Deployment of in silico and in vitro safety assays in early-stage drug discovery

2012 ◽  
Vol 4 (10) ◽  
pp. 1211-1213 ◽  
Author(s):  
Yvonne Will ◽  
Thomas Schroeter
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Early Stage

ADMET Profiling in Drug Discovery and Development: Perspectives of in silico, in vitro and integrated approaches

2021 ◽  
Vol 22 ◽  
Author(s):  
Nour El-Huda Daoud ◽  
Pobitra Borah ◽  
Pran Kishore Deb ◽  
Katharigatta N. Venugopala ◽  
Wafa Hourani ◽  
...  
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Predictive Power ◽  
Early Stage ◽  
In Vitro Models ◽  
Prediction Ability ◽  
Drug Discovery And Development ◽  
In Silico Admet

: In the drug discovery setting, undesirable ADMET properties of a pharmacophore with good predictive power obtained after a tedious drug discovery and development process may lead to late-stage attrition. The early-stage ADMET profiling has introduced a new dimension to leading development. Although several high-throughput in vitro models are available for ADMET profiling, however, the in silico methods are gaining more importance because of their economic and faster prediction ability without the requirements of tedious and expensive laboratory resources. Nonetheless, in silico ADMET tools alone are not accurate and, therefore, ideally adopted along with in vitro and or in vivo methods in order to enhance predictability power. This review summarizes the significance and challenges associated with the application of in silico tools as well as the possible scope of in vitro models for integration to improve the ADMET predictability power of these tools.

scholarly journals Miniaturization and Automation of a Human In Vitro Blood–Brain Barrier Model for the High-Throughput Screening of Compounds in the Early Stage of Drug Discovery

Pharmaceutics ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 892
Author(s):  
Elisa L. J. Moya ◽  
Elodie Vandenhaute ◽  
Eleonora Rizzi ◽  
Marie-Christine Boucau ◽  
Johan Hachani ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Blood Brain Barrier ◽  
Early Stage ◽  
Molecular Transport ◽  
Brain Barrier ◽  
Blood Brain ◽  
Cns Drugs ◽  
The Impact ◽  
The Brain

Central nervous system (CNS) diseases are one of the top causes of death worldwide. As there is a difficulty of drug penetration into the brain due to the blood–brain barrier (BBB), many CNS drugs treatments fail in clinical trials. Hence, there is a need to develop effective CNS drugs following strategies for delivery to the brain by better selecting them as early as possible during the drug discovery process. The use of in vitro BBB models has proved useful to evaluate the impact of drugs/compounds toxicity, BBB permeation rates and molecular transport mechanisms within the brain cells in academic research and early-stage drug discovery. However, these studies that require biological material (animal brain or human cells) are time-consuming and involve costly amounts of materials and plastic wastes due to the format of the models. Hence, to adapt to the high yields needed in early-stage drug discoveries for compound screenings, a patented well-established human in vitro BBB model was miniaturized and automated into a 96-well format. This replicate met all the BBB model reliability criteria to get predictive results, allowing a significant reduction in biological materials, waste and a higher screening capacity for being extensively used during early-stage drug discovery studies.

Recommended Guidelines for Developing, Qualifying, and Implementing Complex In Vitro Models (CIVMs) for Drug Discovery

2020 ◽  
Vol 25 (10) ◽  
pp. 1174-1190
Author(s):  
Jason E. Ekert ◽  
Julianna Deakyne ◽  
Philippa Pribul-Allen ◽  
Rebecca Terry ◽  
Christopher Schofield ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Early Stage ◽  
Cell Types ◽  
In Vitro Models ◽  
Success Rates ◽  
Lead Discovery ◽  
Cellular Models ◽  
Discovery Phase ◽  
Early Drug

The pharmaceutical industry is continuing to face high research and development (R&D) costs and low overall success rates of clinical compounds during drug development. There is an increasing demand for development and validation of healthy or disease-relevant and physiological human cellular models that can be implemented in early-stage discovery, thereby shifting attrition of future therapeutics to a point in discovery at which the costs are significantly lower. There needs to be a paradigm shift in the early drug discovery phase (which is lengthy and costly), away from simplistic cellular models that show an inability to effectively and efficiently reproduce healthy or human disease-relevant states to steer target and compound selection for safety, pharmacology, and efficacy questions. This perspective article covers the various stages of early drug discovery from target identification (ID) and validation to the hit/lead discovery phase, lead optimization, and preclinical safety. We outline key aspects that should be considered when developing, qualifying, and implementing complex in vitro models (CIVMs) during these phases, because criteria such as cell types (e.g., cell lines, primary cells, stem cells, and tissue), platform (e.g., spheroids, scaffolds or hydrogels, organoids, microphysiological systems, and bioprinting), throughput, automation, and single and multiplexing endpoints will vary. The article emphasizes the need to adequately qualify these CIVMs such that they are suitable for various applications (e.g., context of use) of drug discovery and translational research. The article ends looking to the future, in which there is an increase in combining computational modeling, artificial intelligence and machine learning (AI/ML), and CIVMs.

Integration of in Silico and in Vitro Tools for Scaffold Optimization during Drug Discovery: Predicting P-Glycoprotein Efflux

2013 ◽  
Vol 10 (4) ◽  
pp. 1249-1261 ◽  
Author(s):  
Prashant V. Desai ◽  
Geri A. Sawada ◽  
Ian A. Watson ◽  
Thomas J. Raub
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
P Glycoprotein

New in silico and conventional in vitro approaches to advance HIV drug discovery and design

2012 ◽  
Vol 8 (1) ◽  
pp. 83-92 ◽  
Author(s):  
Giovanni Maga ◽  
Nevena Veljkovic ◽  
Emmanuele Crespan ◽  
Silvio Spadari ◽  
Jelena Prljic ◽  
...  
Keyword(s):  
Drug Discovery ◽  
In Silico

scholarly journals Computational and micro-analytical techniques to study the in vitro and in silico models of novel therapeutic drugs

2016 ◽  
Author(s):  
◽  
Njabulo Joyfull Gumede
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Pharmacophore Model ◽  
Binding Affinities ◽  
Ic50 Values ◽  
Database Screening ◽  
Pharmacophore Models ◽  
Novel Therapeutic

In drug discovery and development projects, metabolism of new chemical entities (NCEs) is a major contributing factor for the withdrawal of drug candidates, a major concern for other chemical industries where chemical-biological interactions are involved. NCEs interact with a target macro-molecule to stimulate a pharmacological or toxic response, known as pharmacodynamics (PD) effect or through the Adsorption, Distribution, Metabolism, and Excretion (ADME) process, triggered when a bio-macromolecule interacts with a therapeutic drug. Therefore, the drug discovery process is important because 75% of diseases known to human kind are not all cured by therapeutics currently available in the market. This is attributed to the lack of knowledge of the function of targets and their therapeutic use in order to design therapeutics that would trigger their pharmacological responses. Accordingly, the focus of this work is to develop cost saving strategies for medicinal chemists involved with drug discovery projects. Therefore, studying the synergy between in silico and in vitro approaches maybe useful in the discovery of novel therapeutic compounds and their biological activities. In this work, in silico methods such as structure-based and ligand-based approaches were used in the design of the pharmacophore model, database screening and flexible docking methods. Specifically, this work is presented by the following case studies: The first involved molecular docking studies to predict the binding modes of catechin enantiomer to human serum albumin (HSA) interaction; the second involved the use of docking methods to predict the binding affinities and enantioselectivity of the interaction of warfarin enantiomers to HSA. the third case study involved a combined computational strategy in order to generate information on a diverse set of steroidal and non-steroidal CYP17A1 inhibitors obtained from literature with known experimental IC50 values. Finally, the fourth case study involved the prediction of the site of metabolisms (SOMs) of probe substrates to Cytochrome P450 metabolic enzymes CYP 3A4, 2D6, and 2C9 making use of P450 module from Schrödinger suite for ADME/Tox prediction. The results of case study I were promising as they were able to provide clues to the factors that drive the synergy between experimental kinetic parameters and computational thermodynamics parameters to explain the interaction between drug enantiomers and thetarget protein. These parameters were correlated/converted and used to estimate the pseudo enantioselectivity of catechin enantiomer to HSA. This approach of combining docking methodology with docking post-processing methods such as MM-GBSA proved to be vital in estimating the correct pseudo binding affinities of a protein-ligand complexes. The enantioselectivity for enantiomers of catechin to HSA were 1,60 and 1,25 for site I and site II respectively. The results of case study II validates and verifies the preparation of ligands and accounting for tautomers at physiological pH, as well as conformational changes prior to and during docking with a flexible protein. The log KS = 5.43 and log KR = 5.34 for warfarin enantiomer-HSA interaction and the enantioselectivity (ES = KS/KR) of 1.23 were close to the experimental results and hence referred to as experimental-like affinity constants which validated and verified their applicability to predict protein-ligand binding affinities. In case study III, a 3D-QSAR pharmacophore model was developed by using 98 known CYP17A1 inhibitors from the literature with known experimental IC50 values. The starting compounds were diverse which included steroidal and non-steroidal inhibitors. The resulting pharmacophore models were trained with 69 molecules and 19 test set ligands. The best pharmacophore models were selected based on the regression coefficient for a best fit model with R2 (ranging from 0.85-0.99) & Q2 (ranging from 0.80-0.99) for both the training and test sets respectively, using Partial Least Squares (PLS) regression. On the other hand, the best pharmacophore model selected was further used for a database screening of novel inhibitors and the prediction of their CYP17A1 inhibition. The hits obtained from the database searches were further subjected to a virtual screening workflow docked to CYP17A1 enzyme in order to predict the binding mode and their binding affinities. The resulting poses from the virtual screening workflow were subjected to Induced Fit Docking workflow to account for protein flexibility during docking. The resulting docking poses were examined and ranked ordered according to the docking scores (a measure of affinity). Finally, the resulting hits designed from an updated model from case study III were further synthesized in an external organic chemistry laboratory and the synthetic protocols as well as spectroscopic data for structure elucidation forms part of the provisional patent specification. A provisional patent specification has been filed (RSA Pat. Appln. 2015/ 07849). The case studies performed in this thesis have enabled the discovery of non-steroidal CYP17A1 inhibitors.

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