Development of an Affordable, Sustainable and Efficacious Plant-Based Immunomodulatory Food Ingredient Based on Bell Pepper or Carrot RG-I Pectic Polysaccharides.

The prevalence of acute respiratory infections and their impact on quality of life underlies the need for efficacious solutions that are safe, sustainable and economically viable. Polysaccharides in several (traditional) plant extracts have been shown to be immunostimulatory, and some studies suggest beneficial effects against respiratory infections. The aim of this study was to (i) identify the active polysaccharide constituents from affordable and renewable crops (bell pepper and carrot) using activity-guided fractionation, (ii) evaluate in vitro effects on innate immune responses (phagocytosis and cytokine secretion), microbiota modulation and production of short chain fatty acids, followed by (iii) the evaluation of effects of a bell pepper extract enriched for the active component in a human proof of concept study. We identified rhamnogalacturonan-I (RG-I) as the nutricophore responsible for the immunostimulatory activity with substantial structural and functional equivalence between bell pepper (bp) and carrot (c). The in vitro studies showed that bpRG-I and cRG-I comprise similar immune- and microbiota modulatory potential and the human study demonstrated that bpRG-I was well tolerated and enhanced innate immune responsiveness in vivo. This is an important step towards testing the efficacy of RG-I from bpRG-I or cRG-I in an infection trial in humans.

Novel natural and synthetic inhibitors of solute carriers SGLT1 and SGLT2.

Selective analogs of the natural glycoside phloridzin are marketed drugs that reduce hyperglycemia in diabetes by inhibiting the active sodium glucose cotransporter SGLT2 in the kidneys. In addition, intestinal SGLT1 is now recognized as a target for glycemic control. To expand available type 2 diabetes remedies, we aimed to find novel SGLT1 inhibitors beyond the chemical space of glycosides. We screened a bioactive compound library for SGLT1 inhibitors and tested primary hits and additional structurally similar molecules on SGLT1 and SGLT2 (SGLT1/2). Novel SGLT1/2 inhibitors were discovered in separate chemical clusters of natural and synthetic compounds. These have IC50-values in the 10-100 µmol/L range. The most potent identified novel inhibitors from different chemical clusters are (SGLT1-IC50 Mean ± SD, SGLT2-IC50 Mean ± SD): (+)-pteryxin (12 ± 2 µmol/L, 9 ± 4 µmol/L), (+)-ε-viniferin (58 ± 18 µmol/L, 110 µmol/L), quinidine (62 µmol/L, 56 µmol/L), cloperastine (9 ± 3 µmol/L, 9 ± 7 µmol/L), bepridil (10 ± 5 µmol/L, 14 ± 12 µmol/L), trihexyphenidyl (12 ± 1 µmol/L, 20 ± 13 µmol/L) and bupivacaine (23 ± 14 µmol/L, 43 ± 29 µmol/L). The discovered natural inhibitors may be further investigated as new potential (prophylactic) agents for controlling dietary glucose uptake. The new diverse structure activity data can provide a starting point for the optimization of novel SGLT1/2 inhibitors and support the development of virtual SGLT1/2 inhibitor screening models.

Identification of novel small molecule inhibitors for solute carrier SGLT1 using proteochemometric modeling.

Sodium-dependent glucose co-transporter 1 (SGLT1) is a solute carrier responsible for active glucose absorption. SGLT1 is present in both the renal tubules and small intestine. In contrast, the closely related sodium-dependent glucose co-transporter 2 (SGLT2), a protein that is targeted in the treatment of diabetes type II, is only expressed in the renal tubules. Although dual inhibitors for both SGLT1 and SGLT2 have been developed, no drugs on the market are targeted at decreasing dietary glucose uptake by SGLT1 in the gastrointestinal tract. Here we aim at identifying SGLT1 inhibitors in silico by applying a machine learning approach that does not require structural information, which is absent for SGLT1. We applied proteochemometrics by implementation of compound- and protein-based information into random forest models. We obtained a predictive model with a sensitivity of 0.64 ± 0.06, specificity of 0.93 ± 0.01, positive predictive value of 0.47 ± 0.07, negative predictive value of 0.96 ± 0.01, and Matthews correlation coefficient of 0.49 ± 0.05. Subsequent to model training, we applied our model in virtual screening to identify novel SGLT1 inhibitors. Of the 77 tested compounds, 30 were experimentally confirmed for SGLT1-inhibiting activity in vitro, leading to a hit rate of 39% with activities in the low micromolar range. Moreover, the hit compounds included novel molecules, which is reflected by the low similarity of these compounds with the training set (< 0.3). Conclusively, proteochemometric modeling of SGLT1 is a viable strategy for identifying active small molecules. Therefore, this method may also be applied in detection of novel small molecules for other transporter proteins.