Compugen Ltd (CGEN) 2011 Q1 法說會逐字稿

Ladies and gentlemen, thank you for standing by. Welcome to the Compugen Ltd. first-quarter 2011 financial results conference call.

All participants are at present in a listen-only mode. Following management's formal presentation instructions will be given for the question-and-answer session. (Operator Instructions) As a reminder this conference is being recorded May 11, 2011.

With us online today are Mr. Martin Gerstel, Chairman of the Board; Dr. Anat Cohen-Dayag, President and CEO; and Ms. Dikla Czaczkes Axselbrad, CFO.

I would like to remind everyone that the Safe Harbor language contained in today's press release also pertains to all content of this conference call. If you have not received a copy of today's release and would like to do so, please contact Dikla Czaczkes Axselbrad at telephone number 972-3765-8595.

Mr. Gerstel, would you like to begin?

Yes, thank you very much. On behalf of my associates and all the employees of Compugen, welcome to our first-quarter 2011 conference call.

As stated in our press release of today, we will be basing our formal remarks on a slide presentation. The presentation is now available on our website www.cgen.com. In order to access it go to our homepage at www.cgen.com, click on Events, and then click on the presentation link.

Please note that in the window which opens after entering your personal information you should elect the bottom option, Web Participant Application. This option will enable you to connect without installing software on your computer. This information regarding access to the slide presentation is also provided in today's press release.

Today's presentation will begin with a review of our pipeline program by Anat, following which I will provide a quick review of why we have established this very unique company and how we are accomplishing our objectives. Dikla will then provide a financial overview and outlook, and we will open the call for Q&A. Anat?

Thank you, Martin. Slide number five.

As of today, our pipeline program initiated late last year consists of more than 30 product candidates at various stages of evaluation. The program begins with a market-driven approach to identify pathway protein families and target of high industry interest. Then we harness all of our relevant capabilities to predict and select molecules that we believe to could be superior product candidates for that need.

In addition to our efforts in advancing this early-stage therapeutic pipeline we continue to undertake additional candidate discovery programs to both increase the number of candidates in the pipeline and to replace those that failed. And to maintain our leadership position in predictive discovery our research team continues to enhance our unique discovery infrastructure through the development of additional algorithms and platforms. However, we will not be reviewing these other activities in our call today.

Slide number six. As already disclosed, our pipeline program is focused primarily on oncology and immunology, including autoimmune and inflammatory conditions, and consisting of drug target candidates for monoclonal antibody therapy and candidates for protein and peptide therapeutics.

Slide number seven. As part of our pipeline program, we have decided to advance selected product candidates for up to an additional 18 months post animal disease model studies towards IND. We expect that this will result in higher value partnerships for advanced product candidates and will provide further support of the effectiveness and power of our unique predictive discovery approach.

I would like now to review our pipeline program providing some additional information regarding the product candidates, their therapeutic indications, the stage of our validation and development studies, and the discovery capabilities underlying this discovery.

Slide number eight. It is critical to understand that our primary and unique competitive advantage is the stage of in silico prediction and selection. Our success as a company will be determined almost entirely by the results from this stage.

The better the prediction and selection, the better the pipeline in terms of quantity of products, probability of success, and relative superiority to other products for the same indication. And, of course, predictions based on models can only improve over time.

It is true that much remains to be done to reach the marketplace following the prediction and selection, but all of the remaining activities are widely available in the industry. Therefore, in reviewing our pipeline I would also like to provide some insight into a few of the prediction and selection methodologies that we utilize.

In general, the in vitro and in vivo stages are similar or the same as those used by other companies for the type of discovery or indication of interest. The in vitro studies are either conducted at Compugen or in outside expert laboratories in each of the relevant fields.

Following successful in vitro validation, the molecules are further advanced to either in vivo proof-of-concept studies in diseased animal models. Or, in the case of drug target, additional target validation studies confirming the target's expression profile followed by mAB generation and in vivo proof-of-concept studies in diseased animal models.

Molecules selected to be advanced to (inaudible) and the activities then enter the stage of fleet candidate optimization and selection. For peptides and proteins this would mean the selection of the final therapeutic form of the molecules to be used at later development stages. For mAB targets selected to advance to the next stage this would be the generation of a therapeutic mAB either humanized or fully human.

Slide number nine. I will first describe our pipeline program for oncology.

CGEN-928 is a novel protein predicted to serve as drug targets for mAB therapy in the field of multiple myeloma, specifically in advanced stages and for drug-resistant subtypes of the disease which is a significant unmet medical need. This target is now at the stage of mAB generation for further evaluation of the target therapeutic potential.

Slide number 10. The results to date continue to validate our prediction and have demonstrated that this candidate is indeed expressed on cells derived from multiple myeloma patients with significantly more pronounced expression in advanced stages of the disease. Although most multiple myeloma cells are positive to both CD138, a known marker for multiple myeloma, and CGEN-928, the more advanced stage patients have a substantial subset of tumor cells which are positive to CGEN-928 only.

These findings that a mAB targeting CGEN-928 could target a larger population of multiple myeloma cells which are both the more aggressive disease and at the more advanced stages inside currently suggests a potential to answer this clinical unmet medical need. In addition to its possible diagnostic use as a biomarker for multiple myeloma, these results support its role as a therapeutic drug target candidate.

In experiments testing the combination of a polyclonal antibody we have developed for this target candidate with a standard-of-care therapy we observed and enhanced death of cells suggesting that combination with mAB directly towards CGEN-928 may allow reduced dosage of standard-of-care therapy or achieve higher efficacy.

Slide number 11. We currently have six undisclosed mAB target candidates in the program, all of which are slight variants of either known cancer targets or known proteins with potential to show superior characteristics over the known cancer target or protein.

Slide number 12. Compugen's mAB target discovery capability that has resulted in these product candidates is an excellent example of how we can combine different discovery systems to address a given need. In this case, our mAB target capability is based on two proprietary infrastructure platforms LEADS, which is our predictor of the human transcriptome, proteome, and peptidome, and MED, which is our disease-oriented gene expression database.

The mAB discovery capability integrating these two infrastructures serves as the basis upon which we incorporate information from pathway analysis, protein-protein interactions, transcription [regulation], and more -- all of which are analyzed by specialized algorithms. This allows the selection of disease-specific proteins, new protein family members, spliced variant protein targets, and other mAB target candidates.

Slide number 13. The next target is CGEN-15001T, a membrane protein predicted to serve as a target for mAB therapy in small cell lung cancer, which is currently at the stage of validation at the protein level and is in the process of mAB generation.

Slide number 14. This target was predicted to belong to the family of B7/CD28 negative co-stimulatory proteins which play an important role in the modulation of the immune system in cancer and autoimmune diseases. Therefore, this is an area of significant current industry interest.

Our new members of protein families discovery capability was designed to systematically meet this challenge. The algorithm making up this capability are applied sequentially starting from our proprietary database of all predicted human proteins and are designed to specifically focus on those proteins that are predicted to be present on surface of cells, namely membrane proteins.

On this subset of proteins we apply algorithms with the aim to identify proteins sharing the same family characteristics. In order to identify family members that are not already known, we have developed powerful and sensitive novel algorithms identifying both known and unknown structural and sequence-based features of this family.

In the next stage we predict whether these proteins are expressed in a disease state. In this case, some of the proteins we have identified have the potential to be expressed in cancer cells, a characteristic that would make them suitable candidates for targeting peripheral immunity.

Peripheral immunity is considered as attractive mechanisms for the development of new drugs for oncology and immunology. The application of this process of the B7/CD28 family of proteins resulted in the prediction of multiple potential novel family members. This is a significant achievement, particularly in view of the low [sequentomology] found between members of this protein family.

Slide number 15. Next our addition mAB target candidates, most of which are still undisclosed and at early stages of in vitro validation. As you can see, most of the mAB targets in our oncology program, including these five, are targeting epithelial tumors addressing a large unmet clinical need and one of high industry interest.

Slide 16. Epithelial cancers, which are also referred to as carcinomas, account for approximately 85% of all cancers including the 10 most prevalent cancers in the Western world, such as breast, colorectal, lung, ovary, and prostate.

Slide number 17. The next candidate is CGEN-25017, a predicted peptide blocker for the angiopoietin Tie2 pathway which is involved in the process of angiogenesis, the formation of new blood vessels. Angiogenesis is the subject of significant research and this candidate has potential use for the treatment of various cancers, but also inflammatory disorders and recognized disorders.

Slide number 18. Due to its predicted role as a blocker of angiogenesis, we selected a diseased animal model of retinopathy as the first model to test our predictive capability with respect to CGEN-25017. We have compared the effect of our molecules to deaths observed with and anti-VEGF mAB representing Avastin or Lucentis.

In this study we showed that the therapeutic effect of our molecule is better than that of anti-VEGF alone. In addition, the combination of anti-VEGF with our peptide resulted a further reduction in neurovascularization, suggesting a therapeutic advantage to the combination. It is also worth noting that as a short peptide our molecule may possess an advantage over large molecules like Avastin or VEGF-Trap, including better bioavailability to the back of the eye and the possibility of less invasive forms of administration.

Slide number 19. The next molecules in our pipeline for oncology are four peptide candidates predicted to target and antagonize biological pathways which are known to be involved in cancer biology. These pathways are of high industry interest and are the subject of the development of new therapeutic approaches.

Slide number 20. In summary, most of our product candidates in the field of oncology have not yet been disclosed by us. Our mAB target candidates consists of several types of targets. The first and second types are novel proteins or known proteins that were predicted by us to serve as drug targets.

The third type are spliced variants of known proteins or drug targets. These candidates should be suitable to various approaches that utilize our basic monoclonal antibody, including technologies of antibody drug conjugates or enhanced antibody-dependent cell-induced cytotoxicity.

Note that in order to advance our mAB target candidates to the stage of lead candidate selection and pre-IND we will need to partner with a company having expertise in therapeutic mAB generation technology. The peptide candidates all predicted to target biological pathways of high industry interest may serve as candidates for either peptides therapeutics or as an internal research tool for the development of protein [therapeutics].

Slide number 21. I will now move to review our pipeline for immunology and several other indications. CGEN-15001, our novel molecule predicted to serve as a negative co-stimulator and modulator of the immune response, was already shown to inhibit disease in animal models of both multiple sclerosis and rheumatoid arthritis.

This molecule was recently selected by us to be advanced to pre-IND stage and is now in the process of lead candidate selection. This test consists of the selection of the exact molecular structure of the compound to be tested in the clinic.

The lead candidate will be later incorporated into preclinical studies and will eventually be used for clinical studies. This test is highly important to optimize future clinical efficacy and safety of the drug candidate and should be completed before the initiation of manufacturing process development and pre-IND stage.

Slide number 22. As you probably remember, these impressive results were observed by Professor Stephen Miller from Northwestern University, indicating a long-term abolishment of relapses following short-term administration of our molecule in an animal model of multiple sclerosis.

Slide number 23. In the next slide are the results observed by Dr. Richard Williams from the UK Imperial College indicating a similar efficacy as that of Enbrel, one of the leading drugs for this indication, in an animal model of rheumatoid arthritis. Since our molecule seems to act as a negative co-stimulator and immunomodulator, it should be devoid of general immunosuppression seen with Enbrel or other TNF-blockers and it does have potentially less adverse effects.

Slide number 24. As a result of the confirmation of CGEN-15001 prediction, all eight additional molecules predicted to belong to this family of proteins by this discovery platform have now been incorporated in our pipeline and are undergoing in vitro validation.

Slide number 25. These specific molecules currently not disclosed are being studied under an expanded arrangement with Northwestern University intended to test their potential to serve as protein therapeutics for autoimmune and inflammatory diseases.

Slide number 26. Next in our pipeline is CGEN-25007, a peptide predicted to block heat shock protein named gp96. The therapeutic effect of this peptide in inflammatory disease was confirmed in an animal model of inflammatory bowel disease. The very different type of approach on which the DAC blockers discovery platform responsible for this discovery is based is illustrated in the next slide.

Slide number 27. Confirmational changes in proteins have a central role in regulating activity. Therefore, blocking confirmational changes in biologically active proteins holds therapeutic promise.

You can imagine confirmational changes as two simple states as described in the slide -- an inactive open stage and an active closed stage. The goal underlying the development of the DAC blocker's discovery platform was to identify interacting segments within the proteins that are required to adopt a certain active confirmational state. Then, based on these sequences and without the need of three-dimensional structure of the protein, peptides could be predicted that would interfere with this interaction by preventing the protein from [forging] into its disease confirmation.

The method at the core of the DAC blockers platform was published in a scientific publication in 2009 followed by a 2010 review article describing several approaches for therapeutic peptide discovery developed and implemented by Compugen. Through the use of this platform we have identified both CGEN-25007 and CGEN-25017 as peptide blockers demonstrating positive therapeutic potential.

Slide number 28. The next product candidates also tested to have positive therapeutic potential in various pathological conditions, including fibrosis, hypertension, and cardiovascular indications, were predicted by a different peptide discovery capability, our GPCR peptide ligand discovery platform.

Slide number 29. Approximately 40% of all known drugs rely on the modulation of GPCRs for their effectiveness. Therefore, it is not surprising that this is one of the most active areas of pharmaceutical research.

Compugen's GPCR peptide ligand discovery platform relies on a series of sequential computational biology models and machine-learning capabilities for the prediction and selection of peptide product candidates. The platform is based on predicting cleavage site that are present in precursor proteins and may cause their cleavage under natural or pathological human body conditions.

This protein cleavage prediction test is performed on the secreted set of human proteins creating and in silico prediction of thousands of novel putative human peptides predicted to be endogenous. The discovery platform then utilizes proprietary machine learning algorithms trained on known GPCR peptide ligands to analyze this peptidome and to identify peptides likely to activate GPCRs.

The approach and results underlying this discovery platform were published by us in few scientific papers. The human peptidome created by this platform may also serve as the basis for the discovery of peptides activating other protein families of interest besides the GPCR.

Slide number 30. The next candidate is CGEN-25017, which as I showed you previously, has potential use for the treatment of various cancers and also inflammatory disorders and retinal disorders.

Slide number 31. In summary, about half of our product candidates in the field of oncology have not yet been disclosed by us. Also, in this case the peptide candidate may serve as candidates for either peptide therapeutics or as an internal research tool for development of protein therapeutics.

Slide number 32. This completes my review of our pipeline program consisting of over 30 product candidates focused primarily in the fields of oncology, autoimmune, and inflammatory diseases. As noted, specific results for the majority of these product candidates have not yet been disclosed.

Updates about the progress and status of individual product candidates will be disclosed from time to time and it is our current expectation that the next overall review of the program will be following the third quarter of this year. Although our pipeline is currently early stage and not all of its molecules will be successfully developed into commercial products, in our opinion the number of initial candidates with expected superiority in areas of high industry interest and unmet needs coupled with the broadly applicable and constantly improving discovery infrastructure represents a unique and valuable asset for our industry.

It is our intent in general to license out or enter into other collaborations with our product candidates towards the end of their pipeline program activities. Although, in specific cases, we may choose to take certain molecules for further development or enter into collaborations at an early stage as done in the past.

Thank you for your attention and interest. I will now turn the call over to Dikla.

To me.

To Martin, sorry.

Thank you, Anat. Although the use of ethical drugs worldwide is certainly going to expand significantly for the indefinite future, it's interesting that essentially all major biopharma or biotech, which were once the darlings of the financial community, are facing uncertain futures.

They are still very profitable -- this is slide 34. They are still very profitable, but as everyone knows facing the major patent expirations, the limited late-stage pipelines, the ability to license in clinical products seems to be very limited.

There are pricing pressures on me-too products. This is something that people following the industry may not fully be aware of, but most of the profits in the industry in the past have come from me-too products. Not the first product in the field but the second, or third, or the fourth.

It's generally understood and agreed that the opportunities to get premium pricing for me-too products in the future is rapidly disappearing. It's interesting that in the face of all this what the industry is doing is cutting back on research, cutting back on the attempts to find new drugs.

The underlying cause of all of these issues is that there has been a long-term reliance, largely on high throughput-based discovery methodologies. And all of these by definition means that you get the low-hanging fruit first. You find the easy ones and then it must get harder and harder over time, which is what the industry is now faced with.

In fact, if you think about the industry, there is only one key need that is missing in our industry. Everything is available out there in most cases, and perhaps in all cases, in excess supply and available at a price which you could -- which would make it cheaper for you to buy it than to build it yourself.

The one thing that is missing is a systematic, constantly improving discovery methodology. This has never existed in our industry and a company that created this over a period of time I think has the opportunity to dominate the industry.

This leads to our mission, as stated on slide 37, is to be the Company that provides an increasing flow of superior drug and diagnostic product candidates to the industry through leadership and science-driven predictive discovery.

I first want to focus on the word superior in our mission statement. I think if you -- those of you who recall the results that we have been publishing with respect to some of our initial candidate products I think will recognize that we are proving that we do have that capability, at least with respect to up to the stage of disease animal models.

For example, if you go to slide 3 -- and each of these product candidates was disclosed or were described by Anat in her talk. But with respect -- if you look at slide 38, Dr. James Berenson who is the Head of the Institute of Myeloma & Bone Cancer Research says that this particular product is very sensitive to multiple myeloma tumors, very drug resistant, highly aggressive, detecting and treating cells that are most aggressive and resistant to currently available drugs.

You go to slide 39 talking about CGEN-15001, Dr. Williams, who is an expert in this field, stated that the results are very impressive. Same level of efficacy as Enbrel and has a potential therapy for multiple autoimmune diseases and explained by Anat with the same level of efficacy as Enbrel, but there is reason to believe that it should have substantially less side effects.

Slide 41. Professor John Penn, with respect to 25017, fairly rare finding, falls within the top 10% of a test compounds passed through our hands. And they are one of the centers for this type of research in the world. Warrants further development and study.

Then slide 42. Professor Neurath, with respect to 25007, never saw such dramatic effects. So I think, at least with respect to the stage of development that our initial products have reached at this point, I think we can clearly state that we are in fact accomplishing our mission of having a flow of superior product candidates.

Moving to slide 43. The business model that I think is the obvious one, given this unique capability of a continuing increasing flow of product candidates and a situation where everything else that is necessary in the industry is available and in excess supply, our business model is to maximize the number of our product candidates in the development pipelines of or under co-development with drug and diagnostic companies worldwide pursuant to milestone royalty or other revenue sharing agreements.

And with time we want to be and expect to be a primary source of product candidates for the industry's future. So how are we able to do this?

If you move to slide 44, our science-driven predictive discovery, and then slide 45. Instead of relying on the experimentally-based hit-or-miss or serendipity for a discovery, all of Compugen's efforts are based on a science-driven approach. We start with an unmet medical need of interest and from that we then go to our understandings of how life works at the molecular level based on our decade of fundamental research in this area and predict possible candidates.

After predicting possible candidates, again we use the knowledge that we have and that exists in the world with respect to whatever it is that we are looking at, combined with our exceptional computational biology capabilities algorithm development, to select the most probable candidates. It's important to understand that as of that point in time we now have multiple product candidates for the unmet medical need of interest and have not done a single laboratory experiment.

We then look at the ones that we believe are the most likely to be successful and move forward with experimental validation.

Moving to slide 46. In a sense, this is -- those of you who studied science will recognize that this is what is called the scientific method. It is the way to solve this -- to meet this unmet need in the industry of having a systematic constantly improving source of product candidates. This is the way that you do this.

Unfortunately, for the industry it has proven to be much easier to say than to do. Many, many companies in the past, particularly since the beginning of the genomic revolution, have attempted to do this and essentially all of them have completely failed.

It raises the question, so why is Compugen seemingly able to accomplish this where so many people and the expenditure of such a large amount of resources have failed in the past? If you go to slide 47, it's based solely on one reason.

And that is that starting more than a decade ago we began to pioneer the understanding of key biological phenomenon at the molecular level. Rather than essentially trying to be lucky through high throughput experimentation, we pioneered the predictive understanding of how life works at this level.

Moving to slide 48. Of course, at that point in time we had the scientific understanding. In order to make it usable we had to create sort of that technology bridge. And that technology bridge involves creating predictive models based on these scientific understandings and then integrating all of these individual pieces so they begin to show you a picture of the complete jigsaw.

This has taken a long time but we are now seeing the benefits of it. It's interesting to note that being in Israel, which is well-known for its algorithm specialists and computational skills, we have access to some of the best people in this area in the world.

However, even if some other organization found better, somehow, people skilled in algorithms, computer science, mathematics, whatever, they couldn't compete with us because the primary reason for our competitive advantage is that these algorithm specialist have available to them this proprietary understanding of the science. And it's the proprietary understanding of the science that is built into these algorithms and that causes -- allows us to do what essentially has never been done before.

If you move to slide 49, just a symbolic show that all of this underlying capability, the science and the technology represented by the platforms and the algorithms, lead to our ability to address pretty much any of the unmet needs in medicine. And that is sort of on a one-at-a-time basis. Not talked about we -- as of now we are focusing on specific areas and specific types of molecules, but this capability is of course very, very broadly applicable.

In spite of everything that I said, the proof is in the pudding. The question about talking about all of this and saying how great we are and how wonderful the science is, the fact is does it work? And if you go to slide 50, it works.

You can see the period of time building this capability -- the science, the technology. And then you can see what has been happening over the last few years as our initial product candidates demonstrate in the well-recognized disease animal models that these novel molecules, 100% predicted by a computer, do exactly what we said they should do when put into a living system. So, obviously, it has been very rewarding for us to see this growing proof of this unique capability.

Going to 51, I think the advantages of this approach are straightforward and obvious. 52, instead of largely random word it's hypothesis-driven and systematic.

Slide 53, if you do experimental work, if you are fortunate and you see something, great, you may discover something wonderful. But if not, which is the majority of times, you learn very little from your failures. Our approach we learn from everything that we do, from both successes and failures.

Going to 54, sort of the bottom line on it is that -- and this has been now proven in the industry, but it has to be that doing things experimentally gets more and more difficult over time as you harvest the low-hanging fruit. Whereas when you do -- when you approach it the way we are, and I think again our early results show that this is the case, that every discovery you make makes it easier to make the next one rather than harder.

Moving to slide 55. Just schematically, when I showed you the diagram of how we do things I left off a very, very critical arrow and that is the arrow on the bottom where there is feedback which allows this capability -- not only allows it but essentially forces this capability to improve over time. All of the results, the positives and the negatives, result in information data that gets fed back into the model making better predictions and selections for the future.

If you then go to slide 57, this -- I think given the results and the description of our initial pipeline by Anat and some of the capabilities that have been developed here and recognizing the core infrastructure that is creating all of this, I think it's very fair for us to say that we are now establishing a new paradigm in drug discovery. One that is systematic, constantly improving.

You can start on a market-driven; you don't have to just see what you can discover but you have flexibility in selecting the areas that you want to go after. And given the way we do it, it doesn't really matter where the area is easy or hard to do experimentally because we are not doing it experimentally. When you do it theoretically and through this hypothesis-driven approach, the hard ones and the easy ones are basically all the same.

As we move forward it clearly deals with biological complexity as our models continue to improve. It allows a very powerful integration of different discovery approaches and platforms, so you can both come at the same problem from different directions. And by doing that it is a situation where clearly from a knowledge standpoint it's one plus one equals three because each of these approaches support each other.

And as we have shown, it is already resulting not only in novel product candidates, but clearly better superior product candidates. And so with that what I would like to do is turn the call over to Dikla for a brief discussion of our financial status and outlook.

Thank you, Martin. I will start with slide 59.

As previously stated, for 2011 we are anticipating a gross cash outlay of approximately $10 million. This represents a significant increase over recent years in view of our increased activities supporting our pipeline progress. The $10 million is before revenues and, therefore, this amount would be reduced by any research revenues, initial fees, or milestone payments received during the year.

Slide 60. I am certain that those of you not familiar with Compugen, but familiar with the cost of pharmaceutical R&D are surprised and impressed by the relatively low expenditures supporting such an impressive output in terms of product candidates of high industry interest. This is, of course, a direct result of the fact that all of our discoveries -- actually not discovery but prediction and selection as -- are performed by computer as described by Martin.

And since only one to three months are required for prediction and selection of multiple candidates, based on our capabilities our efforts are probably orders of magnitude less costly than traditional experimentally-based methodologies. After the selection of multiple product candidates, our validation efforts are in most cases similar to those undertaken by the rest of the industry costing us less than $50,000 for in vitro validation and less than $100,000 for a disease model or equivalent experimental validation for a monoclonal antibody product candidate.

As we now move towards preclinical activities, this per candidate cost may increase substantially. Research and development expenses of $1.7 million for the most recent quarter compared with $1.6 million for the first quarter of 2010 remain our largest expense, representing approximately 70% of total operating expense for the first quarter of 2011 and approximately 60% of total operating expense for the first quarter of 2010. The amount for the first quarter of 2011 is before deducting $48,000 of governmental and added (inaudible).

Slide 61. We ended the first quarter with approximately $30 million of sellable resources to fund our future operations. This amount includes the averaging share we still own.

Slide 62. Looking ahead, our future expenditures will be largely dependent upon the progress of our pipeline program. Products and arrangements; potential source of short-term revenues in the amounts required to achieve cash breakeven include upfront fees from licenses, research revenues, and payments related to strategic-type arrangements.

Based on current expenditures -- based on current expectations and [cautions], possible timing for such revenues in the amount required to achieve breakeven is later this year or in the first half of 2012.

We will now open the call for any questions that you may have.

(Operator Instructions) Brett Rice, Janney Montgomery Scott.

Hi, everybody. I appreciate all the time and effort that went into the slide presentation, but in the midst of it I was e-mailed a question from a client and I have it myself. When can we expect to see the licensing out of some of these molecules?

We may continue having some licensing out that could occur kind of at any time. But with respect to the primary business model of the Company at the present time, the aspect of the pipeline program in taking things 12 to 24 months -- 12 to 18 months further towards pre-IND, which we believe will have a significant increase in the value of the arrangements that we can make, if you just sort of add -- we began the pipeline program the end of last year.

You do the 12 to 18 months, that is the end of this year, first half of next year. So what you should expect and what we expect is that we will begin to see the initial products reaching this stage and hopefully being able to enter into some fairly substantial kinds of arrangements.

Separately from that, of course, we continue to talk with various companies about different types of arrangements utilizing our capabilities rather than licensing out our direct products. We have spoken about them in the past as discovery on demand or other types of what I would call strategic-like arrangements rather than product-based arrangements.

Has the frequency of big pharma companies that are contacting you now increased within the last few months than prior times?

Dramatically.

Okay, I appreciate it. I am going to step back in queue.

Keay Nakae, Chardan.

Good morning. With respect to CGEN-15001, you talked about it briefly but I am wondering if can give us a little more color on what current activities you are engaged in with respect to that molecule in order to advance it to pre-IND stage.

Yes, of course. One of the activities is what I just explained in the call. This is the activity of candidate selection in which we have to do experiments in order to set what is the right molecule structure that you go into further preclinical and clinical testings.

The molecule is, as probably most of you know by now, is a molecule that is a combination of an extracellular domain of a membrane protein fused to an Fc, which is the tail of an antibody. We have to decide which is the right Fc tail of the molecule we should use in order to get the best efficacy and also to attach it in different ways to the extracellular domain. So this is one of the things that we are currently doing.

We are also in the process of selection of companies to manufacture this protein for doing optimization of a manufacturing process for us and conduct the manufacturing. I guess we will be able to decide in the next few months who is the company we are going to work with.

We are also conducting additional experiments, both in the field of multiple sclerosis and rheumatoid arthritis, and we aim to do additional experiments in additional autoimmune disorders.

We are also testing -- continuing to learn about the biology of the protein in terms of the mechanism of action that we are conducting as part of our long-term agreement with Professor Stephen Miller from Northwestern University. These are experiments that are done in his laboratory and part of the experiments are also done in the laboratory of Professor Richard William from the UK Imperial College.

Also, we have some internal efforts here, computational efforts, in order to identify the receptor to which this molecule binds which may elucidate additional biological understanding as to the pathway that this molecule acts. So in general this is a summary of most of the activities that are done here in the Company in order to -- and outside the company in order to move ahead with this molecule.

In terms of selecting the structure of the molecule, how much of the factor, design factor is [route of] administration in your decision there?

This is something that would be the result of the structures that we will decide upon of course. This is always in our mind, but we are working with the industry experts that are really experts in the fields of Fc fusions.

We are doing this type of activities with other soluble therapeutic proteins. We are taking into consideration the parameter that you have just described, but also additional parameters that should aim in order to allow us to identify the optimal molecule that would result in the best efficacy and as much less as possible side effects.

Okay. So appreciate your thoughts there. So given where you are at at this point, how would you characterize your level of enthusiasm for that compound versus where it was on previous calls?

We are very enthusiastic with this program. It's not only related to our last call. This is something that is of high interest here in the Company in any case.

Not to say that the other molecules in the pipeline are not -- that we incorporated into the pipeline are not attractive enough. But this molecule is quite advanced based on as compared to the others, and we are very happy with the results that we are getting.

Okay, thank you for those comments.

[Ken Farber].

Thank you. I am a shareholder and I have some rather basic questions that you might be able to help me with.

From the presentations it's very impressive on the scientific side, but it leads to questions as to what sort of expertise is within the Company in terms of the number of resources in different areas. It might be helpful at some point to better explain how many folks are devoted in different areas and how you -- it almost seems as though the science can move so quickly in what you are doing. How do you ensure that you have adequate resources to address the potential that the algorithms are providing?

And I guess the second somewhat related question, while there has been a significant discussion on science there has been very limited discussion on collaboration, both in terms of how many collaborations are ongoing currently and the process that you are using for developing collaborations internally. So if you could sort of cover those topics it would be very helpful.

Of course. This is an excellent question and I will answer the first one and I will then defer to Martin to answer for the second one.

It is true that we didn't relate to that in the presentation and we had some thoughts as to what is it that we should incorporate into the presentation. We had so many issues to cover and we selected what we already -- what you know that we presented. But this is a very good question.

As you know, we are experts in the field of predictive discovery and this is what we know to do best. In order for us to be able to validate our discovery capabilities, both in vitro and in vivo, we had to be able to develop here in the Company in the last two or three years the capability to perform these assays in well established systems in quality that is not less good than what is done in pharma companies and to bring results that would be highly appreciated in the industry.

And for that matter, we were developing here expertise in the management of outsourcing efforts to do this type of activities. It is either done by CROs or done by experts in the industry, in the academy. We are doing that based on service agreements, nothing related to handing IP to any third party, and we know how to do that very successfully.

This is for design. But now we had to face the issue of how we do that with respect to development. We used in the last year, since we launched this pipeline program, we used our understanding and expertise that we developed in outsourcing activities in order to do exactly the same for the development activity.

So on one hand we recruited few personnel that has the experience and understanding of how to take biologics into Phase 2 and even into Phase 3, but we also hired some that would be able to manage the activities that will be conducted outside the Company. These activities had to do with preclinical trials, with manufacturing CMC activities, and also with regulation and submission to FDA. Currently, we feel that we are equipped in the right way in order to perform these studies.

In effect, for 15001 we are already in the middle of these activities talking with the production companies that should manufacture these proteins and talking with the people that are experts in FDA regulation, and also talking with experts specializing in preclinical trials. Also in clinical trials, in order to be able to take into considerations parameters that will be of relevance in clinical trials that we should know them now and relate to them now and design in the right way our preclinical studies and submissions to FDA.

So in general this is what we were doing in the last year or so since we launched this pipeline program. I agree with you this was missing in the presentation, but as I said in the beginning we had to select what we are focusing on from all the amount of the activities that are done in the Company. So I am thanking you for asking this question.

Martin, will you relate to the second one?

Yes, thank you, Anat. We have a number of ongoing discussions and collaborations with respect to some of our early-stage prior discoveries.

But the key focus of the Company now is, with respect to the moving forward, the pipeline program, and entering into arrangements, more substantial arrangements based on the more advanced products that we will have at the end of this pipeline program, and on early discussions with respect to I mentioned before kind of strategic, more strategic-type arrangements that aren't specific product-oriented, as you remember, if you remember, we have made a lot of very interesting discoveries in the past.

Now as we focus our efforts, at least short term, in the fields and on the types of molecules that we have and on the pipeline program, we, to some degree, are looking for ways to make use of and commercialize or gain value, monetize some of our earlier work, mainly which arose out of our research and out of our validation efforts. But the real collaborations that most likely are going to be the ones that are going to really make the difference for our company, I believe, will be set in one of the two categories.

Either the strategic type, which most likely -- I am sorry, the pipeline products which most likely would begin at the end of this year, early next year, or strategic type things, which they can occur at any time but I don't want to -- I will state now but I won't state any more in the future, the discussions that are ongoing are very, very early stages.

Thank you.

(Operator Instructions) There are no further questions at this time. Before I ask Mr. Gerstel to go ahead with his closing statement I would like to remind participants that a replay of the call is scheduled to begin in two hours for a period of 72 hours.

In the US, please call 1-888-295-2634. In Israel, please call 3-92-55-928. Internationally, please call 972-3-92-55-928. Mr. Gerstel, would you like to make your concluding statement?

Yes, thank you. I just want to thank everybody for participating. We appreciate your -- we realize that we provided a lot of information in the call and actually we had a couple of questions about some additional information that we perhaps could have and should have included.

So we just wanted to thank you again for your interest, for your support. We hope that today's presentation was of interest and provided more -- some valuable information to you with respect to who we are and where we are going. Thank you very much.

Thank you. This concludes the Compugen Ltd. first-quarter 2011 financial results conference call. Thank you for your participation. You may go ahead and disconnect.