ReviewInformaticsComputational drug repositioning for rare diseases in the era of precision medicine
Introduction
Most rare diseases have a genetic etiology, affect a small proportion of the population (usually less than 1/1500 in the USA or 1/2000 in Europe) but are severe and life-threatening 1, 2, 3. Although rare diseases are themselves infrequent by definition, collectively they are a common occurrence. There are more than 7000 rare diseases based on the European Organization for Rare Diseases (EURORDIS) statistics (http://www.eurordis.org/about-rare-diseases). However, there have only been ∼600 treatment options available since the Orphan Drug Act of 1983 was passed [4]. The average time to diagnosis of a rare disease is more than 7 years. Over one-third of children with a rare disease will not live more than 5 years, and about 35% of these children will die within the first year of life [5].
The fundamental challenge of orphan drug development is a lack of knowledge about pathophysiology, etiology and the natural history of rare diseases. Few patients are available and, together with their geographical dispersal, clinical trials are often impractical [6]. Also, researchers have great difficulty in gauging the genetic origin of rare diseases [1]. The causative genetic mutations are either hereditary (even when the disease has a late onset in the patient’s life) or they are caused by a new mutation (de novo) [7]. Like common diseases, heterogeneity also exists in rare diseases, which makes it extremely challenging to distinguish patients with different morphological features or genetic variants and then look for the right treatment options. One example is cystic fibrosis (CF), which is accounted for by the genetic mutation of the transmembrane conductance regulator (CFTR) gene. There are ∼2000 identified mutations within the CFTR gene from CF patients. Among the 2000 identified CFTR mutations, F508del and G551D are major mutations that are carried by >90% of CF patients. However, the associated phenotypic outcomes of the two mutations are distinct. The F508del mutation is mainly associated with CFTR folding impairment, stability at the endoplasmic reticulum and plasma membrane, and chloride channel gating. The G551D mutation is mainly related to channel gating alternation 8, 9. The only FDA-approved drug, ivacaftor, is only effective in patients with the 33 genetic mutation types such as the G551D mutation, which only covers a total of 6% of CF patients (https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm559212.htm). Meanwhile, there are still a substantial number of CF patients carrying the F508del mutation without a treatment option.
The advent of next-generation sequencing (NGS) has changed the landscape of rare disease research, presenting the opportunity for the causative genes of rare diseases to be identified at an unprecedented pace and resolution [1]. NGS is also considered as a key technology for advancing precision medicine [10]. Many genetic variants of rare diseases have been detected and the data are publicly accessible. However, there are still many undetected genes associated with rare diseases 7, 11, 12. Ongoing efforts are being made and will lead to substantial improvement in our understanding of the genetic origin of rare diseases. For example, the International Rare Diseases Research Consortium (IRDiRC) set a goal of developing the capacity to diagnose all of the rare diseases and to establish 200 new or repurposed therapies for rare diseases by the year 2020 [12].
How to translate the accumulated genetic knowledge to facilitate rare disease treatment development is still an open question [13]. First, to identify and validate therapeutic targets of rare diseases is a great challenge. Even if a causative genetic mutation in a patient with rare diseases is detected, there is no guarantee that a therapeutic option might arise from this knowledge. This is because the mutated protein might be unsuitable as a therapeutic target for a variety of reasons such as inaccessibility or lack of suitability as a small molecule target [14]. In this context, the current drug design paradigm has proved generally successful in inhibiting therapeutic targets in rare diseases with gain-of-function mutations [15]. Rare diseases with gain-of-function mutations, like most common diseases, are defined as the activation of specific pathways or the ectopic activity in relation to the proteins, which aligns well with the current concept of target identification. However, there are many rare diseases that are caused by loss-of-function where the impairment of a particular protein drives the etiology [15]. Therefore, a novel approach for translating knowledge of loss-of-function genetic variants into clinical use is urgently needed.
Drug repositioning that aims to find new uses for existing drugs is considered as an effective and alternative paradigm of drug development [16]. Computational drug repositioning provides a systematic and rational solution for identifying treatment options as compared with conventional drug repositioning approaches arising from serendipity or close clinical observation 17, 18, 19, 20. Linking the genetic findings of rare disease and drug repositioning into the same framework to accelerate drug development for rare diseases is imperative and is also a necessary practice for precision medicine. In this review, we summarize the current progress in research on the genetic origins of rare diseases. Then, we propose several novel strategies to integrate these accumulated genetic findings into computational drug repositioning frameworks for the development of treatments for rare diseases (Fig. 1). Finally, we discuss the remaining challenges and future perspectives in this field.
Section snippets
Genetic landscape of rare diseases
During the past decade, much progress has been made in the detection of the genetic origin of rare diseases even though patient recruitment is a challenge for obtaining samples and for carrying out clinical studies for the development of treatment options. This has resulted from the advancement of new techniques, the assistance of social media and the policy shifts of regulatory agencies 1, 21, 22. Particularly, NGS techniques have greatly enabled the detection of the possible genetic basis of
Application of NGS for rare diseases
NGS techniques have enabled the comprehensive sequencing of DNA that is relevant to rare diseases at a much higher throughput and much lower costs than previously possible. Compared with the conventional genetic mutation detection methodologies 40, 41, 42, 43, 44, NGS techniques provide more-detailed and high-resolution information of genetic variants (see Fig. S1 in Supplementary material online). There are three major NGS techniques: whole genome sequencing (WGS), whole exome sequencing (WES)
Paths toward rare disease therapy
The emerging techniques have accelerated the pace of the identification of rare disease genetic variants [1]. However, the majority of detected variants remains to be translated into treatment options. Here, we summarize and propose several computational drug repositioning approaches for facilitating this process (Fig. 1).
Verification of repurposing candidates
Computational drug repositioning provides a rapid turnaround list of repositioning candidate drugs. The challenge is to experimentally verify the efficacy and safety of these and to move the drugs forward into clinical trials. Currently, most in silico drug repositioning approaches are verified by animal-based in vitro or in vivo models 90, 91, 119, 120, 121. Moving these in silico findings toward clinical application is challenging owing to difficulties in patient recruitment, which are
Concluding remarks
The NGS technologies have driven a dramatic shift in our understanding of rare diseases at a genome-wide scale [123]. Bioinformatics plays a central part and has become an important component in NGS data analysis, generating many algorithms and workflows. However, building a standard bioinformatics solution for NGS analysis and application to clinical practice remains to be carried out. Accurate and reliable NGS analysis ensures patients with rare diseases receive the correct diagnosis and
Conflicts of interest
Dr Ruth Roberts is co-founder and co-director of Apconix, an integrated toxicology and ion channel company that provides expert advice on nonclinical aspects of drug discovery and drug development to academia, industry and not-for-profit organizations.
The views presented in this article do not necessarily reflect current or future opinion or policy of the FDA or NIH. Any mention of commercial products is for clarification and not intended as endorsement.
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