The fragmented Digitalization landscape Life Sciences firms have chosen to adopt over the last few years has resulted in multiple sources of truth and the Chain of Custody often breaking at a Product Value Chain level. For instance, a company implements Document Management System supplied by one vendor and a Learning Management System supplied by a different one and perhaps a LIMS supplied by a third vendor. Each vendor adopts custom data models, thereby resulting in three sources of truth – the compliance footprint surrounding documentation, another surrounding Training and a third surrounding the Laboratory workbench.
While it is hugely inefficient and counter-productive to the desired benefits of any Digitalization initiative, further, any insights companies attempt to derive on the back of this fragmented data narrative is fraught with risk, redundancy, and unreliable insights.
A.i.zen Algo’s ZenVector™ platform through its integrated narrative, delivers one single source of truth that leadership and users alike can bank on to build a Data and Insights driven Enterprise.
Our custom AI use cases deliver Hyperautomation benefits to users reducing Errors due to Poor Training or mere Subjectivity, procedural non-compliance etc.
A backend Natural Language Programme (NLP) combined with an AI algorithm mines all documents residing inside the Document Management System and connects the meta data and tags to training courses on the Training Management System while also drawing related Quality Events from Quality Management System to deliver a 3600 learning platform for the trainee. Combine this with all Internal and External Audit (FDA, MHRA) observations to make the trainee aware of the real-world benchmarks of performance and not be content about answering a Questionnaire.
Launch an internal audit on AuditZen. Type your findings from an internal audit. An advanced Neural Network AI model in the backend will parse this input and compares them with recent global Audit observations (483s, Warning Letters etc.) to give you the perfect profile of your current risk when looked through a regulatory ‘prism’.
All historical incident and related CAPA data is stored inside QualZen. An NLP model will mine all this data and match them with an incoming incident description and offers recommended work flows to users in a real time basis. Further, a supporting AI model also recommends corresponding CAPA further leading to workflows on the QualZen platform.
Presently, choosing the number of timepoints in a BE study is manual and timepoints are derived considering the formulation and parameters of the molecule viz., Tmax, Thalf, Strength and Intra Subject coefficient of Variation (ISCV) by adhering to the regulatory (USFDA, ANVISA, EU etc.) guidelines. This process needs a highly experienced professional to maintain uniformity and consistency in successfully deriving the timepoints manually. Our Clinical Automation suite has inbuilt Machine Learning models that optimize the number of timepoints required to achieve Bioequivalence, thus resulting in lesser volunteer pain, blood loss and faster processing times. There are several others. Please reach out to us to know more about the bleeding edge work our team is doing to save millions of dollars for Life Science firms.
