Compliance & Validation

Validated and regulatory compliant approach: All required software and business support processes should meet local and country-specific validation and compliance standards. Our approach covers validated and regulatory compliant internal databases, SAS routines, modules and programming libraries for Phases I to IV trial data analysis and reporting across studies

Health authority compliant procedures such as HL7, CDISC, CFRs (Part 11, etc.), cGLP, cGMP, cGXP, etc. are followed by the company in all its engagements.

Standard Operating Procedures (SOPs) and Work Instructions: Our SMEs are specialized in the development and documentation of SOPs and Work Instructions for various Clinical Trail Management areas, software development and validation practices, etc. Some of the SOPs our SMEs have contributed to include Preparation and Conduct of Clinical Trials, Visit Planning, Study Initiation and Monitoring, Compliance and Reporting at all levels, Site, Protocols, Compounds and Study Management. We can also assist you in understanding your current practices better and provide audit-compliant SOPs and Work Instructions

Audit trial capture and reporting is necessary to comply with FDA’s CFR Part11 requirements. Working with clients, we provide necessary design, implementation and execution of system or process related events to be captured using necessary technology and database driven calls for analysis and reporting. Audit trails are also classified according to various categories based on the system and divisional context.

Data Validation: An inherent, but invisible part of successful structuring and coding of SAS programs is related to ensuring validity of data. We consider several documented, SAS-recommended best practices (http://www.sas.com/news/newsletter/tech/2006_02_14.pdf) in our processes for data validation, preparation, presentation and reporting. Some of the issues we consider while checking for data integrity include the following: Data sampling errors and bias, missing values, non-representative data in datasets based on study objective, outliers, etc. Based on sponsor feedback, we take appropriate action to correct suspect data points or incorporate other suggestions such as ignoring missing values or perform different types of logical analysis to compare results.

Validation documents (IQ/OQ/PQs): Working with clients and their internal validation teams, we provide necessary IQs, OQs, PQs, validation plans, reports and summaries based on required SDLC parameters and the nature of the project.