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Citation: Hsieh, C.-L.; Yu, S.-J.; Lai,
K.-L.; Chao, W.-T.; Yen, C.-Y. IFN-γ,
IL-17A, IL-4, and IL-13: Potential
Biomarkers for Prediction of the
Effectiveness of Biologics in Psoriasis
Patients. Biomedicines 2024,12, 1115.
https://doi.org/10.3390/
biomedicines12051115
Academic Editor: Chia-Jung Li
Received: 28 April 2024
Revised: 10 May 2024
Accepted: 14 May 2024
Published: 17 May 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
biomedicines
Article
IFN-γ, IL-17A, IL-4, and IL-13: Potential Biomarkers for
Prediction of the Effectiveness of Biologics in Psoriasis Patients
Ching-Liang Hsieh 1,2, Sheng-Jie Yu 3, Kuo-Lung Lai 4, Wei-Ting Chao 5and Chung-Yang Yen 6,7,8,*
1Chinese Medicine Research Center, China Medical University, Taichung City 404, Taiwan;
clhsieh0826@gmail.com
2Department of Chinese Medicine, China Medical University Hospital, Taichung City 404, Taiwan
3Department of Medical Research, Taichung Veterans General Hospital, Taichung City 407, Taiwan;
shengjieyu@vghtc.gov.tw
4Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine,
Taichung Veterans General Hospital, Taichung City 407, Taiwan; kllaichiayi@yahoo.com.tw
5Department of Life Science, Tunghai University, Taichung City 407, Taiwan; wtchao@thu.edu.tw
6Department of Dermatology, Taichung Veterans General Hospital, Taichung City 407, Taiwan
7School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
8Integrated Care Center of Psoriatic Disease, Taichung Veterans General Hospital, Taichung City 407, Taiwan
*Correspondence: vernayen@yahoo.com.tw; Tel.: +886-4-2359-2525 (ext. 5303); Fax: +886-4-24621357
Abstract:
Biologics are widely used to treat moderate-to-severe psoriasis. However, we have unmet
needs for predicting individual patient responses to biologics before starting psoriasis treatment. We
investigate a reliable platform and biomarkers for predicting individual patient responses to biologics.
In a cohort study between 2018 and 2023 from a referral center in Taiwan, twenty psoriasis patients
with or without psoriatic arthritis who had ever experienced two or more biologics were enrolled.
Peripheral blood mononuclear cells obtained from these patients were treated with Streptococcus
pyogenes and different biologics. The PASI reduction rate was strongly correlated with the reduction
rate in the IL-13 level (p= 0.001) and the ratios of IFN-
γ
to IL-13 (p< 0.001), IFN-
γ
to IL-4 (p= 0.019),
and IL-17A to IL-13 (p= 0.001). The PASI reduction difference was strongly correlated with the
difference in the IFN-
γ
level (p= 0.002), the difference in the ratios of IFN-
γ
to IL-4 (p= 0.041), the
difference in the ratios of IFN-
γ
to IL-13 (p= 0.006), the difference in the ratios of IL-17A to IL-4
(p= 0.011), and the difference in the ratios of IL-17A to IL-13 (p= 0.029). The biomarkers IFN-
γ
, IL-13,
IFN-
γ
/IL4, IFN-
γ
/IL13, IL-17A/IL-4, and IL-17A/IL-13 are representative of the effectiveness of
psoriasis treatment.
Keywords: psoriasis; biologics; biomarker
1. Introduction
Psoriasis is an autoimmune skin inflammation disease with a prevalence between
0.14% and 5.32% worldwide [1,2]. In recent decades, psoriasis treatment has substantially
improved with the introduction of several new biologics [
3
,
4
]. Psoriasis Area Severity Index
(PASI) scores can be reduced by 90% from baseline (PASI 90) or 100% from baseline (PASI
100) with the use of biologics. The estimated PASI 90 response rates at 44 to 60 weeks after
beginning treatment were 79% for risankizumab, 76.5% for guselkumab, 74.0% for bro-
dalumab, 73.9% for ixekizumab, 71.3% for secukinumab, 52.4% for ustekinumab, 46.2% for
adalimumab, 40.1% for infliximab, 33.4% for etanercept, and 16.0% for apremilast [
5
]. These
overall response rates, however, do not represent the real responses of individual patients
to each biological agent. More information about selecting biologics was investigated, such
as difficult-to-treat psoriasis areas, short-term effectiveness, and long-term drug survival
on IL-17 and IL-23 inhibitors in biologics-naïve patients or after adalimumab failure [
6
–
8
].
Biologics-naive patients weighing
5
65 kg could be considered potential candidates for a
Biomedicines 2024,12, 1115. https://doi.org/10.3390/biomedicines12051115 https://www.mdpi.com/journal/biomedicines
Biomedicines 2024,12, 1115 2 of 13
half dose of risankizumab in treating psoriasis [
9
]. Additionally, crucial factors influencing
an individual patient’s clinical response include the patient’s genetics, environmental fac-
tors, and autoantibody profile, as well as whether the patient is biologics-naïve [
10
–
12
]. In
2015, Boehncke et al. mentioned a need for a reliable platform for predicting individual
patient responses to biologics before starting psoriasis treatment [13].
The medical treatment of psoriasis and psoriatic arthritis incurs substantial costs every
year. Some patients do not respond favorably to a first clinical treatment with biological
agents. These patients must then change to another biological agent to continue treatment.
Due to the lack of a pre-administration evaluation and a screening platform, patients are
often prescribed biological agents according to standard treatment guidelines instead of
according to their individual cases. An improper selection of biologics causes the patient
physical discomfort and psychological stress, as well as wasting health care resources.
A prescreen is the first step toward personal, precise treatment. Several studies
investigated genes, including HLA-related and non-HLA-related single-nucleotide poly-
morphisms, tissue markers, and serum markers [14]. An HLA-C*06:02 negative genotype
was more likely to respond to adalimumab than ustekinumab [
15
]. Conversely, patients
achieved a PASI75 outcome with ustekinumab; 92% of HLA-C*06:02 positive patients
achieved this compared with 67% of HLA-C*06:02 negative patients [
16
]. Several SNPs
were identified as potential biomarkers in response to tumor necrosis factor (TNF)-
α
inhibitor biologics [
17
]. In recent studies, tissue and serum markers seem to be controver-
sial [
18
]. So far, there is no useful or reliable platform or biomarker that can predict the
effectiveness of biologics before starting treatment.
Peripheral blood mononuclear cells (PBMCs), which are composed of lymphocytes,
macrophages, dendritic cells, and basophils, are widely used for immune mechanism
surveys and drug selection. Each individual’s PBMCs present that individual’s immune
function and characteristics. Therefore, PBMCs are useful in designing an individualized
platform for screening the efficacy of biologics before starting treatment. Streptococcus
pyogenes (S. pyogenes) can trigger immune responses to activate psoriasis outbreaks [
19
,
20
].
Furthermore, the innate immune system has been shown to be activated by S. pyogenes
in both guttate and chronic plaque psoriasis [
21
]. The specific IgA response against
S. pyogenes was correlated with a cutaneous lymphocyte-associated antigen+ T cell-dependent
IL-17F response [
22
]. In this study, we use S. pyogenes for PBMC induction to simulate a
real clinical psoriasis outbreak and treat it with different biologics. We survey different
biomarkers that can represent clinical skin change in terms of the PASI score.
2. Materials and Methods
2.1. Participants
Our criteria for participating in this study were patients with psoriasis aged from 20
to 65 years old who had experienced at least 2 biological agents. We excluded patients with
active infections or potential malignancies. A total of 20 patients with psoriasis with or
without psoriatic arthritis were enrolled in this study from 2018 to 2023. All participants
were from the clinic of either the Department of Allergy, Immunology, and Rheumatology
or the Department of Dermatology at Taichung Veterans General Hospital. All partici-
pants provided written informed consent, and the study protocol was approved by the
Institutional Review Board of Taichung Veterans General Hospital (TCVGH-CE16265B;
TCVGH-CE20043B). The PASI score was accessed before and after the biologic treatments.
We recorded the PASI score on day 0 and the PASI score closest to the time of PBMC testing
on experienced biologics (at least 180 days) or the PASI score on day 180 for inexperienced
biologics or experienced biologics (within 180 days).
2.2. Materials
S. pyogenes group A was identified and prepared by the Department of Pathology
and Laboratory Medicine of Taichung Veterans General Hospital. S. pyogenes group A was
inactivated by heat and then placed on blood agar plates for 1 week.
Biomedicines 2024,12, 1115 3 of 13
2.3. Cell Culture
To prepare the PBMC culture, 16 mL of blood was obtained from each patient and
collected in sodium citrate tubes (Vacutainer CPT, BD Biosciences, Franklin Lakes, NJ,
USA). Then, PBMCs were purified using centrifugation at 1800
×
gfor 20 min at room
temperature and with the brake off to generate distinct plasma, PBMCs, gel plugs, and
red blood cell (RBC) layers. The cells were washed with phosphate-buffered saline and
subsequently cultured in RPMI-1640 supplemented with 10% fetal bovine serum and 1%
penicillin/streptomycin at 37 ◦C and 5% CO2.
2.4. Cell Viability Test
Patient PBMCs were cultured with 5
×
10
6
, 1
×
10
7
, and 2
×
10
7
CFU concentra-
tions of S. pyogenes for 24 h, and then 0.5 mg/mL of 3-(4,5-dimethylthiazol-2-yl)-2,5-
diphenyltetrazolium bromide was added. After reacting for 1 h, the mixtures were cen-
trifuged, and the supernatants were removed. Subsequently, 200
µ
L of dimethyl sulfoxide
was added to lyse the cells and dissolve purple formazan crystals. We analyzed cell viability
with an enzyme-linked immunosorbent assay reader at a wavelength of 570 nm.
2.5. Mutiplex Assay for Cytokine Levels
A total of 6
×
10
5
cells per milliliter were then cultured in a 12-well plate and treated
for 24 h with the following biologics: control, S. pyogenes only, S. pyogenes + adalimumab
(4
µ
g/mL), S. pyogenes + golimumab (0.5
µ
g/mL), S. pyogenes + certolizumab (20
µ
g/mL),
S. pyogenes + ustekinumab (0.25
µ
g/mL), S. pyogenes + ixekizumab (3.5
µ
g/mL), S. pyo-
genes + secukinumab (16.7
µ
g/mL), S. pyogenes + secukinumab (34
µ
g/mL), S. pyogenes +
guselkumab (1.2
µ
g/mL), or S. pyogenes + risankizumab (2
µ
g/mL). Supernatants were
collected for the subsequent measurement of cytokine levels. The concentrations of the
biological agents we tested are the trough serum concentrations at a steady state indicated
in the pharmacokinetic section of the reference list. The two concentrations of secuk-
inumab (16.7 or 34
µ
g/mL) reflect the two common clinical doses of 150 mg or 300 mg per
month, respectively.
To measure cytokine levels, culture supernatants were collected, and the concentra-
tions of IL-1
β
, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12, IL-13, IL-17A, IFN-
γ
, TNF-
α
,
monocyte chemoattractant protein (MCP)-1, macrophage inflammatory protein (MIP)-1
α
,
MIP-1
β
, platelet-derived growth factor-BB, and chemokine (CC motif) ligand 5 (RANTES)
were determined using a protein multiplex immunoassay system (Bio-Plex Cytokine Array
System, Bio-Rad Laboratories, Hercules, CA, USA).
2.6. Statistical Analysis
All statistical analyses were performed using SPSS version 22 (IBM, Armonk, NY,
USA). Cytokine and chemokine levels in psoriasis patients and psoriatic arthritis patients
were analyzed using the Mann–Whitney U test. Cytokine expression and PASI were
analyzed with Spearman’s rho. Data were presented as the mean
±
standard deviation.
Two-sided pvalues of 0.05 or less were considered to indicate statistical significance.
3. Results
3.1. Cell Viability Test
Five patients’ PBMCs were cultured with 5
×
10
6
, 1
×
10
7
, and 2
×
10
7
CFU con-
centrations of S. pyogenes for 24 h. Similar cell viability to the control was recorded for
the concentration of 5
×
10
6
, 1
×
10
7
, and 2
×
10
7
CFU/mL. Next, 2
×
10
7
CFU/mL of
S. pyogenes group A was selected for similar cell viability and proper induction response
(Figure 1).
Biomedicines 2024,12, 1115 4 of 13
Biomedicines 2024, 12, x FOR PEER REVIEW 4 of 13
3. Results
3.1. Cell Viability Test
Five patients’ PBMCs were cultured with 5 × 106, 1 × 107, and 2 × 107 CFU concentra-
tions of S. pyogenes for 24 h. Similar cell viability to the control was recorded for the con-
centration of 5 × 106, 1 × 107, and 2 × 107 CFU/mL. Next, 2 × 107 CFU/mL of S. pyogenes
group A was selected for similar cell viability and proper induction response (Figure 1).
Figure 1. PBMCs isolated from psoriasis patients (n = 5) incubated with 5 × 106, 1 × 107, and 2 × 107
CFU S. pyogenes for 24 h. Cell viability was measured by an MTT assay.
3.2. Clinical Association between Clinical PASI and Laboratory Profile by Reduction Rate and
Difference
Twenty patients were enrolled in our study. The demographic, time of PBMC testing,
order of biologic sequences, and clinical characteristics of the psoriasis patients are listed
in Table 1. The cytokine analyses of patients’ PBMCs and PASI scores are listed in Table 2.
In this preliminary pilot study, the PASI reduction rate was strongly correlated with the
reduction rate in the IL-13 level (p = 0.001) and the ratios of IFN-γ to IL-13 (p < 0.001), IFN-
γ to IL-4 (p = 0.019), and IL-17A to IL-13 (p = 0.001). The PASI reduction difference was
strongly correlated with the difference in the IFN-γ level (p = 0.002), the difference in the
ratios of IFN-γ to IL-4 (p = 0.041), the difference in the ratios of IFN-γ to IL-13 (p = 0.006),
the difference in the ratios of IL-17A to IL-4 (p = 0.011), and the difference in the ratios of
IL-17A to IL-13 (p = 0.029; Table 3).
Table 1. Demographic and clinical characteristics of psoriasis patients (n = 20).
Pt Age/
Gender
PsO/
PsA
Time of PBMC
Test
Course and Order
Sequence of Biologics
PASI Results (Consumption
Time of Drugs on Cell Test) Other Systemic Disease
A 52 y/M +/− 10th month of
adalimumab
6 months of
ustekinumab
12 months of
adalimumab
PASI 19
(6 months)
PASI 100
(10 months)
-
B 43 y/M +/+ 2nd month of
ustekinumab
6 months of
adalimumab
6 months of
ustekinumab
PASI-7
(6 months)
PASI 99
(6 months)
Alcoholic hepatitis
TAILS
C 44 y/M +/− 5th month of
adalimumab
6 months of
ustekinumab
24 months of adalimumab
PASI 8
(6 months)
PASI 96
(6 months)
-
D 59 y/M +/+ 12th month of
adalimumab
12 months of adalimumab
18 months of
ustekinumab *
PASI-100
(12 months)
PASI 89
-
Cell viability (fold of Control)
Figure 1.
PBMCs isolated from psoriasis patients (n= 5) incubated with 5
×
10
6
, 1
×
10
7
, and
2×107CFU S. pyogenes for 24 h. Cell viability was measured by an MTT assay.
3.2. Clinical Association between Clinical PASI and Laboratory Profile by Reduction Rate
and Difference
Twenty patients were enrolled in our study. The demographic, time of PBMC testing,
order of biologic sequences, and clinical characteristics of the psoriasis patients are listed
in Table 1. The cytokine analyses of patients’ PBMCs and PASI scores are listed in Table 2.
In this preliminary pilot study, the PASI reduction rate was strongly correlated with the
reduction rate in the IL-13 level (p= 0.001) and the ratios of IFN-
γ
to IL-13 (p< 0.001),
IFN-
γ
to IL-4 (p= 0.019), and IL-17A to IL-13 (p= 0.001). The PASI reduction difference was
strongly correlated with the difference in the IFN-
γ
level (p= 0.002), the difference in the
ratios of IFN-
γ
to IL-4 (p= 0.041), the difference in the ratios of IFN-
γ
to IL-13 (p= 0.006),
the difference in the ratios of IL-17A to IL-4 (p= 0.011), and the difference in the ratios of
IL-17A to IL-13 (p= 0.029; Table 3).
Table 1. Demographic and clinical characteristics of psoriasis patients (n= 20).
Pt Age/
Gender
PsO/
PsA
Time of PBMC
Test
Course and Order
Sequence of Biologics
PASI Results
(Consumption Time of
Drugs on Cell Test)
Other Systemic Disease
A 52 y/M +/−10th month of
adalimumab
6 months of
ustekinumab
12 months of
adalimumab
PASI 19
(6 months)
PASI 100
(10 months)
-
B 43 y/M +/+ 2nd month of
ustekinumab
6 months of
adalimumab
6 months of
ustekinumab
PASI-7
(6 months)
PASI 99
(6 months)
Alcoholic hepatitis
TAILS
C 44 y/M +/−5th month of
adalimumab
6 months of
ustekinumab
24 months of adalimumab
PASI 8
(6 months)
PASI 96
(6 months)
-
D 59 y/M +/+ 12th month of
adalimumab
12 months of adalimumab
18 months of
ustekinumab *
PASI-100
(12 months)
PASI 89
(6 months)
-
E 51 y/M +/+ 6th month of
guselkumab
26 months of adalimumab
6 months of guselkumab
8 months of
secukinumab *
PASI 52
(26 months)
PASI 46
(6 months)
PASI 85
(6 months)
-
F 55 y/F +/+ 29th month of
golimumab
30 months of golimumab
18 months of adalimumab *
PASI 90
(29 months)
PASI 80
(6 months)
-
Biomedicines 2024,12, 1115 5 of 13
Table 1. Cont.
Pt Age/
Gender
PsO/
PsA
Time of PBMC
Test
Course and Order
Sequence of Biologics
PASI Results
(Consumption Time of
Drugs on Cell Test)
Other Systemic Disease
G 44 y/M +/+ 20th month of
secukinumab
54 months of
ustekinumab
24 months of
secukinumab
12 months of
ixekizumab *
PASI 60
(54 months)
PASI 89
(20 months)
PASI 100
(6 months)
-
H 65 y/F +/+ 15th month of
secukinumab
52 months of
adalimumab
24 months of
ustekinumab
24 months of
secukinumab
PASI 26
(52 months)
PASI 69
(24 months)
PASI 93
(15 months)
-
I 57 y/F +/+ 1st month of
secukinumab
47 months of
adalimumab
15th month of
secukinumab
PASI 47
(47 months)
PASI 100
(6 months)
TAILS
J 40 y/M +/−1st month of
ixekizumab
24 months of
ustekinumab
12 months of
ixekizumab
PASI 100
(24 months)
PASI 92
(6 months)
-
K 54 y/M +/−1st month of
ixekizumab
24 months of
ustekinumab
6 months of
ixekizumab
PASI 75
(24 months)
PASI 100
(6 months)
HBV
L 42 y/M +/−2st week of
ixekizumab
24 months of
ustekinumab
6 months of
ixekizumab
PASI 89
(24 months)
PASI 94
(6 months)
-
M 65 y/F +/−18th month of
secukinumab
27 months of adalimumab
28 months of
secukinumab
PASI 74
(27 months)
PASI 94
(18 months)
Tb
N 54 y/M +/+ 10th month of
ixekizumab
24 months of
ustekinumab
19 months of golimumab
12 months of
ixekizumab
PASI 89
(24 months)
PASI 66
(19 months)
PASI 100
(10 months)
-
O 39 y/M +/+
30th month of
ustekinumab
(2nd course of
ustekinumab)
36 months of
ustekinumab
6 months of
secukinumab
30 months of
ustekinumab
PASI 49
(6 months)
PASI 84
(30 months in 2nd course)
Kikuchi
Fujimoto
disease
MI
P 43 y/M +/−20th month of
guselkumab
6 months of
ixekizumab
24 months of guselkumab
PASI 100
(6 months)
PASI 100
(20 months)
HTN
CKD
Q 32 y/M +/+ 33th month of
guselkumab
24 months of
ustekinumab
33 months of guselkumab
PASI 92.5
(24 months)
PASI 100
(33 months)
-
R 42 y/M +/−21th month of
risankizumab
24 months of
ustekinumab
21 months of risankizumab
PASI 100
(24 months)
PASI 100
(21 months)
-
S 43 y/M +/−12th month of
ixekizumab
24 months of
ustekinumab
12 months of ixekizumab
PASI 42
(24 months)
PASI 100
(12 months)
Hyperlipidemia
T 43 y/M +/−0th month of
certolizumab
12 months of
ustekinumab
12 months of risankizumab
PASI 80
(12 months)
PASI 93
(12 months)
-
PsO = psoriasis; PsA = psoriatic arthritis; HBV = hepatitis B virus; TAILS = TNF-
α
inhibitor-induced systemic lupus
erythematosus; Tb = tuberculosis; MI = myocardial infarction; CKD = chronic kidney disease. Patients received a
PBMC test before biologics labeled with *. Patient O received a PBMC test in the 2nd course of ustekinumab.
Biomedicines 2024,12, 1115 6 of 13
Table 2. Laboratory profiles of patients with administered biologics (n= 20).
Pt Before After
Bio Induction After
Bio Induction After
Bio Induction After
Bio Induction After
Bio Induction After
Bio Biologics
Absolute
PASI score IFN-γ(pg/mL) IL13
(pg/mL) IFN-γ/IL13 IL4 (pg/mL) IFN-γ/IL4
A
3.2 2.6
13.72
12.43
4.19
3.41
3.27
3.46 0.09
0.09
0.2 ada
3.2 0 11.97 3.55 3.36 OOR< ust
B
15 16
74.46
88.28
1.24
0.81 62.47 108.99
5.54
3.56
13.44
24.8 ada
15 0.2 71.02 1.24 57.27 5.45 13.03 ust
C
13 0.5
22.9
19.71
0.5
0.59
45.8
33.41
6.01
4.76
3.81
4.14 ada
13 12 21.97 0.5 43.94 5.6 3.92 ust
D
7.5 15
200.39
222.11
5.19
4.31
38.61
51.53
13.94
13.64
14.38
16.28 ada
7.5 0.8 201.66 5.28 38.19 14.48 13.93 ust
E
25 12
92.6
76.99
1.53
1.53
60.52
50.32
12.17
9.41
7.61
8.18 ada
8 4.3 85.17 1.34 63.56 12.34 6.9 gus
8 1.2 80.12 1.71 46.85 12.94 6.19 sec
F
4.1 0.8
107.72
105.21
2.99
2.22
33.69
47.39
10.94
8.62
9.85
12.2 ada
4.1 0.4 107.68 2.22 48.5 8.72 12.35 gol
G
31.2 0
100.13
76.76
8.44
5.69
11.86
13.49
11.74
11.01
8.53
6.97 ixe
56.5 22.4 102.31 6.73 15.2 11.44 8.94 ust
22.4 2.4 84.74 7.78 10.89 11.49 7.38 sec
H
11.4 3.5
85.68
89.51
2.74
2.91
31.27
30.76
11.35
11.43
7.55
7.83 ust
15.5 11.4 102.91 2.04 50.45 9.76 10.54 ada
15.3 1 66.92 2.57 26.35 11.23 5.96 sec
I
3.8 2
244.94
290.14
3.05
2.73
80.31
106.28
10.73
10.82
22.83
26.82 ada
2 0 269.05 4.27 63.01 13.1 20.54 sec
J15 1.2
273.76
297.09
2.15
2.24
127.33
132.63
11.74
12.17
23.32
24.41 ixe
16 0 268.46 2.24 119.85 12.02 22.33 ust
K
15 3.7
367.78
365.71
6.14
5.86
59.9
62.41
13.51
13.31
27.22
27.48 ust
11.7 0 383.17 6.27 61.11 13.81 27.75 ixe
L
36.8 4
194.87
134.85
1.98
1.62
98.41
83.24
14.22
11.55
13.7
11.68 ust
16 1 81.47 1.44 56.57 9.97 8.17 ixe
M
20 5.2
247.16
262.11
1.71
2.06
144.54
127.24
7.77
9.77
31.81
26.83 ada
16 0.9 297.59 3.9 76.3 15.15 19.64 sec
N27
3.1
66.37
61.69
1.76
1.76
37.71
35.2
3.82
3.53
17.37
17.65 ust
9.1 66.72 1.45 46.01 3.46 19.28 gol
0 63.72 1.86 34.26 3.85 16.55 ixe
O 19.2
3
249.87
194.12
1.44
0.84
173.52
231.1
14.67
12.02
17.03
16.15 ust
9.8 188.89 1.25 151.11 11.7 16.14 sec
P13.1
0
50
53.26
1.66
1.66
30.12
32.08
3.23
3.41
15.48
15.62 ixe
0 40.67 1.86 21.87 3.01 13.51 gus
Q16 1.2
32.1
40
0.16
0.59
200.6
71.3
2.35
2.44
13.7
17.2 ust
27 0 24.2 0.24 100.8 1.88 12.9 gus
R
26 0
52.6
49.8
0.32
0.32
164.3
104.3
2.48
1.56
21.2
31.3 ust
16.4 0 31.2 0.46 97.5 1.41 22.1 ris
S
7.8 4.5
29
27.64
0.34
0.2
85.3
84
4.06
3.95
7.14
7.21 ust
8.5 0 28.5 0.34 138.2 3.96 7 ixe
T18.4
3.6
41.02
31.78
0.42
0.2
97.67
158.9
2.01
2.42
20.4
13.1 ust
1.2 27.98 0.56 49.96 2 13.99 ris
Pt = patient; PASI = psoriasis area and severity index; OOR< = out of range below; Bio = biologics; ada = adalimumab;
ust = ustekinumab; gus = guselkumab; ris = risankizumab; gol = golimumab; ixe = ixekizumab; sec = secukinumab.
Biomedicines 2024,12, 1115 7 of 13
Table 3.
Clinical association between clinical PASI and laboratory profile by reduction rate
and difference.
Reduction PASI IFN-r IL-13 IL-4 IL17A
Ratio rspValue rspValue rspValue rspValue rspValue
PASI 1.00 0.22 0.152 −0.48 0.001 ** −0.05 0.753 0.12 0.444
IFN-r 0.22 0.152 1.00 0.18 0.255 0.40 0.007 ** 0.49 0.001 **
IL-13 −0.48 0.001 ** 0.18 0.255 1.00 0.33 0.030 * 0.21 0.161
IL-4 −0.05 0.753 0.40 0.007 ** 0.33 0.030 * 1.00 0.74 <0.001 **
IFN-r/IL13 0.52 <0.001 ** 0.35 0.022 * −0.76 <0.001 ** 0.01 0.958 0.10 0.516
IFN-r/IL4 0.29 0.061 0.51 0.001 ** −0.09 0.581 −0.40 0.009 ** −0.13 0.394
IL17A 0.12 0.444 0.49 0.001 ** 0.21 0.161 0.74 <0.001 ** 1.00
IL17A/IL13 0.47 0.001 ** 0.27 0.081 −0.60 <0.001 ** 0.25 0.108 0.51 <0.001 **
IL17A/IL4 0.36 0.019 * 0.54 <0.001 ** 0.02 0.914 0.27 0.087 0.71 <0.001 **
IL-6 0.04 0.805 0.66 <0.001 ** 0.22 0.145 0.52 <0.001 ** 0.51 <0.001 **
Reduction PASI IFN-r IL-13 IL-4 IL-17A
Difference rspvalue rspvalue rspvalue rspvalue rspvalue
PASI 1.00 0.45 0.002 ** −0.14 0.366 0.13 0.392 0.10 0.512
IFN-r 0.45 0.002 ** 1.00 0.18 0.251 0.49 0.001 ** 0.48 0.001 **
IL-13 −0.14 0.366 0.18 0.251 1.00 0.57 <0.001 ** 0.41 0.006 **
IL-4 0.13 0.392 0.49 0.001 ** 0.57 <0.001 ** 1.00 0.77 <0.001 **
IFN-r/IL13 0.41 0.006 ** 0.30 0.051 −0.62 <0.001 ** −0.04 0.780 −0.05 0.753
IFN-r/IL4 0.32 0.041 * 0.44 0.004 ** −0.25 0.115 −0.29 0.063 −0.21 0.191
IL17A 0.10 0.512 0.48 0.001 ** 0.41 0.006 ** 0.77 <0.001 ** 1.00
IL17A/IL13 0.33 0.029 * 0.32 0.032 * −0.48 0.001 ** 0.16 0.295 0.25 0.098
IL17A/IL4 0.39 0.011 * 0.49 0.001 ** −0.02 0.894 0.15 0.347 0.47 0.002 **
IL-6 0.2 0.195 0.58 <0.001 ** 0.25 0.104 0.53 <0.001 ** 0.4 0.007 **
Spearman’s rho. * p< 0.05, ** p< 0.01.
The PASI results represent the mean relative PASI. The results of PASI and consump-
tion time were parallel to previous excel columns for different biologics.
In every patient, we showed the absolute PASI score before (day 0) and after biologic
treatment. Laboratory profiles showed induction after S. pyogenes and treatment with
biologics in different markers. Patient I received secukinumab 150 mg monthly, and
patients E and G received secukinumab 300 mg monthly. The patients’ PBMCs were treated
with secukinumab at 16.7 and 34 µg per milliliter, respectively.
The reduction rate of IFN-
γ
/IL-13 was calculated after treatment with biologics as
compared with S. pyogenes induction only as follows:
(IFN-γ/IL-13 w/S.pyogenes & biologics)−(IFN-γ/IL-13 w/S.pyogenes only)
(IFN-γ/IL-13 w/S.pyogenes only)(1)
* The difference in IFN-
γ
/IL-13 was calculated after treatment with biologics compared
with S. pyogenes induction only as follows:
(IFN-γ/IL-13 w/S. pyogenes and biologics) −(IFN-γ/IL-13 w/S. pyogenes only) (2)
There is the same rationale for IFN-γ/IL4 and IL-17A/IL13 as above.
3.3. Correlation between Clinical PASI and Biomarkers among Different Biologics
The level of IL-6 was weakly correlated with the PASI score reduction but was not
significant. The levels of IL-1
β
, IL-2, IL-5, IL-7, IL-8, IL-10, IL-12, TNF-
α
, MCP-1, MIP-1
α
,
MIP-1
β
, platelet-derived growth factor-BB, and RANTES were not correlated. The relation
of biomarkers in different subgroup biologics was that the reduction ratios of IFN-
γ
to IL-13
were dominant with the PASI reduction difference in ustekinumab (p= 0.047; Figure 2A);
the difference in the IL-17A level and the ratio of IL-17A to IL4 were correlated with the
PASI difference but were not significant in ustekinumab (p= 0.068, p= 0.052; Figure 2B).
The difference in the IL-13 level and the ratio of IFN-
γ
to IL4 were dominant with the PASI
Biomedicines 2024,12, 1115 8 of 13
difference in adalimumab (p= 0.01, p= 0.047; Figure 2D). There was a lack of significance
for ixekizumab, risankizumab, and guselkumab.
Biomedicines 2024, 12, x FOR PEER REVIEW 8 of 13
3.3. Correlation between Clinical PASI and Biomarkers among Different Biologics
The level of IL-6 was weakly correlated with the PASI score reduction but was not
significant. The levels of IL-1β, IL-2, IL-5, IL-7, IL-8, IL-10, IL-12, TNF-α, MCP-1, MIP-1α,
MIP-1β, platelet-derived growth factor-BB, and RANTES were not correlated. The relation
of biomarkers in different subgroup biologics was that the reduction ratios of IFN-γ to IL-
13 were dominant with the PASI reduction difference in ustekinumab (p = 0.047; Figure
2A); the difference in the IL-17A level and the ratio of IL-17A to IL4 were correlated with
the PASI difference but were not significant in ustekinumab (p = 0.068, p = 0.052; Figure
2B). The difference in the IL-13 level and the ratio of IFN-γ to IL4 were dominant with the
PAS I difference in adalimumab (p = 0.01, p = 0.047; Figure 2D). There was a lack of signif-
icance for ixekizumab, risankizumab, and guselkumab.
Figure 2. Correlation between clinical PASI and biomarkers among different biologics. Heatmaps
showing correlation coefficients with different colors and p values with stars. (A) Reduction ratios
of PASI and biomarkers for ustekinumab. (B) Reduction difference of PASI and biomarkers for
ustekinumab. (C) Reduction ratios of PASI and biomarkers for adalimumab. (D) Reduction differ-
ence of PASI and biomarkers for adalimumab. * p < 0.05, ** p < 0.01, *** p < 0.001.
3.4. Correlation between Clinical PASI and Biomarkers in PsO Only and PsO + PsA Groups
We further investigated biomarkers in the PsO only and PsO + PsA subgroups. Sim-
ilar biomarkers, including IFN-r, IL-13, IFN-r/IL-4, IFN-r/IL-13, IL-17A/IL-4, and IL-
17A/IL-13, were significantly associated with clinical PASI, despite fewer samples in each
group (Table 4).
Figure 2.
Correlation between clinical PASI and biomarkers among different biologics. Heatmaps
showing correlation coefficients with different colors and pvalues with stars. (
A
) Reduction ratios
of PASI and biomarkers for ustekinumab. (
B
) Reduction difference of PASI and biomarkers for
ustekinumab. (
C
) Reduction ratios of PASI and biomarkers for adalimumab. (
D
) Reduction difference
of PASI and biomarkers for adalimumab. * p< 0.05, ** p< 0.01, *** p< 0.001.
3.4. Correlation between Clinical PASI and Biomarkers in PsO Only and PsO + PsA Groups
We further investigated biomarkers in the PsO only and PsO + PsA subgroups. Similar
biomarkers, including IFN-r, IL-13, IFN-r/IL-4, IFN-r/IL-13, IL-17A/IL-4, and IL-17A/IL-
13, were significantly associated with clinical PASI, despite fewer samples in each group
(Table 4).
Table 4. Correlation between clinical PASI and biomarkers in PsO only and PsO + PsA groups.
PsO Only (n= 10) PsO + PsA (n= 10)
PASI PASI PASI PASI
Ratio rspValue Difference rspValue Ratio rspValue Difference rspValue
PASI 1 PASI 1 PASI 1 PASI 1
IFN-r 0.02 0.935 IFN-r 0.46 0.040 * IFN-r 0.35 0.093 IFN-r 0.53 0.008 **
IL-13 −0.35 0.128 IL-13 −0.4 0.079 IL-13 −0.46 0.023 * IL-13 −0.03 0.88
IL-4 0.27 0.257 IL-4 0.1 0.678 IL-4 −0.31 0.147 IL-4 0.11 0.598
IFN-r/IL13 0.3 0.199 IFN-r/IL13 0.63 0.003 ** IFN-r/IL13 0.56 0.005 ** IFN-r/IL13 0.35 0.094
IFN-r/IL4 −0.26 0.297 IFN-r/IL4 0.23 0.369 IFN-r/IL4 0.5 0.012 * IFN-r/IL4 0.38 0.066
IL17A 0.27 0.241 IL17A 0.12 0.609 IL17A −0.14 0.525 IL17A 0.09 0.661
IL17A/IL13 0.47 0.038 * IL17A/IL13 0.35 0.132 IL17A/IL13 0.32 0.124 IL17A/IL13 0.43 0.036 *
IL17A/IL4 0.36 0.137 IL17A/IL4 0.27 0.278 IL17A/IL4 0.25 0.238 IL17A/IL4 0.56 0.005 **
IL-6 0.05 0.833 IL-6 0.05 0.843 IL-6 0.1 0.627 IL-6 0.34 0.106
Spearman’s rho. * p< 0.05, ** p< 0.01.
Biomedicines 2024,12, 1115 9 of 13
4. Discussion
Psoriasis is a Th1/Th17-predominant disease. IFN-
γ
is a crucial cytokine in the patho-
genesis of psoriasis. IFN-
γ
-producing Th1 lymphocytes can cause neutrophil infiltration,
vessel proliferation, and keratinocyte hyperproliferation [
23
]. Kryczek et al. found that
IFN-
γ
is a potent promoter of human IL-17+ T cell function, induction, and trafficking [
24
].
IFN-
γ
synergizing with IL-17 can enhance
β
-defesin-2 secretion, suggesting that Th1 and
Th17 cells can cooperate to contribute to the pathogenesis of psoriasis [
25
]. IFN-
γ
injections
in the unaffected skin of a patient with psoriasis can cause pathogenic T cell infiltration,
dendritic cell infiltration, and IL-23 secretion [
26
]. Therefore, IFN-
γ
is a major cytokine in
Th1 and plays a pivotal role in the Th17 pathway. In our pilot research, IFN-
γ
level was
a primary marker reflecting the outcome of biologics before starting treatment. The Th17
pathway is another major effector cytokine in the pathogenesis of psoriatic disease [
27
]. The
relationship between the ratio of IL-17A to IL-13 and PASI was also statistically significant.
The ratio of IL-17A to IL-13 seems to be another potential marker. Th17/1 cells, which
originate from Th17, will be activated in the chronic phase, re-challenged with stress, and
then IL-17A and IFN-
γ
will be secreted to exacerbate clinical disease [
28
]. Taken together,
IFN-γas well as IL-17A are important markers in our research.
Our results suggest that psoriasis is not only associated with Th1 and Th17 but also
Th2. An imbalance of Th1 and Th2 tends to exacerbate psoriasis symptoms. In 2003,
Ghoreschi et al. developed an innovative therapy where IFN-
γ
-producing Th1 cells were
selectively skewed toward IL-4-producing Th2 cells. Rather than suppressing the immune
system, the researchers attempted to achieve immune balance by using IL-4 and found
improvements in psoriasis treatment outcomes [
29
]. IL-4 and IL-13 are major cytokines in
the Th2 reaction. In the present study, we analyzed the ratios of IFN-
γ
to IL-4, IFN-
γ
to
IL-13, and IL-17A to IL-13, which were all significantly correlated with clinical PASI scores.
By contrast, the levels of IL-8 were not correlated with the PASI score. The relationship
between clinical severity and IL-8 concentration has been controversial in some previous
studies [
30
,
31
]. In our study, IL-8 levels were highly sensitive in S. pyogenes induction, and
some data appeared out of range in multiplex testing. Beyond that, our data showed no
correlation with clinical PASI scores. Some biomarkers similar to IL-8, such as MCP-1,
MIP-1
α
, and MIP-1
β
, were very sensitive to induction by S. pyogenes, independent of PASI
results. IL-8, MCP-1, MIP-1
α
, and MIP-1
β
are essential for chemotaxis. In our opinion,
variation in Th1 or Th17 upstream responses appears to correlate more with clinical PASI
scores than cell chemotaxis.
In our subgrouping data, different markers appear to be present in different biologics.
IFN-
γ
/IL-4 and IL-13 are important biomarkers in adalimumab. IFN-
γ
/IL-13 is an impor-
tant one in ustekinumab. In different biologic mechanisms, they share the same biomarkers
on IFN-
γ
and IL-13. Additionally, IL-17A seemed to be another marker for ustekinumab
from our subgroup data. IFN-
γ
plays a pivotal role in the Th1 pathway. Adalimumab is a
TNF-
α
inhibitor, a major cytokine in the Th1 pathway. It is reasonable that IFN-
γ
is an im-
portant marker of adalimumab. Ustekinumab blocks IL-12 and IL-23, which are upstream
of Th17. It is logical that IL-17A is a better marker for ustekinumab than adalimumab.
IFN-
γ
levels have a role in determining disease severity, as shown in a previous study [
32
].
The IL-17 levels of psoriasis patients were significantly higher than those of the controls
and correlated to the severity of the disease [
33
]. IL-13 is an important cytokine of Th2,
which plays important parts in both ustekinumab and adalimumab in our study. All of
them are potentially important predictive biomarkers for guiding treatment selection. The
lack of significance for ixekizumab, risankizumab, and guselkumab, respectively, may be
due to the small sample size.
Appropriate biological therapy for individual patients with psoriasis can interrupt
multiple inflammatory loops. TNF-
α
inhibitors could block dendritic cell activation, thereby
inhibiting Th1-induced IFN-
γ
secretion and Th17-induced IL-17 secretion [
34
]. The ex-
hausted phenotype of regulatory T cell dysfunction comprised reduced TGF-
β
release
and increased IFN-
γ
production. Blocking IL-17 will reverse the exhausted phenotype of
Biomedicines 2024,12, 1115 10 of 13
regulatory T cells to reverse the increased IFN-
γ
levels [
35
–
38
]. Furthermore, the induction
of IL12p70 production on macrophages by IL-17 potentiates IFN-
γ
production by CD4+
cells. IL-17 inhibitor biologics could reduce IFN-
γ
production from macrophages and CD4+
cells [
39
]. IL-23, or IL-12/23, is the upstream cytokine of IL-17; the inhibitors of these share
the same mechanism for blocking IFN-
γ
. In clinical data, we also observed suppression
of serum IFN-
γ
levels with ustekinumab treatment in systemic lupus erythematosus pa-
tients [
40
]. The mechanisms of different biologics on IL-4 and IL-13 levels in individual
patients with psoriasis remain unclear and need to be elucidated in the future. The role
of immunity in Th1, Th17, and Th2 is frequently seen as a Yin and Yang [
41
]. Selecting
a biologic that downregulates IFN-
γ
and IL-17A and upregulates IL-13 and IL-4 leads to
better outcomes in individually tailored treatments. By prescreening patients, we could
select a suitable biologic for an individual patient by following the flow chart in Figure 3.
This study has some limitations. First, the results of the study must be validated in a larger
group to assess the variability and validity of our findings. Second, some measurement
errors can occur when the concentrations of IL-13 or IL-4 are extremely low, which may
have caused the extremely large ratios of IL-17A to IL-13 and IFN-
γ
to IL-13 or IL-4 in some
of our observations. More clinical research is necessary to address these limitations.
Biomedicines 2024, 12, x FOR PEER REVIEW 10 of 13
Adalimumab is a TNF-α inhibitor, a major cytokine in the Th1 pathway. It is reasonable
that IFN-γ is an important marker of adalimumab. Ustekinumab blocks IL-12 and IL-23,
which are upstream of Th17. It is logical that IL-17A is a beer marker for ustekinumab
than adalimumab. IFN-γ levels have a role in determining disease severity, as shown in a
previous study [32]. The IL-17 levels of psoriasis patients were significantly higher than
those of the controls and correlated to the severity of the disease [33]. IL-13 is an important
cytokine of Th2, which plays important parts in both ustekinumab and adalimumab in
our study. All of them are potentially important predictive biomarkers for guiding treat-
ment selection. The lack of significance for ixekizumab, risankizumab, and guselkumab,
respectively, may be due to the small sample size.
Appropriate biological therapy for individual patients with psoriasis can interrupt
multiple inflammatory loops. TNF-α inhibitors could block dendritic cell activation,
thereby inhibiting Th1-induced IFN-γ secretion and Th17-induced IL-17 secretion [34].
The exhausted phenotype of regulatory T cell dysfunction comprised reduced TGF-β re-
lease and increased IFN-γ production. Blocking IL-17 will reverse the exhausted pheno-
type of regulatory T cells to reverse the increased IFN-γ levels [35–38]. Furthermore, the
induction of IL12p70 production on macrophages by IL-17 potentiates IFN-γ production
by CD4+ cells. IL-17 inhibitor biologics could reduce IFN-γ production from macrophages
and CD4+ cells [39]. IL-23, or IL-12/23, is the upstream cytokine of IL-17; the inhibitors of
these share the same mechanism for blocking IFN-γ. In clinical data, we also observed
suppression of serum IFN-γ levels with ustekinumab treatment in systemic lupus erythe-
matosus patients [40]. The mechanisms of different biologics on IL-4 and IL-13 levels in
individual patients with psoriasis remain unclear and need to be elucidated in the future.
The role of immunity in Th1, Th17, and Th2 is frequently seen as a Yin and Yang [41].
Selecting a biologic that downregulates IFN-γ and IL-17A and upregulates IL-13 and IL-
4 leads to beer outcomes in individually tailored treatments. By prescreening patients,
we could select a suitable biologic for an individual patient by following the flow chart in
Figure 3. This study has some limitations. First, the results of the study must be validated
in a larger group to assess the variability and validity of our findings. Second, some meas-
urement errors can occur when the concentrations of IL-13 or IL-4 are extremely low,
which may have caused the extremely large ratios of IL-17A to IL-13 and IFN-γ to IL-13
or IL-4 in some of our observations. More clinical research is necessary to address these
limitations.
Figure 3. Suggested protocol for biologic selection before psoriasis treatment.
Figure 3. Suggested protocol for biologic selection before psoriasis treatment.
5. Conclusions
Psoriasis is a complex disease associated with genetics, environmental factors, and
immune reactions, influencing diverse clinical characteristics and responses to treatment.
We created an innovative, economic, and convenient platform to prescreen the efficacy
of biological treatments for individual patients. The markers IFN-
γ
, IL-13, IFN-
γ
/IL13,
IL-17A/IL-13, IFN-
γ
/IL4, and IL-17A/IL-4 are representative of predicting the efficacy
of psoriasis treatment. These markers imply the importance of not only the Th1/Th17
pathway but also the Th2 pathway. Although the cohort analyzed was small, this study
may be a valuable step toward predicting the responses of patients with psoriasis to a given
therapy and, thus, toward individually tailored treatments.
Author Contributions:
C.-L.H. participated in protocol design and manuscript discussion; S.-J.Y.
and W.-T.C. performed the experiments; K.-L.L. participated in protocol design and discussion; and
C.-Y.Y. performed the experiments, designed the protocol, and wrote the manuscript. All authors
have read and agreed to the published version of the manuscript.
Biomedicines 2024,12, 1115 11 of 13
Funding:
This work was funded by the Ministry of Science and Technology in Taiwan (MOST
109-2320-B-039-044). This work was also financially supported by the “Chinese Medicine Research
Center, China Medical University” from the Featured Areas Research Center Program within the
framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan
(CMRC-CENTER-0). The funders had no role in the study design, data collection and analysis,
interpretation of findings, manuscript writing, or target journal selection.
Institutional Review Board Statement:
We comply with the guidelines for human studies and
should include evidence that the research was conducted ethically in accordance with the World
Medical Association Declaration of Helsinki. All patients have given their written informed consent,
and the study protocol was approved by the Institutional Review Board of Taichung Veterans General
Hospital (TCVGH-CE16265B; TCVGH-CE20043B).
Informed Consent Statement:
Written informed consent was obtained from patients to publish
this paper.
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author due to the privacy of research participants.
Acknowledgments:
We thank all the participants who volunteered for this study, the Biostatistics
Task Force of Taichung Veterans General Hospital for the analyses, and the Department of Pathology
and Laboratory Medicine of Taichung Veterans General Hospital for S. pyogenes group A identification.
Conflicts of Interest: The authors declare no conflicts of interest.
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