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Dendrimer Space Concept: A Futuristic Vision in Nanomedicine to Develop New Drugs
Published in Anne-Marie Caminade, Cédric-Olivier Turrin, Jean-Pierre Majoral, Phosphorus Dendrimers in Biology and Nanomedicine, 2018
Serge Mignani, Jean-Pierre Majoral
One of the utmost important challenges of drug discoverers is to identify and expand chemical areas that contain specific biologically active derivatives with adequate physicochemical or topological properties. Thus, the concept of boundaries of the continuum chemical space, the navigation and the exploration inside this vast space has been outstandingly pointed out by Lipinski [26]. The far- ranging goal is to identify new, discrete chemical regions (called clusters, subtractions,...) in order to find and develop original drugs, especially for challenging targets. The relationship between the continuum ot chemical space and several clusters occupied by biological compounds with specific mechanism ot action is represented in Fig. 13.1. These ditterent spaces are mapped onto coordinates ot chemical descriptors based on physicochemical or topological properties. Thus, tor instance, oral drug-like space based on Ro5 parameters can be used. These criteria determine the boundaries ot each chemical cluster and are used as early-stage tool filters to select appropriate potential druggable final drugs. The overlapping ot the drug-likeness chemical space continuum (Ro5) and the 2D “target classes,” including, tor instance, PPI space, kinase space, a “poor” druggable target, etc., defined an overlap volume (truncated space) tor which all the compounds (virtual or real) within this space are druggable. The anti-overlap area corresponds to the poorly druggable compounds. The same parameters used to define the boundaries of druggable compounds (e.g., Ro5) can be used to define a specific target space, including drugs. By analogy, other cubes can be drawn for drugs destined for other administrative routes such as ocular, inhalation, and transdermal [40]. In addition, several other spaces (vide supra) can be defined (clusterized) such as natural product chemical space and commercial library chemical space (existing and virtual compounds), not shown in Fig. 13.1. In summary, an anti-overlap between the drug-like space and chemical space defines the chemical space of a “poor” drug lead’s properties, while the overlap between drug-like space and chemical space defines a good chemical property space on which druggable active compounds can be developed. Importantly, once the boundaries of each chemical cluster is defined, and based on guidelines such as Ro3-5, the navigation inside each cluster can be performed using metrics-filters, such as ligand efficiency (LE) or others [41], in order to select the “best” druggable compounds within each cluster.
Genetic functional algorithm model, docking studies and in silico design of novel proposed compounds against Mycobacterium tuberculosis
Published in Egyptian Journal of Basic and Applied Sciences, 2020
The first stage for the design and synthesis of novel hypothetical compounds with enhanced anti-tubercular activity and less toxicity/side effect as to with the approaches and methods that will consider the rate of experimental runs and time factor. Reference to the design of novel drug candidate, computer-aided drug design has demonstrated a crucial part for the discovery of new molecules in pharmaceutical design, drug metabolism, and medicinal chemistry [13]. This approach had facilitated the improvement in the course of optimization of chemical structures with well-defined purposes [14]. Quantitative structure-activity relationship study (QSAR) and molecular docking are one of the computer-aided drug design approaches which had been broadly utilized in the design, improvement and synthesis of first-hand drug [2]. QSAR investigation had shown to be an expedient technique for forecasting biological/inhibition activities, properties of any chemical compound by making use of an experimental data and molecular descriptors. This idea is based on the correlation between the information derived from any chemical space or structural molecule illustrated by the descriptor and well-defined experimental data provided. Meanwhile, molecular docking technique help to foresee the binding location and affinity of the existing interaction between the molecule (ligand) and the target. Thereby providing an idea to design prospective drug with better activity against the target [2]. Therefore, the study aimed to build a Genetic Functional Algorithm model, carry out molecular docking studies and in silico design of novel proposed compounds against Mycobacterium tuberculosis
Revisiting the Meyer-Overton rule for drug-membrane permeabilities
Published in Molecular Physics, 2019
Roberto Menichetti, Tristan Bereau
The overwhelming number of possible compounds thus calls for the introduction of new strategies that allow to more comprehensively cover large regions of chemical space. In this perspective, we recently proposed the use of coarse-grained (CG) models as a powerful instrument to preserve accurate thermodynamics while ‘clustering’ chemical space, in this way enabling a more systematic and efficient in silico exploration of chemical diversity [22,23].