scholarly journals Prediction of ARA/PPI Drug-Drug Interactions at the Drug Discovery and Development Interface

2019 ◽  
Vol 108 (1) ◽  
pp. 87-101 ◽  
Author(s):  
Stephanie Dodd ◽  
Sivacharan Kollipara ◽  
Manuel Sanchez-Felix ◽  
Hyungchul Kim ◽  
Qingshuo Meng ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Drug Interactions ◽  
Drug Discovery And Development

scholarly journals Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

2020 ◽  
Vol 10 (7) ◽  
pp. 2376 ◽  
Author(s):  
Rob C. van Wijk ◽  
Rami Ayoun Alsoud ◽  
Hans Lennernäs ◽  
Ulrika S. H. Simonsson
Keyword(s):  
Drug Discovery ◽  
Drug Interactions ◽  
Development Strategy ◽  
Drug Exposure ◽  
Rapid Development ◽  
Drug Combinations ◽  
Pharmacodynamic Modeling ◽  
Drug Discovery And Development

The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.

Development of an ADME and drug–drug interactions knowledge database for the acceleration of drug discovery and development

2006 ◽  
Vol 1 (7) ◽  
pp. 737-751 ◽  
Author(s):  
François Petitet ◽  
Olivier Barberan ◽  
Elodie Dubus ◽  
Ismail Ijjaali ◽  
Mary Donlan ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Drug Interactions ◽  
Drug Discovery And Development ◽  
Knowledge Database

Recent progress on 1H-1,2,3-triazoles as potential antifungal agents

2021 ◽  
Vol 21 ◽  
Author(s):  
Nisha Poonia ◽  
Aman Kumar ◽  
Vijay Kumar ◽  
Monika Yadav ◽  
Kashmiri Lal
Keyword(s):  
Drug Discovery ◽  
Side Effects ◽  
Drug Interactions ◽  
Broad Spectrum ◽  
Recent Progress ◽  
Development Process ◽  
Antifungal Agents ◽  
Present Review ◽  
Drug Discovery And Development ◽  
Triazole Derivatives

: To overcome ever increasing cases of antifungal resistance and circumventing side effects and drug interactions related with currently available drugs has impelled the need to expedite the process of drug discovery and development of novel antifungals. 1,4-Disubstituted 1,2,3-triazole has gained tremendous interest in last two decades mainly because of its ease of synthesis via copper(I)-catalyzed azide-alkyne cycloaddition (CuAAC) and its broad spectrum of chemotherapeutic potential. 1,2,3-Triazole is an excellent pharmacophore which has been used as a bioisostere for obtaining libraries of new medicinally important scaffolds. The present review focus on the recent advances (2016-2021) of 1,2,3-triazole derivatives obtained by CuAAC as potential antifungal agents that may facilitate triazole based antifungal development process.

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