Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform.

A literature curated dataset containing 24 distinct metal oxide (MexOy) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of MexOy NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by MexOy NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the MexOy conduction band (EC), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⟂ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project's Integrated Approach to Testing and Assessment (IATA).

Fe-Doped ZnO nanoparticle toxicity: assessment by a new generation of nanodescriptors.

In the search for novel tools to combat cancer, nanoparticles (NPs) have attracted a lot of attention. Recently, the controlled release of cancer-cell-killing metal ions from doped NPs has shown promise, but fine tuning of dissolution kinetics is required to ensure specificity and minimize undesirable toxic side-effects. Theoretical tools to help in reaching a proper understanding and finally be able to control the dissolution kinetics by NP design have not been available until now. Here, we present a novel set of true nanodescriptors to analyze the charge distribution, the effect of doping and surface coating of whole metal oxide NP structures. The polarizable model of oxygen atoms enables light to be shed on the charge distribution on the NP surface, allowing the in detail study of the factors influencing the release of metal ions from NPs. The descriptors and their capabilities are demonstrated on a Fe-doped ZnO nanoparticle system, a system with practical outlook and available experimental data.

In Silico Design of Optimal Dissolution Kinetics of Fe-Doped ZnO Nanoparticles Results in Cancer-Specific Toxicity in a Preclinical Rodent Model.

Cancer cells have unique but widely varying characteristics that have proven them difficult to be treated by classical therapeutics and calls for novel and selective treatment options. Nanomaterials (NMs) have been shown to display biological effects as a function of their chemical composition, and the extent and exact nature of these effects can vary between different biological environments. Here, ZnO NMs are doped with increasing levels of Fe, which allows to finely tune their dissolution rate resulting in significant differences in their biological behavior on cancer or normal cells. Based on in silico analysis, 2% Fe-doped ZnO NMs are found to be optimal to cause selective cancer cell death, which is confirmed in both cultured cells and syngeneic tumor models, where they also reduce metastasis formation. These results show that upon tuning NM chemical composition, NMs can be designed as a targeted selective anticancer therapy.

Identification of several high-risk HPV inhibitors and drug targets with a novel high-throughput screening assay.

Human papillomaviruses (HPVs) are oncogenic viruses that cause numerous different cancers as well as benign lesions in the epithelia. To date, there is no effective cure for an ongoing HPV infection. Here, we describe the generation process of a platform for the development of anti-HPV drugs. This system consists of engineered full-length HPV genomes that express reporter genes for evaluation of the viral copy number in all three HPV replication stages. We demonstrate the usefulness of this system by conducting high-throughput screens to identify novel high-risk HPV-specific inhibitors. At least five of the inhibitors block the function of Tdp1 and PARP1, which have been identified as essential cellular proteins for HPV replication and promising candidates for the development of antivirals against HPV and possibly against HPV-related cancers.

Macrocyclic peptidomimetics with antimicrobial activity: synthesis, bioassay, and molecular modeling studies.

Novel, cyclic peptidomimetics were synthesized by facile acylation reactions using benzotriazole chemistry. Microbiological testing of the synthesized compounds revealed an exceptionally high activity against Candida albicans with a minimum inhibitory concentration (MIC) two orders of magnitude lower than the MIC of the antifungal reference drug amphotericin B. A strikingly high activity was also observed against three Gram-negative bacterial strains (Pseudomonas aeruginosa, Klebsiella pneumoniae and Proteus vulgaris), two of which are known human pathogens. Thus the discovered chemotype is a potential polypharmacological agent. The toxicity against mammalian tumor cells was found to be low, as demonstrated in five different human cell lines (HeLa, cervical; PC-3, prostate; MCF-7, breast; HepG2, liver; and HCT-116, colon). The internal consistency of the experimental data was studied using 3D-pharmacophore and 2D-QSAR.

Theoretical modeling of HPV: QSAR and novodesign with fragment approach.

Structure-activity relationships in a data set of HPV6-E1 helicase ATPase inhibitors were investigated based on two different sets of descriptors. Statistically significant four parameter Quantitative Structure-Activity Relationships (QSAR) models were constructed and validated in both cases (R(2)=0.849; R(2) cv=0.811; F=52.20; s(2)=0.25; N=42). A Fragment based QSAR (FQSAR) approach was applied for developing a fragment-QSAR equation, which enabled the construction of virtual structures for novel ATPase inhibitors with desired or pre-defined activity.