel-therapeutic-cost-factors-affecting-drug-development-costs-in-the-pharma-industry-a-mini-report/

How much does developing a novel therapeutic cost? – Factors Affecting Drug Development Costs across the Pharma Industry: A mini-Report

Introduction

Data evaluating the costs associated with developing novel therapeutics within the pharmaceutical industry can be used to identify trends over time and can inform more accurate budgeting for future research projects. However, the cost to develop a drug therapeutic is difficult to accurately evaluate, resulting in varying estimates ranging from hundreds of millions to billions of US dollars between studies. The high cost of drug development is not purely because of clinical trial expenses. Drug discovery, pre-clinical trials, and commercialisation also need to be factored into estimates of drug development costs.

There are limitations in trying to accurately assess these costs. The sheer number of factors that affect estimated and real costs means that studies often take a more specific approach. For example, costs will differ between large multinational companies with multiple candidates in their pipeline and start-ups/SMEs developing their first pharmaceutical. Due to the amount and quality of available data, many studies work mostly with data from larger multinational pharmaceutical companies with multiple molecules in the pipeline. When taken out of context, the “$2.6 billion USD cost for getting a single drug to market” can seem daunting for SMEs. It is very important to clarify what scale these cost estimates represent, but cost data from large pharma companies are still relevant for SMEs as they can used to infer costs for different scales of therapeutic development.

This mini-report includes what drives clinical trial costs, methods to reduce these costs, and then explores what can be learned from varying cost estimates.

What drives clinical trial costs?

There is an ongoing effort to streamline the clinical trial process to be more cost and time efficient. Several studies report on cost drivers of clinical trials, which should be considered when designing and budgeting a trial. Some of these drivers are described below:

Study size

Trial costs rise exponentially with an increasing study size, which some studies have found to be the single largest driver in trial costs1,2,3. There are several reasons for varying sample sizes between trials. For example, study size increases with trial phase progression as phases require different study sizes based on the number of patients needed to establish the safety and/or effectiveness of a treatment. Failure to recruit sufficient patients can result in trial delays which also increases costs4.

Trial site visits

A large study size is also correlated with a larger overall number of patient visits during a trial, which is associated with a significant increase in total trial costs2,3. Trial clinic visits are necessary for patient screening, treatment and treatment assessment but include significant costs for staff, site hosting, equipment, treatment, and in some cases reimbursement for patient travel costs. The number of trial site visits per patient varies between trials where more visits may indicate longer and/or more intense treatment sessions. One estimate for the number of trial visits per person was a median of 11 in a phase III trial, with $2 million added to estimated trial costs for every +1 to the median2.

Number & location of clinical trial sites

A higher number of clinical trial study sites has been associated with significant increase in total trial cost3. This is a result of increased site costs, as well as associated staffing and equipment costs. These will vary with the size of each site, where larger trials with more patients often use more sites or larger sites.

Due to the lower cost and shorter timelines of overseas clinical research5,6, there has been a shift to the globalisation of trials, with only 43% of study sites in US FDA-approved pivotal trials being in North America7. In fact, 71% of these trials had sites in lower cost regions where median regional costs were 49%-97% of site costs in North America. Most patients in these trials were either in North America (39.7%), Western Europe (21%), or Central Europe (20.4%).

Median cost per regional site as a percentage of North American median cost for comparison.

However, trials can face increased difficulties in managing and coordinating multiple sites across different regions, with concerns of adherence to the ethical and scientific regulations of the trial centre’s region5,6. Some studies have reported that multiregional trials are associated with a significant increase in total trial costs, especially those with sites in emerging markets3. It is unclear if this reported increase is a result of lower site efficiency, multiregional management costs, or outsourcing being more common among larger trials.

Clinical Trial duration

Longer trial duration has been associated with a significant increase in total trial costs3,4, where many studies have estimated the clinical period between 6-8 years8,9,10,11,12,13. Longer trials are sometimes necessary, such as in evaluating the safety and efficacy of long-term drug use in the management of chronic and degenerative disease. Otherwise, delays to starting up a trial contribute to longer trials, where delays can consume budget and diminish the relevance of research4. Such delays may occur as a result of site complications or poor patient accrual.

Another aspect to consider is that the longer it takes to get a therapeutic to market (as impacted by longer trials), the longer the wait is before a return of investment is seen by both the research organisation and investors. The period from development to on-market, often referred to as cycle time, can drive costs per therapeutic as interest based on the industry’s cost of capital can be applied to investments.

Therapeutic area under investigation

The cost to develop a therapeutic is also dependent on the therapeutic area, where some areas such as oncology and cardiovascular treatments are more cost intensive compared with others1,2,5,6,12,14. This is in part due to variation in treatment intensity, from low intensity treatments such as skin creams to high intensity treatments such as multiple infusions of high-cost anti-cancer drugs2. An estimate for the highest mean cost for pivotal trials per therapeutic area was $157.2M in cardiovascular trials compared to $45.4M in oncology, and a lowest of $20.8M in endocrine, metabolic, and respiratory disease trials1. This was compared to an overall median of $19M. Clinical

trial costs per therapeutic area also varied by clinical trial phase. For example, trials in pain and anaesthesia have been found to have the lowest average cost of a phase I study while having the highest average cost of a phase III study6.

It is important to note that some therapeutic areas will have far lower per patient costs when compared to others and are not always indicative of total trial costs. For example, infectious disease trials generally have larger sample sizes which will lead to relatively low per patient costs, whereas trials for rare disease treatment are often limited to smaller sample sizes with relatively high per patient costs. Despite this, trials for rare disease are estimated to have significantly lower drug to market costs.

Drug type being evaluated

As mentioned in the therapeutic areas section above, treatments may vary in intensity from skin creams to multiple rounds of treatment with several anti-cancer drugs. This can drive total trial costs due to additional manufacturing and the need for specially trained staff to administer treatments.

In the case of vaccine development, phase III/pivotal trials for vaccine efficacy can be very difficult to run unless there are ongoing epidemics for the targeted infectious disease. Therefore, some cost estimates of vaccine development include from the pre-clinical stages to the end of phase IIa, with the average cost for one approved vaccine estimated at $319-469 million USD in 201815.

Study design & trial control type used

Phase III trial costs vary based on the type of control group used in the trial1. Uncontrolled trials were the least expensive with an estimated mean of $13.5 million per trial. Placebo controlled trials had an estimated mean of $28.8 million, and trials with active drug comparators had an estimated mean cost of $48.9 million. This dramatic increase in costs is in part due to manufacturing and staffing to administer a placebo or active drug. In addition, drug-controlled trials require more patients compared to placebo-controlled, which also requires more patients than uncontrolled trials2.

Reducing therapeutic development costs

Development costs can be reduced through several approaches. Many articles recommend improvements to operational efficiency and accrual, as well as deploying standardised trial management metrics4. This could include streamlining trial administration, hiring experienced trial staff, and ensuring ample patient recruitment to reduce delays in starting and carrying out a study.

Another way to reduce development costs can take place in the thorough planning of clinical trial design by a biostatistician, whether in-house or external. Statistics consulting throughout a trial can help to determine suitable early stopping conditions and the most appropriate sample size. Sample size calculation is particularly important as underestimation undermines experimental results, whereas overestimation leads to unnecessary costs. Statisticians can also be useful during the pre-clinical stage to audit R&D data to select the best available candidates, ensure accurate R&D data analysis, and avoid pursuing unsuccessful compounds.

Other ways to reduce development costs include the use of personalised medicine, clinical trial digitisation, and the integration of AI. Clinical trial digitisation would lead to the streamlining of clinical trial administration and would also allow for the integration of artificial intelligence into clinical trials. There have been many promising applications for AI in clinical trials, including the use of electronic health records to enhance the enrolment and monitoring of patients, and the potential use of AI in trial diagnostics. More information about this topic can be found in our blog “Emerging use-cases for AI in clinical trials”.

For more information on the methodology by which pharmaceutical development and clinical trials costs are estimated and what data has been used please see the article: https://anatomisebiostats.com/biostatistics-blog/estimating-the-costs-associated-with-novel-pharmaceutical-development-methods-and-limitations/

Cost breakdown in more detail: How is a clinical trial budget spent?

Clinical trial costs can be broken down and divided into several categories, such as staff and non-staff costs. In a sample of phase III studies, personnel costs were found to be the single largest component of trial costs, consisting of 37% of the total, whereas outsourcing costs made up 22%, grants and contracting costs at 21%, and other expenses at 21%3.

From a CRO’s perspective, there are many factors that are considered in the cost of a pivotal trial quotation, including regulatory affairs, site costs, management costs, the cost of statistics and medical writing, and pass-through costs27. Another analysis of clinical trial cost factors determined clinical procedure costs made up 15-22% of the total budget, with administrative staff costs at 11-29%, site monitoring costs at 9-14%, site retention costs at 9-16%, and central laboratory costs at 4-12%5,6. In a study of multinational trials, 66% of total estimated trial costs were spent on regional tasks, of which 53.3% was used in trial sites and the remainder on other management7.

Therapeutic areas and shifting trends

Therapeutic area had previously been mentioned as a cost driver of trials due to differences in sample sizes and/or treatment intensity. It is however worth mentioning that, in 2013, the largest number of US industry-sponsored clinical trials were in oncology (2,560/6,199 active clinical trials with 215,176/1,148,340 patients enrolled)4,14. More recently, there has been a shift to infectious disease trials, in part due to the needed COVID-19 trials9.

Clinical trial phases

Due to the expanding sample size as a trial progresses, average costs per phase increase from phase I through III. Median costs per phase were estimated in 2016 at $3.4 million for phase I, $8.6 million for phase II, and $21.4 million for phase III3. Estimations of costs per patient were similarly most expensive in phase III at $42,000, followed by phase II at $40,000 and phase I at $38,50014. The combination of an increasing sample size and increasing per patient costs per phase leads to the drastic increase in phase costs with trial progression.

In addition, studies may have multiple phase III trials, meaning the median estimated cost of phase III trials per approved drug is higher than per trial costs ($48 million and $19 million respectively)2. Multiple phase III trials can be used to better support marketing approval or can be used for therapeutics which seek approval for combination/adjuvant therapy.

There are fewer cost data analyses available on phase 0 and phase IV on clinical trials. Others report that average Phase IV costs are equivalent to Phase III but much more variable5,6.

Orphan drugs

Drugs developed for the treatment of rare diseases are often referred to as orphan drugs. Orphan drugs have been estimated to have lower clinical costs per approved drug, where capitalised costs per non-orphan and orphan drugs were $412 million and $291 million respectively17. This is in part due to the limit to sample size imposed upon orphan drug trials by the rarity of the target disease and the higher success rate for each compound. However, orphan drug trials are often longer when compared to non-orphan drug trials, with an average study duration of 1417 days and 774 days respectively.

NMEs

New molecular entities (NMEs) are drugs which do not contain any previously approved active molecules. Both clinical and total costs of NMEs are estimated to be higher when compared to next in class drugs13,17. NMEs are thought to be more expensive to develop due to the increased amount of pre-clinical research to determine the activity of a new molecule and the increased intensity of clinical research to prove safety/efficacy and reach approval.

Conclusion & take-aways

There is no one answer to the cost of drug or device development, as it varies considerably by several cost drivers including study size, therapeutic area, and trial duration. Estimates of total drug development costs per approved new compound have ranged from $754 million12 to $2.6 billion10 USD over the past 10 years. These estimates do not only differ based on the data used, but also due to methodological differences between studies. The limited availability of comprehensive cost data for approved drugs also means that many studies rely on limited data sets and must make assumptions to arrive at a reasonable estimate.

There are still multiple practical ways that can be used to reduce study costs, including expert trial design planning by statisticians, implementation of biomarker-guided trials to reduce the risk of failure, AI integration and digitisation of trials, improving operational efficiency, improving accrual, and introducing standardised trial management metrics.

References

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.1 Moore T, Zhang H, Anderson G, Alexander G. Estimated Costs of Pivotal Trials for Novel Therapeutic Agents Approved by the US Food and Drug Administration, 2015-2016. JAMA Internal Medicine. 2018;178(11):1451-1457.

2. Moore T, Heyward J, Anderson G, Alexander G. Variation in the estimated costs of pivotal clinical benefit trials supporting the US approval of new therapeutic agents, 2015–2017: a cross-sectional study. BMJ Open. 2020;10(6):e038863.

3. Martin L, Hutchens M, Hawkins C, Radnov A. How much do clinical trials cost?. Nature Reviews Drug Discovery. 2017;16(6):381-382.

4. Bentley C, Cressman S, van der Hoek K, Arts K, Dancey J, Peacock S. Conducting clinical trials—costs, impacts, and the value of clinical trials networks: A scoping review. Clinical Trials. 2019;16(2):183-193.

5. Sertkaya A, Birkenbach A, Berlind A, Eyraud J. Examination of Clinical Trial Costs and Barriers for Drug Development [Internet]. ASPE; 2014. Available from: https://aspe.hhs.gov/reports/examination-clinical-trial-costs-barriers-drug-development-0

6. Sertkaya A, Wong H, Jessup A, Beleche T. Key cost drivers of pharmaceutical clinical trials in the United States. Clinical Trials. 2016;13(2):117-126.

7. Qiao Y, Alexander G, Moore T. Globalization of clinical trials: Variation in estimated regional costs of pivotal trials, 2015–2016. Clinical Trials. 2019;16(3):329-333.

8. Monitor Deloitte. Early Value Assessment: Optimising the upside value potential of your asset [Internet]. Deloitte; 2020 p. 1-14. Available from: https://www2.deloitte.com/content/dam/Deloitte/be/Documents/life-sciences-health-care/Deloitte%20Belgium_Early%20Value%20Assessment.pdf

9. May E, Taylor K, Cruz M, Shah S, Miranda W. Nurturing growth: Measuring the return from pharmaceutical innovation 2021 [Internet]. Deloitte; 2022 p. 1-28. Available from: https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/life-sciences-health-care/Measuring-the-return-of-pharmaceutical-innovation-2021-Deloitte.pdf

10. DiMasi J, Grabowski H, Hansen R. Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics. 2016;47:20-33.

11. Farid S, Baron M, Stamatis C, Nie W, Coffman J. Benchmarking biopharmaceutical process development and manufacturing cost contributions to R&D. mAbs. 2020;12(1):e1754999.

12. Wouters O, McKee M, Luyten J. Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018. JAMA. 2020;323(9):844-853.

13. Prasad V, Mailankody S. Research and Development Spending to Bring a Single Cancer Drug to Market and Revenues After Approval. JAMA Internal Medicine. 2017;177(11):1569-1575.

14. Battelle Technology Partnership Practice. Biopharmaceutical Industry-Sponsored Clinical Trials: Impact on State Economies [Internet]. Pharmaceutical Research and Manufacturers of America; 2015. Available from: http://phrma-docs.phrma.org/sites/default/files/pdf/biopharmaceutical-industry-sponsored-clinical-trials-impact-on-state-economies.pdf

15. Gouglas D, Thanh Le T, Henderson K, Kaloudis A, Danielsen T, Hammersland N et al. Estimating the cost of vaccine development against epidemic infectious diseases: a cost minimisation study. The Lancet Global Health. 2018;6(12):e1386-e1396.
16. Hind D, Reeves B, Bathers S, Bray C, Corkhill A, Hayward C et al. Comparative costs and activity from a sample of UK clinical trials units. Trials. 2017;18(1).

17.Jayasundara K, Hollis A, Krahn M, Mamdani M, Hoch J, Grootendorst P. Estimating the clinical cost of drug development for orphan versus non-orphan drugs. Orphanet Journal of Rare Diseases. 2019;14(1).

19. Speich B, von Niederhäusern B, Schur N, Hemkens L, Fürst T, Bhatnagar N et al. Systematic review on costs and resource use of randomized clinical trials shows a lack of transparent and comprehensive data. Journal of Clinical Epidemiology. 2018;96:1-11.

20. Light D, Warburton R. Demythologizing the high costs of pharmaceutical research. BioSocieties. 2011;6(1):34-50.

21. Adams C, Brantner V. Estimating The Cost Of New Drug Development: Is It Really $802 Million?. Health Affairs. 2006;25(2):420-428.

22. Thomas D, Chancellor D, Micklus A, LaFever S, Hay M, Chaudhuri S et al. Clinical Development Success Rates and Contributing Factors 2011–2020 [Internet]. BIO|QLS Advisors|Informa UK; 2021. Available from: https://pharmaintelligence.informa.com/~/media/informa-shop-window/pharma/2021/files/reports/2021-clinical-development-success-rates-2011-2020-v17.pdf

23. Wong C, Siah K, Lo A. Estimation of clinical trial success rates and related parameters. Biostatistics. 2019;20(2):273-286.
24. Chit A, Chit A, Papadimitropoulos M, Krahn M, Parker J, Grootendorst P. The Opportunity Cost of Capital: Development of New Pharmaceuticals. INQUIRY: The Journal of Health Care Organization, Provision, and Financing. 2015;52:1-5.
25. Harrington, S.E. Cost of Capital for Pharmaceutical, Biotechnology, and Medical Device Firms. In Danzon, P.M. & Nicholson, S. (Eds.), The Oxford Handbook of the Economics of the Biopharmaceutical Industry, (pp. 75-99). New York: Oxford University Press. 2012.
26. Zhuang J, Liang Z, Lin T, De Guzman F. Theory and Practice in the Choice of Social Discount Rate for Cost-Benefit Analysis: A Survey [Internet]. Manila, Philippines: Asian Development Bank; 2007. Available from: https://www.adb.org/sites/default/files/publication/28360/wp094.pdf
27. Rennane S, Baker L, Mulcahy A. Estimating the Cost of Industry Investment in Drug Research and Development: A Review of Methods and Results. INQUIRY: The Journal of Health Care Organization, Provision, and Financing. 2021;58:1-11.
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Emerging use-cases for AI in clinical trials

Overview

Clinical trials are becoming more expensive where, according to a Deloitte’s report1, the average cost to get a drug to market in the USA was $1.188 billion in 2010, and $1.981 billion in 2019. This increase in cost reflects the difficulties that are associated with current linear clinical trial designs. Clinical trials can take a long time due to difficulty in finding suitable/eligible patients for each study and the growing amount of data available used to plan or inform a trial. Current clinical trials are still evolving to make use of the rapidly developing technologies, scientific methods, and data availability of recent years.

One possible way to improve and transform the clinical trial process as we know it would be through the implementation of Artificial Intelligence (AI). AI incorporates all intelligence demonstrated by machines and includes important aspects such as Machine Learning and Natural Language Processing. It is already widely used in modern technology, such as in smartphones and online website searches, but has also been used more recently to innovate the drug discovery process. AI implementation could be of benefit across diverse tasks is the planning, execution and analysis stages of clinical trials to improve cost effectiveness, study time, treatment efficacy, and quality of data2.

Many emerging developments in artificial intelligence (AI) have the potential to benefit the clinical trials landscape.

AI in Adaptive clinical trial design

Many clinical trials are designed linearly, however adaptive designs are being used to allow predetermined changes during a study in response to ongoing trial data. Adaptive designs provide the flexibility to optimise resource allocation, end an unproductive trial early, and better characterise a treatment’s efficacy and safety through multiple endpoints. AI can help to inform and optimise adaptive designs through the analysis of healthcare data to select optimal endpoints, determine and monitor parameters for early stopping, and identify appropriate protocols for the trial3. These study design changes can increase the efficiency of a study, resulting in a trial which is more cost and time effective while maintaining high quality data collection and analysis.

Meta-analysis

AI can also enhance the exploratory analysis of data from previous trials. AI-enabled technology can collect, organise, and analyse increasing amounts of data, which could be applied to data from previous trials. This is normally performed manually by a biostatistician as part of a meta-analysis, but AI could help to gather and perform initial analyses before a more in-depth statistical approach is taken. This could highlight potentially important patterns in collected evidence which would then be used in informing trial design.

Synthetic control arms

Current clinical trials typically compare an experimental treatment to placebo and established treatments, assigning enrolled patients to either a treatment or control group. Synthetic control arms are an AI-driven solution for having a control group in a single-arm trial, which usually have only a treatment group. Synthetic control arms use data from previous studies to simulate the control treatment in patients. This would allow for all patients in a trial to receive active treatment to provide more evidence for the treatment efficacy and safety at each clinical trial stage. This may also have an impact on patient enrolment as patients generally show less interest in enrolling on placebo-controlled trials4,5.

As synthetic control arms are relatively novel, comparisons should be made with traditional control groups. In a typical blinded trial, patients are unaware if they are receiving the experimental active treatment, or a placebo. This is to test that the new treatment is causative of any clinically meaningful response. Synthetic control arms could create more single-arm clinical trials, and the fact that patients are aware of receiving active treatment would be a factor given clinically meaningful results are found.

Site selection

Identification of suitable sites and investigators to perform a trial is an important factor in study efficiency and feasibility. A study site should be amply equipped to carry out a study, must be of a suitable size to process the needs of study participants, and be located in an accessible area to potential participants and investigators. Identification of target locations and investigators can be optimised through AI implementation, which also enables real-time monitoring of site performance once the trial has started. Study sites can be evaluated and compared through the development of a points-based algorithm which could factor in location, site size, and equipment.

Patient enrolment/recruitment

Most patients enrol on a clinical trial if they have not responded to existing treatments in a clinically meaningful way. However, there are often strict eligibility criteria required for patients to enrol for a trial, including diagnostic tests, biomarker profiles, and demographics. Currently, patients find out about clinical trials either through manually searching online databases or, on occasion, through a clinician’s recommendation. This puts a lot of responsibility on patients to search for potential trials, just to be faced with trying to understand eligibility criteria full of medical jargon. This can lead to low patient recruitment.

AI and natural language processing offer a potential solution for this issue. Natural language processing could be used to match patients with trials based on eligibility criteria and patient electronic health records. Potentially suitable trials could then be suggested to patients or their clinicians, making it easier for patients to find suitable trials and for trials to recruit patients. While this is an improvement to the current recruitment method, natural language processing may initially have some difficulty with clinical notes due to heavy use of acronyms, medical jargon, and deciphering of hand-written clinical notes. These problems aren’t specific to AI though, as currently patients looking for suitable trials have the same difficulties.

AI-driven patient recruitment could also be used to reduce population heterogeneity and use prognostic and predictive enrichment to increase study power. While reduced heterogeneity can be beneficial (e.g. in testing the safety of drugs for patients unable to enrol on early clinical trials), caution should be taken with limiting patient diversity. Selectively enrolling patients who are more likely to respond well to treatment initially could lead to advancing a treatment that may not be as wel tolerated in the post-market patient population.

AI balancing of diversity in clinical trials could be used to balance this. It is important to test safety and efficacy in with differing demographics for a treatment to be properly characterised. This may include different ethnic groups, body types (height, weight, BMI), ages, and sexes. For example, a drug intended for female patients should specify dosage programs and any specific adverse effects in female patients before approval.

Patient diagnostics

Trial data often uses diagnostic tests to measure a patient’s response to treatment. AI can be used to improve diagnostic accuracy and objectivity during trials, reduce the potential for bias, and help with blinding a trial. AI programs have already been shown to provide more accurate diagnoses compared to clinicians6, which could be extended to use in clinical trials. AI can also be used to integrate multiple biomarkers or large data sets (e.g. bioinformatics data) to better monitor and understand a patient’s response, and to make any needed changes in dosing.

Another application of AI in clinical trials would be in patient management. Wearable devices or apps could be used to provide real time safety and effectiveness data to both clinicians and patients, leading to better data quality and higher patient retention.

Patient monitoring, retainment, & medication adherence

AI could be used to monitor patients through automatic data capture and digital clinical assessments. Automated monitoring with AI can allow for personalised adherence alerts, and wearable devices could provide real time safety and efficacy data shared with both patient and clinician to increase retainment and adherence rates. Video consultations with clinicians could improve retainment by reducing travelling required from patients, but there would still be a risk of drop due to travel as some diagnostic tests would need to be done at an appropriate site, which may warrant additional costs if tests for research purposes are not covered by a patient’s health insurance or national health service.

Currently, medication adherence is mostly dependent on each patient’s diary/record keeping or memory, which is then discussed with clinicians during routine appointments. This can make it difficult to accurately track adherence. Digitising this through use of a website/app would allow for more accurate adherence data to be obtained, in addition to providing patients with notifications, educational content, and adherence records. Other medical devices such as timed medication bottles could also be used to ensure medication is used in appropriate intervals, and smart bottles could be used to synchronise this with a smartphone app if applicable.

Data Cleaning

Data cleaning for clinical trials is typically performed by trained biostatisticians and is essential to ensure that collected data is consistently formatted and free from inputting errors. However, the data cleaning process can be time-consuming, especially with large datasets collected during clinical trials. AI could be implemented through machine learning methods to identify and correct errors found in clinical trial datasets7, leading to better quality data which is optimised for analysis. An AI approach may also reduce the amount of time spent on data cleaning.

AI implementation and clinical trial digitisation

AI implementation is relevant in several aspects of clinical trials, be it in study design, patient diagnostics, or trial management. Several tech giants (including Apple & Google) have invested in developing solutions to process electronic health records, monitor patients remotely, and integrate healthcare data into devices. By improving the cost and time effectiveness of clinical trials, both patients and pharma-tech companies benefit with more affordably priced treatment costs for patients and greater return of investment for companies.

However, for clinical trials to implement AI successfully, many aspects of clinical trials would first require digitisation. Many trials still use paper documents instead of digital alternatives, which results in lost documents and slowed trial progression. Integrating electronic health records, digital copies of clinicians’ notes, and digital patient monitoring alone would help in designing and managing a trial. There is a concern that a switch to digital may be difficult for patients unfamiliar with technology, or for those who might prefer to keep paper diaries. However, digital solutions would allow for the development and implementation of AI-based solutions which would modernise and streamline the clinical trial process.

References

  1. Taylor K, Properzi F, Cruz M, Ronte H, Haughey J. Intelligent clinical trials [Internet]. www2.deloitte.com. 2020 [cited 16 March 2022]. Available from: https://www2.deloitte.com/content/dam/insights/us/articles/22934_intelligent-clinical-trials/DI_Intelligent-clinical-trials.pdf

  2. Glass L, Shorter G, Patil R. AI IN CLINICAL DEVELOPMENT [Internet]. IQVIA. 2019 [cited 16 March 2022]. Available from: https://www.iqvia.com/-/media/iqvia/pdfs/library/white-papers/ai-in-clinical-development.pdf
  • Bhatt A. Artificial intelligence in managing clinical trial design and conduct: Man and machine still on the learning curve?. Perspectives in Clinical Research. 2021;12(1):1-3. Available from: https://doi.org/10.4103/picr.PICR_312_20

Clinical Trial Phases in Drug Development


The development of new drugs starts far before they are even seen in clinical trials. The discovery of multiple candidate drugs occur early on in the development process, often as a result of new information about how a disease functions, large-scale screening of small molecules, or the release of a new technology.

After a promising drug has been found, pre-clinical studies can be performed. A pre-clinical study for a new drug is used to determine important information about toxicity and suitable dosage amounts. These studies can be in vitro (in cell culture) and/or in vivo (in animal models) and determine whether a treatment will continue to the clinical trials stage.

Clinical trials test whether these experimental treatments are safe for use in humans, and whether they are more effective in treating or preventing a disease when compared to existing treatments. Clinical trials consist of several stages, called phases, where each phase is focused on answering a different clinical question: Progression of a treatment to the next phase requires the study to meet several parameters to ensure a treatment’s safety or efficacy.

  • Phase 0: Is the new treatment safe to use in humans in small doses?
  • Phase I: Is the new treatment safe to use in humans in therapeutic doses?
  • Phase II: Is the new treatment effective in humans?
  • Phase III: Is the new treatment more effective than existing treatments?
  • Phase IV: Does the new treatment remain safe and effective post-market?
Key phases of a pharmaceutical clinical trial

Phase 0: Small dose safety

Phase 0 studies can help to streamline the other clinical trial phases. Phase 0 consists of giving a few patients small, sub-therapeutic doses of the new treatment. This is to make sure that the new treatment behaves as expected by researchers and isn’t harmful to humans prior to using higher doses in phase I trials.

Phase I: Therapeutic dose safety

Phase I studies evaluate the safety of various doses of the new treatment in humans. This takes several months with typically around 20-80 healthy volunteers. In some cases, such as in anti-cancer drug trials, the study participants are patients with the targeted cancer type. A treatment may not pass phase I if the treatment leads to any serious adverse events.

Initial dosages in phase I studies can be informed based on data obtained during pre-clinical animal studies, and adjustments can be made to investigate the treatment’s side effect profile and develop an optimal dosing program. This could also include comparing different methods of giving a drug to patients (e.g., oral, intravenous etc.).

Phase II: Treatment efficacy

After passing phase I trials and having proven safety in humans, a new treatment advances to phase II studies designed to assess whether it may prevent or treat a disease. This phase can take between several months to 2 years, testing the new treatment in up to several hundred patients with the disease. Using a larger number of patients over a longer time period provides researchers with additional safety and effectiveness data, which is essential for the design of phase III trials.

To further test safety and efficacy, it is common to have a control group that receives either a placebo (a harmless pill or injection without the new treatment) or other current treatment (in trials where the disease is fatal unless treated e.g., cancer).

Phase III: Comparing to current treatments

Phase III studies are the last stage of a clinical trial before a new treatment can be approved for market use. The primary focus of a phase III study is to compare the safety and efficacy of a new treatment with current, existing treatments in patients with the target disease. Anywhere from several hundred to 3,000 patients may be included in a phase III study for between 1 to 4 years. Due to the scale of this phase, long-term or rare side effects are more likely to be uncovered.

Phase III studies are often randomised control trials, where patients will be randomly designated to different treatment groups. These groups may receive placebo, a current treatment (control group), the new treatment, or variations of the new treatment (e.g., different drug combinations). Randomised control trials are often double-blinded, where both the patient and the clinician administering their treatment do not know which treatment group they are assigned to.

A new treatment may continue to market and phase IV trials if the results prove it is as safe and effective as an existing treatment.

Phase IV: Post-market surveillance

If a new treatment passes phase III and is approved by the MHRA, FDA, or other national regulatory agency, it can be put to market. Phase IV is carried out in the post-market surveillance of the new treatment to keep updated on any emerging or long-term safety and efficacy concerns. This may include rare or long-term adverse side effects that were not yet discovered, or long-term analyses to see if the new treatment improves the life expectancy of a patient after recovery from disease.

Summary

Clinical trials are ultimately designed to mitigate risk. This includes the risk to the safety of trial participants by limiting the use of potentially unsafe treatments to small doses in a small number of patients before scaling up to testing therapeutic dose safety. Risk mitigation is not only for patient safety but also for preventing financial misspending as a treatment that is deemed unsafe in phase 0 would not proceed to the later, more costly clinical trial phases.

Not all clinical trials are the same, however, as each trial will have a different disease and treatment context. Trials for medical devices are somewhat different from pharmaceutical trials (for more information about the differences between medical device and pharma trials, click here). In addition, while sample sizes expand with phase progression, the required sample size for each trial and each phase is dependent on several factors including disease context (a rare disease may require lower sample sizes), patient availability (location of trial), trial budget and effect size. The sample size values mentioned earlier in this blog are purely indications of what each phase may use (for more information on how a biostatistician determines a suitable sample size, click here).

References

https://www.fda.gov/patients/drug-development-process/step-3-clinical-research

https://www.healthline.com/health/clinical-trial-phases