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Translational Bioinformatics Approaches to Drug Development.

ABSTRACT: A majority of therapeutic interventions occur late in the pathological process, when treatment outcome can be less predictable and effective, highlighting the need for new precise and preventive therapeutic development strategies that consider genomic and environmental context. Translational bioinformatics is well positioned to contribute to the many challenges inherent in bridging this gap between our current reactive methods of healthcare delivery and the intent of precision medicine, particularly in the areas of drug development, which forms the focus of this review.A variety of powerful informatics methods for organizing and leveraging the vast wealth of available molecular measurements available for a broad range of disease contexts have recently emerged. These include methods for data driven disease classification, drug repositioning, identification of disease biomarkers, and the creation of disease network models, each with significant impacts on drug development approaches.An important bottleneck in the application of bioinformatics methods in translational research is the lack of investigators who are versed in both biomedical domains and informatics. Efforts to nurture both sets of competencies within individuals and to increase interfield visibility will help to accelerate the adoption and increased application of bioinformatics in translational research.It is possible to construct predictive, multiscale network models of disease by integrating genotype, gene expression, clinical traits, and other multiscale measures using causal network inference methods. This can enable the identification of the "key drivers" of pathology, which may represent novel therapeutic targets or biomarker candidates that play a more direct role in the etiology of disease.

SUBMITTER: Readhead B

PROVIDER: S-EPMC3817001 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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Translational Bioinformatics Approaches to Drug Development.

Readhead Ben B Dudley Joel J

Advances in wound care 20131101 9

<h4>Significance</h4>A majority of therapeutic interventions occur late in the pathological process, when treatment outcome can be less predictable and effective, highlighting the need for new precise and preventive therapeutic development strategies that consider genomic and environmental context. Translational bioinformatics is well positioned to contribute to the many challenges inherent in bridging this gap between our current reactive methods of healthcare delivery and the intent of <i>prec ...[more]

PMID: 24527359

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Project description:BackgroundWith the surge of translational medicine and computational omics research, complex disease diagnosis is more and more relying on massive omics data-driven molecular signature detection. However, how to detect and prevent possible diagnostic biases in translational bioinformatics remains an unsolved problem despite its importance in the coming era of personalized medicine.MethodsIn this study, we comprehensively investigate the diagnostic bias problem by analyzing benchmark gene array, protein array, RNA-Seq and miRNA-Seq data under the framework of support vector machines for different model selection methods. We further categorize the diagnostic biases into different types by conducting rigorous kernel matrix analysis and provide effective machine learning methods to conquer the diagnostic biases.ResultsIn this study, we comprehensively investigate the diagnostic bias problem by analyzing benchmark gene array, protein array, RNA-Seq and miRNA-Seq data under the framework of support vector machines. We have found that the diagnostic biases happen for data with different distributions and SVM with different kernels. Moreover, we identify total three types of diagnostic biases: overfitting bias, label skewness bias, and underfitting bias in SVM diagnostics, and present corresponding reasons through rigorous analysis. Compared with the overfitting and underfitting biases, the label skewness bias is more challenging to detect and conquer because it can be easily confused as a normal diagnostic case from its deceptive accuracy. To tackle this problem, we propose a derivative component analysis based support vector machines to conquer the label skewness bias by achieving the rivaling clinical diagnostic results.ConclusionsOur studies demonstrate that the diagnostic biases are mainly caused by the three major factors, i.e. kernel selection, signal amplification mechanism in high-throughput profiling, and training data label distribution. Moreover, the proposed DCA-SVM diagnosis provides a generic solution for the label skewness bias overcome due to the powerful feature extraction capability from derivative component analysis. Our work identifies and solves an important but less addressed problem in translational research. It also has a positive impact on machine learning for adding new results to kernel-based learning for omics data.

Project description:Parkinson's disease (PD) is a progressive neurodegenerative disorder that primarily affects elderly people and constitutes a major source of disability worldwide. Notably, the neuropathological hallmarks of PD include nigrostriatal loss and the formation of intracellular inclusion bodies containing misfolded α-synuclein protein aggregates. Cardinal motor symptoms, which include tremor, rigidity and bradykinesia, can effectively be managed with dopaminergic therapy for years following symptom onset. Nonetheless, patients ultimately develop symptoms that no longer fully respond to dopaminergic treatment. Attempts to discover disease-modifying agents have increasingly been supported by translational molecular imaging concepts, targeting the most prominent pathological hallmark of PD, α-synuclein accumulation, as well as other molecular pathways that contribute to the pathophysiology of PD. Indeed, molecular imaging modalities such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT) can be leveraged to study parkinsonism not only in animal models but also in living patients. For instance, mitochondrial dysfunction can be assessed with probes that target the mitochondrial complex I (MC-I), while nigrostriatal degeneration is typically evaluated with probes designed to non-invasively quantify dopaminergic nerve loss. In addition to dopaminergic imaging, serotonin transporter and N-methyl-D-aspartate (NMDA) receptor probes are increasingly used as research tools to better understand the complexity of neurotransmitter dysregulation in PD. Non-invasive quantification of neuroinflammatory processes is mainly conducted by targeting the translocator protein 18 kDa (TSPO) on activated microglia using established imaging agents. Despite the overwhelming involvement of the brain and brainstem, the pathophysiology of PD is not restricted to the central nervous system (CNS). In fact, PD also affects various peripheral organs such as the heart and gastrointestinal tract - primarily via autonomic dysfunction. As such, research into peripheral biomarkers has taken advantage of cardiac autonomic denervation in PD, allowing the differential diagnosis between PD and multiple system atrophy with probes that visualize sympathetic nerve terminals in the myocardium. Further, α-synuclein has recently gained attention as a potential peripheral biomarker in PD. This review discusses breakthrough discoveries that have led to the contemporary molecular concepts of PD pathophysiology and how they can be harnessed to develop effective imaging probes and therapeutic agents. Further, we will shed light on potential future trends, thereby focusing on potential novel diagnostic tracers and disease-modifying therapeutic interventions.

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Translational Bioinformatics Approaches to Drug Development.

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Translational Bioinformatics Approaches to Drug Development.

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