Speakers:
* Mr. Eamonn Warren, VP, API and Dry Products Manufacturing, Eli Lilly and Company,
USA
* Dr. Despina Solomonidou, EVP, Global Head Technical Research & Development,
Novartis, Switzerland
* Ms. Rashmi Kumar, SVP & Chief Information Officer, Medtronic, USA
* Dr. Peter Owotoki, Cofounder & CEO, EmpathicAI.life Ltd, Germany
* Prof. Kil To Chong, Founder & CEO, JuYoungBio Corporation, South Korea
* Dr. Kenneth Barr, SVP, Head of Strategic Collaborations, Head of SynVent, Syngene
Moderator: Mr. Shriram Chary- AI leader Europe ( Life sciences & partner, EY)
Below is the full transcription of the session:
(MR.SHIRAM CHARY)
(0:00) What an exciting time to be in the biopharma healthcare space. (0:06) Good morning everyone, my name isShreep Chari. (0:08) I am a partner at WISE Healthcare Life Sciences Practice.
(0:13) And I get the opportunity to meet all these wonderful stalwarts of our industry and host the panel today. (0:20) We’re living in a paradox, let me explain what that means. (0:25) This company is moving at a breakneck speed, but yet our…
(0:29) This is panel discussion. (0:33) That’s the topic that we’ll be talking about today. (0:36) We’ve realized that a breakthrough in a lab is just a promise, but only becomes a viable product when science, systems, software, and supply act as a single interwoven and an integrated entity.
(0:51) At EY, we’re calling this bioware. (0:54) The interplay across all these is what we’re going to talk through today as we introduce most of our panelists today. (1:01) So let me introduce the team of avengers that we’ve got assembled from the industry today.
(1:08) I’m going to introduce each one of them, and also talk about the unique super strength that they’ll bring to the table today as we move through the discussion. (1:16) I’ll start with Eamon. (1:18) Eamon’s super strength is he’s a scaler.(1:21) He’s an expert in moving discoveries from the lab to scaling and building this interwoven supply chain. (1:27) Eamon is the Group Vice President of Manufacturing at ELISA Game Company. (1:31) Next to Eamon, we’re joined by Dr. Jaspeena. (1:35) Her unique superpower is she’s the architect. (1:38) Transforming traditional R&D into integrated technical platforms is what she’s pioneering. (1:45) Dr. Jaspeena is the Executive Vice President, Global Technical R&D Lead for Novartis. (1:52) Next to her, we’re joined by Rashmi Kumar. (1:56) Rashmi’s super strength is she’s the digital engineer. (1:58) She’s known for driving hyper automation and AI-assisted surgery and patient care.
(2:05) Rashmi is the Senior Vice President at Global CIO at Medtronic. (2:09) Next to Rashmi, we’re joined by Dr. Peter Otoki. (2:13) Peter’s super strength is he’s the ethicist.
(2:16) His expertise lies in measuring psychological patient conditions to ensure empathetic care. (2:22) Peter’s also the co-founder and CEO of EmpatheticAI.life. Next to Peter, we’ve got Professor Chong. (2:29) Professor Chong is a pioneer in computational modeling and use of natural products for drug discovery.
(2:36) He’s also the founder and CEO of Juyung Bio, a research company. (2:41) And to finish off the panel, we’ve got Dr. Kenneth Barr. (2:45) His superpower is he’s the connector.
(2:48) Mastering partnerships and alliances to accelerate target-containing timelines is what Dr. Kenneth has pioneered. (2:54) Dr. Kenneth is the Senior Vice President of Strategic Collaborations at Cengi in India. (3:01) If you notice, each of our panelists today represent each part of the value chain.
(3:06) From drug discovery to manufacturing to software systems at scale, can also bring ethical new considerations in order to strategic alliances and partnerships. (3:16) We look forward to a conversation about how all of these aspects of the value chain will define the next-gen healthcare and life-science innovation. (3:32) Oh, you’ve got a mic?
(3:33) Okay. (3:34) Alright. (3:35) I guess we’re taking the mics then.
(3:37) So, why don’t we start with the heart of the issue. (3:41) The new architecture is going to require breaking down the walls between R&D and manufacturing. (3:46) So, why don’t we start with Dave and you and Dr. Bisbeha. (3:49) The question to you is, you both represent the opposite technical spectrum. (3:54) Manufacturing and R&D. (3:56) Historically, breakthroughs have come from the R&D labs.
(3:59) You push it over to manufacturing, hoping they’ll find a way to scale it. (4:04) As we hope to scale a billion doses, which do you think wall has to come down first? (4:10) Is it a software problem?
(4:12) Is it a data problem? (4:13) Or do you think it’s a cultural issue? (4:15) Why don’t we start with you, Dr. Bisbeha.
(DR. DESPINA SOLOMONIDOU)
(4:25) I hope that I’m audible. (4:26) Wonderful. (4:28) Thanks for the question, Sri, and it’s interesting.
(4:30) I’m sitting in between my colleague from manufacturing and Dave. (4:38) I don’t think it’s… (4:40) Is it working now?
(4:41) Okay. (4:43) So, it’s an interesting question that you are asking, in fact, about the wall, right? (4:49) And whether it is a throwing over the fence product.
(4:55) I think historically, it has been maybe a matter of organizational charts, R&D and commercial production. (5:04) In my view, it’s more, let’s say, a belief or a philosophical conversation about what great science means. (5:16) And I think R&D stands for optimizing for novelty, for innovation, proof of principle, and for speed.
(5:25) Whereas my colleagues at commercial production are likely to optimize for robustness, reliability. (5:32) I’m going to call it out as well, cost-effectiveness, right? (5:36) So, if I were to prioritize among all the elements that you offer, Sri, I first would go probably for culture.
(5:46) Because it has to do with the trust and the co-ownership. (5:52) It has to do with knowledge generation, but also knowledge use. (5:56) And in my mind, it’s all about the history of development.
(6:03) A collection of data that is being transferred to operations. (6:08) It’s not just a product or a process. (6:12) So, the one that I would prioritize is trust and ownership as it creates this joint responsibility for developing a product.
(6:22) And it goes along with followed by data, which up to date, it has been a collection of numerous of pages and reports. (6:35) I hope that in the future, it will be translatable into a digital thread where systems come into play. (6:43) That will be handed over as a long history of a product development to the commercial organization.(6:52) To take it forward into production. (6:55) That’s the way I would look into the future. (6:57) And in fact, that’s the way we live, I think, today.
(7:01) R&D and production. (7:04) At least in the organization that I’m representing. (7:07) We have the colleagues from commercial production being part of our R&D efforts.
(7:13) From the beginning, where we built in, I call it, manufacturability in the design of the project, in the design of the process. (7:22) Having in mind, how can we scale up? (7:25) How can we control this?
(7:27) What could be the control strategies? (7:29) How do we make a product affordable on the long run?
(MR.SHIRAM CHARY)
(7:34) Eamon, what do you think you want to add to what the supply said?
(MR.EAMONN WARREN)
(7:39) Okay, it’s different. (7:40) Yeah, I agree with a lot of what you said, Nespina. (7:42) I think the important question initially is, why is this important?
(7:47) I think it’s important for us to get products to patients quickly. (7:50) Because if we don’t, patients don’t get the medicine they need. (7:53) And our ability to scale, to develop and scale products quickly.
(7:58) To be able to serve not just one, but millions of patients is super important. (8:02) So the patients are waiting, and it’s up to us to figure out ways to make this process more efficient. (8:06) I think when you look at it across different aspects, I think it requires so many different things to do this effectively.
(8:14) It requires science and engineering to be able to effectively develop these processes and scale them up. (8:20) It requires digital, using PLM, Project Life Cycle Management, and electronic plant records, which are all relevant, to be able to make high-quality, repeatable processes. (8:30) It also requires innovative record-keeping to be able to launch record-keeping dossiers across multiple countries simultaneously, and launch not just to one country, but to all countries at the same time.
(8:44) But I think, going back to what Nespina said, I think the cultural aspect for me is probably really, really core to what we do. (8:51) And I think that you’re having that cultural change to where speed is paramount, and the primary focus is really important. (9:01) Both the teamwork between development and manufacturing, but then also the co-location of development and manufacturing is super important.
(9:10) To be able to have that organic interchange of ideas and developing innovative solutions to problems quickly is super important.
(MR.SHIRAM CHARY)
(9:19) Thanks, David. (9:20) How about we look at this same issue from a very different lens? (9:23) I’d like to call upon Professor Chong and Dr. Kenneth here. (9:27) So, Professor Chong, your computational models are now discovering molecules at a frequent speed, which would have been impossible about five years back. (9:37) But Kenneth, as a CRDMO, can the lab and the supply chain keep up with the digital speed, at the speed at which UGenBio is discovering molecules and discoveries in the lab? (9:49) How do we stop the supply from being the brake on science?
(9:54) Why don’t we start with Professor Chong? (9:55) Maybe if you could speak to talk about the speed of computational discovery, and then we’ll go over to Dr. Kenneth.
(PROF. KIL TO CHONG)
(10:02) Thank you. (10:05) I’m doing the end-to-end drug discovery using AI. (10:10) One of the tools that we have developed, since once we’ve got a target protein, we can produce the optimized molecule in 10 minutes.
(10:24) So what we do, using the target protein, we can generate new molecules. (10:30) Then we will combine with the protein and molecule binding ability, and we do toxin testing, ultimate testing, and mDNA all together in one of the platforms. (10:43) So we could develop a kind of a lead compound in 10 minutes.
(10:48) However, we have to consider how we can make it drug. (10:54) So we need the CD, CRO companies that they can do the lab and everything. (11:02) And also, we have to do kind of supplies together.
(11:08) So that for catalyze the AI into the real drug, we need kind of the cooperation from the beginning. (11:18) Usually the AI can determine the not good compounds at the beginning. (11:24) So that AI can speed up the development of the drug.
(11:28) However, we can interrupt things that cannot be the drug at the end. (11:34) So start from the beginning, we are using the AI and interrupt things that are not good for the drug compound. (11:42) So then we need the cooperation with the CRO.
(MR.SHIRAM CHARY)
(11:52) Dr. Kim, is there anything you’d like to add in terms of the role of the CRO in how we make drug discovery?
(DR. KENNETH BARR)
(11:59) Sure, thank you. (12:01) First of all, I really like the way that Professor Tong framed the question. (12:06) Because the first speakers were taking the word scale in the context of large-scale production, which is an important question to address.
(12:13) But the other part of scale, which is on the discovery side, the R&D side, which is where we spend our time, is about the number of successful programs that you can bring forward, eventually making it to the patients and to the clinic. (12:26) So how can you increase the scale across the continuum of therapeutic areas that you’re serving, the number of projects that are going to go from ideation through to success in the clinic. (12:37) And so, for an organization such as ours, and the company I work for is CINTI, we’re what we call a fully integrated contract discovery, development, manufacturing organization.
(12:47) And we have reframed the conversation from segmenting discovery is here, development is here, and manufacturing is there, to what we now call modality-based service lines. (13:01) And in modality-based service lines, you have your small molecule that runs all the way from discovery across through development and manufacturing. (13:07) Likewise with biologics, likewise with peptides, polybinoleucleotides, ADCs, targeted degradation.
(13:15) So the idea is that you build in the capability and the capacity aggregated into one organization to be able to accelerate that process. (13:23) And one way or the other, I think most people in this room are in the business of science and the business of discovery. (13:29) So we all know what the speed, quality, cost triangle is.
(13:33) And today the conversations that we’re having are almost entirely focused on speed and quality in the context of reasonable cost. (13:43) So I think we’ve now covered a pretty good part of the spectrum when we talk about scale. (13:48) Thank you.
(MR.SHIRAM CHARY)
(13:48) Why don’t we move to maybe at the heart of the problem, right? (13:52) It comes down to data. (13:54) Data is now unstructured, pluggable, available in different forms.
(13:58) I guess my next question is to Rashmi and Peter. (14:01) As we talk about the invisible capital, which is data, how is electronic harnessing all these unstructured data from the lab to supply chain and manufacturing to ensure resilience? (14:13) And maybe the second part of the question for you, Peter, is as we leverage so much data, is there a component of human and psychological behavior and modeling that we may be missing or not fully taking into consideration as we design the lab?
(14:28) So I’ll start with Rashmi and then get to Peter.
(MS. RASHMI KUMAR)
(14:38) Integrating that. (14:43) I hope this is better. (14:44) Yeah.
(14:45) So the way we think about data and platforms is one part is during the science and the engineering and the development of the science, but at the same time connecting the dots of the science and development to manufacturing. (14:58) And with the automation we have done over years, we are sitting on a lot of data which is coming to us, which now with the advancement of technology and availability of AI can be leveraged to define future iterations of how these end-to-end processes are going to look and how we reimagine that. (15:18) In our product development, we are a little unique compared to the panelist here where we are a medical technology company.
(15:26) We have 73 different therapies. (15:29) And what we are seeing as an advantage with the advancement of AI is our ability to leverage our imaging data better to create different level of automation through robotics and in surgeries, right? (15:44) I think it died.
(15:55) Oh, no. (15:56) So with the availability of data platforms and capabilities in engineering, it plays very well on both sides of it and product development through imaging and robotics and digital touch surgery that we have already out there, but at the same time is improving that entire end-to-end value chain from scientific research to development to manufacturing.
(MR.SHIRAM CHARY)
(13:32) Thank you, Ashwini. (13:34) Peter, anything you want to add in terms of using data for human behavior modeling and how we take ethical considerations into?
Speaker 4(DR. PETER OWOTOKI)
(13:47) Can I add? (13:49) I think this is fine. (13:50) Yeah, so thank you very much.
(13:51) This is a very important question, right? (13:54) And it kind of closes the loop, right? (13:56) From molecules to getting it through the research process to manufacturing, now I come to the human aspect of it, right?
(14:04) How does it work in the real world, right? (14:07) Using data as the lifeblood of innovation, the software of innovation, the kinds of data we collect, how we use those data really matters for the real-world impact and the scalability of the innovation. (14:23) Imagine Professor Till invests the molecule that is awesome, both with the AI and walks through the clinical trial process, but it gets into the real world and it misses the mark for six billion people, and why?
(14:43) Because the intentionality to have representation (14:48) in the data pool such that the effects, (14:54) the side effects, and how those dosages, (14:58) how they’re gonna have an impact in the real world, (15:01) if they are not considered intentionally, right, (15:04) that would be, I mean, a classical example is, (15:07) you know, women’s health, right, (15:09) where the majority of cell lines (15:11) that many of the innovation is based upon (15:13) are just male cell lines, (15:16) and then you have a drug that works, (15:18) but in the real world, (15:19) it’s not working so well for women, right? (15:23) Similarly for people of color like myself, right? (15:26) Making sure, especially in this age of AI, where AI, Professor Till’s AI will accelerate innovation.
(15:36) This amazing connection of these processes will bring them back to the market quicker, but it could miss the mark for billions of people. (15:43) It will not be robust, it will not scale. (15:45) So thinking a lot more about the ethics, right, how we innovate for eight billion people with AI, using intentionality that there is representation by age, by gender, by ethnicity, by social classes, and this is something that drives the work that we do at Envati, right?
(MR.SHIRAM CHARY)
(16:06) Very interesting. (16:07) Let’s maybe build on that a little bit more, right? (16:10) So, Professor Chong, you use very advanced AI to do drug discovery, right?
(16:15) And you’re studying AI through the lens of chemistry, cheminformatics, bioinformatics, so forth. (16:22) And Peter, you’re looking at AI to study human behavior, fundamentally, based on what you just spoke about. (16:28) Is there a way, or is there a world where these two worlds collide?
(16:33) Is there a potential digital twin in the future where we’re looking at chemistry and human psychological models together? (16:39) Why don’t we start with Professor Chong first, and I’ll come back to you, Peter.
(PROF. KIL TO CHONG)
(16:47) Jeremy? (16:49) By using AI, we can determine the small molecule, but it could kill the disease. (16:58) However, AI can determine the autism and other side things, so that the psychology problem or neurological problems, if we’re using the omics data, we can, it is possible to determine the biomarkers or some kind of a trend in the data, omics data.
(17:21) So it’s possible, it is the next generation of our direction that we can develop psychological solutions so that Peter, that he could manage that by his way, and I can provide the biomarkers or some kind of the information using the AI. (17:43) So it could be a good, next generation therapeutical work. (17:51) That’s what I guess.
(DR. PETER OWOTOKI)
(17:53) Peter, anything you’d like to add? (17:55) I think that’s really fantastic description of what both the genomics or multi-omics kind of data combined with behavioral data can achieve for many conditions of the mind. (18:09) Autism is one space where we’ve done a lot of work.
(18:13) Today, there is really no clear biomarkers. (18:17) They don’t exist, right? (18:19) There’s a lot of acceleration today with AI.
(18:22) Diagnosis is based on the observations of human experts. (18:26) Oftentimes, they’re biased, right? (18:30) Or they’re just based on the subjective observations of these experts.
(18:38) Now imagine that with the work that Dr. Cheng’s doing, you could actually combine the behavioral information with genomics information to identify truly objective biomarkers for autism. (18:55) What would that lead to? (18:58) Four times, men are diagnosed more frequently than women with autism, right?
(19:05) Either because women are very good at masking, right? (19:08) And the observers don’t kind of catch that. (19:12) Combining the genomics with the behavioral will take that bias away.
(19:17) You’ll have very clear biomarkers. (19:19) And this is not just for autism, it’s just the stats, that it affects things that’s gonna touch everyone first, Alzheimer’s, other neurodegenerative situations. (19:27) Being able to combine behavioral information and genomics information to find clear biomarkers, and then you get targets, right, that can address these conditions and make everyone healthier.
(MS. RASHMI KUMAR)
(19:38) Thank you. (19:39) I’ll just add to it. (19:40) The regulations also need to accelerate because pharmacogenomics is available to personalize treatment for these types of biomarker symptoms that are seen subjectively, right?
(19:54) But that’s where data and evidence is also important for regulators who are building biomarkers law to say that is it approved to treat a patient based on what we are seeing? (20:08) And then the whole ethical AI part of it is, is that not going to impact that person’s ability to work and be contributing to the society at the level that they can do it, right? (20:21) So one part is science, one part is software, other part is how do we take the information and data and enable the regulatory aspects of the treatment?
(MR.SHIRAM CHARY)
(20:32) Thank you, Rashmi. (20:33) I’m glad you brought all those three aspects back together. (20:36) Kind of tease up the next topic we want to talk about.
(20:39) One topic, since we’re in India, right? (20:41) The next topic is more about India’s role in survival as all those three capabilities need to come together symbiotically, right? (20:49) So I guess my question to, we’ll start with India, Dr. Kenneth. (20:53) India is often called the world’s factory. (20:56) But looking at the biome and model, it’s not just about scaling manufacturing. (21:00) It’s about bringing software like science with leading manufacturing capabilities being brought together to really deliver a printed doses at scale.
(21:09) So Amy, on the global perspective, what’s your perspective on the case scale as you think about the biome?
(MR. EAMONN WARREN)
(21:19) Yeah, I think India, it’s amazing. (21:22) The whole ecosystem in India around manufacturing in particular is phenomenal. (21:28) I think this week we got to visit some of our partners here in India.
(21:33) The scale that they operate at and the ability to take products from lab to pilot to manufacturing scale is phenomenal. (21:41) I think the big thing I take away from India is India really knows how to make medicine and do it really well. (21:48) So I think that’s something that we’re hoping to see more of that going forward in India next several years.
(MR.SHIRAM CHARY)
(21:55) Dr. Keren, anything you want to add to it as you think about integrated CRDMOs or the evolution of CRDMOs in this journey?
(DR. KENNETH BARR)
(22:03) Sure, thank you. (22:05) So evolution, Sri, is the right word. (22:09) So I spent the first 25 years of my career in US pharma and biotech.
(22:15) And a large percentage of that, I was responsible for running drug discovery programs that were outsourced to India and China. (22:21) And I can tell you that when we first started, it was primarily about the cost arbitrage. (22:28) And in fact, when I was working at Merck, procurement used to walk the hallways and ask the question, how many Simgene scientists would it take to equal the productivity of a Merck scientist?
(22:38) I could do not. (22:39) But now having been seven years on the provider side and seen a tremendous evolution in the industry, where we are today is people have started to use the phrase IIT, or Integrated Innovation Partner, right? (22:51) And what that means in practice, and this is where I think the whole industry is headed in certain Simgene is working very hard in this direction, is to effectively be today an 8,500 person biopharmaceutical company for hire.
(23:04) We have built in each of the capabilities to go from early drug discovery through to the candidate selection, preclinical evaluation, clinical trials. (23:13) And we can do that as a partner with the client. (23:17) And we can even do that now on behalf of the client.
(23:19) And Simgene is not alone. (23:20) There are other organizations that are working in that direction as well. (23:23) But importantly, and we talked about this earlier, it has to be that people are now coming to India not because we’re very low cost.
(23:31) There is an advantage of working in a low cost operating region for sure, but they have to come to us because we create value. (23:37) And that’s where I think India’s on a very strong trajectory and I’m really proud to be part of it. (23:43) And thank you very much for including me in the conversation because I think this really reflects what we’re trying to get to.
(MR.SHIRAM CHARY)
(23:49) Thank you. (23:51) Let’s sort of switch back to the work. (23:53) We were so far talking about the scale of S in manufacturing.
(23:57) Let’s maybe shift to the scale of access. (24:00) Access is going to be equally important or the scale of access is going to be important for all these intended medicines to deliver the value that they’re supposed to. (24:08) So question to Dr. Justina and Peter. (24:12) We’ve talked about scale in terms of what? (24:15) Through manufacturing and CRD more strategies. (24:18) How do we think about scale in terms of patient access and healthcare access?
(24:23) And how do you think about democratized care, Peter, as you think about ethical use of AI, democratic use of AI? (24:31) Maybe we’ll start with Dr. Despina first and then get to Peter.
(DR. DESPINA SOLOMONIDOU)
(24:36) Thank you. (24:37) Thank you, Sri, for the question. (24:39) Actually, two questions, right?
(24:41) First, speaking about scale. (24:43) And I’m glad that we are shifting the discussion from volumes to more value, building on what Ken just said, the value of the supply chain. (24:54) And access to me, it’s not just pricing, reimbursement, policies, and government.
(25:02) As you said, access is about making the drug available to patients, to those who need it. (25:08) And when I think about access, I look at it like an operational challenge that has to address the supply chain, and we can speak about that with him later, which is the reliability, the robustness of the manufacturing process, the ability to deliver reproducible high quality to patients at any time. (25:34) And in order to do that, one of the areas to look at is obviously science, but also how to integrate AI and technology into high quality products.
(25:49) I’m thinking of reducing the cost of goods, not simply by cutting costs, but more importantly, by improving the process in such a way that the yield is higher, for example, or we have automation
integrated in the manufacturing process that allows the product to be manufactured in a more cost effective manner. (26:12) But then the other thing, it’s about beyond affordability, it’s also about availability. (26:17) And in a truly global world, (26:20) and you mentioned at the beginning of this session (26:23) with some more peculiar modalities (26:27) like radiolegal therapies, cell therapies, (26:30) but also the biologics and gene therapies, (26:34) the question to me becomes, (26:36) how can we come closer to the patients, to the end users, (26:41) which is an element of, (26:44) can we reimagine the manufacturing architecture (26:48) from a single global hub (26:50) to more regional distributed manufacturing sites (26:54) where the logistics is not becoming (26:57) the rate limiting step and it’s not a cost factor as well.
(27:01) So these are three elements that I would like to put together when we speak about access in operational language, as opposed to pricing and reimbursement. (27:12) Just to sum it up, supply, affordability, or reliable supply, I should rather say affordability, and more about a distributed manufacturing network. (27:27) The second question I think was about democratizing.
(MR.SHIRAM CHARY)
(27:30) No, I think you summarized it pretty well. (27:33) Maybe sort of pivot a little bit, I don’t know how big Peter is. (27:37) Peter, do you want to talk about how you could potentially democratize access at the care of diagnosis itself and talk about some of the work that you’ll be leading in that space?
(27:46) Absolutely. (27:49) You might want to bring the mic closer to you.
(DR. PETER OWOTOKI)
(27:53) Okay, so I mean, a key part of democratizing access is leveraging technology. (28:03) Leveraging technology, but leveraging platforms of innovation as well. (28:09) So, I mean, in my earlier conversation, (28:12) it’s about the six billion to the eight billion, (28:17) giving access, but making sure that even those voices (28:21) get embedded at every part of the value chain, (28:27) not just in manufacturing, (28:29) but also in the very early designs, (28:32) that you’re leveraging platforms of innovation (28:35) to make sure that those solutions, (28:38) those therapies that will be created, right, (28:40) including the advanced personalized therapies, (28:43) that these voices are represented from the very get-go. (28:46) And that applies, it applies here in India, it applies in Africa, it applies everywhere. (28:51) I talked about women’s health, for example, where, I mean, four billion, 50% of the population, but only 1% of R&D spent, right, is going into that, right?
(29:02) How can you ensure that there is more at the very early stages, right? (29:06) Even right here in Hyderabad, but in other places, (29:10) that innovation is happening at the very early stages, (29:13) a lot more collaboration, collaborative platforms, (29:16) the likes that’s been built by like EBE, by Novartis, (29:19) that is leveraging the diversity, (29:21) and by other companies as well, (29:22) is leveraging the diversity of representation, (29:26) and getting these voices in early on, right, (29:29) to make sure that the cell lines, the data, (29:33) the, you know, insights, right, (29:36) that will make sure that these solutions scale, (29:38) that they’re coming in early on, right? (29:40) And it will lead to some innovations, right? (29:42) Some of what we’re doing, some of the ventures in our studio, is to think about how do we democratize access to diagnosis, right, in ways that touches the end of the aisle, right?
(29:56) Using things like digital biomarkers, so your voice signature, right, as a way to better understand the aisle, right? (30:03) Your social, emotional stimuli, and interacting with them, betterunderstand the state of the aisle, right? (30:10) Making sure that these type of innovation, they feel through the power of the platforms that’s represented here, but ensuring that also the funding gets in early enough, right, through these collaborative platforms, you know, with different voices.
(MR.SHIRAM CHARY)
(30:26) All right, it’s great, Peter, thank you for that. (30:28) I mean, all of you represented in this stage today represent different parts of the value chain, and you have equally a vantage point of what access has to do here, right? (30:37) I mean, Rashmi was giving a great example, that breakout where she had this app.
(30:42) I don’t know if, Rashmi, you want to talk about that, or if you think what that is.
(MS. RASHMI KUMAR)
(30:45) Yeah, we have a app called the Contact Surgery, which is the most, one of the most used medical app. (30:53) Maybe open evidence will be there soon, (30:56) but it is about training surgeons on surgery, (31:00) and we are integrating it with our uber robotic system, (31:04) robotic surgery systems, (31:06) where it improves access (31:10) because people who don’t have access to robotic surgery, (31:14) at that point, can train themselves on that capability (31:17) so that when they have access to that technology, (31:21) they can leverage it, you know, leverage it going forward. (31:24) I just want to add to Peter’s point about access and women health. (31:30) Our head of Structure Heart Party (31:32) at one of the operating units, (31:35) product leaders, created a campaign (31:37) called Letter to Your Mother, (31:40) and it is interesting, in part, (31:43) everybody considered the same, (31:44) versus a structure of a female heart (31:47) is very different than a male heart, (31:48) and the sizes of valves and technologies that needed, (31:52) and the other part is, socially, (31:55) heart health focuses on men and it’s not on women, right? (31:58) So, Metonic, we had a campaign started, Letter to Your Mother, which, starting from our CEO to every leader, every president, every employee, we
encouraged them to write a letter to their mother to take care of their heart health and go for checkups and make sure that it reflects in their regular, at least, symptomatic treatments around that topic.
(MR. EAMONN WARREN)
(32:25) Anyone else like to add anything? (32:27) We talked about democratizing access to medicine. (32:31) I think a really important part of that is scale.
(32:34) You know, I think we’ve been able to scale up and scale out these new medicines. (32:38) I think, for too long, a lot of medicines haven’t been accessible to many parts of the world. (32:44) So, I think India offers a really good opportunity here to help us, you know, help the pharma industry develop efficient and effective processes that can be scaled rapidly and out to all parts of the world at the same time.
(32:57) So, I think talking about India being the hub for vaccine manufacturing in the past, there’s a huge footprint here that can be leveraged to help the rest of the world learn and explore some of the know-how around making medicine to other parts of the world to manufacture products at scale and then improve access to patients. (33:16) That’s great.
(MR.SHIRAM CHARY)
(33:16) Anything else? (33:17) Dr. Kennett, anything you want to add? (33:19) Or Professor John?
(DR. KENNETH BARR)
(33:28) Thinking about the comments now in the context of the session, which is on catalystic scale, on the technology side, on the innovation side that you’re referring to, that we’re trying to bring back into earlier, obviously, there’s going to be a great emphasis now on leveraging AI, different automation tools, robotics. (33:49) It’s about how quickly and efficiently we can move forward. (33:53) So, one of the ways in which we’re trying to do that now, and again, I don’t think we’re unique, but I do think it’s important to call out, is if you think about in discovery, how we call it the design, make, test, analyze cycle, the DMTA cycle.
(34:08) So, the purpose, to Professor Chong’s point, is can we reduce the number of cycles required? (34:14) Because every cycle takes time, and the goal is to get to the therapeutic as fast as possible, but the right therapeutic. (34:20) So, if we’re leveraging AI computational tools, we should be able to run fewer cycles to get to the right molecule.
(34:28) Combined with that, every single part of that DMTA cycle can benefit from some degree of automation. (34:35) So, for example, the first step would be the design of the molecule. (34:39) But if we’re talking about a small molecule, the second step would be the retrosynthetic analysis.
(34:44) How would you make that molecule? (34:46) The third step would be, where can you access the chemical reagents and the starting materials required to synthesize that molecule? (34:54) How rapidly can you synthesize and then characterize the molecule in the biological test?
(34:59) How rapidly can you analyze the data so that it goes back into the loop? (35:03) So, those things are, that’s what we’re trying to do now. (35:06) So, it’s worth accelerating the cycles, butreducing the number of cycles.
(35:11) And then, also to Professor Chong’s point, at the end of the day, it’s not only about designing a molecule that is active, that binds to the surface of the protein, but there are so many different characteristics that have to be optimized in order for it to become a successful drug, including developability. (35:29) Can you scale it into manufacturing? (35:31) There too, we’re leveraging these tools to help us make better decisions.
(35:35) So, I think that ties really in together with the purpose of the session.
(MR.SHIRAM CHARY)
(35:39) Thank you, Dr. Kenneth. (35:40) Anything to add, Dr. Chong?
(DR. PETER OWOTOKI)
(35:45) Actually, the real catalyst is the start from the beginning. (35:53) Science and the software and supplies, they have to start at the beginning so that the success of a drug discovery is right up. (36:07) When we start from the beginning, all of these kind of collaboration.
(36:15) So, from this discussion, I wish we can realize that how important it is that start from the beginning, all of this combination together, collaboration together.
(MR.SHIRAM CHARY)
(36:29) Thank you. (36:30) So, we’ve talked about scale and manufacturing. (36:32) That’s the first S.
(36:34) We’ve talked about stimulant access. (36:35) That’s the second S. (36:37) And now we’re gonna get, a little biased, scale and software, which seems to be a pretty topical discussion right now.
(36:43) So, here’s a question to Christina and Rashmi. (36:47) So, both, I think about software and science as almost like dueling the same package. (36:52) If you both could go back in time and code a perfect ecosystem for the next five years, what would you prioritize?
(37:00) That is science, smarter software, or if you could add in, I don’t want to keep you hanging out, if you could add better supply, how would you go about prioritizing and what would you do differently? (37:11) Maybe we’ll start with Dr. Christina, who’s given us some of the most breakthrough platforms that it seems to be at the beginning of our history.
(DR. DESPINA SOLOMONIDOU)
(37:19) Right, so you want me to pick one of those? (37:21) I could not. (37:23) I think you need it all.
(37:25) So, if I were to code a new system, I would code something that integrates all what you said. (37:34) Science, the software, and the supply. (37:38) In my mind, science determines the direction.
(37:42) And yes, we do have good science. (37:44) Actually, science becomes even better as technology is evolving. (37:49) We understand better biology.
(37:51) We are gaining more insights. (37:53) Science becomes eventually more predictable. (37:56)
However, software is the one that accelerates our understanding.
(38:03) The AI that we are talking about, the technology advancements in data science, these all integrate, allows us to expedite drug discovery, as Professor Chong said, allows us to better understand and predict eventually the clinical efficacy through other differently designed clinical trials. (38:26) We heard about quality, improving quality. (38:30) So, software to me is the accelerator.
(38:34) And then supply defines the boundaries. (38:37) We spoke about accessibility. (38:39) We spoke about availability.
(38:41) This is what is determined by the supply. (38:44) So, it’s a framework. (38:46) So, in my next life, if I were to program, to code something, I would do it in an integrated fashion of all these three together.
(38:54) And I believe in this environment that we speak to, the Indian ecosystem does allow us to do it.
(MR.SHIRAM CHARY)
(39:01) Fascinating. (39:02) Rashmi, from your vantage point as a global CIO…
(MS. RASHMI KUMAR)
(39:05) The way I look at it would be fine in terms of supply. (39:09) Because breed science cannot reach to the patients and the customers. (39:14) What’s the point of it, right?
(39:16) Both are equally critical. (39:17) And software is becoming the flow through the pipes, becoming the foundation through which we can develop science and we can get it to the patients. (39:29) So, I’ll talk about a couple of science that we have.
(39:31) And we are a little different than genomic or pharma molecules. (39:37) I’m going to explain it a little bit on the science side. (39:40) Either we talk about our stealth access system, which is the spine surgery, or our recent acquisition, CatWorx, for cardiac diagnosis.
(39:51) So, CatWorx takes a very invasive process where you have to get through the human artery into the heart to figure out what’s the propensity of the blockage to cause a heart attack. (40:06) It has now gone to an AI-driven imaging platform where, through pictures, we are able to identify the FFR number, which is the flow reduction number, and you don’t have to send a catheter inside the human body. (40:22) Our stealth access spine surgery system creates a planning platform for the surgeon, for a particular patient, depending on their images of the spine and comparing with numerous surgeries which are done before and what kind of rod or screws that patient might need.
(40:41) So, if you look at software and AI, we have numerous such examples in our pacemakers, in diabetes systems, and our GI genius for colonoscopy, where science has been tremendously enhanced in real time because of the AI software that we are able to leverage to enable that. (41:05) It’s a true digital twin of the human or the organ. (41:09) Our Afera cardiac ablation system is another example.
(41:14) But at the same time, when we look at, we call it mission, purpose, and being a company goes hand in hand, right? (41:23) The software and AI in our supply systems (41:26) and how do we transition these technologies into the real life, (41:30) either it’s distributed, digital, clinical trial, (41:33) or it’s about regulatory compliance and medical approvals (41:37) across different bodies globally(41:39) so that we can bring this therapy to the patient, (41:43) or it is about how do we forecast, (41:46) how do we manage logistics, (41:48) how do we transfer this science into a real life manufacturing. (41:52) Because, again, we are very different than Pharmaware. (41:55) It’s a very high-end throughput manufacturing versus us.
(42:00) It’s more discrete. (42:00) Our cardiac ablation system is a little spherical mesh with nine electrodes in it, and every electrode plays a different role when we are ablating a patient for the heart issues. (42:12) So if we look at our logistics ability, simple systems like transportation management system becomes extremely critical for us.
(42:19) Planning and forecasting is absolutely critical because these devices have sharp lines, and we need to understand where do we need how much of it. (42:28) So when we talk about supply chain, automated visual inspection of these medical products, we have seen that AI and vision technologies driven by AI creates better product qualities for our patients and customers eventually. (42:48) So when we look at this software AI technological advancement which has happened, it enables both the science, the R&D, and our ability to transition it into the manufacturing and production and get it to our patients and customers in a very resilient, reliable, and frictionless customer experience.
(MR.SHIRAM CHARY)
(43:09) Thank you. (43:11) Go ahead.
(MR. EAMONN WARREN)
(43:12) As you said before, we make two things. (43:15) The manufacturing makes the product, and we make paper or documentation. (43:19) So I think there’s a huge opportunity, and we’re seeing this already, around changing that paper into digital and having electronic fax records and having
electronic review and release of those records where it’s much more error-proof and much less prone to human error if you’re filling out these paper documents and creating a whole big paper trail around that as well.
(43:40) So I think we’re going to scale at these scales. (43:43) We need to have those kind of error-proof systems built in and digitized up front, and then be able to overlay all these AI and predictive analytics tools and everything else on top of that to be able to drive better outcomes for the process overall.
(MR.SHIRAM CHARY)
(43:55) It’s the last code scale, scale and prediction. (43:58) Excellent. (43:59) Very good.
(43:59) I’m getting the nudge on the time here, but before I leave you all to a quick rapid fire, a few minutes to each one of you. (44:10) It is 2030, so we fast forward four years. (44:13) What is one thing we do today in life sciences and healthcare that would look absolutely primitive and slow in four years from now?
(44:22) Your best predictions or most provocative predictions at the end.
(MR. EAMONN WARREN)
(44:26) Start with you, Amy. (44:27) I’m going to build off my last points. (44:28) I’m going to say getting rid of paper from manufacturing processes.
(44:31) That’s going to look antiquated, hopefully, in four years’ time. (44:34) It sounds like a lot of pre-safe, fully digital processes. (44:38) Thanks, Amy.
(DR. DESPINA SOLOMONIDOU)
(44:39) I’d like to say that probably I would love to see our industry being paperless as it concerns batteries, but also regulatory submissions. (44:49) I’m dreaming of that. (44:51) We are handing over a disk or a memory or whatever you call it, an algorithm that summarizes the history of development for a product as opposed to 10 binders full of paper.
(45:05) Perfect.
(MS. RASHMI KUMAR)
(45:07) I’ll be provocative as you asked. (45:10) Two words in our six-word mission statement is extend life. (45:14) I do think 2030 will be a turning point in our lives to really see extended lives implemented, because of what our pharma colleagues are doing, what we are doing in MedTech and other communities.
(45:29) David?
(DR. PETER OWOTOKI)
(45:34) I would go for personalization at scale. (45:38) Basically, having data that is morerepresentative and combines different modalities. (45:47) Genomics information, imaging information, behavioral information, to build a model of mind that then helps to better diagnose as we get older, that we have better cognition, that we are able to address these conditions better.
(46:07) And we wonder, already in 2030, why didn’t we do that earlier? (46:10) Why didn’t we bring all this data together? (46:11) Why were we not representative earlier?
(46:15) Dr. Kenneth?
(DR. KENNETH BARR)
(46:20) Just to build on that a bit, I think by 2030, hopefully, we will be much harder along in terms of leveraging the data that comes from, say, like genetics and proteomics, from computational tools and algorithms, from human tissue-based organoid systems, so that we believe we will have much better predictability and probability of success for patient outcomes. (46:47) Professor Chong, lastly.
(DR. PETER OWOTOKI)
(46:50) Science and software and supplies are working right now, but in five, six years later, they are going to be one team, so that science, software, supplies, they work together, so that the success rate will increase. (47:10) Thank you. (47:11) So, yeah, this is the end of our panel.
(MR.SHIRAM CHARY)
(47:14) I just want to conclude with two things. (47:15) One, we’ve got a highly diversified panel that represents the overall life science healthcare ecosystem. (47:22) We’ve got MedTech, manufacturing
CDMOs, drug discovery start-ups, driven here.
(47:28) It kind of reinforces the interval with interdependency value chain that we think, which is BioWeave, which will define the future of this sector and broaden the future of this sector. (47:38) So with that, a round of applause to all our panelists today, and hopefully you found the panel interesting. (47:44) Thank you.Panel Discussion 1- Catalysts that Actually Scale: Science, Software & Supply
Speakers:
* Mr. Eamonn Warren, VP, API and Dry Products Manufacturing, Eli Lilly and Company,
USA
* Dr. Despina Solomonidou, EVP, Global Head Technical Research & Development,
Novartis, Switzerland
* Ms. Rashmi Kumar, SVP & Chief Information Officer, Medtronic, USA
* Dr. Peter Owotoki, Cofounder & CEO, EmpathicAI.life Ltd, Germany
* Prof. Kil To Chong, Founder & CEO, JuYoungBio Corporation, South Korea
* Dr. Kenneth Barr, SVP, Head of Strategic Collaborations, Head of SynVent, Syngene
Moderator: Mr. Shriram Chary- AI leader Europe ( Life sciences & partner, EY)
Below is the full transcription of the session:
(MR.SHIRAM CHARY)
(0:00) What an exciting time to be in the biopharma healthcare space. (0:06) Good morning everyone, my name isShreep Chari. (0:08) I am a partner at WISE Healthcare Life Sciences Practice.
(0:13) And I get the opportunity to meet all these wonderful stalwarts of our industry and host the panel today. (0:20) We’re living in a paradox, let me explain what that means. (0:25) This company is moving at a breakneck speed, but yet our…
(0:29) This is panel discussion. (0:33) That’s the topic that we’ll be talking about today. (0:36) We’ve realized that a breakthrough in a lab is just a promise, but only becomes a viable product when science, systems, software, and supply act as a single interwoven and an integrated entity.
(0:51) At EY, we’re calling this bioware. (0:54) The interplay across all these is what we’re going to talk through today as we introduce most of our panelists today. (1:01) So let me introduce the team of avengers that we’ve got assembled from the industry today.
(1:08) I’m going to introduce each one of them, and also talk about the unique super strength that they’ll bring to the table today as we move through the discussion. (1:16) I’ll start with Eamon. (1:18) Eamon’s super strength is he’s a scaler.(1:21) He’s an expert in moving discoveries from the lab to scaling and building this interwoven supply chain. (1:27) Eamon is the Group Vice President of Manufacturing at ELISA Game Company. (1:31) Next to Eamon, we’re joined by Dr. Jaspeena. (1:35) Her unique superpower is she’s the architect. (1:38) Transforming traditional R&D into integrated technical platforms is what she’s pioneering. (1:45) Dr. Jaspeena is the Executive Vice President, Global Technical R&D Lead for Novartis. (1:52) Next to her, we’re joined by Rashmi Kumar. (1:56) Rashmi’s super strength is she’s the digital engineer. (1:58) She’s known for driving hyper automation and AI-assisted surgery and patient care.
(2:05) Rashmi is the Senior Vice President at Global CIO at Medtronic. (2:09) Next to Rashmi, we’re joined by Dr. Peter Otoki. (2:13) Peter’s super strength is he’s the ethicist.
(2:16) His expertise lies in measuring psychological patient conditions to ensure empathetic care. (2:22) Peter’s also the co-founder and CEO of EmpatheticAI.life. Next to Peter, we’ve got Professor Chong. (2:29) Professor Chong is a pioneer in computational modeling and use of natural products for drug discovery.
(2:36) He’s also the founder and CEO of Juyung Bio, a research company. (2:41) And to finish off the panel, we’ve got Dr. Kenneth Barr. (2:45) His superpower is he’s the connector.
(2:48) Mastering partnerships and alliances to accelerate target-containing timelines is what Dr. Kenneth has pioneered. (2:54) Dr. Kenneth is the Senior Vice President of Strategic Collaborations at Cengi in India. (3:01) If you notice, each of our panelists today represent each part of the value chain.
(3:06) From drug discovery to manufacturing to software systems at scale, can also bring ethical new considerations in order to strategic alliances and partnerships. (3:16) We look forward to a conversation about how all of these aspects of the value chain will define the next-gen healthcare and life-science innovation. (3:32) Oh, you’ve got a mic?
(3:33) Okay. (3:34) Alright. (3:35) I guess we’re taking the mics then.
(3:37) So, why don’t we start with the heart of the issue. (3:41) The new architecture is going to require breaking down the walls between R&D and manufacturing. (3:46) So, why don’t we start with Dave and you and Dr. Bisbeha. (3:49) The question to you is, you both represent the opposite technical spectrum. (3:54) Manufacturing and R&D. (3:56) Historically, breakthroughs have come from the R&D labs.
(3:59) You push it over to manufacturing, hoping they’ll find a way to scale it. (4:04) As we hope to scale a billion doses, which do you think wall has to come down first? (4:10) Is it a software problem?
(4:12) Is it a data problem? (4:13) Or do you think it’s a cultural issue? (4:15) Why don’t we start with you, Dr. Bisbeha.
(DR. DESPINA SOLOMONIDOU)
(4:25) I hope that I’m audible. (4:26) Wonderful. (4:28) Thanks for the question, Sri, and it’s interesting.
(4:30) I’m sitting in between my colleague from manufacturing and Dave. (4:38) I don’t think it’s… (4:40) Is it working now?
(4:41) Okay. (4:43) So, it’s an interesting question that you are asking, in fact, about the wall, right? (4:49) And whether it is a throwing over the fence product.
(4:55) I think historically, it has been maybe a matter of organizational charts, R&D and commercial production. (5:04) In my view, it’s more, let’s say, a belief or a philosophical conversation about what great science means. (5:16) And I think R&D stands for optimizing for novelty, for innovation, proof of principle, and for speed.
(5:25) Whereas my colleagues at commercial production are likely to optimize for robustness, reliability. (5:32) I’m going to call it out as well, cost-effectiveness, right? (5:36) So, if I were to prioritize among all the elements that you offer, Sri, I first would go probably for culture.
(5:46) Because it has to do with the trust and the co-ownership. (5:52) It has to do with knowledge generation, but also knowledge use. (5:56) And in my mind, it’s all about the history of development.
(6:03) A collection of data that is being transferred to operations. (6:08) It’s not just a product or a process. (6:12) So, the one that I would prioritize is trust and ownership as it creates this joint responsibility for developing a product.
(6:22) And it goes along with followed by data, which up to date, it has been a collection of numerous of pages and reports. (6:35) I hope that in the future, it will be translatable into a digital thread where systems come into play. (6:43) That will be handed over as a long history of a product development to the commercial organization.(6:52) To take it forward into production. (6:55) That’s the way I would look into the future. (6:57) And in fact, that’s the way we live, I think, today.
(7:01) R&D and production. (7:04) At least in the organization that I’m representing. (7:07) We have the colleagues from commercial production being part of our R&D efforts.
(7:13) From the beginning, where we built in, I call it, manufacturability in the design of the project, in the design of the process. (7:22) Having in mind, how can we scale up? (7:25) How can we control this?
(7:27) What could be the control strategies? (7:29) How do we make a product affordable on the long run?
(MR.SHIRAM CHARY)
(7:34) Eamon, what do you think you want to add to what the supply said?
(MR.EAMONN WARREN)
(7:39) Okay, it’s different. (7:40) Yeah, I agree with a lot of what you said, Nespina. (7:42) I think the important question initially is, why is this important?
(7:47) I think it’s important for us to get products to patients quickly. (7:50) Because if we don’t, patients don’t get the medicine they need. (7:53) And our ability to scale, to develop and scale products quickly.
(7:58) To be able to serve not just one, but millions of patients is super important. (8:02) So the patients are waiting, and it’s up to us to figure out ways to make this process more efficient. (8:06) I think when you look at it across different aspects, I think it requires so many different things to do this effectively.
(8:14) It requires science and engineering to be able to effectively develop these processes and scale them up. (8:20) It requires digital, using PLM, Project Life Cycle Management, and electronic plant records, which are all relevant, to be able to make high-quality, repeatable processes. (8:30) It also requires innovative record-keeping to be able to launch record-keeping dossiers across multiple countries simultaneously, and launch not just to one country, but to all countries at the same time.
(8:44) But I think, going back to what Nespina said, I think the cultural aspect for me is probably really, really core to what we do. (8:51) And I think that you’re having that cultural change to where speed is paramount, and the primary focus is really important. (9:01) Both the teamwork between development and manufacturing, but then also the co-location of development and manufacturing is super important.
(9:10) To be able to have that organic interchange of ideas and developing innovative solutions to problems quickly is super important.
(MR.SHIRAM CHARY)
(9:19) Thanks, David. (9:20) How about we look at this same issue from a very different lens? (9:23) I’d like to call upon Professor Chong and Dr. Kenneth here. (9:27) So, Professor Chong, your computational models are now discovering molecules at a frequent speed, which would have been impossible about five years back. (9:37) But Kenneth, as a CRDMO, can the lab and the supply chain keep up with the digital speed, at the speed at which UGenBio is discovering molecules and discoveries in the lab? (9:49) How do we stop the supply from being the brake on science?
(9:54) Why don’t we start with Professor Chong? (9:55) Maybe if you could speak to talk about the speed of computational discovery, and then we’ll go over to Dr. Kenneth.
(PROF. KIL TO CHONG)
(10:02) Thank you. (10:05) I’m doing the end-to-end drug discovery using AI. (10:10) One of the tools that we have developed, since once we’ve got a target protein, we can produce the optimized molecule in 10 minutes.
(10:24) So what we do, using the target protein, we can generate new molecules. (10:30) Then we will combine with the protein and molecule binding ability, and we do toxin testing, ultimate testing, and mDNA all together in one of the platforms. (10:43) So we could develop a kind of a lead compound in 10 minutes.
(10:48) However, we have to consider how we can make it drug. (10:54) So we need the CD, CRO companies that they can do the lab and everything. (11:02) And also, we have to do kind of supplies together.
(11:08) So that for catalyze the AI into the real drug, we need kind of the cooperation from the beginning. (11:18) Usually the AI can determine the not good compounds at the beginning. (11:24) So that AI can speed up the development of the drug.
(11:28) However, we can interrupt things that cannot be the drug at the end. (11:34) So start from the beginning, we are using the AI and interrupt things that are not good for the drug compound. (11:42) So then we need the cooperation with the CRO.
(MR.SHIRAM CHARY)
(11:52) Dr. Kim, is there anything you’d like to add in terms of the role of the CRO in how we make drug discovery?
(DR. KENNETH BARR)
(11:59) Sure, thank you. (12:01) First of all, I really like the way that Professor Tong framed the question. (12:06) Because the first speakers were taking the word scale in the context of large-scale production, which is an important question to address.
(12:13) But the other part of scale, which is on the discovery side, the R&D side, which is where we spend our time, is about the number of successful programs that you can bring forward, eventually making it to the patients and to the clinic. (12:26) So how can you increase the scale across the continuum of therapeutic areas that you’re serving, the number of projects that are going to go from ideation through to success in the clinic. (12:37) And so, for an organization such as ours, and the company I work for is CINTI, we’re what we call a fully integrated contract discovery, development, manufacturing organization.
(12:47) And we have reframed the conversation from segmenting discovery is here, development is here, and manufacturing is there, to what we now call modality-based service lines. (13:01) And in modality-based service lines, you have your small molecule that runs all the way from discovery across through development and manufacturing. (13:07) Likewise with biologics, likewise with peptides, polybinoleucleotides, ADCs, targeted degradation.
(13:15) So the idea is that you build in the capability and the capacity aggregated into one organization to be able to accelerate that process. (13:23) And one way or the other, I think most people in this room are in the business of science and the business of discovery. (13:29) So we all know what the speed, quality, cost triangle is.
(13:33) And today the conversations that we’re having are almost entirely focused on speed and quality in the context of reasonable cost. (13:43) So I think we’ve now covered a pretty good part of the spectrum when we talk about scale. (13:48) Thank you.
(MR.SHIRAM CHARY)
(13:48) Why don’t we move to maybe at the heart of the problem, right? (13:52) It comes down to data. (13:54) Data is now unstructured, pluggable, available in different forms.
(13:58) I guess my next question is to Rashmi and Peter. (14:01) As we talk about the invisible capital, which is data, how is electronic harnessing all these unstructured data from the lab to supply chain and manufacturing to ensure resilience? (14:13) And maybe the second part of the question for you, Peter, is as we leverage so much data, is there a component of human and psychological behavior and modeling that we may be missing or not fully taking into consideration as we design the lab?
(14:28) So I’ll start with Rashmi and then get to Peter.
(MS. RASHMI KUMAR)
(14:38) Integrating that. (14:43) I hope this is better. (14:44) Yeah.
(14:45) So the way we think about data and platforms is one part is during the science and the engineering and the development of the science, but at the same time connecting the dots of the science and development to manufacturing. (14:58) And with the automation we have done over years, we are sitting on a lot of data which is coming to us, which now with the advancement of technology and availability of AI can be leveraged to define future iterations of how these end-to-end processes are going to look and how we reimagine that. (15:18) In our product development, we are a little unique compared to the panelist here where we are a medical technology company.
(15:26) We have 73 different therapies. (15:29) And what we are seeing as an advantage with the advancement of AI is our ability to leverage our imaging data better to create different level of automation through robotics and in surgeries, right? (15:44) I think it died.
(15:55) Oh, no. (15:56) So with the availability of data platforms and capabilities in engineering, it plays very well on both sides of it and product development through imaging and robotics and digital touch surgery that we have already out there, but at the same time is improving that entire end-to-end value chain from scientific research to development to manufacturing.
(MR.SHIRAM CHARY)
(13:32) Thank you, Ashwini. (13:34) Peter, anything you want to add in terms of using data for human behavior modeling and how we take ethical considerations into?
Speaker 4(DR. PETER OWOTOKI)
(13:47) Can I add? (13:49) I think this is fine. (13:50) Yeah, so thank you very much.
(13:51) This is a very important question, right? (13:54) And it kind of closes the loop, right? (13:56) From molecules to getting it through the research process to manufacturing, now I come to the human aspect of it, right?
(14:04) How does it work in the real world, right? (14:07) Using data as the lifeblood of innovation, the software of innovation, the kinds of data we collect, how we use those data really matters for the real-world impact and the scalability of the innovation. (14:23) Imagine Professor Till invests the molecule that is awesome, both with the AI and walks through the clinical trial process, but it gets into the real world and it misses the mark for six billion people, and why?
(14:43) Because the intentionality to have representation (14:48) in the data pool such that the effects, (14:54) the side effects, and how those dosages, (14:58) how they’re gonna have an impact in the real world, (15:01) if they are not considered intentionally, right, (15:04) that would be, I mean, a classical example is, (15:07) you know, women’s health, right, (15:09) where the majority of cell lines (15:11) that many of the innovation is based upon (15:13) are just male cell lines, (15:16) and then you have a drug that works, (15:18) but in the real world, (15:19) it’s not working so well for women, right? (15:23) Similarly for people of color like myself, right? (15:26) Making sure, especially in this age of AI, where AI, Professor Till’s AI will accelerate innovation.
(15:36) This amazing connection of these processes will bring them back to the market quicker, but it could miss the mark for billions of people. (15:43) It will not be robust, it will not scale. (15:45) So thinking a lot more about the ethics, right, how we innovate for eight billion people with AI, using intentionality that there is representation by age, by gender, by ethnicity, by social classes, and this is something that drives the work that we do at Envati, right?
(MR.SHIRAM CHARY)
(16:06) Very interesting. (16:07) Let’s maybe build on that a little bit more, right? (16:10) So, Professor Chong, you use very advanced AI to do drug discovery, right?
(16:15) And you’re studying AI through the lens of chemistry, cheminformatics, bioinformatics, so forth. (16:22) And Peter, you’re looking at AI to study human behavior, fundamentally, based on what you just spoke about. (16:28) Is there a way, or is there a world where these two worlds collide?
(16:33) Is there a potential digital twin in the future where we’re looking at chemistry and human psychological models together? (16:39) Why don’t we start with Professor Chong first, and I’ll come back to you, Peter.
(PROF. KIL TO CHONG)
(16:47) Jeremy? (16:49) By using AI, we can determine the small molecule, but it could kill the disease. (16:58) However, AI can determine the autism and other side things, so that the psychology problem or neurological problems, if we’re using the omics data, we can, it is possible to determine the biomarkers or some kind of a trend in the data, omics data.
(17:21) So it’s possible, it is the next generation of our direction that we can develop psychological solutions so that Peter, that he could manage that by his way, and I can provide the biomarkers or some kind of the information using the AI. (17:43) So it could be a good, next generation therapeutical work. (17:51) That’s what I guess.
(DR. PETER OWOTOKI)
(17:53) Peter, anything you’d like to add? (17:55) I think that’s really fantastic description of what both the genomics or multi-omics kind of data combined with behavioral data can achieve for many conditions of the mind. (18:09) Autism is one space where we’ve done a lot of work.
(18:13) Today, there is really no clear biomarkers. (18:17) They don’t exist, right? (18:19) There’s a lot of acceleration today with AI.
(18:22) Diagnosis is based on the observations of human experts. (18:26) Oftentimes, they’re biased, right? (18:30) Or they’re just based on the subjective observations of these experts.
(18:38) Now imagine that with the work that Dr. Cheng’s doing, you could actually combine the behavioral information with genomics information to identify truly objective biomarkers for autism. (18:55) What would that lead to? (18:58) Four times, men are diagnosed more frequently than women with autism, right?
(19:05) Either because women are very good at masking, right? (19:08) And the observers don’t kind of catch that. (19:12) Combining the genomics with the behavioral will take that bias away.
(19:17) You’ll have very clear biomarkers. (19:19) And this is not just for autism, it’s just the stats, that it affects things that’s gonna touch everyone first, Alzheimer’s, other neurodegenerative situations. (19:27) Being able to combine behavioral information and genomics information to find clear biomarkers, and then you get targets, right, that can address these conditions and make everyone healthier.
(MS. RASHMI KUMAR)
(19:38) Thank you. (19:39) I’ll just add to it. (19:40) The regulations also need to accelerate because pharmacogenomics is available to personalize treatment for these types of biomarker symptoms that are seen subjectively, right?
(19:54) But that’s where data and evidence is also important for regulators who are building biomarkers law to say that is it approved to treat a patient based on what we are seeing? (20:08) And then the whole ethical AI part of it is, is that not going to impact that person’s ability to work and be contributing to the society at the level that they can do it, right? (20:21) So one part is science, one part is software, other part is how do we take the information and data and enable the regulatory aspects of the treatment?
(MR.SHIRAM CHARY)
(20:32) Thank you, Rashmi. (20:33) I’m glad you brought all those three aspects back together. (20:36) Kind of tease up the next topic we want to talk about.
(20:39) One topic, since we’re in India, right? (20:41) The next topic is more about India’s role in survival as all those three capabilities need to come together symbiotically, right? (20:49) So I guess my question to, we’ll start with India, Dr. Kenneth. (20:53) India is often called the world’s factory. (20:56) But looking at the biome and model, it’s not just about scaling manufacturing. (21:00) It’s about bringing software like science with leading manufacturing capabilities being brought together to really deliver a printed doses at scale.
(21:09) So Amy, on the global perspective, what’s your perspective on the case scale as you think about the biome?
(MR. EAMONN WARREN)
(21:19) Yeah, I think India, it’s amazing. (21:22) The whole ecosystem in India around manufacturing in particular is phenomenal. (21:28) I think this week we got to visit some of our partners here in India.
(21:33) The scale that they operate at and the ability to take products from lab to pilot to manufacturing scale is phenomenal. (21:41) I think the big thing I take away from India is India really knows how to make medicine and do it really well. (21:48) So I think that’s something that we’re hoping to see more of that going forward in India next several years.
(MR.SHIRAM CHARY)
(21:55) Dr. Keren, anything you want to add to it as you think about integrated CRDMOs or the evolution of CRDMOs in this journey?
(DR. KENNETH BARR)
(22:03) Sure, thank you. (22:05) So evolution, Sri, is the right word. (22:09) So I spent the first 25 years of my career in US pharma and biotech.
(22:15) And a large percentage of that, I was responsible for running drug discovery programs that were outsourced to India and China. (22:21) And I can tell you that when we first started, it was primarily about the cost arbitrage. (22:28) And in fact, when I was working at Merck, procurement used to walk the hallways and ask the question, how many Simgene scientists would it take to equal the productivity of a Merck scientist?
(22:38) I could do not. (22:39) But now having been seven years on the provider side and seen a tremendous evolution in the industry, where we are today is people have started to use the phrase IIT, or Integrated Innovation Partner, right? (22:51) And what that means in practice, and this is where I think the whole industry is headed in certain Simgene is working very hard in this direction, is to effectively be today an 8,500 person biopharmaceutical company for hire.
(23:04) We have built in each of the capabilities to go from early drug discovery through to the candidate selection, preclinical evaluation, clinical trials. (23:13) And we can do that as a partner with the client. (23:17) And we can even do that now on behalf of the client.
(23:19) And Simgene is not alone. (23:20) There are other organizations that are working in that direction as well. (23:23) But importantly, and we talked about this earlier, it has to be that people are now coming to India not because we’re very low cost.
(23:31) There is an advantage of working in a low cost operating region for sure, but they have to come to us because we create value. (23:37) And that’s where I think India’s on a very strong trajectory and I’m really proud to be part of it. (23:43) And thank you very much for including me in the conversation because I think this really reflects what we’re trying to get to.
(MR.SHIRAM CHARY)
(23:49) Thank you. (23:51) Let’s sort of switch back to the work. (23:53) We were so far talking about the scale of S in manufacturing.
(23:57) Let’s maybe shift to the scale of access. (24:00) Access is going to be equally important or the scale of access is going to be important for all these intended medicines to deliver the value that they’re supposed to. (24:08) So question to Dr. Justina and Peter. (24:12) We’ve talked about scale in terms of what? (24:15) Through manufacturing and CRD more strategies. (24:18) How do we think about scale in terms of patient access and healthcare access?
(24:23) And how do you think about democratized care, Peter, as you think about ethical use of AI, democratic use of AI? (24:31) Maybe we’ll start with Dr. Despina first and then get to Peter.
(DR. DESPINA SOLOMONIDOU)
(24:36) Thank you. (24:37) Thank you, Sri, for the question. (24:39) Actually, two questions, right?
(24:41) First, speaking about scale. (24:43) And I’m glad that we are shifting the discussion from volumes to more value, building on what Ken just said, the value of the supply chain. (24:54) And access to me, it’s not just pricing, reimbursement, policies, and government.
(25:02) As you said, access is about making the drug available to patients, to those who need it. (25:08) And when I think about access, I look at it like an operational challenge that has to address the supply chain, and we can speak about that with him later, which is the reliability, the robustness of the manufacturing process, the ability to deliver reproducible high quality to patients at any time. (25:34) And in order to do that, one of the areas to look at is obviously science, but also how to integrate AI and technology into high quality products.
(25:49) I’m thinking of reducing the cost of goods, not simply by cutting costs, but more importantly, by improving the process in such a way that the yield is higher, for example, or we have automation
integrated in the manufacturing process that allows the product to be manufactured in a more cost effective manner. (26:12) But then the other thing, it’s about beyond affordability, it’s also about availability. (26:17) And in a truly global world, (26:20) and you mentioned at the beginning of this session (26:23) with some more peculiar modalities (26:27) like radiolegal therapies, cell therapies, (26:30) but also the biologics and gene therapies, (26:34) the question to me becomes, (26:36) how can we come closer to the patients, to the end users, (26:41) which is an element of, (26:44) can we reimagine the manufacturing architecture (26:48) from a single global hub (26:50) to more regional distributed manufacturing sites (26:54) where the logistics is not becoming (26:57) the rate limiting step and it’s not a cost factor as well.
(27:01) So these are three elements that I would like to put together when we speak about access in operational language, as opposed to pricing and reimbursement. (27:12) Just to sum it up, supply, affordability, or reliable supply, I should rather say affordability, and more about a distributed manufacturing network. (27:27) The second question I think was about democratizing.
(MR.SHIRAM CHARY)
(27:30) No, I think you summarized it pretty well. (27:33) Maybe sort of pivot a little bit, I don’t know how big Peter is. (27:37) Peter, do you want to talk about how you could potentially democratize access at the care of diagnosis itself and talk about some of the work that you’ll be leading in that space?
(27:46) Absolutely. (27:49) You might want to bring the mic closer to you.
(DR. PETER OWOTOKI)
(27:53) Okay, so I mean, a key part of democratizing access is leveraging technology. (28:03) Leveraging technology, but leveraging platforms of innovation as well. (28:09) So, I mean, in my earlier conversation, (28:12) it’s about the six billion to the eight billion, (28:17) giving access, but making sure that even those voices (28:21) get embedded at every part of the value chain, (28:27) not just in manufacturing, (28:29) but also in the very early designs, (28:32) that you’re leveraging platforms of innovation (28:35) to make sure that those solutions, (28:38) those therapies that will be created, right, (28:40) including the advanced personalized therapies, (28:43) that these voices are represented from the very get-go. (28:46) And that applies, it applies here in India, it applies in Africa, it applies everywhere. (28:51) I talked about women’s health, for example, where, I mean, four billion, 50% of the population, but only 1% of R&D spent, right, is going into that, right?
(29:02) How can you ensure that there is more at the very early stages, right? (29:06) Even right here in Hyderabad, but in other places, (29:10) that innovation is happening at the very early stages, (29:13) a lot more collaboration, collaborative platforms, (29:16) the likes that’s been built by like EBE, by Novartis, (29:19) that is leveraging the diversity, (29:21) and by other companies as well, (29:22) is leveraging the diversity of representation, (29:26) and getting these voices in early on, right, (29:29) to make sure that the cell lines, the data, (29:33) the, you know, insights, right, (29:36) that will make sure that these solutions scale, (29:38) that they’re coming in early on, right? (29:40) And it will lead to some innovations, right? (29:42) Some of what we’re doing, some of the ventures in our studio, is to think about how do we democratize access to diagnosis, right, in ways that touches the end of the aisle, right?
(29:56) Using things like digital biomarkers, so your voice signature, right, as a way to better understand the aisle, right? (30:03) Your social, emotional stimuli, and interacting with them, betterunderstand the state of the aisle, right? (30:10) Making sure that these type of innovation, they feel through the power of the platforms that’s represented here, but ensuring that also the funding gets in early enough, right, through these collaborative platforms, you know, with different voices.
(MR.SHIRAM CHARY)
(30:26) All right, it’s great, Peter, thank you for that. (30:28) I mean, all of you represented in this stage today represent different parts of the value chain, and you have equally a vantage point of what access has to do here, right? (30:37) I mean, Rashmi was giving a great example, that breakout where she had this app.
(30:42) I don’t know if, Rashmi, you want to talk about that, or if you think what that is.
(MS. RASHMI KUMAR)
(30:45) Yeah, we have a app called the Contact Surgery, which is the most, one of the most used medical app. (30:53) Maybe open evidence will be there soon, (30:56) but it is about training surgeons on surgery, (31:00) and we are integrating it with our uber robotic system, (31:04) robotic surgery systems, (31:06) where it improves access (31:10) because people who don’t have access to robotic surgery, (31:14) at that point, can train themselves on that capability (31:17) so that when they have access to that technology, (31:21) they can leverage it, you know, leverage it going forward. (31:24) I just want to add to Peter’s point about access and women health. (31:30) Our head of Structure Heart Party (31:32) at one of the operating units, (31:35) product leaders, created a campaign (31:37) called Letter to Your Mother, (31:40) and it is interesting, in part, (31:43) everybody considered the same, (31:44) versus a structure of a female heart (31:47) is very different than a male heart, (31:48) and the sizes of valves and technologies that needed, (31:52) and the other part is, socially, (31:55) heart health focuses on men and it’s not on women, right? (31:58) So, Metonic, we had a campaign started, Letter to Your Mother, which, starting from our CEO to every leader, every president, every employee, we
encouraged them to write a letter to their mother to take care of their heart health and go for checkups and make sure that it reflects in their regular, at least, symptomatic treatments around that topic.
(MR. EAMONN WARREN)
(32:25) Anyone else like to add anything? (32:27) We talked about democratizing access to medicine. (32:31) I think a really important part of that is scale.
(32:34) You know, I think we’ve been able to scale up and scale out these new medicines. (32:38) I think, for too long, a lot of medicines haven’t been accessible to many parts of the world. (32:44) So, I think India offers a really good opportunity here to help us, you know, help the pharma industry develop efficient and effective processes that can be scaled rapidly and out to all parts of the world at the same time.
(32:57) So, I think talking about India being the hub for vaccine manufacturing in the past, there’s a huge footprint here that can be leveraged to help the rest of the world learn and explore some of the know-how around making medicine to other parts of the world to manufacture products at scale and then improve access to patients. (33:16) That’s great.
(MR.SHIRAM CHARY)
(33:16) Anything else? (33:17) Dr. Kennett, anything you want to add? (33:19) Or Professor John?
(DR. KENNETH BARR)
(33:28) Thinking about the comments now in the context of the session, which is on catalystic scale, on the technology side, on the innovation side that you’re referring to, that we’re trying to bring back into earlier, obviously, there’s going to be a great emphasis now on leveraging AI, different automation tools, robotics. (33:49) It’s about how quickly and efficiently we can move forward. (33:53) So, one of the ways in which we’re trying to do that now, and again, I don’t think we’re unique, but I do think it’s important to call out, is if you think about in discovery, how we call it the design, make, test, analyze cycle, the DMTA cycle.
(34:08) So, the purpose, to Professor Chong’s point, is can we reduce the number of cycles required? (34:14) Because every cycle takes time, and the goal is to get to the therapeutic as fast as possible, but the right therapeutic. (34:20) So, if we’re leveraging AI computational tools, we should be able to run fewer cycles to get to the right molecule.
(34:28) Combined with that, every single part of that DMTA cycle can benefit from some degree of automation. (34:35) So, for example, the first step would be the design of the molecule. (34:39) But if we’re talking about a small molecule, the second step would be the retrosynthetic analysis.
(34:44) How would you make that molecule? (34:46) The third step would be, where can you access the chemical reagents and the starting materials required to synthesize that molecule? (34:54) How rapidly can you synthesize and then characterize the molecule in the biological test?
(34:59) How rapidly can you analyze the data so that it goes back into the loop? (35:03) So, those things are, that’s what we’re trying to do now. (35:06) So, it’s worth accelerating the cycles, butreducing the number of cycles.
(35:11) And then, also to Professor Chong’s point, at the end of the day, it’s not only about designing a molecule that is active, that binds to the surface of the protein, but there are so many different characteristics that have to be optimized in order for it to become a successful drug, including developability. (35:29) Can you scale it into manufacturing? (35:31) There too, we’re leveraging these tools to help us make better decisions.
(35:35) So, I think that ties really in together with the purpose of the session.
(MR.SHIRAM CHARY)
(35:39) Thank you, Dr. Kenneth. (35:40) Anything to add, Dr. Chong?
(DR. PETER OWOTOKI)
(35:45) Actually, the real catalyst is the start from the beginning. (35:53) Science and the software and supplies, they have to start at the beginning so that the success of a drug discovery is right up. (36:07) When we start from the beginning, all of these kind of collaboration.
(36:15) So, from this discussion, I wish we can realize that how important it is that start from the beginning, all of this combination together, collaboration together.
(MR.SHIRAM CHARY)
(36:29) Thank you. (36:30) So, we’ve talked about scale and manufacturing. (36:32) That’s the first S.
(36:34) We’ve talked about stimulant access. (36:35) That’s the second S. (36:37) And now we’re gonna get, a little biased, scale and software, which seems to be a pretty topical discussion right now.
(36:43) So, here’s a question to Christina and Rashmi. (36:47) So, both, I think about software and science as almost like dueling the same package. (36:52) If you both could go back in time and code a perfect ecosystem for the next five years, what would you prioritize?
(37:00) That is science, smarter software, or if you could add in, I don’t want to keep you hanging out, if you could add better supply, how would you go about prioritizing and what would you do differently? (37:11) Maybe we’ll start with Dr. Christina, who’s given us some of the most breakthrough platforms that it seems to be at the beginning of our history.
(DR. DESPINA SOLOMONIDOU)
(37:19) Right, so you want me to pick one of those? (37:21) I could not. (37:23) I think you need it all.
(37:25) So, if I were to code a new system, I would code something that integrates all what you said. (37:34) Science, the software, and the supply. (37:38) In my mind, science determines the direction.
(37:42) And yes, we do have good science. (37:44) Actually, science becomes even better as technology is evolving. (37:49) We understand better biology.
(37:51) We are gaining more insights. (37:53) Science becomes eventually more predictable. (37:56)
However, software is the one that accelerates our understanding.
(38:03) The AI that we are talking about, the technology advancements in data science, these all integrate, allows us to expedite drug discovery, as Professor Chong said, allows us to better understand and predict eventually the clinical efficacy through other differently designed clinical trials. (38:26) We heard about quality, improving quality. (38:30) So, software to me is the accelerator.
(38:34) And then supply defines the boundaries. (38:37) We spoke about accessibility. (38:39) We spoke about availability.
(38:41) This is what is determined by the supply. (38:44) So, it’s a framework. (38:46) So, in my next life, if I were to program, to code something, I would do it in an integrated fashion of all these three together.
(38:54) And I believe in this environment that we speak to, the Indian ecosystem does allow us to do it.
(MR.SHIRAM CHARY)
(39:01) Fascinating. (39:02) Rashmi, from your vantage point as a global CIO…
(MS. RASHMI KUMAR)
(39:05) The way I look at it would be fine in terms of supply. (39:09) Because breed science cannot reach to the patients and the customers. (39:14) What’s the point of it, right?
(39:16) Both are equally critical. (39:17) And software is becoming the flow through the pipes, becoming the foundation through which we can develop science and we can get it to the patients. (39:29) So, I’ll talk about a couple of science that we have.
(39:31) And we are a little different than genomic or pharma molecules. (39:37) I’m going to explain it a little bit on the science side. (39:40) Either we talk about our stealth access system, which is the spine surgery, or our recent acquisition, CatWorx, for cardiac diagnosis.
(39:51) So, CatWorx takes a very invasive process where you have to get through the human artery into the heart to figure out what’s the propensity of the blockage to cause a heart attack. (40:06) It has now gone to an AI-driven imaging platform where, through pictures, we are able to identify the FFR number, which is the flow reduction number, and you don’t have to send a catheter inside the human body. (40:22) Our stealth access spine surgery system creates a planning platform for the surgeon, for a particular patient, depending on their images of the spine and comparing with numerous surgeries which are done before and what kind of rod or screws that patient might need.
(40:41) So, if you look at software and AI, we have numerous such examples in our pacemakers, in diabetes systems, and our GI genius for colonoscopy, where science has been tremendously enhanced in real time because of the AI software that we are able to leverage to enable that. (41:05) It’s a true digital twin of the human or the organ. (41:09) Our Afera cardiac ablation system is another example.
(41:14) But at the same time, when we look at, we call it mission, purpose, and being a company goes hand in hand, right? (41:23) The software and AI in our supply systems (41:26) and how do we transition these technologies into the real life, (41:30) either it’s distributed, digital, clinical trial, (41:33) or it’s about regulatory compliance and medical approvals (41:37) across different bodies globally(41:39) so that we can bring this therapy to the patient, (41:43) or it is about how do we forecast, (41:46) how do we manage logistics, (41:48) how do we transfer this science into a real life manufacturing. (41:52) Because, again, we are very different than Pharmaware. (41:55) It’s a very high-end throughput manufacturing versus us.
(42:00) It’s more discrete. (42:00) Our cardiac ablation system is a little spherical mesh with nine electrodes in it, and every electrode plays a different role when we are ablating a patient for the heart issues. (42:12) So if we look at our logistics ability, simple systems like transportation management system becomes extremely critical for us.
(42:19) Planning and forecasting is absolutely critical because these devices have sharp lines, and we need to understand where do we need how much of it. (42:28) So when we talk about supply chain, automated visual inspection of these medical products, we have seen that AI and vision technologies driven by AI creates better product qualities for our patients and customers eventually. (42:48) So when we look at this software AI technological advancement which has happened, it enables both the science, the R&D, and our ability to transition it into the manufacturing and production and get it to our patients and customers in a very resilient, reliable, and frictionless customer experience.
(MR.SHIRAM CHARY)
(43:09) Thank you. (43:11) Go ahead.
(MR. EAMONN WARREN)
(43:12) As you said before, we make two things. (43:15) The manufacturing makes the product, and we make paper or documentation. (43:19) So I think there’s a huge opportunity, and we’re seeing this already, around changing that paper into digital and having electronic fax records and having
electronic review and release of those records where it’s much more error-proof and much less prone to human error if you’re filling out these paper documents and creating a whole big paper trail around that as well.
(43:40) So I think we’re going to scale at these scales. (43:43) We need to have those kind of error-proof systems built in and digitized up front, and then be able to overlay all these AI and predictive analytics tools and everything else on top of that to be able to drive better outcomes for the process overall.
(MR.SHIRAM CHARY)
(43:55) It’s the last code scale, scale and prediction. (43:58) Excellent. (43:59) Very good.
(43:59) I’m getting the nudge on the time here, but before I leave you all to a quick rapid fire, a few minutes to each one of you. (44:10) It is 2030, so we fast forward four years. (44:13) What is one thing we do today in life sciences and healthcare that would look absolutely primitive and slow in four years from now?
(44:22) Your best predictions or most provocative predictions at the end.
(MR. EAMONN WARREN)
(44:26) Start with you, Amy. (44:27) I’m going to build off my last points. (44:28) I’m going to say getting rid of paper from manufacturing processes.
(44:31) That’s going to look antiquated, hopefully, in four years’ time. (44:34) It sounds like a lot of pre-safe, fully digital processes. (44:38) Thanks, Amy.
(DR. DESPINA SOLOMONIDOU)
(44:39) I’d like to say that probably I would love to see our industry being paperless as it concerns batteries, but also regulatory submissions. (44:49) I’m dreaming of that. (44:51) We are handing over a disk or a memory or whatever you call it, an algorithm that summarizes the history of development for a product as opposed to 10 binders full of paper.
(45:05) Perfect.
(MS. RASHMI KUMAR)
(45:07) I’ll be provocative as you asked. (45:10) Two words in our six-word mission statement is extend life. (45:14) I do think 2030 will be a turning point in our lives to really see extended lives implemented, because of what our pharma colleagues are doing, what we are doing in MedTech and other communities.
(45:29) David?
(DR. PETER OWOTOKI)
(45:34) I would go for personalization at scale. (45:38) Basically, having data that is morerepresentative and combines different modalities. (45:47) Genomics information, imaging information, behavioral information, to build a model of mind that then helps to better diagnose as we get older, that we have better cognition, that we are able to address these conditions better.
(46:07) And we wonder, already in 2030, why didn’t we do that earlier? (46:10) Why didn’t we bring all this data together? (46:11) Why were we not representative earlier?
(46:15) Dr. Kenneth?
(DR. KENNETH BARR)
(46:20) Just to build on that a bit, I think by 2030, hopefully, we will be much harder along in terms of leveraging the data that comes from, say, like genetics and proteomics, from computational tools and algorithms, from human tissue-based organoid systems, so that we believe we will have much better predictability and probability of success for patient outcomes. (46:47) Professor Chong, lastly.
(DR. PETER OWOTOKI)
(46:50) Science and software and supplies are working right now, but in five, six years later, they are going to be one team, so that science, software, supplies, they work together, so that the success rate will increase. (47:10) Thank you. (47:11) So, yeah, this is the end of our panel.
(MR.SHIRAM CHARY)
(47:14) I just want to conclude with two things. (47:15) One, we’ve got a highly diversified panel that represents the overall life science healthcare ecosystem. (47:22) We’ve got MedTech, manufacturing
CDMOs, drug discovery start-ups, driven here.
(47:28) It kind of reinforces the interval with interdependency value chain that we think, which is BioWeave, which will define the future of this sector and broaden the future of this sector. (47:38) So with that, a round of applause to all our panelists today, and hopefully you found the panel interesting. (47:44) Thank you.
