Feliza Mirasol is the science editor for 

Feliza Mirasol

Biopharma quality control is being revolutionized by advanced analytics and digital transformation through a synergy that boosts quality and scalability.

Drug Digest: Closing the Gap in Modern Process Control and Automation for Quality

Outsourcing is driving biopharma efficiency and scale, including in the drug discovery area and early phase development. Forming strategic partnerships helps integrate specialized workflows, linking advanced therapy formulation and manufacturing to de-risk clinical progression. External contract research organization and contract development and manufacturing organization expertise are helping to accelerate complex modalities (e.g., cell and gene therapy, RNA, next-gen biologics, etc.) via optimized process development and integrated antibody discovery platforms. This episode of PharmTech Drug Digest will explore how this critical leveraging of partnership expertise speeds drug candidates from discovery to first-in-human clinical trials and beyond.

Dr. Delubac is a technical leader with a strong track record in building transformational laboratory products and techbio platforms across AI, software, robotics, and process engineering. He has built some of the most impactful healthcare products in non-invasive prenatal testing at Counsyl, liquid biopsy at Guardant Health, early cancer detection at Freenome, patient-derived models of cancer at Xilis, and the first AI-agentic synthetic chemistry platform at Chemify. Dr. Delubac trained as an applied physicist, then earned a PhD in Biomedical Engineering from Carnegie Mellon University. At iOrganBio, he is rethinking cell-based products from an engineering and data-driven perspective and building the next generation, AI-enabled infrastructure designed to manufacture the exact same cells we have in our body—that will model biology 

 accurately, restore degraded bodily function, cure disease, and power population-scale, affordable, holistic, regenerative medicine.

® editors, who interview industry experts to discuss the emerging opportunities, obstacles, and advances in the pharmaceutical and biopharmaceutical industry for the research, development, formulation, analysis, upstream and downstream processing, manufacturing, supply chain, and packaging of drug products.

 Formulation and Development for Small Molecules

Editor's note: This transcript is a direct, unedited rendering of the original audio/video content. It may contain errors, informal language, or omissions as spoken in the original recording.

MJH, pharmaceutical technology presents drug digest, a tech talk with the pharmaceutical technology editors all about emerging opportunities, obstacles and advances in the pharmaceutical, biopharmaceutical and biotech industries. Join us as we discuss with industry experts the research, development, formulation, analysis, upstream and downstream, processing, manufacturing, supply chain and packaging, as well as business strategies and regulatory issues.

Hello everyone, and welcome to the drug digest video series. I'm the Pharm tech group science editor feliza mirasol In this episode, I'll explore how that critical leveraging of partnership expertise speeds drug candidates from discovery to first in human clinical trials and beyond. But before we get into it, here's a few words from our sponsors. This episode of drug digest is sponsored by quotient sciences and piramal pharma solutions. Quotient sciences is a global CR DMO with integrated drug product development, manufacturing and clinical testing capabilities in the US and UK, leveraging its translational pharmaceutics platform to accelerate clinical programs and time to market while maintaining rigorous quality and control. Piramal pharma Solutions is a global cdmo and part of piramal pharma Ltd, providing end to end drug development and manufacturing services across the product life cycle through an integrated network of facilities in North America, Europe and Asia. Now we'll take a closer look at how strategic outsourcing is transforming the biopharmaceutical landscape by driving unprecedented efficiency across drug discovery and early phase development by forming strategic partnerships. Companies are integrating specialized workflows that link Advanced Therapy formulation directly to manufacturing effectively de risking the path to clinical progression. This collaborative approach leverages external expertise to accelerate complex modalities such as cell and gene therapies and RNA using optimized process development and integrated discovery platforms to discuss how these critical partnerships speed drug candidates from the lab to first in human clinical trials. I am joined today by Dr Daniel duluback, the CEO of I organ bio, a US based biotech firm, Daniel, why don't you go ahead and introduce yourself and give our audience some background into your expertise and industry experience.

Thank you. Felisa. I'm Daniel dillabeck, currently the Chief Executive Officer of I organ bio. My background has been heavily on the engineering side of a PhD in Biomedical Engineering from Carnegie Mellon. I've been a technologist at Council, garden, health and freedom on the diagnostic side. I've most recently built the technology platform at chemify before founding co founding I organ bio with professors Sri Bing Shan and shelling.

Shen, Now, Daniel, I'd like to kick off the conversation by asking you, How do strategic outsourcing partnerships improve biopharma efficiency and scale?

Yeah, I think this is a great question in the context of drug discovery and drug development, and later, the breadth of expertise, a the set of quasi independent challenges that have very little technical or operational overlap that needs to be tackled are extremely broad, Right? So as an example, being exceptional at sample, sourcing, model creation, drug design and running clinical trial at the same time is not a correlated outcome a they are independent function and independent capabilities that needs to be developed. So a well thought out strategic outsourcing strategy or approach is one that can enable access to the very best technologies, the most appropriate capabilities to advance a particular program, and so many of those programs also have different dependencies, right? So building a small molecule drug versus a biology versus a cell therapy actually depends on a different set of technology. So being able to strategically select within the market. Place the most appropriate technologies, which themselves are independently scalable, right? So running one clinical trial versus 10 clinical trial shares the same expertise, and so we can reach scalable subsystems, if you will, within the overall ecosystem to build a very streamlined and scalable infrastructure for the entire industry, and thus sponsors right drug discovery company can then select the right partners and concatenate them to create very efficient programs for drug discovery.

Now, in what ways do partnerships reduce risks during early clinical development.

I think one way to think about risk reduction in early drug development, and at least the way I think of it, is minimizing the likelihood that the drug fails in clinical trials and does not become a successful compound in the clinic, or modality in the in the clinic. And so any investment that moves the needle towards increasing the likelihood that the drug is successful ends up becoming a good a good investment. And so similarly to my answer a little bit earlier, having access to the most advanced, the most powerful technologies to select the right compound to advance through ind in human trials, is when it's to be thought of very carefully throughout those programs. And so strategic outsourcing is the natural way to do this, specifically in how the ecosystem is built. Right through strategic outsourcing, the access to proprietary IP is really what is being discussed right and so accessing the right technology through outsourcing to increase the likelihood of success is, I think, that the primary way to achieve this and how the ecosystem can help. Another way to think about risk in more economic terms is, of course, the cost of approaching inhuman trial, and part of the cost is, of course, weighted by time, right, how long it takes to get there. And so engineering relationship that can move fast, right, and concatenating all of the partners and as a sponsor, ensuring that all those partners work efficiently together to advance a program is, I think, something that we can tack on to risk reduction, cost reduction and more rapid advancement of compounds in the ecosystem.

And how does external expertise accelerate development for advanced and next gen therapies?

I think this is a nice follow up question in advanced modalities, right boundary modalities that are the cusp of what we've done before and where we still need technology development to establish optimality in our collective ecosystem level ability to advanced compound the dependency on the right technology increases right, and particularly in the context of startups, right, where most of the disruptive innovation is happening on the emerging technologies that are dedicated to improving our capabilities for advanced and next gen therapies becomes even more reliant. There is, of course, more risk for those right. This is not just technology risk, but, you know, efficacy, safety regulatory, etc, etc. So the combinatorial of all those components, we're very aware, for example, in ex vivo cell therapy, is that manufacturing itself is is a risk that needs to be overcome throughout the development process to propose to the market, to patients and a safe, effective and affordable therapy that will have strong impact on the marketplace. So I'll refer back to this question, but with a heightened emphasis, sorry to my answer to your previous question, but with a heightened emphasis of accessing through partnerships, the most appropriate, the most valuable technology, the one that have either demonstrated or a high likelihood of impacting a drug development process for the better and selecting those from the marketplace on a program per program basis is, of course, for advanced modality, a riskier value proposition, if you will, but one that I think is is most appropriate the likelihood that the. A single organization, no matter how big can crack independently, in particular, in high innovation areas, everything that needs to be done for successfully delivering a drug to the market is lower and less efficient, as a matter of fact, in terms of overall capital allocation than relying on independent companies that are solving each one of those problems independently of one another. And that priory is not obvious which company is going to be or which technology, which the company is built on, is going to be the best position to solve a particular problem. So from a sponsor perspective, being able to assess all those technology, test those technology through pilots, before putting in on the critical programs and what have you, creates an overall system level efficiency that large companies and drug development company can leverage when it best suits their own internal capabilities as a complement to what they can do without without partners.

So now let's go back for a minute about your comment on sponsors looking for partnerships that meet their technology needs. How do these sponsors or drug developers or what have you evaluate and decide or select on which company best fits their needs? How does that process work?

Yeah, great question. So I think this is a problem driven and priority driven process, a as a sponsor, as a drug developer, as a big pharma, as a program manager. One knows the end to end process, and typically, one has experiences from previous programs as to what went well and what did not, and what a priori right from a first principle scientific perspective, are the key risks of a particular program. And so going back to your question about risk mitigation and de risking, it is thinking ahead as to what problems, what challenges, what risk will be part of this program, and working with the market to assess the technologies that could have an impact on that risk and then narrowing to the one that will most likely decrease that risk and help the sponsor overcome those challenges. And so it becomes what typically the external innovation arm of pharma company focus on the technology that allow them to overcome some very well understood and characterized challenge that those organization face,

and why are integrated workflows between partners critical for drug discovery success

in this potentially envision setup right and one that myself and some people that I know across the industry think is optimal, the concatenation of experts in very specific steps within the drug discovery process is The best way to a cross drug discovery, then the interface of companies working on the same program becomes critical, right, and the upstream and downstream integration of both physical components, models, drugs, biologics, what have you right that needs to go from partner to partner, but just as importantly, of data right, and the compilation of everything that has been learned along the way into one FDA submission, for example, to use a canonical milestone in The process, but generally speaking, making sure that there's no loss of know how no loss of information throughout the entire process becomes critical, right? And thinking of this as integration is, I think, the right question to ask, right or the right problem statement to optimize the relationship between partners. And I think this is really where the sponsors role becomes prominent as they select the right partners to work together. So one can envision a centralized model where everything flows through the sponsor run gets allocated to different partner, which is a very effective model, the more distributed version to this. Sorry, off this approach is to get the partners to work with one another directly. I don't think this is prominent in the space and specifically for IP reasons. It may not realize. Eyes, but for overall system efficiency, for ecosystem fluidity of both material and data, this is a model that might be worth exploring, for for for sponsors

and of course, how do strategic partnerships help Biotherapeutics reach first in human trials faster.

Thanks for this question, and thanks for this last statement as a corollary to your previous question, right? The drug discovery process is well established as a series of milestones that needs to be reached towards drug selection, to ind, to, you know, phase 123, trials, etc, etc, right? So we have a good understanding of all the milestone that the drug needs to go through. And so do strategic partnership allow us to move faster? I would say, if one considers the cost and time to develop the technologies that enhance the selection process, design and selection process of the right drugs and running the trials, and running those trial faster, I think so, right? We were making the interface between all the players a little less smooth, right? It takes time to, for example, put in place a contract to exchange information, etc, etc. So one could make the arguments that running a program fully internally would go faster. But of course, that comes at the expense of accessing the right technology. With that said, those technologies within each one of the buckets that need to come together for a successful program. Each one of those could move faster. So I think there's a bit of trade off here, and the impact of programs on, sorry, of outsourcing, on the speed of a particular program, and so would have to do maybe a little bit of one off assessments to answer this, but I think a well thought out and a well oiled machine to run those trials could get the best of both worlds right, making sure all the contracting is in place, making sure that the various strategic partners are well embedded within a pre established process, we could approximate the overall speed of an internally run program. I think there's through strategic partnership, also the opportunity for arbitrage to actually go much faster. And I think nothing here would be more apparent than the example of running clinical trials internationally for American based company were relying on strategic partners for particular blocks of the development, again, specifically clinical trials, their opportunity to shave off years in the overall process, if one continued to only leverage the historical way of approaching drug discovery.

Now, Daniel, what are your thoughts on how AI and digital technologies are accelerating drug discovery?

I think there are two elements that I'd like to bring to the table as my answer one is the ultimate realization of AI in drug discovery. Don't know how long it's going to take, how many years or decades, but by the time our AI predictive system are as accurate as they will inevitably become throughout the process, we can envision quasi immediate drug discovery processes. Right we measure the right biomarkers for a patient, we leverage our understanding of biology again through AI and drug design again through AI. Those were both expertises. They're too advanced, too complex for the human mind together, independently, but not for AI, right? So by the time we have reached that level of optimality, slash, quasi perfection, in the predictive capabilities of AI for human health and perturbation of human health through external compounds or biologics or cell therapies, we technically will be able to go from diagnosis to treatment on a personalized basis, quasi immediately, by designing the drug fully computationally, knowing that it's going to be safe and effective computationally and deliver this to the patient. So the promise is there. It's inevitable that is going to happen, but it's going to be hard, and it's going to take the whole ecosystem working together to move the entire field in that direction. Now, the process together is going to be long. Where are we today? But we're. Seeing last year and this year a major inflection point in the deployment of AI technologies throughout the drug discovery process, from more efficient clinical trials to primarily the drug design aspect and part of the drug discovery process, but we see the introduction of AI in almost every single node that makes the entire process of drug discovery, from designing and manufacturing cell therapies to designing and manufacturing non animal models for drug discovery, to computational assessments of drug safety, to computational assessment of drug efficacy, etc, etc. And so it's having, today already an enormous impact, and it is likely that we have passed the threshold of AI being AI, sorry, value, additive, value, accurate throughout the entire process for every component. So we don't have generalizable AI that can solve the entirety of the drug discovery process, but we are seeing a lot of localized AI that are having very meaningful, very impactful, very measurable improvements on each one of the steps that make the end to end drug discovery process and development,

I'm afraid that's all the time we have. Thank you, Daniel for your deep insights into the outsourcing and partnership landscape in today's modern and complex biopharma environment. Thank you to our audience for watching And a huge thank you to our sponsors, quotient sciences and piramal pharma solutions for supporting today's episode. You can catch all the Pharm tech drug digest videos@www.pharmtech.com Until next time, have a great day.

Did you enjoy this episode? If you did, please share this video with colleagues who you think would enjoy the content. Thank you to our editors and experts for sharing their insight. Thank you as well to our sponsors for sponsoring this episode. If you want to stay in touch with pharmaceutical technology team, subscribe to this podcast as well as to our E newsletters. When you sign up for our newsletters, you will be updated about future episodes of drug digest. Receive our magazines and more. Thanks to everyone for joining us for this episode of drug digest. We will see you next time you.

Videos

Strategic outsourcing utilizes external expertise to accelerate the biopharma pipeline, integrating specialized workflows that drive efficiency from discovery through early clinical trials.

Drug Digest:  Outsourcing Partnerships Fuel Global Biopharma Discovery and Scale-Up

China’s National Medical Products Administration has approved the 

 (mAb) mepolizumab (brand name Nucala) as an add-on maintenance therapy for adults with inadequately controlled chronic obstructive pulmonary disease (COPD) characterized by elevated blood eosinophils, GSK announced on Jan. 5, 2026 (1). The approval marks a regulatory milestone for 

 in respiratory disease management in the country and reflects growing 

 of targeted immunologic approaches for a condition historically managed with inhaled therapies alone (1,2).

The approval was supported by data from two Phase III trials (MATINEE and METREX), which evaluated the mAb in patients with COPD who continue to experience exacerbations despite optimal inhaled triple therapy. According to GSK, these studies demonstrated statistically significant reductions in moderate and severe exacerbations compared with placebo, with a safety profile comparable between treatment groups.

“Given the high incidence of COPD in China and a mortality rate that is above the global average, there is a clear need for novel options to address COPD,” said Kaivan Khavandi, senior vice-president and global head, Respiratory, Immunology & Inflammation R&D, GSK, in a company press release (1). “The approval of Nucala offers patients in China a monthly add-on maintenance treatment to reduce exacerbations, including those leading to emergency department visits and/or hospitalizations, which account for a large proportion of annual direct medical costs.”

What does China’s approval mean for biologics in COPD treatment?

With this approval, mepolizumab becomes the first monthly biologic approved in China for COPD and the first evaluated across a broad patient population with blood eosinophil counts starting at 150 cells/µL, GSK stated in its release (1). The company noted that this threshold is clinically relevant because a substantial proportion of patients with persistent exacerbations fall into this category, despite receiving 

COPD affects an estimated 100 million people in China, where the disease accounts for more than 30% of global mortality attributed to the disease (3). Recurrent exacerbations are associated with accelerated lung function decline, increased hospitalization and readmission rates, and higher healthcare costs, creating both clinical and economic pressures on health systems (4,5).

How did the Phase III trials shape regulatory confidence?

In both pivotal trials, mepolizumab met primary endpoints by reducing the annualized rate of moderate or severe exacerbations when added to inhaled triple therapy. In the MATINEE study, the rate ratio was 0.79, while the METREX study demonstrated a rate ratio of 0.82, indicating consistent benefit across study populations with an eosinophilic phenotype (1).

Importantly for regulators and payers, the MATINEE study also showed reductions in exacerbations requiring hospitalization or emergency department visits, outcomes that drive a disproportionate share of COPD–related costs. The incidence of adverse events was similar between treatment and placebo groups, reinforcing the therapy’s safety profile in long-term use (1).

 heterogeneity, enrolling patients with chronic bronchitis, emphysema, or overlapping presentations, according to GSK. This breadth may have contributed to confidence in generalizability, particularly in a healthcare environment managing large, diverse patient populations (1).

Why does eosinophil stratification matter to the industry?

This approval underscores the increasing role of biomarker-driven stratification in respiratory drug development. Approximately 67% of patients with COPD who remain uncontrolled on triple inhaled therapy have blood eosinophil counts above 150 cells/µL, identifying a sizable subgroup that may benefit from targeted biologic intervention, according to GSK (1).

In addition, this decision highlights a shift toward precision medicine in chronic respiratory diseases and may influence development strategies, trial design, and regulatory engagement for future biologics. It also signals a growing willingness by regulators to expand biologic use beyond traditional indications such as asthma, potentially reshaping treatment paradigms for COPD globally (1,2).

Nucala (mepolizumab) Approved in China for Use in Adults with Chronic Obstructive Pulmonary Disease (COPD)

The Management of Type 2 Inflammatory Respiratory Diseases: A Chinese Expert Consensus [2024]

Wang, C.; Xu, J.; Yang, L.; et al. Prevalence and Risk Factors of Chronic Obstructive Pulmonary Disease in China (the China Pulmonary Health [CPH] Study): A National Cross-Sectional Study. 

Yin, P.; Wang, H.; Vos, T.; et al. A Subnational Analysis of Mortality and Prevalence of COPD in China From 1990 to 2013: Findings from the Global Burden of Disease Study 2013. 

Advancing COPD Care in China Through a Comprehensive Approach

Articles

China's approval of mepolizumab as an add-on therapy for COPD marks a significant advancement in respiratory disease management, emphasizing the shift towards targeted biologics. Supported by Phase III trials, this approval highlights the efficacy of mepolizumab in reducing exacerbations in patients with elevated eosinophil levels. The decision reflects a growing acceptance of precision medicine in chronic respiratory diseases, potentially influencing future biologic development and regulatory strategies. This milestone underscores the importance of biomarker-driven stratification in addressing unmet needs in COPD treatment.

China clears GSK’s Nucala (mepolizumab) for COPD, highlighting growing acceptance in the country for biomarker-driven therapies.


China Clears Mepolizumab for COPD

Digital transformation and advanced analytics are revolutionizing biopharmaceutical quality control (QC). By synergistically combining formulation and analytical methods, the industry enhances quality, scalability, and innovation. This crucial shift is driving R&D investment, making advanced biotech equipment essential for scalable drug production. This episode of Drug Digest will explore this modernization of QC and examine the role that advanced analytics and digital transformation play in this unfolding revolution.

, Fellow, Regulatory Compliance Associates

At Regulatory Compliance Associates (RCA), Schniepp is responsible for training and advising RCA staff and clients on current trends and issues facing the pharmaceutical, biotechnology, and medical device industries. Her background includes current good manufacturing practices, audit readiness, change control, investigations, supplier quality, and batch record review. She has more than 40 years’ experience in quality assurance, serving in leadership roles at Allergy Laboratories, Oso BioPharmaceuticals, Searle, Abbott, and Hospira. She has been on the Parenteral Drug Association (PDA) Board of Directors from 2011–2013 and from 2016–2019 and currently serves as the board’s chair. Schniepp has served on numerous planning committees, including the PDA/FDA Joint Regulatory Conference Planning Committee since 2002.

, PhD, Vice President, Technical, Parexel International

At Parexel, Dr. Schmitt provides consulting services to the healthcare industry on all aspects of regulatory compliance, particularly the design and implementation of quality management systems. The services cover the entire product lifecycle, from the clinical to the commercial phase, for all aspects of the good practices. His areas of expertise include all aspects of quality and compliance for systems, processes, facilities and operations for drug substances and drug products. Specifically, he consults on compliant quality systems that integrate industry best practices with future-looking compliance approaches. His career spans over 35 years in industry. He is an accomplished editor, author of nearly 200 publications, trainer and presenter.

Hello everyone, and welcome to the joke digest video series. I'm Felice Mirza, science editor for pharmaceutical technology, pharmaceutical technology Europe and BioPharm International. In this episode, industry experts will discuss how digital transformation and advanced analytics are revolutionizing biopharmaceutical quality control and the challenges that the industry faces undergoing this transformation, joining me today will be Susan schneep from regulatory compliance Associates, and Dr Siegfried Schmidt from pyrexel International. But first a few words from our sponsors. This episode of drug digest is sponsored by LGM pharma, Nelson labs, the parenteral Drug Association, Thermo Fisher Scientific and beltek Associates. LGM Pharma is a US based cdmo, providing integrated drug substance sourcing and drug product development and manufacturing from early stage programs through commercial production. Nelson labs, a sotera health company, is an industry leading global provider of laboratory testing and expert advisory services that performs more than 900 rigorous microbiological and analytical laboratory tests across the medical device, pharmaceutical, protective barriers and tissue industries, the parenteral Drug Association is the leading global provider of science, technology and regulatory information, and since its founding in 1946 as a nonprofit organization, has been creating awareness and understanding of important issues facing the pharma community and delivering high quality, relevant education to the industry, as well as scientifically sound, practical technical information and expertise to advance pharma, manufacturing, science and regulation. Thermo Fisher Scientific is the world leader in serving science, enabling its customers to make the world healthier, cleaner and safer. Bell tech Associates is a single source supplier providing solutions for pharmaceutical or biotechnology contamination control requirements. And now, without further ado, let's get to our speakers.

Hi everyone. My name is Sue Schneep. I'm a Distinguished Fellow with regulatory compliance Associates, a division of Nelson laboratories. I've been in the industry about 40 years, so I guess I'm old and crabby, but I've learned a lot, and we're here with Siegfried Schmidt to impart some of our wisdom and knowledge onto the audience here with that, I'm going to turn it over to Siegfried to introduce himself.

Thank you. So my name is Siegfried Schmidt, and I'm Vice President technical at PAREXEL, and I've got over 35 years in industry and over 18 years with powerexcel, and so my expertise is really from clinical to commercial, all matters of compliance any regulatory system in the world. So yes, and it's it's always fun and a pleasure to share some of our expertise with the rest. Thank you.

Now for my first question, I'd like to throw this to Siegfried first, and then Susan, so Siegfried, how are advanced analytical techniques changing the definition of analytical best practices in biopharmaceutical quality control today?

Well, you see these techniques on the one side, they represent the state of the art, and on the other side, they help expedite analysis. And why do I mention state of the art? Because state of the art is actually a regulatory requirement. This doesn't necessarily mean that all the methods must be cutting edge, but you need to use the methods that are established by industry. So we are no longer talking about thin layer chromatography. We're talking. Talking about really modern analytical methods, HPLC, UHPLC, hyphenated techniques and so on. So from the perspective of the analytical laboratory, you want fast and accurate technologies because they help an increase of throughput of the analysis. It supports the fast creation of certificates of analysis, because these you want to have as early as possible, because you want to release your product or the product for the next step and so on. And being faster also means you reduce cost and you increase revenue, very important, particularly in R and D and so these technologies, they typically run on highly automated equipment, which, again, is an important factor, because Electronic Data Management helps, or is actually essential for data integrity. I am sure sue you have additional information you want to share on this?

Well, yeah, Siegfried, I think you bring up some excellent points what this new analytical technology is doing, which is, to me, very important, is driving to real time release in a lot of cases, because you've got in line monitoring procedures and processes you've got, you know, the rapid microbiology results that come along with this, and I think driving to real time release. And, you know, like, as you're making it, you're releasing it on the back end, you're manufacturing, you're putting it together on the front end, and you're shipping it out on the back end, all within, you know, 24 hours. I think it's important, because as we get into the biologics and the atmps, I think we're dealing with very sensitive products that may or may not have, you know, a stability, a longevity of stability, you may have to get it out the door relatively quickly in order for it to be viable with the patient, right? I mean, that's kind of where I think we're driving. And I think these are going to get more and more sophisticated, and some of our traditional thinking around compliance to the Compendia is going to need to go away, because the Compendia is not going to be able to keep up, at least under their current operating processes, right with the rapid changes going on with these techniques.

So you're, you're very right there to go and combine not just the thinking about the analytical method, but about the processes, because you need to look at it holistically, at both what is happening in operations and what is happening in the laboratory. You can't think of things separately. They are integral to each other.

For this next question, let's start with yusu. What specific impact has the use of innovative tools had on improving quality control?

I think the impact has been we've been able to get results faster and evaluate the state of the product faster. This becomes important. If you have like a defect or investigation deviation during manufacturing, you can get a result or resolution to that quicker, hopefully, which will allow you to longer you keep that subjective batch running through your process. Hopefully, you know, you don't get to the end, because that's a waste of money. If you have to abort the batch, you want to do it sooner in the process, rather than later. So I think it allows us to kind of close deviations, maybe investigations a little bit quicker. But I think there's a downside to it too, in terms of we know more about the product. In terms of, like, if we did impurity profiling, we probably are picking up with these pieces of equipment, a lot more impurities than we ever picked up with the traditional HPLC. And so now we're chasing, I don't want to say chasing our tails, but chasing our tails, trying to figure out whether the impurities are harmful, you know, and and. At some point we almost know too much about the product that almost negates it from being safe, right? It's kind of that catch 22

that's very interesting what you say, because the more precise, the more detail an analytical method can detect, the more you can find, but we clearly also have limits defined what is acceptable, what is not acceptable, and just because we can dig deeper doesn't mean do we always have to, but perhaps another point you already brought up is the speed of things. And I think these tools not only help improve the analytical methods to be faster and better, but it's also the processes that they support. They become faster, more reliable, more efficient. And what I recently saw was absolutely astonishing. Some of the tools that big pharma are developing and using, they use, for example, ultrasound, to move samples of meniscus volume in the laboratory so there is no more manual handling. It's ultrasound driven. Sample goes from a to b, phenomenal. And of course, when we talk about tools, we also have to think about, for example, the media that may be needed in the microbiology laboratory, and there are modern methods that help you understand the variability of the various media in the short time. So what we're talking about is something that helps us, not just with the actual analytical method as such, but with all the media that we use around them, the methodologies, the processes, and maybe I'm just repeating what I said before, but we need to look at it holistically. We can't just look at one element only, and I think this is where it's also important for the industry. If you want to improve, say, your processes in the operations part, then you also need to look at your laboratories. Can the laboratory support this? Can they not or the other way around? You may have a super laboratory, but a lousy process operations, and what is the value of a fast laboratory? If your process is still slow,

I totally agree with you. Sigma freed what and you what you said raised an issue with me on these new pieces of equipment and moving it with ultrasound. And there's also an element of, we're saving resources, right? We're not using as much. So there's an environmental friendly and sustainability quality about many of these new analytical tools that are coming down the line where we're not using, you know, benzene to or acetone, we're using a lot less resources, a lot less waste, which is, you know, more sustainable.

How would you say companies are leveraging the synergy between formulation development and analytics to enhance product quality, scalability and innovation throughout the entire drug development process. And I'll toss it to whichever one of you wants to start first,

what I've seen, and I find it quite interesting, is that formulation development rather sticks to standard formulations using standard excipients and formulation processes, I see limited innovation. Perhaps this is mostly with intention to save time and provide an acceptable formulation quickly for clinical trials and commercialization. Now, of course, if you have modern analytical methods and tools, you may get faster, more accurate analytical result. So So overall, I think the key driver is speed is not necessarily innovation,

and what core R D investments in advanced equipment are most essential for achieving scalable drug production in the next

decade? I don't know that R and D investment. I think it's going to be in the manufacturing line investments, not necessarily R and D. I think it's the manufacture. Lines that are R and D usually tends to have a lot of creative and clever and cutting edge equipment kind of built into their budgets, and so they're fiddling with, you know, things like the rapid micro. It's what I worry about, is the manufacturing facilities that are a little bit lagging. We tend to not update them on a traditional schedule. You know, we have equipment that's been around for 30 years. It's kind of like one of those cars that you get a little plaque if you get over, you know, 500,000 miles, and you're lucky, it's still running. I think the game to winning this game is going to need to manifest itself into key investments and purchases of new equipment in the manufacturing arena. Now, all is not lost, because there are several companies that are starting to build, you know, Greenfield manufacturing facilities around the world that would accommodate these new biologics that are coming out. But at some point, some of the older biologics are going to be transitioning to contract manufacturing arenas, and they're the ones that are really lagging in terms of updated equipment and facility, at least last time I checked, I'm going to throw it to Siegfried and see if he can correct my misinterpretations on this.

No, no, no. So yeah, you're right there. You're right. It's not the R and D. R and D can use design of experiments and all sorts, and they have a lot of nice tools, is here really is about commercial manufacture and see what we what you really need to get better and more consistent and more reliable manufactures that we need in line, online, nearline analytics, with feedback loops for continuous, if possible, real time analysis, because otherwise we will be stuck, as you say, with equipment that's 30 years old. Now the vessel itself is not the issue. It is the instrumentation. It's the automation. And when I say automation, I also think it's the integration of automated systems. If you don't have the integration, if you don't have the transmission of the data from one system to another, then you may wait, and it's not unusual days weeks for an analytical result. And what happens in the meantime with production? Nothing, it stops. And just to give you an example, and this was an R and D plant, unfortunately. So this plant that I visited, it takes 15 minutes for a person to manually transport a sample from the line to the laboratory. Yeah. I mean, this is not sustainable.

I totally agree, Siegfried, and one thing you prompted in my mind is this continuous loop as we're getting information faster and faster, but we're also getting interpretation. We're adding AI to the whole process, and virtual learning and digital twins and all that stuff that you know we're talking about in the AI. The AI world, and so I wonder how fast we, the human can learn, and we've got the machine learning quicker than we can right? Just some thoughts on that from you.

Well, so AI, everyone mentions AI, and I totally believe that AI will play a role in in the facilities of the future, say the laboratories, and where I think AI really works well is how to manage things, yeah, because these tools are really good at identifying optimal processes. Say, in the laboratory, you have a series of systems available, you have a number of samples waiting, you have a number of analysts and trying to figure out what is the optimal way which sample should go on, which equipment be analyzed by which analyst, etc, etc. We can do it, but slow AI systems are very fast at that, so that's great. And these tools may, may be useful for QA too, not for say, release decisions. But to support the review activities, and again, I have to say it's a connectivity that is absolutely essential to make these systems work. Because again, I see so many companies where analytical results are manually typed into a limbs to create a certificate of analysis. I mean, it's beyond belief. I have to say this is just antiquated, archaic. This is not the way to work.

Now, in both of your experience, what are the primary challenges contributing to the gap between hype and implementation of digital transformation, particularly concerning process control, automation and process analytical technology?

That's a very interesting question, and I'm really going to be fascinated to hear siegfried's take on this. But from my perspective, we hold ourselves back in a lot of ways, because we're we're afraid of the regulatory implications or filings or compliance that we need to achieve when we're adding new and innovative technologies, even AI into the even putting in a new line in our manufacturing facility, people tend to shy away because they're afraid of the regulatory implications or doing something, and then having the agencies Say, oh no, no, you didn't do it right? I don't think that should drive our thought processes here. I think what we need to and this is going to sound real old school and old fashioned, but in my way of thinking, what we need to do is revive our skills when it comes to risk based decision making. I think we have lost that talent in quality and regulatory areas of professionals, and we've driven ourselves over the last 20 years to what I would call a checkbox mentality. And we need to go back to those appropriate, risk based decisions and understand that if I can put in a new manufacturing line, it's going to be more beneficial and good for the patient as well as the product, as well as the company. And how do you do that? You need to look at the regulations and figure out which ones apply and how so let's just take an example. I if you're as as I alluded to before, if you're a contract manufacturing organization, you have multiple clients. You're producing multiple products. The traditional pharma thinking has been that in order to put in a new line, you need to build up two years of inventory of all the product that you're making, then you need to decommission the line, then you need to put in the new line, but you got to get permission, a prior approval supplement from in this case, it would be the FDA to put in that new line that may take you a year. So you may be down on a line for two years while you figure out this interesting dilemma of putting in a new line to improve productivity, reduce scrap and improve patient safety. Now, if you read the regulations in the US, you find out that you can do it relatively easy in four months. If you interpret the regulation. So for instance, if I have an old traditional line with what we called shower curtain barriers, and I want to put in a RABs restricted access barrier system, I can do that as an annual reportable, because there is in that particular guideline something that says, you know, any increase between the product and the human, any increase of intervention, which certainly a restricted access barrier, is a lot better than shower curtain, right? That can be an annual reportable so go ahead and make that change as a positive. So I think in some of all of this improvement, the quagmire is one we've made ourselves, and we need to develop a little bit more risk based decision making. Siegfried, I'd like your take on that that was a lot of ways saying we were stuck in the mud.

We are. We are so and yes, if I hear someone saying, Oh, I don't know, well exactly, why don't you do a risk assessment? Where are the facts? Where the data don't just use good feeling, give us the facts. If it if you say. The regulations don't allow it. Where does it say? Have you spoken to the agency? But I think when we look at these concepts like pa t, they have been around for two decades, and I know facilities where they have been implemented for two decades, and they work, and it's it actually is nothing new. But then we have to ask ourselves, why aren't they everywhere? And something you mentioned is the upgrading and upgrading an older facility to make the facility ready for full digitalization. That isn't trivial. That is really a tricky thing. And again, if you don't have an alignment between analytical and manufacturing technology, this will all be difficult. And so what? What I see, and I'm sure you've seen the same so is a lot of these facilities have absolutely minimal automation and digitalization in there. Yeah, there's very little integration of systems. And there's, there's reliance on laboratories rather than on online, in line analytical systems. And I remember nearly 30 years ago, we put an HPLC right next to the line. We didn't take the sample for 15 minutes to the lab, and perhaps they have some time, some time to give us a result, yeah. And what it seems to me is that the cost benefits of the digital transformation aren't really fully understood by so many companies. Yeah, but when you look at Big Pharma, the picture, to me looks very different, because these companies definitely embrace technology, and they profit greatly from it.

I really like what you touched on to kind of exemplify a little bit of where we're at. And Siegfried said it really well, some of these lines we're talking about, the manufacturing lines are very old and haven't been updated, and it's so it's like taking a 1968 Mustang and then trying to put in all the computer equipment and sensors of 1925 BMW, right? It's going to take a really long time and a lot of work and a lot of money to bring that kind of technology to an old manufacturing facility or place, right? And that's what we're talking about. So in some ways, you have to ask yourself, Do I scrap the facility and go green field, or do I try to do this very expensive upgrade, and maybe it works, maybe it doesn't right. So I think that was a really key critical point you brought up.

Siegfried, now Sue going off your previous comment. Have you actually seen any examples where companies like cdmos have made that decision, in other words, to go Greenfield, with a facility, or go with another strategy?

No, I haven't seen that decision. I think what's kind of happening, although I don't know right, because I haven't been but my guess, or from what I hear in the scuttlebutt, is we're kind of limping along there by adding technology slowly at certain points in the process. You know, so, but the whole transition that really needs to be able to keep up with some of these at Advanced therapeutic, medicinal products that are coming out, it's it's going to be next to impossible, I think, in some ways, to achieve that vision. But Siegfried is much more the expert than I throwing it over to him.

Well, cdmos find it very difficult to change a facility because it usually affects too many clients. So building a new facility, almost is the only solution. And when we suddenly what I've seen, and sometimes you get the facility of the Year Award for this is new facilities, and they're mainly built by large Big Pharma. They are the ones that are really automated, that leverage all what can. Be leveraged from these tools, and so really, they try not to upgrade the old equipment that's already 30 years old, because it's difficult. It's much easier to build a new one. However, on the other hand, I see many new plants, new facilities where the operators on their mobile phone have more computing power than what's on the equipment, and it doesn't make sense in some way. And what Sue mentioned about atmps, selling gene therapy manufacturing that there has to be a much higher level of automation built in right from the start, because, again, as Sue said, You cannot wait two weeks for an analytical result when you have the cells ready and it's either inject into the patient or put in the waste, either or. And you need results there and then. And so this can only be achieved through rapid methods, through fast, automated systems.

Well, thank you both, and I'm afraid that's all the time we have today. Thank you to Susan and Siegfried for being on today's episode and sharing their experience and insights. I'd also like to thank our sponsors, LGM pharma, Nelson labs, the parenteral Drug Association, Thermo Fisher, scientific and veltic Associates, for supporting today's episode. You can catch all our drug digest videos at www.pharmtech.com thank you for watching.

Did you enjoy this episode? If you did, please share this video with colleagues who you think would enjoy the content. Thank you to our editors and experts for sharing their insight. Thank you as well to our sponsors for sponsoring this episode. If you want to stay in touch with pharmaceutical technology team, subscribe to this podcast as well as to our E newsletters. When you sign up for our newsletters, you will be updated about future episodes of drug digest. Receive our magazines and more. Thanks to everyone for joining us for this episode of drug digest, we will see you next time you.

Drug Digest: Closing the Gap in Modern Process Control and Automation for QC/QA

Sanjay Konagurthu AAPS Interview Part Two

, held from Nov. 9–12 in San Antonio, Texas, 

, PhD, senior director,Science and Innovation, Thermo Fisher Scientific, about the presentation “

AI/ML Model Informed Early-Stage Drug Development.

 Dr. Konagurthu talks about integrating a systematic 

 framework to accelerate decision making regarding 

He says this framework provides insights into a final dosage form, as drug loading can impact both physical and chemical stability—and how much drug substance can be contained in a 

“Drug loading is an important aspect when it comes to physical and chemical stability,” Dr. Konagurthu says. “With some of these modeling tools we have deployed and implemented, we can predict not only the favorable excipients and polymers to screen prior to any experimentation, we can also predict how much drug loading one can achieve.”

Dr. Konagurthu also addresses how molecular dynamics simulations and quantum mechanical calculations inform complex 

“If the drug is bound too tight, it won't release,” he says. “If it's too loose, it doesn't really stabilize in the amorphous form. So, these are the series of calculations and various descriptors we assign in addition to the physical chemical properties ... to be able to do these calculations and narrow down the formulation design space. And all of this is done 

 of Dr. Konagurthu’s interview with PharmTech Group, in which he discusses how artificial intelligence and machine learning can help solve the dilemma of poor solubility.

 for all of our AAPS PharmSci 360 coverage.

Editor's note: This transcript is a lightly edited rendering of the original audio/video content. It may contain errors, informal language, or omissions as spoken in the original recording.

Hello, I'm Sanjay Konagurthu. I'm Senior Director of Science and Innovation at Thermo Fisher.

When we do things like quantum calculations, we look at the confirmer space. So what that does is give us insight into how a molecule folds upon itself. That gives us an idea about the accessible surface area of the molecule and the excipient polymers to interact. So that's one piece of it.

And we look at the various descriptors, like the classical hydrogen bond donors, acceptors, aromatic, hydrophobic. We look at potential energy surface charge models. These are like the sticky points of a molecule that essentially give us insight into how a molecule and an excipient or a polymer are interacting at a molecular level.

And then we look at molecular dynamic. Simulations, looking at interactions between the drug and the polymer. If the drug is bound too tight, it won't release. If it's too loose, it doesn't really stabilize in the amorphous form. So these are the series of calculations and various descriptors we assign in addition to the physical chemical properties, like I mentioned, to be able to do these calculations and narrow down the formulation design space.

. And so we are saving an incredible amount of trial-and-error and unnecessary experimentation and getting to the goal quicker.

Drug loading is an important aspect when it comes to physical and chemical stability. With some of these modeling tools we have deployed and implemented, we can predict not only the favorable excipients and polymers to screen prior to any experimentation, we can also predict how much drug loading one can achieve.

So what that does is, our priority is, you get an insight into what your final dosage form is going to look like because the drug loading can impact both physical and chemical stability, and how much drug loading you have can impact how much drug you can get in a tablet or a capsule. Those are the interplays that we can get insight into based on these AI/ML modeling tools that we have implemented at Thermo Fisher.

The conclusion of this interview with Sanjay Konagurthu focuses on simulations and calculations that go beyond simple screening.

Molecular Dynamics and Quantum Calculations: AAPS Thermo Fisher Interview, Part Two

Sanjay Konagurthu AAPS Interview, Part One

, PhD, senior director, Science and Innovation, Thermo Fisher Scientific, about the conference presentation “

In the above interview—the first in a two-part installment—Dr. Konagurthu discusses the potential of 

 using artificial intelligence (AI) and machine learning (ML).

“The big advantage is time and cost savings and resource savings for the industry and our customers,” Konagurthu says. “Time is money, literally speaking, and what we bring to the table is reducing risk in product development, saving time and resources, minimizing API consumption. It's an ultra-material-sparing approach by leveraging some of these 

 modeling tools like AI, leveraging AI/ML for supporting product development and formulation development.”

How can AI/ML improve early-stage drug development?

In particular, Konagurthu says, AI/ML can help sift through a field of drug candidates in which the percentage that are 

 has increased in the past several decades from 30% to as high as 90%.

“It's a challenging environment and the probability of success of getting a poorly soluble NCE [new chemical entity] to a commercial product is pretty low,” he says. “It's way less than 10%, probably more like 5%. So, by leveraging some of these AI/ML tools that we've developed, we aim and strive to reduce the risk in product development.”

Check back for Part Two of PharmTech Group’s interview with Konagurthu, in which he drills down further on molecular dynamics simulations and quantum mechanical calculations as tools to be deployed beyond simple screening.

Hello, I'm Sanjay Konagurthu. I'm Senior Director of Science and Innovation at Thermo Fisher. I've been in the industry going on now 27 years.

Background, I'm a chemical engineer by education, PhD. My experience, broadly speaking, is in small-molecule oral drug delivery areas like solubility, bioenhancements, spray drying, hot-melt extrusion, nanotechnology, as well as modified control release, complex dosage forms, etc., ranging from early development through commercialization.

I also lead a predictive modeling and simulation team at Thermo Fisher. We are a global team. We deal with the predictive modeling and simulations for drug product development.

That's a great question. I like to tell people when I first started working in this industry over 20-plus years ago, the number of candidates that were poorly soluble were estimated to be 30% or so. Now, depending on the source that people quote, 70% to 90% of molecules are poorly soluble, so it's more than doubled, if not tripled.

It's a challenging environment and the probability of success of getting a poorly soluble NCE to a commercial product is pretty low. It's way less than 10%, probably more like 5%. So by leveraging some of these AI/ML tools that we've developed, we aim and strive to reduce the risk in product development.

We can get to accelerating the early phase especially by leveraging these 

 AI/ML tools for selection of technologies. What's the best technology that might work for any given molecule? And it's all dependent on the type of molecule. Every molecule is unique.

And also in formulation selection, for example, we deal with complex modalities like protacs, which are on the larger end of the spectrum, and the traditional small molecules. And all these molecules violate, typically, Lipinski's rule of five. So they're literally the brick dust, greaseball-type molecules. By leveraging these AI/ML tools we again, as I said, reduce the risk in product development, get to the milestones faster, and shorten timelines, etc.

The big advantage is time and cost savings and resource savings, for the industry and our customers. Time is money, literally speaking, and what we bring to the table is reducing risk in product development, saving time and resources, minimizing API consumption. It's an ultra-material sparing approach by leveraging some of these 

 modeling tools like AI, leveraging AI/ML for supporting product development and formulation development.

Every API, as I mentioned, is unique. So your structural features are very important, obviously the chemical structure, also fundamental properties like the melting point which have bearing on the crystal lattice, [the] energy of the molecule [is] important. Other properties like pKa, LogP, LogD, all of those are important.

Also, things like permeability are important. So typically we try to get data measured in 

 assays like KCO2 or MDCK, and we can leverage that to predict what's happening in the human body. These are some of the features that are important in some of these calculations that we do.

Sanjay Konagurthu of Thermo Fisher Scientific discusses how AI and ML can help solve the dilemma of poor solubility.

Leveraging AI/ML to Reduce Risk in Drug Development: Part One

Mechanistic models and hypothesis-driven strategies generate optimized efficient solutions for drug development, says Catalent’s Nathan Bennette at AAPS PharmSci 360.

Shifting from Trial-and-Error to Rational Design Drives Efficiency in Pharmaceutical Formulation

American Association of Pharmaceutical Scientists

, director, Scientific and Technical Advisory, Catalent Pharma Services, discusses a paradigm shift toward rational design—a method grounded in conceptual and mechanistic modeling. According to Bennette, the rational design approach brings clarity and efficiency to the development of new drug 

Instead of the traditional trial-and-error method, Bennette advocates a structured approach. “The rational design concept is to use models—conceptual models and mechanistic models—to develop more focused hypotheses and then targeted experimentation to more efficiently get at the solution,” he explains. This shift begins with the creation of conceptual models to understand the problem in depth, followed by mechanistic (quantitative or mathematical) models that reveal the fundamental physical and chemical processes at play, he explains.

Semi-empirical models, often realized via computer simulations, Bennette notes, further refine hypothesis testing and solution optimization. Bennette shares a recent example in which his team faced a drug with poor oral absorption. Through 

 experimentation, the team isolated the rate-limiting step—diffusion across the unstirred water layer—and designed formulations specifically to enhance that process. Bennette notes that the 

How are rational design models transforming pharmaceutical problem solving?

Bennette believes this efficiency-first mindset yields tangible industry advantages. “If you can come up with these really focused hypotheses and work on those problem statements, you get there faster. You get there with less expenditure of time and energy and resource, and, ultimately, I think you come up with a better product,” he says.

Bennette adds that, as pharmaceutical companies face mounting pressure to deliver innovations with speed and efficacy, rational design frameworks promise to shape industry standards for problem solving and formulation optimization.

Nathan Bennette, Director, Scientific and Technical Advisory, Catalent Pharma Services

Bennette is a subject matter expert on establishing developability and non-parenteral formulation strategies for poorly bioavailable molecules, including protein degraders and others described as “beyond rule of 5”. He has worked in drug development for over 15 years in a range of technical and leadership roles and has broad experience with formulation of small-molecule therapeutics. Bennette applies his passion for mechanistic understanding of drug delivery to design systems tailored to each molecule and target product profile, utilizing a range of oral technologies such as amorphous solid dispersions, controlled-release platforms, nanocrystalline formulations, multiparticulates, and lipid-based systems. He has experience working across the entire drug development lifecycle from preclinical and early phase development to late phase clinical studies and commercialization.

Hi, Nathan Bennette, I work in our scientific advisory function at catalent pharma solutions, and my role there is to be sort of an in house consultant working with customers to define the problem statement and develop technical strategy for formulations.

So in sort of traditional problem solving and pharma development and formulation and process development in particular, a lot of times, we have historically done a lot of trial and error, where you form a hypothesis, but then you run a large number of experiments trying everything under the sun to develop a solution to the problem. The rational design concept is to use models, conceptual models and mechanistic models to develop more focused hypotheses and then targeted experimentation to more efficiently get at the solution.

The question, it's principally around efficiency. Those trial and error experimentation methods require huge amounts of effort and to arrive at a robust solution eventually. But these mechanistic, driven, hypothesis, driven experiment designs allow us to use the best practices in science to build out our understanding of the system and more efficiently come to a more optimal solution.

I think of it in sort of three layers. First, you have conceptual models, you have to form a picture in your head of what is actually happening in the system that you're studying. Once you've done that, you can start to form a real hypothesis about what might be going wrong with your with your molecule, for example, as you're trying to develop your formulation, all formulation development is a problem solving exercise, and it's important to know what problem you're trying to solve. So this conceptual model allows you to have a framework for forming a hypothesis. Then there's another level that I call mechanistic models. Some people call them quantitative models, mathematical models. These are the basic physics that underlie the physical situation of your drug molecule and the question you're trying to answer maybe it's from an absorption perspective or from a stability perspective. And so once you have your quantitative mechanistic model, you can start to form a more specific hypothesis. How fast does this process happen to? What extent can we expect this process to proceed. And then finally, we have sort of empirical or semi empirical models where that mechanism and concept have been reduced to an actual computer program, and you can run simulations. So these tools are all available for a range of different questions that are relevant to pharma development, like pharmacokinetic absorption or chemical stability or process modeling for different pharmaceutical manufacturing processes. So we use the starting from conceptual to mechanistic to semi empirical models. We use this whole framework to describe the problems that we're trying to solve and develop actual formulations to resolve that.

I have a really good one from recent work that does both. It helped us to get much more quickly to an optimized solution that actually we tested it out in vivo, and it worked really well. So we were developing this compound that had very, very poor oral absorption. And we asked the question first, why? And we started with the conceptual model that every molecule has to get into the GI tract. It has to dissolve it then has to diffuse across the unstirred water layer, up against the epithelium and then partition across the epithelium to get into systemic absorption. So that's our conceptual model. And we asked, Well, why is this molecule having a hard time doing that? What? What is the fundamental rate limiting step? And we learned through a series of in vitro experiments that the rate limiting step was diffusion across that unstirred water layer, the mucus layer. And so with that in mind, we didn't, we didn't go out and try every formulation concept under the sun that would be a trial and error approach. Let's try this. Let's try this. Let's try this. We said, Okay, we know the key problem is diffusion rate. So what formulations can we develop that enhance the diffusion rate? Because that's the key step. So we went in the lab and we developed a few formulations that we proposed would help with that, and we tested them in vitro and demonstrated that they did, in fact, enhance the diffusion rate. And then we took the best of that set into an in vivo study. And what was really cool was that the extent to which. We enhanced diffusion with those formulations, in vitro correlated very well with the in vivo enhancement factor as well. So we saw, we figured out what the right problem was. We developed a conceptual framework for how to resolve that, and we went and tested that specific hypothesis, and much more quickly, much more efficiently, came to a workable solution.

I think I've been hitting a lot on the efficiency, the efficiency of the process of coming with a workable formulation. I think that translates to faster timelines if you're if you're spending enormous amounts of time trying everything in order to find the one thing that works. Then you spend a lot of resource. You spend a lot of time working on stuff that isn't isn't going to be the answer. But if you can come up with these sort of really focused hypotheses and work on those problem statements, you get there faster. You get there with less expenditure of time and energy and resource and ultimately, I think you come up with a better product. It's a it's a more optimized outcome.