Monday, March 05, 2018
Thursday, February 22, 2018
New FDA Guidance Documents for Drug Development in Neurological Conditions Aiming to Ease the Drug Approval Paths
“Today I’m pleased to issue five guidance documents that benefited from the streamlined approach of this pilot as part of a broader, programmatic focus on advancing treatments for neurological disorders that aren’t adequately addressed by available therapies. These guidance documents provide details on how researchers can best approach drug development for certain neurological conditions – Duchenne muscular dystrophy (DMD) and closely related conditions, migraine, epilepsy, AD and ALS. These guidance documents provide our current thinking and sound regulatory and scientific advice for product developers so that safe and effective treatments can ultimately be made available to patients. These documents are each a culmination of thoughtful scientific collaboration within the agency and incorporate important input from patients, researchers and advocates. We hope that providing up-to-date, clear information about our scientific expectations, such as clinical trial design and ways to measure effectiveness, will save companies time and resources and ultimately, bring effective new medicines to patients more efficiently.”
Duchenne muscular dystrophy (DMD) and related conditions
Amyotrophic lateral sclerosis (ALS)
Monday, February 12, 2018
In FDA guidance 'Multiple Endpoints in Clinical Trials', the Bonferroni Method was described as the following:
The Bonferroni method is a single-step procedure that is commonly used, perhaps because of its simplicity and broad applicability. It is a conservative test and a finding that survives a Bonferroni adjustment is a credible trial outcome. The drug is considered to have shown effects for each endpoint that succeeds on this test. The Holm and Hochberg methods are more powerful than the Bonferroni method for primary endpoints and are therefore preferable in many cases. However, for reasons detailed in sections IV.C.2-3, sponsors may still wish to use the Bonferroni method for primary endpoints in order to maximize power for secondary endpoints or because the assumptions of the Hochberg method are not justified. The most common form of the Bonferroni method divides the available total alpha (typically 0.05) equally among the chosen endpoints. The method then concludes that a treatment effect is significant at the alpha level for each one of the m endpoints for which the endpoint’s p-value is less than α /m. Thus, with two endpoints, the critical alpha for each endpoint is 0.025, with four endpoints it is 0.0125, and so on. Therefore, if a trial with four endpoints produces two-sided p values of 0.012, 0.026, 0.016, and 0.055 for its four primary endpoints, the Bonferroni method would compare each of these p-values to the divided alpha of 0.0125. The method would conclude that there was a significant treatment effect at level 0.05 for only the first endpoint, because only the first endpoint has a p-value of less than 0.0125 (0.012). If two of the p-values were below 0.0125, then the drug would be considered to have demonstrated effectiveness on both of the specific health effects evaluated by the two endpoints. The Bonferroni method tends to be conservative for the study overall Type I error rate if the endpoints are positively correlated, especially when there are a large number of positively correlated endpoints. Consider a case in which all of three endpoints give nominal p-values between 0.025 and 0.05, i.e., all ‘significant’ at the 0.05 level but none significant under the Bonferroni method. Such an outcome seems intuitively to show effectiveness on all three endpoints, but each would fail the Bonferroni test. When there are more than two endpoints with, for example, correlation of 0.6 to 0.8 between them, the true family-wise Type I error rate may decrease from 0.05 to approximately 0.04 to 0.03, respectively, with negative impact on the Type II error rate. Because it is difficult to know the true correlation structure among different endpoints (not simply the observed correlations within the dataset of the particular study), it is generally not possible to statistically adjust (relax) the Type I error rate for such correlations. When a multiple-arm study design is used (e.g., with several dose-level groups), there are methods that take into account the correlation arising from comparing each treatment group to a common control group.
The guidance also discussed the weighted Bonferroni approach:
The Bonferroni test can also be performed with different weights assigned to endpoints, with the sum of the relative weights equal to 1.0 (e.g., 0.4, 0.1, 0.3, and 0.2, for four endpoints). These weights are prespecified in the design of the trial, taking into consideration the clinical importance of the endpoints, the likelihood of success, or other factors. There are two ways to perform the weighted Bonferroni test:
These two approaches are equivalent
- The unequally weighted Bonferroni method is often applied by dividing the overall alpha (e.g., 0.05) into unequal portions, prospectively assigning a specific amount of alpha to each endpoint by multiplying the overall alpha by the assigned weight factor. The sum of the endpoint-specific alphas will always be the overall alpha, and each endpoint’s calculated p-value is compared to the assigned endpoint-specific alpha.
- An alternative approach is to adjust the raw calculated p-value for each endpoint by the fractional weight assigned to it (i.e., divide each raw p-value by the endpoint’s weight factor), and then compare the adjusted p-values to the overall alpha of 0.05.
- Clinical importance of the endpoints
- The likelihood of success
- Other factors
- With two primary efficacy endpoints, the expectation for regulatory approval for one endpoint is greater than another
- Sample size calculation indicates that the sample size that is sufficient for primary efficacy endpoint #1 is overestimated for the primary efficacy endpoint #2
The study was to be considered positive if either of the two coprimary end points, progression free or overall survival, was significantly longer with durvalumab than with placebo. Approximately 702 patients were needed for 2:1 randomization to obtain 458 progression-free survival events for the primary analysis of progressionfree survival and 491 overall survival events for the primary analysis of overall survival. It was estimated that the study would have a 95% or greater power to detect a hazard ratio for disease progression or death of 0.67 and a 85% or greater power to detect a hazard ratio for death of 0.73, on the basis of a log-rank test with a two-sided significance level of 2.5% for each coprimary end point.
However, in the original study protocol, the weighted Bonferroni method was used and unequal alpha levels were assigned to OS and PFS.
The two co-primary endpoints of this study are OS and PFS. The control for type-I error, a significance level of 4.5% will be used for analysis of OS and a significance level of 0.5% will be used for analysis of PFS. The study will be considered positive (a success) if either the PFS analysis results and/or the OS analysis results are statistically significant.
Here, aweight of 0.9 (resulting in an alpha 0.9 x 0.05 = 0.045) was given to OS and a weight of 0.1 (resulting in an alpha 0.1 x 0.05 = 0.005) was given to PFS.
The initial assumptions for the primary end-point were an annual rate of 21% on placebo with a risk reduced by 36% (hazard ratio (HR) 0.64) with bosentan and a negligible annual attrition rate. In addition, it was planned to conduct a single final analysis at 0.04 (two-sided), taking into account the existence of a co-primary end-point (change in 6MWD at 16 weeks) planned to be tested at 0.01 (two-sided). Over the course of the study, a number of amendments were introduced based on the evolution of knowledge in the field of PAHs, as well as the rate of enrolment and blinded evaluation of the overall event rate. On implementation of an amendment in 2007, the 6MWD end-point was change from a co-primary end-point to a secondary endpoint and the Type I error associated with the single remaining primary end-point was increased to 0.05 (two-sided).
RESPIRE 1 Study:
- Hypothesis 1: ciprofloxacin DPI for 28 days on/off treatment regimen versus pooled placebo (alpha=0.025)
- Hypothesis 2: ciprofloxacin DPI for 14 days on/off treatment regimen versus pooled placebo (alpha=0.025)
RESPIRE 2 Study:
- Hypothesis 1: ciprofloxacin DPI for 28 days on/off treatment regimen versus pooled placebo (alpha=0.001)
- Hypothesis 2: ciprofloxacin DPI for 14 days on/off treatment regimen versus pooled placebo (alpha=0.049)
Thursday, February 01, 2018
There are situations where the protocol deviations are on the site level, not the subject level. For example, many study protocols have a specific requirement for handling the study drugs (or IP - investigational products). The study drug must be stored under the required temperatures. An temperature excursion occurs when a time temperature sensitive pharmaceutical product is exposed to temperature outside the ranges prescribed for storage. The temperature excursion may result in inactivation of the study drug efficacy or cause safety concern. If there are multiple subjects enrolled in the problematic site, the protocol deviation associated with temperature excursion will have impact on all subjects at this site - this is called the site level protocol deviation.
There is no specific discussion about documenting and handling site level protocol deviations in ICH and CDISC guidelines.
According to CDISC SDTM, Protocol Deviations should be captured in DV domain. According to current SDTM standard, all tabulation data sets including DV are designed for subject data (with the only exception of Trial Design info).
For site level deviations, the deviations are not associated with any specific subjects, they can not be directly included in the DV data set. There may be two ways to handle the site level protocol deviations:
- Document the site level protocol deviations separately from the subject level protocol deviations. Then document them in Clinical Study Report (CSR) and in Study Data Reviewer's Guide (SDRG) if applicable.
- If any site level deviation has impact on all or multiple subjects enrolled at that site, the specific deviation can be repeated for each affected subject
Tuesday, January 23, 2018
- Inclusion/exclusion violations, protocol deviations, protocol deviation waiver, rescreening, and others
- Protocol Deviation versus Protocol Violation and its Classifications (minor, major, critical, important)
5.14.1 Considerations Regarding Usage of a Protocol Deviations CRF
The general recommendation is to avoid the creation of a Protocol Deviations CRF (individual sponsors can determine whether it is needed for their particular company), as this information can usually be determined from other sources or derived from other data. As with all domains, Highly Recommended fields are included only if the domain is used. The DV domain table was developed as a guide that clinical teams could use for designing a Protocol Deviations CRF and study database should they choose to do so.
If a sponsor decides to use a Protocol Deviations CRF, the sponsor should not rely on this CRF as the only source of protocol deviation information for a study. Rather, they should also utilize monitoring, data review and programming tools to assess whether there were protocol deviations in the study that may affect the usefulness of the datasets for analysis of efficacy and safety.
SDTM requires a protocol deviation (PD) domain for tabulation data set (pd.xpt) no matter how the original protocol deviation data is collected.
In ADAM, whether or not creating an analysis data set for protocol deviation is up to each individual's decision. It is not required. If a summary table for protocol deviation is needed, it can be programmed from the SDTM PV data set and ADAM ADSL data set.
Thursday, January 11, 2018
Axovant Sciences (NASDAQ:AXON) today announced a correction to the data related to the Company’s investigational drug nelotanserin previously reported in its January 8, 2018 press release. In the results of the pilot Phase 2 Visual Hallucination study, the post-hoc subset analysis of patients with a baseline Scale for the Assessment of Positive Symptoms - Parkinson's Disease (SAPS-PD) score of greater than 8.0 was misreported. The previously reported data for this population (n=19) that nelotanserin treatment at 40 mg for two weeks followed by 80 mg for two weeks resulted in a 1.21 point improvement (p=0.011, unadjusted) were incorrect. While nelotanserin treatment at 40 mg for two weeks followed by 80 mg for two weeks did result in a 1.21 point improvement, the p-value was actually 0.531, unadjusted. Based on these updated results, the Company will continue to discuss a larger confirmatory nelotanserin study with the U.S. Food and Drug Administration (FDA) that is focused on patients with dementia with Lewy bodies (DLB) with motor function deficits. The Company may further evaluate nelotanserin for psychotic symptoms in DLB and Parkinson’s disease dementia (PDD) patients in future clinical studies.
(note: PE: pulmonary exacerbation; PEBAC: pulmonary exacerbation blinded adjudication committee)
Re-Assessment of Outcomes
Following database lock and unblinding of treatment assignment, the Applicant performed additional data assessments due to errors identified in the programming/data entry that impacted identification of PEs. This led to changes in the final numbers of PEs. Based on discussion the Applicant had with the PEBAC Chair, it was decided that 10 PEs initially adjudicated by the PEBAC were to be re-adjudicated by the PEBAC using complete and final subject-level information. This led to a re-adjudication by the PEBAC who were blinded to subject ID, site ID, and treatment. Result(s) of prior adjudication were not provided to the PEBAC.
Efficacy results presented in Section 7.3 reflect the revised numbers. Further details regarding the reassessment by the PEBAC are discussed in Section 7.3.6.
7.3.6 Primary Endpoint Changes after Database Lock and Un-Blinding
Following database lock and treatment assignment un-blinding, the Applicant performed additional data assessments leading to changes in the final numbers of PEs. Specifically, per the Applicant, during a review of the ORBIT-3 and ORBIT-4 data occurring after database locking and data un-blinding (for persons involved in the data maintenance and analyses), ‘personnel identified errors in the programming done by Accenture Inc. (data analysis contract research organization (CRO)) and one data entry error that impacted identification of PEs. Because of the programming errors, the Applicant states that they chose to conduct a ‘comprehensive audit of all electronic Case Report Forms (eCRFs) entries for signs, symptoms or laboratory abnormalities as entered in the PE worksheets for all patients in ARD-3150-1201 and ARD-3150-1202’ (ORBIT-3 and ORBIT-4). From this audit, the Applicant notes ‘that no further programming errors’ were identified but instead 10 PE events (three from ORBIT-4 and seven from ORBIT-3) were found for which the PE assessment by the PEBAC was considered potentially incorrect. This was based on the premise that subject-level data provided to the PEBAC during the original PE adjudication were updated at the time of the database lock. Reasons provided are: 1) the clinical site provided update information to the eCRF after
the initial PEBAC review (2 PEs), 2) incorrect information was supplied to the PEBAC during initial adjudication process (2 PEs), 3) inconsistency between visit dates and reported signs and symptoms (6 PEs). After discussion with the PEBAC Chair, it was decided that these 10 PEs initially deemed PEs by the PEBAC were to be re-assessed by the PEBAC using complete and final subject-level information. This led to a re-adjudication by the PEBAC during a closed session on January 25, 2017. This re-adjudication was coordinated by Synteract (Applicant’s CRO) who provided data to the PEBAC that were blinded to subject ID, site ID, and treatment. In addition, result(s) of prior adjudication were not provided. While the PEBAC was provided with subject profiles for other relevant study visits, the PEBAC focus was only on the selected visits for which data were updated or corrected.
Because of the identified programming errors and PEBAC re-adjudication, there were two new first PEs added to the Cipro arm in ORBIT-3 and two new first PEs added to the placebo arm in ORBIT-4. Given these changes, the log-rank p-value in ORBIT-4 changed from 0.058 to 0.032 (when including sex and prior PEs strata). The p-value in ORBIT-3 changed from 0.826 to 0.974 remaining insignificant. These changes are summarized in Table 9. Note that there were no overall changes in the results of the secondary endpoints analyses from changes in PE status described above.
- Independent validation process (double programming): The probability for two independent people to make the same mistake is very very low.
- Dry-run process: using the dirty data, perform the statistician analysis using the dummy randomization schedule, i.e., perform the statistical analysis with the real data, but fake treatment assignment. The purpose is to do the programming work up front and to check the data upfront so that the issues and mistakes can be identified and corrected.
Tuesday, January 02, 2018
- When should one report adverse event?
- GCPHelpDesk: Adverse Events & Serious Adverse Events
- Adverse Event Reporting: During the Study
Adverse Event (or Adverse Experience) Any untoward medical occurrence in a patient or clinical investigation subject administered a pharmaceutical product and which does not necessarily have to have a causal relationship with this treatment. An adverse event (AE) can therefore be any unfavourable and unintended sign (including an abnormal laboratory finding, for example), symptom, or disease temporally associated with the use of a medicinal product, whether or not considered related to the medicinal product.
A. Commonly, the study period during which the investigator must collect and report all AEs and SAEs to the sponsor begins after informed consent is obtained and continues through the protocol-specified post-treatment follow-up period. Since the ICH E2A guidance document defines an AE as “any untoward medical occurrence in a patient or clinical investigation subject administered a pharmaceutical product…” This definition clearly excludes the period prior to the IMP’s administration (in this context a placebo comparator used in a study is considered an IMP. Untoward medical occurrences in subjects who never receive any study treatment (active or blinded) are not treatment emergent AEs and would not be included in safety analyses. Typically, the number of subjects “evaluable for safety” comprises the number of subjects who received at least one dose of the study treatment. This includes subjects who were, for whatever reason, excluded from efficacy analyses, but who received at least one dose of study treatment.
There are situations in which the reporting of untoward medical events that occur after informed consent but prior to the IMP’s administration may be mandated by the protocol and/or may be necessary to meet country-specific regulatory requirements. For example, it is considered good risk management for sponsors to require the reporting of serious medical events caused by protocol-imposed screening/diagnostic procedures, and medication washout or no treatment run-in periods that precede IMP administration. For example, a protocol-mandated washout period, during which subjects are taken off existing treatments (such as during crossover trials) that they are receiving before the test article is administered, may experience withdrawal symptoms from removal of the treatment and must be monitored closely. If the severity and/or frequency of AEs occurring during washout periods are considered unacceptable, the protocol may have to be modified or the study halted. Some protocols may also require the structured collection of signs and symptoms associated with the disease under study prior to IMP administration to establish a baseline against which post-treatment AEs can be compared. In some countries, regulatory authorities require the expedited reporting of these events to assess the safety of the human research.
QUESTION: What are the investigator's responsibilities in terms of reporting the post-discontinuation adverse events? On one hand, since the patient discontinued from the study, some think that the investigator has no right to review the patient's clinical record under HIPAA (authorization terminated) or informed consent regulations (consent withdrawn) and consequently has no authority or responsibility to report the adverse events. On the other hand, there does not appear to be any variances to an investigator's IND obligations (even when a patient discontinues from the study) with respect to reporting adverse events according to 21 CFR 312.64. Also, would the investigator's reporting responsibilities be the same for Situation A and Situation B?
ANSWER:In summary, the AE collection can be depicted as the following where TEAE stands for treatment-emergent adverse event:
FDA has stated that clinical investigators need to capture information about adverse effects resulting from the use of investigational products, whether or not they are conclusively linked to the product. The fact that a subject has voluntarily withdrawn from the study does not preclude FDA's need for such information. In fact, withdrawal is often due to adverse effects, some already realized and others beginning and that will later progress. For your first scenario, that is obviously not a real problem since the investigator is also the individual's private physician and obviously has this information. While you are correct to worry about privacy issues in both scenarios, the public welfare is a larger issue. Failure to capture and report adverse effects, particularly serious adverse effects, will not only be a problem for the individual in question but potentially for other actual and potential study subjects. It is also essential to capture the information so that the total picture is available to FDA when a marketing decision is imminent. The individual in question may be one of very few who would evidence the particular adverse effect, particularly given the limited number of individuals included in a study. However, this information could have major ramifications for the potentially large population of users of the drug once legally marketed. How to best go about collecting the details of the adverse effect is obviously a different issue.