The problem of multi-resistant bacteria is increasing and the healthcare costs and implications are a global concern, notes Magnus Jansson, PhD, Chief Scientific Officer, SymCel Sverige AB. Consequently, the search is on for novel solutions.

Novel Tools for Antibiotics Development and Monitoring
The use of a sensitive label-free cell-based assay that can measure bacterial activity in real-time in complex setups with minimal effort is needed. Enter calorimetry! The properties of calorimetry based cell monitoring, and the data produced are uniquely well suited to the development of novel antibiotics.

Calorimetry measures the power produced in a cell culture at any given time as Joules/second (W). The heat produced is a measurement of the metabolic processes in the cells and gives a true phenotype fingerprint of the organism measured. Different bacteria and different treatments give rise to unique heat profiles, revealing significant information about the system tested (Figures 1 and 2).

Heat flow curves of serial dilutions of S. typhimurium demonstrating the shift in lag time and consistent maximal heat output. Curve shape is strain- and media-specific.

Figure 1: Heat flow curves of serial dilutions of S. typhimurium demonstrating the shift in lag time and consistent maximal heat output. Curve shape is strain- and media-specific.

Integration with time for the data from Figure 1. By plotting the accumulated energy release, a growth curve can be derived. Fitting the data to growth models allows the determination of lag-phase time and maximal growth rate. The maximum growth rate corresponds to the maximum slope of the heat over time curve. The duration of the lag phase is measured as the time from the start of the experiment to the interception of a tangent to the maximum growth rate point and the baseline.

Figure 2: Integration with time for the data from Figure 1. By plotting the accumulated energy release, a growth curve can be derived. Fitting the data to growth models allows the determination of lag-phase time and maximal growth rate. The maximum growth rate corresponds to the maximum slope of the heat over time curve. The duration of the lag phase is measured as the time from the start of the experiment to the interception of a tangent to the maximum growth rate point and the baseline.

Calorimetry has the advantage of being a label-free and non-destructive measurement, making post-experimental analysis possible whilst being totally independent of sample morphology. Consequently, assays can be performed on bacteria in solution as well as on solid media, including three-dimensional matrixes such as bone biopsies, surgical and dental implant materials, while being totally indifferent to turbidity and/or fluorescence.

One of the unique properties of the calorimetry based metabolic monitoring of bacterial growth is that the pattern of energy expenditure is species-, as well as strain-specific — each bacteria giving rise to a specific growth pattern as heat production with time. The metabolic output assay thus becomes both quantitative as well as qualitative. Even minor changes in growth behaviour such as metabolic pathway mutations are detected, as well as biofilm formation and, most importantly, antimicrobial sensitivity.

By integrating the metabolic power with time to accumulated heat over time (in Joules), a growth curve is established that’s equivalent to a traditional growth curve (as measured by the optical density of the culture). From this data, it is possible to calculate both the lag time and the maximal growth rate of the culture, which is the basis for determining the effect of antibiotic treatment (Figures 3 and 4).

Heat flow curves of E. coli growth without antibiotic and two different concentrations of antibacterial compound. The bacterial inoculum is identical for all three samples. The blue curve depicts an untreated sample. The red curve represents a low concentration of antibiotics, and demonstrates a prolonged lag-phase but a similar maximum peak power. The green curve depicts a high antibiotic concentration, showing a shift in lag-phase time as well as growth-rate and peak output power.

Figure 3: Heat flow curves of E. coli growth without antibiotic and two different concentrations of antibacterial compound. The bacterial inoculum is identical for all three samples. The blue curve depicts an untreated sample. The red curve represents a low concentration of antibiotics, and demonstrates a prolonged lag-phase but a similar maximum peak power. The green curve depicts a high antibiotic concentration, showing a shift in lag-phase time as well as growth-rate and peak output power.

Integration over time for the Figure 3 data. The derived growth curves demonstrate that the low antibiotic concentration has a shift in lag-phase time but very similar maximal growth-rate, indicative of bactericidal action (red curve). The green curve, with a high initial concentration, shows a prolonged lag-phase as well as a significantly decreased growth-rate, indicative of an additional bacteriostatic effect at the high concentration.

Figure 4: Integration over time for the Figure 3 data. The derived growth curves demonstrate that the low antibiotic concentration has a shift in lag-phase time but very similar maximal growth-rate, indicative of bactericidal action (red curve). The green curve, with a high initial concentration, shows a prolonged lag-phase as well as a significantly decreased growth-rate, indicative of an additional bacteriostatic effect at the high concentration.

The bioavailability of a novel compound is an important factor. But, as the calorimetric measurement is a measurement of total metabolism, bioavailability is a non-issue: it’s accounted for within the measurement. In addition, calorimetric monitoring is completely independent of sample morphology.

Colonization in complex matrices such as bone is notoriously hard to assay. Normal assays cannot give a representative sample of bacteria colonizing three-dimensional surfaces. Consequently, microscopy, fluorescence and molecular methods are prone to large deviations. The heat produced by bacterial metabolism in 3D matrix settings is readily measured regardless of the sample properties, giving access to whole new areas of investigation. Bacterial growth assays using calorimetry can be performed in both liquid and solid media, allowing the investigation of different properties during colonization of, for example, dental and surgical implant materials.

One of the standard dilemmas of antibiotic development is the correct quantification of the number of cells, not to mention the number of living cells. As many bacteria give rise to clustered cells, biofilms etc., the use of standard plating/growth analyses is likely to give a misrepresentative result. What is counted for as a single colony may originate from a cluster of living bacteria, thus giving false efficacy numbers.

Calorimeter-based assays only account for the actual number of metabolically active, live cells. This has implications for comparison with DNA- or protein-based assays where it can be difficult to distinguish between the number of live active cells and DNA/protein remaining in inactive/dead cells protected by a biofilm. It is easy to monitor the metabolic activity for prolonged times using calorimetry; a typical assay can run from a couple of hours up to days or weeks if needed — all with continuous monitoring. This allows the monitoring of persister-cells or cells derived with antibiotic resistance by natural selection pressure.

Persister cells will give rise to metabolic activity at a lower but constant rate during a prolonged time and can be distinguished in the assay. Development in more native conditions such as a serum supplemented growth media, mimicking the conditions in the human body, is also a proposition for calorimetric assay. The possible degradation and instability of tested compounds gives rise to the regrowth of persister cells, all easily monitored by following the total metabolic state of the culture for prolonged periods.

There is a growing interest in the use of potentiating treatments to increase the efficacy of antibiotics. Multiple modes of action of combined therapies, as well as the use of potentiating compounds, with no inherent antibiotic properties are easily monitored using calorimetric assays. The co-operative effects of two or more compounds are readily followed and, as there is no need to know the mechanism of action prior to the experiment, there is an unbiased phenotype screening only. Calorimetry is also a very ’open’ type of cell-based assay that is not limited to a specific cell type or setting. It is a valid proposal to test the toxicity of novel chemical entities on mammalian cell systems using the same equipment used for testing the antibacterial potency, which makes the system a good value proposition.