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Historically, natural products (NPs) have played a key
role in drug discovery, especially for cancer and infec-
tious diseases1,2, but also in other therapeutic areas,
including cardiovascular diseases (for example, statins)
and multiple sclerosis (for example, fingolimod)3–5.
NPs offer special features in comparison with con-
ventional synthetic molecules, which confer both advan-
tages and challenges for the drug discovery process.
NPs are characterized by enormous scaffold diversity
and structural complexity. They typically have a higher
molecular mass, a larger number of sp3 carbon atoms and
oxygen atoms but fewer nitrogen and halogen atoms,
higher numbers of H- bond acceptors and donors, lower
calculated octanol–water partition coefficients (cLogP
values, indicating higher hydrophilicity) and greater
molecular rigidity compared with synthetic compound
libraries1,6–9. These differences can be advantageous; for
example, the higher rigidity of NPs can be valuable in
drug discovery tackling protein–protein interactions10.
Indeed, NPs are a major source of oral drugs ‘beyond
Lipinski’s rule of five’11. The increasing significance of
drugs not conforming to this rule is illustrated by the
increase in molecular mass of approved oral drugs over
the past 20 years12. NPs are structurally ‘optimized’
by evolution to serve particular biological functions1,
including the regulation of endogenous defence mech-
anisms and the interaction (often competition) with
other organisms, which explains their high relevance for
infectious diseases and cancer. Furthermore, their use
in traditional medicine may provide insights regarding
efficacy and safety. Overall, the NP pool is enriched with
‘bioactive’ compounds covering a wider area of chemical
space compared with typical synthetic small- molecule
libraries13.
Despite these advantages and multiple successful
drug discovery examples, several drawbacks of NPs have
led pharmaceutical companies to reduce NP- based drug
discovery programmes. NP screens typically involve a
library of extracts from natural sources (FIG.1), which
may not be compatible with traditional target- based
assays14. Identifying the bioactive compounds of inter-
est can be challenging, and dereplication tools have to
be applied to avoid rediscovery of known compounds.
Accessing sufficient biological material to isolate and
characterize a bioactive NP may also be challenging15.
Furthermore, gaining intellectual property (IP) rights
for (unmodified) NPs exhibiting relevant bioactivities
can be a hurdle, since naturally occurring compounds
in their original form may not always be patented (legal
frameworks vary between countries and are evolving)16,
although simple derivatives can be patent- protected
(BOX1). An additional layer of complexity relates to the
regulations defining the need for benefit sharing with
countries of origin of the biological material, framed
in the United Nations 1992 Convention on Biological
Diversity and the Nagoya Protocol, which entered into
force in 2014 (REF.17), as well as recent developments con-
cerning benefit sharing linked to use of marine genetic
resources18.
Although the complexity of NP structures can be
advantageous, the generation of structural analogues to
explore structure–activity relationships and to optimize
sp3 carbon atoms
Tetravalent carbon atoms
forming single covalent bonds
with other atoms within the
molecular structure. A higher
fraction of sp3 carbons within
molecules is a descriptor that
indicates more complex
3D structures.
Natural products in drug discovery:
advances and opportunities
AtanasG.Atanasov
1,2,3,4 ✉ , SergeyB.Zotchev2, VerenaM.Dirsch
2, the International
Natural Product Sciences Taskforce* and ClaudiuT.Supuran
5 ✉
Abstract | Natural products and their structural analogues have historically made a major
contribution to pharmacotherapy, especially for cancer and infectious diseases. Nevertheless,
natural products also present challenges for drug discovery, such as technical barriers to screening,
isolation, characterization and optimization, which contributed to a decline in their pursuit by
the pharmaceutical industry from the 1990s onwards. In recent years, several technological and
scientific developments — including improved analytical tools, genome mining and engineering
strategies, and microbial culturing advances — are addressing such challenges and opening up
new opportunities. Consequently, interest in natural products as drug leads is being revitalized,
particularly for tackling antimicrobial resistance. Here, we summarize recent technological
developments that are enabling natural product- based drug discovery, highlight selected
applications and discuss key opportunities.
1Institute of Genetics and
Animal Biotechnology of the
Polish Academy of Sciences,
Jastrzebiec, Poland.
2Department of
Pharmacognosy, University
of Vienna, Vienna, Austria.
3Institute of Neurobiology,
Bulgarian Academy of
Sciences, Sofia, Bulgaria.
4Ludwig Boltzmann Institute
for Digital Health and Patient
Safety, Medical University
of Vienna, Vienna, Austria.
5Università degli Studi di
Firenze, NEUROFARBA
Dept, Sezione di Scienze
Farmaceutiche, Florence, Italy.
*A full list of members and
affiliations is presented
at the end of the paper.
✉e- mail: a.atanasov.
mailbox@gmail.com;
claudiu.supuran@unifi.it
https://doi.org/10.1038/
s41573-020-00114- z
REvIEwS
Nature reviews
|
Drug Discovery
NP leads can be challenging, particularly if synthetic
routes are difficult. Also, NP- based drug leads are
often identified by phenotypic assays, and deconvolu-
tion of their molecular mechanisms of action can be
time- consuming19. Fortunately, there have been sub-
stantial advances20 both in the development of screening
assays (for example, harnessing the potential of induced
pluripotent stem cells and gene editing technologies)
and in strategies to identify the modes of action of active
compounds (reviewed previously21–23).
Here, we discuss recent technological and scien-
tific advances that may help to overcome challenges in
NP- based drug discovery, with an emphasis on three
areas: analytical techniques, genome mining and engi-
neering, and cultivation systems. In the concluding sec-
tion, we highlight promising future directions for NP
drug discovery.
Application of analytical techniques
Classical NP- based drug research starts with biological
screening of ‘crude’ extracts to identify a bioactive ‘hit’
extract, which is further fractionated to isolate the active
NPs. Bioactivity- guided isolation is a laborious process
with a number of limitations, but various strategies and
technologies can be used to address some of them (FIG.2).
For example, to create libraries that are compatible
Problem 1
Not possible to culture the
organism out of the natural
habitat
Solutions for 1 and 2
• New methods for culturing
• New methods for in situ
analysis
Problem 2
Relevant NPs are not produced
when the organism is taken out
of the natural habitat
Problem 3
Presence of NPs that are known
Problem 6
Unknown molecular
targets for NPs
identified with
phenotypic assays
Solution for 6
New methods
for molecular
mode of action
elucidation
Problem 4
Presence of NPs that do not
have drug-like properties
Problem 5
Bioactive NPs are present in
insufficient amounts
Organisms Crude
extracts Fractions
Multiple
rounds of
fractionation
Single
compounds
Bioactivity
evaluation
Solution for 2
New methods for NP synthesis
induction and heterologous
expression of biosynthetic genes
Solution for 3
New methods for
dereplication
Solution for 4 and 5
New methods for
extraction and
pre-fractionation
of extracts
Fig. 1 | Outline of traditional bioactivity-guided isolation steps in natural product drug discovery. Steps in the
process are shown in purple boxes, with associated key limitations shown in red boxes and advances that are helping to
address these limitations in modern natural product (NP)- based drug discovery shown in green boxes. The process begins
with extraction of NPs from organisms such as bacteria. The choice of extraction method determines which compound
classes will be present in the extract (for example, the use of more polar solvents will result in a higher abundance of polar
compounds in the crude extract). To maximize the diversity of the extracted NPs, the biological material can be subjected
to extraction with several solvents of different polarity. Following the identification of a crude extract with promising
pharmacological activity, the next step is its (often multiple) consecutive bioactivity- guided fractionation until the pure
bioactive compounds are isolated. A key limitation for the potential of this approach to identify novel NPs is that many
potential source organisms cannot be cultured or stop producing relevant NPs when taken out of their natural habitat.
These limitations are being addressed through development of new methods for culturing, for insitu analysis, for NP
synthesis induction and for heterologous expression of biosynthetic genes. At the crude extract step, challenges include
the presence in the extracts of NPs that are already known, NPs that do not have drug- like properties or insufficient
amounts of NPs for characterization. These challenges can be addressed through the development of methods for
dereplication, extraction and pre- fractionation of extracts. Finally, at the last stage, when bioactive compounds are
identified by phenotypic assays, significant time and effort are typically needed to identify the affected molecular targets.
This challenge can be addressed by the development of methods for accelerated elucidation of molecular modes of action,
such as the nematic protein organization technique (NPOT), drug affinity responsive target stability (DARTS), stable isotope
labelling with amino acids in cell culture and pulse proteolysis (SILAC- PP), the cellular thermal shift assay (CETSA) and an
extension known as thermal proteome profiling (TPP), stability of proteins from rates of oxidation (SPROX), the similarity
ensemble approach (SEA) and bioinformatics- based analysis of connectivity (connectivity map, CMAP)23,189–192.
Lipinski’s rule of five
This guideline for the likelihood
of a compound having oral
bioavailability is based on
several characteristics
containing the number 5.
It predicts that a molecule is
likely to have poor absorption
or permeation if it has more
than one of the following
characteristics: there are
>5 H- bond donors and
>10 H- bond acceptors; the
molecular weight is >500;
or the partition coefficient
LogP is >5. Notably, natural
products were identified as
common exceptions at the
time of publication in 1997.
Dereplication
Pharmacological screening of
natural product extracts yields
hits potentially containing
multiple natural products that
need to be considered for
further study to identify
the bioactive compounds.
Dereplication is the process of
recognizing and excluding from
further study such hit mixtures
that contain already known
bioactive compounds.
Phenotypic assays
Assays that rely on the ability
of tested compounds to exert
desired phenotypic changes
in cells, isolated tissues, organs
or animals. They offer a
complementary strategy
to target- based assays for
identifying new potential drugs.
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Reviews
with high- throughput screening, crude extracts can be
pre- fractionated into sub- fractions that are more suit-
able for automated liquid handling systems. In addi-
tion, fractionation methods can be adjusted so that
sub- fractions preferentially contain compounds with
drug- like properties (typically moderate hydrophilicity).
Such approaches can increase the number of hits com-
pared with using crude extracts, as well as enabling more
efficient follow- up of promising hits24.
Metabolomics was developed as an approach to
simultaneously analyse multiple metabolites in biologi-
cal samples. Enabled by technological developments in
chromatography and spectrometry, metabolomics was
historically applied first in other research fields, such as
biomedical and agricultural sciences2. Advances in the
analytical instrumentation used in NP research25,26, cou-
pled with computational approaches that can generate
plausible NP analogue structures and their respective
simulated spectra27, have also enabled application of
‘omics’ approaches such as metabolomics in NP- based
drug discovery. Metabolomics can provide accurate infor-
mation on the metabolite composition in NP extracts,
thus helping to prioritize NPs for isolation, to accelerate
dereplication28,29 and to annotate unknown analogues
and new NP scaffolds. Moreover, metabolomics can
detect differences between metabolite compositions in
various physiological states of producing organisms and
enable the generation of hypotheses to explain them,
and can also provide extensive metabolite profiles to
underpin phenotypic characterization at the molecular
level30. Both options are very useful in understanding the
molecular mechanisms of action ofNPs.
For metabolite profiling, NP extracts are analysed by
NMR spectroscopy or high- resolution mass spectrom-
etry (HRMS), or respective combined methods involv-
ing upstream liquid chromatography (LC)31,32, such as
LC–HRMS, which can separate numerous isomers pres-
ent in NP extracts33. Moreover, such combined methods
might integrate HRMS and NMR, allowing the simulta-
neous use of the advantages of both techiques34,35. NMR
analysis of NP extracts is simple and reproducible, and
provides direct quantitative information and detailed
structural information, although it has relatively low sen-
sitivity, meaning that it generally enables profiling only
of major constituents33. The applications of NMR in NP
research are versatile36 and the technique is used both
directly for metabolomics of unfractionated NP extracts
and for structural characterization of compounds and
fractions obtained with appropriate separation methods,
most often LC. HRMS is the gold standard for qualita-
tive and quantitative metabolite profiling33 and is most
commonly applied in combination with LC. HRMS can
also be used in the direct infusion mode (called DIMS)37,
whereby samples are directly profiled by MS without a
chromatography step, or in MS imaging (MSI)38, which
enables determination of the spatial distribution of NPs
within living organisms. HRMS enables routine acqui-
sition of accurate molecular mass information, which
together with appropriate heuristic filtering can pro-
vide unambiguous assignment of molecular formulae
for hundreds to thousands of metabolites within a sin-
gle extract over a dynamic range that may exceed five
Box 1 | Natural products that activate the KEAP1/NRF2 pathway
An example of a pathway affected by diverse natural products (NPs) is the KEAP1/NRF2
pathway. This pathway regulates the expression of networks of genes encoding proteins
with versatile cytoprotective functions and has essential roles in the maintenance
of redox and protein homeostasis, mitochondrial biogenesis and the resolution of
inflammation196–199.
Activation of this pathway can protect against damage by most types of oxidants and
pro- inflammatory agents, and it restores redox and protein homeostasis200. The pathway
has therefore attracted attention for the development of drugs for the prevention
and treatment of complex diseases, including neurological conditions such as
relapsing–remitting multiple sclerosis201 and autism spectrum disorder202.
Dimethyl fumarate (DMF), the methyl ester of the NP fumarate (a tricarboxylic acid
(TCA) cycle intermediate that is found in both animals and plants), is one of the earliest
discovered inducers of the KEAP1/NRF2 pathway203,204. The origins of the development of
DMF as a drug date back to the use in traditional medicine of the plant Fumaria officinalis.
Initially, fumaric acid derivatives were used for the treatment of psoriasis as it was
thought that psoriasis is caused by a metabolic deficiency in the TCA cycle that could
be compensated for by repletion of fumarate205. Despite this erroneous assumption,
DMF is effective in treating psoriasis, both topically and orally, and is the active principle
of Fumaderm, which has been used clinically for several decades in the treatment of plaque
psoriasis in Germany. More recently, a DMF formulation developed by Biogen has been
tested in other immunological disorders, with successful phase III trials in multiple
sclerosis206,207 leading to its approval by the FDA and EMA in 2013.
The isothiocyanate sulforaphane, isolated from broccoli (Brassica oleracea)208, is among
the most potent naturally occurring inducers of the KEAP1/NRF2 pathway209 and has
protective effects in animal models of Parkinson210, Huntington211 and Alzheimer212
diseases, traumatic brain injury213, spinal cord contusion injury214, stroke215, depression216
and multiple sclerosis217. Sulforaphane- rich broccoli extract preparations are being
developed as preventive interventions in areas of the world with unavoidable exposure
to environmental pollutants, such as China; the initial results of a randomized clinical trial
showed rapid and sustained, statistically significant increases in the levels of excretion
of the glutathione- derived conjugates of benzene and acrolein218, and a follow- up trial
(NCT02656420) also demonstrated dose–response- dependent benzene detoxification219.
In a placebo- controlled, double- blind, randomized clinical trial in young individuals
(age 13–27 years) with autism spectrum disorder, sulforaphane reversed many of the
clinical abnormalities202; these encouraging findings led to a recently completed clinical
trial in children (age 3–12 years) (NCT02561481; results of the trial are not yet publicly
available). An α- cyclodextrin complex of sulforaphane known as SFX-01 (developed by
Evgen Pharma) is being clinically studied for its potential to reverse resistance to endocrine
therapies in patients with ER+HER2- metastatic breast cancer (phase II trial completed220)
and in patients with subarachnoid haemorrhage (phase II trial NCT02614742 recently
completed; results not yet publicly available). Currently, a clinical trial of SFX-01 in patients
hospitalized with COVID-19 is in its final stages of preparation.
Finally, the pentacyclic triterpenoids bardoxolone methyl (also known as RTA 402) and
omaveloxolone (RTA 408), which are semi- synthetic derivatives of the NP oleanolic acid,
are the most potent (active at nanomolar concentrations) activators of the KEAP1/NRF2
pathway known to date221. These compounds have shown protective effects in numerous
animal models of chronic disease222, and are currently in clinical trials for a wide range
of indications, such as chronic kidney disease in type 2 diabetes, pulmonary arterial
hypertension, melanoma, radiation dermatitis, ocular inflammation and Friedreich’s
ataxia200. Most recently, bardoxolone methyl has entered a clinical trial in patients
hospitalized with confirmed COVID-19 (NCT04494646).
O
O
N
O
O
H
HH
Bardoxolone methyl (RTA 402)
Sulforaphane
S
N
C
OS
O
O
N N
H
H
HHO
FF
Omaveloxolone (RTA 408)
Dimethyl fumarate
O
O
O
O
Nature reviews
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Drug Discovery
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orders of magnitude31,39. However, challenges remain in
data mining and in the unambiguous identification of
the metabolites using various workflows relying on open
web- based tools40.
Dereplication of secondary metabolites in bioactive
extracts includes the determination of molecular mass
and formula and cross- searching in the literature or
structural NP databases with taxonomic information,
which greatly assists the identification process. Such
metadata, which are difficult to query in the literature,
are often compiled in proprietary databases, such as the
Dictionary of Natural Products, which encompasses
all NP structures reported with links to their biological
sources (see Related links). However, a comprehensive
experimental tandem mass spectrometry (MS/MS) data-
base of all NPs reported to date does not exist, and a
search for experimental spectra across various platforms
is hindered by the lack of standardized collision energy
conditions for fragmentation in LC–MS/MS25.
In this respect, the Global Natural Products Social
(GNPS) molecular networking platform developed
inthe Dorrestein laboratory is an important addition
to the toolbox41. Molecular networking organizes thou-
sands of sets of MS/MS data recorded from a given set
of extracts and visualizes the relationship of the ana-
lytes as clusters of structurally related molecules. This
improves the efficiency of dereplication by enabling
annotation of isomers and analogues of a given metab-
olite in a cluster42. The recorded experimental spectra
can be searched against putative structures and their
corresponding predicted MS/MS spectra generated
by tools such as competitive fragmentation modelling
(CFM- ID)43. Based on such approaches, vast databases
of theoretical NP spectra have been created and applied
in dereplication44. TheGNPS molecular networking
approach has limitations, however, such as better appli-
cability to some classes of NPs than others and the
uncertainty of structural assignment among possible
Bacterial strains isolated from
marine sediment from the
coastal areas of Panama were
used for the preparation of
234 NP extracts
a
Image-based phenotypic
bioactivity profiling of the
234 NP extracts in HeLa cells
LC–HRMS-based metabolomics
data also recorded with the 234
NP extracts
Integration and clustering of the
biological and chemical datasets
revealed 13 unique clusters
One of the clusters was
prioritized for further study, and
a combination of LC–MS and NMR
analysis led to the identification
of quinocinnolinomycins A–D
N
N
O
N
H
O
OH
R
Me
Quinocinnolinomycin A R =
Quinocinnolinomycin B R =
Quinocinnolinomycin C R =
Quinocinnolinomycin D R =
156 FACs generated containing
unique BGCs from three
species from the Aspergillus
genus
b
Selected 56 FACs predicted
to contain uncharacterized
BGCs (i.e. BGCs with no
known product or well-
characterized homologue)
Metabolomics data analysed
with FAC-Score algorithm
that filters out signals present
in host extracts or in more
than one FAC strain
56 FACs transformed into the
heterologous expression host
(A. nidulans) and NP extracts
prepared
NP extracts subjected to
untargeted LC–HRMS
15 new metabolites and their
BGCs were characterized through
combination of gene deletions
within the BGCs and additional
LC–MS and NMR analysis
Fig. 2 | Applications of advanced analytical technologies empowering modern natural product-based drug discovery.
a | An illustrative example of the application of liquid chromatography–high- resolution mass spectrometry (LC–HRMS)
metabolomics in the screening of natural product (NP) extracts is the work of Kurita etal.58, in which 234 bacterial extracts
were subjected to image- based phenotypic bioactivity screening and LC–HRMS metabolomics. Clustering of the resulting
data allowed prioritization of promising extracts for further analysis, resulting in the discovery of the new NPs, quinocin-
nolinomycins A–D. b | Another illustrative example of LC–HRMS screening of NP extracts is the work of Clevenger etal.85,
who obtained novel NP extracts through heterologous expression of fungal artificial chromosomes (FACs) containing
uncharacterized biosynthetic gene clusters (BGCs) from diverse fungal species in Aspergillus nidulans. Analysis of the
LC–HRMS metabolomics data with a FAC- Score algorithm directed the simultaneous discovery of 15 new NPs and
the characterization of their BGCs.
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predicted candidates. Efforts to address such issues are
ongoing45–47, including overlaying molecular networks of
large NP extract libraries with taxonomic information to
improve the confidence of annotation48. Overall, molec-
ular networking mainly allows better prioritization
ofthe isolation of unknown compounds by strengthen-
ing the dereplication process and elucidating relation-
ships between NP analogues, and rigorous structure
elucidation for NPs of interest should not beneglected.
Another useful platform for metabolite identifica-
tion is METLIN49, which includes a high- resolution
MS/MS database with a fragment similarity search
function that is useful for identification of unknown
compounds. Other databases and in silico tools such
as Compound Structure Identification (CSI): FingerID
and Input Output Kernel Regression (IOKR) can be used
to search available fragment ion spectra, as well as to
generate predicted spectra of fragment ions not present
in current databases50. A novel computational platform
for predicting the structural identity of metabolites
derived from any identified compound has also been
recently reported51, which should increase the searchable
chemical space of NPs.
To accelerate the identification of bioactive NPs in
extracts, metabolomics data can be matched to the bio-
logical activities of these extracts52. Various chemometric
methods such as multivariate data analysis can correlate
the measured activity with signals in the NMR and MS
spectra, enabling the active compounds to be traced in
complex mixtures with no need for further bioassays53–55.
Furthermore, several analytical modules involving dif-
ferent bioassays and detection technologies can be
linked to allow simultaneous bioactivity evaluation and
identification of compounds present in small amounts
(analytical scale) in complex compound mixtures34,35.
Metabolomics data can be integrated with data
obtained by other omics techniques such as transcrip-
tomics and proteomics and/or with imaging- based
screens. For example, Acharya etal. used this approach
to characterize NP- mediated interactions between a
Micromonospora species and a Rhodococcus species56.
In another interesting example, Kurita etal. developed a
compound activity mapping platform for the prediction
of identities and mechanisms of action of constituents
from complex NP extract libraries by integrating cyto-
logical profiling57 with untargeted metabolomics data
from a library of extracts58, and identified quinocinno-
linomycins as a new family of NPs causing endoplasmic
reticulum stress58 (FIG.2a).
Analytical advances that enable the profiling of
responses to bioactive molecules at the single- cell
level can also accelerate NP- based drug discovery. Irish,
Bachmann, Earl and colleagues developed a high-
throughput platform for metabolomic profiling of bio-
activity by integrating phospho- specific flow cytometry,
single- cell chemical biology and cellular barcoding with
metabolomic arrays (characterized chromatographic
microtitre arrays originating from biological extracts)59.
Using this platform, the authors studied the single- cell
responses of bone marrow biopsy samples from patients
with acute myeloid leukaemia following exposure to
microbial metabolomic arrays obtained from extracts
of biosynthetically prolific bacteria, which enabled the
identification of new bioactive polyketides59.
Finally, advances in analytical technologies con-
tinue to support the rigorous structure determination
of NPs of interest. The progressive development of
higher- field NMR instruments and probe technology60,61
has enabled NP structure determination from very small
quantities (below 10 µg)62,63, which is important, as the
available quantities of NPs are often limited. In addi-
tion, microcrystal electron diffraction (MicroED) has
recently emerged as a cryo- electron microscopy- based
technique for unambiguous structure determination
of small molecules64 and is already finding important
applications in NP research65. The increased resolution
and sensitivity of analytical equipment can also help
address problems associated with ‘residual complex-
ity’ of isolated NPs; that is when biologically potent
but unidentified impurities in an isolated NP sample
(which could include structurally related metabolites or
conformers) lead to an incorrect assignment of structure
and/or activity66,67. To avoid futile downstream develop-
ment efforts, Pauli and colleagues recommended that
lead NPs should undergo advanced purity analysis at an
early stage using quantitative NMR and LC–MS67.
Genome mining and engineering
Advances in knowledge on biosynthetic pathways for NPs
and in developing tools for analysing and manipulating
genomes are further key drivers for modern NP- based
drug discovery. Two key characteristics enable the iden-
tification of biosynthetic genes in the genomes of the
producing organisms. First, these genes are clustered in
the genomes of bacteria and filamentous fungi. Second,
many NPs are based on polyketide or peptide cores,
and their biosynthetic pathways involve enzymes —
polyketide synthases (PKSs) and nonribosomal peptide
synthetases (NRPSs), respectively — that are encoded by
large genes with highly conserved modules68.
‘Genome mining’ is based on searches for genes
that are likely to govern biosynthesis of scaffold struc-
tures, and can be used to identify NP biosynthetic gene
clusters69–71. Prioritization of gene clusters for further
work is facilitated by advances in biosynthetic know-
ledge and predictive bioinformatics tools, which can pro-
vide hints about whether the metabolic products of the
clusters have chemical scaffolds that are new or known,
thereby supporting dereplication72,73. Such predictive
tools for gene cluster analysis can be applied in combi-
nation with spectroscopic techniques to accelerate the
identification of NPs65 and determine the stereochem-
istry of metabolic products66. Furthermore, to extend
genome mining from a single genome to entire genera,
microbiomes or strain collections, computational tools
have been developed, such as BiG- SCAPE, which enables
sequence similarity analysis of biosynthetic gene clusters,
and CORASON, which uses a phylogenomic approach
to elucidate evolutionary relationships between gene
clusters74.
Phylogenetic studies of known groups of talented
secondary metabolite producers can also empower
discovery of novel NPs. Recently, a study comparing
secondary metabolite profiles and phylogenetic data in
Phylogenomic approach
The use of genomic data to
reveal evolutionary
relationships. In the context of
natural product drug discovery,
the use of phylogenomics is
based on the assumption that
organisms that have closer
evolutionary relationships are
more likely to produce similar
natural products.
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Drug Discovery
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myxobacteria demonstrated a correlation between the
taxonomic distance and the production of distinct sec-
ondary metabolite families75. In filamentous fungi, it was
likewise shown that secondary metabolite profiles are
closely correlated with their phylogeny76. These organ-
isms are rich in secondary metabolites, as demonstrated
by LC–MS studies of their extracts under laboratory
conditions77. Concurrent genomic and phylogenomic
analyses implied that even the genomes of well- studied
organism groups harbour many gene clusters for sec-
ondary metabolite biosynthesis with as yet unknown
functions78. The phylogeny of biosynthetic gene clusters,
together with analysis of the absence of known resistance
determinants, was recently used to prioritize members of
the glycopeptide antibiotic family that could have novel
activities. This led to the identification of the known
antibiotic complestatin and the newly discovered cor-
bomycin as compounds that act through a previously
uncharacterized mechanism involving inhibition of
peptidoglycan remodelling79.
Many microorganisms cannot be cultured, or tools
for their genetic manipulation are not sufficiently
developed, which makes it more challenging to access
their NP- producing potential. However, biosynthetic
gene clusters for NPs can be cloned and heterologously
expressed in organisms that are well- characterized
and easier to culture and to genetically manipulate
(such as Streptomyces coelicolor, Escherichia coli and
Saccharomyces cerevisiae)80. The aim is to achieve
higher production titres in the heterologous hosts
than in wild- type strains, improving the availability of
lead compounds80–82. Vectors that can carry large DNA
inserts are needed for the cloning of complete NP bio-
synthetic gene clusters. Cosmids (which can have inserts
of 30–40 kb), fosmids (which can harbour 40–50 kb) and
bacterial artificial chromosomes (BACs; which can have
inserts of 100 kb to >300 kb) have been developed83. For
fungal gene clusters, self- replicating fungal artificial
chromosomes (FACs) have been developed, which can
have inserts of >100 kb (REF.84). FACs in combination
with metabolomic scoring were used to develop a scal-
able platform, FAC- MS, allowing the characterization
of fungal biosynthetic gene clusters and their respec-
tive NPs at unprecedented scale85. The application of
FAC- MS for the screening of 56 biosynthetic gene clus-
ters from different fungal species yielded the discovery
of 15 new metabolites, including a new macrolactone,
valactamide A85 (FIG.2b).
Even in culturable microorganisms, many biosyn-
thetic gene clusters may not be expressed under con-
ventional culture conditions, and these silent clusters
could represent a large untapped source of NPs with
drug- like properties86. Several approaches can be pur-
sued to identify such NPs. One approach is sequencing,
bioinformatic analysis and heterologous expression
of silent biosynthetic gene clusters, which has already
led to the discovery of several new NP scaffolds from
cultivable strains87. Direct cloning and heterologous
expression was also used to discover the new antibiotic
taromycin A, which was identified upon the transfer
of a silent 67 kb NRPS biosynthetic gene cluster from
Saccharomonospora sp. CNQ-490 into S. coelicolor88.
To transfer a biosynthetic gene cluster of such size, a plat-
form based on transformation- associated recombination
(TAR) cloning was developed. This platform enables
direct cloning and manipulation of large biosynthetic
gene clusters in S. cerevisiae, maintenance and manipula-
tion of the vector in E. coli, and heterologous expression
of the cloned gene clusters in Actinobacteria (such as
S. coelicolor) following chromosomal integration88, and
is an alternative to BACs for heterologous expression of
large biosynthetic gene clusters.
Heterologous expression has limitations, such as
the need to clone and manipulate very large genome
regions occupied by biosynthetic gene clusters and the
difficulty of identifying a suitable host that provides all
conditions necessary for the production of the corre-
sponding NPs. These limitations can be circumvented
by activating biosynthetic gene clusters directly in the
native microorganism through targeted genetic manip-
ulations, generally involving the insertion of activating
regulatory elements or deletion of inhibitory elements
such as repressors or their binding sites. For example,
a derepression strategy of deleting gbnR, a gene for a
transcriptional repressor in Streptomyces venezuelae
ATCC 10712 was used by Sidda etal. in the discovery of
gaburedins, a family of γ- aminobutyrate- derived ureas89.
An example of the activator- based strategy is the consti-
tutive expression of the samR0484 gene in Streptomyces
ambofaciens ATCC 23877, which led to the discovery
of stambomycins A–D, 51- membered cytotoxic glyco-
sylated macrolides72. Alternatively, silent biosynthetic
gene clusters can be activated using repressor decoys90,
which have the same DNA nucleotide sequence as the
binding sites for the repressors that prevent the expres-
sion of the clusters. When these decoys are introduced
into the bacteria, they sequester the respective repres-
sors, and the ‘endogenous’ binding sites in the genome
remain unoccupied, leading to derepression of the pre-
viously silent biosynthetic genes and production of the
corresponding NPs. This approach has been applied to
activate eight silent biosynthetic gene clusters in multi-
ple streptomycetes and led to the characterization of a
novel NP, oxazolepoxidomycin A90. The repressor decoy
strategy is simpler, easier and faster to perform than the
deletion of genes encoding regulatory factors. However,
it has the same limitation as other approaches that rely
on the introduction of recombinant DNA molecules into
cells: it is necessary to develop protocols for efficient
introduction of DNA into the targeted host strain, and
the decoy must be maintained on a high- copy plasmid
to ensure efficient repressor sequestration.
Another approach focused on exchange of regu-
latory elements is based on the CRISPR–Cas9 technol-
ogy. The promise of this technique is exemplified in a
recent work by Zhang etal., which demonstrated that
CRISPR–Cas9- mediated targeted promoter introduction
can efficiently activate diverse biosynthetic gene clusters
in multiple Streptomyces species, leading to the produc-
tion of unique metabolites, including a novel polyketide
in Streptomyces viridochromogenes91. The CRISPR–Cas9
technology was also used to knock out genes encoding
two well- known and frequently rediscovered anti-
biotics in several actinomycete strains, which led to the
Taxonomic distance
The distance of compared taxa
on a constructed phylogenetic
tree (also known as an
evolutionary tree). Closer
distance of compared taxa
indicates a closer evolutionary
relationship.
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Reviews
production of different rare and previously unknown
variants of antibiotics that were otherwise obscured,
including amicetin, thiolactomycin, phenanthroviridin
and 5- chloro-3- formylindole92.
Approaches that rely on sequencing, bioinformat-
ics and heterologous expression can also enable the
identification of novel NPs from bacterial strains that
have not yet been cultivated (FIG.3a). For example,
Hoveretal. searched the metagenomes of 2,000 soil
samples for biosynthetic gene clusters for lipopeptides
with calcium- binding motifs. This led to the discov-
ery of malacidins, members of the calcium- dependent
antibiotic family, via heterologous expression of a 72 kb
biosynthetic gene cluster from a desert soil sample ina
Streptomyces albus host strain93 (FIG.3b). However, in
comparison with some of the other above- discussed
strategies72,89,90, this metagenome- based discovery
approach is more suited to finding new members of
known NP classes rather than discovery of entirely
new classes. In another study, Chu etal. developed a
human microbiome- based approach that identified
nonribosomal linear heptapeptides called humimycins
as novel antibiotics active against methicillin- resistant
Staphylococcus aureus (MRSA)94 (FIG.3c). The structure
of the NPs was predicted via bioinformatics analysis of
gene clusters found in human commensal bacteria, fol-
lowed by their chemical synthesis. A major strength of
this innovative approach is that it is entirely independent
of microbial cultivation and heterologous gene expres-
sion. Nevertheless, there are limitations related to the
accuracy of computational chemical structure predic-
tions and the feasibility of total chemical synthesis if
structures are complex.
The genomes of plants or animals can also be mined
for novel NPs. For example, mining of 116 plant genomes
enabled by identification of a precursor gene for the
biosynthesis of lyciumins, a class of branched cyclic
ribosomal peptides with hypotensive action produced
by Lycium barbarum (popularly known as goji), identi-
fied diverse novel lyciumin chemotypes in seven other
plants, including crops such as soybean, beet, quinoa and
eggplant95. Genome mining in the animal kingdom is
exemplified by the work of Dutertre etal., which used
an integrated transcriptomics and proteomics approach
to discover thousands of novel venom peptides from
Conus marmoreus snails96. Proteomics analysis revea-
led that the vast majority of the conopeptide diversity
was derived from a set of ~100 genes through variable
peptide processing96.
Some bioactive compounds initially isolated from
marine organisms might be products of symbionts, and
genome mining can facilitate the characterization of
such NPs. For example, it has been shown that bio active
compounds from the sponge Theonella swinhoei are pro-
duced by bacterial symbionts97, and characterization of
the symbiont ‘Candidatus Entotheonella serta’ using
single- cell genomics led to the discovery of gene clus-
ters for misakinolide and theonellamide biosynthesis98.
Another example of a marine NP produced by a bacte-
rial symbiont is ET-743 (trabectedin), originally isolated
from the tunicate Ecteinascidia turbinate. Ameta-omics
approach developed by Rath etal. revealed that the
producer of this clinically used anticancer agent is the
bacterial symbiont ‘Candidatus Endoecteinascidia
frumentensis’99.
Similarly, plant microbiomes also represent a large
reservoir for the identification of novel bioactive NPs
(such as the antitumour agents maytansine, paclitaxel
and camptothecin, which were initially isolated from
plants and later shown to be produced by microbial
endophytes)100 that can be tapped by genome mining
approaches. An illustrative example is a recent work
by Helfrich etal. that identified hundreds of novel bio-
synthetic gene clusters by genome mining of 224bac-
terial strains isolated from Arabidopsis thaliana leaves101.
Acombination of bioactivity screening and imaging mass
spectrometry was used to select a single species for fur-
ther genomic analysis and led to the isolation of a NP with
an unprecedented structure, the trans- acyltransferase
PKS- derived antibiotic macrobrevin101.
Targeted genetic engineering of NP biosynthetic gene
clusters can be of high value if the producing organism
is difficult to cultivate or the yield of a NP is too low
to allow comprehensive NP characterization. Rational
genetic engineering and heterologous expression con-
tributed to increase the production of vioprolides, a
depsi peptide class of anticancer and antifungal NPs in
the myxobacterium Cystobacter violaceus Cb vi35, by
several orders of magnitude. In addition, non-natural
vio prolide analogues were generated by this approach102.
Similarly, promoter engineering and heterologous
expres sion of biosynthetic gene clusters was reported
to result in a 7- fold increase in the production of the
cytotoxic NP disorazol103, and a 328- fold increase in
the production of spinosad, an insecticidal macrolide
produced by the bacterium Saccharopolyspora spinosa104.
Besides increasing NP yields, targeted gene manip-
ulation can also be used to alter biosynthetic pathways
in a predictable manner to produce new NP analogues
with improved pharmacological properties, such as
higher specific activity, lower toxicity and better pharma-
cokinetics. Such biosynthetic engineering approaches
depend on a solid understanding of the biosynthetic
pathway leading to a specific NP, access to the genes
specifying this pathway and the ability to manipulate
them in either the original or a hetero logous host.
Recent advances in biosynthetic engineering have
enabled faster and more efficient production of NP
analogues, including the development of methods for
accelerated engineering and recombination of modules
of PKS gene clusters105, NRPSs106,107 and NRPS–PKS
assembly lines108, as well as elucidation of mechanisms
for polyketide chain release that are contributing to
NP structural diversification109,110. Examples of bio-
synthetic engineering applied to several important NPs
include the generation of analogues of the immuno-
suppressant rapamycin111, the antitumour agents
mithramycin112 and bleomycin113, and the antifungal
agent nystatin114.
It should be noted that biosynthetic engineering has
limitations regarding the parts of the NP molecule that
can be targeted for modifications, and the chemical
groups that can be introduced or removed. Considering
the complexity of many NPs, however, total synthesis
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|
Drug Discovery
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DNA isolation from
specific microorganism/
environment/microbiota
Sequenced
metagenomes of
2,000 soil samples
Identified a predicted BGC
and transferred into
Streptomyces albus using
transformation-associated
recombination
Analysed sequences for BGCs
that code for lipopeptides
with calcium-binding motifs
and clustered to create a
phylogenetic tree
Selected a soil sample rich in BGCs
from a tree branch not associated
with the BGCs for known
calcium-binding antibiotics and
cloned DNA in a cosmid library
Malacidins isolated from cultures and
their structures elucidated using a
combination of mass spectrometry and
NMR data, supported by bioinformatics
analysis of the BGC
Extracts from cultures of S. albus
harbouring the BGC found to
show antibacterial activity
against Staphylococcus aureus
Sequencing Bioinformatics analysis
(genome mining)
Heterologous
expression
NP isolation and
characterization
NP chemical
synthesis
Genetic manipulations
of the native host
a
b
R = Me
R = Et
Malacidin A
Malacidin B
Analysed genomic sequence
data from human microbiome
for gene clusters predicted
to encode large (≥5 residues)
nonribosomal peptides
Tested the 25 NP-like
compounds for activity
against a panel of common
human commensal and
pathogenic bacteria
Identified 57 unique
NRPS gene clusters
Chemically synthesized 25
‘synthetic–bioinformatic’
NP-like compounds predicted
to be encoded by the analysed
gene clusters
Identified humimycins as
new antibiotics active
against methicillin-resistant
S. aureus
c
OH
H
N
O
N
H
O
OH
H
N
O
N
H
OR1R2
OH OH
H
N
O
OH
N
H
O
H
N
O
OH
O
Humimycin A
Humimycin B
R1 = L-Phe, R2 = L-Val
R1 = L-Tyr, R2 = L-Ile
H
N
O
N
H
HO
O
O
H
NO
HN
O
NH
OH
O
OH
O
O
N
HN
O
N
H
O
H
N
O
N
H
OH
O
NH2
O
H
N
O
OH
O
R
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Reviews
may be prohibitively costly, and a combined approach
of biosynthetic engineering and chemical modification
can provide a viable alternative for identifying improved
drug candidates. For example, biosynthetic engineering
may create a ‘handle’ for addition of a beneficial chem-
ical group by synthetic chemistry, as demonstrated for
the biosynthetically engineered analogues of nystatin
mentioned above; further synthetic chemistry modifi-
cations resulted in compounds with improved invivo
pharmacotherapeutic characteristics compared with
amphotericin B115,116.
Advances in microbial culturing systems
The complex regulation of NP biosynthesis in response
to the environment means that the conditions under
which producing organisms are cultivated can have a
major impact on the chance of identifying novel NPs87.
Several strategies have been developed to improve the
likelihood of identifying novel NPs compared with
mono culture under standard laboratory conditions
and to make ‘uncultured’ microorganisms grow in a
simulated natural environment117 (FIG.4).
One well- established approach to promote the identi-
fication of novel NPs is the modulation of culture condi-
tions such as temperature, pH and nutrient sources. This
strategy may lead to activation of silent gene clusters,
thereby promoting production of different NPs. The
term ‘One Strain Many Compounds’ (OSMAC) was
coined for this approach about 20 years ago118, but the
concept has a longer history119, with its use being routine
in industrial microbiology since the 1960s120.
While OSMAC is still widely used for the identifi-
cation of new bioactive compounds121,122, this approach
has limited capacity to mimic the complexities of natu-
ral habitats. It is difficult to predict the combination of
cues (which might also involve metabolites secreted by
other members of the microbial community) to which
the microorganism has evolved to respond by switching
metabolic programmes. To account for such kinds of
interactions, co- culturing using ‘helper’ strains can be
applied123. This can enable the production and identifica-
tion of new NPs, as illustrated by recent studies in which
particular fungi were co- cultured with Streptomcyes
species124,125.
Study of the molecular mechanisms underlying
the ability of helper strains to increase the cultivabil-
ity of previously uncultured microbes can lead to the
identification of specific growth factors, allowing expan-
sion of the number of species that can be successfully
cultured. This strategy was used by D’Onofrio etal. for
the identification of new acyl- desferrioxamine sidero-
phores (iron- chelating compounds) as growth factors
produced by helper strains promoting the growth of
previously uncultured isolates from marine sedi-
ment biofilm117,126. The siderophore- assisted growth is
based on the property of these compounds to provide
iron for microbes unable to autonomously produce
siderophores themselves, and the application of this
approach led to the isolation of previously uncultivated
microorganisms126. The development of strategies to
cultivate microbial symbionts that produce NPs only
upon interaction with their hosts can promote access
to new NPs. Microbial symbionts interacting with
insects or other organisms are a highly promising reser-
voir for the discovery of novel bioactive NPs produced
in a unique ecological context127–130. To stimulate NP
production, culturing strategies can be developed that
better mimic the native environment of microbial sym-
bionts of insects, including the use of media containing
either lyophilized dead insects131 or - proline, a major
constituent of insect haemolymph132.
Strategies to mimic the natural environment even
more closely by harnessing insitu incubation in the
environment from which the microorganism is sam-
pled have been developed, dating back to more than
20 years ago with the biotech companies OneCell and
Diversa. They developed platforms that allowed the
growth of some previously uncultivated microbes from
various environments based on diluting out and sus-
pension in a single drop of medium120,133. More recently,
such strategies have been highlighted by the develop-
ment and application of a platform dubbed the iChip,
in which diluted soil samples are seeded in multiple
small chambers separated from the environment with
a semipermeable membrane134. After seeding, the iChip
is placed back into the soil from which the sample was
taken for an insitu incubation period, allowing the cul-
tured microorganisms to be exposed to influences from
their native environment. The power of this culturing
approach was demonstrated by the discovery of a new
antibiotic, teixobactin, produced by a previously uncul-
tured soil bacterium135,136 (FIG.4a). This platform may be
of great significance for NP drug discovery, given that
it has been estimated that only 1% of soil organisms
have so far been successfully cultured using traditional
culturing techniques137.
The omics strategies discussed in previous sections
can complement efforts to explore NPs produced upon
microbial interactions. The application of such a strategy
is illustrated in the work of Derewacz etal., who analysed
the metabolome of a genome- sequenced Nocardiopsis
bacterium upon co- culture with bacteria of the genera
Escherichia, Bacillus, Tsukamurella and Rhodococcus138.
Around 14% of the metabolomic features found in
co- cultures were undetectable in monocultures, with
many of those being unique to specific co- culture gen-
era, and the previously unreported polyketides ciro-
micin A and B, which possess an unusual pyrrolidinol
substructure and displayed moderate and selective
Fig. 3 | Strategies for genome mining-driven discovery of natural products and
natural product-like compounds. a | Genome mining- based approaches to explore
the biosynthetic capacity of microorganisms rely on DNA extraction, sequencing and
bioinformatics analysis. The vast majority of microbes from different environments
and microbiota communities have not been cultured, and their capacity to produce
natural products (NPs) was largely inaccessible until recently. In the case of unculturable
microorganisms, the bioinformatics analysis step can be followed by either targeted
heterologous expression of biosynthetic gene clusters (BGCs) prioritized as being likely
to yield relevant new NPs or direct chemical synthesis of ‘synthetic–bioinformatic’
NP- like compounds. b,c | These two approaches are exemplified by the recent discoveries
of malacidins (panel b) and humimycins (panel c), respectively93,94. A major strength of the
‘synthetic–bioinformatic’ approach is that it is entirely independent of microbial culture
and gene expression. Its limitations are the accuracy of computational chemical structure
predictions and the feasibility of total chemical synthesis. NRPS, nonribosomal peptide
synthetase.
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|
Drug Discovery
Reviews
cytotoxicity, were identified138. Other examples include
a ‘culturomics’ approach that combines multiple culture
conditions with MS profiling and 16S rRNA- based tax-
onomy to identify prokaryotic species from the human
gut139, and an ultrahigh- throughput screening platform
based on microfluidic droplet single- cell encapsulation
and cultivation followed by next- generation sequenc-
ing and LC–MS, which allows investigation of pairwise
interactions between target microorganisms140. The lat-
ter approach enabled identification of a slow- growing
oral microbiota species that inhibits the growth of
S. aureus140.
Historically early- adopted microbial culturing
approaches led to a bias reflected in the predominant
discovery of NPs from microorganisms that are easy to
cultivate (such as streptomycetes and some common fil-
amentous fungi). As a result, a vast number of NPs from
such ‘easy to culture’ microbes have already been charac-
terized, and conventional screening efforts tend to yield
disappointing returns associated with frequent rediscov-
ery of known NPs and their closely related congeners.
Therefore, culturing strategies aimed at previously unex-
plored (or under- investigated) microbial groups, with
the potential to produce NPs with entirely new scaffolds
and bioactivities (such as Burkholderia, Clostridium and
Xenorhabdus) are of high interest141,142. Closthioamide,
the first secondary metabolite from a strictly anaerobic
bacterium, was discovered from Clostridium cellulo-
lyticum by this approach143. Targeted isolation of such
species is important, and a genome- guided approach
to achieve this goal has recently been demonstrated
for Burkholderia strains in environmental samples144.
Another highly innovative approach to the isolation
and cultivation of previously uncultured bacteria was
iChip device with diluted soil
samples incubated in soil to
simultaneously grow and
isolate uncultured bacteria
Extracts from 10,000 iChip
isolates screened for
antimicrobial activity
against Staphylococcus
aureus
a
Compound isolation from the
E. terrae extract, and structure
elucidation by NMR and
advanced Marfey’s analysis
yielded teixobactin, a new
antibiotic with activity against
Gram-positive bacteria
An extract from a new
bacterial species, Eleftheria
terrae, showed good
antimicrobial activity
against S. aureus
Microbial species selected
from human oral microbiome
for targeted isolation based
on genomic sequence data
b
Antigens representative of the
selected extracellular protein
domains designed, synthesized
and injected into rabbits for
antibody production
IgG purified from the rabbit
serum and fluorescently
labelled
Bioinformatics analysis
identified membrane proteins
with extracellular domains that
could serve as antigens for
antibody development
Oral microbiota samples stained
with fluorescently labelled
antibodies, followed by flow
cytometry and cell sorting to
isolate the targeted bacterial
species
Three species of Saccharibacterium
isolated along with their interacting
Actinobacterium hosts, as well as
SR1 bacteria that are members of
a candidate phylum with no
previously cultured representatives
Teixobactin
H
N
N
H
O
H
N
O
OH
N
H
O
ONH2
H
N
O
N
H
O
H
N
O
OH
N
H
O
O
O
H
N
O
O
N
H
HN
O
N
H
NH
NH
Fig. 4 | Application of advanced microbial culturing approaches to identify new natural products. New strategies
for isolating previously uncultured microorganisms can enable access to new natural products (NPs) produced by them.
a | To recapitulate the effect of complex signals coming from the native environment, microorganisms can be cultivated
directly in the environment from which they were isolated. This concept is used with the iChip platform, in which diluted
environmental samples are seeded in multiple small chambers separated from the native environment with a semipermeable
membrane. The potential of this approach is illustrated by the recent discovery of teixobactin, a new antibiotic with
activity against Gram- positive bacteria134,135. b | Another important recent development involves obtaining information from
environmental samples using omics techniques such as metagenomics to identify and partially characterize microorganisms
present in a specific environment before culturing. An approach relying on such preliminary information was recently
used to engineer the capture of antibodies based on genetic information, which resulted in the successful cultivation of
previously uncultured bacteria from the human mouth145. This reverse genomics workflow was validated by the isolation
and cultivation of three species of Saccharibacteria (TM7) along with their interacting Actinobacteria hosts, as well as
SR1 bacteria that are members of a candidate phylum with no previously cultured representatives.
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Reviews
recently reported by Cross etal.145, who used genomic
information to engineer antibodies predicted to target
selected microorganisms and to specifically capture
these microorganisms from complex communities
and to isolate them in pure cultures. This approach
was validated by isolation and cultivation of previ-
ously uncultured bacteria from the human oral cavity145
(FIG.4b), and it could be applicable to a wide range of
target organisms if suitable cultivation conditions can
be identified for the isolated cells.
Despite these advances in culturing strategies,
artificial conditions still do not fully represent the
complex environment of natural habitats. To circum-
vent this problem, microbial and NP diversity can
also be accessed via extraction of organisms and/or
their NPs insitu. To directly gain compounds pro-
duced in the natural marine environment (which may
be missed otherwise), resin capture technology can be
used to capture compounds on inert sorbent supports
ready to be desorbed, analysed and tested for bio-
logical activity146. Sustainable approaches for insitu
extraction with green solvents, such as glycerol or nat-
ural deep eutectic and ionic solvents (NADES), could
be used directly during field work147,148. To improve
dereplication, analytical equipment miniaturization
is also facilitating insitu analysis; examples include
the introduction of devices for physicochemical data
analysis, such as micro- MS and portable near infrared
spectroscopy149,150.
Outlook for NPs in drug discovery
The technological advances discussed above have the
potential to reinvigorate NP- based drug discovery in
both established and emerging areas. NPs have long
been the key source of new drugs against infectious dis-
eases, especially antibiotics (reviewed elsewhere151,152).
Selected NPs with antimicrobial properties discovered
by leveraging advances discussed in the sections above,
including strategies to exploit the human microbiome
for novel NPs94,153 are highlighted in FIGS3,4. Along
with the search for new NPs with antimicrobial activi-
ties, researchers are continuing to develop and optimize
already known NP classes, making use of advances
in biosynthetic engineering154, total synthesis155 or
semi- synthetic strategies156,157. In addition, antivirulence
strategies could represent an alternative approach to
fighting infections158, for which NPs targeting bacterial
quorum sensing could be of interest159.
NPs also have a successful history as cancer therapeu-
tics, which has been well covered in other reviews160–163.
An important new opportunity in this field is the capac-
ity of some NPs to trigger a selective yet potent host
immune reaction against cancer cells, particularly given
the intense interest at present in strategies that could
improve response rates to immune checkpoint inhibi-
tors by turning ‘cold’ tumours ‘hot’164. For example, NPs
such as cardiac glycosides165 can increase the immuno-
genicity of stressed and dying cancer cells by triggering
immunogenic cell death, characterized by the release
of damage- associated molecular patterns (DAMPs),
which could open new avenues for drug discovery or
repurposing166–168.
Botanical therapies containing complex mixtures of
NPs have long attracted interest owing to the potential
for synergistic therapeutic effects of components within
the mixture169,170. However, the variability of the NP
composition in the starting plant material owing to fac-
tors such as environmental variations in the location at
which the plants were collected is a major challenge for
the development of botanical drugs1. With the advances
in technology for their characterization, such as metab-
olomics discussed above, as well as development of
regulatory guidance for complex mixtures of NPs (see
Related links), it is becoming more feasible to develop
such mixtures as therapeutics, rather than to identify
and purify a single active ingredient171.
Since gut microbiota are considered to play a major
role in health and disease172–174, and NPs are known to
affect the gut microbiome composition175–178, this area is
an emerging opportunity for NP- based drug discovery.
However, drug discovery efforts in this area are still in
their infancy, with many open questions remaining179.
A future direction may be the characterization of single
microbiota- derived species for particular therapeutic
applications, and the advances in culturing strategies,
genome mining and analytics discussed above will be of
great importance in this respect.
Many advances discussed above are supported
by computational tools including databases (such as
genomic, chemical or spectral analysis data; see REF.180
for a recent review on NP databases) and tools that ena-
ble the analysis of genetic information, the prediction
of chemical structures and pharmacological activities181,
the integration of data sets with diverse information
(such as tools for multi- omics analysis)182 and machine
learning applications183.
Although this Review focuses on technologies that
enable the discovery of novel NPs, it is important to
acknowledge that unmodified NPs may possess sub-
optimal efficacy or absorption, distribution, metab-
olism, excretion and toxicity (ADMET) properties.
So, for development of NP hits into leads and ultimately
into successful drugs, chemical modification may be
required. In addition, bringing a compound into clini-
cal development requires a sustainable and economically
viable supply of sufficient quantities of the compound.
Total chemical synthesis, semi- synthesis using a NP as a
starting point for analogue generation and biosynthetic
engineering modifying biosynthetic pathways of the
producing organism will be of great importance in this
context (FIG.5). Recent advances in chemical synthesis
and biosynthetic engineering technologies are strongly
empowering NP- based drug discovery and develop-
ment by enabling property optimization of complex NP
scaffolds that were previously regarded as inaccessible.
This allows the enrichment of screening libraries with
NPs, NP hybrids, NP analogues and NP- inspired mole-
cules, as well as superior structure functionalization
approaches (including late- stage functionalization) for
optimization of NP leads94,105–108,184–188.
Finally, although NP- based drug discovery offers a
unique niche for diverse forms of academia–industry
collaboration, a key challenge is that scientific and
technological expertise is often scattered over many
Nature reviews
|
Drug Discovery
Reviews
NP with suboptimal
pharmacological properties
Chrysomycin A was identified as a hit
in a high-throughput screen against
multidrug-resistant tuberculosis strains
Total chemical synthesis
A 10-step scalable synthesis of
chrysomycin A was developed,
which also enabled the
synthesis of 33 new analogues
Semisynthesis of analogues Biosynthetic engineering for
production of analogues
Chemical derivatization
NP analogues with superior
pharmacological properties
One of the new analogues exhibited
fivefold stronger activity against
multidrug-resistant tuberculosis strains
a
b
Chrysomycin A
O
O
HO
HO
OH
OH O
O
O
(+)-64
O
O
OH O
O
O
OH
OH
OH
Arylomycins are a class of NP
antibiotics with weak activity
and limited spectrum
Chemical derivatization of
arylomycin A-C16 led to the
discovery of G0775
G0775 exhibits an unprecedented molecular
mechanism of action and activity against
multidrug-resistant Gram-negative clinical
isolates in vitro and in vivo
c
Arylomycin A-C16
HO
OH
N
O
H
N
HN
O
O
OH
H
O
H
N
O
N
H
O
N
OH
O
G0775
O
O
N
O
H
N
HN
O
O
H
N
H
O
H
N
O
H2N
NH2
N
NH2
N
N
Streptomyces mobaraensis DSM40847 was identified
by genome mining as a new bleomycin producer
Targeted manipulation of the
biosynthetic pathway of
bleomycin in S. mobaraensis
led to the production of
6′-deoxy-BLM A2
6′-deoxy-BLM A2 resulted in more rapid
DNA cleavage than BLM A2
d
BLM A2
N
S
S
N
H
N
O S
NH
O
H
N
HO
O
HO
N
H
O
O
N
HN
HN
O
NN
H2N
H
N
O
H2N
NH2
O
NH2
O
OH
O
OH
O
OH
O
OH
OH
OH
O
NH2
6′-deoxy-BLM A2
N
S
S
N
H
N
OS
NH
O
H
N
HO
O
HO
N
H
O
O
N
HN
HN
O
NN
H2N
H
N
O
H2N
NH2
O
NH2
O
OH
CH3
O
OH
O
OH
O
OH
OH
O
NH2
www.nature.com/nrd
Reviews
academic institutions and companies. Focused efforts
are needed to support translational NP research in aca-
demia, which has become more difficult in recent years
given the decline in the number of large companies
actively engaged in NP research. A conventional solution
to improve academia–industry interaction is to focus
the relevant expertise under one umbrella and in close
spatial proximity. For example, the Phytovalley Tirol,
centred in Innsbruck, Austria, brings together several
research institutions and companies (among others, the
Austrian Drug Screening Institute (ADSI), the Michael
Popp Resea rch Institute for New Phyto- Entities, Biono-
rica Research and Biocrates Life Sciences AG) with the
aim of accelerating NP- based drug discovery. Another
solution could be virtual consortia, such as the Inter-
natio nal Natural Product Sciences Taskforce (INPST)
that we have recently established (see Related links),
which provides a platform for integration of exper-
tise, technology and materials from the participating
academic and industrial entities.
In conclusion, NPs remain a promising pool for the
discovery of scaffolds with high structural diversity
and various bioactivities that can be directly developed
or used as starting points for optimization into novel
drugs. While drug development overall continues to be
challenged by high attrition rates, there are additional
hurdles for NPs due to issues such as accessibility, sus-
tainable supply and IP constraints. However, we believe
that the scientific and technological advances discussed
in this Review provide a strong basis for NP- based drug
discovery to continue making major contributions to
human health and longevity.
Published online xx xx xxxx
Fig. 5 | Strategies to obtain natural product analogues with superior properties.
Unmodified natural products (NPs) often possess suboptimal properties, and superior
analogues need to be obtained in order to yield valuable new drugs. a | NP analogues
can be accessed through the development of total chemical synthesis followed by
chemical derivatization, through semisynthesis using a NP as a starting point for the
introduction of chemical modifications, and through biosynthetic engineering using
manipulations of biosynthetic pathways of the producing organism to generate NP
analogues. b,c | Tetracyclines are an example of NP- derived antibiotics that have already
yielded several generations of successfully marketed semisynthetic and synthetic
derivatives. The first generation of tetracyclines (such as chlortetracycline and tetracycline)
were unmodified NPs, while the two subsequent generations of analogues with optimized
properties were semisynthetic (second- generation, doxycycline, minocycline; third-
generation, tigecycline) and the most recently developed fourth- generation analogues
(eravacycline) are entirely synthetic, accessed via total synthesis193,194. More recent
examples of property optimization of other classes of NPs through total chemical synthesis
followed by chemical derivatization or through semisynthesis are illustrated by studies
focused on analogues of chrysomycin A (panel b)195 and arylomycins (panel c)157, respectively.
d | The biosynthetic engineering approach has also shown potential; for example, in
the generation of analogues of rapamycin111, bleomycin113 (panel d) and nystatin114.
6′- deoxy- BLM A2, 6′- deoxy- bleomycin A2; BLM A2, bleomycin A2.
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Acknowledgements
This paper is affectionately dedicated in memory of DrMariola
Macías (1984–2020) M.D., Ph.D. in Immunology, Emergency
Physician at Hospital Punta Europa, Algeciras (Cadiz), Spain
and active member of a research team working against
SARS- CoV-2. An excellent professional and a better person.
Her humanity, kindness, special and unmistakable smile, gen-
erosity, dedication and professionalism will never be forgotten.
The authors are grateful to P. Kirkpatrick for his editorial con-
tribution, which resulted in a greatly improved manuscript.
A.G.A. acknowledges support from the Austrian Science Fund
(FWF) project P25971- B23 (‘Improved cholesterol efflux by
natural products’). R.B. acknowledges support by a grant from
the Austrian Science Fund (FWF) P27505. V.B. acknowledges
support by a grant from the Austrian Science Fund (FWF)
P27682- B30. N.B. is recipient of an Australian Research
Council DECRA Fellowship. A.C. and E.I. thank the Ministerio
de Ciencia, Innovación y Universidades, Spain (Project
AGL2017-89417- R) for support. M. Diederich is supported by
the National Research Foundation (NRF) (grant number
019R1A2C1009231), by a grant from the MEST of Korea for
Tumour Microenvironment Global Core Research Center
(GCRC) (grant number NRF-2011-0030001), by the
Creative- Pioneering Researchers Program through Seoul
National University (Funding number: 370C-20160062), by
the Brain Korea 21 (BK21) PLUS programme, by the
‘Recherche Cancer et Sang’ foundation, by the ‘Recherches
Scientifiques Luxembourg’ association, by the ‘Een Häerz fir
kriibskrank Kanner’ association, by the Action LIONS ‘Vaincre
le Cancer’ association and by Télévie Luxembourg. The
research work of A.T.D.- K. is funded by Cancer Research UK
(C20953/A18644), the Biotechnology and Biological Sciences
Research Council (BB/L01923X/1), Reata Pharmaceuticals,
and Tenovus Scotland (T17/T14). B.L.F. acknowledges BMBF
(TUNGER 036/FUCOFOOD) and AIF (AGEsense) for support-
ing his research. M.I.G. acknowledges financial support from
the European Union’s Horizon 2020 research and innovation
programme, project PlantaSYST (SGA No 739582 under FPA
No. 664620) and the BG05M2OP001-1.003-001- C01 proj-
ect, financed by the European Regional Development Fund
through the ‘Science and Education for Smart Growth’
Operational Programme. K.M.G. is supported by the UK
Medical Research Council (MC_UU_12011/4), the National
Institute for Health Research (NIHR Senior Investigator
(NF- SI-0515-10042) and the NIHR Southampton Biomedical
Research Centre), the European Union (Erasmus+
Capacity- Building ENeA SEA Project and Seventh Framework
Programme (FP7/2007-2013), projects EarlyNutrition and
ODIN (grant agreements 289346 and 613977), the
US National Institute On Ageing of the National Institutes of
Health (award no. U24AG047867) and the UK ESRC and
BBSRC (award no. ES/M00919X/1). Research in the labora-
tory of C.W.G. is supported by the Austrian Science Fund (FWF)
through project P32109 and a NATVANTAGE grant 2019 by
the Wilhelm Doerenkamp- Stiftung. A.K. acknowledges support
by national funds through FCT- Foundation for Science and
Technology of Portugal within the scope of UIDB/04423/2020
and UIDP/04423/2020. A.L. acknowledges HKBU SDF16-
0603- P02 for supporting this research. F.A.M. acknowledges
the support by Ministerio de Economia y Competitividad,
Spain (project AGL2017-88083- R). A.M. acknowledges
the support by a grant of the Romanian Ministry of Research
and Innovation, CNCS – UEFISCDI, project number PN-III-
P1-1.1-PD-2016-1900 – ‘PhytoSal’, within PNCDI III. G.P.
acknowledges the support by NIH G12- MD007591, Kleberg
Foundation and NIH R01- AG066749. M.R. acknowledges
support by the Swiss National Science Foundation (Schweiz-
erischer Nationalfonds, SNF), and by the Horizon 2020 pro-
gramme of the European Union. J.M.R. acknowledges
the support from the Austrian Science Fund (FWF: P24587), the
Natvantage grant 2018 and the University of Vienna, Austria.
G.L.R. acknowledges the group of Cellular and Molecular
Nutrition (BJ- Lab) at the Institute of Food Sciences, National
Research Council, Avellino, Italy. A.S.S. acknowledges the sup-
port by UIDB/00211/2020 with funding from FCT/MCTES
through national funds. D.S. acknowledges the support by
FWF S10711. D.S. is an Ingeborg Hochmair Professor at the
University of Innsbruck. K.S.W. is supported by the National
Centre for Research and Development (4/POLTUR-1/2016)
and the National Science Centre (2017/27/B/NZ4/00917) and
Medical University of Lublin, Poland. E.S.S. thanks Universidad
Central de Chile, through Dirección de Investigación y
Postgrado, for supporting this research. H. Stuppner acknowl-
edges support by the Austrian Research Promotion Agency
(FFG), the Austrian Science Fund (FWF) and the Horizon 2020
programme of the European Union (RISE, 691158). A.S. was
granted by Instituto de Salud Carlos III, CIBEROBN
(CB12/03/30038) and EU- COST Action (CA16112). M.W.
acknowledges the support by DFG, BMBF, EU, CSC, DAAD,
AvH and Land Baden Württemberg. J.L.W. is grateful to the
Swiss National Science Foundation (SNF) for supporting
its natural product metabolomics projects (grants nos.
310030E-164289, 31003A_163424 and 316030_164095).
S.B.Z. acknowledges the support by University of Vienna,
Vienna, Austria. M.H. acknowledges an EPSRC CASE
Award (with Pukka Herbs Ltd, UK as industrial partner).
I.B.- N. acknowledges the support of Competitivity
Operational Program, 2014–2020, entitled ‘Clinical and eco-
nomical impact of personalized targeted anti-microRNA
therapies in reconverting lung cancer chemoresistance’ —
CANTEMIR, No. 35/01.09.2016, MySMIS 103375; project
PNCDI III 2015-2020 entitled ‘Increasing the performance of
scientific research and technology transfer in translational
medicine through the formation of a new generation of young
researchers’ — ECHITAS, no. 29PFE/18.10.2018. This work
was also funded by the Italian Ministry for University and
Research (MIUR), grant PRIN: rot. 2017XYBP2R (to C.T.S).
Competing interests
A.G.A. is executive administrator of the International Natural
Product Sciences Taskforce (INPST) and Digital Health and
Patient Safety Platform (DHPSP). M. Banach has served on the
speakers’ bureau of Abbott/Mylan, Abbott Vascular, Actavis,
Akcea, Amgen, Biofarm, KRKA, MSD, Novo- Nordisk, Novartis,
Sanofi- Aventis, Servier and Valeant, has served as a consultant
to Abbott Vascular, Akcea, Amgen, Daichii Sankyo, Esperion,
Freia Pharmaceuticals, Lilly, MSD, Novartis, Polfarmex,
Resverlogix, Sanofi- Aventis, and has received grants from
Amgen, Mylan, Sanofi and Valeant. R.B. collaborates with
Bayer Consumer Health and DrWillmar Schwabe GmbH &
Co. KG, and is scientific advisory committee member of
PuraPharm International (HK) Limited and ISURA. G.K.B. is a
board member of Bionorica SE. M. Daglia has received consul-
tancy honoraria from Pfizer Italia and Mylan for training
courses for chemists, and is a member of the INPST board of
directors. A.T.D.- K. is a member of the Scientific and Medical
Advisory Board of Evgen Pharma plc. I.E.O. is Dean of Faculty
of Pharmacy, Gazi University, Ankara, Turkey, member of the
Traditional Chinese Medicine Experts Group in European
Pharmacopeia, and principal member of Turkish Academy
of Sciences (TUBA). B.L.F. is a member of the INPST Board of
Directors and has received research funding from DrWillmar
Schwabe GmbH & Co. KG. K.M.G. has received reimburse-
ment for speaking at conferences sponsored by companies
selling nutritional products and is part of an academic consor-
tium that has received research funding from Abbott Nutrition,
Nestec and Danone. C.W.G. is chairman of the scientific advi-
sory board of Cyxone AB, SE. M.H.’s research group has
received charitable donations from DrWillmar Schwabe GmbH
& Co. KG and recently completed a research project sponsored
by Pukka Herbs, UK. A.L. is a member of the board of direc-
tors of Kaisa Health. M.J.S.M. is president of Kaiviti Consulting
and consults for Gnosis by LeSaffre. F.N. is cofounder and
shareholder of OncoNox and Aura Biopharm. G.P. is on the
board of Neurotez and Neurotrope. M.R. serves as an adviser
for the Nestlé Institute of Health Sciences. G.L.R. is a member
of the board of directors of INPST. N.T.T. is Founder and
CEO of NTZ Lab Ltd and advisory board member of INPST.
M.W. collaborates with Finzelberg GmbH and Schwabe GmbH.
J.L.W. collaborates with Nestlé and Firmenich. M.A.P. is CEO
and owner of Bionorica SE. J.H. is an employee of and holds
shares in UCB Pharma Ltd. M.M. is Founder and Chairman of
Sami–Sabinsa Group of Companies. D.S.B. is an employee
of Janssen R&D. M. Bodkin is an employee of Evotec
(UK) Ltd.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional
claims in published maps and institutional affiliations.
RELATED LINKS
Dictionary of Natural Products: http://dnp.chemnetbase.com/
faces/chemical/ChemicalSearch.xhtml
FDA botanical drug development guidance for industry:
https://www.fda.gov/regulatory- information/search- fda-
guidance- documents/botanical- drug- development- guidance-
industry
INPST: https://inpst.net/
© Springer Nature Limited 2021
www.nature.com/nrd
Reviews
the International Natural Product Sciences Taskforce
Ilkay Erdogan Orhan6, Maciej Banach7, Judith M. Rollinger2, Davide Barreca8, Wolfram Weckwerth9,10, Rudolf Bauer11,12, Edward A. Bayer13, Muhammed Majeed14,15,16,
Anupam Bishayee17, Valery Bochkov18, Günther K. Bonn19, Nady Braidy20, Franz Bucar11, Alejandro Cifuentes21, Grazia D’Onofrio22, Michael Bodkin23, Marc Diederich24,
Albena T. Dinkova- Kostova25,26, Thomas Efferth27, Khalid El Bairi28, Nicolas Arkells29, Tai- Ping Fan30,31, Bernd L. Fiebich32, Michael Freissmuth33, Milen I. Georgiev34,35,
Simon Gibbons36, Keith M. Godfrey37, Christian W. Gruber33, Jag Heer38, Lukas A. Huber39,40, Elena Ibanez21, Anake Kijjoa41, Anna K. Kiss42, Aiping Lu43, Francisco A. Macias44,
Mark J. S. Miller45, Andrei Mocan46, Rolf Müller47,48, Ferdinando Nicoletti49, George Perry50, Valeria Pittalà51, Luca Rastrelli52, Michael Ristow53, Gian Luigi Russo54,
Ana Sanches Silva55,56, Daniela Schuster57,58, Helen Sheridan59, Krystyna Skalicka- Woźniak60, Leandros Skaltsounis61, Eduardo Sobarzo- Sánchez62,63, David S. Bredt64,
Hermann Stuppner65, Antoni Sureda66,67, Nikolay T. Tzvetkov68,69, Rosa Anna Vacca70, Bharat B. Aggarwal71, Maurizio Battino72,73, Francesca Giampieri72,74,75, Michael Wink76,
Jean- Luc Wolfender77,78, Jianbo Xiao73,79, Andy Wai Kan Yeung80, Gérard Lizard81, Michael A. Popp82, Michael Heinrich83,84, Ioana Berindan- Neagoe85,86, Marc Stadler48,87,
Maria Daglia73,88 and Robert Verpoorte89
6Department of Pharmacognosy, Faculty of Pharmacy, Gazi University, Ankara, Turkey. 7Polish Mother’s Memorial Hospital Research Institute (PMMHRI), Łodz, Poland. 8Department of Chemical,
Biological, Pharmaceutical and Environmental Sciences, Università degli Studi di Messina, Messina, Italy. 9Molecular Systems Biology (MOSYS), Department of Evolutionary and Functional
Ecology, University of Vienna, Vienna, Austria. 10Vienna Metabolomics Center (VIME), University of Vienna, Vienna, Austria. 11Institute of Pharmaceutical Sciences, Department of Pharmacognosy,
University of Graz, Graz, Austria. 12BioTechMed- Graz, Graz, Austria. 13Department of Biomolecular Sciences, The Weizmann Institute of Science, Rehovot, Israel. 14Sami Labs Limited,
19/1, 19/2, First Main, Second Phase, Peenya Industrial Area, Bangalore, Karnataka, India. 15Sabinsa Corporation, East Windsor, NJ, USA. 16Sabinsa Corporation, Payson, UT, USA. 17Lake Erie
College of Osteopathic Medicine, Bradenton, FL, USA. 18Institute of Pharmaceutical Sciences, Department of Pharmaceutical Chemistry, University of Graz, Graz, Austria. 19Institute of Analytical
Chemistry and Radiochemistry, Leopold- Franzens University of Innsbruck and Austrian Drug Screening Institute — ADSI, CCB — Center of Chemistry and Biomedicine, Innsbruck, Austria.
20Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia. 21Laboratory of Foodomics, Bioactivity and Food Analysis
Department, Institute of Food Science Research CIAL (UAM- CSIC), Madrid, Spain. 22Clinical Psychology Service, Health Department, Fondazione IRCCS ‘Casa Sollievo della Sofferenza’, San
Giovanni Rotondo, Italy. 23Evotec (UK) Ltd, Oxford, UK. 24Department of Pharmacy, College of Pharmacy, Seoul National University, Seoul, South Korea. 25Jacqui Wood Cancer Centre, Division
of Cellular Medicine, School of Medicine, University of Dundee, Dundee, UK. 26Department of Pharmacology and Molecular Sciences and Department of Medicine, Johns Hopkins University
School of Medicine, Baltimore, MD, USA. 27Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Mainz, Germany.
28Cancer Biomarkers Working Group, Oujda, Morocco. 29International Natural Product Sciences Taskforce (INPST), Jastrzebiec, Poland. 30Department of Pharmacology, University of Cambridge,
Cambridge, UK. 31College of Life Sciences, Northwest University, Xi’an, China. 32Neuroimmunology and Neurochemistry Research Group, Department of Psychiatry and Psychotherapy, Medical
Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany. 33Institute of Pharmacology and the Gaston H. Glock Research Laboratories for Exploratory
Drug Development, Center of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria. 34Laboratory of Metabolomics, The Stephan Angeloff Institute of Microbiology,
Bulgarian Academy of Sciences, Plovdiv, Bulgaria. 35Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria. 36Research Department of Pharmaceutical and Biological Chemistry,
UCL School of Pharmacy, London, UK. 37MRC Lifecourse Epidemiology Unit and NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital
Southampton NHS Foundation Trust, Southampton, UK. 38UCB Pharma Ltd, Slough, UK. 39Institute for Cell Biology, Biocenter, Medical University of Innsbruck, Innsbruck, Austria. 40Austrian
Drug Screening Institute- ADSI, Innsbruck, Austria. 41ICBAS- Instituto de Ciências Biomédicas Abel Salazar & CIIMAR, Universidade do Porto, Porto, Portugal. 42Department of Pharmacognosy
and Molecular Basis of Phytotherapy, Medical University of Warsaw, Warsaw, Poland. 43School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China. 44Allelopathy Group,
Department of Organic Chemistry, Institute of Biomolecules (INBIO), Campus de Excelencia Internacional (ceiA3), School of Science, University of Cadiz, Cadiz, Spain. 45Kaiviti Consulting, LLC,
Dallas, TX, USA. 46Department of Pharmaceutical Botany, ‘Iuliu Haţieganu’ University of Medicine and Pharmacy, Cluj- Napoca, Romania. 47Department of Microbial Natural Products,
Helmholtz- Institute for Pharmaceutical Research Saarland, Helmholtz Centre for Infection Research and Department of Pharmacy, Saarland University, Saarbrücken, Germany. 48German Centre
for Infection Research (DZIF), Partner Site Hannover, Braunschweig, Germany. 49Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy. 50Department
of Biology, The University of Texas at San Antonio, San Antonio, TX, USA. 51Department of Drug Science, University of Catania, Catania, Italy. 52Dipartimento di Farmacia, University of Salerno,
Fisciano, Italy. 53Energy Metabolism Laboratory, Institute of Translational Medicine, Swiss Federal Institute of Technology (ETH) Zurich, Schwerzenbach, Switzerland. 54Institute of Food Sciences,
National Research Council, Avellino, Italy. 55National Institute for Agricultural and Veterinary Research (INIAV), Vila do Conde, Portugal. 56Center for Study in Animal Science (CECA), ICETA,
University of Porto, Porto, Portugal. 57Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Paracelsus Medical University Salzburg, Salzburg, Austria. 58Institute of
Pharmacy/Pharmaceutical Chemistry and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck, Austria. 59The NatPro Centre, School of Pharmacy and
Pharmaceutical Sciences, Trinity College Dublin, Dublin, Ireland. 60Independent Laboratory of Natural Products Chemistry, Medical University of Lublin, Lublin, Poland. 61Department
of Pharmacognosy and Natural Products Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimioupolis Zografou, Athens, Greece. 62Laboratory of
Pharmaceutical Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, Santiago de Compostela, Spain. 63Instituto de Investigación y Postgrado en Salud, Facultad de Ciencias
dela Salud, Universidad Central de Chile, Santiago, Chile. 64Janssen Pharmaceuticals Research & Development, San Diego, CA, USA. 65Institute of Pharmacy/Pharmacognosy, Center for
Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck, Austria. 66Research Group on Community Nutrition and Oxidative Stress, and Health Research Institute of the
Balearic Islands (IdISBa), Department of Fundamental Biology and Health Sciences, University of Balearic Islands, Palma de Mallorca, Spain. 67CIBEROBN (Physiopathology of Obesity and
Nutrition), Instituto de Salud Carlos III, Madrid, Spain. 68Institute of Molecular Biology ‘Roumen Tsanev’, Department of Biochemical Pharmacology and Drug Design, Bulgarian Academy of
Sciences, Sofia, Bulgaria. 69Pharmaceutical Institute, University of Bonn, Bonn, Germany. 70Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, Italian National Council
of Research, Bari, Italy. 71Inflammation Research Center, San Diego, CA, USA. 72Department of Clinical Sciences, Università Politecnica delle Marche, Ancona, Italy. 73International Research
Center for Food Nutrition and Safety, Jiangsu University, Zhenjiang, China. 74Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi Arabia. 75College of
Food Science and Technology, Northwest University, Xi’an, Shaanxi, China. 76Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, Heidelberg, Germany. 77School
of Pharmaceutical Sciences, University of Geneva, CMU, Geneva, Switzerland. 78Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, CMU, Geneva, Switzerland.
79Nutrition and Bromatology Group, Department of Analytical Chemistry and Food Science, Faculty of Food Science and Technology, University of Vigo — Ourense Campus, Ourense, Spain.
80Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China. 81Team Bio- PeroxIL, ‘Biochemistry
of the Peroxisome, Inflammation and LipidMetabolism’ (EA7270)/University Bourgogne Franche- Comté/Inserm, Dijon, France. 82Bionorica SE, Neumarkt/Oberpfalz, Germany. 83Research Group
‘Pharmacognosy and Phytotherapy’, UCL School of Pharmacy, London, UK. 84‘Graduate Institute of Integrated Medicine, College of Chinese Medicine’, and ‘Chinese Medicine Research Center’,
China Medical University, Taichung, Taiwan. 85Research Center for Functional Genomics, Biomedicine and Translational Medicine, Institute of Doctoral Studies, ‘Iuliu Hatieganu’ University of
Medicine and Pharmacy, Cluj- Napoca, Romania. 86Department of Experimental Pathology, ‘Prof. Dr.Ion Chiricuta’, The Oncology Institute, Cluj- Napoca, Romania. 87Helmholtz- Center for
Infection Research, Department of Microbial Drugs, Braunschweig, Germany. 88Department of Pharmacy, University of Naples Federico II, Naples, Italy. 89Natural Products Laboratory, Institute
of Biology, Leiden University, Leiden, Netherlands.
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