Sharif Digital Repository

A total of 6289 drug-like anticancer molecules were collected from Binding database and were analyzed by using the classification techniques. The collected molecules were encoded to a diverse set of descriptors, spanning different physical and chemical properties of the molecules. A combination of genetic algorithms and counterpropagation artificial neural networks was used for navigating the generated drug-like chemical space and selecting the most relevant molecular descriptors. The proposed method was used for the classification of the molecules according to their therapeutic targets and activities. The selected molecular descriptors in this work define discrete areas in chemical space,... 

Biological cell injection is a sensitive and important work which is implemented in injection of foreign materials into individual cells. Microinjection is significantly developed in the field of drug discovery and genetics so predicting the behavior of cell in microinjection is remarkably important because a tiny excessive manipulation force can destroy the tissue of the biological cell. There are a few analytical methods available to simulate the cell injection, hence the numerical methods such as FEM are suitable to be used to model the microinjection. In this study, a new spherical super element is presented to model the biological cells and deformation of a specific cell under an... 

Biological cell injection is a sensitive and important work which is implemented in injection of foreign materials into individual cells. Microinjection is significantly developed in the field of drug discovery and genetics so predicting the behavior of cell in microinjection is remarkably important because a tiny excessive manipulation force can destroy the tissue of the biological cell. There are a few analytical methods available to simulate the cell injection, hence the numerical methods such as FEM are suitable to be used to model the microinjection. In this study, a new spherical super element is presented to model the biological cells and deformation of a specific cell under an... 

Development of predictive multi-organ models before implementing costly clinical trials is central for screening the toxicity, efficacy, and side effects of new therapeutic agents. Despite significant efforts that have been recently made to develop biomimetic in vitro tissue models, the clinical application of such platforms is still far from reality. Recent advances in physiologically-based pharmacokinetic and pharmacodynamic (PBPK-PD) modeling, micro- and nanotechnology, and in silico modeling have enabled single- and multi-organ platforms for investigation of new chemical agents and tissue-tissue interactions. This review provides an overview of the principles of designing... 

The pharmaceutical industry is facing enormous challenges due to high drug attribution rates. For the past decades, novel methods have been developed for safety and efficacy testing, as well as for improving early development stages. In vitro screening methods for drug-receptor binding are considered to be good alternatives for decreasing costs in the identification of drug candidates. However, these methods require lengthy and troublesome labeling steps. Biosensors hold great promise due to the fact that label-free detection schemes can be designed in an easy and low-cost manner. In this paper, for the first time in the literature, we aimed to compare the potential of label-free optical and... 

Organ-on-chip (OOC) platforms have attracted attentions of pharmaceutical companies as powerful tools for screening of existing drugs and development of new drug candidates. OOCs have primarily used human cell lines or primary cells to develop biomimetic tissue models. However, the ability of human stem cells in unlimited self-renewal and differentiation into multiple lineages has made them attractive for OOCs. The microfluidic technology has enabled precise control of stem cell differentiation using soluble factors, biophysical cues, and electromagnetic signals. This study discusses different tissue- and organ-on-chip platforms (i.e., skin, brain, blood–brain barrier, bone marrow, heart,... 

Single-cell resolution technologies warrant computational methods that capture cell heterogeneity while allowing efficient comparisons of populations. Here, we summarize cell populations by adding features’ dispersion and covariances to population averages, in the context of image-based profiling. We find that data fusion is critical for these metrics to improve results over the prior alternatives, providing at least ~20% better performance in predicting a compound’s mechanism of action (MoA) and a gene’s pathway. © 2019, The Author(s)