Bioprocessing has traditionally been a slow, empirical discipline: engineers tweak a bioreactor’s temperature, pH or feed rate, wait days for a batch to finish, measure the outcome, and adjust again for the next run. That cycle is now being compressed and, in leading facilities, partly replaced by software. Digital twins — virtual, continuously updated replicas of a physical manufacturing process — are converging with artificial intelligence and Process Analytical Technology (PAT) to cut batch variability in biologics production, and the pace of that convergence is accelerating faster than most of India’s domestic manufacturing base has yet adapted to.
The technical leap is significant. A digital twin ingests real-time sensor data — temperature, dissolved oxygen, nutrient levels, cell density — and runs it through a computational model that can simulate how a bioprocess will behave under different parameters before those changes are made on the physical plant floor. Where earlier statistical models offered rough correlations, the most advanced cell models now used for prediction incorporate thousands of genes, reactions and metabolites, delivering a level of mechanistic precision that was simply unavailable to process engineers a decade ago. In practice, this lets manufacturers test dozens of parameter combinations virtually, identify the optimal process window, and implement it on the real line with far less trial-and-error — shrinking both development timelines and the risk of an out-of-specification batch.
For India’s biopharma manufacturing base — anchored heavily around Hyderabad’s Genome Valley and the broader CDMO ecosystem discussed elsewhere in this issue — the strategic question is not whether this technology matters, but whether domestic capacity can be built out fast enough to remain competitive on real-time release testing, the regulatory-grade capability that lets a batch be released for shipment based on in-process data rather than lengthy end-product lab testing. Real-time release is increasingly what global sponsors expect from a serious CDMO partner, and it is precisely the kind of infrastructure investment that requires both capital and specialised talent that Indian facilities are still assembling.
Global industry leadership on this question was on display this August at Cambridge Healthtech Institute’s Bioprocessing Summit in Boston, where senior quality executives from Gilead Sciences, GSK and Pfizer headlined sessions on bringing digital and AI tools into biopharmaceutical quality organisations without compromising regulatory rigour. Gilead’s Anthony Mire-Sluis, delivering the summit’s opening plenary, framed the challenge explicitly around leadership, governance and organisational change — a reminder that the barrier to AI-enabled manufacturing is rarely the algorithm itself, but the quality culture and regulatory trust required to let software make decisions that once required a human quality officer’s sign-off.
India is not standing entirely still on this front. Anurag Rathore, who coordinates the Department of Biotechnology’s Centre of Excellence for Biopharmaceutical Technology at IIT Delhi, has argued that bioprocess control is a far more comprehensive undertaking than automation alone — encompassing system architecture, software, hardware and interfaces, all of which must be optimised together while accounting for regulatory constraints and production costs. That framing matters because it cautions against treating digital-twin adoption as a simple software purchase; it is, instead, an integration challenge that touches process design, quality systems and workforce capability simultaneously. India’s BioE3 policy framework has also begun backing AI-omics hubs aimed at democratising advanced manufacturing capability across multiple sectors, a signal that policymakers recognise the stakes, even if implementation remains at an early stage relative to the scale of Chinese and American investment in the same technologies.
Closer to home, the momentum is visible in industry gatherings too. Hyderabad hosted a major pharma-technology summit this year — backed by automation majors including Honeywell and Rockwell Automation, alongside domestic technology firms — explicitly built around connected operations, manufacturing intelligence and enterprise transformation for the pharma sector. That such an event anchors itself in Hyderabad rather than Mumbai or Bengaluru reflects the city’s emergence as India’s genuine biopharma manufacturing hub, a status this magazine has tracked across CDMO investment, R&D centre expansion and now digital-manufacturing summits alike.
There is also a workforce dimension that receives less attention than the technology itself. Operating a digital twin effectively requires process engineers equally comfortable with bioreactor chemistry and with the machine-learning methods used to build and validate predictive models — a hybrid skill set India’s biotechnology pipeline has only recently begun producing at scale. PDA’s Global Pharmaceutical Quality Summit, held in Mumbai earlier this year, made this explicit: panels on enterprise-wide AI adoption brought together digital officers from Dr. Reddy’s, Alkem, Lupin and Mankind Pharma alongside Microsoft India’s manufacturing leadership, precisely because closing the skills gap is inseparable from closing the technology gap. Without engineers who can interrogate a model’s assumptions rather than simply trust its output, digital-twin investment risks becoming expensive infrastructure that sits underused.
The regulatory dimension compounds this. India’s drug regulator has historically evaluated manufacturing quality through inspection-based, batch-record frameworks rather than the continuous, streaming verification real-time release testing requires. Aligning domestic regulatory expectations with where the US FDA and European regulators are moving is not simply a matter of Indian companies adopting new software; it requires parallel evolution in how Indian regulators audit and approve AI-assisted quality systems — a multi-year undertaking that the Mumbai summit’s own panel on global regulatory perspectives, featuring officials from India, the UK and Brazil, suggests is only just beginning in earnest.
The risk for Indian manufacturers who delay this transition is not abstract. As global regulators increasingly expect real-time release testing and AI-assisted quality assurance as baseline expectations rather than differentiators, facilities that have not invested in digital-twin and PAT infrastructure may find themselves excluded from the highest-value manufacturing contracts even if their production costs remain competitive. Cost advantage alone no longer guarantees a seat at the table when sponsors choose which CDMO gets their next complex biologics programme; digital manufacturing maturity is becoming a qualifying criterion in its own right. For an industry that has spent two decades competing primarily on price, that is a genuinely new kind of pressure — one that domestic capital, talent pipelines and regulatory alignment will need to answer within the next few years, not the next decade.
– Dr. Jagan Mohan Somagoni
