This work was supported by the Australian National Health and Medical Research Council (NHMRC; Program Grant No. GNT1149990), the Australian Centre for HIV and Hepatitis Virology Research (ACH2), as well a gift from the estate of R&#233;jane Louise Langlois. F.C. acknowledges the award of a National Health and Medical Research Council (NHMRC) Senior Principal Research Fellowship (GNT1135806). Figure 1 and Figure 2 were created with BioRender.com.

1. Choosing the appropriate stream
<ol>
	<li>Follow the decision tree outlined in Figure 1 to determine the best workflow (stream) (Figure 2) for the experimental setup used. Refer to the discussion for further comments on this choice of stream.</li>
	<li>If following the Cytometer Stream, continue with steps 2.1.1-2.2.7. If following the Bulk Stream, continue with steps 3.1.1.1-3.1.5.7.</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/64259/64259fig01.jpg" /> 
Figure 1: Workstream decision tree. The decision as to which Stream to use depends primarily on the carrier type of interest. Larger carriers and carriers with high scattering properties can more easily be detected individually on cytometers, thus making them suitable for quantification using the Cytometer Stream. The Bulk Stream is suitable for all other carrier types. <a href="https://www.jove.com/files/ftp_upload/64259/64259fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/64259/64259fig02.jpg" /> 
Figure 2: Overview of workstreams. This protocol is split into two different Streams. The Cytometer Stream uses a sensitive cytometer to count the carriers in suspension, measure their individual fluorescence, and then determine the fluorescence of cells incubated with carriers. The Bulk Stream uses non-cytometry-based techniques, such as Nanoparticle Tracking Analysis, to count the carriers in suspension. The individual carrier fluorescence is then quantified using a microplate reader or spectrofluorometer. The use of the flow cytometer is, therefore, restricted to measuring the final fluorescence of cells incubated with carriers, a measurement that can be done on a wider range of cytometers and that is independent of the carrier type used. Abbreviations: MESF = Molecules of Equivalent Soluble Fluorochrome; MFI = median fluorescence intensity. <a href="https://www.jove.com/files/ftp_upload/64259/64259fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. The Cytometer Stream
<ol>
	<li>Carrier counting 
	NOTE: Any flow cytometer can be used for this measurement, provided the flow rate (&#181;L/s) is known. If the flow rate is unknown and cannot be determined, do not proceed with this step. Instead, proceed with step 3.1. Counting the carriers in suspension allows accurate and reproducible determination of the number of carriers incubated in each cell experiment.
	<ol>
		<li>Set up the cytometer to detect carriers, both by an optical scattering channel (typically side scatter [SSC]) and fluorescence. Make sure to adjust the threshold to allow detection of the carriers. 
		NOTE: Iteration through different optical scattering channels may be required if SSC does not provide a clear signal (e.g., forward scatter [FSC]).</li>
		<li>Run a diluent-only sample to quantify the background event count in both the SSC and fluorescent channels. 
		NOTE: Ideal background event counts are &#60;100 events/s.</li>
		<li>Prepare the carriers for flow cytometry.
		<ol>
			<li>Ensure the carriers are well suspended by vortexing or sonication, depending on the carrier system involved.</li>
			<li>If possible, ensure the carrier concentration is between 1,000 carriers/&#181;L and 10,000 carriers/&#181;L. An event count of one to two orders of magnitude higher than the background is a good start. If the order of magnitude of the carrier concentration is unknown, a good start is to prepare a 1:1,000 dilution from stock. Use the initial results as feedback to inform future sample dilutions. 
			NOTE: A cloudy suspension is generally too concentrated.</li>
		</ol>
		</li>
		<li>Load the first carrier sample onto the cytometer and start recording.</li>
		<li>Compare event counts resulting from both the SSC and fluorescence channels; these should be approximately equal (&#60;10% difference). If not, check the cytometer settings, e.g., photomultiplier tube (PMT) settings and laser intensity for the fluorescent channel. Alternatively, use other methods such as confocal microscopy to validate that the fluorescent labeling of the carriers is present and uniform.</li>
		<li>Repeat steps 2.1.3-2.1.5 two or more additional times with different dilutions from the stock. Ensure that the event count in each sample is at least one order of magnitude higher than the background event count.</li>
		<li>Verify that three or more samples show a linear trend, that is, a two-fold sample dilution should result in a corresponding two-fold reduction in the measured carrier concentration.</li>
		<li>Use the samples within the linear range, corresponding dilution factors, and the known cytometer flow rate to calculate the stock carrier concentration according to equation (1): 
		<img alt="Equation 1" src="/files/ftp_upload/64259/64259eq01.jpg" style="margin: auto;" />&#160; &#160; &#160;(1) 
		where C is the stock carrier concentration in carriers/mL. It is recommended to use the event count derived from optical scattering detection rather than fluorescence.</li>
	</ol>
	</li>
	<li>Flow cytometry readout of the carrier-cell experiment, including determination of fluorescence intensity per carrier 
	NOTE: Ideally, the fluorescent intensity per carrier will be determined as close as possible to the carrier-cell experiment. This is to ensure that the MFIs obtained for individual carriers can directly be compared to the MFIs of cells associated with the carriers. In practice, a cytometer will usually generate similar results when used on consecutive days using the same PMT voltages, but this cannot be guaranteed.
	<ol>
		<li>Design the carrier-cell experiment. Use the carrier concentration determined in step 2.1 to administer the desired dose of carriers.</li>
		<li>Set up the flow cytometer for the final carrier-cell experiment by determining optimal PMT voltage settings in the relevant channels. Set the thresholds to allow carrier detection.</li>
		<li>Run the carriers in suspension to determine the fluorescence intensity per carrier under the current PMT settings.</li>
		<li>If needed, change the cytometer thresholds to detect the cells and not the carriers.</li>
		<li>Run a negative control sample-cells not incubated with carriers-to determine the background fluorescence (autofluorescence) of the cells.</li>
		<li>Run the carrier-cell samples to determine the fluorescence intensity per cell. This fluorescence is a linear combination of cellular autofluorescence and the presence of fluorescent carriers.</li>
		<li>Calculate the number of carriers per cell using the following equation (2): 
		<img alt="Equation 2" src="/files/ftp_upload/64259/64259eq02.jpg" style="margin: auto;" />&#160; &#160; &#160;(2) 
		where Passoc is the number of carriers associated per cell, FIcell is the MFI of cells incubated with carriers, FIbackground is the MFI of cells not incubated with carriers, and FIcarrier is the MFI of carriers in suspension.</li>
	</ol>
	</li>
</ol>
3. The Bulk Stream
<ol>
	<li>Carrier counting: nanoparticle tracking analysis 
	NOTE: In the Bulk Stream, carrier counting is a necessary step to quantify the absolute fluorescence intensity per carrier (see step 3.1.4). In addition, counting the carriers in suspension allows the accurate and reproducible determination of the number of carriers incubated in each cell experiment.
	<ol>
		<li>Preparation
		<ol>
			<li>Mount the flow cell onto the laser module and lock the entire laser module in place inside the instrument.</li>
			<li>Slowly (not faster than 0.1 mL/s) flush the flow cell with ~1 mL of distilled water. If bubbles form within the flow cell, retract the suspension partially to merge the bubble with the air-liquid interface before proceeding.</li>
			<li>Start the camera roughly halfway through flushing; be sure to confirm that carrier debris is washed out. Select Capture to open the Capture Settings tab and click Start Camera.</li>
			<li>Dry the system with 1 mL of air. If any static carriers are visible on the screen, clean the flow cell according to the manufacturer&#39;s instructions.</li>
			<li>Prepare the carriers for Nanoparticle Tracking Analysis by ensuring the carriers are well suspended via vortexing or sonication, depending on the carrier system involved. If the order of magnitude of the stock concentration is unknown, prepare a 1:100 dilution from the stock and use the initial results as feedback to inform future sample dilutions. Dilute the carriers in water and not phosphate-buffered saline (PBS) to prepare at least ~0.6-1 mL of each sample with a carrier concentration between 1 &#215; 107 carriers/mL and 1 &#215; 109 carriers/mL. 
			NOTE: A cloudy suspension is generally too concentrated. Buffers and salts can generate high background noise.</li>
		</ol>
		</li>
		<li>Measurement
		<ol>
			<li>Take out the laser module and place it upright.</li>
			<li>Draw up the first carrier sample into a 1 mL syringe. Attach the syringe to the tube inlet and carefully load the sample into the flow cell. If bubbles form within the flow cell, retract the suspension partially to merge the bubble with the air-liquid interface before proceeding. Ensure the entire flow cell is filled with liquid, then pause.</li>
			<li>Adjust the camera focus if needed to visualize individual carriers. Make coarse focus adjustments with the rotating knob on the right-hand side of the instrument. Make finer adjustments by selecting the Hardware tab | Pumps/Stage. Change the focus by adjusting the Focus slider.</li>
			<li>Adjust the camera level to make sure there is no oversaturation. Within the Capture tab, select the optimal Camera Level by adjusting the slider.</li>
			<li>If the instrument is equipped with this accessory, load the syringe containing the carrier sample into the Syringe Pump to ensure continuous sample flow during measurements.</li>
			<li>Under the SOP tab, select Standard Measurement to take five captures of 30 s each. Enter Base filename and, if desired, add additional sample information by clicking the Advanced button (which opens a modal dialogue with a variety of choices). 
			NOTE: If a dilution factor is entered, the final carrier concentration measurement will be adjusted automatically by the software. Entering this factor is advised against. Instead, perform the adjustment manually, which facilitates the analysis and allows the assessment of whether each dilution falls within the dynamic range of the instrument (step 3.1.3.4).</li>
			<li>Press Create and Run Script and wait for a pop-up to appear asking to Please advance sample.</li>
			<li>If using the Syringe Pump, select the Hardware tab | Syringe Pump tab | set the Infusion Rate at 30-80 and press Infuse. If not using the Syringe Pump, manually advance the sample.</li>
			<li>In the pop-up window, select OK to start capturing. After each of the five captures, when the Please advance sample pop-up reappears, check that the sample is still moving through the flow cell, either manually or via the Syringe Pump. Then, select OK to proceed with the next capture. 
			NOTE: After five captures, the software automatically opens the Process tab and opens a pop-up asking to adjust the process settings.</li>
		</ol>
		</li>
		<li>Analysis
		<ol>
			<li>In the Process tab, adjust the Detection Threshold slider (between 4 and 8) to correctly identify distinct carriers visible on the screen. Adjust the Screen Gain too to aid visualization; it will not affect the downstream analysis. Use the slider under the capture screen to scroll through multiple frames of the video to aid detection threshold setting. 
			NOTE: The detection threshold should be set once and, subsequently, should not be altered between measurements or samples.</li>
			<li>In the pop-up (note after step 3.1.2.9), press OK to initiate tracking analysis. Monitor the progress of the analysis by clicking on the Analysis tab | Single Analysis tab.</li>
			<li>Once the analysis is finished, look for an Export Settings prompt to appear, in which Include PDF and Include Experiment Summary should be selected by default. Select any other export formats as desired.</li>
			<li>In the Results section of the PDF data export, to ensure the concentration measured is reliable, verify that the measured carrier concentration is between 1 &#215; 107 carriers/mL and 1 &#215; 109 carriers/mL-the dynamic range of the instrument-and check for any error messages or messages of caution underneath the concentration measurement result.</li>
			<li>Repeat steps 3.1.2.1-3.1.3.4 two or more times with different dilutions from the stock. Ensure that the concentration of each sample falls within the linear range of the instrument.</li>
			<li>Select three or more samples that show a linear trend, that is, a two-fold sample dilution should result in a corresponding two-fold reduction in the measured carrier concentration. Use the selected samples and corresponding dilution factors to calculate the stock carrier concentration.</li>
		</ol>
		</li>
		<li>Determination of the absolute fluorescence intensity per carrier 
		NOTE: Since the fluorescence of individual carriers in this stream cannot be characterized directly, the fluorescence intensity is quantified in bulk. This method relies on the fact that fluorescence intensity is linearly related to the fluorochrome concentration according to the Lambert-Beer law. When such bulk quantification of carriers in suspension is done on a suspension of known carrier concentration (see step 3.1), the fluorescence per carrier can be derived. This step can be done on either a fluorescence plate reader or a spectrofluorometer. The fluorescence intensity is compared to a standard curve of samples with known absolute fluorescence, given in number of MESFs.
		<ol>
			<li>Use a solution of the free fluorochrome to label the carrier: resuspend the dye in the appropriate buffer (e.g., DMSO) and perform further dilutions in the same buffer as the carrier diluent. Alternatively, use a solution of an antibody conjugated to the fluorochrome. Calculate the concentration of the stock solution (MESF/mL) from the concentration in mg/mL, the molecular weight in mg/mole, and Avogadro&#39;s number using equation (3). Perform a serial dilution in the carrier diluent to generate standard curve samples. 
			<img alt="Equation 3" src="/files/ftp_upload/64259/64259eq03.jpg" style="margin: auto;" />&#160; &#160; &#160;(3) 
			NOTE: Use a fluorochrome-conjugated antibody only if the degree of labeling, i.e., the molar ratio between the fluorochrome and antibody in the solution, is known. Initially, generate a standard curve with a wide range, as the fluorescence intensity of the carrier sample is still unknown. From here, narrow it down to include the range required.</li>
			<li>Prepare the carrier samples. 
			NOTE: The best practice is to test two or more carrier dilutions to validate that the measurements are linear and fall within the range of the standard curve.</li>
			<li>Measure the fluorescence of equal volumes of each sample, i.e., both carrier and standard curves.</li>
			<li>Generate a standard curve and deduct the bulk absolute fluorescence intensity in MESF/mL for the carrier samples measured.</li>
			<li>Calculate the absolute fluorescence intensity per carrier (MESF/carrier) by dividing the bulk fluorescence (MESF/mL) by the carrier concentration (carriers/mL) as in equation (4): 
			<img alt="Equation 4" src="/files/ftp_upload/64259/64259eq04.jpg" style="margin: auto;" />&#160; &#160; &#160;(4)</li>
		</ol>
		</li>
		<li>Cell experiment (including the determination of the equivalent fluorescence intensity per carrier) 
		NOTE: In this step, flow cytometry quantitation beads are used to generate a standard curve of the relationship between MESF and MFI. These quantitation beads consist of multiple bead populations with a known number of MESF per bead, and these individual beads can be detected by any cytometer. Ideally, the MESF standard curve is determined at the same time as the readout of the carrier-cell experiments. This is to ensure that the MFI values calculated for individual carriers can directly be compared to the MFI of cells associated with carriers. In practice, a cytometer will usually generate similar results when used on consecutive days using the same PMT voltages, but this cannot be guaranteed.
		<ol>
			<li>Design the carrier-cell experiment. Use the carrier concentration determined in section 3.1.3 to administer the desired dose of carriers.</li>
			<li>Set up the flow cytometer for the final carrier-cell experiment by determining optimal PMT voltage settings in the relevant channels.</li>
			<li>Run a negative control sample, that is, cells not incubated with carriers, to determine the background fluorescence.</li>
			<li>Prepare and resuspend the flow cytometry quantitation beads. Use the same buffer as used for the cell samples (e.g., PBS). If the bead populations are provided separately, pool them together.</li>
			<li>Run the flow cytometry quantitation bead sample.</li>
			<li>Run the carrier-cell samples to determine the fluorescence intensity per cell.</li>
			<li>Use the quantitation bead sample to generate a standard curve converting the absolute fluorescence intensity (MESF) into MFI. Use this standard curve and the results from step 3.1.4 to calculate the theoretical MFI of the carriers. Calculate the number of carriers per cell using equation (5): 
			<img alt="Equation 5" src="/files/ftp_upload/64259/64259eq05.jpg" style="margin: auto;" />&#160; &#160; &#160;(5) 
			Where Passoc is the number of carriers associated per cell, FIcell is the MFI of cells incubated with carriers, FIbackground is the MFI of cells not incubated with carriers, and FIcarrier&#160;is the calculated MFI of carriers in suspension (step 3.1.4).</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>

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The authors have no conflicts of interest to disclose.

Characterizing the interactions between drug carriers and cells is becoming increasingly important in the development of novel drug delivery systems. Specifically, to allow the rational evaluation and comparison of various carrier constructs, absolute quantification of the performance of said carrier to interact with target and off-target cells is critical. This protocol describes a two-stream methodology that allows any researcher working with a drug carrier to convert relative, semiquantitative flow cytometry data on c...

The design of drug delivery constructs that exhibit specific, designed behavior depending on what cell type they encounter has attracted substantial research interest. Potential drug delivery constructs or &#34;carriers&#34; include lipid formulations, nano-grown inorganics, polymeric assemblies, extracellular vesicles, functionalized bacterial cells, or modified viruses. All of these can exhibit organ, tissue, or cell specificity due to physical properties, surface properties, or engineered chemical functionalizations such as antibody attachment1,2.
A nearly ubiquitous step in in vitro carrier evaluation is to incubate cells with a suspension containing said drug-loaded carrier. Post incubation, carrier performance is measured via a functional readout of the drug cargo&#39;s performance, for example, transfection efficiency or toxicity. Functional readouts are useful, as they are a downstream measure of carrier effectiveness. However, for more complex drug delivery constructs, it is increasingly important to move beyond functional readouts and separately quantify the degree of carrier interaction with the cell of interest. There are a few reasons for this.
First, there is increasing interest in discovering (and iteratively improving) &#34;platform&#34; carrier technologies, which can carry a variety of cargo. For example, lipid nanoparticles (LNPs) designed to encapsulate RNA can exchange one RNA sequence for another with few caveats3. Thus, to iteratively improve the carrier technology, it is critical to quantify its performance independent of the cargo functionality. Second, functional readouts may not be straightforward for the cargo of interest, compromising the ability to rapidly iterate and evaluate carrier formulations. While one could perform in vitro optimization using a model cargo with a straightforward functional readout (for instance, fluorescence), changing the cargo can change the biological response to a carrier4 and may, thus, not yield representative results. Third, many carriers are designed to interact with and be taken up by a specific cell type. Such targeting capability of a carrier can and should be differentiated from the performance of its therapeutic cargo&#160;post targeting. To continue the LNP example, an RNA cargo might be extremely potent, but if the LNP is unable to bind to the cell, be internalized, and release the RNA, no downstream functional effect will be observed. This can be an issue particularly for carriers intended to target hard-to-transfect cell types, such as T cells5. Conversely, an LNP could target extremely effectively, but the RNA cargo might not function. A downstream assay that just measures cargo functionality will be unable to differentiate between these two situations, thus complicating the development and optimization of carrier drug delivery systems.
In this work, how to absolutely quantify carrier association is discussed. Association is a term that refers to the experimentally measured degree of interaction between a carrier and a cell. Association does not differentiate between membrane binding and internalization-a carrier may be associated because it is bound to the cell surface or because the cell has internalized it. Association is commonly measured as part of cell-carrier incubation experiments. Historically, association has been reported either in arbitrary fluorescent units (typically &#34;median fluorescence intensity&#34; or MFI) or as &#34;percent association,&#34; metrics whose limitations have been previously discussed6. In short, these measurements are not comparable between experiments, laboratories, and drug carriers due to differences in experimental protocols, flow cytometer settings, and the labeling intensities of different carriers. Efforts have been made to overcome the former by calibrating the cytometer, thereby converting the relative measure of MFI into an absolutely quantitative measure of fluorescence7. However, this method does not account for the variability in the labeling intensity of various carriers and, thus, does not allow the rational comparison of various carrier performances in a target cell of choice8.
Here, how to practically convert from relative, arbitrary fluorescent units to the absolute quantitative metric of the &#34;number of carriers per cell&#34; is demonstrated by performing a small number of additional characterization experiments. If another metric of carrier concentration is desired (e.g., carrier mass per cell or carrier volume per cell), it is straightforward to convert from carriers per cell, provided carrier characterization has been done. For brevity and to avoid jargon, the word &#34;carrier&#34; is used within this work to refer to the vast assortment of drug delivery constructs. These quantification techniques are equally applicable, whether applied to a nano-engineered gold particle or a bio-engineered bacteria.
A few facts enable the conversion from arbitrary fluorescent units to carriers per cell. First, the measured fluorescence intensity is proportional to the concentration of a fluorophore9 (or a fluorescently labeled carrier), assuming the fluorescence is within the detection limits of the instrument and the instrumentation settings are the same. Thus, if the fluorescence of a carrier and the fluorescence of a sample are known, one can determine how many carriers are present in that sample if all the measurements were performed under the same settings and conditions. However, especially for smaller carriers, it may not be possible to measure carrier fluorescence, cell autofluorescence, and cell-associated-with-carriers fluorescence on the same instrument with the same settings. In this case, there is a second requirement to make it possible to convert between measured fluorescence on one instrument and measured fluorescence on another. To do so, a standard curve of fluorophore concentration can be established to measure the fluorescence intensity on both instruments, taking advantage of the Molecules of Equivalent Soluble Fluorochrome (MESF) standard9. This then allows measurement of the carrier fluorescence in bulk on a non-cytometer, a measurement that can be done on carriers of any size or characteristic. When such bulk quantification is done on a carrier suspension of known concentration, the number of carriers per cell of a sample can, once again, be calculated.
While this work demonstrates the process for measuring carrier association (as determined by measured fluorescence intensity), an analogous protocol could be performed for other measures of cell-carrier interaction (e.g., an experimental protocol that differentiates internalized and membrane-bound carriers). Additionally, this protocol would be largely the same if association was measured through a non-fluorescent assay (for instance, through mass cytometry).

<table><tbody><tr><td>Alexa Fluor 647 C2 Maleimide</td><td>Invitrogen</td><td>A20347</td><td>pH-stable dye used to label 150 nm, 235 nm, or 633 nm PMA&lt;sub&gt;SH&lt;/sub&gt; carriers; example of good dye to use in cell-carrier association studies</td></tr><tr><td>Apogee A50 Microflow</td><td>Apogee</td><td></td><td>Sensitive flow cytometer capable of detecting small carriers for counting</td></tr><tr><td>CytoFLEX S Flow Cytometer</td><td>Beckman Coulter</td><td></td><td>Sensitive flow cytometer capable of detecting small carriers for counting and read out for final cell-barrier experiments</td></tr><tr><td>FCS Express</td><td>De Novo Software</td><td></td><td>Software used to analyze flow cytometry data, i.e., perform gating and derive median fluorescence intensity values of populations of choice. Alternatives include FlowJo, OMIQ, Python</td></tr><tr><td>Infinite 200 PRO</td><td>Tecan Lifesciences</td><td></td><td>Standard microplate reader instrument used for bulk fluorescence measurements of carriers in solution</td></tr><tr><td>LSRFortessa Cell Analyzer</td><td>BD Biosciences</td><td></td><td>Less sensitive flow cytometer, but one more generally available to researchers. Can be used to read out final cell-carrier experiment</td></tr><tr><td>NanoSight NS300</td><td>Malvern Panalytical</td><td></td><td>Instrument used for Nanoparticle Tracking Analysis</td></tr><tr><td>Prism 8</td><td>GraphPad</td><td></td><td>Software used to graph and calculate standard curves. Alternatives include Microsoft Excel, Origin, Minitab, Python amongst many others</td></tr><tr><td>Quantum MESF kits Alexa Fluor 647</td><td>Bangs Laboratories</td><td>647</td><td>Absolute quantitation beads for flow cytometery. Used to convert fluorescence intensities measured in bulk on a microplate reader to fluorescence intensities measured on a flow cytometer using the MESF standard</td></tr></tbody></table>

experimental quantification of interactions between drug delivery systems and cells in vitro: a guide for preclinical nanomedicine evaluation

A major component of designing drug delivery systems concerns how to amplify or attenuate interactions with specific cell types. For instance, a chemotherapeutic might be functionalized with an antibody to enhance binding to cancer cells (&#34;targeting&#34;) or functionalized with polyethylene glycol to help evade immune cell recognition (&#34;stealth&#34;). Even at a cellular level, optimizing the binding and uptake of a drug carrier is a complex biological design problem. Thus, it is valuable to separate how strongly a new carrier interacts with a cell from the functional efficacy of a carrier&#39;s cargo once delivered to that cell.
To continue the chemotherapeutic example, &#34;how well it binds to a cancer cell&#34; is a separate problem from &#34;how well it kills a cancer cell&#34;. Quantitative in vitro assays for the latter are well established and usually rely on measuring viability. However, most published research on cell-carrier interactions is qualitative or semiquantitative. Generally, these measurements rely on fluorescent labeling of the carrier and, consequently, report interactions with cells in relative or arbitrary units. However, this work can be standardized and be made absolutely quantitative with a small number of characterization experiments. Such absolute quantification is valuable, as it facilitates rational, inter- and intra-class comparisons of various drug delivery systems-nanoparticles, microparticles, viruses, antibody-drug conjugates, engineered therapeutic cells, or extracellular vesicles.
Furthermore, quantification is a prerequisite for subsequent meta-analyses or in silico modeling approaches. In this article, video guides, as well as a decision tree for how to achieve in vitro quantification for carrier drug delivery systems, are presented, which take into account differences in carrier size and labeling modality. Additionally, further considerations for the quantitative assessment of advanced drug delivery systems are discussed. This is intended to serve as a valuable resource to improve rational evaluation and design for the next generation of medicine.

As discussed previously, different drug carrier types require the use of different techniques for the absolute quantification of cell-carrier association. For example, 633 nm disulfide-stabilized poly(methacrylic acid) (PMASH) core-shell particles are large and dense enough for detection using a sensitive flow cytometer. As such, these particles were labeled fluorescently, then gated and counted using side-angle light scattering (SALS, analogous to SSC), as well as the appropriate fluorescent channel (<strong ...

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Experimental Quantification of Interactions Between Drug Delivery Systems and Cells In Vitro: A Guide for Preclinical Nanomedicine Evaluation

A workflow is demonstrated for the absolute quantification of drug carrier-cell interactions using flow cytometry to allow better rational evaluation of novel drug delivery systems. This workflow is applicable to drug carriers of any type.

Experimental Quantification of Interactions Between Drug Delivery Systems and Cells In Vitro: A Guide for Preclinical Nanomedicine Evaluation

A major component of designing drug delivery systems concerns how to amplify or attenuate interactions with specific cell types. For instance, a ...

experimental-quantification-interactions-between-drug-delivery

Research

JoVE Journal

Bioengineering

1.8K Views.  The University of Melbourne at the Peter Doherty Institute for Infection and Immunity. Dit protocol biedt kwantitatieve gegevens over hoe dragersystemen interageren met cellen. Kwantitatieve gegevens zijn de sleutel tot engineering en optimalisatie. Het stelt ons in staat om van ja of nee biologische vragen, naar hoeveel vragen te gaan.Deze techniek is nuttig om resultaten van preklinische prestaties van geneesmiddelendragers, die kwalitatief waren, om te zetten in kwantitatieve resultaten. En dit werkt zelfs wanneer u uw drager op een ander systeem karakteriseert dan wa...

Video: Experimental Quantification of Interactions Between Drug Delivery Systems and Cells In Vitro: A Guide for Preclinical Nanomedicine Evaluation

Microfluidic Device for Recreating a Tumor Microenvironment in Vitro

We have developed a microfluidic device that mimics the delivery and systemic clearance of drugs to heterogeneous three-dimensional tumor tissues in vitro. Nutrients delivered by vasculature fail to reach all parts of tumors, giving rise to heterogeneous microenvironments consisting of viable, quiescent and necrotic cell types. Many cancer drugs fail to effectively penetrate and treat all types of cells because of this heterogeneity. Monolayers of cancer cells do not mimic this heterogeneity, making it difficult to test cancer drugs with a suitable in vitro model. Our microfluidic devices were fabricated out of PDMS using soft lithography. Multicellular tumor spheroids, formed by the hanging drop method, were inserted and constrained into rectangular chambers on the device and maintained with continuous medium perfusion on one side. The rectangular shape of chambers on the device created linear gradients within tissue. Fluorescent stains were used to quantify the variability in apoptosis within tissue. Tumors on the device were treated with the fluorescent chemotherapeutic drug doxorubicin, time-lapse microscopy was used to monitor its diffusion into tissue, and the effective diffusion coefficient was estimated. The hanging drop method allowed quick formation of uniform spheroids from several cancer cell lines. The device enabled growth of spheroids for up to 3 days. Cells in proximity of flowing medium were minimally apoptotic and those far from the channel were more apoptotic, thereby accurately mimicking regions in tumors adjacent to blood vessels. The estimated value of the doxorubicin diffusion coefficient agreed with a previously reported value in human breast cancer. Because the penetration and retention of drugs in solid tumors affects their efficacy, we believe that this device is an important tool in understanding the behavior of drugs, and developing new cancer therapeutics.

Microfluidic Device for Recreating a Tumor Microenvironment in Vitro

We present the procedure for fabrication and operation of a microfluidic device that recreates heterogeneous tumor microenvironments in vitro. The variability in apoptosis within tumor tissue was quantified using fluorescent stains and the effective diffusion coefficient of the chemotherapeutic drug doxorubicin into tumor tissue was evaluated.

We have developed a microfluidic device that mimics the delivery and systemic clearance of drugs to heterogeneous three-dimensional tumor tissues in ...

Improved Visualization and Quantitative Analysis of Drug Effects Using Micropatterned Cells

To date, most HCA (High Content Analysis) studies are carried out with adherent cell lines grown on a homogenous substrate in tissue-culture treated micro-plates. Under these conditions, cells spread and divide in all directions resulting in an inherent variability in cell shape, morphology and behavior. The high cell-to-cell variance of the overall population impedes the success of HCA, especially for drug development. The ability of micropatterns to normalize the shape and internal polarity of every individual cell provides a tremendous opportunity for solving this critical bottleneck 1-2.
To facilitate access and use of the micropatterning technology, CYTOO has developed a range of ready to use micropatterns, available in coverslip and microwell formats. In this video article, we provide detailed protocols of all the procedures from cell seeding on CYTOOchip micropatterns, drug treatment, fixation and staining to automated acquisition, automated image processing and final data analysis. With this example, we illustrate how micropatterns can facilitate cell-based assays. Alterations of the cell cytoskeleton are difficult to quantify in cells cultured on homogenous substrates, but culturing cells on micropatterns results in a reproducible organization of the actin meshwork due to systematic positioning of the cell adhesion contacts in every cell. Such normalization of the intracellular architecture allows quantification of even small effects on the actin cytoskeleton as demonstrated in these set of protocols using blebbistatin, an inhibitor of the actin-myosin interaction.

Adhesive micropatterns that normalize cellular architecture can be used to increase sensitivity in the detection of drug effects, improve reproducibility and simplify automated image acquisition and analysis. Such technology will benefit drug/siRNA screening assays, performed on conventional cell culture supports and consequently suffering from excessive cell-to-cell variability.

To date, most HCA (High Content Analysis) studies are carried out with adherent cell lines grown on a homogenous substrate in tissue-culture treated ...

A Comprehensive Procedure to Evaluate the In Vivo Performance of Cancer Nanomedicines

Inspired by the success of previous cancer nanomedicines in the clinic, researchers have generated a large number of novel formulations in the past decade. However, only a small number of nanomedicines have been approved for clinical use, whereas the majority of nanomedicines under clinical development have produced disappointing results. One major obstacle to the successful clinical translation of new cancer nanomedicines is the lack of an accurate understanding of their in vivo performance. This article features a rigorous procedure to characterize the in vivo behavior of nanomedicines in tumor-bearing mice at systemic, tissue, single-cell, and subcellular levels via the integration of positron emission tomography-computed tomography (PET-CT), radioactivity quantification methods, flow cytometry, and fluorescence microscopy. Using this approach, researchers can accurately evaluate novel nanoscale formulations in relevant mouse models of cancer. These protocols may have the ability to identify the most promising cancer nanomedicines with high translational potential or to aid in the optimization of cancer nanomedicines for future translation.

A Comprehensive Procedure to Evaluate the In Vivo Performance of Cancer Nanomedicines

The poor understanding of the in vivo performance of nanomedicines stymies their clinical translation. Procedures to evaluate the in vivo behavior of cancer nanomedicines at systemic, tissue, single-cell, and subcellular levels in tumor-bearing immunocompetent mice are described here. This approach may help researchers to identify promising cancer nanomedicines for clinical translation.

Inspired by the success of previous cancer nanomedicines in the clinic, researchers have generated a large number of novel formulations in the past ...

Cancer Research

Sample Extraction and Simultaneous Chromatographic Quantitation of Doxorubicin and Mitomycin C Following Drug Combination Delivery in Nanoparticles to Tumor-bearing Mice

Combination chemotherapy is frequently used in the clinic for cancer treatment; however, associated adverse effects to normal tissue may limit its therapeutic benefit. Nanoparticle-based drug combination has been shown to mitigate the problems encountered by free drug combination therapy. Our previous studies have shown that the combination of two anticancer drugs, doxorubicin (DOX) and mitomycin C (MMC), produced a synergistic effect against both murine and human breast cancer cells in vitro. DOX and MMC co-loaded polymer-lipid hybrid nanoparticles (DMPLN) bypassed various efflux transporter pumps that confer multidrug resistance and demonstrated enhanced efficacy in breast tumor models. Compared to conventional solution forms, such superior efficacy of DMPLN was attributed to the synchronized pharmacokinetics of DOX and MMC and increased intracellular drug bioavailability within tumor cells enabled by the nanocarrier PLN. 
To evaluate the pharmacokinetics and bio-distribution of co-administered DOX and MMC in both free solution and nanoparticle forms, a simple and efficient multi-drug analysis method using reverse-phase high performance liquid chromatography (HPLC) was developed. In contrast to previously reported methods that analyzed DOX or MMC individually in the plasma, this new HPLC method is able to simultaneously quantitate DOX, MMC and a major cardio-toxic DOX metabolite, doxorubicinol (DOXol), in various biological matrices (e.g., whole blood, breast tumor, and heart). A dual fluorescent and ultraviolet absorbent probe 4-methylumbelliferone (4-MU) was used as an internal standard (I.S.) for one-step detection of multiple drug analysis with different detection wavelengths. This method was successfully applied to determine the concentrations of DOX and MMC delivered by both nanoparticle and solution approaches in whole blood and various tissues in an orthotopic breast tumor murine model. The analytical method presented is a useful tool for pre-clinical analysis of nanoparticle-based delivery of drug combinations.

This protocol describes an efficient and convenient analytical process of sample extraction and simultaneous determination of multiple drugs, doxorubicin (DOX), mitomycin C (MMC) and a cardio-toxic DOX metabolite, doxorubicinol (DOXol), in the biological samples from a preclinical breast tumor model treated with nanoparticle formulations of synergistic drug combination.

Combination chemotherapy is frequently used in the clinic for cancer treatment; however, associated adverse effects to normal tissue may limit its ...

Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation

Potentiation of hostile monoclonal antibodies (mAb) by chemotherapeutic agents constitutes a valuable strategy for designing effective and safer therapy against cancer. Here we provide a protocol to identify a rational combination at the preclinical step. First, we describe a cell-based assay to assess the synergism between anticancer mAb and cytotoxic drugs, that uses the combination index equation of Chou and Talalay1. This includes the measurement of tumor cell drug- and antibody-sensitivity using an MTT assay, followed by an automated computer analysis to calculate the combination index (CI) values. CI values of &lt;1 indicate synergism between tested mAbs and cytotoxic agents1. To corroborate the in vitro findings in vivo, we further describe a method to assess the combination regimen efficacy in a xenograft tumor model. In this model, the combined regimen significantly delays tumor growth, which results in a significant extended survival in comparison to single-agent controls. Importantly, the in vivo experimentation reveals that the combination regimen is well tolerated. This protocol allows the effective evaluation of anticancer drug combinations in preclinical models and the identification of rational combination to evaluate in clinical trials.

This protocol describes how to assess synergism between an anticancer antibody and antineoplastic drugs in preclinical models by using the combination index equation of Chou and Talalay.

Potentiation of hostile monoclonal antibodies (mAb) by chemotherapeutic agents constitutes a valuable strategy for designing effective and safer therapy ...

Models and Methods to Evaluate Transport of Drug Delivery Systems Across Cellular Barriers

Sub-micrometer carriers (nanocarriers; NCs) enhance efficacy of drugs by improving solubility, stability, circulation time, targeting, and release. Additionally, traversing cellular barriers in the body is crucial for both oral delivery of therapeutic NCs into the circulation and transport from the blood into tissues, where intervention is needed. NC transport across cellular barriers is achieved by: (i) the paracellular route, via transient disruption of the junctions that interlock adjacent cells, or (ii) the transcellular route, where materials are internalized by endocytosis, transported across the cell body, and secreted at the opposite cell surface (transyctosis). Delivery across cellular barriers can be facilitated by coupling therapeutics or their carriers with targeting agents that bind specifically to cell-surface markers involved in transport. Here, we provide methods to measure the extent and mechanism of NC transport across a model cell barrier, which consists of a monolayer of gastrointestinal (GI) epithelial cells grown on a porous membrane located in a transwell insert. Formation of a permeability barrier is confirmed by measuring transepithelial electrical resistance (TEER), transepithelial transport of a control substance, and immunostaining of tight junctions. As an example, ~200&#160;nm polymer NCs are used, which carry a therapeutic cargo and are coated with an antibody that targets a cell-surface determinant. The antibody or therapeutic cargo is labeled with 125I for radioisotope tracing and labeled NCs are added to the upper chamber over the cell monolayer for varying periods of time. NCs associated to the cells and/or transported to the underlying chamber can be detected. Measurement of free 125I allows subtraction of the degraded fraction. The paracellular route is assessed by determining potential changes caused by NC transport to the barrier parameters described above. Transcellular transport is determined by addressing the effect of modulating endocytosis and transcytosis pathways.

Many therapeutic applications require safe and efficient transport of drug carriers and their cargoes across cellular barriers in the body. This article describes an adaptation of established methods to evaluate the rate and mechanism of transport of drug nanocarriers (NCs) across cellular barriers, such as the gastrointestinal (GI) epithelium.

Sub-micrometer carriers (nanocarriers; NCs) enhance efficacy of drugs by improving solubility, stability, circulation time, targeting, and release. ...

Manufacture and Drug Delivery Applications of Silk Nanoparticles

Silk is a promising biopolymer for biomedical and pharmaceutical applications due to its outstanding mechanical properties, biocompatibility and biodegradability, as well its ability to protect and subsequently release its payload in response to a trigger. While silk can be formulated into various material formats, silk nanoparticles are emerging as promising drug delivery systems. Therefore, this article covers the procedures for reverse engineering silk cocoons to yield a regenerated silk solution that can be used to generate stable silk nanoparticles. These nanoparticles are subsequently characterized, drug loaded and explored as a potential anticancer drug delivery system. Briefly, silk cocoons are reverse engineered first by degumming the cocoons, followed by silk dissolution and clean up, to yield an aqueous silk solution. Next, the regenerated silk solution is subjected to nanoprecipitation to yield silk nanoparticles &#8211; a simple but powerful method that generates uniform nanoparticles. The silk nanoparticles are characterized according to their size, zeta potential, morphology and stability in aqueous media, as well as their ability to entrap a chemotherapeutic payload and kill human breast cancer cells. Overall, the described methodology yields uniform silk nanoparticles that can be readily explored for a myriad of applications, including their use as a potential nanomedicine.

Nanoparticles are emerging as promising drug delivery systems for a broad range of indications. Here, we describe a simple yet powerful method to manufacture silk nanoparticles using reverse engineered Bombyx mori silk. These silk nanoparticles can be readily loaded with a therapeutic payload and subsequently explored for drug delivery applications.

Silk is a promising biopolymer for biomedical and pharmaceutical applications due to its outstanding mechanical properties, biocompatibility and ...

Phase Change Dimethyldioctadecylammonium-Shelled Microdroplets as a Promising Drug Delivery System: Results on 3D Spheroids of Mammalian Tumor Cells

Significant improvement of phase-change perfluorocarbon microdroplets (MDs) in the vast theranostic scenario passes through the optimization of the MDs composition with respect to synthesis efficiency, stability, and drug delivery capability. To this aim, decafluoropentane (DFP) MDs stabilized by a shell of dimethyldioctadecylammonium bromide (DDAB) cationic surfactant were designed. A high concentration of DDAB-MDs was readily obtained within a few seconds by pulsed high-power insonation, resulting in low polydisperse 1 &#181;m size droplets. Highly positive &#950;-potential, together with a long, saturated hydrocarbon chains of the DDAB shell, are key factors to stabilize the droplet and the drug cargo therein. The high affinity of the DDAB shell with cell plasma membrane allows for localized chemotherapeutics delivery by increasing the drug concentration at the tumor cell interface and boosting the uptake. This would turn DDAB-MDs into a relevant drug delivery tool exhibiting high antitumor activity at very low drug doses.
In this work, the efficacy of such an approach is shown to dramatically improve the effect of doxorubicin against 3D spheroids of mammalian tumor cells, MDA-MB-231. The use of&#160;three-dimensional (3D) cell cultures developed in the form of multicellular tumor spheroids (i.e., densely packed cells in a spherical shape) has numerous advantages compared to 2D cell cultures: in addition to have the potential to bridge the gap between conventional in vitro studies and animal testing, it will improve the ability to perform more predictive in vitro screening assays for preclinical drug development or evaluate the potential of off-label drugs and new co-targeting strategies.

Decafluoropentane microdroplets developed with a shell of dimethyldioctadecylammonium bromide exhibited an exceptional colloidal stability and an actractive biointerface. DDAB-MDs proved to be efficient drug reservoirs characterized by a high affinity to plasma membranes together with enhanced uptake and antitumor activity of Doxorubicin against human triple-negative breast cancer (MDA-MB-231) 3D model.

Significant improvement of phase-change perfluorocarbon microdroplets (MDs) in the vast theranostic scenario passes through the optimization of the MDs ...

Chemistry

Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo

Due to the high level of heterogeneity and mutations inherent in human cancers, single agent therapies, or combination regimens which target the same pathway, are likely to fail. Emphasis must be placed upon the inhibition of pathways that are responsible for intrinsic and/or adaptive resistance to therapy. An active field of investigation is the development and testing of DNA repair inhibitors that promote the action of, and prevent resistance to, commonly used chemotherapy and radiotherapy. We used a novel protocol to evaluate the effectiveness of BRCA2 inhibition as a means to sensitize tumor cells to the DNA damaging drug cisplatin. Tumor cell metabolism (acidification and respiration) was monitored in real-time for a period of 72 hr to delineate treatment effectiveness on a minute by minute basis. In combination, we performed an assessment of metastatic frequency using a chicken embryo chorioallantoic membrane (CAM) model of extravasation and invasion. This protocol addresses some of the weaknesses of commonly used in vitro and in vivo methods to evaluate novel cancer therapy regimens. It can be used in addition to common methods such as cell proliferation assays, cell death assays, and in vivo murine xenograft studies, to more closely discriminate amongst candidate targets and agents, and select only the most promising candidates for further development.

Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo

Here, real-time monitoring of tumor cell metabolism, combined with an in vivo chicken embryo chorio-allantoic membrane (CAM) model of metastasis, are used to evaluate novel anti-cancer targets/agents for their ability to sensitize tumor cells to DNA damaging chemotherapeutics.

Due to the high level of heterogeneity and mutations inherent in human cancers, single agent therapies, or combination regimens which target the same ...

Medicine

An Organotypic High Throughput System for Characterization of Drug Sensitivity of Primary Multiple Myeloma Cells

In this work we describe a novel approach that combines ex vivo drug sensitivity assays and digital image analysis to estimate chemosensitivity and heterogeneity of patient-derived multiple myeloma (MM) cells. This approach consists in seeding primary MM cells freshly extracted from bone marrow aspirates into microfluidic chambers implemented in multi-well plates, each consisting of a reconstruction of the bone marrow microenvironment, including extracellular matrix (collagen or basement membrane matrix) and stroma (patient-derived mesenchymal stem cells) or human-derived endothelial cells (HUVECs). The chambers are drugged with different agents and concentrations, and are imaged sequentially for 96 hr through bright field microscopy, in a motorized microscope equipped with a digital camera. Digital image analysis software detects live and dead cells from presence or absence of membrane motion, and generates curves of change in viability as a function of drug concentration and exposure time. We use a computational model to determine the parameters of chemosensitivity of the tumor population to each drug, as well as the number of sub-populations present as a measure of tumor heterogeneity. These patient-tailored models can then be used to simulate therapeutic regimens and estimate clinical response.

We describe a high-throughput drug sensitivity assay for primary multiple myeloma cells. It consists of a reconstruction of the bone marrow microenvironment (including extracellular matrix and stroma) in multi-well plates, and a non-invasive method for longitudinal quantification of cell viability.

In this work we describe a novel approach that combines ex vivo drug sensitivity assays and digital image analysis to estimate chemosensitivity and ...

Experimental Quantification of Interactions Between Drug Delivery Systems and Cells In Vitro: A Guide for Preclinical Nanomedicine Evaluation

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