Kaido Tämm

Aqueous biphasic systems. Partitioning of organic molecules: A QSPR treatment

The partitioning of 29 small organic probes in a PEG-2000/(NH4)2SO4 biphasic system was investigated using a quantitative structure-property relationship (QSPR) approach. A three-descriptor equation with the squared correlation coefficient (R2) of 0.97 for the partition coefficient (log D) was obtained. All descriptors were derived solely from the chemical structure of the compounds. Using the same descriptors, a three-parameter model was also obtained for log P (octanol/water, R2=0.89); predicted log P values were used as an external descriptor for modeling log D.

Prediction of Cell-Penetrating Peptides using Artificial Neural Networks

An investigation of cell-penetrating peptides (CPPs) by using combination of Artificial Neural Networks (ANN) and Principle Component Analysis (PCA) revealed that the penetration capability (penetrating/non-penetrating) of 101 examined peptides can be predicted with accuracy of 80%-100%. The inputs of the ANN are the main characteristics classifying the penetration. These molecular characteristics (descriptors) were calculated for each peptide and they provide bio-chemical insights for the criteria of penetration. Deeper analysis of the PCA results also showed clear clusterization of the peptides according to their molecular features.

Application of the QSPR approach to the boiling points of azeotropes.

CODESSA Pro derivative descriptors were calculated for a data set of 426 azeotropic mixtures by the centroid approximation and the weighted-contribution-factor approximation. The two approximations produced almost identical four-descriptor QSPR models relating the structural characteristic of the individual components of azeotropes to the azeotropic boiling points. These models were supported by internal and external validations. The descriptors contributing to the QSPR models are directly related to the three components of the enthalpy (heat) of vaporization.

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.

Fragment-Based Development of HCV Protease Inhibitors for the Treatment of Hepatitis C

A novel computational technology based on fragmentation of the chemical compounds has been used for the fast and efficient prediction of activities of prospective protease inhibitors of the hepatitis C virus. This study spans over a discovery cycle from the theoretical prediction of new HCV NS3 protease inhibitors to the first cytotoxicity experimental tests of the best candidates. The measured cytotoxicity of the compounds indicated that at least two candidates would be suitable further development of drugs.

Estimating the toxicities of organic chemicals in activated sludge process

The experimental logEC50 toxicity values of 104 compounds causing bioluminescent repression of the bacterium strain Pseudomonas isolated from an industrial wastewater were studied. Using the Best Multilinear Regression method implemented in CODESSA PRO, models with up to 8 theoretical descriptors were obtained. Utilizing a rigorous descriptor selection and validation procedure a reliable QSAR model with four parameters was selected as best. The proposed model emphasizes the importance of the halogen atoms presented in each compound, the possibility of H-bond formation and the flexibility and degree of branching of the molecules. As pointed out by many researchers, the contribution of the octanol-water partition coefficient to the explanation of the toxicity effect was also found to be significant. In addition, the model currently proposed was compared to those reported earlier and its advantages were discussed in detail.

NMR and DFT Study of the Copper(I)-Catalyzed Cycloaddition Reaction: H/D Scrambling of Alkynes and Variable Reaction Order of the Catalyst

Mechanism of an efficient and easily applicable catalytic system for the copper(I)‐catalyzed azide–alkyne cycloaddition (CuAAC) reaction, consisting of phosphane‐ligated CuI carboxylates and apolar/aprotic solvent was investigated by means of 1H NMR reaction monitoring techniques, isotope exchange studies, and DFT calculations (at the M06L/6‐311++G(d,p)//B97D/cc‐pVDZ (SDD) level of theory). Kinetic analysis indicates 1st order kinetics with respect to [Azide] and nonlinear positive order in [Cu]. H/D scrambling between alkynes reveals a quickly reached equilibrium existing between CuI–carboxylates and CuI–acetylides and that proton transfer processes are mediated by acetate/acetic acid system. According to the computational results, the Cu–triazolide forms a dinuclear structure that equalizes the copper atoms in the catalytic complex.

An Integrated Data-Driven Strategy for Safe-by-Design Nanoparticles: The FP7 MODERN Project

The development and implementation of safe-by-design strategies is key for the safe development of future generations of nanotechnology enabled products. The safety testing of the huge variety of nanomaterials that can be synthetized is unfeasible due to time and cost constraints. Computational modeling facilitates the implementation of alternative testing strategies in a time and cost effective way. The development of predictive nanotoxicology models requires the use of high quality experimental data on the structure, physicochemical properties and bioactivity of nanomaterials. The FP7 Project MODERN has developed and evaluated the main components of a computational framework for the evaluation of the environmental and health impacts of nanoparticles. This chapter describes each of the elements of the framework including aspects related to data generation, management and integration; development of nanodescriptors; establishment of nanostructure-activity relationships; identification of nanoparticle categories; hazard ranking and risk assessment.

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.

Subchronic Oral and Inhalation Toxicities: a Challenging Attempt for Modeling and Prediction

The article deals with a challenging attempt to model and predict “difficult” properties as long‐term subchronic oral and inhalation toxicities (90 days) using nonlinear QSAR approach. This investigation is one of the first to tackle such multicomplex properties where we have employed nonlinear models based on artificial neural network for the prediction of NOAEL (no observable adverse effect level). Despite the complex nature of the NOAEL property based on in vivo rat experiments, the successful models can be used as alternative tools to non‐animal tests for the initial assessment of these chronic toxicities. The model for oral subchronic toxicity is able to describe 88 %, and the inhalation model 87 % of the statistical variance. For the sake of future predictions, we have also defined in a quantitative way the applicability domain of all neural network models.