Rationale
We briefly introduce the conventional approach to diagnosis and management of inherited metabolic diseases (IMDs), the problems and consequences associated with these conventional approaches, the overall objectives of Recon4IMD to overcome these problems, and our ambition to impact patients across Europe.
Accelerated diagnosis of Inherited Metabolic Diseases
Setting
Inherited metabolic diseases (IMDs) are a collection of ~1,450 rare diseases, each with a prevalence < 0.2% and each of which is caused by a genetic defect in a metabolic pathway. For a considerable proportion of IMDs, specific therapies are available that can dramatically improve patient outcomes, so it is essential to achieve an accurate and timely diagnosis.
Convention
Diagnosis of IMDs is usually driven by descriptive and intuitive methodologies. The conventional approach is to initially classify symptomatic patients in terms of clinical symptoms and signs and then classify patients based on the results of a series of targeted biochemical and genetic tests. Each of these tests is targeted either to quantify the concentration of a small number of metabolites, measure the activity of a specific enzyme, or sequence a small number of genes for a small number of rare diseases, though genomic approaches are becoming increasingly adopted.
Problem
Descriptive and intuitive diagnostic methods are conditioned by human limitations. IMDs are associated with a large, heterogeneous set of symptoms that have considerable overlap with common diseases, and with ~4,500 other rare diseases, i.e., not associated with an IMD gene. The heterogeneity of IMD symptoms and the large number of differential diagnoses make laboratory tests essential for accurate diagnosis. However, an extensive list of targeted tests and the impossible challenge for each clinician to be able to interpret each test result in the context of a patient’s phenotype means that timely diagnosis is often difficult to achieve. From a patient perspective, this phenomenon is referred to as a diagnostic odyssey, which requires an average of five years from the first symptom to a correct diagnosis.
Consequences
The incidence of all IMDs combined is ~1:800 newborns4. Delayed diagnosis is a problem, which has an impact on patients, their families, and society. For patients, every erroneous initial diagnosis leads to prolonged disease manifestations that are potentially avoidable with appropriate treatment and may lead to additional harm due to inappropriate therapies that aggravate their symptoms. If the patient is a child, for families, this leads to stress, anxiety, and uncertainty about the future. For clinicians, a delayed diagnostic process diverts energy away from optimising therapy. For society, a diagnostic delay is a substantial waste of human and economic resources and moreover, it is a failure to sufficiently translate existing and ongoing academic technological advances for the betterment of mankind.
Overall Recon4IMD objective
To accelerate the diagnosis of patients at risk of an IMD by computational modelling of genetic risk, enzyme structure, and metabolic networks, personalised using genomic, proteomic and metabolomic data.
Setting
All IMDs have a wide spectrum of disease severity, from life-threatening events in the first days of life to death in the first decade, up to a chronic progressive disorder that may not manifest until late adulthood. For a small group of diseases (~50), newborn biochemical screening is available, allowing early detection and pre-symptomatic treatment (mostly symptomatic, based on diet modification, enzyme replacement, or small molecule therapies), which slows or halts the progression of the disease. However, treatment is often not personalised but rather based on strict protocols and clinical experience. For the remaining ~1400 diseases, patients are exposed to delay, incorrect diagnosis, or both, sometimes followed by inappropriate therapies, which could potentially harm the patient or accelerate the progression of the disease.
Convention
Clinical management of IMDs is usually driven by the identification of signs and symptoms, which might be related to poor control of the metabolic equilibrium or to the progression of the disease. Although several national and international guidelines exist, available treatments are rarely personalised. Follow-ups are mainly planned to routinely check the medical status, generally according to guidelines, when patients are severely affected by metabolic decompensation, or hospitalisation is planned for a complex procedure.
Problem
Even when a diagnosis is available, for almost all the conditions, it is quite difficult, if not impossible, to predict the optimal therapeutic approach for each patient. Correlation between genotype and phenotype is often poor owing to the high number of mutations and the low number of patients. Furthermore, during routine follow-up, the testing of patients does not involve a full -omic analysis and is limited to the minimal tests needed to assess metabolic control. Only a very limited number of biomarkers might be checked, hence, stratification of patients is not possible due to the lack of data. Optimal selection of therapeutic approach is also of importance for the diseases detected by newborn screening, where a genetic variant is identified of unknown significance. Indeed, for patients with the same genetic diagnosis, even within families, related individuals with the same causative genetic defect can have dramatically different severity of clinical phenotypes.
Consequences
The absence of a proper stratification of patients describing the genotype/phenotype correlation and the difficulty to administer a personalised therapeutic approach severely impact the quality of life of patients and families, especially because the central nervous system is affected by many IMDs (~70%). Furthermore, non-personalised therapies limit their benefits, since they are supplied at standard dosage, without taking into consideration the real need of the patients and his/her level of response.
Overall Recon4IMD objective
To stratify a focussed set of IMD patients by identification of clinically actionable compensatory and aggravating metabolic mechanisms, using personalised computational modelling.
Personalised clinical management of Inherited Metabolic Diseases
