Agilent Technologies, Inc.

中文摘要目的   优化表达抗人IL-17A/F单克隆抗体（单抗）的中国仓鼠卵巢（Chinese hamster ovary，CHO）细胞的培养基，筛选获得高表达且质量可比抗体的国产培养基。方法   用1株表达抗IL-17A/F单抗的CHO细胞，首先筛选出较优的基础培养基和补料培养基组合，并对该组合进行进一步补料策略优化和2 L生物反应器筛选，再经反应器工艺确认比较抗体表达量和产品质量。结果   基础培养基BM-01、补料培养基FM-01和FM-02组合为重组CHO细胞表达抗IL-17A/F单抗最优的培养基组合；其中补料培养基FM-01补料策略为培养第3/6/8/10/12天（3%~7%），补料培养基FM-01∶FM-02的补加体积比例为10∶1。在2 L生物反应器中最终抗体表达量超过6.5 g/L，为原工艺的2倍以上，且产品质量一致性好。结论   使用基础培养基BM-01、补料培养基FM-01和FM-02组合，实现了抗IL-17A/F单抗在CHO细胞中的高表达且质量可比，为国产培养基用于规模化生产抗IL-17A/F单抗奠定了基础。正文IL-17是促炎因子细胞家族，共有6个已知成员：IL-17A、IL-17B、IL-17C、IL-17D、IL-17E（也称IL-25）和IL-17F。IL-17家族细胞因子结构相似，对靶细胞产生相似的作用，其中IL-17A的致炎性最强，被研究最多。IL-17A与IL-17F具有55%的结构同源性和重叠的生物学功能，可以IL-17A/A和IL-17F/F同源二聚体的形式存在，也可以IL-17A/F异源二聚体的形式存在，并通过相同的IL-17RA/RC受体复合物介导信号传递，从而促进炎症反应。有研究表明，在免疫介导的炎症疾病，例如银屑病、银屑病关节炎、强直性脊柱炎和哮喘中，IL-17A和IL-17F功能失调。目前全球已有IL-17靶向药物被批准用于治疗银屑病和强直性脊柱炎，其中首个治疗中重度银屑病的IL-17A/F双重抑制剂bimekizumab已在日本和美国获批上市。Ⅲ期临床试验结果显示，bimekizumab治疗的皮肤清除效果优于现有疗法，安全性良好，治疗效果显著。单克隆抗体（单抗）药物因靶向性高、特异性强而受到广泛关注。每年大约有40种新型单抗进入临床试验，其中大部分使用中国仓鼠卵巢（Chinese hamster ovary，CHO）细胞生产。目前，重组抗体的表达水平和产量是制约抗体药物发展的主要瓶颈之一。珠海市丽珠单抗生物技术有限公司已构建并筛选出1株稳定表达抗人IL-17A/F抗体的重组单克隆CHO细胞。为提高抗体产量，本研究拟对该细胞株进行培养基筛选，期望筛选出高产的细胞培养基组合，为后续优化细胞培养工艺奠定基础，并为后续降低产业化生产成本提供科学依据和指导。1材料与方法1.1细胞株本研究所用细胞株为珠海市丽珠单抗生物技术有限公司提供的表达抗人IL-17A/F抗体的重组CHO细胞。1.2培养基本研究选取16种商业化培养基组合，补料体积为初始体积的3%~7%，具体信息如表1所示。1.3主要仪器Vi-Cell XR细胞计数仪购自美国Beckman coulter life sciences公司，Rapidpiont 500血气分析仪购自德国Siemens公司，ISF1-XC细胞培养摇床购自瑞士Kuhner公司，Ez-control生物反应器购自荷兰Applikon公司，1260高效液相色谱仪购自美国Agilent公司，Infinite F200 pro多功能酶标仪购自瑞士Tecan公司。1.4细胞培养1.4.1　细胞复苏、扩增和培养　从液氮罐取出冻存的细胞，经水浴后转移至含有10 mL基础培养基的离心管中，经300×g离心5 min后弃去上清液。用含25 μmol/L蛋氨酸亚氨基代砜的Dynamis培养基重悬细胞，将细胞悬液转移至125 mL摇瓶中，置于36.5 ℃、5% CO2培养箱并于125 r/min转速培养。对种子细胞进行扩增，细胞扩增足量后接种培养。1.4.2　摇瓶培养基筛选　根据表1的培养基组合，以（0.3~1.0）×106 mL-1的密度接种于含不同培养基的250 mL摇瓶中，培养体积70 mL。摇床培养参数为36.5 ℃、5% CO2和125 r/min。细胞培养接近平台期时，培养温度调至33 ℃。每天取样检测活细胞密度、细胞存活率和葡萄糖浓度；按表1的补料策略进行补料操作，补料体积按照摇瓶的初始培养体积进行计算。培养期间葡萄糖浓度控制不低于5.6 mmol/L。1.4.3　摇瓶补料策略研究　基于1.4.2筛选的结果，选择适合细胞生长的培养基组合进行补料策略筛选实验，研究方案见表2。补料体积按照摇瓶的初始培养体积计算，补充3%~7%。细胞培养接近平台期时，培养温度调至33 ℃。培养期间葡萄糖浓度控制不低于5.6 mmol/L。1.4.4　2 L反应器培养基筛选和工艺确认　种子细胞按照（0.3~1.0）×106 mL-1的初始接种密度接种至反应器。工艺参数按照实验设计进行设定，pH通过级联碳酸钠溶液和CO2进行关联调节，pH控制在6.80~7.20。细胞培养接近平台期时，培养温度调至33 ℃。每天取样检测活细胞密度、细胞存活率和葡萄糖浓度，补料策略如表3所示，补料体积按照反应器的初始培养体积进行计算。培养期间葡萄糖浓度控制不低于5.6 mmol/L。根据培养产生的泡沫情况，适量添加消泡剂。根据结果选择新工艺为BR03进行工艺确认，与原工艺进行各项参数比较。1.5抗人IL-17A/F抗体纯化培养上清液中的抗体采用Protein A亲和层析柱进行纯化。使用pH7.2的三羟甲基氨基甲烷〔tris（hydroxymethyl）aminomethane，Tris〕-盐酸缓冲液平衡层析柱，取约20 mL经离心过滤的样品上样，然后用pH7.2的Tris-盐酸缓冲液冲洗层析柱，最后用pH3.6的Tris-醋酸缓冲液洗脱抗体，并用1 mol/L缓冲液将收集的抗体洗脱液pH值调至5.5左右。1.6分析方法1.6.1　蛋白浓度检测　采用Protein A-高效液相色谱法检测培养上清液中抗体的浓度。使用Applied Biosystems POROS Protein A（2.1 mm×30 mm）色谱柱，在流动相A（1×PBS，pH7.0）条件下，培养上清液经9 000×g、5 min离心后，取上清液20 μL注入高效液相色谱仪，柱温20 ℃，将抗体特异性结合到色谱柱上，用流动相B（51 mmol/L甘氨酸，150 mmol/L NaCl，pH2.5）进行洗脱，通过280 nm紫外光检验吸收强度计算上清液中抗体的浓度。1.6.2　抗人IL-17A/F抗体纯度检测　层析样品采用凝胶排阻色谱法进行检测。使用TOSOH TSK gel G3000SWXL（7.8 mm×300 mm，5 μm）色谱柱，以pH6.5的0.1 mol/L Na2HPO4+0.2 mol/L NaCl为流动相，柱温25 ℃，检测波长280 nm，将供试品稀释至约1 mg/mL，经离心取上清液20 μL注入高效液相色谱仪，按面积归一化法计算。1.6.3　抗人IL-17A/F抗体糖型分析　层析样品采用超高效液相色谱法进行检测。使用PNGase F酶将糖链从糖蛋白上酶切下来，加入乙醇沉淀蛋白，回收纯化糖链，真空离心浓缩，得到糖链粉末，加入2-AB衍生试剂对糖基进行衍生，进行色谱分析，选用Acquity UPLC BEH Amide（2.1 mm×100 mm，1.7 µm）色谱柱，以pH4.5的100 mmol/L甲酸铵和纯乙腈作为流动相，流速0.4 mL/min，梯度洗脱50 min，经荧光检测器激发波长330 nm、发射波长420 nm的信号，得到各糖型的分布及相对含量。1.6.4　抗人IL-17A/F抗体电荷异质性检测　层析样品采用弱阳离子交换色谱法进行检测。采用ProPac WCX-10（4 mm×250 mm，0.3 µm）色谱柱，以pH6.2的20 mmol/L 吗啉乙磺酸+20 mmol/L NaCl为流动相A、pH6.2的20 mmol/L 吗啉乙磺酸+200 mmol/L NaCl为流动相B，流速1.0 mL/min，柱温25 ℃，检测波长280 nm，将供试品稀释至约1 mg/mL，取20 μL注入高效液相色谱仪，按峰面积归一化法计算主峰、酸性峰及碱性峰百分含量。由于碱性羧肽酶B可以识别并结合到抗体C末端的赖氨酸残基上，通过催化水解抗体C末端的赖氨酸残基，因此，可以通过对比羧肽酶B酶切前后抗体的碱性峰含量来确定抗体中赖氨酸变体的含量。1.6.5　抗人IL-17A/F抗体报告基因活性检测　层析样品采用萤光素酶报告基因法检测。将HeLa（Nlucp/NF-κB-RE）重组细胞浓度调至3×105 mL-1，按100 μL/孔加入96孔全白细胞培养板，于37 ℃、5% CO2培养箱培养16～24 h。用含IL-17A/F的阳性对照液梯度稀释分析标准品和供试品至首孔浓度600 μg/mL后，按3.5倍梯度稀释，共11个浓度梯度，每个浓度3个复孔。以50 μL/孔加至细胞板中，于37 ℃、5% CO2培养箱孵育4~6 h。以50 μL/孔加入显色液，室温避光反应5~30 min。用酶标仪化学发光模式读板，测定发光强度，使用软件计算供试品相对结合活性。分析标准品同法操作。与分析标准品相比相对活性应为60%~140%。1.6.6　抗人IL-17A/F抗体结合活性检测　层析样品采用ELISA检测。将供试品稀释至首孔500 ng/mL，1.9倍梯度稀释，共12个梯度，以100 μL/孔加至已包被500 ng/mL重组人IL-17A的96孔酶标板，共孵育2 h±30 min，洗涤后加辣根过氧化物酶标记的羊抗人IgG酶标二抗100 μL/孔，孵育1 h±10 min，显色（33±3） min，终止反应后用酶标仪在450 nm/620 nm双波长处测定光密度值，使用软件计算供试品相对结合活性。分析标准品同法操作。与分析标准品相比相对活性应为70%~130%。除包被蛋白为500 ng/mL重组人IL-17F外，抗人IL-17F抗体采用相同方法进行结合活性检测。1.7统计学方法利用SPSS 18.0统计软件对实验数据进行统计分析，检验方差齐性后用独立样本t检验进行组间比较，以P<0.05为差异有统计学意义。2结果2.1摇瓶培养基筛选以表达重组抗人IL-17A/F抗体的CHO细胞为实验对象，以最高活细胞密度、细胞存活率、蛋白表达量、纯度、N-糖型和弱阳离子交换为考察指标，综合筛选出可支持重组CHO细胞生长良好的培养基，进行以下阶段补料的优化实验。使用16种培养基组合进行14 d批次培养，细胞性能参数如图1所示，部分组合细胞由于生长状态差，因此提前结束培养。B02和B08实验组细胞生长表现优于原工艺（图1A）；大部分组合细胞存活率维持较好，与原工艺相近（图1B）；不同的培养基组合抗体表达量存在差异，B02、B08、B10、B12、B13和B15的蛋白表达量比原工艺高，其中B02组合蛋白表达量较原工艺提高约70%，达到5.6 g/L（图1C）。将蛋白表达量较优的组合（B02、B08、B10、B12、B13和B15）细胞培养液经纯化制备得到的亲和层析样品进行纯度、N-糖型和电荷异质性等质量检测，检测结果如表4—6所示，单体含量与原工艺无明显差异；Man5含量和碱性峰含量均比原工艺高；电荷异质体主峰含量和岩藻糖含量较原工艺低。以上结果表明，B02、B08、B10、B12、B13和B15实验组的工艺性能优于原工艺，而N-糖型和电荷异质性质量属性与原工艺有差异。2.2摇瓶补料策略研究基于摇瓶培养基筛选的实验结果，以B02、B08、B10、B12和B15实验组的培养基组合进行补料策略研究，包括原工艺共进行11组摇瓶实验，由于B12和B13实验组培养基组合为同一厂家生产，因此只选择B12实验组进行补料策略研究。细胞性能参数如图2所示，FB01-1和FB01-2活细胞密度峰值和细胞生长趋势均优于原工艺（图2A）；各实验组细胞存活率均维持在90%以上，与原工艺相近（图2B）；各实验组的蛋白表达量均高于原工艺，其中FB01-1的蛋白表达量最高，约为原工艺的2倍，达8.4 g/L（图2C）。各实验组间，FB01-1、FB02-1、FB03-1、FB04-1的蛋白表达量高于FB01-2、FB02-2、FB03-2、FB04-2组，表明FB01、FB02、FB03和FB04组在培养第3天（D3）/6/8/10/12补料（3%~7%）更有利于CHO细胞表达IL-17A/F，而FB05-1的蛋白表达量比FB05-2低，表明FB05组在D3/5/7/9/11补料（3%~7%）更有利于蛋白表达。检测细胞培养液亲和层析纯化样品的纯度、N-糖型和电荷异质性，结果如表7—9所示，各实验组的单体含量均在99%以上，与原工艺相近；各实验组N-糖型中Man5含量均比原工艺高，岩藻糖含量均比原工艺低；各实验组电荷异质性酶切前主峰含量和酸性峰含量比原工艺低，酶切后各实验组的主峰含量与酶切前比升高，而碱性峰含量有所降低，说明重组蛋白C末端Lys被不完全去除。2.32 L反应器培养基筛选基于补料策略研究结果，选择FB01-1、FB03-1、FB04-1实验组培养工艺在反应器上进行培养基筛选及补料策略实验。细胞性能参数如图3所示，BR02和BR03组的活细胞密度峰值均高于原工艺，而BR01组与原工艺基本一致（图3A）；各实验组细胞存活率均在95%以上，与原工艺相近（图3B）；各实验组的蛋白表达量均高于原工艺，其中BR02和BR03分别为6.3和5.7 g/L，即原工艺的3.0和2.7倍（图3C）。检测细胞培养液亲和层析纯化样品的纯度、N-糖型和电荷异质性，结果如表10—12所示，实验组的单体含量均在99%以上，与原工艺相似；各实验组Man5含量比原工艺高，岩藻糖含量比原工艺低，其中BR01和BR03组岩藻糖含量相当；BR03电荷异质性酶切前后主峰含量与原工艺相当，酶切后各实验组酸性峰含量升高，而碱性峰含量降低，说明重组蛋白C末端Lys被不完全去除。综合上述结果，BR03实验组在质量方面更接近原工艺且蛋白表达量更高。2.4反应器工艺确认基于反应器培养基筛选结果选择B03培养工艺进行工艺确认，结果如图4所示，新工艺活细胞密度峰值比原工艺约高1倍（图4A），细胞存活率趋势优于原工艺（图4B），蛋白表达量从3.2 g/L提高至6.6 g/L（图4C），差异有统计学意义（t=50.81，P<0.001）。细胞培养液经纯化后得到的亲和层析样品进行纯度、N-糖型和电荷异质性检测，原工艺和新工艺单体含量均达99%以上（图4D），差异无统计学意义（t=2.14，P=0.099）；N-糖型中岩藻糖含量比原工艺低（图4E），且差异有统计学意义（t=9.92，P<0.001）；新工艺电荷异质性主峰含量与原工艺相似，酸性峰（t=8.47，P=0.001）和碱性峰（t=3.58，P=0.020）含量差异均有统计学意义（图4F）。由于N-糖型和电荷异质性与原工艺存在差异，因此进一步检测重组细胞活性和IL-17A/F结合活性，结果显示，新工艺重组细胞活性相对比活和IL-17A/F结合活性均与原工艺标准品相当（表13）。综上，新工艺产品N-糖型和电荷异质性与原工艺的部分差异并不影响抗体的生物学活性。3讨论CHO细胞是1种广泛应用于生物制药领域的细胞系，其培养基的质量和性能对细胞培养和产物表达至关重要。虽然目前我国的大部分CHO细胞培养基市场被国外品牌占据，但随着我国生物医药产业的快速发展，以及在疫情和国际大环境的影响下，培养基的国产化替代已经成为趋势。培养基的国产化替代不仅可以降低生物制药企业的生产成本，还可以提高培养基的质量和稳定性，从而提高生物药的生产效率和质量。此外，培养基的国产化替代也有助于提高中国生物制药行业的竞争力和创新能力，促进行业健康发展。在重组蛋白生产中，细胞培养基的开发和优化通常被视为提高细胞密度和蛋白产量、减少副产物以及提高产品质量的有用策略（细胞密度和抗体滴度通常是培养基研究的主要报告结果）。目前，哺乳动物细胞生产单抗主要在大型生物反应器中通过分批补料的工艺方式进行，以达到最大的活细胞密度和最高蛋白产量。抗体产量直接影响药物未来商业化生产的制造成本，为了提高抗体产量，本实验在重组表达抗人IL-17A/F单抗的CHO细胞基础上，对商业化基础培养基和补料培养基进行筛选和优化，综合评估并筛选出可使抗人IL-17A/F抗体产量更高的商业化培养基组合。本研究通过培养基筛选等实验综合考察抗体表达和产品质量属性发现，新的细胞培养工艺虽不影响抗体的纯度，但糖型和电荷异质性存在部分差异，糖型的种类和含量可能影响抗体的有效性、安全性和稳定性。由于IL-17A/F为分泌型细胞因子，自然状态下无膜上表达IL-17A/F的靶细胞存在，因此难以介导抗体依赖的细胞介导的细胞毒和补体依赖的细胞毒效应。新工艺糖型中岩藻糖含量略有差异，不会对Fc功能活性造成影响，推测对产品有效性和安全性影响风险小。电荷异质性的分布发生变化虽可能会对抗体的稳定性和活性造成影响，新工艺的酸性峰比例较原工艺偏高，碱性峰比例较原工艺偏低，但体外的生物学活性测定显示，新工艺的生物学活性与原工艺标准品相当，表明新工艺的糖型和电荷异质性差异不影响抗体的活性。本研究未对抗体稳定性进行评估，以后可进一步优化工艺。IL-17作为银屑病、银屑病关节炎和强直性脊柱炎治疗的靶点，抗人IL-17A/F单抗能够阻断其与IL-17受体A及C的结合，从而抑制其下游促炎细胞因子以及效应细胞的表达，是具有广阔前景的抑制免疫性疾病发展的药物。本研究筛选出了可使细胞高度表达抗人IL-17A/F抗体的国产培养基组合，为后续细胞培养工艺优化和放大生产奠定了基础，降低了抗IL-17A/F单抗细胞培养工艺产业化的成本。作者吕美影1  蒋俊凯1  黄平周1  张航烨1  王迎港1  梁育松1  潘旭耀1  邓钦培2  冯春艳2  许方岩1  胡振湘31珠海市丽珠单抗生物技术有限公司工艺开发部，珠海 519045；2珠海市丽珠单抗生物技术有限公司分析技术部，珠海 519045；3珠海市丽珠单抗生物技术有限公司，珠海 519045通信作者：胡振湘Email：huzhenxiang@livzon.cn引用本文：吕美影, 蒋俊凯, 黄平周, 等. CHO细胞表达重组抗人IL-17A/F单克隆抗体的培养基优化和质量评价 [J]. 国际生物制品学杂志, 2025, 48(2): 82-90.  DOI: 10.3760/cma.j.cn311962-20240704-00045识别微信二维码，添加生物制品圈小编，符合条件者即可加入生物制品微信群！请注明：姓名+研究方向！版权声明本公众号所有转载文章系出于传递更多信息之目的，且明确注明来源和作者，不希望被转载的媒体或个人可与我们联系(cbplib@163.com)，我们将立即进行删除处理。所有文章仅代表作者观不本站。

CHICAGO--(BUSINESS WIRE)--Cellworks Group Inc., a leader in Personalized Therapy Decision Support and Best-in-Class PTRS, today announced compelling results from a new study demonstrating the ability of the Cellworks Platform to identify patients with high microsatellite instability (MSI-H) who may not respond to immune checkpoint inhibitors (ICIs), despite MSI-H status. Results from the study were showcased in a poster presentation titled, Use of Biosimulation to Predict Immune Checkpoint Inhibitor Resistance in Patients with High Microsatellite Instability as part of the AACR Annual Meeting 2025 taking place April 25-30, 2025 at the McCormick Place Convention Center in Chicago. While immune checkpoint inhibitors (ICIs) such as pembrolizumab are considered a standard-of-care for MSI-H cancers, MSI-H status alone is not a definitive predictor of treatment success. In this study, Cellworks applied its unique mechanistic Computational Biology Model (CBM) to biosimulate patient-specific responses to ICIs. The computational biosimulation process in the study uncovered molecular signatures of resistance in MSI-H patients who were predicted to have poor response to ICIs, providing a deeper understanding of why some MSI-H patients fail to benefit from immunotherapy. Key Findings Efficacy Scores Significantly Higher in MSI-H Patients. MSI-H patients demonstrated significantly higher pembrolizumab efficacy scores compared to microsatellite stable (MSS) patients in both STAD (average ES: 20.5 vs. 3.2, p < 0.001) and CRC (average ES: 13.4 vs. 2.4, p < 0.001). Large Subset of MSI-H Patients Predicted to Have Low ICI Response. Despite being MSI-H, 59% of STAD and 81% of CRC patients were identified as low pembrolizumab responders. Molecular Drivers of Resistance Identified. In MSI-H patients classified as low pembrolizumab responders, higher rates of NOTCH2, EGFR, and EZH2 amplifications, along with TP53 loss-of-function mutations, were identified. In MSI-H/ES-L CRC patients, MYC amplification was significantly enriched ( p < 0.05). “These findings highlight the power of using patient-specific drug response methods to move beyond MSI-H status and identify critical molecular drivers of immune checkpoint inhibitor resistance,” said Dr. James Wingrove, Chief Development Officer at Cellworks and presenting author of the study. “By identifying patients unlikely to respond to ICIs, we can help oncologists personalize treatment strategies and improve outcomes for MSI-H patients who may otherwise receive ineffective therapies.” “This study demonstrates the importance of looking beyond MSI status to understand immune checkpoint inhibitor resistance at a molecular level,” said Dr. Michael Castro, Chief Medical Officer at Cellworks. “Our biosimulation revealed that MSI-H patients with low predicted response to pembrolizumab frequently harbored alterations such as NOTCH2, EGFR, and EZH2 amplifications, as well as TP53 loss-of-function mutations in STAD, and MYC amplifications in CRC. Identifying these resistance-associated biomarkers can help guide clinicians in selecting more effective, personalized treatment strategies for MSI-H patients who may not benefit from ICIs alone.” Study Design Cellworks developed a mechanistic Computational Biology Model (CBM) that can be personalized based on a patient’s tumor-based genomic profile, revealing signaling pathway dysregulation and patient-specific drug response. Output from the model was used to identify MSI-H patients who may have a poorer response to ICIs. Computational biosimulation was performed using real-world retrospective cohorts of 423 STAD patients and 534 CRC patients (TCGA). MSI measurements were provided by TCGA. Efficacy scores based on biosimulated composite cell growth in response to disease and therapy were generated on all patients for pembrolizumab. Molecular rationales for ICI resistance were identified for MSI-H patients with low pembrolizumab efficacy scores. The Cellworks Platform The Cellworks Platform performs computational biosimulation of protein-protein interactions, enabling in silico modeling of tumor behavior using comprehensive genomic data. This allows for the evaluation of how personalized treatment strategies interact with the patient’s unique tumor network. Multi-omic data from an individual patient or cohort is used as input to the in silico Cellworks Computational Biology Model (CBM) to generate a personalized or cohort-specific disease model. The CBM is a highly curated mechanistic network of 6,000+ human genes, 30,000 molecular species and 600,000 molecular interactions. This model along with associated drug models are used to biosimulate the impact of specific compounds or combinations of drugs on the patient or cohort and produce therapy response predictions, which are statistically modeled to produce a qualitative therapy response score for a specific therapy. The Cellworks CBM has been tested and applied against various clinical datasets with results provided in over 125 presentations and publications with global collaborators. About Cellworks Group Cellworks Group, Inc. is a leader in Personalized Therapy Decision Support and Precision Drug Development. The Cellworks Platform predicts therapy response for individual patients and patient cohorts using a breakthrough Computational Biology Model (CBM) and biosimulation technology. Backed by Artiman Ventures, Bering Capital, Sequoia Capital, UnitedHealth Group and Agilent Ventures, Cellworks has the world’s strongest trans-disciplinary team of molecular biologists, cellular pathway modelers and software engineers working toward a common goal – attacking serious diseases to improve the lives of patients. The company is based in South San Francisco, California with a CLIA-certified computational laboratory in Franklin, Tennessee and a research and development facility in Bangalore, India. For more information, visit www.cellworks.life. All trademarks and registered trademarks in this document are the properties of their respective owners.

Can AI make an avatar of Mikael Dolsten? The former chief scientific officer of Pfizer will try to find out now that he’s starting a new chapter at Formation Bio after nearly 16 years at the New York pharma giant. In one of his first interviews since leaving Pfizer, Dolsten exclusively told Endpoints News that he is leading a drug-picking committee at Formation Bio, an AI-focused biopharma. In addition to chairing that group, he will serve as a strategic advisor to Formation Bio, a deeply ambitious company that has raised over $600 million, including a $372 million Series D last June . Formation Bio CEO Benjamine Liu said Dolsten brings 30-plus years of experience knowing what a great drug program looks like. He’ll help Formation Bio — which is pursuing a Roivant-esque model of picking up assets — select which drugs to in-license. But Liu said he also hopes Dolsten’s input can help teach AI models to basically imitate how Dolsten and other veteran drug hunters make these calls. “The thing we’re trying to do with Mikael is: How do we recapitulate what an AI Mikael looks like?” Liu said in an interview, saying he hopes experts like Dolsten can train the AI system how to make decisions and evaluate opportunities. Dolsten, 66, has wasted little time kicking off his post-Pfizer tenure. His last day at the drugmaker was Feb. 28, and he started working with Formation Bio on March 1. His role at Formation Bio will be announced later on Wednesday. He’s also building “Mikael’s ecosystem,” a mix of about 10 life sciences companies that he’s working with. Dolsten described his Pfizer tenure as “a fabulous run exceeding my wildest aspirations,” adding that he started to plan his departure about two years ago. Dolsten’s exit was first announced in July, and he helped choose Chris Boshoff as his successor. Leaving Pfizer was his own decision, he said, and wasn’t influenced by Starboard Value’s activist campaign. All told, Dolsten brought more than 35 products through approval, including the Covid-19 vaccine, Comirnaty. “I just felt that it would be hard to beat a lot of those amazing things we accomplished,” Dolsten said. “I felt intrigued by working in a new environment.” Dolsten’s other roles include board seats at the instrument maker Agilent Technologies, the New York AI biotech Immunai , and the Danish macrocycle biotech Orbis Medicines . He said he is also helping launch a new biotech startup with a serial entrepreneur from Yale University. That said, he’s spending relatively more time on his Formation Bio role, estimating he spends about a day per week at the company. Also on Formation’s drug-picking committee is its newly-hired chief business officer David Steinberg, a co-founder and former executive at PureTech Health. Steinberg joined in October. Dolsten described his conversations with Liu over the past few months as a key factor in his decision to join the company. He described Liu as a “high-energy AI magician with a vision.” One of Formation’s newest projects is using AI, particularly the latest generation of deep research tools , to create an atlas of all possible drug targets. This includes scouring press releases, scientific conference abstracts, company websites and more to aggregate up-to-date reports for each target. The goal, Liu said, is an “equivalent of RA Capital’s atlas , but instead of 50 PhDs, we have AI systems go through target by target.” With about 150 employees right now, Formation Bio is betting that AI-based workflows can be more efficient than a 100,000-person pharma company. Dolsten said he’s impressed by what today’s AI systems can produce, compared to the conventional way that large drugmakers like Pfizer and AstraZeneca operated while he worked there. “So much of that work required advisory committees, multiple business development meetings, and now can happen in the instant of a moment,” Dolsten said, estimating that about 80% of that work can happen in 30 minutes using these AI systems.