Drug resistance and treatment failure in leishmaniasis: A 21st century challenge

Reevaluation of treatment guidelines for Old and New World leishmaniasis is urgently needed on a global basis because treatment failure is an increasing problem. Drug resistance is a fundamental determinant of treatment failure, although other factors also contribute to this phenomenon, including the global HIV/AIDS epidemic with its accompanying impact on the immune system. Pentavalent antimonials have been used successfully worldwide for the treatment of leishmaniasis since the first half of the 20th century, but the last 10 to 20 years have witnessed an increase in clinical resistance, e.g., in North Bihar in India. In this review, we discuss the meaning of “resistance” related to leishmaniasis and discuss its molecular epidemiology, particularly for Leishmania donovani that causes visceral leishmaniasis. We also discuss how resistance can affect drug combination therapies. Molecular mechanisms known to contribute to resistance to antimonials, amphotericin B, and miltefosine are also outlined

Chimeric piggyBac transposases for genomic targeting in human cells.

Integrating vectors such as viruses and transposons insert transgenes semi-randomly and can potentially disrupt or deregulate genes. For these techniques to be of therapeutic value, a method for controlling the precise location of insertion is required. The piggyBac (PB) transposase is an efficient gene transfer vector active in a variety of cell types and proven to be amenable to modification. Here we present the design and validation of chimeric PB proteins fused to the Gal4 DNA binding domain with the ability to target transgenes to pre-determined sites. Upstream activating sequence (UAS) Gal4 recognition sites harbored on recipient plasmids were preferentially targeted by the chimeric Gal4-PB transposase in human cells. To analyze the ability of these PB fusion proteins to target chromosomal locations, UAS sites were randomly integrated throughout the genome using the Sleeping Beauty transposon. Both N- and C-terminal Gal4-PB fusion proteins but not native PB were capable of targeting transposition nearby these introduced sites. A genome-wide integration analysis revealed the ability of our fusion constructs to bias 24% of integrations near endogenous Gal4 recognition sequences. This work provides a powerful approach to enhance the properties of the PB system for applications such as genetic engineering and gene therapy

A Partnership for Health: Minorities & Biomedical Research

The National Institute of Allergy and Infectious Diseases (NIAID) has long recognized that minority populations bear a disproportionate burden of sickness and disease in the United States. Differences in racial and ethnic backgrounds can affect susceptibility to infectious and immunologic diseases, including acquired immunodeficiency syndrome (AIDS), asthma, sexually transmitted infections, and kidney disease. Moreover, minority populations often do not fully benefit from research advances that have helped improve the health of other Americans. For more than 50 years, NIAID has progressed in understanding, treating, and preventing infectious and immunologic diseases known to occur disparately in minority populations. As outlined in its Strategic Plan for Addressing Health Disparities, NIAID continues to prioritize basic, clinical, and epidemiological research in addressing the health disparities in minority populations. Specifically, NIAID supports efforts to increase the participation of minority scientists in its research, increase the participation of the minority community in clinical research, and design targeted outreach activities for minority communities that communicate research developments and health risk

Translational Oncogenomics and Human Cancer Interactome Networks

An overview of translational, human oncogenomics, transcriptomics and cancer interactomic networks is presented together with basic concepts and potential, new applications to Oncology and Integrative Cancer Biology. Novel translational oncogenomics research is rapidly expanding through the application of advanced technology, research findings and computational tools/models to both pharmaceutical and clinical problems. A self-contained presentation is adopted that covers both fundamental concepts and the most recent biomedical, as well as clinical, applications. Sample analyses in recent clinical studies have shown that gene expression data can be employed to distinguish between tumor types as well as to predict outcomes. Potentially important applications of such results are individualized human cancer therapies or, in general, ‘personalized medicine’. Several cancer detection techniques are currently under development both in the direction of improved detection sensitivity and increased time resolution of cellular events, with the limits of single molecule detection and picosecond time resolution already reached. The urgency for the complete mapping of a human cancer interactome with the help of such novel, high-efficiency / low-cost and ultra-sensitive techniques is also pointed out

PRN OPINION PAPER: Application of precision medicine across pharmacy specialty areas

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149551/1/jac51107_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149551/2/jac51107.pd

Understanding exposure to pharmacogenetically actionable opioids in primary care

Indiana University-Purdue University Indianapolis (IUPUI)Pharmacogenetic testing has the potential to improve pain management through addressing wide interindividual variations in responses to pharmacogenetically actionable opioids, ultimately decreasing costly adverse drug effects and improving responses to these medications. A recent review of pharmacogenomics in the nursing literature highlighted the need for nurses to more fully embrace the burgeoning field of pharmacogenomics in nursing research, clinical practice, and education. Despite the promise of pharmacogenetic testing, significant challenges exist for evaluating outcomes related to its implementation, including oversimplification of medication exposure, the complexity of patients' clinical profiles, and the characteristics of healthcare contexts in which medications are prescribed. A better understanding of these challenges could enhance the assessment and documentation of the benefits of pharmacogenetic testing in guiding opioid therapies. This dissertation is intended to address the challenges of evaluating outcomes of pharmacogenetic testing implementation and the need for nurses to lead pharmacogenomic-related research. The dissertation purpose was to advance the sciences of nursing, pain management, and pharmacogenomics through the development of a typology of common patterns of medication exposure to known pharmacogenetically actionable opioids (codeine & tramadol). A qualitative, person-oriented approach was used to retrospectively analyze six months of electronic health record and pharmacogenotype data in 30 underserved adult patients. An overarching typology with eight groups of patients that had one of five opioid prescription patterns (singular, episodic, switching, sustained, or multiplex) and one of three types of medical emphasis of care (pain, comorbidities, or both) were identified. This typology consisted of a description of multiple common patterns that compare and contrast salient factors of exposure and the emphasis of why individuals were seeking care. Furthermore, in an aggregate descriptive analysis evaluating key clinical profile factors, these patients had complex medical histories, extensive healthcare utilization, and experienced significant polypharmacy. These findings can aid in addressing challenges related to the implementation of pharmacogenetic testing in clinical practice and point to ways in which nurses can take the lead in pharmacogenomics research. Findings also provide a foundation for future studies aimed at developing medication exposure measures to capture its dynamic nature and identifying and tailoring interventions in this population

Applying antibodies inside cells: Principles and recent advances in neurobiology, virology and oncology

To interfere with cell function, many scientists rely on methods that target DNA or RNA due to the ease with which they can be applied. Proteins are usually the final executors of function but are targeted only indirectly by these methods. Recent advances in targeted degradation of proteins based on proteolysis-targeting chimaeras (PROTACs), ubiquibodies, deGradFP (degrade Green Fluorescent Protein) and other approaches have demonstrated the potential of interfering directly at the protein level for research and therapy. Proteins can be targeted directly and very specifically by antibodies, but using antibodies inside cells has so far been considered to be challenging. However, it is possible to deliver antibodies or other proteins into the cytosol using standard laboratory equipment. Physical methods such as electroporation have been demonstrated to be efficient and validated thoroughly over time. The expression of intracellular antibodies (intrabodies) inside cells is another way to interfere with intracellular targets at the protein level. Methodological strategies to target the inside of cells with antibodies, including delivered antibodies and expressed antibodies, as well as applications in the research areas of neurobiology, viral infections and oncology, are reviewed here. Antibodies have already been used to interfere with a wide range of intracellular targets. Disease-related targets included proteins associated with neurodegenerative diseases such as Parkinson's disease (α-synuclein), Alzheimer's disease (amyloid-β) or Huntington's disease (mutant huntingtin [mHtt]). The applications of intrabodies in the context of viral infections include targeting proteins associated with HIV (e.g. HIV1-TAT, Rev, Vif, gp41, gp120, gp160) and different oncoviruses such as human papillomavirus (HPV), hepatitis B virus (HBV), hepatitis C virus (HCV) and Epstein-Barr virus, and they have been used to interfere with various targets related to different processes in cancer, including oncogenic pathways, proliferation, cell cycle, apoptosis, metastasis, angiogenesis or neo-antigens (e.g. p53, human epidermal growth factor receptor-2 [HER2], signal transducer and activator of transcription 3 [STAT3], RAS-related RHO-GTPase B (RHOB), cortactin, vascular endothelial growth factor receptor 2 [VEGFR2], Ras, Bcr-Abl). Interfering at the protein level allows questions to be addressed that may remain unanswered using alternative methods. This review addresses why direct targeting of proteins allows unique insights, what is currently feasible in vitro, and how this relates to potential therapeutic applications

Comparison of Classifier Fusion Methods for Predicting Response to Anti HIV-1 Therapy

BACKGROUND: Analysis of the viral genome for drug resistance mutations is state-of-the-art for guiding treatment selection for human immunodeficiency virus type 1 (HIV-1)-infected patients. These mutations alter the structure of viral target proteins and reduce or in the worst case completely inhibit the effect of antiretroviral compounds while maintaining the ability for effective replication. Modern anti-HIV-1 regimens comprise multiple drugs in order to prevent or at least delay the development of resistance mutations. However, commonly used HIV-1 genotype interpretation systems provide only classifications for single drugs. The EuResist initiative has collected data from about 18,500 patients to train three classifiers for predicting response to combination antiretroviral therapy, given the viral genotype and further information. In this work we compare different classifier fusion methods for combining the individual classifiers. PRINCIPAL FINDINGS: The individual classifiers yielded similar performance, and all the combination approaches considered performed equally well. The gain in performance due to combining methods did not reach statistical significance compared to the single best individual classifier on the complete training set. However, on smaller training set sizes (200 to 1,600 instances compared to 2,700) the combination significantly outperformed the individual classifiers (p<0.01; paired one-sided Wilcoxon test). Together with a consistent reduction of the standard deviation compared to the individual prediction engines this shows a more robust behavior of the combined system. Moreover, using the combined system we were able to identify a class of therapy courses that led to a consistent underestimation (about 0.05 AUC) of the system performance. Discovery of these therapy courses is a further hint for the robustness of the combined system. CONCLUSION: The combined EuResist prediction engine is freely available at http://engine.euresist.org