Recursion Pharmaceuticals Inc (RXRX) 2024 Q1 法說會逐字稿

Hi, everybody. I’m Chris Gibson, Co-Founder and CEO of Recursion; and I’m so delighted you’ve joined us today for our Q1 2024 earnings call -- learnings call, as we call it. And I want to run through some of the exciting updates from the company over the last quarter as we move forward to achieve our mission of decoding biology to radically improve lives.

大家好。我是 Recursion 聯合創始人兼執行長 Chris Gibson；我很高興您今天加入我們的 2024 年第一季財報電話會議（我們稱之為學習電話會議）。我想回顧一下公司上個季度的一些令人興奮的更新，因為我們正在努力實現解碼生物學以從根本上改善生活的使命。

And so with that, of course, some disclaimers. And Recursion, I think, is uniquely positioned to hit the TechBio escape velocity. And what I mean by that is I think we have pretty uniquely put together the pipeline, the platform, and the people that are giving us the opportunity over the coming quarters and years to really start to demonstrate a shift in the pace, the scale, and hopefully, also the probability of success of drug discovery and development.

當然，還有一些免責聲明。我認為，遞歸在達到 TechBio 逃脫速度方面具有獨特的優勢。我的意思是，我認為我們已經非常獨特地將管道、平台和人員組合在一起，為我們提供了在未來幾個季度和幾年內真正開始展示速度、規模、希望還有藥物發現和開發成功的可能性。

And we’re just delighted to be in this position to be helping to lead TechBio because we feel so confident about what the future looks like. We feel that the future of TechBio is the inevitable future of biotech. And we are delighted to be leading that and so thankful that all of you are supporting us.

我們很高興能夠幫助領導 TechBio，因為我們對未來充滿信心。我們覺得TechBio的未來就是生技的必然未來。我們很高興能夠領導這項工作，並非常感謝大家對我們的支持。

So with that, I’m going to go ahead and dive in to a little bit of what we’re working on, and I’m going to start with the pipeline. And the pipeline, in particular, I think, is very exciting because we have an opportunity to start to demonstrate catalysts on a roughly quarterly cadence. These catalysts are going to start in Q3 with our first Phase 2 readout. And then again, we’re going to start to be able to demonstrate Phase 2 POC readouts on a quarterly cadence.

因此，我將繼續深入研究我們正在做的一些事情，我將從管道開始。我認為，這個管道尤其令人興奮，因為我們有機會開始以大約每季的節奏展示催化劑。這些催化劑將於第三季開始，我們將發布第一個第二階段的數據。再說一次，我們將開始能夠以季度節奏展示第 2 階段 POC 讀數。

We’ll kick off with REC-994, the SYCAMORE trial in cerebral cavernous malformation. This is a first-in-disease opportunity where we’re really leading out as the first institutional sponsored program that’s gone through the FDA. We are nearly complete not only with the study, but enrolling almost all of the patients into a long-term extension.

我們將從 REC-994 開始，這是針對腦海綿狀血管瘤的 SYCAMORE 試驗。這是一個首個疾病機會，我們真正成為第一個通過 FDA 批准的機構贊助計畫。我們不僅接近完成了這項研究，而且還招募了幾乎所有患者進行長期擴展。

And what we’ll be looking for in the context of this trial is that looking across all the evidence from all of these exploratory end points, not only at safety and tolerability, where we believe we’ve got a really strong opportunity, but certainly at a variety of different potential efficacy readouts that we could work with the FDA to move forward with to try and get this medicine to patients as quickly as we can.

我們在這次試驗中要尋找的是，審視所有這些探索性終點的所有證據，不僅是安全性和耐受性，我們相信我們在這方面有一個非常強大的機會，但當然，在各種不同的潛在療效讀數中，我們可以與FDA 合作，盡快嘗試將這種藥物提供給患者。

We’ll follow that up with another program, REC-2282 in neurofibromatosis type 2, where we’ve given guidance that we think we’ll be reading out the preliminary safety and efficacy in Q4. We’ve got then REC-4881 with a preliminary safety and efficacy readout in the first half of 2025.

我們將跟進另一個項目，即治療 2 型神經纖維瘤病的 REC-2282，我們已經給出了指導，我們認為我們將在第四季度宣讀初步的安全性和有效性。我們已經在 2025 年上半年獲得了 REC-4881 的初步安全性和有效性讀數。

We’ve got another REC-4881 program, again, with another preliminary safety and efficacy readout in the first half of 2025 and then a number of other programs coming behind that, either initiating Phase 2 studies or moving into IND submission in the near-term. So really excited about this pipeline. I think it gives Recursion a unique opportunity to start to demonstrate, in a really robust way, shots on goal.

我們再次啟動了另一個REC-4881 項目，並在2025 年上半年公佈了另一個初步的安全性和有效性結果，隨後還有許多其他項目，要么啟動2 期研究，要么在不久的將來進入IND提交階段-學期。對這條管道非常興奮。我認為這給了 Recursion 一個獨特的機會來開始以非常強大的方式展示射門。

And of course, we know all of these programs won’t be successful, but we believe some may be. And ultimately, what’s exciting to us is not only the opportunity to bring medicines to patients in many cases, first-in-disease, but the opportunity to begin to learn, the opportunity to begin to take the data from these trials, feed it back into the platform, and, whether it’s a success or a failure, to be able to improve the platform. Because we really are in a multi-decade journey to build what we think will be the TechBio company that defines the future.

當然，我們知道所有這些計劃都不會成功，但我們相信有些計劃可能會成功。最終，令我們興奮的不僅是在許多情況下為患者提供藥物的機會，而且是開始學習的機會，開始從這些試驗中獲取數據的機會，將其反饋到平台中，並且無論成功還是失敗，都能夠改進平台。因為我們確實正在經歷數十年的旅程，以打造我們認為定義未來的 TechBio 公司。

And it’s not only our pipeline that we’re excited about; it’s our platform. And we’re delighted to share with today’s earnings, a lot like the deal we did with Tempus back in December, a deal here with Helix. Helix is a fantastic company. We’ve been getting to know them for a while.

我們不僅對我們的管道感到興奮，而且對我們的管道也感到興奮。這是我們的平台。我們很高興分享今天的收益，就像我們去年 12 月與 Tempus 達成的交易（這裡與 Helix 達成的交易）一樣。Helix 是一家出色的公司。我們對他們的了解已經有一段時間了。

They’ve partnered with healthcare systems all across the country to bring really significant scale of exome, genomics, and longitudinal health data into a robust software environment where, like we did with the Tempus data, we’ve now signed a collaboration with Helix. That’s going to give us access to hundreds of thousands of de-identified records along with omics data that we can put together with all of the rest of the data we generated at Recursion and start to continue to train these causal AI models to help understand the gene networks that are underlying now, not only oncology diseases but also non-oncology diseases.

他們與全國各地的醫療保健系統合作，將真正大規模的外顯子組、基因組學和縱向健康數據引入強大的軟體環境，就像我們對 Tempus 數據所做的那樣，我們現在已經與螺旋。這將使我們能夠存取數十萬條去識別化的記錄以及組學數據，我們可以將這些數據與我們在 Recursion 中生成的所有其他數據放在一起，並開始繼續訓練這些因果 AI 模型來幫助了解現在潛在的基因網絡，不僅包括腫瘤疾病，還包括非腫瘤疾病。

And what’s fantastic about this collaboration is that, as you know, we’ve got large partnerships in the context of Roche-Genentech and neuroscience and maybe other partnerships coming down the line in non-oncology areas. And this collaboration will allow us to be generating even more robust hypotheses, using patient data to help drive our really, really exciting platform internally.

如你所知，這次合作的奇妙之處在於，我們在羅氏基因泰克和神經科學領域建立了大型合作關係，也許還有非腫瘤學領域的其他合作夥伴關係。這種合作將使我們能夠產生更可靠的假設，利用患者數據來幫助推動我們真正非常令人興奮的內部平台。

So very excited to announce this collaboration with the Helix team, multi-year collaboration here. And we’re going to be kicking that off imminently.

非常高興地宣布與 Helix 團隊的合作，這是多年的合作。我們將立即開始這項工作。

Second, we shared this on social a couple of weeks ago, but I just want to emphasize. Recursion pioneered the use of phenomics to try and understand biology. But we now also have started to scale multiple other layers of omics, transcriptomics, working on proteomics.

其次，幾週前我們在社群媒體上分享了這一點，但我只是想強調一下。遞歸開創了使用表型組學來嘗試和理解生物學的先河。但我們現在也開始擴大組學、轉錄組學的多個其他層次，致力於蛋白質體學。

We have enviromics. We have the patient data, like I just talked about. But I really want to emphasize here our work in transcriptomics.

我們有環境。正如我剛才所說，我們有患者數據。但我真的想在這裡強調我們在轉錄組學方面的工作。

We’ve now sequenced our one millionth transcriptome at Recursion. And we are leveraging our unique take on doing transcriptomes in order to work across a whole genome, transcriptomics map, the first of potentially many in that space, and also starting to layer in data from transcriptomics across chemical perturbations as well. We think this will be a really exciting orthogonal data layer to what we’re building with phenomics, what we’re going to build with proteomics, what we’re building with enviromics in the animal organism step and what we’ve got with genome scale, population scale data not only with Tempus, but now with Helix.

我們現在已經在 Recursion 上對第一百萬個轉錄組進行了測序。我們正在利用我們在轉錄組方面的獨特做法，以便在整個基因組、轉錄組圖譜中開展工作，這是該領域潛在的許多轉錄組圖譜中的第一個，並且也開始將來自化學擾動的轉錄組學的資料分層。我們認為這將是一個非常令人興奮的正交數據層，與我們正在用表型組學構建的內容、我們將用蛋白質組學構建的內容、我們在動物有機體步驟中用環境組學構建的內容以及我們將要建立的內容相比，這將是一個非常令人興奮的正交資料層。

And so not only are we training our AI models within each of these data layers, but increasingly starting to train AI models between and across these different data layers, these multimodal omics data sets that we’re generating, I think, pretty uniquely here at Recursion. So really exciting, and congratulations to the team for the incredible scale that they put together with our transcriptomics technology.

因此，我們不僅在每個資料層中訓練人工智慧模型，而且越來越多地開始在這些不同資料層之間訓練人工智慧模型，我認為，我們生成的這些多模式組學資料集在這裡非常獨特在遞歸時。非常令人興奮，祝賀我們的團隊與我們的轉錄組學技術結合在一起實現了令人難以置信的規模。

Finally, another exciting opportunity on the platform side, we’ve really been leaning into active learning over the past quarter. And I think you’re going to see us continue to increase the cadence of this work, this research work coming out of the team at Valence Labs and being done here at Recursion, which is allowing us to move away from having to do every experiment towards making predictions about what experiment would give us the highest amount of learning.

最後，平台方面的另一個令人興奮的機會是，我們在過去的季度中一直在真正傾向於主動學習。我想你會看到我們繼續加快這項工作的節奏，這項研究工作來自 Valence Labs 團隊，並在 Recursion 完成，這使我們不必做每件事進行實驗來預測什麼實驗會給我們帶來最高的學習量。

And what you can see here is one of our early pilots looking across just about 50 or 100 genes where by using this iterative approach of active learning, we can get about 80% of the value, 80% of the information you see on that top line, only doing about 40% of the experiments. And we think this is going to be a transformational tool for us to deploy with our unique scaled wet lab environment.

你在這裡可以看到，我們的早期試點之一正在研究大約50 或100 個基因，透過使用這種主動學習的迭代方法，我們可以獲得大約80% 的價值，即你在頂部看到的80%的資訊線，只做了大約40%的實驗。我們認為這將成為我們獨特的規模化濕實驗室環境中部署的變革工具。

Because what this is going to allow us to do is start to explore this incredible high-dimensional space of biological perturbations, chemical perturbations, time-based perturbations across multiple different layers of omics. And it starts to become trillions and trillions and trillions -- in fact, trillions times trillions times trillions of different possible combinations one could explore, far too much to ever brute force.

因為這將使我們能夠開始探索這個令人難以置信的高維度空間，其中包括跨多個不同組學層的生物擾動、化學擾動、基於時間的擾動。它開始變得數萬億、數萬億、數萬億——事實上，數萬億乘數萬億乘數萬億人們可以探索的不同可能組合，遠遠超出了暴力的程度。

And using these kinds of tools and technologies we’re building out of Recursion, I think we’re going to be uniquely positioned to do the best next experiment to use our resources in the most efficient way to broadly map drug discovery, to broadly map biology, to broadly map chemistry, and ultimately bring medicines to patients more quickly.

使用我們在遞歸基礎上構建的這些工具和技術，我認為我們將處於獨特的地位，可以進行最好的下一個實驗，以最有效的方式利用我們的資源來廣泛地繪製藥物發現圖，廣泛地繪製生物學圖譜，廣泛地繪製化學圖譜，並最終更快地為患者提供藥物。

And then I also want to talk a little bit about some exciting news around BioHive 2. Again, we announced a little bit of this on social. We partnered with NVIDIA last year. They brought an equity investment in. And since then, we’ve been working so closely with that team, announced in the fall that we were going to build a BioHive 2, our next supercomputer.

然後我還想談談有關 BioHive 2 的一些令人興奮的消息。我們再次在社交媒體上宣布了這一點。我們去年與 NVIDIA 合作。他們引入了股權投資。從那時起，我們一直與團隊密切合作，並在秋季宣布我們將建造 BioHive 2，這是我們的下一個超級電腦。

And you can see here that we actually now have built out this supercomputer. We benchmarked the supercomputer at 23.32 petaflops,. We did that once all the materials were in place. We built it out and benchmarked it in three weeks thanks to an incredible partnership with the NVIDIA team.

你可以在這裡看到，我們現在實際上已經建造了這台超級電腦。我們對超級電腦進行了基準測試，速度為 23.32 petaflops。一旦所有材料就位，我們就這樣做了。由於與 NVIDIA 團隊的良好合作關係，我們在三週內完成了它的建置並進行了基準測試。

And if we were to take that benchmark and compare it to the previous top 500 list, it would put us right around 30th position there. So we’ll see, as the new top 500 list comes out, where we end up, probably in the top 50. But this means Recursion now owns and operates the two of the fastest supercomputers in all of biopharma.

如果我們採用該基準並將其與先前 500 強的名單進行比較，我們將排在第 30 位左右。因此，隨著新的 500 強名單公佈，我們將拭​​目以待，我們最終可能會進入前 50 名。但這意味著 Recursion 現在擁有並經營生物製藥領域最快的兩台超級電腦。

We'll be combining BioHive 1 with BioHive 2. We’ll rebenchmark that later and see if we can continue to move up the path. But I think in the in the space of TechBio, where we know scaling laws apply, where we know both data and computer are going to be critical, Recursion's really uniquely positioned with one of the most compelling data sets, one of the fastest-growing data sets, one of the most purpose-built for machine learning data sets alongside one of the most robust sets of on-premise compute in the industry. And putting those two things together, we think, gives us a really, really robust mode.

我們將結合 BioHive 1 和 BioHive 2。稍後我們將重新進行基準測試，看看我們是否可以繼續沿著這條路前進。但我認為在TechBio 領域，我們知道縮放法則適用，我們知道資料和電腦都將至關重要，遞歸確實具有獨特的地位，是最引人注目的資料集之一，也是成長最快的資料集之一資料集，是最專門為機器學習而建構的資料集之一，也是業界最強大的本地運算集之一。我們認為，將這兩件事放在一起會給我們一個非常非常強大的模式。

So kudos to the team for all of their hard work getting this done. And now I want to finish by talking just a little bit about the people. Because ultimately, without the people, we can’t drive this mission forward.

感謝團隊為完成這項工作所做的所有辛勤工作。現在我想簡單談談人民。因為最終，如果沒有人民，我們就無法推動這項使命向前發展。

We had a couple of really big announcements this last quarter. The first was Najat Khan, I think one of the icons of TechBio, moving from J&J to join us here at Recursion. She’s already joined our Board of Directors. And in the next 6 to 10 weeks, she’ll be moving over as our Chief R&D Officer and Chief Commercial Officer here at Recursion.

上個季度我們發布了幾項重大公告。第一個是 Najat Khan，我認為他是 TechBio 的偶像之一，他從強生公司跳槽到 Recursion 加入我們。她已經加入我們的董事會。在接下來的 6 到 10 週內，她將擔任 Recursion 的首席研發長和商務長。

She brings, I think, incredible vision from hit and target discovery all the way through to using digital tools for commercialization, marketing, and distribution problems. And that visionary arc across every step of drug discovery and development, her experience doing pipeline strategy at J&J -- and I think J&J really one of the leaders in thinking about digital tools in the biopharma space. We’re just delighted to have Najat join us here at Recursion and can’t wait for the next stage with her.

我認為，她帶來了令人難以置信的願景，從命中和目標發現一直到使用數位工具解決商業化、行銷和分銷問題。這種遠見貫穿藥物發現和開發的每一步，以及她在強生公司製定管道策略的經驗——我認為強生確實是考慮生物製藥領域數位工具的領導者之一。我們很高興 Najat 加入我們的 Recursion，並且迫不及待地等待與她一起進入下一階段。

And then we also announced Michael Bronstein, the deep mine Professor of artificial intelligence at Oxford, has joined us as a Recursion valence adviser. And we’re so excited to join him in London in a few weeks as we formally open our London office, where we think there’s just such an exciting talent arbitrage. So much great talent in the computational biology space exists in London, and we aim to consolidate a lot of that great talent as we advance this exciting mission forward for Recursion.

然後我們也宣布牛津大學深礦人工智慧教授 Michael Bronstein 加入我們，擔任遞歸顧問。幾週後我們將正式開設倫敦辦事處，我們非常高興能在倫敦與他會面，我們認為那裡有如此令人興奮的人才套利。倫敦在計算生物學領域擁有如此多的優秀人才，我們的目標是在推進這一令人興奮的 Recursion 使命時整合許多優秀人才。

Before I move to questions, I just want to end with some high-level guidance. I think as I shared before, Recursion really uniquely leading TechBio. And we believe that over the coming quarters and coming years, I think Recursion has the opportunity with a wide variety of catalysts to really start to demonstrate the potential of our philosophy for drug discovery.

在開始提問之前，我只想以一些高級指導作為結束。我認為正如我之前分享的那樣，Recursion 確實以獨特的方式領先於 TechBio。我們相信，在未來幾季和未來幾年，我認為遞歸有機會透過各種催化劑真正開始展示我們藥物發現理念的潛力。

On the pipeline side, we’ve got multiple Phase 2 trial starts in starts in 2024, multiple Phase 2 readouts over the next 18 months on roughly a quarterly cadence. And our hope is that if our pipeline continues to operate, we’ll be able to meet or exceed that cadence in the future. We've got multiple INDs that we think are going to happen in the near term.

在管道方面，我們已於 2024 年開始啟動多個第 2 階段試驗，並在接下來的 18 個月內以大約每季度的節奏進行多個第 2 階段的讀數。我們希望，如果我們的管道繼續運行，我們將來能夠達到或超過這個節奏。我們有多個 IND，我們認為短期內將會發生。

On the partnership side, we continue to move forward with our colleagues at Roche Genentech on a really pioneering visionary collaboration. And so excited to be working with both the G-red and P-red teams. We’ve got the potential for near-term program and map options on top of the program option we already announced in oncology last fall.

在合作關係方面，我們繼續與羅氏基因泰克的同事一起推進真正開創性的富有遠見的合作。很高興能與 G-red 和 P-red 團隊合作。除了我們去年秋天已經宣布的腫瘤學計畫選項之外，我們還有近期計畫和地圖選項的潛力。

With our ongoing collaboration with Bayer, we see significant opportunity in the near term in the space of undruggable oncology targets for some program options. We’ve got a fantastic collaboration with Tempus, and we’ve already identified some pretty exciting targets in the context of non-small cell lung cancer. And we’re starting to integrate larger chunks of their data for broader kind of pan-cancer causal AI models that are giving us really exciting hypotheses in oncology.

透過與拜耳的持續合作，我們看到近期在某些項目選項的不可成藥腫瘤學目標領域存在重大機會。我們與 Tempus 進行了出色的合作，並且我們已經在非小細胞肺癌的背景下確定了一些非常令人興奮的目標。我們開始將他們的大量數據整合到更廣泛的泛癌症因果人工智慧模型中，這些模型為我們提供了真正令人興奮的腫瘤學假設。

And then as we’ve shared, we see the opportunity for additional transformational partnerships in the context of non-oncology areas, areas perhaps like cardiovascular and metabolism. And so we’ll be looking forward, we hope, to being able to share more details there in the near future.

然後，正如我們所分享的，我們看到了在非腫瘤學領域（可能是心血管和新陳代謝等領域）建立更多轉型夥伴關係的機會。因此，我們希望能夠在不久的將來在那裡分享更多細節。

And then finally, as you saw as we shared our Phenom-Beta foundation model for image-based drug discovery with NVIDIA on BioNeMo, we continue to do business experiments with our data, using it as a value driver, and have the potential to put some of our data, some of our tools into a variety of different marketplaces, a variety of different partnerships. And we hope to be able to share more there soon.

最後，正如您所看到的，我們在BioNeMo 上與NVIDIA 分享了用於基於圖像的藥物發現的Phenom-Beta 基礎模型，我們繼續利用我們的數據進行商業實驗，將其用作價值驅動因素，並有潛力將我們的一些數據、一些工具進入了各種不同的市場、各種不同的合作關係。我們希望很快能夠在那裡分享更多內容。

And then finally, our platform, we’re moving the Recursion OS from automated discovery, which is where I would argue we are today. Increasingly, workflows and processes that are being driven in an automated way towards autonomous discovery.

最後，在我們的平台上，我們正​​在將遞歸作業系統從自動發現轉移出來，這就是我認為我們今天所處的位置。工作流程和流程越來越多地以自動化方式驅動，以實現自主發現。

And our low platform that we unveiled at JPMorgan is a stepping stone in this direction towards autonomous discovery, where AI agents will be leveraging the tools that we built at Recursion, both wet lab and dry lab, to automatically hypothesize about biology, automatically look for high areas of unmet need, and automatically prioritize experimentation to five us the fastest route to impact for patients. We feel like that’s where the industry is going to move, and we want to make sure we’re leading that.

我們在摩根大通推出的低平台是朝著自主發現方向邁出的墊腳石，人工智慧代理將利用我們在 Recursion 中建立的工具（包括濕實驗室和乾實驗室）來自動假設生物學，自動尋找未滿足需求的高領域，並自動將實驗優先排序為五個我們對患者產生影響的最快途徑。我們覺得這就是產業將要發展的方向，我們希望確保我們處於領先地位。

And then finally, I just want to share some of the things that I feel like are on our road map in the near term. Active learning, I’ve talked about; proteomics, I’ve talked about. But we’re also doing a lot of exploration around the potential for organoids and steroids to help us increase both translation and kind of predictive ADME Tox at scale at Recursion. Those are kind of bottlenecks that we’re working on now.

最後，我只想分享一些我認為近期路線圖上的事情。我已經談過主動學習；我已經談過蛋白質體學。但我們也在圍繞類器官和類固醇的潛力進行了大量探索，以幫助我們在 Recursion 中大規模增加翻譯和預測性 ADME Tox。這些都是我們現在正在努力解決的瓶頸。

And then, of course, on the automated synthesis side, we continue to think that the time that it takes to synthesize small molecules, 8 to 12 weeks is -- we’ve been bringing that down through our collaboration with Enamine, but we see a real opportunity to continue to accelerate that through things like automated synthesis and automated microsynthesis.

當然，在自動化合成方面，我們仍然認為合成小分子所需的時間（8 到 12 週）——我們一直在透過與 Enamine 的合作來降低這一時間，但我們看到一個真正的機會，可以透過自動化合成和自動化微合成等方式持續加速這一進程。

And finally, we’re ending Q1 with nearly $300 million in cash at the end of Q1. And I think that gives us robust runway and Recursion really feels poised with some of these milestones that I’ve just mentioned in the near term, these potential milestones giving us pretty significant runway extension as we begin to hit those.

最後，我們在第一季末擁有近 3 億美元的現金。我認為這為我們提供了強大的跑道，而遞歸確實對我剛剛在近期提到的一些里程碑做好了準備，當我們開始實現這些里程碑時，這些潛在的里程碑為我們提供了相當大的跑道延伸。

So we feel like we’re in a really fantastic space. We’re honored to be helping to lead the TechBio evolution as we move biotech into TechBio. And we’re so thankful for all of your questions and support.

所以我們感覺我們身處在一個非常奇妙的空間。當我們將生物技術引入 TechBio 時，我們很榮幸能夠幫助引領 TechBio 的發展。我們非常感謝您提出的所有問題和支持。

And with that, I’m going to go ahead and move over to start answering some questions.

說到這裡，我將繼續並開始回答一些問題。

So let’s dive right in. Looks like we’ve got a question from Dante Noah, Eric Joseph on the team at JPM, Gil Blum, et cetera. Similar question here, and they’re asking, what does success look like for each of your upcoming CCM, NF2, FAP, AXIN1 APC Phase 2 trial readouts, and what might your next steps be?

那麼就讓我們開始吧。看來我們收到了 JPM 團隊的 Dante Noah、Eric Joseph、Gil Blum 等人的問題。這裡有類似的問題，他們問的是，您即將推出的 CCM、NF2、FAP、AXIN1 APC 第 2 期試驗讀數的成功是什麼樣子，以及您的下一步可能是什麼？

And I think just given the time today, I won’t go through each of these programs individually. But what I will share is for each of these programs, we are likely to be first in disease or near first in disease. And when you have an opportunity like that, I think we really have to work with our colleagues at the agency, work with key opinion leaders, work with patient advocates to look at the sum of the evidence.

我想，鑑於今天的時間，我不會單獨逐一討論這些程序。但我要分享的是，對於這些項目中的每一個，我們很可能在疾病方面處於領先地位，或者接近處於疾病領先地位。當你有這樣的機會時，我認為我們確實必須與該機構的同事合作，與關鍵意見領袖合作，與患者倡導者合作，以研究證據的總和。

And of course, we’ll be looking for a therapeutic window, but we’ll be looking for early signals of efficacy across a variety of different readouts. In the context of CCM, for example, we could imagine looking for objective improvement in things like hemosiderin deposition around lesions as well as subjective improvements in things like patient-reported outcomes or other kind of neurologist-reported outcome tools that we have in the secondary end points.

當然，我們將尋找治療窗口，但我們將在各種不同的讀數中尋找療效的早期訊號。例如，在CCM 的背景下，我們可以想像在病變周圍的含鐵血黃素沉積等方面尋求客觀的改善，以及在患者報告的結果或我們在二級中擁有的其他類型的神經科醫生報告的結果工具等方面尋求主觀的改善。

And I think as we look at the sum of the evidence for each of these, we’ll work very closely with key opinion leaders and patients and the FDA. And what we want to see is moving the biology. If we see that we’re moving in the biology in a way that is going to be meaningful for patients potentially, that’s going to be the signal we want to see to drive forward and have discussions with the agency.

我認為，當我們研究每一項證據的總和時，我們將與關鍵意見領袖、患者以及 FDA 密切合作。我們希望看到的是改變生物學。如果我們發現我們正在以一種對患者潛在有意義的方式在生物學方面取得進展，那麼這將成為我們希望看到的信號，以推動前進並與該機構進行討論。

And the next steps will be to aggressively pursue whatever it is we can to move these medicines to patients. In some context, like NF2, it might be moving to start our Phase 3 trial and in consultation with the agency. In other context, we might even have discussions with the agency about the potential for accelerated approval. But we’re going to really need to see what the data looks like, and we’ll be looking forward to reporting that in Q3, Q4, in the first half of 2025 with more programs coming in the future.

接下來的步驟將是積極尋求盡我們所能，將這些藥物轉移給患者。在某些情況下，例如 NF2，可能會開始我們的第 3 階段試驗並與該機構協商。在其他情況下，我們甚至可能與該機構討論加速批准的可能性。但我們確實需要看看數據是什麼樣的，我們期待在 2025 年上半年的第三季、第四季報告這些數據，未來還會推出更多計畫。

All right. Moving on to another question here. Steve Dechert from KeyBanc and Vikram Purohit from Morgan Stanley are asking, how should we think about the significance of your Phase 2 readout for REC-994 in terms of validating your platform and the potential for other programs in your pipeline?

好的。這裡繼續討論另一個問題。KeyBanc 的 Steve Dechert 和摩根士丹利的 Vikram Purohit 問，我們應該如何考慮 REC-994 第 2 階段讀數對於驗證您的平台以及管道中其他項目的潛力的重要性？

This is a great question and one we get asked pretty frequently So Steve, Vikram, what I can say is I think we’ve already got a lot of leading indicators of the power of this platform. As I’ve shared before, you can go back and look at the paper that we published a preprint in April of 2020, where now, we are 9 for 10 at predicting the outcome of FDA-approved drug in the context of SARS-CoV-2 virus. And we made all of those predictions well ahead of time.

這是一個很好的問題，我們經常被問到。 所以史蒂夫、維克拉姆，我能說的是，我認為我們已經獲得了很多關於這個平台力量的領先指標。正如我之前分享的，您可以回去看看我們在 2020 年 4 月發表的預印本論文，現在，我們以 9 比 10 的比例預測 FDA 批准的藥物在 SARS 背景下的結果—— CoV-2 病毒。我們提前做出了所有這些預測。

But as we all know, there’s a lot that goes into every clinical trial, a lot of resources and trial design that can really influence the outcome. And the probability of success of the average Phase 2 is somewhere between 25% and 35%. And so we know that they will -- we hope -- be both successes, and we know there may be some failures.

但眾所周知，每項臨床試驗都涉及許多內容，大量資源和試驗設計可以真正影響結果。第二階段的平均成功機率在 25% 到 35% 之間。所以我們知道，我們希望它們都會成功，但我們也知道可能會失敗。

Ultimately, I think there’s an uncorrelated opportunity with each of the programs we have moving forward. And by that, I mean technically uncorrelated. Because even though we’re using the same platform to identify each potential opportunity, we are validating those opportunities against the same gold standards, standard animal models, PDX oncology models, et cetera, that anyone else in the industry would use.

最終，我認為我們正在推進的每個專案都存在一個不相關的機會。我的意思是技術上不相關。因為即使我們使用相同的平台來識別每個潛在機會，我們也會根據業內其他任何人都會使用的相同黃金標準、標準動物模型、PDX 腫瘤學模型等來驗證這些機會。

And so while we certainly think a positive trial gives people a lot of optimism around what we’re building, if any of these trials are negative, then we certainly could imagine that the technical risk is relatively uncorrelated between each of these. And that’s why we’re pushing so hard to advance a whole pipeline of programs with readouts coming on a quarterly cadence.

因此，雖然我們當然認為積極的試驗讓人們對我們正在建構的東西抱持著很大的樂觀態度，但如果這些試驗中的任何一個是消極的，那麼我們當然可以想像這些試驗之間的技術風險相對不相關。這就是為什麼我們如此努力地推進整個計劃流程，並每季發布一次讀數。

What’s more, we’ve got programs that are now moving forward with pharma partners. We’ve got the potential for additional programs to move forward. And we’ve got the potential for driving value through our data and through our computational and software opportunities. So I think Recursion, unlike a traditional biopharma company really doesn’t have the same bimodal risk that many other companies in this space do, who will typically advance one or two drugs to this big Phase 2 to read out.

此外，我們現在正在與製藥合作夥伴一起推進一些專案。我們有潛力推進其他計劃。我們有潛力透過我們的數據以及我們的計算和軟體機會來推動價值。因此，我認為 Recursion 與傳統的生物製藥公司不同，它確實不存在與該領域許多其他公司相同的雙峰風險，這些公司通常會將一兩種藥物推進到這個大的第二階段進行測試。

Great. Thanks for that question. Next, I’m going to go to Mary [F] and Gil. What success have you had to date with using the Tempus data? What other population genomics data might you look to access? And how could such data complement what you were able to learn from Tempus?

偉大的。謝謝你提出這個問題。接下來，我要去找瑪莉 [F] 和吉爾。到目前為止，您在使用 Tempus 資料方面取得了哪些成功？您可能希望存取哪些其他群體基因組學數據？這些數據如何補充您從 Tempus 學到的知識？

Great question. So we’ve already leveraged the Tempus data. We signed that collaboration in either late November, early December of last year. We had data coming in within weeks. The team worked over the holidays, and we had deployed some of our early AI models onto Tempus data in the first -- really by the start of J.P. Morgan in the first couple of weeks of January.

很好的問題。所以我們已經利用了 Tempus 數據。我們在去年 11 月底或 12 月初簽署了該合作協議。我們在幾週內就收到了數據。該團隊在假期期間一直在工作，我們一開始就將一些早期的人工智慧模型部署到了 Tempus 數據上——實際上是在一月份的前幾週摩根大通開始的時候。

We’ve continued to refine that work. And as I shared earlier, we’ve already identified an exciting novel opportunity in the context of non-small cell lung cancer. We have a program that’s now moving forward using the Tempus data. And I think we’re really uniquely positioned to take the Tempus data alongside the proprietary data we’ve generated at Recursion to bring those together to identify targets that, really, you wouldn’t be able to identify without these complementary data sets.

我們繼續完善這項工作。正如我之前分享的，我們已經在非小細胞肺癌領域發現了一個令人興奮的新機會。我們有一個計劃，現在正在使用 Tempus 數據推進。我認為我們確實處於獨特的地位，可以將 Tempus 數據與我們在 Recursion 中生成的專有數據結合在一起，以識別目標，如果沒有這些補充數據，您實際上將無法識別這些目標套。

And so, you’ll see us continue to move programs forward that way. And we hope as those programs hit kind of the preclinical stage, we’ll be able to share more about them.

因此，您會看到我們繼續以這種方式推進專案。我們希望當這些項目進入臨床前階段時，我們將能夠分享更多有關它們的資訊。

But as we just announced today in our collaboration with Helix, we’re also now looking at non-oncology scaled, population scale genomics -- transcriptomics data. And we think that’s a really fascinating opportunity. Not only for the same play can we take that data looking across large non-oncology diseases, maybe in neuroscience, maybe in cardiovascular metabolism, combine it with our internal data to identify this combination of forward and reverse genetics that can move the company forward, probably in some of our partnerships, either existing or future partnerships.

但正如我們今天剛與 Helix 合作宣布的那樣，我們現在也在研究非腫瘤規模、群體規模的基因組學——轉錄組學數據。我們認為這是一個非常令人著迷的機會。不僅對於同一個遊戲，我們還可以利用這些數據來研究大型非腫瘤疾病，也許是神經科學，也許是心血管代謝，將其與我們的內部數據相結合，以確定可以推動公司前進的正向和反向遺傳學的組合，可能在我們的一些合作關係中，無論是現有的還是未來的合作關係。

But also, I would say, I think there’s some opportunity in oncology and non-oncology space to actually use both the Tempus and the Helix data along with our underlying data to get a sense of how these genetic -- these gene networks really work. Knowing how they’re perturbed in the context of oncology settings and how they’re perturbed in the context of non-oncology settings, I think, will give us a really robust field from which to work, robust substrate.

但我想說的是，我認為在腫瘤學和非腫瘤學領域有一些機會實際使用 Tempus 和 Helix 數據以及我們的基礎數據來了解這些遺傳——這些基因網絡是如何運作的。我認為，了解它們在腫瘤學背景下如何受到干擾以及它們在非腫瘤學背景下如何受到干擾，將為我們提供一個真正強大的工作領域，強大的基礎。

It takes a while to take a discovery program and get it into the preclinical space. But rest assured, Mary and Gil, as we get those programs into IND-enabling studies, we look forward to being able to share quite a bit more. All right.

實施發現計劃並將其進入臨床前空間需要一段時間。但請放心，瑪麗和吉爾，當我們將這些項目納入 IND 支持研究時，我們期待能夠分享更多資訊。好的。

Next, we’re going to go to Laura, who asks about our London office. What are we looking for in the London office? Why are we opening a London office? And what are our international growth plans beyond that?

接下來，我們要去拜訪勞拉，她詢問我們倫敦辦事處的狀況。我們在倫敦辦事處尋找什麼？我們為什麼要開設倫敦辦事處？除此之外，我們的國際成長計畫是什麼？

Great question, Laura. We like to operate in cities, in communities where we feel like there’s an arbitrage, where there’s great talent and maybe fewer companies that are leveraging that talent. We’re based in Salt Lake City. We’ve got fantastic teams, really focused in software engineering in Toronto, and other related areas, digital chemistry as well.

好問題，勞拉。我們喜歡在城市、社區開展業務，因為我們覺得那裡有套利空間，那裡有優秀的人才，但利用這些人才的公司可能更少。我們的總部位於鹽湖城。我們擁有出色的團隊，真正專注於多倫多的軟體工程以及其他相關領域和數位化學。

We’ve got a great team in AI and AI research in Montreal. We’ve got a fantastic team in San Jose, Milpitas, with our InVivo facility. And London felt like an opportunity for us to accelerate our computational biology talent. We think the UK has done a really tremendous job of training folks at the intersection of data science and computation with biology and chemistry, really probably ahead of the universities in the US in terms of that integrated training.

我們在蒙特婁擁有一支優秀的人工智慧和人工智慧研究團隊。我們在米爾皮塔斯聖荷西擁有一支出色的團隊，並擁有 InVivo 工廠。倫敦感覺對我們來說是一個加速我們計算生物學天賦的機會。我們認為英國在數據科學和計算與生物學和化學交叉領域的人員培訓方面做得非常出色，在綜合培訓方面可能確實領先於美國的大學。

And we had nearly 300 applicants in just the first couple of days when we announced our London office for just a couple of dozen positions that we posted, and these were extraordinarily talented folks. So we feel like that bet is already paying off with fantastic talent in London.

當我們宣佈設立倫敦辦事處並招聘幾十個職位時，僅僅頭幾天就有近 300 名申請者，而這些人都是非常有才華的人。所以我們覺得這個賭注已經得到了回報，倫敦擁有出色的人才。

As far as other international plans, I think that office is probably going to be a fantastic step for us internationally. We’re still only a team of 530 or 540 folks, so I don’t think you’ll see us do a lot of additional international growth in the near term.

就其他國際計劃而言，我認為該辦事處可能將是我們在國際上邁出的重要一步。我們仍然只有 530 或 540 人的團隊，因此我認為您不會在短期內看到我們在國際上實現大量成長。

But certainly, as the company begins to move into development, begins to scale our development ambitions, maybe even thinks about commercialization in the intermediate to long term, we’ll have an opportunity to grow in places like Asia, Western Europe, and beyond. So I think those are more intermediate to long-term plans. Thanks, Laura.

但當然，隨著公司開始進入發展階段，開始擴大我們的發展雄心，甚至可能考慮中長期的商業化，我們將有機會在亞洲、西歐等地發展。所以我認為這些是更中期到長期的計劃。謝謝，勞拉。

All right. Next up, we’ve got a question from Lucille [M], who asks, what do you think about Xaira being founded? And how much are they a competitor for Recursion?

好的。接下來，Lucille [M] 向我們提出了一個問題，她問，您對 Xaira 的成立有何看法？他們在多大程度上是遞歸的競爭對手？

Great question. So for those who don’t know, there’s an exciting new TechBio company with a great cast of characters that got announced a couple weeks ago. They’ve got significant funding, really, Marc Tessier-Lavigne and others who are leading that organization.

很好的問題。對於那些不知道的人來說，幾週前宣布了一家令人興奮的新 TechBio 公司，該公司擁有許多優秀的角色。他們確實獲得了大量資金，包括馬克·泰西爾-拉維涅 (Marc Tessier-Lavigne) 和領導該組織的其他人。

There’s a lot of disease that needs to be treated, needs to be cured. So we welcome everybody to the space. And our belief is that the biopharma industry in a decade is going to look a lot more like scaled versions of companies like Recursion than it does today.

有很多疾病需要治療、需要治癒。因此，我們歡迎大家來到這個空間。我們相信，十年後的生物製藥產業將比今天更像 Recursion 這樣的公司的規模化版本。

And so we welcome companies like Xaira to the space. We look forward to potentially collaborating with those companies, competing with those companies.

因此，我們歡迎像 Xaira 這樣的公司進入這個領域。我們期待與這些公司合作、競爭。

What I can say is that we believe in TechBio. The primary bottleneck will be data. We’re seeing that where data exists, companies are making extraordinarily rapid progress with computational tools like machine learning and AI. And where data is sparse, it’s much, much more difficult.

我能說的是我們相信TechBio。主要瓶頸將是數據。我們看到，只要有資料存在，企業就可以利用機器學習和人工智慧等運算工具取得異常快速的進展。在資料稀疏的地方，事情就變得非常非常困難。

And so what we think Xaira will have to do is generate and aggregate, high-quality data sets to make progress there. And the reality is that cells take time to grow. Organoids take time to grow. And so we know that they’ve got an incredible team, and we look forward to seeing how they start to work in that space, continue to work to build the right data sets.

因此，我們認為 Xaira 必須做的是產生和匯總高品質的資料集，以便在這方面取得進展。現實是細胞需要時間才能生長。類器官的生長需要時間。因此，我們知道他們擁有一支令人難以置信的團隊，我們期待看到他們如何開始在該領域工作，繼續努力建立正確的數據集。

And certainly from our perspective, the more the merrier. We look forward to leading the space. And we’re so glad to see so many super competent companies joining us and others as we move towards what we see as an inevitable future. I think I’m going to do two more here.

當然，從我們的角度來看，越多越好。我們期待引領該領域。我們很高興看到這麼多超級有能力的公司加入我們和其他公司，共同邁向我們認為不可避免的未來。我想我還要在這裡再做兩件事。

We’ve got a question from [Hamida Alghazwi], who says, my daughter has Batten disease, CLN6, an ultra-rare genetic disease. Is Recursion willing to help labs who are interested in helping these kids, since we all know that pharmaceutical companies would not work for 35 patients? And how do you establish that kind of relationship?

我們收到 [Hamida Alghazwi] 提出的問題，她說，我女兒患有巴頓氏症 (CLN6)，這是一種極為罕見的遺傳疾病。既然我們都知道製藥公司不會為 35 名患者服務，Recursion 是否願意幫助有興趣幫助這些孩子的實驗室？如何建立這種關係？

Thanks, Hamida, for the question. My heart goes out to you, your daughter, your family, everybody else with Batten disease, and everybody else with a rare disease. I think Recursion believes that by building maps of biology, by decoding biology, there will be a path forward to working across many of these diseases.

謝謝哈米達提出的問題。我的心與你、你的女兒、你的家人、所有患有巴頓病的人以及其他患有罕見疾病的人同在。我認為遞歸相信，透過建立生物學圖譜，透過解碼生物學，將會有一條解決許多這些疾病的道路。

We have a track record of working with patient groups. You can reach out to us via [partnering@recursion.com] and get connected to our patient advocacy team. There are scenarios where we have used our maps to work directly with patient advocates to try and advance programs forward.

我們擁有與患者團體合作的良好記錄。您可以透過 [partnering@recursion.com] 與我們聯繫，並聯繫我們的患者倡導團隊。在某些情況下，我們使用地圖直接與患者倡導者合作，嘗試推進專案。

And ultimately, we do believe that companies like Recursion and others, as TechBio comes into the space, even if we don’t have a clear hypothesis today around CLN6 -- and I don’t know. I don’t have the map pulled up right now. But even if we don’t have a clear hypothesis around CLN6 today or other areas of Batten disease, these kinds of approaches, these scaled approaches, are going to be really, really exciting in the medium to long term.

最終，我們確實相信像 Recursion 和其他公司（例如 TechBio）會進入這個領域，即使我們今天沒有關於 CLN6 的明確假設——我也不知道。我現在還沒拿出地圖。但即使我們目前對 CLN6 或 Batten 病的其他領域沒有明確的假設，從中長期來看，這些方法、這些規模化的方法也將非常非常令人興奮。

And I know that’s no consolation to you and your daughter today. But my hope is that in 5 to 10 years, it’s not going to be hard to see a biopharma company working across diseases that have 35 patients or maybe even less. Thank you so much for your question. And again, reach out to partnering@recursion.com.

我知道這對今天的你和你的女兒來說並沒有什麼安慰。但我希望在 5 到 10 年內，不難看到一家生物製藥公司致力於治療擁有 35 名甚至更少患者的疾病。非常感謝你的提問。再次聯絡partnering@recursion.com。

All right. And finally, we’ve got a final question here. There’s a question about my beard, which I’m going to not answer, and I will move on to a question from -- but thank you to Alec at Bank of America for the question about my beard. I’ll go to [Amir Shahin], who asked, what’s on the wish list, the next big pieces of the puzzle, that we need to get put in place in the wider community over a five-year horizon and a 15-year horizon in order for us to progress as fast as possible in using state-of-the-art computation for drug discovery?

好的。最後，我們還有最後一個問題。有一個關於我的鬍子的問題，我不會回答這個問題，我將繼續回答一個問題——但感謝美國銀行的亞歷克提出關於我的鬍子的問題。我會去找[Amir Shahin]，他問，願望清單上有什麼，下一個重要的難題是什麼，我們需要在五年內將其落實到更廣泛的社區中，並且為了讓我們在使用最先進的計算進行藥物發現方面盡快取得進展，需要15 年的時間嗎？

Amir, I think it really comes down to the data sets. And we believe that, ultimately, to fully understand biology, people are going to need to build out really deep, broad data sets. And you’re not going to need to build out the hundreds of these. You’re going to need to build out a dozen or two dozen technologies.

阿米爾，我認為這實際上取決於數據集。我們相信，最終，為了充分理解生物學，人們需要建立真正深入、廣泛的資料集。而且您不需要建造數百個這樣的設備。您將需要建造一打或兩打技術。

Maybe its phenomics, proteomics, metabolomics, lipidomics, transcriptomics, invivomics at some scale, alongside predictive ADME datasets, tox datasets, alongside automated synthesis, and on the large molecule side, moving in the direction of other modalities, RNAi therapies, antibody therapies, other kind of -- gene therapies. I think there will be a dozen or two dozen scaled technologies.

也許是一定規模的表型組學、蛋白質組學、代謝組學、脂質組學、轉錄組學、體內組學，以及預測性ADME 數據集、毒物數據集、自動化合成，在大分子方面，正朝著其他模式、RNAi 療法、抗體療法的方向發展，另一種——基因療法。我認為將會有十幾個或兩打規模化技術。

And the company who can bring together the highest number of those over the next five to 10 years in a disciplined and robust way is going to start to be able to pull out compounding efficiencies. So that even if you only make each step of drug disOcovery and development 5%, 20% better than it was before, as you start to layering these technologies together as I think Recursion is really doing, at least my belief, more and better than any other TechBio start up in the space, you are going to start pulling together these compounding efficiencies. And that’s going to create this flywheel of momentum and opportunity.

能夠在未來五到十年內以嚴格而穩健的方式聚集最多人數的公司將開始能夠提高複合效率。因此，即使你只使藥物發現和開發的每一步都比以前好5%、20%，當你開始將這些技術分層在一起時，我認為遞歸確實在做，至少我相信，比以前更好。這將創造動力和機會的飛輪。

And of course, we’ve got programs that are going to be reading out from our first-generation platform over the coming quarters. We’re excited then for our second-generation molecules to start reading out after that. And we hope a third, a fourth, a fifth generation and at each stage will be able to demonstrate higher scale, lower cost, more rapid translation of these programs. And ultimately, the biggest lever will be probability of success.

當然，我們的程式將在未來幾季從我們的第一代平台上讀出。我們很高興第二代分子在那之後開始讀出。我們希望第三代、第四代、第五代在每個階段都能展示這些專案的更大規模、更低成本、更快速的轉換。最終，最大的槓桿將是成功的機率。

As you all know, 90% of drugs fail in the clinics today from start to getting to the market. And if we can get as an industry to 80% failure and then 70% failure and then 60% failure, we’re going to dramatically improve the access to medicines and dramatically reduce the price of medicines over the coming decades. And we want to make sure that we are doing an experiment to ask and answer whether the kinds of tools that we’re building can help lead out with that kind of vision.

眾所周知，今天90%的藥物從開始到進入市場在診所都失敗了。如果我們整個產業能夠先經歷 80% 的失敗，然後再經歷 70% 的失敗，然後再經歷 60% 的失敗，那麼我們將在未來幾十年內大幅改善藥品的取得並大幅降低藥品價格。我們希望確保我們正在進行一項實驗，以詢問並回答我們正在建立的工具是否可以幫助實現這種願景。

So watch for us to continue to build the vertical with small molecules. And then as we make a lot of progress in that space, we start to demonstrate successes in that space. You can see Recursion thinking about moving into complementary modalities as well so we can go after a broader range of diseases, both with our internal pipeline and with our biopharma partners.

因此，請關注我們繼續建立小分子垂直領域。然後，當我們在該領域取得巨大進展時，我們開始展示該領域的成功。您可以看到 Recursion 也在考慮進入補充模式，以便我們可以透過我們的內部管道和我們的生物製藥合作夥伴來追蹤更廣泛的疾病。

All right. Thanks, everybody. It’s been fantastic to connect with you all for these 35 minutes here at our Q1 2024 earnings. Please follow us on social. Please post questions at our future learnings calls. And please engage with us at conferences and in other ways.

好的。謝謝大家。很高興能在這 35 分鐘內與大家交流我們的 2024 年第一季財報。請在社交上關注我們。請在我們未來的學習電話中提出問題。請透過會議和其他方式與我們互動。

We’re so excited to be having this conversation with all of you and to be leading the TechBio field as we move biotech into TechBio. Thanks, everybody, and have a fantastic evening. Bye-bye.

我們非常高興能夠與大家進行這次對話，並在我們將生物技術引入 TechBio 的過程中引領 TechBio 領域。謝謝大家，祝您有個美好的夜晚。再見。