It is widely accepted that drug discovery often requires a systems-level polypharmacology approach to tackle problems such as lack of efficacy and emerging resistance of single-targeted compounds. Network pharmacology approaches are increasingly being developed and applied to find new therapeutic opportunities and to re-purpose approved drugs. However, these recent advances have been relatively slow to be translated into the field of natural products. Here, we argue that a network pharmacology approach would enable an effective mapping of the yet unexplored target space of natural products, hence providing a systematic means to extend the druggable space of proteins implicated in various complex diseases. We give an overview of the key network pharmacology concepts and recent experimental-computational approaches that have been successfully applied to natural product research, including unbiased elucidation of mechanisms of action as well as systematic prediction of effective therapeutic combinations. We focus specifically on anticancer applications that use in vivo and in vitro functional phenotypic measurements, such as genome-wide transcriptomic response profiles, which enable a global modelling of the multi-target activity at the level of the biological pathways and interaction networks. We also provide representative examples of other disease applications, databases and tools as well as existing and emerging resources, which may prove useful for future natural product research. Finally, we offer our personal view of the current limitations, prospective developments and open questions in this exciting field.
Neuroimaging and neuropsychological studies have revealed that the primary motor cortex (PMC) and the extramotor cortical areas are functionally abnormal in motor neuron disease (MND, amyotrophic lateral sclerosis), but the nature of the cortical lesions that underlie these changes is poorly understood. In particular, there have been few attempts to quantify neuronal loss in the PMC and in other cortical areas in MND. We used SMI-32, an antibody against an epitope on non-phosphorylated neurofilament heavy chain, to analyse the size and density of SMI-32-positive cortical pyramidal neurons in layer V of the PMC, the dorsolateral prefrontal cortex (DLPFC) and the supragenual anterior cingulate cortex (ACC) in 13 MND and eight control subjects. There was a statistically significant reduction in the density of SMI-32-immunoreactive (IR) pyramidal neurons within cortical layer V in the PMC, the DLPFC and the ACC in MND subjects compared with controls [t (19) = 2.91, P = 0.009; estimated reduction 25%; 95% CI = 8%, 40%]. In addition, we studied the density and size of interneurons immunoreactive for the calcium-binding proteins calbindin-D(28K) (CB), parvalbumin (PV) and calretinin (CR) in the same areas (PMC, DLPFC and ACC). Statistically significant differences in the densities of CB-IR neurons were observed within cortical layers V (P = 0.003) and VI (P = 0.001) in MND cases compared with controls. The densities of CR- and PV-IR neurons were not significantly different between MND and control cases, although there were trends towards reductions of CR-IR neuronal density within the same layers and of PV-IR neuronal density within cortical layer VI. Loss of pyramidal neurons and of GABAergic interneurons is more widespread than has been appreciated and is present in areas associated with neuroimaging and cognitive abnormalities in MND. These findings support the notion that MND should be considered a multisystem disorder.
Multiple studies across global populations have established the primary symptoms characterising Coronavirus Disease 2019 (COVID-19) and long COVID. However, as symptoms may also occur in the absence of COVID-19, a lack of appropriate controls has often meant that specificity of symptoms to acute COVID-19 or long COVID, and the extent and length of time for which they are elevated after COVID-19, could not be examined. We analysed individual symptom prevalences and characterised patterns of COVID-19 and long COVID symptoms across nine UK longitudinal studies, totalling over 42,000 participants. Conducting latent class analyses separately in three groups (‘no COVID-19’, ‘COVID-19 in last 12 weeks’, ‘COVID-19 > 12 weeks ago’), the data did not support the presence of more than two distinct symptom patterns, representing high and low symptom burden, in each group. Comparing the high symptom burden classes between the ‘COVID-19 in last 12 weeks’ and ‘no COVID-19’ groups we identified symptoms characteristic of acute COVID-19, including loss of taste and smell, fatigue, cough, shortness of breath and muscle pains or aches. Comparing the high symptom burden classes between the ‘COVID-19 > 12 weeks ago’ and ‘no COVID-19’ groups we identified symptoms characteristic of long COVID, including fatigue, shortness of breath, muscle pain or aches, difficulty concentrating and chest tightness. The identified symptom patterns among individuals with COVID-19 > 12 weeks ago were strongly associated with self-reported length of time unable to function as normal due to COVID-19 symptoms, suggesting that the symptom pattern identified corresponds to long COVID. Building the evidence base regarding typical long COVID symptoms will improve diagnosis of this condition and the ability to elicit underlying biological mechanisms, leading to better patient access to treatment and services.
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