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.

What's New in FDA's Draft Guidance "Use of Data Monitoring Committees in Clinical Trials"?

In early 2024, the FDA unveiled its latest drug guidance for the industry titled "Use of Data Monitoring Committees in Clinical Trials." This forthcoming guidance will supplant the existing DMC guidance, "Establishment and Operation of Clinical Trial Data Monitoring Committees," which dates back to March 2006. Notably, the title of the guidance has been modified.

What distinguishes this newly issued guidance? And why the necessity for a fresh approach to DMC guidance?

The subsequent articles endeavor to tackle these queries.

• RAPS.org FDA issues new draft guidance on data monitoring committees
• FDA Law Blog: How to Run DMC? It’s Tricky – FDA’s New Draft Guidance Provides Updated Recommendations on How to Best Use Data Monitoring Committees in Clinical Trials
• RAPs.org (April, 2024) Industry advises practical fixes for new FDA guidance on data monitoring committees

The new guidance actually mentioned the following "Significant changes in DMC structure and practice since the 2006 guidance was issued include: 

• The increased use of DMCs in trials (Califf et al. 2012) of modest size as reflected in the clinical trials data bank housed at ClinicalTrials.gov 
• A trend for DMC charters to become longer and more detailed
• An increased use of DMCs to implement certain adaptive clinical trial designs
• An increased use of some DMCs to oversee an entire clinical development program rather than a single clinical trial
• The potential for expansion of functions of a DMC; for example, for review of aggregate data for safety reporting for trials under an investigational new drug  application (IND) 
• An increased globalization of medical product development and use of multiregional trials with DMCs

In the early 2000s, the concept of Data Monitoring Committees (DMCs) began gaining traction, inspired by their use in NIH studies since the 1980s, albeit without formal FDA acknowledgment until later. These committees, born from the need for independent oversight, particularly in trials with serious outcomes or severely ill patients, initially operated with considerable flexibility, leading to varied implementations. Recognizing the evolving landscape, stakeholders engaged in discussions, culminating in the Clinical Trial Transformation Initiative's partnership with the FDA in 2015. Through a series of meetings, including one in Washington, recommendations were made to address the observed diversity in DMC practices. This dialogue ultimately contributed to the drafting of a new guidance document in 2024, aimed at refining DMC usage. The proposed revisions underscore not only the importance of study rigor and integrity but also prioritize participant safety, potentially influencing the early termination or modification of trials. Moreover, the guidance acknowledges the increasing prevalence of adaptive trial designs, suggesting a responsiveness to emerging trends. The new guidance suggested adaptation committee for clinical trials utilizing the adaptive design. The adaptive committee may or may not be the same as the DMC. Overall, the iterative process of refining DMC guidance reflects a collaborative effort to adapt to evolving clinical trial practices and prioritize patient well-being.

In recent years, there has been considerable discourse surrounding the creation of a distinct committee, often referred to as a safety assessment committee, tasked with the ongoing review of aggregate safety data. The requirement was spurred by the FDA guidance "Safety Assessment for Investigational New Drug Application Safety Reporting;" in 2015 that was subsequently withdrew. I have contended that the oversight of aggregate safety data can effectively be carried out by the independent Data Monitoring Committee (DMC), obviating the need for an additional safety assessment committee. The newly issued DMC guidance reflects this perspective, affirming that either the DMC or an independent safety team may undertake the review of aggregate safety data. The guidance specifies the following:

We are witnessing a growing prevalence of DMC utilization across various types of clinical trials, spanning from those focusing on rare diseases to trials characterized by high morbidity and mortality rates, as well as trials featuring innovative designs like adaptive designs. Furthermore, DMCs may find application in open-label studies, dose escalation studies, gene therapy trials, and beyond. Occasionally, sponsors may opt to incorporate DMCs as a means to enhance the study's credibility.

New champion for the most expensive drug in the US

It's widely acknowledged that medications for rare diseases and gene therapies come with a hefty price tag. In the United States, the FDA oversees drug approval, but there's no governmental oversight on pricing. After FDA approval, drug sponsors and manufacturers have the autonomy to propose and set the drug's price. Typically, pricing considerations revolve around factors like patient population, investment in drug development and approval, and anticipated financial gains, rather than the drug's effectiveness or its benefit-risk profile.

Before this month (March, 2024), the top 10 most expensive drugs in the US are:

Brand Name	Cost	Manufacturer	Indication	FDA approval date	Reason for high cost
Hemgenix	$3.5 million per one-time dose	CSL Behring	Hemophilia B	November 22, 2022	Gene therapy
Elevidys	$3.2 million per one-time dose	Sarepta Therapeutics	Duchenne Muscular Dystrophy (DMD)	June 22, 2023	Gene therapy
Skysona	$3 million per one-time dose	Bluebird Bio, Inc.	Cerebral Adrenoleukodystrophy (CALD)	September 16, 2022	Gene therapy
Zynteglo	$2.8 million per one-time dose	Bluebird Bio, Inc	Beta-thalassemia	September 16, 2022	Gene therapy
Zolgensma	$2.1 million per one-time dose	Novartis	Spinal Muscular Atrophy	May 24, 2019	Gene therapy
Myalept	$1.3 Million annually	Amryt Pharmaceuticals	Lipodystrophy/Leptin deficiency	February 24, 2014	Ultra rare disease
Danyelza	$1.2 million annually	Y-mAbs Therapeutics	Neuroblastoma	November 25, 2020	Ultra rare disease
Zokinvy	$1.2 million annually	Eiger Biopharmaceuticals	Progeria and Progeroid Laminopathies	November 20, 2020	Ultra rare disease
Kimmtrak	$1.1 million annually	Immunocore	Uveal Melanoma	January 25, 2022	Ultra rare disease
Luxturna	$850,000 per one-time dose	Spark Therapeutics	Biallelic RPE65-Mediated Inherited Retinal Disease	December 19, 2017	Gene therapy

In the past month, a new titleholder emerged for the most expensive drug in the US. Orchard Therapeutics, a subsidiary of Japan's Kyowa Kirin, now claims the top spot for producing the priciest medication.

Brand Name	Cost	Manufacturer	Indication	FDA approval date	Reason for high cost
Lenmeldy	$4.25 million per one-time treatment	Orchard Therapeutics, a unit of Kyowa Kirin	Metachromatic Leukodystrophy (MLD)	March 18, 2024	Gene therapy

There's an organization known as the Institute for Clinical and Economic Review (ICER), tasked with suggesting drug prices based on health economic assessments or Health Technology Assessments (HTAs). However, manufacturers often diverge from ICER's recommendations, frequently setting prices significantly higher. Take, for instance, a recent case where Merck, the drug's manufacturer, proposed a price nearly ten times higher than ICER's recommendation.

These exorbitant prices must ultimately be endorsed by health insurance companies or the Centers for Medicare & Medicaid Services (CMS). Consequently, it's the insurance companies or the Medicare & Medicaid program that bear the burden of these steep costs, as patients are unable to afford medications with such astronomical price tags.

Advancing Psychedelic Clinical Study Design - Virtual Public Meeting Organized by Reagan-Udall Foundation

Over the past decade, psychedelic compounds like psilocybin and ecstasy have emerged as potentially life-changing treatments for mental illnesses, including major depressive disorder and posttraumatic stress disorder. These psychedelic products may be synthetic compounds or extracts from natural products (such as magic mushroom). They usually have hallucinations effects and belongs to the substance control products. 

Academics and pharmaceutical/biotech companies are now interested in developing the psychedelic products for therapeutic uses in treating diseases like major depression, PTSD, Pakington's disease,...

In order to obtain the regulatory approval, various phases of clinical trials need to be conducted. The clinical trial designs for psychedelic drugs are more complicated than typical drugs because of its known side effects and the nature of the substance-controlled products. 

Reagan-Udall Foundation recently organized a virtual public meeting to discuss "advancing psychedelic clinical study design". During this public meeting, attendees discussed the experience of scientists working with psychedelics in FDA-authorized clinical studies and drug development, considerations for psychedelics in clinical trial designs, and perspectives and current research in psychedelic clinical trials. FDA presenters provided an overview of its newly issued guidance "Psychedelic Drugs: Considerations for Clinical Investigations."

Video recording of this virtual public meeting is available on Reagan-Udall Foundation website: 

Advancing Psychedelic Clinical Study Design
Virtual Public Meeting