Task shifting roles, interventions and outcomes for kidney and cardiovascular health service delivery among African populations: a scoping review.

BACKGROUND: Human resources for health (HRH) shortages are a major limitation to equitable access to healthcare. African countries have the most severe shortage of HRH in the world despite rising communicable and non-communicable disease (NCD) burden. Task shifting provides an opportunity to fill the gaps in HRH shortage in Africa. The aim of this scoping review is to evaluate task shifting roles, interventions and outcomes for addressing kidney and cardiovascular (CV) health problems in African populations. METHODS: We conducted this scoping review to answer the question: "what are the roles, interventions and outcomes of task shifting strategies for CV and kidney health in Africa?" Eligible studies were selected after searching MEDLINE (Ovid), Embase (Ovid), CINAHL, ISI Web of Science, and Africa journal online (AJOL). We analyzed the data descriptively. RESULTS: Thirty-three studies, conducted in 10 African countries (South Africa, Nigeria, Ghana, Kenya, Cameroon, Democratic Republic of Congo, Ethiopia, Malawi, Rwanda, and Uganda) were eligible for inclusion. There were few randomized controlled trials (n = 6; 18.2%), and tasks were mostly shifted for hypertension (n = 27; 81.8%) than for diabetes (n = 16; 48.5%). More tasks were shifted to nurses (n = 19; 57.6%) than pharmacists (n = 6; 18.2%) or community health workers (n = 5; 15.2%). Across all studies, the most common role played by HRH in task shifting was for treatment and adherence (n = 28; 84.9%) followed by screening and detection (n = 24; 72.7%), education and counselling (n = 24; 72.7%), and triage (n = 13; 39.4%). Improved blood pressure levels were reported in 78.6%, 66.7%, and 80.0% for hypertension-related task shifting roles to nurses, pharmacists, and CHWs, respectively. Improved glycaemic indices were reported as 66.7%, 50.0%, and 66.7% for diabetes-related task shifting roles to nurses, pharmacists, and CHWs, respectively. CONCLUSION: Despite the numerus HRH challenges that are present in Africa for CV and kidney health, this study suggests that task shifting initiatives can improve process of care measures (access and efficiency) as well as identification, awareness and treatment of CV and kidney disease in the region. The impact of task shifting on long-term outcomes of kidney and CV diseases and the sustainability of NCD programs based on task shifting remains to be determined.

Rationally designed inhibitors of the Musashi protein-RNA interaction by hotspot mimicry.

RNA-binding proteins (RBPs) are key post-transcriptional regulators of gene expression, and thus underlie many important biological processes. Here, we developed a strategy that entails extracting a "hotspot pharmacophore" from the structure of a protein-RNA complex, to create a template for designing small-molecule inhibitors and for exploring the selectivity of the resulting inhibitors. We demonstrate this approach by designing inhibitors of Musashi proteins MSI1 and MSI2, key regulators of mRNA stability and translation that are upregulated in many cancers. We report this novel series of MSI1/MSI2 inhibitors is specific and active in biochemical, biophysical, and cellular assays. This study extends the paradigm of "hotspots" from protein-protein complexes to protein-RNA complexes, supports the "druggability" of RNA-binding protein surfaces, and represents one of the first rationally-designed inhibitors of non-enzymatic RNA-binding proteins. Owing to its simplicity and generality, we anticipate that this approach may also be used to develop inhibitors of many other RNA-binding proteins; we also consider the prospects of identifying potential off-target interactions by searching for other RBPs that recognize their cognate RNAs using similar interaction geometries. Beyond inhibitors, we also expect that compounds designed using this approach can serve as warheads for new PROTACs that selectively degrade RNA-binding proteins.

Rationally designed inhibitors of the Musashi protein-RNA interaction by hotspot mimicry.

RNA-binding proteins (RBPs) are key post-transcriptional regulators of gene expression, and thus underlie many important biological processes. Here, we developed a strategy that entails extracting a "hotspot pharmacophore" from the structure of a protein-RNA complex, to create a template for designing small-molecule inhibitors and for exploring the selectivity of the resulting inhibitors. We demonstrate this approach by designing inhibitors of Musashi proteins MSI1 and MSI2, key regulators of mRNA stability and translation that are upregulated in many cancers. We report this novel series of MSI1/MSI2 inhibitors is specific and active in biochemical, biophysical, and cellular assays. This study extends the paradigm of "hotspots" from protein-protein complexes to protein-RNA complexes, supports the "druggability" of RNA-binding protein surfaces, and represents one of the first rationally-designed inhibitors of non-enzymatic RNA-binding proteins. Owing to its simplicity and generality, we anticipate that this approach may also be used to develop inhibitors of many other RNA-binding proteins; we also consider the prospects of identifying potential off-target interactions by searching for other RBPs that recognize their cognate RNAs using similar interaction geometries. Beyond inhibitors, we also expect that compounds designed using this approach can serve as warheads for new PROTACs that selectively degrade RNA-binding proteins.

Machine learning classification can reduce false positives in structure-based virtual screening.

With the recent explosion in the size of libraries available for screening, virtual screening is positioned to assume a more prominent role in early drug discovery's search for active chemical matter. In typical virtual screens, however, only about 12% of the top-scoring compounds actually show activity when tested in biochemical assays. We argue that most scoring functions used for this task have been developed with insufficient thoughtfulness into the datasets on which they are trained and tested, leading to overly simplistic models and/or overtraining. These problems are compounded in the literature because studies reporting new scoring methods have not validated their models prospectively within the same study. Here, we report a strategy for building a training dataset (D-COID) that aims to generate highly compelling decoy complexes that are individually matched to available active complexes. Using this dataset, we train a general-purpose classifier for virtual screening (vScreenML) that is built on the XGBoost framework. In retrospective benchmarks, our classifier shows outstanding performance relative to other scoring functions. In a prospective context, nearly all candidate inhibitors from a screen against acetylcholinesterase show detectable activity; beyond this, 10 of 23 compounds have IC50 better than 50 µM. Without any medicinal chemistry optimization, the most potent hit has IC50 280 nM, corresponding to Ki of 173 nM. These results support using the D-COID strategy for training classifiers in other computational biology tasks, and for vScreenML in virtual screening campaigns against other protein targets. Both D-COID and vScreenML are freely distributed to facilitate such efforts.

Cellular Growth Arrest and Persistence from Enzyme Saturation.

Metabolic efficiency depends on the balance between supply and demand of metabolites, which is sensitive to environmental and physiological fluctuations, or noise, causing shortages or surpluses in the metabolic pipeline. How cells can reliably optimize biomass production in the presence of metabolic fluctuations is a fundamental question that has not been fully answered. Here we use mathematical models to predict that enzyme saturation creates distinct regimes of cellular growth, including a phase of growth arrest resulting from toxicity of the metabolic process. Noise can drive entry of single cells into growth arrest while a fast-growing majority sustains the population. We confirmed these predictions by measuring the growth dynamics of Escherichia coli utilizing lactose as a sole carbon source. The predicted heterogeneous growth emerged at high lactose concentrations, and was associated with cell death and production of antibiotic-tolerant persister cells. These results suggest how metabolic networks may balance costs and benefits, with important implications for drug tolerance.