Speaker 1: Maternal out already reporting a narrow with an expected first
Speaker 1: quarter loss cost cutting of setting a steep decline in
Speaker 1: its COVID business. Maternal expecting to receive US approval for
Speaker 1: its second product, an RSV vaccine, in the coming days,
Speaker 1: joining us now as the Maternal CEO. Stephan Bansal, Stephan,
Speaker 1: wonderful to catch up with you, sir. The stock is
Speaker 1: just about positive in the pre market. Can you talk
Speaker 1: to me about how you're balancing cost cutting with investing
Speaker 1: in innovation given what's in the pipeline.
Speaker 2: Sure, well, good morning, Thank you for having me so
Speaker 2: very pleased with a quote. We basically try to focus
Speaker 2: on how do we drive sales, how do we drive
Speaker 2: R and D, how do we prioritize opportunities, which is
Speaker 2: the way. For example, we announced that we are stopping
Speaker 2: the partnership with Metagenom in research engine editing.
Speaker 3: Same thing if you look.
Speaker 2: At the portfolio, we're looking very carefully at all investments.
Speaker 2: And the good thing about those vaccines, like respiratory vaccines
Speaker 2: is your only pay the face free study. Whats so
Speaker 2: if you think about COVID, we still up sells from COVID,
Speaker 2: but the investment in the idea of COVID has come
Speaker 2: down a lot. As you said ours, we we are
Speaker 2: anticipating a launch this spring, but we're not going to
Speaker 2: do another Phase three four URSV, so you can still
Speaker 2: basically have a lot of new studies going on. We're
Speaker 2: using the capital you used to put in the other
Speaker 2: products before. And then if you look at oncology, as
Speaker 2: you know, we're in a fifty to fifty profit share
Speaker 2: with Merk, so merk is paying half of a phase
Speaker 2: free study. So that's how we're managing when we're seeing
Speaker 2: a lot in technology. You might have seen last week
Speaker 2: an announcement with open Ai. We actually more than seven
Speaker 2: hundred fifty GPT is going and that is helping us
Speaker 2: a lot scale the company across not only science but
Speaker 2: surd having a lot of productivity in manufacturing in commercial
Speaker 2: illegal So that's kind of how we're doing it.
Speaker 1: So Stephan, let's talk about something that our colleagues here
Speaker 1: at Bloomberger extremely focused on, and that's your RS three shot,
Speaker 1: which according to our colleagues, some data is showing that
Speaker 1: maybe it doesn't last as long as others in the market.
Speaker 1: What we all want to know here at Bloomberger is
Speaker 1: whether that raises questions about the promise of your technology
Speaker 1: in treating other diseases. How would you answer that?
Speaker 2: So will first say that if you look at the
Speaker 2: data the duration of the over vaccines, they are very similar.
Speaker 2: So I don't think it is scientifically correct to say
Speaker 2: that one of a vaccine doesn't last as long as
Speaker 2: your ones of a too that are improved and ours.
Speaker 2: Look at the data. This will be debated at the
Speaker 2: CDC meeting at the end of drewne that for reccommodations.
Speaker 2: So this doesn't worry me. If you look at duration,
Speaker 2: the duration of vaccination is induced by T cell. If
Speaker 2: you look at cancer product, the only reason it works
Speaker 2: is T cells, not antibodies. Antibodies don't have a row
Speaker 2: in cancer. It's about T cells going and attacking your cancer.
Speaker 2: If our vaccine technology didn't have good T cell response,
Speaker 2: the cancer product will not look as good as it is.
Speaker 2: So I'm not worried at all about duration.
Speaker 4: Pretty much every time we speak, Stefan, I ask you, basically,
Speaker 4: have we couraged cancer yet? So I'm glad that you
Speaker 4: went there because that's been sort of one of the
Speaker 4: big questions in the hope for a lot of the
Speaker 4: MR and A vaccines. You have this Melanima vaccine in
Speaker 4: the works, what more do you have to do to
Speaker 4: get it sort of set up for the approval process
Speaker 4: to apply for that and are you using artificial intelligence
Speaker 4: to extradite.
Speaker 3: That great question.
Speaker 2: So if you look at cancer treatment in melanoma, we've
Speaker 2: said that we need to achieve free things to be
Speaker 2: able to talk to regulator about accelerated approval. So the
Speaker 2: face to day ties data we shared on the show
Speaker 2: several times, we see duration. If you remember in December
Speaker 2: we shad a three year survival. It was better than
Speaker 2: the two year survival. So the difference between people on
Speaker 2: all treatment and people that are just getting cathedral is
Speaker 2: getting wider. So there's a very strong evidence that the
Speaker 2: drug is working.
Speaker 3: So that's number one.
Speaker 2: Number two is we need a phase free study to
Speaker 2: be substantially enrolled, and so we are working very actively.
Speaker 2: Face free study started two months earlier plan last summer
Speaker 2: and so when we are substantially enrolled, we will meet
Speaker 2: that criteria and it could be late this year. And
Speaker 2: the third one is a plant because of course we
Speaker 2: need to file in the restrection du all the information
Speaker 2: about the manufacturing process BFD the day you file is
Speaker 2: allowed to go, of course, audit your plant. That plant
Speaker 2: is being built. I had a chance to go there
Speaker 2: two weeks ago. The team is working NonStop, scheduling literally
Speaker 2: by the days, a bit like we did during COVID
Speaker 2: during the pandemic, and so I anticipate that potentially sometime
Speaker 2: next year, you know, the if a regulator was willing
Speaker 2: to look at the accelerated approval file, we should have
Speaker 2: this product available to help a lot of people, because
Speaker 2: one in two people benefit with no thises coming back
Speaker 2: or our deaths compared to the best drug available today
Speaker 2: to them on the market.
Speaker 4: Stephan, can you just give us a sense of you
Speaker 4: talk about artificial intelligence. Everyone's talking about artificial intelligence. Could
Speaker 4: you just talk about how much that could expedite generally
Speaker 4: some of the drug production that we're seeing. Just how
Speaker 4: much that could really get us to achieve you know,
Speaker 4: that cure for cancer, that cure for als, cure for Alzheimer's.
Speaker 4: You know, it's funny you're talking about sex and city.
Speaker 4: I sit around and I worry about these things. You know,
Speaker 4: what are we to cure these things? So I'm just wondering,
Speaker 4: you know, this is going to be in our lifetime
Speaker 4: in the next couple of years because of some of
Speaker 4: the machine learning.
Speaker 2: Yes, so I think there's a few things to tear
Speaker 2: part in your in your great question. First is I
Speaker 2: think machine learning in academic labs, in research labs, in
Speaker 2: industry is helping accelerate the understanding of a human body.
Speaker 2: If you think about you know, this is Alzheimer and
Speaker 2: all those complicated disease that we do not have solutions
Speaker 2: for yet as a society. It's because we do not
Speaker 2: understand the biology. We do not understand how the disease happened,
Speaker 2: how the disease evolved, and so we are just trying
Speaker 2: things and some work, but very few work. Most of
Speaker 2: them don't work because we're just trying and guessing. If
Speaker 2: you look at a biology, once we understand that something works,
Speaker 2: then the industry can comes with very very good actions
Speaker 2: to deal with those. So I think AI will accelerate
Speaker 2: the understanding of biology, which would be fundamental to bring
Speaker 2: new drug. Then AI is already used to accelerate discovery
Speaker 2: in terms of what tool do you go after a
Speaker 2: disease once you understand it. At modern already we have
Speaker 2: different chemical matters that are generated by our AI system
Speaker 2: that are helping us to accelerate the work that humans
Speaker 2: are doing. So it's an accelerator to the teams. And
Speaker 2: then there's a huge chapter on productivity. If you think
Speaker 2: about clinical development phase one, two and three, it's basically
Speaker 2: doing experiment in human, getting the data, finding the doors,
Speaker 2: doing more experiment, and when you have all studied on,
Speaker 2: you get all the data and you submit by relator.
Speaker 3: My point is it's all about data.
Speaker 2: They are literally hundreds of business processes that need to happen,
Speaker 2: and I think many of those, if not most of those,
Speaker 2: you've got to be able to apply AI to shrink
Speaker 2: time to go faster. An example we shared in March
Speaker 2: in a vaccine. Then the team wrote a GPT to
Speaker 2: help us to do those selections. When you do clinical
Speaker 2: study your phase one, you try several doses and then
Speaker 2: based on the data you get in a clinic, you
Speaker 2: decide which jos go into your phase three. Well, it
Speaker 2: used to take around a month to do that by
Speaker 2: having people and meeting and experts looking at the data
Speaker 2: while we develop a GPT that basically get all the
Speaker 2: data from the clinical study and suggests to us a
Speaker 2: those in literally a minutes or two. That is already
Speaker 2: a tool that has been developed that I've seen used
Speaker 2: at the company.
Speaker 3: That's just one example. So here you go to shrink
Speaker 3: them off.
Speaker 2: And if you do that on the hundreds of business
Speaker 2: processes that have to happen in preparing the drug for
Speaker 2: the clinic, the clinical testing, the analyzing of the data,
Speaker 2: the communication with the FDA, I think you can save
Speaker 2: a lot of time. I don't know yet, because only
Speaker 2: history will show us in the next few years, can
Speaker 2: you shave thirty percent, forty percent, fifty percent of how
Speaker 2: many years it takes you to develop a drug?
Speaker 3: I think it's going to be very significant.
Speaker 1: Stephan, We've got to leave you there. It's fantastic to
Speaker 1: catch up with you, so amazing to listen to you
Speaker 1: talk about the efforts taking place at Maderna. Madenna CEO
Speaker 1: Stefan Bansell