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\begin{document}

\title{Impact of Ethanol on Continuous Inline Diafiltration of Liposomal Drug
Products}



\author[1]{Rob Worsham}%
\author[2]{Vaughan Thomas}%
\author[3]{Suzanne Farid}%
\affil[1]{Affiliation not available}%
\affil[2]{University College London}%
\affil[3]{UCL}%


\vspace{-1em}



  
  \date{May 22, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Liposomal drug products are playing an increasing role in the field of
drug delivery. With this increased demand comes the need to increase the
capabilities and capacity of manufacturing options. Continuous
manufacturing techniques present a significant opportunity to address
these needs for liposomal manufacturing processes. Liposomal
formulations have unique considerations that impact translation from
batch to continuous process designs. This article examines aspects of
converting to a continuous design that were previously viewed as
inconsequential in a batch process. The batch process involves the
removal of ethanol through tangential flow filtration (TFF). Ethanol was
found to reduce the permeability of the hollow fibers used for TFF. This
effect was determined to have minimal impact on the overall batch
process design but considerable influence on the design of continuous
TFF such as inline diafiltration (ILDF). Using a pilot scale setup,
ethanol was found to decrease permeability in an inverse manner to
ethanol concentration. Further assessment found that dilution of the
ethanol levels prior to diafiltration can significantly reduce the
amount of ILDF stages needed and that a continuous design requires less
buffer to the commensurate batch design.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Liposomes are nano-scale spherical-shaped vesicles comprised of one or
more phospholipid bilayers, that have the ability to encapsulate
hydrophilic or lipophilic drugs for the purposes of targeted drug
delivery (Akbarzadeh et al. 2013). A number of strategies have been
demonstrated for liposome manufacture but have suffered from the lack of
reliable methods with sufficient throughput to enable a commercial scale
(Mozafari 2005, Maherani et al. 2011, Worsham et al. 2019). Strategies
for liposome synthesis focus on addressing and optimizing one or several
of the key driving forces of vesicle assembly including the component
solubilities, concentrations, and process thermodynamic parameters
(i.e., temperature, pressure, etc.) (Mozafari 2005, Maherani et al.
2011). Manufacturing methods can be designed to fine-tune liposomes with
various properties and, in doing so, can lend both advantages and
disadvantages amenable to large-scale processing. In addition, selection
of the manufacturing method often depends on the end product
requirements for clinically efficacy including liposome size and size
distribution, lipid composition, and the drug release characteristics,
which together dictate the pharmacokinetic demonstration of adsorption,
distribution, metabolism, and elimination (ADME).

The most successful examples of scaled methods for liposome manufacture
to date have followed the principles of alcohol injection (Figure 1A) or
crossflow techniques (Figure 1B), wherein dissolved lipids are
precipitated from an organic solvent into an aqueous solution
(anti-solvent) by means of reciprocal diffusion of the organic and
aqueous phases (Jaafar-Maalej et al. 2010, Wagner et al. 2002, Wagner et
al. 2002, Wagner et al. 2011, Wagner et al. 2002). A change in the local
solubility of the lipids during this process ultimately leads to the
spontaneous formation of liposomes that encapsulate a small volume of
the aqueous solution. Depending on the chemical nature of the API, it
can be encapsulated in the aqueous core or embedded in the lipid layer.
The critical parameters for the formation of liposomes by this method
are residence time and geometry of the mixing/intersection of
organic-solvated lipid and the antisolvent which are dictated by
programmed flow conditions. After liposome formation, the mixture
containing undesired organic solvent and unencapsulated API can then be
refined to the desired formulation strength and composition using TFF or
similar methods (Wagner et al. 2002, Kim et al. 2012, Li et al. 2011).

The aforementioned production methods were designed to operate as a
batch process, but the crossflow method is based on a liposome formation
step which is continuous in its inherent mechanism (Figure 1B). So long
as each feed stream is continuously fed, liposomes will be continuously
generated. With continuous formulation of the feed solutions, the
liposome formation step can proceed indefinitely. By implementing a
continuous version of TFF, which supports refinement of the drug product
to the desired end formulation, continuous manufacturing of liposomal
drug products is a feasible concept.

Continuous versions of TFF have been explored for similar applications
in the biologics sector. For continuous perfusion cell culture, the
industry has moved from internal spin-filters to external retention
devices such as alternating tangential flow (ATF) or TFF systems for
media exchange (Pollock et al. 2013, Castilho 2015, Whitford 2015).
Single pass tangential flow filtration (SPTFF) has been evaluated for
cell culture harvest concentration and for protein concentration
allowing this process step to happen in a continuous fashion instead of
the batch mode required by traditional TFF (Arunkumar et al. 2017, Casey
et al. 2011, Brower et al. 2015, Jungbauer 2013, Dizon-Maspat et al.
2012, Subramanian 2015). TFF concentrates product through multiple
passes of a recirculating loop while SPTFF concentrates in an inline
fashion with a single pass through multiple TFF cassettes in series.
SPTFF enables product to be continuously fed to the next unit operation
or process step with the additional benefits of lower system hold-up
volumes. Designs for multiple SPTFFs in series, such as the Cadence\selectlanguage{ngerman}®
In-line Diafiltration Module (ILDF), are becoming available and have
been explored (Gjoka et al. 2017). Applying the ILDF design as the
TFF/formulation refinement step in a continuous process is shown in
Figure 1C.

Previous work with continuous TTF designs has focused on biologics
manufacture. To date, no examination of continuous TFF designs have been
performed for liposomal drug product manufacture. This paper
investigates the impact of the organic solvent used in liposome
formation, namely ethanol, on the design of continuous inline
diafiltration for a liposomal drug product. To explore the continuous
TFF/formulation refinement for liposomal drug product, a pilot scale
ILDF system mimicking Figure 1C was explored (Figure 1D). The set up in
Figure 1D evaluated a simulated ILDF configuration by analysing each
concentration pass and dilution step individually with the objective of
determining the number of passes/stages and the buffer consumption
needed to achieve target solvent removal. The results were compared to
the batch process option.

\textbf{Methods}

\emph{Feed Materials}

The amikacin solutions were prepared by dissolving amikacin sulfate in
water-for-injections (WFI) or deionized water at 45 mg/ml amikacin base
and pH adjusted to 6.7 using NaOH.

The lipid solution were prepared by dissolving DPPC and cholesterol at a
2:1 weight ratio in 100\% ethanol at 20mg/ml.

The buffer solution consists of 1.5\% NaCl in water-for-injections (WFI)
or deionized water.

\emph{Batch Processing}

The batch process for manufacturing liposomal drug product, as depicted
in Figure 1B, involves mixing/infusing streams of lipid solution and
amikacin solution at the crossflow point in an approximate 1:2 ratio.
The magnitude of these flow rates is dependent on the scale of the
process. The output of the crossflow point (``liposome mixture'') is fed
into a central vessel concurrently with the buffer solution. Constant
volume diafiltration (CVDF) is performed using a 50kDa GE hollow fiber
cartridge (UFP-500-E-85MSM) until six diavolumes have been processed.
The product is then concentrated through the hollow fiber membrane until
the retentate reaches 70mg/ml amikacin base.

\emph{Simulated ILDF System}

The simulated ILDF system consists of the same or similar equipment used
in the batch process. The equipment was arranged in a manner supporting
multiple independent concentration/buffer exchange passes with
opportunity for sampling and dilution with buffer in between each pass
(Figure 1D). The hollow fiber cartridge was not replaced in between
passes. A new hollow fiber was used for each bulk run on the simulated
ILDF system.

The starting solutions and liposome mixture as described previously were
used as a starting material. The buffer solution was used to pre-dilute
the liposome mixture as described in the Results Section.

\emph{Analytical Methods}

Total amikacin concentrations were measured by high-performance liquid
chromatography (HPLC) using a Hypersil GOLD C18 column (175 Å, 3 \selectlanguage{greek}µ\selectlanguage{english}m, 150
mm \selectlanguage{ngerman}× 4.6 mm) with a mobile phase of 65\% methanol, 35\% water, and 0.3\%
penta\selectlanguage{english}fluoropropionic acid (PFPA). An aliquot of each liposome suspension
was centrifuged in an Amicon Ultra-0.5 centrifugal \selectlanguage{english}filter unit with
Ultracel-30 KDa membrane to separate free amikacin, and the amount of
unencapsulated amikacin was then determined by HPLC.

Lipid concentrations were measured by HPLC using an XBridge C8 column
(130A, 3.5\selectlanguage{greek}µ\selectlanguage{english}m, 150mm \selectlanguage{ngerman}× 4.6mm) with a mobile phase A consisting of 49.9\%
acetonitrile, 49.9\% water, 0.1\% acetic acid, and 0.1\% triethylamine
and mobile phase B consisting of 44.9\% acetonitrile, 45\% isopropyl
alcohol, 10\% water, 0.1\% acetic acid, and 0.1\% triethylamine.

Residual ethanol or ethanol concentrations were measured by using gas
chromatography (GC) using a Duraguard DB.

\textbf{Results}

In this section, the batch process diafiltration was assessed keeping in
mind the desire to convert to a continuous diafiltration or ILDF
configuration. These learnings were then applied to an experimental set
up (Figure 1D), designed to mimic the ILDF configuration. These results
were evaluated for trends and optimal conditions with respect to an ILDF
process.

Batch Process

In order to properly compare batch to continuous TFF performance, the
batch TFF must be better understood. The process begins with the
liposome formation mixture entering the central vessel at the same time
as the buffer. At the completion of the liposome formation step, the
central vessel contains approximately 30\% ethanol, which is slightly
less than the undiluted liposome formation mixture due to the dilution
from the added buffer stream.

The batch TFF operates as constant volume diafiltration (CVDF) with
constant inlet pressure to the hollow fiber. Permeate flow is fully open
with no applied back pressure. The buffer stream flow matches the
permeate flow to maintain constant volume.

Evaluation of the batch TFF performance showed an unexpected phenomenon.
Regardless of the constant inlet pressure, the permeate flow showed an
increasing pattern as the diavolumes progressed as opposed to a constant
flow. Shown in Figure 2A, permeate flow increased and leveled off
through the run. Further assessment of the permeate, showed that the
ethanol concentration decreased linearly as the permeate flow increased
(Figure 2A), indicating permeate flow and ethanol concentration may be
interrelated.

Ethanol (EtOH) would be expected to be removed during TFF in a manner
following the standard diafiltration equation (Equation 1) such
that\(c_{0}\)= initial EtOH concentration, \(c\)=
final EtOH concentration, \selectlanguage{greek}α \selectlanguage{english}= EtOH rejection coefficient and
\(\frac{V_{b}}{V_{s}}\) = diavolumes.

\(c=c_{0}e^{-(1-\sigma)\frac{V_{b}}{V_{s}}}\) (Equation 1)

It is common to assume the species being removed is free flowing and has
a rejection coefficient of 0. The results in Figure 2A indicate a
relationship between the ethanol concentration and the permeate flow.
The ethanol most likely has an impact on hollow fiber permeability,
which could be expressed as ethanol having a partial rejection
coefficient (\selectlanguage{greek}α\selectlanguage{english}). This is not unprecedented as ethanol has shown hollow
fiber swelling and impact on permeability in other industrial
applications (Otitoju T.A. et al. 2017, Kochan J. et al. 2009).

To assess the ethanol rejection coefficient, the batch process was
assessed with the Equation 1 model. Using an inline density measurement
on the permeate, the EtOH concentration was calculated across the
diavolumes. Applying mass balance for the system, the retentate EtOH
concentration was calculated, and the curve fitted to Equation 1 to
determine the EtOH rejection coefficient. As shown in Figure 2B, the
rejection coefficient (\selectlanguage{greek}α\selectlanguage{english}) calculated to approx. 0.5 as opposed to the
zero coefficient that would have been assumed. Additionally, the fit
showed the rejection coefficient to be slightly greater than 0.5 early
in the process and slightly less than 0.5 later in the process,
indicating an ethanol concentration dependency to the rejection
coefficient.

While not having a significant impact on the overall batch process, an
ethanol concentration dependent rejection coefficient is interesting to
consider in a continuous TTF/ILDF system. In the system shown in Figure
1C, the initial hollow fiber pass/stage of the ILDF system would contain
higher concentrations of ethanol and would presumably be less
efficient/permeable than the later hollow fiber passes/stages. Thus,
more passes would be needed to remove the first portion of the ethanol
as compared to later portions. Based on this, additional upfront
dilution of the initial liposome formation mixture should increase
permeability and efficiency and reduce the amount of passes/stages
needed in a continuous design.

The ethanol concentration dependency phenomenon was further explored
regarding its impact on the continuous ILDF arrangement under the guise
that dilution/increased permeability decreases the number of
passes/stages needed and increases overall efficiency/benefit of the
system.

Continuous Inline Diafiltration

\emph{Hollow Fiber Passes and Ethanol Rejection Coefficient}

Continuous ILDF was explored using the arrangement shown in Figure 1D to
mimic the arrangement in Figure 1C, but with the ability to analyze the
output of each pass/stage. The entirety of the bulk of the liposome
formation mixture was fed through the hollow fiber as a single pass into
the second vessel. The retentate and permeate were analyzed, buffer
added to replace the permeate, then the entirety of the adjusted bulk
returned to the first vessel in preparation for another pass. This was
repeated until the target ethanol removal was achieved. The intent was
to simulate the arrangement in Figure 1C with the ability to assess the
output of each pass/stage discretely.

The assessment of the continuous ILDF arrangement involved processing
the post-liposome formation bulk mixture with various levels of
pre-dilution from undiluted (36\% EtOH) to significantly diluted (5\%
EtOH). The ethanol concentration was assessed after each pass/stage and
the process repeated.

Figures 3A and B show the results of the various initial ethanol
concentrations and the ethanol removal curves over the repeated passes.
As expected, ethanol concentration was reduced with each pass and the
number of passes needed to remove the ethanol decreased with decreased
initial ethanol concentrations. For example, an initial concentration of
24\% EtOH required 36 passes for target removal while starting at 5\%
EtOH required 9 passes. The behavior of the curves followed the pattern
of the diafiltration model (Equation 1) with passes in place of
diavolumes, but with a notable exception. Unexpectedly, the fitted
curves for the simulated ILDF results showed a variable exponent. The
exponent, where the rejection coefficient is contained in the
traditional diafiltration equation (Equation 1), was expected to be
constant, but instead, decreased as the initial ethanol concentration
decreased. This supports the previous notion of an ethanol concentration
dependent rejection coefficient.

From this, a continuous ILDF equation (Equation 2) was derived
where\(c_{0}\)= initial EtOH concentration, c\selectlanguage{ngerman} =
final EtOH concentration, \(P\) = simulated ILDF passes and
\selectlanguage{greek}α \selectlanguage{english}= ethanol rejection coefficient. The ethanol rejection coefficient is
dependent on the initial ethanol concentration, \selectlanguage{greek}α \selectlanguage{english}= \(f(c_{0})\).
This function was derived from the results in Figures 3A and B as shown
in Figure 3C and represented with Equation 3.

\(c=c_{0}e^{-\left(1-\alpha\right)P}\) (Equation 2)\(\alpha=f\left(c_{0}\right)=0.059\ln\left(c_{0}\right)+0.99\)(Equation 3)

\emph{Simulated ILDF Permeate}

Before fully applying the above equations to continuous ILDF systems,
aspects of the permeate stream should be considered. Figure 3D showed
the amount of permeate collected with each pass for the various initial
ethanol concentration runs (not all are show for visual simplicity). As
initial ethanol concentration decreased, the amount of permeate
collected increased. Additionally, as the ethanol concentration
decreased with each pass, the amount of permeate collected increased and
plateaued, but did not reach the same level as the plateaus for other
initial concentration runs. For example, 24\% initial ethanol
concentration started at 4.5kg/pass and plateaued at approx. 8kg/pass,
15\% initial ethanol concentration started at 7kg/pass and plateaued at
approx. 10kg/pass, and 7.5\% initial ethanol concentration started at
10.5kg/pass and plateaued at approx. 12kg/pass.

Figure 3E shows the permeate flow rates versus the retentate ethanol
concentration for each pass for various initial ethanol concentration
runs (not all are show for visual simplicity). Similar to the permeate
collected in Figure 3D, flow rates increased as ethanol concentrations
decreased and peak flow rates increased with decreasing initial ethanol
concentration. Specifically, 24\% initial ethanol concentration started
at 4kg/min permeate flow and peaks at approx. 9kg/min while starting
with 7.5\% ethanol gave 10.7kg/min and peaked with approx. 13kg/min. It
would be expected that a given ethanol concentration would yield a given
flow rate, similar to the ethanol concentration/permeate flow rate
results in Figure 2A (Permeate Flow vs. Ethanol concentration). Instead,
when the initial ethanol concentrations of 24\%, 15\%, and 7.5\% were
reduced to 5\%, for example, the flow rates were 7.5, 9.0 and
11.3kg/min, respectively.

This indicates that hollow fiber permeability is not only impacted and
dependent on ethanol concentration during the process but is set by the
initial ethanol concentration exposure and only able to recover a
limited amount of performance. Stated another way, when the ethanol
concentration levels between runs are equivalent in the retentate, it
does not correlate to a specific permeability, but rather permeability
is set by the initial ethanol concentration and will only improve a
relative amount. This aligns with the other plot in Figure 2A (Permeate
flow rate vs. DV). Figure 2A captures the plateau effect, but not the
effect of the initial ethanol concentration since there is only one
initial concentration.

Once the hollow fiber was exposed to the initial concentration in the
Figure 1D set up, the permeability was set and overall performance
limited. Therefore, the Figure 1D set up is not a fully valid
representation of a continuous ILDF process. The Figure 1D set up used
the same single hollow fiber to facilitate all passes/stages for each
run, whereas a continuous ILDF process like Figure 1C would have
individual hollow fibers for each pass/stage. More appropriately, the
permeate values for only the initial ethanol concentration passes should
be used and extrapolated; these values being accurate representations of
the initial ethanol exposures of some of the independent hollow fibers
in a continuous ILDF set up (Figure 1C).

\emph{Extrapolated ILDF stage requirements}

Using the permeate removal data for the initial pass of the five
starting ethanol concentrations examined, a continuous ILDF (Figure 1C)
was modelled and the number of passes/stages for the target ethanol
removal calculated. Figure 4A shows the permeate removed for each of the
measured initial ethanol concentration passes with an extrapolated
curve/function. This function was used to model each independent
pass/stage of a continuous ILDF process until the target ethanol removal
was achieved (Figure 4B). Each pass/stage was modelled using the
following equations:

\(c_{p+1}=\frac{c_{p}R-c_{p}f\left(c_{p}\right)}{R}\) (Equation 4)\(f\left(c_{p}\right)=-5.696\ln\left(c_{p}\right)-3.8359\)(Equation 5)

where \(c_{p}\)= ethanol concentration at the beginning of a
pass,\(c_{p+1}\)= ethanol concentration at the end of a pass,
\(R\)= mass of the retentate, and \(f(c_{p})\) = the
extrapolated ethanol concentration dependent permeate removal function.
Using Equations 4 and 5, starting ethanol concentrations were selected
and the ethanol concentrations calculated after each subsequent
pass/stage. The calculation continued until the target ethanol removal
was achieved and the number of passes/stages determined.

The number of passes needed when starting at different concentrations
was calculated and are shown in Figure 4B. Figure 4C compares the
calculated pass/stage data to the pass/stage data determined with the
Figure 1D set up. The modelled continuous ILDF passes required are
significantly less than the Figure 1D set up, but the difference becomes
smaller with lower initial ethanol concentrations. This shows that the
impact of carrying through the Figure 1D initial ethanol concentration
limitation on permeability was mitigated by calculating the passes
independently. Note that the continuous ILDF ethanol reduction curve
does not follow the diafiltration equation, while the Figure 1D set up
did. This is most likely due to the Figure 1D reuse of the same hollow
fibers for each run, which caused the permeability limitations similar
to a CVDF batch process. The extrapolated ILDF model's use of new hollow
fibers for each pass/stage leveled out the rejection coefficient effect
into a linear reduction as the permeability of each pass/stage was set
independently. Overall, this showed that a continuous ILDF process would
minimize any impact of the ethanol rejection coefficient or permeability
reduction as compared to the batch CVDF process.

\emph{Continuous ILDF Buffer Consumption}

Another means of evaluating the continuous ILDF process versus the batch
process involves comparing buffer consumption. Buffer consumption, in
this case, included the buffer needed for the initial dilution of the
starting liposome formation mixture as well as that needed to perform
the diafiltration to the target ethanol concentration. Figure 5 shows
the buffer consumption (dilution and diafiltration) for theoretical
batch processes of equivalent scale with rejection coefficients of 0 and
0.5 (the approximate value of previously calculated batch scenario) and
compares them to the continuous ILDF model used to generate the results
in Figures 4B. Surprisingly, the continuous ILDF set up is in line with
the zero rejection coefficient batch process. Previous evaluations of
similar set ups in the realm of biologics have shown continuous designs
to require more buffer than batch (Kavara, et al. 2020, Jabra, et al.
2019). The Figure 5 results again show how the use of independent
passes/stages offsets the impact of ethanol on the hollow fiber
permeability.

\textbf{Discussion}

The assessment of the continuous ILDF set up for liposomal drug product
formulation refinement was successful and provided a range of intriguing
results. The notion of the permeate being impacted by the ethanol
concentration in the batch process was reinforced by the results from
the continuous ILDF experiment(s).

The use of the simulated ILDF (Figure 1D) showed that reduction of the
initial ethanol concentration produced an ethanol concentration
dependent rate of reduction in the number of passes/stages necessary to
achieve the target ethanol removal. This led to the derivation of an
equation (Equation 2) for the simulated ILDF concentration/dilution
arrangement (Figure 1D) including the derivation of a function for
ethanol concentration dependency of the rejection coefficient (Equation
3).

While these equations prove applicable and predictable under the
simulated ILDF set up from Figure 1D, these equations proved less
applicable to a real-world set up such as that shown in Figure 1C. The
simulated ILDF method showed that the permeability of the hollow fiber
was limited by the initial exposure of ethanol. The permeability then
only improved a limited amount as the ethanol concentration decreased
with each pass. By using the same hollow fiber to simulate each pass of
a ILDF system, the efficiency of each subsequent pass was limited and
not representative of a true ILDF arrangement. By using the data from
the initial passes of the separate runs, a true ILDF data set was
extrapolated and assessed.

The continuous ILDF model showed an almost linear reduction as compared
to the exponential reduction in the simulated ILDF model (Figure 4B vs.
Figure 3B). This may be because the permeability of the hollow fibers in
the continuous ILDF model were set independently and had no impact on
the subsequent hollow fibers as the ethanol concentration decreased.
Additionally, the linear reduction of the ethanol in the continuous ILDF
model limited the overall buffer consumption to that of a batch process
where the rejection coefficient is zero. This showed that continuous
ILDF would be more efficient than a solvent-based batch TFF with respect
to buffer required.

Based on these findings, the impact of ethanol on the continuous ILDF
design is less driven by a traditional rejection coefficient concept and
more on ethanol's effect on hollow fiber permeability. By reducing the
initial exposure of each individual hollow fiber in a continuous ILDF
system, the performance and overall efficiency of the system is
improved. The most optimal design for a liposomal continuous ILDF
process would involve significant upfront dilution (i.e. 5\% initial
ethanol concentration) in order to start the ILDF with minimal ethanol
concentration. This would minimize the amount of ILDF stages needed and
buffer required. This is similar to the dilution strategy recommended
for albumin diafiltration though optimization is not specifically
correlated to the impact of ethanol concentration on permeability
(Jaffrin et al. 1994, Paulen et al. 2011). Additionally, these finding
may prove applicable and beneficial to mRNA-LNP vaccines, which often
look to minimize mRNA exposure to organic solvents as well as having
massive production demands (Hou et al. 2021, Schoenmaker et al. 2021,
Verma et al. 2023), which a continuous process design could help to
meet.

\textbf{Conclusions}

Continuous manufacturing designs for liposomal drug products will help
meet the demand for these formulations, which are critical for future
drug delivery options. Understanding what impacts these continuous
manufacturing designs has benefit to their optimization. What is viewed
to be a minor phenomenon in a batch process design could have
significant impact to a continuous design. The deduction of an ethanol
concentration-dependent rejection coefficient in the batch process has
led to a greater understanding of how ethanol concentration will impact
a continuous TFF/ILDF process. Ethanol reduces and limits hollow fiber
permeability with the impact increasing with concentration. It was
determined that by diluting the post-liposome formation bulk and
reducing the initial ethanol concentration that fewer passes/stages of
ILDF and less buffer would be needed to achieve target ethanol removal.
In understanding this impact, continuous ILDF presents as a competitive
alternative to the batch process that can be scaled at will.

\textbf{Acknowledgements}

UCL Biochemical Engineering hosts the Future Targeted Healthcare
Manufacturing Hub in

collaboration with UK universities and with funding from the UK
Engineering \& Physical

Sciences Research Council (EPSRC) and a consortium of industrial users
and sector

organisations (Grant Ref: EP/P006485/1).

\textbf{Conflict of interest}

The authors declare no financial or commercial conflict of interest.

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