RARE DISEASE
Alkaptonuria
Alkaptonuria
Alkaptonuria
Synonyms: Hereditary ochronosis, Homogentisic acid oxidase deficiency
Synonyms: Hereditary ochronosis, Homogentisic acid oxidase deficiency
Synonyms: Hereditary ochronosis, Homogentisic acid oxidase deficiency
Drug discovery
4
drugs
With orphan designations
Overview
Alkaptonuria (AKU) is a rare autosomal recessive disorder caused by HGD gene mutations, resulting in deficient homogentisate 1,2-dioxygenase. This leads to systemic accumulation of homogentisic acid (HGA), causing ochronosis (blue-black connective tissue pigmentation) and early-onset osteoarthritis. Complications include cardiac valve calcification, renal/prostatic stones, and osteoporosis [1][2][7]. Diagnosis involves urinary HGA quantification, genetic testing, and imaging for arthropathy. First-line therapy with nitisinone reduces HGA production, supplemented by symptomatic management (analgesics, joint replacement) [3][6][8].
Burden
Severe quality-of-life impact: Chronic pain, mobility loss, and frequent surgeries (50% require joint replacement by age 55) [4][8].
Increased risks: Cardiovascular disease (22% aortic stenosis), renal stones, and Parkinson’s disease (20× higher prevalence) [9][16][18].
Economic burden: High costs from repeated surgeries and lifelong multidisciplinary care [4][20].
Categories: rare genetic diseases, rare inborn errors of metabolism, rare ophthalmic disorders, rare skin diseases
Research Papers
270 drug discovery papers related to Alkaptonuria, with 3 first-in-class and 3 next-in-class early-stage therapies forecasted to outperform the average preclinical success rate. Recent publications:
270 drug discovery papers related to Alkaptonuria, with 3 first-in-class and 3 next-in-class early-stage therapies forecasted to outperform the average preclinical success rate. Recent publications:
2026-06-30 | SMILES-based degree molecular descriptors and machine learning for QSPR modeling of anti-alkaptonuria drugs.
Quantitative Structure-Property Relationship (QSPR) modelling provides an efficient computational framework for predicting physicochemical properties of drug molecules when experimental data are limited. In this study, we investigate the predictive capability of degree-based topological indices (TIs) derived from SMILES (Simplified Molecular Input Line Entry System) representations for modelling physicochemical properties of anti-alkaptonuria drugs. Nine representative compounds, including Nitisinone, Ascorbic Acid, Ibuprofen, Naproxen, Paracetamol, Tramadol, Methotrexate, Sulfasalazine, and Glucosamine, were analysed using several molecular descriptors such as molecular weight, logP, hydrogen bond donors and acceptors, rotatable bonds, and polar surface area. A total of 58 regression models were developed using Linear Regression (LR) and two machine learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost, abbreviated XGB). Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination R 2 . The results demonstrate that machine learning models significantly outperform classical regression, with XGB achieving the most accurate and stable predictions for the investigated physicochemical properties. This study introduces a machine learning-driven QSPR framework that integrates SMILES-derived degree-based topological indices with ensemble learning techniques for predicting physicochemical properties of anti-alkaptonuria drugs. The proposed approach demonstrates improved predictive performance on small datasets and highlights the effectiveness of combining graph-theoretic molecular descriptors with advanced machine learning methods.
2026-06-02 | Liver-directed AAV gene therapy metabolically corrects AKU in Hgd deficient mice
Abstract Background Alkaptonuria (AKU) is a rare autosomal recessive metabolic disorder caused by deficiency of homogentisate 1,2-dioxygenase (HGD), resulting in systemic accumulation of homogentisic acid (HGA), ochronosis, and progressive multisystem disease. Although nitisinone (NTBC) lowers HGA levels, it does not correct the underlying genetic defect and induces hypertyrosinemia, highlighting the need for curative treatment approaches. We evaluated liver-directed adeno-associated virus (AAV)-mediated HGD gene therapy as a potential treatment for AKU. Methods Hgd-deficient ( Hgd -/- ) mice received liver-directed AAV2/8 vectors expressing codon-optimized human HGD under a liver-specific promoter. Reporter vectors were first used to assess hepatic biodistribution and transduction efficiency. Therapeutic efficacy was subsequently evaluated following AAV2/8-HGD administration (1 x 10 12 vg/mouse). HGD expression was assessed by DNAscope, Western blotting, and RT-qPCR. Metabolic correction was determined using targeted LC-MS/MS and untargeted LC-HRMS metabolomics and compared with NTBC-treated Hgd -/- mice. Results Reporter studies demonstrated liver-predominant transduction, with dose-dependent hepatocyte transduction reaching 89-93% at the highest dose. AAV2/8-HGD treatment produced robust hepatic HGD expression, with codon-optimized human HGD transcript levels approximately 33-fold higher than endogenous murine Hgd expression. Twelve weeks after treatment, plasma and urinary HGA levels were significantly reduced, with plasma HGA restored to near wild-type concentrations. Untargeted metabolomics further demonstrated marked reductions in HGA-derived phase I and II metabolites and revealed significant modulation of tricarboxylic acid cycle metabolism, consistent with partial restoration of metabolic homeostasis. Compared with NTBC-treated mice, AAV2/8-HGD achieved comparable plasma HGA reduction without elevation of upstream tyrosine pathway metabolites. Conclusions Liver-directed AAV2/8-HGD gene therapy achieved substantial biochemical correction in Hgd -/- mice and restored metabolic flux without inducing hypertyrosinemia. These findings provide proof-of-concept supporting AAV-mediated HGD replacement as a promising long-term therapeutic strategy for AKU.
2026-01-27 | Effect of Nitisinone on Aortic Stenosis Disease Progression in Patients With Alkaptonuria: An Analysis of the Suitability of Nitisinone in Alkaptonuria (SONIA) 2 Study.
Background and aim Alkaptonuria (AKU) is a rare metabolic disorder characterised by the accumulation of homogentisic acid (HGA). Deposition of HGA in the aortic valve leading to progressive aortic stenosis is a serious complication. Nitisinone has been shown to improve morbidity and slow disease progression in AKU, but the effects of this treatment on aortic stenosis progression have not yet been described. The objective of this study was to evaluate whether treatment with nitisinone attenuated the progression of aortic stenosis, as assessed by peak trans-aortic valve pressure (Pmax), in patients with AKU. This post-hoc analysis used longitudinal echocardiographic data from the Suitability of Nitisinone in Alkaptonuria (SONIA) 2, a four-year multicenter randomised controlled trial, to examine aortic stenosis disease progression. Methods Data were obtained from echocardiograms performed on 138 patients over 48 months of follow-up. A linear mixed-effects regression model was used to ascertain the difference in the maximal trans-aortic valve pressure gradient (Pmax) at baseline and 48 months between the treatment and control groups, adjusting for baseline Pmax and other covariates. Results At baseline, 18/138 patients (13.0%) had aortic stenosis of varying degrees of severity, and 25/138 (18.1%) had aortic sclerosis. The difference in Pmax between the control (N=69) and treatment (N=69) groups at baseline was 0.063 mmHg [95% CI: -0.054 mmHg to 0.18 mmHg) and did not reach statistical significance (p=0.23). At the end of the four-year treatment period, the difference in Pmax was 0.10 mmHg (95% CI: -0.0007 mmHg to 0.20 mmHg) (p = 0.05), representing a modest but statistically significant between-group treatment effect. Conclusion Nitisinone may attenuate the progression of aortic stenosis in patients with AKU. Given the small absolute effect size and post-hoc nature of the analysis, these findings should be interpreted as exploratory and hypothesis-generating rather than clinically definitive. Additional research is needed to determine whether nitisinone produces clinically meaningful outcomes for aortic stenosis in this population.
2026-06-30 | SMILES-based degree molecular descriptors and machine learning for QSPR modeling of anti-alkaptonuria drugs.
Quantitative Structure-Property Relationship (QSPR) modelling provides an efficient computational framework for predicting physicochemical properties of drug molecules when experimental data are limited. In this study, we investigate the predictive capability of degree-based topological indices (TIs) derived from SMILES (Simplified Molecular Input Line Entry System) representations for modelling physicochemical properties of anti-alkaptonuria drugs. Nine representative compounds, including Nitisinone, Ascorbic Acid, Ibuprofen, Naproxen, Paracetamol, Tramadol, Methotrexate, Sulfasalazine, and Glucosamine, were analysed using several molecular descriptors such as molecular weight, logP, hydrogen bond donors and acceptors, rotatable bonds, and polar surface area. A total of 58 regression models were developed using Linear Regression (LR) and two machine learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost, abbreviated XGB). Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination R 2 . The results demonstrate that machine learning models significantly outperform classical regression, with XGB achieving the most accurate and stable predictions for the investigated physicochemical properties. This study introduces a machine learning-driven QSPR framework that integrates SMILES-derived degree-based topological indices with ensemble learning techniques for predicting physicochemical properties of anti-alkaptonuria drugs. The proposed approach demonstrates improved predictive performance on small datasets and highlights the effectiveness of combining graph-theoretic molecular descriptors with advanced machine learning methods.
2026-06-02 | Liver-directed AAV gene therapy metabolically corrects AKU in Hgd deficient mice
Abstract Background Alkaptonuria (AKU) is a rare autosomal recessive metabolic disorder caused by deficiency of homogentisate 1,2-dioxygenase (HGD), resulting in systemic accumulation of homogentisic acid (HGA), ochronosis, and progressive multisystem disease. Although nitisinone (NTBC) lowers HGA levels, it does not correct the underlying genetic defect and induces hypertyrosinemia, highlighting the need for curative treatment approaches. We evaluated liver-directed adeno-associated virus (AAV)-mediated HGD gene therapy as a potential treatment for AKU. Methods Hgd-deficient ( Hgd -/- ) mice received liver-directed AAV2/8 vectors expressing codon-optimized human HGD under a liver-specific promoter. Reporter vectors were first used to assess hepatic biodistribution and transduction efficiency. Therapeutic efficacy was subsequently evaluated following AAV2/8-HGD administration (1 x 10 12 vg/mouse). HGD expression was assessed by DNAscope, Western blotting, and RT-qPCR. Metabolic correction was determined using targeted LC-MS/MS and untargeted LC-HRMS metabolomics and compared with NTBC-treated Hgd -/- mice. Results Reporter studies demonstrated liver-predominant transduction, with dose-dependent hepatocyte transduction reaching 89-93% at the highest dose. AAV2/8-HGD treatment produced robust hepatic HGD expression, with codon-optimized human HGD transcript levels approximately 33-fold higher than endogenous murine Hgd expression. Twelve weeks after treatment, plasma and urinary HGA levels were significantly reduced, with plasma HGA restored to near wild-type concentrations. Untargeted metabolomics further demonstrated marked reductions in HGA-derived phase I and II metabolites and revealed significant modulation of tricarboxylic acid cycle metabolism, consistent with partial restoration of metabolic homeostasis. Compared with NTBC-treated mice, AAV2/8-HGD achieved comparable plasma HGA reduction without elevation of upstream tyrosine pathway metabolites. Conclusions Liver-directed AAV2/8-HGD gene therapy achieved substantial biochemical correction in Hgd -/- mice and restored metabolic flux without inducing hypertyrosinemia. These findings provide proof-of-concept supporting AAV-mediated HGD replacement as a promising long-term therapeutic strategy for AKU.
2026-01-27 | Effect of Nitisinone on Aortic Stenosis Disease Progression in Patients With Alkaptonuria: An Analysis of the Suitability of Nitisinone in Alkaptonuria (SONIA) 2 Study.
Background and aim Alkaptonuria (AKU) is a rare metabolic disorder characterised by the accumulation of homogentisic acid (HGA). Deposition of HGA in the aortic valve leading to progressive aortic stenosis is a serious complication. Nitisinone has been shown to improve morbidity and slow disease progression in AKU, but the effects of this treatment on aortic stenosis progression have not yet been described. The objective of this study was to evaluate whether treatment with nitisinone attenuated the progression of aortic stenosis, as assessed by peak trans-aortic valve pressure (Pmax), in patients with AKU. This post-hoc analysis used longitudinal echocardiographic data from the Suitability of Nitisinone in Alkaptonuria (SONIA) 2, a four-year multicenter randomised controlled trial, to examine aortic stenosis disease progression. Methods Data were obtained from echocardiograms performed on 138 patients over 48 months of follow-up. A linear mixed-effects regression model was used to ascertain the difference in the maximal trans-aortic valve pressure gradient (Pmax) at baseline and 48 months between the treatment and control groups, adjusting for baseline Pmax and other covariates. Results At baseline, 18/138 patients (13.0%) had aortic stenosis of varying degrees of severity, and 25/138 (18.1%) had aortic sclerosis. The difference in Pmax between the control (N=69) and treatment (N=69) groups at baseline was 0.063 mmHg [95% CI: -0.054 mmHg to 0.18 mmHg) and did not reach statistical significance (p=0.23). At the end of the four-year treatment period, the difference in Pmax was 0.10 mmHg (95% CI: -0.0007 mmHg to 0.20 mmHg) (p = 0.05), representing a modest but statistically significant between-group treatment effect. Conclusion Nitisinone may attenuate the progression of aortic stenosis in patients with AKU. Given the small absolute effect size and post-hoc nature of the analysis, these findings should be interpreted as exploratory and hypothesis-generating rather than clinically definitive. Additional research is needed to determine whether nitisinone produces clinically meaningful outcomes for aortic stenosis in this population.
Access all drug discovery articles and probability of success in trials forecasts:
Access all drug discovery articles and probability of success in trials forecasts:
Drug Discovery Landscape
4 orphan drug designations for Alkaptonuria, including 1 approved therapy.
4 orphan drug designations for Alkaptonuria, including 1 approved therapy.
Drug | Therapy type | Regulator | Orphan designation | Approval | Sponsor |
|---|---|---|---|---|---|
nitisinone [Harliku] | small molecules | FDA | 2023-06-12 | 2025-06-10 | Cycle Pharmaceuticals Ltd. |
Methotrexate | small molecules | EMA | 2016-08-29 | — | aimAKU (Associazione Italiana Malati di Alcaptonuria) |
Nitisinone | small molecules | EMA | 2002-03-13 | — | Swedish Orphan Biovitrum AB (publ) |
nitisinone | small molecules | FDA | 2001-10-19 | — | Swedish Orphan Biovitrum AB (publ) |
Let's accelerate rare disease drug discovery
Let's accelerate drug discovery
Get access to Explority AI's forecasts to outperform average preclinical success rates. Whether you're expanding your R&D pipeline, evaluating a partnership, or simply have a question — we'd love to hear from you.