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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.Most rare diseases still lack approved treatments. This article analyses the main therapeutic modalities available to researchers interested in translating advances in the scientific understanding of rare diseases into therapies, highlights progress so far and discusses overarching issues in drug development for rare diseases.

The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we presented a new network-based algorithm for drug repositioning, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug–disease associations by quantifying the interplay between the drug targets and the disease-specific proteins in the human interactome via a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Specifically, we applied SAveRUNNER on a panel of 14 selected diseases with a consolidated knowledge about their disease-causing genes and that have been found to be related to COVID-19 for genetic similarity (i.e., SARS), comorbidity (e.g., cardiovascular diseases), or for their association to drugs tentatively repurposed to treat COVID-19 (e.g., malaria, HIV, rheumatoid arthritis). Focusing specifically on SARS subnetwork, we identified 282 repurposable drugs, including some the most rumored off-label drugs for COVID-19 treatments (e.g., chloroquine , hydroxychloroquine , tocilizumab , heparin ), as well as a new combination therapy of 5 drugs ( hydroxychloroquine , chloroquine , lopinavir , ritonavir , remdesivir ), actually used in clinical practice. Furthermore, to maximize the efficiency of putative downstream validation experiments, we prioritized 24 potential anti-SARS-CoV repurposable drugs based on their network-based similarity values. These top-ranked drugs include ACE-inhibitors, monoclonal antibodies (e.g., anti-IFNγ, anti-TNFα, anti-IL12, anti-IL1β, anti-IL6), and thrombin inhibitors. Finally, our findings were in-silico validated by performing a gene set enrichment analysis, which confirmed that most of the network-predicted repurposable drugs may have a potential treatment effect against human coronavirus infections.

Identification of potential drug-associated indications is critical for either approved or novel drugs in drug repositioning. Current computational methods based on drug similarity and disease similarity have been developed to predict drug-disease associations. When more reliable drug- or disease-related information becomes available and is integrated, the prediction precision can be continuously improved. However, it is a challenging problem to effectively incorporate multiple types of prior information, representing different characteristics of drugs and diseases, to identify promising drug-disease associations. In this study, we propose an overlap matrix completion (OMC) for bilayer networks (OMC2) and tri-layer networks (OMC3) to predict potential drug-associated indications, respectively. OMC is able to efficiently exploit the underlying low-rank structures of the drug-disease association matrices. In OMC2, first of all, we construct one bilayer network from drug-side aspect and one from disease-side aspect, and then obtain their corresponding block adjacency matrices. We then propose the OMC2 algorithm to fill out the values of the missing entries in these two adjacency matrices, and predict the scores of unknown drug-disease pairs. Moreover, we further extend OMC2 to OMC3 to handle tri-layer networks. Computational experiments on various datasets indicate that our OMC methods can effectively predict the potential drug-disease associations. Compared with the other state-of-the-art approaches, our methods yield higher prediction accuracy in 10-fold cross-validation and de novo experiments. In addition, case studies also confirm the effectiveness of our methods in identifying promising indications for existing drugs in practical applications.

Repurposing existing drugs to treat new diseases is a cost-effective alternative to de novo drug development, but there are millions of potential drug-disease combinations to be considered with only a small fraction being viable. In silico predictions of drug-disease associations can be invaluable for reducing the size of the search space. In this work we present a novel network of drugs and the diseases they treat, compiled using a combination of existing textual and machine-readable databases, natural-language processing tools, and hand curation, and analyze it using network-based link prediction methods to identify potential drug-disease combinations. We measure the efficacy of these methods using cross-validation tests and find that several methods, particularly those based on graph embedding and network model fitting, achieve impressive prediction performance, significantly better than previous approaches, with area under the ROC curve above 0.95 and average precision almost a thousand times better than chance.

Measuring similarity between diseases plays an important role in disease-related molecular function research. Functional associations between disease-related genes and semantic associations between diseases are often used to identify pairs of similar diseases from different perspectives. Currently, it is still a challenge to exploit both of them to calculate disease similarity. Therefore, a new method (SemFunSim) that integrates semantic and functional association is proposed to address the issue. SemFunSim is designed as follows. First of all, FunSim (Functional similarity) is proposed to calculate disease similarity using disease-related gene sets in a weighted network of human gene function. Next, SemSim (Semantic Similarity) is devised to calculate disease similarity using the relationship between two diseases from Disease Ontology. Finally, FunSim and SemSim are integrated to measure disease similarity. The high average AUC (area under the receiver operating characteristic curve) (96.37%) shows that SemFunSim achieves a high true positive rate and a low false positive rate. 79 of the top 100 pairs of similar diseases identified by SemFunSim are annotated in the Comparative Toxicogenomics Database (CTD) as being targeted by the same therapeutic compounds, while other methods we compared could identify 35 or less such pairs among the top 100. Moreover, when using our method on diseases without annotated compounds in CTD, we could confirm many of our predicted candidate compounds from literature. This indicates that SemFunSim is an effective method for drug repositioning.

ObjectivesThere is a need to identify effective treatments for rheumatic diseases, and while genetic studies have been successful it is unclear which genes contribute to the disease. Using our existing Capture Hi-C data on three rheumatic diseases, we can identify potential causal genes which are targets for existing drugs and could be repositioned for use in rheumatic diseases.MethodsHigh confidence candidate causal genes were identified using Capture Hi-C data from B cells and T cells. These genes were used to interrogate drug target information from DrugBank to identify existing treatments, which could be repositioned to treat these diseases. The approach was refined using Ingenuity Pathway Analysis to identify enriched pathways and therefore further treatments relevant to the disease.ResultsOverall, 454 high confidence genes were identified. Of these, 48 were drug targets (108 drugs) and 11 were existing therapies used in the treatment of rheumatic diseases. After pathway analysis refinement, 50 genes remained, 13 of which were drug targets (33 drugs). However considering targets across all enriched pathways, a further 367 drugs were identified for potential repositioning.ConclusionCapture Hi-C has the potential to identify therapies which could be repositioned to treat rheumatic diseases. This was particularly successful for rheumatoid arthritis, where six effective, biologic treatments were identified. This approach may therefore yield new ways to treat patients, enhancing their quality of life and reducing the economic impact on healthcare providers. As additional cell types and other epigenomic data sets are generated, this prospect will improve further.

Drug repositioning helps fully explore indications for marketed drugs and clinical candidates. Here we show that the clinical side-effects (SEs) provide a human phenotypic profile for the drug, and this profile can suggest additional disease indications. We extracted 3,175 SE-disease relationships by combining the SE-drug relationships from drug labels and the drug-disease relationships from PharmGKB. Many relationships provide explicit repositioning hypotheses, such as drugs causing hypoglycemia are potential candidates for diabetes. We built Naïve Bayes models to predict indications for 145 diseases using the SEs as features. The AUC was above 0.8 in 92% of these models. The method was extended to predict indications for clinical compounds, 36% of the models achieved AUC above 0.7. This suggests that closer attention should be paid to the SEs observed in trials not just to evaluate the harmful effects, but also to rationally explore the repositioning potential based on this \"clinical phenotypic assay\".

Objective Ascertain the optimal interaction scoring criteria for the Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun drug repurposing to improve benchmarking performance, thereby enabling more accurate prediction of novel therapeutic drug-indication pairs. Results We have investigated and enhanced the interaction scoring criteria in the bioinformatic docking protocol in the newest version of our platform (v1.5), with the best performing interaction scoring criterion yielding increased benchmarking accuracies from 11.7% in v1 to 12.8% in v1.5 at the top10 cutoff (the most stringent one) and correspondingly from 24.9 to 31.2% at the top100 cutoff.

Much of the literature on trends and factors affecting biopharmaceutical innovation has focused overwhelmingly on the development and approval of never-before approved drugs and biologics. Little attention has been paid to new uses for already-approved compounds, which can be an important form of innovation. This paper aimed to determine and analyze recent trends in the number and type of new or modified US indication approvals for drugs and biologics. We also examine regulatory approval-phase times for new-use efficacy supplements and compare them to approval-phase times for original-use approvals over the same period. We developed a data set of efficacy supplements approved by the US Food and Drug Administration (FDA) from 1998 to 2011 that includes information on the type, approval-phase time (time from submission to the FDA of an application for marketing approval to approval of the application), and FDA therapeutic-significance rating for the approved application, which we obtained from an FDA Web site. This data set was merged with a Tufts Center for the Study of Drug Development (CSDD) data set of US new drug and biologics approvals. We developed descriptive statistics on trends in the number and type of new-use efficacy supplements, on US regulatory approval-phase times for the supplements, and on original new drug and biologics approvals over the study period and for the time from original- to new-use approval. The total number of new-use efficacy-supplement approvals did not exhibit a marked trend, but the number of new pediatric-indication approvals increased substantially. Approval-phase times for new-use supplements varied by therapeutic class and FDA therapeutic-significance rating. Mean approval-phase times were highest for central nervous system compounds (13.8 months) and lowest for antineoplastics (8.9 months). The mean time from original to supplement approval was substantially longer for new pediatric indications than for other new uses. Mean approval-phase time during the study period for applications that received a standard review rating from the FDA was substantially shorter for supplements compared to original uses, but the differences for applications that received a priority review rating from the FDA were negligible. Development of and regulatory approval for new uses of already-approved drugs and biologics is an important source of innovation by biopharmaceutical firms. Despite rising development costs, the output of new-use approvals has remained stable in recent years, driven largely by the pursuit of new pediatric indications. FDA approval-phase times have generally declined substantially for all types of applications since the mid-1990s following legislation that provided a new source of income for the agency. However, while the resources needed to review supplemental applications are likely lower in general than for original-use approvals, the approval-phase times for important new uses are no lower than for important original-use applications.

Based on in vitro data and results of a recent drug repositioning study, some medications approved by the FDA for the treatment of various non-malignant disorders were demonstrated to have anti-SCLC activity in preclinical models. The aim of our study is to confirm whether use of these medications is associated with survival benefit. Consecutive patients with pathologically confirmed, stage 4 SCLC were analyzed in this retrospective study. Patients that were prescribed statins, aspirin, clomipramine (tricyclic antidepressant; TCA), selective serotonin reuptake inhibitors (SSRIs), doxazosin or prazosin (α1-adrenergic receptor antagonists; ADRA1) were identified. There were a total of 876 patients. Aspirin, statins, SSRIs, ADRA1, and TCA were administered in 138, 72, 20, 28, and 5 cases, respectively. A statistically significant increase in median OS was observed only in statin-treated patients when compared to those not receiving any of the aforementioned medications (OS, 8.4 vs. 6.1 months, respectively; p = 0.002). The administration of SSRIs, aspirin, and ADRA1 did not result in a statistically significant OS benefit (median OS, 8.5, 6.8, and 6.0 months, respectively). The multivariate Cox model showed that, besides age and ECOG PS, radiotherapy was an independent survival predictor (Hazard Ratio, 2.151; 95% confidence interval, 1.828-2.525; p <0.001). Results of drug repositioning studies using only preclinical data or small numbers of patients should be treated with caution before application in the clinic. Our data demonstrated that radiotherapy appears to be an independent survival predictor in stage 4 SCLC, therefore confirming the results of other prospective and retrospective studies.