Semi-quantitative models for identifying potent and selective transthyretin amyloidogenesis inhibitors

Article abstract of DOI:10.1016/j.bmcl.2017.05.080

Rate-limiting dissociation of the tetrameric protein transthyretin (TTR), followed by monomer misfolding and misassembly, appears to cause degenerative diseases in humans known as the transthyretin amyloidoses, based on human genetic, biochemical and pharmacologic evidence. Small molecules that bind to the generally unoccupied thyroxine binding pockets in the native TTR tetramer kinetically stabilize the tetramer, slowing subunit dissociation proportional to the extent that the molecules stabilize the native state over the dissociative transition state—thereby inhibiting amyloidogenesis. Herein, we use previously reported structure-activity relationship data to develop two semi-quantitative algorithms for identifying the structures of potent and selective transthyretin kinetic stabilizers/amyloidogenesis inhibitors. The viability of these prediction algorithms, in particular the more robust in silico docking model, is perhaps best validated by the clinical success of tafamidis, the first-in-class drug approved in Europe, Japan, South America, and elsewhere for treating transthyretin aggregation-associated familial amyloid polyneuropathy. Tafamidis is also being evaluated in a fully-enrolled placebo-controlled clinical trial for its efficacy against TTR cardiomyopathy. These prediction algorithms will be useful for identifying second generation TTR kinetic stabilizers, should these be needed to ameliorate the central nervous system or ophthalmologic pathology caused by TTR aggregation in organs not accessed by oral tafamidis administration.

Full text of DOI:10.1016/j.bmcl.2017.05.080

Accepted Manuscript
Semi-Quantitative Models for Identifying Potent and Selective Transthyretin
Amyloidogenesis Inhibitors
Stephen Connelly, David E. Mortenson, Sungwook Choi, Ian A. Wilson, Evan
T. Powers, Jeffery W. Kelly, Steven M. Johnson
PII:
S0960-894X(17)30578-4
DOI:
http://dx.doi.org/10.1016/j.bmcl.2017.05.080
BMCL 25025
Reference:
To appear in:
Bioorganic & Medicinal Chemistry Letters
Received Date:
Revised Date:
Accepted Date:
8 April 2017
22 May 2017
26 May 2017
Please cite this article as: Connelly, S., Mortenson, D.E., Choi, S., Wilson, I.A., Powers, E.T., Kelly, J.W., Johnson,
S.M., Semi-Quantitative Models for Identifying Potent and Selective Transthyretin Amyloidogenesis Inhibitors,
Bioorganic & Medicinal Chemistry Letters (2017), doi: http://dx.doi.org/10.1016/j.bmcl.2017.05.080
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Semi-Quantitative Models for Identifying Potent and
Selective Transthyretin Amyloidogenesis Inhibitors
a
b,c
e
a,d
a
Stephen Connelly, David E. Mortenson, Sungwook Choi, Ian A. Wilson, Evan T. Powers,
b,c,d
f
Jeffery W. Kelly, * Steven M. Johnson *
a
c
b
Department of Integrative Structural and Computational Biology, Department of Chemistry,
d
Department of Molecular Medicine, and The Skaggs Institute for Chemical Biology, The
Scripps Research Institute, 10550 N. Torrey Pines Rd., La Jolla, CA 92037
e
Department of New Drug Discovery and Development, Chungnam National University, 99
Daehak-ro Yuseong-gu, Daejon, 305-764, Republic of Korea
f
Department of Biochemistry & Molecular Biology, Indiana University, School of Medicine,
Van Nuys Medical Sciences Building, MS 0013D, 635 Barnhill Dr., Indianapolis, IN 46202
*
Correspondence: johnstm@iu.edu, Tel: 317-274-2458, Fax: 317-274-4686
jkelly@scripps.edu, Tel: 858-784-9605, Fax: 858-784-9610
1

Abstract:
Rate-limiting dissociation of the tetrameric protein transthyretin (TTR), followed by
monomer misfolding and misassembly, appears to cause degenerative diseases in humans known
as the transthyretin amyloidoses, based on human genetic, biochemical and pharmacologic
evidence. Small molecules that bind to the generally unoccupied thyroxine binding pockets in
the native TTR tetramer kinetically stabilize the tetramer, slowing subunit dissociation
proportional to the extent that the molecules stabilize the native state over the dissociative
transition state—thereby inhibiting amyloidogenesis. Herein, we use previously reported
structure-activity relationship data to develop two semi-quantitative algorithms for identifying
the structures of potent and selective transthyretin kinetic stabilizers / amyloidogenesis
inhibitors. The viability of these prediction algorithms, in particular the more robust in silico
docking model, is perhaps best validated by the clinical success of tafamidis, the first-in-class
drug approved in Europe, Japan, South America, and elsewhere for treating transthyretin
aggregation-associated familial amyloid polyneuropathy. Tafamidis is also being evaluated in a
fully-enrolled placebo-controlled clinical trial for its efficacy against TTR cardiomyopathy.
These prediction algorithms will be useful for identifying second generation TTR kinetic
stabilizers, should these be needed to ameliorate the central nervous system or ophthalmologic
pathology caused by TTR aggregation in organs not accessed by oral tafamidis administration.
2

Human genetic, biochemical and pharmacologic evidence implicates rate-limiting
transthyretin (TTR) tetramer dissociation, followed by rapid monomer misfolding and
misassembly, as the cause of several degenerative diseases exhibiting overlapping phenotypes,
1
-16
collectively referred to as the transthyretin amyloidoses.
The amyloidogenic TTR monomer
misassembles into a variety of aggregate structures during amyloidogenesis, including cross-β-
1
7-19
sheet amyloid fibrils, for which these diseases are named.
Amyloidogenesis of wild-type
(
WT) TTR or aggregation of certain mutants along with WT-TTR in heterozygotes leads to
cardiomyopathies, affecting up to 500,000 individuals (disorders historically called senile
1
4, 20
systemic amyloidosis (SSA) and familial amyloid cardiomyopathy (FAC), respectively).
Amyloidogenesis of distinct TTR mutants along with WT-TTR in heterozygotes results in a
primary autonomic and peripheral neuropathy, often called familial amyloid polyneuropathy
(
FAP). The latter disease has historically been treated by liver transplant-mediated gene therapy,
wherein the mutant-TTR / WT-TTR liver (which secretes destabilized TTR heterotetramers) is
replaced by a WT-TTR / WT-TTR liver (which secretes a more stable WT-TTR homotetramer).
Interestingly, slowing the course of peripheral disease progression by liver transplantation has
led to the appearance of TTR aggregation in the central nervous system (CNS) and eyes, which
2
1-26
manifests as a consequence of treatment-associated lifespan extension.
Another strategy to prevent TTR amyloidogenesis is to fashion small molecules that bind
selectively in human blood to one or both of the thyroxine (T ) binding sites comprising the
4
tetramer made up of WT or mutant and WT subunits. Selective binding to the native tetrameric
ground state of TTR over the dissociative transition state raises the kinetic barrier for subunit
dissociation, substantially slowing TTR aggregation. The extent of kinetic stabilization of
2
7-31
tetrameric TTR determines the extent to which amyloidogenesis is inhibited.
A placebo-
3

controlled clinical trial in V30M FAP patients (a prominent mutation causing tetramer
destabilization), along with a 12-month extension study, demonstrates the efficacy of this
3
2, 33
strategy in slowing the progression of autonomic and peripheral neuropathy.
Our studies carried out over the last two decades to develop small molecule TTR
amyloidogenesis inhibitors have revealed that optimal TTR kinetic stabilizers are typically
2
8, 29, 34-55
composed of two aryl rings joined by linkers of variable chemical composition.
Figure
S1 and Table S1 in the Supporting Information contain compilations of the structures and
experimental results for the majority of the inhibitors procured or synthesized by the Kelly
laboratory during this period. Binding of these small molecules to one or both of the generally
unoccupied, funnel-shaped, T
4
binding pockets strengthens the weaker dimer-dimer interface of
TTR by non-covalently bridging adjacent monomeric subunits through specific hydrophobic and
electrostatic interactions, as exemplified in the TTR•(201)
gauge the efficacy of candidate molecules to bind to the T
2
crystal structure (Figure 1). To
4
pockets and kinetically stabilize the
TTR tetramer from dissociating and aggregating in complex biological environments, we rely on
two primary assays: 1) an in vitro acid-mediated TTR aggregation assay carried out with
recombinant TTR in buffer; and 2) an ex vivo TTR immunoprecipitation / HPLC assay to
quantify the stoichiometry of a candidate kinetic stabilizer bound to TTR in blood plasma. These
two assays are briefly explained below, with complete experimental details presented in the
5
6, 57
Supporting Information).
Figure 1: X-ray structure of the TTR•(201)
2
complex (PDB ID 5TZL) highlights the
interactions known to be important for tight binding to TTR. Compound 201 is bound in its
equivalent symmetry-related binding modes (grey and green, respectively), which results from
ligand binding along the crystallographic 2-fold axis. The omit F
O
-F density (contoured at +/-
C
3
.5ꢀ) for 201 is shown in Figure S3 of the Supporting Information. The binding pocket is
characterized by a smaller inner cavity and a larger outer cavity, throughout which are
distributed three pairs of symmetric hydrophobic depressions, referred to as the halogen binding
pockets (HBPs). The iodine and chlorine atoms of 201 reside within HBPs 1 and 3. Primed
4

amino acids or HBPs refer to symmetry-related monomers of TTR comprising each T binding
4
pocket. The phenolate of 201 makes charged interactions with the Lys 15 and 15' residues in the
outer cavity; however, it is known that other kinetic stabilizers composed of phenols exhibiting a
higher pK
a
preferentially bind in the opposite orientation so that the phenols can hydrogen bond
with the Ser-117 and 117' residues in the inner binding cavity (details of this phenomenon have
46-50
been previously reported).
The acid-mediated TTR aggregation assay (pH 4.4; 100 mM acetate buffer) probes the
ability of a candidate small molecule kinetic stabilizer to bind tetrameric TTR in vitro and
prevent amyloidogenesis under denaturing conditions that destabilize the tetramer, and largely
convert the dissociated TTR monomers to aggregates within 3 days in the absence of a kinetic
stabilizer. The best inhibitors prevent >90% of TTR aggregation over a 72 h time course when
5

incubated at a concentration twice that of tetrameric TTR (i.e., 7.2 µM candidate inhibitor versus
.6 µM tetrameric TTR). We have traditionally presented results as % fibril formation (% FF),
3
with complete arrest of TTR aggregation (i.e., 100% inhibition) corresponding to 0% FF.
Since plasma TTR is the form that undergoes amyloidogenesis and causes degenerative
phenotypes in the periphery, a kinetic stabilizer candidate must bind selectively to TTR over all
the other proteins in blood plasma to be efficacious. The ex vivo TTR immunoprecipitation /
HPLC quantification assay probes the ability of candidate kinetic stabilizers to bind selectively
to TTR over the 4000+ other proteins that are present in human blood plasma. Selectively
binding to TTR over albumin, which is the primary T
4
transport protein in the blood, can be
particularly challenging: TTR represents <0.5% by mass of the total plasma proteome, whereas
5
8
albumin comprises ~57%. Potent TTR kinetic stabilizers can display average plasma TTR
binding stoichiometry values, also referred to as the Plasma Binding Selectivity (PS) values,
between zero (i.e., the candidate small molecule binds predominantly to plasma proteins other
than TTR) and the theoretical maximum of two (i.e., the candidate kinetic stabilizer binds very
selectively to TTR in blood plasma and exhibits a slow dissociation rate), owing to the presence
of the two T binding sites per TTR tetramer. A PS value of 0 renders a compound useless as a
4
TTR amyloidogenesis inhibitor in vivo, whereas a PS value of 1 or higher is acceptable as our
previous studies show that only one inhibitor bound per TTR tetramer is necessary to kinetically
2
9
stabilize the WT-TTR tetramer against amyloidogenesis. In addition to mitigating off-target
toxicity during the envisioned life-long use of a kinetic stabilizer to ameliorate a TTR amyloid
disease, achieving a high PS value may also allow administration of lower amounts of drug while
still maintaining efficacy.
6

Our early studies looked at the potential of repurposing approved non-steroidal anti-
inflammatory drugs (NSAIDs) to serve as TTR kinetic stabilizers, and thus inhibit the process of
3
8-41
TTR amyloidogenesis.
During these investigations, we discovered that diflunisal (compound
3
89) is a viable TTR kinetic stabilizer and inhibitor of TTR aggregation in vitro. Diflunisal is an
effective TTR kinetic stabilizer in humans because it can achieve extremely high plasma
concentrations, which makes up for its modest binding constant and mediocre binding
selectivity. Since diflunisal is already an approved drug in many countries around the world, this
discovery was instrumental in setting up an international academic clinical trial to test the
efficacy of TTR kinetic stabilizers as a strategy to treat FAP. While the clinical trial of tafamidis
to ameliorate V30M FAP (see below) was completed first, the diflunisal FAP trial (all mutations)
ultimately showed that treatment with diflunisal reduces the rate of progression of neurological
5
9
impairment and preserves the quality of life in polyneuropathy patients. However, the long
term therapeutic use of diflunisal for the treatment of the transthyretin amyloidoses is limited by
5
9-64
the fact that, as an NSAID, it can trigger gastrointestinal bleeds and impair renal flow,
which
is already diminished in cardiomyopathy patients. Hence, diflunisal is contraindicated for TTR
cardiomyopathy patients. Thus, we focused on developing lead TTR kinetic stabilizers /
amyloidogenesis inhibitors that did not inhibit cyclooxygenase activity.
Early on in our TTR kinetic stabilizer development program, it became clear that some of
the most potent TTR kinetic stabilizers under evaluation also exhibited binding to the thyroid
hormone receptors, which would likely produce undesirable metabolism and heart rate side-
effects in the envisioned long-term use of TTR amyloidogenesis inhibitors. Thus, we also sought
TTR kinetic stabilizers that were neither thyroid agonists nor antagonists.
7

Through extensive medicinal chemistry optimization efforts and counter-screens to avoid
NSAID or thyroid hormone receptor activity, one TTR kinetic stabilizer emerged as a lead drug
candidate, tafamidis (compound 250), which has since been approved by regulatory agencies in
>30 countries for treating FAP, causatively linked to over 100 TTR mutations. The 18-month-
long clinical trial, the 12-month extension study, and observations of more than 1000 patients,
some for longer than 8 years, indicate a very favorable safety profile and clearly demonstrate the
therapeutic efficacy of tafamidis at delaying neurologic impairment and preserving nutritional
3
2, 33, 64, 65
status in polyneuropathy patients, while enhancing quality of life and likely lifespan.
Unfortunately, a drawback to current therapies is that a subset of liver transplant patients with
FAP, and some tafamidis-treated patients, present with continued ocular and CNS deposition of
TTR aggregates (tafamidis does not enter the eye or the brain upon oral administration). It seems
that the majority of FAP patients treated by liver transplant-mediated gene therapy develop CNS
amyloid pathology after more than a decade of treatment. Thus, it may be necessary to develop a
second generation, tafamidis-like molecule that enters the eye and the brain while still
maintaining TTR kinetic stabilization in the periphery.
To increase the success of developing second generation transthyretin kinetic stabilizers /
amyloidosis inhibitors, in the present study, we have developed two semi-quantitative models to
predict the structures of potent TTR kinetic stabilizers / amyloidogenesis inhibitors that bind
with high selectivity to TTR in the blood, and that exhibit minimal off-target effects, such as
binding to the thyroid hormone receptor or the cyclooxygenase enzymes. The development of
these two prediction models will allow us to more effectively focus future research efforts on
optimizing the CNS and ocular permeability of TTR kinetic stabilizers, since we can be
8

confident that, even before any synthesis commences, proposed lead candidates will have a high
probability of selectively targeting transthyretin.
Figure 2. Summary of the structure-activity relationships (data derived) from a trio of previous
libraries designed to screen the three substructures of a typical small molecule TTR amyloidosis
4
6-48
inhibitor (i.e., Aryl-X, Linker-Y, and Aryl-Z).
Substructures are quantitatively ranked
according to the average experimental efficacy scores (EES) as determined using Equation 1,
which evaluates the ability of a substructure to afford potent aggregation inhibitors in vitro that
bind selectively to TTR in human blood plasma ex vivo.
Previously, we synthesized and evaluated three libraries of TTR kinetic stabilizer
candidates in an effort to optimize the three substructures of a typical TTR amyloidogenesis
46-
inhibitor – the two aromatic rings and the linker joining them (Figure S1, compounds 1-134).
4
8
The structure-activity relationship (SAR) data from these three mini-libraries (detailed in the
9

4
6-48
previous studies and summarized in Figure 2)
enabling one to predict the potency and selectivity of a larger combinatorial library of
theoretical” TTR amyloidogenesis inhibitors. For the envisioned predictive algorithm,
were envisioned to contain the information
“
Equation 1 was developed, incorporating an equal weighting of inhibitor potency (based on %
FF) and plasma TTR binding selectivity (PS), to semi-quantitatively rank compounds by what
we call an Experimental Efficacy Score (EES).
Equation 1
ꢃ100% − %ꢄꢄꢅ × ꢃ1 + ꢆꢂꢅ
ꢁꢁꢂ =
3
00%
EESs range from 0 (least potent and selective inhibitors) to a maximum of 1, which corresponds
to a compound displaying maximal amyloidogenesis inhibition (0% FF) and a maximum binding
selectivity value of 2 (i.e., both T binding sites are occupied by a kinetic stabilizer in blood
4
plasma). The EES values for the Aryl-X, Aryl-Z, and Linker-Y substructures can then be used to
calculate what we refer to as a Predicted Efficacy Score (PES) for a theoretical candidate
molecule (Equation 2).
Equation 2
ꢆꢁꢂ = ꢇ + ꢇ + ꢊ + ꢋ + ꢋ
ꢉ
ꢈ
ꢉ
ꢈ
In this algorithm, the Aryl-X substructure EESs have been further subdivided into X
s
and X
p
EESs (values are presented in Figure 2), to differentiate contributions from substituents and the
substitution patterns, respectively. The Aryl-Z substructure EESs have been similarly subdivided
into Z
calculate PES values for a given molecule is presented in the Supporting Information (page 16).
Briefly, to calculate a molecule’s PES, the EES values for the X , X , Y, Z , and Z substructures
that characterize the molecule (as defined in Figure 2) are simply added together (Equation 2).
s
and Z
p
EESs (values are presented in Figure 2). A detailed description of how to
s
p
s
p
1
0

With EESs for the X
s
, X
p
, Y, Z
s
, and Z parameters (9, 6, 10, 10, and 8 substructures,
p
4
6-48
respectively) derived from the three small libraries consisting of 134 molecules,
we are able
to calculate PESs for 43,200 theoretical TTR kinetic stabilizer small molecules, although some
structural overlap occurs owing to molecular symmetry, which reduces the total number of
distinct chemical entities. In theory, PES values can range from 0 (least potent and selective
inhibitors) to a maximum of 5 (most potent and selective inhibitors); however, the current
practical range spans between ~0.5 - 3.0 as EESs never reach the limits of 0 or 1 for any of the
individual molecular substructures.
Next, we scrutinized whether the PES algorithm could enhance our ability to chemically
synthesize a high proportion of potent and selective TTR amyloidogenesis inhibitors. As our test
4
9
case, we employed a library of stilbene and dihydrostilbene compounds reported previously
with calculated PESs in the 1.9-2.9 range (i.e., at the higher end of the practical PES range). In
this test library, 93% of the stilbenes (52/56, compounds 135-174) and 100% of the
dihydrostilbenes (34/34, compounds 175-199) displayed EESs greater than 0.667, an important
cutoff that represents a scenario of 0% FF and a binding stoichiometry of ≥1 molecule per TTR
tetramer. A complete tabulation of % FF, PS, EES, and PES values for the stilbene and
dihydrostilbene compounds can be found in Table S1 in the Supporting Information.
When EESs are plotted vs. PESs for those compounds containing substructures
encompassed within this prediction model (i.e., substructures presented in Figure 2) – from the
4
6-49
four aforementioned small molecule libraries (1-199)
along with 19 new compounds reported
28, 29, 35-41, 43-45, 51-55
here (200-218) and previously evaluated compounds (243-314)
– it becomes
evident that enrichment of compounds with EESs above the 0.667 cutoff occurs for compounds
with PESs >1.8 (Figure 3; shaded area). Solid black data points (see rightmost Y-axis in Figure 3)
1
1

represent the percentage of compounds exceeding the EES = 0.667 cutoff (PES data plotted in
increments of 0.2). Thus, focusing synthetic efforts on compounds with PESs ≥1.8 should
enhance the development of a higher proportion of potent and selective TTR amyloidogenesis
inhibitors. While this PES approach appears to have value in terms of directing synthetic efforts
towards developing potent TTR kinetic stabilizers that display high binding selectivity in blood
plasma, and presumably in cerebrospinal fluid (to be determined), experimental evidence
suggests that these types of predictions may not directly translate into developing the most
promising clinical candidates, as exemplified by the situation for compound 201.
Figure 3. Examination of the correlation between Predicted Efficacy Scores (PES) of kinetic
stabilizers and their Experimental Efficacy Scores (EES). EES and PES values for individual
compounds were calculated using Equations 1 and 2, respectively. The dashed line at EES =
0
.667 represents a scenario where an inhibitor binds with a stoichiometry of 1 molecule per TTR
tetramer, which has been shown sufficient to completely inhibit TTR aggregation (i.e., 0% fibril
2
9
formation). Solid black data points represent the percentage of compounds within the 0.2 PES
bins that exceed the EES=0.667 cutoff. The area shaded grey represents the PES region where
significant enrichment of molecules above this cutoff occurs: that is, where the greatest
proportion of highly desirable, potent and selective TTR aggregation inhibitors are predicted
(
PES > 1.8). Please refer to Table S1 in the Supporting Information for a complete tabulation of
all values.
1
2

Compound 201 (PES = 2.682) was one of the first candidates to undergo further
evaluation as a result of this substructure optimization study and embodies the pinnacle of TTR
aggregation inhibition and blood plasma TTR binding selectivity (EES = 0.97). Examination of
the TTR•(201)
2
co-crystal structure (Figure 1) reveals that 201 complements the T binding site
4
nearly perfectly, justifying its binding affinity and plasma selectivity. However, it fails as a
promising drug candidate because it binds significantly to the thyroid hormone (TH) nuclear
receptor (relative TH receptor binding = 0.93 – i.e., it displaces 93% of radiolabled T [the
3
endogenous thyroid hormone] from the receptor at a concentration of 10 µM), which is an
undesirable characteristic for a molecule that would be used daily for the remainder of a patient’s
life. Forewarning of this off-target binding was not possible using the PES approach, since most
of the compounds in the three libraries from which the X
s
, X
p
, Y, Z
Of the 70 highly potent and selective
TTR kinetic stabilizers evaluated for TH receptor binding efficacy in the aforementioned 3
s
, and Z
p
parameters were
4
6-48
derived lacked any appreciable TH receptor binding.
1
3

libraries, only 3 compounds displaced T by >20% (compounds 46, 110, and 111, with relative
3
TH receptor activities of 0.28, 0.21, and 0.28, respectively).
4
9
Prior to the creation of the fourth library, the stilbene and dihydrostilbene library,
emerging evidence from a panel of compounds designed to validate this lead candidate
predictive algorithm (compounds 200-218 and others that were later incorporated into the
stilbene and dihydrostilbene scaffold) suggested that molecules at the high end of the PES range
stood a greater chance of binding to the TH nuclear receptor. However, it appeared that
investigating compounds with sub-optimal PES values could alleviate this issue. To probe what
appeared to be the transition zone from low-to-high TH receptor binding, we investigated the
4
9
stilbene and dihydrostilbene compound series. Of the 21 stilbenes tested, 15 (68%) display
relative TH receptor binding activities exceeding the 0.2 threshold; however, only 17% of the
dihydrostilbene compounds tested (2/12) exceeded the 0.2 threshold. A correlation is evident
from plotting the accumulated relative TH receptor binding results as a function of PES for all
compounds evaluated to date (Figure 4). TH receptor binding increases significantly at PES
values >2.5. As evidenced by the stilbene/dihydrostilbene results (∆PES~0.115), decreasing the
PES by a small amount can have a profound effect on the likelihood that a candidate TTR kinetic
stabilizer will bind the thyroid hormone receptor.
Figure 4. Examination of the correlation between kinetic stabilizer Predicted Efficacy Scores
(
PES) of the candidate kinetic stabilizers and their Relative Thyroid Hormone (TH) receptor
binding. A cutoff for undesirable TH receptor binding has been selected at 0.2, represented by
the dashed line. Solid black data points represent the percentage of compounds within the 0.2
PES bins that are below this threshold. The area shaded grey represents the PES region where
significant enrichment of molecules below this cutoff occurs: that is, where the greatest
proportion of highly desirable molecules that do not bind to the TH receptor are predicted (PES <
2
.5). Please refer to Table S1 in the Supporting Information for a complete tabulation of all
values.
1
4

Given that the most promising clinical candidates are not necessarily those with the
highest Predicted Efficacy Scores, we reassessed the scoring algorithm to incorporate a “negative
design” component, i.e., to select against compounds exhibiting TH receptor binding capacity.
Toward this end, the Experimental Efficacy Scoring function of Equation 1 was modified to
integrate relative TH receptor binding results and compute what we refer to as a Thyroid
Hormone Receptor Experimental Efficacy Score, or THREE Score (Equation 3).
Equation 3
ꢃ100%ꢏ − ꢏ%ꢄꢄꢅꢏ×ꢏꢃ1 + ꢆꢂꢅ
ꢌꢍꢎꢁꢁꢏꢂꢐꢑꢒꢓ =
− ꢎꢓꢔꢕ ꢌꢍꢎ
3
00%
Consistent with data in Figures 3 and 4, plotting THREE Scores as a function of PES (Figure 5)
demonstrates an enrichment of compounds that exceed the 0.667 threshold within the 1.8-2.5
PES range (shaded area), with a maximum occurring at a PES of ~2.2 (refer to Table S1 in the
Supporting Information for a tabulation of all THREE Score results).
1
5

Figure 5. Examination of the correlation between the Predicted Efficacy Scores (PES) of the
candidate kinetic stabilizers and their Thyroid Hormone Receptor Experimental Efficacy Scores
(
THREE Scores). THREE Score values for individual compounds were calculated using
Equation 3. The cutoff line at 0.667 represents 1 inhibitor bound per TTR tetramer as described
in Figure 3. Solid black data points represent the percentage of compounds within the 0.2 PES
bins that exceed the 0.667 cutoff. As in Figure 3, the highest proportion of potent and selective
TTR aggregation inhibitors occurs at PES values >1.8; however, at PES values above 2.5,
binding to the TH receptor decreases the THREE score values significantly. Thus, the most
promising potent and selective TTR amyloidogenesis inhibitors that display minimal binding to
the TH receptor are predicted in the PES=1.8-2.5 range (i.e., the area shaded grey). Please refer
to Table S1 in the Supporting Information for a complete tabulation of all values.
While these Predicted Efficacy Scores are useful for identifying candidate TTR kinetic
stabilizers, this semi-quantitative optimization algorithm has several deficiencies that limit its
effectiveness. For one, this algorithm is limited to predicting the efficacies of compounds
encompassed within the scope of the scaffold substructures included in the Aryl-X, Aryl-Z, and
4
6-48
Linker-Y optimization studies (i.e., limited to the substructures presented in Figure 2).
Furthermore, PES calculations can be ambiguous owing to molecular symmetry that does not
permit unambiguous assignment of Aryl-X and Aryl-Z rings; thus, scoring in both directions is
possible, which may not give the same PES values (this issue is described in more detail with
1
6

compound 88 in Figure S2B of the Supporting Information). Scaffolds such as the benzoxazoles,
featuring substituent patterns that do not fall within the general classification of ortho, meta, and
para, also complicate PES calculations. PES comparisons are further complicated by the
differences in linker lengths and angles between the aryl rings (e.g., biphenyls vs. a urea linker
vs. a biphenyl ether linker, etc.), distinctions in substituent orientation on different scaffolds, and
the orientation differences of the candidate kinetic stabilizers within the TTR T
4
binding sites,
which this algorithm assumes are equivalent. We also note that carboxylates were not highly
4
6-48
represented in the three mini-libraries that serve as the basis for this strategy.
In prior SAR
binding
-4-OH aryl-X group was employed that
preferred binding in the outer cavity; thus, the carboxylates on the aryl-Z ring were unfavorably
positioned into the inner binding cavity, leading to their assignment of low Z scores. We know
studies, carboxylate-bearing aryls strongly favor binding to the outer cavity of the T
4
4
8
site. However, in one of the mini-libraries, a 3,5-Br
2
s
that for many inhibitors, carboxylates are not as much of a liability as this scoring algorithm
would suggest. This deficiency is highlighted by the results of tafamidis (compound 250),
which, while proving to be an excellent drug for treating FAP, would not have been predicted as
a lead candidate using this algorithm: PES = 1.558, EES = 0.597, and THREE Score = 0.597.
Thus, if we had evaluated a larger data set, carboxylate substituents might have been suggested
as more favorable and tafamidis would likely have fallen within the 1.8-2.5 PES range for
optimal candidates to select for further pre-clinical and clinical evaluation.
To counter the deficiencies inherent in the PES algorithm described above, we evaluated
the viability of an in silico docking model for predicting the efficacy of TTR kinetic stabilizers.
In this approach, we obtained docking energies for all of the compounds listed in Figure S1 and
Table S1 by docking them to the unliganded TTR derived from the TTR•(201) complex
2
1
7

6
6-68
structure using AutoDock 4.
because 201 exhibits one of the highest experimental efficacy scores that we have ever observed
EES = 0.97) – the idea being that TTR adopts this structure because of complementarity to an
We used the apo-TTR from the TTR•(201)
2
crystal structure
(
ideal kinetic stabilizer from a TTR binding perspective. The detailed protocol employed for
compound docking to TTR is outlined in the Supporting Information. Briefly, compound
structures were created using ChemDraw, then their conformational energy was minimized using
Accelrys Discovery Studio 4. The compound docking files, the receptor docking files, and
docking parameter files were prepared using AutoDockTools 1.5.6. The receptor grid maps were
established using the AutoGrid4 program, with a grid box large enough to encompass both of the
thyroxine binding sites so that the molecules could dock to either site in a single simulation.
Compounds were then docked in the receptor employing triplicate simulations and the docking
conformations and energies were evaluated in AutoDockTools. If the docked conformations
were considered reasonable (i.e., bound in conformations that we would expect based on our
prior experience observing numerous TTR•(Inhibitor) co-crystal structures, but in general with
2
RMSDs < 8Å – see validation discussion below), the docking energies were recorded in Table
S1 (representing the average of the three docking energies). In 92.2% of the dockings, the
lowest energy conformations were considered reasonable and bound in conformations we would
expect. For the remaining 7.8% of the dockings, the lowest energy conformations had the
molecules either not positioned within the binding site or in conformations that we have not
previously observed; however, in all cases, we were able to observe reasonable binding
conformations for higher energy docked structures. Therefore, we recorded these higher energy
docking conformations in Table S1, rather than the lowest docking energies for the
conformations that were deemed unreasonable.
1
8

Figure 6. The lowest energy docked conformation of inhibitor 201 calculated using Autodock 4
superimposes nearly identically with the TTR•(201) co-crystal structure (RMSD = 0.44 Å;
2
calculated using the Accelrys Discovery Studio 4 crystallographic visualization program).
We validated that the docking of inhibitors provided reasonable binding conformations
by comparing the docked conformations of 30 inhibitors with their established TTR•(kinetic
stabilizer) co-crystal structures. Of the 30 docked compounds, 23 of them (77%) had RMSD
2
values that differed by <2.5 Å compared to their X-ray co-crystal structures (refer to Figure S4
in the Supporting Information). Compound 201 docked with an RMSD of 0.44 Å compared to
the established TTR•(201)
2
crystal structure (Figure 6). The remaining compounds had higher
binding site
RMSD values (but still < 8Å) owing to binding in the opposite orientation in the T
4
relative to that observed in the crystal structures (i.e., binding backwards in the TTR thyroid
hormone binding pocket). While this indicates a failure of AutoDock to re-capitulate the
conformations observed in these seven TTR•(Inhibitor) co-crystal structures, we still considered
2
these docking conformations reasonable as we have often seen compounds binding in
orientations opposite to what we might have predicted based on historical SAR. Furthermore,
we have also observed compounds binding in mixed forward and reversed orientations in the
1
9

same structures, indicating the energetic differences between the two orientations can be small,
with both likely sampled in the solution state. Based on these results, we had high confidence
that AutoDock 4 was effective at predicting reasonable binding conformations and that the
docking model would provide robust and physiologically relevant binding rankings.
The initial docking scores for each of the compounds (i.e., raw docking scores as
computed by AutoDock 4, presented in Table S1) were plotted against in vitro biochemical
results to evaluate potential correlations (Figure S5, left panels, in the Supporting Information).
Autodock 4 generates calculated docking scores as binding energies (in kcal/mol units); thus,
lower docking scores (i.e., more negative) correspond to tighter binding kinetic stabilizers. Upon
closer examination of the correlations, it appears that the scoring algorithm that AutoDock 4 uses
to calculate docking energies was over-estimating the contributions that –CO
2
H and –NO
2
substituents made to the docking energies (at least in this context of binding to TTR). Thus, we
decided to assign “correction factors” to the AutoDock 4 calculated docking energies for
compounds bearing –CO
for the carboxyl and nitro substituents, respectively. Since we are perturbing these docking
energies using correction factors for the –CO H and –NO substituents, we feel that representing
the corrected values as energies is not entirely valid, and thus we represent the results as docking
scores” omitting the units (kcal/mol). A detailed description of how we determined these
correction factors” and how they are used to compute corrected docking scores is presented in
the Supporting Information.
As with the Predicted Efficacy Scores, we compared the corrected in silico docking
scores for all the inhibitors against the various biochemical results and scoring functions (Figure
). As shown in Figure 7A, the most potent TTR kinetic stabilizers (aggregation inhibitors) in
2
H and –NO
2
substituents, which came to be +1.03 and +2.37 kcal/mol
2
2
“
“
7
2
0

vitro are those with corrected docking scores lower than -7. The corrected docking scores also
correlated with the candidate kinetic stabilizer plasma binding stoichiometries (Figure 7B) and,
consequently, the Experimental Efficacy Scores (Figure 7C). There was also a trend noted that
the lower the corrected docking score, the greater the chance a molecule had for binding to the
thyroid hormone receptors (Figure 7D). From these correlations, it appears that the “sweet spot”
for predicting the most promising lead candidate kinetic stabilizers is associated with a docking
score ranging from -7.5 to -9.0 (Figure 7E). Perhaps not surprisingly, the corrected docking
score for tafamidis is -7.83, which falls within this range. Thus, this in silico optimization
algorithm further supports that tafamidis is an excellent transthyretin kinetic stabilizer, a
prediction borne out by its clinical efficacy.
While we would like to include a “negative design” component to this in silico algorithm
that incorporates scores from docking to the thyroid hormone receptors and cyclooxygenases, the
scale of this endeavor renders it beyond the scope of what can be included in the initial paper.
Ideally, we would like more than ten X-ray crystal structures of candidate TTR kinetic stabilizers
bound to the thyroid hormone receptors and cyclooxygenases, and these data are lacking. While
other thyroid hormone receptor and cyclooxygenase crystal structures are available (i.e. those
without TTR kinetic stabilizer ligands), we fear that docking results using these structures may
not prove reliable. Future studies linking thyroid hormone receptors and cyclooxygenase co-
crystal structures and docking experiments with TTR kinetic stabilizer candidates are warranted
to potentially add this negative design component.
Figure 7. Examination of the correlations between corrected in silico docking scores of
candidate kinetic stabilizers and their in vitro % inhibition of TTR aggregation (A); ex vivo
plasma TTR binding stoichiometry (B); Experimental Efficacy Scores (C); Relative Thyroid
Hormone receptor binding (D); and Thyroid Hormone Receptor Experimental Efficacy Scores
(
E). Please refer to Table S1 in the Supporting Information for a complete tabulation of all
values. In panels A, B, C, and E, solid black data points represent the percentage of compounds
2
1

within the 0.5 docking score bins that are above the respective thresholds (90% for inhibition of
TTR aggregation for panel A; 1 molecule bound per TTR tetramer for panel B; and 0.667 for C
and E, representing 100% inhibition of TTR aggregation and 1 molecule bound per tetramer). In
panel D, solid black data points represent the percentage of compounds within the 0.5 docking
score bins that are below the 0.2 threshold for undesirable thyroid hormone receptor binding.
Areas shaded grey represent the docking score regions where significant enrichment of
molecules above the respective cutoffs occur (or below with respect to thyroid hormone receptor
binding). In panel E, the greatest proportion of highly desirable, potent, and selective TTR
aggregation inhibitors are predicted within the -7.5 to -9.0 docking score region.
2
2

2
3

In conclusion, we have developed two semi-quantitative models for predicting the
optimal structures of potent and selective transthyretin kinetic stabilizers / amyloidogenesis
inhibitors. While in our initial Predicted Efficacy Scoring model, tafamidis fell slightly out of
the range for predicting the best lead candidates to pursue for further pre-clinical development, it
did fall within the optimized inhibitor zone using our in silico docking model, which we believe
is the more robust of the two predictive algorithms. Thus, not only is tafamidis predicted to be a
bona fide lead candidate, but the clinical success of tafamidis also supports this algorithm as a
useful tool for predicting promising lead transthyretin amyloidogenesis inhibitors for treating the
transthyretin amyloidoses. These prediction algorithms should be useful for identifying second
generation TTR kinetic stabilizers, should these be needed to ameliorate CNS or ophthalmologic
challenges caused by TTR aggregation from TTR synthesized by the choroid plexus and the
retinal pigment epithelial cells, respectively.
Supporting Information
Supporting information associated with this article can be found in the online version, which
includes a compilation of compounds developed in the Kelly lab for which % fibril formation
and Plasma Selectivity values are available; tabulated values of experimental results for these
compounds; guidelines for calculating compound Predicted Efficacy Scores (PESs); synthesis
and evaluation procedures for new molecules presented in this manuscript; X-ray crystallography
procedures and structure data for the TTR•(201)
docking of molecules to the unliganded TTR from the TTR•(201)
AutoDock 4; comparison of the lowest energy docked conformations of molecules with their
2
X-ray co-crystal structure; procedures for
2
crystal structure using
2
4

TTR•(molecule)
CO H and –NO
2
co-crystal structures; and a description of the procedure for determining the –
substituent correction factors and calculation of corrected docking scores.
2
2
Acknowledgements
We are grateful for the support of the NIH (DK 46335), the Skaggs Institute for Chemical
Biology and the Lita Annenberg Hazen Foundation (JWK), as well as startup funding from the
Indiana University School of Medicine, Department of Biochemistry and Molecular Biology
(
SMJ). David Mortenson is supported by a postdoctoral fellowship from the Hewitt Foundation.
While tafamidis was discovered at The Scripps Research Institute, it was developed at FoldRx
Pharmaceuticals by the substantial expertise of Richard Labaudinière and his colleagues.
FoldRx Pharmaceuticals is now owned by Pfizer. The technical expertise of Ted Foss, Maria
Dendle, and Mike Saure from the Kelly lab is greatly appreciated. Crystallographic data were
collected at beamline 11-1 at the Stanford Synchrotron Radiation Lightsource (SSRL), a national
user facility operated by Stanford University on behalf of the U.S. Department of Energy, Office
of Basic Energy Sciences. The SSRL Structural Molecular Biology Program is supported by the
Department of Energy, Office of Biological and Environmental Research and by the National
Institutes of Health, National Center for Research Resources, Biomedical Technology Program,
and the National Institute of General Medical Sciences. The authors would also like to thank
Drs. Xiaoping Dai, Andre Schiefner and Xueyong Zhu from the Wilson laboratory for assistance
in data collection.
2
5

References:
1
.
.
Buxbaum, J. N. The systemic amyloidoses. Curr. Opin. Rheumatol. 2004, 16, 67-75.
Buxbaum, J. N.; Tagoe, C. E. The genetics of the amyloidoses. Annu. Rev. Med. 2000,
2
5
1, 543-69.
3
. Jacobson, D. R.; Pastore, R. D.; Yaghoubian, R.; Kane, I.; Gallo, G.; Buck, F. S.;
Buxbaum, J. N. Variant-sequence transthyretin (isoleucine 122) in late-onset cardiac amyloidosis
in black Americans. N. Engl. J. Med. 1997, 336, 466-73.
4
.
Jiang, X.; Buxbaum, J. N.; Kelly, J. W. The V122I cardiomyopathy variant of
transthyretin increases the velocity of rate-limiting tetramer dissociation, resulting in accelerated
amyloidosis. Proc. Natl. Acad. Sci. U.S.A. 2001, 98, 14943-8.
5
.
Gambetti, P.; Russo, C. Human brain amyloidoses. Nephrol. Dial. Transplant 1998, 13
Suppl 7, 33-40.
6
.
Westermark, P.; Sletten, K.; Johansson, B.; Cornwell, G. G., 3rd. Fibril in senile systemic
amyloidosis is derived from normal transthyretin. Proc. Natl. Acad. Sci. U.S.A. 1990, 87, 2843-5.
7
.
Ikeda, S.; Nakazato, M.; Ando, Y.; Sobue, G. Familial transthyretin-type amyloid
polyneuropathy in Japan: clinical and genetic heterogeneity. Neurology 2002, 58, 1001-7.
8
.
Sekijima, Y.; Hammarstrom, P.; Matsumura, M.; Shimizu, Y.; Iwata, M.; Tokuda, T.;
Ikeda, S.; Kelly, J. W. Energetic characteristics of the new transthyretin variant A25T may
explain its atypical central nervous system pathology. Lab. Invest. 2003, 83, 409-17.
9
.
Sousa, A.; Coelho, T.; Barros, J.; Sequeiros, J. Genetic epidemiology of familial
amyloidotic polyneuropathy (FAP)-type I in Povoa do Varzim and Vila do Conde (north of
Portugal). Am. J. Med. Genet. 1995, 60, 512-21.
1
0.
McCutchen, S. L.; Lai, Z.; Miroy, G. J.; Kelly, J. W.; Colon, W. Comparison of lethal
and nonlethal transthyretin variants and their relationship to amyloid disease. Biochemistry 1995,
3
1
4, 13527-36.
1. Hammarstrom, P.; Sekijima, Y.; White, J. T.; Wiseman, R. L.; Lim, A.; Costello, C. E.;
Altland, K.; Garzuly, F.; Budka, H.; Kelly, J. W. D18G transthyretin is monomeric, aggregation
prone, and not detectable in plasma and cerebrospinal fluid: a prescription for central nervous
system amyloidosis? Biochemistry 2003, 42, 6656-63.
1
2.
Sekijima, Y.; Wiseman, R. L.; Matteson, J.; Hammarstrom, P.; Miller, S. R.; Sawkar, A.
R.; Balch, W. E.; Kelly, J. W. The biological and chemical basis for tissue selective amyloid
disease. Cell 2005, 121, 73-85.
1
3.
Sipe, J. D. Amyloidosis. Crit. Rev. Clin. Lab. Sci. 1994, 31, 325-54.
2
6

1
4.
Westermark, P.; Bergstrom, J.; Solomon, A.; Murphy, C.; Sletten, K. Transthyretin-
derived senile systemic amyloidosis: clinicopathologic and structural considerations. Amyloid
2
003, 10 Suppl. 1, 48-54.
5. Johnson, S. M.; Connelly, S.; Fearns, C.; Powers, E. T.; Kelly, J. W. The transthyretin
1
amyloidoses: from delineating the molecular mechanism of aggregation linked to pathology to a
regulatory-agency-approved drug. J. Mol. Biol. 2012, 421, 185-203.
1
6.
Johnson, S. M.; Wiseman, R. L.; Reixach, N.; Paulsson, J.; Choi, S.; Powers, E. T.;
Buxbaum, J. N.; Kelly, J. W. Understanding and Ameliorating the Transthyretin Amyloidoses. In
Protein Misfolding Diseases: Current and Emerging Principles and Therapies, Ramirez-
Alvarado, M.; Kelly, J. W.; Dobson, C. M., Eds. John Wiley & Sons: 2010; pp 967-1003.
1
1
1
7.
8.
9.
Dobson, C. M. Protein folding and misfolding. Nature 2003, 426, 884-90.
Selkoe, D. J. Folding proteins in fatal ways. Nature 2003, 426, 900-4.
Kelly, J. W. Alternative conformations of amyloidogenic proteins govern their behavior.
Curr Opin Struct Biol 1996, 6, 11-7.
2
0.
Dharmarajan, K.; Maurer, M. S. Transthyretin cardiac amyloidoses in older North
Americans. J. Am. Geriatr. Soc. 2012, 60, 765-74.
2
1.
Sandgren, O.; Kjellgren, D.; Suhr, O. B. Ocular manifestations in liver transplant
recipients with familial amyloid polyneuropathy. Acta Ophthalmol. 2008, 86, 520-4.
2
2.
Ohya, Y.; Okamoto, S.; Tasaki, M.; Ueda, M.; Jono, H.; Obayashi, K.; Takeda, K.;
Okajima, H.; Asonuma, K.; Hara, R.; Tanihara, H.; Ando, Y.; Inomata, Y. Manifestations of
transthyretin-related familial amyloidotic polyneuropathy: long-term follow-up of Japanese
patients after liver transplantation. Surg. Today 2011, 41, 1211-8.
2
3.
Sekijima, Y. Transthyretin (ATTR) amyloidosis: clinical spectrum, molecular
pathogenesis and disease-modifying treatments. J. Neurol. Neurosurg. Psychiatry 2015, 86,
1
2
036-43.
4. Ando, Y.; Coelho, T.; Berk, J. L.; Cruz, M. W.; Ericzon, B. G.; Ikeda, S.; Lewis, W. D.;
Obici, L.; Plante-Bordeneuve, V.; Rapezzi, C.; Said, G.; Salvi, F. Guideline of transthyretin-
related hereditary amyloidosis for clinicians. Orphanet J. Rare Dis. 2013, 8, 31.
2
5.
Sekijima, Y.; Yazaki, M.; Oguchi, K.; Ezawa, N.; Yoshinaga, T.; Yamada, M.;
Yahikozawa, H.; Watanabe, M.; Kametani, F.; Ikeda, S. Cerebral amyloid angiopathy in
posttransplant patients with hereditary ATTR amyloidosis. Neurology 2016, 87, 773-81.
2
6.
Maia, L. F.; Magalhaes, R.; Freitas, J.; Taipa, R.; Pires, M. M.; Osorio, H.; Dias, D.;
Pessegueiro, H.; Correia, M.; Coelho, T. CNS involvement in V30M transthyretin amyloidosis:
clinical, neuropathological and biochemical findings. J. Neurol. Neurosurg. Psychiatry 2015, 86,
1
59-67.
2
7

2
7.
Hammarstrom, P.; Wiseman, R. L.; Powers, E. T.; Kelly, J. W. Prevention of
transthyretin amyloid disease by changing protein misfolding energetics. Science 2003, 299, 713-
6
2
.
8.
Johnson, S. M.; Wiseman, R. L.; Sekijima, Y.; Green, N. S.; Adamski-Werner, S. L.;
Kelly, J. W. Native state kinetic stabilization as a strategy to ameliorate protein misfolding
diseases: a focus on the transthyretin amyloidoses. Acc. Chem. Res. 2005, 38, 911-21.
2
9.
Wiseman, R. L.; Johnson, S. M.; Kelker, M. S.; Foss, T.; Wilson, I. A.; Kelly, J. W.
Kinetic stabilization of an oligomeric protein by a single ligand binding event. J. Am. Chem. Soc.
2
005, 127, 5540-51.
0. Foss, T. R.; Kelker, M. S.; Wiseman, R. L.; Wilson, I. A.; Kelly, J. W. Kinetic
3
stabilization of the native state by protein engineering: Implications for inhibition of transthyretin
amyloidogenesis. J. Mol. Biol. 2005, 347, 841-54.
3
1.
Foss, T. R.; Wiseman, R. L.; Kelly, J. W. The pathway by which the tetrameric protein
transthyretin dissociates. Biochemistry 2005, 44, 15525-33.
3
2.
Coelho, T.; Maia, L. F.; Martins da Silva, A.; Waddington Cruz, M.; Plante-Bordeneuve,
V.; Lozeron, P.; Suhr, O. B.; Campistol, J. M.; Conceicao, I. M.; Schmidt, H. H.; Trigo, P.;
Kelly, J. W.; Labaudiniere, R.; Chan, J.; Packman, J.; Wilson, A.; Grogan, D. R. Tafamidis for
transthyretin familial amyloid polyneuropathy: a randomized, controlled trial. Neurology 2012,
7
9, 785-92.
3
3. Coelho, T.; Maia, L. F.; da Silva, A. M.; Cruz, M. W.; Plante-Bordeneuve, V.; Suhr, O.
B.; Conceicao, I.; Schmidt, H. H.; Trigo, P.; Kelly, J. W.; Labaudiniere, R.; Chan, J.; Packman,
J.; Grogan, D. R. Long-term effects of tafamidis for the treatment of transthyretin familial
amyloid polyneuropathy. J. Neurol. 2013, 260, 2802-14.
3
4.
Klabunde, T.; Petrassi, H. M.; Oza, V. B.; Raman, P.; Kelly, J. W.; Sacchettini, J. C.
Rational design of potent human transthyretin amyloid disease inhibitors. Nat. Struct. Biol. 2000,
7
3
, 312-21.
5. Miroy, G. J.; Lai, Z.; Lashuel, H. A.; Peterson, S. A.; Strang, C.; Kelly, J. W. Inhibiting
transthyretin amyloid fibril formation via protein stabilization. Proc. Natl. Acad. Sci. U.S.A.
1
996, 93, 15051-6.
3
6. Oza, V. B.; Petrassi, H. M.; Purkey, H. E.; Kelly, J. W. Synthesis and evaluation of
anthranilic acid-based transthyretin amyloid fibril inhibitors. Bioorg. Med. Chem. Lett. 1999, 9,
1
-6.
3
7.
Oza, V. B.; Smith, C.; Raman, P.; Koepf, E. K.; Lashuel, H. A.; Petrassi, H. M.; Chiang,
K. P.; Powers, E. T.; Sachettini, J.; Kelly, J. W. Synthesis, structure, and activity of diclofenac
analogues as transthyretin amyloid fibril formation inhibitors. J. Med. Chem. 2002, 45, 321-32.
3
8.
Baures, P. W.; Peterson, S. A.; Kelly, J. W. Discovering transthyretin amyloid fibril
inhibitors by limited screening. Bioorg. Med. Chem. 1998, 6, 1389-401.
2
8

3
9.
Baures, P. W.; Oza, V. B.; Peterson, S. A.; Kelly, J. W. Synthesis and evaluation of
inhibitors of transthyretin amyloid formation based on the non-steroidal anti-inflammatory drug,
flufenamic acid. Bioorg. Med. Chem. 1999, 7, 1339-47.
4
0.
Adamski-Werner, S. L.; Palaninathan, S. K.; Sacchettini, J. C.; Kelly, J. W. Diflunisal
analogues stabilize the native state of transthyretin. Potent inhibition of amyloidogenesis. J. Med.
Chem. 2004, 47, 355-74.
4
1.
Miller, S. R.; Sekijima, Y.; Kelly, J. W. Native state stabilization by NSAIDs inhibits
transthyretin amyloidogenesis from the most common familial disease variants. Lab. Invest.
2
004, 84, 545-52.
2. Purkey, H. E.; Palaninathan, S. K.; Kent, K. C.; Smith, C.; Safe, S. H.; Sacchettini, J. C.;
4
Kelly, J. W. Hydroxylated polychlorinated biphenyls selectively bind transthyretin in blood and
inhibit amyloidogenesis: rationalizing rodent PCB toxicity. Chem. Biol. 2004, 11, 1719-28.
4
3.
Razavi, H.; Palaninathan, S. K.; Powers, E. T.; Wiseman, R. L.; Purkey, H. E.;
Mohamedmohaideen, N. N.; Deechongkit, S.; Chiang, K. P.; Dendle, M. T.; Sacchettini, J. C.;
Kelly, J. W. Benzoxazoles as transthyretin amyloid fibril inhibitors: synthesis, evaluation, and
mechanism of action. Angew. Chem. Int. Ed. Engl. 2003, 42, 2758-61.
4
4.
Razavi, H.; Powers, E. T.; Purkey, H. E.; Adamski-Werner, S. L.; Chiang, K. P.; Dendle,
M. T.; Kelly, J. W. Design, synthesis, and evaluation of oxazole transthyretin amyloidogenesis
inhibitors. Bioorg. Med. Chem. Lett. 2005, 15, 1075-8.
4
5.
Petrassi, H. M.; Klabunde, T.; Sacchettini, J. C.; Kelly, J. W. Structure-based design of
N-phenyl phenoxazine transthyretin amyloid fibril inhibitors. J. Am. Chem. Soc. 2000, 122,
2
4
178-2192.
6. Johnson, S. M.; Connelly, S.; Wilson, I. A.; Kelly, J. W. Biochemical and structural
evaluation of highly selective 2-arylbenzoxazole-based transthyretin amyloidogenesis inhibitors.
J. Med. Chem. 2008, 51, 260-70.
4
7.
Johnson, S. M.; Connelly, S.; Wilson, I. A.; Kelly, J. W. Toward optimization of the
linker substructure common to transthyretin amyloidogenesis inhibitors using biochemical and
structural studies. J. Med. Chem. 2008, 51, 6348-58.
4
8.
Johnson, S. M.; Connelly, S.; Wilson, I. A.; Kelly, J. W. Toward optimization of the
second aryl substructure common to transthyretin amyloidogenesis inhibitors using biochemical
and structural studies. J. Med. Chem. 2009, 52, 1115-25.
4
9.
Choi, S.; Reixach, N.; Connelly, S.; Johnson, S. M.; Wilson, I. A.; Kelly, J. W. A
substructure combination strategy to create potent and selective transthyretin kinetic stabilizers
that prevent amyloidogenesis and cytotoxicity. J. Am. Chem. Soc. 2010, 132, 1359-70.
5
0.
of kinetic stabilizers that ameliorate the transthyretin amyloidoses. Curr. Opin. Struct. Biol.
010, 20, 54-62.
Connelly, S.; Choi, S.; Johnson, S. M.; Kelly, J. W.; Wilson, I. A. Structure-based design
2
2
9