Mr. Ramesh Makkena
Managing Member, CRC Pharma LLC, NJ, USA
Interviwed by Dr. Priyanka Ingle-Jadhav
Translational Clinical Pharmacologist, CRC Pharma LLC, NJ, USA
About Mr. Ramesh Makkena, Managing Member, CRC Pharma LLC.
Ramesh Makkena has over 18 years of clinical reporting experience at small and large Pharma and Biotech companies. Mr. Makkena has proficiency and excellent technical knowledge of the SAS language, HL7, ICH CDISC as well as ADaM standards and principles. He has also performed and supervised data set deidentification for various clients. Mr. Makkena has provided programming expertise to six NME’s, including reporting of Phase IIV individual trials, pooled analysis (e.g. Summary of Clinical Safety, Summary of Clinical Efficacy), exploratory analyses, and responses to Regulatory Authority queries. His diverse experience provides him with indepth perspective on clinical trial disclosure methods and intricacies involved in it.
Q. 1. In the domain of clinical trial disclosure, in your opinion, which were some of the important and impactful events ?
Recognizing the need for increased transparency, need to share the trial results with participants and public, irrespective of the outcome, EFPIA (European Federation of Pharmaceutical Industries and Associations) and PhRMA (Pharmaceutical Research and Manufacturers of America) member companies committed to “Principles for Responsible Clinical Trial Data Sharing” in July 2013.
In May 2014, European Trial Regulation (EU No. 536/2014) introduced new requirements for authorization, conduct, reporting and transparency of clinical trials wherein atleast one site is in European Union (EU) member state. This regulation mandated sponsors to provide results of clinical trials in a summary that could be understood by any lay person. It also stated that these summaries will be accessible to all through an EUwide database. Following this event, European Medicines Agency (EMA) adopted policy 0070, effective from Jan 1, 2015. This policy required proactive publication of entire anonymized clinical trial reports and clinical submission documents if the drug receives market authorization. In addition to CSR (clinical study report), companies are required to submit risk analysis report describing deidentification methods and their impact on data quality. Amongst all these past efforts, this was one of the most revolutionary move to rekindle the focus of pharmaceutical industry to clinical trial transparency.
This been said, FDAMA (Food and Drug Administration Modernization Act) 1997 mandated the registration of publicly and privately funded clinical trials, resultant of which National Library of Medicine (an institute of National Institutes of Health) launched ClinicalTrials.gov in February 2000. Till date, there are more than 230,000 trials registered on the website. In 2007, Congress expanded submission of summary results to the website, including adverse events, under FDAAA (Food and Drug Administration Amendments Act). Eventually, in November 2014 HHS (Department of Health and Human Services) issued notice of changes to procedures for registration and reporting of summary trial reports to cover unapproved, unlicensed and uncleared products as well. Meanwhile NIH (National Institutes of Health) proposed policy to get all NIHfunded clinical trials registered and submit summary results to ClinicalTrials.gov. So, as you can see, the sponsors are required to produce to the authorities and to public with anonymized version of CSRs for public information.
Q. 2. What are these documents and from where can any lay person get access to this information ?
As mentioned earlier, Clinical study reports, raw data sets (eg, SDTM/(Study Data Tabulation Model), reporting analysis (eg, ADaM/ Analysis Data Model) datasets, including lay language summaries can be accessed by the participants and the public at large. This information, either in full or in part is available freely or on request at EU online portal and at various iopharmaceutical companies’ websites (e.g. Glaxo, Pfizer, Shire, Novartisetc). Collaborative websites are been created by Industry and Research Groups to provide centralized source of such information, which includes CSDRs (Clinical Study Data Request), Project Data Sphere, OpenTrials, etc. By 2016, nearly 3000 trials were available to be accessed via suitable systems.
Q.3. While abiding to the regulatory landscape, what are the measures undertaken to ensure patient privacy ?
Utmost importance is given to make sure that the patient privacy is paramount. There are various organizations working in this direction, to strike the balance between data usability and nonymization processes. PhUSE (Pharmaceutical Users Software Exchange) working group, IOM (Institute of Medicine), TransCelerate, MRCT (Multi Regional Clinical Trials Center at Harvard), has laid down guidelines, toolkits to ensure deidentification methods, ensure protection of personal data in CSRs, and develop lay language summaries. In addition to rigorous deidentification process, adding a layer of security by ensuring registering or signing in process to access this databases will be crucial.
During this process, the challenge is of losing the data value or utility for analysis, especially with unstructured data. PhUSE guidelines, for example, use two deidentification methods (US HIPAA legislation: Safe Harbor and Expert Determination) to CDISC (Clinical Data Interchange Standards Consortium) files utilizing twopass process. TransCelerate guidelines also support the CSR anonymization using simple method and ensures CCI (Commercially Confidential Information) protection simultaneously.
Q. 4. For better understanding, can you elaborate on dataset anonymization process by giving an example ?
Patient level data collected in clinical trials are anonymised according to the standards set forth by PhUSE deidentification group for CDISC SDTM 3.2. These standards will ensure compliance with current privacy laws and regulatory guidance while allowing data to be shared with researchers. There are a number of data elements enumerated in the “Privacy Rule” under the Health Insurance Portability and Accountability Act (HIPAA) of 1996 and other guidance from European General Data Protection Regulation which can be used to identify individuals. The process of anonymizing can be thought of as permanently removing the ability to use any of these elements to identify individual participants. Direct and indirect identifiers are removed thereby making it unlikely to allow any individual to be identified by combining data. Adherence to the framework of these standards will minimize the risks of encroaching on the privacy and confidentiality of research participants. We have developed a SAS based tool to deidentify and anonymize the datasets. As seen in example below few measures for purpose of illustration are demonstrated in (A) as non-anonymized data set while (B) is the anonymized data set. For future data set analysis, we decoded SUBJID to a unique identifier, all the dates were offset, site IDs were dropped, and instead of country continent wise distribution could be done. The methods can be customized depending on the nature of study or the requirements of the sponsor. In any case, the key is to maintain the method consistent roughout the study to maintain data quality. For ages where patient identification could be done relatively easily, either in pediatric or geriatric populations, range of age can be used.
Sample Source Input dataset:
Sample Anonymized Output Dataset:
Q. 5. How do you see CRC Pharma can contribute to the objective of providing patients with trial information while safeguarding their privacy ?
We, at CRC Pharma, LLC, are aggressively gearing up to support the immense demand of services in this sector. With diverse expertise inhouse, we are capable to provide wide scale solutions including anonymization of data set, CSR redaction and authoring and reviewing lay language summary to convey trial results to the public. In conclusion, I think this is the time to gear up for change in Clinical Trials Disclosure Domain. The industry will have to comply with the changing rules, guidance and ensure that high quality data is generated, shared after anonymization, ensuring patient data privacy at the highest level.