1932

Abstract

In the high-stakes arena of drug discovery, the journey from bench to bedside is hindered by a daunting 92% failure rate, primarily due to unpredicted toxicities and inadequate therapeutic efficacy in clinical trials. The FDA Modernization Act 2.0 heralds a transformative approach, advocating for the integration of alternative methods to conventional animal testing, including cell-based assays that employ human induced pluripotent stem cell (iPSC)-derived organoids, and organ-on-a-chip technologies, in conjunction with sophisticated artificial intelligence (AI) methodologies. Our review explores the innovative capacity of iPSC-derived clinical trial in a dish models designed for cardiovascular disease research. We also highlight how integrating iPSC technology with AI can accelerate the identification of viable therapeutic candidates, streamline drug screening, and pave the way toward more personalized medicine. Through this, we provide a comprehensive overview of the current landscape and future implications of iPSC and AI applications being navigated by the research community and pharmaceutical industry.

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2025-01-23
2025-06-14
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Literature Cited

  1. 1.
    Singh N, Vayer P, Tanwar S, Poyet J-L, Tsaioun K, Villoutreix BO. 2023.. Drug discovery and development: introduction to the general public and patient groups. . Front. Drug Discov. 3::1201419
    [Crossref] [Google Scholar]
  2. 2.
    Villoutreix BO. 2021.. Post-pandemic drug discovery and development: facing present and future challenges. . Front. Drug Discov. 1::728469
    [Crossref] [Google Scholar]
  3. 3.
    Freedman LP, Cockburn IM, Simcoe TS. 2015.. The economics of reproducibility in preclinical research. . PLOS Biol. 13::e1002165
    [Crossref] [Google Scholar]
  4. 4.
    Hay M, Thomas DW, Craighead JL, Economides C, Rosenthal J. 2014.. Clinical development success rates for investigational drugs. . Nat. Biotechnol. 32::4051
    [Crossref] [Google Scholar]
  5. 5.
    Dowden H, Munro J. 2019.. Trends in clinical success rates and therapeutic focus. . Nat. Rev. Drug Discov. 18::49596
    [Crossref] [Google Scholar]
  6. 6.
    Prinz F, Schlange T, Asadullah K. 2011.. Believe it or not: How much can we rely on published data on potential drug targets?. Nat. Rev. Drug Discov. 10::712
    [Crossref] [Google Scholar]
  7. 7.
    Zushin P-JH, Mukherjee S, Wu JC. 2023.. FDA Modernization Act 2.0: transitioning beyond animal models with human cells, organoids, and AI/ML-based approaches. . J. Clin. Investig. 133::e175824
    [Crossref] [Google Scholar]
  8. 8.
    Stresser DM, Kopec AK, Hewitt P, Hardwick RN, Van Vleet TR, et al. 2024.. Towards in vitro models for reducing or replacing the use of animals in drug testing. . Nat. Biomed. Eng. 8::93035
    [Crossref] [Google Scholar]
  9. 9.
    Ahmed SM, Shivnaraine RV, Wu JC. 2023.. FDA Modernization Act 2.0 paves the way to computational biology and clinical trials in a dish. . Circulation 148::30911
    [Crossref] [Google Scholar]
  10. 10.
    Waring MJ, Arrowsmith J, Leach AR, Leeson PD, Mandrell S, et al. 2015.. An analysis of the attrition of drug candidates from four major pharmaceutical companies. . Nat. Rev. Drug Discov. 14::47586
    [Crossref] [Google Scholar]
  11. 11.
    Morelli MB, Bongiovanni C, Da Pra S, Miano C, Sacchi F, et al. 2022.. Cardiotoxicity of anticancer drugs: molecular mechanisms and strategies for cardioprotection. . Front. Cardiovasc. Med. 9::847012
    [Crossref] [Google Scholar]
  12. 12.
    Arrowsmith J, Miller P. 2013.. Trial watch: phase II and phase III attrition rates 2011–2012. . Nat. Rev. Drug Discov. 12::569
    [Crossref] [Google Scholar]
  13. 13.
    Mensah GA, Fuster V, Murray CJL, Roth GA, Mensah GA, et al. 2023.. Global burden of cardiovascular diseases and risks, 1990–2022. . J. Am. Coll. Cardiol. 82::2350473
    [Crossref] [Google Scholar]
  14. 14.
    Paratz ED, Mundisugih J, Rowe SJ, Kizana E, Semsarian C. 2023.. Gene therapy in cardiology: Is a cure for hypertrophic cardiomyopathy on the horizon?. Can. J. Cardiol. 40::77788
    [Crossref] [Google Scholar]
  15. 15.
    Tschöpe C, Elsanhoury A. 2022.. Treatment of transthyretin amyloid cardiomyopathy: the current options, the future, and the challenges. . J. Clin. Med. 11::2148
    [Crossref] [Google Scholar]
  16. 16.
    Bhatnagar A. 2017.. Environmental determinants of cardiovascular disease. . Circ. Res. 121::16280
    [Crossref] [Google Scholar]
  17. 17.
    Hartiala JA, Hilser JR, Biswas S, Lusis AJ, Allayee H. 2021.. Gene-environment interactions for cardiovascular disease. . Curr. Atheroscler. Rep. 23::75
    [Crossref] [Google Scholar]
  18. 18.
    Seok J, Warren HS, Cuenca AG, Mindrinos MN, Baker HV, et al. 2013.. Genomic responses in mouse models poorly mimic human inflammatory diseases. . PNAS 110::350712
    [Crossref] [Google Scholar]
  19. 19.
    Sayed N, Liu C, Wu JC. 2016.. Translation of human-induced pluripotent stem cells: from clinical trial in a dish to precision medicine. . J. Am. Coll. Cardiol. 67::216176
    [Crossref] [Google Scholar]
  20. 20.
    Craveiro NS, Lopes BS, Tomás L, Almeida SF. 2020.. Drug withdrawal due to safety: a review of the data supporting withdrawal decision. . Curr. Drug Saf. 15::412
    [Crossref] [Google Scholar]
  21. 21.
    Onakpoya IJ, Heneghan CJ, Aronson JK. 2016.. Post-marketing withdrawal of 462 medicinal products because of adverse drug reactions: a systematic review of the world literature. . BMC Med. 14::10
    [Crossref] [Google Scholar]
  22. 22.
    Giordano D, Biancaniello C, Argenio MA, Facchiano A. 2022.. Drug design by pharmacophore and virtual screening approach. . Pharmaceuticals 15::646
    [Crossref] [Google Scholar]
  23. 23.
    Ruiz-Moreno AJ, Dömling A, Velasco-Velázquez MA. 2021.. Reverse docking for the identification of molecular targets of anticancer compounds. . Methods Mol. Biol. 2174::3143
    [Crossref] [Google Scholar]
  24. 24.
    Lo YC, Senese S, Damoiseaux R, Torres JZ. 2016.. 3D chemical similarity networks for structure-based target prediction and scaffold hopping. . ACS Chem. Biol. 11::224453
    [Crossref] [Google Scholar]
  25. 25.
    Mamoshina P, Volosnikova M, Ozerov IV, Putin E, Skibina E, et al. 2018.. Machine learning on human muscle transcriptomic data for biomarker discovery and tissue-specific drug target identification. . Front. Genet. 9::242
    [Crossref] [Google Scholar]
  26. 26.
    Muslu O, Hoyt CT, Lacerda M, Hofmann-Apitius M, Frohlich H. 2022.. GuiltyTargets: prioritization of novel therapeutic targets with network representation learning. . IEEE/ACM Trans. Comput. Biol. Bioinform. 19::491500
    [Crossref] [Google Scholar]
  27. 27.
    Zhavoronkov A, Ivanenkov YA, Aliper A, Veselov MS, Aladinskiy VA, et al. 2019.. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. . Nat. Biotechnol. 37::103840
    [Crossref] [Google Scholar]
  28. 28.
    Swanson K, Walther P, Leitz J, Mukherjee S, Wu JC, et al. 2024.. ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries. . Bioinformatics 40::btae416
    [Crossref] [Google Scholar]
  29. 29.
    Liu R, Wei L, Zhang P. 2021.. A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data. . Nat. Mach. Intell. 3::6875
    [Crossref] [Google Scholar]
  30. 30.
    Kavalci E, Hartshorn A. 2023.. Improving clinical trial design using interpretable machine learning based prediction of early trial termination. . Sci. Rep. 13::121
    [Crossref] [Google Scholar]
  31. 31.
    Pun FW, Ozerov IV, Zhavoronkov A. 2023.. AI-powered therapeutic target discovery. . Trends Pharmacol. Sci. 44::56172
    [Crossref] [Google Scholar]
  32. 32.
    Nishiga M, Liu C, Qi LS, Wu JC. 2022.. The use of new CRISPR tools in cardiovascular research and medicine. . Nat. Rev. Cardiol. 19::50521
    [Crossref] [Google Scholar]
  33. 33.
    Drakos SG, Badolia R, Makaju A, Kyriakopoulos CP, Wever-Pinzon O, et al. 2023.. Distinct transcriptomic and proteomic profile specifies patients who have heart failure with potential of myocardial recovery on mechanical unloading and circulatory support. . Circulation 147::40924
    [Crossref] [Google Scholar]
  34. 34.
    Burridge PW, Matsa E, Shukla P, Lin ZC, Churko JM, et al. 2014.. Chemically defined generation of human cardiomyocytes. . Nat. Methods 11::85560
    [Crossref] [Google Scholar]
  35. 35.
    Thomas D, Cunningham NJ, Shenoy S, Wu JC. 2022.. Human-induced pluripotent stem cells in cardiovascular research: current approaches in cardiac differentiation, maturation strategies, and scalable production. . Cardiovasc. Res. 118::2036
    [Crossref] [Google Scholar]
  36. 36.
    Serrano R, Feyen DAM, Bruyneel AAN, Hnatiuk AP, Vu MM, et al. 2023.. A deep learning platform to assess drug proarrhythmia risk. . Cell Stem Cell 30::8695.e4
    [Crossref] [Google Scholar]
  37. 37.
    Yu J, Vodyanik MA, Smuga-Otto K, Antosiewicz-Bourget J, Frane JL, et al. 2007.. Induced pluripotent stem cell lines derived from human somatic cells. . Science 318::191720
    [Crossref] [Google Scholar]
  38. 38.
    Takahashi K, Tanabe K, Ohnuki M, Narita M, Ichisaka T, et al. 2007.. Induction of pluripotent stem cells from adult human fibroblasts by defined factors. . Cell 131::86172
    [Crossref] [Google Scholar]
  39. 39.
    Musunuru K, Sheikh F, Gupta RM, Houser SR, Maher KO, et al. 2018.. Induced pluripotent stem cells for cardiovascular disease modeling and precision medicine: a scientific statement from the American Heart Association. . Circ. Genom. Precis. Med. 11::e000043
    [Google Scholar]
  40. 40.
    Yildirim Z, Kojic A, Yan CD, Wu MA, Vagelos R, Wu JC. 2022.. Generation of two induced pluripotent stem cell lines from dilated cardiomyopathy patients caused by heterozygous mutations in the HCN4 gene. . Stem Cell Res. 65::102951
    [Crossref] [Google Scholar]
  41. 41.
    Jimenez-Tellez N, Vera CD, Yildirim Z, Vicente Guevara J, Zhang T, Wu JC. 2023.. Generation of two iPSC lines from long QT syndrome patients carrying SNTA1 variants. . Stem Cell Res. 66::103003
    [Crossref] [Google Scholar]
  42. 42.
    Lian X, Bao X, Al-Ahmad A, Liu J, Wu Y, et al. 2014.. Efficient differentiation of human pluripotent stem cells to endothelial progenitors via small-molecule activation of WNT signaling. . Stem Cell Rep. 3::80416
    [Crossref] [Google Scholar]
  43. 43.
    Orlova VV, van den Hil FE, Petrus-Reurer S, Drabsch Y, Ten Dijke P, Mummery CL. 2014.. Generation, expansion and functional analysis of endothelial cells and pericytes derived from human pluripotent stem cells. . Nat. Protoc. 9::151431
    [Crossref] [Google Scholar]
  44. 44.
    Patsch C, Challet-Meylan L, Thoma EC, Urich E, Heckel T, et al. 2015.. Generation of vascular endothelial and smooth muscle cells from human pluripotent stem cells. . Nat. Cell Biol. 17::9941003
    [Crossref] [Google Scholar]
  45. 45.
    Williams IM, Wu JC. 2019.. Generation of endothelial cells from human pluripotent stem cells. . Arterioscler. Thromb. Vasc. Biol. 39::131729
    [Crossref] [Google Scholar]
  46. 46.
    Guadix JA, Orlova VV, Giacomelli E, Bellin M, Ribeiro MC, et al. 2017.. Human pluripotent stem cell differentiation into functional epicardial progenitor cells. . Stem Cell Rep. 9::175464
    [Crossref] [Google Scholar]
  47. 47.
    Zhang J, Tao R, Campbell KF, Carvalho JL, Ruiz EC, et al. 2019.. Functional cardiac fibroblasts derived from human pluripotent stem cells via second heart field progenitors. . Nat. Commun. 10::2238
    [Crossref] [Google Scholar]
  48. 48.
    Cheung C, Bernardo AS, Pedersen RA, Sinha S. 2014.. Directed differentiation of embryonic origin-specific vascular smooth muscle subtypes from human pluripotent stem cells. . Nat. Protoc. 9::92938
    [Crossref] [Google Scholar]
  49. 49.
    Wanjare M, Kuo F, Gerecht S. 2013.. Derivation and maturation of synthetic and contractile vascular smooth muscle cells from human pluripotent stem cells. . Cardiovasc. Res. 97::32130
    [Crossref] [Google Scholar]
  50. 50.
    Zhang Q, Jiang J, Han P, Yuan Q, Zhang J, et al. 2011.. Direct differentiation of atrial and ventricular myocytes from human embryonic stem cells by alternating retinoid signals. . Cell Res. 21::57987
    [Crossref] [Google Scholar]
  51. 51.
    Shen M, Zhao SR, Khokhar Y, Li L, Zhou Y, et al. 2023.. Protocol to generate cardiac pericytes from human induced pluripotent stem cells. . STAR Protoc. 4::102256
    [Crossref] [Google Scholar]
  52. 52.
    Shen M, Liu C, Wu JC. 2022.. Generation of embryonic origin-specific vascular smooth muscle cells from human induced pluripotent stem cells. . Methods Mol. Biol. 2429::23346
    [Crossref] [Google Scholar]
  53. 53.
    Ackermann M, Rafiei Hashtchin A, Manstein F, Carvalho Oliveira M, Kempf H, et al. 2022.. Continuous human iPSC-macrophage mass production by suspension culture in stirred tank bioreactors. . Nat. Protoc. 17::51339
    [Crossref] [Google Scholar]
  54. 54.
    Hinson JT, Chopra A, Nafissi N, Polacheck WJ, Benson CC, et al. 2015.. Titin mutations in iPS cells define sarcomere insufficiency as a cause of dilated cardiomyopathy. . Science 349::98286
    [Crossref] [Google Scholar]
  55. 55.
    Karakikes I, Stillitano F, Nonnenmacher M, Tzimas C, Sanoudou D, et al. 2015.. Correction of human phospholamban R14del mutation associated with cardiomyopathy using targeted nucleases and combination therapy. . Nat. Commun. 6::6955
    [Crossref] [Google Scholar]
  56. 56.
    Lan F, Lee AS, Liang P, Sanchez-Freire V, Nguyen PK, et al. 2013.. Abnormal calcium handling properties underlie familial hypertrophic cardiomyopathy pathology in patient-specific induced pluripotent stem cells. . Cell Stem Cell 12::10113
    [Crossref] [Google Scholar]
  57. 57.
    Hong L, Zhang M, Ly OT, Chen H, Sridhar A, et al. 2021.. Human induced pluripotent stem cell-derived atrial cardiomyocytes carrying an SCN5A mutation identify nitric oxide signaling as a mediator of atrial fibrillation. . Stem Cell Rep. 16::154254
    [Crossref] [Google Scholar]
  58. 58.
    Seeger T, Shrestha R, Lam CK, Chen C, McKeithan WL, et al. 2019.. A premature termination codon mutation in MYBPC3 causes hypertrophic cardiomyopathy via chronic activation of nonsense-mediated decay. . Circulation 139::799811
    [Crossref] [Google Scholar]
  59. 59.
    Garg P, Oikonomopoulos A, Chen H, Li Y, Lam CK, et al. 2018.. Genome editing of induced pluripotent stem cells to decipher cardiac channelopathy variant. . J. Am. Coll. Cardiol. 72::6275
    [Crossref] [Google Scholar]
  60. 60.
    Ma N, Zhang JZ, Itzhaki I, Zhang SL, Chen H, et al. 2018.. Determining the pathogenicity of a genomic variant of uncertain significance using CRISPR/Cas9 and human-induced pluripotent stem cells. . Circulation 138::266681
    [Crossref] [Google Scholar]
  61. 61.
    Wu SM, Hochedlinger K. 2011.. Harnessing the potential of induced pluripotent stem cells for regenerative medicine. . Nat. Cell Biol. 13::497505
    [Crossref] [Google Scholar]
  62. 62.
    Grafton F, Ho J, Ranjbarvaziri S, Farshidfar F, Budan A, et al. 2021.. Deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes. . eLife 10::e68714
    [Crossref] [Google Scholar]
  63. 63.
    Vera E, Studer L. 2015.. When rejuvenation is a problem: challenges of modeling late-onset neurodegenerative disease. . Development 142::308589
    [Crossref] [Google Scholar]
  64. 64.
    Kim C, Wong J, Wen J, Wang S, Wang C, et al. 2013.. Studying arrhythmogenic right ventricular dysplasia with patient-specific iPSCs. . Nature 494::10510
    [Crossref] [Google Scholar]
  65. 65.
    Lundy SD, Zhu WZ, Regnier M, Laflamme MA. 2013.. Structural and functional maturation of cardiomyocytes derived from human pluripotent stem cells. . Stem Cells Dev. 22::19912002
    [Crossref] [Google Scholar]
  66. 66.
    Lewis-Israeli YR, Wasserman AH, Gabalski MA, Volmert BD, Ming Y, et al. 2021.. Self-assembling human heart organoids for the modeling of cardiac development and congenital heart disease. . Nat. Commun. 12::5142
    [Crossref] [Google Scholar]
  67. 67.
    Abilez OJ, Yang H, Tian L, Wilson KD, Lyall EH, et al. 2022.. Micropatterned organoids enable modeling of the earliest stages of human cardiac vascularization. . bioRxiv 2022.07.08.499233. https://doi.org/10.1101/2022.07.08.499233
  68. 68.
    Liu C, Oikonomopoulos A, Sayed N, Wu JC. 2018.. Modeling human diseases with induced pluripotent stem cells: from 2D to 3D and beyond. . Development 145::dev156166
    [Crossref] [Google Scholar]
  69. 69.
    Sato T, Vries RG, Snippert HJ, van de Wetering M, Barker N, et al. 2009.. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. . Nature 459::26265
    [Crossref] [Google Scholar]
  70. 70.
    Sato T, Clevers H. 2013.. Growing self-organizing mini-guts from a single intestinal stem cell: mechanism and applications. . Science 340::119094
    [Crossref] [Google Scholar]
  71. 71.
    Zhang D, Shadrin IY, Lam J, Xian HQ, Snodgrass HR, Bursac N. 2013.. Tissue-engineered cardiac patch for advanced functional maturation of human ESC-derived cardiomyocytes. . Biomaterials 34::581320
    [Crossref] [Google Scholar]
  72. 72.
    Schaaf S, Shibamiya A, Mewe M, Eder A, Stöhr A, et al. 2011.. Human engineered heart tissue as a versatile tool in basic research and preclinical toxicology. . PLOS ONE 6::e26397
    [Crossref] [Google Scholar]
  73. 73.
    Nunes SS, Miklas JW, Liu J, Aschar-Sobbi R, Xiao Y, et al. 2013.. Biowire: a platform for maturation of human pluripotent stem cell-derived cardiomyocytes. . Nat. Methods 10::78187
    [Crossref] [Google Scholar]
  74. 74.
    Voges HK, Mills RJ, Elliott DA, Parton RG, Porrello ER, Hudson JE. 2017.. Development of a human cardiac organoid injury model reveals innate regenerative potential. . Development 144::111827
    [Google Scholar]
  75. 75.
    Tiburcy M, Hudson JE, Balfanz P, Schlick S, Meyer T, et al. 2017.. Defined engineered human myocardium with advanced maturation for applications in heart failure modeling and repair. . Circulation 135::183247
    [Crossref] [Google Scholar]
  76. 76.
    Thomas D, Kim H, Lopez N, Wu JC. 2021.. Fabrication of 3D cardiac microtissue arrays using human iPSC-derived cardiomyocytes, cardiac fibroblasts, and endothelial cells. . J. Vis. Exp. 169::e61879
    [Google Scholar]
  77. 77.
    Richiardone E, Van den Bossche V, Corbet C. 2022.. Metabolic studies in organoids: current applications, opportunities and challenges. . Organoids 1::85105
    [Crossref] [Google Scholar]
  78. 78.
    Eberle C, Stichling S. 2022.. Environmental health influences in pregnancy and risk of gestational diabetes mellitus: a systematic review. . BMC Public Health 22::1572
    [Crossref] [Google Scholar]
  79. 79.
    Mills RJ, Titmarsh DM, Koenig X, Parker BL, Ryall JG, et al. 2017.. Functional screening in human cardiac organoids reveals a metabolic mechanism for cardiomyocyte cell cycle arrest. . PNAS 114::E837281
    [Crossref] [Google Scholar]
  80. 80.
    Lindoso RS, Kasai-Brunswick TH, Monnerat Cahli G, Collino F, Bastos Carvalho A, et al. 2019.. Proteomics in the world of induced pluripotent stem cells. . Cells 8::703
    [Crossref] [Google Scholar]
  81. 81.
    Alarcon-Barrera JC, Kostidis S, Ondo-Mendez A, Giera M. 2022.. Recent advances in metabolomics analysis for early drug development. . Drug Discov. Today 27::176373
    [Crossref] [Google Scholar]
  82. 82.
    Aragam KG, Jiang T, Goel A, Kanoni S, Wolford BN, et al. 2022.. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. . Nat. Genet. 54::180315
    [Crossref] [Google Scholar]
  83. 83.
    Silva S, Nitsch D, Fatumo S. 2023.. Genome-wide association studies on coronary artery disease: a systematic review and implications for populations of different ancestries. . PLOS ONE 18::e0294341
    [Crossref] [Google Scholar]
  84. 84.
    Wells MF, Nemesh J, Ghosh S, Mitchell JM, Salick MR, et al. 2023.. Natural variation in gene expression and viral susceptibility revealed by neural progenitor cell villages. . Cell Stem Cell 30::31232.e13
    [Crossref] [Google Scholar]
  85. 85.
    Mitchell JM, Nemesh J, Ghosh S, Handsaker RE, Mello CJ, et al. 2020.. Mapping genetic effects on cellular phenotypes with “cell villages. .” bioRxiv 2020.06.29.174383. https://doi.org/10.1101/2020.06.29.174383
  86. 86.
    Wu JC, Woo YJ, Mayerle M, Harrington RA, Quertermous T. 2019.. Stanford Cardiovascular Institute: at the forefront of cardiovascular research. . Circ. Res. 124::142024
    [Crossref] [Google Scholar]
  87. 87.
    Tucker NR, Chaffin M, Fleming SJ, Hall AW, Parsons VA, et al. 2020.. Transcriptional and cellular diversity of the human heart. . Circulation 142::46682
    [Crossref] [Google Scholar]
  88. 88.
    Wang L, Yu P, Zhou B, Song J, Li Z, et al. 2020.. Single-cell reconstruction of the adult human heart during heart failure and recovery reveals the cellular landscape underlying cardiac function. . Nat. Cell Biol. 22::10819
    [Crossref] [Google Scholar]
  89. 89.
    Litviňuková M, Talavera-López C, Maatz H, Reichart D, Worth CL, et al. 2020.. Cells of the adult human heart. . Nature 588::46672
    [Crossref] [Google Scholar]
  90. 90.
    Chaffin M, Papangeli I, Simonson B, Akkad AD, Hill MC, et al. 2022.. Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy. . Nature 608::17480
    [Crossref] [Google Scholar]
  91. 91.
    Koenig AL, Shchukina I, Amrute J, Andhey PS, Zaitsev K, et al. 2022.. Single-cell transcriptomics reveals cell-type-specific diversification in human heart failure. . Nat. Cardiovasc. Res. 1::26380
    [Crossref] [Google Scholar]
  92. 92.
    Knight-Schrijver VR, Davaapil H, Bayraktar S, Ross ADB, Kanemaru K, et al. 2022.. A single-cell comparison of adult and fetal human epicardium defines the age-associated changes in epicardial activity. . Nat. Cardiovasc. Res. 1::121529
    [Crossref] [Google Scholar]
  93. 93.
    Friedman CE, Nguyen Q, Lukowski SW, Helfer A, Chiu HS, et al. 2018.. Single-cell transcriptomic analysis of cardiac differentiation from human PSCs reveals HOPX-dependent cardiomyocyte maturation. . Cell Stem Cell 23::58698.e8
    [Crossref] [Google Scholar]
  94. 94.
    Wang J, Morgan W, Saini A, Liu T, Lough J, Han L. 2022.. Single-cell transcriptomic profiling reveals specific maturation signatures in human cardiomyocytes derived from LMNB2-inactivated induced pluripotent stem cells. . Front. Cell Dev. Biol. 10::895162
    [Crossref] [Google Scholar]
  95. 95.
    Cheng S, Brenière-Letuffe D, Ahola V, Wong AOT, Keung HY, et al. 2023.. Single-cell RNA sequencing reveals maturation trajectory in human pluripotent stem cell-derived cardiomyocytes in engineered tissues. . iScience 26::106302
    [Crossref] [Google Scholar]
  96. 96.
    Sallam K, Thomas D, Gaddam S, Lopez N, Beck A, et al. 2022.. Modeling effects of immunosuppressive drugs on human hearts using induced pluripotent stem cell-derived cardiac organoids and single-cell RNA sequencing. . Circulation 145::136769
    [Crossref] [Google Scholar]
  97. 97.
    Ho DLL, Lee S, Du J, Weiss JD, Tam T, et al. 2022.. Large-scale production of wholly cellular bioinks via the optimization of human induced pluripotent stem cell aggregate culture in automated bioreactors. . Adv. Healthc. Mater. 11::e2201138
    [Crossref] [Google Scholar]
  98. 98.
    Palmer JA, Smith AM, Gryshkova V, Donley ELR, Valentin JP, Burrier RE. 2020.. A targeted metabolomics-based assay using human induced pluripotent stem cell-derived cardiomyocytes identifies structural and functional cardiotoxicity potential. . Toxicol. Sci. 174::21840
    [Crossref] [Google Scholar]
  99. 99.
    Lin W, Mousavi F, Blum BC, Heckendorf CF, Moore J, et al. 2023.. Integrated metabolomics and proteomics reveal biomarkers associated with hemodialysis in end-stage kidney disease. . Front. Pharmacol. 14::1243505
    [Crossref] [Google Scholar]
  100. 100.
    Zhang A, Wu Z, Wu E, Wu M, Snyder MP, et al. 2023.. Leveraging physiology and artificial intelligence to deliver advancements in health care. . Physiol. Rev. 103::242350
    [Crossref] [Google Scholar]
  101. 101.
    Zhang A, Xing L, Zou J, Wu JC. 2022.. Shifting machine learning for healthcare from development to deployment and from models to data. . Nat. Biomed. Eng. 6::133045
    [Crossref] [Google Scholar]
  102. 102.
    Daina A, Michielin O, Zoete V. 2017.. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. . Sci. Rep. 7::42717
    [Crossref] [Google Scholar]
  103. 103.
    Mayr A, Klambauer G, Unterthiner T, Hochreiter S. 2016.. DeepTox: toxicity prediction using deep learning. . Front. Environ. Sci. 3::80
    [Crossref] [Google Scholar]
  104. 104.
    Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE. 2017.. Neural message passing for quantum chemistry. . PMLR 70::126372
    [Google Scholar]
  105. 105.
    Wu Z, Ramsundar B, Feinberg EN, Gomes J, Geniesse C, et al. 2018.. MoleculeNet: a benchmark for molecular machine learning. . Chem. Sci. 9::51330
    [Crossref] [Google Scholar]
  106. 106.
    Yang K, Swanson K, Jin W, Coley C, Eiden P, et al. 2019.. Analyzing learned molecular representations for property prediction. . J. Chem. Inf. Model. 59::337088
    [Crossref] [Google Scholar]
  107. 107.
    Miljković F, Martinsson A, Obrezanova O, Williamson B, Johnson M, et al. 2021.. Machine learning models for human in vivo pharmacokinetic parameters with in-house validation. . Mol. Pharm. 18::452030
    [Crossref] [Google Scholar]
  108. 108.
    Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, et al. 2019.. Applications of machine learning in drug discovery and development. . Nat. Rev. Drug Discov. 18::46377
    [Crossref] [Google Scholar]
  109. 109.
    Biswas N, Chakrabarti S. 2020.. Artificial intelligence (AI)-based systems biology approaches in multi-omics data analysis of cancer. . Front. Oncol. 10::588221
    [Crossref] [Google Scholar]
  110. 110.
    Baumgartner R, Arora P, Bath C, Burljaev D, Ciereszko K, et al. 2023.. Fair and equitable AI in biomedical research and healthcare: social science perspectives. . Artif. Intel. Med. 144::102658
    [Crossref] [Google Scholar]
  111. 111.
    Khan B, Fatima H, Qureshi A, Kumar S, Hanan A, et al. 2023.. Drawbacks of artificial intelligence and their potential solutions in the healthcare sector. . Biomed. Mater. Devices 1::73138
    [Crossref] [Google Scholar]
  112. 112.
    Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, et al. 2020.. A deep learning approach to antibiotic discovery. . Cell 180::688702.e13
    [Crossref] [Google Scholar]
  113. 113.
    Öztürk H, Özgür A, Ozkirimli E. 2018.. DeepDTA: deep drug–target binding affinity prediction. . Bioinformatics 34::i82129
    [Crossref] [Google Scholar]
  114. 114.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, et al. 2000.. The Protein Data Bank. . Nucleic Acids Res. 28::23542
    [Crossref] [Google Scholar]
  115. 115.
    Jumper J, Evans R, Pritzel A, Green T, Figurnov M, et al. 2021.. Highly accurate protein structure prediction with AlphaFold. . Nature 596::58389
    [Crossref] [Google Scholar]
  116. 116.
    Corso G, Stärk H, Jing B, Barzilay R, Jaakkola T. 2022.. DiffDock: diffusion steps, twists, and turns for molecular docking. . arXiv:2210.01776 [q-bio.BM ]
  117. 117.
    Powers AS, Yu HH, Suriana P, Koodli RV, Lu T, et al. 2023.. Geometric deep learning for structure-based ligand design. . ACS Cent. Sci. 9::225767
    [Crossref] [Google Scholar]
  118. 118.
    Jain M, Raparthy SC, Hernandez-Garcia A, Rector-Brooks J, Bengio Y, et al. 2023.. Multi-objective GFlowNets. . PMLR 202::1463153
    [Google Scholar]
  119. 119.
    Swanson K, Liu G, Catacutan D, Zou J, Stokes J. 2024.. Generative AI for designing and validating easily synthesizable and structurally novel antibiotics. . Nat. Mach. Intell. 6:33853
    [Google Scholar]
  120. 120.
    Albanese A, Swaney JM, Yun DH, Evans NB, Antonucci JM, et al. 2020.. Multiscale 3D phenotyping of human cerebral organoids. . Sci. Rep. 10::21487
    [Crossref] [Google Scholar]
  121. 121.
    Beghin A, Grenci G, Sahni G, Guo S, Rajendiran H, et al. 2022.. Automated high-speed 3D imaging of organoid cultures with multi-scale phenotypic quantification. . Nat. Methods 19::88192
    [Crossref] [Google Scholar]
  122. 122.
    Metzger JJ, Pereda C, Adhikari A, Haremaki T, Galgoczi S, et al. 2022.. Deep-learning analysis of micropattern-based organoids enables high-throughput drug screening of Huntington's disease models. . Cell Rep. Methods 2::100297
    [Crossref] [Google Scholar]
  123. 123.
    Kandasamy K, Chuah JK, Su R, Huang P, Eng KG, et al. 2015.. Prediction of drug-induced nephrotoxicity and injury mechanisms with human induced pluripotent stem cell-derived cells and machine learning methods. . Sci. Rep. 5::12337
    [Crossref] [Google Scholar]
  124. 124.
    Yang H, Obrezanova O, Pointon A, Stebbeds W, Francis J, et al. 2023.. Prediction of inotropic effect based on calcium transients in human iPSC-derived cardiomyocytes and machine learning. . Toxicol. Appl. Pharmacol. 459::116342
    [Crossref] [Google Scholar]
  125. 125.
    Kowalczewski A, Sakolish C, Hoang P, Liu X, Jacquir S, et al. 2022.. Integrating nonlinear analysis and machine learning for human induced pluripotent stem cell-based drug cardiotoxicity testing. . J. Tissue Eng. Regen. Med. 16::73243
    [Crossref] [Google Scholar]
  126. 126.
    Juhola M, Penttinen K, Joutsijoki H, Aalto-Setälä K. 2021.. Analysis of drug effects on iPSC cardiomyocytes with machine learning. . Ann. Biomed. Eng. 49::12938
    [Crossref] [Google Scholar]
  127. 127.
    Theodoris CV, Li M, White MP, Liu L, He D, et al. 2015.. Human disease modeling reveals integrated transcriptional and epigenetic mechanisms of NOTCH1 haploinsufficiency. . Cell 160::107286
    [Crossref] [Google Scholar]
  128. 128.
    Sayed N, Liu C, Ameen M, Himmati F, Zhang JZ, et al. 2020.. Clinical trial in a dish using iPSCs shows lovastatin improves endothelial dysfunction and cellular cross-talk in LMNA cardiomyopathy. . Sci. Transl. Med. 12::eaax9276
    [Crossref] [Google Scholar]
  129. 129.
    Peng BY, Singh AK, Tsai CY, Chan CH, Deng YH, et al. 2023.. Platelet-derived biomaterial with hyaluronic acid alleviates temporal-mandibular joint osteoarthritis: clinical trial from dish to human. . J. Biomed. Sci. 30::77
    [Crossref] [Google Scholar]
  130. 130.
    Blinova K, Schocken D, Patel D, Daluwatte C, Vicente J, et al. 2019.. Clinical trial in a dish: personalized stem cell-derived cardiomyocyte assay compared with clinical trial results for two QT-prolonging drugs. . Clin. Transl. Sci. 12::68797
    [Crossref] [Google Scholar]
  131. 131.
    Thomas D, Choi S, Alamana C, Parker KK, Wu JC. 2022.. Cellular and engineered organoids for cardiovascular models. . Circ. Res. 130::1780802
    [Crossref] [Google Scholar]
  132. 132.
    Wu F, He Q, Li F, Yang X. 2023.. A review of protocols for engineering human cardiac organoids. . Heliyon 9::e19938
    [Crossref] [Google Scholar]
  133. 133.
    Kim H, Kamm RD, Vunjak-Novakovic G, Wu JC. 2022.. Progress in multicellular human cardiac organoids for clinical applications. . Cell Stem Cell 29::50314
    [Crossref] [Google Scholar]
  134. 134.
    Mitchell JM, Nemesh J, Ghosh S, Handsaker RE, Mello CJ, et al. 2020.. Mapping genetic effects on cellular phenotypes with “cell villages. .” bioRxiv 2020.06.29.174383. https://doi.org/10.1101/2020.06.29.174383
  135. 135.
    Richards DJ, Li Y, Kerr CM, Yao J, Beeson GC, et al. 2020.. Human cardiac organoids for the modelling of myocardial infarction and drug cardiotoxicity. . Nat. Biomed. Eng. 4::44662
    [Crossref] [Google Scholar]
  136. 136.
    Lancaster MA, Knoblich JA. 2014.. Organogenesis in a dish: modeling development and disease using organoid technologies. . Science 345::1247125
    [Crossref] [Google Scholar]
  137. 137.
    Chinta MS, desJardins-Park HE, Wan DC, Longaker MT. 2020.. “ Tissues in a dish”: a review of organoids in plastic surgery. . Plast. Reconstruct. Surg. Glob. Open 8::e2787
    [Crossref] [Google Scholar]
  138. 138.
    Bershteyn M, Kriegstein AR. 2013.. Cerebral organoids in a dish: progress and prospects. . Cell 155::1920
    [Crossref] [Google Scholar]
  139. 139.
    Thomas D, de Jesus Perez VA, Sayed N. 2022.. An evidence appraisal of heart organoids in a dish and commensurability to human heart development in vivo. . BMC Cardiovasc. Disord. 22::122
    [Crossref] [Google Scholar]
  140. 140.
    Hargrove-Grimes P, Low LA, Tagle DA. 2021.. Microphysiological systems: stakeholder challenges to adoption in drug development. . Cells Tissues Organs 211::26981
    [Crossref] [Google Scholar]
  141. 141.
    Ingber DE. 2022.. Human organs-on-chips for disease modelling, drug development and personalized medicine. . Nat. Rev. Genet. 23::46791
    [Crossref] [Google Scholar]
  142. 142.
    Wang P, Wang Y, Qin J. 2023.. Multi-organ microphysiological system: a new paradigm for COVID-19 research. . Organs Chip 5::100029
    [Crossref] [Google Scholar]
  143. 143.
    Trapecar M. 2022.. Multiorgan microphysiological systems as tools to interrogate interorgan crosstalk and complex diseases. . FEBS Lett. 596::68195
    [Crossref] [Google Scholar]
  144. 144.
    Abulaiti M, Yalikun Y, Murata K, Sato A, Sami MM, et al. 2020.. Establishment of a heart-on-a-chip microdevice based on human iPS cells for the evaluation of human heart tissue function. . Sci. Rep. 10::19201
    [Crossref] [Google Scholar]
  145. 145.
    Gu B, Han K, Cao H, Huang X, Li X, et al. 2024.. Heart-on-a-chip systems with tissue-specific functionalities for physiological, pathological, and pharmacological studies. . Mater. Today Bio 24::100914
    [Crossref] [Google Scholar]
  146. 146.
    Tang Y, Tian F, Miao X, Wu D, Wang Y, et al. 2023.. Heart-on-a-chip using human iPSC-derived cardiomyocytes with an integrated vascular endothelial layer based on a culture patch as a potential platform for drug evaluation. . Biofabrication 15::015010
    [Crossref] [Google Scholar]
  147. 147.
    Marsano A, Conficconi C, Lemme M, Occhetta P, Gaudiello E, et al. 2016.. Beating heart on a chip: a novel microfluidic platform to generate functional 3D cardiac microtissues. . Lab Chip 16:(3):599610
    [Crossref] [Google Scholar]
  148. 148.
    Kujala VJ, Pasqualini FS, Goss JA, Nawroth JC, Parker KK. 2016.. Laminar ventricular myocardium on a microelectrode array-based chip. . J. Mater. Chem. B 4::353443
    [Crossref] [Google Scholar]
  149. 149.
    Zhang YS, Arneri A, Bersini S, Shin SR, Zhu K, et al. 2016.. Bioprinting 3D microfibrous scaffolds for engineering endothelialized myocardium and heart-on-a-chip. . Biomaterials 110::4559
    [Crossref] [Google Scholar]
  150. 150.
    Ronaldson-Bouchard K, Teles D, Yeager K, Tavakol DN, Zhao Y, et al. 2022.. A multi-organ chip with matured tissue niches linked by vascular flow. . Nat. Biomed. Eng. 6::35171
    [Crossref] [Google Scholar]
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