Combination therapeutics in complex diseases.
Ontology highlight
ABSTRACT:
SUBMITTER: He B
PROVIDER: S-EPMC5134672 | biostudies-other | 2016 Dec
REPOSITORIES: biostudies-other
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Project description:BackgroundHuman genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials.MethodsWe previously used Gentrepid (http://www.gentrepid.org) as a platform to predict 1,497 candidate genes for the seven complex diseases considered in the Wellcome Trust Case-Control Consortium genome-wide association study; namely Type 2 Diabetes, Bipolar Disorder, Crohn's Disease, Hypertension, Type 1 Diabetes, Coronary Artery Disease and Rheumatoid Arthritis. Here, we adopted a simple approach to integrate drug data from three publicly available drug databases: the Therapeutic Target Database, the Pharmacogenomics Knowledgebase and DrugBank; with candidate gene predictions from Gentrepid at the systems level.ResultsUsing the publicly available drug databases as sources of drug-target association data, we identified a total of 428 candidate genes as novel therapeutic targets for the seven phenotypes of interest, and 2,130 drugs feasible for repositioning against the predicted novel targets.ConclusionsBy integrating genetic, bioinformatic and drug data, we have demonstrated that currently available drugs may be repositioned as novel therapeutics for the seven diseases studied here, quickly taking advantage of prior work in pharmaceutics to translate ground-breaking results in genetics to clinical treatments.
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Project description:Around 350 million people worldwide suffer from rare diseases. These may have a genetic, infectious, or autoimmune basis, and several include an inflammatory component. Launching of effective treatments can be very challenging when there is a low disease prevalence and limited scientific insights into the disease mechanisms. As a key trigger of inflammatory processes, complement has been associated with a variety of diseases and has become an attractive therapeutic target for conditions involving inflammation. In view of the clinical experience acquired with drugs licensed for the treatment of rare diseases such as hereditary angioedema and paroxysmal nocturnal hemoglobinuria, growing evidence supports the safety and efficacy of complement therapeutics in restoring immune balance and preventing aggravation of clinical outcomes. This review provides an overview of the candidates currently in the pharmaceutical pipeline with potential to treat orphan diseases and discusses the molecular mechanisms triggered by complement involved with the disease pathogenesis.
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Project description:Current treatment strategies for rheumatoid arthritis (RA) consisting of disease-modifying anti-rheumatic drugs or biological agents are not always effective, hence driving the demand for new experimental therapeutics. The antiproliferative capacity of proteasome inhibitors (PIs) has received considerable attention given the success of their first prototypical representative, bortezomib (BTZ), in the treatment of B cell and plasma cell-related hematological malignancies. Therapeutic application of PIs in an autoimmune disease setting is much less explored, despite a clear rationale of (immuno) proteasome involvement in (auto)antigen presentation, and PIs harboring the capacity to inhibit the activation of nuclear factor-?B and suppress the release of pro-inflammatory cytokines such as tumor necrosis factor alpha and interleukin-6. Here, we review the clinical positioning of (immuno) proteasomes in autoimmune diseases, in particular RA, systemic lupus erythematosus, Sjögren's syndrome and sclerodema, and elaborate on (pre)clinical data related to the impact of BTZ and next generation PIs on immune effector cells (T cells, B cells, dendritic cells, macrophages, osteoclasts) implicated in their pathophysiology. Finally, factors influencing long-term efficacy of PIs, their current (pre)clinical status and future perspectives as anti-inflammatory and anti-arthritic agents are discussed.
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Project description:Research and drug development concerning rare diseases are at the cutting edge of scientific technology. To date, over 7,000 rare diseases have been identified. Despite their individual rarity, 1 in 10 individuals worldwide is affected by a rare condition. For the majority of these diseases, there is no treatment, much less cure; therefore, there is an urgent need for new therapies to extend and improve quality of life for persons who suffer from them. Here we focus specifically on rare neuromuscular diseases. Currently, genetic medicines using short antisense oligonucleotides (ASO) or small interfering ribonucleic acids that target RNA transcripts are achieving spectacular success in treating these diseases. For Duchenne muscular dystrophy (DMD), the state-of-the-art is an exon skipping therapy using an antisense oligonucleotide, which is prototypical of advanced precision medicines. Very recently, golodirsen and viltolarsen, for treatment of DMD patients amenable to skipping exon 53, have been approved by regulatory agencies in the USA and Japan, respectively. Here, we review scientific and clinical progress in developing new oligonucleotide therapeutics for selected rare neuromuscular diseases, discussing their efficacy and limitations.
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Project description:Nucleic acid-based therapeutics present huge potential in the treatment of pulmonary diseases ranging from lung cancer to asthma and chronic pulmonary diseases, which are often fatal and widely prevalent. The susceptibility of nucleic acids to degradation and the complex structure of lungs retard the effective pulmonary delivery of nucleic acid drug. To overcome these barriers, different strategies have been exploited to increase the delivery efficiency using chemically synthesized nucleic acids, vector encapsulation, proper formulation, and administration route. However, several limitations regarding off-target effects and immune stimulation of nucleic acid drugs hamper their translation into the clinical practice. Therefore, their successful clinical application will ultimately rely on well-developed carriers and methods to ensure safety and efficacy. In this review, we provide a comprehensive overview of the nucleic acid application for pulmonary diseases, covering action mechanism of the nucleic acid drugs, the novel delivery systems, and the current formulation for the administration to lungs. The latest advances of nucleic acid drugs under clinical evaluation to treat pulmonary disorders will also be detailed.
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Project description:Inherited retinal diseases (IRDs) are a heterogenous group of orphan eye diseases that typically result from monogenic mutations and are considered attractive targets for gene-based therapeutics. Following the approval of an IRD gene replacement therapy for Leber's congenital amaurosis due to RPE65 mutations, there has been an intensive international research effort to identify the optimal gene therapy approaches for a range of IRDs and many are now undergoing clinical trials. In this review we explore therapeutic challenges posed by IRDs and review current and future approaches that may be applicable to different subsets of IRD mutations. Emphasis is placed on five distinct approaches to gene-based therapy that have potential to treat the full spectrum of IRDs: 1) gene replacement using adeno-associated virus (AAV) and nonviral delivery vectors, 2) genome editing via the CRISPR/Cas9 system, 3) RNA editing by endogenous and exogenous ADAR, 4) mRNA targeting with antisense oligonucleotides for gene knockdown and splicing modification, and 5) optogenetic approaches that aim to replace the function of native retinal photoreceptors by engineering other retinal cell types to become capable of phototransduction.
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Project description:Biologic therapeutics such as protein/polypeptide drugs are conventionally administered systemically via intravenous injection for the treatment of diseases including lung diseases, although this approach leads to low target site accumulation and the potential risk for systemic side effects. In comparison, topical delivery of protein drugs to the lung via inhalation is deemed to be a more effective approach for lung diseases, as proteins would directly reach the target in the lung while exhibiting poor diffusion into the systemic circulation, leading to higher lung drug retention and efficacy while minimising toxicity to other organs. This review examines the important considerations and challenges in designing an inhaled protein therapeutics for local lung delivery: the choice of inhalation device, structural changes affecting drug deposition in diseased lungs, clearance mechanisms affecting an inhaled protein drug's lung accumulation, protein stability, and immunogenicity. Possible approaches to overcoming these issues will also be discussed.
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Project description:PURPOSE:The amount of available next-generation sequencing data of tumors, in combination with relevant molecular and clinical data, has significantly increased in the last decade and transformed translational cancer research. Even with the progress made through data-sharing initiatives, there is a clear unmet need for easily accessible analyses tools. These include capabilities to efficiently process large sequencing database projects to present them in a straightforward and accurate way. Another urgent challenge in cancer research is to identify more effective combination therapies. METHODS:We have created a software architecture that allows the user to integrate and analyze large-scale sequencing, clinical, and other datasets for efficient prediction of potential combination drug targets. This architecture permits predictions for all genes pairs; however, Food and Drug Administration-approved agents are currently lacking for most of the identified gene targets. RESULTS:By applying this approach, we performed a comprehensive study and analyzed all possible combination partners and identified potentially synergistic target pairs for 38 approved targets currently in clinical use. We further showed which genes could be synergistic prediction markers and potential targets with MAPK/ERK inhibitors for the treatment of melanoma. Moreover, we integrated a graph analytics technique in this architecture to identify pathways that could be targeted synergistically to enhance the efficacy of certain therapeutics in cancer. CONCLUSION:The architecture and the results presented provide a foundation for discovering effective combination therapeutics.
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Project description:BackgroundDesign of drug combination cocktails to maximize sensitivity for individual patients presents a challenge in terms of minimizing the number of experiments to attain the desired objective. The enormous number of possible drug combinations constrains exhaustive experimentation approaches, and personal variations in genetic diseases restrict the use of prior knowledge in optimization.ResultsWe present a stochastic search algorithm that consisted of a parallel experimentation phase followed by a combination of focused and diversified sequential search. We evaluated our approach on seven synthetic examples; four of them were evaluated twice with different parameters, and two biological examples of bacterial and lung cancer cell inhibition response to combination drugs. The performance of our approach as compared to recently proposed adaptive reference update approach was superior for all the examples considered, achieving an average of 45% reduction in the number of experimental iterations.ConclusionsAs the results illustrate, the proposed diverse stochastic search algorithm can produce optimized combinations in relatively smaller number of iterative steps. This approach can be combined with available knowledge on the genetic makeup of the patient to design optimal selection of drug cocktails.
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Project description:Extracellular vesicles (EV) deliver cargoes such as nucleic acids, proteins, and lipids between cells and serve as an intercellular communicator. As it is revealed that most of the functions associated to EVs are closely related to the immune response, the important role of EVs in inflammatory diseases is emerging. EVs can be functionalized through EV surface engineering and endow targeting moiety that allows for the target specificity for therapeutic applications in inflammatory diseases. Moreover, engineered EVs are considered as promising nanoparticles to develop personalized therapeutic carriers. In this review, we highlight the role of EVs in various inflammatory diseases, the application of EV as anti-inflammatory therapeutics, and the current state of the art in EV engineering techniques.
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