In Silico 기반임상연구 신재민 신테카바이오 2016-11-04 @ 데이터그랜드컨퍼런스
목차 1 About 신테카바이오 2 In-silico 임상시험, 필요한가? 3 In-silico 임상연구동향 3-1. In-silico 임상연구사례 4 요약 3-2. In-silico 임상연구분야 3-3. In-silico 응용연구 2
1 About 신테카바이오 3
신테카바이오개요 회사개요 신테카바이오는 2009년에설립된바이오벤처회사로서 2014년한국전자통신연구원 (ETRI) 의 유전자검사전용슈퍼컴퓨팅 기술을출자받은연구소기업입니다. 빅데이터기반알고리즘개인유전체맵플랫폼기술 (PMAP) 을보유하고있으며, in silico 에기반한유전체빅데이터연구소기업으로고성능 고효율의슈퍼컴퓨터를이용한유전체분석과대량의바이오데이터를관리 분석하는회사입니다. 유전질환검사스크리닝 기술로정부가인증하는보건신기술 (NET, New Excellent Technology) 을획득하였으며, 현재본사대전유전체데이터통합센터와서울 KIST R&D센터, 서울비즈니스센터, 용인인실리코의학연구센터, 청주슈퍼컴센터로운영되고있습니다. 회사연혁 2016년 2015년 2014년 2013년 2012년 2009년 보건신기술인증 ( 보건복지부 ) - 차세대시퀀싱통합데이터플랫폼기반유전질환스크리닝기술 PMAP ( 개인유전체맵 ) 자동생성 Beta 버전완성 한국전자통신연구원 (ETRI) 연구소기업선정 바이오빅데이터 2020 미래 100 대기술과주역 선정 복지부차세대맞춤의료유전체사업단유전체통합용역수행 신테카바이오설립및기술보증기금벤처인증 4
신테카바이오센터소개 대전유전체데이터통합센터 본사 _ 대덕테크노밸리 광화문비즈니스센터 KIST R&D 센터 청주슈퍼컴센터 용인인실리코의학연구센터 용인시동백골드프라자내 신재민 Ph.D. KAIST Ph.D. 한효과학기술원, NIH( 미 ), 분자설계연구소인실리코의학연구현 신테카바이오인실리코의학연구센터소장 5
신테카바이오사업모델 Prenatal Testing & Newborn Screening In silico trial 컴퓨터모델링을통해신약개발, 임상등에적용, Phenotype Responder/Non-responder 민간유전자검사 (DTC) Direct To Consumer Genetic Testing 전장유전체통합서비스 Integration of whole genome data 맞춤유전자패널암예측, 대부분유전질환, 및만성질환, 하플로타이핑 (HLA, DMET, KIR, 등 ) Overseas Expansion PMAP : Personal Genome Map for Healthcare HLA : Human Leukocyte Antigen DMET : Drug Metabolism Enzyme and Transporter KIR : Killer-cell immunoglobulin-like receptors 6
In silico ( 예비 ) 임상시험 : 정의 컴퓨터 모델을통한 의약 / 의료기기의개발및 효용성 / 독성 / 부작용등을평가하는것. -- wiki (ISCT, in silico Clinical Trial) 현재, 완전한사용또는대체가되지는않고있지만, 확실히유용한정보와이점을주고있음. 7
2 ISCT 필요성 8
ISCT 동기 빅데이터분석환경 환자, 유전체, 약물, ADME/T, 작용 / 부작용메커니즘, 임상실험데이터축적. 과거부정형 / 부정확한데이터와는 양적 / 질적 으로차원이다른데이터가기하급수적으로축적. EMR (Electronic Medical Records), HER(Electronic Health Records) 약물 / 의료장비의상업적처방 / 사용에따른잠재적위험감소, 임상비용절감, 개발실패확률최소화필요 in vivo 테스트에대한부담 ( 생명윤리 / 도덕적인어려움, 비용및기간 ) 특히, Low-frequency side-effects 는 개발 / 전임상 / 임상단계에서미리예상하는것은매우어려움 탈리도마이드 ( 진정수면제 동물시험에서는무독성 vs. 사람에게서 1만명이상의기형아출산 ) Rofecoxib(==Vioxx: Cox-2 저해제 광범위한심혈관질환부작용 ) 9
Exabytes 빅데이터 1EB 는 8TB HDD 약 12 만개 = 약 32KM 어떤전문가도기존 정보 를 다알수없으므로자신의판 단에대해계속불안함. 10
Google - AlphaGo Alpha-Go 는 바둑 10 단 의경지 경험과직관보다는 Logic-Model 이더경쟁적임. 가능한모든 가능성 을평가해볼수있음. 11
인공지능 (in silico)-watson Many Un-Structured Biomedical Data 수백만의학교과서, 논문, 치료법정보학습 영상검사정보판독 (X-Ray, CT, MRI, 조직검사데이터 ) 2014년미국암임상학회테스트결과, 82.6% 의정확도로진단. (2014년당시, Watson 이숙련된전문의보다우수하다고판정하지는않았지만 현재는?) Customized Therapy 암, 당뇨, 고지혈, 고혈압등복합질병에대한환자맞춤최적치료법 ( 정밀의료, Precision Medicine) 제시. 12
Withdrawn: Database URL: http://www.koreahealthlog.com/?p=6682 Database 기반분석결과, 부작용에대해 왜 어떻게 에대한답을미리제시 / 확인할수있음. V. B. Siramshetty et. al., D1080 D1086 Nucleic Acids Research, 2016, Vol. 44, Database issue http://cheminfo.charite.de/withdrawn/ 13
3 빅데이터기반인실리코임상 (ISCT) 동향 2-1. In-silico 임상연구사례 2-2. In-silico 임상연구분야 2-3. In-silico 응용연구 - QSAR-Toxicology - Others - Drug-Repositioning 14
Y2016 미국의회예산법안 2016년도의회예산제출예산자료를근거로, 가능한신약및신의료기기활용에 in silico 임상 을 FDA 에촉구. http://www.vph-institute.org/news/significant-step-for-in-silico-medicine-as-insilico-clinical-trials-are-highlighted-in-a-us-senate-.html 15
Y2016 국제심포지움 16
European Regulatory Toxicology Conference 17
3-1 ISCT 연구사례 18
ISCT 관련연구이력 1955: Solomon & Gold - Potassium transport in human erythrocytes: evidence for a three compartment system 1980s 2000s: Population-Specific Stochastic Models to predict values 2000s-: patient-specific models for population ecology - Y2002: Chabaud, S., et. al., Clinical trial simulation using therapeutic effect modeling: application to ivabradine efficacy in patients with angina pectoris. - Y2007-: VPH (Virtual Physiological Human) Digital Patient Roadmap Y2009: In Silico Preclinical Trials: A Proof of Concept in Closed-Loop Control of Type 1 Diabetes - Kovatchev BP, Breton MD, Dalla Man C, Cobelli C. In silico model and computer simulation environment approximating the human glucose/insulin utilization. Food and Drug Administration Master File MAF 1521. 2008. Y2013: ASME (FDA/NIH/NSF) - Verification & Validation in Computational Modeling of Medical Devices (V&V-40) - 수학적근거, 실험적근거, 통계적근거에의한성능검증기준협의체가동 19
ISCT: 적용사례 -1 20
ISCT: 적용사례 -2 US-FDA will Modernize Toxicology.. (01/07/2014) 1. Develop better models of human adverse response: 2. Identify and evaluate biomarkers and endpoints that can be used in non-clinical and clinical evaluations: 3. Use and develop computational methods and in silico modeling: - Improve the use of chemical Structure-Activity Relationship (SAR) models in the prediction of human risk and integrate this analysis into the review process; - Develop and implement approaches to link chemical structures and substructures to a wide range of information about product safety, disease targets, and toxicity mechanisms; - Develop clinical trial simulation models that can reveal interactions between drug or device effects, patient characteristics, and disease variables influencing outcomes - Develop computer models of cells, organs, and systems to better predict product safety and efficacy; - Implement computer models that integrate pharmacokinetic, pharmacodynamic, materials science, or mechanistic safety data to predict clinical risk-benefit and confirm post-marketing safety in different patient populations; and - Develop and apply data mining, knowledge building, and data visualization tools to inform computer model development, clinical risk prediction, and regulatory decision-making. 21
ISCT: 적용사례 -3 Best practices applying to experimental studies - Experimental studies include (but are not limited to) studies in silico, in vitro, ex-vivo, with instrumental or biochemical methods, studies conducted on volunteers, investigator evaluations, sensory evaluations, etc. Different types of experimental studies can be used to provide data on the performance of cosmetic products. It is useful to take into consideration existing relevant guidelines, e.g. guidelines relating to instrumental clinical techniques, other European or international guidelines or standards (e.g. CEN, ISO, etc.). 22
ISCT: 적용사례 -4 23
ISCT: 적용사례 - 5 How new technologies could transform Existing Approaches (Y2007) Emerging fields also include systems biology, a powerful approach that uses computational models and laboratory data to describe and understand biologic systems as a whole and how they operate. Another important field is bioinformatics, which applies computational techniques to vast amounts of data to understand how cells and cell systems work.. 24
3-2 ISCT 연구분야 25
ISCT 연구분야 Virtual Human (VPH, Entelos, ) Genome-Wide Patient-Population Analysis Virtual Organ/Tissue/Cell ADME/T Prediction or Assessment for Biomedical Compounds/Device (NIH, EPA, EU,..) QSAR/Machine-Learning Data Driven Model/Statistics Metabolite/Pharmacogenomics Mechanism/MOA (Withdrawn DB 등수많은 DB) Early-Stage Filtering Drug-Reposition/Disposition 26
Avicenna ISCT Roadmaps: From ISCT to in silico medicine 3D Organ Printing Organ-on-Chip Big-Data Systems Biology Mobile Health and personal health forecasting 27
3-3 ISCT 응용연구 - QSAR 28
In silico 독성예측 인실리코독성학 은, 알려진화합물의독성정보를이용, 약물설계단계에서부터향후발생가능한실패를줄이는것. 29
In silico Model (SAR) End-Points Data Descriptors 30
Y=F(x) 에서 Y 와 x 는? 31
Y = F(x) 에서 F 는? F(x) Y 32
In silico 모델개발 In silico 모델개발과정에서복잡한데이터 / 표현자가공과정이필요한이유는? Junk-In, Junk-Out Ref: WIREs Comput Mol Sci 2016, 6:147 172. doi: 10.1002/wcms.1240 33
중요한 Descriptors Selection Ref: WIREs Comput Mol Sci 2016, 6:147 172. doi: 10.1002/wcms.1240 34
QSAR Example -1 신장독성예측모델개발에서, Metabolites 를고려하면훨씬더좋은 ( 신뢰할만한 ) 모델을얻을수있음. Ref: dx.doi.org/10.1021/tx400249t Chem. Res. Toxicol. 2013, 26, 1652-1659 35
QSAR Example -2 Tissue-Targeting Peptide Sequence 를예측하는모델개발에서, 방법론 (Methodology) 뿐만아니라 Descriptor 의선택이중요함. 36
3-3 ISCT 응용연구 - Others 37
(Sub)-Genotyping for Viral Sequence 유행또는유행예상바이러스변종에서열에대해 in silico Sub-Genotyping 정보를제공. 현재 Data/Tool 기준으로는좀더정확한 Validation 이가능할것임. http://www.flugenome.org/genotyping.php 38
FDA 2013 용출시험조건 in silico Model 개발 39
FDA 용출시험조건 in silico Model 개발 - 2 40
3-3 ISCT 응용연구 - Drug Repositioning (ISDR) 41
ISDR - 개념 Cmp Structure & Composition Cmp A (CMP.A) Target-A 에결합하는것으로알려짐 Target Structure &/or Sequence Com with Similar Structures Published Binding & Affinities Potential Targets Subjected to Virtual Screening against CMP.A Filtering QASR Data of Target Selectivity Proteins with Similar Structures Potential Targets with Significant Affinities Candidate Targets in vitro Screening against CMP.A In silico screening Potential Targets with Published or Proven Validation 42
Recent Review on ISDR 43
ISDR Anticancer Examples Shim & Liu, Int. J. Biol. Sci. 2014, Vol. 10 44
Why ISDR? 신약개발비용증가에비해신약 (NCEs) 은늘어나지않고있음. 따라서, NCEs 개발보다는독성등부작용이알려진물질을 새로운용도 로개발. 개발비용 / 개발기간 / 부작용가능성감소등에서많은이점이있음 45
Similar Structure Similar Activity 신테카바이오 Compounds-Map 수퍼컴 / 빅데이터기반모든유사화합물탐색및알려진모든타겟검색 / 평가 질병, 표적 & Pathway, MOA 등알려진정보를 DB에서검색 / 평가 46
신테카바이오 FDADrug-Map 모든유사화합물탐색및알려진모든 Target 검색후, 알려진 Drug-Target 에 3 차원구조 Mapping 자동화된 Protocol 에의해, 탐색된모 든화합물의가능성있는 Target 에대 한결합가능성분석 47
4 요약 48
요약 ISCT 는, 점점중요한 Validation 도구로 인식되고있음 ISCT 는 Low-frequency side-effects 에대한불안감을해 소할수있는 꼭필요한개념 으로인식되고있지만, 좀더 다양한확증사례가필요함. 시간문제임. Tox/ADME/Pharmacogenomics 등특정영역에서는이미 충분히성숙된도구로인정되고있음. 49
감사합니다.