Identification of effective natural PIK3CA H1047R inhibitors by computational study.

Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer with a poor prognosis and a high recurrence rate. PIK3CA gene is frequently mutated in breast cancer, with PIK3CA H1047R as the hotspot mutation reported in TNBC. We used the ZINC database to screen natural compounds that could be structurally modified to develop drugs targeting the PIK3CA H1047R mutant protein in the PI3K pathway. The LibDock module showed that 2,749 compounds could strongly bind to the PIK3CA H1047R protein. Ultimately, the top 20 natural ligands with high LibDock scores were used for further analyses including assessment of ADME (absorption, distribution, metabolism, and excretion), toxicity, stability, and binding affinity. ZINC000004098448 and ZINC000014715656 were selected as the safest drug candidates with strong binding affinity to PIK3CA H1047R, no hepatotoxicity, less carcinogenicity, better plasma protein binding (PPB) properties, and enhanced intestinal permeability and absorption than the two reference drugs, PKI-402 and wortmannin. Moreover, their lower potential energies than those of PIK3CA H1047R confirmed the stability of the ligand-receptor complex under physiological conditions. ZINC000004098448 and ZINC000014715656 are thus safe and stable leads for designing drugs against PIK3CA H1047R as part of a targeted therapeutic approach for patients with TNBC.

Size-adjustable micelles co-loaded with a chemotherapeutic agent and an autophagy inhibitor for enhancing cancer treatment via increased tumor retention.

Autophagy plays a key role in the stress response of tumor cells, which contributes to cancer cell survival and resistance to chemotherapy by degrading cytoplasmic proteins to provide energy and clear the hazardous substances. Therefore, combined treatment of chemotherapeutics and autophagy inhibitors is thought to obtain a desirable antitumor effect. Nanoparticles (NPs) show potential in tumor-targeting drug delivery because of the enhanced permeability and retention (EPR) effect. However, NPs with fixed particle size cannot achieve optimal delivery effect. Herein, a strategy based on Cu (I)-catalyzed click chemistry-triggered aggregation of azide/alkyne-modified micelles was developed for the co-delivery of the chemotherapeutic drug doxorubicin (Dox) and the autophagy inhibitor wortmannin (Wtmn). In vitro experiments showed that the size of micelles increased in a time-dependent manner, which enhanced micelle accumulation in both B16F10 and 4 T1 cells. The fluorescence resonance energy transfer (FRET) experiment and biodistribution study further demonstrated that the aggregation of micelles through click cycloaddition significantly improved the accumulation of drug-loading micelles at the tumor region. Furthermore, the decreased amount of autophagosomes observed by transmission electron microscopy (TEM), the declined expression of LC3-II, and the increased level of p62 by western blotting and immunohistochemistry (IHC) confirmed the obvious inhibition of autophagy induced by Dox/Wtmn co-loaded size-adjustable micelles, which had a synergistic effect in cancer suppression. In addition, the co-loaded size-adjustable micelles showed outstanding cytotoxicity and antitumor effect. Therefore, this strategy effectively suppressed melanoma and breast cancer in mice. STATEMENT OF SIGNIFICANCE: The therapeutic effects of chemotherapy can be limited by autophagy; hence, combined use of autophagy inhibitors with chemotherapeutics achieves desirable anticancer efficacy. In the present study, we designed size-adjustable micelles by modifying the click reaction substrate azide group and the alkyne group on the surface of micelles, and subsequently, the autophagy inhibitor wortmannin and the chemotherapeutic drug doxorubicin were co-loaded. The micelles could aggregate by click reaction at the tumor site when the catalysts were intratumorally injected. The results showed that the size-adjustable micelles achieved efficient drug delivery, penetration, and retention in tumors; through the combined effect of wortmannin-mediated autophagy inhibition and doxorubicin-mediated cytotoxicity, this strategy exerted significant anticancer effect in melanoma and breast cancer treatment.

Wortmannin and Wortmannine Analogues from an Undescribed Niesslia sp.

In the course of our studies of coprophilous fungi as sources of antifungal agents, a strain of an undescribed species in the genus Niesslia (TTI-0426) was isolated from horse dung collected in Texas. An extract from fermentation cultures of this strain afforded two new antifungal wortmannin derivatives, wortmannins C and D (1 and 2), as well as four additional new related compounds, wortmannines B1-B4 (3-6), containing an unusual ring system. The structures of these metabolites were established mainly by analysis of HRESIMS and 2D NMR data. Relative configurations were assigned using NOESY data, and the structure assignments were supported by NMR comparison with similar compounds. Wortmannins C and D showed activity against Cryptococcus neoformans and Candida albicans in disk assays, but low MIC potency observed for 1 was suggested to be due in part to efflux processes on the basis of assay results for a Schizosaccharomyces pombe efflux mutant in comparison to wild-type.

Predicting protein targets for drug-like compounds using transcriptomics.

An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation of set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions.