# Research Pulse — 2026-09-05 _Generated 2026-09-05 07:30 KST · AI × Bioresearch Daily synthesis_ Brown Biotech daily research pulse — multi-front synthesis across CRISPR / gene editing, AI drug discovery, protein AI, single-cell foundation models, FDA/regulatory, and spatial transcriptomics. Sources: company filings, peer-reviewed journals, FDA guidance, conference disclosures. --- ### Post 1: 오늘의 전체 흐름 🧬 **AI × Bioresearch Daily — 9월 5일 (토요일) · Diversifying(다양화 Day)** 9/5(다양화) → 9/4(전이) → 9/3(집적) → 9/2(성숙) → 9/1(교정) → 8/31(수렴) → 8/30(매핑) → 8/29(평가) → ... → 7/14(가속). Transitioning(9/4)된 기술들이 **새로운 적용 영역과 방식으로 다양화(Diversifying)하는 날** — Prime editing PE-eVLPs로 in vivo transient delivery实现, 눈 질환 모델에서 부분적 시력 회복 확인(An Nature Biotechnology 2024), Prime editing PE1→PE7 방법론 진화 체계적 분석, in vivo delivery 플랫폼별 전략 종합(Jong BMB Reports 2026), PertAdapt으로 single-cell foundation model의 perturbation prediction에서 condition-sensitive adaptation 구현, 7개 perturbation dataset에서 FM baseline 능가 확인(Bai Bioinformatics 2026), Bach et al.으로 CAR-T·TIL convergence paradigm 재확인, lifileucel(TIL, melanoma) · afamitresgene(TCR, synovial sarcoma) FDA 승인 2개确认 + 3단계 barrier framework(Access·Recognition·Persistence) 체계화(Cancer Cell International 2026), Cai et al.로 AI preclinical drug discovery의 translational hurdles 종합, data quality · model interpretability · patient heterogeneity · regulatory adaptation 체계 분석(Pharmaceuticals 2026), Shahzad et al.으로 FDA CGT 38개 through 2025 전수 분석, 73.4% postmarketing requirement + 17.9% primary efficacy 문제 재確認(Oo + Shahzad Clinical Pharmacology 2026)이 동시에 진행된다. Diversifying은 Transitioning의 다음 단계다. 오늘 6개 전선은 **전이된 기술들이 새로운 적용 영역과 방식으로 다양화하는 날**이다. *출처: [An PE-eVLPs Prime Editing Nature Biotechnology 2024](https://consensus.app/papers/details/6b8d23d2890a5bef8732ed76db9ed0b3/), [Jong Prime Editing Updates BMB Reports 2026](https://consensus.app/papers/details/11216802c06153dca45549e3d4ff98f5/), [Bai PertAdapt Bioinformatics 2026](https://consensus.app/papers/details/1b9008e455eb5a8099daee1c1036700a/), [Bach CAR-T TIL Convergence Cancer Cell International 2026](https://consensus.app/papers/details/d4d687ec1a6250e9a6f0c2bcca81592f/), [Cai AI Preclinical Drug Discovery Pharmaceuticals 2026](https://consensus.app/papers/details/9db7efc33cb250c4b08b448e73fcdd17/), [Shahzad FDA CGT Assessment Clinical Pharmacology Therapeutics 2026](https://consensus.app/papers/details/24705d5037e85bcb9d9863f1aabca040/), [Oo FDA Gene Therapy Approvals Journal Clinical Pharmacology 2026](https://consensus.app/papers/details/181658e93c825ea581a0664845a98d12/)* *#DiversifyingDay #An #NatureBiotechnology #PEeVLP #PrimeEditing #InVivo #TransientDelivery #Retina #VisualFunction #Jong #BMBReports #PE7 #PrimeEditingEvolution #Bai #PertAdapt #ConditionSensitive #Bioinformatics #PerturbationPrediction #FoundationModel #Bach #CancerCellInternational #CART #TIL #Convergence #lifileucel #afamitresgene #SolidTumor #3StageBarrier #Access #Recognition #Persistence #Cai #Pharmaceuticals #TranslationalHurdles #DataQuality #Interpretability #Shahzad #ClinicalPharmacologyTherapeutics #FDA #CGT #38Approvals #Postmarketing #17Percent #PrimaryEfficacy #Oo #JournalClinicalPharmacology #PlatformAligned #Diversifying #Transitioning #Industrialization #ClinicalTranslation #GeneEditing #DeliveryPlatform #PerturbationAtlas #SingleCell #FoundationModel #CellTherapy #SolidTumor #Immunotherapy* --- ### Post 2: CRISPR & Gene Editing — PE-eVLPs In Vivo Transient Delivery와 Prime Editing PE1→PE7 방법론 진화 🪄 **[CRISPR/Gene Editing] Prime editing engineered virus-like particles(PE-eVLPs)로 in vivo transient delivery實現, 눈 질환 모델에서 부분적 시력 회복 확인(An Nature Biotechnology 2024) + Prime editing PE1→PE7 방법론 진화 체계적 분석, in vivo delivery 플랫폼별 전략 종합(Jong BMB Reports 2026) + 136건 CRISPR trial + 36건 in vivo non-viral vector 전환 재確認(Lummerstorfer Drug Delivery 2026): CRISPR의 Diversifying(다양화) 동시 확인** CRISPR 유전자 편집 기술의**다양화(Diversifying)**가 출처에서 동시에 확인된다[1][2][3]. **PE-eVLPs: Virus-Like Particles로 Prime Editor In Vivo Transient Delivery[1]** Meirui An et al.(Nature Biotechnology 2024)의 연구는 prime editor engineered virus-like particles(PE-eVLPs)를 제시한다. **Prime editing의 장점**: double-strand breaks 없이 정확한 genomic substitutions, insertions, deletions 가능 — uncontrolled editing outcomes(混合物, large deletions, chromosomal rearrangements) 회피. **eVLP 혁신**: Cas9 nickase + reverse transcriptase fusion protein + pegRNA + nicking sgRNA를 transient ribonucleoprotein complexes로 전달. **v3·v3b PE-eVLPs**: 기존 base editor eVLP 대비 human cells에서 65-170배 높은 editing efficiency. **遗传性 눈 질환 모델**: 두 종류의 마우스 모델에서 단일 injection. **시력 회복**: therapeutically relevant levels의 prime editing + protein expression restoration + partial visual function rescue. **안전성 우위**: off-target editing 위험 감소 + oncogenic transgene integration 가능성 배제. 핵심: PE-eVLPs로 prime editing의 in vivo transient delivery 실현 — CRISPR Diversifying. **Prime Editing: PE1→PE7 방법론 진화와 In Vivo Delivery 전략[2]** U. Jong et al.(BMB Reports 2026)의 review는 prime editing의 방법론 진화와 in vivo delivery 전략을 종합한다. **PE1→PE7 진화**: editing efficiency 향상 + editable target range 확대 + large genomic regions correction 가능. **Delivery platforms**: viral vectors, lipid nanoparticles, virus-like particles, electroporation. **In vivo 적용 분야**: genetic blindness, metabolic disorders, cardiovascular diseases. **핵심 메시지**: PE1→PE7로 prime editing의 산업화 가능성 확대 + platform별 delivery 전략 체계화 — CRISPR Diversifying. **CRISPR Clinical Trials: 136건 진행 + Non-Viral 전환 가속[3]** M. Lummerstorfer et al.(Drug Delivery 2026)의 review: 136건 CRISPR trials 진행 중(2025년 12월 기준), 36건 in vivo non-viral vector로의 전환 확인. **핵심**: in vivo CRISPR therapy에서 non-viral vectors 산업적 전환 가속 — CRISPR Diversifying. **9월 4일 Transitioning vs 오늘의 차별점**: 9/4가 **Yang MedComm**으로 **ionizable lipid nanoparticles 93% knockdown**을, **Makhijani Discover Nano**으로 **base/prime/epigenome editing methodology 종합**을 제시했다면, 오늘은 **An Nature Biotechnology**으로 **PE-eVLPs로 prime editing의 in vivo transient delivery 실현**을(새 delivery 플랫폼), **Jong BMB Reports**으로 **PE1→PE7 방법론 진화 + platform별 delivery 전략 체계화**를(방법론 Diversifying), **Lummerstorfer Drug Delivery**으로 **136 trial + non-viral 전환 재確認**을 동시에 제시한다. Transitioning(9/4) → CRISPR Diversifying(9/5). 실무 함의: 유전자 치료 전달팀에서 An et al.의 PE-eVLPs 접근법을 분석하여 prime editing in vivo delivery pipeline 전략을 수립해야 한다. *출처: [An PE-eVLPs Prime Editing Nature Biotechnology 2024](https://consensus.app/papers/details/6b8d23d2890a5bef8732ed76db9ed0b3/), [Jong Prime Editing Updates BMB Reports 2026](https://consensus.app/papers/details/11216802c06153dca45549e3d4ff98f5/), [Lummerstorfer Non-Viral CRISPR Clinical Trials Drug Delivery 2026](https://consensus.app/papers/details/747d35e9e5bc5ca9a9854998c7acd326/)* *#CRISPR #GeneEditing #An #NatureBiotechnology #PEeVLP #PrimeEditing #VLP #InVivo #TransientDelivery #VisualFunction #Retina #GeneticBlindness #Jong #BMBReports #PE7 #PrimeEditingEvolution #InVivoDelivery #Lummerstorfer #DrugDelivery #136Trials #NonViral #HitAndRun #OffTarget #TransgeneIntegration #DiversifyingDay #Ribonucleoprotein #Cas9Nickase #ReverseTranscriptase #pegRNA #GeneticDisease #TherapeuticIndex #Biodegradable #ViralVector #LipidNanoparticle #Electroporation #Industrialization #ClinicalTrials #GeneCorrection #PrecisionMedicine* --- ### Post 3: AI 신약 — AI Preclinical Drug Discovery의 Translational Hurdles 종합과 Multimodal AI·Digital Twins 전망 💊 **[AI 신약] AI preclinical drug discovery의 translational hurdles 체계적 분석, data quality · model interpretability · patient heterogeneity · regulatory adaptation 종합(Cai Pharmaceuticals 2026) + AI drug discovery의 Phase IIa/Phase I 이중성 재확인(Mao Frontiers Pharmacology 2026) + fully AI-discovered drug 0개 승인 현실 + AI-originated candidates 임상 진행 확인(Seyhan Critical Reviews Oncology Hematology 2026): AI drug discovery의 Diversifying(다양화) 동시 확인** AI-Driven Drug Discovery의**다양화(Diversifying)**가 출처에서 동시에 확인된다[4][5][6]. **AI Preclinical Drug Discovery: Translational Hurdles 체계적 분석[4]** Meng Cai et al.(Pharmaceuticals 2026)의 review는 AI의 preclinical drug discovery 적용에서 임상 이행까지의 translational hurdles를 체계적으로 분석한다. **적용 분야**: drug-target interaction prediction, structure prediction, de novo design, virtual screening, drug repurposing, ADMET forecasting. **핵심 hurdles 4가지**: (i) data quality — 불완전하거나 편향된 training data, (ii) model interpretability — black-box nature로 regulatory confidence 부족, (iii) patient heterogeneity — 임상시험 participants와 training data 간 불일치, (iv) regulatory adaptation — 기존 framework로 AI model 평가 어려움. **미래 방향**: multimodal AI · digital twins · closed-loop automation. **핵심**: AI preclinical drug discovery의 4대 translational hurdle 체계적 분석 — AI drug discovery Diversifying. **AI Drug Discovery: Phase IIa/Phase I 이중성 재확인[5]** Zihan Mao et al.(Frontiers in Pharmacology 2026)의 review: AI-discovered candidate가 Phase IIa에서 encouraging efficacy 달성, 다른 candidate는 Phase I에서 safety signals로 정체. **Toxicity assessment**: AI가 adverse effects를 earlier에 포착 가능. **Multimodal data + explainable AI**: transparency · regulatory confidence를 위한 필수 조건. 핵심: AI drug discovery Phase IIa/I 이중성 재확인 — AI drug discovery Diversifying. **AI Drug Discovery: 현주소 + AI-Originated Candidates 임상 진행[6]** A. Seyhan et al.(Critical Reviews in Oncology/Hematology 2026): fully AI-discovered/designed drug 0개 승인 현실, AI-originated candidates 임상 개발 진행 중. **AlphaFold · MoleR · PocketCrafter · single-cell FMs · LLMs** 활용. 핵심: AI drug discovery 현주소 종합 — Diversifying. **9월 4일 Transitioning vs 오늘의 차별점**: 9/4가 **Singh Current Opinion**으로 **AlphaFold-like model의 protein-ligand data gap**을, **Adhao Innovative Insights**으로 **AI-pharma partnership 구조화**를 제시했다면, 오늘은 **Cai Pharmaceuticals**으로 **AI preclinical drug discovery의 4대 translational hurdle 체계적 분석**을(data quality · interpretability · heterogeneity · regulatory), **Mao Frontiers Pharmacology**으로 **Phase IIa/I 이중성 Diversifying**을, **Seyhan Critical Reviews**으로 **fully AI-discovered drug 0개 + AI-originated 임상 진행 현황**을(현실 인식 Diversifying) 동시에 제시한다. Transitioning(9/4) → AI drug discovery Diversifying(9/5). 실무 함의: AI drug discovery 협업팀에서 Cai et al.의 translational hurdle 분석을 참조하여 AI candidate clinical translation strategy를 수립해야 한다. *출처: [Cai AI Preclinical Drug Discovery Pharmaceuticals 2026](https://consensus.app/papers/details/9db7efc33cb250c4b08b448e73fcdd17/), [Mao AI Drug Discovery Frontiers Pharmacology 2026](https://consensus.app/papers/details/ca5321af0bb656e095b6108766231764/), [Seyhan AI Drug Discovery Critical Reviews Oncology Hematology 2026](https://consensus.app/papers/details/0e6b685fd2be56fb8a814115873a73d5/)* *#AI #DrugDiscovery #Cai #Pharmaceuticals #TranslationalHurdles #DataQuality #Interpretability #PatientHeterogeneity #RegulatoryAdaptation #Mao #FrontiersPharmacology #PhaseIIa #PhaseI #SafetySignals #Seyhan #CriticalReviews #OncologyHematology #ZeroAIApproval #AIOriginated #ClinicalTrials #MultimodalAI #DigitalTwins #ClosedLoopAutomation #DiversifyingDay #DeNovoDesign #VirtualScreening #ADMET #DrugRepurposing #AlphaFold #MoleR #PocketCrafter #FoundationModel #LLM #ExplainableAI #Transparency #RegulatoryScience #ClinicalTranslation #Pipeline #AttritionRate* --- ### Post 4: 단백질 AI — AlphaFold CDK20 Inhibitor Discovery와 AI-Based Protein Structure Drug Discovery Impacts 🔬 **[단백질 AI] AlphaFold-powered drug discovery로 CDK20 small molecule inhibitor 30일 내 discovery, Kd 9.2μM → 566.7nM까지 potency 향상(Ren Chemical Science 2023) + AI-based protein structure prediction의 drug discovery 적용 Impact와 Challenges 종합(Schauperl Journal Chemical Information 2022) + AlphaFold2 applications in biology and medicine 종합(Yang Signal Transduction 2023): 단백질 AI의 Diversifying(다양화) 동시 확인** 단백질 AI의**다양화(Diversifying)**가 출처에서 동시에 확인된다[7][8][9]. **AlphaFold-Powered Drug Discovery: CDK20 Inhibitor 30일 Discovery[7]** Feng Ren et al.(Chemical Science 2023)의 연구는 AlphaFold-powered end-to-end AI drug discovery engine를 제시한다. **PandaOmics + Chemistry42**: target identification → hit identification까지. **대상**: cyclin-dependent kinase 20(CDK20), hepatocellular carcinoma(HCC) 치료 표적. **AlphaFold 사용**: experimental structure 부재 표적의 structure prediction. **1차 screening**: 7개 화합물 synthesis 후 Kd 9.2 ± 0.5 μM. **2차 최적화**: ISM042-2-048, Kd 566.7 ± 256.2 nM. **세포 실험**: HCC cell line(Huh7) IC50 208.7 nM, control cell line(HEK293) IC50 1706.7 nM — selectivity 확인. **기간**: target selection에서 hit identification까지 30일. **핵심**: AlphaFold로 experimental structure 없는 표적의 inhibitor discovery 실현 — 단백질 AI Diversifying. **AI-Based Protein Structure Prediction: Drug Discovery Impact and Challenges[8]** Michael Schauperl et al.(Journal of Chemical Information and Modeling 2022)의 review는 AlphaFold2의 drug discovery 적용 impact와 challenges를 분석한다. **50년 난제 해결**: protein structure prediction의 breakthrough. **SBDD 적용**: drug discovery에서 protein structure 활용. **남은 도전**: domain-domain orientations, conformational states, posttranslational modifications, protein binding partners의 influence. **향후 방향**: advanced ML methods의 pharmaceutical industry 적용 확대. 핵심: AlphaFold2 drug discovery 적용 impact + challenges 종합 — 단백질 AI Diversifying. **AlphaFold2 Applications: Biology and Medicine 종합[9]** Zhenyu Yang et al.(Signal Transduction and Targeted Therapy 2023)의 review는 AlphaFold2의 biology and medicine applications를 종합한다. **200M+ proteins**: AlphaFold predicted structures 공개. **적용 분야**: drug discovery, protein design, protein function prediction. **한계**: conformational changes, dynamic interactions modeling의 어려움. 핵심: AlphaFold2 applications 종합 — 단백질 AI Diversifying. **9월 4일 Transitioning vs 오늘의 차별점**: 9/4가 **Zhang Biotechnology Advances**으로 **AlphaFold peptide conformational dynamics 한계 + MD 보완**을, **Fadini Nature Methods**으로 **ROCKET cryo-EM-AF2 통합**을 제시했다면, 오늘은 **Ren Chemical Science**으로 **AlphaFold-powered CDK20 inhibitor 30일 discovery**를(실제 drug discovery 적용 사례), **Schauperl Journal Chemical Information**으로 **AlphaFold2 drug discovery impact + challenges 체계적 분석**를(계산-실험 통합), **Yang Signal Transduction**으로 **AlphaFold2 applications 종합**을(범용 적용) 동시에 제시한다. Transitioning(9/4) → 단백질 AI Diversifying(9/5). 실무 함의: 전산단백질 설계팀에서 Ren et al.의 AlphaFold-powered drug discovery 접근법을 분석하여 target-based drug discovery strategy를 수립해야 한다. *출처: [Ren AlphaFold CDK20 Inhibitor Chemical Science 2023](https://consensus.app/papers/details/09302901f5bf5be0b993976de891c60f/), [Schauperl AlphaFold Drug Discovery Journal Chemical Information 2022](https://consensus.app/papers/details/f8020225d4dc5b97a38eee53c8359a0c/), [Yang AlphaFold2 Biology Medicine Signal Transduction 2023](https://consensus.app/papers/details/53e13aa17e195388a8afb65dffe7312a/)* *#ProteinAI #AlphaFold #Ren #ChemicalScience #CDK20 #Inhibitor #HCC #HepatocellularCarcinoma #PandaOmics #Chemistry42 #30Days #Kd566nM #Schauperl #JournalChemicalInformation #DrugDiscovery #SBDD #ProteinStructure #AlphaFold2 #Yang #SignalTransduction #TargetedTherapy #200Million #ProteinStructure #DiversifyingDay #DrugDesign #ProteinDesign #EnzymeEngineering #ConformationalChanges #DynamicInteractions #ComputationalBiology #StructuralBiology #Biotechnology #MedicinalChemistry #HitIdentification #LeadOptimization #Selectivity #IC50 #Kd* --- ### Post 5: Single-Cell Multi-Omics Foundation Model — PertAdapt Condition-Sensitive Adaptation과 Perturbation Response Prediction Benchmark 🬬 **[Single-Cell Multi-Omics Foundation Model] PertAdapt으로 single-cell foundation model의 perturbation prediction에서 condition-sensitive adaptation 구현, 7개 dataset에서 FM baseline 능가(Bai Bioinformatics 2026) + 27 methods 29 datasets 6 metrics benchmark로 perturbation response prediction의 generalizability 체계적 평가(Wei Nature Methods 2025) + Single-cell perturbation modeling methods comprehensive review로 CRISPR · compound perturbation modeling 동시 분석(Gavriilides Computational Structural Biotechnology 2024): foundation model의 Diversifying(다양화) 동시 확인** Single-Cell Foundation Model의**다양화(Diversifying)**가 출처에서 동시에 확인된다[10][11][12]. **PertAdapt: Condition-Sensitive Adaptation for Single-Cell Foundation Models[10]** Ding Bai et al.(Bioinformatics 2026)의 연구는 PertAdapt framework를 제시한다. **문제**: 기존 single-cell foundation models가 unseen genetic perturbations(knockouts, variants)에 대한 transcriptional responses 예측에서 pretrained knowledge를 효과적으로 transfer하지 못함. **PertAdapt 혁신**: (i) plugin perturbation adapter — gene-similarity-masked attention mechanism으로 perturbation conditions + contextualized representations 통합. (ii) adaptive loss — perturbation-sensitive genes를 동적으로 reweighting. **평가**: 7개 perturbation datasets(single-gene · double-gene 모두). **성과**: non-pretrained · FM baselines 모두 consistent outperform. **강점**: multiplexed gene interactions modeling, limited-data regime generalization, backbone size robustness. 핵심: PertAdapt으로 condition-sensitive adaptation 실현 — foundation model Diversifying. **Perturbation Response Prediction Benchmark: 27 Methods · 29 Datasets · 6 Metrics[11]** Zhiting Wei et al.(Nature Methods 2025)의 연구는 perturbation response prediction methods의 comprehensive benchmark를 수행한다. **대상**: 27 methods(including foundation models), 29 datasets, 6 complementary performance metrics. **평가 scenario**: multiple generalization scenarios — diverse · previously unseen cellular contexts · perturbation scenarios. **주요 발견**: foundation models의 claimed performance에 대한 의문 제기. **실제 generalizability**: cellular context embedding approaches 필요. **실무 guidance**: method selection을 위한 실질적 지침 제공. 핵심: 27 methods benchmark로 perturbation response prediction의 실제 generalizability 평가 — foundation model Diversifying. **Single-Cell Perturbation Modeling: Methods Comprehensive Review[12]** George I. Gavriilidis et al.(Computational and Structural Biotechnology Journal 2024)의 review는 single-cell perturbation modeling methods를 종합한다. **perturbation modeling 목적**: disease onset · molecular knock-outs · external stimulants의 효과를 comprehensive하게 파악. ** perturbation technologies**: CRISPR, compounds. **computational methods**: classical statistical inference → ML/DL architectures → gene regulatory networks-based approaches → ensemble learning. **foundation models trend**: large foundational models의 부상. **미래 방향**: perturbation atlases, multi-omics · spatial datasets, causal ML for interpretability, multi-task learning. 핵심: perturbation modeling methods 종합 — foundation model Diversifying. **9월 4일 Transitioning vs 오늘의 차별점**: 9/4가 **Demir ArXiv**으로 **CellxPert MCMC steering**을, **Li ArXiv**으로 **25 dataset benchmark**를 제시했다면, 오늘은 **Bai Bioinformatics**으로 **PertAdapt condition-sensitive adaptation**을(새 adaptation 방법론), **Wei Nature Methods**으로 **27 methods · 29 datasets benchmark**를(더 큰规模的 평가), **Gavriilides Computational Structural Biotechnology**으로 **perturbation modeling methods comprehensive review**를(방법론 Diversifying) 동시에 제시한다. Transitioning(9/4) → Foundation model Diversifying(9/5). 실무 함의: 단일세포 연구팀에서 Bai et al.의 PertAdapt 접근법을 분석하여 perturbation prediction method selection strategy를 수립해야 한다. *출처: [Bai PertAdapt Bioinformatics 2026](https://consensus.app/papers/details/1b9008e455eb5a8099daee1c1036700a/), [Wei Perturbation Benchmark Nature Methods 2025](https://consensus.app/papers/details/2252f23023815170ad69618a33936f87/), [Gavriilides Perturbation Modeling Computational Structural Biotechnology 2024](https://consensus.app/papers/details/6485fdef49c25e899dad041c23c49f3e/)* *#SingleCell #FoundationModel #Bai #PertAdapt #Bioinformatics #ConditionSensitive #Adaptation #PerturbationAdapter #GeneSimilarityMasked #Attention #AdaptiveLoss #PerturbationPrediction #Wei #NatureMethods #Benchmark #27Methods #29Datasets #6Metrics #Generalizability #FoundationModel #Gavriilides #ComputationalStructuralBiotechnology #CRISPR #CompoundPerturbation #MachineLearning #GeneRegulatoryNetworks #EnsembleLearning #DiversifyingDay #FineTuning #ZeroShot #Multiplexed #GeneInteraction #LimitedData #BackboneSize #CellularContext #Embeddings #PerturbationAtlas #MultiOmics #Spatial #CausalML #Interpretability* --- ### Post 6: FDA/규제 — CAR-T·TIL Convergence Paradigm과 FDA CGT 38개 Postmarketing Evidence 문제 📋 **[FDA/규제] CAR-T·TIL convergence paradigm 재확인, lifileucel(TIL, melanoma) · afamitresgene(TCR, synovial sarcoma) FDA 승인 2개 확인 + 3단계 barrier framework(Access·Recognition·Persistence) 체계화(Bach Cancer Cell International 2026) + FDA CGT 38개 through 2025 전수 분석, 73.4% postmarketing requirement + 17.9% primary efficacy endpoint 문제 재確認(Shahzad Clinical Pharmacology Therapeutics 2026) + FDA gene therapy approvals의 platform-aligned risk-based framework 구체화(Oo Journal Clinical Pharmacology 2026): FDA/규제의 Diversifying(다양화) 동시 확인** FDA/규제의**다양화(Diversifying)**가 출처에서 동시에 확인된다[13][14][15]. **CAR-T·TIL Convergence Paradigm: 3-Stage Barrier Framework[13]** Duc-Hiep Bach et al.(Cancer Cell International 2026)의 review는 CAR-T와 TIL therapy의 convergence paradigm을 분석한다. **2024년 milestone**: lifileucel FDA 승인 — 고형종양 최초 세포 치료 승인. **CAR-T vs TIL**: hematologic malignancies에서는 CAR-T 성공, solid tumors에서는 도전. **3-stage barrier framework**: (i) Access — stromal · vascular barriers로 T-cell tumor infiltration 제한, (ii) Recognition — antigen heterogeneity · immune evasion으로 malignant cells 식별 어려움, (iii) Persistence — immunosuppressive TME 내에서 T-cell function 유지 어려움. **Convergence paradigm**: CAR-T와 TIL이 동일한 biology barriers에 대해 complementary engineering · biological strategies로 대응. **Biomarker-guided patient selection**: tumor의 dominant biological barriers 기반으로 therapeutic selection. 핵심: CAR-T·TIL convergence + 3-stage barrier framework 체계화 — FDA 규제 Diversifying. **FDA CGT: 38개 Through 2025 Postmarketing Evidence 문제[14]** Mahnum Shahzad et al.(Clinical Pharmacology & Therapeutics 2026): FDA CGT 38개 through December 31, 2025 전수 분석. **Postmarketing requirements**: 73.4%가 ≥1 postmarketing requirement/commitment — 총 52 studies. **문제**: 17.9%만 primary clinical efficacy endpoint를 가진 postmarketing study 보유. **대부분 ongoing**: 현재 진행 중인 studies. 핵심: FDA CGT의 postmarketing requirement 문제 재확인 — FDA 규제 Diversifying. **FDA Gene Therapy Approvals: Platform-Aligned Risk-Based Framework[15]** C. Oo et al.(Journal of Clinical Pharmacology 2026): FDA gene therapy approvals의 진화와 future directions 분석. **최근 approvals 가속**: RNA-based agents, viral/non-viral in vivo platforms, ex vivo genetically modified cell therapies. **FDA initiatives**: plausible mechanism framework, CMC flexibility initiative, advanced manufacturing technologies program. **AI/ML-enabled analytics**: dose selection, safety evaluation, durability prediction 지원. 핵심: FDA gene therapy의 platform-aligned risk-based framework 구체화 — FDA 규제 Diversifying. **9월 4일 Transitioning vs 오늘의 차별점**: 9/4가 **Moreno Lancet Regional Health Europe**으로 **solid tumour cell therapy European milestone + FDA 승인 2개**를, **Shahzad Clinical Pharmacology Therapeutics**으로 **CGT postmarketing requirement 문제**를 제시했다면, 오늘은 **Bach Cancer Cell International**으로 **CAR-T·TIL convergence paradigm + 3-stage barrier framework**를(새 paradigm), **Shahzad Clinical Pharmacology Therapeutics**으로 **CGT postmarketing 문제 재確認**을(지속적 문제), **Oo Journal Clinical Pharmacology**으로 **platform-aligned risk-based framework 구체화**를(규제 Diversifying) 동시에 제시한다. Transitioning(9/4) → FDA 규제 Diversifying(9/5). 실무 함의: 규제 기획팀에서 Bach et al.의 convergence paradigm 분석을 참조하여 cell therapy regulatory strategy를 수립해야 한다. *출처: [Bach CAR-T TIL Convergence Cancer Cell International 2026](https://consensus.app/papers/details/d4d687ec1a6250e9a6f0c2bcca81592f/), [Shahzad FDA CGT Assessment Clinical Pharmacology Therapeutics 2026](https://consensus.app/papers/details/24705d5037e85bcb9d9863f1aabca040/), [Oo FDA Gene Therapy Approvals Journal Clinical Pharmacology 2026](https://consensus.app/papers/details/181658e93c825ea581a0664845a98d12/)* *#FDA #Regulatory #Bach #CancerCellInternational #CART #TIL #Convergence #Paradigm #3StageBarrier #Access #Recognition #Persistence #lifileucel #afamitresgene #Melanoma #SynovialSarcoma #SolidTumor #Biomarker #PatientSelection #Shahzad #ClinicalPharmacologyTherapeutics #CGT #38Approvals #Postmarketing #Requirement #17Percent #PrimaryEfficacy #Oo #JournalClinicalPharmacology #PlatformAligned #RiskBased #PlausibleMechanism #CMC #AdvancedManufacturing #AI #ML #DurableBenefit #EquitableAccess #DiversifyingDay #CellTherapy #GeneTherapy #RegulatoryScience #Manufacturing #Immunotherapy #Hematologic #SolidTumor #ImmuneEvasion #TME #StromalBarrier #VascularBarrier #AntigenHeterogeneity* --- ### Post 7: Spatial Transcriptomics — Pan-Cancer 12 Cancer Types 56 LCPs 13 Niches와 TME Profiling의 Clinical Translation 🗺️ **[Spatial Transcriptomics] Pan-cancer 12 cancer types 373 samples에서 56 LCPs · 13 recurrent niches 확인, Niche_4(poor prognosis) · Niche_11(better survival) biomarker 체계 재확인(Li Cell Reports Medicine 2026) + Spatial omics로 dynamic TME profiling 가능성 분석, cross-platform integration · data standardization · computational scalability 도전 과제 확인(Nguyen Clinical Translational Immunology 2026) + Pan-cancer 131 tumor sections 6 cancer types spatial heterogeneity 분석, 3D tumor structure reconstruction 확인(Mo Nature 2024): spatial transcriptomics의 Diversifying(다양화) 동시 확인** Spatial Transcriptomics의**다양화(Diversifying)**가 출처에서 동시에 확인된다[16][17][18]. **Pan-Cancer Spatial TME: 12 Cancer Types · 56 LCPs · 13 Niches[16]** Jiarong Li et al.(Cell Reports Medicine 2026): 12 cancer types · 373 samples에서 pan-cancer spatial transcriptomic analysis 수행. **56개 Local Cellular Programs(LCPs)**: tumor spatial microenvironment(TSME) 내에서. **13개 recurrent niches**: niche-shared · niche-specific interactions. **Niche_4(종양-M2 macrophage colocalization)**: poor prognosis + immunotherapy resistance. **Niche_11(면역세포와 colocalization)**: better survival + treatment response. **종양세포 + macrophages의 위치 의존성**: gene expression이 위치에 따라 결정. 핵심: Pan-cancer 12 types · 56 LCPs · 13 niches 통합 + Niche_4/Niche_11 prognostic biomarkers 체계 — spatial TME Diversifying. **Spatial Omics for Dynamic TME Profiling: Clinical Translation 도전[17]** Hao Nguyen et al.(Clinical & Translational Immunology 2026)의 review는 spatial omics의 TME profiling 적용을 분석한다. **ST · SP technologies**: tumor microenvironment에서 RNA · protein distributions mapping. **dynamic interactions**: healthy · diseased tissues의 physiological processes 분석. **AI 통합**: biological · translational applications를 위한 AI의 잠재력. **도전 과제 3가지**: (i) cross-platform integration — 서로 다른 플랫폼 간 데이터 통합 어려움, (ii) data standardization — 방법론 차이, (iii) computational scalability — high-plex single-cell datasets의 확장성 문제. **미래 방향**: standardized workflows, cost-effective pipelines, rigorous validation, explainable AI models. 핵심: spatial omics의 TME profiling 가능성 + clinical translation 도전 과제 분석 — spatial TME Diversifying. **3D Tumor Structure: 131 Tumor Sections · 6 Cancer Types[18]** Chia-Kuei Mo et al.(Nature 2024): 131 tumor sections · 78 cases · 6 cancer types에서 Visium spatial transcriptomics 분석. ** tumor microregions**: spatially distinct cancer cell clusters — stromal components로 분리. ** spatial subclones**: genetic alterations로 grouping. **3D tumor structure reconstruction**: 48 serial sections co-registering. ** immune neighborhoods**: unsupervised deep learning으로 immune hot/cold neighborhoods 식별. 핵심: 3D tumor structure reconstruction으로 spatial tumor evolution 분석 — spatial TME Diversifying. **9월 4일 Transitioning vs 오늘의 차별점**: 9/4가 **Zhang Cell Reports Medicine**으로 **SCLC 600K cells + endothelial-immune crosstalk**을, **Gao Nature Communications**으로 **gastric cancer LAR biomarker**을 제시했다면, 오늘은 **Li Cell Reports Medicine**으로 **12 cancer types · 56 LCPs · 13 niches 체계 재확인**을(통합 분석), **Nguyen Clinical & Translational Immunology**으로 **spatial omics TME profiling의 clinical translation 도전 3가지**를(표준화 과제), **Mo Nature**으로 **3D tumor structure reconstruction**을(공간적 분석 확대) 동시에 제시한다. Transitioning(9/4) → Spatial transcriptomics Diversifying(9/5). 실무 함의: spatial transcriptomics 연구팀에서 Nguyen et al.의 clinical translation 도전 과제 분석을 참조하여 spatial omics workflow standardization strategy를 수립해야 한다. *출처: [Li Pan-Cancer Spatial TME Cell Reports Medicine 2026](https://consensus.app/papers/details/f420ab985e1257e18352cac9e0c425cf/), [Nguyen Spatial Omics TME Clinical & Translational Immunology 2026](https://consensus.app/papers/details/1b906e5abaaf5e789aaabde458127265/), [Mo Tumor Evolution 3D Space Nature 2024](https://consensus.app/papers/details/ea9b582ed3605dfe9c3d452b2fddf227/)* *#SpatialTranscriptomics #Li #CellReportsMedicine #PanCancer #12CancerTypes #56LCPs #13Niches #Niche4 #Niche11 #Macrophages #Prognosis #Survival #Nguyen #ClinicalTranslationalImmunology #SpatialOmics #TME #Profiling #CrossPlatform #Integration #Standardization #Scalability #AI #ExplainableAI #Mo #Nature #3D #TumorStructure #Reconstruction #SpatialSubclones #CopyNumber #ImmuneHotCold #DeepLearning #CODEX #Visium #DiversifyingDay #SpatialAtlas #TME #Microenvironment #Biomarker #Immunotherapy #Resistance #PrecisionOncology #SpatialHeterogeneity #SolidTumor #Metastasis #TertiaryLymphoid #TLS #CancerEcosystem* --- ### Post 8: 오늘의 요약 — Diversifying(다양화 Day) 핵심 정리 🧬 **오늘 6개 영역 핵심 요약:** 9/5(Diversifying)의 핵심: Transitioning(9/4)된 기술들이 **새로운 적용 영역과 방식으로 다양화(Diversifying)하는 날**이었다. **1. PE-eVLPs Prime Editing In Vivo Transient Delivery(An Nature Biotechnology 2024)**: v3·v3b PE-eVLPs로 65-170배 높은 editing efficiency + genetic blindness 마우스 모델에서 partial visual function rescue — CRISPR Diversifying. [An Nature Biotechnology 2024] **2. PertAdapt Condition-Sensitive Adaptation(Bai Bioinformatics 2026) + 27 Methods Benchmark(Wei Nature Methods 2025)**: PertAdapt으로 gene-similarity-masked attention + adaptive loss로 FM baseline consistent outperform + 27 methods · 29 datasets · 6 metrics benchmark로 foundation models의 실제 generalizability 평가 — foundation model Diversifying. [Bai Bioinformatics 2026; Wei Nature Methods 2025] **3. CAR-T·TIL Convergence Paradigm + 3-Stage Barrier Framework(Bach Cancer Cell International 2026)**: lifileucel(TIL) + afamitresgene(TCR) FDA 승인 2개 확인 + Access·Recognition·Persistence 3단계 barrier framework 체계화 + biomarker-guided patient selection — FDA 규제 Diversifying. [Bach Cancer Cell International 2026] **4. AI Preclinical Drug Discovery Translational Hurdles 4가지(Cai Pharmaceuticals 2026)**: data quality · model interpretability · patient heterogeneity · regulatory adaptation 4대 hurdle 체계적 분석 + multimodal AI · digital twins · closed-loop automation 미래 방향 제시 — AI drug discovery Diversifying. [Cai Pharmaceuticals 2026] **5. AlphaFold-Powered CDK20 Inhibitor 30일 Discovery(Ren Chemical Science 2023) + AlphaFold2 Drug Discovery Impact·Challenges(Schauperl Journal Chemical Information 2022)**: AlphaFold로 experimental structure 없는 CDK20 표적에서 30일에 Kd 566.7nM inhibitor discovery + drug discovery 적용 impact + challenges 종합 — 단백질 AI Diversifying. [Ren Chemical Science 2023; Schauperl Journal Chemical Information 2022] **6. Pan-Cancer 12 Cancer Types · 56 LCPs · 13 Niches 재확인(Li Cell Reports Medicine 2026) + Spatial Omics Clinical Translation 도전 3가지(Nguyen Clinical & Translational Immunology 2026) + 3D Tumor Structure Reconstruction(Mo Nature 2024)**: Niche_4/Niche_11 prognostic biomarkers 체계 + cross-platform integration · data standardization · computational scalability 도전 + 3D tumor structure reconstruction — spatial TME Diversifying. [Li Cell Reports Medicine 2026; Nguyen Clinical & Translational Immunology 2026; Mo Nature 2024] **9월 6일 전망**: Diversifying된 기술들의 새로운 적용 영역 확대 속도와 임상 이행 체계화가 핵심 변수. 특히 PE-eVLPs의 추가 질환 모델 적용, PertAdapt의 임상 데이터 integration, CAR-T·TIL convergence의 biomarker-guided patient selection 실용화, spatial omics의 standardized workflow 정착이 관전. *출처: [An PE-eVLPs Nature Biotechnology 2024](https://consensus.app/papers/details/6b8d23d2890a5bef8732ed76db9ed0b3/), [Jong Prime Editing BMB Reports 2026](https://consensus.app/papers/details/11216802c06153dca45549e3d4ff98f5/), [Lummerstorfer Non-Viral CRISPR Drug Delivery 2026](https://consensus.app/papers/details/747d35e9e5bc5ca9a9854998c7acd326/), [Cai AI Preclinical Pharmaceuticals 2026](https://consensus.app/papers/details/9db7efc33cb250c4b08b448e73fcdd17/), [Mao AI Drug Discovery Frontiers Pharmacology 2026](https://consensus.app/papers/details/ca5321af0bb656e095b6108766231764/), [Seyhan AI Drug Discovery Critical Reviews 2026](https://consensus.app/papers/details/0e6b685fd2be56fb8a814115873a73d5/), [Ren AlphaFold CDK20 Chemical Science 2023](https://consensus.app/papers/details/09302901f5bf5be0b993976de891c60f/), [Schauperl AlphaFold Drug Discovery JChemInfo 2022](https://consensus.app/papers/details/f8020225d4dc5b97a38eee53c8359a0c/), [Yang AlphaFold2 Signal Transduction 2023](https://consensus.app/papers/details/53e13aa17e195388a8afb65dffe7312a/), [Bai PertAdapt Bioinformatics 2026](https://consensus.app/papers/details/1b9008e455eb5a8099daee1c1036700a/), [Wei Perturbation Benchmark Nature Methods 2025](https://consensus.app/papers/details/2252f23023815170ad69618a33936f87/), [Gavriilides Perturbation Modeling ComputStructBioTech 2024](https://consensus.app/papers/details/6485fdef49c25e899dad041c23c49f3e/), [Bach CAR-T TIL Convergence Cancer Cell International 2026](https://consensus.app/papers/details/d4d687ec1a6250e9a6f0c2bcca81592f/), [Shahzad FDA CGT Clinical Pharmacology Therapeutics 2026](https://consensus.app/papers/details/24705d5037e85bcb9d9863f1aabca040/), [Oo FDA Gene Therapy Journal Clinical Pharmacology 2026](https://consensus.app/papers/details/181658e93c825ea581a0664845a98d12/), [Li Pan-Cancer TME Cell Reports Medicine 2026](https://consensus.app/papers/details/f420ab985e1257e18352cac9e0c425cf/), [Nguyen Spatial Omics Clinical & Translational Immunology 2026](https://consensus.app/papers/details/1b906e5abaaf5e789aaabde458127265/), [Mo 3D Tumor Structure Nature 2024](https://consensus.app/papers/details/ea9b582ed3605dfe9c3d452b2fddf227/)* *#DiversifyingDay #Summary #An #PEeVLP #PrimeEditing #VisualFunction #Jong #PE7 #Bai #PertAdapt #ConditionSensitive #PerturbationPrediction #Wei #Benchmark #27Methods #29Datasets #Gavriilides #PerturbationModeling #Bach #CART #TIL #Convergence #3StageBarrier #lifileucel #afamitresgene #Cai #TranslationalHurdles #DataQuality #Interpretability #Mao #PhaseIIa #Seyhan #ZeroAIApproval #Ren #AlphaFold #CDK20 #30Days #Schauperl #AlphaFold2 #Yang #Li #PanCancer #56LCPs #13Niches #Niche4 #Niche11 #Nguyen #SpatialOmics #Standardization #Mo #3D #TumorStructure #Diversifying #Transitioning #Industrialization #ClinicalTranslation #Diversification #GeneEditing #DeliveryPlatform #PerturbationAtlas #SingleCell #FoundationModel #CellTherapy #SolidTumor #Immunotherapy #Regulatory #PrecisionMedicine #SpatialBiology #TME #Biomarker #Prognosis #Survival* --- ## Sources [1] [An et al. — Engineered virus-like particles for transient delivery of prime editor ribonucleoprotein complexes in vivo. Nature Biotechnology 2024](https://consensus.app/papers/details/6b8d23d2890a5bef8732ed76db9ed0b3/) [2] [Jong et al. — Prime editing updates: technological evolution, methodological expansion, and delivery strategies for in vivo applications. BMB Reports 2026](https://consensus.app/papers/details/11216802c06153dca45549e3d4ff98f5/) [3] [Lummerstorfer et al. — Non-Viral CRISPR carriers: transient delivery with lasting effects. Drug Delivery 2026](https://consensus.app/papers/details/747d35e9e5bc5ca9a9854998c7acd326/) [4] [Cai et al. — From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation. Pharmaceuticals 2026](https://consensus.app/papers/details/9db7efc33cb250c4b08b448e73fcdd17/) [5] [Mao et al. — Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation. 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Clinical & Translational Immunology 2026](https://consensus.app/papers/details/1b906e5abaaf5e789aaabde458127265/) [18] [Mo et al. — Tumour evolution and microenvironment interactions in 2D and 3D space. Nature 2024](https://consensus.app/papers/details/ea9b582ed3605dfe9c3d452b2fddf227/)
Research Pulse·2026-09-05
Research Pulse — 2026-09-05
28 journals × 7 topics · fibrosis · OXPHOS · ferroptosis · sarcopenia · senescence
Raw data + scoring: Daily Tech Digest (separate feed)
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