Tag Archives: FDA

FDA approved new drug to treat heart failure

On July 7, 2015 the U.S. Food and Drug Administration approved Entresto (sacubitril/valsartan) tablets for the treatment of heart failure. The drug has been shown to reduce the rate of cardiovascular death and hospitalization related to heart failure.

Heart failure is a common condition affecting about 5.1 million people in the United States. It is a condition in which the heart can’t pump enough blood to meet the body’s needs. Heart failure generally worsens over time as the heart’s pumping action grows weaker. The leading causes of heart failure are diseases that damage the heart, such as heart attacks and high blood pressure.

Entresto is manufactured by Novartis, based in East Hanover, New Jersey.

For more information go to FDA website @FDA

Source: FDA website

Disclaimer: The EDC Developer blog is “one man’s opinion”. Anything that is said on the report is either opinion, criticism, information or commentary. If making any type of investment or legal decision it would be wise to contact or consult a professional before making that decision

FDA strengthens warning that non-aspirin nonsteroidal anti-inflammatory drugs (NSAIDs) can cause heart attacks or strokes

The U.S. Food and Drug Administration (FDA) is strengthening an existing label warning that non-aspirin nonsteroidal anti-inflammatory drugs (NSAIDs) increase the chance of a heart attack or stroke. Based on our comprehensive review of new safety information, we are requiring updates to the drug labels of all prescription NSAIDs. As is the case with current prescription NSAID labels, the Drug Facts labels of over-the-counter (OTC) non-aspirin NSAIDs already contain information on heart attack and stroke risk. We will also request updates to the OTC non-aspirin NSAID Drug Facts labels.

Patients taking NSAIDs should seek medical attention immediately if they experience symptoms such as chest pain, shortness of breath or trouble breathing, weakness in one part or side of their body, or slurred speech.

To learn more, please visit: NSAIDs.

Disclaimer: The EDC Developer blog is “one man’s opinion”. Anything that is said on the report is either opinion, criticism, information or commentary. If making any type of investment or legal decision it would be wise to contact or consult a professional before making that decision

Data Management Plan in Clinical Trials

 

The preparation of the data management plan (DMP) is a simple, straightforward approach designed to promote and ensure comprehensive project planning.

The data management plan typically contains the following items. They are:

  1. Introduction/Purpose of the document
  2. Scope of application/Definitions
  3. Abbreviations
  4. Who/what/where/when
  5. Project Schedule/Major Project Milestones
  6. Updates of the DMP
  7. Appendix

The objective of this guidelines is to define the general content of the Data Management Plan (DMP) and the procedures for developing and maintaining this document.

The abbreviation section could include all acronyms used within a particular study for further clarification.

e.g. CRF = Case Report Form
TA = Therapeutic Area

The Who/What/Where/When section should describe the objective of the study specific data management plans for ABC study. This section provides detail information about the indications, the number of subjects planned for the study, countries participating in the clinical trial, monitoring guidelines (SDV) or partial SDV, if any CROs or 3rd party are involved in the study (e.g. IVRS, central labs), which database will be used to collect study information (e.g. Clintrial, Oracle Clinical, Medidata Rave or Inform EDC).

The Appendix provides a place to put supporting information, allowing the body of the DMP to be kept concise and at more summary levels. For example, you could document Database Access of team members, Self-evident correction plan, Data Entry plan if using Double-data entry systems or Paper-Based clinical trials systems.

Remember, this is a living document and must be updated throughout the course of the clinical trial.

If problems arise during the life of a project, our first hunch would be that the project was not properly planned.

Reference: Role of Project Management in Clinical Trials
Your comments and questions are valued and encouraged.
Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica, Open Source and Oracle Clinical.

To hire me for services, you may contact me via Contact Me OR Join me on LinkedIn

Disclaimer: The legal entity on this blog is registered as Doing Business As (DBA) – Trade Name – Fictitious Name – Assumed Name as “GAMBOA”.

Got EDC?

Clinical trials play a key role in the pharmaceutical, biotechnology and medical device industry. With a large number of drugs coming off patent, companies are under pressure to develop and test new drugs as swiftly and efficiently as possible. This requires an increase in clinical trials and a reduction in the time cycle of those trials.

What is Electronic Data Capture?

Electronic Data Capture or EDC is the gathering of data collected by humans into computer systems without the need for manual data re-entry. EDC systems can speed time-to-market, reduce data entry errors, provide for early analysis and trend monitoring.

Data entry can be achieved using a number of mechanisms. Users can enter data directly into an electronic device such as a laptop PC, handheld device, tablet PC, and touch screen or tone dialing system such as IVRS.

EDC has been around for more than 10 years and still only about a third of all studies use it – the rest using paper-based data collection process.

While EDC has many advantages, a barrier to success is the expectation that the system is ready at the time of the enrollment of the first subject (patient). In order to achieve this target, one must have agreement on the data to be collected during the trial. I have found that this requirement frequently changes between the finalization of the protocol and first subject in. When paper case report forms are used, these changes to data can be more easily accommodated.

For EDC to become increasingly used in the pharmaceutical industry, they need to address the challenges prior to implementation. However, while pilot studies have been successful, pharmaceutical companies have not yet implemented EDC across the majority of their clinical trials. They are constrained by a lack of strategic planning, the varying requirements of each trial, the relative immaturity and fragmentation of the EDC software market and the need to address both process and change management.

Challenges:

  • What data are you gathering?
  • Is the site and clinical personal fully trained? What hardware do they have available?
  • Validation – Is validation of input data required?
  • Workflow – Do the data need validating, reviewing, approving and releasing for general consumption locally and centrally?
  • Integration – Does the system need integrating with other computer systems? Regulations. Does the system have to conform to any externally imposed regulations, such as 21 CFR Part 11, Good Manufacturing Practice, or Good Laboratory Practice?

EDC eliminate the transcription error (paper-based errors) and therefore transcription errors.

Benefits:

  • Double-Data Entry – The manual re-entry of data recorded on paper is expensive and unreliable
  • Validation – Immediate validation of data entry
  • User Friendly Web Forms – Data entry is quick and efficient
  • Access to real time data – quick executive level decision and information

EDC helps reduce invalid data entries and speed up the availability of drug trial information.

While the EDC technology still faces some challenges, benefits will drive acceptance. A leading stimulation to growth will be the reduction in price and increased sophistication and power of small handheld devices. These developments are only useful if accepted by the users. Not all EDC systems are created equally, and one must carefully pick a system that best meets your need.

If a company changes from paper-based system to EDC system, this ‘innovation’ will have a human side. Successful implementation is not just a matter of installing the software and announcing the change. Stakeholders, who are responsible for collecting, processing and communicating clinical trial data must adopt and adapt to the new systems – ensuring that a technical innovation is actually successfully adopted.

With current standards CDISC initiatives, it is inevitable that the FDA will eventually demand all information be provided in this format — although no date has yet been set.

Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.

Disclaimer: The legal entity on this blog is registered as Doing Business As (DBA) – Trade Name – Fictitious Name – Assumed Name as “GAMBOA”.

CDISC Will Be Required by Kit Howard

Over the past decade or so, a tremendous amount of development work has gone into creating standards for data to be submitted electronically to the FDA. There are those, however, who question whether these standards will ever be required, and believe that unless the FDA requires them, the standards don’t have to be adopted. Indeed, in the absence of such a requirement, there is sometimes a good business case for not adopting the standards, for example, if the company already has their own robust standards, or when the exit strategy is to achieve proof of concept and then sell. The writing is on the wall, however. Standards will be required, at least for data submitted to FDA, and that includes CDER (drugs), CBER (biologics) and CDRH (devices). This article will present evidence of that, and cover sources for the agency’s expectations.

In February 2012, FDA issued a Notice of Proposed Rule Making called Electronic Submission of Data From Studies Evaluating Human Drugs and Biologics. This Notice outlined the FDA’s intention to require that all data submitted in support of clinical trials would have to be sent in “an electronic format that FDA can process, review, and archive.” This will cover new drug applications (NDAs, usually submitted to CDER), biologic license applications (BLAs, usually submitted to CBER and sometimes jointly to CDRH), and abbreviated NDAs (ANDAs, usually submitted to CDER and CBER, and sometimes jointly to CDRH), and all of the associated supplements and amendments. Later in the Notice, it states that “FDA’s proposed rule would address the submission of study data in a standardized electronic format.” While CDISC is not specifically mentioned, it is referenced in several other FDA documents.

There isn’t currently a rule specifically mentioning CDRH requiring standards, but there is a very strong commitment internally to adopting CDISC, so the probability is good that there will eventually be some kind of regulatory requirement.

Guidance
Providing Regulatory Submissions in Electronic Format — Standardized Study Data http://www.fda.gov/downloads/Drugs/ GuidanceComplianceRegulatoryInformation/ Guidances/UCM292334.pdf

FDA is issuing a series of guidances designed to help sponsors submit data electronically, and the most recent is a draft guidance titled Providing Regulatory Submissions in Electronic Format — Standardized Study Data. Its primary purpose is to state that data submitted to the agency must be submitted electronically and in a standardized structure. The draft was issued in February, and the comments period closed on April 23.

The guidance covers a much wider set of submissions than the proposed rule, including both clinical and nonclinical data submitted in investigational new drug applications (INDs), NDAs, ANDAs and BLAs, and also medical device data in investigational device exemptions (IDEs), pre‐ marketing notifications (510(k)s), and premarketing approval applications (PMAs). It also includes all supplements and amendments. It effectively means that all clinical and nonclinical data going to CDER, CBER and CDRH are covered.

The guidance describes the kinds of data the agency receives and why it will require that the data be standardized. The reasons are much as you’d expect ‐ they can receive, import, analyze and report on the data much more efficiently and spend much less time working out what data are where. The guidance then goes on to discuss processes for data that were captured in a standardized format and those that weren’t.

It is important to note that the guidance does not state that CDISC standards must be used, but rather says that standards must be used, and to see the FDA’s standards page for the specific standards that are currently required. This permits changes to the standards to happen without having to update the guidance. Most of the data‐specific examples use CDISC, and the strong hint is that this is what should be used.

Here are some key points:

  • The guidance clearly states that collecting data in the same standardized format in which they will be submitted is preferable to converting after collection, a practice that is discouraged.
  • For both clinical and nonclinical studies, the sponsor should outline their standards plan in the IND or IDE. This means that the standards strategy must be established before the clinical program starts. This has implications for companies that currently expect to do only a few studies and license the product out ‐ we should expect to see conformance to standards become a part of the assessment of quality of study data.
  • When study data are submitted, the cover letter should include a description of the standards used. The guidance specifically states that it does not define what should be collected, just that everything that is collected should be submitted in a standardized fashion.
  • Each submission should contain a Reviewers’ Guide that orients the user to the structure and content of the datasets. There is no guidance currently available as to the desired content of the guide at this time.
  • Controlled terminologies must be used, and the submission must note what ones were used. The guidance discusses the value of such terminologies.
  • In cases where non‐standard data are mapped to the standards, any variable that cannot be fully or exactly mapped must be documented and explained. Data validation as per the guidance is the process of making sure that the data conform to the standards rules. It is one aspect of data quality. There are 2 types of validation rules ‐ technical validation rules (e.g., is the variable name correct) and business validation rules (do the data make sense, e.g., is the heart rate compatible with life?).
  • The guidance notes that different parts of the FDA are adopting standards at different rates, and there‐ fore there may be exceptions to the requirements. Sponsors can request meetings with the review division to determine what the requirements will be for their studies/submissions.

Online Resources for FDA’s Standards Expectations

The FDA is developing a robust set of online resources to help organizations understand and adopt standards in the way that is most useful for all parties involved. Most can be found via the primary data standards page, which is FDA Resources for Data Standards, http://www.fda.gov/ForIndustry/DataStandards/ default.htm, and can be accessed from the main FDA page by clicking on For Industry, and scrolling down to the For Industry Topics to Data Standards. Note that the FDA websites are reorganized periodically, so this may change in time. Among other things, this includes links to the pages for the Structured Product Labeling and Individual Case Safety Report (terminology below), the Study Data Standards page (discussed below), and a link to the FDA’s Data Council, which has overall responsibility for FDA standards. Be aware that there are no links back to the Data Standards page from any of the linked pages, which is rather annoying.

This part of this article lists and describes some of the resources that are available on this page a number of them that may be of particular use.

The Study Data Standards page has the largest number of standards resources. Its url is: http://www.fda.gov/ForIndustry/DataStandards/StudyDataStandards/default.htm. Here are some of the resources you can find there.

Study Data Specifications: a highly technical description of the software format the datasets should use (SAS XPORT), the file naming conventions, conventions for splitting large files, the format for the data tabulations (CDISC SDTM (human trials) or SEND (animal trials)), and the structure for data listings and patients profiles (this is quite limited). While CDISC ADaM is not specified, there are a number of rules for structuring the analysis datasets, including the need to provide both text and numeric versions of coded variables, and dates should be numeric rather than ISO8601. A define.xml is required that describes the dataset structures, an annotated CRF must be included, and a folder structure for the files is defined.

Study Data File Format Standards: a list of the file for‐ mats and what can be used to send what to whom (e.g., send study datasets to CDER, CBER and CDRH using SAS XPORT, send SDTM and ADaM define.xml to CBER and CDER using XML v1.0)

Study Data Exchange and Analysis Standards: speci‐ fies SDTM versions 1.1 and 1.2, the SDTM IG v3.1.1 and 3.1.2, SEND IG v3.0, and ADaM v2.1.

Study Data Terminology Standards: these are the code lists and dictionaries that should be used, including CDISC Terminology v 2011‐06‐10 or later, MedDRA v8 or later, CDRH Device Problem codes, and others.

Study Data Validation Rules: this is a list of rules to which submission data must conform, or it will be rejected by the agency’s data uploader.
There are also center‐specific pages that may be useful.

CBER: http://www.fda.gov/BiologicsBloodVaccines/ DevelopmentApprovalProcess/ucm209137.htm. This page has a very specific list of the steps you need to take to be ready to submit data to CBER in electronic format. It is critical that sponsors review and follow this list, as it is the first thing that the CBER contact (Amy Malla) will ask you. CBER started accepting CDISC‐formatted data in December 2010.

CDER: http://www.fda.gov/Drugs/DevelopmentApprovalProcess/FormsSubmissionRequirements/ElectronicSubmissions/ ucm248635.htm. The webpage starts with the following statement:

CDER strongly encourages IND sponsors and NDA applicants to consider the implementation and use of data standards for the submission of applications. Such implementation should occur as early as possible in the product development lifecycle, so that data standards are accounted for in the design, conduct, and analysis of studies.

It can’t get much blunter than that. It then references the Study Data Specifications document discussed above, the CDISC SDTM, SEND and ADaM, and includes a list of Data Standards Common Issues that lists the problems that CDER most commonly sees with respect to the standardization of submissions. Sponsors can use this to ensure they do not commit these errors. This document also suggests that CDASH‐style CRFs are best for simplifying the process of creating SDTM domains.

CDER began accepting CDISC‐formatted data some number of years ago.

  • CDRH: there isn’t a standards‐specific page for CDRH but they started accepting CDISC‐formatted data in May 2011. The same person (Amy Malla) is the contact for submitting data electronically to CDRH, and the list of steps on the CBER page is intended for use for CDRH data as well. CDRH has some standards terminologies defined that sponsors are ex‐ pected to use.

Some other resources that may be helpful are as follows.

  • A consolidated list of guidances (both draft and final) relating to electronic submissions http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm064994.htm
  • FDA terminology hosted by the National Cancer Institute’s Enterprise Vocabulary Services (the same organization that hosts CDISC’s controlled terms) http://www.cancer.gov/cancertopics/cancerlibrary/ terminologyresources/fda
  • Structured product labeling terms, in‐ cluding Mechanism of Action, Physiologic Effect, Structural Class and Problem List Subset
  • Unique Ingredient Identifier (UNII)
  • Individual Case Safety Report
  • CDRH’s terminology, including Event Problem Codes that cover Patient Prob‐ lem Codes, Device Component Codes and Device Problem Codes. Note that these are listed with the link of Center for Devices and Radiological Health, rather than by the name of the code lists.

For the sake of completeness, here are the links for the CDISC standards

These are currently available resources, but of course this will grow as the agency develops more.

Kit Howard is a Clinical Data Standards, Quality and Management consultant for Kestrel Consultants.

Reference: The Proposed Rule

Disclaimer: The legal entity on this blog is registered as Doing Business As (DBA) – Trade Name – Fictitious Name – Assumed Name as “GAMBOA”.

FDA and Clinical Trials

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SDTM Terminology

The current version of the Study Data Tabulation Model Implementation Guide Version 3.1.2 (SDTMIG v3.1.2) answers to CDSH questions to comply with SDTM terminology.

Some key points to remember:

  • You need to define which codelists will be applied to which questions
  • Most data entry systems require a concise list of potential terms per variable/field
  • SDTM terminology codelists are big, e.g. C66786 – COUNTRY which covers all potential COUNTRYs for all clinical trials / site management
  • Controlled terminology gap – we need to develop new terms

So how SDTM terminology become possible?

In order to improve the efficiency of human drug review through required electronic submissions and standardization of electronic drug application data, FDA and industry leaders are working together in this initiative.

Format in EXCEL


SDTM controlled terminology is extracted from the NCit by an automated procedure that creates a report organized into terminology codelists. These codelists correspond to CDISC variables.

To access SDTM Controlled Terminology visit CDISC website and click on Standards & Innovations –>; Terminology

This is the superset of codelists that are used to both collect and submit SDTM data

Excel Format – Column descriptions in the Controlled Terminology (SDTM subset)

Column. Description
Code (column A) Unique numeric code randomly generated by NCI Thesaurus (NCIt) and assigned to individual CDISC controlled terms.
Codelist Code (Column B) Unique numeric code randomly generated by NCI Thesaurus (NCIt) and assigned to the SDTM parent codelist names. This code is repeated for each controlled term (aka permissible value) belonging to a codelist. As of 9/22/2008, this code was dropped for parent codelist entries, where it created confusion.
**NOTE – light blue highlighting is used to identify the beginning of a new SDTM codelist and its applicable term set.
Codelist Extensible (Yes/No) (Column C) Defines if controlled terms may be added to the codelist. New terms may be added to existing codelist values as long as they are not duplicates or synonyms of existing terms. The expectation is that sponsors will use the published controlled terminology as a standard baseline and codelists defined as “extensible” (or “Yes”) may have terms added by the sponsor internally.
Codelist Name (Column D) Contains the descriptive name of the codelist which is also referred to as the codelist label in the SDTM IG. As with the Codelist Code, the Codelist Name is repeated for each controlled term belonging to a codelist.
CDISC Submission Value (Column E) IMPORTANT COLUMN: Currently (as per SDTMIG 3.1.2) this is the specific value expected for submissions. Each value corresponds to a SDTM Codelist Name as indicated by light blue shading.
CDISC Synonym(s) (Column F) This identifies the applicable synonyms for a CDISC Preferred Term in Column F. **NOTE – this is especially important in instances where a Test name or Parameter Test name contains a corresponding Test Code or Parameter Test Code.
CDISC Definition (Column G) This identifies the CDISC definition for a particular term. In many cases an existing NCI definition has been used. The source for a definition is noted in parentheses (e.g. NCI, CDISC glossary, FDA).
NCI Preferred Term (Column H) This identifies the NCI preferred name for a term as identified in NCIt. **NOTE – This column designates the human readable, fully specified preferred term corresponding to the NCI c-code, and is especially helpful for searching NCIt to get the entire concept with links to all instances of the term.

Above example of the spreadsheet for the ‘Route of Administration‘ codelist.

Reference: Clinical Data Interchange Standards Consortium, Inc (CDISC) Representing Controlled Terminology in CDASH

Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.

Safety Reporting in Clinical Trials – (Sample pages)

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Source: ClinfoSource

CDISC Clinical Research “A” Terminology

acronym: A word formed from the beginning letters (e.g., ANSI) or
a combination of syllables and letters (e.g., MedDRA) of a name or phrase.
admission criteria:Basis for selecting target population for a clinical trial.
Subjects must be screened to ensure that their characteristics match a list of admission criteria and that none of their characteristics match any single one of the exclusion criteria set up for the study.
algorithm: Step-by-step procedure
for solving a mathematical problem;
also used to describe step-by-step
procedures for making a series of
choices among alternative decisions to
reach a calculated result or decision.
amendment: A written description
of a change(s) to, or formal clarification
of, a protocol.
analysis dataset:An organized collection of data or
information with a common theme arranged in rows and columns and
represented as a single file; comparable to a database table.
analysis variables: Variables used
to test the statistical hypotheses
identified in the protocol and analysis
plan; variables to be analyzed.
approvable letter:An official communication from FDA to an
NDA/BLA sponsor that lists issues to be resolved before an approval can be issued.
[Modified from 21 CFR 314.3;Guidance to Industry and FDA Staff

arm: A planned sequence of elements,
typically equivalent to a treatment
group.

attribute (n): In data modeling,
refers to specific items of data that can
be collected for a class.
audit:A systematic and independent
examination of trial-related activities
and documents to determine whether
the evaluated trial-related activities were
conducted and the data were recorded,
analyzed, and accurately reported
according to the protocol, sponsor’s
standard operating procedures (SOPs),
good clinical practice (GCP), and the
applicable regulatory requirement(s).
[ICH E6 Glossary]
audit report: A written evaluation by
the auditor of the results of the audit.
[Modified from ICH E6 Glossary]
audit trail. A process that captures
details such as additions, deletions,
or alterations of information in an
electronic record without obliterating the original record. An audit trail
facilitates the reconstruction of the
history of such actions relating to the
electronic record.

Source:Applied Clinical Trials

Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.

The Drug Discovery Process

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Source: PhRMA Press