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.

scholarly journals Utilizing in vitro transporter data in IVIVE-PBPK: an overview

ADMET & DMPK ◽  
2017 ◽  
Vol 5 (4) ◽  
pp. 201-211 ◽  
Author(s):  
Pankajini Mallick
Keyword(s):  
Drug Discovery ◽  
Drug Transporters ◽  
Drug Discovery And Development ◽  
Pbpk Models ◽  
Physiologically Based Pharmacokinetic ◽  
Human Pharmacokinetic ◽  
Development Processes ◽  
Key Aspects

In vitro-in vivo extrapolation (IVIVE) integrated in physiologically-based pharmacokinetic (PBPK) models have been increasingly used during drug discovery and development processes to predict human pharmacokinetic (PK) parameters. Drug transporters can influence drug pharmacokinetics and are key aspects contributing to the development of a successful drug. This review provides a snapshot of challenges or shortcomings of in vitro and in vivo techniques for understanding the contribution of drug transporters to a drug’s pharmacokinetics. The paper also describes the potential of IVIVE-PBPK models as prospective approaches to predict the role of drug transporters in drug discovery and development.

scholarly journals Nitroxoline as a promising alternative drug for the treatment of Lyme disease based on an in-vitro study

2021 ◽  
Author(s):  
Hector S. Alvarez-Manzo ◽  
Yumin Zhang ◽  
Wanliang Shi ◽  
Ying Zhang
Keyword(s):  
Lyme Disease ◽  
Stationary Phase ◽  
Drug Exposure ◽  
Drug Combinations ◽  
Stationary Phase Culture ◽  
Promising Alternative ◽  
Alternative Drug ◽  
Tract Infections

AbstractLyme disease (LD) is the most common vector-borne disease in USA and Europe and is caused by Borrelia burgdorferi. Despite proper treatment, approximately one fifth of patients will develop post-treatment LD syndrome (PTLDS), a condition which is poorly understood. One of the possible causes is thought to be due to persister forms of B. burgdorferi that are not effectively killed by the current Lyme antibiotics. In this study, we evaluated nitroxoline, an antibiotic used to treat urinary tract infections, for its activity against a stationary-phase culture enriched with persister forms of B. burgdorferi. Nitroxoline was found to be equivalent in activity against B. burgdorferi to cefuroxime (standard Lyme antibiotic) in different experiments. Moreover, we found that the three-drug combination cefuroxime + nitroxoline + clarithromycin eradicated 98.3% of stationary phase bacteria in the drug-exposure experiment and prevented the regrowth in the subculture study after drug exposure, as well as two-drug combinations cefuroxime + nitroxoline and clarithromycin + nitroxoline. These drug combinations should be further evaluated in a LD mouse model to assess if eradication of persister forms of B. burgdorferi in-vivo is possible and if so, whether nitroxoline could be repurposed as an alternative drug for the treatment of LD.

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.

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