Absci Corporation (NASDAQ:ABSI) Q1 2024 Earnings Call Transcript

Absci Corporation (NASDAQ:ABSI) Q1 2024 Earnings Call Transcript May 14, 2024

Operator: Ladies and gentlemen, thank you for standing by. Welcome to the Absci First Quarter 2024 Business Update Call. At this time, all participants are in a listen-only mode. After the speakers’ presentation, there will be a question-and-answer session. [Operator instructions] Please be advised that today’s conference is being recorded. [Operator Instructions] I would like now to turn the conference over to Alex Khan, Vice President Finance and Investor Relations. Please go ahead.

Alex Khan: Thank you. Earlier today Absci released financial and operating results for the quarter ended March 31 2024. If you haven’t received this news release or if you’d like to be added to the Company’s distribution list, please send an email to investors.absi.com. An archived webcast of this call will be available for replay on Absci’s Investor Relations website at investors.absci.com for at least 90 days after this call. Joining me today are Sean McClain, Absci’s Founder and CEO; and Zach Jonasson, Chief Financial Officer and Chief Business Officer, Christian Stegmann, Absci’s SVP of Drug Creation will also join for Q&A following prepared remarks. Before we begin, I’d like to remind you that management will make statements during this call that are forward-looking statements within the meaning of the Federal Securities laws.

These statements involve material risks and uncertainties that could cause actual results or events to materially differ from those anticipated and you should not place undue reliance on forward-looking statements. Additional information regarding these risks, uncertainties and factors that could cause results to differ appears in the section entitled “Forward-looking Statements” in the press release Absci issued today and the documents and reports filed by Absci from time-to-time with the Securities and Exchange Commission. Except as required by law, Absci disclaims any intention or obligation to update or revise any financial or product pipeline projections or other forward-looking statements either because of new information, future events, or otherwise.

This conference call contains time-sensitive information and is accurate only as of the live broadcast May 14, 2024. With that, I will turn the call over to Sean.

Sean McClain: Thanks, Alex. Good morning and thank you to everyone for joining us today for our first business update conference call since 2021. A lot has changed since then. While we have been active at investor conferences and other venues sharing our progress, we are excited to welcome you to our quarterly update call, which we will host on a regular basis moving forward. There are a few reasons for this change. Among them, we are pleased with the expansion of our shareholder base and research analyst coverage. We want to provide an open setting to share our latest progress and details and allow for interactive dialogue. Additionally, our business model previously revolved almost exclusively around partner programs where our communication was influenced by our partner.

Q&A Session

Our new hybrid business model, including our internal pipeline gives us more opportunities to discuss the exciting progress we are making on each program. With that in mind, I’d like to begin with a brief recap of the achievements we have made in 2023 before discussing our programs in 2024. Later in the call, Zach will provide more details on the status and outlook for each of our internal programs and our business as a whole. We are often asked about our differentiating features. What sets us apart and positions us as leaders in AI drug discovery for biologics and a partner of choice across the industry. Any discussion of our integrated drug creation platform starts and stops with the data. The ability to generate and screen massive amounts of scalable biological data is an industry breakthrough that enables our platform to operate as it does.

Based on these pillars of data that train AI creates and will that be validate our platform is designed to continuously learn and improve as a result of our data generation capabilities and integration with our wet lab operations. Looking back on our accomplishments from the first quarter of 2024, it is clear they were only made possible due to the groundwork laid by our dedicated team in the previous year. The ability of our platform to rapidly design and create differentiated antibody candidates in a capital-efficient manner, while achieving epitope specificity is the direct result of our team’s efforts through 2023. We started 2023 with the release of a foundational manuscript demonstrating a first in creating and validating de novo antibodies zero-shot generative AI.

Over the year, we expanded our models, harnessing the proprietary wet lab technologies to generate scalable biological data for model training and validation. We continue to build our talented team, optimize our organization and integrate all of our platform capabilities deeply. In line with our hybrid business model, these does aimed to leverage our platform to create a pipeline of assets and establish validating partnerships with companies like AstraZeneca and Almirall. In October of last year, at our inaugural R&D Day, we reached the pivotal moment by unveiling our internal pipeline of asset programs including potential best-in-class and first-in-class antibody programs. We are excited to continue advancing each of these programs, which Zach will be discussing in greater detail later.

But stepping back, it is humbling and gratifying to see the translation of an idea using Generative AI with our platform to create differentiated antibody candidates into reality. We demonstrate our platform’s ability to create a differentiated antibody like ABS-101, our anti-TL1A antibody program with potential best-in-class properties in a very time and capital-efficient manner, compared to industry standards. At the beginning of 2024, we unveiled positive preclinical data for this program and by the end of February, we initiated our IND-enabling studies. As we advance this program, I want to highlight our platform’s efficiency in generating this program and the potential vast implications. In just 14 months, we generated and selected our drug candidate at a cost of less than $5 million.

ABS-101 is an early example of the power of our platform and we see additional opportunities to demonstrate similar results through our other current and future pipeline programs. At a high level, our platform unlocks the potential for us, powerful, novel biotech business model, we can generate a differentiated antibody candidate and complete IND-enabling studies at a cost of approximately $15 million. This could enable a new paradigm in biotech where capital typically allocated for one asset could be spread across multiple candidates, improving the overall probability of success. In addition to applying our platform to our internal pipeline, we are encouraged by the industry reception, including recent partnerships with industry leaders. It is humbling to count companies like Merck and AstraZeneca among our partners who bring invaluable talent, skill and expertise to our collaborations.

This year and beyond, we look forward to entering into additional partnerships with pharma and biotech companies. These partnerships will leverage our platform to enable and advance their drug discovery programs, but potentially shortening timeframes, lowering costs, improving success probabilities and unlocking new biologies previously thought unachievable. I am incredibly proud of our team at Absci for these achievements and excited to see the promise of our platform flight to further internal programs demonstrating the potential for disruptive improvements in biotech economics. At Absci, we come to work every day with a mindset focused on innovation and continuous improvement. We are driven by our mission to create better biologics for patients faster.

A few months ago, we decided to further strengthen our balance sheet and capital position through an underwritten public offering of common stock. We were encouraged by the positive reception and support from a large and diverse group of investors, both new and existing. This support will help us continue to pursue our vision as we look ahead to the rest of the year, I’m very excited by what lies ahead. We remain laser focused on execution across all aspects of our business, including partnered and internal programs, with innovation and the pursuit of being possible at the core of everything we do. With that, I’ll turn the call over to Zach to walk through each of our programs, provide our outlook, and give an update on our financials. Zach?

Zach Jonasson: Thanks Sean. As Sean discussed, this past quarter we closed an underwritten public offering of common stock raising gross proceeds of approximately 86.4 million. This additional capital will further support our ability to advance our internal pipeline of asset programs. This strategy reflects the hybrid business model that we introduced last year wherein we intend to develop our internal programs to a certain value to certain value inflection points, for example, through a Phase 1 or potentially Phase 2 clinical trial, before selling, partnering, or out-licensing set asset. A primary rationale for our strategies stems from our platform’s ability to create differentiated antibody drug candidates in a highly efficient manner.

We believe our strategy will allow us to create and capture more of the overall value of these internally generated programs. As we have said in the past, every program is unique and there is no one size fits all strategy for these assets. As the best practice and guidance strategy, we will look for the right partner at the right time. As Sean mentioned earlier, we generated the ABS-101 candidate in just 14 months at a cost of less than $5 million. By comparison, pharma industry estimates we have seen pin such figures at three plus years to reach a drug candidate and at a cost of $30 million to $50 million and as our platform continues to improve via our data generation and screening cycles, we believe over time, that we will even further reduce the time it takes us to generate additional drug candidates.

Turning back to our current internal program pipeline. Our lead program ABS-101 is a potential best-in-class anti-TL1A antibody. In January, we presented early preclinical data from three advanced leads from this program. This data showed properties consistent with a potentially superior product profile, including demonstrated high affinity, high potency, favorable developability and extended half life. We used our de novo AI model to design ABS-101 leads towards a specific epitope of TL1A with the objective of creating a drug candidate with superior potency and lower immunogenicity. This target product profile, combined with anticipated high bioavailability could ultimately improve patient experience with easier, less frequent dosing. Following further confirmatory PK studies.

In February, we selected a primary and a backup development candidate to advance into IND-enabling studies. We also recently completed studies demonstrating ABS-101 candidate’s ability to bind both the TL1A monomer and trimer, which could potentially lead to differentiated clinical efficacy. We plan to share additional preclinical data, including data from non-human primate studies in the next few months. We then expect to initiate Phase 1 clinical studies in early 2025 with an interim data readout expected in the second half of 2025. Next, ABS-201, a potentially best-in-class antibody for an undisclosed dermatology target is designed for an undisclosed dermatological indication with significant unmet need where the efficacy for the pharmacological standard-of-care is not satisfactory.

We anticipate selecting the development candidate for this program in the second half of 2024. Finally, ABS-301, a potentially first-in-class antibody for an undisclosed immuno-oncology target is a fully human antibody designed to bind to a novel target discovered through our reverse immunology platform. This antibody inhibits an immunosuppressive cytokine and is believed to stimulate an innate immune response. ABS-301 is being evaluated for broad applicability to a variety of oncology indications and comprehensive profiling of this program is in progress. We anticipate completion of the mode of action validation studies for this program in the second half of 2024. As a reminder, our reverse immunology platform is designed to discover novel antibody targets, based on the analysis of Tertiary Lymphoid Structures or TLS from patient samples.

Within this platform, we mine the antibody repertoires from the TLS samples derived from patients who have exhibited an extraordinary immune response. We discovered the ABS-301 novel human antibody and its corresponding target using this platform. We look forward to sharing additional details later this year. In addition to further development of ABS-101, ABS-201 and ABS-301, we continue to expect to advance at least one additional internal asset program to a lead stage in 2024. Further, in line with our hybrid business model, we continue to execute and make solid progress on our existing drug creation partnerships. I am pleased to see the close collaboration between our partners and our own R&D teams. And while we cannot disclose much detail about our partners’ programs, the work we are doing with these partners is progressing well and according to our expected timelines.

We look forward to sharing more details about these programs at a later date. Additionally, we continue to anticipate finding additional drug creation partnerships with at least four partners in 2024 including one or more multi-program partnerships. I am also pleased to share that we have a robust and diverse pipeline of potential partners and we look forward to updating you on these over the course of the year. Turning now to our financials, revenue in the first quarter of 2024 was $900,000 as we continue to progress our partnered an internal programs concurrently. Research and development expenses in the first quarter of 2024 were 12.2 million, as compared to $12.7 million in the prior year period. This decrease was primarily driven by lower personnel cost, offset by an increase in stock compensation expense.

Selling, general and administrative expenses were $8.7 million in the first quarter of 2024, as compared to $9.6 million in the prior year period. This decrease was due to a lower personnel cost and continued reduction in administrative costs, offset by an increase in stock compensation expense. Turning to our balance sheet, we ended the quarter with $161.5 million in cash, cash equivalents and short-term investments as compared to $97.7 million as of December 31st 2023. For 2024, we continue to expect a gross use of cash, cash equivalents and short-term investments of approximately $80 million, inclusive of the expected costs associated with completing the IND-enabling studies for ABS-101 with a third-party CRO. Based on our current plans, we believe our existing cash, cash equivalents and short-term investments will be sufficient to fund our operations into the first half of 2027.

Altogether, we are very encouraged by the progress we have made on our internal programs and confident in our ability to execute on these and our partnered programs over the course of 2024. With that, I’ll turn it back to Sean.

Sean McClain : Thanks, Zach. 2023 was a pivotal and successful year for Absci and our company’s evolution. In 2024, we will continue to focus on execution and further demonstrate the power of our platform to create differentiated antibody assets. We are thrilled to advance each of our programs with the goal of creating better medicines for patients faster and fundamentally improving the economics of biotech. We look forward to updating you along the way. With that, I’ll turn it back to the operator to begin Q&A. Operator? Question-And-Answer Session Thank you. [Operator Instructions] One moment for the first question. The first question comes from Kripa Devarakonda with Truist Securities. Your line is open.

Kripa Devarakonda : Hey guys. Thank you so much for taking my question. I have a question about ABS-101. We recently saw exciting preclinical data from the program. I was just wondering if you can talk about your level of confidence that this drug is differentiated versus the other drugs in the same class. Do you – do we truly understand the biology about the importance of targeting monomer versus trimer and what sort of an edge do you have in understanding that part of it? And then I have a follow-up question.

Sean McClain: Yeah, absolutely. Thank you, Kripa. I’ll hand that over to Christian to talk about the differentiated properties that we see with the TL1A and in particular with the monomer versus trimer.

Christian Stegmann : Yeah, thank you, Sean. Absolutely. First off, we do think that our extended half-life is an absolutely critical and very differentiated parameter that will address patient convenience topics. But more importantly also on the monomer trimer question, I think it’s important to highlight that our AI guided antibody design strategy is really based on epitope-specific targeting using 3D structures and specifically for 3D structures of TL1A. So we selectively chose an epitope on the monomer that shows no discrimination for monomer versus trimer binding and we designed antibodies using AI. So we completely avoid epitopes that’s span multiple sub-units. And this allowed us to produce a development candidate that equally targets both TL1A states and it also addresses to our knowledge, all known monomer isoforms. This may have relevance in clinical trials, because it’s known that certain monomers are expressed differentially in a certain patient populations.

Kripa Devarakonda : Great. Thank you so much. Sean, I have a question for you, a big bigger picture question. You were talking about using Generative AI models. I was just wondering with the improved Generative AI models and new data, do you think you can continue to improve efficiency and reduce the drug development timelines? I know, 14 months is already great and $5 million to develop a drug is already great, but just wondering about that. Thank you.

Sean McClain: Yeah, absolutely. As the models get more and more accurate with the more data we’re training and as we continue to improve the AI architectures and our models, we do see these timelines continuing to decrease over time and the overall cost decreasing. And this is a very important metric for us internally these cycle times and you are going to continue to see over time these overall cost decrease both on how long it takes us to get to a drug candidate, as well as the overall cost associated with generating a drug candidate. So do expect that in the future.

Kripa Devarakonda : Great. Thank you so much.

Operator: Next question comes from George Farmer with Scotia Bank. Your line is open.

George Farmer: Hi, good morning, everyone. Thanks for taking my question. I was wondering if you could give us a heads up on what we can expect to see from the non-human primate studies ongoing with ABS-101 later this year.

Sean McClain: Yeah, absolutely. Christian, do you want to take that?

Christian Stegmann : Yeah, absolutely. So our non-human primate pharmacokinetic studies are expected to demonstrate that we indeed are able to show an extended half-life of our antibody that’s de-risking the pharmacokinetic profile in humans. So we, in the next few months we will be able to demonstrate that our antibody engineering approach to extend the half-life of the antibody have worked.

George Farmer: Okay. Anything else from those studies we should be looking out for the PD markers, anything like that that can kind of shed light on the differentiation of the antibody from competitors?

Christian Stegmann : Yes, we will absolutely measure the non-pharmacodynamic market, as well, obviously, and exactly as you mentioned, it’s demonstrating an extended effect on the pharmacodynamic biomarker will then obviously also de-risk efficacies, Very good point.

George Farmer: Okay. Great. And then, maybe a bit on cash. Your guidance implies, I think some funding coming in probably from partnerships is probably the best guess. Can you kind of elaborate a little bit more on that relative to how we should think about cash usage through 2027?

Zach Jonasson : Yeah, we do see.

Sean McClain: Yeah, that’s for Zach.

Zach Jonasson : Yeah, I was going to say, George, we continue to reiterate our guidance of $80 million of gross cash usage for 2024. That’s obviously a gross figure and that includes the complete the cost associated with completing the IND-enabling studies for ABS-101. Are forecasts that take within to 2027 includes some modest assumptions around partnering on a regular cadence. But it doesn’t include any assumptions around a significant partnership deal. So it’s to kind of our typical run rate assumptions built into that. And then, I would point out too, if you look at our net cash usages, that typically comes in well under gross. So, for example, if you look at H2 of 2023, the net cash usage for that second half of last year was roughly $27 million in total.

And the net cash usage for Q1 of this year was roughly $15.9 million, which is a little higher given that we paid bonuses in March. So as can see our net cash usage is coming well in – coming well under the gross cash usage.

George Farmer: Yeah, okay. Thanks very much, Zach.

Operator: One moment for the next question. The next question comes from Jacqueline Kisa with TD Cowen. Your line is open.

Jacqueline Kisa: Hi, this is Jacqueline Kisa on for Steven Mah. Thanks so much for taking the question. With the ongoing bipartisan discussions regarding biosecurity, can you give us any color on the third party CRO you’re using for your IND studies? Is it WuXi Biologics or a China-based CRO?

Sean McClain: Yeah, that’s a great question. We are using WuXi at the current moment and given the recent discussions that have been ongoing with that, we do believe that the relationship with WuXi will not put us at risk with the current program. But we are engaging with other CROs and do have backup strategies to mitigate any other potential tailwinds that may occur with the new legislation that that may come out.

Jacqueline Kisa: Great. Thank you. I appreciate the color. And regarding your fourth internal asset, can you give us any insight on the disease area you’re looking to focus on? Is it one you’ve targeted before or new therapeutic area? And is the company right-sized to serve both your internal and partnered programs?

Sean McClain: Yeah, it’s a great question. So, we are focusing on I&I as well as oncology. So we’ll fall in one of those therapeutics areas. And then, additionally, we are currently right-sized to be able to continue to take on more programs as we continue – as our model continues to get more and more accurate. We’re able to do more with less resources. But one area that we are going to continue to grow in that does not correlate to the model itself is on the disease biology and the translational side. So we’re going to continue to build out our drug creation team again both on the disease biology of the translational side and clinical side. But we see that as modest growth as these programs are being undertaken.

Jacqueline Kisa: Great. Thank you. And then, if I could just squeeze one more in? Technologies in the AI drug discovery are pretty fragmented. Are there any white spaces in the tech stack that you could fill inorganically? Or do you expect to build on your tech in-house?

Sean McClain: Yeah, one of the areas that we see as big differentiation is the epitope specificity so being able to landscape an epitope and target an epitope of interest or be able to test epitopes that may give you a new novel on biology with standard approaches like stage display, immunization you have no control over this epitope specificity. And to the best of our knowledge, this is the only technology that exists out there that allows you to hone in on these epitopes of interest and allow you to elucidate potential new novel biologies from those epitopes. And so, we see this as a major differentiation. This is really what’s driven partnerships with AstraZeneca, Almirall, Merck and we will continue to drive our partnership pipeline, but also drive our own internal development, as well. And we have applied that to the internal programs, as well.

Jacqueline Kisa: Great. Thank you so much. I appreciate it.

Operator: One moment for the next question. The next question comes from Steve Dechert with KeyBanc. Your line is open.

Steve Dechert : Hey guys. Thanks for the question. Could you give us some more color on how discussions are going with potential partners? Is there a wait to get into your goal of four partners this year?

Christian Stegmann: Yeah, I can comment on that, Sean. I’d say well, signing partnerships is always a little bit lumpy and if you look at our cadence last year, it’s hard to have them come out on a even cadence throughout the year. But I would say that our pipeline of discussions is robust and covers both large pharma, and mid and small biotech, as well as leading academic institutions. And as we have these discussions and prosecute the BD strategy, we’re really looking for partners who bring a strong synergy to the table. And typically that means really robust and deep knowledge of the target biology. That’s where we see a really fertile ground for partnering. So, I would say, we feel like we’re well on track to hitting the metrics that you mentioned.

Steve Dechert : Okay. Thanks. And then, are there any new capabilities that you’re investing in, as it relates to your platform? Thanks.

Sean McClain: Yeah, one of the areas that we’re continuing to invest in, not only on the de novo AI side, but actually on the reverse neurology side. And one of the areas that we see as a as a bottleneck that AI could really unlock for us is the de-orphaning process. So once we take antibodies from a patient, we do a protean panel screen to find out what these antibodies are binding to. And this is a very laborious and time-consuming steps and what we want to do is actually going to reverse direction of the de novo model sensor is going targeting antibody, we go antibody to target this would allow us to rapidly de-orphan and discover new novel targets much faster than previous and we could scale patients’ data and hospital partnerships, as well. And so, this is a kind of another key area of focus for us on the AI development front.

Operator: Please stand by for the next question. The next question comes from Li Chen with H.C. Wainwright. Your line is open.

Li Chen : Hello. Good morning. This is Li Chen. Can you hear me well?

Sean McClain: Yes.

Li Chen : Hi, I have two questions. One is to expand on previous discussions on the upsize differentiation. So, since the release of the 101 data, have you seen any shifts in the nature of the inbound partnerships or partner interests? What I mean is that you said that previous partnerships were primarily driven by the epitope-specific antibody design. So, anything – any other capabilities that your partner is interested with primary focus on antibody design? And I have another question around 201.

Sean McClain: Yeah, Zach can speak to the interest on ABS-101 and the discussions that we’ve been having on that. But I can say that they are very robust discussions. But Zach, I’ll hand that over to you.

Zach Jonasson : Yeah, we have had a lot of medics inbound interest around that asset and continue to have discussions. Our strategy as Sean illustrated earlier is to move that asset forward into Phase 1 clinical studies. But we’re certainly entertaining discussions now and happy to see the interest from potential partners in that area. And the second part of your question, I’ll just mention, I think this epitope specificity it’s been really intriguing to potential partners. So, highlighting the capabilities of our platform and designing that asset I think has been quite been interesting to a number of potential partners and existing partners as well as our ability to design in unique features. So in some of our other assets and case studies, we’ve shown an ability to design in pH-dependent binding multi band with the unique properties that can be differentiating in a clinical setting. So I think that’s an exciting area for us.

Li Chen : Great. Thank you very much. And my second question is on 201. Can you give us some color on the current competitive landscape of 201 in that undisclosed disease area? And what’s your confidence of the differentiation factors from the current SOC and pipeline drugs? Thank you.

Sean McClain: Yeah, absolutely. CHRISTIAN do you want to take that one?

Christian Stegmann : Yes. Thank you. My audio was cutting off from and so your question around ABS-201?

Li Chen : Yes. 201 around the competitive landscape and differentiation factors.

Christian Stegmann : Yes. So, we have not disclosed the precise indication for ABS-201 yet. But I will share that this is an indication of high unmet medical needs where this current standard-of-care is unsatisfactory. And we also plan to employ extended half-life antibody technology to basically improve patient convenience, as well just like this for ABS-101. And so, in essence, we will deliver a best-in-class profile, not only from an efficacy standpoint platform, also from a patient convenience standpoint.

Sean McClain: Yeah, and I will also mention, as well this target is a very underappreciated derm target. And in this case, we would be a second to the clinic. And so, it’s not a very crowded space. It’s a very underappreciated target, I’d say almost very similar to TL1A.

Li Chen : Great. Thank you very much.

Operator: I show no further questions at this time. This will conclude today’s conference call. Thank you for participating. You may now disconnect.