Carbohydrate Polymers 339 (2024) 122251

Contents lists available at ScienceDirect

Carbohydrate Polymers
journal homepage: www.elsevier.com/locate/carbpol

A quality by design approach to optimise disulfide-linked hyaluronic
acid hydrogels
Saketh Reddy Ranamalla a, c, Shima Tavakoli b, Alina Silvia Porfire c, Lucia Ruxandra Tefas c,
Manuela Banciu d, Ioan Tomuța c, *, Oommen P. Varghese b, *aDoctoral School in Integrative Biology, Faculty of Biology and Geology, “Babeș-Bolyai” University, 400015 Cluj-Napoca, Romania
Macromolecular Chemistry Laboratory, Department of Chemistry-Ångstrom, Uppsala University, Uppsala 751 21, Sweden
Department of Pharmaceutical Technology and Biopharmacy, Faculty of Pharmacy, University of Medicine and Pharmacy Iuliu Hațieganu, 400010 Cluj-Napoca,
Romaniad
Department of Molecular Biology and Biotechnology, Center of Systems Biology, Biodiversity and Bioresources, Faculty of Biology and Geology, “Babeș-Bolyai”
University, 400015 Cluj-Napoca, Romaniabc

A R T I C L E I N F O

A B S T R A C T

Keywords:
Hyaluronic acid
Hydrogels
Quality by design
Failure mode and effects analysis
Design of Experiments

In this study, the disulfide-linked hyaluronic acid (HA) hydrogels were optimised for potential application as a
scaffold in tissue engineering through the Quality by Design (QbD) approach. For this purpose, HA was firstmodified by incorporating the cysteine moiety into the HA backbone, which promoted the formation of disulfide
cross-linked HA hydrogel at physiological pH. Utilising a Design of Experiments (DoE) methodology, the criticalfactors to achieve stable biomaterials, i.e. the degree of HA substitution, HA molecular weight, and coupling
agent ratio, were explored. To establish a design space, the DoE was performed with 65 kDa, 138 kDa and 200
kDa HA and variable concentrations of coupling agent to optimise conditions to obtain HA hydrogel withimproved rheological properties. Thus, HA hydrogel with a 12 % degree of modification, storage modulus of≈2321 Pa and loss modulus of ≈15 Pa, was achieved with the optimum ratio of coupling agent. Furthermore,biocompatibility assessments in C28/I2 chondrocyte cells demonstrated the non-toxic nature of the hydrogel,underscoring its potential for tissue regeneration. Our findings highlight the efficacy of the QbD approach indesigning HA hydrogels with tailored properties for biomedical applications.

1. Introduction
Hyaluronic acid (HA), a non-sulfated glycosaminoglycan, is made up
of alternating units of N-acetyl-D-glucosamine and D-glucuronic acidthat are connected by alternating β − 1,4 and β − 1,3 glycosidic linkages(Xu et al., 2012). HA is commonly found in the extracellular matrix(ECM) of many connective tissues and is synthesised by the hyaluronan
synthase in the presence of UDP-N-acetylglucosamine (UDP-GlcNAc)
within the plasma membrane of the cells. The HA is then secreted into
the ECM space by exocytosis (Li et al., 2020). Due to its biocompatibility,
high water-retention capacity, viscoelasticity, and ability to support cell
development and modify the immunological response (Pérez et al.,2021; Zamboni et al., 2018), HA has been used in a wide range of
biomedical applications, from lubrication in the dry eye to viscosupplementation in osteoarthritis (OA), cosmetic products, and wounddressings. However, uncrosslinked HA has poor mechanical properties

and is easily degraded by oxidation or enzymatically, limiting someclinical applications (Pérez et al., 2021). HA can be physically or
chemically cross-linked to form hydrogels with improved mechanicalcharacteristics and degradation rates (Bajic et al., 2024; Segura et al.,2005; Wang et al., 2022). HA hydrogels can offer a three-dimensional(3D) structure that resembles the extracellular environment in humantissue that facilitates cell proliferation, differentiation, and growth,
making them an ideal material for tissue engineering and regenerative
medicine (Burdick et al., 2011; Collins & Birkinshaw, 2013). Thus, HA
hydrogels have been utilised for drug delivery and stem cell delivery forvarious therapeutic applications, such as for tissue regeneration (Alonso
et al., 2021; Dong et al., 2020; Fang et al., 2021; Hamilton et al., 2021;
Koivusalo et al., 2019). Such hydrogels are particularly interesting forthe controlled release of drug molecules and biologics, highlighting theirability to facilitate the cargo release at the desired target tissue (Bayer,2020). Some examples include the tuneable release of recombinant Bone* Corresponding authors.
E-mail addresses: tomutaioan@umfcluj.ro (I. Tomuța), oommen.varghese@kemi.uu.se (O.P. Varghese).
https://doi.org/10.1016/j.carbpol.2024.122251
Received 27 January 2024; Received in revised form 7 May 2024; Accepted 8 May 2024
Available online 11 May 2024
0144-8617/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

S.R. Ranamalla et al.

Carbohydrate Polymers 339 (2024) 122251

formulation and process variables. Thus, Quality by Design (QbD), a
systematic and proactive strategy, could be applied to ensure HA
hydrogel-based medicinal products’ quality, safety, and effectiveness.
QbD is a systematic approach to product development that focuses onunderstanding the relationship between product attributes and processparameters to ensure product quality, safety, and efficacy. It is initiatedby establishing a Quality Target Product Profile (QTPP), which is acomprehensive summary of the desired attributes of a drug or medical
product. Thereafter, the key physical, chemical, or biological charac­
teristics that directly affect the product’s quality, safety, and efficacy,
called Critical Quality Attributes (CQAs), are recognised. Risk assess­ment is then followed by identifying the key characteristics of raw
materials, called critical material attributes (CMAs), and the key vari­ables that affect the manufacturing process, called the critical processparameters (CPPs), which influence CQAs together. These parameters
are then optimised by utilising the design of experiments (DoE), which
involves varying input factors (variables) systematically to understandtheir impact on the output response (Yu et al., 2014).
Numerous studies have documented the synthesis and characterisa­
tion of various disulfide cross-linked HA hydrogels (Bian et al., 2016;
Ossipov et al., 2010; Shu et al., 2002), demonstrating their potential forforming stable hydrogels at varying cross-linking densities. However,these methodologies typically necessitate extensive chemical modifica­
tions (often exceeding 50 %) using diverse thiolating reagents. Recently,
our group reported on a novel approach involving cysteine-modified
disulfide cross-linked HA hydrogels, which achieved stable gelation
with reduced chemical modification by capitalising on the pKa of
cysteine thiols (Bermejo-Velasco et al., 2019). Despite the promise ofthis methodology, achieving reproducibility remains challenging due tothe sensitivity of carbodiimide coupling and disulfide formation chem­
istries to external factors such as pH (especially due to the presence of
free amines and hydrazides as nucleophiles), oxygen content, and re­agent concentrations. This study aims to address these challenges bydeveloping an optimised synthetic protocol using the Quality by Design(QbD) approach (Fig. 1). Specifically, we aim to establish a reliable
method for generating stable hydrogels by employing carbodiimide
coupling chemistry to create disulfide cross-linked HA hydrogels, uti­
lising hydrazide-modified cysteine reagents.
Although the QbD concept has been implemented in numerous
studies on pharmaceutical development, it has not been well explored tosystematically develop biomaterials for drug/cell delivery applications,albeit for industrial-scale development (Alonso et al., 2021). In thiswork, we show for the first time how QbD principles could be applied tothe design of HA hydrogels, identifying and reducing the risk factors insynthesising disulfide-bearing HA scaffolds using carbodiimide chem­
istry. This approach is expected to provide valuable insights into reac­tion conditions for carbodiimide coupling chemistry that is generally
applied to derivatise HA with nucleophilic reagents. Optimised bio­materials were also used to encapsulate chondrocyte cells and evaluate
their biocompatibility. QbD strategy for optimising the development ofbiomaterials will yield novel insights for solving such challenges related
to synthesis conditions. It will facilitate the creation of scaffolds
designed for delivering cells and drug molecules, thereby holding
promise for scaling to potential commercial applications.

Fig. 1. A) Schematic representation of the carbodiimide coupling reaction withhydrazide-modified cysteine reagent to develop thiol-modified HA derivative;
and B) Schematic representation of disulfide bond formation to obtain stable
HA hydrogel at physiological conditions.

Morphogenetic Protein-2 (rhBMP-2) (Bhakta et al., 2012; Yao et al.,2022), anti-bacterial drugs like vancomycin (Censi et al., 2019), andeven anti-cancer drugs like trastuzumab (Xu et al., 2015). Because of theunique ability of HA to target the CD44 receptor, it has also been utilised
to develop targeted drug delivery systems (Rangasami et al., 2021). We
have previously demonstrated that tuning the protonation of HAcarboxylate makes it possible to sequester rhBMP-2 and modulate thecontrolled release of the drug moiety, thereby enhancing in-vivo boneformation at lower protein concentrations (Yan et al., 2018). Our groupalso investigated HA hydrogels cross-linked with various chemistries forin vivo BMP-2 delivery. We observed that diverse functional groups onpolymer-based materials could induce functional variances, affecting
the stability and bioactivity of the encapsulated therapeutic proteins(Paidikondala et al., 2019).
HA-based hydrogels find significant biomedical utility, notably as
injectable gels for alleviating osteoarthritis pain, a condition marked bycartilage degradation and inflammation. Despite available treatments,
joint replacement remains the primary long-term solution, encouraging
the quest for regenerative therapies. Tissue engineering attempts tosolve this problem by harnessing scaffolds like hydrogels to deliver stem
cells or drugs to stimulate tissue regrowth (Koh et al., 2020; Roseti et al.,2019). Since HA is one of the main components of the cartilage ECM(Koh et al., 2020), it is an ideal choice to develop biomaterials for
cartilage repair. If such materials have shear thinning properties and canform in situ gel upon intraarticular injection, it will minimise compli­
cations and facilitate tissue repair using minimally invasive procedures.
For this purpose, we envisaged the development of thiol-modified HA
that could form hydrogels by disulfide cross-linking under physiologicalconditions. Such hydrogels could deliver stem cells, which is expected topromote the remodelling of the extracellular environment for cartilage
repair (Kim & Guilak, 2022).
Hydrogels based on dynamic covalent coupling (DCC) chemistryhaving a reversible and adaptable polymer network, are an interesting
strategy that allows the development of hydrogels with tuneable phys­
ical and mechanical characteristics and stimuli-responsive properties (e.
g. shear-thinning and self-healing properties) (Wang et al., 2022). The
disulfide bond formation in thiol-modified polymers triggered at specificpH is one such method that allows in situ dynamic cross-linking of thehydrogel. In Nature, the process of disulfide formation involves sulfhy­
dryl side chains of two cysteine residues in a protein or peptide molecule
to form a disulfide bridge to build a covalent dynamic bond (–S–S–)(Swaisgood, 2005). These disulfide linkages are essential for intracel­lular signalling, targeted cellular processes, structural integrity, redox
responsiveness and stability of macromolecules (proteins) (Go & Jones,
2013). Similarly, disulfide cross-linked hydrogels are redox responsive,
exhibit tissue mimicry, and are easily cleaved by enzymes, which makes
them suitable for tissue engineering applications (Shu et al., 2003;
Tavakoli et al., 2024).
Producing HA hydrogels with predictable and controllable charac­
teristics is difficult due to different cross-linking rates and other

2. Materials and methods
2.1. Materials
HA sodium salt (65 kDa, 138 kDa, 200 kDa) was purchased from
Lifecore Biomedical (Chaska, MN, USA). All reagents were in the highest
purity available from Sigma-Aldrich (Steinheim, Germany). The organic
solvents were purchased from Fisher Scientific (Loughborough, UK).
Dialysis membranes Spectra/Por 6 (1000 g/mol cutoff) were from VWR
International (Compton, CA, USA).

2

S.R. Ranamalla et al.

Carbohydrate Polymers 339 (2024) 122251

the software to obtain an experimental design. The degree of modifi­
cation (DM) and the rheological properties (G′, storage modulus and G″,
loss modulus) were found to be CQAs of the HA hydrogel and considered
to be the responses. The molecular weight of HA and the molar ratio
between the coupling agent and HA were the critical factors influencing
these responses and considered variables for optimisation through DoE.
For the design, three types of HA were used, i.e., types 1, 2 and 3, whichindicate the different molecular weights of the HA, namely 65 kDa, 138
kDa and 200 kDa, respectively. The coupling reagent molar ratio, withrespect to the HA mole equivalent, was set at three levels coded as − 1,
0 and +1. Here, − 1 stands for 0.2 equivalents to the HA, which was 0.1
mmol, 0 stands for 0.3 equivalents to the HA, which was 0.15 mmol, and+1 stands for 0.4 equivalents to the HA, which was 0.2 mmol.
The factors were then entered into the software, and a D-optimal
design was suggested, with 18 runs, including three centre points. The
runs of this design are detailed in Table 3.

Table 1QTPP of the disulfide cross-linked HA hydrogels.
Profile requirements

TargetJustification

Indication

Chronic joint diseasesuch as osteoarthritis

Route ofadministration
Dosage form

Intra-articular
injection
In situ hydrogel

Time of gelation

Immediate afteradministration

pH for thioloxidation andcross-linking

pH <8Stability

High

Mechanicalproperties

G′ > 2000 Pa
G″ > 15 Pa

Cytotoxicity

Non-cytotoxic, cell
viability >80 %Degenerative, progressive disease
with high prevalence mainly
among the elderly population
Administration at the site of thediseaseEase of administration andenhanced local retention;
biocompatible and biodegradable.
Faster disulfide bond formation
would help in cross-linking andgelation at physiologicalconditions.
Hydrogel formation at
physiological pH, such as thedisulfide cross-linking, is expected
to have less toxic effects on thesurrounding tissue.
High stability at the physiologicalconditions while allowing it to bebiodegradable
High mechanical stability andrheological properties are
essential for cushioning the joints
and facilitating the motion
between the bones.
High viability of cells for thesafety of the hydrogels used fortissue regeneration

2.4. Preparation of thiol-modified HA polymers
2.4.1. Synthesis of dihydrazide used for modification of HA with cysteinegroupsThe dihydrazide used for the modification of the HA with cysteinegroups, (3,3′-dithiodi(2-aminopropanehydrazide)), was synthesised ac­
cording to previously reported procedures by a three-step process(Bermejo-Velasco et al., 2019). Briefly, the reaction starts with N-[(tertbutoxy)carbonyl]-L-cysteine methyl ester reducing to form dimethyl
3,3′-dithiodi(2-(tert-butoxycarbonyl)-aminopropanoate). Then, uponreaction with hydrazine hydrate, it was reduced to 3,3′-dithiodi(2-(tertbutoxycarbonyl) aminopropanehydrazide). This tertiary butoxy
carbonyl (BoC) derivative of amino propane hydrazide was then acidi­
fied with HCl and washed with cold ethanol to obtain the BoC-free de­rivative (3,3′-dithiodi(2-amino propane hydrazide)). This synthesised
dihydrazide with disulfide bonds was then coupled with HA in thepresence of a coupling reagent, namely 1-ethyl-3-(3-dimethylamino­
propyl)carbodiimide (EDC), as described in detail in the next section.

2.2. Application of the Quality by Design (QbD)
The QbD approach in the development of hydrogels starts withdefining the QTPP, identifying CQAs, CMAs, CPPs, performing a risk
assessment and then moving on to product design, development, andproduction (Yu et al., 2014). According to prior research and existing
literature, we first established the QTPP for HA hydrogels (Table 1).
Then, as a risk management technique, Failure Modes and Effects
Analysis (FMEA) was used in the following phase to identify and assess
the probable failure modes of a product or process and to determine theimpact of those failures on the system and its users (Table 2). Morespecifically, the CMAs and CPPs related to the process have been iden­tified and evaluated in terms of their impact on the CQAs of the HA
hydrogel. In this approach, a risk priority number (RPN) is calculated foreach important parameter by multiplying the score values for failuremodes and consequences that occur (O), are severe (S), and are
detectable (D). The probability of occurrence (O) of an event is divided
into five categories: common (5), probable (4), infrequent (3), remote(2), and improbable (1). The severity can be (5) catastrophic, (4) critical,(3) serious, (2) minor, and (1) negligible. The measure of detectability(D) of the failure mode can differ between (5) for hard to detect, (4) for a
low chance of detection, (3) for moderately detectable, (2) for highly
detectable, and (1) for easy to detect. The parameters with high RPN are
usually optimised using the Design of Experiments (DoE), and thencontrol parameters are set to ensure the QTPP (Yu et al., 2014). The
implementation of QbD requires a significant amount of upfront work interms of understanding the material attributes and process parametersthat impact the product, designing experiments, and extensive data
analysis. QbD heavily relies on analytical tools to quantify the process
outcomes and situations where these tools are not sensitive enough, QbD
implementation may be limited.

2.4.2. HA modification with cysteine dihydrazide
200 mg of HA sodium salt with various molecular weights weredissolved separately in 25 mL of deionised water, and 0.9 HA-equivalentmillimoles of 3,3′-dithiodi(2-amino propane hydrazide), were added tothe clear solution. The pH was adjusted to 4.5 using a 2 M NaOH solu­
tion. The coupling reaction was initiated by adding EDC in different
molar ratios, as specified in Table 3. The EDC was added while onmagnetic stirring, which continued overnight. Then, 4.5 HA-equivalentmillimoles of dithiothreitol was added to the above solution and the pH
was adjusted to 8.5 by adding 5 M NaOH solution and kept on magnetic
stirring overnight. The above solution was diluted to a concentration of
4 mg/mL HA, and the pH was acidified to 3 by adding 2 M HCl. The
solution was then transferred to a 1 kDa dialysis bag and dialysedagainst a dilute HCl solution containing 0.1 M NaCl at pH 3 for 24 h withnitrogen purging. The dialysis bag was then transferred into a dilute HClsolution of pH three and dialysed for 24 h with nitrogen purging. The
solution was transferred from the dialysis bag into a round bottom flask,
shifted to a freeze dryer, and lyophilised for 72 h.
2.4.3. NMR spectroscopy for the determination of DM of HA polymers
The chemical characterisation of the obtained HA derivatives was
carried out using 1H NMR analysis. The thiol-modified HA polymer was
dissolved in D2O to obtain the NMR spectra. All NMR spectra wererecorded on a JEOL JNM-ECP Series FT-NMR spectrometer at magnetic
field strength 9.4 T, operating at 400 MHz for 1H NMR. The residual
solvent peak was used as the internal reference when possible. Chemical
shifts for the (–CHNH2) and (–CH2SH) used to quantify the DM wereexpressed in ppm. Appropriate integration was done, and the degree ofsubstitution was calculated by measuring the area of the peak at 4.36
ppm (–CHNH2) and 2.88 ppm (–CH2SH) against a standard value of 1

2.3. Experimental design
MODDE 13 (Sartorius Stedim, Germany) was used for experimental
data analysis, experimental design, and disulfide cross-linked HA
hydrogels optimisation. The variables and responses were entered into3

S.R. Ranamalla et al.

Carbohydrate Polymers 339 (2024) 122251

Table 2
Failure Modes and Effects Analysis (FMEA).
S.
No

CMA/CPPHA characteristics
1
Quantity of Sodium hyaluronate
dissolved [200 mg/reaction]
2
The molecular weight[65 kDa, 138 kDa, 200 kDa]

Hydrazide characteristics
3
Molar ratio of thiol hydrazide [0.9 eq.
to HA]
4
Type of thiol hydrazide [HA-Cys, HAActCys, HA-SH]

Coupling reaction
5
pH adjustment during the addition of
EDC [pH 4.5]
6
The molar ratio of a Coupling reagent(EDC) [0.2 to 0.4 eq.]
7
Stirring time (Reaction) after the
addition of Coupling reagent (EDC)[overnight]
Reducing reaction (DTT)8
The molar ratio [4.5 eq.]9
pH adjustment after the addition [pH8.5]
10
Stirring (Reaction) time [overnight]
Reaction flow
11
Order of addition of Co-coupling
reagent and reducing agent [EDC →DTT]Dilution
12
Dilution of the product [4 mg/mL]
13

pH adjustment after dilution [pH 3]

Dialysis
14
Dialysis membrane used [1000 g/mol
cutoff]
15
pH during dialysis [pH 3 & then pH
3.5]
16
Dialysis time [24 h]
17
Nitrogen purging time [24 h]

Lyophilisation
18
Freezing temperature [− 80 ◦ C]
19
Chamber pressure
20
Residual moistureGel formation
21
The pH of the buffer used [pH 7.4]
22
The concentration of the final solution[1 mg/mL]

Potential causes

Failure mode

Failure effects

Controlmethods

O

S

D

RPN

Insufficient or excess(Measuring error)
Reaction potential ofdifferent molecularweights

Unreacted reagents

Lower reaction yield, degree ofsubstitution
Lower reaction yield, degree ofsubstitution (Failure to form agood gel)Calibration

1

3

2

6

Screening

4

4

4

64

Insufficient

Unreacted reagents

Validation

1

4

1

4

Reaction potential ofdifferent batches ofhydrazides

Unreacted reagents

Lower reaction yield, degree ofsubstitution, pKa
Lower reaction yield, degree ofsubstitution, pKa

Screening

1

1

1

1

Insufficient or excess(Instrument error)
Insufficient or excess

Inactivated coupling

Lower reaction yield

Calibration

1

4

1

4

Unreacted reagents

Validation

2

5

4

40

Insufficient

Unreacted reagents

Lower reaction yield, degree ofsubstitution
A lower DM

Validation

1

4

2

8

InsufficientInsufficient or excess

Unreacted reagents
Unreacted reagents

Low disulfide formation
Low disulfide formation

Validation
Calibration

1
1

5
5

1
3

5
15

Insufficient

Unreacted reagents

Low disulfide formation

Validation

1

3

2

6

Insufficient or excess

Unreacted reagents

Low reaction yield, degree ofsubstitution, higher pKa, Disulfide
formation

Screening

1

2

3

6

Excess or insufficient
dilution
Unstable pH

Precipitation

Impurity in the final product

Validation

1

1

1

1

Precipitation

Higher pKa, lower disulfide
formation

Calibration

1

2

3

6

High or low MolecularweightHigh or Low pH

Low-quality product

Impurity or loss of product

Screening

1

2

4

8

Unstable product

Lower disulfide formation

Calibration

1

4

3

12

Impurity in the final product
Reduction in the product, leading
to reduced pKa

Screening
Screening

1
1

3
4

3
2

9
8

Unreacted reagents

InsufficientInsufficientHigh temperature
High or low pressure
Excess

Low-quality product

Impurity in the final product
Impurity in the final productWeight of the final product

Calibration
Calibration
Calibration

1
1
1

2
2
1

1
1
1

2
2
1

High or Low pH
Insufficient or excess

Unstable productConcentrated or
diluted product

Lower disulfide formation
Variability in the rheologicalproperties, swelling, degradation
rate

Calibration
Validation

1
1

5
5

3
2

15
10

CMA – Critical Material Attributes, CPP – Critical Process Parameters, O – Occurrence, S – Severity, D –Detectability, RPN – Risk Priority Number, eq. – equivalents.

for (–CHNH2) and 2 for (–CH2SH) for 100 % substituted polymers.

three-dimensional network. The cysteine-modified HA derivatives ob­
tained following the DoE plan were hydrated at physiological pH. Irre­
spective of the DM, all the thiol-modified HA polymers were first
dissolved in degassed, acidified phosphate buffer (pH 5). Then, the pH
was adjusted to 7.4 using the 5 M NaOH solution. The solutions werethen transferred into cylindrical moulds with a 12 mm diameter, sealed

2.5. Hydrogel preparation
To prepare the HA hydrogels, the derived polymers were dissolved inmedia (2 %), facilitating disulfide cross-linking and the formation of a4

S.R. Ranamalla et al.

Carbohydrate Polymers 339 (2024) 122251

Table 3
Responses matrix for the design of experiments.
Exp
no

Exp
name

The
molecular
weight of
HA (kDa)
X1

Coupling
reagent to
HA molarratio (Eq)
X2

DM
(%)
Y1

Storagemodulus,G′ (Pa)Y2

Lossmodulus,G″ (Pa)
Y3

1

N1

65

0.2

10

2

N2

65

0.2

8

3

N3

65

0.4

16

4

N4

65

0.4

16

5

N5

65

0.3

11

6

N6

138

0.2

8

7

N7

138

0.4

18

8

N8

138

0.4

11

9

N9

138

0.3

9

10

N10

200

0.2

12

11

N11

200

0.2

12

12

N12

200

0.4

19

13

N13

200

0.4

18

14

N14

200

0.3

12

15

N15

200

0.2

10

16

N16

200

0.3

14

17

N17

200

0.3

15

18

N18

200

0.3

16

1580.94± 251.86
1933.4 ±87.88
1604.3 ±143.231519.26± 63.071412.07± 59.67
2299.84± 114.08
1749.36± 49.471743.84± 103.40
1819.19± 105.31
2745.42± 212.78
2622.87± 204.00
2181.74± 9.841453.7 ±35.18
2768.14± 347.55
2449.07± 165.66
2555.47± 237.60
2204.47± 455.81
1701.19± 174.04

8.66 ±
1.95
7.04 ±
1.33
4.68 ±
1.16
12.08 ±
3.56
5.06 ±
0.69
14.04 ±
8.30
10.18 ±
2.66
8.78 ±
1.57
5.99 ±
1.08
20.9 ±
6.91
18.71 ±
10.45
32.25 ±
5.66
14.11 ±
5.67
18.91 ±
2.92
11.27 ±
6.91
26.32 ±
12.87
21.23 ±
9.95
8.88 ±
4.34

Fig. 2. Coefficients plot, where X1 indicates the Molecular weight of HA, Type
1–65 kDa, Type 2–138 kDa, Type 3–200 kDa, and X2 indicates the coupling
agent molar ratio to the HA. Y1, Y2 and Y3 represent the DM, G′ and G″
respectively.

adjustment, and the gap was lowered to a size of 500 μm. The time atwhich G′ cross G″ was defined as the gelation time.
To understand the relationship between the network structure andthe rheological data, we calculated the average mesh size (ξ) using thefollowing Eq. (1) applicable to hydrogels with elastic behaviour.
ξ=

R2

Q2

MV

MR

p-ValueLack of fit

DM
G′
G″

0.78
0.86
0.74

0.61
0.76
0.53

0.92
0.79
0.84

0.68
0.84
0.66

1.2853e− 04
2.0339e− 05
4.8126e− 04

7.1807e− 01
4.2387e− 01
5.3517e− 01(1)where NA is the Avogadro constant (6.022 × 1023 mol− 1), R is the molar
gas constant (8.314 J mol− 1 K− 1), T is the temperature (298 K), and G′ isthe hydrogel storage modulus (Pa).
2.6.2. Cytotoxicity study
To assess the biocompatibility of the proposed HA hydrogels, thechondrocyte cell line C28/I2 is encapsulated in HA hydrogels, and thecell viability was evaluated by Live/Dead staining and lactate dehy­
drogenase (LDH) assays. The cells were cultured in T-75 cell culture
flasks in Dulbecco’s Modified Eagle Medium, Nutrient Mixture F-12
(DMEM-F12, Gibco) with 10 % fetal bovine serum (Gibco, USA), 1 %Penicillin-Streptomycin and 200 μM ascorbic acid, in a cell culture
incubator, at 37 ◦ C and 5 % CO2. The medium was changed every
alternate day. TrypLE Select (Gibco, USA) was used to detach the cells
from the flasks during passage. Cells were passaged upon reachingaround 80 % confluency. Hydrogels were synthesised under aseptic
conditions in order to reduce the bioburden to the greatest extent
possible. Additionally, before encapsulating the cells inside the hydro­gels, the freeze-dried samples were sterilised under UV light for 20 min
and dissolved in sterile PBS. Following dissolution, the pH of the solu­
tion was adjusted to 7.4, and a predetermined number of cells wereadded, which were thoroughly mixed with the hydrogel solution by
gentle pipetting. The cell-hydrogel mixture was then cast into a 96-wellplate. After a brief incubation period of 30–45 min to allow complete
gelation, cell culture media was added on top of the hydrogel in eachwell. After encapsulating the cells in a 3D configuration within thehydrogel matrix, cell culture media was changed every two days tomaintain cell viability and provide the necessary nutrients.
Cells were encapsulated in 100 μL hydrogels at 1 × 106 cells/mL in a
96-well plate for both assessments. The encapsulated cells were cultured
for seven days, with a medium change every alternate day. The viabilityof cells and changes in their morphology were evaluated by Live/Deadstaining (ThermoFisher, USA) using a fluorescence microscope. To carryout the Live/Dead staining, the medium was aspirated from the wells
and the hydrogel samples were washed with 1× PBS twice. 200 μL Live/
Dead staining solution containing two μM Calcein AM and one μM
Ethidium homodimer in 1× PBS was added to the wells, and the plates

Table 4
Summary of fit and ANOVA data.
Response( ʹʹ )− 1/3
G NA
RTDM, DM; G′, Storage modulus; G″, Loss modulus; MV, Model validity; MR, Model
reproducibility.

with paraffin film and left in a humid chamber at room temperature
overnight.
2.6. Characterisation of hydrogels
2.6.1. Rheological properties
The cross-linked hydrogels from the previous step were evaluated fortheir rheological properties using a Discovery Hybrid Rheometer 2(DHR2) from TA Instruments (USA). The cylindrical hydrogels weretransferred from the moulds to the rheometer plate. The rheologicalproperties were determined with amplitude sweep at 25 ◦ C using 12 mm
parallel plate geometry. The G′ and G″ were reported at 1 % of strain. The
time sweep measurement was also performed using the same rheometerto determine the hydrogel gelation time. All the gelation kinetics mea­
surements were performed inside a humidity chamber to protecthydrogels from drying. For this, the fresh solutions of the HA derivatives(400 μL) were immediately transferred to the rheometer after pH
5

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Carbohydrate Polymers 339 (2024) 122251

Fig. 3. 2D and 3D Response contour plots for the responses Y1 – DM (top – A, B), Y2 – G′ (middle – C, D) and Y3 – G″ (bottom – E, F) against factors X1 indicating themolecular weight of HA, and X2 indicating the coupling agent molar ratio to the HA at different levels.

were incubated at 37 ◦ C for 1.5 h. Post-incubation, the hydrogels were
washed with 1× PBS once and imaged using a 10× objective and a Nikon
Eclipse Ts-2 fluorescence microscope. Live/Dead staining was per­formed on days 1, 3 and 7 after the encapsulation of the cells. The
transparency of the hydrogels allowed for clear visualisation of theencapsulated cells within the matrix. Images obtained from the micro­
scope were post-processed using ImageJ software. Moreover, CytoTox
96® Non-Radioactive Cytotoxicity assay (Promega, USA) was used tomeasure the release of LDH, an indicator of cytotoxicity. Similarly, at 1,
3, and 7 days, the LDH released was estimated based on manufacturer
protocol.

materials with well-distinguished characteristics. For this purpose, the
QbD-based strategy could assist in optimising the biomaterial design
parameters. Firstly, the QTPP of HA hydrogel had to be defined, out­lining its desired characteristics, including its physical and chemical
properties, which serve as a guide during the development,
manufacturing, and testing of a product in the QbD approach.
In the case of hydrogels, the QTPP for a hydrogel product has tospecify the desired mechanical properties, such as elasticity andstrength, as well as the desired swelling behaviour, degradation rate andbiocompatibility (von Lospichl et al., 2017; Zhao et al., 2022). It also
considers gelation duration, cross-linking effectiveness, and mechanicalqualities in disulfide cross-linked HA hydrogels. We focused on devel­
oping hydrogels with optimum mechanical strength, and the loading
and release characteristics were not considered.
For our current study, the QTPP of the HA hydrogel detailed inTable 1 was established based on prior reported results and experi­mental conditions available in the literature for HA-based hydrogels. As
previously mentioned, the main focus of this study was to develop thiol-

3. Results and discussion
3.1. Quality Target Product Profile (QTPP)The rational development of HA hydrogels in terms of synthesis,characterisation and stability is crucial to developing customisable
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the DM. For hydrazide-specific conjugation using EDC, we employedcoupling at pH ~4.5, which is expected to reduce side products that canbe obtained by amide formation (amino coupling) because of the sig­
nificant differences in pKa between hydrazide (~4.5) and amine (~9)
residues (Pérez et al., 2023). Thiol oxidation leads to the forming of thedisulfide bonds necessary for cross-linking and gelation. As we haveshown previously (Bermejo-Velasco et al., 2019), the pKa of the thiol
residue directly influences the rate of thiol oxidation, which impacts therate of disulfide formation. Hence, such hydrogel that cross-links at a pH
less than eight and closer to physiological pH is considered beneficial forin vivo applications.
Furthermore, the cross-linking, mechanical properties and biocom­
patibility of hydrogels constitute crucial considerations in the develop­
ment of biomaterials and are considered to be the CQAs of the hydrogel.
Biocompatibility was evaluated through cell encapsulation studies
within the hydrogel matrices, followed by comprehensive cell viability
assays designed to simulate the in vivo environment to which the cells
would be exposed, thereby ensuring their compatibility between living
cells and the HA-based hydrogels. The mechanical properties, essential
for ensuring the physical stability of the scaffolds under physiologicalconditions, were characterized by assessing the rheological properties,
including storage modulus (G′) and loss modulus (G″), of the hydrogels.
These measurements provide insight into the structural integrity andelasticity of hydrogels, indicating their ability to withstand various
stress while maintaining their functional integrity for effective cell anddrug delivery. Notably, the optimisation of hydrazone cross-linked HA
hydrogels for protein delivery has recently been facilitated using DoE(Mozipo et al., 2024). While the hydrazone chemistry shares similarities
with other dynamically cross-linked HA hydrogels across various ap­
plications, each hydrogel necessitates unique properties tailored to its
intended use. Designing an appropriate hydrogel for biomedical appli­
cations entails meticulous consideration of diverse design criteria.
Through the application of DoE, it becomes feasible to design promisingbiomaterials that ensure reproducibility with minimal deviation fromthe desired characteristics.

Fig. 4. Contour plots for determining the relationship between the DM (Y1)
and the G′ (Y2) for different molecular weights of hyaluronic acid and at various
molar ratios of the coupling reagent X2 at 0.2 (left), 0.3 (middle), and
0.4 (right).

Fig. 5. Prediction plot predicting the responses Y1, Y2 and Y3 representing theDM, G′ and G″ respectively against Molecular weights of HA (X1) - right andratio of coupling reagent (X2) – left.

3.2. Failure Modes and Effects Analysis (FMEA)
FMEA aims to assess each process step and material attributes
associated with forming the HA hydrogel. Therefore, we have listed thepotential causes of failure and their effects on product characteristics inTable 2. As mentioned earlier, each of these CMAs and CPPs was thenevaluated for occurrence (O), severity (S) and detectability (D) of fail­ure. The RPN was then calculated by multiplying the values of the O, S
and D. A RPN > 30 was considered high risk.
3.3. Design of experiments
FMEA indicated that the molecular weight of the HA and the molar
ratio of the coupling reagent (EDC) with respect to HA influence the
CQAs, namely the DM and the rheological properties like the G′ and theG″. The D-optimal design was adopted based on FMEA results. A series of
18 experiments with different molecular weights of the HA and different
molar ratios of EDC to HA were used, as specified by the MODDE 13
software. D-optimal design, also known as D-efficiency design, is a sta­tistical method used to optimise the design of experiments. D-optimal
design aims to find the design that maximises the determinant of theinformation matrix (also known as the “D-optimality criterion”) to
obtain the most precise estimates of the model parameters. The deter­minant of the information matrix is a measure of the parameter spacevolume defined by the design and is directly related to the precision of
the parameter estimates. D-optimal designs are useful when estimating asmall number of parameters with high accuracy (Atkinson & Donev,
1989). The responses obtained from the experiments were recorded inTable 3 and fed back into the software for further statistical analysis.
Parallelly, the MODDE software has also been used to visualise theFig. 6. Design space plot indicating the ranges of variables with the least
probability of failure (%) to obtain the desired level of responses

modified HA-based hydrogels, which, when injected in the intraarticular joint, could gelate in situ and act as a scaffold to reduce thefriction between the bones. The cross-linking depends on the HA’s De­
gree of Modification (DM), which relies on the coupling reagent. The pH
directly influences the ionisation and reaction of these reagents,
affecting cross-linking and gelation (Pérez et al., 2023). Therefore, theconcentrations of the reagents and the pH were considered essential to7

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Carbohydrate Polymers 339 (2024) 122251

Fig. 7. A) NMR spectrograph of the optimum formulation indicating the peaks around 4.23 ppm (–CHNH2) and 2.77 ppm (–CH2SH) against a standard value of
1.85 for (–CH2SH); B) Rheology trend of the optimum formulation plotted against the oscillation strain (%); and C) Time sweep rheology trend of the optimum
formulation, the crossover point of G′ and G″ shows the gelation point.

and the molar ratio of the coupling reagent to HA on the G′ and G″ of thehydrogels and the DM of the polymer. It was observed that the DM
ranged from 8 to 19%. The G′ ranged from 1412.07 to 2768.14 Pa. The
G″ had a range of 4.68 to 32.25 Pa but was observed to have a larger
standard deviation.

Table 5Comparison between the targeted and the observed response values.
Responsename

Set minvalue

Predicted
target values

Set max
value

Observed
valuesDeviation(%)

DM (%)G′ (Pa)G″ (Pa)

10
2000
15

12
2691.48
22.02

15
–
–

12
2321.7815.54

0− 13.74− 29.433.3.1. Summary of fit
Summary of fit statistics was presented in four parameters (R2, Q2,
Model validity (MV) and Reproducibility (MR)), where 1, or 100 %, isperfect. R2 shows the model fit. A model with an R2 of 0.5 has relatively
low significance. Q2 shows an estimate of the future prediction preci­sion. Q2 should be >0.1 for a significant model and >0.5 for a goodmodel. A value <0.25 for model validity indicates statistically signifi­
cant model problems, such as outliers, an incorrect model, or a trans­
formation problem. A low value may also indicate missing a term, suchas an interaction or square. When the pure error is minimal (replicateswere almost identical), the model validity can be low even though themodel is good and complete because the variation of the replicates iscompared to overall variability. The reproducibility should be >0.5

responses’ contour and design space plots.
Disulfide cross-linked hydrogels with high G′ were prepared by otherresearch groups but in different working conditions, such as a high de­gree of chemical modifications, basic pH or in the presence of oxidants(Bian et al., 2016; Shu et al., 2002, 2003). In our design strategy, theelectron-withdrawing group at the β-position (such as the amino groupin cysteine) facilitated disulfide formation at physiological pH due to thelowering of thiol pKa (Bermejo-Velasco et al., 2019). By employing a
QbD-based approach, we investigated the influence of HA chain length
8

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Carbohydrate Polymers 339 (2024) 122251

Fig. 8. A) Day-wise cell viability (%) for the C28/I2 cells encapsulated inside the HA-hydrogel by LDH assay; B) Microscopic images of live and dead staining
depicting the cell viability of the C28/I2 cells inside the hydrogels corresponding to the optimum set point (10×). From left to right, cells are on days 1, 3, and 7. Red
indicates the dead cells, and green indicates the live cells.

(Tefas et al., 2023). The summary of fit data for the study is listed inTable 4.
R2 for the responses, namely DM, G′ and G″, was found to be 0.784,
0.860 and 0.735, respectively, which shows the model fits well with thedata for all the responses. Q2 for all the responses was higher than 0.5,
which signifies the precision of future predictions, so the model could be
used to determine the responses for different levels of factors selected forthis design. Also, the responses’ model validity and reproducibility
indicate no outliers and less variability.

storage and loss modulus (Snetkov et al., 2020). As expected, the G′ ofthe HA hydrogel was greater than the G″. In the present study, it was also
observed that the G′ (Y2) and G″ (Y3) increased by increasing the mo­lecular weight of the HA i.e., type1 < type2 < type3, although only type
1 and 3 HA had a significant effect on the rheological properties of thehydrogel. Higher molecular weight HA tends to result in a higher crosslinking density in the hydrogel, leading to a tighter network andreducing the mobility of polymer chains, resulting in a stiffer hydrogelwith a higher storage modulus. Another explanation could be that thehigher molecular weight HA tends to have longer polymer chains withmore hydrogen bonds, increasing the likelihood of entanglement be­
tween the chains (Snetkov et al., 2020). This polymer chain entangle­
ment might also contribute to the overall stiffness of the hydrogel andhigh storage modulus. This should allow the retention of incorporated
cells inside the hydrogel while not affecting their proliferation (Antich
et al., 2020). We have recently demonstrated the stability of disulfidecross-linked HA hydrogels in PBS over 28 days (Tavakoli et al., 2024).
These experiments revealed that although such gels show limited
swelling in PBS (35 %) over 28 days, the presence of hyaluronidase or
reducing environment (glutathione) results in diverse degradation
behaviour, making these materials conducive for in vivo applications(Tavakoli et al., 2024). Notably, the degradation kinetics are intricately
linked to the concentrations of hyaluronidase and glutathione, as well as
the degree of cross-linking and chain entanglements within the HA
hydrogel.
The contour plots in Fig. 3 also showed the direct relationship be­
tween coupling reagent molar ratio to HA and the DM, which was ex­pected. Where the G′ (Y2) appeared to be the highest for the higher
molecular weight HA modified using lower coupling reagent ratios, andG″ (Y3), similar to G′, seemed to be highest at higher molecular weight
HA. It appears that there is no impact of the coupling agent to HA ratio
on G″. The coupling agent ratio to HA, influenced G′ inversely i.e., anincrease in the molar ratio of the coupling agent to HA led to a decreasein the response G′. Theoretically, increasing the coupling agent con­centration causes a greater DM of the HA chains, leading to more crosslinking and an increase in G′ (Del-Olmo et al., 2021). However, the highcross-linking beyond the threshold might cause the HA polymer chains
to form more covalent bonds between them, restricting the mobility ofthe polymers and making the hydrogel rigid and brittle. When externalpressure is applied to such hydrogels, the storage modulus tends to be

3.3.2. ANOVAANOVA is generally used to assess the overall variability between
groups or factors. At the same time, the lack of fit test focuses on thediscrepancy between the observed data and the fitted model (Tefas et al.,2015). The ANOVA can be used to test the significance of the lack of fit,comparing the variability between the observed data and the variability
within the regression model using an F-test. The ANOVA and Lack of fit
test data for the study were tabulated in Table 4. The ANOVA indicatesthat the model p-value is <0.05 for all the responses, while the lack of fit
is >0.05. This suggests no significant lack of fit, proving that the models
were significant.
3.3.3. Coefficients, contour and design space plots
The coefficients plots provide a graphical presentation of the sig­
nificance of the model terms. A significant term has a considerable
distance from y = 0 and an uncertainty level that does not extend acrossy = 0. A non-significant model term is close to y = 0, with an uncertainty
level crossing y = 0. The coefficients plot is given in Fig. 2. The plot
indicates that the DM (Y1), the type 3 HA (highest molecular weight),
had the highest DM and is positively influenced by the coupling agent to
HA molar ratio.
The mechanical properties of hydrogels are critical for ensuring
clinical performance. The viscoelasticity of hydrogels can be quantita­
tively expressed as storage modulus (G′) and loss modulus (G″). The
storage modulus G′ represents the measure of stored elastic energy
within the material when subjected to deformation (Stojkov et al.,2021), while the loss modulus G″ shows the viscous-dominant charac­
teristics of the hydrogel (Hong et al., 2023). In the case of HA-basedhydrogels, the molecular weight of HA affects the cross-linking density
and mesh size of the hydrogel network, which, in turn, influences the9

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Carbohydrate Polymers 339 (2024) 122251

reduced than expected, similar to the previous observation with divinyl
sulfone cross-linked HA hydrogels (Del-Olmo et al., 2021).
Interestingly, in Fig. 4, the 4D response contour plot also shows arelation between the DM (Y1) and G′ (Y2) for hydrogels formed using
different molecular weights of HA (X1) at different coupling agent molar
ratios. The plot indicates that at a higher rate of DM (>12 %), the storage
modulus tends to decrease for the higher molecular weight HA mole­cules at any coupling ratio. Typically, the G′ increases with the DM of apolymer due to more available cross-linking sites. Therefore, a more
densely cross-linked hydrogel should appear more rigid (Tsanaktsidou
et al., 2019). However, the storage modulus decreased at a higher DM,
similar to the previous observation. In the case of low molecular weight
HA, an increase in coupling agent ratios leads to an increase in the DM;
however, there were no significant differences between the storage
modulus recorded.

0.12 at 4.36 ppm (–CHNH2) and 0.23 at 2.88 ppm (–CH2SH) against a
standard value of 1 for (–CHNH2) and 2 for (–CH2SH) for 100 %substituted polymers. The degree of substitution was calculated as 12 %,and the recorded spectra are represented in Fig. 7A. The rheologicalparameters, including G′ and G″ were recorded and presented in Fig. 7B.
Additionally, by considering the G′ values obtained from the optimum
run the mesh size of the network is theoretically calculated as 12.09 nm.
Time sweep measurement was also performed for the optimum run andrepresented in Fig. 7C, and the crossover point of G′ and G″ wasconsidered the gelation point. The gelation time was found to be be­
tween 50 and 60 min. The targeted values for the set point and theobtained values for the DM and rheological properties of the HA
hydrogels obtained from the modified polymers according to the opti­
mum set point are given in Table 5.
The properties of the HA hydrogels were within the mentioned target
range of responses, the DM and the G′. Due to the high standard devi­
ation obtained for the G″ throughout the study, the G″ could not be
accurately predicted. However, it passed the minimum required set
value. Hence, the model could successfully predict the DM and thehydrogel’s rheological properties for the given variables’ given levels.

3.4. Prediction plot
The prediction plot helps predict the response range, including thelower confidence interval (L.conf.int) and the higher confidence interval(H.conf.int) with varying levels of the factors. The prediction plot also
indicates observations similar to those of the coefficient plots. The
prediction plot is given in Fig. 5, in which the coupling agent to HA ratio
is directly proportional to the DM and is inversely proportional to the G′.
The molecular weight of the HA significantly impacts the G′ in the order
type1 < type2 < type3.
We observe that the DM of the HA polymers directly affects thestorage modulus of the hydrogel. At concentrations >0.2 mmol equiv­
alent EDC to HA, the DM started to increase from 8 % to 16 % for 65 kDa
HA, 8 % to 18 % for 138 kDa HA and 10 % to 19 % for 200 kDa HA chain,
whereas the storage modulus is reduced from 1933.4 Pa to 1519.26 Pa,
2299.84 Pa to 1743.84 Pa, 2768.14 Pa to 1453.70 Pa. As explained
earlier, this significant reduction in the storage modulus might be due tothe restricted mobility of the polymer chains, leading to increased ri­
gidity and the loss of storage modulus. In conclusion, the prediction
plots suggest a lower coupling agent to HA ratio and high molecularweights of HA for yielding a DM that could give high G′ and G″.

3.5.2.1. Cell viability. To validate the HA hydrogels’ biocompatibility
for potential osteoarthritis treatment, we encapsulated the humanchondrocyte cell line C28/I2, a suitable cell model for cartilage regen­eration. For this purpose, the C28/I2 cells were loaded into the 3D
matrix, and we evaluated their cytotoxicity by LDH assay. We visualised
live and dead cells by staining with fluorescent dyes Calcein AM (ace­toxymethyl ester) and Ethidium homodimer.
LDH is commonly present in the cytoplasm of the cells and is released
when plasma membrane damage suggests cytotoxicity. LDH assay thus
represents cell viability of the chondrocytes inside the hydrogel. LDH
was determined for three samples per group of cells encapsulated in thehydrogel, represented in Fig. 8(A). The cell viability on day 1 corre­
sponds to 98.96 ± 0.96 %, day 3 to 90.30 ± 1.38 % and day 7 to 87.20± 2.09 %, respectively. The results show that the cell viability was
higher than 85 % even after seven days of encapsulation in the HA
hydrogel.
To complement the LDH assay, we have also performed the live anddead staining. Calcein AM converts it into Calcein, emitting green
fluorescence after penetration into live cells. The Ethidium homodimer
penetrates only dead or damaged cells with compromised cell mem­branes emitting red fluorescence. The pictures in Fig. 8(B) indicate thatlive cells, highlighted by the green stain, outnumbered dead cells
marked with red. The viability percentage was also quantified in thisstudy using ImageJ software. In this regard, the viability percentage wasobtained by comparing the number of green cells to the total number ofcells at each time point. In order, the viability of encapsulated cells is
99.08 ± 0.24 %, 96.85 ± 2.04 %, and 90.61 ± 1.15 % for days 1, 3, and7. This visual representation signifies a predominance of cell viability inthe hydrogels. These data suggest this HA hydrogel’s cytocompatibility
over time, making it an ideal candidate for cell delivery and tissue en­gineering applications.

3.5. Optimisation
To get the optimal amount of modification with the maximum
achievable storage and loss modulus for the hydrogels, the optimiser
available in the MODDE software was utilised. Based on the study data,
the software set the predicted maximum storage modulus as 2691.48 Pa,
and we limited it to a minimum set value of 2000 Pa. Similarly, thesoftware set the target loss modulus as 22.02 Pa, and we limited it to aminimum set value of 10 Pa. Since the DM influences the rheologicalproperties, the target was fixed at 12 %.
3.5.1. Design space plot and setpoints
In the design, the factors, molecular weight of the HA (qualitative)
and coupling agent to HA molar ratio (quantitative) were used to searchfor set points yielding maximum rheological properties for the HA
hydrogel with the least probability of failure. A design space plot tovisualise the range of the variables indicated that using a higher mo­
lecular weight HA and a coupling agent at a lower molar equivalent ratio
to the HA yielded a high storage modulus, with a <5 % failure proba­
bility, as indicated in Fig. 6. A set point with type 3 molecular weight of
HA (200 kDa) and the coupling agent to HA molar ratio of 0.1 M wassuggested, to obtain a HA hydrogel with a target of 12 % of the DM andthe G′ of >2000 Pa.

4. Conclusion
This manuscript presents an optimised synthetic strategy to create arobust hyaluronic acid hydrogel with optimal rheological properties forpotential application as a scaffold in tissue engineering, specifically inaddressing joint diseases. For this purpose, we investigated the QbD
approach to optimise the disulfide-linked hyaluronic acid hydrogels forcell delivery. Such optimisation of modified HA that could yield ahydrogel with the desired quality target profile was possible throughidentifying, systematically characterising, and controlling the formula­tion parameters. Both quantitative and qualitative variables introducedin the experimental design influenced the DM and the rheological3.5.2. Optimum run
The optimum run was performed at the set parameters, as the soft­
ware suggested. The degree of substitution of the obtained HA de­rivatives was explored using 1H NMR analysis. We obtained a value of10

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Carbohydrate Polymers 339 (2024) 122251

properties that were selected as CQAs of the hydrogel. The risk assess­ment results of FMEA were further confirmed through DoE.
The influence of the coupling reagent and the HA’s molecular weightsignificantly impacted the DM and the rheological properties. The
higher molecular weight of the HA due to its higher cross-linking density
and higher polymer entanglement gave the best rheological measure­ments. Interestingly, the coupling agent concentration hypothesised toincrease the storage and loss modulus had an inverse effect. Thisdecrease in the rheology parameters was directly correlated to the de­gree of substitution, which made the polymer chains more rigid,
restricting their mobility. Using the higher molecular weight of HA (200kDa) and an optimum ratio of coupling agent to HA, a HA hydrogel withdesired characteristics was achieved, i.e., a 12 % DM and a G′ of 2321.78
Pa and a G″ of 15.54 Pa. Furthermore, the hydrogel was non-cytotoxic to
the C28/I2 chondrocyte cells for at least a week. These HA hydrogels,with their good mechanical and biological properties, would be an ideal
candidate as a scaffold for tissue engineering applications in joint
diseases.

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CRediT authorship contribution statement
Saketh Reddy Ranamalla: Writing – review & editing, Writing –original draft, Methodology, Investigation, Formal analysis, Data cura­tion. Shima Tavakoli: Writing – review & editing, Writing – originaldraft, Validation, Methodology, Investigation, Formal analysis, Data
curation. Alina Silvia Porfire: Writing – review & editing, Visualiza­
tion, Supervision. Lucia Ruxandra Tefas: Writing – review & editing,
Supervision, Methodology, Formal analysis. Manuela Banciu: Writing –review & editing, Visualization, Supervision, Methodology, Formal
analysis. Ioan Tomuța: Writing – review & editing, Supervision, Project
administration, Methodology, Funding acquisition, Conceptualization.
Oommen P. Varghese: Writing – review & editing, Supervision,
Conceptualization.
Declaration of competing interest
There are no conflicts to declare.
Data availability
Data will be made available on request.
Acknowledgements
The present study is a part of the CARTHAGO project that hasreceived funding from the European Union’s Horizon 2020 research andinnovation programme under the Marie Skłodowska-Curie grant
agreement No 955335. Financial support from Swedish Strategic
Research ‘StemTherapy’ (Dnr 2009-1035), Swedish Research Council(Dnr 2020-04025) is also acknowledged. The C28/I2 cells were a kind
gift from Prof Mary Goldring, Professor of Cell and Developmental
Biology at Weill College Graduate School of Medical Sciences, New
York, United States. The authors would also like to acknowledge Prof.
Laura Creemers and Ms. Katrin Agnes Münzebrock for their support inproviding the cells for the cytotoxicity studies.
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