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\begin{document}

\title{Antibody mimetics: the next generation antibody engineering, a
retrospective and prospective analysis}



\author[1]{Siran Zhang}%
\author[2]{Longjiang Wu}%
\author[2]{Mei Dang}%
\affil[1]{Xi’an Middle School of Shaanxi Province}%
\affil[2]{Shaanxi University of Technology}%


\vspace{-1em}



  
  \date{June 15, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Antibody mimetics is a novel antibody engineering approach after the
development of polyclonal, monoclonal antibodies, and genetically
engineered antibody fragments. Inspired by the structure and function of
natural antibodies, antibody mimetics offer many advantages over
conventional antibodies and can be constructed by protein-directed
evolution, peptide design and synthesis, or fusion of
complementarity-determining regions through intervening framework
regions. A series of parent protein/peptide structures and technical
roadmaps have been established to induce better recognising properties,
superior affinity, stability, penetrability, and cost-effectiveness of
the designed mimetics. This article aims to summarise the evolution of
antibody mimetics engineering, illustrate the highlights and hotpots in
this research field using scientometric analysis, and give an
anticipatory analysis on this increasing research topic.%
\end{abstract}%



\sloppy


Antibody mimetics: the next generation antibody engineering, a
retrospective and prospective analysis

Siran Zhang\textsuperscript{1,2,3}, Longjiang Wu\textsuperscript{4}, Mei
Dang\textsuperscript{4,5}

\textsuperscript{1}Xi'an Middle School of Shaanxi Province, Fengcheng
Wulu 69, Weiyang, Xi'an, China

\textsuperscript{2}Department of Genetics, Stanford University, Palo
Alto, CA 94304, USA

\textsuperscript{3}HSS, Stanford University, Stanford, Palo Alto,
California 94305, USA

\textsuperscript{4}College of Biological Sciences and Engineering,
Shaanxi University of Technology, Hanzhong 723000, China

\textsuperscript{5}Department of Biological Sciences, Faculty of
Science, National University of Singapore; 10 Keng Ridge Crescent,
119260, Singapore

\textbf{Correspondence:} Siran
Zhang\emph{siran-zhang@outlook.com},\emph{siran777@stanford.edu}

Mei Dang\emph{e0269788@u.nus.edu}

\textbf{Siran Zhang and Longjiang Wu contribute equally.}

Abstract

Antibody mimetics is a novel antibody engineering approach after the
development of polyclonal, monoclonal antibodies, and genetically
engineered antibody fragments. Inspired by the structure and function of
natural antibodies, antibody mimetics offer many advantages over
conventional antibodies and can be constructed by protein-directed
evolution, peptide design and synthesis, or fusion of
complementarity-determining regions through intervening framework
regions. A series of parent protein/peptide structures and technical
roadmaps have been established to induce better recognising properties,
superior affinity, stability, penetrability, and cost-effectiveness of
the designed mimetics. This article aims to summarise the evolution of
antibody mimetics engineering, illustrate the highlights and hotpots in
this research field using scientometric analysis, and give an
anticipatory analysis on this increasing research topic.

Keywords: antibody engineering, antibody mimetics, synthetic antibodies,
peptidomimetics, protein engineering, peptide design, scientometric
analysis

\section*{1 INTRODUCTION}

{\label{introduction}}

Following polyclonal and full-length monoclonal antibodies as well as
genetically engineered antibody fragments (\emph{Shahidian et al., 2020}
), antibody mimetics have become a major modality in antibody
engineering. It serves as a promising solution to offer more efficient
antibody-like molecules, which could be considered as the next
generation of antibody engineering, this will be discussed in depth in
this article.

Antibody mimetics the property of antibody to recognise and/or
neutralise a target molecule, and thus can be used for a wide range of
immunoassays and therapeutic purposes. They are designed, modified, or
refined from their parent structures, constructed basically by protein
directed evolution, peptide design, or complementary determining regions
(CDR) fusion through framework region (FR) in different sequences
(\emph{Baloch et al., 2016} ;\emph{Van Holsbeeck et al., 2022} ). To
date, several types of antibody mimetics have been developed, such as
affibodies, anticalins, DARPins, nanofitins, fynomers, avimers,
adnectin, peptide aptamer, affimer, affitin, and so forth (\emph{Van
Holsbeeck et al., 2022} ;\emph{Yu et al., 2017} ).\selectlanguage{english}
\begin{longtable}[]{@{}l@{}}
\toprule
\textbf{Box 1 \textbar{} Glossary}\tabularnewline
\midrule
\endhead
\textbf{Polyclonal antibody (First generation antibody):} composed of
biochemically distinct paratopes, the antigen-binding sites on the
extended arm of the antibody, capable of specifically binding to
different binding sites on the same antigen (\emph{Lipman et al.,
2005}). \textbf{Monoclonal antibody (Second generation antibody):} an
immunoglobulin secreted from a single B lymphocyte clone and
specifically recognizes a single epitope of an antigen (\emph{Nelson et
al., 2000}). \textbf{Genetically engineered antibody fragments (Third
generation antibody):} also known as recombinant antibodies, antibody
molecules expressed by transfecting appropriate recipient cells with
genes encoding antibodies that have been modified and reassembled
according to different needs using recombinant DNA and protein
engineering techniques (\emph{Moore, 1989}; \emph{Wright et al., 1992}).
For example, chimeric antibody, humanized antibody, fully humanized
antibodies, single-chain variable fragment (scFv), and bispecific
antibody (BsAb) are representatives of the genetically engineered
antibody fragments. \textbf{Synthetic antibody:} affinity reagents,
which provide therapeutics and diagnostics, engineered entirely \emph{in
vitro}, the production, selection, maturity or application is no longer
limited by the natural immune system (\emph{Adams \& Sidhu, 2014}).
\textbf{Antibody mimetics (Fourth generation antibody):} compounds that
act similarly to antibodies and bind specific antigens; they are
artificial polypeptides and are not related to antibodies (\emph{Zhang
\& Diraviyam, 2017}). \textbf{Peptide:} short chain of amino acids
linked by peptide (amide) bonds; naturally occurring molecules that
regulate many signaling pathways in the body (\emph{Fosgerau \&
Hoffmann, 2015}). \textbf{Peptidomimetics:} modified peptides; leading
peptides are optimized by introducing functional modifications that can
address the inherent shortcomings of the peptide while maintaining the
structural features responsible for the biological activity (\emph{Lenci
\& Trabocchi, 2020}). \textbf{Affibody:} one of the extensively used
antibody mimetics with a protein scaffold derived from the B region of
staphylococcal protein A; the B region consists of 58 amino acids that
form into 3 alpha-helices recognizing the Fc domain of IgG (\selectlanguage{ngerman}\emph{Ståhl
et al., 2017}). \textbf{Peptide aptamer:} short peptide sequences with
an inert and rigid scaffold; they can be generated for low molecular
weight targets, including small molecule ligands (\emph{Stojanovic et
al., 2001}; \emph{Yang et al., 2014}) or even metal ions (\emph{Qu et
al., 2016}). \textbf{Affimer:} aptamers with improved performance;
representing complementary affinity reagents for antibodies used in a
variety of molecular and cellular biology applications (\emph{Tiede et
al., 2017}).\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}

Generation of polyclonal antibodies is relatively simple and cheap as
the use of animals (such as chicken, horses, goats, and rabbits) enables
the recovery of large quantity of antibodies from serum or egg Yolk
(\emph{Yakhkeshi et al., 2022} ). Polyclonal antibodies arising from
diverse B-cell clones constitute a heterogeneous mixture that manifests
varying binding affinities. In contrast, monoclonal antibodies are
homogeneous population originated from a single B-cell clone, resulting
in well-defined binding specificities and affinities. However, at some
point a fresh batch will be sought as the original stock diminishes,
which inevitably leads to a batch-to-batch variation in terms of the
specificity, sensitivity, and reproducibility (\emph{Bradbury \&
Plückthun, 2015} ). This might include differences in antibody
reactivity and titre, and thus polyclonal reagents often lack
reproducibility. In comparison, the continuous culture of B cell
hybridomas overs a reproducible and potentially inexhaustible supply of
antibody with exquisite specificity. Consequently, monoclonal antibodies
enable the development of standardised and secure immunoassay system.

Genetically engineered antibody fragments are alternative to full-length
antibody in diagnostics and therapeutics for a variety of diseases due
to their various advantages (\emph{Holliger \& Hudson, 2005} ). Firstly,
their small size and simpler structure make them ideal for large-scale
production in eukaryotic systems, such as mammalian and insect cells.
The antibody fragments have known sequences and are reproducible,
verifiable, and manufacturable. The single-chain variable fragment
(scFv) is a successful example of a genetically engineered antibody
fragment. As the variable light chain and variable heavy chain coding
sequences are genetically linked in a single transcript, there is no
need to balance the expression of the light chain and heavy chain.
Engineered single domain antibodies (VHH) enable the rapid generation of
antibody fragments at higher yields and lower cost compared to full-size
monoclonal antibodies (mAbs) that typically require mammalian expression
systems. Their small size also facilitates tissue penetration and access
to cryptic epitopes, making them particularly useful for tumour
penetration in cancer immunotherapy (\emph{Zinn et al., 2023} ).
Moreover, the lack of an Fc region removes the risk of bystander immune
cell activation and antibody effector functions such as
antibody-dependent cellular cytotoxicity (ADCC), antibody-dependent
cellular phagocytosis (ADCP), or complement-dependent cytotoxicity
(CDC), allowing the molecule to bind its target without activating the
host's immune system (\emph{Gravina et al., 2023} ). However, the
absence of an Fc region is a double-edged sword. The disadvantage is
that it not only reduces thermostability and enhances propensity for
aggregation, thereby increasing the risk of immunogenicity, but also
shortens half-life due to a lack of FcRn-mediated recycling (\emph{Park
et al., 2016} ). This can lead to the need for higher and more frequent
dosing. Fusion of the scFv to albumin or polyethylene glycol (PEG) can
be used to improve half-life (\emph{Albrecht et al., 2004}
;\emph{Vazquez-Lombardi et al., 2015} ). However, such fusions can
offset the advantages that an scFv holds over a mAb due to cost and the
increase in size (\emph{Bradbury \& Plückthun, 2015} ).

The fourth generation of antibody engineering: antibody mimetics is not
generated by the body immune system naturally but share a common feature
with natural antibody: they shared characteristic of complementary shape
at the binding site with the antigen. Similar to enzyme and substrate,
the binding interactions between antibody mimetics and antigens can be
explained by three models, including the classical lock and key theory,
the induced fit mode, and the conformational selection model. Typically,
the classical lock and key theory reflects the initial recognition and
specifically between antibody mimetics and antigens. The induced fit
model suggests that both antibody mimetics and antigens undergo
conformational changes upon binding, in which the dynamic interaction
allows the active sites of antibody mimetics to adapt to their shape,
optimizing the fit with the antigen. Additionally, the conformational
selection model proposes a conformational change occurs prior to the
binding of antigens, in which the antigen seems to select and stabilize
a higher-energy conformation of an antibody mimetic for binding. Of
note, these models are not mutually exclusive, but rather represent
different aspects of interactions between antibody mimetics and
antigens. Antibody mimetics represent a class of engineered molecules
mimic the functions and properties of natural antibodies while offering
advantages such as enhanced stability, smaller size, improved production
yields. These mimetics can manifest in various forms, like smaller
antibody fragments synthesized based on the functional regions of
antibody fragments such as scFv or Fab. It is possible to design novel
proteins with binding properties through site-directed mutagenesis and
random mutagenesis. We summarized and compared the features of all the
four generations of in terms of stability, specificity, affinity, and so
forth (Table 1).

Now it may be the right time to review and analyse the field of antibody
mimetics retrospectively and prospectively. Given that scientometric
analysis can quantitively evaluate and investigate on all aspects of the
literature at different development stages (\emph{Mooghali et al., 2011}
), this article aims to provide a vigorous roadmap for antibody mimetics
research through this method to identify the major players and their
cooperation networks, including countries, academic groups, and
individuals; to analyse research status and hotspots, especially the key
study findings; to discuss the potentially valuable research directions.

\textbf{Table 1} Antibody production \& diversified antibody generation
platforms\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllllll@{}}
\toprule
& \textbf{Antibody properties} & \textbf{Representative
source/technology} & \textbf{Stability} & \textbf{Specificity} &
\textbf{Affinity} & \textbf{Production time} & \textbf{Reproducibility}
& \textbf{Half-time} & \textbf{Penetrability} & \textbf{Cost
effectiveness} & \textbf{Immunogenicity} & \textbf{Immunogenicity} &
\textbf{Animal Welfare}\tabularnewline
\midrule
\endhead
Polyclonal antibody & Polyclonal full length & Antiserum; IgY antibody
extracted hen egg yolk & +++ & + & +++ & + & + & + & + & ++++ & +++ & +
& +\tabularnewline
Monoclonal antibody & Monoclonal full length & Full length IgG hybridoma
technique & ++ & +++ & ++ & ++ & ++ & ++ & + & +++ & +++ & + &
+\tabularnewline
Genetically engineered antibody fragments & Monoclonal functional
fragments & Fragment antigen-binding (Fab); scFv (single chain variable
fragments); VHH (heavy chain variable domain or single-domain
antibodies) & + & +++ & +++ & +++ & +++ & + & ++ & ++ &
\textsubscript{++} & ++ & ++\tabularnewline
Antibody mimetics & & & +++ & +++ & ++++ & +++ & +++ & +++ & +++ & + & +
& +++ & +++\tabularnewline
\bottomrule
\end{longtable}

Notes: The relative degree of antibody properties of each generation is
displayed using symbol of ``+'', the larger number of ``+'' displayed,
the better property possessed. The table is based on a summary of
literatures (\emph{Ascoli \& Aggeler, 2018} ; \emph{Baloch et al., 2016}
;\emph{Ge et al., 2022} ;\emph{Khalili et al., 2013} ;\emph{Mullard,
2021} ;\emph{Wrapp et al., 2020} ;\emph{Yu et al., 2017} ).



\section*{2 METHODOLOGY}

{\label{methodology}}

\subsection*{2.1 Data Collection and Search
Strategy}

{\label{data-collection-and-search-strategy}}

Web of Science (WoS, Clarivate Analytics) is the authoritative database
for bibliometric investigation owning to the existence of the Journal
Citation Reports (JCR) and the large amount of citation data. Web of
Science Core Collection (WoSCC), as the premier resource on the WoS
database, was used to collect information on literature about antibody
mimetics. The searched time span is 1986-2022 (Data as of 31 Dec. 2022).
All key words related to the topic were combined with Boolean operators
before starting the search for documents as which serves as an effective
search strategy. The search terms and retrieval strategies were
developed as follows: TS (Topical Subject) = (antibody mimetics OR
antibody mimetic OR affibody OR adnectin OR aptamer OR affimer OR
affitin OR affilin OR anticalin OR atrimer OR Avimer OR Knottin) OR TI
(Title) = (antibody mimetics OR antibody mimetic OR affibody OR adnectin
OR aptamer OR affimer OR affitin OR affilin OR anticalin OR atrimer OR
Avimer OR Knottin) OR AK  (Author Keywords) = (antibody mimetics OR
antibody mimetic OR affibody OR adnectin OR aptamer OR affimer OR
affitin OR affilin OR anticalin OR atrimer OR Avimer OR Knottin) OR AB
(Abstract) = (antibody mimetics OR antibody mimetic OR affibody OR
adnectin OR aptamer OR affimer OR affitin OR affilin OR anticalin OR
atrimer OR Avimer OR Knottin); the language type = English; and the
document type = articles or reviews or book or book chapter. All data
obtained were saved in text format and imported into software for
analysis. To ensure the comprehensiveness of our study, we
systematically collected and analysed the terms relevant to antibody
mimetics from published reviews to define the keywords, aiming to
minimize the risk of omitting important information or introduce
irrelevant papers.

\subsection*{2.2 Statistical analysis}

{\label{statistical-analysis}}

Microsoft Office Excel (v.2016) was conducted for literature quantity
analysis. CiteSpace 6.1.R6 software was used to depict network maps of
keywords, journals, and countries. Basic elements in the map include
labels and lines. Items are represented by circular labels and the size
of the label determined by the weight of the item, which demonstrate the
importance of the items. Lines between items represent links, which is a
connection or a relation between two items (\emph{Wu et al., 2022} ).
The distance between two keywords in the figure demonstrates the rate of
co-occurrence of them on a paper or the relationship between two
research areas (\emph{Chen, 2006} ).

\section*{3 RESULTS}

{\label{results}}

\subsection*{3.1 Publication analysis}

{\label{publication-analysis}}

A total of 24 356 related papers were retrieved from the database WoS,
including 20 981 research articles and 2 420 review articles. Overall,
the number of publications reflects an upward trend from 1986 to 2022,
with an exponential increase since 2005 (Fig. 1).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Fig. 1} Publication trend on antibody mimetics between 1986 and
2022

\subsection*{3.2 Keyword Analysis}

{\label{keyword-analysis}}

Keywords demonstrate major research topics in an area with the analysis
of burstiness and cluster. Burstiness analysis retrieves keywords with a
frequency surge in a particular time period, reflecting stages of rise,
bloom and decline of a research topic. Cluster analysis groups keywords
into different categories, in which keywords within the same category
have high frequency to be co-occurred in an article compared with
others. The quality of clusters is determined by the value of
silhouette, meaning that clusters with silhouette closest to 1 have the
most similarities and homogeneities within cluster and least overlap
with other clusters (\emph{Chen, 2004} ).

The occurrence of major keywords throughout the development of antibody
mimetics is visualized over the timeline. Although the attention of
academic community on fundamental research including monoclonal
antibody, ligands, gene expression and phage display remain sustained
and relatively stable over decades, it is worth noting that research in
this field began to enter the stage of therapeutic and diagnostic
applications around 2005 (\emph{Chu et al., 2006} ; \emph{Xu et al.,
2005} ).

Cluster analysis showed 1435 keywords with 6039 links (Fig. 2B), of
which 18 keywords appear 100 times or more (aptamer, DNA, nanoparticles,
binding, selection, biosensor, gold nanoparticles, \emph{in
vitro}selection, assay, protein, aptasensor, ligands, molecules,
recognition, sensor, label free, expression, \emph{in vitro} ). 1435
keywords were clustered into 11 categories with silhouette vales ranging
from 0.641 to 0.982, indicating high cluster credibility and relatively
high differentiation of branched research areas within this field. The
inherent selectivity of antibody mimetics for their antigen (ligand
analyte) confers desirable properties on antibodies to develop
technologies that exploit binding events, especially in immunoassays and
therapeutics. Clusters \#1 ``aptasensor'', \#2 ``food safety'', \#7
``fluorescence anisotropy'', Clusters \#8 ``Alzheimer's disease'', \#9
``circulating tumor cells'' are all focusing on immunoassay. It is
notable that the application of immunoassay in various fields has been
fully developed as evidenced by the large number of publications in each
cluster. Specifically, it acts as important biomarkers for diagnosis of
cancers, degenerative diseases such as Alzheimer's' disease and
infectious disease such as malaria. Recently, antibody mimetics has been
increasingly engineered for detecting bacterial pathogens present in
food, water and the environment. This research boom is driven by the
development of biosensor technology (\emph{Li et al., 2019} ), in which,
antibody mimetics that interact with target analyte are combined with
physiochemical transducer that transforms the result of the interaction
into an optical, piezo-electrical, or electrochemical signal. For
example, antibody-nanoparticle bioconjugates have proven to be an ideal
vessel for bioconjugation and biosensor purposes due to their unique
surface characteristics, optical properties, stability, and consistency.
Apart from the wide application in immunoassay, antibody mimetics also
play roles in drug delivery for cancer diseases and neurological
disorders. Antibody-conjugated drug-loaded nanoparticles can selectively
target cells and release large amounts of drugs to treat diseases like
cancers (\emph{Arruebo et al., 2009} ). This is because they combine
both functions of the nanoparticles such as liposomes, polymeric
nanoparticles, dendrimers, and metallic nanoparticles, which control
drug release, increase drug solubility, protect drug from degradation,
and the antibody mimetics, which can bind to their targets with high
affinity and better cell penetration (\emph{Busch et al., 2019} ).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Fig. 2} Keywords burstiness and cluster analysis

Notes: (A) Height of each peak represents the frequency of occurrence of
a stated keyword within two years. Different keywords are labelled with
different colour. (B) Cluster labels represent the focus themes of each
cluster.

One of the most striking information reflected from this visualization
is that molecular detection for food safety and increasing application
of newly developed immunoassay technology has become the new research
trends in recent five years.

\subsection*{3.3 Country and Institution
Analysis}

{\label{country-and-institution-analysis}}

A total of 112 countries participated in the study of antibody mimetics
(Fig. 3A). Collaborations among countries were relatively small as the
centrality of all countries are less than 1.00, and most of which were
established after 2007. There were no regular patterns relating to the
geographical locations of different countries in this study. The top
five countries which have strong networking with others were Brazil,
Peru, Netherland, Iraq, and Egypt (centrality value of 0.70, 0.66, 0.53,
0.48, and 0.45). USA, Italy, Japan, Canada, UK, Netherlands, Germany,
Austria, Switzerland, and France contributed to the emergence of
antibody engineering with the earliest publications averaging between
1990 and 1993. And the number of publications in all these countries has
remained consistently high over the past 36 years. Interestingly,
collaborations among these leading countries were extremely rare with
few links established between nodes. China, USA, Germany, Japan, South
Korea were the most productive countries with the highest publication
number of 9615, 5811, 1192, 1103, and 1044, respectively.

A total of 921 links are established between 953 institutions which
involved in the study of antibody mimetics, interestingly,
collaborations among institutions within the same country were much more
intensive than international collaboration (Fig. 3B). In addition,
compared with country analysis, the proportion of the leading
institutions with full colour spectrum was much lower, many institutions
like Harvard University reduced their research in recent five years. The
top five institutions with the highest publication number were all in
China, and many newly emerging institutions were also found in China.
The top five institutions with the greatest paper citation number are
Laboratory of Molecular Biology, Medical Research Council, Cambridge, UK
contributing to phage display technology, Department of Chemical and
Biomolecular Engineering, University of California, Los Angeles, USA on
protein engineering and the design of novel scaffolds, Institute of
Biochemistry and Biotechnology, Martin-Luther University Halle-W for
development of artificial binding proteins based on non-antibody
scaffolds and their applications in biotechnology and medicine,
Laboratory of Chemical Biology and Signal Transduction, The Rockefeller
University, New York, USA on synthetic small molecules and
peptidomimetics for the modulation of protein-protein interactions and
therapeutic applications and Department of Biotechnology, Indian
Institute of Technology, Madras, India based on novel scaffolds such as
spider silk.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Fig. 3} Country and institution analysis on antibody mimetics
between 1986 and 2022

Notes: Node size represents the number of publications. Node color:
average time to appear. The purple circles indicate that these countries
have a significant role in the study.

\subsection*{3.4 Journal Analysis}

{\label{journal-analysis}}

Journal selection is an indicator to reflect the research focus and
tendency of antibody mimetics study which is coincide with keywords
analysis. A total of 554 journals were retrieved, the most productive
journals are Biosensors \& Bioelectronics (1146 publications) and
Analytical Chemistry (942 publications) (Fig. 4). The most cited
journals (\emph{Analytical Chemistry} , \emph{Journal of the American
Chemical Society} , \emph{Nature} , \emph{Science} , \emph{PNAS} ,
\emph{and Biosensors \& Bioelectronics} ) have been cited more than
10,000 times. In addition, \emph{Journal of Biological Chemistry}
,\emph{Biochemistry-US} , \emph{Journal of Molecular Biology}
,\emph{PNAS} , \emph{Nature} , \emph{Science} , \emph{Acs Sensors} ,
and\emph{Nature Biotechnology} produced a high citation burst,
indicating their critical role in the field (Table 2). Interestingly,
increasing emerging papers were published on food related journals
like\emph{Foods} , \emph{Food Chemistry} , \emph{Journal of Food
Measurement and Characterization} and \emph{Microchemical Journal} ,
indicating a new application field exploited. Meanwhile, a growing
number of interdisciplinary journals relevant to antibody mimetics
studies span biology, chemistry, and physics.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Fig. 4} Journal analysis on antibody mimetics between 1986 and
2022

Notes: (A) Co-cited journals. (B) Co-cited journals clustering. Each
node represents a journal; the size of the node is determined by the
citation frequency of the journal.

\textbf{Table 2} The top 20 journals in terms of number of publications
and citations\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Rank} & \textbf{Citations} & \textbf{Journals} &
\textbf{Publications} & \textbf{Journals}\tabularnewline
\midrule
\endhead
1 & 12254 & ANAL CHEM & 147 & P NATL ACAD SCI USA\tabularnewline
2 & 11601 & J AM CHEM SOC & 136 & CANCER RES\tabularnewline
3 & 10680 & NATURE & 135 & NATURE\tabularnewline
4 & 10612 & SCIENCE & 132 & ANAL CHEM\tabularnewline
5 & 10451 & P NATL ACAD SCI USA & 131 & J AM CHEM SOC\tabularnewline
6 & 10363 & BIOSENS BIOELECTRON & 130 & SCIENCE\tabularnewline
7 & 8646 & ANGEW CHEM INT EDIT & 126 & NUCLEIC ACIDS RES\tabularnewline
8 & 7585 & CHEM COMMUN & 122 & BIOSENS BIOELECTRON\tabularnewline
9 & 7570 & NUCLEIC ACIDS RES & 115 & CHEM COMMUN\tabularnewline
10 & 6202 & ANAL CHIM ACTA & 111 & ANGEW CHEM INT EDIT\tabularnewline
11 & 6166 & ANALYST & 111 & ANAL CHIM ACTA\tabularnewline
12 & 5853 & J BIOL CHEM & 111 & BIOCONJUGATE CHEM\tabularnewline
13 & 5847 & SENSOR ACTUAT B-CHEM & 109 & PLOS ONE\tabularnewline
14 & 5474 & TALANTA & 107 & J BIOL CHEM\tabularnewline
15 & 5332 & BIOCHEMISTRY-US & 105 & CELL\tabularnewline
16 & 4914 & CHEM REV & 102 & CLIN CANCER RES\tabularnewline
17 & 4868 & ANAL BIOANAL CHEM & 102 & J CLIN ONCOL\tabularnewline
18 & 4766 & ACS NANO & 99 & ACS APPL MATER INTER\tabularnewline
19 & 4742 & PLOS ONE & 99 & NAT BIOTECHNOL\tabularnewline
20 & 4621 & ACS APPL MATER INTER & 98 & BIOMATERIALS\tabularnewline
\bottomrule
\end{longtable}

\textbf{4 DISCUSSION}

Antibody mimetics is a research heated topic at a rapid development
stage witnessed by bibliometrics analysis, and it is predicted that this
trend will continue in the following years. Although the first article
retrieved from the Web of Science appeared at the year of 1986 according
to the keywords, the first ever research ``A synthetic peptide mimotope
of the hepatitis C virus NS3 protein induces HCV-reactive antibodies and
cytokine-secreting T cells in mice'' using molecules that mimic the
function of antibody was in 1995 (\emph{Wilson et al., 2001} ). The year
of 2005 was a landmark moment in the development of antibody mimetics,
indicating the transition of methodology and basic research into
application stage (Figs. 1, 2). Antibody mimetics is applied into more
fields such as neuroscience and contamination monitoring in recent years
(Fig. 2). The technical focus of the application of antibody mimetics
has shifted from basic biological problems such as protein structure
prediction and molecular interaction into some physically and chemically
based technology such as nanoparticles and quantum technology (Figs. 2,
4). The combination of these newly developed techniques widens the
fields of applications by providing antibody mimetics with more
desirable properties at various conditions. Therefore, it is predicted
that the development of antibody mimetics will be highly influenced by
other technologies. Researchers with interdisciplinary backgrounds may
be greatly promote the combinations of new technologies into antibody
mimetics.

In antibody engineering, a series of antibody features have been
highlighted and pursued, which are all demonstrated by the high
frequency of related terms retrieved, for example, high affinity (704),
high stability (364), high specificity (110), high penetration, good
host mediation, simple preparation, high repeatability, and low cost in
mass production, in which affinity is the highly emphasized (Fig. 2).

The challenges encountered in establishing appropriate keywords for the
bibliometric analysis stem primarily from inherent limitations in the
field. Specifically, the inclusion of all articles, reviews, and book
chapters related to antibody mimetics proved challenging due to
variations in terminology and categorization. Some authors focused on
specific aspects of engineered proteins without explicitly labeling them
as antibody mimetics. In addition, the broad nature of the keywords
employed in the analysis, as highlighted by the editor, may have
resulted in the inclusion of irrelevant papers. To avoid of this issue
as much as possible, we conducted comprehensive artificial screening
process to refine and confine the selection of the papers for analysis.
Moreover, it is important to note that the term ``antibody mimetics'' is
not uniformly adopted as a standardized terminology within this research
field. Researchers often interpret protein engineering techniques from
different perspectives, leading to the usage of diverse terms such as
``synthetic antibody'', ``engineered antibody'', and ``designed drug
molecules''. Concepts and terminologies in this field are various mainly
due to the dynamic and expending understanding on antibody molecules,
and novel antibody formats designed, as well as the continuous
invention, combination, and modification of different technologies in
antibody generation from molecular design to mass manufacturing. For
instance, only within the last 20 years, the advent of synthetic
diversity libraries provides a `chemical solution' to the biological
constraints on natural repertoire diversity (\selectlanguage{polish}\emph{Šácha et al., 2016}\selectlanguage{ngerman}
). Synthetic approaches are particularly attractive to those seeking to
understand the fundamentals of antibody--antigen interactions or to
engineer desired antibody properties, as they enable precise control
over the composition of diversity incorporated into antigen-binding
sites (\emph{Miersch \& Sidhu, 2012} ). This explains why ``synthetic
antibody'' has been used as a general term referring to part of antibody
mimetics (Fig. 5). At the same time, with the increase of application
scenarios and the diversification of molecular types of the referenced
parent structures, the scope of these terms has also taken on a broader
meaning. The molecular structures of antibody mimetics generally include
peptides and proteins, while aptamer is an exception, it is a short
oligonucleotide sequence (DNA or RNA), which can specifically bind to
its target on proteins and other molecules, or the entire cells, for
further therapeutic or diagnostic interventions (\emph{Yan et al., 2021}
). Meanwhile, it is worth noting that the functional repertoire of
antibody mimetics has significantly expanded beyond their traditional
role as antigen binders. They have evolved into versatile tools capable
of recognizing and binding specific targets for the purpose of detecting
analytes in biological samples (\selectlanguage{polish}\emph{Šácha et al., 2016}\selectlanguage{ngerman} ;\selectlanguage{polish}\emph{Šubr et
al., 2021}\selectlanguage{english} ;\emph{Yu et al., 2017} ), as well as in separation methods
(\emph{Olson et al., 2012} ), cancer therapy (\emph{Guillard et al.,
2017} ; \emph{Park et al., 2000} ), targeted drug delivery
(\emph{Balmforth et al., 2021} ), and \emph{in vivo} imaging
(\emph{Chomet et al., 2021} ;\emph{Dietrich et al., 2021} ). This
diversification of functionality showcases the adaptability and
potential applications of antibody mimetics in various research fields.
Peptide antibody mimetics is a subset of peptidomimetics which provide
alternatives to natural biopolymers (Fig. 5). Both antibody mimetics and
peptidomimetics are designed to mimic the structure and/or function of
antibodies or peptides, respectively. However, there are several
differences between them. Antibody mimetics is typically larger in size
and more complex in structure than peptidomimetics, as they often
incorporate multiple binding domains and/or structural motifs.
Peptidomimetics, on the other hand, are usually smaller and simpler in
structure, consisting of one or a few modified amino acids. Antibody
mimetics is designed to recognize and bind to specific targets, such as
proteins or cells, with high affinity and specificity. The development
of computational modeling and structure-based design methods, such as
conformational constraint and incorporation of non-natural amino acids,
has facilitated the rational design and optimization of peptidomimetics,
allowing the peptidomimetics to achieve binding specificity on par with
antibodies. In addition, given that peptidomimetics offer advantages
such as smaller size, easier synthesis, and better stability compared to
antibodies. These attributes make them attractive candidates for drug
development, where they can target challenging protein-protein
interactions or intracellular targets that are not easily accessible to
antibodies. Antibody mimetics can have various modes of action, such as
blocking protein-protein interactions (\emph{Fernandes et al., 2022} ),
inhibiting enzymatic activity (\emph{Khalili et al., 2016} ), or
inducing cell signaling (\emph{Shan et al., 2020} ). Peptidomimetics, on
the other hand, typically act as ligands or modulators of specific
receptors or signaling pathways (\emph{Goodman et al., 2007} ).
Throughout the development, researchers from different discipline
backgrounds and perspectives have been involved, chemists focusing on
the design of chemical conjugates for drug delivery may not interpret
these structures as antibody mimetics, as this terminology was arisen
from immunology research.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Fig. 5} Evolutionary and integrative perspective and definition
of the scope of antibody mimetics

Notes: green, orange, and red ovals represent the assemblies of
peptides, peptidomimetics, and ligands, respectively. Boxes are
representatives of antibody-derived (deep green) and non-antibody
derived (red) protein/peptides, nucleic acids and their mimics (cyan),
as well as other components (purple) like ions, chemical molecules, and
other developing structures. Antibody mimetics originally evolved from
artificially manipulated antibodies, including antibody fragments and
synthetic antibodies. Later, this term expanded and focused more on
refining the existing parent structures from functional fragments to
smaller, more stable, higher affinity, more reproducible and penetrable
ones. For instance, natural peptides, peptidomimetics and ligands
partially belong to the family of antibody mimetics. As antibody mimetic
engineering evolved, different concepts and terminology emerged, but
each may better reflect the state of research at that time.
Retrospectively, some terms, such as synthetic antibody, are blurry. To
certain extent, the term ``antibody mimetics'' also could not reflect
the entire intension and extension of this dynamic concept as increasing
non-antibody sourced and non-protein and peptide sourced structures have
been introduced into this field. As highlighted by cyan and orange boxes
in the figure, aptamer and other substances beyond the scope of
biomolecules deserve consideration for inclusion in antibody mimetics
repertoire. Therefore, further classification and definition of this
field are urgently needed and expected. Here we propose the
protein/peptide-based antibody mimetics as the core concept of this
field.

Compared to traditional antibodies, the fourth generations of antibody
engineering offer antibodies with a range of properties, providing much
diverse formats such as small peptides, protein scaffolds, or synthetic
polymers, for antibody design which allows for greater flexibility in
their application. At the same time, they are smaller in size, which
allows for better tissue penetration and target access (\emph{Jiang et
al., 2009} ). They can be produced using simpler and more cost-effective
methods, which enables mass production. Additionally, they have improved
stability and can be engineered to have higher specificity and affinity
for their target antigen. Through artificial modification and screening,
antibody mimetics have demonstrated commendable attributes in comparison
to traditional antibodies. These modifications may involve targeted
alteration within antigen-binding sites or conjugation of specific
proteins/peptides, thereby facilitating the development of more
efficacious therapeutic or diagnostic reagents and expediting their
market penetration. Such reagents were summarized in the Table 3, and
here we only mentioned two compelling representatives. Qiu \emph{et al.}
, fused antibody mimetics with the bacterial toxin colicin Ia, resulting
in the creation of ``pheromonicins'' that specifically inhibit tumor
growth. it displayed superior tumor targeting and penetration
capabilities surpassing those of their parent antibodies (\emph{Qiu et
al., 2007} ). Similarly, Wold \emph{et al.} synthesized antibody
mimetics by site-specific binding of small molecules with high affinity
and specificity for disease-associated antigens to Fc fragments,
enabling to develope antibody-based drugs with enhanced efficacy
(\emph{Wold et al., 2015} ). By harnessing the unique properties of
antibody mimetics, such as their modifiability and specificity, they
hold promise for bringing antibody-based drugs to the market. However,
there are also some disadvantages to using mimetic antibodies. They may
not have the same efficacy as traditional antibodies, particularly in
complex biological systems. Additionally, their potential for off-target
effects and toxicity may need to be carefully evaluated (\emph{Khatib \&
Salla, 2022} ). Moreover, the antibody mimetic engineering still
requires sophisticated design and complicated biopanning process, which
also lead to high cost and limits its application and commercialization.
The potential for antibody mimetics to replace outdated or obsolete
antibody drugs and revive their presence in the market is theoretically
possible. However, it must be recognized that reintroducing an antibody
taken from the market due to safety concerns entails multifaceted
considerations. Safety issues may originate from various factors,
encompassing adverse effects, lack of efficacy, or other complications.
If the advancements of antibody mimetics could address the specific
safety concerns or enable safer alternatives to withdrawn antibodies, it
has the potential to usher in novel and improved therapeutic options.
Nevertheless, each case requires an extensive reassessment of the safety
profile, including careful evaluation, rigorous testing, and regulatory
scrutiny to ensure the safety and efficacy of the developed antibody
mimetic prior to its contemplation for reintroduction into the market.
As depicted in Table 3, we present a summary of antibody mimetics drugs
that have entered the clinic or are undergoing clinical validation.

Computational analysis has emerged as a promising strategy for
accelerating the development of novel antibody mimetics, enhancing their
capabilities while reducing the cost associated with traditional
trial-and-error approaches (\emph{Kadonosono et al., 2020}
;\emph{Raybould et al., 2019} ). Notably, prominent computational
methods such as RossettaAntibody and AntBo have been employed to achieve
various purposes in this context. These methods enable to discern and
characterize the crucial attributes of existing antibodies, including
affinity, specificity, stability, and immunogenicity. Additionally, they
facilitate the determination of key residues responsible for antigen
binding. By harnessing computational analysis, it becomes possible to
predict and propose modifications that can enhance binding affinity and
specificity (\emph{Kuroda et al., 2012} ;\emph{Wang et al., 2021} ).
Moreover, the integration of machine learning and deep sequencing
techniques (\emph{Sloth et al., 2022} ) has proven to be efficient in
the realm of biopanning and optimization of phage display technology
(Fig. 6). These state-of-the-art methodologies provide valuable insights
into the discovery and refinement of antibody mimetics.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Fig. 6} Application of artificial intelligence in antibody
mimetics

Notes: Each node represents a keyword; the size of the keyword is
determined by the rate of occurrence.

Furthermore, artificial intelligence technology, such as Alphafold (Fig.
6) (\emph{Tunyasuvunakool et al., 2021} ), is applied to accurately
predict the structure and conformation of an unknown protein based on
regular binding and folding patterns collected from related database
such as Protein Data Bank. In particular, the three-dimensional
structure of an antibody can already be predicted from its gene.
However, there is a big proportion of antibodies without corresponding
gene available, furthermore, dynamic conformational change of antibodies
when binding with antigens or receptors cannot be predicted yet
(\emph{Abanades et al., 2022} ). It will greatly diminish time and cost
of antibody design if protein structure could be predicted based on the
structure of the antigens or receptors, while the conformational change
and more limited data of the antibody-antigen complex increase the
challenge for this breakthrough. However, there are still a lot of
limitation of computational approach restricting its further
optimization and application in antibody engineering. For example,
computational analysis and AI algorithms require large amounts of data
to learn and make accurate predictions. However, there is limited data
available on antibody-antigen interactions, which can limit the accuracy
of predictions made by these methods. Computational models used for
predicting the structure and function of antibody mimetics are based on
assumptions and simplifications, which may not accurately represent the
complexity of real-world systems. This can lead to inaccuracies in
predictions and suboptimal designs, Computational design methods often
rely on the availability of templates for the design of new antibody
mimetics. However, the diversity of available templates is limited,
which can limit the range of possible designs.

\textbf{Table 3} Antibody mimetics currently approved by the FDA or in
clinical trials\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Antibody mimetics}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Scaffold}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Molecular mass (kDa)}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Example drug}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Clinical Trials}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Publications}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Applications}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Affibody molecules\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Z domain of Protein A\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
ABY-025\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Phase II (NCT01216033)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Tumor imaging\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Affilins (Anticalins)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Ubiquitin or Lipocalins\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
10-20\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
PRS-080\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Phase I/II (NCT02754167)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Anemia\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Affimers (Adhirons)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Cystatin\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
12--14\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Anti-PD-L1 Affimer\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
(\emph{Basran \& Stanley, 2018})\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Cancer treatment\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Alphabodies\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Triple helix coiled coil\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
CMPX-1023\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
(\emph{Desmet et al., 2014})\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Autoimmune disease\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Avimers\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
A domains of various membrane receptors\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
9-18\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
C326 (AMG220)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Phase I (NCT00353756)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Crohn's disease\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
DARPins\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Ankyrin repeat motif\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
14-21\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Abicipar\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Phase III (NCT02462486, NCT02462928)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Age-related macular degeneration (AMD)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Domain antibodies\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Engineered human single-domain Ig scaffold\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
11-15\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
CEP37247/PN0621/ ART621\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Phase II NCT00928317\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Treat autoimmune diseases\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Fynomers\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Src homology 3 (SH3) domain of Fyn\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
COVA322\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Phase I/II (NCT02243787)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Plaque psoriasis\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Kunitz domain peptides\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Kunitz domains of various protease inhibitors\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Ecallantide (DX88) Depelstat (DX890)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
FDA approved in 2012 Phase II (NCT00455767)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Hereditary angioedema Pulmonary fibrosis\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Knottin\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Three anti-parallel \selectlanguage{greek}β-\selectlanguage{english}strands connected\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Ziconotide (Prialt)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
FDA approved in 2012\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Neuropathic pain\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Monobodies (Adnectins)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
10th type III domain of fibronectin\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
BMS-986089\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Phase II (NCT02515669)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Duchenne muscular dystrophy\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\selectlanguage{greek}β-\selectlanguage{english}Hairpin mimetics\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\selectlanguage{greek}β-\selectlanguage{english}Hairpin motif\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1-2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
POL6326\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Phase I (NCT01837095)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Tumor suppressor\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Monobodies (Adnectins)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
10th type III domain of fibronectin\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
BMS-986089\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Phase II (NCT02515669)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Duchenne muscular dystrophy\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Notes: All the data were derived through thorough investigation of the
existing literature, including review articles and research papers, as
well as the information available on the Drugbank website regarding
antibody mimetic drugs. From these sources, we carefully selected
representative compounds from each category, taking into consideration
the variations in scaffold structure, and provided an overview of the
clinical status for each of the selected drugs.

\section*{5 CONCLUSIONS}

{\label{conclusions}}

Antibody mimetics has shown widespread applications in immunoassays and
therapeutics, a trend that will continue and have real impact. Antibody
mimetic research relies on the advancement of interdisciplinary
approach, especially computational analysis, which plays a practical and
proficient role in prediction. However, it shows great limitations in
predicting the structure of antibody mimetics. Increasing and
diversified institutions and countries have been involved in this area
with strong collaboration and networking exhibited.



\section*{ACKNOWLEDGEMENTS}

{\label{acknowledgements}}

We sincerely thank Prof. Dr. Brian Sutton, King's College London for his
guidance and patient discussion before and during the preparation of
this article. We thank Prof. Dr. Xiaoying Zhang, University of Guelph,
for his valuable suggestions and revision of this article.



\section*{FUNDING}

{\label{funding}}

This research did not receive any specific grant from funding agencies
in the public, commercial, or not-for-profit sectors.



\section*{CONFLICT OF INTEREST}

{\label{conflict-of-interest}}

The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.



\section*{AUTHOR CONTRIBUTIONS}

{\label{author-contributions}}

Siran Zhang and Longjiang Wu contribute equally. Siran Zhang:
Conceptualization, Methodology, Investigation, Validation, Data
curation, Writing -- original draft. The endeavors undertaken by Siran
Zhang have been duly acknowledged and wholeheartedly supported by her
parents. For further details, we kindly direct your attention to the
attached informed consent letter, which can be found in a separate file.

Longjiang Wu: Conceptualization, Methodology, Writing -- review \&
editing. Mei Dang: Conceptualization, Writing -- review \& editing,
Supervision.



\section*{CONFLICT OF INTEREST}

{\label{conflict-of-interest-1}}

Details of all funding sources should be provided, including grant
numbers if applicable. Please ensure to add all necessary funding
information, as after publication this is no longer possible.



\section*{DATA AVAILABILITY STATEMENT}

{\label{data-availability-statement}}

Data is contained within the manuscript.

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\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig/Fig}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fih/Fih}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fii/Fii}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fij/Fij}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fik/Fik}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fil/Fil}

\end{center}
\end{figure}

\selectlanguage{english}
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\end{document}

