Chemical genetics of Plasmodium falciparum

Abstract

Malaria caused by Plasmodium falciparum is a disease that is responsible for 880,000 deaths per year worldwide. Vaccine development has proved difficult and resistance has emerged for most antimalarial drugs. To discover new antimalarial chemotypes, we have used a phenotypic forward chemical genetic approach to assay 309,474 chemicals. Here we disclose structures and biological activity of the entire library-many of which showed potent in vitro activity against drug-resistant P. falciparum strains-and detailed profiling of 172 representative candidates. A reverse chemical genetic study identified 19 new inhibitors of 4 validated drug targets and 15 novel binders among 61 malarial proteins. Phylochemogenetic profiling in several organisms revealed similarities between Toxoplasma gondii and mammalian cell lines and dissimilarities between P. falciparum and related protozoans. One exemplar compound displayed efficacy in a murine model. Our findings provide the scientific community with new starting points for malaria drug discovery.

Full text

ARTICLES
Chemical genetics of Plasmodium falciparum
W. Armand Guiguemde
1
, Anang A. Shelat
1
, David Bouck
1
, Sandra Duffy
2
, Gregory J. Crowther
3
, Paul H. Davis
4
,
David C. Smithson
1
, Michele Connelly
1
, Julie Clark
1
, Fangyi Zhu
1
, Marı
´
a B. Jime
´nez-Dı
´
az
5
, Marı
´
a S. Martinez
5
,
Emily B. Wilson
6
, Abhai K. Tripathi
7
, Jiri Gut
8
, Elizabeth R. Sharlow
9
, Ian Bathurst
10
, Farah El Mazouni
11
,
Joseph W. Fowble
12
, Isaac Forquer
13
, Paula L. McGinley
14
, Steve Castro
14
,In
˜igo Angulo-Barturen
5
, Santiago Ferrer
5
,
Philip J. Rosenthal
8
, Joseph L. DeRisi
6
, David J. Sullivan Jr
7
, John S. Lazo
9
, David S. Roos
4
, Michael K. Riscoe
13
,
Margaret A. Phillips
11
, Pradipsinh K. Rathod
12
, Wesley C. Van Voorhis
3
, Vicky M. Avery
2
& R. Kiplin Guy
1
Malaria caused by Plasmodium falciparum is a disease that is responsible for 880,000 deaths per year worldwide. Vaccine
development has proved difficult and resistance has emerged for most antimalarial drugs. To discover new antimalarial
chemotypes, we have used a phenotypic forward chemical genetic approach to assay 309,474 chemicals. Here we disclose
structures and biological activity of the entire library—many of which showed potent in vitro activity against drug-resistant P.
falciparum strains—and detailed profiling of 172 representative candidates. A reverse chemical genetic study identified 19
new inhibitors of 4 validated drug targets and 15 novel binders among 61 malarial proteins. Phylochemogenetic profiling in
several organisms revealed similarities between Toxoplasma gondii and mammalian cell lines and dissimilarities between P.
falciparum and related protozoans. One exemplar compound displayed efficacy in a murine model. Our findings provide the
scientific community with new starting points for malaria drug discovery.
The widespread resistance of P.falciparum to many antimalarialdrugs,
the dependence of all new drug combinations on artemisinins (for
which resistance may have emerged)
1,2
, and new efforts to eradicate
malaria all drive the need to develop new, effective and affordable
antimalarial drugs
3
. Although our understanding of the parasite’s bio-
logy has increased with the sequencing of the Plasmodium genome
4
and the development of new technologies to study resistance
acquisition
5–7
, few new drug targets or classes of drugs have been
clinically validated
8
. The lack of publicly accessible antimalarial che-
motypes with differing modes of action has significantly hindered
efforts to discover and develop new drugs
9
. To address this urgent
need, we have developed a forward chemical genetic approach to
identify novel antimalarials (Supplementary Fig. 1).
The forward chemical genetic screen
A library containing 309,474 unique compounds, designed at the scaf-
fold level to provide diverse, comprehensive coverage of bioactive
space
10,11
, was screened against Plasmodium falciparum strain 3D7 at
a fixed concentration of 7 mM (Supplementar y Information)
12
. Fidelity
of the assay was examined by receiver operator characteristic (ROC)
analysis and other metrics (Supplementary Figs 2 and 3), demonstrat-
ing good discriminatory power (area under the curve ,0.85) and
indicating that a cutoff of $80% activity would retain most of the true
positives. The strength of the assay was further determined by testing a
set of bioactive compounds including known antimalarials, all of
which were re-identified (Supplementary Table 3), demonstrating
that the method was very likely to identify any molecule acting by
a known mechanism. The primary screen gave approximately 1,300
hits with activity .80%. These compounds were serially diluted and
tested against both the chloroquine-sensitive 3D7 strain and the
chloroquine-resistant K1 strain, giving 1,134 validated hits that had
saturated dose–response curves. Chemical structure analysis of vali-
dated hits by topology mapping and clustering
10
revealed a wide distri-
bution of chemotypes in the active chemical space, with several
displaying promising structure–activity relationships (Fig. 1).
Although all known antimalarial scaffolds (aminoquinolines, quino-
lones, bis-amidines) present in the screening collection were identified,
providing positive controls for the screen, most of the chemotypes
identified were new. A total of 561 of the validated hits had half-
maximum effective concentration (EC
50
) values #2mM against either
3D7 or K1 and a therapeutic window $10-fold against two mammalian
cell lines (HepG2 and BJ). From this set, 228 structurally distinct, pure
compounds were re-purchased in powder form for further studies.
Antimalarial potencies of ,75% of these compounds (172) were re-
confirmed to within tenfold (Bland–Altman analysis, Supplementary
Fig. 4) by three laboratories using distinct methods providing the cross-
validated hit set used for all subsequent experiments.
Combination with antimalarial drugs
Owing to rapid resistance acquisition, the World Health Organiza-
tion (WHO) recommends combination therapy
13
.Theagonisticand
antagonistic synergies of the cross-validated set were therefore quan-
tified by measuring EC
50
shifts in the presence of a fixed fraction of
potency (EC
10
) concentration of chloroquine, mefloquine, artemisinin
and atovaquone. Most cross-validated compounds were additive in
effect or had minor synergies with existing drugs. Two classes demon-
strated strong synergies (EC
50
values reduced by $10-fold): the dia-
minonaphthoquinones with artemisinin, and the dihydropyridines
1
Department of Chemical Biology and Therapeutics, St Jude Children’s Research Hospital, Memphis, Tennessee 38105, USA.
2
Discovery Biology, Eskitis Institute for Cell and Molecular
Therapies, Griffith University, Brisbane 4111, Australia.
3
Department of Medicine, University of Washington, Seattle, Washington 98195-7185, USA.
4
Department of Biology and the
Penn Genome Frontiers Institute, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
5
GlaxoSmithKline, Tres Cantos Medicines Development Campus, Diseases of
Developing World, Tres Cantos 28760, Spain.
6
Department of Biochemistry and Biophysics, University of California, San Francisco, California 94158-2542, USA.
7
W. Harry Feinstone
Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA.
8
Department of Medicine, San
Francisco General Hospital, University of California, San Francisco, California 94143, USA.
9
Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh,
Pennsylvania 15261, USA.
10
Medicines for Malaria Venture, Geneva 1215, Switzerland.
11
Department of Pharmacology, University of Texas Southwestern Medical Center at Dallas,
Dallas, Texas 75390-9041, USA.
12
Department of Chemistry, University of Washington, Seattle, Washington 98195-7185, USA.
13
Experimental Chemotherapy Lab, Portland VA
Medical Center, Portland, Oregon 97239, USA.
14
Department of Chemistry and Chemical Biology Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA.
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with mefloquine (Fig. 2). One diaminonaphthoquinone and a cyclo-
guanil analogue displayed antagonism with chloroquine and meflo-
quine, respectively.
Reverse chemical genetics
The advantages of phenotypic screens for the identification of novel
chemotypes are that no a priori assumptions are made concerning
drug targets and that active compounds inherently have cellular bio-
availability. Becauseinsight into the mechanism of action is helpful for
drug development, we also investigated the interaction of the cross-
validated set with 66 potential targets using enzyme inhibition assays
and thermal melt shift assays (to detect binding).
Three high-priority, well characterized biological targets were
evaluated in activity assays (Fig. 3, left): P. falciparum dihydroorotate
dehydrogenase (PfDHOD), haemozoin formation and P. falciparum
falcipain-2 (PfFP-2). PfDHOD catalyses the oxidation of dihydroor-
otate to orotate in de novo pyrimidine biosynthesis, which is essential
for parasite viability
14,15
. Three compounds inhibited this enzyme: two
O
O
O
N
H
F
O
O
O
N
O
N
O
N
S
R3N
H
R2
O
R3
R1
R3N
H
R2
O
R3
R1
R4
R3N
H
R2
O
R1
R4
R3
R
3
N
H
R
2
O
R
4
R
3
R
1
SJ000025081
SJ000101247
O
N
N
S
O
O
NHO
N
O
S
Cl
O
N
N
N
S
R
2
N
NHO
R
3
R
4
O
S
R
1
O
N
N
S
R
2
N
O
O
S
NH
R
1
R
2
N
O
O
S
NH
R
1
R
3
N
O
O
S
NH
R
2
R
1
R
3
N
O
O
S
NH
R
1
R
2
O
N
O
NH
O
N
O
N
N
O
O
O
O
N
NH
O
O
Br
OH
O
O
NH
N
O
O
Br
NH
O
O
NN
N
O
O
N
N
O
N
S
N
R
1
NH
N
O
O
R
1
NH
N
O
O
R
2
N
N
OO
O
R
1
SJ000030570
CoreScaffold
Molecule
HN
Br
N
N
NN
Analogues to
known
DHOD
inhibitor
NN
HN
I
N
N
HN
N
N
NN
N
HN
NCl
OH
Amodiaquine
K1 EC50
4 nM40 nM 400 nM >4 μM
3D7 EC50
4 nM40 nM 400 nM >4 μM
3D7 EC50
Core to core (Tanimoto)
Core to scaffold (parent/child)
Scaffold to scaffold (substructure)
Scaffold to molecule
(parent/child)
Molecule to molecule
(common parent scaffold)
a
b
c
O
O
O
O
N
H
F
O
Figure 1
|
Chemical structure network graph and antimalarial potencies of
the 1,300 primary screen hits. Topologically similar molecules cluster
together in the branches of the network. To construct the graph, molecules
were first abstracted to scaffolds and then further to cores using the Murcko
algorithm
10
. Each of these structural entities is represented as a node, and
nodes are connected via edges according to topological relationships with
closeness being defined using the Tanimoto coefficient. Molecular nodes are
coded to reflect potency against P. falciparum strains K1 (low, white; high,
blue) and 3D7 (low, small; high, large). The highly branched structure of the
full network graph (bottom half of the figure) indicates that the 1,300
compounds are organized into clusters of clusters: cores are well sampled by
multiple scaffolds, and the cores themselves are grouped into families of
related chemotypes. Previously reported antimalarial compounds are
highlighted in the lower centre. The top half of the figure provides greater
detail on three potent chemotypes with well-developed structure activity
relationships: a, tetrahydroisoquinoline; b, diaminonaphthoquinone;
c, dihydropyridine. Data are in Supplementary Information.
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triazolopyrimidines, structurally related to known PfDHOD inhibi-
tors with comparable potencies
14
, and a dihydropyridine, structurally
related to the calcium blocker felodipine. The potency of these com-
pounds against PfDHOD strongly correlated with their antimalarial
activities (Supplementary Table 5). Furthermore, these compounds
were inactive against transgenic parasites expressing Saccharomyces
cerevisiae dihydroorotate dehydrogenase (Supplementary Table 6).
Next, haemozoin formation inhibition was investigated. The parasite
digests host haemoglobin to provide amino acids, detoxifying the
resulting haem molecules by conversion to a crystallized form known
as haemozoin. Haem detoxification is believed to be the target of many
antimalarial drugs
16
. Twelve compounds showed appreciable efficacy
in an in vitro haemozoin formation assay
17
, including analogues of
quinazoline, benzofuran, benzimidazole and carbazole as well as
amodiaquine, a known haemozoin formation inhibitor present in
our library. The correlation between enzyme inhibitory potency and
antimalarial potency was similar to that displayed by the positive con-
trols quinine and amodiaquine (Supplementary Table 5). The third
enzyme assayed was PfFP-2, which has a critical role in haemoglobin
degradation
18
. Falcipains are redundant in P. falciparum,withfour
known homologues including two (falcipain-2 and falcipain-3) that
seem to have key roles in erythrocytic stage parasites
19
. Three weakly
active PfFP-2 inhibitors were identified. Thus, 19 compounds (11%)
were inhibitors of validated antimalarial targets.
To expand the pool of potential targets, the compounds were tested
for binding to 61 recombinant malarial proteins (95% purity or better
after affinity and size exclusion chromatography; Supplementary
Table 1) in a thermal melt shift assay
20
. Fifteen compounds displayed
reproducible thermal shifts with seven malarial proteins (Fig. 3,
right; dissociation constant (K
d
) values in Supplementary Table 2):
6-phosphogluconolactonase, 6-pyruvoyltetrahydropterin synthase,
choline kinase, D-ribulose-5-phosphate 3-epimerase, dUTPase, glycogen
25 nM Chloroquine 20 nM Meoquine
20 nM Artemisinin 37 pM Atovaquone
Δ 3D7 log(EC50)
1.0 2.5–1.0–2.5
Synergy Antagonism
Figure 2
|
Reduced representation of the network map showing synergistic
activities with clinically relevant antimalarials. The size of the nodes
reflects the magnitude of the logarithmic difference between EC
50
in the
presence and absence of EC
10
of exemplar antimalarial drugs. Absolute
differences less than one log unit were not considered significant. Synergistic
and antagonistic compounds are uniformly colour-coded blue and red,
respectively. Highly synergistic compounds can be seen with artemisinin and
mefloquine. Data are in Supplementary Information.
PfDHOD
PfFP-2
PF11_0282
PFC0525c
PF14_0020
PFL0960w
PF14_0511
PVX_114505
PF14_0545
Inactive
Haemozoin formation
Inactive
Figure 3
|
Reduced representation of the network map showing the
interaction of the cross-validated hits with potential biological targets.
The network map on the left displays compounds targeting well-validated
protein targets as measured in inhibition assays (EC
50
#15 mM). The map
on the right shows compounds that bind to purified malarial proteins
according to thermal melt shift experiments. The size of nodes representing
active or binding compounds is increased for clarity. Data are in
Supplementary Information.
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synthase kinase 3 and thioredoxin. Two compounds bound multiple
proteins. Two out of the seven proteins are in essential malarial path-
ways: phosphatidylcholine synthesis
21
(choline kinase) and redox
metabolism
22
(thioredoxin). The remaining five protein targets poten-
tially represent novel antimalarial drug targets.
The potential for cross-resistance
To evaluate the potential for cross-resistance with existing drugs, the
cross-validated compounds were tested against a panel of P. falciparum
strains with different chemosensitivities to known antimalarials,
including strains 3D7 (chloroquine sensitive), K1, W2, V1/S and
Dd2 (all resistant to both chloroquine and to antifolates), and SB-A6
and D10_yDHOD (both chloroquine sensitive and atovaquone resist-
ant). All strains were profiled for sensitivity to a set of antimalarial
drugs to normalize activity (Supplementary Table 3). A total of 58
cross-validated compounds displayed similar potencies (EC
50
shift #3-fold) against 3D7, K1, V1/S and SB-A6, indicating that these
compounds do not share mechanisms of resistance with chloroquine,
atovaquoneor sulphadoxine/pyrimethamine. A subset of the 172com-
pounds that were inactive against drug-resistant P. falciparum strains
with known mutations in target proteins were tested against 3D7 dihy-
drofolate reductase and Plasmodium yoelii cytochrome bc
1
complex in
biochemical assays. Two inhibitors were identified for each protein
(Supplementary Tables 5 and 6).
Phylochemogenetic profiling
To understand relationships between chemical sensitivity of Plasmo-
dium and related parasites, the cross-validated set was tested against
three additional protozoan parasite species—Toxoplasma gondii,
which belongs to the same phylum as Plasmodium (Apicomplexa),
and Leishmania major and Trypanosoma brucei, which are both
Kinetoplastida, unrelated to the Apicomplexa—and an expanded
panel of human cell lines including a Burkitt’s lymphoma line (Raji)
and embryonic kidney fibroblast cells (HEK293). Phylogenetic criteria
predict that chemical sensitivity should correlate with evolutionary
history, due to homology between key protein targets, as is known to
be the case for many antiparasitic drugs
23,24
. Although a few com-
pounds showed activity in other parasites, most were highly selective
for Plasmodium (Fig. 4), whereas Toxoplasma exhibited a chemo-
sensitivity pattern more similar to human cell lines. Similarly,
the highly potent anti-leishmanial benzothiazoles were only weakly
active against the related Trypanosoma. These findings indicate that
chemical sensitivity of pathogens is regulated by a combination of
pathogen genetics, physiology and relationships to host and vector
species in vivo.
Early leads for drug development
To understand the potential for development of the novel chemotypes,
the pharmacokinetic properties of the cross-validated set were
assessed. The majority are reasonably drug-like, with 78% of com-
pounds having no violations of the Lipinski rule of five, a well validated
predictor of oral bioavailability, and 99% having one or fewer viola-
tions
25
. Within the cross-validated set were embedded three chemical
series that had multiple members that together gave structure–activity
relationships that spanned 1,000-fold potency differences, had con-
sistent activity in drug-resistant strains, had very good cellular thera-
peutic windows, and had at least one member with an EC
50
more
potent than 50nM. An exemplar compound was selected from each
series and fully profiled using standard models of in vitro and in vivo
adsorption, distribution, metabolism and toxicity (Supplementary
Table 7). Each possessed reasonable characteristics for developable
hits; indeed, each comes close to passing Medicines for Malaria
Venture (MMV) criteria for ‘late leads’. The compound from these
exemplars with thebest pharmacokinetic profile was further evaluated
to measure in vivo antimalarial activity and displayed efficacy in a
murine malaria model infected with P. yoelii. A twice-daily admini-
stration of 100 mg kg
21
for 3 days resulted in a 90% suppression of the
parasitaemia (Supplementary Fig. 5). Although it is not suggested that
BJ
HEK293
HepG2
Raji
Tg
Lm
Tb
V1/S
K1
3D7a
1 pM 1 μM 50 μM
Eukaryotes
Chordata Homo sapiens
Apicomplexa Toxoplasma gondii (Tg)
Plasmodium falciparum (Pf)
Euglenozoa Trypanosoma brucei (Tb)
Leishmania major (Lm)
Figure 4
|
Phylochemogenetic profiling. Phylochemogenetic analysis of the
cross-validated compounds using a two-way hierarchical clustering of
growth inhibition against P. falciparum strains (3D7, K1, V1/S), other
eukaryotic parasites (Toxoplasma gondii (Tg), Trypanosoma brucei (Tb),
Leishmania major (Lm)) and human cell lines (HEK293, BJ, Raji and
HepG2). Columns represent single compounds and are clustered according
to potency against the cell lines and organisms. Neighbouring compounds
share a similar potency spectrum. Rows represent a single cell line or
organism and are clustered according to their chemosensitivity to the
compounds in the study. A phylogenetic tree of the organisms in this study is
provided for reference. Note that despite the many known examples of
taxonomically conserved pathways, on a global level, phylogeny is a poor
predictor of chemical sensitivity profiles: Toxoplasma responses more
closely parallel human than their evolutionary siblings, Plasmodium. Data
are in Supplementary Information.
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any of the compounds discussed herein are bone fide preclinical can-
didates, all provide reasonable starting points for drug development.
Conclusions
Drug therapy remains a key component in controlling malaria. Curren t
challenges of rapid acquisition of resistance, cross-resistance and
dependence on a limited number of chemical classes of antimalarials
highlight the need to enhance our understanding of the ‘chemical
space’ that can be brought to bear on malaria treatment. Solving this
problem requires understanding the relationships between the struc-
tures of compounds active against malaria parasites, and their potency,
selectivity and targets. We have identified a number of novel com-
pounds and defined these relationships. We expect that these findings
will provide novel paths for drug development and hope that making
this set of well characterized, non-proprietary lead antimalarials
publicly available to the global research community will help to re-
invigorate drug discovery for malaria.
METHODS SUMMARY
The primary screen was carried out by comparing quantities of DNA in treated
and control cultures of Plasmodium falciparum in human erythrocytes after 72 h
incubation with a fixed concentration of 7 mM of the test compounds. The
secondary potency determination was made by using the same assay in a
dose–response mode with 10 concentrations varying from 10 mM to 5 nM.
Chemical sensitivities of the human cell lines and T. brucei were determined
by measuring their ATP content (Cell Titer Glo, Promega). T. gondii parasites
expressing luciferase were cultured and drug sensitivity was determined by
luminescence; L. major promastigote drug susceptibility was tested using a meta-
bolic function assay (Alamar Blue, Promega). Chemicals were assayed for hae-
mozoin formation
17
and PfDHOD
15
and PfFP-2
26
inhibitory activities based on
previously described methods. Thermal shift assays were done at compound
concentrations of 25 mM and protein concentrations of 100 mgml
21
. All data
processing and visualization, and chemical similarity and substructure analysis,
was performed using custom programs written in the Pipeline Pilot platform
(Accelrys, v.7.0.1) and the R program
27
. A complete description of the methods
can be found in Supplementary Information.
The Supplementary Information provides a summary of all relevant data
arising from the phenotypic screen and all secondary screens including relevant
diagnostics and details about the following: cell-based, enzyme and thermal shift
screens, data processing, Bland–Altman analysis, and the algorithm to generate
the chemical structure network graph. Chemical structures annotated with assay
data and high-resolution PDFs of the figures may be downloaded from http://
www.stjuderesearch.org/guy/data/malaria/.
Received 20 January; accepted 21 April 2010.
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Supplementary Information is linked to the online version of the paper at
www.nature.com/nature.
Acknowledgements This work was supported by the American Lebanese Syrian
Associated Charities (ALSAC) and St Jude Children’s Research Hospital (SJCRH,
R.K.G.), the Medicines for Malaria Venture (W.C.V.V. and V.M.A.), National
Institute of Allergy and Infectious Diseases (AI772682 (P.H.D.), AI075517
(R.K.G.), AI067921 (W.C.V.V.) and AI080625 (W.C.V.V.), AI28724 (D.S.R.),
AI53862 (J.L.D.), AI35707 (P.J.R.), AI053680 (M.A.P. and P.K.R.), AI075594
(M.A.P., P.K.R. and I.B.), AI082617 (P.K.R.) and AI045774 (D.J.S.)), the National
Cancer Institute (CA78039 (J.S.L.)), the Welch Foundation (I-1257 (M.A.P.)), the
Doris Duke Charitable Foundation (P.J.R.), and the Ellison Medical Foundation
(D.S.R.). We acknowledge A. B. Vaidya for providing the parasite strain
D10_yDHOD. We acknowledge M. Sigal for assistance in the early leads project
coordination, the SJCRH High Throughput Screening Center, particularly J. Cui; the
SJCRH Lead Discovery Informatics Center, and the SJCRH High Throughput
Analytical Chemistry Center, particularly C. Nelson and A. Lemoff; at UW,
F. Buckner, W. Hol and A. Napuli (AI067921, W. Hol); S. Wei and W. Hao in the UT
Southwestern HTS Center; and the Australian Red Cross Blood Service for the
provision of O1erythrocytes to Griffith University.
Author Contributions W.A.G. and R.K.G. designed and coordinated the project.
A.A.S. wrote the algorithms for the data analysis and generated the figures. Assays
were conceived, performed and analysed by W.A.G. and D.B. (P. falciparum
phenotypic screen), M.C. (human cell lines), D.C.S. (T. brucei), P.H.D. and D.S.R. (T.
gondii), J.S.L. and E.R.S. (L. major), A.K.T. and D.J.S. (haemozoin inhibition), G.J.C.
and W.C.V.V. (thermal melt experiments), M.A.P., P.K.R., F.E.M. and I.B.
(PfDHOD), J.W.F. and P.K.R. (P. falciparum dihydrofolate reductase), J.G. and P.J.R.
(PfFP-2), I.F. and M.K.R. (cytochrome bc
1
), J.C. (P. falciparum mutant drug
sensitivity). E.B.W., S.D., J.L.D. and V.M.A. (independent antimalarial in vitro
experiments), F.Z. (in vitro pharmacokinetics), M.B.J.D., M.S.M., I.A.-B. and S.F. (in
vivo pharmacokinetics and efficacy), I.B. (coordination of technology development
and network development), S.C. and P.L.M. (re-synthesis). W.A.G., A.A.S. and
R.K.G. wrote the manuscript. All authors contributed to the design of the
experiments and the preparation of the manuscript.
Author Information Reprints and permissions information is available at
www.nature.com/reprints. The authors declare no competing financial interests.
Readers are welcome to comment on the online version of this article at
www.nature.com/nature. Correspondence and requests for materials should be
addressed to R.K.G. (kip.guy@stjude.org).
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