Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Application of new approach methodologies for nonclinical safety assessment of drug candidates

Abstract

The development of new approach methodologies (NAMs) and advances with in vitro testing systems have prompted revisions in regulatory guidelines and inspired dedicated in vitro/ex vivo studies for nonclinical safety assessment. This Review by a safety reflection initiative subgroup of the European Federation of Pharmaceutical Industries and Associations (EFPIA)/Preclinical Development Expert Group (PDEG) summarizes the current state and potential application of in vitro studies using human-derived material for safety assessment in drug development. It focuses on case studies from recent projects in which animal models alone proved to be limited or inadequate for safety testing. It further highlights four categories of drug candidates for which alternative in vitro approaches are applicable and discusses progress in using in vitro testing solutions for safety assessment in these categories. Finally, the article highlights new risk assessment strategies, initiatives and consortia promoting the advancement of NAMs. This collective work is meant to encourage the use of NAMs for more human-relevant safety assessment, which should ultimately result in reduced animal testing for drug development.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Categories of drug candidates for safety assessment based on their pharmacological target.
Fig. 2: Example from category 1: animal species lack the target.
Fig. 3: Example from category 2: cross-species target issues.
Fig. 4: Example from category 3: non-mammalian targets.

Similar content being viewed by others

References

  1. Greaves, P., Williams, A. & Eve, M. First dose of potential new medicines to humans: how animals help. Nat. Rev. Drug Discov. 3, 226–236 (2004).

    Article  CAS  PubMed  Google Scholar 

  2. Monticello, T. M. et al. Current nonclinical testing paradigm enables safe entry to first-in-human clinical trials: the IQ consortium nonclinical to clinical translational database. Toxicol. Appl. Pharm. 334, 100–109 (2017).

    Article  CAS  Google Scholar 

  3. Bailey, J., Thew, M. & Balls, M. An analysis of the use of animal models in predicting human toxicology and drug safety. Altern. Lab. Anim. 42, 181–199 (2014).

    Article  CAS  PubMed  Google Scholar 

  4. Waring, M. J. et al. An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat. Rev. Drug Discov. 14, 475–486 (2015).

    Article  CAS  PubMed  Google Scholar 

  5. Martignoni, M., Groothuis, G. M. M. & de Kanter, R. Species differences between mouse, rat, dog, monkey and human CYP-mediated drug metabolism, inhibition and induction. Expert Opin. Drug Met. 2, 875–894 (2006).

    Article  CAS  Google Scholar 

  6. Kararli, T. T. Comparison of the gastrointestinal anatomy, physiology, and biochemistry of humans and commonly used laboratory animals. Biopharm. Drug Dispos. 16, 351–380 (1995).

    Article  CAS  PubMed  Google Scholar 

  7. Bjornson-Hooper, Z. B. et al. A comprehensive atlas of immunological differences between humans, mice and non-human primates. Front. Immunol. 13, 867015 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Krause, C. et al. Preclinical species gene expression database: development and meta-analysis. Front. Genet. 13, 1078050 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Worley, K. C. et al. The common marmoset genome provides insight into primate biology and evolution. Nat. Genet. 46, 850–857 (2014).

    Article  Google Scholar 

  10. Namdari, R. et al. Species selection for nonclinical safety assessment of drug candidates: examples of current industry practice. Regul. Toxicol. Pharm. 126, 105029 (2021).

    Article  CAS  Google Scholar 

  11. European Medicines Agency. Guideline on strategies to identify and mitigate risks for first-in-human and early clinical trials with investigational medicinal products. https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-strategies-identify-mitigate-risks-first-human-early-clinical-trials-investigational_en.pdf (2017).

  12. Food and Drug Administration, HHS. International Conference on Harmonisation; addendum to International Conference on Harmonisation Guidance on S6 Preclinical Safety Evaluation of Biotechnology-Derived Pharmaceuticals; availability. Notice. Fed. Regist. 77, 29665–29666 (2012).

    Google Scholar 

  13. Congress, 117th US. S.5002—FDA Modernization Act 2.0 117th Congress (2021-2022). https://www.congress.gov/bill/117th-congress/senate-bill/5002 (2022).

  14. Avila, A. M. et al. An FDA/CDER perspective on nonclinical testing strategies: classical toxicology approaches and new approach methodologies (NAMs). Regul. Toxicol. Pharmacol. 114, 104662 (2020).

    Article  CAS  PubMed  Google Scholar 

  15. Avila, A. M. et al. Gaps and challenges in nonclinical assessments of pharmaceuticals: an FDA/CDER perspective on considerations for development of new approach methodologies. Regul. Toxicol. Pharmacol. 139, 105345 (2023).

    Article  CAS  PubMed  Google Scholar 

  16. Kitano, H. Biological robustness. Nat. Rev. Genet. 5, 826–837 (2004).

    Article  CAS  PubMed  Google Scholar 

  17. Clark, M. & Steger-Hartmann, T. A big data approach to the concordance of the toxicity of pharmaceuticals in animals and humans. Regul. Toxicol. Pharm. 96, 94–105 (2018).

    Article  CAS  Google Scholar 

  18. NRamos, K. S., Downey, A. & Yost, O. C. (eds). Nonhuman Primate Models in Biomedical Research. https://doi.org/10.17226/26857 (2023).

  19. Pognan, F. et al. The evolving role of investigative toxicology in the pharmaceutical industry. Nat. Rev. Drug Discov. 22, 317–335 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Schaller, T. H. et al. First in human dose calculation of a single-chain bispecific antibody targeting glioma using the MABEL approach. J. Immunother. Cancer 8, e000213 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Ji, Y. et al. Model‐informed drug development for immuno‐oncology agonistic anti‐GITR antibody GWN323: dose selection based on MABEL and biologically active dose. Clin. Transl. Sci. 15, 2218–2229 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Dudal, S. et al. Application of a MABEL approach for a T-cell-bispecific monoclonal antibody. J. Immunother. 39, 279–289 (2016).

    Article  CAS  PubMed  Google Scholar 

  23. Muller, P. Y., Milton, M., Lloyd, P., Sims, J. & Brennan, F. R. The minimum anticipated biological effect level (MABEL) for selection of first human dose in clinical trials with monoclonal antibodies. Curr. Opin. Biotechnol. 20, 722–729 (2009).

    Article  CAS  PubMed  Google Scholar 

  24. Beilmann, M. et al. Optimizing drug discovery by investigative toxicology: current and future trends. ALTEX 36, 289–313 (2018).

    PubMed  Google Scholar 

  25. Mulder, P. et al. Predicting cardiac safety using human induced pluripotent stem cell-derived cardiomyocytes combined with multi-electrode array (MEA) technology: a conference report. J. Pharmacol. Toxicol. Methods 91, 36–42 (2018).

    Article  CAS  PubMed  Google Scholar 

  26. Rana, P., Aleo, M. D., Gosink, M. & Will, Y. Evaluation of in vitro mitochondrial toxicity assays and physicochemical properties for prediction of organ toxicity using 228 pharmaceutical drugs. Chem. Res. Toxicol. 32, 156–167 (2019).

    Article  CAS  PubMed  Google Scholar 

  27. Hynes, J., Carey, C. & Will, Y. Fluorescence‐based microplate assays for in vitro assessment of mitochondrial toxicity, metabolic perturbation, and cellular oxygenation. Curr. Protoc. Toxicol. 70, 2.16.1–2.16.30 (2016).

    Article  PubMed  Google Scholar 

  28. Matsui, T. & Shinozawa, T. Human organoids for predictive toxicology research and drug development. Front. Genet. 12, 767621 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Mina, S. G. et al. Assessment of drug-induced toxicity biomarkers in the brain microphysiological system (MPS) using targeted and untargeted molecular profiling. Front. Big Data 2, 23 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Wang, X. et al. Application of immunocompetent microphysiological systems in drug development: current perspective and recommendations. ALTEX 40, 314–336 (2022).

    PubMed  Google Scholar 

  31. Gjorevski, N. et al. Neutrophilic infiltration in organ-on-a-chip model of tissue inflammation. Lab. Chip 20, 3365–3374 (2020).

    Article  CAS  PubMed  Google Scholar 

  32. McAleer, C. W. et al. Multi-organ system for the evaluation of efficacy and off-target toxicity of anticancer therapeutics. Sci. Transl. Med. 11, eaav1386 (2019).

    Article  CAS  PubMed  Google Scholar 

  33. Carmichael, P. L. et al. Ready for regulatory use: NAMs and NGRA for chemical safety assurance. Altex 39, 359–366 (2022).

    PubMed  Google Scholar 

  34. Mozneb, M. et al. Multi-lineage heart-chip models drug cardiotoxicity and enhances maturation of human stem cell-derived cardiovascular cells. Lab. Chip 24, 869–881 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Liu, S., Fang, C., Zhong, C., Li, J. & Xiao, Q. Recent advances in pluripotent stem cell-derived cardiac organoids and heart-on-chip applications for studying anti-cancer drug-induced cardiotoxicity. Cell Biol. Toxicol. 39, 2527–2549 (2023).

    Article  PubMed  Google Scholar 

  36. Roux, G. L. et al. Proof-of-concept study of drug brain permeability between in vivo human brain and an in vitro iPSCs-human blood-brain barrier model. Sci. Rep. 9, 16310 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Hajal, C. et al. Engineered human blood–brain barrier microfluidic model for vascular permeability analyses. Nat. Protoc. 17, 95–128 (2022).

    Article  CAS  PubMed  Google Scholar 

  38. Harper, J. et al. An approved in vitro approach to preclinical safety and efficacy evaluation of engineered T cell receptor anti-CD3 bispecific (ImmTAC) molecules. PLoS ONE 13, e0205491 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Parng, C. et al. Induction and impact of anti-drug responses elicited by a human recombinant coagulation factor FXaI16L in preclinical species. AAPS J. 21, 52 (2019).

    Article  PubMed  Google Scholar 

  40. Clarke, J. et al. Evaluation of a surrogate antibody for preclinical safety testing of an anti-CD11a monoclonal antibody. Regul. Toxicol. Pharmacol. 40, 219–226 (2004).

    Article  CAS  PubMed  Google Scholar 

  41. Bugelski, P. J. & Martin, P. L. Concordance of preclinical and clinical pharmacology and toxicology of therapeutic monoclonal antibodies and fusion proteins: cell surface targets. Br. J. Pharmacol. 166, 823–846 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Martin, P. L. & Bugelski, P. J. Concordance of preclinical and clinical pharmacology and toxicology of monoclonal antibodies and fusion proteins: soluble targets. Br. J. Pharmacol. 166, 806–822 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Boudousquie, C. et al. Polyfunctional response by ImmTAC (IMCgp100) redirected CD8+ and CD4+ T cells. Immunology 152, 425–438 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Howlett, S., Carter, T. J., Shaw, H. M. & Nathan, P. D. Tebentafusp: a first-in-class treatment for metastatic uveal melanoma. Ther. Adv. Méd. Oncol. 15, 17588359231160140 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Ryan, P. C. et al. In vitro MABEL approach for nonclinical safety assessment of MEDI-565 (MT111). ALTEX Proceedings, 1/12, Proceedings of W8. https://proceedings.altex.org/data/2012-01/085087_Ryan31.pdf (2012).

  46. Moek, K. L. et al. 427P. Phase I study of AMG 211/MEDI-565 administered as continuous intravenous infusion (cIV) for relapsed/refractory gastrointestinal (GI) adenocarcinoma. Ann. Oncol. 29, viii139–viii140 (2018).

    Google Scholar 

  47. Pishvaian, M. et al. Phase 1 dose escalation study of MEDI-565, a bispecific T-cell engager that targets human carcinoembryonic antigen, in patients with advanced gastrointestinal adenocarcinomas. Clin. Colorectal Cancer 15, 345–351 (2016).

    PubMed  Google Scholar 

  48. Kinugasa, T. et al. Expression of four CEA family antigens (CEA, NCA, BGP and CGM2) in normal and cancerous gastric epithelial cells: up‐regulation of BGP and CGM2 in carcinomas. Int. J. Cancer 76, 148–153 (1998).

    Article  CAS  PubMed  Google Scholar 

  49. Teijeira, A. et al. Three-dimensional colon cancer organoids model the response to CEA-CD3 T-cell engagers. Theranostics 12, 1373–1387 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Harter, M. F. et al. Analysis of off-tumour toxicities of T-cell-engaging bispecific antibodies via donor-matched intestinal organoids and tumouroids. Nat. Biomed. Eng. 8, 345–360 (2024).

    Article  CAS  PubMed  Google Scholar 

  51. Kebenko, M. et al. A multicenter phase 1 study of solitomab (MT110, AMG 110), a bispecific EpCAM/CD3 T-cell engager (BiTE®) antibody construct, in patients with refractory solid tumors. Oncoimmunology 7, e1450710 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Wang, L. et al. Efficient tumor regression by adoptively transferred CEA-specific CAR-T cells associated with symptoms of mild cytokine release syndrome. Oncoimmunology 5, e1211218 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Amann, M. et al. Therapeutic window of MuS110, a single-chain antibody construct bispecific for murine EpCAM and murine CD3. Cancer Res. 68, 143–151 (2008).

    Article  CAS  PubMed  Google Scholar 

  54. Park, S. E., Georgescu, A. & Huh, D. Organoids-on-a-chip. Science 364, 960–965 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Bar-Ephraim, Y. E., Kretzschmar, K. & Clevers, H. Organoids in immunological research. Nat. Rev. Immunol. 20, 279–293 (2020).

    Article  CAS  PubMed  Google Scholar 

  56. Hammel, J. H., Cook, S. R., Belanger, M. C., Munson, J. M. & Pompano, R. R. Modeling immunity in vitro: slices, chips, and engineered tissues. Annu. Rev. Biomed. Eng. 23, 461–491 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Ronaldson-Bouchard, K. et al. A multi-organ chip with matured tissue niches linked by vascular flow. Nat. Biomed. Eng. 6, 351–371 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Koning, J. J. et al. A multi-organ-on-chip approach to investigate how oral exposure to metals can cause systemic toxicity leading to Langerhans cell activation in skin. Front. Toxicol. 3, 824825 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Tao, T. et al. Microengineered multi‐organoid system from hiPSCs to recapitulate human liver‐islet axis in normal and type 2 diabetes. Adv. Sci. 9, 2103495 (2022).

    Article  Google Scholar 

  60. Cecen, B. et al. Multi-organs-on-chips for testing small-molecule drugs: challenges and perspectives. Pharmaceutics 13, 1657 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Mandrycky, C. J., Howard, C. C., Rayner, S. G., Shin, Y. J. & Zheng, Y. Organ-on-a-chip systems for vascular biology. J. Mol. Cell Cardiol. 159, 1–13 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Hachey, S. J. et al. A human vascularized micro-tumor model of patient-derived colorectal cancer recapitulates clinical disease. Transl. Res. 255, 97–108 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Kim, S., Wan, Z., Jeon, J. S. & Kamm, R. D. Microfluidic vascular models of tumor cell extravasation. Front. Oncol. 12, 1052192 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Palikuqi, B. et al. Adaptable haemodynamic endothelial cells for organogenesis and tumorigenesis. Nature 585, 426–432 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Yu, J. et al. Perfusable micro-vascularized 3D tissue array for high-throughput vascular phenotypic screening. Nano Converg. 9, 16 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Johnson, D. E. & Wolfgang, G. H. I. Predicting human safety: screening and computational approaches. Drug Discov. Today 5, 445–454 (2000).

    Article  CAS  PubMed  Google Scholar 

  67. Mestres, J. & Gregori-Puigjané, E. Conciliating binding efficiency and polypharmacology. Trends Pharmacol. Sci. 30, 470–474 (2009).

    Article  CAS  PubMed  Google Scholar 

  68. Peón, A., Naulaerts, S. & Ballester, P. J. Predicting the reliability of drug-target interaction predictions with maximum coverage of target space. Sci. Rep. 7, 3820 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Metz, J. T. & Hajduk, P. J. Rational approaches to targeted polypharmacology: creating and navigating protein–ligand interaction networks. Curr. Opin. Chem. Biol. 14, 498–504 (2010).

    Article  CAS  PubMed  Google Scholar 

  70. Gradl, S. et al. Abstract ND04: BAY 2666605: the first PDE3A-SLFN12 complex inducer for cancer therapy. Cancer Res. 82(12 Suppl.), ND04 (2022).

    Article  Google Scholar 

  71. Garvie, C. W. et al. Structure of PDE3A-SLFN12 complex reveals requirements for activation of SLFN12 RNase. Nat. Commun. 12, 4375 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Greulich, H. Velcrin compounds activate the SLFN12 tRNase to induce tomoptosis. Cell Chem. Biol. 31, 1039–1043 (2024).

    Article  CAS  PubMed  Google Scholar 

  73. Lewis, T. A. et al. Discovery of BAY 2666605, a molecular glue for PDE3A and SLFN12. ACS Med. Chem. Lett. 15, 1662–1667 (2024).

    Article  CAS  PubMed  Google Scholar 

  74. Zhu, K. et al. HER2-targeted therapies in cancer: a systematic review. Biomark. Res. 12, 16 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Phillips, G. L. et al. Trastuzumab does not bind rat or mouse ErbB2/neu: implications for selection of non-clinical safety models for trastuzumab-based therapeutics. Breast Cancer Res. Treat. 191, 303–317 (2022).

    Article  Google Scholar 

  76. Dokter, W. et al. Preclinical profile of the HER2-targeting ADC SYD983/SYD985: introduction of a new duocarmycin-based linker-drug platform. Mol. Cancer Ther. 13, 2618–2629 (2014).

    Article  CAS  PubMed  Google Scholar 

  77. Hosseini, V. et al. Healthy and diseased in vitro models of vascular systems. Lab. Chip 21, 641–659 (2021).

    Article  CAS  PubMed  Google Scholar 

  78. Cochrane, A. et al. Advanced in vitro models of vascular biology: human induced pluripotent stem cells and organ-on-chip technology. Adv. Drug Deliv. Rev. 140, 68–77 (2019).

    Article  CAS  PubMed  Google Scholar 

  79. Ceyzériat, K. et al. Learning from the past: a review of clinical trials targeting amyloid, Tau and neuroinflammation in Alzheimer’s disease. Curr. Alzheimer Res. 17, 112–125 (2020).

    Article  PubMed  Google Scholar 

  80. World Health Organization. World -malaria report 2021. https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2021 (2021).

  81. Barber, J. et al. A target safety assessment of the potential toxicological risks of targeting plasmepsin IX/X for the treatment of malaria. Toxicol. Res. 10, 203–213 (2021).

    Article  Google Scholar 

  82. Barber, N. M. et al. Structure-guided design of a synthetic mimic of an endothelial protein C receptor-binding PfEMP1 protein. mSphere 6, e01081-20 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Hewitt, P. et al. An innovative study design with intermittent dosing to generate a GLP-regulatory package in preclinical species for long lasting molecule M5717, inhibitor of Plasmodium eukaryotic translation elongation factor 2. Toxicol. Appl. Pharm. 443, 116006 (2022).

    Article  CAS  Google Scholar 

  84. Baragaña, B. et al. A novel multiple-stage antimalarial agent that inhibits protein synthesis. Nature 522, 315–320 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  85. McCarthy, J. S. et al. Safety, pharmacokinetics, and antimalarial activity of the novel plasmodium eukaryotic translation elongation factor 2 inhibitor M5717: a first-in-human, randomised, placebo-controlled, double-blind, single ascending dose study and volunteer infection study. Lancet Infect. Dis. 21, 1713–1724 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Feng, J. Y. Addressing the selectivity and toxicity of antiviral nucleosides. Antivir. Chem. Chemother. 26, 2040206618758524 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Kiy, R. T., Khoo, S. H. & Chadwick, A. E. Assessing the mitochondrial safety profile of the molnupiravir active metabolite, β-d-N4-hydroxycytidine (NHC), in the physiologically relevant HepaRG model. Toxicol. Res. 13, tfae012 (2024).

    Article  CAS  Google Scholar 

  88. Honkoop, P., Scholte, H. R., de Man, R. A. & Schalm, S. W. Mitochondrial injury. Drug Saf. 17, 1–7 (1997).

    Article  CAS  PubMed  Google Scholar 

  89. Fenaux, M. et al. Antiviral nucleotide incorporation by recombinant human mitochondrial RNA polymerase is predictive of increased in vivo mitochondrial toxicity risk. Antimicrob. Agents Chemother. 60, 7077–7085 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Johnson, K. A. & Dangerfield, T. Mechanisms of inhibition of viral RNA replication by nucleotide analogs. Enzymes 49, 39–62 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Soldatow, V. Y., LeCluyse, E. L., Griffith, L. G. & Rusyn, I. In vitro models for liver toxicity testing. Toxicol. Res. 2, 23–39 (2012).

    Article  Google Scholar 

  92. Schofield, C. A. et al. Evaluation of a three-dimensional primary human hepatocyte spheroid model: adoption and industrialization for the enhanced detection of drug-induced liver injury. Chem. Res. Toxicol. 34, 2485–2499 (2021).

    Article  CAS  PubMed  Google Scholar 

  93. Gough, A. et al. Human biomimetic liver microphysiology systems in drug development and precision medicine. Nat. Rev. Gastroenterol. 18, 252–268 (2021).

    Article  Google Scholar 

  94. Hilpert, J. et al. Hepatotoxicity of AKR1C3 inhibitor BAY1128688: findings from an early terminated phase IIa trial for the treatment of endometriosis. Drugs R. D. 23, 221–237 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Howell, B. A. et al. In vitro to in vivo extrapolation and species response comparisons for drug-induced liver injury (DILI) using DILIsymTM: a mechanistic, mathematical model of DILI. J. Pharmacokinet. Pharmacodyn. 39, 527–541 (2012).

    Article  PubMed  Google Scholar 

  96. Shoda, L. K. M., Woodhead, J. L., Siler, S. Q., Watkins, P. B. & Howell, B. A. Linking physiology to toxicity using DILIsym®, a mechanistic mathematical model of drug‐induced liver injury. Biopharm. Drug Dispos. 35, 33–49 (2014).

    Article  CAS  PubMed  Google Scholar 

  97. Barrile, R. et al. Organ‐on‐chip recapitulates thrombosis induced by an anti‐CD154 monoclonal antibody: translational potential of advanced microengineered systems. Clin. Pharmacol. Ther. 104, 1240–1248 (2018).

    Article  CAS  PubMed  Google Scholar 

  98. Xie, J. H. et al. Engineering of a novel anti-CD40L domain antibody for treatment of autoimmune diseases. J. Immunol. 192, 4083–4092 (2014).

    Article  CAS  PubMed  Google Scholar 

  99. Nieskens, T. T. G. et al. Nephrotoxic antisense oligonucleotide SPC5001 induces kidney injury biomarkers in a proximal tubule-on-a-chip. Arch. Toxicol. 95, 2123–2136 (2021).

    Article  CAS  PubMed  Google Scholar 

  100. LaLone, C. A. et al. International consortium to advance cross‐species extrapolation of the effects of chemicals in regulatory toxicology. Environ. Toxicol. Chem. 40, 3226–3233 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. InSphero. InSphero and pharmaceutical companies form pre-competitive consortium to advance development of in vitro tools to screen and predict drug-induced liver injury (DILI). https://insphero.com/x-species-consortium/ (2021).

  102. Jang, K.-J. et al. Reproducing human and cross-species drug toxicities using a liver-chip. Sci. Transl. Med. 11, eaax5516 (2019).

    Article  CAS  PubMed  Google Scholar 

  103. European Union. Regulation (EC) No 1223/2009 of the European Parliament and of the Council of 30 November 2009 on cosmetic products (recast) (Text with EEA relevance)Text with EEA relevance. https://data.europa.eu/eli/reg/2009/1223/2022-12-17 (2022).

  104. European Union. Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC and repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94 as well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC (Text with EEA relevance)Text with EEA relevance. https://data.europa.eu/eli/reg/2006/1907/2022-12-17 (2022).

  105. Worth, A. P. & Patlewicz, G. Validation of alternative methods for toxicity testing. Adv. Exp. Med. Biol. 856, 317–342 (2016).

    Article  CAS  PubMed  Google Scholar 

  106. OECD. Guideline No. 497: defined approaches on skin sensitisation. OECD Guidelines for the Testing of Chemicals, Section 4. https://doi.org/10.1787/b92879a4-en (2021).

  107. Delrue, N. et al. The adverse outcome pathway concept: a basis for developing regulatory decision-making tools. Altern. Lab. Anim. 44, 417–429 (2016).

    Article  PubMed  Google Scholar 

  108. Leist, M. et al. Adverse outcome pathways: opportunities, limitations and open questions. Arch. Toxicol. 91, 3477–3505 (2017).

    Article  CAS  PubMed  Google Scholar 

  109. Muller, P. Y. & Milton, M. N. The determination and interpretation of the therapeutic index in drug development. Nat. Rev. Drug Discov. 11, 751–761 (2012).

    Article  CAS  PubMed  Google Scholar 

  110. Naga, D., Parrott, N., Ecker, G. F. & Olivares-Morales, A. Evaluation of the success of high-throughput physiologically based pharmacokinetic (HT-PBPK) modeling predictions to inform early drug discovery. Mol. Pharm. 19, 2203–2216 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. CNAM Working Group. Catalyzing the development and use of novel alternative methods. Report to the Advisory Committee to the NIH Director. https://acd.od.nih.gov/documents/presentations/Working_Group_Report.pdf (2023).

  112. Baier, V. et al. A model‐based workflow to benchmark the clinical cholestasis risk of drugs. Clin. Pharmacol. Ther. 110, 1293–1301 (2021).

    Article  CAS  PubMed  Google Scholar 

  113. Haid, R. T. U. & Reichel, A. Transforming the discovery of targeted protein degraders: the translational power of predictive PK/PD modeling. Clin. Pharmacol. Ther. 116, 770–781 (2024).

    Article  PubMed  Google Scholar 

  114. Wu, F. et al. Computational approaches in preclinical studies on drug discovery and development. Front. Chem. 8, 726 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Watkins, P. B. DILIsym: quantitative systems toxicology impacting drug development. Curr. Opin. Toxicol. 23, 67–73 (2020).

    Article  Google Scholar 

  116. Vamathevan, J. et al. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov. 18, 463–477 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Dara, S., Dhamercherla, S., Jadav, S. S., Babu, C. M. & Ahsan, M. J. Machine learning in drug discovery: a review. Artif. Intell. Rev. 55, 1947–1999 (2022).

    Article  PubMed  Google Scholar 

  118. Oualikene-Gonin, W. et al. Artificial intelligence integration in the drug lifecycle and in regulatory science: policy implications, challenges and opportunities. Front. Pharmacol. 15, 1437167 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  119. Ganesh, S. et al. Cytokine storm in a phase 1 trial of the anti-CD28 monoclonal antibody TGN1412. N. Engl. J. Med. 355, 1018–1028 (2006).

    Article  Google Scholar 

  120. Vessillier, S. et al. Development of the first reference antibody panel for qualification and validation of cytokine release assay platforms—report of an International Collaborative Study. Cytokine X 2, 100042 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Hargrove-Grimes, P., Low, L. A. & Tagle, D. A. Microphysiological systems: stakeholder challenges to adoption in drug development. Cell Tissues Organs 211, 269–281 (2022).

    Article  CAS  Google Scholar 

  122. Jensen, K. B. & Little, M. H. Organoids are not organs: sources of variation and misinformation in organoid biology. Stem Cell Rep. 18, 1255–1270 (2023).

    Article  Google Scholar 

  123. Mohammadi, S. et al. Assessing donor-to-donor variability in human intestinal organoid cultures. Stem Cell Rep. 16, 2364–2378 (2021).

    Article  Google Scholar 

  124. Zhao, Z. et al. Organoids. Nat. Rev. Methods Prim. 2, 94 (2022).

    Article  CAS  Google Scholar 

  125. Zhou, C. et al. Standardization of organoid culture in cancer research. Cancer Med. 12, 14375–14386 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  126. Kato, Y. et al. Analysis of reproducibility and robustness of OrganoPlate® 2-lane 96, a liver microphysiological system for studies of pharmacokinetics and toxicological assessment of drugs. Toxicol. Vitr. 85, 105464 (2022).

    Article  CAS  Google Scholar 

  127. Lim, A. Y. et al. Reproducibility and robustness of a liver microphysiological system PhysioMimix LC12 under varying culture conditions and cell type combinations. Bioengineering 10, 1195 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. NIH National Center for Advancing Translational Sciences. Tissue Chip Testing Centers. https://ncats.nih.gov/research/research-activities/tissue-chip/projects/centers (2016).

  129. Rusyn, I. et al. Microphysiological systems evaluation: experience of TEX-VAL Tissue Chip Testing Consortium. Toxicol. Sci. 188, 143–152 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. TissUse. Press release: Novel liver ring trial set to revolutionize drug safety assessment. https://www.tissuse.com/en/news/press-releases/ (2024).

  131. Scannell, J. W. et al. Predictive validity in drug discovery: what it is, why it matters and how to improve it. Nat. Rev. Drug Discov. 21, 915–931 (2022).

    Article  CAS  PubMed  Google Scholar 

  132. Proctor, W. R. et al. Utility of spherical human liver microtissues for prediction of clinical drug-induced liver injury. Arch. Toxicol. 91, 2849–2863 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Fäs, L. et al. Physiological liver microtissue 384-well microplate system for preclinical hepatotoxicity assessment of therapeutic small molecule drugs. Toxicol. Sci. 203, 79–87 (2024).

    Article  PubMed Central  Google Scholar 

  134. Ewart, L. et al. Performance assessment and economic analysis of a human liver-chip for predictive toxicology. Commun. Med. 2, 154 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  135. Congress, 118th. H.R.7248 - FDA Modernization Act 3.0. https://www.congress.gov/bill/118th-congress/house-bill/7248/text#:~:Text=To%20amend%20the%20Federal%20Food,product%20or%20other%20drug%2C%20and (2024).

  136. Nelson, C. P. et al. Advancing alternative methods to reduce animal testing. Science 386, 724–726 (2024).

    Article  CAS  PubMed  Google Scholar 

  137. FDA. Potential approaches to drive future integration of new alternative methods for regulatory decision-making. https://www.fda.gov/media/182478/download?attachment (2024).

  138. FDA. FDA’s ISTAND Pilot Program accepts a submission of first organ-on-a-chip technology designed to predict human drug-induced liver injury (DILI). https://www.fda.gov/drugs/drug-safety-and-availability/fdas-istand-pilot-program-accepts-submission-first-organ-chip-technology-designed-predict-human-drug (2024).

  139. Ball, N. et al. A framework for chemical safety assessment incorporating new approach methodologies within REACH. Arch. Toxicol. 96, 743–766 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Baudy, A. R. et al. Liver microphysiological systems development guidelines for safety risk assessment in the pharmaceutical industry. Lab. Chip 20, 215–225 (2019).

    Article  PubMed  Google Scholar 

  141. Dalsbecker, P., Adiels, C. B. & Goksör, M. Liver-on-a-chip devices: the pros and cons of complexity. Am. J. Physiol. Gastrointest. Liver Physiol. 323, G188–G204 (2022).

    Article  CAS  PubMed  Google Scholar 

  142. Fu, J., Qiu, H. & Tan, C. S. Microfluidic liver-on-a-chip for preclinical drug discovery. Pharmaceutics 15, 1300 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Hassan, S. et al. Liver‐on‐a‐chip models of fatty liver disease. Hepatology 71, 733–740 (2020).

    Article  PubMed  Google Scholar 

  144. Yang, Z. et al. Liver-on-a-chip: considerations, advances, and beyond. Biomicrofluidics 16, 061502 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Vilas-Boas, V. et al. Primary hepatocytes and their cultures for the testing of drug-induced liver injury. Adv. Pharmacol. 85, 1–30 (2019).

    Article  CAS  PubMed  Google Scholar 

  146. Cho, S., Discher, D. E., Leong, K. W., Vunjak-Novakovic, G. & Wu, J. C. Challenges and opportunities for the next generation of cardiovascular tissue engineering. Nat. Methods 19, 1064–1071 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Jiao, Y.-C. et al. Advances in the differentiation of pluripotent stem cells into vascular cells. World J. Stem Cell 16, 137–150 (2024).

    Article  Google Scholar 

  148. Roland, T. J. & Song, K. Advances in the generation of constructed cardiac tissue derived from induced pluripotent stem cells for disease modeling and therapeutic discovery. Cells 13, 250 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Pointon, A. et al. Cardiovascular microphysiological systems (CVMPS) for safety studies—a pharma perspective. Lab. Chip 21, 458–472 (2021).

    Article  CAS  PubMed  Google Scholar 

  150. Ashammakhi, N. et al. Gut-on-a-chip: current progress and future opportunities. Biomaterials 255, 120196 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. Carvalho, M. R. et al. Gastrointestinal organs and organoids-on-a-chip: advances and translation into the clinics. Biofabrication 15, 042004 (2023).

    Article  CAS  Google Scholar 

  152. McCoy, R. et al. In vitro models for investigating intestinal host–pathogen interactions. Adv. Sci. 11, 2306727 (2024).

    Article  CAS  Google Scholar 

  153. Peters, M. F. et al. Developing in vitro assays to transform gastrointestinal safety assessment: potential for microphysiological systems. Lab. Chip 20, 1177–1190 (2020).

    Article  CAS  PubMed  Google Scholar 

  154. Yin, Y.-B., de Jonge, H. R., Wu, X. & Yin, Y.-L. Mini-gut: a promising model for drug development. Drug Discov. Today 24, 1784–1794 (2019).

    Article  CAS  PubMed  Google Scholar 

  155. Hoffmann, S. et al. Validation of an MPS based intestinal cell culture model for the evaluation of drug-induced toxicity. Front. Drug Discov. https://doi.org/10.3389/fddsv.2024.1459424 (2024).

  156. Phillips, J. A. et al. A pharmaceutical industry perspective on microphysiological kidney systems for evaluation of safety for new therapies. Lab. Chip 20, 468–476 (2020).

    Article  CAS  PubMed  Google Scholar 

  157. Soo, J. Y.-C., Jansen, J., Masereeuw, R. & Little, M. H. Advances in predictive in vitro models of drug-induced nephrotoxicity. Nat. Rev. Nephrol. 14, 378–393 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Musah, S. et al. Mature induced-pluripotent-stem-cell-derived human podocytes reconstitute kidney glomerular-capillary-wall function on a chip. Nat. Biomed. Eng. 1, 0069 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Pașca, S. P. et al. A nomenclature consensus for nervous system organoids and assembloids. Nature 609, 907–910 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  160. Rouleau, N., Murugan, N. J. & Kaplan, D. L. Functional bioengineered models of the central nervous system. Nat. Rev. Bioeng. 1, 252–270 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  161. Kang, Y. J., Xue, Y., Shin, J. H. & Cho, H. Human mini-brains for reconstituting central nervous system disorders. Lab. Chip 23, 964–981 (2023).

    Article  CAS  PubMed  Google Scholar 

  162. Tan, H.-Y., Cho, H. & Lee, L. P. Human mini-brain models. Nat. Biomed. Eng. 5, 11–25 (2021).

    Article  PubMed  Google Scholar 

  163. De, A. et al. Lung-on-chip: its current and future perspective on pharmaceutical and biomedical applications. J. Drug Deliv. Sci. Technol. 78, 103930 (2022).

    Article  CAS  Google Scholar 

  164. Lin, K.-C., Yen, C.-Z., Yang, J.-W., Chung, J. H. Y. & Chen, G.-Y. Airborne toxicological assessment: the potential of lung-on-a-chip as an alternative to animal testing. Mater. Today Adv. 14, 100216 (2022).

    Article  CAS  Google Scholar 

  165. Francis, I. et al. Recent advances in lung-on-a-chip models. Drug Discov. Today 27, 2593–2602 (2022).

    Article  CAS  PubMed  Google Scholar 

  166. Ainslie, G. R. et al. Microphysiological lung models to evaluate the safety of new pharmaceutical modalities: a biopharmaceutical perspective. Lab. Chip 19, 3152–3161 (2019).

    Article  CAS  PubMed  Google Scholar 

  167. Lam, M., Lamanna, E., Organ, L., Donovan, C. & Bourke, J. E. Perspectives on precision cut lung slices—powerful tools for investigation of mechanisms and therapeutic targets in lung diseases. Front. Pharmacol. 14, 1162889 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  168. Tomlinson, L. et al. Considerations from an International Regulatory and Pharmaceutical Industry (IQ MPS Affiliate) Workshop on the standardization of complex in vitro models in drug development. Adv. Biol. 8, e2300131 (2024).

    Article  Google Scholar 

  169. Baran, S. W. et al. Perspectives on the evaluation and adoption of complex in vitro models in drug development: workshop with the FDA and the pharmaceutical industry (IQ MPS Affiliate). ALTEX 39, 297–314 (2022).

    PubMed  Google Scholar 

  170. Stressor, D. M. et al. Towards in vitro models for reducing or replacing the use of animals in drug testing. Nat. Biomed. Eng. 8, 930–935 (2024).

    Article  Google Scholar 

  171. Roth, A. Setting the scene—the investigative toxicology landscape in the European pharmaceutical industry. Toxicol. Lett. 280, S76 (2017).

    Article  Google Scholar 

  172. Low, L. A., Mummery, C., Berridge, B. R., Austin, C. P. & Tagle, D. A. Organs-on-chips: into the next decade. Nat. Rev. Drug Discov. 20, 345–361 (2021).

    Article  CAS  PubMed  Google Scholar 

  173. Fabre, K. et al. Introduction to a manuscript series on the characterization and use of microphysiological systems (MPS) in pharmaceutical safety and ADME applications. Lab. Chip 20, 1049–1057 (2020).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank the European Federation of Pharmaceutical Industries and Associations (EFPIA)/Preclinical Development Expert Group (PDEG), especially P. Brinck, B. Haenen and E. Vock for initiating the establishment of this safety reflection working group.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mario Beilmann.

Ethics declarations

Competing interests

M.B., K.A., H.C.M.B., P.H., W.H., R.M., S.M., P.R., T.S.-H., R.V., T.v.V. are employees of pharmaceutical companies. R.V. holds equity in Emulate. The authors declare no further competing interests.

Peer review

Peer review information

Nature Reviews Drug Discovery thanks Anna-Karin Sjögren, Szczepan Baran and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

3RsC: https://3rc.org/

ASPIS cluster: https://aspis-cluster.eu/

Biotechnology Innovation Organization: https://www.bio.org/

CleanCut: https://nc3rs.org.uk/crackit/cleancut

CrossDART: https://nc3rs.org.uk/crackit/crossdart

FDA news release in April 2025: https://www.fda.gov/news-events/press-announcements/fda-announces-plan-phase-out-animal-testing-requirement-monoclonal-antibodies-and-other-drugs

In vitro TDAR: https://nc3rs.org.uk/crackit/vitro-tdar

IQ Consortium: https://iqconsortium.org/

IQ MPS Afffiliate: https://www.iqmps.org/

NC3Rs: https://nc3rs.org.uk/

ONTOX: https://ontox-project.eu/

PrecisionTox: https://precisiontox.org/

Retinal 3D: https://nc3rs.org.uk/crackit/retinal-3d

Risk-Hunt3R: https://www.risk-hunt3r.eu/

SensOoChip: https://nc3rs.org.uk/crackit/sensoochip

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Beilmann, M., Adkins, K., Boonen, H.C.M. et al. Application of new approach methodologies for nonclinical safety assessment of drug candidates. Nat Rev Drug Discov 24, 705–725 (2025). https://doi.org/10.1038/s41573-025-01182-9

Download citation

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41573-025-01182-9

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research