Modern approaches to designing novel CD80 inhibitors that mimic biological effects of Abatacept

 

 

Article By

 

Asha Kulkarni Almeida
E. Bharathy
T. T. Matan
Chandrika B-Rao

 

Abstract

Disruption of CD80-CD28 interactions is an established target for rheumatoid arthritis, transplant rejection, systemic lupus erythematosus, non-Hodgkin’s lymphoma and other inflammatory disorders where suppression of T cell activity is desirable. In this study, we developed pharmacophore models for small molecule inhibitors of this interaction, with a view to identifying novel chemical scaffolds that would necessarily block the T cell activation as induced by Abatacept. We screened the Asinex database using these models. We tested 31 of the in silico hits in vitro and identified 7 active scaffolds, which were different from those used for developing the pharmacophore models. These 7 scaffolds may provide good starting points for further medicinal chemistry efforts to develop novel and potent CD80 inhibitors.

 

Introduction

T-cell activation plays a critical role in driving normal and pathogenic immune responses[1]. The interaction between cell surface co-stimulatory molecules on antigen-presenting  cells and the T cells is essential for modulating cell to cell communication  and the immune response. Two structurally and functionally well characterized T cell surface co-stimulatory molecules are CD28 and CD152 (CTLA-4), both of which bind the ligands CD80 and CD86, on the antigen-presenting cells (APCs) (Figure 1)[2-4]. Disruption of CD80-CD28 interactions (which is involved in T cell activation) is an established target for rheumatoid arthritis (RA), transplant rejection, non-Hodgkin’s lymphoma (NHL) and other  applications where suppression of T cell activity is desirable[5].

TC- Apr 2016 - 022 - Mechanism of T cell mediated interactions

CD28  is  constitutively  expressed  on  naive  T-cells while CD80 expression  is upregulated  in established  disease[6]. CD28  is  believed to  be involved in many activities other  than  just immune  response and  hence targeting it may result in unpredictable consequences[7,8]. The dangerous outcomes  of targeting  T  cell activation through inhibition of CD28 were clearly seen in the Phase-1 clinical trial results with the CD28 superagonist antibody TGN1412 (developed by TeGenero) in March 2006[9]. Treatment with the CD28  superagonist  antibody  TGN1412 along-side blocking of the CD28 receptors expressed on activated T cells, also induced a life-threatening cytokine-release syndrome  in  six volunteers recruited  in  this  Phase I clinical trial. A retrospective investigation on the devastating effects of the massive cytokine storm generated in human     volunteers  who  received a  single infusion  of  the  humanized  monoclonal  antibody  TGN1412 and  comparative  rodent  studies in  mice as well as rats  has  suggested that TGN1412 rapidly induced a marked T cell lymphopenia by trapping  T cells in the spleen and lymph nodes of mice. Possibly, a similar dramatic redistribution  of T cells simulated  profound  T cell lymphopenia observed in human  recipients of TGN1412. The abject failure of targeting CD28 receptor and the unpredictable nature of targeting T cells which are indispensable in mounting cell mediated  immune  responses, brings  focus towards the CD28 co-stimulating receptor CD80, which is expressed on antigen presenting  cells. Incidentally, galiximab (anti-CD80 antibody)-induced  growth inhibition  and  prolongation  of survival in vivo of B-NHL tumor  xenografts[10]. What further strengthens prospects of targeting CD80 is the success of Abatacept.

Abatacept is a biologic (a fusion protein  comprising  of  CTLA4 bound  to  immunoglobulin) which  mimics  CD152 (CTLA4) the  co-stimulatory molecule that  down regulates expression of CD80-CD28, thus suppressing an inflammatory response. Abatacept when it binds to CD80/CD86 instead, it opposes co-stimulatory molecules namely  CD80 – CD28 and prevents  or  suppresses T-cell activation. The mechanism of Abatacept binding  to the CD80/CD86 markers downregulates the CD28 expression on T cells. It has been approved for RA in patients with an inadequate response to anti-tumor  necrosis factor (anti-TNF) therapy as a second line approach. Side effects include infections such as respiratory or  urine  tract  infections, and  also tuberculosis is a potential problem similar to other anti-TNF drugs. The side effects are therefore similar to all other biologic drugs. Among multiple ongoing clinical trials, an interim 6-month analysis of clinical trial data from ACTION (Abatacept In Routine Clinical Practice); a prospective observational study assessing effectiveness, safety, and tolerability of Abatacept in patients with RA, suggests that it offers an effective and well-tolerated treatment  option  for patients  with RA, including those who have previously failed anti-TNF treatment.  In  addition,  higher  retention  rates and effectiveness outcomes were observed when Abatacept treatment  was initiated earlier in the course of the disease[33]. Further to responses in RA patients, a clinical trial to assess the efficacy and safety of a 24-week course of Abatacept in the treatment  of active lupus nephritis  appears promising.

CD80 inhibition, therefore, represents a logical choice of target for a small-molecule program in autoimmune disease. There is a clear, unmet medical need for a new oral therapeutic for rheumatoid  arthritis,  which intervenes early in  the disease process, treats  the  causes and  not  the symptoms of   disease, has minimal side-effects and  leaves the  immune  system functional[11-13]. Inhibition of CD80-CD28 interaction may be a promising strategy to fulfill this need.

Several anti-CD80 monoclonal antibodies are in clinical trials, but the major drawbacks of these protein-based therapies are their high treatment cost, reactivation  of dormant  tuberculosis,  induction  of lymphomas  on  long term  exposure and requirement for parenteral administration with side-effects[14]. A series of condensed aromatic peptide inhibitors of CD80 with increased cellular activity was identified. However, these peptides showed nonspecific binding, and hence were thought to be poor lead candidates[15]. Small molecule inhibitors of CD80-CD28 interaction have been  reported[16],  but  were found  to  lack potency under physiological conditions, possibly due to fast dissociation of ligand from protein. Hence it was suggested that new small molecule inhibitors should have much slower dissociation rates[17].

Activity data  for several CD80 small molecule inhibitors that belong to diverse chemical classes are currently available[6,16,17]. One of these, Rhudex, an  orally bioavailable small molecule inhibitor  of CD80, was in Phase II clinical trials to prevent  inflammation  in rheumatoid  arthritis[6,18]. However, the trial was terminated due to death of a subject participating in the trial who suffered cardiac arrest.  There is no other small molecule in clinical trials for this target, although there  are  many  in  biological testing[21].  Recent developments   involve  collaborations   between the  company  Medigene with  Dr.  Falk Pharma for further Phase II studies with Rhudex in the treatment  of gastrointestinal  and  hepatological disorders. The aim of this study was to identify new  classes of  molecules for  CD80 inhibition and hence, hitherto-unknown  starting structures for researchers to use in designing novel inhibitors. Towards this end, we used virtual screening as a time- and cost-saving approach to shortlist compounds for biological screening. Among the different virtual screening approaches available, docking is perhaps the most popular. However, crystal structure information  for interactions of CD80-CD28 and CD80-small molecule complexes are not available. Pharmacophore modeling is an  alternate  effective, inexpensive and  fast approach to discover useful starting points in drug discovery research. This prompted us to develop three-dimensional pharmacophore models using known CD80 inhibitors for virtual screening of a large compounds database.

A  “pharmacophore”  is  the  three  dimensional (3D) arrangement of atoms or functional groups essential for the molecule to bind to a specific receptor.  Pharmacophore  models  can  be  used to discover or design new bioactive compounds. The screening of compounds databases using pharmacophore  models  is  a  method  that  has been successfully applied in many drug-discovery programs[19,20]. In this study, 3D pharmacophore model(s) were developed from known CD80 inhibitors, validated using an external test set of known inhibitors, and used for virtual screening of the Asinex database. Some of the virtual hits were tested in biological assays and found to be active. These compounds have scaffolds that are completely different from those used for developing the pharmacophore models, and may provide novel starting points for future research.

Eight  molecules[6,16,17,21]    with  known  activities (Figure 2) were used to generate the pharmacophore models.

TC- Apr 2016 - 023 - Chemical structures of CD80 inhibitors

The molecules were built using Maestro (version 8.0) 2D/3D visualizer using standard procedures. [22-26]  These were loaded  into  the  Phase  (v2.5) module. A single low energy conformation  was generated for each molecule[22].

For each of the eight molecules shown in Figure 2, we identified pharmacophoric features using the Phase program. Combined pharmacophore models containing three, four and five pharmacophoric features were then generated. Two sets of 7 molecules each provided two reasonably good pharmacophoric hypothesis, which covered all 8 molecules in the training set.

The two four-point variants were ARRR (A – Acceptor, R – Ring aromatic) and AARR.  Hypothesis with the highest survival scores from each of the 2 sets (4.733 & 4.264 respectively) were selected as Hypothesis 1 and Hypothesis 2, respectively.  The features and distance patterns of the two hypotheses are shown in Figure 3. We found that fitness scores were higher for Hypothesis 1 than for Hypothesis 2, and this is reflected in the survival score also. The selectivity score for Hypothesis 1 was also slightly higher than that for Hypothesis 2. Hence, Hypothesis 1 was expected to perform better than Hypothesis 2.

TC- Apr 2016 - 024 - Distance pattern

The molecule that  contributes  to the reference pharmacophore  is the  reference  ligand  having highest fitness of 3.00. The reference ligands for Hypotheses 1 and 2 are compounds 3 and 8 respectively. (Figure 3). The alignment for Hypothesis 1 appears to be better than that for Hypothesis 2 (Figure 4).

TC- Apr 2016 - 025 - Alignment of seven molecules

The pharmacophore hypothesis obtained were validated against a set of 39 molecules from Prous/Integrity  database[21]. We found  that  Hypothesis 1 was able to pick up all the molecules with fitness score above 2.00, whereas Hypothesis 2 was able to pick up only 26 molecules out of 39. This is because the aromatic ring feature attached to the tri-cyclic fused ring (Figures 2 & 3) is not present in 9 of the molecules and also the H-bond acceptor feature (present in the tricyclic ring on nitrogen) corresponds to a donor feature in the molecules that were rejected.

After confirming  their  reliability, we employed Hypotheses 1 & 2 as 3D search queries on the Asinex database (containing 127,281 molecules) which was already Lipinski-filtered and had pre- generated 3D conformations  and  pharmacophoric  sites of  compounds.  Fitness  score, which measures how well the conformer  matches the pharmacophoric  hypothesis, was used to select “hits”. Thus, 61,791 hits were obtained  for Hypothesis 1 and 65,654 hits for Hypothesis 2. Then we applied a further fitness score filter, to select only the molecules that have fitness greater than or equal to 2.00. The number of hits then reduced to 200 and 207 for Hypothesis 1 and 2, respectively. Interestingly, there was no common molecule between the two sets of virtual screening hits. This indicates that  the two hypothesis are independent and compounds missed by one may be picked up by the other.  Hence, it is important to use both the hypothesis to increase chances of picking up all true positives, particularly those with novel scaffolds not used in the training set.

For the purpose of biological testing, 31 compounds were randomly selected from the 407 potential hits. Interestingly, all of these compounds had scaffolds that were different from those of compounds in the training set, demonstrating the validity of virtual screening using pharmacophoric models for finding novel active scaffolds from a database of molecules.

The selected compounds were screened in a human  peripheral  blood  mononuclear  cell (hPBMC) assay at a concentration of 100µM, and the functional end-point of T-cell proliferation inhibition was measured (see Figure 1). PBMCs were isolated from the peripheral blood of healthy human  volunteers  as  previously reported[27].  The PBMC were treated with the compounds for 30 minutes prior to stimulation.  T-cell proliferation was induced in presence of phytohemagglutinin and phorbol ester. Finally, the extent of proliferation was measured by BrdU labeling of proliferating T-cell DNA[28]. Dexamethasone (Sigma) and anti-human  CD80 monoclonal antibody (Southern Biotech, USA) were used as standards in the biological testing.

Sixteen compounds showed 50% or higher inhibition of T-cell proliferation (Figure 5). The effect was equivalent to the effect of monoclonal antibody to  human-CD80  at 25µg/ml (62.2±12.4% inhibition).  We used dexamethasone as a standard small molecule inhibitor of CD28 – CD80 interactions because it has been reported to downregulate CD80 and CD28 receptors on APCs and T cells respectively[29,30].  Dexamethasone at 10µM showed 72.3±5.9% inhibition of T-cell proliferation in parallel biological experiments.

TC- Apr 2016 - 026 - Effect of compounds

The 31 tested compounds could be classified into 11 scaffold classes, of which 5 contained  more than 1 compound. 3 out of 4 compounds in scaffold class 1 (SC1) showed more than 50% inhibition in T-cell proliferation assay. Only 2 of the 6 singleton scaffolds showed more than 50% inhibition, giving a total of 7 active scaffolds.

 

Conclusion

In  summary,  the  work  described  here  identified two four-point  pharmacophore  models for disrupting  the interaction  of CD80 with CD28. Further,  preliminary  results  of  biological testing of virtual screening hits, provided new hits from  7 different scaffolds which were hitherto unknown for this activity, and may provide good starting scaffolds for the synthesis of novel and potent  CD80 inhibitors  by researchers. Recent advances with abatacept describe the success of this antibody to the T-cell co-stimulatory molecule CD80, in inducing remission in five patients with focal segmental glomerulosclerosis (FSGS) resistant to rituximab and glucocorticoids (one patient with primary FSGS and four with recurrent  FSGS after transplantation).  The rationale for using Abatacept was that  CD80 is induced in podocytes in primary and recurrent  FSGS as well as in patients with membranous  nephropathy[31]. Similarly reports from clinical trials with patients diagnosed for rheumatoid  arthritis  are available. The clinical data suggests that Abatacept plus methotrexate (MTX) demonstrated robust efficacy compared with MTX alone in early RA, with a good safety profile. The achievement of sustained remission following withdrawal of all RA therapy suggests an effect of abatacept’s mechanism on autoimmune  processes. Interestingly, withdrawal from  the  antibody  treatment has not generated undesirable effects but has also sustained remission suggesting that the immune mechanisms targeted by the antibody successfully achieve the desired remission and end points desirable in RA patients whereas in contrast the patients exposed to the drug MTX do not exhibit similar remission patterns[32]. This further underscores the significance of the target in immune modulatory responses with abatacept. In context with our results the role of CD28-CD80 interaction is once again prominently  highlighted and is therefore suggested as a good target for novel drug designing.

 

Acknowledgements

We would like to thank Dr. Jyothi Subramanian for her  suggestions and  Dr. Periyasamy Giridharan  for his excellent technical assistance. We are grateful to Dr. Somesh Sharma for many useful discussions and directions.

 

Disclosure

The authors  have no conflict of interest to declare.

TC- Apr 2016 - 027 - Writers Art pg 41

 

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