CURRENT TRENDS AND FUTURE DIRECTIONS IN

BIOMARKER DISCOVERY AND

STANDARDIZATION.

ABSTRACT

1. Introduction
o Definition and importance of biomarkers.
o Applications across various fields (e.g., diagnostics, drug development,
personalized medicine).
o Overview of the current state of biomarker discovery and challenges.
o Types and Sources of Natural Biomarkers
2. Materials and methods
3. Current trends and workflow for Natural Biomarker Discovery
3.1. Problem Identification and Study Design
3.1.1. Objective Setting:
o Define the purpose of biomarker discovery (e.g., diagnostic, prognostic,
therapeutic).
o Identify specific diseases, conditions, or environmental factors to target.
3.1.2. Selection of Natural Sources: Choose plants, microbes, marine organisms, or
other biological sources based on literature, ethnopharmacology, or traditional
knowledge.
3.2. Sample Collection and Preparation
3.2.1. Collection of Biological Material:
o Obtain samples from selected sources (e.g., plant leaves, microbial
cultures, marine algae).
o Ensure ethical approvals and regulatory compliance.
3.2.2. Sample Preparation:
o Dry, grind, or homogenize samples as necessary.
o Use solvent extraction, distillation, or other suitable methods to isolate
bioactive components.
3.2.3. Storage: Store samples under optimal conditions to maintain biomarker
integrity (e.g., cryopreservation or lyophilization).
3.3. Screening for Bioactivity
3.3.1. High-Throughput Screening:
o Use assays to identify bioactivity (e.g., antimicrobial, antioxidant,
anticancer).
o Techniques:
Plate-based assays for enzymatic or cell-based activity.
In vitro assays to assess bioactive potential.
3.3.2. Prioritization of Hits:
 o Select samples with significant bioactivity for further analysis.
3.4. Biomarker Identification
The detection and analysis of biomarkers in plant tissues can be accomplished using
a variety of laboratory-based methods. In these approaches, the biomarker or the
genes encoding it are examined (Pérez-Clemente et al. 2013). Wyoming blotting,
MALDI-TOF, SDS-PAGE, 2D-GE, northern blotting, ELISA, LC-MS, and
polymerase chain reaction (PCR) are a few examples of these methods (Yang et al.
2021).

3.4.1. Isolation and Purification

Isolation and purification of plant-based biomarkers:
Plant-based biomarkers are isolated and purified using a series of procedures
intended to extract, separate, and purify bioactive molecules (biomarkers)
from plant material for application in research, food, cosmetics, and
pharmaceuticals.

Plant species are chosen for collection based on prior study or ethnobotanical
expertise. Plant components, such as leaves, roots, bark, etc., are dried under
carefully monitored circumstances before being pulverized into a powder to
enhance surface area.

BiomarkerExtraction:

The goal is to use the right solvents to solubilize the target biomarker. The
techniques are such as Maceration is the process of soaking plant powder at
room temperature in a solvent. Continuous hot extraction with organic
solvents is known as Soxhlet extraction. By causing physical disruption,
ultrasound-assisted extraction (UAE) and microwave-assisted extraction
(MAE) increase extraction efficiency. Supercritical Fluid Extraction (SFE) is
perfect for non-polar chemicals, it uses CO₂ at high pressure [51]. Choice of
Solvents depending on the compound's polarity, it may be ethanol or methanol
for polar compounds or hexane or chloroform for non-polar ones.
Techniques for Final Purification are HPLC, or high-performance liquid
chromatography, provides excellent precision and resolution. For volatile
substances, gas chromatography (GC) is utilized. Thin Layer Chromatography
(TLC): To facilitate rapid comparison and analysis. Using preparative TLC or
HPLC, particular amounts of pure chemicals can be isolated.Spectroscopic
techniques like these are used for identification and characterization. UV-Vis,
FTIR, NMR (1H and 13C), Spectrometry by mass (MS)

Isolation and purification of animal-based biomarkers:

Proteins, nucleic acids, and extracellular vesicles are examples of animal-
based biomarkers that must be isolated and purified for use in diagnostics,
biomarker discovery, and treatment development.

Purification Using Spin Columns

This technique preferentially binds nucleic acids in high-salinity environments

by using silica membranes. Nucleic acids adhere to the silica matrix upon cell
lysis, and impurities are removed by washing. Purified DNA or RNA is
 obtained by elution using a low-salt buffer. This method's simplicity and speed
make it popular.
TRIzol/AGPC Removal

RNA, DNA, and proteins can be separated into different phases using a liquid-
liquid extraction method called the acid guanidinium thiocyanate-phenol-
chloroform (AGPC) method. Proteins and DNA separate into the
organic/interphase phase, whereas RNA separates into the aqueous phase.
High-quality RNA, including tiny RNA species like miRNA and siRNA, can
be isolated with this technique.

Affinity Chromatography

Using certain interactions between the target molecule and a ligand fixed on a
solid matrix, this method separates biomolecules. Common types include
lectin affinity chromatography, which uses proteins that bind carbohydrates,
and immunoaffinity chromatography, which uses antibodies. It may attain
great purity and is very selective.

Isolation and purification of marine based biomarkers (Fucoxanthin):

 One of the oxygenation derivatives of carotenoids is fucoxanthin, a naturally
occurring lutein. Fucoxanthin's polyene chain has an uncommon allenic bond,
a conjugated carbonyl group, 5,6-mono epoxide, and hydroxyl, giving it a
distinctive structure [1]. Its structural diversity enables it to display a wide
range of health-promoting qualities, such as anti-inflammatory, anti-cancer,
anti-obesity, anti-diabetic, anti-angiogenic, anti-malarial, hepatoprotective,
cardioprotective, and neuroprotective activities [2–4].

 By using silica gel-based prepared high-performance liquid chromatography,

Xia et al. [15] developed a quick extraction technique for fucoxanthin that
allowed it to be isolated and purified from crude pigment extractions of the
marine diatom, Odontella aurita. Using an n-hexane:acetone (6:4, v/v) eluent,
the natural pigment extracts were eluted using open silica gel column
chromatography. An orange-red fraction high in fucoxanthin was isolated, and
the mixture's fucoxanthin purity was only 86.7%. Preparative high-
performance liquid chromatography was used to further get 97% pure
fucoxanthin.

 Fucoxanthin was extracted from Tisochrysis lutea using ethanol and ultrasonic
assisted extraction by Raimundo et al. [16]. Using centrifugal partition
chromatography in conjunction with flash liquid chromatography, a two-step
purification process for fucoxanthin was refined. This technique was able to
produce up to 99% pure fucoxanthin.

 In order to isolate fucoxanthin from the brown algae Sargassum horneri, Ye et

al. [17] used an environmentally friendly technique. Ethanol: water (9:1, v/v)
was used as the gradient eluent in octadecylsilyl column chromatography to
separate the ethanol extract high in fucoxanthin. Fucoxanthin with a purity of
91.07% was then obtained using ethanol precipitation studies.
 The unicellular sea diatom Phaeodactylum tricornutum has a fucoxanthin
level that is more than ten times that of macroalgae, as well as polyunsaturated
fatty acids such eicosapentaenoic acid (EPA) [10]. Fucoxanthin is mostly
isolated and refined on a big scale from different brown algae because
industrial manufacture of the compound is still difficult because of the
intricacy and low efficiency of chemical synthesis [11]. There are now
numerous techniques for purifying fucoxanthin, including high-performance
liquid chromatography, preparative thin-layer chromatography, and column
chromatography [6,12].

o Fractionate crude extracts using chromatography techniques (e.g., HPLC, GC,

TLC).

In natural product research, fractionation is a crucial stage that

separates complex plant crude extracts into simpler constituents. Often
known as plant biomarkers, this aids in the separation, identification,
and characterization of bioactive substances (e.g., alkaloids,
flavonoids, terpenoids, phenolics). Because chromatographic
techniques can effectively separate complicated mixtures based on
physical and chemical features including polarity, volatility, and
molecular size, they are frequently utilized for this purpose.

 TLC technique:
Of all the popular chromatographic techniques, TLC, often known as
"planar" or "flat-bed" chromatography, is the easiest to execute. In
TLC experiments, a finely split material coated on a rigid substrate,
such as glass or aluminum, serves as the stationary phase. Capillary
action allows the mobile phase, which can be a pure solvent or a
combination of solvents, to flow over the stationary phase.
TLC can isolate a component fast and easily based on its nature and
standard separation techniques [18-20]. To analyze the flavonoids and
phenolic acids in a methyl extract of leaves, several solvent systems
were developed.

 HPLC technique:
The most popular modes of separation in natural product isolation are
normal phase (polar stationary phase and non-polar mobile phase) and
reversed phase (non-polar stationary phase and polar mobile phase)
chromatography. Normal phase chromatography has limited
application to natural product isolation because some of the solvents of
this technique strongly absorb UV light. Preparative pressure liquid
chromatography (HPLC) is a versatile, robust, and widely used
technique for the isolation of natural products. HPLC has been applied
to the isolation of virtually all classes of natural products.
  GC technique:
Purification of volatile chemicals is the primary application for prep-
GC.Both capillary columns and packed columns can be used to
accomplish separation. Higher flow rates of carrier gas are used, with
ranges of 100 ml/min to 1000 ml/min depending on column size.
Isothermal mode alone is used for separation with a packed column.
Open tubular columns are recommended for the isolation of tiny
amounts of chemicals [87].

o Employ bioassay-guided fractionation to focus on active components.

3.4.2. Structural Characterization
Use advanced analytical tools:
o Mass spectrometry (MS) for molecular weight determination.

A strong analytical method that is frequently used to ascertain the molecular

weight and structure of substances, including plant biomarker chemicals, is
mass spectrometry (MS). An outline of MS's use in this situation is provided
below:
Mass spectrometry measures the mass-to-charge ratio (m/z) of charged
molecules or fragments produced by ionizing chemical substances. The end
product is a mass spectrum that gives details about the structure and
molecular weight.
Solvents (such as methanol, ethanol, and water) are commonly used to
remove plant material in order to separate the biomarker chemicals.
ionization Methods for ionizing plant metabolites include Electrospray
Ionization (ESI) and Matrix-Assisted Laser Desorption/Ionization (MALDI).
Mass analysers that are often used include the quadrupole is useful for
focused analysis. The TOF, or time of flight measurement of mass with high
resolution. Use of Orbitrap and FT-ICR allowed for extremely high
resolution and precise mass determination. The detector creates a mass
spectrum by logging the m/z values and intensities.

Advanced MS Techniques:

The focused MS technique known as Multiple Reaction Monitoring (MRM)

improves the sensitivity and quantification of particular analytes, which
qualifies it for use in clinical diagnostics.A more sophisticated version of
MRM that is advantageous for complex clinical samples, parallel reaction
monitoring (PRM) offers increased sensitivity and specificity. Using
isotopically labelled standards, Stable Isotope Dilution MS (SID-MS)
accurately quantifies biomarkers in biological matrices. For the analysis of
tiny biomolecules and peptides, capillary electrophoresis–mass spectrometry
 (CE–MS) is the best method since it combines high-resolution separation
with MS detection [24].

o Nuclear Magnetic Resonance (NMR) for structural elucidation.

Two-Dimensional (2D) NMR Spectroscopy:

By establishing a correlation between signals from several nuclei, 2D NMR
techniques improve the investigation of complicated compounds.
Correlation spectroscopy (COSY) is a crucial experiment that helps
determine proton-proton connection by identifying spin-spin interactions
between protons. Total Correlation Spectroscopy (TOCSY): This technique
is helpful for examining spin systems in biomolecules since it uses scalar
couplings to provide information on proton networks. The ability to
determine three-dimensional structures through the application of NOESY
(Nuclear Overhauser Effect Spectroscopy) reveals the spatial proximity
between nuclei. Protons with heteronuclei such as ^13C or ^15N are
correlated by HSQC (Heteronuclear Single Quantum Coherence), which
makes it easier to assign resonances in compounds with isotope labels. The
structures of complicated biomolecules including lipids, peptides, and
natural products must be clarified using these methods [25-26].

High-Resolution Magic Angle Spinning (HR-MAS) NMR:

MAS for HR To analyse semi-solid samples, including tissue biopsies or cell
suspensions, NMR uses the magic angle to spin the sample in order to
average anisotropic interactions. This technique facilitates the investigation
of metabolic alterations linked to diseases by providing high-resolution
spectra of metabolites in their natural habitats.

Dynamic Nuclear Polarization (DNP) NMR:

Low-abundance metabolites can be detected thanks to DNP NMR's
considerable sensitivity enhancement, which transfers polarization from
unpaired electrons to adjacent nuclei. This method is especially helpful for
imaging and metabolic profiling applications.

Triple-Resonance NMR Spectroscopy:

To sequentially assign resonances in proteins and other biomolecules, triple-
resonance studies are necessary. These experiments usually involve ^1H,
^13C, and ^15N nuclei. The determination of proteins' and nucleic acids'
three-dimensional structures depends on these tests.

Micro coil and Cryoprobe NMR:

The sensitivity and resolution of micro coil probes are increased by their
ability to analyse small sample quantities. Cryoprobes are perfect for
examining rare or valuable materials because they further boost sensitivity
by cooling the probe electronics to lower thermal noise.

Covariance NMR Spectroscopy:

To improve resolution and sensitivity, especially in complex mixtures,
covariance NMR integrates statistical analysis with conventional NMR data.
 For the identification and measurement of metabolites in biological samples,
this method is useful in metabolomics.

o Infrared (IR) spectroscopy and UV-Vis for functional group identification.

Functional groups in biomarker chemicals are frequently identified using
infrared spectroscopy, especially Fourier-transform infrared (FTIR). Among
the crucial uses are
Flavonoids and Phenolic Compounds the FTIR spectra show distinctive
peaks for aromatic rings (C–H bend about 1450 cm⁻¹) and hydroxyl groups
(O–H stretch around 3200–3550 cm⁻¹). As an example, Dillenia
pentagyna's FTIR examination revealed flavonoids and phenolic chemicals,
demonstrating their antioxidant properties [27].
Anthocyanins: The conjugated double-bond systems of anthocyanins are
represented by absorption maxima in their UV-Vis spectra, which are
located between 500 and 550 nm. FTIR spectra can help clarify structures by
further confirming the existence of carbonyl and hydroxyl groups [28].
Terpenoids and Carotenoids: Terpenoids’ FTIR spectra reveal the C=O and
C–H spans are around 1735 cm⁻¹ and 2900 cm⁻¹, respectively.
Characteristic C=C lengths of about 1640 cm⁻¹ are seen in carotenoids.
These characteristics are essential for determining which plant extracts
contain bioactive substances [29].
Functional groups can be distinguished via UV-Vis spectroscopy by their
electronic changes. Among the notable applications are
Flavonoids and Phenolic Compounds: Flavonoids' UV-Vis spectra reveal
absorption maxima in the 250–280 nm (B-ring) and 320–370 nm (A-ring)
ranges. These characteristics help distinguish particular subclasses, such as
flavonols and flavones[27].
Anthocyanins: The conjugated complexes of anthocyanins show absorption
maxima in their UV-Vis spectra at 520–550 nm. This information helps
identify the type and quantity of acylation and glycosylation.
Authentication of Medicinal Plants: To ensure quality and avoid
adulteration, medicinal plant materials from various geographic locations
have been authenticated using UV-Vis spectroscopy in conjunction with
chemometric approaches such as principal component analysis (PCA).

3.5. Data Analysis and Integration

3.5.1. Omics Technologies:
Genomics, Proteomics and metabolomics to identify molecular signatures

The three omics technologies—genomics, proteomics, and metabolomics—are

essential for determining the molecular signatures of biomarkers, providing
information on disease causes, diagnostics, and potential treatment targets. An
outline of each technology's contribution to biomarker discovery is provided
here, along with reading recommendations.

Genomics: Whole-Genome and Whole-Exome Sequencing (WGS/WES)

These high-throughput sequencing techniques offer detailed information on
genetic variations, such as structural variants, insertions, deletions, and single
nucleotide polymorphisms (SNPs). Finding uncommon mutations, novel
disease-associated variations, and possible therapeutic targets is made possible
in large part by WGS and WES [29].

Proteomics: A potent method for determining the molecular signatures of

biomarkers, proteomics provides information on prognosis, early detection,
illness causes, and potential treatment targets. Proteomics makes it possible to
identify disease-specific protein patterns that can function as trustworthy
biomarkers by examining the protein content of biological samples.

Mass Spectrometry (MS)-Based Approaches: Chromatography–Tandem Mass

Spectrometry, or LC-MS/MS, is a high-throughput method that offers
comprehensive peptide sequencing and separates intricate protein mixtures. It
is frequently employed to detect and measure proteins in a range of biological
materials.
SELDI-TOF/MS (Surface-Enhanced Laser Desorption/Ionization Time-of-
Flight Mass Spectrometry): captures particular proteins using protein chips,
then analyzes the results using mass spectrometry. It can be used to profile
serum or plasma samples in order to find possible biomarkers.
MALDI-TOF/MS (Matrix-Assisted Laser Desorption/Ionization Time-of-
Flight Mass Spectrometry): enables quick protein identification by ionizing
proteins in a matrix using a laser. For the analysis of high-molecular-weight
proteins, it is especially helpful [30].
Gel-Based Techniques: In 2D-DIGE (Two-Dimensional Difference Gel
Electrophoresis), proteins are labeled with various fluorescent dyes, separated
according to their molecular weight and isoelectric point, and their levels of
protein expression are compared across samples. Proteins are separated in two
dimensions using 2D-gel electrophoresis, which creates a visual representation
of the patterns of protein expression. For protein identification, it is frequently
used with MS.
Quantitative Proteomics: Stable Isotope Labeling by Amino Acids in Cell
Culture, or SILAC, allows for the relative quantification of proteins under
various conditions by incorporating isotopically labeled amino acids into
 proteins during cell culture. Proteins can be simultaneously quantified and
identified using iTRAQ (Isobaric Tags for Relative and Absolute
Quantitation), which labels peptides from various samples using isobaric tags
[31].
Metabolomics: Profiling small-molecule metabolites in biological samples
using the potent analytical technique of metabolomics provides a thorough
overview of the metabolic state of an organism. Metabolomics makes it
possible to uncover prospective biomarkers for diagnosis, prognosis, and
therapy monitoring by detecting and quantifying metabolites. This results in
the discovery of molecular signatures linked to a variety of disorders [32].

3.5.2. Bioinformatics Tools:

Utilize AI and machine learning for data integration and predictive modeling.
The application of machine learning (ML) and artificial intelligence (AI) to
data integration and biomarker predictive modeling is transforming precision
medicine, diagnostics, and biomedical research. In order to find and evaluate
biomarkers linked to the development, progression, and response to treatment
of disease, these technologies allow the synthesis of several data types,
including imaging, proteomics, metabolomics, genomes, and clinical records.

Neurological Disorders: AI models analyzing neuroimaging biomarkers in

Alzheimer's disease have shown high accuracy in predicting disease
progression; Parkinson's Disease: Using AI to integrate motion analysis data
with traditional clinical measurements has shown promise in improving
disease monitoring and management;[33] Cancer: AI models integrating
multi-omics data, radiomics, and clinical information have improved
performance in cancer diagnosis and prognosis, aiding in personalized
treatment strategies[34]; Cardiovascular Disease: Machine learning models
incorporating lipidomic and proteomic data have enhanced risk prediction,
outperforming traditional models in accuracy[35].

3.5.3. Database Comparisons:

Compare findings with existing biomarker databases (e.g., PubChem, HMDB).
3.6. Validation of Biomarkers
3.6.1. Analytical Validation:
Assess reproducibility, sensitivity, and specificity of assays.

For plant biomarker assays to reliably and accurately detect plant diseases,
stress responses, and quality control in agricultural and pharmaceutical
applications, it is crucial to evaluate the assays' reproducibility, sensitivity, and
specificity. An outline of these important performance indicators is provided
below, with examples from different test types:
Reproducibility: In biological assays, the U.S. Food and Drug Administration
(FDA) and the European Medicines Agency (EMA) generally accept a CV of
10–15% for quality control samples; however, newer guidelines for single-
cell-based assays suggest a higher CV acceptance of up to 30%, reflecting the
 inherent variability in live cell-based immune assays. Reproducibility is the
consistency of assay results across different laboratories, analysts, or
instruments, and is commonly quantified using the coefficient of variation
(CV), which is the ratio of the standard deviation to the mean, expressed as a
percentage [36].
Sensitivity: By detecting biomarkers at concentrations as low as 1 picogram
per milliliter, enzyme-linked immunosorbent assays (ELISA) demonstrate
high sensitivity in detecting specific proteins in complex biological samples.
Sensitivity measures an assay's ability to accurately identify true positives, i.e.,
its capacity to detect the presence of a biomarker when it is actually present
[37].
Specificity: By accurately identifying true negatives, an assay's specificity
shows how well it can separate the target biomarker from other substances in
the sample. Reversed-phase high-performance liquid chromatography (RP-
HPLC) was developed to analyze bioactive markers in a polyherbal
formulation. The specificity of the method was verified by comparing the
markers' retention times with those found in the samples, guaranteeing precise
identification in complex matrices [38]
3.6.2. Biological Validation:

Test biomarkers in relevant biological systems (e.g., cell lines, animal

models).

Plant biomarkers are molecular indicators such as genes, proteins, secondary

metabolites, or transcripts used to monitor physiological states, stress
responses, disease resistance, or the presence of bioactive compounds.
Biological validation of these biomarkers involves confirming their functional
relevance and consistency across different experimental models, including
plant cell lines, tissues, and whole plants.

When it comes to disease diagnosis, (Table:1) [39,40] prognosis, and

treatment monitoring, biomarkers are quantifiable indicators of biological
states or conditions. A crucial step in confirming the usefulness of biomarkers
and comprehending their function is testing them in relevant biological
systems, which usually entails simulating human physiological and
pathological conditions using cell lines, animal models, or organoids.

Testing Biomarkers in Cell Lines: Due to their affordability, ease of use, and
capacity for chemical and genetic manipulation, cell lines—immortalized cells
cultured in vitro—are a popular platform for biomarker testing. A fundamental
stage in translational research is biomarker validation in cell lines, which
frequently comes before investigations in more intricate systems like animal
models or human tissues. In order to investigate biomarker behaviour under
diverse treatments or genetic alterations, plant cell lines—such as suspension
cultures and callus cultures—are produced from differentiated plant tissues.
Purpose of Using Cell Lines for Biomarker Testing
 Analyse expression to see if the biomarker is present in a setting that is
relevant to the disease.
Functional Studies: Use overexpression or knockdown techniques (e.g.,
siRNA, CRISPR) to examine the biomarker's biological function.
Drug Response Testing: Determine whether biomarker levels are associated
with drug sensitivity or resistance.
Pathway Elucidation: Use molecular tests (such as western blotting, qPCR,
and reporter assays) to identify the signalling pathways linked to the
biomarker.

Table 1.

Cell Line Tissue/Origin Common Biomarkers

Studied
MCF-7 Breast cancer Oestrogen receptor (ER),
HER2
HeLa Cervical cancer p53, HPV oncoproteins
A549 Lung adenocarcinoma EGFR, KRAS
HepG2 Liver carcinoma AFP (alpha-fetoprotein),
cytochrome P450 enzymes
PC3, LNCaP Prostate cancer PSA (prostate-specific
antigen), AR (androgen
receptor)

Use of Plant Cell Lines in Biomarker Validation: In order to investigate biomarker

behavior under diverse treatments or genetic alterations, plant cell lines—such as
suspension cultures and callus cultures—are produced from differentiated plant
tissues.
 Studying the expression of biomarkers in response to abiotic stressors, such as
salinity or drought, is known as stress response.

 Using pathogen challenge assays, disease resistance biomarkers are assessed after
being exposed to microbial pathogens.

 Measuring the amounts of secondary metabolites associated with certain

biosynthetic gene expression.
Some examples are Auxin, jasmonate, and other hormone-induced gene expression
responses are validated using tobacco BY-2 cells. Arabidopsis T87 Cells are used to
screen genes for function and to clarify pathways in large quantities. Western blotting
for protein markers, qRT-PCR, RNA-seq for transcript biomarkers, and HPLC/GC-
MS for metabolite biomarkers are the methods employed[41].
Other Relevant Systems are included:
a. Complete Plant Models
To verify that biomarkers found in vitro apply to the entire organism, they are
validated in model plants such as Solanum lycopersicum (tomato), Oryza sativa (rice),
and Arabidopsis thaliana.[42].

b. Lines of transgenesis
In transgenic plants, potential biomarkers can be overexpressed or silenced to validate
their functionality in an environment similar to that of the native species.

c. Research on certain organelles

In isolated organelles or targeted lines, chloroplast or mitochondrial biomarkers are
examined to evaluate subcellular reactions [43].

3.6.3. Clinical Validation:

Conduct studies on human cohorts to confirm clinical relevance.
Assessing the relevance and dependability of plant-derived biomarkers in human
populations is necessary for clinical validation in order to guarantee their potential
use in therapeutic settings. The efficacy and applicability of the biomarker are
established via a number of stages in this procedure, which usually include
discovery, analytical validation, and clinical validation [44].

Stages of Biomarker Validation:

i. The discovery phase involves finding possible biomarkers using a variety of
methods, including proteomics, metabolomics, and genomes. ii. Analytical
validation is the process of evaluating the biomarker's performance attributes, such
as sensitivity, specificity, and repeatability, frequently with the use of platforms
such as tandem mass spectrometry and liquid chromatography. iii. Clinical
validation is the process of assessing a biomarker in human cohorts to determine its
clinical relevance and validate whether it can predict the presence, course, or
responsiveness to therapy of a disease. iv. Clinical Utility is the assessing if the
biomarker offers useful data that can affect choices about patient care.

Although there aren't many studies specifically looking at plant-derived biomarkers,

studies on metabolomic biomarkers in human populations offer valuable
information about the validation process:

1. Lung Cancer Detection: A study [45] used plasma metabolite profiles to

differentiate between healthy controls and patients with non-small cell lung cancer
(NSCLC). The model's high sensitivity and specificity showed how useful
metabolomic indicators might be for early cancer detection.

2. Metabolomic Panels for Cancer Risk Assessment: When paired with clinical
information [46], a metabolomic panel created by another study correctly identified
cases of resectable lung cancer. For improved diagnostic accuracy, this emphasizes
 how crucial it is to incorporate metabolites produced from plants into larger
biomarker panels.

Difficulties in Validating Biomarkers:

Pre-Analytical Challenges: Sample selection and handling should be done correctly

before the method measuring the analyte of interest is analytically validated.
Without taking pre-analytical parameters into account, confounders may be
introduced into the results, decreasing the analytical method's perceived accuracy
and reproducibility. These pre-analytical difficulties can be found in both targeted
and untargeted metabolomics methods based on LC-MS. To guarantee appropriate
sample collection and handling, it is necessary to investigate the effects of various
pre-analytical parameters on the target metabolites.
 Patient Selection: Drugs, lifestyle, food, and pollution are examples of these
environmental influences [48,49]. As a result, no two individuals will have
exactly the same metabolome because everyone is unique in their genetic
makeup and experiences. Despite the fact that every patient sample will have
a unique metabolism, it is nevertheless vital to reduce confounding factors
like medications and comorbidities that could significantly affect the
relevant analytes. Although it is impossible to account for every conceivable
confounding factor, it is crucial to reduce the impacts wherever possible.
 Sample Collection and Storage: The quality of the samples will be affected
by the collecting and storage methods. It is crucial to make sure that the
standard operating procedures (SOPs) are created specifically for
metabolomics analysis and that they are adhered to. Otherwise, metabolomic
analysis may be highly variable due to improper sample treatment,
metabolite degradation, and other reasons. As a result, the analytical method
may not be validated and analyte values may become inconsistent.
Analytical Validation: A new guideline [50] on the validation of
bioanalytical methods, including LC-MS/MS-based methods, was made
clear and harmonized by the International Council for Harmonization (ICH)
in January 2023.
 Calibration curve
 Matrix effect
 Stability
 Reinjection reproducibility
 Sensitivity and specificity
 Accuracy precision
 Carryover
 Dilution integrity

3.7. Biobanking and big data
4. Standardization of Natural Biomarkers
4.1. Challenges in Standardization
4.2. Analytical Standardization
 4.3. Regulatory Frameworks
4.4. Role of Biobanks and Reference Materials
5. Applications of Natural Biomarkers
5.1. In Healthcare and Medicine
5.2. In Agriculture
5.3. In Environmental Monitoring
5.4. Food processing
6. Future perspectives
6.1. Technological Innovations: CRISPR-based biomarker discovery, biosensors,
and nanotechnology.
6.2. Multi-Omics and Systems Biology Approaches: Comprehensive profiling for
biomarker panels.
6.3. Sustainability in Biomarker Research: Green chemistry and eco-friendly
extraction processes.
6.4. Global Collaboration and Data Sharing: Open databases for biomarker
research and discovery.
6.5. Precision Biomarkers for Personalized Applications: Tailored diagnostics and
treatments.
7. Conclusion
 References

 Pérez-Clemente RM, Vives V, Zandalinas SI, López-Climent MF, Muñoz V, Gómez-

Cadenas A (2013) Biotechnological approaches to study plant responses to stress.
Biomed Res Int 2013:654120. https://doi.org/10.1155/2013/654120

 Yang Y, Saand MA, Huang L, Abdelaal WB, Zhang J, Wu Y, Li J, Sirohi MH, Wang F
(2021) Applications of multi-omics technologies for crop improvement. Front Plant
Sci 12:563953

 1. Mikami, K.; Hosokawa, M. Biosynthetic Pathway and Health Benefits of

Fucoxanthin, an Algae-Specific Xanthophyll in Brown Seaweeds. Int. J. Mol. Sci.
2013, 14, 13763–13781. [CrossRef] [PubMed]

 2. Van Chuyen, H.; Eun, J.-B. Marine carotenoids: Bioactivities and potential benefits
to human health. Crit. Rev. Food Sci. Nutr. 2017, 57, 2600–2610. [CrossRef]
[PubMed].

 3. Rengarajan, T.; Rajendran, P.; Nandakumar, N.; Balasubramanian, M.P.; Nishigaki,

I. Cancer Preventive Efficacy of Marine Carotenoid Fucoxanthin: Cell Cycle Arrest
and Apoptosis. Nutrients 2013, 5, 4978–4989. [CrossRef] [PubMed]

 4. Zhang, H.; Tang, Y.; Zhang, Y.; Zhang, S.; Qu, J.; Wang, X.; Kong, R.; Han, C.;
Liu, Z. Fucoxanthin: A Promising Medicinal and Nutritional Ingredient. Evid.-Based
Complement. Complement. Med. 2015, 2015, 723515. [CrossRef] [PubMed]

 5. Kim, S.-K.; Pangestuti, R. Biological Activities and Potential Health Benefits of

Fucoxanthin Derived from Marine Brown Algae. Adv. Food Nutr. Res. 2011, 64, 111–
128. [CrossRef]

 6. Lourenço-Lopes, C.; Garcia-Oliveira, P.; Carpena, M.; Fraga-Corral, M.; Jimenez-

Lopez, C.; Pereira, A.; Prieto, M.; Simal-Gandara, J. Scientific Approaches on
Extraction, Purification and Stability for the Commercialization of Fucoxanthin
Recovered from Brown Algae. Foods 2020, 9, 1113. [CrossRef]
 7. Shannon, E.; Abu-Ghannam, N. Optimisation of fucoxanthin extraction from Irish
seaweeds by response surface methodology. J. Appl. Phycol. 2016, 29, 1027–1036.
[CrossRef]

 8. Petrushkina, M.; Gusev, E.; Sorokin, B.; Zotko, N.; Mamaeva, A.; Filimonova, A.;
Kulikovskiy, M.; Maltsev, Y.; Yampolsky, I.; Guglya, E.; et al. Fucoxanthin
production by heterokont microalgae. Algal Res. 2017, 24, 387–393. [CrossRef]

 9. Peng, J.; Yuan, J.-P.; Wu, C.-F.; Wang, J.-H. Fucoxanthin, a Marine Carotenoid
Present in Brown Seaweeds and Diatoms: Metabolism and Bioactivities Relevant to
Human Health. Mar. Drugs 2011, 9, 1806–1828. [CrossRef]

 10. Kim, S.M.; Jung, Y.-J.; Kwon, O.-N.; Cha, K.H.; Um, B.-H.; Chung, D.; Pan, C.-
H. A Potential Commercial Source of Fucoxanthin Extracted from the Microalga
Phaeodactylum tricornutum. Appl. Biochem. Biotechnol. 2012, 166, 1843–1855.
[CrossRef]

 11. Piovan, A.; Seraglia, R.; Bresin, B.; Caniato, R.; Filippini, R. Fucoxanthin from
Undaria pinnatifida: Photostability and Coextractive Effects. Molecules 2013, 18,
6298–6310. [CrossRef] [PubMed]

 12. Raji, V.; Loganathan, C.; Sadhasivam, G.; Kandasamy, S.; Poomani, K.;
Thayumanavan, P. Purification of fucoxanthin from Sargassum wightii Greville and
understanding the inhibition of angiotensin 1-converting enzyme: An in vitro and in
silico studies. Int. J. Biol. Macromol. 2020, 148, 696–703. [CrossRef] [PubMed]

 13. Gómez-Loredo, A.; Benavides, J.; Rito-Palomares, M. Partition behavior of

fucoxanthin in ethanol-potassium phosphate two-phase systems. J. Chem. Technol.
Biotechnol. 2014, 89, 1637–1645. [CrossRef]

 14. Xiao, X.; Si, X.; Yuan, Z.; Xu, X.; Li, G. Isolation of fucoxanthin from edible
brown algae by microwave-assisted extraction coupled with high-speed
countercurrent chromatography. J. Sep. Sci. 2012, 35, 2313–2317. [CrossRef]

 15. Xia, S.; Wang, K.; Wan, L.; Li, A.; Hu, Q.; Zhang, C. Production,
Characterization, and Antioxidant Activity of Fucoxanthin from the Marine Diatom
Odontella aurita. Mar. Drugs 2013, 11, 2667–2681. [CrossRef]

 16. de Oliveira-Júnior, R.G.; Grougnet, R.; Bodet, P.-E.; Bonnet, A.; Nicolau, E.;
Jebali, A.; Rumin, J.; Picot, L. Updated pigment composition of Tisochrysis lutea and
purification of fucoxanthin using centrifugal partition chromatography coupled to
flash chromatography for the chemosensitization of melanoma cells. Algal Res. 2020,
51, 102035. [CrossRef]

 17. Ye, Y.; Sun, J.; Wang, L.; Zhu, J.; Cui, W.; Hou, H.; Zhang, J.; Zhou, C.; Yan, X.
Isolation and Purification of Fucoxanthin from Brown Seaweed Sargassum horneri
Using Open ODS Column Chromatography and Ethanol Precipitation. Molecules
2021, 26, 3777. [CrossRef]
 18.Tokarek, W.; Listwan, S.; Pagacz, J.; Le´ sniak, P.; Latowski, D. Column
chromatography as a useful step in purification of diatom pigments. Acta Biochim.
Pol. 2016, 63, 443–447. [CrossRef]

 19 Qi, F.-F.; Ma, T.-Y.; Fan, Y.-M.; Chu, L.-L.; Liu, Y.; Yu, Y. Nanoparticle-based
polyacrylonitrile monolithic column for highly efficient micro solid-phase extraction
of carotenoids and vitamins in human serum. J. Chromatogr. A 2020, 1635, 461755.
[CrossRef]

 20.Sherma J, Fried B. Hand Book of TLC. 2nd ed. New York: Marcel Dekker, Inc.
1996.

 21.Remington: The Science and Practice of Pharmacy. 19th ed. Penn sylvania: Mack
Publishing Company 1995; Vol. I.

 22.Canell RJP. Natural Product Isolation. New Jersey: Humana Press 1998.
[http://dx.doi.org/10.1007/978-1-59259-256-2]

 23. Hostettmann K, Marston A, Hostettmann M. Preparative chroma tography

techniques: Applications in natural product isolation. Ber lin: Springer-Verlag 1998.
[http://dx.doi.org/10.1007/978-3-662-03631-0].

 24. Lin, M. (2016). Key Mass Spectrometry Techniques Used in Clinical Biomarker
Research. Translating Molecular Biomarkers into Clinical Assays: Techniques and
Applications, 153-161.

 25. Zhang, S., Gowda, G. N., Ye, T., & Raftery, D. (2010). Advances in NMR-based
biofluid analysis and metabolite profiling. Analyst, 135(7), 1490-1498.

 26. Crook, A. A., & Powers, R. (2020). Quantitative NMR-based biomedical

metabolomics: Current status and applications. Molecules, 25(21), 5128.

 27. Patle, T. K., Shrivas, K., Kurrey, R., Upadhyay, S., Jangde, R., & Chauhan, R.
(2020). Phytochemical screening and determination of phenolics and flavonoids in
Dillenia pentagyna using UV–vis and FTIR spectroscopy. Spectrochimica Acta Part
A: Molecular and Biomolecular Spectroscopy, 242, 118717.

 28. Saha S, Singh J, Paul A, Sarkar R, Khan Z, Banerjee K. Anthocyanin Profiling

Using UV-Vis Spectroscopy and Liquid Chromatography Mass Spectrometry. J
AOAC Int. 2020 Jan 1;103(1):23-39. doi: 10.5740/jaoacint.19-0201. PMID:
31462350.

 29. Lozada-Ramírez, J. D., Ortega-Regules, A. E., Hernández, L. R., & Anaya de

Parrodi, C. (2021). Spectroscopic and Spectrometric Applications for the
Identification of Bioactive Compounds from Vegetal Extracts. Applied
Sciences, 11(7), 3039. https://doi.org/10.3390/app11073039.

 29. Matsuoka, T., & Yashiro, M. (2024). Bioinformatics Analysis and Validation of
Potential Markers Associated with Prediction and Prognosis of Gastric
 Cancer. International Journal of Molecular Sciences, 25(11), 5880.
https://doi.org/10.3390/ijms25115880.

 30. Alharbi RA. Proteomics approach and techniques in identification of reliable

biomarkers for diseases. Saudi J Biol Sci. 2020 Mar;27(3):968-974. doi:
10.1016/j.sjbs.2020.01.020. Epub 2020 Jan 27. PMID: 32127776; PMCID:
PMC7042613.

 31. Paul D, Kumar A, Gajbhiye A, Santra MK, Srikanth R. Mass spectrometry-based

proteomics in molecular diagnostics: discovery of cancer biomarkers using tissue
culture. Biomed Res Int. 2013;2013:783131. doi: 10.1155/2013/783131. Epub 2013
Mar 17. PMID: 23586059; PMCID: PMC3613068.

 32. Liu HN, Liu TT, Wu H, Chen YJ, Tseng YJ, Yao C, Weng SQ, Dong L, Shen XZ.
Serum microRNA signatures and metabolomics have high diagnostic value in
colorectal cancer using two novel methods. Cancer Sci. 2018 Apr;109(4):1185-1194.
doi: 10.1111/cas.13514. Epub 2018 Feb 26. PMID: 29363233; PMCID:
PMC5891204.

 33. Rudroff, T., Rainio, O., & Klén, R. (2024). AI for the prediction of early stages of
Alzheimer's disease from neuroimaging biomarkers–A narrative review of a growing
field. Neurological sciences, 1-11.

 34. Ozaki, Y., Broughton, P., Abdollahi, H., Valafar, H., & Blenda, A. V. (2024).
Integrating Omics Data and AI for Cancer Diagnosis and Prognosis. Cancers, 16(13),
2448. https://doi.org/10.3390/cancers16132448

 35. Kasartzian, D.-I., & Tsiampalis, T. (2025). Transforming Cardiovascular Risk

Prediction: A Review of Machine Learning and Artificial Intelligence
Innovations. Life, 15(1), 94. https://doi.org/10.3390/life15010094.

 36.Masucci, G. V., Cesano, A., Hawtin, R., Janetzki, S., Zhang, J., Kirsch, I., ... &
Thurin, M. (2016). Validation of biomarkers to predict response to immunotherapy in
cancer: volume I—pre-analytical and analytical validation. Journal for
immunotherapy of cancer, 4, 1-25.

 37. Nimse, S. B., Sonawane, M. D., Song, K. S., & Kim, T. (2016). Biomarker
detection technologies and future directions. Analyst, 141(3), 740-755.

 38. Healy, D. A., Hayes, C. J., Leonard, P., McKenna, L., & O’Kennedy, R. (2007).
Biosensor developments: application to prostate-specific antigen detection. TRENDS
in Biotechnology, 25(3), 125-131.

 39. Sharma, S. V., Haber, D. A., & Settleman, J. (2010). Cell line-based platforms to
evaluate the therapeutic efficacy of candidate anticancer agents. Nature reviews
cancer, 10(4), 241-253.
 40. Masters, J. R. (2002). HeLa cells 50 years on: the good, the bad and the
ugly. Nature Reviews Cancer, 2(4), 315-319.

 41. Galieni, A., D'Ascenzo, N., Stagnari, F., Pagnani, G., Xie, Q., & Pisante, M.
(2021). Past and future of plant stress detection: an overview from remote sensing to
positron emission tomography. Frontiers in Plant Science, 11, 609155.

 42. Yang, Q., Zhang, A. H., Miao, J. H., Sun, H., Han, Y., Yan, G. L., ... & Wang, X.
J. (2019). Metabolomics biotechnology, applications, and future trends: a systematic
review. RSC advances, 9(64), 37245-37257.

 43. Nagata, T., Nemoto, Y., & Hasezawa, S. (1992). Tobacco BY-2 cell line as the
“HeLa” cell in the cell biology of higher plants. In International review of
cytology (Vol. 132, pp. 1-30). Academic Press.

 44. Malczewska, A., Kidd, M., Matar, S., Kos-Kudla, B., & Modlin, I. M. (2018). A
comprehensive assessment of the role of miRNAs as biomarkers in
gastroenteropancreatic neuroendocrine tumors. Neuroendocrinology, 107(1), 73-90.

 45. Zhang, L., Zheng, J., Ahmed, R., Haince, J., Torres-Calzada, C., Mandal, R., ... &
Wishart, D. S. (2024). Clinical Validation of Metabolite Markers for Early Lung
Cancer Detection. medRxiv, 2024-11.

 46. Joubert, P., Zhang, L., Boullier, C., Plante, S., Biardel, S., Mandal, R., ... &
Maksymiuk, A. W. (2023). Large retrospective validation study of metabolomic
biomarkers for resectable lung cancer detection and risk assessment.

 47. Li, S., Looby, N., Chandran, V., & Kulasingam, V. (2024). Challenges in the
Metabolomics-Based Biomarker Validation Pipeline. Metabolites, 14(4), 200.
https://doi.org/10.3390/metabo14040200

 48. Vuckovic, D. Improving Metabolome Coverage and Data Quality: Advancing

Metabolomics and Lipidomics for Biomarker Discovery. Chem. Commun. 2018, 54,
6728–6749. [Google Scholar] [CrossRef] [PubMed]

 49.Monteiro, M.S.; Carvalho, M.; Bastos, M.L.; Guedes De Pinho, P. Metabolomics

Analysis for Biomarker Discovery: Advances and Challenges. Curr. Med.
Chem. 2013, 20, 257–271

 50. Committee for Medicinal Products for Human Use. ICH Guideline M10 on
Bioanalytical Method Validation and Study Sample Analysis Step5; European
Medicines Agency: Amsterdam, The Netherlands, 2022

 51. Najmi, A., Javed, S. A., Al Bratty, M., & Alhazmi, H. A. (2022). Modern
approaches in the discovery and development of plant-based natural products and
their analogues as potential therapeutic agents. Molecules, 27(2), 349.