The pursuit for effective treatment modalities for cancer rides on sustained efforts with catalyzed adoption of technological advancements that surpass traditional paradigms. Cancer, with nearly 10 million deaths yearly, is the leading cause of death worldwide. However, the beam of comfort is the intensive research work being conducted in the oncology space. Cancer survivors living in the U.S. increased from 3.0 million to 15.5 million between 1971 and January 2016, with 70 per cent and more of the gains in cancer survival due to novel medicines, highlighting the importance of innovative clinical trials as vital tools in the pursuit of improved health.
The pursuit for effective treatment modalities for cancer rides on sustained efforts with catalysed adoption of technological advancements that surpass traditional paradigms. Cancer, with nearly 10 million deaths1 yearly, is the leading cause of death worldwide. Every year, there are 23.6 million2 new cancer cases, with an estimated 250 million disability-adjusted life years (DALYs) due to cancer.
However, the beam of comfort is the intensive research work being conducted in the oncology space. Cancer survivors living in the U.S. increased from 3.0 million to 15.5 million between 1971 and January 2016, with 70 per cent and more of the gains in cancer survival due to novel medicines, highlighting the importance of clinical trials as a vital tool in the pursuit of improved health.
There have been sustained efforts to make clinical trials faster and efficient to facilitate augmented adoption of drug products. About 85 per cent of the oncology medicines in development are likely to be first-in-class, while more than 6,500 cancer drugs are in the R&D pipeline, with oncology investigational drugs accounting for 37 per cent of agents in clinical development, in line with the high unmet need.
Despite some significant success achieved, the challenges in cancer research are monumental. Cancer therapeutics currently have the lowest clinical trial success rate of all major diseases. Of the oncology agents that enter Phase I trials, only 3 per cent eventually receive U.S. Food and Drug Administration (FDA) approval. The answer to this critical problem lies in novel approaches to cancer clinical trials, which not only increases the success rate but also recognises failure in a faster time and lesser cost, to allow the resources and efforts to be directed in an optimised way. This need to be applied from the outset of the clinical development program, and this paper focuses on innovative designs in early phase oncology trials (Phase 1 and 2). While study designs are the focus area, innovation can be extended to other aspects as well like patient selection, endpoints as well as study logistics.
The traditional approach to clinical development follows a fixed straight-line path and tests a specific scientific assumption throughout the study. Whereas innovative designs are dynamic in nature and allow updation of the study protocol based on the data generated
Although in theory, the concept of innovative designs was formed nearly two decades ago, there was gradual acceptance with increasing evidence-based proof.
The European Medicines Agency (EMA) in 2007 came up with guidelines on the introduction of adaptive measures in trials. As a part of their Life Sciences Industrial Strategy in 2017, the U.K. Government committed investment towards clinical trials that incorporate “novel methodology”, in partnership with the pharmaceutical industry.
The U.S. Food and Drug Administration (FDA) released a draft guidance around master protocols and adaptive designs in 2018. The FDA also started a pilot program to support complex innovative design (CID) trials that accelerate drug development in areas of unmet need.
EMA on Complex Clinical Trials (2022):“The rationale for the complexity of the design and conduct of a complex clinical trial needs to be explained in clear terms and justified in the protocol(s) and related documentation; such information should be made available to investigators, regulators, and provided in lay language to clinical trial participants. It should also be explained why the same objectives are not pursued using more conventional, non-complex designs.”
FDA on Master Protocols in Oncology (2022):“The potential advantage of a master protocol is flexibility and efficiency in drug development, consistent with FDA’s goal of helping to make safe and effective drugs and drug combination treatments available to the public. A master protocol provides an opportunity to incorporate efficient approaches, such as a shared control arm and/or the use of centralised data capture systems to enhance efficiency. However, a master protocol can also create challenges in the conduct and analysis of the trial that, if not properly addressed, can increase risk to human subjects or delay the development of the drug”.
MTD estimation in phase 1 dose-escalation trial can be based on either Rule Based Designs or Model Based Designs.
Rule-based designs are commonly used and referred to as the 3+3 designs. Patients are enrolled into small cohorts (usually three patients), with a starting dose initially defined by the data from animal models. Provided that no predefined dose-limiting toxicities (DLTs) occur in the first three patients (i.e., <33 per cent in the first cycle), the dose is escalated to the next dose level using a predefined algorithm. Dose escalation proceeds until a DLT occurs; at this point, the dose level is either expanded (if ≤1 DLT) or patients are accrued to a lower dose level (if ≥2 DLT). If there is further DLT, then that is the Multiple Ascending Dose (MAD) and the dose level below is usually considered there commended Phase 2 dose (RPTD).
While this trial design continues to be the most commonly used , it has been criticised on the grounds of inefficiency, lack of statistical foundation, ethical considerations (the number of patients treated at low doses) and applicability to targeted agents where acute DLT in cycle one may not be relevant.
Newer designs, such as accelerated titration (AT), attempts to address this by enrolling a single patient to each early dose level until a prespecified level of toxicity (e.g., ≥ grade 2) is observed. Thereafter, three patients per dose level are accrued until the MAD/ RPTD is reached. Simon and colleagues demonstrated that AT designs have the potential to reduce the number of patients necessary to determine the RPTD and increase the number of patients receiving a potentially therapeutic dose. Unfortunately, these theoretical advantages may not translate into clinical practice. In our view, between the traditional versus AT design, the proportion of patients that were treated below the MTD was lower for the AT design compared with the 3+3 design (58 vs 71 per cent). However, the total number of dose levels was higher for the AT design, while the length of study was similar for both designs
Model-based designs adapt to data insights that become available during the clinical trial. Proponents consider them to have the potential to increase efficiency, treat more patients at or near optimal doses and address several questions within the context of a single trial. Although there are no direct comparisons of efficiency between model-based designs and traditional designs, reviews suggest that the new trial designs might result in fewer cohorts, or fewer patients treated at lower dose level.
Potential reasons for this include the added complexity of the trial and the need for biostatistical support during the conduct of the study. Commonly discussed examples are the continual reassessment method (including trivariate continual reassessment model [Tri-CRM]) and escalation with overdose control (EWOC).
The continual reassessment method selects the first dose near the predicted RPTD based on statistical modeling. Toxicity data obtained from the first patient enrolled onto the trial are then used to reassess the probability of a DLT occurring at a specific dose level; this information is then used to select the next appropriate dose. Proponents of the model believe that it offers a more accurate and precise measurement of the MTD with fewer patients experiencing DLT. However, there are concerns that patients would be exposed to toxic doses of the experimental agent due to the rapid increase in dosing that the model proposes.
The EWOC design minimises the risk of overdose by specifically setting the probability of a dose above that desirable to a preset low level. The trial design then proceeds in a similar manner to approach the RPTD efficiently. Simulation studies have demonstrated that a greater proportion of patients are treated at optimal doses compared with rule-based design and has the potential additional safeguard over the continual reassessment method.
Traditional 3+3 method. Used most often, but lacks precision, yields an the true MTD. It involves an excessive number of escalation steps, which results in a large proportion of patients who are treated at potentially subtherapeutic doses while few patients actually receive doses at or near the recommended dose for phase II trials.
Other Rule Based Designs (Accelerated titration, Pharmacologically Guided Dose Escalation etc.) These designs may be better than conventional 3+3 method, but still have the inherent disadvantage of rule-based designs. For example, these designs may be inefficient in establishing the dose that meets a specific target toxicity level. In addition, the decision of dose allocation for future patients as well as the definition of the RP2D rely on information from the current dose level and do not use all available information.
Modified continual reassessment method: This method has several advantages including precisely defining target toxicity level, faster dose escalation, utilising all available information from all patients, and accounting for late-onset toxicities.
The Continual Reassessment Method (CRM) is an adaptive method and can better identify the target dose. Unfortunately, many stakeholders are hesitant to use CRM as it is considered a completely unknown territory. The solution is to engage a CRO partner experienced in CRM at an early stage.
Biomarkers, especially tissue-based ones, may be incorporated into Phase I clinical trials for a number of reasons. They may be used to confirm that the agent is achieving the desired molecular effect (proof of principle) or penetrating into the tumor. They may also be used to help define the recommended Phase 2 dose (RPTD), either by demonstrating an effect estimate, based on data obtained from preclinical studies, or by demonstrating a dose-response (or lack thereof ).
This is especially useful for drugs that are associated with little toxicity. Finally, their use in the early clinical trials setting may allow early identification of subsets of patients most likely to benefit. This can be further explored in the Phase II/III setting.
In the development of the DNA repair protein polyADP ribose polymerase (PARP) inhibitor, Fong et al. not only utilised biomarkers in tumor and surrogate tissues to demonstrate proof of principle of the PARP inhibitor, but, based on preclinical data, enriched the trial with patients known to have germline BRCA mutations demonstrating an impressive relative risk (R.R) in this subset of patients. Such data provided insights for further development of this agent. Biomarkers were also of use in the development of other agents such as bortezomib
Innovations in early phase oncology trials design and in clinical drug development, moving towards the implementation of adaptive designs and master protocols, are encouraged by regulatory agencies.
The biggest advantages of adaptive designs and master protocol are flexible decision-making, accelerated timelines and cost efficiencies. The early phase adaptive trial design will also help in the later stages of clinical trial.
Early phase adaptive clinical trial success story continues to inspire and influence the development of nextgeneration trial designs in oncology as well as other therapeutic areas.
These flexible methodologies in adaptive clinical trial design are critical enablers of scientific breakthroughs that will aid in faster pace of drug development.
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