Earlier this year, the World Health Organization published new findings from 115 countries that revealed how heavy the burden of cancer is globally. The WHO estimates that around one in five people develop cancer in their lifetime, with 35 million new cancer cases predicted in 2050; a 77% increase from the estimated 20 million cases in 2022 (the most recent year it has data available).
While there has been a general reduction in mortality as cancer care has improved, the incidence of six of the top ten cancers continues to increase; rates of breast, pancreas, uterine, prostate, liver, and kidney cancers, and HPV-associated mouth cancer, have all risen. Cancer rates are thought to be higher due to a combination of improved diagnostics, changes in lifestyle factors like growing rates of obesity, and increasing life expectancy globally – with the risk of cancer being much higher in older populations.
Given the colossal impact on all nations, it’s not surprising that oncology remains a priority area for clinical development. There is significant demand for new therapies and though available treatments continue to advance, they cannot hold back the tide on the rising number of cases. The clinical development industry therefore continues to invest, with 2,287 oncology products under development in 2022 – 2.6 times as many as in 2013.
The resources the industry has dedicated to oncology have led to breakthroughs, and oncology trials are frequently at the forefront of discovery. Particularly with immunotherapies like CAR-T and mRNA vaccines, and stem-cell-based therapies against certain types of cancer. At the same time, advances in precision medicine are expanding the frontiers of individualized treatment regimens for cancer patients.
This culture of innovation has and continues to have concomitant effects on the oncology clinical trial process. As the parameters of trials shift to adopt a precision medicine approach, patient populations are becoming narrower, and consequently identifying the right investigators and sites is becoming progressively more challenging. Additionally, with greater innovation comes greater regulatory scrutiny, and like so many other areas, oncology is challenged by failed, costly, and delayed trials.
Sponsors must balance innovation with the need to reduce costs, minimize amendments, and get new cancer therapies to patients faster – and sophisticated clinical data science powered by big data and artificial intelligence will be essential to achieving this goal.
The State of Cancer Clinical Development in 2024
Oncology makes up a major proportion of clinical research activity. Data from Phesi finds that in 2022 and 2023, cancers accounted for three of the five most-studied indications (Table 1). In 2022, breast cancer, solid tumors, and prostate cancer were in the top five, alongside stroke and COVID-19. In 2023, an analysis of 65,749 trials revealed a similar picture, but with COVID-19 falling down the list. In its place, solid tumor trials that explore potential drug candidates when a specific cancer type has not yet been identified rose the list. Increased investment in solid tumor therapies is an exciting move, with these trials likely to be the foundation of innovative cancer therapies.
In a new analysis of data from the first six months of 2024, the scenario has shifted again. A review of 66,935 clinical trials reveals COVID-19 has dropped out of the top five altogether, to be replaced by type 2 diabetes. But significantly, the same three oncology indications of breast cancer, solid tumor, and prostate cancer remain.
Rising Attrition Rates
Throughout the clinical development industry, the years between 2021 and 2023 saw a surge in trial terminations at Phase II, with the attrition rate at 28% at the end of 2023 – substantially higher than pre-pandemic levels of 20% (Figure 1). Higher attrition caused by the disruption to global clinical development from the pandemic was to be expected. But worryingly, Phesi’s review of the first six months of 2024 reveals that the rate has not stabilized or reverted to pre-pandemic levels. It has continued to increase – reaching 32% in June. There may be an improvement by the end of the year, and we await those data with interest, but the fact remains that an attrition rate of around a third may indeed be the new normal for clinical development.
Given the prominence of oncology in clinical development, it is clear cancer trials have been impacted by these high attrition rates, delaying the introduction of new therapies to market and leading to a steady increase in costs. Moreover, costs are expected to rise further due to a growing likelihood of Phase III cancellations in the coming years; struggling to meet Phase II objectives calls into question why a candidate is advanced to Phase III.
Poor Investigator Site Selection
In a separate analysis, Phesi reviewed 11,755 Phase I, II, and III global oncology trial sites open for recruitment since 2019, revealing a 49% rise in recruiting sites. The US remains the leader in oncology research, while China has significantly increased its presence, with a 374% rise in recruiting sites. The UK and Canada saw the smallest growth at 20%. Among the top five countries with the highest growth after the US, three are in Asia: China (374%), Korea (83%), and Taiwan (69%). Brazil (158%) and Spain (87%) also showed significant increases.
While it is reassuring for patients that so much focus is on cancer R&D, it puts pressure on investigator sites. Many sponsors are looking beyond the US for trial sites due to intense competition for patients and investigators. Yet, despite advances in precision medicine, investigator site selection and country allocation remain imprecise. Saturation of sites in certain areas leads to more non-performing and underperforming sites, causing trial failures.
A deep dive into non-small cell lung cancer (NSCLC) clinical trials underlines the need to precisely select investigator sites and identify experienced investigators. Among the 11,755 trials analyzed were 471 recruiting Phase I NSCLC trials, each targeting more than 20 specific genetic markers. A fifth (20%) of the investigators in these 471 NSCLC trials were shown to specialize in different areas of oncology, with no history of strong recruitment in lung cancer studies. Moreover, the top 100 lung cancer investigator sites in the US each were recruiting for 39 trials in parallel on average. A single investigator cannot meaningfully recruit patients for 39 trials simultaneously. Ultimately, overburdened investigator sites will predictably have a detrimental impact on oncology clinical performance.
Recruitment Issues Remain
At a treatment level, a greater understanding of the genetic drivers of cancer leads to more targeted therapies. As a result, new cancer treatments may more often be designed for smaller patient sub-populations. As oncology research is increasingly focusing on cancers with specific genetic markers and mutations, recruiting the right patients in a desired time frame for a trial is a growing challenge and the industry must adapt to the practical implications that come with this.
Yet, trial site performance in oncology remains sluggish. A 2023 Phesi analysis of 173 cancer clinical trials found that over three years, nearly one in five investigator sites recruited just a single patient. These findings correlate with research from the Tufts Center for the Study of Drug Development, which found that 37% of sites under-enroll.
Applying Clinical Data Science To Overcome Challenges
To mitigate the difficulties outlined here and improve the performance of oncology trials, sponsors must embrace clinical data science. That means using data gathered from historical and current various clinical settings to move from “perception-led” to “data-led” portfolio and protocol design, and execution. A data-led outlook puts the patient at the very center, and this begins with a Digital Patient Profile (DPP). Using information derived from a DPP, sponsors can assess numerous elements in a protocol, such as inclusion/exclusion criteria, selections of control/comparator, treatment outcome(s), and treatment duration. This alignment between a protocol design and the patient population it intends to treat has proven to eliminate unnecessary and ad hoc costly amendments.
A DPP offers a granular data breakdown of the patient population, including sub-populations, for a target indication – giving sponsors a statistical view of patient attributes, including typical age, sex, ethnicity, comorbidities, and many other variables, along with the investigators/physicians caring for them. This enables the identification of countries and investigator sites with higher precision to recruit the right patients and eliminate non-active and non-enrolling sites. Importantly, the data delivered by a DPP can be used to inform predictive analytics for trial simulation and scenario modeling. This allows sponsors to accurately predict trial outcomes and risk factors – including likely side effects safety risks, and efficacy. These deep analyses minimize overall operational costs and enhance submission packages for new drug approvals.
Where there are few patients – as is the case for many rare diseases and cancer indications with specific biomarkers – clinical data science and clinical analytics can be further used to support single-arm clinical trials; eliminating the need for external comparator arms to reduce patient burden. Oncology drug approvals are increasingly supported by single-arm trials. An analysis by JAMA Oncology revealed that between 2002 and 2021, 31% of new oncology drug indications approved by the FDA were based on data from single-arm trials. One example is GSK’s single-arm trial assessing dostarlimab for a rare form of locally advanced rectal cancer, where a total of 12 patients completed treatment with a clinical complete response. Digital twins can be deployed to better interpret the results from those single-arm clinical trials.
Cancer trials benefit from using DPPs to inform the construction of digital twins to serve as an external control arm. Derived and constructed from extensive real-world data, digital twins improve trial implementation by enabling sponsors to model a study from early screening to protocol design, to removing a comparator arm. The good news for sponsors is the growing positive regulatory sentiment from the FDA and European Medicines Agency (EMA) towards digital twins. Automated external control arms can be used across various indications to address ethical concerns around the use of placebos and to reduce patient burden. Additionally, removing the need for a control arm decreases the number of patients required for a trial, reducing costs and shortening cycle times.
Accelerating Innovation with Clinical Data Science
Oncology leads to innovation in both science and clinical development. By adopting a comprehensive data-led, patient-centered view from the outset, innovation doesn't have to be costly or risky. The data to accelerate trials, cut costs, and reduce patient burden is available – it’s the change management curve that is harder. But with best practices being established now by industry leaders, and deep expertise in clinical data science available to sponsors, there is no reason not to follow this approach.
Today, with advances in artificial intelligence and clinical data science, it is for the first time possible for the industry to have complete confidence in predictive data science. The benefits are clear to sponsors: to optimize protocol design, select better investigator sites with acute precision, and future-proof the organization with the use of digital twins and external control arms – all sitting on top of a clinical trial simulation. But the biggest benefit will come from the ultimate result – ensuring much-needed therapies get to patients faster.
Author Details
Dr. Gen Li- President, Phesi
Publication Details
This article appeared in Pharmaceutical Outsourcing:Vol. 25, No.4 Oct/Nov/Dec 2024Pages: 8-10