Executive Interview with Shilpa Thosar, Former Executive Director, Cara Therapeutics

[0:03] Hi everyone and welcome to a new interview with Oxford Global. Today we are joined by Shilpa, an independent consultant and Former Executive Director of Cara Therapeutics.

She has very broad experience in developing drug delivery systems and guiding therapies through every stage of development, all the way from the pre-clinical stages to the commercial launch. In this interview, we will be unpacking shelters personal journey and experience in this space, and her opinions and insights on AI and drug discovery and drug development.

So Shilpa, thank you for joining us today. And I guess the first question from me would be having worked in Big Pharma and in small biotechs, and now you're an independent consultant, how has this shaped your outlook on the challenges of drug development today?

[0:56] Yeah, first of all, thank you so much for having me here as well as a moderator in Oxford Global conference, I'm really looking forward and enjoying this conversation with you. So in three decades of my experience in pharma and biotech, I have worked with large pharmaceutical companies on another extreme. I have worked with small biotechs, and last one year or so, I have my own consulting so in a large pharma companies, I learned that they are at a massive scale, global footprint.

They have rigor, because of a lot of expertise in people and also very defined processes. And also they have to look for global regulatory compliance because of their global footprint. They also have lot of programs, so pipeline in different clinical areas, different commercial programs, as well as the huge capacity. However, they can be slower to pivot the small biotech companies.

On the other hand, they can move very fast, so they have agility. They have innovation, but they are limited by their resources, funding and other challenges. Also, they are in a very limited space, so they can have, like one or two disease area or a very specialized pipeline. Now, being in a consulting now, I see both the worlds, and I would say overall, the success for any company, whether it's large or small, depends not only on their science and execution, but also for the companies who can integrate innovation and compliance as well as strategic foresight to move the therapies faster to the patients. So that is what I have to say. Thank you.

[2:44] Great answer. Thank you very much. And as someone with experience taking a candidate all the way from pre clinical to commercial phase, in your opinion, what is the most overlooked scientific challenge?

[2:57] Yeah, first of all, I had a tremendous privilege to take the molecules from pre clinical stage to clinical to all the way commercial, enjoy the experience. One of the most important overlook scientific challenge is when the companies are translating their early, pre clinical or clinical promising data the events they get that data, in the excitement of getting that positive data, one of the thing that is often overlooked is, how will you translate this early data now into a product which is manufacturable, scalable and also globally approvable, or at least approvable in your target market.

And the reason I say it because most of the time in the pre clinical space, or even in the early clinical space, companies underestimate the complexity of developing a formulation and process that is not just phase appropriate, but also that should be able to translate or robust enough to go through the other life cycle aspect, like a scale up, tech transfer, global regulatory submissions.

So those are very important thing. They may not be as important in the beginning, but they become very important as you go through different development phases. So to me, in my experience, programs that are successful are the ones which can build this kind of CMC strategy and regulatory consideration upfront from day one, rather than waiting it down the road.

[4:37] That's great. Thank you very much. And in your opinion, how do CDMOs fit into the drug development equation? And I guess in your opinion, why is it so important to select the right one and be smart and critical about this?

Yeah, so CDMOs have become very very critical in today's pharma biotech world, because lot of the small companies, and sometimes even medium sized and large companies, don't have their own facilities to develop a product or manufacture the product, whether it's a clinical manufacturing or commercial manufacturing, so they rely heavily on CDMOs.

So CDMO should be not just your vendor, but your strategic partner. And it's very, very important to pick the right one. The right one you should pick not only for their technical capability, but also learn what their culture is, how they operate and how they can adapt to your program as your programs and program need evolves. And the reason I say this is very important to select right CDMO is because the CDMO switching cost to switch to another one, if one doesn't work out, is very high. And when I say high switching cost, I just don't mean the dollar. That is true, but also in terms of time, resources and sometimes even regulatory hurdles when you transfer from one side to another, and that could become the big cost for you, especially in the later stages of your development.

Therefore, in my view, the successful partnerships are the ones what I have seen are the ones where both the sides are transparent, aligned and working toward the same long term goals.

[6:29] Thank you very much. And recently, you obtained a certificate from MIT in the use of AI within pharma and biotech. So what were your main takeaways from this course?

[6:44] Yeah, first of all, I liked the course very much. It was pretty intense, but I really enjoyed going through it. The course was structured into giving some AI basics, so I learned there. But also mainly it was focused on the areas, key areas of AI applications in biopharma.

So some of the examples of these applications were drug discovery, disease modelling, clinical trials, biomarkers and patient stratification. So I enjoyed the broad overview of AI applications, but also it was a huge eye opener for me, in the sense that, just like everybody else, I see, that AI will be the way or the future, we will do most things same in our pharma, biotech industry. So it was a huge eye opener for me, and I think we all will have to be ready for it.

[7:42] Thank you very much. And we touched on previously that some biotechs may not be so keen on open access AI systems and would be more willing to develop their own internal systems. What is your general opinion on this? Yeah.

[8:02] So first of all, I do see why some small biotechs are not willing to use open AI system, and because in pharma and biotech, we have challenges and unique issues like IP, which is intellectual property protection, trade secret data security, confidential information, not just about your compound, but about your formulation, process, clinical data, patients, privacy and that related information.

So first of all, I think it is a very, very valid concern, but the approach I would recommend is a hybrid so hybrid model means yes, these companies can develop their own AI specific systems for handling their confidential or private or sensitive information, but at the same time, they could also use the open AI system to their advantage in the places where they can So, for example, regulatory intelligence. All the regulatory global guidances are available on the public domain, in the public domain, so the AI system can be used to condense this regulatory intelligence, knowing the regional differences.

Also, there are a lot of research publications, they form a good basis for technologies to be developed. So all this published research article and historical databases that are publicly available, those are the ones they could use in the open AI system. But in this hybrid approach, still, they need to keep these two systems completely separate, so there is no cross contamination that way. So that is what my advice would be.

[9:49] Thank you very much. And as we all know, the biopharma industry is one that's marked by constant change. So how would you do you see the role of consultants? Is evolving, as you mentioned, with biotechs facing funding challenges, new AI technology emerging maybe a little bit on the political landscape, particularly in the US, shifting at the moment. How do you see this impacting consultants like yourself?

[10:20] Yeah, so certainly, I think consultants can help in all the three challenges that you described. So let me just tackle them one by one. So first of all, is a funding challenge. So currently, biotechs are under immense funding pressure, and when company has a funding limitation, even if they want, they can't hire the permanent resources and expertise.

So I think consultant offer excellent stopgap type of arrangement where companies can hire consultant when they need, as they need, and for the hours they need, as well as for the expertise they need. And once they have the critical mass or the critical funding available, then they can venture into hiring more permanent resources. So that's one thing for the funding. Another thing about AI, I think AI is going to change the way we are doing everything.

So in terms of AI, I think the consultant who are the domain expert, as well as those who know how to use AI to their advantage, as well as to their customers. Which are these companies efficiency as well as advantage would be the big big will add big value. So those consultant who can work with AI and leverage AI for these companies are going to be a big success.

Also, the third thing, a real challenge right now is shifting politics, and because of that, policies. So when this kind of tremendous shift happen, that brings lot of uncertainty, and when the times are so uncertain, I think consultants, because of their experience, expertise, and because they have gone through all these economic cycles, historic cycles, they can bring lot of flexibility, cost efficiency and stability that companies are looking for in these uncertain times. So I think consultants would be very valuable and stabilizing force even under this current uncertain landscape.

[12:23] Thank you very much. And final question from me, Shilpa, so we're very excited to be welcoming you to Oxford Global's discovery and development event taking place in San Diego soon, and you will be participating in a panel discussion on digitization and automation of formulation processes. What would be the message you would like the audience to remember from this discussion?

[12:49] First of all, I'm really looking forward to attending this Oxford global us 2025 Discovery and Development Conference in San Diego. So my panel has a charge of taking on this topic, which is very interesting topic called as digitisation and automation of the formulation processes.

So we are going to have this in a question answer pattern, and we have very good panellist who has experience in AI using it in a formulation. So we are going to do this in three different topics and related questions. So the first one is development with AI within formulations. So how would you use AI for formulation activities such as screening of [unclear], prediction of stability, optimisation of process parameters, and because AI models can learn from historical formulation and manufacturing data to predict the future outcomes. Can we use AI to reduce our experimental burden? We can't eliminate it, of course. And can we increase our technical success because of use of AI? That's the thing we will tackle in the first subtopic, the second subtopic is about utilising the latest technologies.

So we know that the latest technologies such as high throughput robotics, digital twins and advanced analytic platforms are being explored and, of course, successfully, even used in some cases in pharma today. So how do we use this recent AI advancement, such as, like no code or low code AI systems that we can use for some of the formulation activities? And what are those technologies? So that's another thing we will discuss.

The third one is little bit more futuristic. So for example, AI and ML for personalised formulation. So, what is important in the personalised formulation space is on demand formulations, where you can tweak your dose or certain aspect of formulation as per individual patient need, as well as flexible benefits. To produce those so now knowing that, how could we use AI or ML to provide us this flexibility? So that would be another thing. And some examples where you could be used is in rare diseases or paediatrics, where such individual, individual formulation is needed.

 So in conclusion, I would say, with three decades of my experience in product CMC and regulatory CMC, I see that AI is not going to replace subject matter expert or scientist, but somebody with the knowledge of AI could, and therefore we all need to learn about the AI and advancement and how to use it to our advantage.

[15:47] That's great. Thank you very much, and we're very excited for your panel. And thank you for sharing your insights with us today on formulation and the potential on how AI can transform drug development, and we look forward to welcoming you in San Diego very soon.

[16:06] Thank you