sj-docx-1-gcq-10.1177_00169862231201604 – Supplemental material for Influences on Career Development for Gifted Adolescent Girls in Selective Academic Programs in Australia

Supplemental material, sj-docx-1-gcq-10.1177_00169862231201604 for Influences on Career Development for Gifted Adolescent Girls in Selective Academic Programs in Australia by Rebecca D. Napier, Jane M. Jarvis, Julie Clark and R. John Halsey in Gifted Child Quarterly</p

Shared Consensus Machine Learning Models for Predicting Blood Stage Malaria Inhibition

The development of new antimalarial therapies is essential, and lowering the barrier of entry for the screening and discovery of new lead compound classes can spur drug development at organizations that may not have large compound screening libraries or resources to conduct high-throughput screens. Machine learning models have been long established to be more robust and have a larger domain of applicability with larger training sets. Screens over multiple data sets to find compounds with potential malaria blood stage inhibitory activity have been used to generate multiple Bayesian models. Here we describe a method by which Bayesian quantitative structure–activity relationship models, which contain information on thousands to millions of proprietary compounds, can be shared between collaborators at both for-profit and not-for-profit institutions. This model-sharing paradigm allows for the development of consensus models that have increased predictive power over any single model and yet does not reveal the identity of any compounds in the training sets

Inhibition of uptake of 100 μM [³H] D-glucose (Glc, black bars), 12.5 μM [³H] hypoxanthine (Hyp, stippled bars), 1 μM [³H] uridine (Urd, white bars), and 100 μM [³H] L-proline (Pro, square-hatched bars) by Compounds 1 (A), 7 (B), and 13 (C).

<p>Concentrations of each inhibitor are shown on the x-axis; DMSO represents the control using the DMSO vehicle without any inhibitor. Proline uptake was not measured at 0.1 or 1.0 μM concentrations of inhibitors, hence the cross-hatched bar representing proline uptake does not appear for these concentrations.</p

Dose-response curves for inhibition of glucose uptake by PfHT and GLUT1.

<p>Compounds 1 (A), 7 (B), and 13 (C) were applied over a range from 10^-9–10^-5 M to Δ<i>lmxgt1-3</i> null mutants expressing either PfHT (filled circles) or GLUT1 (open circles), and uptake of 100 μM [³H] D-glucose was measured in a 1 min uptake assay. Results are plotted as the mean and standard deviation (error bars) from 3 replicate uptake determinations. Data were fitted to a sigmoidal inhibition curve.</p

Analogs of Compound 1 were tested for inhibition of uptake of 100 μM [³H] D-glucose by PfHT and GLUT1, as in Fig 2.

<p>NI indicates no inhibition, and NA indicates does not apply. Other abbreviations are as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123598#pone.0123598.g002" target="_blank">Fig 2</a>.</p

<i>In vitro</i> absorption-distribution-metabolism-excretion (ADME) properties of Compounds 1, 10, 12, 13 and control compounds.

<p>Aqueous solubility, permeation and retention in artificial membrane, metabolic stability against mouse liver microsomes (t_1/2 or half-life), and intrinsic clearance (Clint) were determined as described in Materials and Methods. Values were also determined for albendazole, carbamazepine, ranitidine, and verapamil as control drugs. ND indicates not done.</p><p><i>In vitro</i> absorption-distribution-metabolism-excretion (ADME) properties of Compounds 1, 10, 12, 13 and control compounds.</p

Flow chart for screen of TCAMS library.

<p>The TCAMS library of 13,533 compounds with demonstrated growth inhibitory activity against intraerythrocytic <i>Plasmodium falciparum</i> parasites was screened by sequential criteria. The steps included: 1) proliferation inhibitory screen (>65% inhibition at 3 μM concentration or >20% differential inhibition among the three strains) of PfHT, LmxGT2, and GLUT1 reporter strains to produce 401 primary hits; 2) 96-well plate assays for compounds (20–30 μM) that inhibited uptake of 200 μM [³H] D-glucose by ≥90%; 3) individual uptake assays for compounds (10 μM) that inhibited glucose uptake by ≥50%; 4) individual uptake assays for compounds (10 μM) that inhibited uptake of 100 μM [³H] L-proline by ≤10%; 5) dose-response curves for compounds that selectively inhibited uptake of glucose through PfHT versus GLUT1 (1 compound plus 1 additional hit that emerged from analysis of analogs). Numbers in parentheses represent the number of positive hits obtained after each sequential step.</p

Compounds from the TCAMS (1–6) and Malaria Box (7–9) libraries that were selective inhibitors of glucose, but not proline, transport.

<p>Dose-response curves for inhibition of uptake of 100 μM [³H] D-glucose by each compound were performed for PfHT and GLUT1, and the IC₅₀ values are tabulated. SI_glucose indicates Specificity Index for glucose uptake and is IC₅₀ for GLUT1/IC₅₀ for PfHT. The reported EC₅₀ values (PubChem web site, <a href="http://pubchem.ncbi.nlm.nih.gov/" target="_blank">http://pubchem.ncbi.nlm.nih.gov/</a>) for inhibition of growth of <i>P</i>. <i>falciparum</i> strain 3D7 intraerythrocytic forms by each compound are also tabulated.</p

Inhibition of glucose uptake by PfHT by Compounds 1, 7, and 13.

<p>Substrate saturation curves (A, B, C) were performed for PfHT in the presence of various concentrations of Compounds 1, 7, and 13, respectively. Data represent the mean and standard deviation of 3 replicate uptake assays. Data were fitted to the Michaelis-Menten equation by non-linear regression.</p

MOESM1 of Diversity-oriented natural product platform identifies plant constituents targeting Plasmodium falciparum

Additional file 1. Specified UPLC conditions, resulting chromatograms and analytical data including NMR, UV and MS spectra for the purified natural products