🤖🧬 Where Technology Meets Science

#datascience #centralizedmonitoring #clinicaltrials #technologyinpharma #pharmatransformationIn this episode, Jennifer Krohn, Associate Director of Risk-Based Quality Management at Gilead Sciences, shares how centralized monitoring is transforming clinical trial oversight, improving data quality, participant safety, and trial efficiency. From statistical tools and open-source innovation to CRA training and AI advancements, Jenn shares what it takes to detect critical data signals earlier, ensure trial integrity, and foster cross-functional collaboration in pharma through the open-source community.Materials shared in the episode:PHUSE RBQM Working GroupRBQM Education ProjectPHUSE CM White Papers:Centralized Monitoring: Exploring the Considerations and Challenges of ImplementationCan the Value of Centralized Monitoring be QuantifiedJennifer Krohnhttps://www.linkedin.com/in/jenn-krohn/ Nat Chrzanowskahttps://www.linkedin.com/in/nat-chrzanowska/ __________________________________________More about Appsilon:► https://www.appsilon.com/Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.Schedule a free consultation with our expert ► https://www.appsilon.com/contact-us_________________________________________For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:► http://appsilon.com/blog LinkedIn: https://www.linkedin.com/company/appsilon/

#datascience #genAI #clinicaltrials #clinicalreporting #AIinpharma #GenerativeAI #Roche #AIautomation #pharmaceuticalindustry #AIchallenges #AIbot #chatbots #copilot #codingassistantIn this episode, we speak with Vincent Shen, Senior Principal Data Scientist at Roche, about the actual impact of generative AI in clinical data analysis. Vincent shares insights from his work implementing AI-powered tools, such as knowledge chatbots and coding assistants, that support clinical reporting at Roche. We discuss the evolving role of AI, the challenges of integrating it into clinical trial workflows, and the importance of rethinking the processes to  ensure effective AI use.In this episode:AI will augment rather than replace data scientists in clinical trial workflows.Roche has successfully deployed a GenAI-powered knowledge bot, saving an estimated 25 hours per 100 questions.Coding assistants help data science teams accelerate time-to-insight.Providing detailed specifications is essential for AI to deliver accurate and meaningful results.Training AI models requires extra effort to ensure they correctly interpret context and domain-specific requirements.The next big step is to rethink how processes are designed, not just automate existing ones.Vincent Shenhttps://www.linkedin.com/in/vincent-shen-vs/ Nat Chrzanowskahttps://www.linkedin.com/in/nat-chrzanowska/ __________________________________________More about Appsilon:► https://www.appsilon.com/Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.Schedule a free consultation with our expert ► https://www.appsilon.com/contact-us We design scalable and user-friendly Shiny dashboards to help you make data-driven decisions.► https://www.appsilon.com/services/data-dashboards We design and implement Statistical Computing Environment for R and Python for efficient data analysis. ► https://www.appsilon.com/services/sceWe design, implement, and optimise data analysis environments so you can focus focus on insights and innovation. ► https://www.appsilon.com/services/platformWhere Technology Meets Science podcast is available on all podcasting platforms:► Spotify: https://hubs.li/Q037KddD0 ► Apple Podcasts: https://hubs.li/Q037KlLl0 ► YouTube: https://hubs.li/Q037Kpth0 __________________________________________For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog ► http://appsilon.com/blogLinkedIn: https://www.linkedin.com/company/appsilon/ 

#datascience #dataanalysis #technology #pharmabrief #clinicaltrials #technologyinpharma #pharma #pharmanewsPharma Brief’s fourth edition spotlights generative AI’s rapid expansion across pharma and regulatory landscapes. This issue covers the FDA’s planned rollout of generative AI tools across all centers, including the launch of Elsa to streamline reviews and inspections. We explore Anthropic’s Model Context Protocol as a new standard for context-aware AI, plus pharma’s growing use of autonomous agents in clinical operations and medical writing, with insights from BCG’s latest report.Benchling’s integration of Claude into biotech R&D workflows highlights significant time savings and smarter data management. Catch a recap of the AWS Life Sciences Symposium keynote and FDA’s public call for comments on the CDISC Dataset-JSON standard, which could reshape study data exchange.Open-source tools like {mergen}, {kuzco}, and {cheetahR} are featured to empower your R workflows with AI, computer vision, and blazing-fast data rendering.You can follow Pharma Brief on LinkedIn: https://www.linkedin.com/newsletters/pharma-brief-7300489155535380480/Now also available in audio on your favorite podcast platforms.Links from the episode:FDA’s Generative AI Rollout: https://www.fda.gov/news-events/press-announcements/fda-announces-completion-first-ai-assisted-scientific-review-pilot-and-aggressive-agency-wide-aiBCG Report on AI Agents in Pharma: https://www.linkedin.com/posts/bcg-platinion_ai-agents-and-the-model-context-protocol-activity-7330864988070309891-zhCZBenchling + Claude Integration: https://www.anthropic.com/customers/benchlingAWS Life Sciences Symposium 2025 & Keynote: https://aws.amazon.com/blogs/industries/7th-annual-aws-life-sciences-symposium/https://www.youtube.com/watch?v=3hohXZF_3ys FDA Request for Comments on CDISC Dataset-JSON: https://www.federalregister.gov/documents/2025/04/09/2025-06051/electronic-study-data-submission-data-standards-clinical-data-interchange-standards-consortiumOpen Source Highlights:{muttest} https://github.com/jakubsob/muttest{mergen} 

https://bioinformatics.mdc-berlin.de/mergen/gptstudio & gpttools https://github.com/MichelNivard/gptstudio {kuzco} 
https://github.com/frankiethull/kuzco{cheetahR} https://github.com/cynkra/cheetahRUpcoming Events:AI and SaMD in Healthcare Webinar | 18 June 2025 | VirtualPHUSE Single Day Event | 21 June 2025 | Hyderabad, IndiaCOSA Spotlight Q2 2025 | 24 June 2025 | VirtualValue-Driven Clinical Data Review Webinar | 26 June 2025 | VirtualPSI/PHUSE Change Management Event | 3 July 2025 | Cambridge, UKPSI Webinar on iRISE Consortium Results | 24 July 2025 | VirtualOpen Call for Papers: R/Pharma 2025 https://sessionize.com/rpharma-2025Subscribe on LinkedIn: https://www.linkedin.com/newsletters/pharma-brief-7300489155535380480/ Nat Chrzanowska https://www.linkedin.com/in/nat-chrzanowska/ __________________________________________More about Appsilon: ► https://www.appsilon.com/Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.Schedule a free consultation with our expert ► https://www.appsilon.com/contact-us __________________________________________For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog: ► http://appsilon.com/blog LinkedIn: https://www.linkedin.com/company/appsilon/ 

#datascience #dataanalysis #technology #machinelearning #clinicaltrials #placeboIn this episode, Jing Dai, Director of Biostatistics at Jazz Pharmaceuticals, shares insights from the PHUSE US Connect conference and her work on applying machine learning to neuroscience clinical trials. She discusses challenges like high placebo response and attrition, the value of interdisciplinary collaboration, and how AI/ML can shape trial design, improve regulatory readiness, and move the field toward more objective, data-driven outcomes.In this episode, you will learn:How machine learning can help address high placebo response and attrition in neuroscience clinical trials.Why traditional statistical models struggle with high-dimensional clinical data.Key regulatory frameworks (GxP, GMLP) for ensuring AI/ML models meet compliance standards in drug development.Practical tips for fostering interdisciplinary collaboration between biostatisticians, clinicians, and data scientists.Nat Chrzanowska https://www.linkedin.com/in/nat-chrzanowska/Jing Dai https://www.linkedin.com/in/jingdai1009/ __________________________________________More about Appsilon: ► https://www.appsilon.com/Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments. Schedule a free consultation with our expert► https://www.appsilon.com/contact-us __________________________________________For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog: ► http://appsilon.com/blog LinkedIn: https://www.linkedin.com/company/appsilon/ 

#datascience #dataanalysis #technology #datascience #opensource #pharmaverse #pharma #dataanalysis #clinicaltrialsIn this episode, Ben Straub, Principal Programmer at GSK, explores the shift from proprietary software to open source tools in the pharmaceutical industry. He shares insights into regulatory challenges, the rise of R, and the impact of {admiral} and other open-source packages on clinical data analysis and submissions. From pilot programs to enterprise-wide adoption, learn why collaboration is key to reducing risk and shaping the future of regulatory workflows.In this episode, you will learn:Lessons learned from GSK’s open source adoption journey,How collaboration via pharmaverse, PHUSE, and R Consortium accelerates regulatory submissionsHow {admiral} package for ADaM datasets was one of the triggers for widespread open source adoption in pharmaWhy R is a dominant language in pharmaWhat are the next milestones to modernize outdated regulatory standardsWebinar about GSK's R Journey: From Pilot Projects to Enterprise Adoption: https://www.youtube.com/watch?v=xDrt6txplekAdmiral package: https://pharmaverse.github.io/admiral/ __________________________________________More about Appsilon: ► https://www.appsilon.com/Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments. Schedule a free consultation with our expert► https://www.appsilon.com/contact-us __________________________________________For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:► http://appsilon.com/blog LinkedIn: https://www.linkedin.com/company/appsilon/

#datascience #dataanalysis #technology #pharmabrief #clinicaltrials #technologyinpharma #pharma #pharmanewsPharma Brief is back with its third edition, packed with essential industry insights and the latest developments in pharma and biotech. This issue covers the FDA’s plan to phase out animal testing for monoclonal antibodies in favor of AI models and NAMs, insights from Stanford’s AI Index 2025 and McKinsey’s analysis of AI in clinical development, plus new tools like CDISC Dataset Generator, scMultiSim, open-source releases from Novo Nordisk and Genentech and more! You can follow Pharma Brief on LinkedIn: https://www.linkedin.com/newsletters/pharma-brief-7300489155535380480/And now it’s available in audio on all your favorite podcasting platforms. Links from the episode:FDA’s New Plan for Phasing Out Animal Testing: https://www.fda.gov/news-events/press-announcements/fda-announces-plan-phase-out-animal-testing-requirement-monoclonal-antibodies-and-other-drugsStanford’s AI Index Report 2025: https://hai-production.s3.amazonaws.com/files/hai_ai_index_report_2025.pdf Summary & takeaways: https://hai.stanford.edu/ai-index/2025-ai-index-reportMcKinsey & Company’s article on clinical development with AI & ML: https://www.mckinsey.com/industries/life-sciences/our-insights/unlocking-peak-operational-performance-in-clinical-development-with-artificial-intelligenceCDISC Dataset Generator for Synthetic Data: https://cdiscdataset.com/scMultiSim for Omics Data Simulation: https://www.nature.com/articles/s41592-025-02651-0Revolutionising Participant Safety Monitoring with Advanced Solutions (Risk Based Quality Management PHUSE Working Group): https://phuse.s3.eu-central-1.amazonaws.com/Archive/2025/Webinar/Worldwide/Virtual/REC_CF12.mp4 Slides: https://phuse.s3.eu-central-1.amazonaws.com/Advance/Community+Forums+/Revolutionizing+Participant+Safety+Monitoring+with+Advanced+Solutions.pdf Clinical Data Analysis: Open Source in Pharma (Free eBook): https://hubs.li/Q03kf2Pk0FDA Pilots Session (ShinyGatherings): https://youtu.be/zZMGFq57wnEOpen-Source packages:Novo Nordisk’s {connector} Package: https://novonordisk-opensource.github.io/connector/Genentech’s BRAID Foundation Models: https://github.com/Genentech/BRAIDbslib v0.9.0 is on CRAN: https://rstudio.github.io/bslib/news/index.html?_gl=1*125lu4v*_ga*NzczNzkyMTAxLjE3Mzc1NTYwMzA.*_ga_8QJS108GF1*MTc0NTkzMzIzMy4xLjAuMTc0NTkzMzIzOC4wLjAuMA..*_ga_2C0WZ1JHG0*MTc0NTkzMzIzMy45LjAuMTc0NTkzMzIzOC4wLjAuMA..#bslib-090Chores package: https://simonpcouch.github.io/chores/ Upcoming Events:Domino RevX Life Science Edition | 20 May 2025 | Philadelphia (PA), United StatesPHUSE Computational Science Symposium 2025 | 20-21 May 2025 | Utrecht, NetherlandsShinyGatherings x Pharmaverse: Presenting aNCA: From Idea to Clinical Impact | 27 May 2025 | VirtualPharmaSUG US Conference | 1-4 June 2025 | San Diego (CA), United StatesVeeva Summit | 4-5 June 2025 | Madrid, SpainPSI 2025 | 8-11 June 2025 | London, United KingdomPHUSE Single Day Event | 11 June 2025 | Boston (MA), United StatesSubscribe on LinkedIn: https://www.linkedin.com/newsletters/pharma-brief-7300489155535380480/  Nat Chrzanowska https://www.linkedin.com/in/nat-chrzanowska/ __________________________________________ More about Appsilon: ► https://www.appsilon.com/Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments. Schedule a free consultation with our expert ► https://www.appsilon.com/contact-us  __________________________________________ For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog: ► http://appsilon.com/blog LinkedIn: https://www.linkedin.com/company/appsilon/

Ari Siggaard Knoph from Novo Nordisk shares how the company transitioned from SAS to R for FDA submissions. From early Shiny apps to full-scale open-source workflows, learn how this shift accelerated innovation, attracted top talent, and redefined clinical programming. In this episode, you will learn:How Novo Nordisk transitioned from legacy systems to R for regulatory submissionsHow Shiny applications became the catalyst for internal buy-inHow open-source adoption has broadened the company’s talent pool and improved productivityFuture directions in pharma tech__________________________________________More about Appsilon:► https://www.appsilon.com/Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.Schedule a free consultation with our expert► https://www.appsilon.com/contact-us __________________________________________or more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:► http://appsilon.com/blog LinkedIn: https://www.linkedin.com/company/appsilon/

#datascience #dataanalysis #technology #clinicaltrials #rshiny #shinyforpython #pharmatechShiny paved the way for R users to create interactive, production-ready applications without switching stacks. In this episode, Eric Nantz reflects on Shiny’s origins, its "lazy by design" reactivity model, and how the ecosystem matured. We dive into how Shiny for Python expands this power to new audiences, and how Shiny is becoming key to modern clinical trial workflows. Eric shares real-world examples, user reactions, and the future of interactive data science.In this episode, you will learn:How Shiny became a game changer for people working with RHow the framework evolved from a simple prototyping tool to a critical asset in life sciencesHow open source is surpassing proprietary softwareThe future of Shiny in drug developmentLinks for the episode:R-Podcast Episode 18 Interview with Joe Cheng https://r-podcast.org/018-interviews-with-the-rstudio-team/ Joe Cheng - The Past and Future of Shiny (rstudio::conf 2022 keynote) https://www.youtube.com/watch?v=HpqLXB_TnpI echarts4r - Interactive visualizations for R via Apache ECharts  https://echarts4r.john-coene.com reactable -Interactive data tables  https://glin.github.io/reactable/ htmlwidgets Gallery https://gallery.htmlwidgets.org/ renv - Project environments for R  https://rstudio.github.io/renv/articles/renv.html  rix - Reproducible data science environments for R with Nix https://docs.ropensci.org/rix/ R Weekly https://rweekly.org R Weekly Highlights podcast https://serve.podhome.fm/r-weekly-highlights Shiny Developer Series https://shinydevseries.com__________________________________________More about Appsilon: ► https://www.appsilon.com/Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.Schedule a free consultation with our expert ► https://www.appsilon.com/contact-us_________________________________________For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:► http://appsilon.com/blog LinkedIn: https://www.linkedin.com/company/appsilon/

Pharma Brief is back with the latest edition, packed with valuable insights and upcoming events you don’t want to miss. This month, we’re exploring AI’s impact on clinical development (trials could be 30% faster!), along with innovative tools that streamline processes. We’ve also highlighted open-source packages to optimize your workflows.You can follow Pharma Brief on LinkedIn: https://www.linkedin.com/newsletters/pharma-brief-7300489155535380480/ And now it’s available in audio on all your favorite podcasting platforms.Links from the episode:NVIDIA’s “State of AI in Healthcare and Life Sciences” Report: https://blogs.nvidia.com/blog/ai-healthcare-life-sciences-survey-2025/McKinsey & Company’s “Faster, Smarter Trials: Modernizing Biopharma’s R&D IT Applications”: https://www.mckinsey.com/industries/life-sciences/our-insights/faster-smarter-trials-modernizing-biopharmas-r-and-d-it-applicationsNovoScribe by Novo Nordisk: https://www.mongodb.com/solutions/customer-case-studies/novo-nordisk'Not a cost-cutting exercise': Novo Nordisk Chief Scientific Officer Marcus Schindler explains rationale behind pharma's R&D shake-up: https://www.fiercebiotech.com/biotech/not-cost-cutting-exercise-novo-nordisk-reshapes-rd-setupNovartis joins the Pharmaverse Council: https://pharmaverse.github.io/blog/posts/2025-03-14_welcoming__.../welcoming__novartis_to_the__pharmaverse__council!.htmlJohnson & Johnson's Open Source Journey with R in Clinical Trials | Webinar | Posit https://www.youtube.com/live/_bqeYh2kNgYWhy Clinical Trials Fail | Where Technology Meets Science Podcast | Appsilon https://youtu.be/UXfUUA1vo4IOpen-Source packages:mall: https://mlverse.github.io/mall/querychat: https://github.com/posit-dev/querychatmultideploy: https://r-pkg.thecoatlessprofessor.com/multideploy/chatlas: https://posit.co/blog/announcing-chatlas/nipals: https://github.com/johnsonandjohnson/open_nipalsEvents:Real World Data Spring Event 2025  |  9–10 April 2025  |  VirtualShinyConf 2025: Life Sciences/Pharma track  |  9-11 April, 2025  |  VirtualPHUSE Risk Based Quality Management Working Group Webinar “Revolutionising Participant Safety Monitoring with Advanced Solutions”  |  23 April, 2025  |   VirtualShiny Gathering: FDA Pilots: Key Insights, Lessons Learned, and What’s Next for 2025  |  29 April, 2025  |   VirtualPHUSE Single Day Event  |   2 May, 2025  |   Yerevan, ArmeniaPHUSE Single Day Event   |   9 May, 2025  |   Denver (CO), United StatesPHUSE Single Day Event   |    9 May, 2025  |   Beijing, ChinaOpen Call for Papers:R/Medicine 2025 https://rconsortium.github.io/RMedicine_website/Abstracts.htmlDeadline: Friday, 11 AprilPHUSE EU https://www.phuse-events.org/attend/frontend/reg/tOtherPage.csp?pageID=47086&ef_sel_menu=4278&eventID=74 Deadline: Friday, 25 AprilSubscribe on LinkedIn: https://www.linkedin.com/newsletters/pharma-brief-7300489155535380480/ Nat Chrzanowskahttps://www.linkedin.com/in/nat-chrzanowska/ _________________________________________More about Appsilon: ► https://www.appsilon.comAppsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.Schedule a free consultation with our expert► https://www.appsilon.com/contact-us __________________________________________For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:► http://appsilon.com/blog LinkedIn: https://www.linkedin.com/company/appsilon/ 

#datascience #dataanalysis #technology #clinicaltrials #opensource #rshiny #techtransformationThe opinions shared here do not represent the official position of Roche or Novartis.We explore why clinical trials lag behind in innovation—even as the research side of drug development makes massive leaps. We’ll uncover why attracting top talent is critical to driving the industry forward, particularly in adopting open-source technologies. Dive into tools like R, test automation frameworks, and data abstractions, and learn how they’re reshaping tech transformation in clinical trials. Joining us are Novartis experts Orla Doyle and James Black to unpack the challenges and opportunities ahead.In this episode, you will learn:Why R and open-source tools are dethroning outdated proprietary systemsHow modern tools attract new talent and retain seasoned expertsThe persistent inefficiency puzzle in clinical data analysisDecentralized trials: balancing accessibility with data integrityBuilding regulatory trust in open-source and agile workflowsAI’s future in trials, decentralized models, and global standardsOrla Doyle► https://www.linkedin.com/in/orla-doyle-53a5019a/James Black► https://www.linkedin.com/in/epijim/Nat Chrzanowskahttps://www.linkedin.com/in/nat-chrzanowska/ __________________________________________More about Appsilon:► https://www.appsilon.com/Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments. Schedule a free consultation with our expert► https://www.appsilon.com/contact-us __________________________________________For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:► http://appsilon.com/blog LinkedIn: https://www.linkedin.com/company/appsilon/ 

Pharma Brief delivers your monthly pulse on pharma’s evolving landscape. Stay ahead with curated insights on FDA/EMA updates, AI-driven trends, and must-attend global events. We unpack breakthroughs—from novel drug approvals to open-source tools like R/Shiny—and arm you with actionable resources to in tech transformations in clinical trials. Whether it’s GxP compliance strategies or clinico-genomic advancements, we’re here to sharpen your expertise.You can follow it on LinkedIn: https://www.linkedin.com/newsletters/pharma-brief-7300489155535380480/  and now it’s available in the audio on all your favorite podcasting platforms.Links from the episode:The FDA draft guidance on AI: https://www.fda.gov/news-events/press-announcements/fda-proposes-framework-advance-credibility-ai-models-used-drug-and-biological-product-submissionsAWS re:Invent Talk about Natera’s AI-Powered Data Extraction: https://www.youtube.com/watch?v=csbfwFbg8zE2024 CDER Novel Drug Approvals: https://www.fda.gov/drugs/novel-drug-approvals-fda/novel-drug-approvals-2024360i Kick off CDISC recording: https://www.cdisc.org/events/webinar/360i-program-kickoff-enabling-standards-driven-automation-study-design-throughThe 360i Program is a transformative initiative designed to automate the entire clinical research data lifecycle. You can watch the recording here and the slide deck here.Top clinical trials for 2025: https://www.nature.com/articles/s41591-024-03383-y/tables/1A new study on personalized cancer treatment: https://www.nature.com/articles/s41467-024-55251-5Generative Artificial Intelligence (AI) on AWS: https://aws.amazon.com/developer/generative-ai/?ref=dc&id=b2You can now learn and build AI applications with Amazon Bedrock, SageMaker, and more.Johnson & Johnson's Open Source Journey with R in Clinical Trials”: https://www.youtube.com/watch?v=IloY1jiDD_YAbout Pharmaverse: https://pharmaverse.org/Overview of CDISC standards and their role in supporting clinical research: https://www.cdisc.org/standardsRecordings from the recent GxP Validation Summit: https://www.youtube.com/playlist?list=PLexAKolMzPcr5rd9bBF5HGoHdCznE2LcTContribute to the aNCA package GitHub: https://github.com/pharmaverse/aNCAEvents:35th Annual European Pharma Congress: 10 - 11 March, 2025  |  Rome, ItalyPHUSE Single Day Event: 13 March, 2025  |  Frankfurt, GermanyPHUSE US Connect 2025: 16 - 19 March, 2025  |  Orlando, USPHUSE Single Day Event: 22 March, 2025  |  Pune, IndiaReal World Data Spring Event 2025: 9 - 10 April, 2025  |  VirtualPharma 2025: 9 - 11 April, 2025   |   Barcelona, SpainPHUSE Single Day Event: 17 April, 2025  |   Utrecht, NetherlandsNat Chrzanowskahttps://www.linkedin.com/in/nat-chrzanowska/ __________________________________________More about Appsilon: ► https://www.appsilon.com/Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments. Schedule a free consultation with our expert► https://www.appsilon.com/contact-us __________________________________________For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:► http://appsilon.com/blog LinkedIn: https://www.linkedin.com/company/appsilon/ 

#datascience #dataanalysis #GxP #PharmaTech #SoftwareValidation #FDARegulations #ClinicalTrials #LifeSciences #R #PharmaInnovation #TechInPharmaGxP compliance is shaping the future of pharmaceutical software engineering, yet many companies still struggle with implementation. In this episode, Appsilon’s co-founder, Paweł Przytuła, unpacks GxP best practices, software validation, and how R-based submissions are gaining traction with regulatory bodies like the FDA. He also shares industry insights from Roche, Novo Nordisk, and GSK, explores emerging trends like AI in validation, and discusses how pharma companies can leverage open-source tools while ensuring compliance. Don’t miss this deep dive into the intersection of software and life sciences!In this episode, you will learn:What is GxP in software engineering and why it’s critical for regulatory approval in life sciencesWhat GxP compliance means for pharma softwareHow leading pharma companies approach R-based submissionsWhat are the risks of ignoring GxP in software developmentHow AI, statistical computing environments (SCEs), and open-source innovations are transforming software validation in pharmaHow to build a strong GxP-ready data science team  Guest: Paweł Przytuła https://www.linkedin.com/in/pawelprzytulaYour host: Nat Chrzanowska https://www.linkedin.com/in/nat-chrzanowska/ __________________________________________Mentioned resources:Paper on R-based Submissions to the FDA: https://pharmasug.org/proceedings/2024/SS/PharmaSUG-2024-SS-344.pdfPresentation on Novo Nordisk's Journey towards open source: https://phuse.s3.eu-central-1.amazonaws.com/Archive/2023/Connect/EU/Birmingham/PRE_TT12.pdfGSK's R Journey to Open Source: https://www.youtube.com/watch?v=xDrt6txplek&ab_channel=PositPBCRoche's End-to-End R Journey to Submission: https://www.youtube.com/watch?v=BlJNILSoZlM&ab_channel=PositPBC __________________________________________More about Appsilon:► https://www.appsilon.com/Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments. Schedule a free consultation with our expert► https://www.appsilon.com/contact-us __________________________________________For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:► http://appsilon.com/blog LinkedIn: https://www.linkedin.com/company/appsilon/ 

In this episode of Technology Meets Science, we welcome David Meza, Head of Analytics at NASA’s Human Capital Office. David shares how data science and AI are transforming talent management at NASA, ensuring the right individuals are matched with critical missions. From knowledge graphs to generative AI, he explains how NASA is leveraging technology to optimize workforce planning, upskilling, and recruitment. Tune in for insights on AI-driven talent analytics, the challenges of data accessibility, and what it takes to land a career at NASA.In this episode, you will learn:How NASA uses data science, analytics, knowledge graphs, and AI to align employees' skills and competencies with space mission rolesHow NASA tackles cleaning and structuring workforce dataHow AI models help to assess employee skills, identify gaps, and recommend training programs to upskill the workforce.How open-source tech stack, including Python, R, SQL, Databricks, and knowledge graphs enable smarter decision-makingWhat it takes to work at NASA, from passion-driven careers to the importance of continuous learning__________________________________________More about Appsilon:► https://www.appsilon.com/Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.Schedule a free consultation with our expert► https://www.appsilon.com/contact-us __________________________________________For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:► http://appsilon.com/blogLinkedIn: https://www.linkedin.com/company/appsilon/ 

Together with Eric Genevois-Marlin, former Head of Data Sciences in R&D and Biostatistics at Sanofi, we uncover the reasons behind clinical trial failures. Drawing from his 20+ years of experience leading science and technology teams in bringing new drugs to market, we'll examine real-world examples and extract lessons from both successes and failures.In this episode, you will learn:What the risks are in clinical trialsThe main causes of clinical trial failuresExamples of success storiesLessons learned from clinical trials that go wrongHow to minimize the risks of failure__________________________________________More about Appsilon:► https://www.appsilon.com/Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.Schedule a free consultation with our expert► https://www.appsilon.com/contact-us __________________________________________For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:► http://appsilon.com/blog LinkedIn: https://www.linkedin.com/company/appsilon/ 

"Where Technology Meets Science" is a podcast series diving into the intersection of technology and life sciences. We bring together industry experts, innovators, and thought leaders to explore how advanced technologies are reshaping data science processes in biotechnology, biopharma, environmental sciences, genetics, and similar fields. Together with experts from companies like Roche, Novartis, NASA, and J&J, we uncover solutions that address complex challenges, accelerate research, and push beyond traditional scientific methods, offering listeners insights into the evolving landscape of scientific discovery.