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In Vitro Resistance-Predicting Studies and In Vitro Resistance-Related Parameters-A Hit-to-Lead Perspective.

Joanna Krajewska¹, Stefan Tyski², Agnieszka E Laudy³

¹Department of Environmental Health and Safety, National Institute of Public Health NIH-National Research Institute, 00-791 Warsaw, Poland.

Pharmaceuticals (Basel, Switzerland)

|August 29, 2024

Summary

Improving in vitro resistance prediction is crucial for developing new antibiotics. This review details methods like mutant prevention concentration (MPC) and adaptive laboratory evolution (ALE) to forecast antibiotic resistance potential.

Keywords:

ALE MPC antimicrobial agents antimicrobial resistance hit-to-lead stage mutant selection resistance-related parameters

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Area of Science:

Microbiology
Pharmacology
Drug Discovery

Background:

The pipeline for new antibiotics is critically low, necessitating improved methods for identifying drug candidates.
Predicting the potential for antibiotic resistance selection in natural environments is essential for drug development.

Purpose of the Study:

To review and highlight in vitro methods for predicting antibiotic resistance.
To discuss the utility of adaptive laboratory evolution (ALE) approaches for comparing antibiotic candidates.
To explore high-throughput ALE setups and fitness evaluation of resistant mutants.

Main Methods:

Presentation of in vitro resistance parameters: frequency of spontaneous mutant selection (FSMS), mutant prevention concentration (MPC), dominant mutant prevention concentration (MPC-D), inferior-mutant prevention concentration (MPC-F), and minimal selective concentration (MSC).
Discussion of various adaptive laboratory evolution (ALE) approaches, including serial transfer, continuous culture, and evolution in spatiotemporal microenvironments.
Consideration of high-throughput ALE setups like robotic workstations, DIY continuous cultivation, MEGA plates, SAGE plates, microfluidic chips, and microdroplet technology.

Main Results:

Various in vitro parameters and ALE approaches can be utilized to compare antibiotic hits.
High-throughput ALE setups offer scalable solutions for the hit-to-lead stage.
Methods for evaluating the fitness of in vitro-generated resistant mutants are available.

Conclusions:

Enhanced in vitro resistance prediction is vital for a sustainable supply of novel antibiotics.
Adaptive laboratory evolution (ALE) and related technologies offer powerful tools for forecasting resistance potential.
Further development and application of these methods can accelerate the discovery of effective antibiotic candidates.