• ContinuousTV: Transforming Life sciences with dialog on AI and GxP

  • By: Nagesh Nama CEO xLM
  • Podcast
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ContinuousTV: Transforming Life sciences with dialog on AI and GxP

By: Nagesh Nama CEO xLM
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  • Summary

  • ContinuousTV episodes are designed to engage professionals in life sciences on recent developments in AI and its impact on GxP. Nagesh Nama is a leading AI Voice in GxP and brings his expertise in developing innovative applications for manufacturing in Pharma/Biotech/Medtech. These apps leverage Computer Vision, LLMs, LAMs, Code Generation in the areas of process control, process automation, computer validation, and regulatory compliance.
    Nagesh Nama, CEO, xLM
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Episodes
  • 004 - ContinuousALM - Application Life Cycle Management for GxP

    004 - ContinuousALM - Application Life Cycle Management for GxP
    Apr 30 2024

    In this episode, xLM's ContinuousALM (Application Life Cycle Management) offering is discussed.


    Host: Nagesh Nama, CEO, xLM Guest: Eric Gavaldo, CEO, xQUAL


    In this episode, Nagesh Nama interviews Eric Gavaldo, the CEO of xQUAL.


    Eric has spent most of his life building and testing software applications.


    They discuss the importance of a good ALM (Application Lifecycle Management) package and the key features that ContinuousALM offers.


    ContinuousALM can be used not only for software development and validation but also for equipment and facilities validation.


    ContinuousALM is built on xQUAL which is one of the industry leading ALM platforms.


    ContinuousALM lets you manage requirements, specifications, traceability, test cases (both manual and automated) as well as deviations. You can:
    1. Structure your releases down to components
    2. Define your business requirements
    3. Write your functional and technical specifications
    4. Manage your projects using Agile/V-model practices
    5. Design your tests and test case procedures
    6. Orchestrate your test and test plans
    7. Plan and execute your test campaigns
    8. Archive and compare your test results
    9. Report on coverage, results, progress, quality etc.
    10. Track your bugs (integrated or third-party)


    Do you have enough tests to cover your product, project or system?
    It takes into account all tests linked to each requirement and also allows testers to refine this coverage estimation on each individual element of the traceability matrix. We also use each item's priority and status to compute an as-reliable-as-possible real-time consolidated coverage.


    What's the overall quality of your product, project or system?

    ContinuousALM uses complex algorithm taking into account status, priority, executability, local coverage of each component in the traceability matrix, latest results on all the elements to calculate a unique Quality Score


    Are you improving your quality?
    ContinuousALM presents the tests distribution (also called tests pyramid) for each product or project. By visualizing the spread in test types, you can immediately assess if your development cycle supports your quality objectives. You also see how your team is embracing proven practices such as Test-Driven Development (TDD), Behavorial-Driven Development (BDD), etc.


    Software Quality Built-in:
    1. Support for automated and manual tests execution
    2. Dedicated exploratory testing module based on Session-Based Test Management (supports FDA CSA)
    3. Test case parameterization (including an implementation of the pairwise algorithm)
    4. Real-time execution graph stored
    5. Dependencies between tests
    6. Automatic scaffolding of tests from Requirements or specifications
    7. Support for Bidirectional Traceability including traceability to Campaign Sessions (Execution Runs)


    Please follow us on LinkedIn, Spotify, YouTube https://www.linkedin.com/company/xlm-llc https://open.spotify.com/show/6xzDAYCaq6rlEFGe09SWUR https://www.youtube.com/@xLMContinuousTV



    #ai #artificialintelligence #alm #aiingxp --- Send in a voice message: https://podcasters.spotify.com/pod/show/nageshnj/message
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    37 mins
  • 003 CAM - Code Action Model - An agent based script automation and validation in CDMS

    003 CAM - Code Action Model - An agent based script automation and validation in CDMS
    Apr 23 2024

    In this episode the implementation of CAM (Code Action Model) in Project MayaCode @ xLM Continuous Labs.
    Host: Nagesh Nama, CEO, xLM Guest: Anand Natarajan, Director of AI/ML Solutions Delivery

    Project MayaCode: Goal: This project aims to develop a GenAI Framework. The purpose of this framework is to automate code generation and comprehensive validation. The focus is on code related to rules governing data acquisition in Electronic Data Capture (EDC) within Clinical Data Management (CDMS).

    Context: Clinical Data Management (CDMS) is the process of collecting, cleaning, and managing clinical trial data in compliance with regulatory standards. The primary objective of CDMS processes is to ensure data accuracy, adherence to protocols, and suitability for statistical analysis. Clinical Data Management Systems (CDMS) are used to capture data for clinical trials. A study translates protocol specific activities into generated data. A study is essentially a road map to handle the data under foreseeable circumstances and describes the CDM activities to be followed in the trial. Concomitant to study design is the definition of rules. Rules play a crucial role in collecting, validating, and identifying discrepancies in trial data. For instance, if the screening criterion for a subject (rule) stipulates that the ECG (Electrocardiogram) readings must be taken within 15 minutes of the collection of vital signs, code will be written for checking those conditions. If for any subject, the condition is not met, a discrepancy will be generated. Rules can also dictate contextual data capture based on subject characteristics (e.g., gender, age, ethnicity). Additionally, rules guide users to relevant forms during clinical trial execution. In effect, rules drive the mechanics of data collection during clinical trials. Use Case: During the design of a study, rules are defined in natural language. System developers then parse these rules and convert them into programmatic instructions with specific syntax. This process is manually intensive, requiring developers to be well-versed in study design, navigation paths, and programming rules. After converting rules to programmatic instructions, the crucial step is validating their correctness. ==**== Please follow us on LinkedIn, Spotify and YouTube 1️⃣https://www.linkedin.com/company/xlm-llc

    2️⃣https://open.spotify.com/show/6xzDAYCaq6rlEFGe09SWUR

    3️⃣https://www.youtube.com/@xLMContinuousTV ==**== #ai #artificialintelligence #cam #xlmcontinuouslabs #aiingxp

    --- Send in a voice message: https://podcasters.spotify.com/pod/show/nageshnj/message
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    24 mins
  • #002: Document Action Model (DAM) - An intelligent GxP document generation agent

    #002: Document Action Model (DAM) - An intelligent GxP document generation agent
    Apr 17 2024

    In this episode Anand Natarajan discusses the implementation of DAM (Document Action Model) in Project Jupiter @ xLM Continuous Labs.


    Host: Anand Natarjan, Director, Director of AI/ML Solutions Delivery, xLM


    Guests: Param Nagda & Tanay Agrawal, Project Jupiter Team Members and AI/ML Engineers @ xLM ContinuousLabs


    Project Jupiter:


    Goal: Develop a GenAI Framework that is agent based with a Chat Interface that can be used to automate generation of complex documents used in GxP workflows.


    Use Case: An example use case is development of a Risk Assessment document for a medical device manufacturing. Any change in the manufacturing environment starts with a CR (Change Request) and a Risk Assessment (RA). Typically, such an RA is a complex document that includes assessing risks to product and process, evaluating the impact of change on existing risks/hazards as well as introduction of new risks/hazards, listing various related references (Product / Process Risk Analysis, etc..) and populating the required approvers based on the scope of change.


    Such an RA induces migraines for the change owner and multiple trips to various document repositories and endless Teams chats with colleagues and QA folks.


    Outcome: A Jupiter sized GenAI Agent that can handle the generation of a complex document like an RA through NLP (Natural Language Processing) chat interface.


    Please follow us on LinkedIn, Spotify and YouTube


    #ai #artificialintelligence #dam #xlmcontinuouslabs #aiingxp

    --- Send in a voice message: https://podcasters.spotify.com/pod/show/nageshnj/message
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    28 mins
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