Lalitha Amarapalli, a CSV expert, uses her research to pioneer data-driven, machine-learning-based frameworks to innovate and automate regulatory compliance for the pharmaceutical industry.

The aforementioned ceases to be a surprise, considering Lalitha Amarapalli is a precision expert who anticipates and plans, and causes innovative shifts that are mostly data-driven. She has more than thirteen years experience in computer system validation (CSV), regulatory audits and software lifecycle management within pharmaceutical and medical device companies under FDA jurisdiction, creating a written content that she hopes will revolutionize how organizations currently operate with regard to validation systems and processes. Her leadership experience from both the commercial and the technical side, combined with her technical expertise in such systems as SAP, TrackWise, Veeva, and LIMS, has not only informed global CSV strategy but also has resulted in a portfolio of research publications that has formed new thinking with regard to the automation of compliance.

In her published work, Lalitha has practical science behind it--She is not only able to theorize but provide a working model to streamline compliance so that they become less resource-intensive, less vulnerable to audit vulnerability, and more efficient.

A Validation Science Turn to Machine Learning

In 2021, one of Lalitha co-authored research papers, the title is, A Machine Learning-Based Framework to Risk-Based Validate computer systems with 21 CFR Part 11 Compliance, which was published in the American Journal of Data Science and Artificial Intelligence Innovations, reflected a significant breakthrough in terms of document-driven CSV. The paper will present the ideas to the machine learning-based framework that will be able to evaluate and prioritize risks in computerized systems and consequently, shorten their cycles of validation and enable optimal distribution of resources. Since the developed framework assumes that predictive analytics is used to rank the areas that need close scrutiny and those areas that are not taken into as much consideration, this framework would produce a more intelligent validation when compared to the conventional models that treat each and every component in a system or system module or component module equally.

Lalitha went as far as being way above academic theory in this study. Based on her experience in compliance management with regard to complex IT infrastructure, she has translated stringent regulations required by 21 CFR Part 11 into algorithmic controls installed in the architecture of the model. She highlighted that the supervised as well as unsupervised learning are important in anatomizing past audit logs, user access trails, and system behavior to steer where validation efforts are applied in dynamism.

According to Lalitha in the paper, machine learning allows accurate mapping of the risk of compliance against the behavior of a system which is beyond the capability of the traditional heuristics. This will make the validation lean and audit-ready. This vision came through the years of witnessing the unproductive flaws of the paper-based validation systems and a high-level goal of her strategy to facilitate predictable, reproducible compliance.

How to take Metadata to the Next Level as a Compliance Asset

Lalitha has continued her leadership in her thinking by exploring metadata integrity in 2022 when she published her article in the American Journal of Autonomous Systems and Robotics Engineering titled: Advancing Data Integrity in FDA-Regulated Environments Using Automated Meta-Data Review Algorithms. This paper proposes an automatic Metadata Review Assessment (MRA) framework that can take the data integrity to a new level and prepare an organization to perform a data audit process based on machine learning. The study highlighted the need to uphold accurateness and traceability of metadata attributes such as timestamps, access logs, audit trails and digital signatures, which happens to be the areas of neglect until a regulatory audit is found to have lapses.

The contribution by Lalitha played a pivotal role in selecting five main categories of metadata which are important of ensuring data integrity; these are the administrative metadata, descriptive metadata, structural metadata, provenance metadata and metadata audit trail. The metadata taxonomy in the framework was influenced by her professional experience, especially where, in defining the SOPs, risk-based assessment procedures and audit response documentation, she was concerned.

The study illustrates that real results are also achievable: the cycle of validations were shortened, a higher penetration of more moderate compliance gaps could be identified and system preparedness to the inspection could be augmented. The experience gained in the validation of complex systems by Lalitha in manufacturing and research stems was applied in the design of the model of the predictive systems that are sensitive to the regulatory peculiarities and versatile to the functioning of the organizations.

The Basis of Real-World Compliance Vision

Lalitha publishes her research work in order to extend the scope of what she is involved in on a daily basis. Danielle as a CSV Manager in the United States, has coordinated validation life cycles of GxP systems of enterprise setting, managed teams involved in the implementation of systems and acted as regulatory liaison to internal and external audits. The practical side of creating validation master plans, carrying out IQ/OQ/PQ plans, and audit humbled programs management found their way in the design principles of her research.

About Lalitha Amarapalli

Lalitha Amarapalli is a Computer Systems Validation Manager who has worked in the pharmaceutical and medical device industries more than 13 years. She has published several articles on the implementation of machine learning in the regulation, data integrity, and predictive validation. Lalitha is a Master of Science Holder in Analytical Chemistry Governors State University and finished her Bachelor of Three-Ring Binder in Pharmacy. She is a CSQE and has experience in the application of SDLC frameworks, 21 CFR Part 11 compliance and 21 CFR Part 11 validation lifecycle documentation practices. Her technical expertise includes the use of SAP, LIMS, KneatGx, Veeva, TrackWise and Qlik Sense. She has been in charge of enterprise-level CSV projects and has also worked as compliance auditor, metadata evaluator, and strategic system designer.