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Antibiotic resistance, defined as the ability of a bacteria to survive an antibiotic drug to which it was initially sensitive, has become increasingly worrying. While the discovery of antibiotics is considered to be one of the most important of all biomedical advances of the twentieth century, bacterial infections are still problematic, and the medical and financial costs that they entail are a great burden to society. In the last 30 years the number of new antibiotic approvals has been decreasing dramatically, leading to a lack of replacements for antibiotics whose effectiveness has been compromised. The drug discovery pipeline is very long and expensive with high rates of attrition at each stage of the process. Massive economic investments have attempted to address this problem but they will bear little fruit if methods are not improved to make the process faster, less expensive, and more efficient. One of the fundamental steps in the evaluation of any new compound is the characterization of the relationship between its pharmacokinetic (PK) and pharmacodynamic (PD) parameters, which will ultimately determine the optimal dosing regimen to be used in clinical trials. A more detailed understanding of these parameters can make a critical difference in early go/no-go decisions, thereby increasing the effectiveness of the drug discovery process. The use of novel assays with single-cell resolution has demonstrated how conventional measurements, taken at the population level, can hide important underlying dynamics of cellular behavior. Since none of the PK/PD models available today allow single-cell measurements, this thesis is focused on the development and implementation of new tools that address this need by enabling the study of essential parameters of antibiotic effectiveness at the single-cell level. We first assessed the effect of different isocratic concentrations of antibiotic by exposing Mycobacterium smegmatis to various concentrations of isoniazid. We find that the higher number of surviving bacteria at lower concentrations of antibiotic is entirely due to increased division rates, as killing rates remain constant. Second, in order to study more realistic pharmacokinetic profiles, we construct a novel microfluidic platform that uses computer-controlled microvalves to enable the administration of time-dependent antibiotic concentration gradients. These gradients serve as input to an array of 960 microchambers in which the cells are imaged by time-lapse microscopy. By studying uropathogenic Escherichia coli (UPEC) treated with ampicillin, we evaluated dosing regimens of intermittently dosed patients. As a proof-of-principle, we accurately predict the pharmacokinetic driver for dose-optimization. Finally, we employ quantitative phase microscopy to complement our single-cell analyses with dry-mass measurements, which give us deeper insights into fundamental processes. Interestingly, we find that this technique can be used as a more rigorous tool to identify dead cells by microscopy.