Dr Gen Li: All areas of the clinical development industry should be aiming to become more data led. Today, we have a wealth of data from clinical trials and patient records at our fingertips. Using such data to inform and optimise cancer trial design and execution, we can reduce patient burden and enable smarter clinical trials, as well as faster drug development.
Data helps investigators make confident decisions that are not based on gut instinct but on facts – such as which country is the best option for new sites and has the required concentration of patients matching your protocol.
We’ve seen extensive research into breast cancer in recent years. In fact, our earlier analysis showed that in 2021, breast cancer was the most studied disease area, with more than 25,000 investigator sites recruiting for breast cancer clinical development. With this in mind, we wanted to investigate any trends that might be appearing in breast cancer clinical trials as they increase in number and sponsors continue to direct resources to breast cancer studies.
To do this, we analysed the data of 2,511,046 patients from 4,674 patient cohorts, mainly those who participated in clinical trials. Our analysis found that since 2014, the number of women younger than 60 years of age has tripled, from 30% to about 90%. We expected to see an increase in younger patients, correlating with progress we’ve made in the disease area in recent years – such as increased mammogram screenings and successful public health awareness campaigns. However, the increase in younger patients is substantial.
Typically, younger patients are living with more aggressive forms of breast cancer, so it’s vital that patient centricity is at the heart of any trial involving such individuals. One of the biggest challenges will be around potential fertility and pregnancy issues. These concerns might have been less of a priority before, but they will now need to be considered in order to convince patients to commit to trialling new treatments over the long term.
There will also be differences in a younger cohort to consider for trial design, including comorbidities and medication history. Sponsors must take advantage of existing patient and trial data to improve enrolment of patients that match protocols. Moreover, breast cancer trials must be dynamically designed around the complexities of this patient profile and be able to adjust in real-time.
As we emerge from COVID-19, sponsors are faced with new challenges when designing any type of clinical trial. Essentially, sponsors are all selecting cohorts from the same limited pool of patients.
We are also seeing disruption in other areas and from other world events, such as the ongoing conflict in Ukraine – which is having a huge impact on clinical development in the region and neighbouring countries.
Another challenge for the industry is getting access to data and – once you have access – figuring out how to analyse data to unlock actionable insights when designing a trial. If you don’t have access to a variety and volume of data, existing challenges – such as finding the right sites, countries, and patients – are only exacerbated.
Synthetic data is collated from similar or identical trials using the same agent to accurately model comparator or placebo outcomes. A synthetic data arm can be used in place of a placebo or comparator arm. This approach offers considerable benefits to the patient, in that it removes the ethically questionable placebo arm of a clinical trial. When patients are in chronic and severe pain, or advanced stages of a disease, placebo arms do nothing to relieve their symptoms – and are especially questionable if the patient has limited time to find an effective treatment.
Synthetic data also reduces the burden on sites and sponsors; fewer patients have to be recruited, and all patients who are recruited will be in the active arm of the trial. Patients are often afraid of being put on a placebo arm, which has long been a hurdle for patient recruitment.
Moreover, synthetic arms make use of the data available to the industry, analysing it to predict and model precise outcomes. They are best used in clinical trials where control group performance has been historically well characterised and where results have been consistent from trial to trial.
For example, trials in late-stage cancers or progressive genetic disorders where a patient’s health would deteriorate were they to receive a placebo rather than the investigational treatment. Synthetic patient data can be used to define the boundaries of a trial, model and predict what types of patients should be included and excluded, and minimise or eradicate the need for placebo patient enrolment.
Patient centricity directly impacts the commercial end of the sponsor, as well as the patients and research subjects along the way. If we can embrace a more patient-centric approach, we have the potential to make trials far less expensive and time and resource consuming, with less burden on the research subjects and the research sites. This is achievable when sponsors take a data-led approach to trials. By adopting a more modern outlook, we can have a profound impact on both patients and industry.
By harnessing the power of real-time dynamic data and AI, we can enable our clients to maximise successful outcomes in drug development. With a data-led approach, sponsors can better predict the outcomes of a clinical trial, rather than relying on guesswork. Once a trial is underway, providing ongoing support enables sponsors to gain deep, real-time insights into how a clinical trial is running.
In this way, we enable confident decision making and rescue under-performing trials. Moreover, we empower clients to prioritise their portfolios and maximise commercial returns, with competitive intelligence, target product profile definitions and objective insights.
We will continue working with clients on upcoming projects, keeping patient centricity at the heart of clinical development. Alongside this, we’re always busy with our own research projects and publications. Our next analysis will take a deep dive into diversity in clinical trials, exploring the crucial role that diversity has to play in patient centricity.
Dr Gen Li founded Phesi in 2007 with the aim of revolutionising clinical trials and the biopharmaceutical industry. Prior to founding Phesi in 2007, Dr Li was head of productivity for Pfizer Worldwide Clinical Development, a position he filled following Pfizer’s acquisition of Pharmacia.
While at Pharmacia and Pfizer, Dr Li contributed significantly to the Centre for Medicines Research (CMR) International database for pharmaceutical R&D performance, ensuring the collection of key clinical trial parameters aligned with the critical path activities. Dr Li was also instrumental in creating the KMR productivity algorithms.
He earned his Ph.D. in Biochemistry from Beijing University, and an MBA from the Johnson Graduate School of Management at Cornell University.
Phesi a data-driven provider of AI-powered clinical development analytics, products and solutions to the biopharmaceutical industry. The company’s integrated offerings cover the entire clinical development process – from development planning and indication assessment to protocol evaluation, site selection, and trial implementation management.
Phesi has the world’s largest real-time clinical development database; delivering patient-centric data science that enables biopharmaceutical companies to predict and optimise clinical development outcomes in any indication. Its database consists of 330,000 completed clinical trials, 604,000 completed research projects, >4.2 million physicians and >600,000 investigator sites worldwide.
Phesi delivers data, insights and answers, enabling smarter trials and faster cures. For more information, please visit Phesi.com.