Latest / BioSpace / The decisions behind scientific progress
Transcript
- speaker-0: My name is Jennifer Smith Parker, Director of Insights at Biospace, and you're listening to Denatured. In this episode, sponsored by Citiva, I speak with Nicola Pivet, VP and GM Manufacturing and Digital Solutions at Citiva. For years, firefarmer organizations have invested heavily in generating more data, increasing visibility, and improving process understanding. Yet many programs still encounter delays, surprises, and scale-up challenges. So what if the real challenge isn't a lack of information, but a lack of confidence in the decisions that guide scientific progress? Today we'll explore why organizations that accelerate science aren't necessarily the ones producing more data, but the ones building confidence in the decisions that shape development, manufacturing, and commercialization. Nicola, thank you so much for joining this episode of Denature. If you can introduce yourself, that would be great. speaker-1: Thank you, Jennifer. Happy to be here. I'm Nicola Pivet, I lead manufacturing and digital solutions for Saitiva. Essentially we provide ⁓ technology that augments the equipment to make it more intelligent so that our customers can benefit integrated solutions that drive connected workflow to improve operations in process development and manufacturing. speaker-0: Perfect. Great start. So first question here, let's go into this. Everyone talks about needing more data. Are organizations really suffering from a lack of information though, or is the bigger challenge something else? speaker-1: Yeah, you know, Jeffrey I would challenge the premise a little. In my experience, most teams are not short of ⁓ data. We often have batch data, analytics, QC analytics deviations, process parameters and dashboard. The harder question is whether that information is connected in a way that helps a team make a better decision at the right time. At Cityb, when we talk about individual solutions or intelligent equipment, the value is not just another data stream. The value is helping customers move from we can see something to we understand what it means and then we know what to do next. This is where confidence is created. speaker-0: Yeah, I think that's important to say that confidence because that also is a great segue into the kind of the next question. So many organizations view scale up challenges as execution problems. Why do you believe those challenges often originate much earlier in development? speaker-1: Yeah, I think many scale up issues are labeled as manufacturing execution problems because this is where they become visible. But the root cause often sits much earlier. It may be an assumption made in process development, a model that was not tested across enough variability, a sensor strategy that was not designed for scale, or even a workflow that works in the lab but becomes fragile in GMP. We do this in bioprocessing all the time. Decisions about automation, analytics, single use. design, process monitoring, they're made early and can either reduce friction later or create that friction. Manufacturing is often where the bill comes due, but not always where the problem started. speaker-0: So it's quite important to be looking at something quite early, the potential for effects quite early. Yeah, I hear this again and again when I'm talking to organizations. So then what separates organizations that move forward with confidence from those that find themselves revisiting decisions as programs advance? speaker-1: Yeah, I think the the difference is decision quality and decision ownership. the stronger organization are not perfect, but they are explicit about what they know and as importantly what they do not know. And what evidence is good enough for them to move forward. They also make decisions in a connected way across development, automation, manufacturing, QC and even commercial. Your own site of strategy work, whether on process analytical technology or predictive maintenance, the same pattern comes back. Progress is faster when teams align early on the use case, the value, the evidence required, and the owner of the next decision. Confidence is not about optimism, it is about a structured alignment around the evidence. speaker-0: You make such an interesting point about alignment, ⁓ because I think going forward with an organization to determine the processes and the programs, as you're saying, if you don't have that alignment early on, you're just gonna have more difficulty down the line. speaker-1: You'll create even more communication issues later on, yeah. speaker-0: Exactly. Exactly. So can you explain the observability gap? Why visibility is only the first step and how does true process understanding improve decision making? speaker-1: It's a first step but a fundamental step. For me, the observability gap is the difference between seeing the process and being able to act on it. Visibility is important, but it's only the first step. A customer can have more sensors, more dashboard and more alarms and still not know which decision to take. The real progression is you observe, you understand and then you decide. In bioprocessing that means moving from raw signals to process understanding. What is changing? Why is it changing? Does it matter what action will improve the outcome? This is why PAT, Process Analytical Technology, Automation Analytics and Workflow Integration, they need to be connected. Otherwise we we risk giving customers more information without giving them more confidence again. speaker-0: Right. So it's really about understanding that data and that information that comes in, as you said, necessarily having so much more data. If you can't understand it and you can't parse that out, it doesn't really mean anything. Yeah. speaker-1: Yeah. It it's under understanding your process and having the data to describe it, yeah. speaker-0: Exactly. Exactly. So how can organizations determine whether they are generating information that supports better decisions versus simply generating more information? speaker-1: Yeah, I mean there's a practical test to ask is what decision will this data improve? You're generating data, but does it have a purpose? If the answer is not clear, the data might still be interesting, but it may not be of value. So I like to frame it around three questions. Does it reduce uncertainty? ⁓ certainty has a lot of value in the pharma industry. Does it change an action? It's gotta be an actionable insight, not just an insight. And third is does it improve an outcome? You've gotta be RI driven in your investment. In sighting of our discussions on PAT sensors, for instance, the the winning proposition is rarely the better measurement, the best technical spec alone. It is improved process control, it's reduced viability, it's fewer manual intervention, it's faster development, and it's really more reliable scalar. That is how you separate information from decision enabling insight. This is really about the outcome. speaker-0: I see. So it's really important to have those at least three factors going into it, these three big questions you want to be asking going forward. So when decisions don't hold that scale, where do organizations most commonly encounter friction that disrupts progress? speaker-1: Absolutely. Well, friction, by definition, usually appears at the interfaces. It is the handoff from PD to manufacturing, that's the usual one. From automation to operations, from equipment design to lifecycle support, or just from a technical proof point to a commercial value prop. Generally assumptions get translated differently by each team. ⁓ a model may work in one context but not transfer as it is. So a workflow may depend on an expert interpretation rather than being embedded in the process. This is why connected workflow matter. They are not just a digital concept, they are a way to reduce rework, to remove ambiguity and to avoid late surprises as programmes move towards care. speaker-0: Okay. When it comes to capabilities here helping organizations create confidence in decisions across development, manufacturing commercializations. What types of capabilities are we talking about? speaker-1: I would group the capabilities in ⁓ in four areas. First it's visibility. We talked about observability before, the ability to see critical process and equipment signals in context. You start with visibility. Then second is process understanding, the ability to translate those signals into meaning, having a proper process modeling, having a proper process experience. Third is we've just talked about it, the connected workflow, the ability to bring Development, manufacturing, automation, quality and service together around the same evidence, sharing the same view, and force its decision support. The ability to make the next best action clearer and faster. At Sativa, this is why we have integrated solutions and integrated intelligent equipment and this is why it's so important. The opportunity is not to sell isolated components, but to help customers build more reliable paths from science to manufacturing. speaker-0: I'm hearing a red line going through of how the communication between these different aspects is so important and as you're saying, not working in silos. It's speaker-1: Right. The connected workflow is the the key glue between the the different organizations. speaker-0: Yes, absolutely, absolutely. So then looking ahead, what will distinguish the organizations that accelerate progress from those that struggle to keep pace? speaker-1: Yeah, the the aviation that will accelerate are the ones that learn faster and make decisions that hold up as they scale. I do not think the differential will simply be more more technology or more dashboard, not just the level of digital investment. It will be the ability to connect data, models, workflow, and teams in a way that improves the quality and speed of decision. And the team connection to it is key. So in bioprocessing that matters because the cost of late learning is very high. And if we can help customers learn earlier, reduce friction and build confidence before the scale, we create value well beyond the equipment or software itself. That is the future I think will matter most, not more information but more reliable progress. speaker-0: And that's great takeaway to say that more reliable progress because that's what everyone in the industry wants, right? Especially in such an uncertain environment. You really want to have that certainty going forward. So, Nicola, thank you so much for joining this episode of Genatured. It's been a pleasure definitely learn quite a bit about not how more information is necessarily better, but really understanding information and having that connectivity throughout an organization to interpret that is is quite important. speaker-1: Yeah. Yeah. Thank you, Jennifer. speaker-0: Thank you. So if you'd like to listen to more episodes of Denatured, please turn to biospace dot com. And if you'd like to send me a pitch, please send me an email at jennifer.smith hyphenparker at biospace dot com. Thank you so much.