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For AI-focused startups seeking out pharma deals, they have to go through people like Greg Meyers, the chief digital and technology officer at Bristol Myers Squibb. Endpoints News recently talked with Meyers about his views on artificial intelligence, including where he’s convinced the technology has value and where it’s mostly hype. At Bristol Myers, he plays a key role in determining what is and isn’t worth the $100 billion pharma company’s time and effort. This interview has been substantially edited for length and clarity. Andrew Dunn : You worked up the ranks at J&J, Novartis and Biogen, and then left the drug industry for Motorola and Syngenta. Why’d you leave the industry and what lessons did you learn? Greg Meyers: I didn’t ever want to be painted as the pharma guy. I wanted to make sure I had a full perspective as a technologist. There’s a lot of constraints on technology in biopharma in terms of the regulations we face. When you go into places where the risks are lower, you start to question if you take another month to do this, do you actually de-risk it? Some of my assumptions — the longer it takes, the safer something is — actually turn out not to be true. In many cases, the faster you move, the safer it is because you have the ability to change course faster. Coming back to life sciences, I’m always measuring up whether we’re moving fast enough, being too conservative or too bureaucratic. Dunn: You hear a lot of pitches from startups using AI in drug discovery. What do you see as the biggest gaps between the AI bio field and what Bristol Myers wants? Meyers : There’s too much media attention on lead identification, probably because it sells headlines: Some big AI lab created a protein-folding tool and now we won’t need scientists anymore. The truth is the vast majority of leads that can be hit will fail. We actually just put a molecule into Phase 1 for sickle cell disease. We’ve built a CELMoD , which is basically a molecule that can degrade proteins. If you can degrade the protein that inhibits the production of fetal hemoglobin, you can ameliorate the symptoms of sickle cell disease. What does that have to do with AI? When you develop a CELMoD, you’ve got an engineering problem. We made subtle changes to the molecule, nothing that would change its functionality, but that allow you to stabilize the molecule so that it could do its job. We’re seeing a ton of value; 100% of our small-molecule programs and 50% of our large molecules won’t make their way to the wet lab until a predictive model suggests that experiment will work. We’re building a lot of this from scratch because the software that you need is inextricably linked to the data you have, and the data is unique. It’s got its own level of dirtiness and cleansing you need to do, so we find using these open-source models and tools really gets us to where we need to go. Dunn: Chai launched earlier this month as the latest AI startup advancing a protein structure model. Is this healthy competition? Or is there a risk of redundancy, given all the different tasks inside the R&D process? From what you’re saying, it sounds like the field could have more impact focusing on optimization. Do we really need another protein language model? Meyers: This competition is great. It’s pretty normal, too. If you rewind the clock to maybe 1998, there were about 12 different search engines. You didn’t know whether Lycos or Yahoo or Google were going to win. The market clears out who really has a substantive product, and who has a solution looking for a problem. It’s probably going to be the same here. I’ll take AlphaFold2 as an example. The new one’s not open-sourced — funny how that works — but AlphaFold2 is something we use almost daily. But we don’t use it the way the media would think you would, to predict proteins. It doesn’t really do that. It does a good job of taking well-characterized proteins, already bound by X-ray crystallography behind the scenes, and allows you to look at adjacencies, like subtle changes, to them. Dunn: A lot of these startups are more about a single model or product than building an end-to-end biopharma company. What does that say about today’s startup landscape? Meyers: There’s a lot of venture money flowing into AI. If we’re honest, there are probably companies that don’t have great product-market fit or great ideas, but because the word GenAI is on the pitch deck, they’re going to get money. In a place like Bristol Myers Squibb, we don’t want to spend time stitching together 100 different point solutions that all think they’re the one-stop shops. You end up having a mall of one-stop shops. You have to engineer these things in a cohesive way if you want to transform your workflow. I don’t think that’s the fault of any startups; that’s just the nature of the lack of maturity of where things are. Dunn: For startups seeking partnerships, what do they need to understand? Meyers: As a startup, when you look at life sciences, there is a systematic oversimplifying in the way we actually operate. I’ll go back to the example of obsessing over lead identification, when in fact being able to predict something like absorption or lipophilicity, these are the things that achieve breakthroughs. But when you look at it from the outside, it looks like what we really need is a scientist in a box. The technology is not in that credible place now. It’s so very complicated. Maybe 5% of cellular interactions have actually been modeled. The other 95% of biology is largely a black box. Dunn: It feels like there’s some conflict with the VC world seeking out huge visions of foundation models and generalizability. Then I’m hearing you say the needs of today’s big pharmas are specific, applied models. Is that going to be a natural tension for the field? Meyers: I think you’re onto something. The venture system probably values simple, easy-to-understand business models. A lot of what exists today tends to be more the ingredients that you need that might be able to achieve a really valuable breakthrough. But that’s not a simple, easy-to-explain business model. Dunn: Given your career in IT, how do you decide when it makes sense to roll out AI-powered technologies, or really any new technology, to thousands of workers at a large company? Meyers: A demo is worth 1,000 slides. Most big companies probably start off with 1,000 slides, and then there’s this huge process to get everyone aligned on whether we should or shouldn’t attempt something. Once you decide to attempt it, it becomes very high-stakes for people. What should be a proof of concept becomes a project where people’s reputations are at stake on it being successful. I like to do things very quietly and small. We’re going to fail nine out of 10 times. The only way you’re going to know what AI can do is getting your hands dirty. You need to make sure there’s no shame or blame or embarrassment in those failures. When you actually have something to show people, you’re either going to get, “This is the dumbest idea I’ve ever heard,” or, “Oh my God, that’s amazing.” You don’t need to write even one slide, because all the organizational energy gets channeled into moving that thing forward. Dunn: You’ve rolled out an internal version of ChatGPT. I know everyone loves to talk about humans in the loop, and that AI won’t replace people. But do you see any jobs or tasks, especially things like medical writing, adverse-event reporting, regulatory submission preparations, at the highest risk of being replaced? Meyers: My base case is not jobs being replaced. I do think tasks will change, but not really any differently than in the past. Adverse events, that’s something we’ve done. We have AI now reading all of our events, and they’re categorizing them. You get a ton of noise in pharmacovigilance. How do you get your best pharmacovigilance people the cases they really need to focus on? This work might have taken days to troll through what you’re getting, but now being able to have them automatically prioritized, it doesn’t require fewer people. It just makes better use of their time. That’s the case for most of these things.

TIS2024第四届自免疾病细胞疗法与干细胞疗法论坛重磅来袭，点击图片查看重磅嘉宾，合作详询：王晨 180 1628 8769 （扫码立即报名获取TIS2024免费票） 编者注：本篇为VVIP投稿~ 2022年11月11日，Nature Review Drug Discovery的一篇新闻稿“Climbing cancer’s MYC mountain”对Omomyc I期临床结果的报道，让靶向c-Myc的药物研发重回大众的视野[1]。 图1：c-Myc/Max 异二聚体- E-box DNA的复合物结构[1] Myc蛋白（又称c-Myc）在70%的肿瘤中表达失调，靶向失调的Myc蛋白有广泛的治疗作用。Myc作为转录因子，是一种固有无序蛋白，单独的Myc蛋白没有稳定的构象和适合小分子结合的位点，也是难成药代表性靶点。Myc蛋白自身稳定性较差，只有结合Max后才具有稳定构象，形成Myc-Max复合物后（图1），与DNA上的E-box序列结合启动下游基因转录[2]。 直接靶向Myc蛋白有一定难度，因此，除了直接靶向Myc通路的Myc-Max相互作用和抑制Myc-Max复合物与DNA的相互作用外，间接调控Myc上下游通路也是治疗Myc相关疾病的重要方法。今天我们就来细说一下这些具有不同modality的Myc抑制剂的研究进展，扒一扒哪些公司在挂羊头卖狗肉，蹭Myc的热点。 目前已有三款药物进入临床研究（Omomyc/PC002/WBC100），多款药物处于临床前研发阶段，药物形式也多种多样，涉及小分子抑制剂、多肽、Protac、分子胶等。 Omomyc 作为本轮热点的“始作俑者”，不得不先说说Omomyc。Soucek教授将c-Myc leucine zipper域中四个极性带电残基（E/E/R/R）突变为中性氨基酸（T/I/Q/N）得到含有91个氨基酸的工程肽Omomyc（图2），Omomyc可以自身二聚，也可以结合内源性Myc，阻断Myc-Max复合物与DNA-Ebox的相互作用，同时Omomyc也能够结合Max蛋白，阻断Myc-Max的相互作用（图3），进而促进内源性Myc蛋白的降解，抑制Myc下游基因转录发挥治疗作用[2]。 图2：Omomyc的序列 OMO-103是基于Omomyc衍生化的细胞穿膜肽，2021年4月启动了一项针对晚期实体瘤的I期临床试验，用以评估其安全性、PK及有效性（NCT04808362）。入组的病人包括胰腺癌、肠癌和非小细胞肺癌等实体瘤病人，剂量从0.48 mg/kg爬坡到9.72 mg/kg，每周一次注射给药，RP2D=6.48mg/kg。截至到2022.10.10，给药9周后进行CT扫描的12名患者中，有8例病人中初步看到有效性（SD，stable disease），DLT是胰腺炎，其他不良反应多与静脉输液相关，安全性目前可接受。另外，临床前研究中发现，在小鼠肿瘤模型中瘤内药物浓度比血液中高四倍，人体PK显示给药50h后在血液里仍能检测到OMO-103，推测药物在人肿瘤中停留的时间可能更长[3]。 Omomyc/OMO-103在不同实体瘤中初步验证阻断Myc功能发挥治疗作用（虽然最好疗效是SD），安全性良好，应用肿瘤范围广，但是目前单药的治疗效果不够亮眼，未来在更大入组人数的表现、寻找合适的biomarker和亚组人群寻找、联用其他药物的结果值得期待。当然，小分子Myc抑制剂能否达到类似的安全性和有效性还有待进一步验证。 图3：Omomyc的可能作用机制[2] PC-002 图4：PC-002结构 临床进展最快的PC-002，又叫Sepantronium bromide（图4），是Cothera Bioscience（科赛睿生物/智康博药）开发用于治疗Myc基因异常淋巴瘤/神经母细胞瘤/神经内分泌前列腺癌的药物。目前正在进行针对高级别B细胞淋巴瘤的二期临床研究（NCT05263583）。Sepantronium bromide原先由Astellas Pharma开发，代号是YM-155，已经停止肿瘤临床开发。据科赛睿生物官网介绍，“PC-002是科赛睿生物公司的核心产品，是针对MYC基因变异肿瘤的first-in-class小分子药物。PC-002通过独特的作用机制（MOA）靶向MYC蛋白降解，选择性诱导MYC依赖的肿瘤细胞凋亡。PC-002以其独特的机理，选择性地杀伤具有MYC基因变异的肿瘤细胞，因而有望成为治疗多种癌症的重磅产品”[4]。 经过检索发现“Sepantronium Bromide是一种有效的survivin抑制剂，通过抑制Survivin启动子活性而发挥作用”。Survivin作为一种抗凋亡蛋白，通常在肿瘤细胞中过度表达，介导肿瘤细胞逃避细胞凋亡，是潜在肿瘤治疗靶点。而c-Myc可以调节Survivin的转录，促进肿瘤的存活[5]。PC-002暂无公开证据表明其能直接靶向Myc而发挥功能，仅从科赛睿生物披露的机制来说，PC-002间接促进Myc的降解，临床选择人群也是Myc基因异常的肿瘤。其官网公开PC-002适应症人群而隐藏了靶点信息，有蹭热点的嫌疑。另外，PC002的化合物专利已经到期（WO 2001060803），科赛睿生物申请了联用和生物标记物专利（CN115605227），保护了PC002联用细胞周期阻滞剂治疗Myc相关肿瘤的用途。其临床前管线中还有一款基于PC-002改造的全新分子PC-002p用于治疗MYC+的肿瘤，可能也是Survivin抑制剂，专利暂未披露。 WBC100 图5：WBC100结构 WBC100是浙大医学院和第二军医大学联合开发的雷公藤甲素的缬氨酸酯衍生物。雷公藤甲素的靶点较多，尚未完全研究清楚其抗肿瘤机制，能够抑制包括NF-κB、HSF1、MDM2等多种靶点，对多种肿瘤细胞具有较强的增殖抑制作用。 杭州卫本药业拥有WBC100的全球权利，已列入开发管线（wb001），目前处于I期临床研究阶段（CTR20211600）。2022年的一篇Adv. Sci.及专利WO2021208787报道WBC100是一种口服Myc分子胶，靶向c-Myc的NLS1-Basic-NLS2区域，通过E3泛素连接酶CHIP介导的26S蛋白酶体途径降解c-Myc蛋白，诱导肿瘤细胞凋亡，对多种c-Myc过表达的血液瘤和包括PDAC、NSCLC、HCC及CRC在内的实体瘤细胞有增殖抑制作用。并在极低剂量（0.1mpk）抑制c-Myc过表达AML、胰腺癌和胃癌肿瘤生长[9]。 笔者并不质疑WBC100能够直接/间接/共价/非共价靶向c-Myc，但是从其结构来看，更相信Myc只是WBC100发挥抗肿瘤作用微不足道的靶点之一。WBC100结构中多个不稳定环氧丙烷结构的体内安全性也需慎重考虑。 GT-19077/GT-19715 苏州开拓药业管线中有两款靶向Myc的药物。其中，GT-19077是小分子Myc-Max蛋白-蛋白相互作用抑制剂，GT-19715是c-Myc/GSPT1双靶点降解剂，已经在IND阶段。 图6：开拓药业专利中代表性结构与来鲁华课题组筛选的Myc抑制剂[6]及结构相似性cIAP1配体[7] 推测GT-19077可能来源于专利（WO2021004391/ WO2022089556），为含有硼酸或硼酸酯类的小分子化合物（图6），其结构基础推测是北大来鲁华课题组虚拟筛选出来的PKUMDL-YC-1205[6]。PKUMDL-YC-1205是Fmoc/Trt保护的天冬酰胺，看起来很是奇怪的一个分子，开拓将其保护基和氨基酸部分进一步改造，获得硼酸或硼酸酯类化合物。但是仔细推敲这些小分子，又像是cIAP1的配体[7]。含有硼酸或硼酸酯的小分子抑制剂可能是通过共价靶向c-Myc上的特定氨基酸增强与c-Myc的结合而发挥作用。 图7：开拓药业专利中代表性结构及分子胶CC-885/CC-90009结构 GT-19715可能来源于开拓药业的降解剂专利（CN115504963/WO2022268066），分析其结构，似乎是在抑制剂基础上再加上Cereblon配体获得，但看起来更像是在GSPT1分子胶CC-90009/885的尾巴上加上Fmoc的类似基团（图7），不知是GT-19715降解谱筛选到了GSPT1，还是在分子胶的研发过程中加上了Myc抑制剂的结构。对于GT-19715是分子胶还是降解剂，开拓在不同的会议中定义不同，猜测GT-19715结构可能是CN115504963中化合物A153。 从抑制剂和降解剂结构上看，虽然是宣传靶向Myc蛋白，但是脱靶风险非常高，当然降解剂已经明确是c-Myc/GSPT1双靶点。从多次会议公布的临床前药效模型来看， GT19630 (DC50=1.5 nM / IC50= 0.33 nM in HL-60；GT19715是 GT19630的盐型) ip给药在HL-60异种移植模型中有效地降解GSPT1，同时完全降解c-Myc，并在低剂量0.3 mg/kg/bid时抑制肿瘤生长，未引起体重明显下降，显示出良好的治疗效果和安全性。 GSPT1降解剂CC-90009的临床适应症也是AML/MS，临床前CC-90009对包括HL-60细胞在内的多种AML细胞具有亚纳摩级别抑制活性[8]。所以，在AML模型中GT19715发挥抑瘤作用究竟是降解Myc还是GSPT1所导致，抑或降解两个蛋白的协同作用，值得进一步探讨，毕竟在细胞增殖抑制中协同作用并不明显。开拓官方在多次会议中也披露GT19715能够抑制前列腺癌LNCap和耐药模型22RV-1肿瘤生长，并且能够促进AR-V7降解。同时，GT19715能够抑制小细胞肺癌细胞增殖和肿瘤生长，并促进c-Myc和N-Myc的降解。同一款分子胶或降解剂，在低浓度下，能够在不同肿瘤中促进包括c-Myc/N-Myc/GSPT1/GSPT2/CK1 alpha /IKZF1/AR-V7等多个蛋白的降解，笔者对这样一款超越分子胶或降解剂降解范围的分子的安全性不禁担忧。 另外，不同于其他蛋白，Myc与Max蛋白间相互作用被阻断后，稳定性变差而降解，GT19715究竟是通过抑制Myc-Max蛋白-蛋白相互作用还是通过泛素-蛋白酶降解系统发挥作用，抑或是共同发挥作用，也是值得进一步验证。但是从公开数据来看，加入蛋白酶体抑制剂MLN9708能够抑制GT19715对Myc的降解，似乎泛素化降解发挥了一定作用。 ICX-101 ICX-101是Incurix开发的一种口服小分子Myc抑制剂，阻断c-Myc-Max异二聚体与DNA-E-box结合。官网披露EMSA/转录组实验验证了ICX-101对Myc具有选择性。 Incurix是韩国国立癌症中心下属子公司，主要研究肿瘤相关转录因子。韩国国立癌症中心专利（WO2012093741/WO2018021810/WO2018021849）显示其结构可能如图8所示，化合物51/56对包括膀胱癌、前列腺癌、肺癌、乳腺癌、白血病、胰腺癌、结直肠癌等肿瘤细胞的增殖抑制在μM级别，在肺癌（NCI-H1299）、前列腺癌（DU145）模型中抑制肿瘤生长。目前正在进行GLP和生物标记物研究，计划于2022年底进行临床试验，当前检索官网，仍未见临床消息。 图8：WO2018021849专利中代表性分子 IDP-121/IDP-410 IDP Pharma 开发了两款肽类Myc抑制剂，其中IDP-121靶向c-Myc用于治疗c-Myc驱动肿瘤。IDP-410靶向N-Myc用于治疗脑胶，IDP-410能搜索到的数据较少。 IDP Pharma专利WO2019048679中披露了靶向c-Myc的拟肽S09、S14、IDP-P1708160和IDP-P1708161用于治疗多发性骨髓瘤（优选S09）。这些拟肽以c-Myc的一段基序为基础，通过“订书肽”形式维持野生肽的α-螺旋结构，增加其体内稳定性，阻断c-Myc与Max间形成复合物发挥抗增殖作用。专利数据显示这些拟肽在多种多发性骨髓瘤（MM）细胞系中具有μM水平的细胞毒作用，强于小分子抑制剂10058-F4，可作为单一药剂或与其他药物联合使用。体内外与标准疗法地塞米松和硼替佐米或环磷酰胺的药物的三联治疗中表现出协同抗肿瘤作用。 目前IDP-121处于IND申请阶段，相较于野生型序列，IDP-121提高了稳定性，但在多发性骨髓瘤模型中单药抑瘤效果较差，与地塞米松、硼替佐米或环磷酰胺的二联或三联疗法中显示出显著的增强效果，临床首选适应症可能会是联用标准疗法用于标准疗法治疗失败的多发性骨髓瘤。 图9：专利WO2019048679中代表性结构 c-Myc 蛋白表达抑制剂 由于 c-Myc 蛋白缺乏稳定的构象和适合的小分子结合口袋，理性设计直接结合Myc的抑制剂具有较高挑战性。因此，也有一些biotech另辟蹊径，从c-Myc的转录和翻译入手，如Arrakis Therapeutics有一款靶向c-Myc mRNA的小分子，通过抑制c-Myc蛋白的表达，从而阻断Myc的功能，目前该项目正处于lead发现阶段。 Anima Biotech同样有一款c-Myc mRNA调节剂，专利WO2022150316披露一系列含硫三环结构分子，能够在细胞水平抑制c-Myc蛋白的翻译，代表性化合物332对c-Myc 依赖性A549肿瘤生长具有显著较强的抑制作用（3 mg/kg twice weekly，ip），目前该项目也处于lead优化阶段。 图10：专利WO2022150316中分子332 c-Myc降解剂 针对Myc这一难成药靶点，直接靶向Myc很难获得高亲和力分子，因此基于弱结合的抑制剂开发降解剂似乎是一个思路。蛋白降解领域的两家先驱Arvinas和Nurix Therapeutics均有一款靶向Myc的药物在临床前研发阶段，目前暂无专利披露。开拓GT19715究竟是Protac还是PPI抑制剂，抑或是c-Myc的分子胶，也还是值得进一步验证。 笔者有个小疑问，c-Myc蛋白或mRNA半衰期低于半个小时，抑制剂干扰Myc-Max的相互作用后即可使Myc蛋白不稳定，促进降解，而在此基础上设计降解剂的有何更深层次的考虑？ 其他小分子 10074-G5和10058-F4是最早一批通过实验筛选发现的直接结合c-Myc蛋白的小分子，也是经分子水平、细胞水平和体内药效验证的c-Myc直接抑制剂。虽然存在荧光或典型的PAINS结构，这些化合物在分子水平通过FP、STD-NMR、2D-NMR、EMSA、CD等结合或竞争性实验验证能够直接结合在c-Myc上，并且明确10074-G5结合在c-Myc的aa363-481，10058-F4结合在aa404-412区域。通过直接结合在c-Myc蛋白上，进一步阻断Myc-Max的相互作用及Myc/Max复合物和DNA的相互作用。在细胞水平，这些分子能够抑制c-Myc依赖性的细胞增殖，下调c-Myc的转录活性，使细胞周期阻滞在G0/G1期等。由于筛选得到分子活性和理化性质不佳，研究者对10074-G5和10058-F4系列进行了多轮改造，3jc48-3的靶标活性和细胞活性略有提高，但是在体内药效并未体现出较优的抗肿瘤效果。但10074-G5和10058-F4结构简单，分子量较小，可作为苗头化合物进一步优化。 化合物MICMI-6/7也经过分子、细胞及体内药效的验证，但是体内抗肿瘤效果一般，从其结构来看，都具有较大的刚性平面，且质子化能力较强，假阳性概率较高，不太适合进一步优化。 KDJ-Pyr-9这类联芳环结构与10074-G5结合在相同c-Myc蛋白序列，通过阻断Myc/Max-DNA相互作用抑制下游基因转录及细胞增殖，但分子刚性太强，分子量过大，有待于进一步优化。 Mycro1系列分子是基于Myc/Max-DNA的FP方法筛选出的化合物，对Myc-Max蛋白-蛋白相互作用具有较强的抑制作用，但对其他转录因子如CEBP/CEBP等的选择性较差。 MYCi361/975是基于已知c-Myc抑制剂构建药效团虚拟筛选、并进一步优化获得的c-Myc抑制剂，结合位点与10074-G5类似，这类分子是目前验证的较为充分的Myc抑制剂，从靶点结合到细胞增殖抑制、周期阻滞、Myc蛋白降解、下游基因转录抑制，系统验证了分子功能。但是此类分子的溶解度极差，单药抗肿瘤活性不强，成药性较差，有待于进一步优化。 KSI-3716是通过EMSA筛选得到的干扰c-Myc-Max复合物与DNA相互作用的小分子，同样能够抑制c-Myc的转录活性、细胞增殖及诱导细胞周期阻滞。KSI-3716本身具有较强的心脏毒性，因此后续开发了WO2018021849专利结构类似的含硫类分子。 Insilico在JPM2023最新披露的管线中也有一款在研c-Myc抑制剂，用于治疗肝癌，目前还处于discovery阶段。 纵观这些早期研究中的小分子，整体上在靶标水平的结合能力不强，都在μM级别，细胞水平对多种c-Myc驱动肿瘤细胞增殖有抑制作用，但活性也不高，多数分子的溶解度、稳定性等理化性质不佳，这些分子多以iv或ip方式给药研究体内药效，在不同的肿瘤模型中，虽然展现出一定的抑瘤作用，但是效果一般，并未见到能消退肿瘤，多数分子的成药性较差。 总结 c-Myc在多种肿瘤中表达异常，应用前景广阔。目前c-Myc直接抑制剂和间接抑制剂的开发尚处于早期阶段，已经发现的小分子抑制剂的成药性差，不足以支撑临床研究，亟待发现成药性更优的抑制剂。无序蛋白的药物开发有很大困难，许多biotech/AI制药公司把c-Myc这类难成药靶点作为管线开发，利用自家的平台优势，挑战难成药靶点药物的开发，一定会推动c-Myc抑制剂的研究进展。 不过换个角度来看，虽然c-Myc在70%的肿瘤中表达失调，但是异常表达的c-Myc是肿瘤的直接驱动因素还是肿瘤发生发展的一个表现？开发c-Myc抑制剂能否在临床上对多种c-Myc失调的肿瘤有治疗效果？c-Myc抑制剂开发的高难度是否与其临床价值所匹配？相信随着越来越多的企业开始关注c-Myc抑制剂的开发，上述问题终将得到解答。 参考文献 [1] Mullard A. 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