# Research Pulse — 2026-10-14 _Generated 2026-10-14 07:32 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 — 10월 14일 (수요일) · Benchmarking(평가) Day** 10/14(평가) → 10/13(표준화) → 10/12(종합) → 10/11(문맥화) → 10/10(실현) → 10/09(임상 전환) → 10/08(성숙) → 10/07(산업화). Standardizing(10/13)된 Framework들이 **각 분야主流 체계에서 실제로 얼마나 성과적으로 이행되는지 평가(Benchmarking)하는 단계로 진입하는 날** — Şahin et al.로 CRISPR global trajectory: 136 trials → 700+ trials注册 + COST GenE-HumDi 4 권고 + accelerated personalized CRISPR for CPS1 deficiency + technical/regulatory/manufacturing/economic challenges( Molecular Therapy. Nucleic Acids 2026), Fay Lin et al.로 Pharma AI 2026 Deals: Iscientific/Recursion/Exscientia/Insilico Medicine-big pharma 다수 계약 체결 + AI platform bets 강화(GEN Biotechnology 2026), Yin et al.로 AlphaFold3 performance: AF2→AF3 architecture evolution + pairwise→single-sequence + AF3 SBDD 적용 사례(Frontiers Molecular Biosciences 2026), Malhotra et al.로 AlphaFold derivatives comparative: AF2/AF3 accuracy + limitations + AF3가 ligand/nucleic acid/ion 예측 확장 + limitations analysis(Computers in Biology Medicine 2025), Ji et al.로 CAPTAIN multimodal: 4M+ cells + 382 proteins + protein imputation/expansion + cell type annotation + COVID-19 immune interaction patterns(Nature Communications 2026), Wang et al.로 SCMBench 23 methods: FMs < DMs in multi-omics integration + lightweight adaptation strategy(Nature Communications 2026), Gao et al.로 gastric cancer LAR: Lymphocyte Aggregated Region + PD1+CD27+CD8 T cells near CD70+LAMP3+ DCs + tertiary lymphoid structures(Nature Communications 2026), Zhang et al.로 SCLC spatial: 600K+ cells + PIHs-1 survival predictor + NLM-enriched malignant 3 subclusters + vascular-immune crosstalk(Cell Reports Medicine 2026)이 동시에 진행된다. Benchmarking은 Standardizing의 다음 단계다. 오늘 6개 전선은 표준화된 Framework들이 **각 분야主流 체계에서 실제 성능·적용 범위·한계를 어떻게 평가(Benchmarking)받는 날**이다. *출처: [Şahin CRISPR Global Trajectory Molecular Therapy Nucleic Acids 2026](https://consensus.app/papers/details/42b076fd7653554582553ab5be003424/), [Fay Lin Pharma AI 2026 Deals GEN Biotechnology 2026](https://consensus.app/papers/details/bc6e9ea3ec2e5025af707131d2fa0ca7/), [Yin AlphaFold3 Performance Frontiers Molecular Biosciences 2026](https://consensus.app/papers/details/880e72fce35255c4887811c76849c03a/), [Malhotra AlphaFold Derivatives Comparative Computers Biology Medicine 2025](https://consensus.app/papers/details/97539d9282a55cc7b207d41360287063/), [Ji CAPTAIN Multimodal Nature Communications 2026](https://consensus.app/papers/details/40361e55d4815c1dae3d5f6a95d4468a/), [Wang SCMBench Nature Communications 2026](https://consensus.app/papers/details/24b273d01c1f508c9a20a5a981ac2e3c/), [Gao Gastric Cancer LAR Nature Communications 2026](https://consensus.app/papers/details/46f3d71acd2e5b6e8816d6886aa485d9/), [Zhang SCLC Spatial Cell Reports Medicine 2026](https://consensus.app/papers/details/dc8b02ddbfcb574fb92b8cf8aed1117/)* *#BenchmarkingDay #Şahin #MolecularTherapy #NucleicAcids #CRISPR #GlobalTrajectory #700Trials #COST #GenEHumDi #CPS1Deficiency #AcceleratedPersonalizedCRISPR #FayLin #GENBiotechnology #PharmaAI #2026Deals #Iscientific #Recursion #Exscientia #InsilicoMedicine #BigPharma #Yin #FrontiersMolecularBiosciences #AlphaFold3 #AF2AF3 #ArchitectureEvolution #Pairwise #SingleSequence #SBDD #Malhotra #ComputersBiologyMedicine #AlphaFold #Derivatives #AF2 #AF3 #Ligand #NucleicAcid #Ion #Ji #CAPTAIN #NatureCommunications #4MillionCells #382Proteins #Imputation #Expansion #COVID19 #Wang #SCMBench #23Methods #FMsLessThanDMs #LightweightAdaptation #Gao #GastricCancer #LAR #LymphocyteAggregatedRegion #PD1 #CD27 #CD8 #CD70 #LAMP3 #DendriticCells #TertiaryLymphoidStructures #Zhang #SCLC #CosMx #600KCells #PIHs1 #VascularImmune #Crosstalk #LNM #Benchmarking #PerformanceEvaluation #BiomedicalAI #DailyReport* --- ### Post 2: CRISPR & Gene Editing — 700+ Trials Trajectory + GenE-HumDi Benchmarking 🪄 **[CRISPR/Gene Editing] Şahin et al.로 CRISPR global trajectory: 136 trials → 700+ trials 등록 + COST GenE-HumDi network 4 권고사항 + accelerated personalized CRISPR for CPS1 deficiency + technical/regulatory/manufacturing/economic challenges( Molecular Therapy. Nucleic Acids 2026) + Fay Lin et al.로 Pharma AI 2026 Deals: Iscientific/Recursion/Exscientia/Insilico Medicine-big pharma 다수 계약 체결(GEN Biotechnology 2026): CRISPR Benchmarking(평가) 동시 확인** CRISPR gene therapy의 **700+ trials global trajectory + GenE-HumDi benchmarks + pharma AI deals(Benchmarking)**이 출처에서 동시에 확인된다[1][2]. **CRISPR Global Trajectory: 700+ Trials + GenE-HumDi 4 Recommendations[1]** Ceren Şahin et al.(Molecular Therapy. Nucleic Acids 2026)의 연구는 CRISPR 치료제 global trajectory를 종합한다[1]. **700+ trials 등록**: ClinicalTrials.gov + EU Clinical Trials Register 기준. ** COST Action GenE-HumDi network**: European Cooperation in Science and Technology — Gene Editing to Treat Human Diseases. **Technical challenges**: off-target effects + delivery systems + scalability. **Regulatory challenges**: prolonged approval timelines. **Manufacturing challenges**: scalable production. **Economic challenges**: cost-effectiveness + patient access. **Accelerated personalized CRISPR**: congenital severe carbamoyl-phosphate synthetase 1(CPS1) deficiency 치료를 위한 가속화 CRISPR — 환자 특이적 편집. **Clinical reality**: first regulatory approval(Casgevy) 이후 broad clinical application까지 제약. **4 GenE-HumDi 권고사항**: network 산출 프레임워크. 핵심: CRISPR 700+ trials trajectory — 전 세계 임상 활동 규모 benchmark(Benchmarking). **Pharma AI 2026 Deals: Big Pharma Platform Bets[2]** Fay Lin et al.(GEN Biotechnology 2026)의 기사는 2026년 pharma AI 계약 현황을 보도한다[2]. **Iscientific, Recursion, Exscientia, Insilico Medicine-big pharma 다수 계약 체결**: 2026년 AI-drug discovery 활발한 계약 활동. **AI platform bets 강화**: 빅파마들의 AI 플랫폼에 대한 전략적 투자 확대. **Industry consolidation**: 주요 빅파마들의 AI biotech와의 제휴 강화. 핵심: Pharma AI 2026 Deals — 빅파마 AI 투자 규모 benchmark(Benchmarking). **10월 13일 Standardizing(Verma GCRC-F + Zahlan 3807 startups + Fay Lin) vs 오늘의 차별점**: 10/13이 **Verma BioTech**으로 GCRC-F 4 pillars 표준화를, **Zahlan NPJ Digital Medicine**으로 3,807개 AI startups drug discovery 2위 투자를, **Fay Lin GEN Biotechnology**으로 pharma AI deals를 제시했다면, 오늘은 **Şahin Molecular Therapy**으로 **136 trials → 700+ trials이라는 5배 증가 글로벌 trajectory + COST GenE-HumDi network 4개 권고사항 + accelerated personalized CRISPR for CPS1 deficiency**라는 CRISPR 임상 규모 benchmark(Benchmarking)를, **Fay Lin 재확인**으로 **Iscientific/Recursion/Exscientia/Insilico Medicine-big pharma 다수 계약 체결**이라는 pharma AI investment规模 benchmark(Benchmarking)를 동시에 제시한다. Standardizing(10/13) → 700+ trials trajectory benchmark + pharma AI deals benchmark Benchmarking(10/14). 실무 함의: 유전자 치료 개발팀에서 Şahin et al.의 700+ trials global trajectory를 참조하여 development strategy benchmark로 활용해야 한다. *출처: [Şahin CRISPR Global Trajectory Molecular Therapy Nucleic Acids 2026](https://consensus.app/papers/details/42b076fd7653554582553ab5be003424/), [Fay Lin Pharma AI 2026 Deals GEN Biotechnology 2026](https://consensus.app/papers/details/bc6e9ea3ec2e5025af707131d2fa0ca7/), [Lummerstorfer Non-Viral CRISPR Carriers Drug Delivery 2026](https://consensus.app/papers/details/747d35e9e5bc5ca9a9854998c7acd326/)* *#CRISPR #GeneEditing #Şahin #MolecularTherapy #NucleicAcids #GlobalTrajectory #700Trials #COST #GenEHumDi #4Recommendations #CPS1Deficiency #AcceleratedPersonalizedCRISPR #TechnicalChallenges #RegulatoryChallenges #ManufacturingChallenges #EconomicChallenges #FayLin #GENBiotechnology #PharmaAI #2026Deals #Iscientific #Recursion #Exscientia #InsilicoMedicine #BigPharma #PlatformBets #Lummerstorfer #NonViral #136Trials #BenchmarkingDay #ClinicalScale #GlobalTrajectory #BiomedicalAI #DailyReport* --- ### Post 3: AI 신약 — FDA-EMA Guiding Principles + AlphaFold3 Performance Benchmarking 💊 **[AI 신약] Hills et al.로 FDA-EMA Guiding Principles: 2026년 1월 공동 발표 + 10개 원칙 + oncology drug development 적용 + good AI practice framework(AI Precision Oncology 2026) + Seyhan et al.로 AI drug discovery review: generative modeling + scaffold-aware tools(AlphaFold/MoleR/PocketCrafter) + 0건 완전 마케팅 승인 + AI clinical candidates 진입(Critical Reviews Oncology Hematology 2026) + Fay Lin et al.로 Pharma AI 2026 Deals 재확인: Iscientific/Recursion/Exscientia/Insilico Medicine-big pharma 다수 계약 체결(GEN Biotechnology 2026): AI Drug Discovery Benchmarking(평가) 동시 확인** AI-Driven Drug Discovery의 **FDA-EMA joint guiding principles + AI platform deals benchmark(Benchmarking)**이 출처에서 동시에 확인된다[3][4][5]. **FDA-EMA Guiding Principles: 10개 원칙 + Oncology AI Framework[3]** Bethany Hills et al.(AI in Precision Oncology 2026)의 article은 FDA와 EMA의 joint AI guiding principles를 oncology drug development에 적용한다[3]. **2026년 1월 공동 발표**: U.S. FDA + European Medicines Agency. **10개 Guiding Principles of Good AI Practice in Drug Development**: responsible AI systems across drug lifecycle. **Shared transatlantic framework**:监管協調. **Operational guidance for oncology researchers/clinicians/institutional leaders**. **Evidence generation 적용**: AI-driven oncology research의 실질적 이행. 핵심: FDA-EMA joint guiding principles — 국제 규제 benchmark framework(Benchmarking). **AI Drug Discovery Review: Clinical Candidates 진입 + 0건 완전 승인[4]** A. Seyhan et al.(Critical Reviews in Oncology/Hematology 2026)의 review를 재확인한다[4]. **Generative modeling**: molecular design加速. **Scaffold-aware tools**: AlphaFold, MoleR, PocketCrafter. **0건 완전 마케팅 승인**: fully AI-discovered and AI-designed drug. **AI-originated candidates clinical development 진입**: translational impact 확인. **AI as collaborative lab partner**: computational/experimental 통합. 핵심: AI drug discovery — 이행의 clinical candidates benchmark(Benchmarking). **Pharma AI 2026 Deals 재확인[5]** (Fay Lin et al. GEN Biotechnology 2026 — Post 2에서 상세 기술). Iscientific/Recursion/Exscientia/Insilico Medicine-big pharma deals. 핵심: pharma AI deals — investment benchmark(Benchmarking). **10월 13일 Standardizing(Zahlan 3807 startups + Fay Lin pharma deals + Ciganek LNP) vs 오늘의 차별점**: 10/13이 **Zahlan NPJ Digital Medicine**으로 3,807개 AI startups drug discovery 2위 투자를, **Fay Lin GEN Biotechnology**으로 pharma AI deals를, **Ciganek Materials Today Bio**으로 LNP immune cell engineering 표준화를 제시했다면, 오늘은 **Hills AI Precision Oncology**으로 **2026년 1월 FDA-EMA joint Guiding Principles 10개 원칙 발표**라는 국제 규제 benchmark framework(Benchmarking)를, **Seyhan 재확인**으로 **0건 완전 마케팅 승인 + AI-originated candidates clinical development 진입**이라는 이행의 임상 candidates benchmark(Benchmarking)를, **Fay Lin 재확인**으로 **pharma AI deals 규모 benchmark**를 동시에 제시한다. Standardizing(10/13) → FDA-EMA 10 principles benchmark + AI clinical candidates benchmark + pharma deals benchmark Benchmarking(10/14). 실무 함의: AI drug discovery 협업팀에서 Hills et al.의 FDA-EMA joint framework를 international AI regulatory strategy benchmark에 반영해야 한다. *출처: [Hills FDA EMA Guiding Principles AI Precision Oncology 2026](https://consensus.app/papers/details/28de9bf5ec6a56c9af30a59ee808c24c/), [Seyhan AI Drug Discovery Critical Reviews Oncology Hematology 2026](https://consensus.app/papers/details/0e6b685fd2be56fb8a814115873a73d5/), [Fay Lin Pharma AI 2026 Deals GEN Biotechnology 2026](https://consensus.app/papers/details/bc6e9ea3ec2e5025af707131d2fa0ca7/)* *#AI #DrugDiscovery #Hills #AIPrecisionOncology #FDA #EMA #GuidingPrinciples #2026 #January #10Principles #Oncology #DrugDevelopment #GoodAIPractice #TransatlanticFramework #Seyhan #CriticalReviews #OncologyHematology #GenerativeModeling #AlphaFold #MoleR #PocketCrafter #ZeroApproval #AIClinicalCandidates #FayLin #GENBiotechnology #PharmaAI #2026Deals #Iscientific #Recursion #Exscientia #InsilicoMedicine #BigPharma #PlatformBets #BenchmarkingDay #RegulatoryBenchmark #ClinicalBenchmark #InvestmentBenchmark #BiomedicalAI #DailyReport* --- ### Post 4: 단백질 AI — AlphaFold3 Performance + AF2/AF3 Comparative Benchmarking 🔬 **[단백질 AI] Yin et al.로 AlphaFold3 performance: AF2→AF3 architecture evolution + pairwise→single-sequence + SBDD 적용 사례(Frontiers Molecular Biosciences 2026) + Malhotra et al.로 AlphaFold derivatives comparative: AF2/AF3 accuracy + limitations + ligand/nucleic acid/ion 예측 확장 + limitations analysis(Computers in Biology Medicine 2025) + Park et al.로 AlphaFold2 beyond: advanced sequence search + protein language models + diverse biomolecular interactions + fundamental physicochemical principles( Current Opinion Structural Biology 2025): 단백질 AI Benchmarking(평가) 동시 확인** 단백질 AI의 **AlphaFold3 performance benchmark + AF2/AF3 comparative benchmarking(Benchmarking)**이 출처에서 동시에 확인된다[6][7][8]. **AlphaFold3 Performance: Architecture Evolution + SBDD Applications[6]** Tianxiang Yin et al.(Frontiers in Molecular Biosciences 2026)의 review는 AlphaFold series의 performance를 종합한다[6]. **AF1(DNN) → AF2(Evoformer) → AF3(Pairformer)**: architecture evolution 추적. **AF2 accuracy**: monomeric protein structure prediction near-experimental. **AF3 expansion**: protein + ligand + nucleic acid + ions + modified residues 모두 예측. **Single-sequence approaches**: ESMFold — 속도와 확장성 우위. **SBDD 적용 사례**: drug discovery에서의 실질적 활용. **Current limitations**: complex folding dynamics + multi-chain interactions. 핵심: AlphaFold3 performance — architecture evolution benchmark + SBDD 적용(Benchmarking). **AlphaFold Derivatives Comparative: AF2 vs AF3 Accuracy + Limitations[7]** Yuktika Malhotra et al.(Computers in Biology and Medicine 2025)의 review는 AlphaFold derivatives를 비교 분석한다[7]. **AF2 accuracy**: revolutionary high accuracy. **AF3 limitations**: AF2에 비해 향상되었으나 여전히挑战. **AF3 ligand/nucleic acid/ion prediction expansion**: drug discovery 적용 범위 확대. **Limitations analysis**: current models의 체계적 한계 분석. **Integration with experimental techniques**: complementary role. 핵심: AlphaFold derivatives comparative — AF2/AF3 accuracy benchmark + limitations(Benchmarking). **AlphaFold2 Beyond: Advanced Sequence Search + Protein Language Models[8]** Sanggeun Park et al.(Current Opinion in Structural Biology 2025)의 review를 재확인한다[8]. **Advanced sequence search techniques**: AF2 성능 향상. **Protein language models**: alternative approaches. **Diverse biomolecular interactions**: 통합 framework. **Physicochemical principles**: 근본적 모델링 원칙. 핵심: AlphaFold2 beyond — advanced methods benchmark(Benchmarking). **10월 13일 Standardizing(Zhang peptide + Singh data gap + Truong CICS) vs 오늘의 차별점**: 10/13이 **Zhang Biotechnology Advances**으로 AF2/AF3 conformational dynamics 한계를, **Singh Current Opinion**으로 protein-ligand data gap hierarchical framework를, **Truong Molecular Therapy**으로 CICS LNP payload distribution을 제시했다면, 오늘은 **Yin Frontiers Molecular Biosciences**으로 **AF2→AF3→ESMFold architecture evolution + single-sequence approaches + SBDD 적용 사례**라는 performance benchmark(Benchmarking)를, **Malhotra Computers in Biology Medicine**으로 **AF2/AF3 comparative accuracy + limitations + ligand/nucleic acid/ion 예측 확장**이라는 comparative benchmark(Benchmarking)를, **Park 재확인**으로 **advanced sequence search + protein language models + physicochemical principles**라는 beyond-AF2 methods benchmark(Benchmarking)를 동시에 제시한다. Standardizing(10/13) → AF3 performance benchmark + AF2/AF3 comparative benchmark + beyond-AF2 methods benchmark Benchmarking(10/14). 실무 함의: SBDD 워크플로우팀에서 Yin et al.의 AlphaFold3 performance benchmark를 tool selection criteria에 반영해야 한다. *출처: [Yin AlphaFold3 Performance Frontiers Molecular Biosciences 2026](https://consensus.app/papers/details/880e72fce35255c4887811c76849c03a/), [Malhotra AlphaFold Derivatives Comparative Computers Biology Medicine 2025](https://consensus.app/papers/details/97539d9282a55cc7b207d41360287063/), [Park AlphaFold2 Beyond Current Opinion Structural Biology 2025](https://consensus.app/papers/details/d7cec8bfc2ee51a69e6ea2c93b86cce0/), [Zhang AlphaFold Peptide Biotechnology Advances 2026](https://consensus.app/papers/details/948caefacacc54fcb7706db4d8660a93/)* *#ProteinAI #AlphaFold #Yin #FrontiersMolecularBiosciences #AlphaFold3 #Performance #ArchitectureEvolution #Evoformer #Pairformer #SBDD #ESMFold #SingleSequence #Malhotra #ComputersBiologyMedicine #AlphaFoldDerivatives #AF2 #AF3 #Accuracy #Limitations #Ligand #NucleicAcid #Ion #Park #CurrentOpinion #StructuralBiology #AdvancedSequenceSearch #ProteinLanguageModels #BiomolecularInteractions #Physicochemical #BenchmarkingDay #PerformanceBenchmark #ComparativeBenchmark #BiomedicalAI #DailyReport* --- ### Post 5: Single-Cell Multi-Omics Foundation Model — CAPTAIN Multimodal + SCMBench FMs< DMs Benchmarking 🬬 **[Single-Cell Multi-Omics Foundation Model] Ji et al.로 CAPTAIN multimodal: 4M+ cells + 382 proteins + cross-modality dependencies modeling + protein imputation/expansion/cell type annotation + batch harmonization + COVID-19 immune patterns(Nature Communications 2026) + Wang et al.로 SCMBench 23 methods: FMs < DMs in multi-omics integration + lightweight adaptation strategy + 4 evaluation metrics(Nature Communications 2026) + Cen et al.로 single-cell spatial omics review: high-throughput/subcellular/multiomics profiling + generative AI + foundation models + clinical applications(MedComm 2026): foundation model Benchmarking(평가) 동시 확인** Single-Cell Foundation Model의 **CAPTAIN multimodal performance + SCMBench benchmarking reality + clinical applications(Benchmarking)**이 출처에서 동시에 확인된다[9][10][11]. **CAPTAIN: Multimodal Transcriptome + Protein Performance[9]** Bo-Ya Ji et al.(Nature Communications 2026)의 연구를 재확인한다[9]. **4M+ cells + 382 proteins**: concurrently measured. **Cross-modality dependencies modeling**: unified multimodal representations. **Protein imputation/expansion**: transcriptomes에서 단백질 예측/보강. **Cell type annotation**: 세포 유형 주석 개선. **Batch harmonization**: 배치 효과 보정. **COVID-19 immune interaction patterns**: 단백질-유전체 통합으로才发现. 핵심: CAPTAIN multimodal — performance benchmark(Benchmarking). **SCMBench: FMs < DMs Benchmarking Reality[10]** Yixuan Wang et al.(Nature Communications 2026)의 SCMBench를 재확인한다[10]. **23개 방법 평가**: 4 evaluation metrics — integration accuracy, biomarker detection, trajectory inference, batch effect correction. **FMs < DMs**: foundation models가 domain-specific models보다 낮은 성능. **Lightweight adaptation strategy**: FM 성능 향상 가능. **DMs 우위 상황**: specific single-cell analysis tasks에서. 핵심: SCMBench — benchmarking reality로 FM vs DM 성능 격차 benchmark(Benchmarking). **Single-Cell Spatial Omics Review: Clinical Applications[11]** Xiaoping Cen et al.(MedComm 2026)의 review를 재확인한다[11]. **High-throughput profiling**: 기술 발전. **Generative AI + foundation models**: computational advancement. **Clinical applications**: biomarker discovery + therapeutic targets + precision medicine. 핵심: single-cell spatial omics — clinical applications benchmark(Benchmarking). **10월 13일 Standardizing(Ji CAPTAIN + Wang SCMBench + Bahrami) vs 오늘의 차별점**: 10/13이 **Ji Nature Communications**으로 CAPTAIN multimodal을, **Wang Nature Communications**으로 SCMBench FMs< DMs를, **Bahrami Cell Systems**으로 compositional modular design을 제시했다면, 오늘은 **Ji 재확인**으로 **CAPTAIN의 4M+ cells + 382 proteins + protein imputation/expansion/cell type annotation + batch harmonization의 구체적 performance benchmark**를(Benchmarking)를, **Wang 재확인**으로 **SCMBench 23 methods 4 evaluation metrics로 FMs< DMs benchmarking reality**를(성능 격차 Benchmarking)를, **Cen MedComm 재확인**으로 **single-cell spatial omics clinical applications benchmark**를(적용 benchmark Benchmarking)를 동시에 제시한다. Standardizing(10/13) → CAPTAIN performance benchmark + SCMBench FM vs DM benchmark + clinical applications benchmark Benchmarking(10/14). 실무 함의: 단일세포 연구팀에서 Wang et al.의 SCMBench benchmarking 결과를 foundation model selection criteria에 반영해야 한다. *출처: [Ji CAPTAIN Multimodal Foundation Nature Communications 2026](https://consensus.app/papers/details/40361e55d4815c1dae3d5f6a95d4468a/), [Wang SCMBench Nature Communications 2026](https://consensus.app/papers/details/24b273d01c1f508c9a20a5a981ac2e3c/), [Cen Single-Cell Spatial Omics MedComm 2026](https://consensus.app/papers/details/c4f998ebe8e15bf498b5fb58997799d6/)* *#SingleCell #FoundationModel #Ji #CAPTAIN #NatureCommunications #4MillionCells #382Proteins #Imputation #Expansion #CellTypeAnnotation #BatchHarmonization #COVID19 #ImmunePatterns #Wang #SCMBench #23Methods #FMsLessThanDMs #LightweightAdaptation #Benchmarking #Cen #MedComm #SingleCell #SpatialOmics #GenerativeAI #FoundationModels #ClinicalApplications #Biomarker #TherapeuticTargets #PrecisionMedicine #BenchmarkingDay #PerformanceBenchmark #BiomedicalAI #DailyReport* --- ### Post 6: FDA/규제 — FDA-EMA Guiding Principles + CGT Regulatory Evaluation 📋 **[FDA/규제] Hills et al.로 FDA-EMA Guiding Principles: 2026년 1월 10개 원칙 joint 발표 + oncology drug development 적용 + good AI practice framework(AI Precision Oncology 2026) + Oo et al.로 FDA 44개 CGT approvals platform-aligned AI/ML analytics 재확인: dose selection + safety evaluation + durability prediction(J Clinical Pharmacology 2026) + Chakraborty et al.로 CRISPR-Cas9 therapeutics: Casgevy UK/MHRA + FDA dual approval + 136 trials global landscape + clinical trials approval landscape(Molecular Therapy Nucleic Acids 2026): FDA/규제 Benchmarking(평가) 동시 확인** FDA 규제 영역의 **FDA-EMA joint guiding principles + CGT approvals benchmark(Benchmarking)**이 출처에서 동시에 확인된다[3][12][13]. **FDA-EMA Guiding Principles 재확인[3]** (Bethany Hills et al. AI Precision Oncology 2026 — Post 3에서 상세 기술). 2026년 1월 joint 발표 + 10개 원칙 + oncology drug development 적용. 핵심: FDA-EMA joint framework — international regulatory benchmark(Benchmarking). **FDA 44개 CGT Approvals Platform-Aligned AI/ML Analytics 재확인[12]** (C. Oo et al. J Clinical Pharmacology 2026 — Post 3에서 상세 기술). 44개 CGT 승인 + platform-aligned + AI/ML analytics. 핵심: FDA CGT approvals — 이행의 regulatory benchmark(Benchmarking). **CRISPR-Cas9 Therapeutics Approval Landscape Benchmarking[13]** Chakraborty et al.(Molecular Therapy. Nucleic Acids 2026)을 재확인한다[13]. **Casgevy dual approval**: UK/MHRA + FDA. **136 trials global landscape**: 임상 활동 규모. **Approval landscape**: first CRISPR therapy approval 이후 현황. 핵심: CRISPR approval landscape — regulatory milestone benchmark(Benchmarking). **10월 13일 Standardizing(Verma GCRC-F 4 pillars + Oo 44 CGT) vs 오늘의 차별점**: 10/13이 **Verma BioTech**으로 GCRC-F 4 pillars 표준화를, **Oo J Clinical Pharmacology**으로 44개 approvals를 제시했다면, 오늘은 **Hills AI Precision Oncology**으로 **2026년 1월 FDA-EMA joint Guiding Principles 10개 원칙**이라는 **신규 국제 규제 benchmark framework**를(Benchmarking)를, **Chakraborty 재확인**으로 **Casgevy dual approval + 136 trials global landscape**라는 regulatory milestone + 임상 규모 benchmark(Benchmarking)를 동시에 제시한다. Standardizing(10/13) → FDA-EMA joint framework + CRISPR approval landscape benchmark Benchmarking(10/14). 실무 함의: Regulatory Affairs팀에서 Hills et al.의 FDA-EMA joint framework를 international AI regulatory strategy benchmark에 반영해야 한다. *출처: [Hills FDA EMA Guiding Principles AI Precision Oncology 2026](https://consensus.app/papers/details/28de9bf5ec6a56c9af30a59ee808c24c/), [Oo FDA CGT Approvals J Clinical Pharmacology 2026](https://consensus.app/papers/details/181658e93c825ea581a0664845a98d12/), [Chakraborty CRISPR Therapeutics Approval Molecular Therapy Nucleic Acids 2026](https://consensus.app/papers/details/68148f0062ec554ba4af1996dd3718cb/)* *#FDA #GeneTherapy #Hills #AIPrecisionOncology #FDA #EMA #GuidingPrinciples #2026 #January #10Principles #Oncology #GoodAIPractice #Transatlantic #Oo #JClinicalPharmacology #44CGTApprovals #PlatformAligned #AIAnalytics #Chakraborty #MolecularTherapy #NucleicAcids #Casgevy #Approval #DualApproval #MHRA #136Trials #BenchmarkingDay #InternationalFramework #CGTBenchmark #RegulatoryBenchmark #BiomedicalAI #DailyReport* --- ### Post 7: Spatial Transcriptomics — Gastric Cancer LAR + SCLC CosMx + Pan-Cancer Benchmarking 🗺️ **[Spatial Transcriptomics] Gao et al.로 gastric cancer LAR: Lymphocyte Aggregated Region + PD1+CD27+CD8 T cells near CD70+LAMP3+ DCs + tertiary lymphoid structures(Nature Communications 2026) + Zhang et al.로 SCLC CosMx: 600K+ cells + PIHs-1 survival predictor + LNM-enriched malignant subclusters 3개 + vascular-immune crosstalk(Cell Reports Medicine 2026) + Li et al.로 pan-cancer spatial: 12개 암종 373개 검체 + 56 LCPs + 13 niches + Niche_4 poor prognosis + Niche_11 better survival(Cell Reports Medicine 2026): spatial transcriptomics Benchmarking(평가) 동시 확인** Spatial transcriptomics의 **gastric cancer LAR + SCLC CosMx + pan-cancer niche benchmarking(Benchmarking)**이 출처에서 동시에 확인된다[14][15][16]. **Gastric Cancer Lymphocyte Aggregated Region: PD1+CD8 near CD70+LAMP3+ DCs[14]** Sen Gao et al.(Nature Communications 2026)의 연구는 gastric cancer의 Lymphocyte Aggregated Region(LAR)을 분석한다[14]. **4개 공간 영역 식별**: tumor microenvironment의 공간적 구조. **Lymphocyte Aggregated Region(LAR)**: tertiary lymphoid structures(TLS) 포함. **PD1+CD27+CD8 T cells**: CD70+LAMP3+ dendritic cells와 근접. **Group A vs Group B differential**: 더 활성화된 lymphocytes가 cancerous regions 근처, 더 resting lymphocytes가 Group B의 cancerous regions 근처. **면역 활성화 공간적 패턴**: immune activation의 공간적 조직. **면역치료 biomarker potential**: PD1+CD27+CD8 near CD70+LAMP3+ 패턴. 핵심: gastric cancer LAR — 공간적 면역 biomarker benchmark(Benchmarking). **SCLC CosMx 600K+ Cells: PIHs-1 + Vascular-Immune Crosstalk[15]** Zi-Cheng Zhang et al.(Cell Reports Medicine 2026)의 연구를 재확인한다[15]. **75명 105개 검체 600K+ cells**: SCLC spatial atlas. **3개 LNM-enriched malignant subclusters**: 림프절 전이 강화 종양 하위 집합. **PIHs-1**: 독립적 survival predictor. **Vascular-immune crosstalk**: endothelial cells가 immune activation orchestrate + cytotoxic T cells와 functional perivascular niches. 핵심: SCLC CosMx — spatial survival predictor benchmark(Benchmarking). **Pan-Cancer Spatial Niche: Niche_4 vs Niche_11 Survival Differential[16]** Jiarong Li et al.(Cell Reports Medicine 2026)의 연구를 재확인한다[16]. **12개 암종 373개 검체**: pan-cancer spatial analysis. **56개 LCPs + 13개 niches**: 공간적 세포 프로그램. **Niche_4(macrophage + tumor cell)**: poor prognosis + immunotherapy resistance. **Niche_11(macrophage + immune cell)**: better survival + treatment response. 핵심: pan-cancer niche — survival differential benchmark(Benchmarking). **Gastric Cancer LAR vs SCLC vs Pan-Cancer Differential**: Gastric cancer LAR(Gao)은 **TLs + PD1+CD27+CD8 near CD70+LAMP3+ DCs**의 공간적 면역 패턴을, SCLC CosMx(Zhang)은 **LNM-enriched subclusters + PIHs-1**의 예후 예측자를, Pan-cancer niche(Li)는 **Niche_4/11 differential**의 보편적 패턴을 제시한다. 동일 공간 면역 영역이지만 gastric-specific immune activation pattern vs SCLC-specific LNM predictor vs pan-cancer universal survival niche로 차별화된다. **10월 13일 Standardizing(Zhang spatial ecotypes + Nguyen TME + Li niche) vs 오늘의 차별점**: 10/13이 **Nguyen Clinical & Translational Immunology**으로 AI/ML cross-platform integration + standardized workflows를, **Zhang Nature**으로 spatial ecotypes를, **Li Cell Reports Medicine**으로 Niche_4/11를 제시했다면, 오늘은 **Gao Nature Communications**으로 **gastric cancer LAR — PD1+CD27+CD8 near CD70+LAMP3+ DCs + tertiary lymphoid structures**라는 **새로운 암종 적용(gastric cancer)에서의 공간적 면역 biomarker benchmark**를(Benchmarking)를, **Zhang 재확인**으로 **SCLC CosMx 600K+ cells + PIHs-1 + LNM-enriched subclusters**라는 **폐소세포암종 특이적 survival predictor benchmark**를(Benchmarking)를, **Li 재확인**으로 **pan-cancer Niche_4/11 survival differential**을(보편적 biomarker benchmark Benchmarking)를 동시에 제시한다. Standardizing(10/13) → gastric cancer LAR spatial biomarker + SCLC PIHs-1 predictor + pan-cancer Niche_4/11 benchmark Benchmarking(10/14). 실무 함의: 면역종양학팀에서 Gao et al.의 gastric cancer LAR spatial pattern을 immunotherapy biomarker strategy에 적용해야 한다. *출처: [Gao Gastric Cancer Lymphocyte Aggregated Region Nature Communications 2026](https://consensus.app/papers/details/46f3d71acd2e5b6e8816d6886aa485d9/), [Zhang CosMx SCLC Cell Reports Medicine 2026](https://consensus.app/papers/details/dc8b02ddbfcb574fb92b8cf8aed1117/), [Li Pan-Cancer Niche Cell Reports Medicine 2026](https://consensus.app/papers/details/f420ab985e1257e18352cac9e0c425cf/)* *#SpatialTranscriptomics #Gao #NatureCommunications #GastricCancer #LAR #LymphocyteAggregatedRegion #PD1 #CD27 #CD8 #CD70 #LAMP3 #DendriticCells #TertiaryLymphoidStructures #TLS #ImmuneActivation #Zhang #CosMx #SCLC #600KCells #PIHs1 #SurvivalPredictor #LNM #MalignantSubclusters #VascularImmune #Crosstalk #Li #PanCancer #56LCPs #13Niches #Niche4 #Niche11 #Macrophage #Tumor #Prognosis #ImmunotherapyResistance #BenchmarkingDay #GastricCancerBiomarker #CancerSpecificPredictor #UniversalSurvivalNiche #BiomedicalAI #DailyReport* --- ### Post 8: 오늘의 요약 — Benchmarking(평가) Day 핵심 정리 🧬 **오늘 6개 영역 핵심 요약:** 10/14(Benchmarking)의 핵심: Standardizing(10/13)된 Framework들이 **각 분야主流 체계에서 실제로 얼마나 성과적으로 이행되는지 평가(Benchmarking)하는 날**이었다. **1. CRISPR 700+ Trials Global Trajectory + GenE-HumDi 4 Recommendations(Şahin Molecular Therapy. Nucleic Acids 2026)**: 136 trials → 700+ trials 등록 + COST GenE-HumDi network 4개 권고사항 + accelerated personalized CRISPR for CPS1 deficiency + technical/regulatory/manufacturing/economic challenges — 글로벌 임상 규모 benchmark[1]. **2. Pharma AI 2026 Deals: Iscientific/Recursion/Exscientia/Insilico Medicine-big pharma Deals(Fay Lin GEN Biotechnology 2026)**: 빅파마 AI 플랫폼 전략적 투자 확대 + AI biotech 제휴 강화 — pharma AI investment benchmark[2]. **3. FDA-EMA Guiding Principles 10개 원칙 + Oncology AI Framework(Hills AI Precision Oncology 2026)**: 2026년 1월 joint 발표 + 10개 good AI practice 원칙 + oncology drug development 적용 — international regulatory framework benchmark[3]. **4. AlphaFold3 Performance + AF2/AF3 Comparative Benchmarking(Yin Frontiers Molecular Biosciences 2026 + Malhotra Computers Biology Medicine 2025)**: AF1→AF2→AF3 architecture evolution + pairwise→single-sequence + SBDD 적용 사례 + AF2/AF3 accuracy + limitations + ligand/nucleic acid/ion prediction — 단백질 AI performance benchmark[6][7]. **5. CAPTAIN Multimodal + SCMBench FMs< DMs Benchmarking(Ji Nature Communications 2026 + Wang Nature Communications 2026)**: CAPTAIN 4M+ cells + 382 proteins + protein imputation/expansion + SCMBench 23 methods FMs< DMs + lightweight adaptation — foundation model performance benchmark[9][10]. **6. Gastric Cancer LAR + SCLC PIHs-1 + Pan-Cancer Niche_4/11(Gao Nature Communications 2026 + Zhang Cell Reports Medicine 2026 + Li Cell Reports Medicine 2026)**: PD1+CD27+CD8 near CD70+LAMP3+ DCs(gastric) + PIHs-1 survival predictor(SCLC) + Niche_4/11 survival differential(pan-cancer) — spatial biomarker benchmark[14][15][16]. 10월 15일 전망: Benchmarking된 Framework들이 다음 단계에서 어떻게 적용 범위를 확대(Scale)하는지가 핵심. 특히 700+ trials 규모 확장, FDA-EMA joint framework의 이행, FMs vs DMs 성능 격차 좁히는 lightweight adaptation이 관전이다. Benchmarking 다음은 **Scaling(규모화)**이다. 오늘의 6개 전선이 제시하는 공통 질문: 평가(Benchmarking)된 Framework들이 어떻게 규모화(Scale)되어 각 분야主流 체계에서 광범위 적용될 수 있는가. 이 질문이 내일 이후 각 전선의 핵심 과제가 된다. *출처: [Şahin CRISPR Global Trajectory Molecular Therapy Nucleic Acids 2026](https://consensus.app/papers/details/42b076fd7653554582553ab5be003424/), [Fay Lin Pharma AI 2026 Deals GEN Biotechnology 2026](https://consensus.app/papers/details/bc6e9ea3ec2e5025af707131d2fa0ca7/), [Hills FDA EMA Guiding Principles AI Precision Oncology 2026](https://consensus.app/papers/details/28de9bf5ec6a56c9af30a59ee808c24c/), [Yin AlphaFold3 Performance Frontiers Molecular Biosciences 2026](https://consensus.app/papers/details/880e72fce35255c4887811c76849c03a/), [Malhotra AlphaFold Derivatives Comparative Computers Biology Medicine 2025](https://consensus.app/papers/details/97539d9282a55cc7b207d41360287063/), [Ji CAPTAIN Multimodal Foundation Nature Communications 2026](https://consensus.app/papers/details/40361e55d4815c1dae3d5f6a95d4468a/), [Wang SCMBench Nature Communications 2026](https://consensus.app/papers/details/24b273d01c1f508c9a20a5a981ac2e3c/), [Gao Gastric Cancer Lymphocyte Aggregated Region Nature Communications 2026](https://consensus.app/papers/details/46f3d71acd2e5b6e8816d6886aa485d9/), [Zhang CosMx SCLC Cell Reports Medicine 2026](https://consensus.app/papers/details/dc8b02ddbfcb574fb92b8cf8aed1117/), [Li Pan-Cancer Niche Cell Reports Medicine 2026](https://consensus.app/papers/details/f420ab985e1257e18352cac9e0c425cf/)* *#BenchmarkingDay #Summary #Şahin #700Trials #GenEHumDi #CPS1Deficiency #AcceleratedPersonalizedCRISPR #FayLin #PharmaAI #2026Deals #Hills #FDA #EMA #GuidingPrinciples #10Principles #Oncology #Yin #AlphaFold3 #ArchitectureEvolution #Malhotra #AF2 #AF3 #Accuracy #Limitations #Ji #CAPTAIN #382Proteins #Wang #SCMBench #FMsLessThanDMs #Gao #GastricCancer #LAR #PD1 #CD27 #CD8 #CD70 #LAMP3 #Zhang #SCLC #PIHs1 #Li #Niche4 #Niche11 #Benchmarking #PerformanceEvaluation #Scale #BiomedicalAI #DailyReport* --- ## Sources (1차 출처 16개 이상) [1] [Şahin C, et al. The landscape and trajectory of global CRISPR therapeutics. Molecular Therapy. Nucleic Acids. 2026.](https://consensus.app/papers/details/42b076fd7653554582553ab5be003424/) [2] [Fay Lin, et al. Pharma Bets Big on AI Platforms with Flurry of 2026 Deals. GEN Biotechnology. 2026.](https://consensus.app/papers/details/bc6e9ea3ec2e5025af707131d2fa0ca7/) [3] [Hills B, et al. From Principles to Practice: Operationalizing FDA and EMA Guiding Principles for AI in Oncology Drug Development. AI in Precision Oncology. 2026.](https://consensus.app/papers/details/28de9bf5ec6a56c9af30a59ee808c24c/) [4] [Seyhan A, et al. AI in drug discovery and development. Critical Reviews in Oncology/Hematology. 2026.](https://consensus.app/papers/details/0e6b685fd2be56fb8a814115873a73d5/) [5] [Yin TX, et al. Protein structure prediction powered by artificial intelligence: from biochemical foundations to practical applications. Frontiers in Molecular Biosciences. 2026.](https://consensus.app/papers/details/880e72fce35255c4887811c76849c03a/) [6] [Malhotra Y, et al. Advancements in protein structure prediction: A comparative overview of AlphaFold and its derivatives. Computers in Biology and Medicine. 2025.](https://consensus.app/papers/details/97539d9282a55cc7b207d41360287063/) [7] [Park S, et al. Advancing protein structure prediction beyond AlphaFold2. Current Opinion in Structural Biology. 2025.](https://consensus.app/papers/details/d7cec8bfc2ee51a69e6ea2c93b86cce0/) [8] [Ji BY, et al. CAPTAIN: a multimodal foundation model pretrained on co-assayed single-cell RNA and protein. Nature Communications. 2026.](https://consensus.app/papers/details/40361e55d4815c1dae3d5f6a95d4468a/) [9] [Wang Y, et al. SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration. 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Research Pulse·2026-10-14
Research Pulse — 2026-10-14
28 journals × 7 topics · fibrosis · OXPHOS · ferroptosis · sarcopenia · senescence
Raw data + scoring: Daily Tech Digest (separate feed)
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