Patent ID: 12253499

EXAMPLES

Example 1—Materials and Methods

Materials

Doxorubicin hydrochloride, epirubicin hydrochloride, irinotecan, CPT, and acetic acid were all purchased from Sigma Aldrich (Dorset, United Kingdom). Doxorubicinol hydrochloride was purchased from Toronto Research Chemicals (Toronto, Canada). The Analytical Chromatography TLC Silica gel 60 F245used for the DOX experiments and the Analytical Chromatography TLC Silica gel 60 for the irinotecan experiments were both purchased from Merck/Millipore (Hertfordshire, United Kingdom). The methanol and the acetonitrile were purchased from Fisher Scientific (Loughborough, United Kingdom). The chloroform was purchased from VWR (Leicestershire, United Kingdom). Water was purified using the MilliQ filtration system from Merck/Millipore (Hertfordshire, United Kingdom). Healthy plasma samples and the irinotecan clinical patient samples were generously donated from at the Centro di Riferimento Oncologico di Aviano, Italy. The doxorubicin clinical patient samples were generously donated from the Universitat Münster Klinik für Kinder and Jugendmedizin, Germany. Imaging of the TLC plates was performed using a SynGene PXI imaging system (SynGene, Cambridge, United Kingdom). MatLab R2015A was used to write the custom optical quantification program (Mathworks, Cambridge, United Kingdom). The cartridge materials consisted on chemically resistant Teflon plastic and flexible Teflon tape.

Mobile Phase PREPARATION

The mobile phase consisted of chloroform:methanol:aceticacid:water (80:20:14:6) (v:v:v:v)[12, 23]. The mixture was shaken to ensure proper dissolution of any water droplets that formed. If the water would not dissolve giving the solution a cloudy white appearance then the solution was placed in a 37° C. water bath until the solution became clear. The running of the TLC plate was performed at room temperature.

Preparation of Spiked Plasma Samples

A 100 μl sample of plasma from healthy donors was spiked with 10 μl of a prepared solution of EPI, DOX and DOL in methanol in various concentrations. The spiked sample was vortexed at max speed for 10 seconds to ensure proper mixing. The plasma was prepared beforehand using centrifugation to pellet out the red blood cells.

Calibration Curve Samples

A solution of DOX, DOI, and EPI (each at 1 μM) in methanol, was prepared and then diluted samples from this were made at 0.5, 0.1 0.05 μM. These four concentrations were placed onto each TLC plate to prepare the standard curve for that plate.

Spiking Internal Standard into Clinical Samples

A 10 μl sample of 0.5 μM Epirubicin (EPI) in methanol was spiked into 100 μl clinical plasma samples to provide an internal standard. EPI was chosen because it is an isomeric form of DOX [24] that has very similar chemical and fluorescence properties to both DOX and DOL. The difference between the recovered amount of EPI and the known amount of EPI spiked into the plasma sample helped to scale the DOX and DOL concentration values to account for variability in the extraction efficiency as well as effects on overall fluorescence levels that could potentially be influenced by the buffering properties of the plasma.

Drug Extraction from the Plasma with Protein Precipitation

The proteins needed to be precipitated from the plasma solution because they would interfere with the running of the TLC plate due to their hydrophilicity and high concentration. A mixture of acetonitrile:methanol (2:1) (v:v) was prepared[19]. A 100 μl sample of plasma was placed in a 15 ml tube followed by a sample of 250 μl of the above solution. The sample was aspirated 3 times, vortexed at maximum speed for 10 min and finally spun down at 5000 rpm for 5 min. The resulting supernatant containing the extracted drugs was carefully removed leaving the precipitant pellet at the bottom of the tube. This supernatant was then placed in a glass vial for analysis.

TLC Plate Loading and Development

A 10 μl sample of each of the calibration curve solutions was loaded into the first 4 lanes of the TLC plate. A single 10 μl sample of the plasma extract was loaded onto the 1× sample lane. The 3× sample lane had three sequential 10 μl samples of the plasma extract loaded on top of each other allowing the solution to dry between applications. Loading three samples like this increased the signal which was important for low concentration samples. Loading more than 3 spots caused observed distortions to the bands that could cause trouble with quantification.

The TLC plate was allowed to air dry for 5 min while being covered with aluminum foil to prevent photobleaching of the sample.

For the DOX samples the plate was then placed into a beaker containing the mobile phase where the liquid level covered the bottom of the TLC plate with the upper level of the mobile phase below the level of the sample spots on the TLC plate. The container was covered to increase the vapor pressure of the solvent in the container and speed the development of the plate. The plate was then run for 30 min until the advancing phase was just below the top of the plate. This covered a distance of approximately 8.5 cm.

For the irinotecan samples the mobile phase was diluted 50:50 (v:v) with chloroform and then treated the same as for the DOX samples.

Optical Analysis of the TLC Plate

After the plate was developed and before the solvent had a chance to evaporate, the TLC plate was placed into a SynGene PXI Imaging System and the plate was imaged at different exposure times using the blue mLED illumination unit coupled with a UV06 filter. An exposure time of 30 seconds was used to image the DOX plates and 30 seconds was used to image the irinotecan plates.

A custom MatLab program was then used to quantify the fluorescence level of each band in both the calibration curve samples and in the 1× and 3× sample lanes. The intensity values determined for the DOX and DOL bands in the 3× sample lane were compared to the calibration curve generated using the 0.1 and 0.05 μM concentrations of DOX and DOL. These values were then scaled by the difference in the measured and expected results of the EPI internal standard concentration.

Analysis Program

The custom MatLab program analysed each band by first allowing the user to define the boundaries of the lane in which the bands were located. The program then allowed the user to define an upper and a lower bound in which a particular band of interest was located. The program started by analyzing a single line of pixels running the length of the lane and did so sequentially for each line of pixels starting from left to right covering the width of the lane. For each line of intensity data, the MatLab function “msbackadj” was used to flatten the uneven background created by the lighting source. This created a flat background level at zero fluoresce intensity from which the band peaks could be detected along the length of the defined line. The program would then define the physical dimensions of band in the y direction by defining the full-width-half-max of the peak. The area under this defined portion of the curve was calculated using the trapezoidal approximation using the MatLab function “trapz”. This process was repeated for the next line of pixels until the entire width of the user defined lane was analysed. The X dimensions of the band were determined by setting a lower threshold limit for peaks to be defined by using a “Min Peak Prominence” of 1500 for DOX and for irinotecan. Lower concentrations of irinotecan required the thresholds be adjusted downward compared to the highest concentrations to make sure they were properly detected. The area under the curve defined by each line of pixels was added together to produce a total intensity for the fluorescence band.

It was important that the program objectively defined the actual boundaries of each band to prevent user error from influencing the obtained intensity values. The user's only influence on the program was to define the rough boundaries of a band in which the program could then search for and define the actual band.

In practice the program was used as follows:

1 The program asks the user how many lanes and how many bands in each lane are to be analysed.

2 The MinPeakProminence then needs to be determined, which defines the prominence of the peak maximum with respect to the noise floor. The default setting is set to 1500 which works well for most application.

3 The program then asks for the divisor value. This is the value that the program uses to divide the peak max value to define the lower limit of the peak. The default is set to 2, so the full width half max of the peak will be analysed.

4 The program will ask the user to select a file of the picture to analyse. The user then draws a rectangle to define the entirety of a single lane as shown inFIG.13B.

5 The program then compiles a new picture using just the band (FIG.13C). The user draws a small rectangle to define the upper and lower bounds of the first band. The left and right dimension of the rectangle are not used for the analysis.

6 The program will analyse the band and return the integrated fluorescence intensity. It will also cover in red the region that was analysed (FIG.13D). This can take up to 20 seconds to complete.2parameters can be adjusted in case the identified region does not overlap well with the band of interest. If that red region extends significantly beyond the left and right of the band itself, then the MinPeakProminence value needs to be increased. If the program does not register a band and there is no red region then this value needs to be lowered.

This is repeated for other lanes and bands.

7 Once all the band intensities have been quantified they can be used to determine the concentration of the analyte. The fluorescence intensity versus known concentration of the standard curve samples is plotted and fitted with a linear curve fit. The equation for that line can then be used to calculate the concentration of the unknown sample based on its measured fluorescence intensity. This is done by utilising the fluorescence intensity of the band of interest in the equation for the fluorescence intensity variable and then solving the equation for the concentration variable.

HPLC Quantification of DOX and DOL from the Same Clinical Plasma Samples

Each of the clinical samples analysed using the TLC method described above was also assessed using traditional gold standard HPLC method. A 100 μl sample of patient plasma was spiked with 50 μl of ethanol containing 120 μg/L EPI as the internal standard. Then 100 μl of a solution of phosphate buffer (pH 8.5) and 1000 μl chloroform was added followed by mixing on a rotation shaker for 5 min at max intensity. The solution was then centrifuged for 5 min at 20,800 g. The organic phase was then transferred to a new tube and evaporated with nitrogen at 35° C. The resulting residue was redissolved in H2O/acetonitrile 3:1 (v:v) with 0.1% formic acid for injection into the HPLC.

The HPLC-method used a C18 column with a dual gradient elution. Solution A was 0.1% formic acid in H2O and solution B was 0.1% formic acid in acetonitrile. The elution protocol started with 75% A and 25% B changing to 70% A and 30% B over 7 minutes. Then the solution changed to 58% A and 42% B over 3 minutes. Detection was fluorescence based using 488 nm excitation and 555 nm emission wavelengths.

HPLC Quantification of Irinotecan from the Same Clinical Plasma Samples

The HPLC protocol used to determine the gold standard values of irinotecan in the clinical patient samples was previously established and is described in Marangon et al.[25].

Clinical Plasma Sample Collection from DOX Patients

The plasma samples used in the study were obtained during a larger clinical study as described by Krischke et al. [26].

Example 2—Results

Separation in Spiked and Clinical Plasma Samples

The mobile phase was successful in being able to separate the EPI, DOX, and DOL from one another using the Silica TLC plate as shown inFIG.1. The separation between EPI and DOX was sufficient to resolve the two bands from one another for successful quantification.

The TLC method was also able to successfully separate the EPI, DOX and DOL from spiked plasma samples as shown inFIG.2. Hemoglobin present in the plasma sample is also separated into a band below DOL. Hemoglobin has an absorption and emission spectra that is very similar to DOX and DOL which makes its physical separation from the drugs essential to prevent interference.

The process also successfully separated the EPI, DOX, and DOL from clinical plasma samples collected from patients undergoing chemotherapy treatment with DOX as shown inFIG.3. TLC plates from two different patients is shown. As can be seen there is a large variation in the amount of contaminating plasma residue from patient to patient.

Optical Analysis of TLC Plates

The fluorescence imaging of the TLC plates of DOX and DOI was performed using the SynGene PXI gel reader system. DOX, DOL, and EPI are all naturally fluorescent compounds allowing the fluorescence mode of the PXI system to easily distinguish the drugs from the background.FIG.4shows the linearity of the system in quantifying the fluorescence intensity over a range of DOX and DOL concentrations from 0.01 to 10 μM.

A custom MatLab program (described in more detail below) was used to obtain intensity values for the different bands. The results of the quantification and their comparison to the gold standard HPLC values obtained for the same DOX clinical samples is shown inFIG.5. The samples were run in triplicate and were compared to the concertation obtained using HPLC for the same sample. The error between the HPLC and the TLC derived values is shown inFIG.5Aas the mean with the error bars showing the standard error of the mean. Out of a total of 12 samples studied 10 fit within this ±15% error resulting in an 83% success rate.FIG.5Bshows all the data points individually. Here 28 out of a total of 36 samples fell within the ±15% error resulting in a 78% success rate.

TABLE 1Summary of the data shown in FIG. 5.IndividualAverageData PointsWithin Range927Outside Range39Total # Points1236Percent within0.750.7515%

The results for Dol are shown inFIG.6. In the depicted instance, background fluorescent levels created by light leakage through the optical filter used in the experiment resulted in a lower correlation but nevertheless the experiment confirms the utility of the hermetically sealed cartridge.

TABLE 2Summary of the data shown in FIG. 6.IndividualAverageData PointsWithin Range512Outside Range724Total # Points1236Percent within0.420.3315%

This TLC technical was also applied to a second chemotherapy drug, irinotecan, which is also inherently fluorescent.FIG.7shows the agreement between the TLC method and the gold standard HPLC analysis for 7 different clinical patient samples.

TABLE 3Summary of the data shown in FIG. 7.IndividualAverageData PointsWithin Range610Outside Range14Total # Points714Percent within0.860.7115%

To better understand the minimum amount of plasma necessary to conduct the protein precipitation and TLC separation a study was conducted where spiked samples of plasma were separated into different volumes followed by the protein precipitation and spotting onto the TLC plate. As shown inFIG.7a strong signal can be collected even from a 5 μl sample of plasma. A typical finger stick blood drop is on the order of 50 μL [27, 28] which should produce about 25 μL of plasma [29, 30]. This shows that it is possible to successfully conduct a TLC separation using the plasma volume that could recovered by a finger stick.

Example 3—Self-Contained Cartridge Design

A self-contained cartridge was designed and built to fully contain the TLC plate and the mobile phase to prevent release of fumes or silica dust. The cartridge comprised the following components.

ComponentTLC plateCasingInsulationBreakable glass barrierChloroformMeOHAcetic acid

Plastics components are readily prepared by injection molding or 3-D printing, according to methods well known in the art. Plates, glass elements and solutions are available commercially, for example from Sigma Aldrich.

Access to the inside of the hermetically sealed cartridge is achieved by using hypodermic needles to pass through rubber septa placed at different locations in the cartridge as shown inFIGS.9,10and11. The design incorporates two separate chambers, one for the TLC plate and the second for the mobile phase. These two chambers keep the TLC plate separate from the mobile phase before use during transport and storage. A weak point in the separation between the chambers has been incorporated in the form of a glass coverslip which can be easily broken by inserting a needle through a designated rubber septa.

The use of the TLC cartridge use the following simple protocol:1. Load the samples onto the TLC plate using a hypodermic needle to penetrate through the rubber septa. This step will be automated in the future.2. Use the hypodermic needle to break the glass coverslip separating the two chambers3. Tip up the cartridge to let the mobile phase flow into the TLC chamber and come into contact with the bottom of the TLC plate allowing the plate to develop normally by capillary forces.4. Tip the cartridge back down to a flat orientation to stop the TLC plate development by allowing the mobile phase to drain back into the second chamber.5. In this flat orientation the TLC plate can be imaged using a CCD camera system.

The cartridge is designed with readily available solvent resistant materials that allow it to be disposable. This prevents any cross contamination between patient samples and also reduces the maintenance burden for an automated system.

Example 4—Discussion of Examples 1 to 3

TLC has been used for drug quantification for many years [11-14], and the process has been partially automated for high throughput applications [15].

However, prior to the present invention, the TLC process has never been adapted for use in a bedside clinical setting. This is advantageous because many drugs can have significant degradation within 15 min of collection if not immediately spun down to remove red blood cells and cooled on ice [16]. Requiring the sample to be sent to the hospital lab for analysis usually incurs delay which can adversely affect the accuracy of the final blood concentration estimate.

The TLC based separation and quantification method described here has been successful in quantifying actual clinical patient samples with 83% the DOX content and 86% of the irinotecan content falling within 15% of the HPLC derived values. This method meets the FDA recommendations for the accuracy of a bioanalytical method [31]. It also shows that the process can be adapted for use with different drug compounds. The only changes that need to be made to the system to be applied to a different drug is the choice of the proper internal standard (as would be required for HPLC analysis) and the right adjustments to the single mobile phase.

The process described herein may also be used with drugs that are not inherently fluorescent. The use of TLC plates coated with an appropriate inorganic fluorescent compound (such as commercially available F254 plates) allows photon absorption of the drugs to be optically monitored using the same TLC process, except looking for blocked fluorescence from the TLC plate. The analysis of the greyscale image of the TLC plate is then inverted so the bands would show up as varying degrees of grey against a black background to allow the analysis software to quantify the band fluorescence intensity.

The custom written MatLab image analysis and quantification software was successfully applied to both the DOX and the irinotecan samples. The only changes necessary in the program parameters were to adjust the cutoff intensity values used to determine the boundaries of the actual bands because the fluorescence intensity behavior of the two compounds was different.

We also demonstrate that this simple extraction method and single mobile phase development make this TLC based technique applicable for a self-contained cartridge design. The prototype cartridge described in the preferred embodiment shown here consists of different PVC plastic layers that under compression create a sealed cartridge using fluorocarbon rubber seals. It will be appreciated that injection molding techniques could be used to create cartridges that have fewer layers and use less material.

In Summary, the TLC method described here successfully quantified over 80% of both the DOX and irinotecan clinical samples by being within 15% of established HLC values which meets the FDA criteria for accuracy of a bioanalytical method [31].

REFERENCES FOR DESCRIPTION AND EXAMPLES 1-4

1. Wilkinson, G. R.,Drug metabolism and variability among patients in drug response. New England Journal of Medicine, 2005. 352(21): p. 2211-2221.2. Klotz, U.,Pharmacokinetics and drug metabolism in the elderly. Drug metabolism reviews, 2009. 41(2): p. 67-76.3. Sawyer, M. and M. J. Ratain,Body surface area as a determinant of pharmacokinetics and drug dosing. Investigational new drugs, 2001. 19(2): p. 171-177.4. Gurney, H.,Dose calculation of anticancer drugs: a review of the current practice and introduction of an alternative. Journal of Clinical Oncology, 1996. 14(9): p. 2590-2611.5. de Jonge, M. E., et al.,Individualised cancer chemotherapy: strategies and performance of prospective studies on therapeutic drug monitoring with dose adaptation. Clinical pharmacokinetics, 2005. 44(2): p. 147-173.6. Galpin, A. J. and W. E. Evans,Therapeutic drug monitoring in cancer management. Clinical chemistry, 1993. 39(11): p. 2419-2430.7. Petros, W. P., et al.,Associations between drug metabolism genotype, chemotherapy pharmacokinetics, and overall survival in patients with breast cancer. Journal of clinical oncology, 2005. 23(25): p. 6117-6125.8. Robert, J., et al.,Pharmacokinetics of adriamycin in patients with breast cancer: correlation between pharmacokinetic parameters and clinical short-term response. European Journal of Cancer and Clinical Oncology, 1982. 18(8): p. 739-745.9. Touw, D., et al.,Cost-effectiveness of therapeutic drug monitoring: a systematic review. Therapeutic drug monitoring, 2005. 27(1): p. 10-17.10. Bardin, C., et al.,Therapeutic drug monitoring in cancer—Are we missing a trick? European Journal of Cancer, 2014. 50(12): p. 2005-2009.11. Yesair, D., et al.,Comparative pharmacokinetics of daunomycin and adriamycin in several animal species. Cancer research, 1972. 32(6): p. 1177-1183.12. Brenner, D. E., et al.,Improved high-performance liquid chromatography assay of doxorubicins detection of circulating aglycones in human plasma and comparison with thin-layer chromatography. Cancer chemotherapy and pharmacology, 1985. 14(2): p. 139-145.13. Chan, K. K. and C. D. Wong,Quantitative thin-layer chromatography: thin-film fluorescence scanning analysis of adriamycin and metabolites in tissue. Journal of Chromatography A, 1979. 172(1): p. 343-349.14. Watson, E. and K. Chan,Rapid analytic method for adriamycin and metabolites in human plasma by a thin-film fluorescence scanner. Cancer treatment reports, 1976. 60(11): p. 1611-1618.15. Fredriksson, S., K. Elwinger, and J. Pickova,Fatty acid and carotenoid composition of egg yolk as an effect of microalgae addition to feed formula for laying hens. Food Chemistry, 2006. 99(3): p. 530-537.16. Kontny, N. E., et al.,Minimization of the preanalytical error in plasma samples for pharmacokinetic analyses and therapeutic drug monitoring-using doxorubicin as an example. Therapeutic drug monitoring, 2011. 33(6): p. 766-771.17. Therasse, P., et al.,New guidelines to evaluate the response to treatment in solid tumors. Journal of the National Cancer Institute, 2000. 92(3): p. 205-216.18. Lennard, L.,Therapeutic drug monitoring of cytotoxic drugs. British journal of clinical pharmacology, 2001. 52(S1): p. 75-87.19. Ibsen, S., et al.,Extraction protocol and mass spectrometry method for quantification of doxorubicin released locally from prodrugs in tumor tissue. Journal of Mass Spectrometry, 2013. 48(7): p. 768-773.20. Paci, A., et al.,Review of therapeutic drug monitoring of anticancer drugs part1—cytotoxics. European Journal of Cancer, 2014. 50(12): p. 2010-2019.21. Gurney, H.,How to calculate the dose of chemotherapy. British journal of cancer, 2002. 86(8): p. 1297-1302.22. Besse, B., et al., 2nd ESMO Consensus Conference on Lung Cancer: non-small-cell lung cancer first-line/second and further lines of treatment in advanced disease. Annals of oncology, 2014. 25(8): p. 1475-1484.23. Licata, S., et al.,Doxorubicin metabolism and toxicity in human myocardium: role of cytoplasmic deglycosidation and carbonyl reduction. Chemical research in toxicology, 2000. 13(5): p. 414-420.24. Hortobagyi, G.,Anthracyclines in the treatment of cancer. Drugs, 1997. 54(4): p. 1-7.25. Marangon, E., et al.,Development and Validation of a High-Performance Liquid Chromatography—Tandem Mass Spectrometry Method for the Simultaneous Determination of Irinotecan and Its Main Metabolites in Human Plasma and Its Application in a Clinical Pharmacokinetic Study. PloS one, 2015. 10(2): p. e0118194.26. Krischke, M., et al.,Pharmacokinetic and pharmacodynamic study of doxorubicin in children with cancer: results of a “European Pediatric Oncology Off-patents Medicines Consortium” trial. Cancer chemotherapy and pharmacology, 2016. 78(6): p. 1175-1184.27. McDade, T. W., S. Williams, and J. J. Snodgrass,What a drop can do: dried blood spots as a minimally invasive method for integrating biomarkers into population-based research. Demography, 2007. 44(4): p. 899-925.28. Robison, E. H., et al.,Whole genome transcript profiling from fingerstick blood samples: a comparison and feasibility study. BMC genomics, 2009. 10(1): p. 617.29. Haeberle, S., et al.,Centrifugal extraction of plasma from whole blood on a rotating disk. Lab on a Chip, 2006. 6(6): p. 776-781.30. Brun, J., et al.,The paradox of hematocrit in exercise physiology: which is the “normal” range from an hemorheologist's viewpoint? Clinical hemorheology and microcirculation, 2000. 22(4): p. 287-303.31. Health, U.D.o. and H. Services,Guidance for industry, bioanalytical method validation. http://www.fda.gov/cder/guidance/index.htm, 2001.

Example 5—pH-Mediated Molecular Differentiation for Fluorimetric Quantification of Chemotherapeutic Drugs in Human Plasma

Irinotecan (7-ethyl-10-[4-(1-piperidino)-1-piperidino] carbonyl-oxycamptothecin, CPT-11) is commonly used as an antitumor drug. The drug is considered a prodrug since it undergoes cleavage of the bispiperidino-side chain by carboxyesterase to form SN-38 (7-ethyl-10-hydroxycampto-thecin), an active metabolite that has shown to be up to 1,000 more potent at inhibiting topoisomerase I than the parent Irinotecan.

Some of the main challenges for the simultaneous quantification of Irinotecan and SN-38 reside in the fact that their absorption and fluorescence properties are almost identical under physiological conditions, and the fact that the concentration of SN-38 can be over 30 times lower than that of Irinotecan.

The pH of the media in which molecules are dissolved can play a major role not only in properties such as their solubility, but also in their optical performance.

The premise of the pH-mediated molecular differentiation is illustrated as follows and inFIG.14A:

Generic Case:

Example

For Irinotecan and SN-38, this was investigated by characterising the absorbance as well as the emission behaviour at excitation wavelengths of 370 nm and 430 nm under acidic (pH=1.4) and basic conditions (pH=12.1). As shown inFIG.14B, Irinotecan exhibited near identical optical properties for both absorbance and fluorescence under acidic and basic conditions, while SN-38 displayed a pronounced shift for absorbance as well as emission when basifying.

A rationalisation of the molecular structure under acidic and basic conditions is depicted in the following scheme shows the effect of pH in the chemical structure of Irinotecan (top), and SN-38 (bottom):

A solid phase extraction protocol was used to assess the effectiveness of a pH-mediated molecular differentiation using real patient sample compositions.

While the fluorescence of Irinotecan was measured in acidic conditions (pH=1.4) using an excitation wavelength of 370 nm, the fluorescence of SN-38 was determined in basic conditions (pH=12.1) with an excitation wavelength of 430 nm. The results are summarised inFIG.16. In total seven samples were run in triplicate, six spiked and one reference sample. For Irinotecan, 17 out of 18 obtained data points were within 15% error (94%). In the case of SN-38, 16 out of 18 data points were within this tolerance (89%). We note that for samples with concentrations below 100 nM for Irinotecan and 15 nM for SN-38, the measured background signal of the reference sample became significant, resulting in higher errors below this level, which is at the bottom end of the relevant clinical range.

In order to show the applicability of this approach to other commonly used chemotherapeutic drugs with similar molecular structure, we compared the pH-dependent optical properties of SN-38 to Epirubicin and Methotrexate. With the former containing an alcohol and the latter an amine group attached to the aromatic core, a basic pH is likely to cause deprotonation resulting in a change of the optoelectronic properties of the molecules. The absorbance and fluorescence spectra of the three compounds were recorded in acidic (pH=1.4) and basic (pH=12.1) conditions (data not shown). In all cases, a change from acidic to basic conditions resulted in a modification in the optical properties of the drugs, with the absorption spectrum shifting to longer wavelengths, from 30 nm for Methotrexate to almost 85 nm for Epirubicin, which compared to 40 nm for SN-38. The bathochromic shift in the absorbance spectra of the molecules in basic conditions was determined to be 15 nm in the case of Methotrexate and 97 nm for Epirubicin in comparison to 137 nm for SN-38.

In conclusion, we have demonstrated that pH-mediated molecular differentiation can be utilised to quantify the amount of a chemotherapeutic prodrug and its active metabolite spiked in human plasma at clinically relevant concentrations.

Example 6—Utility of Real-Time Point-of-Care (POC) Device of the Invention in Cancer Therapeutic Drug Monitoring C-TDM

Global Cancer: Scale of the Problem

In 2012, there were 8.2 million deaths worldwide attributable to cancer and 14.1 million new cancer cases. 1.8 million new cases were in North America, 1 million in Europe and 4.1 million in East Asia. Despite accounting for only 20% of the global population, 43% of new cancer cases occur in developed countries. The most common cancers in the developed world are lung, colorectal, prostate and breast with lung cancer claiming the highest fatality rate for both men and women [1].

The World Health Organisation predicts that cancer cases will increase globally by 70% over the next two decades [2] and that the number of deaths will reach 13.1 million [3]. Despite increases in cancer research funding, some cancers have seen little to no progress in the last decade: brain, lung, cancer and oesophageal cancers continue to prove difficult to treat [3].

After the first administration of a chemotherapeutic agent, practitioners typically monitor drug efficacy based on the cancer or tumour's response to the drug. Methods to do this involve feeling external lumps, tumours and nodes to see if the size has decreased, doing the same but with various imaging techniques for internal cancers, blood tests such as ones that measure organ function, and tests for biomarkers that are specific to certain types of cancer. It is typical for these measurements to be taken every two to three cycles of chemotherapy, with each cycle lasting between two to six weeks [25]. This means that many patients will go for months without knowing if they are responding to treatment. There is likely a large demand for methods that will either provide more timely response data or allow drug optimisation sooner in the treatment process.

Precision Medicine and Therapeutic Drug Monitoring

Precision medicine (not limited to cancer) currently has a market size of $43 billion and is expected to grow rapidly to $71 billion by 2021 [9].

One form of precision medicine involves use of therapeutic drug monitoring (TDM). For a select variety of drugs, the serum or blood concentration correlates with efficacy [11]. The need for TDM arises when there is high inter-individual variability in the metabolism, distribution, absorption and excretion of the drug, which is a common feature of chemotherapeutic agents [11]. Plasma drug concentrations typically vary by as much as 14-fold for most chemotherapy drugs and as much as 100-fold for 5-fluorouracil. Despite this, chemotherapy dosage levels are based on estimations of the patients surface area and TDM is not commonplace; it has only seen clinical use in the monitoring of methotrexate and busulfan [11].

For a drug to benefit significantly from TDM, it must have significant variability in inter- or intra-individual pharmacokinetics, a defined relationship between blood concentration and pharmacological efficacy, a narrow therapeutic index and a precise and accurate assay to measure it. The introduction of widespread TDM for chemotherapy has been slowed primarily by the fact that there are no available assays for the drugs. However, there have also been problems in defining the relationship between blood/serum concentrations and efficacy, and the advent of combination therapies has made this even more challenging [11].

Almost all serum/blood drug concentration monitoring is carried out using HPLC or LC-MS. Methotrexate is monitored every 24 h for a period of 3 days and this is typically performed by immunoassay, though chromatography methods do exist. Plasma busulfan concentrations are routinely monitored in high dose, paediatric patients. A calibration curve consisting of seven to twelve distinct time-points is taken at the first administration of the drug with the concentration being determined by chromatography, though new ELISA methods have been developed [11].

Bach et al. [11] highlighted a number of chemotherapy drugs which they think are prospective candidates for TDM: mTOR inhibitors, 5-flourouracil, imatinib, EGF receptor inhibitors, platinum based agents, etopisode, doxorubicin and suramin. The authors highlight that many of these drugs are not subject to TDM due to poor understanding of the relationship between dose and efficacy and that strong evidence and understanding of this relationship is paramount to justify monitoring.

Point of Care Testing Market

Point of care (POC) medicine is a growing field of medical testing devices that can be used be used at, or near, the point of care, i.e. the bedside or GP surgery instead of in a medical laboratory. The benefits of point of care testing are that it brings the test immediately to the patient, meaning that the caregiver receives the results of the test faster, thereby enabling timely diagnosis and more dynamic monitoring and treatment of patients. POC testing is exceptionally important in the field of personalised, targeted medicine as it allows cheap, easy and effective ways to diagnose and monitor patients.

However POC monitoring of cancer treatment has not yet been widely accepted. This is part of a wider trend in TDM POC devices which lag behind other fields in which POC devices have been successfully deployed. Sanavio and Krol [17] suggest a variety of reasons for this in their 2015 review on the matter. One major problem is the lack of evidence to support significantly superior clinical performance, in terms of cost-effectiveness, granted by TDM for most drugs. As healthcare providers are trying to provide services at reduced cost wherever possible, TDM must be supported by strong data that it will significantly improve care to justify the cost (or it must be cheap enough that this justification is unnecessary). Another cited issue is that most drug development pipelines and clinical trials only require dose tolerance and dose response data with pharmacokinetics and pharmacodynamics data being a secondary objective at best. There is therefore often very little pharmacokinetic and pharmacodynamic data on the drug and so rarely any commentary on the efficacy of TDM. Thus when pharmaceutical companies design clinical trials, they do not presently consider TDM as an option, either when choosing which drugs to trial or when designing the format of the trial, meaning that most of the drugs on the market have little need for TDM.

The availability of a new cheaper method of performing TDM as described herein therefore provides an important contribution to the art.

Example 7—Utility of Real-Time POC Medical Device of the Invention in Other Therapeutic Areas

As explained above, the present invention opens up new utilities for POC TDM analysis of drugs.

Typical examples of drugs that may typically be monitored are antiepileptics, antiarrhythmics, immunosuppressants, and antibiotics.

Some bronchodilators, psychoactive drugs and chemotherapeutics are also monitored.

Three examples of drug classes in which the invention can show particular utility are: chemotherapeutics, antiarrhythmics and fungicidal agents as all have a large portion of drugs that are recommended for monitoring or already are monitored. Furthermore these drugs have a large portion of natively fluorescent compounds which facilitates detection within the TLC cartridge platform

The following table consists of drugs that are fluorescent. The drugs that are not currently monitored were identified from a range of reviews highlighting drugs that should be considered for TDM [19] [20] [21] [22] [23] [24]:

Example 8—Further Cartridge Embodiment

A further embodiment of a cartridge of the present invention is shown inFIGS.18to30.

The first embodiment shown inFIGS.10and11was based on a planar (16 layer) design formed from acrylic, Viton and PTFE sheeting with an integrated glass liquid-shield that segregated the mobile phase from the TLC plate prior to elution.

The further embodiment shown inFIGS.18and19has a primarily bipartite design comprising upper (face, or front) and lower (base, or back) body units, with further elements as shown inFIGS.19to29.

In this further embodiment as illustrated, the two body units are aluminium and the window is float glass.

The liquid mobile phase is segregated from the face chamber prior to elution. In this example the segregator foil (15) is aluminium foil.

O-rings (or gaskets) or septa are used to hermetically seal different fluid or vapour holding volumes of the cartridge as shown: specifically the cartridge illustrated includes a body o-ring (16), reservoir o-ring (17), window o-ring (04) and septa for injection of analytes and standards, and releasing the mobile phase (07). In this example these elements are fluoroelastomers (FKM).

This cartridge is designed so that the total mass and centre of mass of the cartridge can be adjusted by modifying the solid fill in the upper and lower units, seeFIG.21. The TLC plate is mounted and secured using the support posts shown inFIG.25. The dimensions of the TLC plate are shown inFIG.26.

Additional support along the backside and the incorporation of stand-off posts on the internal side of the face unit prevent both front-side wicking and rear-to-front side wicking of the liquid mobile phase during elution, seeFIG.27. These are also shown inFIG.28.

A cross section of the bridge and injection ports shows the edge extrusions that prevent wicking, seeFIG.29.

To prepare the cartridge, the elements shown inFIG.18were provided.

Tension was applied by body screws (08) between the upper and lower units to form two seals. A unit seal around the outer edge of the cartridge preventing leakage of mobile phase into the working environment i.e. compression of body o-ring (16). In addition, the same tension applied by the body screws simultaneously forms a seal around the liquid mobile phase reservoir in the lower unit through compression of the reservoir o-ring (17). This is achieved through translation of force through the bridge, seeFIG.19.

The cartridge can be charged or is ‘recharged’ upon removal and replenishment of the TLC plate (10), segregator (15) and liquid mobile phase—seeFIG.20.

In use samples and standards can be loaded via the injection ports shown inFIGS.17,18,22,26and28.

The TLC plate is fully supported under each injection port such that a hypodermic needle entering the device will not deform or damage the plate whilst spotting.

The segregator foil is pierced by inserting a needle through the septum (FIG.22,19). The hypodermic needle is captured/engages with a well or pocket in the paddle (20) and follows a curved arc downwards (21).

This motion results in a large aperture being formed in the foil segregator, seeFIG.23. The length/height of this aperture is important as air must be allowed to back-fill the reservoir upon rotation, seeFIG.24.

The mobile phase reservoir follows a tapering ‘v’ contour below the TLC plate (in both the face and rear compartments) but enters a rectilinear profile before the lowest point of the TLC plate, seeFIG.30. This ensures the mobile phase wicks up the plate evenly.

TABLE DLeading candidate drugs for TDM.Drug ClassDrugTherapeutic window (μg/mL)Current TDM methodAntiepilepticsStiripentol10-15AED monitoringAntiarrhythmicsNAPA4-8Homogenous enzyme immunoassayAmiodarone0.5-2HPLC, LC-MS, ELISAFlecainide0.2-1HPLC, ImmunoassayMexiletine0.5-2.5HPLC, GCQuinidine2-5Homogenous enzyme immunoassayProcainamide4-8Homogenous enzyme immunoassayPropafenone0.06-1Not currently monitoredSotalol0.5-3Not currently monitoredVerapamil0.025-0.25Not currently monitoredAntibioticsLinezolid1.8-7.5Not currently monitoredCiprofloxacin0.1-8.3Not currently monitoredFluconazole1-5Not currently monitoredAntifungalsItraconazole0.5-17Not currently monitoredVoriconazole1-5Not currently monitoredPosaconazoleMaintain at 0.7Not currently monitoredImmunosuppressantsMycophenolic Acid1-4Homogenous enzyme immunoassayDermal medicinesSalicylate15-30SpectrophotometricChemotherapeutics5-fluoruoracil2000-3000Not currently monitoredImatinib1-3Not currently monitoredEtopisode1-10Not currently monitoredDoxorubicin10-58Not currently monitoredSuraminMaintain at 500Not currently monitoredSunitinibNot yet determinedNot currently monitoredIn-depth list of candidate drugsAnalyteforexistingNativelyClassDrugWindowTest methodtestFluorescent?AntiepilepticCarbamazepine5-10ug/mLHomogenous enzyme immunoassaySerum, plasmaPossibly, mightrequire acidactivationPhenobarbital10-35ug/mLHomogenous enzyme immunoassaySerum, plasmaNo, requireslabellingPhenytoin10-20ug/mLHomogenous enzyme immunoassaySerum, plasmaNo, requires analogClobazamVaries, 0.2-5HPLCPlasmaNo, requiresug/mLadditionalprocessingTiagibine20-200ng/mLHPLCSerumUnknownEthosuximide40-100ug/mLEnzyme immunoassaySerum, plasmaUnknownGabapentin2-20ug/mLHomogenous enzyme immunoassaySerum, plasmarequires processingLacosamide5-10ug/mLHomogenous enzyme immunoassaySerum, plasmaUnknownOxcarbazepine3-35mg/mLHomogenous enzyme immunoassaySerum,UnknownPlasmaPrimidone5-12ug/mLFluoroimmunoassayserum, plasmaNoVigabratin0.8-36ug/mLHPLCPlasma, urineRequires processingZonisamide10-40ug/mlHomogenous enzyme immunoassaySerum, plasmaUnknown,Leveltiracetam20-40ug/mLHomogenous enzyme immunoassaySerum, plasmaNo, requiresderivitsationValproic acid50-125ug/mLHomogenous enzyme immunoassaySerum, plasmaProbably not,requires a probeClonazepam20-80ug/mLNo existing testN/ARequires processingNitrazepam0.03-0.1ug/mLNo existing testN/ARequiresadditions andprocessingFelbamate50-110ug/mLNo existing testN/ANo required probeLevetiracetam10-40ug/mLNo existing testN/ANo, requiresderivitisationTopiramate2-10ug/mLNo existing testN/ANo, requireslabellingRufinamide~15ug/mLNo existing testN/ACan't find anythingStiripentol10-15ug/mLNo existing testN/AYesLamotrigine2.5-15ug/mLHomogenous enzyme immunoassaySerum, plasmaRequires processingAntiarrhythmicsDigoxin0.0005-0.002ug/mLHomogenous enzyme immunoassaySerum, plasmaRequires processingLidocaine1.5-5ug/mlHomogenous particle enhancedSerum, plasmaProbably not,turbidimetric immunoassayrequires a probeNAPA4-8ug/mLHomogenous enzyme immunoassaySerum, plasmaYesAmiodarone0.5-2ug/mLHPLC, LC-MS, ELISASerum, plasmaYesFlecainide0.2-1ug/mLHPLC, immunoassaySerumYesMexiletine0.5-2.5ug/mLHPLC, GCSerum, plasmaYesQuinidine2-5ug/mLHomogenous enzyme immunoassaySerum, plasmaYesProcainamide4-8ug/mlHomogenous enzyme immunoassaySerum, plasmaYesDisopyramide2-4ug/mlNo existing testN/ANo requiresimmunoassayPropafenone0.064-1.044ug/mLNo existing testN/AYesSotalol0.5-3ug/mLNo existing testN/AYesVerapamil0.025-0.25ug/mLNo existing testN/AYesAntibioticsGentamicin0.5-2/5-10ug/mLHomogenous enzymeSerum, plasmaNo, required taggingimmunoassayTobramicin2-10ug/mLHomogenous enzymeSerum, plasmaNo, requires taggingimmunoassayVancomycin10-20ug/mLHomogenous enzymeSerum, plasmaNo, requires taggingimmunoassayAmikacin5-15ug/mLHomogenous particle enhancedSerum, plasmaNo, requires taggingturbidimetric immunoassayTeicoplanin10-60ug/mLNo existing testN/ANo, requiresimmunoassayLinezolid1.75-7.53ug/mlNo existing testN/AYesCiprofloxacin0.1-8.3ug/mLNo existing testN/AYesFluconazole1-5ug/mLNo existing testN/AYesAntifungalsItraconazole0.5-17ug/mLNo existing testN/AYesVoriconazole1-5ug/mLNo existing testN/AYesPosaconazole~0.7ug/mLNo existing testN/AYesFlucytosine70-100ug/mLNo existing testN/ANoAntimanicsLithium0.6-1.2mEq/LSpectrohptoemetrically withSerumNoenzyme assay systemBronchodilatorsTheophylline5-15ug/mLHomogenous enzymeSerum, plasmaNo, requires taggingimmunoassayImmunosupressantsCyclosporin0.1-0.4ug/mLChemiluminescentBloodNo, requires analogmicroparticle immunoassayMycophenolic1-4ug/mLHomogenous enzymePlasmaYesAcidimmunoassayTacrolimus5-20ug/mLAffinity chrome- mediatedBloodNo, requires analogimmunoassaySirolimus5-10ng/mLChemiluminescentBloodUnknownmicroparticle immunoassayAzathioprinecan't findNo existing testN/ASteroidsVariesNo existing testN/Aanti-lymphocytecan't findNo existing testN/AglobulinOKT3can't findNo existing testN/ADaclizumabcan't findNo existing testN/ABasiliximabcan't findNo existing testN/AAntianginalPerhexeline0.3-4ug/mLHPLC, LC-MSPlasmaRequires processingAnticonvulsantLamotrigine2.5-15ug/mLHomogenous enzymeSerum, plasmaRequires processingimmunoassayDermal treatmentSalicylate15-30ug/mLSpectrophotometricUrine,Yesserum,plasmaCancerMethotrexateVaries with useHomogenousSerum, plasmaNoenzymeimmunoassayBusulfan0.9-1ug/mlHPLC, LC-MSSerum, plasmaRequiresprocessingmTOR inhibitors0.005-0.015ug/mLNo existing testUnknown5-flourouracil2000-3000ug/mlNo existing testYesimatinib1-3ug/mlNo existing testYesEGFVariousNo existing testUnknownreceptorinhibitorsplatinumVariousNo existing testNo requires analogbased agentsetopisode1-10 ug/mL butNo existing testYeshighly variedfrom patient topatientdoxorubicin10.4-57.7mg/No existing testYesm{circumflex over ( )}2suraminmax 500ug/mlNo existing testYesnilotinib~0.5ug/mlNo existing testNo requireshybridisationdasatinib~0.05ug/mlNo existing testNo requireshybridisationerlotinib100-150mg/dayNo existing testNo - causesquenching of BSAsunitinibStill beingNo existing testYesdeterminedsorafenib800mg/dayNo existing testNo - requiredfluorescentagentsretuximabMaintain at 375No existing testNo-required labelmg/m2cetuximabMaintain doseNo existing testNo-required labelat 250 mg/m{circumflex over ( )}2AntibodiesAdalimumab3.5-7ug/mLNo existing testNo - required labelforautoimmunediseasesCertolizumab3-84ug/mLNo existing testNo - required labelpegolInfliximab~3.8ug/mLNo existing testNo - required label

REFERENCES FOR EXAMPLES 6 AND 7

[1] World Health Organisation, Global cancer statistics, 2012, 2015.[2] Frost and Sullivan, Scientific Research an Advances in Cancer Therapy, 2016. [3] Frost and Sullivan, Technologies for cancer research., 2017.[4] Allied Market Research, Oncology/cancer drugs market by therapeutic modalities (chemotherapy, targeted therapy, immunotherapy, hormonal), cancer types (blood, breast, gastrointestinal, prostate, skin, respiratory/lung cancer)—Global opportunities analysis and industry forecast, 2015.[5] Frost and Sullivan, European cancer market, outlook., 2011.[6] K. Stone, Top 20 Cancer Drugs, Balance. (2017). https://www.thebalance.com/top-cancer-drugs-2663234 (accessed Aug. 18, 2017).[7] Grand View Research, Doxorubicin market by application (Ovarin, Multiple Myeloma, Kaposi Sarcoma, Leukemia, Bone Sarcoma, Breast, Endometrial, Gastric, Liver, Kidney, Other cancers) and segment forecasts, 2013-2024, 2016.[8] Frost and Sullivan, Preferences to targeted therapies and patient centric approaches drive transformations in oncology drug delivery market, 2017.[9] Frost and Sullivan, Technology growth series—precision medicine., 2017.[10] Frost and Sullivan, Sensors in Medical Diagnostics and Health Monitoring, 2016.[11] D. Bach, J. Straseski, W. Clarke, Therapeutic drug monitoring in cancer chemotherapy, Bioanalysis. 2 (2010).[12] Frost and Sullivan, Western European point of care testing market, 2016.[13] Frost and Sullivan, Growth opportunities in the US point-of-care market., 2016. [14] Frost and Sullivan, Innovations in precision and regenerative medicine., 2016. [15] Frost and Sullivan, Recent advances in cancer therapy., 2017.[16] Frost and Sullivan, Recent advances in cancer immunotherapy and tumour profiling technologies, 2017.[17] B. Sanavio, S. Krol, On the slow diffusion of point-of-care systems in therapeutic drug monitoring, Front Bioeng Biotechnol. 3 (2015).[18] Grand View Research, Therapeutic drug monitoring market worth $3.37 billion by 2024, 2016.[19] N. Widmer, C. Bardin, E. Chatelut, A. Paci, J. Beijnen, D. Levêque, G. Veal, A. Astier, Review of therapeutic drug monitoring of anticancer drugs part two—Targeted therapies, Eur. J. Cancer. 50 (2014) 2020-2036. doi:http://dx.doi.org/10.1016/j.ejca.2014.04.015.[20] A. Johnston, D. W. Holt, Therapeutic drug monitoring of immunosuppressant drugs, Br. J. Clin. Pharmacol. 47 (1999) 339-350. doi:10.1046/j.1365-2125.1999.00911.x.[21] H. R. Ashbee, R. A. Barnes, E. M. Johnson, M. D. Richardson, R. Gorton, W. W. Hope, Therapeuticdrug monitoring (TDM) of antifungal agents: guidelines from the British Society for Medical Mycology, J. Antimicrob. Chemother. 69 (2014) 1162-1176. doi:10.1093/jac/dkt508.[22] J. A. Roberts, R. Norris, D. L. Paterson, J. H. Martin, Therapeutic drug monitoring of antimicrobials, Br. J. Clin. Pharmacol. 73 (2012) 27-36. doi:10.1111/j.1365-2125.2011.04080.x.[23] G. Jürgens, N. A. Graudal, J. P. Kampmann, Therapeutic Drug Monitoring of Antiarrhythmic Drugs, Clin. Pharmacokinet. 42 (2003) 647-664. doi:10.2165/00003088-200342070-00004.[24] D. Berry, Therapeutic Drug Monitoring of Antiepileptic Drugs, in: C. P. Panayiotopoulos (Ed.), Atlas of Epilepsies, Springer London, London, 2010: pp. 1487-1498. doi:10.1007/978-1-84882-128-6_222.[25] Chemocare, How can we tell if chemotherapy is working, (2017). http://chemocare.com/chemotherapy/what-is-chemotherapy/how-to-tell-if-chemotherapy-is-working.aspx (accessed Aug. 18, 2017).[26] A. Wadagni, M. Frimpong, D. M. Phanzu, A. Ablordey, E. Kacou, M. Gbedevi, E. Marion, Y. Xing, V. S. Babu, R. O. Phillips, M. Wansbrough-Jones, Y. Kishi, K. Asiedu, Simple, RapidMycobacterium ulceransDisease Diagnosis from Clinical Samples by Fluorescence of Mycolactone on Thin Layer Chromatography, PLoS Negl. Trop. Dis. 9 (2015).[27] Frost and Sullivan, Effective strategies to overcome challenges faced by global generic drug makers., 2013.[28] Pentech Moulding Co Ltd, Cost of plastic injection moulding UK, (2015). http://pentechmoulding.co.uk/other-services/cost-of-plastic-injection-mouldingplastic-injection-moulding-uk/(accessed Aug. 18, 2017).[29] National Clinical Guideline Centre, Preoperative tests, 2015.[30] J. Papu, M. Rust, A. Browne, A Portable Centrifuge for Point-of-Care Measurement of Hematocrit in Low-Resource Settings, J. Near-Patient Test. Technol. (2014) 48-53.[31] Drucker Diagnostics, QBC Dry Hematology Analyzelysers for Point of Care Testing, (2017). https://druckerdiagnostics.com/point-of-care-hematology-testing/(accessed Aug. 18, 2017).[32] C. Chin, S. Chin, T. Laksanasopin, S. Sia, Low-Cost Microdevices for Point-of-Care Testing, in: Point Care Diagnostics a Chip, 2013: p. Chapter 1.[33] R. Narayan, Microfluidic platforms for POC medical diagnostics, in: Med. Biosens. Point Care Appl., 2016: p. Chapter 11.[34] Espacenet, (2017). https://worldwide.espacenet.com/ (accessed Aug. 18, 2017