making more organs available for successful transplantation · (pgd) affects 15-20% of the lung...

White Paper

Making more organs available for successful transplantation

March 2019 – Version 1.0

Industriepark Zwijnaarde 7C

9052 Zwijnaarde

Belgium

www.mycartis.net P a g e | 1

Timely objective assessment of donor organs during

normothermic perfusion:

Making more organs available for

successful transplantation

MyCartis Wouter Laroy, Chief Scientific Officer – [email protected]

Toon Venneman and Els Decoster

Newcastle University & Newcastle Upon Tyne Hospitals

Andrew J Fisher, Professor of Respiratory Transplant Medicine & Dean of Clinical Medicine, Faculty of

Medical Sciences, Newcastle University and Academic Director and Honorary Consultant Physician,

Institute of transplantation, Freeman Hospital, Newcastle Upon Tyne Hospitals NHS Foundation Trust

Anders Andreasson, Morvern Morrison and Catriona Charlton

MyCartis has made great progress in the development of a fast (<30min) immunoassay to

objectively assess the fitness of a donor organ during perfusion as well as help predict the post-

transplant survival. This is the first assay that meets the demanding analytical performance criteria

required in this emerging field and is ideally suited for repeated testing during the course of an

organ perfusion process. These assay characteristics are made possible through a combination of

high quality DMAT® features as well as unique Evalution® platform concepts.

When implemented, the transplant team would get timely insight on the organ’s

fitness for transplantation and gain confidence in the outcome of this life-saving

procedure.

MyCartis has now set up with a specialized transplant center to run its first pre-clinical tests in the real

environment. As a first model case, donor lungs undergoing perfusion are objectively assessed for

their inflammation status using an ultra-fast and reliable IL-1β assay, a choice based on the findings

of Prof. Dr. Andrew Fisher and team (Newcastle University) who demonstrated that this biomarker

can be predictive for organ fitness as well as subsequent recipient survival. Together with its fast

turnaround time, Evalution®’s specific cartridge form factor allows for repeated testing of the organ,

allowing the transplant team to decide on the right timing for transplantation. The same unique

model assay concept can be easily applied to other biomarkers as well as to other organs, enabling

multiplex organ perfusion panel testing.

The perfusion procedure and companion test has the potential of making

available for transplant 30% more organs from those initially deemed unsuitable

ones and could therefore have a serious positive effect on waiting list size and

mortality.

The market potential of perfusion is growing at a double digit CAGR and expands from lungs into

heart, liver, kidney and other solid organs.

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Contents 1. Introduction ..................................................................................................................................................... 3

1.1. The clinical need for organ perfusion .................................................................................................... 3

1.2. Objective fit-for-purpose testing during Ex-vivo Lung Perfusion (EVLP) ........................................... 5

2. The DMAT® technology ................................................................................................................................. 6

2.1. The DMAT® technology explained ......................................................................................................... 6

2.2. Connection the dots: Organ perfusion testing meets DMAT® .......................................................... 8

3. The IL-1β test for the objective assessment of donor lung quality and readiness during EVLP ........ 9

3.1. Assay setup ................................................................................................................................................. 9

3.2. Analytical sensitivity and specificity ..................................................................................................... 10

3.3. Analytical accuracy and precision ..................................................................................................... 12

3.4. Perfusion sample testing ......................................................................................................................... 12

3.4.1. Precision ................................................................................................................................................ 12

3.4.2. Matrix interference ............................................................................................................................. 13

3.4.3. Technology comparison .................................................................................................................... 14

3.5. Repeated testing during perfusion ...................................................................................................... 15

4. Conclusions and Future directions ............................................................................................................ 17

5. Bibliography .................................................................................................................................................. 18

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Facts

In 2016, 135,860 solid organ transplants (kidney, liver, heart, lung, pancreas and small

bowel) were reported globally, an increase of >7% versus 2015

This covers <10% of the global need for organs

There is a shortage of donor organ supplies. Actually, only 3 in 1000 people die in a

way that currently allows for organ donation. On the other hand, one organ donor can

save 8 lives

Of the organs that become available, only 20-25% (lungs and hearts) to 80% (livers) are

actually used for transplantation

In 2015 and 2016 the US saw over 30,000 transplants per year. Yet, every 10 minutes,

someone is added to the transplant waiting list

Over 25% of patients in need of an organ die while being on the waiting list or are

removed from it. In the US alone, 20 people die each day while waiting for a transplant

1. Introduction

1.1. The clinical need for organ perfusion

Each day, tens of thousands of patients are waiting for that one call that says a match is found and an

organ is available for transplantation. A call that could transform or save their life. However, the Global

Observatory of Donation and Transplantation (GODT) reports that, despite yearly growing transplantation

numbers, organ waiting lists remain growing at an even faster pace (Figure 1). Clearly, there is a shortage

of suitable donor organs for transplantation, as the demand is larger than the availability. As a result,

people stay on a waiting list for longer times with a reduced quality of life, or they die while waiting. This

represents a huge burden for the patient, for his or her direct environment, for the healthcare system and

for the overall community.

According to multiple reports, it is not only the quantity of available donor organs that is the problem, but

also their quality. There are clear problems during organ recovery, storage and transport and these have

a proven effect on transplantation success (e.g. Primary Graft Dysfunction, Acute Rejection).

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Figure 1 – The waiting list growth largely exceeds available donor and transplantation numbers

Europe and global: http://www.transplant-observatory.org/ - US: https://optn.transplant.hrsa.gov/

Sadly, the gap between the number of patients on an organ waiting list and the number of available

and usable organs continues to widen. The shortage of fit-for-purpose donor organs has been globally

recognized as the major limiting factor to organ transplantation (Girlanda, 2016).

To grow the number of available donors, several initiatives have been taken to convince more people

to support the “gift of life” after death, and certain countries have decided to step away from the

consent-based or “opt-in” system and use a presumed consent or “opt-out” system instead. However, it

seems like these actions alone have not dramatically increased the number of donor organs available

or at least not to a level able to halt the waiting list growth.

Improved organ procurement and transplantation procedures (Cantu, 2017) have certainly led to

improved success in treating critically ill patients. It is well recognized that the right preservation solutions

and techniques between procurement and transplantation, actions that can take place 100s or even

1000s of kilometers apart, determine donor organ quality (Salehi, 2018). Reports do indicate a direct link

between organ quality and survival after transplantation. The current clinical gold standard for

preservation of solid organs is still static cold storage (SCS), despite the knowledge that prolonged SCS

increases the risk of early graft dysfunction. Moreover, ischemia occurs in the absence of sufficient

oxygen and glucose supply to the organ.

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017

WAITING LIST (US)

DONORS RECOVERED (US)

TRANSPLANTS PERFORMED (US)

DONORS RECOVERED (EUROPE)

TRANSPLANTS PERFORMED (EUROPE)

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The major challenge in organ preservation is to maintain or regain the viability and function of the organ

in the absence of a blood supply or physiological stimulation. More recently, there has been a trend

towards the use of normothermic or warm machine perfusion systems (Van Raemdonck, 2018; Akateh,

2018) to enhance the functional preservation of solid organs like livers (Nasralla, 2018), lungs (Figure 2)

(Slama, 2017; Andreasson A. , 2017; Van Raemdonck, Ex-vivo lung perfusion, 2015), hearts (Messer, 2015)

and kidneys (Hosgood, 2018; DiRito, 2018). Re-conditioning of solid donor organs by ex-vivo machine

perfusion is becoming increasingly established and is making its move from research to clinical

application, in small as well as large transplant centers (Fisher, 2016; Rosso, 2018; Salehi, 2018).

Figure 2 – A typical Ex-vivo Lung Perfusion (EVLP) setup

1.2. Objective fit-for-purpose testing during Ex-vivo Lung Perfusion (EVLP)

For many patients, lung transplantation is the only effective treatment available for end-stage lung

disease. Of all the donor lungs coming available, about 80% are deemed unsuitable for transplantation

because of clinical impression of sub-optimal quality. Furthermore, severe primary graft dysfunction

(PGD) affects 15-20% of the lung transplant recipients, carrying a high morbidity and mortality risk.

To improve the outcome for patients in need of a donor lung, the use of the pool of donor lungs that

become available needs to be optimized. EVLP has been described as a promising tool to do so (Hsin,

2018; Pan, 2018). Next to the advantageous biological reasons mentioned above, the perfusion fluid is

also an ideal solution to use for the objective assessment of organ quality through measurement of

biomarkers of organ fitness or for PGD prediction. Hence, the continuous or repeated measurement of

those biomarkers during perfusion could help identify more donor organs that are fit for successful

transplantation.

Multiple studies have been conducted to identify biomarkers for different endpoints. Biomarkers to assess

the inflammatory status of the organ (Andreasson A. , 2017), tissue damage markers or PGD prediction

markers (Hashimoto, 2018; Hamilton, 2017) could all add to the target of identifying good organs.

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To allow such biomarkers to get successfully applied, they need to be measured rapidly at multiple

timepoints during perfusion. Not only the timing but also the time to get the result is of critical importance

as the window of opportunity for transplantation is small and hence the transplant team needs to make

a decision on the spot. For the same reason, samples cannot be sent to the central lab and thus

measurements must be done at the site where perfusion occurs. Easy workflows are therefore a must. The

ideal test would need to cover:

• Simultaneous measurement of multiple biomarkers (multiplex technology)

• Timely output (less than 30 minutes between sample taking and result)

• Easy workflow (as little as possible sample work-up)

• Repeated measurements at different timepoints during perfusion

• Demanding analytical specifications (sensitive, specific and robust)

2. The DMAT® technology

2.1. The DMAT® technology explained

Designed to combine accurate detection of clinically relevant biomarkers with fast, robust and

reproducible methods of testing, the Evalution® platform with its DMAT® (Dynamic Multi-Analyte

Technology) technology inside is well positioned to become an important next generation solution in the

immunoassay market (Faclonnet, 2015).

Evalution® is MyCartis’ multiplex analysis platform. Evalution® integrates into a single instrument all the

functions of incubation, washing and optical readout for seamless operation of sophisticated assay

protocols. In one platform, Evalution® combines an extended set of high-end technological features:

• A multiplexing platform requiring small sample volumes

o Tens of biomarkers can be measured from minute amounts of clinical sample. A

theoretical multiplex level of 150 analytes per channel can be reached.

• Fast and easy assays

o Reagents and samples are brought in close contact in the microfluidic channel, fully

eliminating the dependence on diffusion as a driver for assay turnaround time.

Therefore, only short incubation times with no or limited need for washing steps are

required. Evalution® offers an integrated assay workflow where all reactions occur on

board of the instrument, reducing user-induced variation.

• Assays can be assessed at end-point as well as in real time

o Reaction kinetics can be measured, allowing the qualitative next to quantitative

assessment of immune responses through the measurement of affinity or avidity. This can

be done directly from a clinical sample without the need for pre-fractionation.

o Binding reactions can be followed, generating a binding profile which can be used as

a quantitative measure. Such approach allows for ultra-fast turnaround times (minutes)

for biomarker assays that typically take hours.

• Highly flexible cartridge setup

o The 16 channels in the assay cartridge are individually controlled, opening new

opportunities in (multiplex) random access or time course patient assessment.

• High performance assays

o Calibration curve stability

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▪ Stability has been demonstrated for >1 month, obliviating the need for daily

calibration

o Sensitivity:

▪ Typically, single digit to sub-pg/ml analytical sensitivities can be reached in

complex clinical matrices

o Dynamic range:

▪ When using classical workflows with end-point readout, a biomarker dynamic

range of 4-5 logs can be covered, while with more advanced workflows this

number can be exceeded

o Precision:

▪ Intra-assay (replicates on the same cartridge) and inter-assay (replicates on

different cartridges) of <5%CV or <10%CV can be reached after limited

development efforts, when using good quality reagents

o Agreement with other technologies:

▪ Good agreement and clinical concordance with competing technologies and

platforms (ELISA, Luminex, Phadia, MSD…) has been extensively demonstrated

The DMAT® technology on board of the Evalution® platform is built around three major components:

• The encoded multifunctional microparticles

• A disposable microfluidic cartridge

• Software to control the assays and to evaluate the output

The platform’s proprietary encoded microparticles drive the system’s multiplexing capability. By

encoding each microparticle with a physical binary code, the optical system of the instrument is able to

precisely and uniquely identify each microparticle. With 1024 unique digital codes available a maximum

multiplexing capability of 150-plex per channel can be achieved from a single reporter dye. Once the

appropriate capture biomolecules have been covalently attached to the surface of the microparticles,

these functionalized microparticles are loaded into the microfluidic channels of the cartridge. Each code

is associated with a specific assay, which can be combined in multiple populations within the cartridge

channel for multiplexing.

The Evalution® cartridge is a disposable consumable, which has 16 individually actuated microfluidic

channels capable of accepting and processing from 1 to 16 samples concurrently or sequentially. Fast

biological reactions using small sample volumes, are made possible thanks to a specific reaction-limited

binding environment, i.e. diffusion dependence is taken out. Microparticles are retained within the fluidic

channels at the detection zone behind a filter wall where they form a planar layer and precisely located

targets for the optical system, to interrogate the reactions on each individual microparticle in real time.

As such, each microparticle represents an individual reportable reaction or assay, and jointly they create

one assay environment with different (multiplex) and identical (replicates) assays in one single run.

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Figure 3 – The Evalution® platform with DMAT technology inside

The Evalution® platform can accommodate one 16-channel cartridge (top-left). Within each channel a

multiplex analysis can be performed in real-time (right). Microparticles (bottom left) with different codes have

different immunoassays built on top and are imaged for decoding and identification (birght-field) and

quantification (fluorescence).

2.2. Connection the dots: Organ perfusion testing meets DMAT®

Most classical immunoassay technologies and platforms cannot comply to the steep requirements for

EVLP testing. The Evalution® platform with the DMAT® technology inside now provides the necessary

specifications to allow objective testing during organ perfusion:

• Simultaneous measurement of multiple biomarkers (multiplex technology)

o DMAT is a multiplex platform that can measure tens of biomarkers simultaneously

• Timely output (less than 30 minutes between sample taking and result)

o Binding reaction in DMAT do not rely on diffusion and are therefore fast

• Easy workflow (as little as possible sample work-up)

o DMAT needs no or short washes and allows simultaneous incubation of samples and

reagents, largely reducing sample/reagent processing times

• Repeated measurements at different timepoints during perfusion

o DMAT’s cartridge with 16 individually actuated channels allows straightforward

repeated testing

• Demanding analytical specifications (sensitive, specific and robust)

o DMAT can reach clinically relevant analytical specifications

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This study describes a multiplex but yet single biomarker assay for the assessment of the inflammatory

status of a donor lung under perfusion (Andreasson A. , 2017): a robust and sensitive IL-1β assay that

provides results within 30 minutes using a technology that allows for repeated testing. Starting from this

assay concept and using the same assay principals, additional biomarkers can be added to create a

comprehensive testing panel for perfusion testing.

3. The IL-1β test for the objective assessment of donor lung quality and readiness during EVLP

3.1. Assay setup

The DMAT® technology allows for multiplex analysis, through the use of microparticle populations with

different binary codes. To each different code, different molecules are coupled. Next to measuring

multiple specific biomarkers simultaneously, such feature also provides the opportunity to add internal

controls to the assay. Here, a four-plex assay was developed for the specific and reliable quantitation of

IL-1β in lung perfusion samples (Figure 4). To the assay-specific microparticle population, three control

populations were added:

• CODE 1 : anti-hIL-1β

o For the specific capture of IL-1β in samples

• CODE 2 : recombinant hIL-1β

o A positive reagent control

• CODE 3 : Uncoupled

o A negative assay control

• CODE 4 : Mouse Anti-hIL-8

o A negative assay control

o Amongst others, a control for potential Heterophilic Anti-Mouse Antibody (HAMA)

interference

For this assay, a so-called “co-flow” workflow setup was chosen. Such workflow is made possible thanks

to two DMAT® -specific features: (1) the specific binding regime in the microfluidic channel of the assay

cartridge, where binding occurs independent of diffusion, and (2) the binding reaction that occurs under

continuous flow, where assay components are constantly replenished. Next to the advantage of fast

reactions, this also allows for easy workflows where liquid handling steps are reduced to a minimum.

Prior to incubation, the sample (calibrator, QC or perfusate sample) is premixed with the anti-hIL-1β

detection antibody and immediately loaded on the cartridge where the flow over the particles is

induced. After sample incubation under continuous flow and a short and single wash step, the result is

immediately read, processed and provided to the user. A batch-specific pre-loaded calibration curve

allows for immediate conversion of fluorescence values into actual biomarker concentrations and the

in-channel control values confirm the validity of the result.

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Figure 4 - Assay setup and workflow

3.2. Analytical sensitivity and specificity

Six calibration curves are generated over a time period of 3 weeks (Figure 5). One calibration series

consists of STEEN solution spiked with 12 different concentrations of recombinant IL-1β. Each calibration

series is then fitted using a non-linear 4-parameter logistic (4PL) regression model with a weighing factor

of 1/Y². From this, an accuracy & precision profile is generated. For precision, an acceptance level for

the coefficient of variation (CV) of 15% is used in the linear range of the curve and 20% near the limit of

quantitation (LOQ). For accuracy, a ≤15% deviation of the measured or observed concentration from

the expected value (O/E) is accepted in the linear range of the curve and ≤20% near the LOQ.

The lowest calibrator for which precision and accuracy criteria are met contains 5 pg/ml IL-1β. For the

next calibrator (2 pg/ml), this is no longer the case. Hence, the lower limit of quantitation (LLOQ) lays

between 2 and 5 pg/ml. At the highest calibrator concentration level tested (10 ng/ml), acceptance

criteria for precision and accuracy are met and therefore the upper limit of quantitation (ULOQ) is at

least 10 ng/ml. Within the context of this assay, the assay dynamic range between LLOQ and ULOQ is

expected to suffice to test perfusion samples. The robust behavior of the calibration curve over 3 weeks

also demonstrates system stability and indicates that future use of company-provided calibration curves

is feasible.

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Figure 5 - Calibration curve precision, accuracy and quantification range

To determine the limit of detection (LOD) of this assay, 22 blank samples (STEEN solution) are measured.

The average signal supplemented with three times the standard deviation (SD) on these measurements

is back-calculated using the calibration curve. Doing so, an LOD of 2.7 pg/ml was estimated.

The presence of other cytokines in perfusion fluids was illustrated before. Hence, a specificity study is

performed where cross-reactivity with cytokines with shared common ancestry and structural similarity is

tested (Figure 6). To solutions with no or spiked IL-1β content, high concentrations of other cytokines were

added and the effect on the specific signal assessed. No cross-reactivity could be observed, hence this

assay should be considered highly specific for IL-1β.

Figure 6 – The assay specifically measures IL-1β

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3.3. Analytical accuracy and precision

Three independent run validation quality control (QC) samples were included in all 22 assay runs (one

run includes 16 channels in a cartridge) performed over three weeks. A high (1000 pg/ml), mid (100

pg/ml) and low (10 pg/ml) QC sample was prepared and stored frozen in single-use aliquots. From these,

the inter-assay precision as well as the accuracy over 3 weeks and at different concentrations can be

assessed.

For the high-, mid- and low-QC, inter-assay precision and accuracy (Figure 7) are well within acceptance

criteria (same as in 3.2), illustrating a very stable assay and system performance.

Figure 7 – Run validation control accuracy over 3 weeks (n=22) illustrates stable assay performance

3.4. Perfusion sample testing

Lung perfusions were performed using the STEEN solution (Prof. Dr. Andrew Fisher, Newcastle upon Tyne)

and samples were taken at different timepoints and frozen. IL-1β was measured later using the MSD

cytokine assay (V-PLEX Plus Proinflammatory Panel 1 (human) Kit from MesoScale Discovery). Here, 15

random samples from that series, taken at different timepoints, were used for evaluation of the IL-1β assay

on the Evalution® platform.

3.4.1. Precision

On day one, 15 perfusion samples were measured in duplicate. On day two, 9 of these were measured

again in duplicate.

All samples with a quantifiable concentration above LOD showed an intra-day %CV of ≤15%. For all

tested samples covering the full range of IL-1β concentrations measured so far in perfusion fluid, an inter-

day %CV of ≤12% is obtained. Both results fall well within typically accepted acceptance criterium (≤15%)

for this type of assay.

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Figure 8 – Intra- and inter-assay precision

3.4.2. Matrix interference

Three lung perfusion samples with confirmed IL-1β content were serially diluted in STEEN solution to assess

linearity of dilution for this assay and to identify potential matrix effects. Dilution linearity is achieved if the

back-calculated and dilution-corrected concentrations of the different dilutions don’t deviate more

than 20% from the undiluted concentration. For the 3 samples and for all tested dilutions with a value

above LOD, a percent recovery between 93% - 107% was obtained (Figure 9), well within the typical

acceptance range.

Alternatively, linearity of dilution is assessed by linear correlation coefficient (R2) calculation. For all

samples, an R2 of 0.999 was obtained, even when including the non-diluted sample (Figure 9).

Matrix interference can also be assessed using a spike-recovery experiment. Here, a high (1000 pg/mL)

or a low amount (10 pg/mL) of recombinant IL-1β is spiked into four different perfusion samples with

confirmed levels of endogenous IL-1β. The value of the non-spiked lung perfusion sample was subtracted

from the value of the high and low spiked lung perfusion sample. This results in a ‘recovered value’ from

which the % recovery is determined. Where the non-spiked sample had endogenous levels below LOD,

no value was subtracted. The calculated recovery of the spiked material indicates if the expected value

can be measured accurately, without the interference of the matrix it is spiked in. For all spiked lung

perfusion samples a % recovery between 84% – 102% is obtained (Figure 10) which is well within the

acceptance criterium for a good spike/recovery (100 ±20%).

These combined results clearly demonstrate that no matrix interference is observed when analyzing

perfusion samples (STEEN solution) and that perfusion samples can be tested without prior sample dilution.

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Figure 9 – Matrix interference

Figure 10 – Spike & Recovery

3.4.3. Technology comparison

The 15 samples were analyzed in duplicate using the Evalution® method. At the time of that

measurement, MSD results were blinded to the MyCartis operators.

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The data indicates that both methods generate similar results above the Evalution® LOD (2.7

pg/ml)(Figure 11). This is confirmed by a good overall linear correlation coefficient (R²) of 0.894. The only

technology discrepancy is found for one sample for which a value of 9.4 pg/mL is measured with the

Evalution® technology (and a %CV of 6% between the duplicate analysis) and a value of 24 pg/mL is

measured with the MSD method. If this sample is removed from the correlation plot, an R² of 0.977 is

obtained. At this moment, it is unclear what causes the discrepancy in results of this sample. At the low

end of the concentration range, Evalution® seems to still reliably separate samples, whereas MSD seems

to have reached the limit of detection.

Figure 11 – Technology comparison

3.5. Repeated testing during perfusion

As illustrated above, the DMAT® technology is unique in its ability to measure challenging biomarkers with

the right analytical and technical specifications. The fact that robust cytokine readouts can be achieved

within less than 30 minutes with little to no sample workup is unique.

Next to the analytical performance, the cartridge design is ideally suited for repeated testing during

perfusion. Each cartridge contains 16 channels that can be individually actuated (Figure 12). Upon start

of perfusion, such cartridge can be loaded in the Evalution® platform, where it then resides until the end

of the perfusion process (i.e. the decision point for transplantation or rejection). At specific timepoints,

samples can be taken from the perfusion circulation and immediately loaded and analyzed in one of

the channels. This can be repeated until satisfactory values are obtained and a decision is taken to

accept or reject the organ.

To prove that the assay format is stable when stored in the platform and repeatedly used, a cartridge

was loaded in the platform and quality control (QC) samples as well as a real perfusion sample were

loaded and analyzed at different timepoints without intermediate unloading and storage of the

cartridge (Figure 13). Clearly, no deterioration of signal could be observed.

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Figure 12 – Multiplex interval testing during EVLP

Figure 13 – Repeated testing during EVLP is stable

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4. Conclusions and Future directions In this study, a highly performant Evalution® IL-1β assay is described for the timely assessment of

inflammation in donor lungs during perfusion. The assay doesn’t only meet the analytical requirements

but is also practically applicable in a transplant perfusion laboratory while perfusion is ongoing,

generating real-time data on donor organ suitability for transplant. Such information should help the

transplant surgeon and team on the use of an organ, the timing of the surgery and give them confidence

in a positive outcome for the recipient.

This assay can be considered as a benchmark assay for objective testing during organ perfusion. Indeed,

the same assay principle can be applied to other biomarkers in order to build a panel of analytes that

can provide more relevant information on inflammation, tissue damage, risk for primary graft dysfunction

or any other process related to organ fitness. Multiple biomarker selection programs are currently ongoing

in public as well as private labs and this on perfusion fluids from different solid organs like lung, heart, liver

or kidney. Organ-specific panels can be developed as well as general ones for common processes.

Despite a better overall awareness of the importance of organ donation and the implementation of

improved procurement, storage and transplantation conditions, waiting lists for donor transplantation

continue to grow. According to key opinion leaders in the field, the perfusion procedure and a

companion test have the potential of making available for transplant 30% more organs from the initially

rejected ones and could therefore have a serious positive effect on waiting list size and mortality.

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5. Bibliography Akateh, C. (2018). Normothermic ex-vivo liver perfusion and the clinical implications for liver transplantation. J Clin

Transl Hepatol, 6, 276-282.

Andreasson, A. (2017). Profiling inflammation and tissue injury markers in perfusate and bronchoalveolar lavage fluid

during human ex vivo lung perfusion. Eur J Cardiothorac Surg, 51(3), 577-586.

Andreasson, A. (2017). The role of interleukin-1b as a predictive biomarker and potential therapeutic target during

clinical ex vivo lung perfusion. J Heart Lung Transplant, 36, 985–995.

Cantu, E. (2017). Evaluation and Management of the Potential Lung Donor. Clin Chest Med, 38(4), 751-759.

DiRito, J. (2018). The future of marginal kidney repair in the context of normothermic machine perfusion. Am J

Transplant, 18, 2400-2408.

Faclonnet, D. (2015). Rapid, Sensitive and Real-Time Multiplexing Platform for the Analysis of Protein and Nucleic-

Acid Biomarkers. Anal Chem, 87, 1582-1589.

Fisher, A. (2016). An observational study of Donor Ex Vivo Lung Perfusion in UK lung transplantation: DEVELOP-UK.

Health Technol Assess, 20(85), 1-276.

Girlanda, R. (2016). Deceased organ donation for transplantation: Challenges and opportunities. World J Transplant,

6(3), 451-459.

Hamilton, B. (2017). Protein biomarkers associated with primary graft dysfunction following lung transplantation. Am J

Physiol Lung Cell Mol Physiol, 312, L531-L541.

Hashimoto, K. (2018). Higher M30 and high mobility group box 1 protein levels in ex vivo lung perfusate are

associated with primary graft dysfunction after human lung transplantation. J Heart Lung Transplant, 37,

240-249.

Hosgood, S. (2018). Normothermic machine perfusion for the assessment and transplantation of declined human

kidneys from donation after circulatory death donors. Br J Surg, 105, 388-394.

Hsin, M. (2018). Ex vivo lung perfusion: a potential platform for molecular diagnosis and ex vivo organ repair. J Thorac

Dis, 10(Suppl 16), S1871-S1883.

Messer, S. (2015). Normothermic donor heart perfusion: current clinical experience and the future. Transpl Int, 28(6),

634-642.

Nasralla, D. (2018). A randomized trial of normothermic preservation in liver transplantation. Nature, 557(7703), 50-56.

Pan, X. (2018). Application of ex vivo lung perfusion (EVLP) in lung transplantation. J Thorac Dis, 10(7), 4637-4642.

Rosso, L. (2018). Lung transplantation, ex-vivo reconditioning and regeneration: state of the art and perspectives. J

Thorac Dis, 10(Suppl 20), S2423-S2430.

Salehi, S. (2018). Advances in Perfusion Systems for Solid Organ Preservation. Yale J Biol Med, 91, 301-312.

Slama, A. (2017). Standard donor lung procurement with normothermic ex vivo lung perfusion: A prospective

randomized clinical trial. J Heart Lung Transplant, 36(7), 744-753.

Van Raemdonck, D. (2015). Ex-vivo lung perfusion. Transpl Int, 28(6), 643-56.

Van Raemdonck, D. (2018). Machine perfusion of thoracic organs. J Thorac Dis, 10(Suppl 8), S910-S923.

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