Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

Background: Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Results: Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Conclusions: Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Figure not available: see fulltext. © 2015 Freitas et al.; licensee Springer

Cytotoxic activity of new Zn(II), Co(II) and Ni(II) complexes with kojic acid

Metals have been used in the treatment of various diseases since ancient times. The discovery of cisplatin in 1960 - one of the most effective and widely used drugs in current clinical oncology, opened a new page in medical chemistry and stimulated scientists to search for new metal compounds with promising antitumor activity and acceptable tolerance.The aim of our study was to evaluate the influence of Zn(II), Co(II) and Ni(II) complexes with kojic acid on via­bility and proliferation of human cancer (HeLa uterine cervical carcinoma) and non-cancer (Lep-3 embryonic fi­broblasts) cells. The investigations were based on short-term (up to 72 h, with monolayer cultures) and long-term (up to 40 days, with 3D cancer cell colonies) experiments using methods with different cellular/molecular targets and mechanisms of action: MTT test, neutral red uptake cytotoxicity assay, crystal-violet staining, double stain­ing with acridine orange and propidium iodide, Annexin V/FITC assay and colony-forming method. The results obtained revealed that: i) applied at a concentration range of 5 - 200 μg/mL the compounds tested decrease the percent of the viable treated cells (as compared to the control) in a time- and concentration-dependent manner; ii) administered at a concentration of 200 μg/ml the complexes completely inhibit the ability of HeLa uterine cer­vical carcinoma cells to form 3D cell colonies in a semi-solid medium; iii) cancer HeLa cells have been found to be more sensitive to the cytotoxic effect of the compounds examined as compared to the non-cancer Lep-3 fibro­blasts; iv) Co(II) complex with kojic acid shows the highest cytotoxic activity among the compounds investigated and has demonstrated to be more effective than cisplatin.This study was funded by Grant DFNP-17-73/28.07.2017 from the Program `Support of Young Scientists at the Bulgarian Academy of Sciences` and by a mutual project between the Bulgarian Academy of Sciences and the Ro­manian Academy