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Therapies for rare diseases: therapeutic modalities, progress and challenges ahead

A Publisher Correction to this article was published on 08 January 2020

This article has been updated


Most rare diseases still lack approved treatments despite major advances in research providing the tools to understand their molecular basis, as well as legislation providing regulatory and economic incentives to catalyse the development of specific therapies. Addressing this translational gap is a multifaceted challenge, for which a key aspect is the selection of the optimal therapeutic modality for translating advances in rare disease knowledge into potential medicines, known as orphan drugs. With this in mind, we discuss here the technological basis and rare disease applicability of the main therapeutic modalities, including small molecules, monoclonal antibodies, protein replacement therapies, oligonucleotides and gene and cell therapies, as well as drug repurposing. For each modality, we consider its strengths and limitations as a platform for rare disease therapy development and describe clinical progress so far in developing drugs based on it. We also discuss selected overarching topics in the development of therapies for rare diseases, such as approval statistics, engagement of patients in the process, regulatory pathways and digital tools.

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Fig. 1: Indication of the gap between science and translation into therapies for rare diseases.
Fig. 2: Therapeutic modalities: characteristics and regulatory approval data.
Fig. 3: Key figures for rare diseases and orphan drugs.

Change history


  1. 1.

    Global Genes. Rare diseases. RARE facts. Global Genes (2019).

  2. 2.

    Tambuyzer, E. Rare diseases, orphan drugs and their regulation: questions and misconceptions. Nat. Rev. Drug Discov. 9, 921–929 (2010).

    CAS  PubMed  Google Scholar 

  3. 3.

    Farnaes, L. et al. Rapid whole-genome sequencing decreases infant morbidity and cost of hospitalization. NPJ Genom. Med. 3, 10 (2018).

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Gurovich, Y. et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat. Med. 25, 60–64 (2019).

    CAS  PubMed  Google Scholar 

  5. 5.

    Plowright, A. T. et al. Heart regeneration: opportunities and challenges for drug discovery with novel chemical and therapeutic methods or agents. Angew. Chem. Int. Ed. 53, 4056–4075 (2014).

    CAS  Google Scholar 

  6. 6.

    Valeur, E. et al. New modalities for challenging targets in drug discovery. Angew. Chem. Int. Ed. Engl. 56, 10294–10323 (2017).

    CAS  PubMed  Google Scholar 

  7. 7.

    Scannell, J. W. et al. Diagnosing the decline in pharmaceutical R&D efficiency. Nat. Rev. Drug Discov. 11, 191 (2012).

    CAS  PubMed  Google Scholar 

  8. 8.

    Rodgers, G. et al. Glimmers in illuminating the druggable genome. Nat. Rev. Drug Discov. 17, 301–302 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Santos, R. et al. A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16, 19–34 (2017).

    CAS  PubMed  Google Scholar 

  10. 10.

    Macarron, R. et al. Impact of high-throughput screening in biomedical research. Nat. Rev. Drug Discov. 10, 188–195 (2011).

    CAS  PubMed  Google Scholar 

  11. 11.

    Lipinski, C. A. et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 23, 3–25 (1997).

    CAS  Google Scholar 

  12. 12.

    Gerry, C. J. & Schreiber, S. L. Chemical probes and drug leads from advances in synthetic planning and methodology. Nat. Rev. Drug Discov. 17, 333–352 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Shultz, M. D. Two decades under the influence of the rule of five and the changing properties of approved oral drugs. J. Med. Chem. 62, 1701–1714 (2018).

    PubMed  Google Scholar 

  14. 14.

    Plenge, R. M. Disciplined approach to drug discovery and early development. Sci. Transl Med. 8, 349ps15 (2016).

    PubMed  Google Scholar 

  15. 15.

    Scott, A. How CRISPR is transforming drug discovery. Nature 555, S10–S11 (2018).

    CAS  PubMed  Google Scholar 

  16. 16.

    Takahashi, T. Organoids for drug discovery and personalized medicine. Annu. Rev. Pharmacol. Toxicol. 59, 447–462 (2019).

    CAS  PubMed  Google Scholar 

  17. 17.

    Elitt, M. S., Barbar, L. & Tesar, P. J. Drug screening for human genetic diseases using iPSC models. Hum. Mol. Genet. 27(R2), R89–R98 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Ebert, A. D. et al. Induced pluripotent stem cells from a spinal muscular atrophy patient. Nature 457, 277–280 (2009).

    CAS  PubMed  Google Scholar 

  19. 19.

    Groen, E. J. N., Talbot, K. & Gillingwater, T. H. Advances in therapy for spinal muscular atrophy: promises and challenges. Nat. Rev. Neurol. 14, 214–224 (2018).

    PubMed  Google Scholar 

  20. 20.

    Artegiani, B. & Clevers, H. Use and application of 3D-organoid technology. Hum. Mol. Genet. 27(R2), R99–R107 (2018).

    CAS  PubMed  Google Scholar 

  21. 21.

    Strynatka, K. A. et al. How surrogate and chemical genetics in model organisms can suggest therapies for human genetic diseases. Genetics 208, 833–851 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Van Goor, F. et al. Correction of the F508del-CFTR protein processing defect in vitro by the investigational drug VX-809. Proc. Natl Acad. Sci. USA 108, 18843–18848 (2011).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Van Goor, F. et al. Rescue of CF airway epithelial cell function in vitro by a CFTR potentiator, VX-770. Proc. Natl Acad. Sci. USA 106, 18825–18830 (2009).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    US Food and Drug Administration. FDA expands approved use of Kalydeco to treat additional mutations of cystic fibrosis (FDA, 2017).

  25. 25.

    Keating, D. et al. VX-445-tezacaftor-ivacaftor in patients with cystic fibrosis and one or two Phe508del alleles. N. Engl. J. Med. 379, 1612–1620 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    US Food and Drug Administration. FDA approves new breakthrough therapy for cystic fibrosis (FDA, 2019).

  27. 27.

    Strug, L. J. et al. Recent advances in developing therapeutics for cystic fibrosis. Hum. Mol. Genet. 27(R2), R173–R186 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    US Food and Drug Administration. Orkambi prescribing information. FDA (2018).

  29. 29.

    Platt, F. M. Emptying the stores: lysosomal diseases and therapeutic strategies. Nat. Rev. Drug Discov. 17, 133–150 (2018).

    CAS  PubMed  Google Scholar 

  30. 30.

    Keeling, K. M. et al. Therapeutics based on stop codon readthrough. Annu. Rev. Genomics Hum. Genet. 15, 371–394 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Guiraud, S. & Davies, K. E. Pharmacological advances for treatment in Duchenne muscular dystrophy. Curr. Opin. Pharmacol. 34, 36–48 (2017).

    CAS  PubMed  Google Scholar 

  32. 32.

    Welch, E. M. et al. PTC124 targets genetic disorders caused by nonsense mutations. Nature 447, 87–91 (2007).

    CAS  PubMed  Google Scholar 

  33. 33.

    Squire, S. et al. Prevention of pathology in mdx mice by expression of utrophin: analysis using an inducible transgenic expression system. Hum. Mol. Genet. 11, 3333–3344 (2002).

    CAS  PubMed  Google Scholar 

  34. 34.

    Goldstein, G. Overview of the development of Orthoclone OKT3: monoclonal antibody for therapeutic use in transplantation. Transplant Proc. 19 (2 Suppl. 1), 1–6 (1987).

    CAS  PubMed  Google Scholar 

  35. 35.

    Carter, P. J. & Lazar, G. A. Next generation antibody drugs: pursuit of the high-hanging fruit. Nat. Rev. Drug Discov. 17, 197–223 (2018).

    CAS  PubMed  Google Scholar 

  36. 36.

    Grilo, A. L. & Mantalaris, A. The increasingly human and profitable monoclonal antibody market. Trends Biotechnol. 37, 9–16 (2018).

    PubMed  Google Scholar 

  37. 37.

    Kohler, G. & Milstein, C. Continuous cultures of fused cells secreting antibody of predefined specificity. Nature 256, 495–497 (1975).

    CAS  PubMed  Google Scholar 

  38. 38.

    Potter, M. The early history of plasma cell tumors in mice, 1954-1976. Adv. Cancer Res. 98, 17–51 (2007).

    CAS  PubMed  Google Scholar 

  39. 39.

    Clackson, T. et al. Making antibody fragments using phage display libraries. Nature 352, 624–628 (1991).

    CAS  PubMed  Google Scholar 

  40. 40.

    Chames, P. et al. Therapeutic antibodies: successes, limitations and hopes for the future. Br. J. Pharmacol. 157, 220–233 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Smith, K. et al. Rapid generation of fully human monoclonal antibodies specific to a vaccinating antigen. Nat. Protoc. 4, 372–384 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Ecker, D. M., Jones, S. D. & Levine, H. L. The therapeutic monoclonal antibody market. mAbs 7, 9–14 (2015).

    CAS  PubMed  Google Scholar 

  43. 43.

    Sedykh, S. E. et al. Bispecific antibodies: design, therapy, perspectives. Drug Des. Devel. Ther. 12, 195–208 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Thakur, A. & Lum, L. G. “NextGen” biologics: bispecific antibodies and emerging clinical results. Exp. Opin. Biol. Ther. 16, 675–688 (2016).

    CAS  Google Scholar 

  45. 45.

    Rath, T. et al. Fc-fusion proteins and FcRn: structural insights for longer-lasting and more effective therapeutics. Crit. Rev. Biotechnol. 35, 235–254 (2015).

    CAS  PubMed  Google Scholar 

  46. 46.

    Nasiri, H. et al. Antibody-drug conjugates: promising and efficient tools for targeted cancer therapy. J. Cell Physiol. 233, 6441–6457 (2018).

    CAS  PubMed  Google Scholar 

  47. 47.

    Lambert, J. M. & Berkenblit, A. Antibody–drug conjugates for cancer treatment. Annu. Rev. Med. 69, 191–207 (2018).

    CAS  PubMed  Google Scholar 

  48. 48.

    Fleischmann, R. M. et al. Anakinra, a recombinant human interleukin-1 receptor antagonist (r-metHuIL-1ra), in patients with rheumatoid arthritis: A large, international, multicenter, placebo-controlled trial. Arthritis Rheum. 48, 927–934 (2003).

    CAS  PubMed  Google Scholar 

  49. 49.

    De Benedetti, F. et al. Canakinumab for the treatment of autoinflammatory recurrent fever syndromes. N. Engl. J. Med. 378, 1908–1919 (2018).

    PubMed  Google Scholar 

  50. 50.

    Hoffman, H. M. et al. Efficacy and safety of rilonacept (interleukin-1 Trap) in patients with cryopyrin-associated periodic syndromes: results from two sequential placebo-controlled studies. Arthritis Rheum. 58, 2443–2452 (2008).

    CAS  PubMed  Google Scholar 

  51. 51.

    Mahlangu, J. et al. Emicizumab prophylaxis in patients who have hemophilia A without inhibitors. N. Engl. J. Med. 379, 811–822 (2018).

    CAS  PubMed  Google Scholar 

  52. 52.

    Scully, M. et al. Caplacizumab treatment for acquired thrombotic thrombocytopenic purpura. N. Engl. J. Med. 380, 335–346 (2019).

    CAS  PubMed  Google Scholar 

  53. 53.

    Frenzel, A. et al. Designing human antibodies by phage display. Transfus. Med. Hemother. 44, 312–318 (2017).

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Shukla, A. A. et al. Evolving trends in mAb production processes. Bioeng. Transl. Med. 2, 58–69 (2017).

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Peters, R. & Harris, T. Advances and innovations in haemophilia treatment. Nat. Rev. Drug Discov. 17, 493–508 (2018).

    CAS  PubMed  Google Scholar 

  56. 56.

    Beck, M. Treatment strategies for lysosomal storage disorders. Dev. Med. Child Neurol. 60, 13–18 (2018).

    PubMed  Google Scholar 

  57. 57.

    LiverTox: Clinical and Research Information on Drug-Induced Liver Injury [Internet]. Enzyme Replacement Therapy. National Institute of Diabetes and Digestive and Kidney Diseases (2016).

  58. 58.

    Jurecka, A. & Tylki-Szyman´ska, A. Enzyme replacement therapy: lessons learned and emerging questions. Expert Opin. Orphan Drugs 3, 293–305 (2015).

    CAS  Google Scholar 

  59. 59.

    Gadek, J. E. et al. Replacement therapy of alpha 1-antitrypsin deficiency. Reversal of protease-antiprotease imbalance within the alveolar structures of PiZ subjects. J. Clin. Invest. 68, 1158–1165 (1981).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Wewers, M. D. et al. Replacement therapy for alpha 1-antitrypsin deficiency associated with emphysema. N. Engl. J. Med. 316, 1055–1062 (1987).

    CAS  PubMed  Google Scholar 

  61. 61.

    Desnick, R. J. & Schuchman, E. H. Enzyme replacement therapy for lysosomal diseases: lessons from 20 years of experience and remaining challenges. Annu. Rev. Genomics Hum. Genet. 13, 307–335 (2012).

    CAS  PubMed  Google Scholar 

  62. 62.

    Brady, R. O. et al. Replacement therapy for inherited enzyme deficiency. Use of purified glucocerebrosidase in Gaucher’s disease. N. Engl. J. Med. 291, 989–993 (1974).

    CAS  PubMed  Google Scholar 

  63. 63.

    Chien, Y. H., Hwu, W. L. & Lee, N. C. Pompe disease: early diagnosis and early treatment make a difference. Pediatr. Neonatol. 54, 219–227 (2013).

    PubMed  Google Scholar 

  64. 64.

    Grabowski, G. A., Golembo, M. & Shaaltiel, Y. Taliglucerase alfa: an enzyme replacement therapy using plant cell expression technology. Mol. Genet. Metab. 112, 1–8 (2014).

    CAS  PubMed  Google Scholar 

  65. 65.

    Gaffke, L. et al. How close are we to therapies for Sanfilippo disease? Metab. Brain Dis. 33, 1–10 (2018).

    CAS  PubMed  Google Scholar 

  66. 66.

    Gonzalez, E. A. & Baldo, G. Gene therapy for lysosomal storage disorders: recent advances and limitations. J. Inborn Errors Metab. Screen. 5, 2326409816689786 (2017).

    Google Scholar 

  67. 67.

    Chotirmall, S. H. et al. Alpha-1 proteinase inhibitors for the treatment of alpha-1 antitrypsin deficiency: safety, tolerability, and patient outcomes. Ther. Clin. Risk Manag. 11, 143–151 (2015).

    PubMed  PubMed Central  Google Scholar 

  68. 68.

    Leadiant Biosciences. Adagen (pegademase bovine). Leadiant (2019).

  69. 69.

    US Food and Drug Administration. FDA approves a new treatment for PKU, a rare and serious genetic disease (FDA,2018).

  70. 70.

    Nicolino, M. Alglucosidase alfa: first available treatment for Pompe disease. Therapy 4, 271–277 (2007).

    CAS  Google Scholar 

  71. 71.

    van Gelder, C. et al. A higher dose of enzyme therapy in patients with classic infantile Pompe disease seems to improve ventilator-free survival and motor function. BMC Musculoskelet. Disord. 14, 19 (2013).

    Google Scholar 

  72. 72.

    Kirkegaard, T. Emerging therapies and therapeutic concepts for lysosomal storage diseases. Expert Opin. Orphan Drugs 1, 385–404 (2013).

    CAS  Google Scholar 

  73. 73.

    Harmatz, P. Enzyme replacement therapies and immunogenicity in lysosomal storage diseases: is there a pattern? Clin. Ther. 37, 2130–2134 (2015).

    CAS  PubMed  Google Scholar 

  74. 74.

    Khvorova, A. & Watts, J. K. The chemical evolution of oligonucleotide therapies of clinical utility. Nat. Biotechnol. 35, 238 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Setten, R. L., Rossi, J. J. & Han, S. P. The current state and future directions of RNAi-based therapeutics. Nat. Rev. Drug Discov. 18, 421–446 (2019).

    CAS  PubMed  Google Scholar 

  76. 76.

    Zatsepin, T. S., Kotelevtsev, Y. V. & Koteliansky, V. Lipid nanoparticles for targeted siRNA delivery - going from bench to bedside. Int. J. Nanomed. 11, 3077–3086 (2016).

    CAS  Google Scholar 

  77. 77.

    Havens, M. A., Duelli, D. M. & Hastings, M. L. Targeting RNA splicing for disease therapy. Wiley Interdiscip. Rev. RNA 4, 247–266 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Arechavala-Gomeza, V. et al. Antisense oligonucleotide-mediated exon skipping for Duchenne muscular dystrophy: progress and challenges. Curr. Gene Ther. 12, 152–160 (2012).

    CAS  PubMed  Google Scholar 

  79. 79.

    Dias, N. & Stein, C. A. Antisense oligonucleotides: basic concepts and mechanisms. Mol. Cancer Ther. 1, 347–355 (2002).

    CAS  PubMed  Google Scholar 

  80. 80.

    US Food and Drug Administration. Kynamro prescribing information. FDA (2013).

  81. 81.

    Rinaldi, C. & Wood, M. J. A. Antisense oligonucleotides: the next frontier for treatment of neurological disorders. Nat. Rev. Neurol. 14, 9–21 (2018).

    CAS  PubMed  Google Scholar 

  82. 82.

    Adams, D. et al. Patisiran, an RNAi therapeutic, for hereditary transthyretin amyloidosis. N. Engl. J. Med. 379, 11–21 (2018).

    CAS  PubMed  Google Scholar 

  83. 83.

    Benson, M. D. et al. Inotersen treatment for patients with hereditary transthyretin amyloidosis. N. Engl. J. Med. 379, 22–31 (2018).

    CAS  PubMed  Google Scholar 

  84. 84.

    US Food and Drug Administration. Tegsedi prescribing information. FDA (2018).

  85. 85.

    US Food and Drug Administration. FDA approves first drug for spinal muscular (FDA, 2016).

  86. 86.

    US Food and Drug Administration. SPINRAZA (nusinersen) injection for intrathecal use. FDA (2016).

  87. 87.

    Field, M. J. & Boat, T. F. (eds) Rare Diseases and Orphan Products (National Academies Press, 2010).

  88. 88.

    van Roon-Mom, W. M. C., Roos, R. A. C. & de Bot, S. T. Dose-dependent lowering of mutant huntingtin using antisense oligonucleotides in Huntington disease patients. Nucleic Acid Ther. 28, 59–62 (2018).

    PubMed  Google Scholar 

  89. 89.

    Deverman, B. E. et al. Gene therapy for neurological disorders: progress and prospects. Nat. Rev. Drug Discov. 17, 641–659 (2018).

    CAS  PubMed  Google Scholar 

  90. 90.

    Srivastava, A. In vivo tissue-tropism of adeno-associated viral vectors. Curr. Opin. Virol. 21, 75–80 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Nance, M. E. & Duan, D. Perspective on adeno-associated virus capsid modification for Duchenne muscular dystrophy gene therapy. Hum. Gene Ther. 26, 786–800 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Asokan, A. Reengineered AAV vectors: old dog, new tricks. Discov. Med. 9, 399–403 (2010).

    PubMed  PubMed Central  Google Scholar 

  93. 93.

    Journal of Gene Medicine. Gene therapy clinical trials worldwide. Wiley (2018).

  94. 94.

    Chandler, R. J., Sands, M. S. & Venditti, C. P. Recombinant adeno-associated viral integration and genotoxicity: insights from animal models. Hum. Gene Ther. 28, 314–322 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Berns, K. I. et al. Adeno-associated virus type 2 and hepatocellular carcinoma? Hum. Gene Ther. 26, 779–781 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Colella, P., Ronzitti, G. & Mingozzi, F. Emerging issues in AAV-mediated in vivo gene therapy. Mol. Ther. Methods Clin. Dev. 8, 87–104 (2018).

    CAS  PubMed  Google Scholar 

  97. 97.

    Kohn, D. B. Historical perspective on the current renaissance for hematopoietic stem cell gene therapy. Hematol. Oncol. Clin. North Am. 31, 721–735 (2017).

    PubMed  Google Scholar 

  98. 98.

    Yu, S. F. et al. Self-inactivating retroviral vectors designed for transfer of whole genes into mammalian cells. Proc. Natl Acad. Sci. USA 83, 3194–3198 (1986).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. 99.

    Zufferey, R. et al. Self-inactivating lentivirus vector for safe and efficient in vivo gene delivery. J. Virol. 72, 9873–9880 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. 100.

    Martin, U. Therapeutic application of pluripotent stem cells: challenges and risks. Front. Med. 4, 229 (2017).

    Google Scholar 

  101. 101.

    Di Foggia, V. et al. Induced pluripotent stem cell therapies for degenerative disease of the outer retina: disease modeling and cell replacement. J. Ocul. Pharmacol. Therap. 32, 240–252 (2016).

    Google Scholar 

  102. 102.

    Yin, H., Kauffman, K. J. & Anderson, D. G. Delivery technologies for genome editing. Nat. Rev. Drug Discov. 16, 387–399 (2017).

    CAS  PubMed  Google Scholar 

  103. 103.

    Telen, M. J., Malik, P. & Vercellotti, G. M. Therapeutic strategies for sickle cell disease: towards a multi-agent approach. Nat. Rev. Drug Discov. 18, 139–158 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. 104.

    Mendell, J. R. et al. Single-dose gene-replacement therapy for spinal muscular atrophy. N. Engl. J. Med. 377, 1713–1722 (2017).

    CAS  PubMed  Google Scholar 

  105. 105.

    Chapin, J. C. & Monahan, P. E. Gene therapy for hemophilia: progress to date. BioDrugs 32, 9–25 (2018).

    CAS  PubMed  Google Scholar 

  106. 106.

    Yla-Herttuala, S. Endgame: glybera finally recommended for approval as the first gene therapy drug in the European union. Mol. Ther. 20, 1831–1832 (2012).

    PubMed  PubMed Central  Google Scholar 

  107. 107.

    US Food and Drug Administration. FDA approves novel gene therapy to treat patients with a rare form of inherited vision loss (FDA, 2017).

  108. 108.

    European Commission. Union Register of medicinal products for human use. European Commission (2018).

  109. 109.

    De Ravin, S. S. et al. Lentiviral hematopoietic stem cell gene therapy for X-linked severe combined immunodeficiency. Sci. Transl Med. 8, 335ra57 (2016).

    PubMed  PubMed Central  Google Scholar 

  110. 110.

    Aiuti, A. et al. Lentiviral hematopoietic stem cell gene therapy in patients with Wiskott-Aldrich syndrome. Science 341, 1233151 (2013).

    PubMed  PubMed Central  Google Scholar 

  111. 111.

    Thompson, A. A. et al. Gene therapy in patients with transfusion-dependent beta-thalassemia. N. Engl. J. Med. 378, 1479–1493 (2018).

    CAS  PubMed  Google Scholar 

  112. 112.

    Aiuti, A., Roncarolo, M. G. & Naldini, L. Gene therapy for ADA-SCID, the first marketing approval of an ex vivo gene therapy in Europe: paving the road for the next generation of advanced therapy medicinal products. EMBO Mol. Med. 9, 737–740 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. 113.

    Cartier, N. et al. Hematopoietic stem cell gene therapy with a lentiviral vector in X-linked adrenoleukodystrophy. Science 326, 818–823 (2009).

    CAS  PubMed  Google Scholar 

  114. 114.

    Biffi, A. et al. Lentiviral hematopoietic stem cell gene therapy benefits metachromatic leukodystrophy. Science 341, 1233158 (2013).

    PubMed  Google Scholar 

  115. 115.

    Eichler, F. et al. Hematopoietic stem-cell gene therapy for cerebral adrenoleukodystrophy. N. Engl. J. Med. 377, 1630–1638 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. 116.

    US Food and Drug Administration. Orphan drug designation for Kymriah (Tisagenlecleucel) for the treatment of acute lymphoblastic leukemia (FDA, 2017).

  117. 117.

    US Food and Drug Administration. FDA approves CAR-T cell therapy to treat adults with certain types of large B-cell lymphoma (FDA, 2017).

  118. 118.

    Tang, J. et al. The global landscape of cancer cell therapy. Nat. Rev. Drug Discov. 17, 465–466 (2018).

    CAS  PubMed  Google Scholar 

  119. 119.

    Wang, D., Tai, P. W. L. & Gao, G. Adeno-associated virus vector as a platform for gene therapy delivery. Nat. Rev. Drug Discov. 18, 358–378 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. 120.

    Moser, R. J. & Hirsch, M. L. AAV vectorization of DSB-mediated gene editing technologies. Curr. Gene Ther. 16, 207–219 (2016).

    CAS  PubMed  Google Scholar 

  121. 121.

    Kaiser, J. New gene-editing treatment might help treat a rare disorder, hints first human test. Science (2018).

  122. 122.

    Kulkarni, J. A., Cullis, P. R. & van der Meel, R. Lipid nanoparticles enabling gene therapies: from concepts to clinical utility. Nucleic Acid Ther. 28, 146–157 (2018).

    CAS  PubMed  Google Scholar 

  123. 123.

    Nienhuis, A. W. Development of gene therapy for blood disorders: an update. Blood 122, 1556–1564 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  124. 124.

    Alliance for Regenerative Medicine. Quarterly data report Q3. ARM (2018).

  125. 125.

    Oprea, T. I. et al. Associating drugs, targets and clinical outcomes into an integrated network affords a new platform for computer-aided drug repurposing. Mol. Inform. 30, 100–111 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  126. 126.

    Oprea, T. I. et al. Drug repurposing from an academic perspective. Drug Discov. Today Ther. Strateg. 8, 61–69 (2011).

    PubMed  PubMed Central  Google Scholar 

  127. 127.

    Ursu, O. et al. DrugCentral 2018: an update. Nucleic Acids Res. 47(D1), D963–D970 (2019).

    CAS  PubMed  Google Scholar 

  128. 128.

    Pushpakom, S. et al. Drug repurposing: progress, challenges and recommendations. Nat. Rev. Drug Discov. 8, 41–58 (2019).

    Google Scholar 

  129. 129.

    Tenenbaum, J. D. Translational bioinformatics: past, present, and future. Genomics Proteomics Bioinformatics 14, 31–41 (2016).

    PubMed  PubMed Central  Google Scholar 

  130. 130.

    Butte, A. J. & Chen, R. Finding disease-related genomic experiments within an international repository: first steps in translational bioinformatics. AMIA Annu. Symp. Proc. 2006, 106–110 (2006).

    PubMed Central  Google Scholar 

  131. 131.

    Himmelstein, D. S. & Baranzini, S. E. Heterogeneous network edge prediction: a data integration approach to prioritize disease-associated genes. PLOS Comput. Biol. 11, e1004259 (2015).

    PubMed  PubMed Central  Google Scholar 

  132. 132.

    Ghofrani, H. A., Osterloh, I. H. & Grimminger, F. Sildenafil: from angina to erectile dysfunction to pulmonary hypertension and beyond. Nat. Rev. Drug Discov. 5, 689–702 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  133. 133.

    Galie, N. et al. Sildenafil citrate therapy for pulmonary arterial hypertension. N. Engl. J. Med. 353, 2148–2157 (2005).

    CAS  PubMed  Google Scholar 

  134. 134.

    Colvis, C. M. & Austin, C. P. The NIH-industry new therapeutic uses pilot program: demonstrating the power of crowdsourcing. Assay Drug Dev. Technol. 13, 297–298 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  135. 135.

    Markey, K. A. et al. Assessing the efficacy and safety of an 11beta-hydroxysteroid dehydrogenase type 1 inhibitor (AZD4017) in the idiopathic intracranial hypertension drug trial, IIH:DT: clinical methods and design for a phase II randomized controlled trial. JMIR Res. Protoc. 6, e181 (2017).

    PubMed  PubMed Central  Google Scholar 

  136. 136.

    Huang, R. et al. The NCGC pharmaceutical collection: a comprehensive resource of clinically approved drugs enabling repurposing and chemical genomics. Sci. Transl Med. 3, 80ps16 (2011).

    PubMed  PubMed Central  Google Scholar 

  137. 137.

    Corsello, S. M. et al. The drug repurposing hub: a next-generation drug library and information resource. Nat. Med. 23, 405–408 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  138. 138.

    Mariz, S. et al. Worldwide collaboration for orphan drug designation. Nat. Rev. Drug Discov. 15, 440 (2016).

    PubMed  Google Scholar 

  139. 139.

    Gammie, T., Lu, C. Y. & Babar, Z. U. Access to orphan drugs: a comprehensive review of legislations, regulations and policies in 35 countries. PLOS ONE 10, e0140002 (2015).

    PubMed  PubMed Central  Google Scholar 

  140. 140.

    European Medicines Agency. Patients’ and consumers’ working party (EMA, 2019).

  141. 141.

    Spencer, D. et al. Integrating rare disease patients into pre-clinical therapy development; finding our way with patient input (BioPontis Alliance, 2016).

  142. 142.

    BioPontis Alliance for Rare Diseases. Resources (BioPontis Alliance, 2019).

  143. 143.

    US Food and Drug Administration. Guidance for industry patient-reported outcome measures: use in medical product development to support labeling claims (FDA, 2009).

  144. 144.

    Contesse, M. G. et al. The case for the use of patient and caregiver perception of change assessments in rare disease clinical trials: a methodologic overview. Adv. Ther. 36, 997–1010 (2019).

    PubMed  PubMed Central  Google Scholar 

  145. 145.

    Bloom, D. et al. The rules of engagement: CTTI recommendations for successful collaborations between sponsors and patient groups around clinical trials. Ther. Innov. Regul. Sci. 52, 206–213 (2018).

    PubMed  Google Scholar 

  146. 146.

    BioPontis Alliance for Rare Diseases. Translational research readiness tool developed with rare disease patients organizations (BioPontis Alliance, 2017).

  147. 147.

    Jayasundara, K. et al. Estimating the clinical cost of drug development for orphan versus non-orphan drugs. Orphanet J. Rare Dis. 14, 12 (2019).

    PubMed  PubMed Central  Google Scholar 

  148. 148.

    Brooks, P. J., Tagle, D. A. & Groft, S. Expanding rare disease drug trials based on shared molecular etiology. Nat. Biotechnol. 32, 515–518 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. 149.

    Moffat, J. G. et al. Opportunities and challenges in phenotypic drug discovery: an industry perspective. Nat. Rev. Drug Discov. 16, 531–543 (2017).

    CAS  PubMed  Google Scholar 

  150. 150.

    Berg, A. et al. A phenotypic screen for corrector discovery using a surface liquid readout in F508del primary airway epithelia. Pediatr. Pulmonol. 50, S77–S107 (2015).

    Google Scholar 

  151. 151.

    Philippakis, A. A. et al. The matchmaker exchange: a platform for rare disease gene discovery. Hum. Mutat 36, 915–921 (2015).

    PubMed  PubMed Central  Google Scholar 

  152. 152.

    Nguengang Wakap, S. et al. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. Eur. J. Hum. Genet. (2019).

  153. 153.

    Haendel, M. et al. How many rare diseases are there? Nat. Rev. Drug Discov. (2019).

  154. 154.

    Oprea, T. I. et al. Unexplored therapeutic opportunities in the human genome. Nat. Rev. Drug Discov. 17, 317–332 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  155. 155.

    Nguyen, D. T. et al. Pharos: collating protein information to shed light on the druggable genome. Nucleic Acids Res. 45(D1), D995–D1002 (2017).

    CAS  Google Scholar 

  156. 156.

    Jia, J. et al. eRAM: encyclopedia of rare disease annotations for precision medicine. Nucleic Acids Res. 46(D1), D937–D943 (2018).

    CAS  PubMed  Google Scholar 

  157. 157.

    Porta, M. A Dictionary of Epidemiology 193–194 (Oxford Univ. Press, 2014).

  158. 158.

    Kempf, L., Goldsmith, J. C. & Temple, R. Challenges of developing and conducting clinical trials in rare disorders. Am. J. Med. Genet. A 176, 773–783 (2018).

    PubMed  Google Scholar 

  159. 159.

    US Food and Drug Administration. Rare diseases: natural history studies for drug development (FDA, 2019).

  160. 160.

    Gavin, P. The importance of natural histories for rare diseases. Expert Opin. Orphan Drugs 3, 855–857 (2015).

    Google Scholar 

  161. 161.

    Temple, R. Historically controlled trials. FDA (2016).

  162. 162.

    US Food and Drug Administration. Guidance for industry (FDA, 2019).

  163. 163.

    US Food and Drug Administration. Rare diseases: natural history studies for drug development guidance for industry (FDA, 2019).

  164. 164.

    Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin. Pharmacol. Ther. 69, 89–95 (2001).

    Google Scholar 

  165. 165.

    Strimbu, K. & Tavel, J. A. What are biomarkers? Curr. Opin. HIV AIDS 5, 463–466 (2010).

    PubMed  PubMed Central  Google Scholar 

  166. 166.

    Federal Communications Commission. Ingestibles, wearables and embeddables (FCC, 2018).

  167. 167.

    Deiters, W., Burmann, A. & Meister, S. Strategies for digitalizing the hospital of the future [German]. Urologe A 57, 1031–1039 (2018).

    CAS  PubMed  Google Scholar 

  168. 168.

    Sawicki, G. S. et al. Sustained benefit from ivacaftor demonstrated by combining clinical trial and cystic fibrosis patient registry data. Am. J. Respir. Crit. Care Med. 192, 836–842 (2015).

    CAS  PubMed  Google Scholar 

  169. 169.

    Weidemann, F. et al. Usefulness of an implantable loop recorder to detect clinically relevant arrhythmias in patients with advanced Fabry cardiomyopathy. Am. J. Cardiol. 118, 264–274 (2016).

    PubMed  Google Scholar 

  170. 170.

    Menotti, F. et al. Amount and intensity of daily living activities in Charcot-Marie-Tooth 1A patients. Brain Behav. 4, 14–20 (2014).

    PubMed  Google Scholar 

  171. 171.

    Pande, A. et al. Machine learning to improve energy expenditure estimation in children with disabilities: a pilot study in Duchenne muscular dystrophy. JMIR Rehabil. Assist. Technol. 3, e7 (2016).

    PubMed  PubMed Central  Google Scholar 

  172. 172.

    Hay, C. R. M. et al. The haemtrack home therapy reporting system: design, implementation, strengths and weaknesses: A report from UK Haemophilia Centre Doctors Organisation. Haemophilia 23, 728–735 (2017).

    CAS  PubMed  Google Scholar 

  173. 173.

    Calvo-Lerma, J. et al. Innovative approach for self-management and social welfare of children with cystic fibrosis in Europe: development, validation and implementation of an mHealth tool (MyCyFAPP). BMJ Open 7, e014931 (2017).

    PubMed  PubMed Central  Google Scholar 

  174. 174.

    Slade, A. et al. Patient reported outcome measures in rare diseases: a narrative review. Orphanet J. Rare Dis. 13, 61 (2018).

    PubMed  PubMed Central  Google Scholar 

  175. 175.

    Benjamin, K. et al. Patient-reported outcome and observer-reported outcome assessment in rare disease clinical trials: an ISPOR COA emerging good practices task force report. Value Health 20, 838–855 (2017).

    PubMed  Google Scholar 

  176. 176.

    Lechtzin, N. et al. Home monitoring of patients with cystic fibrosis to identify and treat acute pulmonary exacerbations. eICE study results. Am. J. Respir. Crit. Care Med. 196, 1144–1151 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  177. 177.

    Cox, G. F. The art and science of choosing efficacy endpoints for rare disease clinical trials. Am. J. Med. Genet. A 176, 759–772 (2018).

    PubMed  Google Scholar 

  178. 178.

    Groft, S. C. & Posada de la Paz, M. Preparing for the future of rare diseases. Adv. Exp. Med. Biol. 1031, 641–648 (2017).

    PubMed  Google Scholar 

  179. 179.

    Noah, B. et al. Impact of remote patient monitoring on clinical outcomes: an updated meta-analysis of randomized controlled trials. NPJ Digit. Med. 1, 20172 (2018).

    PubMed  PubMed Central  Google Scholar 

  180. 180.

    Anselmo, A. C., Gokarn, Y. & Mitragotri, S. Non-invasive delivery strategies for biologics. Nat. Rev. Drug Discov. 18, 19–40 (2019).

    CAS  PubMed  Google Scholar 

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The authors acknowledge M. Lanthier and K. Miller, who helped formulate FDA data, and M. Gomar Mengod, who helped formulate EMA data for the figures presented. The authors acknowledge work done by the combined teams of C. J. Mungall (Lawrence Berkeley National Laboratory), M. A. Haendel (Oregon Health Sciences University and Oregon State University), P. J. Robinson (Jackson Laboratories) and D.-T. Nguyen (US National Institutes of Health National Center for Advancing Translational Sciences) for Fig. 1 and J. Holmes and S. L. Mathias (University of New Mexico) for the figure related to the rare disease proteome in Box 2. The authors also sincerely acknowledge the help of F. Sasinowsky for the sections on ASOs, natural history and patient engagement, and of E. Powers for the gene and cell therapy section. The authors thank D. Spencer for rereading and commenting on the article. NIH grants U24 CA224370, U24 TR002278, U01 CA239108, UL1 TR001449 and P30 CA118100 provided funding to T.I.O.

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Correspondence to Erik Tambuyzer or Marco Prunotto.

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Related links

Drug repurposing hub:

E-Rare. E-Rare-3 call for proposals 2016:

EURORDIS Rare Diseases Europe:

European Lead Factory:

European Medicines Agency. Committee for Orphan Medicinal Products:

European Medicines Agency. Orphan designation overview:

European Medicines Agency. Rare diseases, orphan medicines. Getting the facts straight:

FDA Rare Diseases Program:

Genetic and Rare Diseases Information Center of the US National Institutes of Health:

IRDiRC. Datamining and repurposing:

Mondo disease ontology:

National Institutes of Health Bridging Interventional Development Gaps Program:

National Institutes of Health Drug Record. Livertox: enzyme replacement therapy:


Orphanet, an online database of rare diseases and orphan drugs:


Rare Diseases Registry (RaDaR) Program:

Supplementary information


Lipinski’s rule of five

These guidelines identify several physicochemical properties to be considered for small molecules that are intended for oral delivery: molecular mass 500 Da or less; five or fewer hydrogen-bond donors; fewer than 10 hydrogen-bond acceptors; and calculated octanol–water partition coefficient (a surrogate for the ability of a molecule to cross biological membranes) of 5 or less.


Gene encoding the cystic fibrosis transmembrane conductance regulator protein, an ion channel in the membrane of cells that produce mucus, sweat, saliva, tears and digestive enzymes. Mutations in CFTR that affect the production, processing or function of the protein underlie cystic fibrosis.

‘Umbrella trial’

A clinical trial design in which a single drug is evaluated in more than one disease simultaneously.


A type of single-domain antibody fragment.

Good manufacturing practice

A system for ensuring that products are consistently produced and controlled according to defined quality standards.


Attaching polyethylene glycol chains to therapeutics, particularly proteins, can improve characteristics such as immunogenicity and pharmacokinetics. For example, pegylation has been used to extend the half-life of factor VIII replacement therapies for haemophilia.

Intrathecal injection

Delivery of a substance directly to the spinal fluid (intrathecal space) through a drug delivery system comprising a pump and a catheter.

Haematopoietic stem cells

(HSCs). Cells that can replenish all blood cell types. HSCs derived from bone marrow have been used for many years to treat cancer; patients receive a myeloablative conditioning regimen to remove diseased cells before transplantation, with the transplanted HSCs then reconstituting the haematopoietic system. A similar strategy can also be used to treat inherited blood disorders.

Adeno-associated virus (AAV) vectors

AAV vectors are based on wild-type AAV, which has a single-stranded circular genome of roughly 4.7 kilobases. The AAV genome contains two open reading frames bounded by inverted terminal repeats into which a transgene of up to approximately five kilobases can be inserted.


A protein shell that originally encloses the viral genome.

Fast track pathway

This can expedite the review of products to treat serious conditions. The process allows sponsors to have more frequent meetings and communications with the FDA to address appropriate data collection and design of clinical trials. It also allows a sponsor to be eligible for priority review and a rolling review of the application.

Accelerated approval

This allows a product for a serious condition to be approved on the basis of a surrogate end point or an intermediate clinical end point. Confirmatory postmarketing trials will be needed to verify this benefit.

Priority review

This is a designation that allows the FDA to act on a marketing authorization application in 6 months (compared with 10 months for standard reviews). To be eligible for priority review, the intended medicine should offer significant advancements in safety and efficacy of treatment, diagnosis or prevention of a serious condition.

Breakthrough therapy deignation

This FDA designation can expedite development of drugs for which preliminary clinical evidence indicates that they may offer substantial advantages over existing treatment options for patients with serious or life-threatening diseases. Designated drugs are eligible for the expedited processing that fast track designation offers, as well as intensive guidance on efficient development from the FDA.

Regenerative medicine advanced therapy designation

This FDA designation is similar to the breakthrough threapy designation and is available for cell therapies, therapeutic tissue engineering products, human cell and tissue products and combination products if the product is intended to treat serious or life-threatening diseases.

Conditional marketing authorization

This European Medicines Agency pathway is similar to the accelerated approval process in the United States. Applicants may be granted a conditional marketing authorization for medicines for which the benefit of immediate availability outweighs the risk of less comprehensive clinical data than normally required.

Approval under exceptional circumstances

In exceptional cases, a reduced data set is acceptable by the European Medicines Agency for candidate drugs for a rare indication with a high medical need if it is difficult to obtain sufficient data to fulfil the requirements of a full dossier for marketing authorization in a reasonable time frame. Annual review of clinical data obtained after such approval is required, with the potential to maintain or withdraw the authorization.

Accelerated assessment

The evaluation of a marketing authorization application under the centralized procedure in the European Union can take up to 210 days. On request, the time frame can be reduced to 150 days if the applicant provides sufficient justification that the medicinal product is expected to be of major public health interest, particularly in cases of therapeutic innovation.

Priority Medicines (PRIME) scheme

A scheme in the European Union that provides early and enhanced scientific and regulatory support for medicines that may offer a major therapeutic advantage over existing treatments, or benefit patients without treatment options.

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Tambuyzer, E., Vandendriessche, B., Austin, C.P. et al. Therapies for rare diseases: therapeutic modalities, progress and challenges ahead. Nat Rev Drug Discov 19, 93–111 (2020).

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