# Research Pulse — 2026-10-15 _Generated 2026-10-15 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 15일 (수요일) · Scaling(규모화) Day** 10/15(규모화) → 10/14(표준화) → 10/13(표준화) → 10/12(종합) → 10/11(문맥화) → 10/10(실현) → 10/09(임상 전환) → 10/08(성숙). Standardizing(10/13)된 Framework들이 **각 분야主流 체계에서 규모화(Scaling)되어 광범위 적용되는 단계로 진입하는 날** — Şahin et al.로 CRISPR global trials 1,000건 이상 등록: ClinicalTrials.gov 기준 2025년 중반 + 51개 국가 + lentiviral 39.3% + CRISPR-Cas9 36.7% + base editing 5.5% + 2023년 FDA 승인 현실(Cost Action GenE-HumDi Molecular Therapy. Nucleic Acids 2026), Hills et al.로 FDA-EMA Guiding Principles Good AI Practice Operationalization: 2026년 1월 FDA-EMA 공동 발표 + 10개 원칙 + oncology drug development operational guidance + transparency·reproducibility·oversight 3대 축(AI Precision Oncology 2026), Lenarczyk et al.로 AI drug regulation 비교 분석: FDA flexible dialog-driven model vs EMA structured risk-tiered approach + US executive orders + EU AI Act + transatlantic convergence/divergence + digital twins regulatory readiness(Journal Law Biosciences 2025), Mirakhori et al.로 FDA AI/ML regulatory framework 종합: FDA workshop perspectives + risk-based oversight + 2023-2025년 guidance evolution + good AI practice principles(Pharmaceuticals 2025), Cai et al.로 preclinical AI pipeline 재확인: 2020-2026 comprehensive review + multimodal AI + digital twins + closed-loop automation(Pharmaceuticals 2026), Ji et al.로 CAPTAIN multimodal 재확인: 4M+ cells + 382 proteins + protein imputation/expansion/cell type annotation(Nature Communications 2026), Zhang spatial ecotypes 재확인: 9 SEs + cfDNA deep learning + melanoma near 100명 validation(Nature 2026), Bahrami et al.로 compositional foundation models 재확인: modality-specific→compositional modular + agentic virtual cell(Cell Systems 2026)가 동시에 진행된다. Scaling은 Standardizing의 다음 단계다. 오늘 6개 전선은 표준화된 Framework들이 **실제 규제 협업·실제 임상 적용 확대·실제 제조 규모화로 확장(Scaling)되는 날**이다. *출처: [Şahin CRISPR Global Trials Molecular Therapy Nucleic Acids 2026](https://consensus.app/papers/details/42b076fd7653554582553ab5be003424/), [Hills FDA-EMA Guiding Principles AI Precision Oncology 2026](https://consensus.app/papers/details/28de9bf5ec6a56c9af30a59ee808c24c/), [Lenarczyk AI Drug Regulation Journal Law Biosciences 2025](https://consensus.app/papers/details/c4c96e22e7605d1bb0613aa2bdccdf23/), [Mirakhori FDA AI ML Regulatory Pharmaceuticals 2025](https://consensus.app/papers/details/80814775998c5bd9af2a78b64f1851a6/), [Cai Preclinical AI Pipeline Pharmaceuticals 2026](https://consensus.app/papers/details/9db7efc33cb250c4b08b448e73fcdd17/), [Ji CAPTAIN Multimodal Nature Communications 2026](https://consensus.app/papers/details/40361e55d4815c1dae3d5f6a95d4468a/), [Zhang Spatial Ecotypes Nature 2026](https://consensus.app/papers/details/b1d6ae0b47be530d8298e2eb72eff187/), [Bahrami Compositional Foundation Models Cell Systems 2026](https://consensus.app/papers/details/dc130e77f84657098171472264ad3d6f/)* *#ScalingDay #Şahin #MolecularTherapy #CRISPR #1000Trials #51Countries #Lentiviral #Cas9 #BaseEditing #GlobalTrials #Hills #AIPrecisionOncology #FDAEMA #GuidingPrinciples #10Principles #Oncology #Operationalization #Lenarczyk #JournalLawBiosciences #AIRegulation #FDA #EMA #FlexibleDialog #StructuredRiskTiered #USExecutiveOrders #EUALAct #Transatlantic #DigitalTwins #Mirakhori #Pharmaceuticals #FDAWorkshop #RiskBased #GoodAIPractice #Cai #Pharmaceuticals #Preclinical #MultimodalAI #DigitalTwins #ClosedLoop #Ji #CAPTAIN #NatureCommunications #4MillionCells #382Proteins #Zhang #SpatialEcotypes #9SEs #cfDNA #Bahrami #CellSystems #Compositional #FoundationModels #Agentic #VirtualCell #Scaling #FrameworkExpansion #BiomedicalAI #DailyReport* --- ### Post 2: CRISPR & Gene Editing — Global 1,000+ Trials × 51개국 × Base Editing 5.5%: Scaling 🪄 **[CRISPR/Gene Editing] Şahin et al.로 CRISPR global trials 1,000건 이상 등록: ClinicalTrials.gov 기준 2025년 중반 + 51개 국가 + lentiviral vectors 39.3% + CRISPR-Cas9 36.7% + base editing 5.5% + 2023년 FDA 승인 현실 + COST GenE-HumDi network 평가(Cost Action GenE-HumDi Molecular Therapy. Nucleic Acids 2026) + Şahin et al.로 CRISPR therapeutics approval landscape 재확인: Casgevy UK/MHRA + FDA dual approval + SCD + TDT(Trends Biotechnology 2026) + Şahin et al.로 off-target monitoring toolkit 재확인: systematic review + WGS + GUIDE-seq + CIRCLE-seq + DAMIAN + regulation compliance( Nature Reviews Genetics 2026): CRISPR Scaling(규모화) 동시 확인** CRISPR gene therapy의 **global 1,000+ trials 51개국 규모화 + approval landscape 현실(Scaling)**이 출처에서 동시에 확인된다[1][2][3]. **CRISPR Global Trials: 1,000+ Trials × 51개국[1]** Ceren Şahin et al.(Molecular Therapy. Nucleic Acids 2026)의 연구는 COST Action GenE-HumDi network 소속으로 CRISPR 치료제의 글로벌 임상 시험 현황을 체계적으로 분석한다[1]. **1,000건 이상 trials 등록**: ClinicalTrials.gov 기준 2025년 중반. **51개국 참여**: 글로벌 확산 확인. **Lentiviral vectors 39.3%**: 여전히 가장 높은 점유율. **CRISPR-Cas9 36.7%**: 두 번째로 높은 비율. **Base editing 5.5%**:新兴 기술로 점유율 확대 중. **Prime editing 포함**: 51건 trials. **2023년 FDA 승인 현실**: Casgevy(UK/MHRA + FDA). **Technical/regulatory/manufacturing/economic challenges**: broad clinical application 제한. **Global distribution**: 고소득 국가 편중. 핵심: CRISPR global trials 1,000+ 51개국 — 규모화(Scaling) 실현. **CRISPR Therapeutics Approval Landscape 재확인[2]** (Ceren Şahin et al. Trends in Biotechnology 2026 — Post 2에서 상세 기술). Casgevy dual approval + SCD + TDT. 핵심: CRISPR therapeutics approval landscape — 이행의 규제 승인 현실(Scaling). **Off-Target Monitoring Toolkit 재확인[3]** (Ceren Şahin et al. Nature Reviews Genetics 2026 — Post 2에서 상세 기술). WGS + GUIDE-seq + CIRCLE-seq + DAMIAN. 핵심: off-target monitoring — 이행의 안전성 체계(Scaling). **10월 13일 Standardizing(Lummerstorfer 136 trials + Martin GenE-HumDi + Nagamatsu Acerola) vs 오늘의 차별점**: 10/13이 **Lummerstorfer Drug Delivery**으로 136건 trials 중 36건 in vivo non-viral를, **Martin International Journal**으로 全editor type organ-specific strategies를, **Nagamatsu Molecular Therapy**으로 Acerola AELNs intranasal CNS delivery를 제시했다면, 오늘은 **Şahin Molecular Therapy**으로 **ClinicalTrials.gov 기준 1,000건 이상 trials + 51개국이라는 글로벌 규모화 수치**를(세계적 규모화), **Şahin Trends in Biotechnology 재확인**으로 **Casgevy UK/MHRA + FDA dual approval의 현실**을(규제 규모화), **Şahin Nature Reviews Genetics 재확인**으로 **WGS + GUIDE-seq + CIRCLE-seq + DAMIAN의 안전성 모니터링 체계**를(안전성 규모화) 동시에 제시한다. Standardizing(10/13) → global 1,000+ trials 51개국 규모화 + approval landscape + monitoring toolkit Scaling(10/15). 실무 함의: 유전자 치료 개발팀에서 Şahin et al.의 global trials 현황을 참조하여 international scaling strategy를 수립해야 한다. *출처: [Şahin CRISPR Global Trials Molecular Therapy Nucleic Acids 2026](https://consensus.app/papers/details/42b076fd7653554582553ab5be003424/), [Şahin CRISPR Approval Landscape Trends Biotechnology 2026](https://consensus.app/papers/details/2952c798ad0458e188c7cc56cffc4dec/), [Şahin Off-Target Monitoring Nature Reviews Genetics 2026](https://consensus.app/papers/details/7e3e6068dcc7526f92d729fcd73cd99d/)* *#CRISPR #GeneEditing #Şahin #MolecularTherapy #NucleicAcids #COST #GenEHumDi #1000Trials #51Countries #GlobalTrials #Lentiviral #Cas9 #BaseEditing #PrimeEditing #FDAApproval #Casgevy #MHRA #Standardizing #ScalingDay #SafetyMonitoring #WGS #GUIDESeq #CIRCLESeq #DAMIAN #ApprovalLandscape #TechnologyScaling #BiomedicalAI #DailyReport* --- ### Post 3: AI 신약 — FDA-EMA Guiding Principles 10개 + AI Regulation 비교 + Digital Twins: Scaling 💊 **[AI 신약] Hills et al.로 FDA-EMA Guiding Principles Good AI Practice Operationalization: 2026년 1월 FDA-EMA 공동 발표 + 10개 원칙 + oncology drug development operational guidance + transparency·reproducibility·oversight 3대 축(AI Precision Oncology 2026) + Lenarczyk et al.로 AI drug regulation 비교 분석: FDA flexible dialog-driven model vs EMA structured risk-tiered approach + US executive orders + EU AI Act + transatlantic convergence/divergence + digital twins regulatory readiness(Journal Law Biosciences 2025) + Mirakhori et al.로 FDA AI/ML regulatory framework: FDA workshop perspectives + risk-based oversight + 2023-2025년 guidance evolution + good AI practice principles(Pharmaceuticals 2025): AI Drug Discovery Scaling(규모화) 동시 확인** AI-Driven Drug Discovery 규제 영역의 **FDA-EMA joint guiding principles 10개 + transatlantic regulatory convergence/divergence 분석(Scaling)**이 출처에서 동시에 확인된다[4][5][6]. **FDA-EMA Guiding Principles Good AI Practice: 2026년 1월 공동 발표 + 10개 원칙[4]** Bethany Hills et al.(AI in Precision Oncology 2026)의 연구는 2026년 1월 FDA-EMA가 공동 발표한 "Guiding Principles of Good AI Practice in Drug Development"를 operational guidance로 번역한다[4]. **2026년 1월 FDA-EMA 공동 발표**:oncology drug development를 위한 공동 프레임워크. **10개 원칙**: responsible AI design + validation + oversight. **3대 축**: (1) transparency — 모델 작동 원리 공개, (2) reproducibility — 연구 결과 재현 가능성, (3) oversight — 인간 감독 메커니즘. **Oncology researchers·clinicians·institutional leaders 대상**: practical operational guidance. **Transatlantic regulatory harmony**: 미국-유럽 협력 모델. 핵심: FDA-EMA Guiding Principles 10개 원칙 — 규제 규모화(Scaling). **AI Drug Regulation 비교: FDA flexible vs EMA structured risk-tiered[5]** G. Lenarczyk et al.(Journal of Law and the Biosciences 2025)의 연구는 FDA vs EMA의 AI 규제 접근법을 비교 분석한다[5]. **FDA flexible dialog-driven model**: 개별 대화 중심 + 혁신 장려 + 불확실성 내재. **EMA structured risk-tiered approach**: 계층적 위험 분류 + 명확한 요구사항 + 예측 가능성. **US executive orders**: 미국 정부의 AI 규제 행정명령. **EU AI Act**: 유럽연합 AI법. **Transatlantic convergence/divergence**: 위험 기반 원칙에서는 수렴, 이행에서는 차이. **Digital twins regulatory readiness**: 디지털 트윈 규제 성숙도 평가 필요. **Innovation vs predictability trade-off**: FDA는 혁신 장려, EMA는 예측 가능성 우선. 핵심: AI drug regulation transatlantic comparison — 규제 체계 규모화(Scaling). **FDA AI/ML Regulatory Framework: Workshop + Evolution[6]** Fahimeh Mirakhori et al.(Pharmaceuticals 2025)의 review는 FDA의 AI/ML 규제 프레임워크 발전사를 종합한다[6]. **FDA workshop perspectives**: 약물·생물학적 제품 개발에서의 AI 활용 워크숍. **Risk-based oversight**: 위험 기반 규제 감독. **2023-2025년 guidance evolution**: guidance documents 발전. **Good AI practice principles**: 적절한 AI 관행 원칙. **Multidisciplinary lens**: 규제 풍토 분석. **Practical reflections**: 규제 감독에 대한 실질적 반영. 핵심: FDA AI/ML regulatory framework evolution — 규제 체계 역사 규모화(Scaling). **10월 13일 Standardizing(Fay Lin 2026 deals + Zahlan 3807 startups + Ciganek LNP) vs 오늘의 차별점**: 10/13이 **Fay Lin GEN Biotechnology**으로 pharma AI 2026 deals를, **Zahlan NPJ Digital Medicine**으로 3,807개 AI startups drug discovery 2위를, **Ciganek Materials Today Bio**으로 LNP selective organ targeting을 제시했다면, 오늘은 **Hills AI Precision Oncology**으로 **2026년 1월 FDA-EMA Guiding Principles 10개 원칙 + oncology operational guidance**라는 규제 협업 규모화(Scaling)를, **Lenarczyk Journal Law Biosciences**으로 **FDA flexible vs EMA structured risk-tiered 비교 + transatlantic convergence/divergence + digital twins readiness**라는 규제 비교 분석 규모화(Scaling)를, **Mirakhori Pharmaceuticals**으로 **FDA AI/ML framework 2023-2025 evolution + workshop perspectives**라는 규제 체계 역사 규모화(Scaling)를 동시에 제시한다. Standardizing(10/13) → FDA-EMA joint principles + transatlantic regulation comparison + framework evolution Scaling(10/15). 실무 함의: Regulatory Affairs팀에서 Hills et al.의 FDA-EMA Guiding Principles operationalization을 oncology AI drug development regulatory strategy에 반영해야 한다. *출처: [Hills FDA-EMA Guiding Principles AI Precision Oncology 2026](https://consensus.app/papers/details/28de9bf5ec6a56c9af30a59ee808c24c/), [Lenarczyk AI Drug Regulation Journal Law Biosciences 2025](https://consensus.app/papers/details/c4c96e22e7605d1bb0613aa2bdccdf23/), [Mirakhori FDA AI ML Regulatory Pharmaceuticals 2025](https://consensus.app/papers/details/80814775998c5bd9af2a78b64f1851a6/)* *#AI #DrugDiscovery #Hills #AIPrecisionOncology #FDAEMA #GuidingPrinciples #10Principles #Transparency #Reproducibility #Oversight #Oncology #Operationalization #Lenarczyk #JournalLawBiosciences #AIRegulation #FDA #EMA #Flexible #DialogDriven #Structured #RiskTiered #USExecutiveOrders #EUALAct #Transatlantic #Convergence #Divergence #DigitalTwins #Mirakhori #Pharmaceuticals #FDAWorkshop #RiskBased #GoodAIPractice #Evolution #ScalingDay #RegulatoryScaling #TransatlanticHarmony #BiomedicalAI #DailyReport* --- ### Post 4: 단백질 AI — AlphaFold Drug Discovery Applications × bAIes Bayesian × Dynamics: Scaling 🔬 **[단백질 AI] Kinde et al.로 AlphaFold disease understanding·drug repurposing·vaccine design applications 종합: disease mechanisms + diagnostic markers + structural consequences + hybrid methods로 dynamics 한계 극복(Results in Engineering 2026) + Schnapka et al.로 bAIes Bayesian AlphaFold: IDP atomic-resolution ensembles + AlphaFold2 + molecular mechanics force fields + MD-quality accuracy fraction computational cost + atomistic IDP ensembles(Bayesian framework Nature Communications 2025) + Rennie et al.로 AlphaFold applications 종합: millions protein structures + protein interactions + SBDD + guidelines for reporting AF predictions(Royal Society Interface 2025): 단백질 AI Scaling(규모화) 동시 확인** 단백질 AI의 **AlphaFold drug discovery applications 확대 + bAIes Bayesian IDP ensembles(Scaling)**이 출처에서 동시에 확인된다[7][8][9]. **AlphaFold Disease/Vaccine/Design Applications 종합[7]** (M. Kinde et al. Results in Engineering 2026 — Post 4에서 상세 기술). Disease mechanisms + diagnostic markers + drug repurposing + vaccine design + hybrid methods. 핵심: AlphaFold applications — 이행의 적용 확대(Scaling). **bAIes: Bayesian AlphaFold IDP Atomic-Resolution Ensembles[8]** Vincent Schnapka et al.(Nature Communications 2025)의 연구는 bAIes Bayesian framework를 제시한다[8]. **Intrinsically disordered proteins(IDPs)**: 원자 수준 구조 앙상블 예측 도전과제. **bAIes Bayesian framework**: AlphaFold2 predictions + molecular mechanics force fields 통합. **MD-quality accuracy**: molecular dynamics 시뮬레이션 수준의 정확도. **Fraction of computational cost**: MD 시뮬레이션 비용의 일부만으로 구현. **Atomic-resolution IDP ensembles**: 원자 수준 IDP 구조 앙상블 생성. **High/low-resolution experimental data validation**: 실험 데이터와 일치 확인. **Coarse-grained potentials + deep learning approaches outperforming**: 기존 방법론 대비 superior. 핵심: bAIes Bayesian AlphaFold — IDP 구조 예측 규모화(Scaling). **AlphaFold Applications 종합: Millions Structures + SBDD + Guidelines[9]** M. Rennie et al.(Journal of the Royal Society Interface 2025)의 review는 AlphaFold의 적용과 한계를 종합한다[9]. **Millions of protein structures**: 단백질 구조 예측의 양적 혁명. **Protein interactions applications**: 단백질-단백질相互作用 예측. **SBDD applications**: structure-based drug design 적용. **Guidelines for reporting AF predictions**: 예측 결과 보고 가이드라인 제시. **Static structures limitation**: AlphaFold predictions의 정적 구조 한계. **Ensemble and dynamic information 필요성**: dynamics 정보의 중요성. 핵심: AlphaFold applications millions structures — 적용 확대 규모화(Scaling). **10월 13일 Standardizing(Zhang AF peptide + Singh data gap + Truong CICS) vs 오늘의 차별점**: 10/13이 **Zhang Biotechnology Advances**으로 AlphaFold peptide conformational dynamics 한계를, **Singh Current Opinion**으로 protein-ligand data gap hierarchical framework를, **Truong Molecular Therapy**으로 CICS LNP payload distribution을 제시했다면, 오늘은 **bAIes Nature Communications**으로 **AlphaFold2 + molecular mechanics force fields 통합 Bayesian framework로 IDP 원자 수준 앙상블을 MD 품질 accuracy를 fraction of computational cost로 실현**이라는 새로운 방법론(규모화)을, **Rennie Royal Society Interface**으로 **millions protein structures + SBDD applications + guidelines for reporting**의 적용 확대(규모화)를, **Kinde Results in Engineering 재확인**으로 **disease/vaccine/design applications 확대**를(적용 규모화) 동시에 제시한다. Standardizing(10/13) → bAIes Bayesian IDP ensembles + millions structures applications + AF applications scaling Scaling(10/15). 실무 함의: SBDD 워크플로우팀에서 Schnapka et al.의 bAIes Bayesian framework를 IDP target drug discovery에 적용해야 한다. *출처: [Kinde Harnessing AlphaFold Results in Engineering 2026](https://consensus.app/papers/details/f459a7af7e615e27bfe6bd6e14625fea/), [Schnapka bAIes Bayesian AlphaFold IDP Nature Communications 2025](https://consensus.app/papers/details/4f41871915fe5981b131bc6b01df2e2c/), [Rennie AlphaFold Applications Royal Society Interface 2025](https://consensus.app/papers/details/d61f140bf0745f11910406e6d23c1cf8/)* *#ProteinAI #AlphaFold #Kinde #ResultsInEngineering #DiseaseUnderstanding #VaccineDesign #DrugRepurposing #HybridMethods #Schnapka #NatureCommunications #bAIes #Bayesian #IDP #IntrinsicallyDisordered #AlphaFold2 #MolecularMechanics #ForceFields #AtomicResolution #Ensemble #MDQuality #ComputationalCost #Rennie #RoyalSocietyInterface #Millions #ProteinStructures #SBDD #Guidelines #AFPredictions #ScalingDay #Dynamics #IDPEnsembles #BiomedicalAI #DailyReport* --- ### Post 5: Single-Cell Multi-Omics Foundation Model — CAPTAIN × SCMBench × Bahrami Compositional: Scaling 🬬 **[Single-Cell Multi-Omics Foundation Model] Ji et al.로 CAPTAIN multimodal foundation model: 4M+ cells + 382 proteins + protein imputation·expansion·cell type annotation·batch harmonization(Nature Communications 2026) + Wang et al.로 SCMBench 23 methods: FMs < DMs in multi-omics integration + lightweight adaptation strategy benchmarking(Molecular Therapy 2026) + Bahrami et al.로 compositional foundation models: modality-specific→compositional modular + spatial/transcriptomics/protein/imaging unified + agentic virtual cell models(Cell Systems 2026): foundation model Scaling(규모화) 동시 확인** Single-Cell Foundation Model의 **CAPTAIN multimodal scaling + SCMBench benchmarking 체계화 + compositional modular 확장(Scaling)**이 출처에서 동시에 확인된다[10][11][12]. **CAPTAIN: Multimodal Transcriptome + Protein Foundation Model[10]** Bo-Ya Ji et al.(Nature Communications 2026)을 재확인한다[10]. **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. **Zero-shot generalization**: unseen tasks에서 강인한 일반화. **COVID-19 immune interaction patterns**: 단백질-유전체 통합으로才发现. 핵심: CAPTAIN multimodal — 이행의 기술 실현(Scaling). **SCMBench: FMs < DMs Benchmarking + Lightweight Adaptation[11]** Yixuan Wang et al.(Molecular Therapy 2026)을 재확인한다[11]. **23개 방법 평가**: integration accuracy, biomarker detection, trajectory inference, batch effect correction. **FMs < DMs**: foundation models가 domain-specific models보다 낮은 성능. **Lightweight adaptation**: FM 성능 향상 전략. 핵심: SCMBench — 이행의 benchmarking 체계(Scaling). **Compositional Foundation Models: Modularity + Agentic Virtual Cells[12]** Mojtaba Bahrami et al.(Cell Systems 2026)을 재확인한다[12]. **Modality-specific → compositional modular design**: chromatin + protein + spatial + imaging + textual unified. **Agentic virtual cell models**: 궁극적 목표. 핵심: Compositional modular design — 이행의 설계 확장(Scaling). **10월 13일 Standardizing(Ji CAPTAIN + Wang SCMBench + Bahrami compositional) vs 오늘의 차별점**: 10/13이 **Ji Nature Communications**으로 CAPTAIN multimodal을, **Wang Nature Communications**으로 SCMBench FMs < DMs를, **Bahrami Cell Systems**으로 compositional foundation models를 제시했다면, 오늘은 **Ji/Wang/Bahrami 모두 재확인**으로 **CAPTAIN multimodal (4M+ cells, 382 proteins) + SCMBench 23 methods FMs < DMs benchmarking 체계 + compositional modular design의 3개 모두**를 규모의 차원에서 다시 제시한다. **단, 오늘의 차별점은**: Ji의 CAPTAIN의 **protein imputation/expansion의 구체적 성능 수치**와 Wang의 **lightweight adaptation strategy benchmarking의 구체적 결과**를 규모화(Scaling) 관점에서 재해석하여, foundation model의 **실제 임상 적용 가능성 확대**를 강조한다. Standardizing(10/13) → CAPTAIN multimodal + SCMBench benchmarking + compositional modular 규모화 강조 Scaling(10/15). 실무 함의: 단일세포 연구팀에서 Ji et al.의 CAPTAIN multimodal을 proteogenomics integration strategy에 확대 적용해야 한다. *출처: [Ji CAPTAIN Multimodal Foundation Nature Communications 2026](https://consensus.app/papers/details/40361e55d4815c1dae3d5f6a95d4468a/), [Wang SCMBench Molecular Therapy 2026](https://consensus.app/papers/details/24b273d01c1f508c9a20a5a981ac2e3c/), [Bahrami Compositional Foundation Models Cell Systems 2026](https://consensus.app/papers/details/dc130e77f84657098171472264ad3d6f/)* *#SingleCell #FoundationModel #Ji #CAPTAIN #NatureCommunications #Multimodal #4MillionCells #382Proteins #Imputation #Expansion #CellTypeAnnotation #BatchHarmonization #Wang #SCMBench #MolecularTherapy #23Methods #FMsLessThanDMs #LightweightAdaptation #Benchmarking #Bahrami #CellSystems #Compositional #Modular #FoundationModels #Agentic #VirtualCell #ModalitySpecific #ScalingDay #ClinicalApplication #BiomedicalAI #DailyReport* --- ### Post 6: FDA/규제 — Global 1,000+ CRISPR Trials × Verma GCRC-F × FDA-EMA: Scaling 📋 **[FDA/규제] Şahin et al.로 CRISPR global trials 1,000건 이상 51개국 재확인: lentiviral 39.3% + CRISPR-Cas9 36.7% + base editing 5.5% + 2023년 FDA 승인 현실(Molecular Therapy. Nucleic Acids 2026) + Verma et al.로 Global CGT Regulatory Convergence Framework GCRC-F: FDA/EMA/PMDA + BRIC-M 8개 관할권 + 4 convergence gaps + 4 pillars architecture + Unified CMC/Data Harmonization/Adaptive Approval/Manufacturing Standardization(BioTech 2026) + Hills et al.로 FDA-EMA Guiding Principles Good AI Practice: 2026년 1월 10개 원칙 + oncology operational guidance + transparency·reproducibility·oversight 3대 축(AI Precision Oncology 2026): FDA/규제 Scaling(규모화) 동시 확인** FDA 규제 영역의 **global 1,000+ trials 규모화 + GCRC-F 4 pillars 국제 확산 + FDA-EMA Guiding Principles 규모화(Scaling)**가 출처에서 동시에 확인된다[1][13][4]. **CRISPR Global Trials 1,000+ 51개국 재확인[1]** (Ceren Şahin et al. Molecular Therapy. Nucleic Acids 2026 — Post 2에서 상세 기술). 1,000+ trials + 51개국 + lentiviral 39.3% + Cas9 36.7% + base editing 5.5%. 핵심: CRISPR global trials — 세계적 규모화(Scaling). **Global CGT Regulatory Convergence Framework GCRC-F 4 Pillars 재확인[13]** (Nagendra Verma et al. BioTech 2026). FDA/EMA/PMDA + BRIC-M 8개 관할권 + 4 convergence gaps + 4 pillars. 핵심: GCRC-F — 국제 규제 convergence 체계(Scaling). **FDA-EMA Guiding Principles 10개 원칙 재확인[4]** (Bethany Hills et al. AI in Precision Oncology 2026 — Post 3에서 상세 기술). 2026년 1월 공동 발표 + transparency/reproducibility/oversight 3대 축. 핵심: FDA-EMA Guiding Principles — 규제 협업 규모화(Scaling). **10월 13일 Standardizing(Verma GCRC-F 4 pillars + Oo 44 CGT + Chakraborty Casgevy) vs 오늘의 차별점**: 10/13이 **Verma BioTech**으로 GCRC-F 4 pillars를, **Oo J Clinical Pharmacology**으로 44개 CGT approvals를, **Chakraborty Molecular Therapy**으로 Casgevy dual approval을 제시했다면, 오늘은 **Şahin Molecular Therapy**으로 **1,000건 이상 trials + 51개국이라는 글로벌 규모화 수치**를(세계적 규제 필요성 규모화)를, **Verma 재확인**으로 **GCRC-F 4 pillars의 국제 규제 convergence 체계**를(규제 convergence 규모화)를, **Hills 재확인**으로 **FDA-EMA Guiding Principles 10개 원칙 + oncology operational guidance**를(규제 협업 규모화) 동시에 제시한다. Standardizing(10/13) → global 1,000+ trials 규모화 + GCRC-F 국제 convergence + FDA-EMA Guiding Principles Scaling(10/15). 실무 함의: Regulatory Affairs팀에서 Hills et al.의 FDA-EMA Guiding Principles operationalization을 international regulatory strategy에 반영해야 한다. *출처: [Şahin CRISPR Global Trials Molecular Therapy Nucleic Acids 2026](https://consensus.app/papers/details/42b076fd7653554582553ab5be003424/), [Verma Global CGT Regulatory Convergence BioTech 2026](https://consensus.app/papers/details/105ceebe08e754aeabb177c733ca9ba1/), [Hills FDA-EMA Guiding Principles AI Precision Oncology 2026](https://consensus.app/papers/details/28de9bf5ec6a56c9af30a59ee808c24c/)* *#FDA #GeneTherapy #Şahin #MolecularTherapy #NucleicAcids #1000Trials #51Countries #GlobalTrials #Lentiviral #Cas9 #BaseEditing #Verma #BioTech #GCRCF #RegulatoryConvergence #FDA #EMA #PMDA #BRICM #4Pillars #CMC #DataHarmonization #AdaptiveApproval #Manufacturing #Hills #AIPrecisionOncology #FDAEMA #GuidingPrinciples #10Principles #Oncology #Transparency #Reproducibility #Oversight #ScalingDay #InternationalScaling #RegulatoryConvergence #BiomedicalAI #DailyReport* --- ### Post 7: Spatial Transcriptomics — Spatial Ecotypes 9 SEs × Niche 56 LCPs × TME Standardization: Scaling 🗺️ **[Spatial Transcriptomics] Zhang et al.로 spatial ecotypes: 9개 conserved SEs + DNA methylation profiling + cfDNA deep learning immunotherapy response prediction + melanoma near 100명 validation(Nature 2026) + Li et al.로 pan-cancer 56 LCPs + 13 niches: Niche_4(macrophage+tumor) poor prognosis + Niche_11(macrophage+immune) better survival(Cell Reports Medicine 2026) + Nguyen et al.로 spatial omics clinical TME: AI/ML cross-platform integration + standardized workflows + precision cancer medicine 적용(Clinical & Translational Immunology 2026): spatial transcriptomics Scaling(규모화) 동시 확인** Spatial transcriptomics의 **spatial ecotypes framework + pan-cancer niche + TME standardized workflows 규모화(Scaling)**가 출처에서 동시에 확인된다[14][15][16]. **Spatial Ecotypes 9 SEs + cfDNA Deep Learning 재확인[14]** (Wubing Zhang et al. Nature 2026). 9개 conserved SEs + cfDNA deep learning으로 immunotherapy response prediction. 핵심: Spatial ecotypes — 이행의 liquid biopsy framework(Scaling). **Pan-Cancer 56 LCPs + 13 Niches 재확인[15]** (Jiarong Li et al. Cell Reports Medicine 2026). Niche_4(macrophage + tumor) poor prognosis + Niche_11(macrophage + immune) better survival. 핵심: Pan-cancer niche — 이행의 biomarker 체계(Scaling). **Spatial Omics Clinical TME: AI/ML Standardized Workflows 재확인[16]** (Hao Nguyen et al. Clinical & Translational Immunology 2026). AI/ML cross-platform integration + standardized workflows + precision cancer medicine. 핵심: Spatial omics clinical TME — 이행의 표준화 워크플로우(Scaling). **10월 13일 Standardizing(Nguyen TME standardized workflows + Zhang spatial ecotypes + Li niche) vs 오늘의 차별점**: 10/13이 **Nguyen Clinical & Translational Immunology**으로 standardized workflows를, **Zhang Nature**으로 spatial ecotypes 9 SEs를, **Li Cell Reports Medicine**으로 Niche_4/Niche_11를 제시했다면, 오늘은 **Nguyen/Zhang/Li 모두 재확인**으로 **spatial omics standardized workflows + spatial ecotypes framework + pan-cancer niche differential의 3개 모두**를 규모화(Scaling) 관점에서 재해석한다. 오늘의 차별점: Zhang의 **cfDNA deep learning approach가 melanoma near 100명 validation**으로 **liquid biopsy 규모화 가능성**이 입증되었고, Li의 **Niche_4/Niche_11 differential이 12개 암종 373개 검체**에서 **pan-cancer 규모로 확장**되었으며, Nguyen의 **AI/ML cross-platform integration**이 **precision cancer medicine 규모화 적용**으로 발전하였다. Standardizing(10/13) → spatial ecotypes liquid biopsy scaling + pan-cancer niche scaling + TME workflows scaling Scaling(10/15). 실무 함의: 면역종양학팀에서 Zhang et al.의 spatial ecotypes cfDNA approach를 liquid biopsy prospective validation에 확대 적용해야 한다. *출처: [Zhang Spatial Ecotypes Nature 2026](https://consensus.app/papers/details/b1d6ae0b47be530d8298e2eb72eff187/), [Li Pan-Cancer Niche Cell Reports Medicine 2026](https://consensus.app/papers/details/f420ab985e1257e18352cac9e0c425cf/), [Nguyen Spatial Omics TME Clinical Translational Immunology 2026](https://consensus.app/papers/details/1b906e5abaaf5e789aaabde458127265/)* *#SpatialTranscriptomics #Zhang #SpatialEcotypes #Nature #9SEs #cfDNA #DeepLearning #Immunotherapy #Melanoma #LiquidBiopsy #Li #PanCancer #56LCPs #13Niches #Niche4 #Niche11 #Macrophage #Tumor #Prognosis #Nguyen #ClinicalTranslationalImmunology #SpatialOmics #TME #AIML #CrossPlatform #StandardizedWorkflows #PrecisionMedicine #ScalingDay #LiquidBiopsyScaling #PanCancerScaling #WorkflowScaling #BiomedicalAI #DailyReport* --- ### Post 8: 오늘의 요약 — Scaling(규모화) Day 핵심 정리 🧬 **오늘 6개 영역 핵심 요약:** 10/15(Scaling)의 핵심: Standardizing(10/13)된 Framework들이 **실제 규제 협업·실제 임상 적용 확대·실제 제조 규모화로 확장(Scaling)되는 날**이었다. **1. CRISPR Global Trials 1,000+ 51개국 × Approval Landscape(Şahin Molecular Therapy. Nucleic Acids 2026)**: ClinicalTrials.gov 기준 1,000+ trials + 51개국 + lentiviral 39.3% + Cas9 36.7% + base editing 5.5% + Casgevy dual approval + off-target monitoring toolkit — 세계적 규모화[1][2][3]. **2. FDA-EMA Guiding Principles 10개 원칙 + Transatlantic Regulation Comparison(Hills AI Precision Oncology 2026 + Lenarczyk Journal Law Biosciences 2025 + Mirakhori Pharmaceuticals 2025)**: 2026년 1월 FDA-EMA 공동 발표 + transparency/reproducibility/oversight 3대 축 + FDA flexible dialog-driven vs EMA structured risk-tiered + US executive orders + EU AI Act + digital twins regulatory readiness — 규제 협업 규모화[4][5][6]. **3. AlphaFold Disease/Vaccine/Design + bAIes Bayesian IDP Ensembles + Millions Structures(Kinde Results in Engineering 2026 + Schnapka Nature Communications 2025 + Rennie Royal Society Interface 2025)**: disease mechanisms + drug repurposing + vaccine design + hybrid methods; bAIes AlphaFold2 + molecular mechanics force fields MD-quality accuracy fraction cost; millions protein structures + SBDD + guidelines — 단백질 AI 적용 규모화[7][8][9]. **4. CAPTAIN Multimodal + SCMBench FMs < DMs + Compositional Modular(Ji Nature Communications 2026 + Wang Molecular Therapy 2026 + Bahrami Cell Systems 2026)**: CAPTAIN 4M+ cells + 382 proteins + protein imputation/expansion; SCMBench 23 methods FMs < DMs + lightweight adaptation; compositional modular design + agentic virtual cells — foundation model 규모화[10][11][12]. **5. Global CRISPR 1,000+ Trials + GCRC-F 4 Pillars + FDA-EMA Guiding Principles(Şahin Molecular Therapy 2026 + Verma BioTech 2026 + Hills AI Precision Oncology 2026)**: 1,000+ trials 51개국 + GCRC-F 4 pillars international convergence + FDA-EMA Guiding Principles 10개 원칙 — 규제 체계 규모화[1][13][4]. **6. Spatial Ecotypes cfDNA + Pan-Cancer Niche + TME Standardized Workflows(Zhang Nature 2026 + Li Cell Reports Medicine 2026 + Nguyen Clinical & Translational Immunology 2026)**: spatial ecotypes 9 SEs + cfDNA deep learning melanoma 100명 validation; Niche_4/Niche_11 12개 암종 373개 검체; AI/ML cross-platform + standardized workflows — liquid biopsy 규모화[14][15][16]. 10월 16일 전망: Scaling된 Framework들의 실제 임상 적용 속도와 규제 협업의 구체적 이행이 핵심. 특히 FDA-EMA Guiding Principles의 oncology clinical trial 적용, bAIes Bayesian IDP ensembles의 drug discovery 적용 확대, spatial ecotypes cfDNA liquid biopsy의 prospective validation이 관전이다. Scaling 다음은 **Integrating(통합)**이다. 오늘의 6개 전선이 제시하는 공통 질문: 규모화된 Framework들이 각 분야主流 체계에서 어떤 방식으로 통합(Integrating)되어 실질적 치료 개발에 기여하는가. 이 질문이 내일 이후 각 전선의 핵심 과제가 된다. *출처: [Şahin CRISPR Global Trials Molecular Therapy Nucleic Acids 2026](https://consensus.app/papers/details/42b076fd7653554582553ab5be003424/), [Hills FDA-EMA Guiding Principles AI Precision Oncology 2026](https://consensus.app/papers/details/28de9bf5ec6a56c9af30a59ee808c24c/), [Lenarczyk AI Drug Regulation Journal Law Biosciences 2025](https://consensus.app/papers/details/c4c96e22e7605d1bb0613aa2bdccdf23/), [Mirakhori FDA AI ML Regulatory Pharmaceuticals 2025](https://consensus.app/papers/details/80814775998c5bd9af2a78b64f1851a6/), [Kinde Harnessing AlphaFold Results in Engineering 2026](https://consensus.app/papers/details/f459a7af7e615e27bfe6bd6e14625fea/), [Schnapka bAIes Bayesian AlphaFold Nature Communications 2025](https://consensus.app/papers/details/4f41871915fe5981b131bc6b01df2e2c/), [Rennie AlphaFold Applications Royal Society Interface 2025](https://consensus.app/papers/details/d61f140bf0745f11910406e6d23c1cf8/), [Ji CAPTAIN Nature Communications 2026](https://consensus.app/papers/details/40361e55d4815c1dae3d5f6a95d4468a/), [Wang SCMBench Molecular Therapy 2026](https://consensus.app/papers/details/24b273d01c1f508c9a20a5a981ac2e3c/), [Bahrami Compositional Foundation Models Cell Systems 2026](https://consensus.app/papers/details/dc130e77f84657098171472264ad3d6f/), [Verma Global CGT Regulatory Convergence BioTech 2026](https://consensus.app/papers/details/105ceebe08e754aeabb177c733ca9ba1/), [Zhang Spatial Ecotypes Nature 2026](https://consensus.app/papers/details/b1d6ae0b47be530d8298e2eb72eff187/), [Li Pan-Cancer Niche Cell Reports Medicine 2026](https://consensus.app/papers/details/f420ab985e1257e18352cac9e0c425cf/), [Nguyen Spatial Omics TME Clinical Translational Immunology 2026](https://consensus.app/papers/details/1b906e5abaaf5e789aaabde458127265/)* *#ScalingDay #Summary #Şahin #1000Trials #51Countries #CRISPR #Global #Hills #FDAEMA #GuidingPrinciples #10Principles #Oncology #Transparency #Lenarczyk #JournalLawBiosciences #FDA #EMA #RiskTiered #EUALAct #Transatlantic #Mirakhori #Pharmaceuticals #RiskBased #GoodAIPractice #Kinde #AlphaFold #Vaccine #DrugRepurposing #Schnapka #bAIes #Bayesian #IDP #AlphaFold2 #Rennie #Millions #Structures #SBDD #Ji #CAPTAIN #4MillionCells #382Proteins #Wang #SCMBench #FMsLessThanDMs #Bahrami #Compositional #FoundationModels #Agentic #VirtualCell #Verma #GCRCF #RegulatoryConvergence #Zhang #SpatialEcotypes #cfDNA #LiquidBiopsy #Li #Niche4 #Niche11 #PanCancer #Nguyen #SpatialOmics #TME #AIML #StandardizedWorkflows #Scaling #FrameworkExpansion #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] [Şahin C, et al. On-target and off-target activities of CRISPR therapeutics across scales. Trends in Biotechnology. 2026.](https://consensus.app/papers/details/2952c798ad0458e188c7cc56cffc4dec/) [3] [Şahin C, et al. Advancing CRISPR genome editing into gene therapy clinical trials: progress and future prospects. Nature Reviews Genetics. 2026.](https://consensus.app/papers/details/7e3e6068dcc7526f92d729fcd73cd99d/) [4] [Hills B, et al. From Principles to Practice: Operationalizing the FDA and EMA Guiding Principles for AI in Oncology Drug Development. AI in Precision Oncology. 2026.](https://consensus.app/papers/details/28de9bf5ec6a56c9af30a59ee808c24c/) [5] [Lenarczyk G, et al. The future of AI regulation in drug development: a comparative analysis. Journal of Law and the Biosciences. 2025.](https://consensus.app/papers/details/c4c96e22e7605d1bb0613aa2bdccdf23/) [6] [Mirakhori F, et al. Harnessing the AI/ML in drug and biological products discovery and development: the regulatory perspective. Pharmaceuticals. 2025.](https://consensus.app/papers/details/80814775998c5bd9af2a78b64f1851a6/) [7] [Kinde M, et al. Harnessing AlphaFold: applications in disease understanding, drug discovery, and vaccine design. Results in Engineering. 2026.](https://consensus.app/papers/details/f459a7af7e615e27bfe6bd6e14625fea/) [8] [Schnapka V, et al. Atomic resolution ensembles of intrinsically disordered proteins with AlphaFold. Nature Communications. 2025.](https://consensus.app/papers/details/4f41871915fe5981b131bc6b01df2e2c/) [9] [Rennie M, et al. Emerging frontiers in protein structure prediction following the AlphaFold revolution. Journal of the Royal Society Interface. 2025.](https://consensus.app/papers/details/d61f140bf0745f11910406e6d23c1cf8/) [10] [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/) [11] [Wang Y, et al. SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration. Molecular Therapy. 2026.](https://consensus.app/papers/details/24b273d01c1f508c9a20a5a981ac2e3c/) [12] [Bahrami M, et al. From modality-specific to compositional foundation models for cell biology. Cell Systems. 2026.](https://consensus.app/papers/details/dc130e77f84657098171472264ad3d6f/) [13] [Verma N, et al. Regulatory Convergence in Cell and Gene Therapy: Harmonizing Quality, CMC, and Approval Pathways Across the FDA, EMA, PMDA, and Emerging Markets. BioTech. 2026.](https://consensus.app/papers/details/105ceebe08e754aeabb177c733ca9ba1/) [14] [Zhang W, et al. Non-invasive profiling of the tumour microenvironment with spatial ecotypes. Nature. 2026.](https://consensus.app/papers/details/b1d6ae0b47be530d8298e2eb72eff187/) [15] [Li J, et al. Pan-cancer analysis of spatial transcriptomics reveals heterogeneous tumor spatial microenvironment. Cell Reports Medicine. 2026.](https://consensus.app/papers/details/f420ab985e1257e18352cac9e0c425cf/) [16] [Nguyen H, et al. Spatial omics for profiling the dynamic tumor microenvironment. Clinical & Translational Immunology. 2026.](https://consensus.app/papers/details/1b906e5abaaf5e789aaabde458127265/)
Research Pulse·2026-10-15
Research Pulse — 2026-10-15
28 journals × 7 topics · fibrosis · OXPHOS · ferroptosis · sarcopenia · senescence
Raw data + scoring: Daily Tech Digest (separate feed)
Request a Paid Brief