Phased clinical trials, seamless clinical trials, phaseless clinical development process

The drug development process encompasses a series of phased clinical trials, typically categorized as phases 0 (optional), 1, 2, 3, and 4. Phases 0, 1, and 2 primarily serve for premarket assessment, while phase 4 focuses on post-marketing evaluation. Phase 1 and 2 trials are often referred to as 'early phase trials,' while phase 3 trials are known as 'late phase trials,' 'pivotal studies,' or 'confirmatory trials.' Notably, the Code of Federal Regulations (Title 21 pertaining to the FDA) does not explicitly describe each phase of clinical trials. Instead, it mandates 'adequate and well-controlled investigations' to substantiate effectiveness. The Code of Federal Regulations does require the specification in IND application form "Identification of the phase or phases of the clinical investigation to be conducted."

The reliance on phased clinical trials can lead to significant delays and cost escalation in the drug development process, potentially impeding timely access to innovative therapies for patients. To address this challenge, incremental innovations, such as adaptive (seamless) clinical trials and cohort expansion designs, have been explored to streamline clinical trial procedures.

The FDA's guidance for industry titled "Demonstrating Substantial Evidence of Effectiveness With One Adequate and Well-Controlled Clinical Investigation and Confirmatory Evidence" acknowledges the evolving landscape. It emphasizes that confirmatory evidence regarding effectiveness may not solely derive from clinical trials but can also encompass other sources like natural history evidence, real-world data, and evidence from expanded access programs. In certain scenarios, the conventional phased approach to clinical development may not be universally applicable. Embracing a phaseless clinical development process may offer a more pragmatic and suitable alternative in specific contexts.

Phased Clinical Trials

In a previous discussion, we delved into the realm of phased clinical trials, exploring the sequential stages denoted as Phases 0, 1, 2, 3, and 4.

Seamless (phases) Clinical Trials

The concept of seamless phases in clinical trials involves the integration of two distinct phases, such as seamless phase 1/2 trials or seamless phase 2/3 trials. It's worth noting that the FDA's guidance document titled "Adaptive Designs for Clinical Trials of Drugs and Biologics" has evolved beyond the specific term "seamless design." Instead, it incorporates seamless elements within the broader framework of "Adaptations to Treatment Arm Selection." This approach encompasses not only dose selection but also the confirmation of efficacy for the selected dose within a single study.

When registering clinical trials on ClinicalTrials.gov, it is necessary to specify the phases of the trial. Categories such as "Phase 1/Phase 2" and "Phase 2/Phase 3" are utilized to denote seamless designs, reflecting the integration of multiple phases within a single trial protocol. There is no option for "Phase 1/Phase 3" and 'Phase 1/2/3'.

The utilization of expansion cohorts design has gained significant traction within oncology drug development and is now widely recognized as a specialized variant of seamless design. Pioneered by Merck and described in NEJM paper by Prowell et al. in 2016, this approach has garnered attention for its potential to streamline the development process for oncology drugs.

In 2022, the FDA released guidance specifically addressing the use of expansion cohorts, titled "Expansion Cohorts: Use in First-In-Human Clinical Trials to Expedite Development of Oncology Drugs and Biologics Guidance for Industry." This guidance outlines best practices for incorporating expansion cohorts into early-phase clinical trials, with the aim of accelerating the development of oncology therapies.

Studies employing expansion cohorts design may be categorized as "Phase 1/Phase 2" or "Phase 1/Phase 3" trials, depending on the primary objectives of the expanded cohorts. If the focus is on assessing anti-tumor activities, the study may be labeled as a "Phase 1/Phase 2" trial. Conversely, if the expanded cohorts are intended to evaluate efficacy endpoints, the study may be classified as a "Phase 1/Phase 3" trial.

In their 2018 paper "Advancing Clinical Trials to Streamline Drug Development," Bates et al. introduced a notable departure from the traditional clinical trial progression. Instead of adhering to the sequential phases of safety evaluation in Phase 1, efficacy assessment in Phase 2, and comparative efficacy testing in Phase 3, they advocated for the adoption of expansion cohorts. This innovative approach created what they termed a "continuum" or "phaseless" trial model, characterized by the seamless integration of various trial components through the use of protocol amendments and ongoing discussions with the FDA.

By embracing expansion cohorts and the phaseless trial concept, the drug development process could expedite the delivery of new therapies to patients. Moreover, this approach showcased the FDA's willingness to embrace flexibility and innovation in regulatory practices.

A particularly intriguing aspect of this "continuum" expansion cohort model is its circumvention of the conventional drug development paradigm and the intricate regulatory framework that often poses challenges for both investigators and regulators. This departure from the norm represents a significant shift towards a more agile and patient-centric approach to drug development, potentially opening avenues for greater efficiency and accessibility in the advancement of medical therapies.

Phaseless Drug Development Process

In certain contexts, traditional phased clinical trials may not be applicable, particularly in the development of therapies for ultra-rare diseases or in the realm of gene therapy. This divergence from the traditional paradigm has led to the emergence of the term "phaseless" to signify scenarios where the sequential numbering of clinical trial phases becomes less relevant.

There are two primary scenarios where the phaseless approach is observed:

• Single-Trial Clinical Development: In this scenario, the entire clinical development program revolves around a single clinical trial, regardless of whether it would conventionally be labeled as phase 1, 2, or 3. Regulatory approval, such as a New Drug Application (NDA) or Biologics License Application (BLA), may rely on the data from this singular study, which can be supplemented by various other types of evidence outlined in FDA guidance documents.

  An example of this approach is seen in the development of the first CRISPR-Cas9 gene-edited therapy by Vertex/CRISPR Therapeutics for severe sickle cell disease (SCD). FDA approval for this product was based on a single-arm, open-label, multi-site, single-dose Phase 1/2/3 study.

• Seamless Phase 1/2/3 Trials: Alternatively, a phaseless approach can involve the integration of all three traditional phases into a single seamless clinical trial. Pfizer/BioNTech's COVID-19 vaccine study serves as an illustrative example of this approach. Their study, labeled as phase 1/2/3, encompassed a randomized, placebo-controlled, observer-blind, dose-finding investigation to identify preferred vaccine candidates and dose levels in phase 1, followed by an expanded cohort and efficacy assessment in phase 2/3.

Phases of clinical trials become vague nowadays and there is a growing sentiment that the reliance on the numbered phases of clinical trials may become outdated and potentially misleading in the era of innovative trial designs. Instead, regulatory drug approval processes should prioritize the totality of evidence, which can stem from a combination of exploratory and confirmatory trial designs, as well as other sources such as natural history data, real-world evidence, expanded access studies, and animal models. In this context, the numbering of clinical trial phases becomes less crucial, emphasizing the need for a more holistic and adaptable approach to evaluating the efficacy and safety of medical interventions.

Phased Clinical Trials - Phases 0, 1, 2, 3, 4

Clinical development programs for drugs and biological products include phased clinical trials ranging from Phase 0, 1, 2, 3, and 4.  Phase 0 study is not typically needed. Phases 1, 2, and 3 studies are typical pre-market clinical trials and Phase 4 studies are post-market clinical trials. There are a lot of articles and books discussing the clinical trial phases. I borrowed some illustrations/slides from FDA's 'Clinical Research Phase Studies" and other web resources:

Phase 0 Clinical Trials:

• Phase 0 trials, also known as exploratory or pre-phase I trials, involve a small number of participants (usually fewer than 15) and are conducted very early in the drug development process.
• The primary goal of Phase 0 trials is to gather preliminary data on how the drug behaves in the human body, including its pharmacokinetics (how the drug is absorbed, distributed, metabolized, and excreted).
• These trials may involve administering subtherapeutic doses of the drug to minimize risks to participants while still providing valuable insight

Phase 1 Clinical Trials:

• Phase 1 trials are the first stage of testing in humans and typically involve a small number of healthy volunteers (or sometimes patients with the target condition).
• The main objectives of Phase 1 trials are to evaluate the safety and tolerability of the drug, determine its pharmacokinetics and pharmacodynamics, and establish an initial dose range for further testing.
• These trials are designed to identify any potential adverse effects and to determine the most appropriate dosage for subsequent studies.
  

Phase 2 Clinical Trials:

• Phase 2 trials involve a larger group of patients (typically several hundred) who have the condition or disease that the drug is intended to treat.
• The primary objectives of Phase 2 trials are to further assess the safety and efficacy of the drug, explore different dosages and dosing regimens, and gather preliminary data on the drug's effectiveness in treating the target condition.
• These trials help to provide more information about the drug's potential benefits and risks and inform the design of larger, more definitive Phase 3 trials

Phase 3 Clinical Trials:

• Phase 3 trials are large-scale studies that involve hundreds to thousands of patients and are designed to provide definitive evidence of the drug's safety and efficacy.
• The main goals of Phase 3 trials are to confirm the effectiveness of the drug in treating the target condition, further evaluate its safety profile, and compare it to existing treatments or placebo.
• Phase 3 trials are crucial for obtaining regulatory approval from health authorities such as the FDA (Food and Drug Administration) in the United States or the EMA (European Medicines Agency) in Europe.

Phase 4 Clinical Trials:

• Phase 4 trials, also known as post-marketing surveillance trials or post-approval studies, are conducted after a drug has been approved for marketing and made available to the general population.
• These trials continue to monitor the drug's safety and effectiveness in real-world settings, identify any rare or long-term adverse effects, and gather additional information about its optimal use.
• Phase 4 trials play a critical role in ensuring the ongoing safety and efficacy of medications after they have been approved for widespread use.

Overall, phased clinical trials are an essential part of the drug development process, providing valuable data at each stage to inform decision-making and ultimately bring safe and effective treatments to patients.

Expanded access, compassionate use, and eINDs

Traditionally, drug approvals relied on at least two adequate and well-controlled studies, each convincing on its own, to establish effectiveness. The adequate and well-controlled (A&WC) studies are referring to the randomized, controlled trials (RCTs). However, in some special situations (such as orphan drug development), one adequate and well-controlled study may be sufficient and the evidences from sources other than RCTs may be considered as substantial evidence of effectiveness. 

In FDAs guidance for industry (September, 2023) "Demonstrating Substantial Evidence of Effectiveness With One Adequate and Well-Controlled Clinical Investigation and Confirmatory Evidence", the following seven types of evidences are mentioned as potential substantial evidence of effectiveness.

The one highlighted is 'evidence from expanded access use of an investigational drug'. FDA guidance explained this: 

FDA has a website "Expanded Access" to define the scope of expanded access and provide guidelines how expanded access application can be obtained. There are similar terminologies in 'expanded access', compassionate use', and emergency investigational new drugs (eINDs). These three terms are explained below: 

Expanded Access:

• Expanded access, also known as "expanded access programs" or "compassionate use programs," refers to a regulatory pathway that allows patients with serious or life-threatening conditions to gain access to investigational drugs outside of clinical trials when no other treatment options are available.
• These programs are typically initiated by pharmaceutical companies or drug sponsors and require approval from regulatory agencies such as the FDA in the United States.
• Expanded access may be granted on a single-patient basis or through larger-scale programs, depending on the circumstances and the availability of the investigational drug.
• The primary goal of expanded access is to provide access to promising therapies to patients who may benefit from them, while also collecting additional data on safety and effectiveness outside of the clinical trial setting.

Compassionate Use:

• Compassionate use is often used interchangeably with expanded access, but it specifically refers to the use of investigational drugs for individual patients facing serious or life-threatening conditions who are unable to participate in clinical trials.
• Compassionate use requests are typically made by healthcare providers on behalf of their patients and are evaluated on a case-by-case basis.
• The decision to grant compassionate use access is based on factors such as the patient's medical condition, the potential benefits and risks of the investigational drug, and the availability of alternative treatments.
• Compassionate use is guided by ethical principles of beneficence and nonmaleficence, with the aim of providing relief to suffering patients while minimizing harm.

eINDs (Emergency Investigational New Drug Applications):

• eINDs are a specific type of expanded access mechanism that allows for the emergency use of investigational drugs in situations where there is an urgent medical need and no approved treatment options are available.
• eINDs are typically requested in emergency or life-threatening situations where waiting for traditional regulatory approval processes would not be feasible.
• These applications are submitted to regulatory agencies like the FDA and are subject to expedited review and approval.
• eINDs are governed by strict regulations and guidelines to ensure patient safety and ethical use of investigational drugs in emergency situations.

In summary, while all three mechanisms—expanded access, compassionate use, and eINDs—serve to provide access to investigational drugs outside of clinical trials, they differ in their specific contexts, processes, and regulatory requirements. However, they share the common goal of addressing unmet medical needs and providing hope and relief to patients facing serious or life-threatening conditions.

In clinicaltrials.gov, expanded access is categorized into three types:

Individual Patients: Allows a single patient, with a serious disease or condition who cannot participate in a clinical trial, access to a drug or biological product that has not been approved by the FDA. This category also includes access in an emergency situation.

Intermediate-size Population: Allows more than one patient (but generally fewer patients than through a Treatment IND/Protocol) access to a drug or biological product that has not been approved by the FDA. This type of expanded access is used when multiple patients with the same disease or condition seek access to a specific drug or biological product that has not been approved by the FDA.

Treatment IND/Protocol: Allows a large, widespread population access to a drug or biological product that has not been approved by the FDA. This type of expanded access can only be provided if the product is already being developed for marketing for the same use as the expanded access use.

Simpson's Paradox - in integrated analyses or in sub-group analyses

 Simpson's paradox is a statistical paradox wherein the successes of groups seem reversed when the groups are combined. Simpson's paradox occurs when we combine data or when we perform the sub-group analyses.

According to Wikipedia, the Simpson's paradox is defined as the following:

Simpson's paradox is a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined. This result is often encountered in social-science and medical-science statistics, and is particularly problematic when frequency data are unduly given causal interpretations. The paradox can be resolved when confounding variables and causal relations are appropriately addressed in the statistical modeling. Simpson's paradox has been used to illustrate the kind of misleading results that the misuse of statistics can generate

It gave several examples of the Simpson's paradox including the kidney stone treatment example:

Kidney stone treatment

Another example comes from a real-life medical study comparing the success rates of two treatments for kidney stones. The table below shows the success rates (the term success rate here actually means the success proportion) and numbers of treatments for treatments involving both small and large kidney stones, where Treatment A includes open surgical procedures and Treatment B includes closed surgical procedures. The numbers in parentheses indicate the number of success cases over the total size of the group.

The paradoxical conclusion is that treatment A is more effective when used on small stones, and also when used on large stones, yet treatment B appears to be more effective when considering both sizes at the same time. In this example, the "lurking" variable (or confounding variable) causing the paradox is the size of the stones, which was not previously known to researchers to be important until its effects were included.

Here are some additional links to explain the Simpson's paradox:

• Bonovas & Piovani (2023) Simpson’s Paradox in Clinical Research: A Cautionary Tale
• 李永乐老师 (辛普森悖论)
• Stanford Encyclopedia of Philosophy (2021) Simpson’s Paradox
• Wagner (1982) Simpson's paradox in real life
• Fenton et al (2019) Simpson's paradox and the implication for medical trials
• Kievit et al (2013) Simpson's paradox in psychological science: a practical guide
• Shi and Chan (2020) Simpson's paradox in meta-analysis - choice of studies and summary statistics
• Simpson's Paradox (and How to Avoid Its Effects)

In clinical trials, Simpson's paradox can occur when the overall treatment effect observed in the entire study population is different from the treatment effect observed within subgroups of the population. This phenomenon can lead to misleading conclusions about the effectiveness of a treatment if not properly addressed.

For example, consider a clinical trial evaluating the effectiveness of two treatments (Drug A and Drug B) for a certain medical condition. The overall analysis of the trial data might suggest that Drug A is more effective than Drug B in improving patient outcomes. However, when the data is stratified by important demographic or clinical variables, such as age, gender, or disease severity, a different picture may emerge.

Let's say that within each age group, Drug B appears to be more effective than Drug A. However, the older age group constitutes a larger proportion of the study population, and within this group, patients tend to have poorer outcomes regardless of the treatment they receive. As a result, the overall analysis might erroneously indicate that Drug A is more effective, when in fact Drug B is more effective within each age group.

In this scenario, Simpson's paradox occurs because the distribution of confounding variables (in this case, age) differs between the treatment groups, leading to a reversal of the observed treatment effects when the data is aggregated. To avoid drawing misleading conclusions from clinical trial data affected by Simpson's paradox, it's crucial to conduct subgroup analyses and consider potential confounding variables that may influence treatment outcomes. Additionally, techniques such as propensity score matching or regression adjustment can help mitigate the impact of confounding variables and provide more accurate estimates of treatment effects.

Simpson's paradox can occur in pooling the data from multiple clinical trials, in meta analysis, in integrated summary of effectiveness (ISE), and in integrated summary of safety (ISS). 

FDA guidance for industry "Integrated Summary of Effectiveness" discussed 'pooled analyses of data from more than one study' and specifically mentioned  

Simpson's paradox can be a subject of the regulatory review comments when pooling the data or combining the data from multiple studies. 

Statistical review on BI's NDA of Empagliflozin for the treatment of type 2 diabetes:

FDA Briefing Document Cardiovascular and Renal Drugs Advisory Committee Meeting July 15, 2021 Roxadustat for the treatment of anemia due to chronic kidney disease (CKD)

FDA summary of review REPATHA for the treatment of Primary Hyperlipidemia and Mixed Dyslipidemia discussed the safety review:

... crude pooling of trials with varying allocation ratios can also lead to confounding by trial (i.e., Simpson’s paradox). 

In a DIA webinar presentation (2024) Integrated Safety Analyses in Drug Marketing Applications: Avoiding Common Mistakes, Mary Nilsson provided an example of Simpson's paradox when pooling the safety data from three randomized controlled clinical trials - Simpson's paradox can obscure the safety signals in the pooled analyses. She suggested that the stratification as an approach to address the Simpson's paradox issue. For the integrated safety analyses, the analytical methods should always be stratified by study - take differences within studies first, then takes the average of those differences.

This same issue was mentioned in a paper by SUH (2009) The use of atypical antipsychotics in dementia: rethinking Simpson’s paradox"

for safety analysis, pooling the data from multiple studies may obscure the finding of the safety signals. 

Science.org had an article in 2018 FDA's revolving door: Companies often hire agency staffers who managed their successful drug reviews. It described a case of pooled data analyses obscuring the signal of sudden cardiac death due to Simpson's paradox.  raise conflict of interest questions

In 2009, for example, an FDA panel weighed whether the agency should approve AstraZeneca's widely prescribed antipsychotic drug quetiapine (Seroquel) for a wider range of conditions. The panel heard from health policy expert Wayne Ray of Vanderbilt University in Nashville, who described his research linking the drug to sudden cardiac death when used with certain other medications. Ray recalls "an FDA staff member who gave a very negative presentation on our paper." And according to the meeting transcript, the agency's then-Director of Psychiatric Products Thomas Laughren, who was instrumental in shepherding Seroquel and similar drugs through the review process and personally signed their FDA approvals, also challenged Ray's results and defended AstraZeneca's clinical trial findings in the discussion that followed. The company's "analysis should have been able to pick up a difference in sudden cardiac death, and they didn't find any difference between drug and placebo," he said.

Ray told Laughren and the panel that AstraZeneca had pooled data from all its trials as though the data were one data set, causing a well-known statistical error called Simpson's paradox. To take the company's conclusion "as definitive" would be "very dangerous," Ray said, according to the transcript. Laughren responded by calling sudden death "a pretty definitive event."

Ultimately, the committee voted overwhelmingly to advise approval of the drug for new indications and made no recommendation on labeling it to warn about sudden cardiac death. Later evidence showed that the cardiac problems Ray described are real, and in 2011, FDA required adding a warning on Seroquel's label.

I consulted ChatGPT about strategies for addressing potential Simpson's paradox, and I wholeheartedly endorse the following responses.

To minimize the potential biases caused by Simpson's paradox, you can employ several strategies during data analysis:

Awareness: Be aware of the possibility of Simpson's paradox and actively look for it, especially when dealing with aggregated data or subgroup analyses.

Visualization: Visualize the data in various ways to explore relationships between variables. Use scatter plots, histograms, box plots, and other visualization techniques to identify patterns and potential confounding variables.

Stratification: Stratify the data by relevant variables to examine relationships within homogeneous subgroups. This allows you to identify any instances where the direction of association changes across subgroups.

Control Variables: Include potential confounding variables as control variables in your analysis. By controlling for these variables, you can assess the relationship between the variables of interest while holding other factors constant.

Causal Inference Techniques: Use causal inference techniques, such as causal mediation analysis or propensity score matching, to account for potential confounding variables and identify true causal relationships.

Sensitivity Analysis: Conduct sensitivity analyses to assess the robustness of your findings to different assumptions and model specifications. This helps you understand the potential impact of confounding variables on your results.

Expert Consultation: Consult with domain experts to ensure that you're considering all relevant variables and potential sources of bias in your analysis.

By incorporating these strategies into your data analysis, you can minimize the potential biases caused by Simpson's paradox and obtain more accurate and reliable results.