Today the average time to bring a new drug to market is a lengthy process that takes up to 12 years. This includes 5-6 years for drug discovery and another 5-7 years for clinical trials. Despite its necessity to ensure safety and efficacy, the prolonged timeline presents challenges for patients in need1.
Also, bringing a new drug to the market has an estimated average cost of $2.6 billion2. This includes the cost of drug failures since the success rate is so low - for every 10,000 preclinical compounds, only 1 makes it to the market3.
Over the last couple of years new technologies, data science, and machine learning are revolutionizing evidence-based medicine, ushering in a new era of 'deep' medicine. However, despite significant advances in these fields, clinical translations in major areas of medicine are lagging behind. The COVID-19 pandemic has exposed inherent limitations in the clinical trial landscape, but it has also led to some positive changes such as new trial designs and a shift toward a more patient-centric and intuitive evidence-generation system. As we move forward, we can look forward to exciting developments in the field of evidence-based medicine, driven by advances in technology and a greater focus on patient needs4.
Clinical studies are extremely complex ecosystems, consisting of hundreds of legal and technical protocols on one side, and hundreds of stakeholders with different interests and pain points on the other side. This complex environment results in inefficiencies, lack of visibility and sharing of important data and information, delays and consequent higher costs.
To understand this complexity better and the reasons behind it, we can start by categorizing the clinical studies’ challenges across three groups: administrative, human and data reasons.
In order to ensure the efficacy and safety of new medicines, clinical studies are subject to strict regulations and regulatory barriers. Before the study can start, the national and/or regional competent authority, the ethics committee and the data protection agency need to give the approval. Documents such as study protocol, containing for example the patient inclusion criteria, investigator brochure, CRF and patient informed consent are part of the extensive preparatory work required.
With the globalization and decentralization of clinical studies, which implies different sites and stakeholders around the world, adhering to the different international, national and local requirements becomes even more challenging.
Approval delays are a common consequence of the complex regulatory requirements, which contribute to the prolonged time and increased costs of a clinical study.
Patient recruitment is currently one of the main reasons for delays and trial failures. It is estimated that about half of all clinical trials are delayed due to recruitment issues, 80% of which are delayed for more than one month, whereas the cost of a delay can go up to $8 million per day for a potential blockbuster drug5. This can amount to a potential opportunity cost up to approximately $480 million (before adjustment to present dollars) for a 2 months delay of a blockbuster. As a result, patient recruitment is the largest cost driver of clinical trials, accounting for 32% of overall costs1.
There are several reasons that render patient recruitment so challenging.
From the patient side, often trials are designed with narrow eligibility criteria that purposely eliminate patients with the investigated disease but that are classified as ineligible due to other characteristics, such as age, level of disease progression, medical history.
From the physician side, there is a limited involvement of community physicians in clinical research and therefore a division between research and clinical practice of medicine6. This results in reduced physician referrals of patients to clinical research studies.
What also influences patient recruitment is site recruitment. Patient access is indeed one of the most important parameters of site selection, together with infrastructure, suitability for the given treatment type and costs. Cost savings are responsible for global clinical trials, as recruiting patients can be significantly less expensive in some countries rather than in others.
Once patients are recruited and enrolled, it is fundamental to ensure patient retention and monitoring/medication adherence, as enrolling additional patients means trial delays and additional costs. Thus because a patient that drops out from the study can’t be simply replaced one-for-one, due to the required statistical power of the trial protocol to assess drug effectiveness.
But patients are not the only human component of the process. The management and coordination of the study team can be as challenging as patient recruitment, considering that the team includes thousands of different players such as research sponsors, clinical investigators, payers, physicians, data monitors and reviewers, geographically dispersed and motivated by different interests. The collaboration and the efficiency of the team is highly impacted by data reasons, soon explained.
Clinical trials generate huge amounts of complex and fragmented data. This is even more true with the recent increasing adoption of real word data in combination with evidence from randomized controlled trials.
Effective data management is therefore fundamental to reach results. Once the data have been collected, what it is often lacking are tools that enable first of all to access and then analyze the data. Frequently, for clinical studies in ophthalmology, an external party, namely a reading center, needs to be involved in the process to access and analyze imaging data. This requires money but especially time, as the reading center can take six months/ one year to answer the clinical questions raised for the trial. The slow speed of the analysis is therefore a significant challenge that impacts the effectiveness of the process.
There is also a general lack of data shareability and interoperability, which directly impacts the study team and the generation of insights, as it doesn’t allow for collaborative research. This is related not only to studies conducted at the present time but also to previous studies, which, if easily accessible, can potentially be a relevant source for data comparison.
Last but not least, clinical data is highly sensitive data and needs to be stored in a secure location compliant with regulatory requirements, to be securely shared and to be verified and verifiable from competent auditors.
Finding ways of tackling these challenges, improving efficiency and cost-effectiveness of clinical studies and being agile and targeted to introduce innovation, are imperatives for the future of drug development.
1 Deloitte. Intelligent Clinical Trials. (2020). https://www2.deloitte.com/content/dam/insights/us/articles/22934_intelligent-clinical-trials/DI_Intelligent-clinical-trials.pdf
2 Di Masi, J.A., Grabowski H. G., Hansen R. W. (2016). Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics, Volume 47, pp. 20–33.
3 The European Federation of Pharmaceutical Industries and Associations (EFPIA). Development of medicines. https://www.efpia.eu/about-medicines/development-of-medicines/#:~:text=For%20every%2010%2C000%20molecules%20identified,for%20patients
4 Vivek Subbiah. (2023). The next generation of evidence-based medicine. Nature Medicine. https://www.nature.com/articles/s41591-022-02160-z
5 Deloitte. (2020). Patient recruitment is often the holy grail for clinical research…could virtual trials improve our chances of reaching it?
6Institute of Medicine (US) Forum on Drug Discovery, Development and Translation (2010). Challenges in clinical research. https://www.ncbi.nlm.nih.gov/books/NBK50888/