abstract: # Background
  .
  The Hedgehog (Hh) signaling pathway is vital to animal development as it mediates the differentiation of multiple cell types during embryogenesis. In adults, Hh signaling can be activated to facilitate tissue maintenance and repair. Moreover, stimulation of the Hh pathway has shown therapeutic efficacy in models of neuropathy. The underlying mechanisms of Hh signal transduction remain obscure, however: little is known about the communication between the pathway suppressor Patched (Ptc), a multipass transmembrane protein that directly binds Hh, and the pathway activator Smoothened (Smo), a protein that is related to G-protein-coupled receptors and is capable of constitutive activation in the absence of Ptc.
  .
  # Results
  .
  We have identified and characterized a synthetic non-peptidyl small molecule, Hh-Ag, that acts as an agonist of the Hh pathway. This Hh agonist promotes cell-type-specific proliferation and concentration-dependent differentiation *in vitro,* while *in utero* it rescues aspects of the Hh-signaling defect in *Sonic hedgehog*-null, but not *Smo*-null, mouse embryos. Biochemical studies with Hh-Ag, the Hh-signaling antagonist cyclopamine, and a novel Hh-signaling inhibitor Cur61414, reveal that the action of all these compounds is independent of Hh-protein ligand and of the Hh receptor Ptc, as each binds directly to Smo.
  .
  # Conclusions
  .
  Smo can have its activity modulated directly by synthetic small molecules. These studies raise the possibility that Hh signaling may be regulated by endogenous small molecules *in vivo* and provide potent compounds with which to test the therapeutic value of activating the Hh-signaling pathway in the treatment of traumatic and chronic degenerative conditions.
author: Maria Frank-Kamenetsky; Xiaoyan M Zhang; Steve Bottega; Oivin Guicherit; Hynek Wichterle; Henryk Dudek; David Bumcrot; Frank Y Wang; Simon Jones; Janine Shulok; Lee L Rubin; Jeffery A Porter
date: 2002
institute: 1Curis, Inc., 61 Moulton Street, Cambridge, MA 02138, USA; 2Columbia University, College of Physicians and Surgeons, 701 West 168 Street, New York, NY 10032, USA
references:
title: Small-molecule modulators of Hedgehog signaling: identification and characterization of Smoothened agonists and antagonists

# Background

The *hedgehog* (*hh*) gene was identified two decades ago in *Drosophila* as a critical regulator of cell-fate determination during embryogenesis \[1\]. Subsequent work in several model systems has defined and characterized the *Hh* gene family that encodes highly conserved secreted signaling proteins (for review see \[2\]). Hedgehog (Hh) proteins are synthesized as approximately 45 kDa precursors that autoprocess in an unprecedented fashion, resulting in the covalent attachment of a cholesterol moiety to the amino-terminal half of the precursor \[2\]. This processed amino-terminal domain, Hh-Np, is responsible for the activation of a unique and complex signaling cascade that is essential for controlling cell fate throughout development and into adulthood \[2\]. In mammals there are three Hh-family proteins: Sonic (Shh), Indian (Ihh), and Desert (Dhh). Gene-targeting experiments in mice have demonstrated that the development and patterning of essentially every major organ requires input from the Hh pathway \[2\].

*In vitro* culture systems of neuronal tissues have been used to characterize the biology of the Hh-signaling pathway. Most notably, the neural-plate explant assay has defined the concentration-dependent role that ventrally expressed Shh plays in opposing dorsally expressed bone morphogenetic proteins (BMPs) to pattern the neural tube \[2\]. The assay demonstrates that the Hh-signaling cascade can distinguish between small concentration differences in the Hh ligand to instruct the differentiation of specific neuronal cell types. Additional insights have been gained by utilizing cultures of postnatal cerebellar neuron precursors \[2\]. These studies have shown that Hh patterns the cerebellum by promoting proliferation of the granule neuron precursors. Given the role that Hh signaling plays in promoting progenitor-cell proliferation, it is not surprising that misregulation of Hh signaling has been implicated in the biology of certain cancers, in particular basal cell carcinoma (BCC) and medulloblastoma.

The Hh-signaling pathway comprises three main components: the Hh ligand; a transmembrane receptor circuit composed of the negative regulator Patched (Ptc) plus an activator, Smoothened (Smo); and finally a cytoplasmic complex that regulates the Cubitus interruptus (Ci) or Gli family of transcriptional effectors. Additional pathway components are thought to modulate the activity or subcellular distribution of these molecules \[2\]. There is positive and negative feedback at the transcriptional level as the *Gli1* and *Ptc1* genes are direct transcriptional targets of activation of the pathway.

Smo is a seven-pass transmembrane protein with homology to G-protein-coupled receptors (GPCRs), while Ptc is a twelve-pass transmembrane protein that resembles a channel or transporter. Consistent with its role as an essential pathway inhibitor, removal of Ptc renders the Hh pathway constitutively 'on', independent of the Hh ligand. Similarly, specific point mutations in the transmembrane helices of Smo are capable of constitutively stimulating the pathway, effectively bypassing Ptc inhibition \[3\]. At present, a controversy surrounds the mechanism by which Ptc inhibits Smo. Although early studies suggested a simple, direct, stoichiometric regulation, more recent data support a more complicated indirect or catalytic model \[2\]. And although it has been demonstrated that Hh directly interacts with \[4\] and destabilizes \[5\] Ptc, the downstream molecular events remain obscure. In particular, little is known about the means by which Ptc exerts its inhibitory effect on Smo, or how Smo communicates with the cytoplasmic Ci/Gli transcription factor complex.

Through a 'chemical genetic' approach of identifying and studying the mechanism of action of small-molecule agonists (and antagonists), we hoped to uncover some of the complexities of the Hh-signaling system. Small-molecule modulators of growth-factor pathways have proven valuable in providing enhanced understanding of the intracellular events that occur subsequent to receptor activation, and in establishing the biological functions of these pathways \[6-8\]. In Hh signaling, multiple insights have been gained through the use of the plant-derived Hh antagonist cyclopamine \[9-16\] and a recently identified synthetic small-molecule Hh-signaling inhibitor, Cur61414 \[17\]. Interestingly, these specific inhibitors of Hh signaling appear to function downstream of Ptc but their precise molecular target(s) and mechanism of action are unknown.

Although genetic manipulations involving gain-of-function point mutations of Smo \[3\] have demonstrated that the pathway can be activated independently of Hh ligand, no small molecules with this capability have been identified. Indeed, it has proven difficult to identify small-molecule agonists of any signaling pathway activated by a protein ligand. Two examples have recently been described, however. One involved identification of a non-peptide activator of the granulocyte colony-stimulating factor (GCSF) pathway that appeared to act via receptor oligomerization \[18\]. Another report described a small-molecule activator of the insulin-signaling pathway that also acts at the level of the receptor \[19\].

Since the Hh receptor, Ptc, serves to inhibit signaling, a small-molecule pathway activator would need to be capable of one of the following: first, interfering with the inhibitory effect that Ptc exerts on Smo; second, activating Smo without affecting Ptc; or third, activating the pathway downstream of Smo. Identifying small molecules with any of these activities would provide useful information concerning the details of Hh signaling and would also provide a simple means of modulating activity of the pathway *in vivo* or *in vitro*.

In this article, we show that a non-peptidyl small-molecule agonist of Hh signaling has been identified that has all the known signaling properties of the recombinant Hh protein. But this agonist, unlike Hh protein, appears to bypass the Ptc-regulatory step, by interacting directly with Smo. Furthermore, studies with the agonist and several antagonists of Hh signaling suggest that Smo can be activated or inhibited by direct interaction with small-molecule ligands. These observations suggest that the Ptc-Smo receptor circuit may incorporate native small-molecule ligands in the regulation of Hh signaling.

# Results

## Isolation of Hh agonists by high-throughput screening

To identify small-molecule agonists of Hh signaling, we established a mammalian-cell-based assay. After testing several cell lines for Hh-dependent induction of the target genes *Ptc1* and *Gli1*\[2\], we identified C3H10T1/2 and TM3 cells as optimal responders. We then introduced into each line a plasmid containing a luciferase reporter downstream of multimerized Gli binding sites and a minimal promoter \[20\]. An isolated stable clone of the 10T1/2 cell transfectants (referred to as clone S12) gave a 10-20-fold up-regulation of luciferase activity (Figure 1a) when stimulated with Hh protein \[21\] for 24 hours. Using this assay system, we screened 140,000 synthetic compounds at a concentrations of 2-5 μM and isolated several putative agonists. One of these molecules - Hh-Ag 1.1 (Figure 1a,b) - was studied further. Hh-Ag 1.1 exhibited half-maximal stimulation (EC~50~) at around 3 μM, and an activation maximum (A~max~) of approximately 35% compared to the Hh protein control (Figure 1a). In the presence of sub-threshold signaling levels of Hh protein (0.3 nM), the EC~50~ of Hh-Ag 1.1 was reduced to around 0.4 μM and the A~max~ approached 70% (Figure 1a).

We next tested whether expression of endogenous Hh-responsive genes was stimulated by the agonist. Using quantitative PCR, Hh-Ag 1.1 was shown clearly to elevate the expression of *Gli1* and *Ptc1* in a dose-dependent manner (Figure 1c).

## Chemical modifications increase potency

In an effort to improve the potency of Hh-Ag 1.1, over 300 derivatives were synthesized and tested in the cell-based reporter assay. The relative potencies of the most active derivatives - 1.2, 1.3, 1.4 and 1.5 - are shown in Figure 1d. The most potent, Hh-Ag 1.5, had an EC~50~ of approximately 1 nM. Thus, potency was increased over 1000-fold by chemical modification. The structures of compounds 1.2 and 1.3 are shown in Figure 1e. Hh-Ag 1.2 was the most stable derivative *in vivo* and *in vitro* (data not shown) and was used for most cell-based assays. Hh-Ag 1.3 showed lower toxicity in embryonic tissue cultures (data not shown) and was used for the neural plate explant assays described below. These experiments suggest that the agonist may have many of the properties of the Hh ligand. To specifically test this, we used two established *in vitro* assay systems that detect the effects of Hh on primary neuronal precursors.

## *In vitro* assay of neuronal precursors

### Proliferation activity of the agonist

It has recently been shown that primary neonatal cerebellar granule neuron (CGN) precursors proliferate in response to Hh stimulation \[2\]. To determine whether the Hh agonist could elicit this response, we monitored \[^3^H\]-thymidine incorporation of cultured rat CGN precursors treated with Hh protein, Hh-Ag 1.1, Hh-Ag 1.2, or vehicle (DMSO). The original active molecule, Hh-Ag 1.1, stimulated thymidine incorporation at 5 μM, but not at 1.75 μM (Figure 1f). The extent of proliferation was around 50% of that seen with a high dose of Hh protein (50 nM). Hh-Ag 1.2 stimulated proliferation at 300 nM and 100 nM to levels comparable to those seen with Hh protein (Figure 1f). These data demonstrate that the agonists can elicit a biological response in primary cells similar to that produced by Hh protein.

### Morphogenic activity of the agonist

Neural progenitors within the intermediate region of the chick neural plate (Figure 2a) respond to increasing concentrations of Hh protein by adopting specific fates. The identity of these cells can be assessed by their distinct expression patterns of a set of transcription factors \[2\]. Three of these transcription factors - Pax7, MNR2 and Nkx2.2 - whose expression is differentially sensitive to increasing concentrations of Hh protein were assayed in response to varying concentrations of the agonist (Hh-Ag 1.3). The dorsal spinal cord marker Pax7 is normally repressed by low concentrations of Hh \[22\]. Pax7 expression was extinguished by 1-10 nM agonist (Figure 2b,c,d,e,f). Higher concentrations of agonist (10-200 nM) induced expression of the motor neuron progenitor marker MNR2 (Figure 2b,g,h,i,j), and yet higher concentrations (20 nM-1 μM) induced the most ventral interneuron progenitor marker Nkx2.2 (Figure 2b,k,l,m,n). This dose-dependent profile of expression closely resembles the response achieved by increasing concentrations of Hh protein \[22-24\], demonstrating that the Hh agonist mimics the concentration-dependent inductive activity of Hh on neural precursors.

## Activity of the agonist *in vivo*

To explore the site of action of the Hh agonist within the Hh pathway, we developed an *in vivo* assay for the agonist that would allow us to test its activity in *Shh-* and *Smo*-mutant mouse embryos *in utero*. First, we compared the expression of *Ptc1* in vehicle- and agonist-treated *Ptc1*^*lacZ*/+^ mouse embryos \[25\]. The *Ptc1*^*lacZ*/+^ mouse expresses β-galactosidase under control of *Ptc1*-regulatory elements and thus reports Hh-pathway activity in mouse tissues. Hh-Ag 1.2 (Figure 1e) was chosen for study on the basis of its relatively low toxicity, long serum half-life and ability to cross the placenta (data not shown). Hh-Ag 1.2 was delivered by oral gavage to pregnant mice at 7.5 and 8.5 days post coitum (7.5 and 8.5 dpc). Embryos were collected at embryonic day (E) 9.5 and analyzed by staining with the β-galactosidase chromogenic substrate X-gal. In vehicle-treated embryos, *Ptc1* expression was confined primarily to the ventral neural tube (Figure 3a,c). In embryos treated with Hh-Ag 1.2, however, expression of *Ptc1*^*lacZ*^ was greatly extended dorsally in the neural tube and throughout the adjacent mesoderm (Figure 3b,d). These embryos also displayed open rostral neural tubes, similar to those of *Ptc1*^-/-^ embryos. These experiments demonstrate that the agonist compound effectively activates Hh signaling *in vivo* following oral administration.

### Agonist site of action *in vivo*

Having established an *in utero* assay for Hh signaling, we next investigated whether the agonist could rescue aspects of *Shh*- or *Smo*-mutant phenotypes, by monitoring *lacZ* expression in *Smo*^-/-^*Ptc1*^*lacZ*/+^ embryos \[26\] and *Ptc1* mRNA levels in *Shh*^-/-^ embryos.

Pregnant mice from *Shh*^+/-^ and *Smo*^+/-^ intercrosses were treated by oral gavage with vehicle or agonist (15 mg/kg) at 6.5 and 7.5 dpc. Embryos were collected at 6-8 somite stages (E8.5) when the midline defects are first detectable in both *Shh*^-/-^ and *Smo*^-/-^ embryos, but prior to any general retardation of growth and development \[26,27\]. In both *Shh*^+/-^ and *Smo*^+/-^*Ptc1*^*lacZ*/+^ embryos, *Ptc1* was detected in ventral neural tube, somites and lateral plate mesoderm (Figure 3e,i,m). Treatment with the agonist dramatically enhanced and expanded the expression of *Ptc1* in these heterozygous embryos (Figure 3f,j,n). This was consistent with what we have observed in wild-type embryos (Figure 3a,b,c,d). It is worth noting that the agonist-treated embryos exhibited overgrowth of the headfolds and hindbrain, reminiscent of *Ptc1*^-/-^ embryos (compare Figure 3e,m with 3f,n).

*Shh*^-/-^ and *Smo*^-/-^ embryos at this stage (6-8 somites) started to show fused ventral lips of the cephalic folds, and a single continuous optic vesicle, indicating lack of a clearly defined midline (red arrow, Figure 3g, and data not shown). As expected, *Ptc1* expression was not detected in the ventral neural tube of the vehicle-treated *Shh*^-/-^ embryos (arrowhead, Figure 3g), whereas expression was seen in lateral plate mesoderm and weakly in somites (Figure 3g,k). This is most likely due to Ihh signaling in these tissues \[26\]. Both Shh and Ihh signaling were dependent on Smo, however, because *Ptc1* expression could not be detected in *Smo*^-/-^ embryos (Figure 3o).

Following agonist treatment, we observed that the neural tube and somite expression of *Ptc1* in *Shh*^-/-^ embryos was greater than vehicle-treated wild-type levels (compare Figure 3h,l with 3e,i). The midline defects in *Shh*^-/-^ embryos were at least partly rescued by agonist treatment (compare Figure 3g and 3h; red arrows). Like *Shh*^+/-^ embryos, *Shh*^-/-^ embryos had overgrown headfolds after administration of the Hh agonist (Figure 3f and h). In contrast, agonist treatment had no detectable effect on either morphology or *Ptc1* expression in *Smo*^-/-^ embryos (compare Figure 3o and p). In summary, these studies demonstrate that agonist activity *in vivo* does not depend upon Shh, but that Smo is absolutely required.

## Mechanism of action

### Chemical epistasis studies

We sought to determine the level at which the agonist acts in the Hh pathway, in cultured cell assays. To begin addressing this question, we used the Hh reporter cell line to conduct competition experiments between the Hh agonist and known Hh- signaling antagonists that block the pathway at different levels (Figure 4a). These include: a Hh-protein-blocking antibody, 5E1 \[23\]; a natural product derivative, cyclopamine \[9,10\] that has recently been shown to act downstream of Ptc, perhaps at the level of Smo \[11\]; a recently identified synthetic small-molecule inhibitor, Cur61414, which has inhibitory properties similar to cyclopamine \[17\]; and forskolin, an adenylate cyclase/protein kinase A activator that is thought to block Hh signaling by stimulating degradation of members of the Gli family of transcriptional activators \[2\].

The Hh-blocking antibody 5E1 had no effect on pathway activation by the agonist (Figure 4b), while forskolin (Figure 4c), cyclopamine (Figure 4d) and Cur61414 (Figure 4e), were all inhibitory. The lack of inhibition by 5E1 eliminates the possibility that the small molecule agonist activates signaling indirectly via stimulation of Hh expression. Furthermore, this supports the data showing that the agonist can activate signaling in *Shh*^-/-^ embryos (Figure 3) and suggests that the agonist function is not only downstream of the Hh protein but also independent of the endogenous Hh-signaling modulators, Tout veloux and HIP, that act via the Hh ligand \[2\]. The competition experiment with forskolin showed identical inhibition curves for Hh protein and the agonist, strongly suggesting that the action of the small molecule is upstream of the protein-kinase-A-sensitive step in the pathway. In contrast, the competition experiments with cyclopamine (Figure 4d) and Cur61414 (Figure 4e) showed that Hh protein and the agonist differ in their sensitivity to these antagonists. Specifically, the agonist appears somewhat resistant to the inhibitory effect of cyclopamine and Cur61414. Identical results were seen using the slightly less active cyclopamine-related natural compound jervine, and the more potent synthetic derivative of cyclopamine, KAAD-cyclopamine (data not shown). These results argue that the agonist activates the pathway downstream of the Hh-Ptc interaction while cyclopamine, Cur61414 and the agonist may act at a similar level in the Hh-signaling cascade.

### Regulation of Ptc and Smo by Hh protein and Hh agonist

Recent work in *Drosophila* tissue culture has shown that endogenous Ptc and Smo proteins are differentially affected by the addition of Hh to the growth medium \[5\]. Ptc was destabilized, while Smo accumulated following post-translational modification. To test whether similar phenomena occur in mammalian cells with Hh protein and agonist, we generated stable cell lines expressing two epitope-tagged proteins, Ptc coupled to green fluorescent protein, Ptc-GFP, and Smo coupled to a fragment of influenza hemagglutinin, HA-Smo. Figure 5a shows an immunoprecipitation (anti-GFP) plus protein blot (anti-Ptc) analysis of extracts from these cells treated for 4, 8 and 24 hours with vehicle, 25 nM Hh protein or 0.2 μM Hh agonist (see Figure 1e; Hh-Ag 1.2). This experiment shows that Ptc-GFP appears to be destabilized by Hh protein but not by the agonist. Similar results were seen at higher doses of agonist (up to 2 μM) and in several independent lines (data not shown). These data further support the idea that Hh protein and the agonist act in distinct ways to stimulate the pathway.

Figure 5b shows an immunoblot (anti-HA) of total extracts from HA-Smo-expressing cells treated for 2, 5, 8 and 20 hours with vehicle, 35 nM Hh protein or 0.5 μM Hh agonist. In contrast to the results with Ptc-GFP, incubation of cells with both Hh protein and the small-molecule agonist resulted in the apparent accumulation of HA-Smo protein after 5 hours of incubation. To test whether the accumulation of HA-Smo in response to Hh protein or the agonist required protein synthesis, a similar study was performed in the presence of cycloheximide (Figure 5c). Under these conditions, HA-Smo accumulation was detectable 5 hours after addition of either Hh protein or the agonist (Figure 5c); this result argues that the effect of Hh protein and the agonist on HA-Smo levels does not require new protein synthesis. Finally, with increasing concentrations of Hh protein and the agonist there is a clear dose-dependent increase of HA-Smo levels (Figure 5d). These effects on epitope-tagged Smo protein were observed in multiple lines (data not shown). Taken together, these data suggest that Hh protein and the agonist share the ability to stabilize Smo, but only Hh protein can destabilize Ptc. Yet the agonist is fully capable of activating the full signaling pathway.

## Testing Smo as the molecular target

### Binding in whole cells

Our biochemistry experiments (above) show that the agonist modulates Smo levels, and thus may activate Hh signaling by directly binding Smo. To explore this possibility we tested whether a tritiated form of the agonist analog Hh-Ag 1.5 could form a complex with Smo, when Smo is transiently overexpressed in 293T cells. Figure 6a shows immunoprecipitable counts of extracts from cells incubated at 37°C for 2 hours with 5 nM \[^3^H\]-Hh-Ag 1.5 either in the absence (columns 1-3) or presence of competitors (columns 4-9).

Immunocomplexes from untransfected control and β-adrenergic-receptor transfected cells did not contain significant counts (Figure 6a, columns 1, 2). Immunocomplexes derived from cells expressing Smo (Figure 6a, column 3) resulted in the recovery of approximately 40,000 of the 800,000 added counts, however. To test the specificity of this apparent Hh-Ag/Smo complex, cells were incubated with 5 μM (1000-fold molar excess) of unlabeled Hh Ag 1.5 or an unlabeled, signaling-inactive but structurally similar compound, an Hh-Ag 1.1 derivative that has a two-carbon linker in place of the cyclohexane ring (Figure 6a; Hh-Ag 1.5, column 4; Hh-Ag control, column 5). The addition of the unlabeled Hh-Ag 1.5, but not the inactive Hh-Ag 1.1 derivative agonist control, resulted in the complete absence of counts in the immunocomplex. These results suggest that a stable, specific interaction can form between Smo and the Hh agonist.

It has been shown that the Hh-pathway antagonists cyclopamine and Cur61414 block signaling in a Ptc-independent manner \[11,17\] and therefore may act directly on Smo. Having established a binding assay for a small-molecule agonist binding to Smo-expressing cells, we next tested whether the Hh antagonists could selectively compete out binding of \[^3^H\]-Hh-Ag 1.5. To perform theses studies, Smo-overexpressing 293T cells were incubated for 2 hours at 37°C with 5 nM \[^3^H\]-Hh-Ag 1.5 in the presence of either KAAD-cyclopamine at 5 μM (Figure 6a, column 6), the related but inactive plant compound tomatadine at 5 μM (Figure 6a; Antag control 1, column 7), Cur61414 at 5 μM (Figure 6a, column 8), or a related but inactive Cur61414 derivative (Figure 6a; Antag control 2, column 9) at 5 μM. These data show that the Hh-signaling inhibitors, but not structurally related inactive compounds, can significantly compete with the binding of the Hh agonist to Smo-expressing cells. This supports the model that all of these small-molecule modulators of Hh signaling are direct ligands of Smo.

We next asked whether a derivative of the Hh agonist carrying a photoactivatable crosslinker could be coupled directly to Smo, to facilitate further biochemical characterization of the binding site. To perform this experiment we synthesized a tritiated diazirine derivative of Hh-Ag 1.2 with an EC~50~ in the cell-based assay of 35 nM (data not shown). We incubated this compound at 0.5 μM with HA-Smo- or control, GFP-transfected 293T cells and subsequently ultraviolet-irradiated them to initiate crosslinking. Fractionation by SDS-polyacrylamide gel electrophoresis and autoradiography of the resulting immunocomplexes from these cells showed crosslinking exclusively to HA-Smo, but with an efficiency of less than 1% (data not shown). This result demonstrates that a Hh-agonist derivative can be covalently crosslinked to Smo in living cells. More efficient crosslinkers are required to extend these studies, however.

### Cell-free membrane-binding assays

To test whether the Hh agonist could interact with Smo *in vitro*, we transiently overexpressed murine Smo, murine Ptc, rat β2-adrenergic receptor and GFP in 293T cells, harvested membranes and performed a filtration membrane-binding assay in a 96-well plate with \[^3^H\]-Hh-Ag 1.5 added at 2 nM. Figure 6b shows a bar graph of the bound counts from these binding assays (murine Smo, column 1; GFP, column 2; βAR, column 3; murine Ptc1, column 4; and a no-membrane plate control, column 5). The no-membrane control (column 5) was included to show the degree of non-specific binding to the filter-plate apparatus. The Smo-containing membranes (column 1) are the only samples that exhibit significant binding above that seen in the absence of membranes.

To assess the specificity of binding, we repeated the experiment in the presence and absence of a 1000-fold molar excess of unlabeled agonist (2 μM). The addition of 'cold' compound completely competed out these counts (Figure 6c, column 1 compared to column 2). To control for this observation, we added an unlabeled, inactive Hh agonist to the binding assay at 2 μM (a 1000-fold molar excess). This compound was unable to compete out the binding of \[^3^H\]-Hh-Ag 1.5 to the Smo-containing membranes (Figure 6c, column 3). These results argue that Smo and the agonist form a specific complex *in vitro*, as predicted by the whole cell/immunocomplex binding assay (Figure 6a).

Having established an *in vitro* binding assay for the Hh agonist to Smo, we next tested whether the Ptc-independent Hh antagonists could selectively compete with the interaction. Binding was assayed in the presence of KAAD-cyclopamine at 10 μM (Figure 6c, column 4), tomatadine at 10 μM (Figure 6c; Antag control 1, column 5), Cur61414 at 10 μM (Figure 6c, column 6), or the inactive Cur61414 derivative (Figure 6c; Antag control 2, column 7) at 10 μM. These data show that the Hh-signaling inhibitors, but not structurally related inactive compounds, can significantly compete with the binding of the Hh agonist to Smo membranes.

## Kinetics, saturation and competition binding analysis

Next, we sought to generate association, dissociation and saturation-binding curves, in order to derive affinity constants for the interaction of the Hh agonist and Smo. To control for nonspecific binding we used either Cur61414 or Hh-Ag 1.5 as unlabeled competitors. Similar results were generated if control membranes (from cells transfected with GFP, βAR, or Ptc) were used to define the non-specific level (data not shown).

First, we performed a kinetic analysis to establish the reversibility of the binding reaction and the approximate incubation time required for equilibrium binding studies. Association assays were performed at 37°C by combining 2 nM \[^3^H\]-Hh-Ag 1.5 with Smo-containing membranes for various times prior to harvesting and counting. Dissociation studies were initiated by adding 2 μM unlabeled Hh-Ag 1.5 after 2 hours of association. Samples were then incubated for 1-26 hours prior to harvesting and counting. Figure 7a shows the association and dissociation phases of agonist binding to Smo-containing membranes. Using the Prism GraphPad software, these data were fit to one-phase exponential association and decay curves, respectively, and gave an association t½ of approximately 1 hour and a dissociation t½ of approximately 10 hours. These results demonstrate that the binding of the agonist to Smo is reversible and that equilibrium binding will require binding reaction times of approximately 50 hours (five times the t½ of dissociation).

Next, we performed a saturation binding experiment. To establish total, nonspecific, and specific binding curves (Figure 7b), we added a range of \[^3^H\]-Hh-Ag 1.5 concentrations (0.01-3 nM) in the presence or absence of unlabeled Hh-Ag-1.5 at 2 μM. Identical results were seen if Cur61414 at 10 μM was used as the competitor (data not shown). On the basis of the binding kinetics, incubations were carried out for approximately 45 hours at 37°C, to allow for equilibrium to be reached. Using the Prism GraphPad software to perform non-linear regression analysis and curve fitting, we concluded that the data best fit a simple one-site binding model with a predicted K~d~ of 0.37 nM for Hh-Ag 1.5. This K~d~ is in general agreement with the EC~50~ values observed in the cell-based assay (0.37 nM as compared to 1 nM).

To further validate our binding results, we performed a competition assay using several agonist derivatives across a range of concentrations (0.01 nM to 1 μM). Figure 7c shows the competition curves for Hh-Ag 1.5, Hh-Ag 1.3, Hh-Ag 1.2, Hh-Ag 1.1, and the signaling-inactive Hh-Ag 1.1 derivative described above. With the exception of the inactive derivative, these compounds all compete out the binding of \[^3^H\]-Hh-Ag 1.5 (0.4 nM) to the Smo-containing membranes. These data are best fit to a single-binding-site competition model that predicts the following K~i~ values: Hh-Ag 1.5, 0.52 nM; Hh-Ag 1.3, 8.4 nM; Hh-Ag 1.2, 22 nM; and Hh-Ag 1.1, 96 nM. These K~i~ values are in general agreement with the agonist EC~50~ values in cell culture for these compounds, with the exception that the Hh-Ag 1.1 compound is not as potent in signaling assays (EC~50~ 2 μM) as its K~i~ (96 nM) might predict. This suggests an uncoupling of binding and signaling for certain agonists. Although binding affinities and signaling efficacy can correspond for certain ligand/receptor complexes, exceptions often arise \[28\] because binding affinity does not necessarily measure the ability of a compound to induce an active receptor conformation. As a control for these binding studies, identical competition experiments were performed with membranes from cells transfected with GFP, or with the β-adrenergic receptor. No specific binding or apparent competition was seen under these conditions (data not shown).

We next compared binding of KAAD-cyclopamine, Cur61414 and Hh-Ag 1.5 to a constitutively active mutant of Smo (Smo^act^) or to wild-type Smo (Smo^wt^). The two Hh-signaling antagonists, KAAD-cyclopamine and Cur61414, have shown decreased potency on Smo^act^-expressing cells, leading to the speculation that they may bind this mutant form of Smo less well than the wild-type form \[11,17\]. We sought to determine whether the Hh agonist binds Smo^act^ with a higher affinity, an observation seen with certain ligands and constitutively active mutants of GPCRs \[29\]. To perform this experiment, we isolated membranes from cells transfected with a cDNA construct encoding a tryptophan-to-leucine mutation at residue 539 (W539L) of murine Smo. This oncogenic mutation has been found in human basal cell carcinoma \[3\] and the correspondingly mutated protein is capable of ligand-independent activation of the Hh pathway in cell-culture assays \[11\]. A kinetic and saturation binding assay with Smo^act^-containing membranes showed that this mutant protein binds the Hh agonist with an affinity identical to that of Smo^wt^ (data not shown).

Using Smo^act^- and Smo^wt^-containing membranes, we then performed competition binding studies by adding increasing concentrations of unlabeled Hh-Ag 1.5, KAAD-cyclopamine or Cur61414 in the presence of \[^3^H\]-Hh-Ag 1.5 (0.4 nM). These binding curves (Figure 7d) can be fit to a single-site competition model. Although the K~i~ for Hh-Ag 1.5 on Smo^act^-containing membranes was essentially identical to that observed for Smo^wt^-containing membranes (approximately 0.5 nM), the K~i~ values of the Hh antagonists were seven-fold higher on the Smo^act^- compared to the Smo^wt^-containing membranes for both KAAD-cyclopamine (38.3 nM versus 5.8 nM) and Cur61414 (309 nM versus 44 nM). These results strongly support the model, initially hypothesized for cyclopamine \[11\], that the reduced potency observed for Hh antagonists on Smo^act^-expressing cells is directly due to a reduced affinity of the antagonist for the mutated Smo protein. The agonist, on the other hand, would be predicted to bind to a site on Smo that is not affected by this gain-of-function Smo^act^ mutation.

# Discussion

Hh signal transduction has been the focus of intense research over the past decade due to the central role it plays in development and its emerging biomedical relevance in areas ranging from regenerative medicine to oncology \[2,30\]. Our goal in these studies was to isolate and characterize small-molecule modulators of Hh signaling in order to understand better the regulation of pathway activation and to generate potential therapeutics. Our work shows firstly that it is possible to identify potent small molecule agonists of Hh signaling, secondly that these compounds can mimic the effects of recombinant Hh protein in multiple assays used to define the properties of Hh signaling, thirdly that these compounds act by binding directly to Smo, and finally that two Ptc-independent inhibitors of Hh signaling compete for this binding to Smo, strongly suggesting they too act directly on Smo.

## Models of Smo-ligand interaction

To interpret the results of the competition binding studies, we assume that the mutation in Smo^act^, like those in constitutively activate mutants of other GPCRs \[29\], indirectly influences ligand binding by creating a change in the normal equilibrium between the different conformations of Smo. Thus, the mutation would not directly influence the binding pocket for either ligand. A simple two-state model (Figure 8a) predicts that the agonist (Ag, green square) and the antagonist (Ant, pink circle) compete for the same site on Smo to activate or inactivate Hh-pathway signaling. It also suggests that antagonists should bind Smo^act^ with a lower affinity than they bind Smo^wt^, while the agonist should bind with a higher affinity, as it prefers the active conformation. Such a model cannot accommodate our observations with the gain-of-function Smo mutant. Thus we introduce a ternary complex model (Figure 8b), used traditionally to describe the behavior of GPCRs in binding studies with agonist and antagonists \[31\], as well as constitutively active receptors \[29\]. The ternary complex model for Hh signaling suggests that there are two independent binding sites on Smo^wt^, one specific for the agonist and another specific for antagonists. Binding at either site would decrease the affinity for interactions at the other site (allosteric binding with high negative cooperativity). The agonist-bound form represents the normal activated state, while the antagonist-bound form is considered the inactive conformation. There are also other conformations that would not be bound, or would be transiently bound, by both ligands. A signaling pathway coupler, or effector (in blue), is proposed to bind the activated state of Smo^wt^ so as to generate a complex competent to initiate Hh signaling. Throughout the discussion the term 'coupler/effector' is used to describe an unknown molecule that binds activated Smo in such a way as to trigger signal transduction. The model further suggests that the Smo^act^ protein resides in a stable conformation in the absence of agonist that is capable of forming an active coupler/effector complex resistant to antagonist, but not agonist, binding.

Specifically, our data suggest that the agonist binds and stabilizes (or induces) an active signaling state of Smo while the antagonists bind and stabilize (or induce) an inactive form. Furthermore, the gain-of-function Smo mutation renders the protein less sensitive to the inhibitors, presumably because the amino-acid substitution directly stabilizes or induces an active conformation. On the basis of a simple two-state model, one might predict an increased affinity of the agonist for the mutant form, but in our studies binding of the agonist, unlike the antagonist, is not affected by the activating mutation, suggesting that a more complex model requiring two binding sites and perhaps multiple active conformations is needed to account for the observations. Thus, we propose a variation of the classic 'ternary complex model' (Figure 8), a decades-old paradigm that has provided the foundation for describing ligand induced conformational changes of GPCRs \[31\].

Briefly, if this model is applied to Hh signaling, it proposes that, firstly, agonist and antagonists act at independent sites to select active and inactive conformations of Smo; secondly, that Smo engages an undefined coupler/effector, when it is in its signaling state; and finally, that the gain-of-function mutant form of Smo, Smo^act^, adopts an abnormal conformation that resembles the coupler/effector-bound signaling state of Smo with low affinity for antagonists but normal affinity for the agonist.

## Activating Hh signaling through the GPCR-like Smo receptor

As a receptor class, GPCRs are considered excellent drug targets because they are often regulated through interactions with small natural ligands \[32,33\]. Specifically, studies of classic GPCRs, such as the β-adrenergic receptors, show that in the absence of endogenous ligand (agonists) these receptors exist in multiple interconvertible conformations that are predominately inactive \[34\]. Upon exposure to their natural ligands, however, the active receptor forms are preferentially stabilized, allowing them to readily engage G-protein couplers and to create signaling-competent complexes. Multiple compound classes have been isolated on the basis of their ability to compete for the binding of β-adrenergic receptors by their natural ligands. These competitors can mimic the natural ligand activity (agonists) or interfere with it (antagonists).

In addition to the binding sites for natural or endogenous agonists (orthosteric sites), many GPCRs have also been found to have allosteric sites \[35\]. These sites can bind natural ligands, as in the case of Zn ions and heparin for the dopamine and neurokinin receptors, respectively, or bind synthetic drugs such gallamine, in the case of the muscarinic receptors \[35\]. Binding of small molecules to these allosteric sites can modulate activity of a receptor without directly mimicking or competing out the interaction of ligands to the orthosteric sites. In summary, GPCRs have an array of potential regulatory binding sites, or potential drug targets.

How does Smo compare with other GPCRs with regard to the properties described above? Although there is clear structural homology between Smo and other GPCRs, endogenous ligands have yet to be discovered. Early models of Hh signaling proposed a Hh-regulated Ptc-Smo complex that directly controlled the conformation of Smo, making endogenous ligands unnecessary. But recent studies argue against this stoichiometric model \[5,36\], indicating that perhaps natural ligands should be considered. Furthermore, regarding G-protein-effector coupling for Smo, the results are also equivocal. Although no compelling data have been presented that directly link classic G-protein activation \[37\] to the canonical Hh pathway involving Ci/Gli stimulation \[2\], recent studies show that under certain conditions Smo can engage G-protein subunits in a Ptc-dependent manner \[38\] and that G-protein-mediated cAMP modulation may underlie certain effects of Hh on neuronal tissue \[39\].

Finally, with respect to pharmacological properties, our studies indicate that Smo behaves like a classic GPCR in many regards and that the models used to describe this large family of receptors can be applied to Hh signaling (Figure 8). Two relatively novel concepts for Hh signaling are raised by these GPCR models: firstly, the importance of considering the active Smo-coupler/effector complexes when modeling pathway regulation, and secondly the potential for endogenous ligands in the regulation of Smo activity.

## GPCR models for Smo and potential mechanisms of Ptc function

A recent study in *Drosophila* suggested that Hh stimulates the pathway via Ptc degradation and, as a result, Smo is stabilized through extensive phosphorylation at the plasma membrane where it initiates signaling \[5\]. This led to speculation that a Ptc-regulated phosphatase may control the subcellular distribution and stability of Smo \[5\]. Although suggestive, the studies in *Drosophila* did not establish that phosphorylation, translocation or stabilization of Smo is required for pathway stimulation. It is plausible that some or all of these effects on Smo result from a feedback inhibition mechanism that targets the activated Smo receptor. Interestingly, while we did observe stabilization of Smo by Hh in our mammalian cell experiments (Figure 5), we did not detect Smo phosphorylation or translocation to the plasma membrane (data not shown). Perhaps the cellular (or species) context dictates the degree to which the active conformations of Smo are associated with such changes. Thus, in considering models of Ptc function based on Hh-stimulated effects it is important to consider whether the form of Smo that is being observed is the active (coupler/effector bound) signaling state, or perhaps a downregulated and inactive form.

Several potential points for regulation by Ptc during the formation of a Smo-coupler/effector complex are apparent in a ternary complex model. The ideas proposed for the *Drosophila* system, in which Ptc may affect the levels of Smo or its subcellular localization, are easily accommodated. Ptc-induced instability of Smo protein would indirectly reduce the concentration of an active Smo-coupler/effector complex. Furthermore, limiting access of Smo to its effector through targeted vesicle trafficking would prevent a signaling-competent complex from forming. Alternatively, Ptc could more directly maintain Smo in an inactive state. On the basis of our studies it is tempting to speculate that native small molecules with properties similar to our agonist and antagonists act directly on Smo in a Ptc-dependent manner. These putative endogenous Smo modulators could represent orthosteric or allosteric ligands.

The simplest model would have Ptc acting catalytically to dock a natural antagonist directly onto (or remove an agonist from) Smo. A more complex model would involve Ptc restricting the distribution of Smo such that it is forced into compartments containing the natural antagonists or lacking the natural agonists. Finally, Ptc could control the distribution of the endogenous small-molecule modulators themselves. Ptc shares sequence homology with molecules associated with vesicle trafficking and transporter activity, namely SCAP and NPC1 \[36,40-42\]; if it also shares the activities of these molecules, as suggested by a recent study \[36\], then the possibility of the existence of endogenous Smo ligands that are docked via Ptc should be explored.

## Therapeutic potential of a Hh-pathway agonist

Various studies in mammals have shown that *Hh* genes are expressed in discrete areas of the adult organism and may function in the normal maintenance of mature organ systems \[43-46\]. In addition, the regenerative healing of vascular and skeletal tissues following acute injuries appears to be aided by re-activating the Hh-signaling cascade \[47,48\]. Taken together, these observations suggest that the Hh pathway may represent a point of intervention for treating certain degenerative disorders. Two recent studies in models of Parkinson's disease and peripheral nerve damage support this claim, by demonstrating that pathway activation with a Hh-protein ligand has therapeutic value \[49,50\]. On the basis of our current understanding of these models and the specific mechanism of action of the Hh agonists, we predict that an agonist-derivative with low toxicity and favorable pharmacokinetics would replicate these positive results. As a drug, a Hh agonist would represent an attractive alternative to an expensive Hh-protein therapeutic. Beyond the economics, for disorders of the central nervous system a small molecule with the potential to cross the blood-brain barrier would eliminate the need for injections directly into the brain, the current delivery mode for central nervous system protein therapies.

# Materials and methods

## Chemical libraries and medicinal chemistry

The compound libraries used in our screens were purchased from a number of commercial vendors and were primarily generated by combinatorial chemistry approaches. The Hh-agonist class was isolated from a library synthesized by Oxford Asymmetry International, now EvotecOAI. The derivatization of this compound class utilized standard procedures, the details of which will be published elsewhere.

## Cultured cell line assays

TM3 and C3H10T1/2 cells (ATCC; Manassas, USA) were maintained according to the instructions of ATCC. Stable Hh-signaling reporter cell lines were established by G418 selection following transfection with a luciferase reporter plasmid \[20\] containing the neomycin-resistance gene. Hh signaling was monitored by plating cells at 70% confluence in growth medium. After 24 hours the cells were changed to 0.5% serum-containing medium, and Hh protein or compounds were added; 24 hours later the cells were either monitored for luciferase activity using the Luc-lite assay kit (Packard Instrument Company, Meriden, USA) or harvested for RNA isolation using an RNA isolation kit (Qiagen; Valencia, USA). RNA was subjected to quantitative RT-PCR analysis (Taqman; Applied Biosystems, Foster City, USA) utilizing *Gli1, Ptc1* and *GAPDH* primers and probes. Assays were run on a Prism 7700 instrument (ABI; Applied Biosystems).

## Recombinant Hh protein

The Hh protein used in the studies described here was bacterially overexpressed amino-terminal human Shh modified at its amino-terminal cysteine by an octyl maleimide moiety \[21\]. This lipophilic Shh form showed comparable potency to native Shh in the cell-based reporter assay (data not shown).

## Retroviral cell lines

Mouse *Smo* and *Ptc1* genes were introduced by a retroviral approach utilizing the pLPCX vector (Clontech; Palo Alto, USA) to limit the copy number per cell. Stable HA-Smo and Ptc-GFP lines were established by puromycin selection following infection of TM3 cells with the respective retroviruses. TM3 cells expressing both epitope-tagged Ptc and Smo were derived by infecting first with an HA-Smo construct and subsequently with a Ptc-GFP construct. The levels of Ptc were relatively low in these lines and a standard immunoprecipitation procedure followed by western blotting was required to detect the Ptc-GFP protein. The HA-Smo protein was highly expressed and was easily detected in western blots of whole cell extracts. The HA tag was sub-cloned into the *Smo* gene so that it would reside immediately after the Smo signal sequence. The GFP tag was inserted before the stop codon of the *Ptc* open reading frame.

## *In utero* Hh-signaling assays

Generation of *Ptc1*^*lacZ*^, *Shh* and *Smo* mutant mice has been described previously \[25,26\]. *Ptc1*^*lacZ*/+^ mice were kindly provided by Matthew Scott. *Shh*^+/-^ and *Smo*^+/-^ mice were kindly provided by Andrew McMahon. Agonist solution was prepared in fine suspension in 0.5% methylcellulose/0.2% Tween 80 at 1.5 mg/ml. Compound was administered by oral gavage to pregnant mice once a day for two days at 100 μl per 10 g body weight. Embryos were collected 24 hours later. Whole-mount *in situ* hybridization with *Ptc1* probe and X-gal staining for whole-mount β-galactosidase detection were performed as described \[26\]. For histology, embryos stained with X-gal were post-fixed in 4% paraformaldehyde, wax-embedded, and 20 μm sections were prepared.

## Primary cerebellar cultures

Cerebellar neurons were dissected out of postnatal (one week) rat brains, and placed into primary cell culture. Briefly, cells were placed in 96-well plates at a density of approximately 150,000 cells per well in basal medium of Eagle (Gibco; Carlsbad, USA) supplemented with 26 mM KCl, 2 mM glutamine and 10% calf serum. Treatment agents were added once, on the first day of culture (0 DIV). Cells were left in culture until 2 DIV, when \[^3^H\]-thymidine was added for 5 hours. Cells were then lysed, and the incorporation of \[^3^H\]-thymidine was determined by scintillation counting.

## Neural plate explant assay

Intermediate regions of the open neural tube (i-explants) were dissected from stage 10-11 chick embryos and embedded in collagen gel \[23\]. Explants were cultured in Ham-F12 supplemented with 3 g/l D-glucose, Mito Serum Extender (Collaborative Research; Bedford, USA), penicillin/streptomycin (Gibco), 2 mM L-glutamine (Gibco) and Hh-Ag 1.3 (0.1, 1, 10, 20, 200 and 1000 nM prepared as 1000X stocks in DMSO; n = 6 explants). As a control, some explants were cultured with vehicle alone or with octylated Hh-N recombinant protein. Cultures were fixed after 22 hours, stained with mouse monoclonal antibodies against Pax7, MNR2 or rabbit polyclonal antibodies against Nkx2.2 and the number of immunoreactive cells per explant counted.

## Whole cell/immunocomplex binding assay

Cultured cells - 70% confluent 293T cells in 6-well plates - were either left untransfected or transfected using Fugene6 with a pCDNA3.1 construct containing HA-tagged Smo or v5-epitope tagged β2AR (Invitrogen; Carlsbad, USA). After 48 hours cells were switched from 10% fetal bovine serum containing DME media to 0.5% FBS containing media supplemented with either 5 nM \[^3^H\]-Hh-Ag 1.5 alone or 5 nM \[^3^H\]-agonist plus 5 μM of various competitors (see Results). After 2 hours of incubation at 37°C, cells were washed one time with PBS and subsequently lysed in 0.5 ml of lysis/wash buffer containing 1% NP40 in Tris-buffered saline plus an EDTA-free protease inhibitor cocktail (Roche; Indianapolis, USA) for 20 minutes at 4°C. Cell extracts were spun at 14,000 rpm in a microcentrifuge and supernatants were incubated for 40-60 minutes with either anti-HA beads (Roche) or anti-v5 antibody (Invitrogen) and Protein A beads (Pierce; Rockford, USA) to form immunocomplexes. Immunobeads were then spun down and washed three times with 0.5 ml lysis/wash buffer per wash. The washed pellets were then resuspended in SDS sample buffer and combined with scintillation fluid. Counts per minute (cpm) for each sample were then determined in a scintillation counter (Packard topcount).

## Membrane binding assays

Membranes were prepared as follows. Briefly, approximately 10^8^ cells were transfected with pcDNA 3.1 constructs (Invitrogen) bearing either murine *Smo* (wild-type or W539L mutant), GFP, rat β2AR (Invitrogen), or murine *Ptc* cDNAs using Fugene 6 (Roche). After 48 hours cells were harvested by scraping in PBS, centrifuged at 1,000 × *g* for 10 minutes, and gently resuspended in around 10 ml of a 50 mM Tris pH 7.5, 250 mM sucrose buffer containing an EDTA-free protease inhibitor cocktail (Roche). This cell suspension was then placed in a nitrogen cavitation device (Parr Instrument Co, Moline, USA) and exposed to nitrogen gas (230 psi) for 10 minutes. Lysed cells were released from the device and centrifuged at 20,000 rpm in an SS34 rotor for 20 minutes at 4°C. Supernatants were discarded and the pellets were resuspended in 10% sucrose, 50 mM Tris pH 7.5, 5 mM MgCl, 1 mM EDTA solution using three 10-second pulses with a Polytron (Brinkman; Westbury, USA) at a power setting of 12. Using these membranes, filtration binding assays were performed according to previously described protocols \[28\]. To reduce nonspecific binding, 96-well filtration plates (fiberglass FB filters; Millipore, Bedford, USA) were pre-coated as suggested by the manufacturer with 0.5% polyethyleneimine + 0.1% BSA and then washed four times with 0.1% BSA.

For association and dissociation studies, membranes (1.5 μg total protein) were incubated in polypropylene tubes with 2 nM \[^3^H\]-Hh-Ag 1.5 in the presence or absence of 2 μM competitor in binding buffer (50 mM Tris pH 7.5, 5 mM MgCl, 1 mM EDTA, 0.1 % bovine serum albumin) plus EDTA-free protease inhibitor cocktail (Roche) in a final volume of 250 μl for 1-26 hours at 37°C. For saturation and competition binding analysis, membranes (1.5 μg total protein) were incubated on the plates with various concentrations of the \[^3^H\]-Hh-Ag 1.5 (plus and minus competitors) in binding buffer plus EDTA-free protease inhibitor cocktail (Roche) at a final volume of 1 ml for approximately 45 hours at 37°C to allow binding to reach apparent equilibrium. Binding reaction mixtures (0.2 ml for association/dissociation studies and 0.75 ml in saturation and competition experiments) were then transferred to the pre-coated 96-well filtration plates (Millipore fiberglass FB filters), filtered and washed over a vacuum manifold with six 300 μl per well washes of binding buffer supplemented with 2% hydoxypropyl cyclodextrin (HPCD; Sigma; ST Louis, USA) + 0.1% BSA to decrease non-specific binding. Identical results were obtained if incubations were done in borosilicate glass or siliconized plastic tubes. Centrifugation assays were also performed that replicate the filtration assay results (data not shown). Additionally, these experiments showed that the extent of ligand depletion was less than 10% in these studies. Binding-kinetics experiments were performed similarly to the saturation and competition studies. All binding data were evaluated using a nonlinear regression analysis program (Prism; GraphPad; San Diego, USA). K~i~ values were calculated using the Cheng-Prusoff correction equation \[28\], where K~i~ = IC~50~/1+ \[L\]/K~d~, and K~d~ for Hh-Ag 1.5 was determined to be 0.37 nM by the saturation analysis.

## Note added in proof

Related results demonstrating the action of cyclopamine on Smo have been reported by Beachy and colleagues \[51,52\].

### Acknowledgments

We thank Douglas Melton, Andrew McMahon, Thomas Jessell and Phillip Beachy for comments on the manuscript. Shh-, Smo- and Ptc-transgenic lines were graciously provided by the labs of Andrew McMahon and Matthew Scott. We thank James Chen and Phillip Beachy for providing us with KAAD-cyclopamine and for sharing their observations that cyclopamine and its derivatives bind directly to Smoothened and that our agonists compete this interaction. Superior medicinal chemistry support as well as screening libraries were provided by Larry Kruse, Andy Boyd, Steven Price and the team at Evotec/Oxford Asymmetry International.

## Figures and Tables


author: Vandana Mehta; C Balachandran; Sudhir Nayak
date: 2008
references:
title: HERPETIFORM PEMPHIGUS CLINICALLY RESEMBLING BULLOUS PEMPHIGOID

Herpetiform pemphigus was first introduced by Jablonska in 1975, as a variant of pemphigus that combines the clinical features of dermatitis herpetiformis with the immunological features of pemphigus.1 Because of its rare and atypical clinical presentation, patients are often initially diagnosed as Dermatitis herpetiformis, Linear IgA bullous disease, Bullous pemphigoid or Pemphigus foliaceus. We report a case of herpetiform pemphigus in an elderly lady which was initially misdiagnosed as bullous pemphigoid, however histology and immunofluorescence helped in clinching the diagnosis.

A 75-year-old woman presented with a one year history of an intensely pruritic eruption which began on her extremities and later expanded to involve the trunk with only partial response to topical corticosteroids and antihistamines. She was otherwise healthy with no accompanying constitutional symptoms and not on any other medications. Cutaneous examination revealed a symmetric polymorphous eruption of old and new lesions, comprising of erythematous urticarial papules and plaques studded with clear and cloudy vesicles (Fig. 1), ulcerated and crusted lesions arranged in herpetiform groups. Oral and genital mucosa were normal. Clinically a differential diagnosis of bullous pemphogoid was entertained, however a Tzanck smear from a clear vesicle showed abundant neutrophils, with only few eosinophils. Biopsy for histopathology showed mild epidermal hyperplasia with a subcorneal blister containing neutrophils and few acantholytic cells. Predominant areas of neutrophilic spongiosis with a superficial perivascular infiltrate of eosinophils and neutrophils were seen in the papillary dermis. Direct and indirect immunofluorescence demonstrated strong intercellular deposits of IgG and C3 in the upper epidermis. Based on the histology and immunofluorescence findings, the diagnosis was revised to herpetiform pemphigus and patient was started on oral steroids.

Pemphigus herpetiformis (PH) is recognized as a distinct subset of pemphigus by its pruritus, rarity of mucosal involvement, invariable presence of eosinophils on histology and its tendency to respond to sulfones. Various terms that have been used to describe it are *acantholytic herpetiform dermatitis*, *pemphigus controlled by sulfapyridine*, *mixed bullous disease* and *eosinophilic spongiosis in pemphigus*. Clinically patients present with severe intractable pruritus and erythematous urticarial papules and plaques with occasional vesicles in a herpetiform arrangement. Even though the clinical picture is reminiscent of dermatitis herpetiformis, the histological findings are variable and include eosinophilic spongiosis, subcorneal pustules with minimal or no apparent acantholyis.2 Though eosinophilic spongoisis is classically seen in pemphigus herpetiformis, neutrophilic spongiosis may also be encountered and should be recognized as an important diagnostic clue3 as was seen in our case. Definitive diagnosis is established by immunopathology which shows *in vivo* bound IgG deposits on the keratinocyte cell surface primarily in the upper epidermis. The auto-antibodies in most patients of PH target desmoglein 1 and because desmoglein 1 is predominantly located in the upper epidermis, there is preferential binding of IgG to the upper epidermis as was seen in our case. Why the auto-antibodies in PH and pemphigus vulgaris give rise to different clinical presentations could be explained by their preferential binding to different epitopes on the same antigen molecule.4

In our case, the presence of severe pruritus in an elderly female with blisters and absence of mucosal involvement made us think of bullous pemphigoid, however, to our surprise the histology and immunofluorescence turned out to be PH. Patient was started on oral steroids along with dapsone and is currently under follow-up.

# References


abstract: # OBJECTIVE
  .
  The Diabetes Prevention Program (DPP) and its Outcomes Study (DPPOS) demonstrated that either intensive lifestyle intervention or metformin could prevent type 2 diabetes in high-risk adults for at least 10 years after randomization. We report the 10-year within-trial cost-effectiveness of the interventions.
  .
  # RESEARCH DESIGN AND METHODS
  .
  Data on resource utilization, cost, and quality of life were collected prospectively. Economic analyses were performed from health system and societal perspectives.
  .
  # RESULTS
  .
  Over 10 years, the cumulative, undiscounted per capita direct medical costs of the interventions, as implemented during the DPP, were greater for lifestyle (\$4,601) than metformin (\$2,300) or placebo (\$769). The cumulative direct medical costs of care outside the DPP/DPPOS were least for lifestyle (\$24,563 lifestyle vs. \$25,616 metformin vs. \$27,468 placebo). The cumulative, combined total direct medical costs were greatest for lifestyle and least for metformin (\$29,164 lifestyle vs. \$27,915 metformin vs. \$28,236 placebo). The cumulative quality-adjusted life-years (QALYs) accrued over 10 years were greater for lifestyle (6.81) than metformin (6.69) or placebo (6.67). When costs and outcomes were discounted at 3%, lifestyle cost \$10,037 per QALY, and metformin had slightly lower costs and nearly the same QALYs as placebo.
  .
  # CONCLUSIONS
  .
  Over 10 years, from a payer perspective, lifestyle was cost-effective and metformin was marginally cost-saving compared with placebo. Investment in lifestyle and metformin interventions for diabetes prevention in high-risk adults provides good value for the money spent.
author: aCorresponding author: Diabetes Prevention Program Coordinating Center, <firstname.lastname@example.com>. [^1][^2][^3][^4][^5]
date: 2012-04
references:
subtitle: : An intent-to-treat analysis of the DPP/DPPOS
title: The 10-Year Cost-Effectiveness of Lifestyle Intervention or Metformin for Diabetes Prevention

Intensive lifestyle and metformin interventions can delay or prevent progression from impaired glucose tolerance (IGT) to type 2 diabetes (1–3). The Diabetes Prevention Program (DPP), a randomized controlled clinical trial, demonstrated that compared with the placebo intervention (placebo), the intensive lifestyle intervention (lifestyle) reduced the incidence of type 2 diabetes by 58%, and the metformin intervention (metformin) reduced the incidence of type 2 diabetes by 31% over 2.8 years (3). The Diabetes Prevention Program Outcomes Study (DPPOS) is a long-term follow-up of the DPP participants to investigate whether the delay in the development of diabetes observed during the DPP is sustained and to assess the long-term effects of the interventions on health.

The DPPOS has followed participants for an additional 7 years during which time participants in lifestyle and metformin were encouraged to continue those interventions, and all participants were offered a group lifestyle intervention (4). The incidence of diabetes during the 10-year average follow-up after randomization was reduced by 34% in those initially randomized to lifestyle and 18% in those initially randomized to metformin compared with placebo (4). Previously, we reported the resource utilization and costs of care in the DPP (5), and the cost per quality-adjusted life-year (QALY) gained over the 3-year timeframe of the randomized controlled clinical trial (6). We also used 3-year DPP data and a computer model to simulate the longer-term cost-effectiveness of the interventions. Although we (7) and others (8,9) suggested that lifestyle would be cost-effective or even cost-saving over the long term, one analysis suggested that it might be too expensive for health plans or a national program to implement (10). In this report, we present a within trial intent-to-treat analysis spanning the combined 10-year DPP/DPPOS timeframe to assess the longer-term cost-effectiveness of lifestyle and metformin for diabetes prevention. All of the DPPOS clinical centers as well as the DPP Coordinating Center had institutional review board approvals. All participants gave written informed consent.

# RESEARCH DESIGN AND METHODS

## Interventions

### DPP.

The DPP enrolled 3,234 participants with IGT and fasting hyperglycemia who were at least 25 years of age and had BMI of 24 kg/m^2^ or higher (22 kg/m^2^ in Asian Americans) (3). Enrollment began in July 1996 and ended in May 1999. Mean age was 51 years of age and mean BMI was 34.0 kg/m^2^ (3). Sixty-eight percent were women, and forty-five percent were members of minority groups (3).

The goals for participants randomized to lifestyle were to achieve and maintain a weight reduction of at least 7% of initial body weight through diet and physical activity of moderate intensity, such as brisk walking, for at least 150-min per week. A 16-session core curriculum (given approximately weekly in individual participant sessions) and subsequent individual sessions (usually monthly) and group sessions with case managers were designed to reinforce the behavioral changes. The medication interventions (metformin and placebo) were initiated at a dose of 850 mg taken orally once a day. At 1 month, the dose was increased to 850-mg twice daily. Adherence was reinforced during individual quarterly visits with case managers. Standard lifestyle recommendations were provided to all groups through written information and an annual 20- to 30-min individual session that emphasized the importance of a healthy lifestyle. Mean follow-up at the end of the DPP was 3.2 years. For the purposes of this analysis, we assumed that all subjects were enrolled in the DPP for exactly 3 years.

### DPP/DPPOS bridge.

At the end of the DPP in July 2001, masked treatment was discontinued and each participant had a 1-h debriefing and closure visit during which he or she was informed of the main DPP results. In light of the proven benefits of lifestyle, all participants were offered a group-implemented 16-session lifestyle intervention between January and July 2002. Forty percent of lifestyle, fifty-eight percent of metformin, and fifty-seven percent of placebo participants attended at least one session (11). Each session lasted 1 h and was taught by one staff member. Participants received reminders for the sessions and the full packet of course materials. The original lifestyle group was offered additional lifestyle support and was not encouraged to take metformin. The original metformin group was encouraged to continue metformin and to participate in the group lifestyle intervention. Those randomized to placebo stopped placebo and were encouraged to participate in the group lifestyle intervention. For the purposes of this analysis, we assumed that year 4 represented the DPP/DPPOS bridge.

### DPPOS maintenance.

The DPPOS maintenance phase started in September 2002. All active participants were eligible for continued follow-up, and 2,766 of 3,150 (88%) enrolled (4). These included 910 from lifestyle, 924 from metformin, and 932 from placebo. During the DPPOS, the group lifestyle intervention was implemented as the Healthy Lifestyle Program (HELP) for all participants. HELP reinforced the original weight loss and physical activity goals and focused on current topics in nutrition, physical activity, stress management, and diabetes prevention. HELP consisted of four quarterly 1-h group visits. All participants received a reminder for HELP sessions. Although all participants were invited to attend all HELP sessions, many chose to attend fewer.

The DPP participants initially randomized to lifestyle were also eligible to receive two additional sessions, referred to as BOOST sessions, per year to reinvigorate their self-management behaviors for weight loss. Those randomized to metformin and placebo were excluded from BOOST sessions. The sessions were more intensive than HELP sessions and reinforced specific behavioral self-management activities (e.g., self-monitoring of fat, calories, and/or physical activity, as well as weight checks) important for weight loss and physical activity adherence and/or maintenance. In addition, the sessions promoted home-based behavioral self-management of weight and physical activity through the use of motivational campaigns. Lifestyle participants received reminders for each BOOST session.

During the DPPOS, metformin participants taking study-provided metformin received an annual complete blood count and serum creatinine for drug safety monitoring. Participants were encouraged to see their personal physicians for treatment of emergent adverse events. Only metformin participants were encouraged to take metformin, and only 1% of nondiabetic participants in lifestyle and 3% of nondiabetic participants in placebo took metformin prescribed outside the study (4). For the purposes of this analysis, we assumed that years 5–10 represented DPPOS follow-up.

### Interventions for participants who developed diabetes.

Participants identified with glucose levels diagnostic of diabetes at their 6-monthly visits were seen within 6 weeks for glucose testing to confirm the diagnosis. Participants with confirmed newly diagnosed diabetes received 1 h of individual counseling focused on self-monitoring of blood glucose, were provided with meters and test strips and encouraged to monitor their glucose levels once daily, and were maintained in their randomized intervention groups. Treatment for diabetes and surveillance for complications and comorbidities were performed by the participants' own health care providers. Medications used by the DPP participants for management of diabetes were recorded every 6 months on a drug summary form.

## Costs

We calculated the total direct medical costs associated with the DPP/DPPOS interventions over each of the 10 years after randomization ([Supplementary Table 1](http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc11-1468/-/DC1)). Direct medical costs of the interventions were estimated from the resources used and unit costs adjusted to 2010 U.S. dollars ([Supplementary Table 2](http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc11-1468/-/DC1)). As a sensitivity analysis, we estimated what the cost of lifestyle might have been during the DPP if it had been administered in a group format rather than individually (DPP group lifestyle intervention). We recalculated the costs of lifestyle assuming that the core curriculum and monthly follow-up visits with the lifestyle case managers, which were conducted individually during the 3 years of the DPP, were conducted as group sessions with 10 participants. Studies have shown that group intervention programs can be as effective as individual programs (12,13). Although metformin was implemented with brand name metformin (Glucophage), we assumed that it was implemented with generically priced metformin throughout the 10 years of the DPP/DPPOS.

As previously described, we also estimated the direct medical cost of care outside the study (5). The direct medical cost of care outside the study included the costs of hospital, emergency room, urgent care, outpatient services, and telephone calls to health care providers. These were determined annually from patient self-report. They also included the cost of prescription medications. This was estimated from the number of prescription medications that participants reported taking at semiannual visits and the mean average wholesale price of a prescription medication dispensed by a large U.S. pharmacy benefit manager in 2009.

Direct nonmedical costs were assessed twice, once during the DPP and once during the DPPOS, and costs were annualized. In estimating the direct nonmedical costs of the interventions, we considered the costs of food, food preparation items (blenders, cookbooks, food scales, freezers, microwave ovens, mixers, popcorn poppers, steamers, and woks), exercise classes, gym memberships, personal trainers, and exercise equipment (bicycles, exercise videos, free weights, golf clubs, home gyms, shoes, stationary bicycles, steps and treadmills) (5). We also considered the costs of transportation to study visits and to medical visits (5). The value of the time that participants spent shopping, cooking, exercising, and traveling to and attending appointments was also assessed (5). The costs of exercise were valued according to whether participants "disliked," were "neutral," or "liked" leisure time physical activity (5,14) ([Supplementary Table 2](http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc11-1468/-/DC1)). Although direct nonmedical costs are not usually paid by private insurers or government health programs, we included them in our cost calculations from a societal perspective.

## Outcomes

We assessed outcomes in terms of QALYs (15). QALYs measure length of life adjusted for quality of life as assessed by the health utility score. By convention, health utility scores are placed on a continuum where perfect health is assigned a value of 1.0 and health judged equivalent to death is assigned a value of 0.0. We assessed health utilities annually using the Self-Administered Quality of Well-Being Index (QWB-SA) (6). The QWB-SA is a widely used, validated, multiattribute utility model that combines preference-weighted values for symptoms and functioning to provide a health utility score. The numerical value assigned by the QWB-SA to quality of life reflects the public's judgment of the desirability of the health state. Mathematically, QALYs are calculated as the sum of the product of the number of years of life and the quality of life, measured in health utilities, in each of those years.

## Perspective

For the base-case analysis, we followed the recommendations of the Panel on Cost-Effectiveness in Health in Medicine (15) and took the perspective of a health system. Thus, we included only direct medical costs in our base-case analysis. We included direct nonmedical costs excluding participant time in a sensitivity analysis from a modified societal perspective and direct nonmedical costs including participant time in a sensitivity analysis from a full societal perspective. These sensitivity analyses assessed the impact of covering the cost of the behavioral interventions implemented by the study participants on society as a whole.

## Analyses

The analyses of lifestyle, metformin, and placebo were based on the design, cost, and clinical effectiveness of the interventions as implemented in the 3 years of the DPP and the 7 years of the DPPOS. For the DPP group lifestyle sensitivity analysis, we estimated what the costs of lifestyle would have been during the 3 years of DPP if the 16-session core curriculum and monthly follow-up visits with the case managers had been conducted as group sessions with 10 participants. We further assumed that outcomes would have been the same as observed for lifestyle. We excluded from the analyses the costs of the research component of the DPP/DPPOS. All costs were expressed as year 2010 U.S. dollars ([Supplementary Table 2](http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc11-1468/-/DC1)). Analyses were performed with a 10-year time horizon. Initial analyses were performed without discounting. Subsequently, both cost and health outcomes were converted to net present value using a 3% discount rate, and incremental cost-effectiveness ratios were calculated using the discounted costs and QALYs.

# RESULTS

## Costs

The annual undiscounted, per capita, direct medical costs of lifestyle, metformin, and placebo over 10 years are summarized in Table 1 as are the costs of the DPP group lifestyle sensitivity analysis. Figure 1A illustrates the cumulative, undiscounted, per participant intervention costs. The cumulative, undiscounted per participant cost of the lifestyle intervention as implemented in the DPP (\$4,601) was substantially greater than the metformin intervention (\$2,300) or the placebo intervention (\$769). The estimated cost of the DPP group lifestyle intervention (\$3,023) was approximately one-third less than that of the lifestyle intervention. The costs of lifestyle were substantially less during the DPPOS than during the DPP because of the change from an individual- to a group-implemented intervention and because fewer visits took place. The costs of placebo were higher during the DPPOS than during the DPP because placebo participants engaged in the group lifestyle intervention. During the DPPOS, lifestyle and metformin each cost approximately \$140 per participant per year. The costs of the interventions during the DPP differ somewhat from those reported previously as we have added the costs of fasting glucose and glucose tolerance testing and incorporated generic pricing for metformin (5). Detailed cost calculations are presented in [Supplementary Table 1](http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc11-1468/-/DC1).

Undiscounted, per capita, direct medical costs of the DPP/DPPOS interventions by intervention group and study year (\$)\*

![](723tbl1)

The cumulative, undiscounted per capita direct medical costs of nonintervention-related medical care by intervention group and year following randomization are shown in Table 2 and Fig. 1B. These are the costs of medical care received outside the DPP/DPPOS. The direct medical costs of nonintervention-related medical care were substantially greater than the costs of the interventions, and within 3 years, the cumulative costs of nonintervention-related medical care exceeded the 10-year cumulative direct medical costs of the interventions. The cumulative per-participant direct medical costs of nonintervention-related medical care increased substantially over time. From the outset of the DPP, the per-participant costs of placebo exceeded those of lifestyle and metformin. The greater cost of nonintervention-related medical care for placebo was largely driven by greater use of outpatient and inpatient services, prescription medications, and by the greater rate of conversion to diabetes with the attendant costs of self-monitoring and laboratory tests (Table 2). Across treatment groups, the direct medical costs of nonintervention-related medical care were 34–44% higher among diabetic participants compared with nondiabetic participants ([Supplementary Table 3](http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc11-1468/-/DC1)). Over 10 years, cumulative, per capita nonintervention-related direct medical costs were greater by \$1,853 and \$2,905 for placebo compared with metformin and lifestyle, respectively.

Undiscounted, per capita, direct medical costs of care outside the DPP/DPPOS by intervention group and study year, and distribution of undiscounted, per capita, 10-year, direct medical costs of care outside the DPP/DPPOS by intervention group and type (\$)

![](723tbl2)

Over 10 years, the cumulative, undiscounted, per capita direct medical costs of the interventions were greater for participants randomized to lifestyle (\$4,601) and metformin (\$2,300) compared with placebo (\$769). In contrast, the cumulative, undiscounted, per capita direct medical costs of nonintervention-related medical care (medical care received outside the DPP/DPPOS) were greater for placebo (\$27,468) than for metformin (\$25,615) or lifestyle (\$24,563). By year 10, the cumulative, undiscounted, per capita, total direct medical costs of the interventions and nonintervention-related medical care were higher for lifestyle than for placebo but were lower for metformin than for placebo (Fig. 1C). They were also lower for the DPP group lifestyle (Fig. 1C).

[Supplementary Table 4](http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc11-1468/-/DC1) summarizes the undiscounted, per capita, cumulative, 10-year direct nonmedical costs by intervention group and type defined as diet-related costs, physical activity–related costs, transportation-related costs, and participant time-related costs. Diet-related costs were substantial but did not differ among intervention groups. As might be expected, physical activity–related costs were greatest for lifestyle. Transportation-related costs were also substantially higher for lifestyle and metformin due to the greater number of study visits. Total diet-, physical activity-, and transportation-related costs were greatest for lifestyle but similar for metformin and placebo. Participant time contributed substantially to direct nonmedical costs. Participant time related to the interventions (time spent traveling to study visits, at study visits, and for intervention-related calls) was greater for lifestyle and metformin than for placebo. Participant time related to medical care outside of the interventions was generally greater for placebo than for metformin or lifestyle. Time spent shopping and cooking was the largest component of participant time but differed little across intervention groups. Although lifestyle subjects spent more time exercising, the adjusted value of the time they spent exercising was less than for either metformin or placebo because of their greater enjoyment of leisure time physical activity and the lower opportunity cost. The total, per capita, 10-year, direct nonmedical costs—including the costs of participant time—were lowest for metformin (\$144,143) and similar for placebo and lifestyle (\$147,043 and \$147,493, respectively).

## Health utilities

Every year after randomization, quality of life was better for lifestyle than for metformin or placebo ([Supplementary Table 3](http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc11-1468/-/DC1)). Across treatment groups, quality of life was worse among participants who developed diabetes ([Supplementary Table 3](http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc11-1468/-/DC1)). Since more placebo participants developed diabetes, the cumulative, undiscounted, per participant quality of well-being score gained over 10 years was greatest for lifestyle (6.81), intermediate for metformin (6.69), and least for placebo (6.67) (Fig. 1D).

## Cost-effectiveness

Table 3 summarizes the differences in total costs and QALYs among the treatment groups and the incremental cost-effectiveness ratios of lifestyle and metformin versus placebo, and lifestyle versus metformin. The incremental cost-effectiveness ratio is also shown for the DPP-group lifestyle versus placebo. From the health system perspective, from the modified societal perspective, and from the societal perspective, lifestyle cost more than placebo but was also more effective as assessed by the QALYs that were gained. From a health system perspective, with both costs and health outcomes discounted at 3% per year, the cost of lifestyle compared with placebo was approximately \$10,000 per QALY gained; however, metformin had slightly lower costs and nearly the same outcome (as assessed by QALYs) as placebo. Compared with metformin, lifestyle cost more but produced better health outcomes. From a health system perspective, with both costs and health outcomes discounted at 3% per year, the cost of lifestyle compared with metformin was approximately \$13,400 per QALY gained. DPP-group lifestyle, like metformin, was generally less expensive and more effective than placebo.

Differences in costs and QALYs and incremental cost-effectiveness ratios for lifestyle and metformin versus placebo and lifestyle versus metformin over 10 years from three alternative perspectives

![](723tbl3)

# CONCLUSIONS

Using 3 years of DPP data and computer simulation modeling, we and others suggested that screening for glucose intolerance in the overweight and obese population and implementing lifestyle and metformin interventions would have favorable cost-effectiveness ratios (7–9,16). However, one analysis suggested that lifestyle might be too expensive for health plans or a national program to implement (10). The current study, a 10-year, within-trial, intention-to-treat analysis of the DPP/DPPOS demonstrates that lifestyle is indeed cost-effective, and metformin is marginally cost-saving or at least cost-neutral compared with placebo. Even when the direct nonmedical costs of the interventions are considered, the interventions are cost-effective. Health and social policies should support the funding of intensive lifestyle and metformin interventions for diabetes prevention in high-risk adults.

When a new treatment is more effective and less costly than usual care, it should be widely adopted and used. Unfortunately, fewer than 1 in 5 new interventions in health and medicine are cost-saving compared with usual care (17). Published cost-effectiveness ratios—that is the cost in dollars per QALY gained for prevention and treatment—range from less than \$10,000 per QALY to greater than \$1 million per QALY with most falling between \$10,000 and \$50,000 per QALY. While influenza immunization has been demonstrated to be cost-saving in the Medicare population, interventions such as mammography, antihypertensive treatment, and cholesterol treatment for secondary prevention of cardiovascular disease have been estimated to cost between \$10,000 and \$60,000 per QALY (18). Widely implemented interventions such as dialysis for end-stage renal disease (\$50,000 to \$100,000 per QALY) and left ventricular assist devices (\$500,000 to \$1.4 million per QALY) are substantially more expensive.

From the perspective of a health system or society, what is the value of delaying or preventing the development of type 2 diabetes? From a health system perspective, it delays or prevents the direct medical costs of diabetes including the costs of diabetes education and nutritional counseling, glucose monitoring, antihyperglycemic treatments, and surveillance and treatment of complications (19–21). From a societal perspective, diabetes prevention also reduces health care–related costs to the individual not reimbursed by the health system and time lost from work and usual activities (19). It also improves quality of life.

The direct medical costs of diabetes are enormous. The American Diabetes Association has estimated that total per capita health care expenditures for people with diabetes are \$11,744 per year, of which \$6,649 is attributed to diabetes (19). This estimate likely overstates the costs of diabetes in DPP participants, since DPP participants who developed diabetes were screen-detected very early in their clinical course and had few complications. The costs of diabetes increase with duration of diabetes and with the presence of complications and comorbidities and would be expected to be lower for persons with short durations of diabetes and for those without complications (22). Patients with new clinically diagnosed diabetes have been reported to have costs 2.1 times those of individuals without diabetes, and the incremental cost of diabetes is apparent from the time of diagnosis (20). In 2000, using data from a single managed-care health plan, we estimated that the median, annual, direct medical cost of care for a man with diet-controlled type 2 diabetes with no microvascular, neuropathic, or cardiovascular risk factors or complications was approximately \$1,700 (23). More recently, using data from approximately 7,100 type 2 diabetic patients enrolled in eight managed-care health plans participating in Translating Research Into Action for Diabetes (TRIAD), we demonstrated that median, annual, per capita, direct medical costs of care were approximately \$2,500 for a man with recent-onset diabetes without complications or comorbidities (R. Li, personal communication). These costs of recent-onset type 2 diabetes are quite consistent with those observed during the DPP/DPPOS. Compared with the substantial costs of diabetes, the costs of lifestyle, metformin, and DPP group lifestyle seem quite small.

It is now clear that the use of a 3-year time horizon in our previous within-trial economic analysis resulted in a higher cost per QALY gained than the current analysis, which used a 10-year time horizon (6). With a 3-year time horizon, treatment costs were higher, and the benefits of lifestyle and metformin in terms of averted diabetes were less. The costs of both lifestyle and metformin were greatest in year 1, decreased substantially in years 2 and 3, and decreased further during years 4 through 10. In contrast, much of the benefit of both lifestyle and metformin, as assessed by both cumulative, nonintervention-related direct medical costs and quality of life, occurred after 3 years of follow-up.

There are a number of limitations to our analyses. First, the DPPOS was an observational follow-up of the DPP, a randomized controlled clinical trial. It is likely that during the DPPOS, when 57% of placebo participants also attended at least one group lifestyle intervention (HELP) session, placebo was more effective than the usual care that nonstudy participants might receive (11). Thus, if real-world usual care was used for comparison, the difference between lifestyle and placebo might have been greater. The fact that 58% of metformin participants also attended at least one HELP session may also have made metformin appear more effective and cost-effective compared with lifestyle (11). Second, the costs of medical care outside the interventions appear low compared with those reported in the literature for people with diabetes (19). This likely reflects the fact that trial participants were earlier in the natural history of diabetes and were healthier than patients with diabetes in the general population. It is important to note that resource utilization and cost were assessed in an identical fashion across intervention groups so the differences among groups, which determine the incremental cost-effectiveness ratios, should be reasonably accurate. Third, in our analysis of the DPP group lifestyle intervention, we assumed that lifestyle could be implemented in a group rather than in an individual format at one-third lower cost and achieve the same outcomes. Although group-implemented lifestyle interventions have been shown to be at least as effective as individual programs for weight loss, there has not been a direct comparison of individual and group lifestyle interventions for diabetes prevention (12,13). Finally, we recognize that in assessing the direct nonmedical costs of the interventions, we have overestimated diet-related costs. Clearly, people need to eat whether or not they participate in a randomized controlled clinical trial. We did not have a good method for distinguishing study-related and nonstudy-related food consumption. Nevertheless, the data are instructive in that they indicate that there were not substantial differences in diet-related costs across intervention groups.

This 10-year, within trial, intention-to-treat economic analysis of the DPP/DPPOS demonstrates that lifestyle, when compared with placebo, is cost-effective, and metformin is marginally cost-saving from a health system and societal perspective. If a DPP group lifestyle intervention could be delivered at one-third lower cost than the DPP lifestyle intervention and achieve the same outcomes, it would also be cost-saving or cost-effective compared with placebo. Even when compared with metformin, lifestyle was cost-effective from both a health system and societal perspective. These analyses should assist health plans and policy makers in comparing the benefit of diabetes prevention to other preventive and palliative interventions. The adoption of diabetes prevention programs by health plans and society will result in important health benefits over 10 years and represents a good value for the money spent.

## Acknowledgments

During the DPPOS, the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health provided funding to the clinical centers and the DPP Coordinating Center for the design and conduct of the study, and collection, management, analysis, and interpretation of the data. The Southwestern American Indian Centers were supported directly by the NIDDK, including its Intramural Research Program, and the Indian Health Service. The General Clinical Research Center Program, National Center for Research Resources, and the U.S. Department of Veterans Affairs supported data collection at many of the clinical centers. Funding was also provided by the National Institute of Child Health and Human Development; the National Institute on Aging; the National Eye Institute; the National Heart, Lung, and Blood Institute; the Office of Research on Women's Health; the National Institute on Minority Health and Health Disparities; the Centers for Disease Control and Prevention; and the American Diabetes Association.

Bristol-Myers Squibb and Parke-Davis provided additional funding and material support during the DPP. Lipha (Merck-Sante) provided medication and LifeScan Inc. donated materials during the DPP and the DPPOS. No other potential conflicts of interest relevant to this article were reported.

W.H.H. researched data, wrote the manuscript, and is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. S.L.E. and M.B.B. researched data, conducted analyses, reviewed and edited the manuscript, and contributed to discussion. R.E.R., M.G.M., R.T.A., T.J.O., M.A.F., P.Z., and C.D.S. researched data, reviewed and edited the manuscript, and contributed to discussion.

Parts of this study were presented in oral form at the 71st Scientific Sessions of the American Diabetes Association, San Diego, California, 24–28 June 2011, and in poster form at the 47th Annual Meeting of the European Association for the Study of Diabetes, Lisbon, Portugal, 12–16 September 2011.

The DPP Research Group gratefully acknowledges the commitment and dedication of the participants of the DPP and the DPPOS.

### APPENDIX

Members of the writing group are William H. Herman, MD, MPH^1^; Sharon L. Edelstein, ScM^2^; Robert E. Ratner, MD^3^; Maria G. Montez, RN, MSHP^4^; Ronald T. Ackermann, MD, MPH^5^; Trevor J. Orchard, MBBCh, MMedSci^6^; Mary A. Foulkes, PhD^2^; Ping Zhang, PhD^7^; Christopher D. Saudek, MD^8†^; and Morton B. Brown, PhD^1^.

From the ^1^University of Michigan, Ann Arbor, Michigan; ^2^The Biostatistics Center, The George Washington University, Rockville, Maryland; the ^3^Medstar Health Research Institute, Washington, D.C.; the ^4^University of Texas Health Science Center at San Antonio, San Antonio, Texas; the ^5^Northwestern University Feinberg School of Medicine, Chicago, Illinois; the ^6^University of Pittsburgh, Pittsburgh, Pennsylvania; the ^7^Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, Georgia; and ^8^The Johns Hopkins University School of Medicine, Baltimore, Maryland.

†Deceased.

# References

[^1]: Clinical trial reg. nos. NCT00038727 (DPPOS) and NCT00004992 (DPP), [clinicaltrials.gov](clinicaltrials.gov).

[^2]: This article contains Supplementary Data online at <http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc11-1468/-/DC1>.

[^3]: A slide set summarizing this article is available online.

[^4]: \*A complete list of the members of the Diabetes Prevention Program Research Group, centers, and staff can be found in the Supplementary Data online, and members of the writing group are listed in the appendix.

[^5]: The opinions expressed in this article are those of the investigators and do not necessarily reflect the views of the funding agencies.