Exscientia PLC (EXAI) 2023 Q1 法說會逐字稿

Hello, everyone. My name is Chris, and I'll be your conference operator today. At this time, I'd like to welcome everyone to Exscientia's business update call for the first quarter 2023. (Operator Instructions)

大家好。我叫克里斯，今天我將擔任你們的會議接線員。在這個時候，我想歡迎大家參加 Exscientia 2023 年第一季度的業務更新電話會議。（運營商說明）

At this time, I'd like to introduce Sara Sherman, Vice President of Investor Relations. Sara, you may begin.

在這個時候，我想介紹投資者關係副總裁 Sara Sherman。薩拉，你可以開始了。

Thank you, operator. A press release and 6-K were issued this morning with our first quarter 2023 financial results and business update. These documents can be found on our website at www.investor.exscientia.ai, along with the presentation for today's webcast.

謝謝你，運營商。今天上午發布了一份新聞稿和 6-K，其中包含我們 2023 年第一季度的財務業績和業務更新。這些文件可在我們的網站 www.investor.exscientia.ai 上找到，連同今天網絡廣播的演示文稿。

Before we begin, I'd like to remind you that we may make forward-looking statements on our call. These may include statements about our projected growth, revenue, business models, preclinical and clinical results and business performance. Actual results may differ materially from those indicated by these statements. Unless required by law, Exscientia does not undertake any obligation to update these statements regarding the future or to confirm these statements in relation to actual results. On today's call, I'm joined by Andrew Hopkins, Chief Executive Officer; Dave Hallett, Chief Scientific Officer; and Ben Taylor, CFO and Chief Strategy Officer; Garry Pairaudeau, Chief Technology Officer; and Mike Krams, Chief Quantitative Medicine Officer will also be available for the Q&A session.

在開始之前，我想提醒您，我們可能會在電話會議上做出前瞻性陳述。這些可能包括關於我們的預期增長、收入、商業模式、臨床前和臨床結果以及業務績效的陳述。實際結果可能與這些陳述所表明的結果存在重大差異。除非法律要求，否則 Exscientia 不承擔更新這些關於未來的聲明或確認這些與實際結果相關的聲明的義務。在今天的電話會議上，首席執行官安德魯·霍普金斯 (Andrew Hopkins) 也加入了我的行列；首席科學官 Dave Hallett；首席財務官兼首席戰略官 Ben Taylor； Garry Pairaudeau，首席技術官；首席定量醫學官 Mike Krams 也將出席問答環節。

And with that, I will now turn the call over to Andrew.

有了這個，我現在將把電話轉給安德魯。

Thank you, Sara. Today, we're going to talk about our differentiated approach to personalized medicine, how we use complex primary patient tissue samples as preclinical models. Combining this with our in-house multi-omics capabilities, we can go from target identification all the way through to the clinic. 2023 is off to an exciting start as we continue to advance our pipeline and strengthen our business.

謝謝你，薩拉。今天，我們將討論我們對個性化醫療的差異化方法，以及我們如何使用複雜的原始患者組織樣本作為臨床前模型。將其與我們內部的多組學能力相結合，我們可以從目標識別一直到臨床。 2023 年是一個令人興奮的開端，因為我們將繼續推進我們的產品線並加強我們的業務。

We've made significant progress across our internal and partnered programs, including advancing to molecules into the clinic, EXS4318 and EXS21546. An additional molecule, DSP-2342 was advanced by Sumitomo Pharma, which was a result of an early collaboration with Exscientia but is now complete. This marks our sixth novel molecule created for Exscientia’s generative AI platform to enter the clinical stage. We have expanded our precision oncology pipeline by initiating IND-enabling programs for EXS74539, an LSD1 inhibitor and EXS73565, a MALT-1 protease inhibitor. More recently, we presented multiple posters at the AACR Annual Meeting, highlighting Researcher continues to validate our end-to-end approach and demonstrates the potential of a platform to rapidly advance high-quality drug candidates towards the clinic.

我們在內部和合作項目中取得了重大進展，包括將分子推進臨床、EXS4318 和 EXS21546。另一種分子 DSP-2342 由 Sumitomo Pharma 開發，這是與 Exscientia 早期合作的結果，但現已完成。這標誌著我們為 Exscientia 的生成人工智能平台創建的第六個新分子進入臨床階段。我們通過針對 EXS74539（一種 LSD1 抑製劑）和 EXS73565（一種 MALT-1 蛋白酶抑製劑）啟動 IND 支持計劃，擴大了我們的精準腫瘤學管線。最近，我們在 AACR 年會上展示了多張海報，強調 Researcher 繼續驗證我們的端到端方法，並展示了平台快速將高質量候選藥物推向臨床的潛力。

Our team's commitment to strong execution has enabled us to rapidly move programs from discovery through to the clinic. We have achieved a number of milestones already this year. In March, we announced 2 new wholly-owned precision design molecules, an LSD1 inhibitor, 539 and a MALT-1 inhibitor '565. Both programs continue to progress through IND-enabling studies. We expect to provide an update on clinical development plans in the second half of this year.

我們團隊對強大執行力的承諾使我們能夠迅速將項目從發現階段推進到臨床階段。今年我們已經實現了一些里程碑。 3 月，我們宣布了 2 個新的全資精密設計分子，一個 LSD1 抑製劑 539 和一個 MALT-1 抑製劑'565。這兩個項目都通過支持 IND 的研究繼續取得進展。我們預計將在今年下半年提供最新的臨床開發計劃。

We remain on track to meet our target of 4 candidates with meaningful economics for Exscientia in clinical development by 2024. In February, Bristol Myers Squibb initiated the first-in-human study of EXS4318, our potential first-in-class selective PKC-theta inhibitor. '4318 was designed by Exscientia and is currently in Phase I clinical trials in the United States. Earlier this month, the first patient was dosed in IGNITE, our Phase I/II trial evaluating EXS21546 or '546, our A2A receptor antagonist. This was the first AI designed immuno-oncology drug in the clinic, and we remain on track to dose the first patient in a Phase I/II study of GTAEXS617, our precision designed CDK7 inhibitor co-owned through GTAPerion in the coming weeks.

我們仍然有望實現我們的目標，即到 2024 年 Exscientia 有 4 名候選人在臨床開發中具有有意義的經濟學意義。2 月，Bristol Myers Squibb 啟動了 EXS4318 的首次人體研究，這是我們潛在的一流選擇性 PKC-theta抑製劑。 '4318 由 Exscientia 設計，目前正在美國進行 I 期臨床試驗。本月早些時候，第一位患者在 IGNITE 中接受了給藥，這是我們的 I/II 期試驗，評估 EXS21546 或 '546，我們的 A2A 受體拮抗劑。這是臨床上第一種 AI 設計的免疫腫瘤藥物，我們仍有望在 GTAEXS617 的 I/II 期研究中為第一名患者給藥，GTAEXS617 是我們在未來幾週內通過 GTAPerion 共同擁有的精密設計的 CDK7 抑製劑。

We also remain well capitalized with $553 million in cash at the end of the quarter. This provides us with several years runway to advance our near-term programs without the need to raise external capital. On today's call, we'd like to provide more detail on our approach of combined and precision designed with personalized medicine. Before handing over to Dave Hallett, our CFO, I want to highlight a recent scientific presented this year's AACR meeting. We presented data further validating our ability to efficiently design high-quality drug candidates and to identify and predict the right patient populations that may benefit from most from treatment.

截至本季度末，我們還擁有 5.53 億美元的現金，資本充足。這為我們提供了數年的時間來推進我們的近期計劃，而無需籌集外部資金。在今天的電話會議上，我們想提供更多關於我們使用個性化藥物進行組合和精確設計的方法的詳細信息。在交給我們的首席財務官 Dave Hallett 之前，我想強調最近在今年的 AACR 會議上展示的一項科學成果。我們提供的數據進一步驗證了我們有效設計高質量候選藥物以及識別和預測可能從治療中獲益最多的正確患者群體的能力。

Firstly, for '546, we presented research on our adenosine burden score or ABS. It showed that 546 reverses the effect of adenosine analogs ex vivo in patient tissue samples and other complex model. The ABS has been validated in our ongoing IGNITE Phase I/II clinical study of '546 and will be discussed further today. IGNITE was designed based on extensive simulations to enable the most effective, continuous reassessment method settings to predict and accurately evaluate the [anti T-Mobile] effects of '546 in combination with checkpoint inhibition. The team also presented preclinical data on EXS74539, our precision designed LSD1 inhibitor.

首先，對於 '546，我們介紹了關於腺苷負荷評分或 ABS 的研究。它顯示 546 在患者組織樣本和其他復雜模型中體外逆轉腺苷類似物的作用。 ABS 已在我們正在進行的 '546 IGNITE I/II 期臨床研究中得到驗證，並將在今天進一步討論。 IGNITE 的設計基於廣泛的模擬，以實現最有效、連續的重新評估方法設置，以預測和準確評估 '546 與檢查點抑制相結合的[反 T-Mobile] 效果。該團隊還提供了我們精確設計的 LSD1 抑製劑 EXS74539 的臨床前數據。

We designed '539 to optimally target LSD1 in future oncology and hematological patient populations. These preclinical data demonstrated that '539 has the potential to overcome significant safety limitations of other LSD1 inhibitors through its differentiated profile, combined with reversibility and brain penetrants. Lastly, we highlighted the benefits of using data generated with Exscientia precision medicine platform in combination with its proprietary methodology for multi-omics and multi-mobile mapping. By better understanding disease mechanisms, these tools combined can be leveraged to improve patient outcomes by uncovering clinically relevant drug targets already at the discovery stage. We will go into more depth in this topic shortly.

我們設計的 '539 是為了在未來的腫瘤學和血液學患者群體中最佳地靶向 LSD1。這些臨床前數據表明，'539 有可能通過其差異化的特徵、結合可逆性和腦滲透劑來克服其他 LSD1 抑製劑的重大安全限制。最後，我們強調了使用 Exscientia 精準醫學平台生成的數據及其專有的多組學和多移動映射方法的好處。通過更好地了解疾病機制，可以利用這些工具組合來發現已經處於發現階段的臨床相關藥物靶點，從而改善患者的治療效果。稍後我們將更深入地討論這個主題。

In summary, we have 5 programs of economics that are either in the clinic or in IND-enabling studies, all are a testament to the power of our platform and our approach. We are thrilled in our recent advances and look forward to sharing more details on our clinical development plans in the second half of 2023. Today, we would like to focus on how we advance them towards our goal of increasing the probability of success within drug discovery and development for an end-to-end patient-centric approach. In our pipeline to date, we have developed precision design compounds with a patient-driven data approach in a faster and more efficient way with existing methods.

總之，我們有 5 個經濟學項目正在臨床或 IND 支持研究中，所有這些都證明了我們平台和方法的力量。我們對我們最近的進展感到興奮，並期待在 2023 年下半年分享更多關於我們臨床開發計劃的細節。今天，我們想重點關注我們如何推動它們實現我們增加藥物發現成功概率的目標並開發端到端以患者為中心的方法。迄今為止，在我們的管道中，我們已經使用現有方法以更快、更有效的方式開發了具有患者驅動數據方法的精密設計化合物。

I'll now hand over to Dave to walk through how we are working towards predicting clinical responses, preclinically.

我現在將交給 Dave 來介紹我們如何在臨床前預測臨床反應。

Thank you, Andrew. We incorporate the concepts of patient-centric drug discovery in development as early as possible in our efforts. Through these of complex primary patient tissue samples as preclinical models, we are able to leverage our clinically predictive functional imaging platform, especially in translational research. While cell lines and organized models are scalable and useful in design and development, they do not capture the complexity of actual disease biology, nor do they represent the diversity of patients seen in the clinic.

謝謝你，安德魯。在我們的努力中，我們儘早將以患者為中心的藥物發現理念融入到開發中。通過將這些複雜的原始患者組織樣本作為臨床前模型，我們能夠利用我們的臨床預測功能成像平台，尤其是在轉化研究中。雖然細胞系和有組織的模型在設計和開發中具有可擴展性和有用性，但它們並沒有捕捉到實際疾病生物學的複雜性，也沒有代表臨床上所見患者的多樣性。

As you can see here on Slide 6, there is a clear difference in the images of the homogeneous cell line compared to the heterogeneous primary patient material we use. We believe that the heavy use of cell lines as translational models has contributed to the high rate of clinical failure we typically see in our industry. Our answer is to strategically leverage primary patient material for decision-making purposes before entering the clinic. By getting as close to the actual patient as possible, we can embrace both the heterogeneity and complexity of disease biology using our patient-derived model systems, coupled with AI-driven technology.

正如您在幻燈片 6 中看到的，與我們使用的異質主要患者材料相比，同質細胞系的圖像存在明顯差異。我們認為，大量使用細胞係作為轉化模型導致了我們行業中常見的高臨床失敗率。我們的答案是在進入診所之前戰略性地利用主要患者材料進行決策。通過盡可能接近實際患者，我們可以使用我們的患者衍生模型系統以及 AI 驅動技術來接受疾病生物學的異質性和復雜性。

In our preclinical studies, we utilize primary material to create complex model systems that better reflect disease and represent patient diversity. These elaborate models are deployed with the goal of identifying indications as well as subpopulations likely to respond to treatment, uncovering patient enrichment and noninvasive pharmacodynamic biomarkers understanding the potential for resistance, combination effects and more. Depending on the program, we take advantage of our precision medicine platform, which has successfully predicted which drugs will work for a given patient as shown in the EXALT study published in Cancer Discovery in 2021.

在我們的臨床前研究中，我們利用主要材料創建複雜的模型系統，以更好地反映疾病並代表患者的多樣性。部署這些精心設計的模型的目的是確定適應症以及可能對治療有反應的亞群，揭示患者的豐富性和非侵入性藥效學生物標誌物，以了解耐藥性、聯合效應等的可能性。根據項目的不同，我們利用我們的精準醫療平台，該平台已成功預測出哪些藥物對特定患者有效，如 2021 年發表在 Cancer Discovery 上的 EXALT 研究所示。

Functional endpoints in our complex systems allow us to simultaneously quantify what a drug or combination of drugs is doing to cancer-immune and non-transformed cells at the single cell level. We can measure anything from cell size to cell depth through to pathway activity depending on what we want to quantify. We then combine this functional data with omics readouts from the same patient samples, such as genetic mutations, expression, fusion and transcriptional events. The omics data provides a molecular understanding of the observed pheno types.

我們複雜系統中的功能終點使我們能夠同時量化藥物或藥物組合在單細胞水平上對癌症免疫細胞和非轉化細胞的作用。我們可以測量任何東西，從細胞大小到細胞深度再到通路活動，具體取決於我們想要量化的內容。然後，我們將這些功能數據與來自同一患者樣本的組學讀數結合起來，例如基因突變、表達、融合和轉錄事件。組學數據提供了對觀察到的表型的分子理解。

The (inaudible) of technologies, functional and multi-omics combined with years of knowledge of how to interpret these data sets in multimodal programs drives a deep understanding of disease biology and population heterogeneity. Exscientia's unique proposition is that these data are derived from primary patient samples. This provides a preclinical understanding of how and why a drug is or just as importantly is not working in a given patient sample, thus enabling patient enrichment hypothesis generation and the generation of molecular signatures. Today, we will describe 2 ways in which we are combining the use of our functional precision medicine platform with our omics data sets.

技術、功能和多組學的（聽不清）與多年在多模式程序中解釋這些數據集的知識相結合，推動了對疾病生物學和人口異質性的深入理解。 Exscientia 的獨特主張是這些數據來自原始患者樣本。這提供了對藥物如何以及為何在給定患者樣本中起作用或同樣重要地不起作用的臨床前理解，從而能夠生成患者富集假設和分子特徵。今天，我們將介紹將功能性精準醫學平台的使用與組學數據集相結合的兩種方式。

Once again, an understanding of the effect of adenosine on the cancer microenvironment ahead of the clinical trial in patients and the other for target discovery. We'll first highlight progress for our A2A receptor antagonist '546, which specifically blocks the recognition of adenosine by immune cells within the cancer microenvironment. Adenosine is an immunosuppressive metabolite produced at high levels within the tumor microenvironment. Adenosine limits the functionality of multiple protective immune infiltrates, including T cells, while enhancing the activity of immunosuppressive cell types, reversing the effects of adenosine driven through the A2A receptor with our antagonist '546 should therefore release the immune system and also help those patients who have become refractory to immune checkpoint inhibition.

再次，在患者臨床試驗之前了解腺苷對癌症微環境的影響，以及其他目標發現。我們將首先強調我們的 A2A 受體拮抗劑 '546 的進展，它專門阻斷癌症微環境中免疫細胞對腺苷的識別。腺苷是一種在腫瘤微環境中以高水平產生的免疫抑制代謝物。腺苷限制包括 T 細胞在內的多種保護性免疫浸潤的功能，同時增強免疫抑制細胞類型的活性，用我們的拮抗劑 '546 逆轉通過 A2A 受體驅動的腺苷的作用，因此應該釋放免疫系統並幫助那些對免疫檢查點抑制變得難以控制。

For patients to benefit from such an approach, 2 critical attributes are required to be present. One, high levels of adenosine in the microenvironment; and two, an immune system primed but suppressed by adenosine. To date, there has been no robust way to measure both immune potential on adenosine levels within the tumor microenvironment. We believe other drug candidates for this target have not achieved clinical success because they fail to enrich for those patients most likely to respond to A2A receptor pathway inhibition.

為了讓患者從這種方法中受益，需要具備 2 個關鍵屬性。一、微環境中腺苷含量高；第二，免疫系統啟動但被腺苷抑制。迄今為止，還沒有可靠的方法來測量腫瘤微環境中腺苷水平的免疫潛力。我們認為針對該靶標的其他候選藥物尚未取得臨床成功，因為它們未能富集最有可能對 A2A 受體通路抑制產生反應的患者。

Leveraging our precision medicine platform and scalable in-house omics capabilities, we have identified a patient enrichment biomarker that correlates with adenosine levels in the tumor micro environment. We call this the adenosine burden score or ABS. This was found through a detailed examination of multiple primary samples at baseline and after perturbation with adenosine pathway activation. All this work has been done in an effort to maximize the probability of success of '546 in the clinic.

利用我們的精準醫學平台和可擴展的內部組學能力，我們已經確定了一種與腫瘤微環境中腺苷水平相關的患者富集生物標誌物。我們稱之為腺苷負荷評分或 ABS。這是通過在基線和腺苷通路激活擾動後對多個原始樣本進行詳細檢查發現的。所有這些工作都是為了最大限度地提高 '546 在臨床上的成功概率。

On this slide, we show 3 different data sets, 2 from human databases and 1 from mouse data. These include The Cancer Genome Atlas, or TCGA, and the react on database. TCGA is a landmark cancer genomics program from the National Cancer Institute and National Human Genome Research Institute that characterize at a molecular level over 20,000 primary cancer and match normal samples spanning 33 cancer types. [Reactome] is an expertly curated database of biological pathways. At the top, in the TCGA data set when filtering for patients with a high ABS, we observed that these same patient samples are low for public signatures related to inflammation, such as the tumor inflammation score or TIS.

在這張幻燈片上，我們展示了 3 個不同的數據集，2 個來自人類數據庫，1 個來自小鼠數據。其中包括癌症基因組圖譜或 TCGA，以及對數據庫的反應。 TCGA 是美國國家癌症研究所和國家人類基因組研究所的一項具有里程碑意義的癌症基因組學計劃，該計劃在分子水平上對超過 20,000 種原發性癌症進行了表徵，並匹配了 33 種癌症類型的正常樣本。 [Reactome] 是一個專業策劃的生物通路數據庫。在頂部，在過濾具有高 ABS 的患者時，在 TCGA 數據集中，我們觀察到這些相同的患者樣本與炎症相關的公共特徵較低，例如腫瘤炎症評分或 TIS。

The TIS has been used to predict anti-PD-1 efficacy. In the middle panel, from the Reactome data set, the ABS anti-correlates with the PD-1 signaling pathway, indicating that where adenosine is high, as measured by the ABS, PD-1 signaling is low, thereby nullifying anti-PD-1 effects. The last chart is an expert curated mouse data set called TISMO, or tumor immune syngeneic mouse data set. This shows that mice considered resistant to checkpoint inhibitor therapy also enriched for higher mouse ABS, highlighting the rationale for combination therapy in our '546 clinical trial.

TIS 已用於預測抗 PD-1 療效。在中間圖中，根據 Reactome 數據集，ABS 與 PD-1 信號通路反相關，表明在腺苷高的地方，如 ABS 所測量的，PD-1 信號低，從而使抗 PD- 1 影響。最後一張圖表是專家策劃的小鼠數據集，稱為 TISMO，或腫瘤免疫同基因小鼠數據集。這表明被認為對檢查點抑製劑治療有抵抗力的小鼠也富含更高的小鼠 ABS，突出了我們 '546 臨床試驗中聯合治療的基本原理。

Taken together, we believe we have discovered a robust, specific and sensitive biomarker for adenosine pathway activation within the tumor microenvironment. This represents a method for enriching patients likely to respond to our selective adenosine A2A receptor antagonist '546. Comparing the left and right panels, we can see that compared to other disclosed signatures, ours is much more robust and reproducible across samples. Our signature is comprised mainly of B-cell genes towards the later stages of B-cell and plasma cell maturation. Similar to that of data from another molecule recently presented at AACR that was discovered retrospectively after a Phase Ib clinical trial.

綜上所述，我們相信我們已經發現了一種用於腫瘤微環境中腺苷通路激活的穩健、特異和敏感的生物標誌物。這代表了一種豐富可能對我們的選擇性腺苷 A2A 受體拮抗劑 '546 作出反應的患者的方法。比較左右面板，我們可以看到，與其他公開的簽名相比，我們的簽名在樣本中更加穩健和可重現。我們的特徵主要由 B 細胞和漿細胞成熟後期的 B 細胞基因組成。類似於最近在 AACR 上展示的另一個分子的數據，該分子是在 Ib 期臨床試驗後回顧性發現的。

Our work was done preclinically and will be validated alongside the IGNITE trial. What we have shown here is that we can generate data ahead of clinical trials using primary patient samples that our peers can only do in the clinical setting. We believe this is a key differentiator for Exscientia as we advance additional programs and have implications well beyond our A2A program. Since our founding, we have aimed to be a learning company with a goal to constantly increase our knowledge from and to reuse all of the data that we produce from discovery through to development.

我們的工作是在臨床前完成的，並將與 IGNITE 試驗一起進行驗證。我們在這裡展示的是，我們可以使用主要患者樣本在臨床試驗之前生成數據，而我們的同行只能在臨床環境中這樣做。我們相信這是 Exscientia 的一個關鍵差異化因素，因為我們推進了額外的項目並且其影響遠遠超出了我們的 A2A 項目。自成立以來，我們的目標是成為一家學習型公司，目標是不斷增加我們的知識，並重新利用我們從發現到開發過程中產生的所有數據。

We've just shown you an example of how we can preclinically identify patient enrichment biomarker hypotheses using a combination of functional and omics data. I'll now take a moment to highlight how we leverage the same approach in our discovery efforts to understand more about disease biology and target discovery. Using the data sets from preclinical studies, which will be supplemented with information from our clinical and precision medicine studies when available, we can work to understand a disease computationally. I will highlight how we use functional and multi-omic data from our primary models to help identify novel targets and druggable pathways for future projects, some of which we believe may help overcome resistance.

我們剛剛向您展示了一個示例，說明我們如何結合功能和組學數據在臨床前識別患者富集生物標誌物假設。現在我將花點時間強調我們如何在我們的發現工作中利用相同的方法來更多地了解疾病生物學和目標發現。使用來自臨床前研究的數據集，這些數據集將在可用時輔以我們的臨床和精準醫學研究的信息，我們可以通過計算來理解疾病。我將強調我們如何使用來自我們的主要模型的功能和多組學數據來幫助確定未來項目的新目標和藥物途徑，我們認為其中一些可能有助於克服耐藥性。

Here, we show an overview of some of the data inputs we use to triangulate and prioritize novel targets. We start with our proprietary data from various programs that take advantage of our functional Precision medicine platform and next-generation sequencing units. All of this data is from patient tissue models, and this differentiates our approach from others. We then combine this with well-annotated public data such as known drug to target annotations allocations taking into account a drug's polypharmacology and protein-protein interactions in a custom unified and extensible computational framework.

在這裡，我們概述了一些我們用來對新目標進行三角測量和優先排序的數據輸入。我們從各種程序的專有數據開始，這些程序利用了我們的功能性精準醫療平台和下一代測序設備。所有這些數據都來自患者組織模型，這使我們的方法與眾不同。然後，我們將其與註釋良好的公共數據（例如已知藥物）結合起來，以在自定義統一且可擴展的計算框架中考慮藥物的多藥理學和蛋白質-蛋白質相互作用，以進行目標註釋分配。

While the use cases of a program that captures the complexity of the disease in silico are vast, the example I want to describe today is focused on target identification. Our patient-centric multi-omic platform has the potential to uncover targets with high clinical relevance at the discovery stage as well as support target validation and biomarker discovery. At the bottom of the slide, we see our functional layer of data, target annotations and interactome come together to prioritize targets using drug sensitivity and protein-protein interactions as a guide to identify convergent targets.

雖然在計算機中捕獲疾病複雜性的程序用例非常多，但我今天要描述的示例側重於目標識別。我們以患者為中心的多組學平台有可能在發現階段發現具有高度臨床相關性的目標，並支持目標驗證和生物標誌物發現。在幻燈片的底部，我們看到我們的數據功能層、目標註釋和相互作用組結合在一起，使用藥物敏感性和蛋白質-蛋白質相互作用作為識別收斂目標的指南來確定目標的優先級。

Here, we put everything together. I want to first show you a diagram of how this data is represented. We use our precision medicine platform to collect functional and multi-omics data from patient tissues in combination with proprietary methodology for multi-omic and multimodal data set mapping. Then we integrate it using our computational framework. The outer layer represents the standard of care drugs we use as tools to probe the potential target landscape. Drugs are connected to their known targets including off targets on the next layer. Finally, known targets are embedded in the curated protein-protein interaction network, allowing us to identify novel targets at the focal points of successful therapies.

在這裡，我們把所有東西放在一起。我想首先向您展示如何表示這些數據的圖表。我們使用我們的精準醫學平台從患者組織中收集功能和多組學數據，並結合用於多組學和多模態數據集映射的專有方法。然後我們使用我們的計算框架集成它。外層代表我們用作探索潛在目標景觀的工具的護理藥物標準。藥物與其已知目標相關聯，包括下一層的非目標。最後，已知靶點嵌入到精心策劃的蛋白質-蛋白質相互作用網絡中，使我們能夠在成功治療的焦點上識別新靶點。

More than that, we are also able to collaborate and refine our findings using a rich layer of multi-omics data such as [phosphoproteomics] and single-cell RNA seq generated under treatment conditions from the same samples. This approach has the potential to uncover targets with high clinical relevance at the discovery stage and lead to improved patient outcomes. What you see here is an example functional screen performed in 20 ovarian cancer patient tissue samples. We wanted to understand the cancer-specific cytotoxic effect of drugs with well-annotated targets. You may recognize these data from one of our recent AACR posters.

不僅如此，我們還能夠使用豐富的多組學數據（例如 [磷酸蛋白質組學] 和在處理條件下從相同樣本生成的單細胞 RNA 序列）來協作和完善我們的發現。這種方法有可能在發現階段發現具有高度臨床相關性的目標，並改善患者的治療效果。您在這裡看到的是在 20 個卵巢癌患者組織樣本中進行的示例功能篩選。我們想了解具有良好註釋靶標的藥物的癌症特異性細胞毒性作用。您可能從我們最近的一張 AACR 海報中認出了這些數據。

On the left, we have identified numerous novel sensitivities to a subset of tyrosine kinase inhibitors, or TKIs, signified by large dark purple circles within a subset of samples. What's important to appreciate here is that the effects we observed for many drugs in patient tissues, the left panel, and not recapitulated in publicly available cell line sensitivity data indicated on the right. This demonstrates how the use of cell lines and other occulted model systems may obscure targetable pathways. This is likely due to oversimplification of tumor biology since the cell lines lack a complex and diverse cancer environments.

在左側，我們已經確定了對酪氨酸激酶抑製劑或 TKI 子集的許多新敏感性，由樣本子集內的深紫色大圓圈表示。此處需要注意的重要一點是，我們在左側面板中觀察到的許多藥物對患者組織的影響，並沒有在右側顯示的公開可用的細胞系敏感性數據中概括。這證明了細胞系和其他隱蔽模型系統的使用可能會如何掩蓋可靶向通路。這可能是由於腫瘤生物學過於簡單化，因為細胞系缺乏複雜多樣的癌症環境。

Instead, our priority model system incorporates multiple cell types and avoid immortalization or amplification in order to better capture the complex biology of the original microenvironment. But what this does not yet tell us is why specific drugs are having an effect and what they have in common, complicated by the fact that many of them have known polypharmacologies. Overlaying our unique functional endpoints with multi-omics data, we use drugs as tools while also mapping sensitive and incentive pathways across multiple molecular layers and begin to reveal novel biology and target spaces.

相反，我們的優先模型系統包含多種細胞類型並避免永生化或擴增，以便更好地捕捉原始微環境的複雜生物學。但這還沒有告訴我們為什麼特定藥物會起作用以及它們有什麼共同點，而這些藥物中有許多已知的多重藥理學這一事實使情況變得複雜。將我們獨特的功能端點與多組學數據疊加，我們使用藥物作為工具，同時繪製跨多個分子層的敏感和激勵途徑，並開始揭示新的生物學和目標空間。

So here we show the actual data with the targets blinded. First, we use network integration of patient tissue functional data to triangulate convergent targets. Then we add a layer of data from multi-omics measurements that lets us further prioritize them by factors such as disease-specific expression, mutation profiles or novelty. The diagram from outer to inner circle shows firstly, global compound sensitivities then known primary targets. And finally, predicted downstream targets. These targets are not impacted by community bias, highlighting first-in-class potential. Keep in mind, this is data from real patient samples, grounding us in complex human biology.

所以在這裡我們展示了目標盲化的實際數據。首先，我們使用患者組織功能數據的網絡集成來三角化會聚目標。然後我們添加一層來自多組學測量的數據，讓我們可以根據疾病特異性表達、突變譜或新穎性等因素進一步確定它們的優先級。從外圈到內圈的圖表首先顯示的是全局化合物敏感性，然後是已知的主要目標。最後，預測下游目標。這些目標不受社區偏見的影響，突出了一流的潛力。請記住，這是來自真實患者樣本的數據，讓我們了解複雜的人類生物學。

This means that we can combine real-time multi-omics data with the functional biology readouts to directly measure drug response from multiple angles on every sample. This helps us identify novel targets. We've demonstrated biological activity that we would not have been able to find with more artificial models or database screening. We already have some targets identified from this approach going through tractability and validation internally, and we look forward to keeping you updated on our truly differentiated platform.

這意味著我們可以將實時多組學數據與功能生物學讀數結合起來，從多個角度直接測量每個樣本的藥物反應。這有助於我們識別新的目標。我們已經證明了我們無法通過更多人工模型或數據庫篩選找到的生物活性。我們已經通過內部易處理性和驗證從這種方法中確定了一些目標，我們期待著讓您了解我們真正差異化的平台的最新信息。

As I mentioned earlier, Exscientia is a learning company, not just in practice, but also through the reuse and redeployment of collected disease modeling data sets. Here, we use a functional profiling as a guide to build computational disease models for target ID. We are also working to redeploy data for target validation, faster patient enrichment biomarker discovery and combination prediction. We've provided examples here on how complex disease-relevant models, combined with a smart analysis and interpretation of many levels of big data can reveal mechanisms of adenosine pathway activation for us to identify patients that may be sensitive to '546 treatment.

正如我之前提到的，Exscientia 是一家學習型公司，不僅在實踐中，而且通過對收集到的疾病建模數據集的重用和重新部署。在這裡，我們使用功能分析作為構建目標 ID 計算疾病模型的指南。我們還致力於重新部署數據以用於目標驗證、更快的患者富集生物標誌物發現和組合預測。我們在這裡提供了一些示例，說明復雜的疾病相關模型如何結合對多層次大數據的智能分析和解釋可以揭示腺苷通路激活機制，從而幫助我們識別可能對 '546 治療敏感的患者。

We are also working on predicting combinations and identifying resistance-breaking characteristics for our CDK7 inhibitor '617. We plan to present '617 data towards the end of this year, and we'll be adding more data to these models as our pipeline grows and as we recruit patients into our clinical studies.

我們還致力於為我們的 CDK7 抑製劑 '617 預測組合併確定抗藥性破壞特徵。我們計劃在今年年底提交 '617 數據，並且隨著我們管道的增長和我們招募患者進入我們的臨床研究，我們將向這些模型添加更多數據。

And with that, I will now turn the call over to Ben to walk through financial highlights.

有了這個，我現在將把電話轉給本，讓他了解財務亮點。

Thank you, Dave. I'll now take a minute to close with highlights from our financial results. Full results are detailed in our press release and Form 6-K. I'll review the results in U.S. dollars using the March 31, 2023 constant currency rate of $1.24 to the pound. We ended the quarter with $553.3 million in cash, equivalents and bank deposits. We believe this provides us with several years of cash runway and the resources to continue investing in our growth. As Andrew noted earlier, we continue to successfully advance our internal and partnered projects. At the same time, we have also been executing cost efficiency programs that are expected to save over $20 million during the course of 2023 and more in 2024.

謝謝你，戴夫。我現在花一點時間來結束我們財務業績的亮點。完整結果詳見我們的新聞稿和 6-K 表。我將使用 2023 年 3 月 31 日 1.24 美元兌英鎊的固定匯率以美元計算結果。本季度結束時，我們擁有 5.533 億美元的現金、等價物和銀行存款。我們相信這為我們提供了數年的現金跑道和繼續投資於我們增長的資源。正如安德魯之前指出的那樣，我們繼續成功推進我們的內部和合作項目。與此同時，我們還一直在執行成本效益計劃，預計將在 2023 年和 2024 年節省超過 2000 萬美元。

This has been a combination of automation through technology and narrowing the focus of our operations on core activities that have a differentiated commercial profile. We remained cautious in the current macroeconomic environment and intend to continue our cost control efforts through the end of the year with a focus on optimizing workflows and automation. We have a robust business development dialogue and maintain our guidance of 2 new deals this year. Earlier in the year, many of the large pharma had substantially slowed their decision-making process for new partnerships, as they conducted pipeline reductions and budget cuts in response to the IRA and other well noted industry trends.

這是通過技術實現自動化和將我們的運營重點縮小到具有差異化商業形象的核心活動的結合。我們對當前的宏觀經濟環境保持謹慎，並打算在年底前繼續我們的成本控制工作，重點是優化工作流程和自動化。我們有一個強有力的業務發展對話，並維持我們對今年 2 筆新交易的指導。今年早些時候，許多大型製藥公司大幅放慢了新合作夥伴關係的決策過程，因為它們為應對 IRA 和其他廣為人知的行業趨勢而進行了渠道縮減和預算削減。

Recently, we have seen a renewed energy and excitement from our potential partners, especially in our core technologies such as personalized medicine and generative AI. It is important to note that we have never stopped investing in new technologies. While we are being intelligent about burn rate, we continue to see substantial technology advancements even on a quarter-to-quarter basis. Dave discussed how we had taken a strong phenotypic translational platform and invested to add multimodal data that now can produce personalized cellular signatures at every stage of discovery and development. And this is only one example of our growth. We have over 200 people in our technology group focused on improving the capabilities and predictive powering of our AI across the company. This is how we intend to stay in our current leadership position.

最近，我們從潛在合作夥伴那裡看到了新的活力和興奮，尤其是在我們的核心技術領域，如個性化醫療和生成人工智能。值得注意的是，我們從未停止對新技術的投資。雖然我們對燃燒率很了解，但我們繼續看到重大的技術進步，即使是按季度計算也是如此。 Dave 討論了我們如何採用強大的表型轉化平台並投資添加多模式數據，這些數據現在可以在發現和開發的每個階段產生個性化的細胞特徵。這只是我們成長的一個例子。我們的技術團隊有 200 多人專注於提高整個公司 AI 的能力和預測能力。這就是我們打算保持目前領導地位的方式。

And with that, I will turn the call back over to Andrew.

有了這個，我會把電話轉回給安德魯。

Thank you, Ben. During our presentation today, we've highlighted the progress of our clinical and preclinical programs. We are bringing new molecules into the clinic and building out our AI-powered precision medicine platform. We are confident that our differentiated tech-enabled approach will yield strong outcomes. To finish, let me add just how proud I am to lead a global team, this talented and determined who help us do everything in our power to deliver on Exscientia's promise to transform the way the industry discovers and develops effective medicines and to deliver the best possible outcomes for as many people as possible around the world.

謝謝你，本。在我們今天的演講中，我們強調了我們的臨床和臨床前項目的進展。我們正在將新分子引入臨床並構建我們的人工智能精準醫療平台。我們相信，我們差異化的技術支持方法將產生強大的成果。最後，讓我補充一下我對領導一個全球團隊感到多麼自豪，這個有才華和決心的人幫助我們竭盡全力兌現 Exscientia 的承諾，改變行業發現和開發有效藥物的方式，並提供最好的藥物世界各地盡可能多的人的可能結果。

With that, we'll open up the call for questions.

有了這個，我們將打開問題電話。

(Operator Instructions) Our first question is from Alec Stranahan with Bank of America.

（操作員說明）我們的第一個問題來自美國銀行的 Alec Stranahan。

I have 2 higher level ones. I saw an interesting quote, I think, from Garry that by the end of this decade, design of all new drug candidates will be augmented by AI. What do you see as being the key points that need to be addressed today for this future to be realized either at the basic science level of programming or regulatory levels. And as a follow-up to that, maybe for Andrew, how does the company (inaudible) drive the most value for shareholders. If this is the direction that the industry is going, is it through more design as a service, such as your collaboration with Sumitomo or driving pipeline assets through approval and commercialization yourself? Any directional commentary would be helpful.

我有2個更高級別的。我從 Garry 那裡看到了一句有趣的話，我認為到本十年末，人工智能將增強所有新候選藥物的設計。您認為今天需要解決的關鍵點是什麼，以便在編程的基礎科學層面或監管層面實現這個未來。作為後續行動，也許對於安德魯來說，公司（聽不清）如何為股東帶來最大價值。如果這是行業的發展方向，是通過更多的設計即服務，例如您與住友的合作，還是通過您自己的審批和商業化來推動管道資產？任何定向評論都會有所幫助。

Thank you so much for your excellent questions, Alec. Really great actually and a very topical point as well. Actually, for the first question, as you did actually direct that to Garry, I'm actually going to have Garry 2 of them outlined as CTO, what he sees actually as sort of the key further challenges to really expand AI's use in pharma for all drugs eventually to be designed by AI. Garry?

亞歷克，非常感謝你提出的精彩問題。實際上真的很棒，也是一個非常熱門的話題。實際上，對於第一個問題，正如你實際上直接向 Garry 提出的那樣，我實際上會讓 Garry 概述其中的 2 位 CTO，他實際上認為這是真正擴大 AI 在製藥業中的使用的關鍵進一步挑戰所有藥物最終都將由人工智能設計。加里？

Yes. Thanks, Andrew. I think -- I mean the first thing is we're incredibly proud that Exscientia that we've now enabled 6 clinical candidates using AI, and that kind of really shows the promise and the power. And you've only got a pickup a newspaper or look anywhere really to see how the entire world and the entire world of drug discovery is starting to embrace the use of artificial intelligence and broader computational methods. So I think there is a natural evolution. I think for us, what's really important to us is how do we stay at the forefront of that.

是的。謝謝，安德魯。我認為——我的意思是，第一件事是我們為 Exscientia 感到無比自豪，我們現在已經讓 6 名臨床候選人使用人工智能，這確實顯示了前景和力量。你只需要拿起一份報紙或到處看看，就會真正看到整個世界和整個藥物發現世界是如何開始接受人工智能和更廣泛的計算方法的使用的。所以我認為這是一種自然的進化。我認為對我們來說，真正重要的是我們如何保持領先地位。

And I think the activities Exscientia is building out at the moment, particularly in linking AI design to physical automation robotics (inaudible) robotics screening is really closing the cycle and enabling us to drive our projects even more quickly in the future. So I think developments like this that are going to enable more broad acceptance and utilization of these kind of technologies in drug discovery. And let's be honest, it has to be a fantastic thing, doesn't it? Really want to bring that (inaudible) patients faster and more effectively as we're demonstrating technology can do.

我認為 Exscientia 目前正在開展的活動，特別是在將 AI 設計與物理自動化機器人（聽不清）機器人篩選聯繫起來方面，確實正在關閉這個循環，使我們能夠在未來更快地推動我們的項目。所以我認為像這樣的發展將使這些技術在藥物發現中得到更廣泛的接受和利用。老實說，這一定是一件了不起的事情，不是嗎？正如我們正在展示的技術可以做到的那樣，真的很想更快、更有效地為那些（聽不清）患者帶來幫助。

Thank you, Garry. Really, I want to underline Garry's answer actually in how we think about things. To answer your second part of the question, Alec, the way we think about it is that we are incredibly pleased to see that sort of our design progress now and bring in 6 molecules of use generative AI approaches now into the clinic. As you said, actually, the latest one actually been with tying with Sumitomo Pharma, which was with an earlier business molecule -- business model called Design as a Service.

謝謝你，加里。真的，我想強調加里的答案實際上是關於我們如何看待事物的。亞歷克，要回答你問題的第二部分，我們的想法是，我們非常高興看到我們現在的設計取得進展，並將 6 個使用生成人工智能方法的分子引入臨床。正如你所說，實際上，最近的一次實際上是與住友製藥的合作，它與早期的商業分子——稱為設計即服務的商業模式有關。

We're always open to do many kinds of deal structures as you've seen, actually, I think our business development progress for the past few years has actually shown that. But the way we see that AI is going to create real value is to think about what that product of the future looks like, what that sort of AI-enabled drug starts to look like. What we see is the hallmark and Exscientia drug is a drug that uses advanced compute, machine learning, AI and physics based methods to design precision design, a high-quality molecule. But also venues and our deep learning, multimodal approaches that Dave was talking about earlier to really define the patient selection strategy, bringing those 2 together in a model-driven adaptive learning approach to learn about the drug, that's what we see.

正如你所看到的，我們總是願意做多種交易結構，實際上，我認為我們過去幾年的業務發展進展實際上已經表明了這一點。但是我們看到人工智能將創造真正價值的方式是思考未來的產品會是什麼樣子，那種支持人工智能的藥物會是什麼樣子。我們看到的是標誌，Exscientia 藥物是一種藥物，它使用先進的計算、機器學習、人工智能和基於物理的方法來設計精密設計，一種高質量的分子。還有場地和我們的深度學習，Dave 之前談到的多模態方法真正定義了患者選擇策略，將這兩者結合在模型驅動的自適應學習方法中以了解藥物，這就是我們所看到的。

So those 2 pieces of key IP, the molecule being designed by AI and use an AI event to design the biomarker. Both coming together is what we think is the hallmark of Exscientia drug, and that's where we believe in the long term, the high-value wealth can be created by effectively creating highly effective medicines of high response by actually designing the best molecule and targeting the right patients.

所以這兩個關鍵 IP，分子由 AI 設計，並使用 AI 事件來設計生物標記。兩者的結合是我們認為 Exscientia 藥物的標誌，這就是我們相信從長遠來看，通過實際設計最好的分子並針對對的患者。

The next question is from Vikram Purohit with Morgan Stanley.

下一個問題來自摩根士丹利的 Vikram Purohit。

This is Steve for Vikram. So I want to ask about the A2A program. Could you discuss the prior treatment (inaudible) for the patient you are enrolling into the trial? And when could we expect to see the initial data? And what's your expectation about the readout.

這是維克拉姆的史蒂夫。所以我想問一下A2A程序。您能否討論您正在參加試驗的患者的先前治療（聽不清）？我們什麼時候可以看到初始數據？您對讀數有何期望？

Thank you very much, Steve. For that question, actually, I want to hand the stage over to Mike Krams, our Chief Quantitative Medicine Officer, who's actually leading our clinical (inaudible). Mike?

非常感謝你，史蒂夫。對於這個問題，實際上，我想把舞台交給我們的首席定量醫學官 Mike Krams，他實際上領導著我們的臨床（聽不清）。麥克風？

Yes. Thank you very much for the question. So we have recruited our first patient into this program. It's a Phase I/II study. And we use simulation guided clinical trial design to come up with an approach where we initially have a dose escalation aiming to make the correct decision at the earliest time point as to what the dose and treatment regimen is that we will take into a dose expansion phase. We're going to learn about the operating characteristics of the investigational compound. But at the same time, we are qualifying the adenosine burn score.

是的。非常感謝你的提問。因此，我們招募了第一位患者參與該計劃。這是一項 I/II 期研究。我們使用模擬引導的臨床試驗設計來提出一種方法，在這種方法中，我們最初進行劑量遞增，目的是在最早的時間點就我們將進入劑量擴展階段的劑量和治療方案做出正確的決定.我們將了解研究化合物的操作特性。但與此同時，我們正在驗證腺苷燃燒分數。

As Andrew pointed out, as our tool to identify which are the correct patients who might benefit from an A2A receptor antagonist in conjunction with a checkpoint inhibitor. As to when data will become available, this is a Phase I/II study in early development in oncology as many others. So it's really very similar to other programs, and we are going to provide further guidance as time progresses.

正如安德魯指出的那樣，作為我們的工具，可以確定哪些患者可能受益於 A2A 受體拮抗劑與檢查點抑製劑的結合。至於何時可以獲得數據，與許多其他研究一樣，這是腫瘤學早期發展的 I/II 期研究。所以它與其他程序非常相似，我們將隨著時間的推移提供進一步的指導。

(Operator Instructions) the next question is from Peter Lawson with Barclays.

（操作員說明）下一個問題來自巴克萊銀行的彼得勞森。

We will move on to the next question, which is from Chris Shibutani with Goldman Sachs.

我們將繼續下一個問題，該問題來自高盛的 Chris Shibutani。

It's Roger on for Chris. Just a quick question on '565, the MALT-1 inhibitor. You're likely aware that J&J, they debuted their Phase I data for their MALT-1 inhibitor in (inaudible) I was just wondering if you could comment a little bit on the inhibition of [UGT1A1]. And where do you expect '565 to come out in terms of differentiation, noting the competitive landscape?

羅傑接替克里斯。只是一個關於'565 的快速問題，MALT-1 抑製劑。你可能知道 J&J，他們在（聽不清）中首次展示了他們的 MALT-1 抑製劑的 I 期數據，我只是想知道你是否可以對 [UGT1A1] 的抑制發表一些評論。注意到競爭格局，您預計 '565 在差異化方面會出現在哪裡？

Thank you much, Roger. So a great question actually has been a key point of how we have been designed a differentiated molecule. I'm actually going to hand this question over to Dave Hallett, our Chief Scientific Officer, to give you some more color on it.

非常感謝，羅傑。因此，一個重要的問題實際上是我們如何設計差異化分子的關鍵點。實際上，我打算將這個問題交給我們的首席科學官 Dave Hallett，以便為您提供更多信息。

Thank you, Andrew, and thank you for the question. I think the publication of the abstract, I think is coming out ahead of a European Oncology Symposium was very timely. So if you recollect the information that we put out very recently around the design criteria around our MALT-1 inhibitor and specifically, the topic of hyperbilirubinemia and driven by inhibition of UGT1A1. If you remember the takeaway story from those that, we strongly believe that our molecule is differentiated from J&J and most likely quite a few other competitor molecules and that it has little to no activity at that particular transporter. It is therefore unlikely to kind of to drive that particular side effect. If you actually look in even into the abstract details, it's pretty apparent from J&J as we would have predicted that they do see hyperbilirubinemia in the clinic. They've had to take account of that in that -- the recommended Phase II dose. I'm sure they would have preferred not to have done that.

謝謝你，安德魯，謝謝你提出這個問題。我認為在歐洲腫瘤學研討會之前發表摘要非常及時。因此，如果您還記得我們最近發布的關於我們 MALT-1 抑製劑設計標準的信息，特別是高膽紅素血症和由 UGT1A1 抑制驅動的主題。如果您還記得那些故事的要點，我們堅信我們的分子與 J&J 以及很可能與其他許多競爭分子不同，並且它在特定的轉運蛋白上幾乎沒有活性。因此，不太可能導致這種特定的副作用。如果你真的查看抽象細節，從強生公司就可以很明顯地看出，正如我們所預測的那樣，他們確實會在診所看到高膽紅素血症。他們必須考慮到這一點——推薦的 II 期劑量。我敢肯定他們寧願不這樣做。

And so I think we stand by, I think that original assertion is that, that was a really important differentiation criteria. I think it will -- our molecule, we believe, should be free of that particular potential toxicity. And more importantly, as I think as we highlighted, is that we actually remember, it's very likely that a MALT-1 inhibitor will be used in combination with other agents like BTK inhibitors and therefore, you need as clean as possible a safety profile so that you could dose that molecule as high as possible. So no, I think it was -- I wish J&J well, I think, obviously, as they take that compound forward into patient studies, but I think it supported our notion about the differentiation angle of our own compound.

所以我認為我們支持，我認為最初的斷言是，這是一個非常重要的差異化標準。我認為它會——我們相信，我們的分子應該沒有那種特定的潛在毒性。更重要的是，正如我所強調的那樣，我們實際上記得，MALT-1 抑製劑很可能會與其他藥物（如 BTK 抑製劑）聯合使用，因此，您需要盡可能乾淨的安全性，所以你可以盡可能高地添加那個分子。所以不，我認為它是——我希望 J & J 好，我想，很明顯，因為他們將該化合物推進到患者研究中，但我認為它支持我們關於我們自己的化合物的分化角度的概念。

The next question is from Peter Lawson with Barclays.

下一個問題來自巴克萊銀行的彼得勞森。

This is [Shae] on for Peter. Just wanted to touch base on the biologics side of your form and maybe some progress there and how you're thinking about balancing your biologics versus small molecule development and maybe even when we could see the first antibody program going into the clinic.

這是彼得的 [Shae]。只是想談談你表格的生物製劑方面，也許在那裡取得了一些進展，以及你如何考慮平衡你的生物製劑與小分子開發，甚至可能在我們看到第一個抗體項目進入臨床時。

Excellent. Thank you very much. I want to hand over this question actually to Garry, who's in team has the algorithms for developing sort of biologics by design -- by discovery are currently being developed. Garry?

出色的。非常感謝。我實際上想把這個問題交給 Garry，他在團隊中擁有通過設計開發某種生物製劑的算法——目前正在通過發現開發。加里？

Yes. Thanks for the question. I mean, we're really excited about the way that we can introduced biologics into our AI design platform and Professor (inaudible) has been working to build out the algorithms and all the technology to actually drive that forward. We're still at the point where we're developing a robust process, and we're starting to run our first pilot project. So I think we're a little bit away from talking about a molecule in the clinic right now. But what I can tell you is we are developing actually, I'd say, world-leading capabilities in the areas of predicting structure and being able to do generative design into the antibody space.

是的。謝謝你的問題。我的意思是，我們對將生物製劑引入我們的人工智能設計平台的方式感到非常興奮，教授（聽不清）一直在努力構建算法和所有技術來真正推動這一進程。我們仍處於開發穩健流程的階段，我們正在開始運行我們的第一個試點項目。所以我認為我們現在離在臨床上談論分子還有點距離。但我可以告訴你的是，我們實際上正在開發，我想說，在預測結構和能夠對抗體空間進行生成設計方面的世界領先能力。

In terms of growing the pipeline, we certainly are now looking to think about how we might bring forward sort of our first programs going at and actually how then we start to map the antibodies or the capabilities we've been built and actually to sort of our key of (inaudible) sort of focus. One exciting thing is that we've already demonstrated is that our precision medicine platform actually also works as antibodies as well as small molecules. And that's a key thing then that allows us then to think about how then as we head towards the clinic, we can also bring to bear our precision medicine technology and I think that's going to bring a unique differentiator as well actually in this particular field for these modalities.

在增加管道方面，我們現在當然正在考慮如何推進我們的第一個項目，實際上我們如何開始繪製抗體或我們已經建立的能力，實際上是我們的（聽不清）重點的關鍵。一件令人興奮的事情是，我們已經證明我們的精準醫療平台實際上也可以作為抗體和小分子使用。這是一件關鍵的事情，它讓我們可以思考，當我們走向診所時，我們還可以利用我們的精準醫學技術，我認為這實際上也會在這個特定領域帶來獨特的差異化因素這些方式。

The next question is from Steve with Morgan Stanley.

下一個問題來自摩根士丹利的史蒂夫。

This is [Gaspar] on for Vikram. I have a question regarding your PKC program. So for the PKC data program in partnership with BMS, I was wondering how much visibility and control do you have now into the path forward for this molecule and how it might progress through early stage development?

這是 Vikram 的 [Gaspar]。我有一個關於您的 PKC 程序的問題。因此，對於與 BMS 合作的 PKC 數據計劃，我想知道您現在對該分子的前進道路有多少可見性和控制力，以及它可能如何通過早期開發取得進展？

So this is Dave Hallett. Thank you for that question. So in terms of public visibility because BMS in-license that particular program, they both now control the clinical development of that project, but also obviously, kind of public disclosures that are related to that. As a trusted partner, a partner [GSE] we will receive kind of updates on that program ourselves. But just to reiterate to everyone who's on the call is that particular asset has begun a healthy human volunteer study in the United States in the early part of this year. And we look forward to kind of receiving updates from BMS as they progress.

這是戴夫哈利特。謝謝你提出這個問題。因此，就公眾知名度而言，因為 BMS 獲得了該特定項目的許可，他們現在都控制著該項目的臨床開發，而且顯然，還控制著與此相關的公開披露。作為值得信賴的合作夥伴，合作夥伴 [GSE]，我們將自己收到有關該計劃的某種更新。但要向所有在電話中的人重申，特定資產已於今年年初在美國開始一項健康的人類誌願者研究。我們期待在 BMS 取得進展時收到更新。

We have no further questions at this time. We'll turn it back to the presenters for any closing remarks.

目前我們沒有其他問題。我們會將其轉回給演示者以徵求任何結束語。

Thank you, Chris. As Exscientia's CEO and founder, I am proud to see our company [#2] into an end-to-end precision medicines business, spanning from discovery into early development and supported at each stage by innovative technology platforms. Our goal is to be as innovative in the clinic as we have been in discovery. Our remarkable progress to date is a testament for the strength of the company. Thank you to everyone today on the call for your continued support and on our journey and for joining us today, and we look forward to continuing to share our progress with you throughout the year.

謝謝你，克里斯。作為 Exs​​cientia 的首席執行官和創始人，我很自豪地看到我們的公司[#2]進入端到端的精準醫療業務，從發現到早期開發，並在每個階段都得到創新技術平台的支持。我們的目標是在臨床上像我們在發現方面一樣創新。我們迄今為止取得的顯著進步證明了公司的實力。今天感謝大家繼續支持我們的旅程，感謝大家今天加入我們，我們期待著繼續與大家分享我們在這一年中取得的進展。

Operator, you may now disconnect.

接線員，您現在可以斷開連接了。

Thank you. Ladies and gentlemen, this concludes today's conference call. Thank you for participating.

謝謝。女士們，先生們，今天的電話會議到此結束。感謝您的參與。