Alkaptonuria: Leading to the Treasure in Exceptions

  • Timothy M. CoxEmail author
Invited Articles
Part of the JIMD Reports book series (JIMD, volume 5)


The brilliant geneticist, William Bateson, a formidable English experimentalist, was the first to recognize the nature of the “inborn” in Archibald Garrod’s errors of metabolism. Bateson’s advice to young scientists: “Treasure your exceptions!” summarizes much of the vigorous empiricism associated with the study of rare disorders.

The first inborn error of metabolism to be so recognized was alkaptonuria, and it is only recently that a proper understanding of this condition as a disease, rather than a biochemical curiosity, has emerged. Abnormal excretion of the reactive tyrosine metabolite, homogentisic acid, not only provides a tangible biomarker of alkaptonuria, but also a focus for detailed mechanistic understanding.

Currently, there is no proven treatment for alkaptonuria but emergence of orphan drug legislation internationally has promoted the licensing of nitisinone (Orfadin™) for an equally rare disorder of tyrosine metabolism – hereditary tyrosinaemia type 1. Nitisinone, a triketone competitive inhibitor of a proximal step leading to the formation of homogentisic acid, has potent therapeutic effects in hereditary tyrosinemia and rapidly ameliorates the primary biochemical abnormality in patients with alkaptonuria.

Here, we discuss the context in which nitisinone should be further explored for the treatment of alkaptonuria. This exceptional disease is a paradigm case, which opens up unusual opportunities for basic and applied research. In modern times, it also shows how the conflation of orphan drug legislation and the emerging power and commitment of patient organizations can synergize effectively to advance basic research and therapeutic development in ultra-orphan diseases.


Fabry Disease Inborn Error Homogentisic Acid Orphan Medicinal Product Acute Porphyria 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© SSIEM and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.University of CambridgeCambridgeUK

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