<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Modica — Insights</title><description>R&amp;D notes and product deep-dives on adaptive digital twins for bioprocesses.</description><link>https://modica.bio/</link><language>en-us</language><item><title>The golden envelope: catching a drifting batch before it goes off-spec</title><link>https://modica.bio/articles/golden-envelope-batch-monitoring/</link><guid isPermaLink="true">https://modica.bio/articles/golden-envelope-batch-monitoring/</guid><description>A golden batch is a statistical envelope, not a single curve. Here&apos;s how multivariate batch monitoring — Hotelling&apos;s T² and DModX — lets a digital twin flag a drifting run early, and forecast where it&apos;s heading.</description><pubDate>Thu, 24 Sep 2026 00:00:00 GMT</pubDate><category>Product</category><category>digital twin</category><category>golden batch</category><category>MVDA</category><category>BSPC</category><category>prediction</category><category>bioprocess</category></item><item><title>An adaptive digital twin is only as good as its data</title><link>https://modica.bio/articles/adaptive-digital-twin-needs-live-data/</link><guid isPermaLink="true">https://modica.bio/articles/adaptive-digital-twin-needs-live-data/</guid><description>Simulation and prediction of a bioprocess depend on clean, standardised, contextualized, live signals. Here&apos;s why the data layer — not the model — is where most digital-twin efforts quietly stall.</description><pubDate>Thu, 17 Sep 2026 00:00:00 GMT</pubDate><category>R&amp;D</category><category>digital twin</category><category>OPC UA</category><category>MTP</category><category>data integration</category><category>NAMUR NOA</category><category>bioprocess</category></item><item><title>We lost the batch. The model was 94% accurate.</title><link>https://modica.bio/articles/pinn-bioprocess-sparse-data/</link><guid isPermaLink="true">https://modica.bio/articles/pinn-bioprocess-sparse-data/</guid><description>Why classical deep learning fails on 10–50 bioprocess runs, and how physics-informed neural networks use mass balance and kinetics to turn sparse data into physically consistent models.</description><pubDate>Mon, 27 Jul 2026 00:00:00 GMT</pubDate><category>R&amp;D</category><category>PINN</category><category>physics-informed machine learning</category><category>hybrid modelling</category><category>machine learning</category><category>bioprocessing</category><category>digital twin</category><category>CHO</category><category>ICH Q8</category></item><item><title>The FDA vs. the black box: can you reconstruct an AI decision from 18 months ago?</title><link>https://modica.bio/articles/fda-black-box-ai-decision-reconstruction/</link><guid isPermaLink="true">https://modica.bio/articles/fda-black-box-ai-decision-reconstruction/</guid><description>Audit trail and data lineage are two different instruments, and an ML-supported release decision needs both — plus the model version, input snapshot, probability score and explanation captured at decision time.</description><pubDate>Wed, 08 Jul 2026 00:00:00 GMT</pubDate><category>Industry</category><category>data integrity</category><category>audit trail</category><category>data lineage</category><category>machine learning</category><category>MLOps</category><category>21 CFR Part 11</category><category>Pharma 4.0</category><category>validation</category><category>pharma manufacturing</category></item><item><title>From pilot to production: a case study on digital twin implementation in CDMOs</title><link>https://modica.bio/articles/cdmo-digital-twin-pilot-to-production/</link><guid isPermaLink="true">https://modica.bio/articles/cdmo-digital-twin-pilot-to-production/</guid><description>Four phases, realistic timelines and real numbers: discovery, shadow-mode pilot, IQ/OQ/PQ deployment and scale-up — plus what happened at a CDMO with eight 2,000 L bioreactors.</description><pubDate>Mon, 11 May 2026 00:00:00 GMT</pubDate><category>Industry</category><category>digital twin</category><category>CDMO</category><category>GxP</category><category>GAMP 5</category><category>IQ/OQ/PQ</category><category>validation</category><category>change control</category><category>bioreactor</category><category>Pharma 4.0</category></item><item><title>Validating the future: a guide to adaptive digital twins in GMP environments</title><link>https://modica.bio/articles/validating-adaptive-digital-twins-gmp/</link><guid isPermaLink="true">https://modica.bio/articles/validating-adaptive-digital-twins-gmp/</guid><description>The regulatory barrier to adaptive AI in GMP rarely materialises when the documentation is in order: PCCP, ICH Q13, the EMA AI reflection paper, phased autonomy and model lifecycle management.</description><pubDate>Fri, 24 Apr 2026 00:00:00 GMT</pubDate><category>Industry</category><category>adaptive digital twin</category><category>GMP</category><category>validation</category><category>PCCP</category><category>ICH Q13</category><category>EMA</category><category>GAMP 5</category><category>model lifecycle management</category><category>ALCOA+</category></item></channel></rss>