Clinical Research Toolkit by NIH and NCI

NIH (National Institute of Health) has been the force in conducting the landmark clinical trials and conducting the clinical trials in the disease areas that pharmaceutical companies are either not interested in or cannot afford to conduct the trials. NCI (National Cancer Institute) plays the prominent role in conducting the clinical trials in various type of cancers.

These government agencies now also design websites to help with the conduct of the clinical trials. Last year, The National Institutes of Health's (NIH) National Institute for Allergy and Infectious Diseases (NIAID) launched a new website meant to make complying with clinical trial regulations around the world substantially easier. The tool is known as ClinRegs (http://clinregs.niaid.nih.gov/index.php). As described by NIAID officials, it's an "online database of country-specific clinical research regulatory information designed to save time and effort in planning and implementing clinical research." With this tool, users can look up clinical data on 12 of the most popular countries for clinical research, including the US, China, India, Brazil and South Africa. Additional countries will be added in the near future according to NIH priorities, the ClinRegs team told Regulatory Focus in a statement.

Various clinical research toolkits are available on NIH’s websites. These toolkits provided the policies, guidance, templates (protocol, ICF, Data Management,…), and other essential documents.

• Toolkit for Clinical Researchers by National Institute of Dental and Craniofacial Research
• Trans NIAID Clinical Research Toolkit by National Institute of Allergy and Infectious Diseases
• R&D Resource for Conducting Clinical Trials by National Cancer Institute

NCI also maintain the CTCAE (Common Terminology Criteria for Adverse Events) that has been the standard for reporting and assessing the AE severity. See my previous post about “Dose Limiting Toxicity (DLT) and Common Toxicity Criteria (CTC) / Common Terminology Criteria for Adverse Events (CTCAE)”

Dose Linearity versus Dose Proportionality

In early phase studies of the drug development, dose linearity and dose proportionality are usually tested. It is essential to determine whether the disposition a new drug are linear or nonlinear. Drugs which behave non-linearly are difficult to use in clinics, especially if the therapeutic window is narrow. if non-linearity is observed for the usual therapeutic concentration range, more clinical studies/tests are needed for the drug development program and drug development can even be stopped. EMA guidance “GUIDELINE ON THE INVESTIGATION OF BIOEQUIVALENCE”, “Guideline on the pharmacokinetic and clinical evaluation of modified release dosage forms”, and FDA Guidance “Bioavailability and Bioequivalence Studies Submitted in NDAs or INDs — General Considerations” specifically requires the test of dose linearity or dose proportionality.

The concept of dose linearity and dose proportionality are often confused because they are very closely related. It can be said that the dose proportionality is a special case of dose linearity or a subset of the dose linearity.

To test the dose linearity or dose proportionality, the clinical trials are often designed as:

• Dose escalation study
• Parallel group study with various dose groups
• Cross-over design with various dose groups

In practice, people usually only test for the dose proportionality. To test for dose proportionality, there are generally four approaches:

Analysis of Variance Approach

In this approach, the dose-normalized PK parameters (AUC or Cmax) will be calculated. The dose-normalized values will then be analyzed by ANOVA approach. Dose normalization is simply the PK parameter divided by dose. With AUC as an example, we can construct the hypothesis as the following: 

          H0: AUC(dose1) / Dose 1 = AUC(dose2) / Dose 2 = AUC(dose3) / Dose 3

If null hypothesis H0 is not rejected, there is no evidence against the dose proportionality. The dose proportionality is then declared. 

Linear Regression Approach

In this approach, the linear regression with quadratic polynomial term of dose will be fit. The PK parameters (AUC or Cmax) will be the dependent variable and dose will be the independent variable.

            Y=alpha + beta1*Dose + beta2*Dose^2 + error

Where the hypothesis is whether beta2 and alpha equal to zero. If either beta2 or alpha is significantly different from zero, the dose proportionality will not be declared. In beta2 is not significant different from zero, the above linear regression is simplified as:

           Y=alpha + beta*Dose + error

If alpha is not significantly different from zero, then dose proportionality is declared

If alpha is significantly different from zero, then dose proportionality cannot declared, but the dose linearity can be declared.

Power Model Approach

In this approach, the relationship between PK parameters (AUC or Cmax) and the dose can be described by the following power model.

          Y=exp(alpha) * Dose^beta * exp(error)

This model can be re-written as:

           ln(Y) = alpha + beta*ln(dose) + error

The slope, beta, measures the proportionality between dose and the PK parameters. If beta=0, it implies that the response is independent from dose. If beta=1, the dose proportionality can be declared. The power essentially tests whether or not the beta = 1.

Equivalence (interval) Approach For Power Model Approach

Based on the power model, Brian Smith et al proposed a bioequivalence approach in their paper “Confidence interval criteria for assessment of dose proportionality”. This approach is concisely described in paper by Zhou et al.

 

EXAMPLE:

In a paper by Campos et al, the dose proportionality was evaluated to compare the 120 mg/kg dose versus 60 mg/kg dose. They first normalized the AUC and Cmax to 60 mg/kg dose. The dose normalized values were then used for ANOVA analysis (mixed model approach as described in previous topic since the study design was a crossover design). They concluded the dose proportionality based on the 90% confidence interval of geometric least square mean ratio (0.83-0.88 for AUC and 0.85-0.92 for Cmax) fell within 80-125% equivalence limits.  

REFERENCES:

• Why should you evaluate dose proportionality?
• Statistics and Pharmacokinetics in Clinical Pharmacology Studies
• Using SAS to Assess Dose Proportionality in Dose Escalation Studies
• Exercise 6: Dose Linearity and Dose Proportionality
• Dose Proportionality Vs Linear Pharmacokinetics

Protocol Deviation versus Protocol Violation and its Classifications (minor, major, critical, important)

Every clinical trial will have a study protocol. The investigator is required to follow the study protocol to conduct the study. However, during the clinical trial, there will always be planned or unplanned deviations from the protocol. ICH GCP requires that these protocol deviations are documented. ICH E6 (section 4.5.3) states “the investigator, or person designated by the investigator, should document and explain any deviation from the approved protocol.” At the end of the study, statistical analysis will include a listing for all protocol deviations and a summary table for protocol deviations by category.

Across various regulatory guidelines, both terms ‘protocol deviations’ and ‘protocol violations’ are used. What is the difference between a protocol deviation and a protocol violation?

For a while, there seems to be a thinking that the protocol deviation is less serious non-compliance and the protocol violation is more serious non-compliance of the protocol. However, the recent documents from the regulatory bodies suggest that both terms are the same and can be used interchangeably. In practice, it will not be wrong if we stick to the term ‘protocol deviation’ and avoid using the term ‘protocol violation’.

In FDA’s “Compliance Program Guidance Manual For FDA Staff - Compliance Program 7348.811 Bioresearch Monitoring: Clinical Investigators” in 2008. It provided a definition for ‘protocol deviation’, however, the term ‘protocol deviation/violation’ was lumped together and did not draw a clear distinction between protocol deviation and protocol violation.

“Protocol deviations. A protocol deviation/violation is generally an unplanned excursion from the protocol that is not implemented or intended as a systematic change. A protocol deviation could be a limited prospective exception to the protocol (e.g. agreement between sponsor and investigator to enroll a single subject who does not meet all inclusion/exclusion criteria). Like protocol amendments, deviations initiated by the clinical investigator must be reviewed and approved by the IRB and the sponsor prior to implementation, unless the change is necessary to eliminate apparent immediate hazards to the human subjects (21 CFR 312.66), or to protect the life or physical wellbeing of the subject (21 CFR 812.35(a)(2)), and generally communicated to FDA. “Protocol deviation” is also used to refer to any other, unplanned, instance(s) of protocol noncompliance. For example, situations in which the investigator failed to perform tests or examinations as required by the protocol or failures on the part of study subjects to complete scheduled visits as required by the protocol, would be considered protocol deviations.”

In ICH E3 Guideline: Structure and Content of Clinical Study Reports Questions & Answers in 2012, both ‘protocol deviation’ and ‘protocol violation’ were used. The document suggested protocol violation is equivalent to important protocol deviation. In other words, the protocol violation is a subset of all protocol deviations.

A protocol deviation is any change, divergence, or departure from the study design or procedures defined in the protocol. Important protocol deviations are a subset of protocol deviations that may significantly impact the completeness, accuracy, and/or reliability of the study data or that may significantly affect a subject's rights, safety, or well-being. For example, important protocol deviations may include enrolling subjects in violation of key eligibility criteria designed to ensure a specific subject population or failing to collect data necessary to interpret primary endpoints, as this may compromise the scientific value of the trial. Protocol violation and important protocol deviation are sometimes used interchangeably to refer to a significant departure from protocol requirements. The word “violation” may also have other meanings in a regulatory context. However, in Annex IVa, Subject Disposition of the ICH E3 Guideline, the term protocol violation was intended to mean only a change, divergence, or departure from the study requirements, whether by the subject or investigator, that resulted in a subject’s withdrawal from study participation. (Whether such subjects should be included in the study analysis is a separate question.) To avoid confusion over terminology, sponsors are encouraged to replace the phrase “protocol violation” in Annex IVa with “protocol deviation”, as shown in the example flowchart below. Sponsors may also choose to use another descriptor, provided that that the information presented is generally consistent with the definition of protocol violation provided above. The E3 Guideline provides examples of the types of deviations that are generally considered important protocol deviations and that should be described in Section 10.2 and included in the listing in Appendix 16.2.2. The definition of important protocol deviations for a particular trial is determined in part by study design, the critical procedures, study data, subject protections described in the protocol, and the planned analyses of study data. In keeping with the flexibility of the Guideline, sponsors may amend or add to the examples of important deviations provided in E3 in consideration of a trial’s requirements. Substantial additions or changes should be clearly described for the reviewer.

When protocol deviations are documented, they are also classified into categories according to the severity and their effect on the subject’s rights, safety, or welfare, or on the integrity of the resultant data.  

ICH E3 “STRUCTURE AND CONTENT OF CLINICAL STUDY REPORTS” requires the important protocol deviation to be described. It does not use the categories of critical, major, or minor. However the descriptions In Section 10.2 suggests the important protocol deviations are those with major or critical categories. Section 10.2 stated:

All important deviations related to study inclusion or exclusion criteria, conduct of the trial, patient management or patient assessment should be described. In the body of the text, protocol deviations should be appropriately summarised by centre and grouped into different categories, such as: 

− those who entered the study even though they did not satisfy the entry criteria;

− those who developed withdrawal criteria during the study but were not withdrawn;

− those who received the wrong treatment or incorrect dose;

− those who received an excluded concomitant treatment. 

In appendix 16.2.2, individual patients with these protocol deviations should be listed, broken down by centre for multicentre studies.

In US, while there is no formal guidance, the protocol deviations are usually classified as major or minor categories. For example, in a FDA presentation about “Avoiding Common Mistakes in Clinical Research”, the protocol deviation spectrum contains minor (a missed lab test, a missed visit) and major (ineligible subject enrolled, safety or efficacy assessments not done, did not report SAE to IRB • • • •).

In EU, EMA guidance “Classification and analysis of the GCP inspection findings of GCP inspections conducted at the request of the CHMP”, the protocol deviations are classified as Critical, Major, and Minor categories.

 

Critical: - Conditions, practices or processes that adversely affect the rights, safety or well-being of the subjects and/or the quality and integrity of data.

- Critical observations are considered totally unacceptable.

- Possible consequences: rejection of data and/or legal action required.

- Remarks: observations classified as critical may include a pattern of deviations classified as major, bad quality of the data and/or absence of source documents. Manipulation and intentional misrepresentation of data belong to this group.

 

• Major: - Conditions, practices or processes that might adversely affect the rights, safety or well-being of the subjects and/or the quality and integrity of data.

- Major observations are serious findings and are direct violations of GCP principles.

             - Possible consequences: data may be rejected and/or legal action required.

             - Remarks: observations classified as major, may include a pattern of deviations

                    and/or numerous   minor observations.

 

•Minor: - Conditions, practices or processes that would not be expected to adversely affect the right, safety or well-being of the subjects and/or the quality and integrity of data.

- Possible consequences: observations classified as minor, indicate the need for improvement of conditions, practices and processes.

            - Remarks: many minor observations might indicate a bad quality and the sum might

                be equal to a major finding with its consequences.

 

In practice, the critical and major protocol deviations may be grouped together. At least this is how it is done in our of NIH studies. See Protocol Deviations CRF Module Instructions

Protocol Deviation Discussion at Firstclinical.com:

• Protocol Deviations Discussion 1
• Protocol Deviations and Clinical Study Reports-2
• Investigator Submission of Protocol Deviations to IRB
• Prospective Protocol Waivers
• Bringing Method to the Madness: Protocol Deviation & Violation Codes
• Protocol Deviation Versus GCP Violation
• Reporting Protocol Violations to the FDA
• Question regarding Informed Consent and Major Protocol Deviations